The Perfect Planet

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Metanarrative is something of a perjorative for postmodern (pomo) critical social theorists, but just because a metanarrative doesn't really explain everything, ...


The Perfect Planet

Jonathan D. Phillips

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The Perfect Planet A Collection of Geoscience Blog Posts Preface and Explanation This document is a collection of Jonathan Phillips’ Geoscience Blog posts from its inception (29 May 2014) through 2 July 2017. As of August 2017 I am continuing to blog; the posts contained here and newer ones are available at: https://geography.as.uky.edu/blogs/jdp What is included (or not) This volume collects most of the blog posts, though some are excluded. Exclusions are generally simple announcements or self-promotion (e.g., an article just published) or observations on some recent event that are largely irrelevant now. They are mostly written in a very informal style and voice, even when addressing serious and scientific topics, though there are some exceptions. The entries are organized into several categories (see Table of Contents), with entries in each category shown in chronological order of when they were posted. They are mostly unchanged from the original posting, though I have corrected a few errors and added a note or two here and there. Caveats •As a collection of originally stand-alone commentaries or essays, there is some redundancy. Sorry. •The various hyperlinks listed in many entries were all working at the time of blog posting, but may or may not active now. •Photographs and graphics are my own unless otherwise indicated. I have tried to provide either a credit or link for all that I lifted from elsewhere. Copyright & Referencing I have not attempted to copyright or otherwise legally protect these contents. However, if you do make use of them, I would appreciate attribution. A suggested bibliographic reference is Phillips, J.D., 2017. The Perfect Planet. Copperhead Road Geosciences, Lexington, KY, U.S.A.



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Table of Contents There are no page numbers here, but you can use the search or find features in the electronic document to get where you want to go

How It’s Done The practice of Earth & environmental sciences--how it is done & how it should be done; Musings and opinions on pedagogy, philosophy & politics of geosciences

Geomiracles Infinite Sand Gators Caroline, the Thermodynamic Miracle The Triad Hydropedology: Flux-Structure Interactions Geoscience Metanarratives Geoscience Metanarratives—Part 2 Quo Vadis Physical Geography? Quo Vadis Physical Geography—Part 2 $how Me the Money Reject, Rewrite, Resubmit! Big Data, Big Deal Disturbing Foundations Some Observations on Observation Geoscientific Storytelling How to Get Scientists to Ignore You The Tao of the River On Being Widely Ignored (Or Not) Some Grumpy Thoughts On Political Ecology & Biophysical Science LaPlace’s Angel Earth Surface System Theory Part 1: Equilibrium & otherwise Adjustedness Dynamic Equilibrium(?) in Rivers Changing Lanes The Balance of Nature, and the Nature of Balance Threshold Modulation vs. Steady-State The (Necessary?) Illusion of Equilibrium



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Earth Surface System Theory Part 2: Nonlinear Dynamics, Complexity, Self-Organization, Power Laws “Taken” Dynamics to the Spatial Domain Convergence, Divergence & Reverse Engineering Power Laws The Dubious Power of Power Laws The Dialectics of Geomorphic Complexity If I Had a Hammer The Top 10 Forms of Complexity in Earth Surface Systems Instability & Complexity Earth Surface System Theory Part 3: Optimality, Selection, and Miscellaneous Scale Ratios Place Similarity Optimality in Earth Surface Systems The Principle of Gradient Selection Reducing Reductionism The Dominant Controls Concept Tipping Points & Other Metaphors Geomorphological Flickering The Perpetual Quest For Efficiency And Stability In Earth Surface Systems The Perpetual Quest For Efficiency Part 2—Gradient & Morphological Selection The Perpetual Quest For Efficiency Part 3—Why Isn’t Everything Always Becoming More Efficient? Providers of Predictability Occam’s Selection Metanarratives, Extremal Principles, & Another Rejection Forest Biogeomorphology Sycamores & Hillslopes Trees Behaving Badly Geomorphic Impacts of Trees Hurricane Matthew & Forest Biogeomorphology More Forest Biogeomorphology & Geoecology Stages of Biogeomorphic Effects Biogeomorphic Niche Construction by Uprooting

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Response to and Effects of Climate and Sea-Level Rise Amplifiers & Filters Climate Change & Environmental Management Teleconnectivity Soil Erosion & Climate Change Climate and History: Geography Matters Polarization Makin’ it Rain Drowning the Coast Climate Change Effects on Karst: It Depends Coevolution of Biota, Soils, & Landforms Phytotaria, Soils, & Landforms OCBILs & YODFELs Resources & Biodiversity Biogeomorphological Selection Rivers and Streams River Restoration & Rehabilitation Froude for Thought The Perfect Floods of Texas Polly’s Bend—Initial Conditions The Curious Expansion of Polly’s Bend Fluviodiversity Bank Full of It Bedrock Channel Erosion Lessons From Icicle Bend Environmental Management and Applied Geoscience The Semantics of Resilience The Inherent Ephemerality of Wetlands Dust Bowl Dynamics Δ Deltas Why Them? Why There? Soil Erosion Rises Again! Soil Erosion: Counting the Costs The Nitrogen Bomb

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Geomorphology The Cycle of Erosion Strat-and-Transition Models Strat-and-Transition Models II Plenty of Peneplains? Romantic Geomorphology Romantic Geomorphology—Part 2 Landscape Evolution Energy Re-enchantment Revisited Axioms of Geomorphology Soils, Regolith, and Karst Connecting the Dot Factors Making Peace With Macropores Karstification at Bowman’s Bend Regolith Mobility A Churning Urn of Burning Funk



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How it's Done The practice of Earth & environmental sciences--how it is done & how it should be done; Musings and opinions on pedagogy, philosophy & politics of geosciences



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GEOMIRACLES Posted 19 May 2014

Science fiction and popular science writer Arthur C. Clarke once wrote that "any sufficiently advanced technology is indistinguishable from magic." Riffing on that theme, I once gave a talk in which I proclaimed that "any sufficiently improbable event is indistinguishable from the miraculous." Some definitions of "miracle" invoke the divine or supernatural, but I have in mind the definition (in this case from the Merriam-Webster dictionary) as: "an extremely outstanding or unusual event, thing, or accomplishment." The point of the argument is that, due to the inescapable, irreducible role of geographical and historical contingency in Earth surface systems, all such systems (landscapes, ecosystems, soils, etc.) are unique in some respects (a formal argument along these lines is presented in this article: Phillips, J.D. 2007. The perfect landscape. Geomorphology 84: 159-169.). Thus the probability of existence of any given state of any given system at a given point in time is infinitesimally low. This exceedingly low probability makes nearly any environment in some senses extremely outstanding and unusual, and thus a miracle. Like any natural scientist, I seek the universal (non-contingent) laws, relationships, and representations that help explain our world. But I am convinced that such laws alone, no matter how advanced they become and how much data, information, and observational detail we have, are not sufficient to explain real-world Earth surface systems. Place matters and history matters. I will use this blog to share some of my ideas along these lines, along with other ideas, suggestions, opinions, etc. on Earth and environmental sciences more generally. As I get older I realize that I have more ideas and such than I will ever publish, even if I doubled my output (and that ain’t happening; if anything I’m headed in the other direction), and worked till I was 90 (that ain’t happening, either). Hopefully these will be of interest or use to someone, but at least I’ll have them off my desk and off my chest.



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INFINITE SAND GATORS Posted 14 August 2014

This unusual bedform was created by the self-organizing dynamics of ocean waves, wind, sand, and shells a couple of days ago. OK, it wasn’t. It is the work of a vacationer at Myrtle Beach. But it got me to thinking, not only about what an awesome sand sculpture it is, but also about uniqueness and probabilities in Earth surface systems. In theoretical physics, the “many worlds in one” (MWO) concept holds that, with unlimited space and time, any outcome not forbidden by the first and second laws of thermodynamics (laws of conservation of mass and energy) will eventually occur (Vilenkin, 2007 is the standard source for MWO; I encountered it via Koonin, 2012). Thus, on some beach, somewhere, some time, waves and wind have independently sculpted a sand alligator.



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However, while Earth science encompasses a lot of space and time, both are quite finite. Thus, while MWO predicts formation of a sand alligator sometime, somewhere in an infinite universe with probability = 1.0, the odds for such an occurrence (without human intervention) on this particular speck of space-time (Earth) are so low as to be essentially zero. Earth surface systems—a sand beach, a pine forest, a karst sinkhole, or whatever-generally include regular, predictable aspects in common with other beaches, forests, and sinkholes around the world, and unique, idiosyncratic characteristics associated with their specific combination of environmental factors and history. Because of this, and the dynamical instabilities and chaos that sometimes lurk within the general or universal governing laws, many, many outcomes are possible. But not everything is possible. I agree with the MWO to the extent that the only outcomes that are absolutely, completely impossible are those forbidden by the conservation laws. However, other general laws and principles relevant to Earth make some outcomes more or less probable, and some essentially impossible. For instance, I can concede, for the sake of argument, that somewhere in an infinite universe weathered rock becomes whole again, but on our planet it ain’t happening. Also, the specifics of geography and history at a given time and place further rule out a

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number of outcomes (or, from the perspective of historical reconstruction, explanations of how something came to be). Much of my research has emphasized the idiosyncracies, individuality, and instabilities of Earth surface systems, based on the unavoidable effects of specifics of geography and history. I typically think from this perspective as geography and history providing opportunities and degrees of freedom for the variety and individuality of Earth systems. However, it is also important to recognize that geographical and historical details (at scales from molecular to planetary) also provide constraints that limit what can happen. -----------------Koonin, E.V., 2012. The Logic of Chance. The Nature and Order of Biological Evolution. Pearson. Vilenkin, A. 2007. Many Worlds in One: The Search for Other Universes. Boston: Hill and Wang.



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CAROLINE, THE THERMODYNAMIC MIRACLE Posted 20 August 2014

"In each human coupling, a thousand million sperm vie for a single egg. Multiply those odds by countless generations, against the odds of your ancestors being alive, meeting, siring this precise son; that exact daughter...until your mother loves a man ...and of that union, of the thousand million children competing for fertilization, it was you, only you...(it's) like turning air to gold... a thermodynamic miracle." Those words, from Alan Moore’s “Watchmen,” indicate that despite the common features of all members of our species, the biological laws and relationships that apply to us all, each of us is unique in some way. I am reminded on this on the occasion of the birth of my first grandchild, Caroline Harper Phillips, yesterday.

Caroline Harper Phillips, age B->C--> . . . .) or as cycles ((A-->B-->C--> . . . -->A). If that accurately represents the system, great—those are the simplest graph structures! However, in some cases the evolutionary sequence is divergent—it splits or forks, as in a biological evolutionary tree, cladogram, or phylogenetic sequence. Divergent evolution has also been documented recently in geomorphic and pedologic systems. Or in some cases, previously existing states reoccur in ways other than a simple cycle. In yet others, more complex mesh-type networks of various transitions among system states may evolve (this is best illustrated by the more complex state-and-transition models). Algebraic graph theory allows us to measure the structural complexity of the historical sequence (via the graph spectral radius), the (inferential) synchronicity and convergence properties (via algebraic connectivity) and the extent that a subgraph is representative of the overall pattern of historical change, which can be useful if you know or suspect that not all relevant transitions have been observed or inferred. I can also determine the extent to which graph complexity is due to the number of possible transitions vs. the specific way the transitions are wired. To me this seems like some great stuff. But, on the other hand, I am not happy with simply measuring or quantifying something because I can. What information or insight can quantifying the complexity of historical sequences of Earth surface system development gain us? What geoscience or ecological problems could it solve, or at least address? That’s what I’m not sure about.



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(http://ux.stackexchange.com/) Abraham Maslow is often credited with the saying that if all you have is a hammer, every problem looks like a nail. Some geoscientists are guilty of this, I suppose, as are other scientists. More often, however, we get or devise a new hammer, metaphorically speaking. We realize that not every scientific problem is a nail, so we grab the hammer and go looking for a nail. That’s what I feel like now. If you know of a nail I might take a whack at with this hammer, or otherwise have thoughts on how this particular hammer might be useful, I’d love to hear from you (at [email protected]). Some of my previous algebraic graph-theory based work on historical networks is available here and here. Note added July 2017: These ideas did eventually result in a publication: Phillips, J.D., 2016. Complexity of Earth surface system evolutionary pathways. Mathematical Geosciences 48: 743-765. DOI 10.1007/s11004-016-9642-1



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THE TOP 10 FORMS OF COMPLEXITY IN EARTH SURFACE SYSTEMS Posted 1 November 2015

When we (scientists) talk and write about complexity in recent years, the focus is on complex nonlinear dynamics, and related phenomena such as deterministic chaos, dynamical instability, some forms of self-organization, fractal geometry, etc. These are forms or sources of complexity that are intrinsic to the structure of dynamical systems, but these are hardly the only things that make systems complex. So, to make sure we don’t forget the elements of complexity that transcend so-called “complexity science,” I present the Top 10 Forms of Complexity in Earth Surface Systems (ESS). ESS is a blanket term that includes geomorphic systems, landscapes, ecosystems, soil systems, etc. Even though the items are numbered, they are actually in no particular order. Many ESS may exhibit only a few of these forms, and still be quite complex!

The list I was gonna do has already been done (http://grogsmovieblogs.com/). Forms of Complexity in Earth Surface Systems 1. Number of components. Earth surface systems may have a very high number of components (landforms, soils, chemical components, organisms, species, microclimates, etc.). 2. Degrees of freedom. The large number of components and dense network of connections and relationships between them means that ESS have many different ways or modes of responding to change, and multiple alternative configurations for a given set of boundary conditions. 3. Mutual adjustments. Due to feedback relationships, ESS components often feature mutual adjustments, whereby components both affect, and are affected by, each other. 4. Changing interconnections. The existence or presence, rates, and intensities of interconnections and feedbacks change. The components are also dynamic.



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5. Multiple Scale Causality. ESS phenomena are not controlled primarily by either bottom-up (microscale to macroscale) or top-down causality. Rather they are determined by multiple processes and controls acting at a range of spatial and temporal scales both larger and smaller. 6. Variability I. Extrinsic factors that influence ESS are strongly heterogeneous in space and time. 7. Variability II. ESS themselves are strongly heterogeneous in space and time. 8. Nonequilibrium. ESS are often in states not in, near, or approaching steady-state, thermodynamic equilibrium, or other equilibria. 9. Historical contingency. The direction and magnitude of change is influenced by preexisting conditions and past events. ESS development is path-dependent. 10. Nonlinearity. ESS are nonlinear, which enables the possibility of complex phenomena such as dynamical instability and deterministic chaos.



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INSTABILITY & COMPLEXITY Posted 19 January 2016

There sometimes exists an intuitive or cognitive disconnect between the idea that Earth surface systems (ESS) may exhibit divergent evolution associated with dynamical instability and deterministic chaos; and the fact that ESS sometimes evolve so as to increase their complexity and interconnectedness. Despite the initial apparent inconsistency, these two phenomena can and do happen simultaneously within the same ESS. Instability/divergence and evolution of increasing complexity are readily reconciled when you realize that instability and chaos are scale-contingent, so that divergence and pseudorandomness occur within firm limits. Also, these phenomena in effects expand the options an ESS has for its development, thus creating more room for evolution of complexity. The ecologist Robert Ulanowicz developed the notion of ascendancy as a measure of the complexity and interconnectedness of a system. Ascendancy is influenced by the quantity of matter and energy throughputs, and the network of mass/energy exhanges between system components. Almost 10 years ago (!) I used the notions of ascendancy and Kolmogorov entropy to show how dynamical instability and chaos can increase ascendancy. This was one of those things that was successful, but not quite enough to justify a publication. Having come back to it now and again over the past decade, I still cannot convince myself there’s enough there for an article. However, I also think it’s too interesting and too good to bury forever. Thus, I attach here my formal demonstration that instability can lead to increasing ascendancy. It’s in the form of a manuscript with the “theory” section fully developed, but nothing else.

Instability, Complexity, and Throughputs In Landscape Evolution

Theory Consider a landscape or geomorphic system in terms of its mass and energy throughputs T. The landscape consists of i = 1, 2, . . ., n components, each with their own mass and energy inputs and outputs such that T = ∑ T . Throughput is controlled by inputs (g) and outputs (f) to each component, and changes in storage (∆s), i

T = g – f + ∆s i

i

i

(1)

i

The proportion of throughput associated with each component (Q) is

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Q = T /T i

(2)

i

The maximum uncertainty or complexity of the fluxes in the system can be measured using the Shannon entropy: H = -∑ Q ln Q i

(3)

i

The mass and energy fluxes can be divided into external inputs (flows into one or more i), external exports (flows from one or more i to the external environment), and internal flows between components. In information theory terms the decrease in uncertainty from knowing the external inputs is given by I = T ∑ g Q ln[g /(∑ g Q )] o

ei

i

ei

ei

(4)

j

i

where g is the proportion of the input to i coming from outside the system. ei

A similar consideration of the internal flux exchanges is sometimes termed integrality, or when applied to ecosystem studies, mutual independence: I = T ∑∑ g Q ln[g /(∑ g Q )] ki

i

ki

kj

(5)

j

i k

where g is the probability that flux at i comes directly from k. ki

The analog of eq. (4) for exports of usable mass and energy (i.e., excluding energy dissipated as heat) is A = T ∑ f Q ln[f /(∑ f Q )] o

je

i

je

ei

(6)

j

J

The proportion of outflow from component j to the external environment is f . je

If the probability of any quantity of flow leaving component i directly contributing to component j is f , then a measure of mutual sustenance is ij

A = T ∑∑ f Q ln[f /(∑ f Q )] ij

k j

i

ij

ij

(7)

j

i

Note that equations (5) and (7) differ by their attention to the probability of inputs coming from a given component (5), versus the likelihood of outputs being directed to a given component (7). In the ecological literature A is referred to as ascendancy, relating to (for example) the complexity and interdependency of ecosystems (e.g. Ulanowicz, 1980).



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The relationship between A and other parameters is A = H – (S + R + A )

(8)

o

Where S is a measured of unfilled mass/energy flux potential: S = (I + I ) – (A + A ) o

(9)

o

R is a measure of redundancy, R = H – (I + I )

(10)

o

These inequalities hold: H > (I + I ) > (A + A ) > 0 o

(11)

o

From eq. (8) we can see that ΔA/Δt = ΔH/Δt – ΔS/Δt – ΔR/Δt – ΔA /Δt

(12)

o

In a dynamical system, the change in Shannon entropy over time is equal to the Kolmogorov entropy, K = ΔH/Δt

(13)

K-entropy is also the sum of the positive Lyapunov exponents (λ) of a dynamical system, where an n-component system has n exponents such that λ > λ > . . . >λ . Because dynamical instability and chaos is indicated by the presence on any positive Lyapunov exponent (λ > 0), positive K-entropy that increases in ascendancy may be associated with dynamical instability. Chaos and instability (ΔH/Δt > 0) is not the only way that ascendancy can increase over time, as changes in S (unfilled storage/flux potential), R (redundancy) and usable exports A could be negative. However, this analysis shows how nonlinear complexity and divergent evolution (i.e., dynamical instability) may play a role in the ascendant development of environmental systems. 1

2

ν

1

o



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Earth Surface System Theory Theories, hypotheses, and other notions of how landform, soil, hydrological, and ecological systems function and evolve

Part 3: Optimality, selection, & miscellaneous



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SCALE RATIOS Posted 12 October 2014

In fluid dynamics the Reynolds Number is the ratio of inertial to viscous forces, and is used to distinguish laminar from turbulent flow. Peter Haff (2007) applied this logic to develop a landscape Reynolds number, and also suggested how other generalized “Reynolds numbers” can be constructed as ratios of large-scale to small-scale diffusivities to measure the efficiencies of complex processes that affect the surface. As far as I know, there has been little follow-up of this suggestion, but the premise seems to me quite promising at an even more general level, to produce dimensionless indices reflecting the ratio of larger to smaller scale sets of processes or relationships. A couple of examples are given below.



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In#fluid#dynamics#the#Reynolds)Number)is#the#ratio#of#inertial#to#viscous#forces,# and#is#used#to#distinguish#laminar#from#turbulent#flow:# Re#=#ρ V#D/µ, where#ρ#is#density#and#µ#the#molecular#viscosity#of#the#fluid,#V#is#velocity,##and#D# is#depth#(or#pipe#diameter).#One#way#to#think#of#it#is#that#Re#is#the#ratio#of#the# effects#of#factors#external#to#the#fluid#driving#flow#(e.g.,#gravity#for#water#in#a# stream#channel)#vs.#the#internal#resistance#of#the#fluid.## Peter#Haff#(2007)#applied#this#logic#to#develop#a#landscape#Reynolds#number,# and#also#suggested#how#other#generalized#“Reynolds#numbers”#can#be# constructed#as#ratios#of#largeMscale#to#smallMscale#diffusivities#to#measure#the# efficiencies#of#complex#processes#that#affect#the#surface.#As#far#as#I#know,#there# has#been#little#followMup#of#this#suggestion,#but#the#premise#seems#to#me#quite# promising#at#an#even#more#general#level,#to#produce#dimensionless#indices# reflecting#the#ratio#of#larger#to#smaller#scale#sets#of#processes#or#relationships.## Take,#for#example,#the#famous#“clorpt”#equation,#articulated#in#this#form#by#Hans# Jenny#from#the#seminal#pedology#ideas#of#V.V.#Dokuchaev:# S#=#f#(cl,#o,#r,#p,#t)#.#.#.#.# where#S#is#soil#type#or#a#soil#property,#cl#=#climate,#o#=#organisms#or#biota,#r#=# relief#(topography),#p#=#parent#material,#t#=#time,#and#the#trailing#dots#indicate# the#possibility#of#other#soil#forming#factors#not#included#in#the#others#that#may# be#locally#important#(e.g.,#seaMlevel#or#salinity#in#coastal#locations).## Time#is#the#only#independent#state#factor,#and#soil,#climate,#organisms,# topography,#and#parent#material#are#in#fact#interdependent,#at#least#globally.#The# “clorpt”#equation,#represented#as#a#networkMgraph,#looks#like#this:#

# The#overall#set#of#relationships#looks#like#this:#



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# # If#we#just#used#the#numbers#of#links#or#edges#in#the#graph#(recalling#that#the# double#arrows#each#represent#two#edges)#to#represent#the#relative#importance#of# the#“global”#set#of#interrelationships#with#respect#to#the#“local”#set#of#state#factor# influences#on#soils:# Scale#ratio#=#20/4#=#5.## Alternatively,#using#the#spectral#radius1#of#the#associated#graphs,# Scale#ratio#=#4/1.732#=#2.31# Of#course,#having#a#clever#measure#of#something#is#a#long#way#from#knowing# what#it#means,#or#putting#it#to#good#use.#However,#I#think#by#applying#this#sort#of# analysis#to#a#number#of#Earth#surface#systems,#some#meaningful#patterns#could# emerge.## For#another#example,#consider#the#ratio#of#crossMsectional#stream#power#in#a# river#channel#(rate#of#work#or#energy#expenditure#for#the#entire#cross#section)#to# the#stream#power#per#unit#weight#of#water#as#a#measure#of#macro/micro#scale:# Scale#ratio#=#γ#A#V#S/VS#=#γ#A# where#γ#is#specific#gravity#of#water,#A#is#crossMsectional#area,#V#is#mean#velocity,# and#S#is#the#energy#grade#slope.#This#tells#us#that#the#macro/micro#ratio#scales#as# a#function#of#channel#crossMsectional#area#(since#specific#gravity#is#more#or#less# constant),#which#is#not#very#surprising#to#any#geomorphologist,#hydrologist,#or# engineer.#On#the#other#hand,#maybe#this#reveals#something#about#the#importance# of#crossMsectional#area#we#haven’t#thought#about#before.### 1Spectral#radius#is#equal#to#the#largest#eigenvalue#of#the#adjacency#matrix#of#a#

graph,#and#is#generally#considered#the#best#index#of#graph#complexity.# # Haff,#P.K.,#2007.#The#landscape#Reynolds#number#and#other#dimensionless# measures#of#Earth#surface#processes.#Geomorphology#91,#178M185.## 103

PLACE SIMILARITY Posted 22 October 2014

I've thought, written, and talked a lot about the need to incorporate geographical and historical contingency--that is, idiosyncratic characteristics of place and history--in geosciences, in addition to (not instead of!) general or universal laws. I've also emphasized the fact that places and environmental systems have elements of uniqueness. This leads to the issue of how to measure or assess place similarity (or the similarity of different, e.g., landscapes, ecosystems, plant communities, soils, etc.). This is a way of thinking about this problem, dressed up with some formal mathematical symbolism. Though I'm personally pretty informal, I'm a big believer in formal statements in science, as it makes arguments at least partly independent of linguistic skills (or lack thereof).



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OPTIMALITY IN EARTH SURFACE SYSTEMS Posted 28 December 2014

A number of theories in geomorphology, ecology, hydrology, etc. are based on the idea that Earth surface systems (ESS) develop according to some optimal principle or goal function. That is, the ESS develops so as to maximize, minimize, equalize, or optimize some quantity—energy, exergy, entropy, work, mass flux, etc. Some of these notions have some explanatory power and have resulted in some important insights. However, they have always bothered me--no one has ever been able to convince me that there is any inherent, a priori, rule, law, or reason that, e.g., a hillslope or a stream channel or a soil would operate so as to optimize anything. The conservation laws for mass, energy, and momentum are the only laws of nature that absolutely must hold everywhere and always. So how does one explain the apparent success of some optimality principles in describing, and even predicting, real ESS behavior? Suppose we use P to represent possible developmental pathways for an ESS. An optimality principle is essentially arguing that a particular P among all those possible is the most likely1. But the sufficient conditions for a particular path need not invoke any extremal or optimal goal functions. Sufficient conditions for preferential development along trend or pathway Po, Pi, i = 1, 2, . . . , n potential pathways are threefold: 1. Po is associated with processes or behaviors that confer advantages or higher probability of persistence or replication relative to other Pi. For instance, with respect to hydrological flow paths, concentrated pathways are favored over diffuse ones; and steeper and hydraulically more efficient routes over gentler and less efficient ones. With respect to hillslope gradients, angles less than the angle of repose persist, while those greater fail. In ecosystem or community composition, for example, more rapid or efficient resource use and cycling may confer competitive or selective advantages. 2. The ESS is at or approaching saturation (i.e., given enough time without change in boundary conditions or disturbance, it will become increasingly saturated). The “saturated” condition is associated with biological saturation (i.e., all available niches are filled) in ecology; with fully developed drainage systems in hydrology; and with relaxation time equilibrium in geomorphology. Condition 2 does not require the ESS to have reached saturation; only that if it has not, then (e.g.) niches will continue to be filled, drainage systems will continue develop, and geomorphic responses will continue until saturation is achieved. 3. There are no significant changes in boundary conditions, or clock-resetting disturbances. In the absence of environmental change (no. 3 above), two conditions (1, 2 above) are

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sufficient. Hypothesized “optimal” development does typically correspond or overlap with pathways that are advantageous in the sense of condition 1 above. Thus observations of supposedly optimal evolution are better explained as emergent outcomes of the simple selection principle indicated in item 1 above, subject to items 2 and 3. I prefer the emergent explanation based on selection to the optimization hypotheses because it is simpler, it works at least as well2, and it requires no suppositions of goal functions for ESS. ----------------------------1

Though in some cases it has been shown that there are many ways an ESS might be configured to achieve a particular optimum. 2

”Works as well” refers to explanation and interpretation. For modeling, assuming a particular optimal condition often simplifies things immensely. However, in those cases the optimality principle should indeed be viewed as a simplifying assumption rather than a truth statement about ESS.



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THE PRINCIPLE OF GRADIENT SELECTION Posted 8 December 2015

Flows of mass and energy occur along the steepest gradients of potentials or concentrations. The principle of gradient selection is simply that features associated with these gradients persist and grow. Take, for instance, the redistribution of excess (i.e., more than the ground can absorb or retain) surface water. Hydraulic selection principles favor the most efficient paths, which we can generally interpret as the fastest pathways. Thus the steepest slopes and/or the routes with the lowest resistance to flow attract more water. The most efficient paths persist and prevail; less efficient options dry up. For example: Standard flow resistance equations are of the general form V = f(RaSbf-c) where R is hydraulic radius (cross-sectional area divided by wetted perimeter; typically roughly equal to mean depth), S is slope (hydraulic gradient), and f is a roughness or frictional resistance factor. The exponents a, b, c < 1. For example, the D’Arcy Weisbach equation is V = 8g R0.5 S0.5 f-0.5 Thus flow pathways where water is concentrated (greater R), slope is steeper (greater S) and resistance is lower (smaller f) are favored. Therefore concentrated flows (greater R, lower f) are favored over diffuse flows and steeper paths over gentler gradients.

Gradient selection at work: even in flat topography concentrated flows tend to evolve and dominate (photo: L. Betts, USDA).



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The force or shear stress exerted by a flow against its boundary is given by mean boundary shear stress (tau):

with rho the density of water, and g the gravity constant. With R in m, tau has units of N m-2.

Hydraulic selection favors pathways with faster V, and also deeper R, and steeper S. Thus hydraulic selection also tends to locally maximize shear stress and stream power. This is not a goal function or a basic principle of fluid flows—rather it is an emergent property; a byproduct. There is no principle that runoff and stream flow seek to maximize local shear stress or stream power; rather that is a corollary of hydraulic selection, which in turn expresses ideas we knew from our earliest experiences as children, on a rainy day or playing in a stream—water follows the steepest slope or the path of least resistance. The emergent local concentration of force and power means that, at least occasionally, force will be sufficient to scour channels. This provides positive feedback reinforcement to hydraulic selection, and channels tend to persist. Steeper, larger, and hydraulically smoother channels are favored over smaller, shallower, rougher, and more gently sloping ones. I may (or may not) have been the first to describe this as hydraulic selection (a specific form of gradient selection; Phillips, 2010), but I was hardly the first to recognize this as a selection phenomenon. Twidale (2004: 170) characterized channel formation as a process of natural selection, and Nanson and Huang (2008) outlined a principle of “survival of the most stable” with respect to channel configurations. The "constructal law" of Bejan (2007) expresses basically the same idea as gradient selection: “For a flow system to persist in time (to survive) it must evolve in such a way that it provides easier and easier access to the currents that flow through it." Bejan (2007) provides examples of natural phenomena that illustrate this type of development,

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including wedge-shaped turbulent shear layers, jets and plumes, the frequency of vortex shedding, B́enard convection in fluids and fluid-saturated porous media, dendritic solidification, and coalescence of solid parcels suspended in a flow. Michael Woldenberg derived these principles for channelized water flows in 1969 (Woldenberg, 1969). Lin (2010) critically reviewed literature on the prevalence of preferential flow paths of water at a wide range of scales, and the tendency for these to either be controlled by, or to evolve into, morphological features in soils and landscapes. By coupling traditional path-of-least-resistance reasoning to persistence of preferred flow paths, constructal-type behavior emerges, though Lin (2010) pointed out that constructal theory does not address the fact that flow patterns in natural hydrologic and pedologic systems are often strongly influenced by factors other than the flow itself. Surface water flow is an archetypal example of gradient selection, but numerous other examples exist, including subsurface flows and associated processes. Water moves along paths of least resistance such as rock joints, macropores, faunaul burrows, zones of higher hydraulic conductivity, etc. Weathering, dissolution, erosion, and sediment transport within these features enhances these pathways at the expense of other nearby zones. This can occur even in homogeneous materials due to unstable growth of minor initial variations in moisture content. This results in unstable wetting fronts, fingered flow, and other preferential flow phenomena, which are manifested not only at the event scale, but often influence regolith and landform development (Dekker and Ritsema, 1994; Liu et al., 1994; Phillips et al., 1996; Ritsema et al., 1998; DiCarlo et al., 1999; Lin, 2010). The work of Heckmann and Schwangert (2013) indicates comparable phenomena at work on other hillslope processes, such as landslides and mass wasting.

The way water forms fingers in homogeneous sandy soils. Images are produced by passing light through sand and converting the different intensities to different colors (black = low moisture content; red = saturation) (http://soilandwater.bee.cornell.edu/Research/pfweb/educators/intro/fingerflow.htm) Over time scales several orders of magnitude faster, air flows follow the “topography” described by isobars, which determine pressure gradients. Wind velocities are higher and air movement is greater where pressure gradients are greater, and vice-versa. Positive feedbacks also occur in these phenomena, as observed in the strengthening of high and low pressure systems, but these run their course in a matter of hours or days.

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Resistance selection Gradient selection as described above can be thought of as positive selective processes, dominated by less resistant paths of flow or change, and positive feedback, which reinforces changes associated with evolving flux pathways. Resistance selection, by contrast, is a negative form of selection dominated by resistant or repellent features, resulting in the persistence of more resistant forms or features. The principle of resistance selection simply states that more resistant features (relative to applied forces, or more generally, drivers of change) are selected for preservation, while less resistant components are preferentially lost or modified. Resistance selection is closely related to, and in some cases simply an inverse way of describing or conceptualizing, gradient selection. Both reflect the effects of variations in the ratio of force (or other drivers of change) to resistance. In some landscapes, however, the resistant residuals--for example karst towers, or the ridgetops in dissected plateaus-rather than the preferred flow paths are the most obvious landscape characteristic. For our purposes, however, resistance selection is considered to be a subset of gradient selection. Natural selection The most famous notion of selection in nature is, of course, the principle of biological evolution by means of natural selection, originally articulated by Charles Darwin and Alfred Russel Wallace. Because of this, some object to the use of selection terminology in other scientific contexts. The use of the term “selection” here in gradient and resistance selection is not meant to imply any analogy between mass flux and landscape change phenomena with organisms. In some cases biota are critical components of the latter, and in some instances organic metaphors may be helpful, but please recognize that differences between, e.g., stream channels or soils and organisms in no way invalidates the application of selection concepts to the former. That is, I ask you to concede that use of the word “selection” does not have to be restricted to biological and organic phenomena. In fact, I searched for a different word, but my thesaurus yielded only “choice,” which carries teleological or anthropomorphic baggage we don’t want. In common with biological natural selection, gradient and resistance selection are emergent properties, arising from tendencies for certain configurations to persist, and for others to decline. I claim no other similarities. Canalization and contingency Once gradient or resistance selection has caused a particular configuration or pathway to be favored, positive feedbacks often reinforce it, as described earlier. This in turn often leads to a type of historical contingency called canalization. Once a channel is scoured or a canal constructed, this profoundly effects (and constrains) future water flows. The idea of canalization is that historical contingencies—for example evolutionary pathways, or development of structural relationships—direct future developments. Canalization originally appeared in evolutionary biology with respect to the notion of genetic



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canalization enhancing stability (Waddington, 1942), but the concept was subsequently broadened. Levchenko and Starobogatov (1997) discussed canalization with respect to biological evolution. The previous development of biological systems prohibits some trajectories, and preferentially favors others. More specifically, they argue that the biosphere canalizes evolution independently of abiotic factors. Levchenko (1999) provided more detailed examples based on energy flows in evolutionary dynamics. Could this be a more direct link between gradient selection and biological selection? -------------------------Bejan, A., 2007. Constructal theory of pattern formation. Hydrology and Earth System Sciences 11, 753–768. Dekker, L.W., Ritsema, C.J., 1994. Fingered flow: the creator of sand columns in dune and beach sands. Earth Surface Processes and Landforms 19, 153–164. DiCarlo, D.A., Bauters, T.W.J., Darnault, C.J.G., Steenhuis, T.S., Parlange, J.-Y., 1999. Lateral expansion of preferential flow paths in sands. Water Resources Research 35, 427–434. Heckmann, T., Schwanghart, W., 2013. Geomorphic coupling and sediment connectivity in an alpine catchment - exploring sediment cascades using graph theory. Geomorphology, 182, 89-103. Levchenko, V.F., 1999. Evolution of life as improvement of management by energy flows. International Journal of Computing Anticipatory Systems 5, 199-220. Levchenko, V.F., Starobogatov, Y.I., 1997. Ecological crises as ordinary evolutionary events canalised by the biosphere. International Journal of Computing Anticipatory Systems 1, 105-117. Lin, H., 2010. Linking principles of soil formation and flow regimes. Journal of Hydrology 393, 3–19. Liu, Y., Steenhuis, T.S., Parlange, Y.-S., 1994. Formation and persistence of fingered flow fields in coarse grained soils under different moisture contents. Journal of Hydrology 159, 187–195. Nanson, G.C., Huang, H.Q., 2008. Least action principle, equilibrium states, iterative adjustment and the stability of alluvial channels. Earth Surface Processes and Landforms 33, 923–942. Phillips, J.D. 2010. The job of the river. Earth Surface Processes and Landforms 35: 305313.



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Phillips, J.D., Perry, D., Carey, K., Garbee, A.R., Stein, D., Morde, M.B., Sheehy, J. 1996. Deterministic uncertainty and complex pedogenesis in some Pleistocene dune soils. Geoderma 73: 147-164. Ritsema, C.J., Dekker, L.W., Nieber, J.L., Steenhuis, T.S., 1998. Modeling and field evidence of finger formation and finger recurrence in a water repellent sandy soil. Water Resources Research 34, 555–567. Twidale, C.R., 2004. River patterns and their meaning. Earth-Science Reviews 67, 159– 218. Waddington, C.H., 1942. Canalization of development and the inheritance of acquired characteristics. Nature 150, 563-565. Woldenberg, M.J., 1969. Spatial order in fluvial systems: Horton's laws derived from mixed hexagonal hierarchies of drainage basin areas. Geological Society of America Bulletin 80, 97–112.



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REDUCING REDUCTIONISM Posted 5 January 2016

In many of my writings I advocate an alternative to reductionist approaches to science. By alternative, I mean a complementary, different way of doing things, not a replacement for reductionism. Many excellent reviews of scientific approaches, viewpoints, and methodological stances exist by historians, philosophers and sociologists of science, and by scientists themselves. I do not intend to review or critique these various approaches here. Further, I have no intent to deny the value or necessity of reductionist science. The crux of my argument is that a reductionist approach, by itself, is inadequate or incomplete for understanding Earth. The American Heritage Dictionary defines reductionism as an attempt or tendency to explain a complex set of facts, entities, phenomena, or structures by another, simpler set, and provides a quote from John Holland: For the last 400 years science has advanced by reductionism ... The idea is that you could understand the world, all of nature, by examining smaller and smaller pieces of it. When assembled, the small pieces would explain the whole. We can distinguish between the practice of reductionist science, pursuing understanding and meaning using the atomistic tools of reductionism, versus a stronger philosophical/theoretical stance. The latter holds that complex systems or ideas can always be reduced to a set of simpler, more fundamental components, and that fundamental or first principles always reside at the smallest level. A belief that reductionism is the primary and preferred path to scientific explanation is by no means rare among scientists, but is less prevalent than a simple acceptance of reductionism as a valid methodology or tool. For instance, the population of biologists using reductionist methods is much larger than the population of biologists who believe that all biological phenomena can ultimately be explained by mechanisms operating at the molecular (or smaller) level. I am arguing, then, not against reductionism as a valid and useful methodology. In fact, I would argue that it is, and always will be, necessary. I do contend that reductionism by itself can never fully explain Earth surface systems, that reductionism is often not be best approach for some problems, and that where alternatives exists, reductionist methods and interpretation are not necessarily superior or preferable. Why not? Multiple Scale Causality In the experimental and laboratory sciences, observed patterns and processes can sometimes be conceptualized straightforwardly as macroscale manifestations of microscale processes. In field-based sciences, however, it is more typical that observed patterns or system states are attributable to multiple processes and controls. Further, those multiple controls operate at multiple spatial and temporal scales--both larger and smaller than the scale of observation.

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Landforms are a good example. They are created by a number of processes acting at molecular and granular scales (e.g., biological and chemical weathering, erosion, and sediment transport), by factors operating over much broader scales (climate, tectonics), and by controls such as geological structure, which may operate at scales ranging from granular to continental. This notion of multiple scale causality (MSC) not only recognizes multiple processes and controls acting at a range of scales, but also recognizes (in contrast to a strict reductionist approach) that the relevant ‘‘first principles’’ may operate at levels other than the smallest microscales. Even among Earth and environmental scientists acknowledgement of MSC is not universal, and some emphasize causality at single, particular scales. However, such emphasis does not necessarily imply the rejection of causality at other levels. The evolutionary ecologist, for instance, does not necessarily deny the importance of microscale processes even as she focuses on macroscale evolutionary phenomena. Similarly, the dynamic climatologist, concerned chiefly with atmospheric physics, is unlikely to reject the relevance of synoptic climatology. Recognition of MSC is often implicit: though particular causal agents and scales may be emphasized, causality at other scales is at least indirectly acknowledged. Accordingly, despite the tradition of reductionism, in the field-based sciences claims of causality residing entirely at a given scale are rare. Ideally, we directly confront MSC, by deliberate attempts to minimize effects at some scales and maximize effects of others, or by isolating scale-contingent causes and explanations. Even when MSC is explicitly recognized, we often seek causal explanation or representation (cartographic, mathematical, conceptual, epistemological, or otherwise) that can accommodate the vast range of potentially relevant scales. Such representations can be reductionist, as in attempts to understand climate on the basis of the fundamental equations of motion or landscape evolution in terms of microscale process mechanics. But reductionists have not cornered the market for attempts at seamless cross-scale

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representations. Certain complex systems theories have been proposed as covering laws or metaprinciples for geomorphology, biogeography, biodiversity, and evolution; or even nature in general. On the other hand, some (including myself) maintain that fundamental changes in controls over process-response relationships occur as spatial and temporal scales change, and that unitary, seamless explanation across scales is not only difficult, but fundamentally impossible or unfeasible. One approach to MSC is via study design. Some are specifically geared toward minimizing—ideally, eliminating—the influence of all but one or a handful of controls or variables. In other cases, investigators simply narrow the focus to minimize variability in broader-scale factors or expand the focus in the sometimes realistic hope that local-scale variations will cancel each other out or be obscured by broader scale factors. Another strategy for coping with MSC involves analytical methods to disentangle the explanatory contributions of variables operating at a range of scales, or at least isolate the variance contribution of a particular scale of interest. Eco- and geoscientists sometimes cope with MSC, especially in practical applications, by acknowledging that variability is influenced by multiple sources and is manifested at a variety of scales, and then seeking to identify the dominant or critical scales in the context of a particular problem. Geostatistical analysis, for example, is often aimed at identifying the area or distance over which spatial variables are dependent or independent. Other approaches seek to determine the resolution or scale at which variance of the phenomenon of interest is minimized—e.g., determination of representative elementary areas and volumes in hydrology. An explicit approach to MSC that has been applied in ecology, biogeography, geomorphology, and other fields is hierarchy theory. The fundamental idea is that a hierarchy of spatial scales is identified. In this hierarchy, at any given level of i, there exist controls or influences operating at adjacent larger and smaller scales, i -1, and i + 1, which may be observed at, and are relevant to, level i. Processes at levels further removed, at either broader or finer scales, operate either too rapidly and finely, or too slowly and broadly, to be observed or manifested at i. Ideally, these hierarchies allow functional relationships to be transferred up and down the scale hierarchy. Other, analytically explicit, efforts to engage MSC, include local forms of spatial analysis and entropy decomposition. These generally deal with the issue of assessing the contribution of global and local factors (defined broadly) to the structure and/or variability of spatial patterns. Independently of formalisms such as hierarchy theory and MSC, defining geographical regions—ecoregions, biomes, agricultural regions, physiographic provinces, etc.--is based on attempts to separate groups of local-scale (within regions) and broader-scale (between regions) controls. In other words, regional delineations attempt to minimize within-region (and maximize between-region) variations.



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Fundamentally, however, the basic point is this: Real-world Earth surface systems are characterized by multiple scale causality, reductionist approaches alone are not sufficient for fully understanding them. The other side of the coin is that methods that deal with the most atomistic scales—reductionist methods—are necessary for understanding them. All Other Things Are Never Equal Basic two-way, cause-and-effect relationships in the sciences are generally based on the principle of ceteris peribus—all other things being equal (AOTBE). For example, AOTBE, increasing CO2 concentrations in the atmosphere have a fertilization effect, increasing plant productivity. Of course, plants ability to use the additional CO2 depends on their having adequate supplies of sunlight, water, and nutrients. As greater atmospheric CO2 influences climatic variables that in turn influence plant growth, and the activities that change atmospheric chemistry also modify nutrients and other edaphic factors, all other things are not equal. In ESS, all other things are never equal. Why? Because everything is connected to everything else. This maxim has been cited as the First Law of Ecology, the First Law of Environmental science, and the First Law of Geography. Everything is not literally related to everything else in the sense of direct, tracable, causal, significant links between any two objects, processes, systems, or places. But everything is related to everything else in the sense that Earth system are characterized by multiple, interrelated components and controls. Our world is seen and analyzed by geoscientists and ecoscientists in terms of maps, webs, matrices, flow charts, multiple equation systems, and other representations of multiple, interconnected, mutually adjusting objects or phenomena. Places and environmental systems are understood as the outcome of multiple, interrelated forcings and controls and complex histories. All humans (or other species), for instance, share common genetic ancestors, and all of us play our roles in the carbon/oxygen cycle with every breath we take, alongside other processes and entities, animate and inanimate. Additionally, long-range interrelationships and teleconnections are typical of ESS: Sea surface temperature anomalies in the equatorial Pacific or the North Atlantic have climatic and oceanographic repercussions around the globe. Commodity price changes in China or Chicago affect land use, soil erosion, and nutrient budgets on fields and paddocks around the world. Coastal landforms in British Columbia or Sumatra may be linked to long-ago, far-away, undersea earthquakes or landslides that triggered tsunamis. Bailey, Bones & Butterflies More than a decade ago, writing along these lines, I spoke of the vast web of connections using the metaphors of Bailey effects, bones, and butterfly effects. The “George Bailey Effect” is named after the protagonist of Frank Capra’s 1946 film ‘‘It’s a Wonderful Life,’’ based on a story by Philip Van Doren Stern. George Bailey suffers a run of terrible luck and self-doubt. In despair and attempting suicide in the belief that his life has not been productive or worthwhile, Bailey is rescued by a guardian angel. He is shown an



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alternative reality; what his community would be like if he had never been born—in the story, a much more desperate, depraved, and depressing place, because the good but relatively routine acts of kindness, bravery and common sense George Bailey committed had not happened. The message: apparently minor actions of an individual may have ripple effects and chain reactions that produce dramatically different outcomes. Bailey Effects are based on conditionality. Whether or not George saves his brother Harry from falling through the ice as a child, determines whether a loaded troop ship is, or is not, saved by the heroic actions of Harry Bailey later on. The conditionality type of contingent connectedness is common in nature. For example, if the surface cold air layer in a winter inversion is shallow rather than deep, then the precipitation is freezing rain instead of sleet. The freezing rain may in turn result in an ice storm, the effects of which then influence forest species composition (Lafon et al. 1999). Many ESS are conditional with respect to the occurrence, frequency, and timing of factors such as storms, fire, sea surface temperature anomalies, or inherited effects of landform singularities.

Complex interrelationships between key geomorphological factors downstream of a dam (Figure 2 from Phillips, J.D. 2003. Toledo Bend Reservoir and geomorphic response in the lower Sabine River. River Research and Applications 19: 137-159).



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The ‘‘bones’’ connections indicate physical, mechanistic, causal interconnectedness, based on the metaphor of a skeleton: the foot bone’s connected to the ankle bone, the ankle bone’s connected to the leg bone, etc. “Bones” connectivity may be long and complicated, but the chain of interrelationships is clear. This type of relatedness is also common in ESS, and arises mainly due to the presence of numerous components or degrees of freedom, hooked together in long, interconnected chains of causality. Visual evidence is apparent in any detailed diagram of the carbon or nitrogen cycle, a watershed system, an ecosytem, marine food web, etc.

Nitrogen cycle in soils--simplified! (http://www.extension.umn.edu). “Butterflies” refers to the famous butterfly effect metaphor from chaos theory. A presentation at a scientific meeting by Edward Lorenz in 1972 was titled ‘‘Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?’’ This was based on Lorenz’s now-famous early work on chaotic dynamics in the atmosphere, where he showed the fundamental equations of motion to be highly sensitive to initial conditions. This sensitivity and chaotic divergence over time gave rise to the notion that a tiny change at a given place and time—e.g., the flap of a butterfly’s wing—might lead to disproportionately large results, far away. Thus ‘‘the butterfly effect,’’ one of the favorite metaphors of chaos and complexity theory. As an aside, as far as I know, no one has ever followed up on my suggestion that “an interesting exercise in trivial geography and chaos theory would be to catalog citations of the butterfly effect, with a map of the different locations of the butterfly and the storm.” Many environmental systems are, or can be, deterministically chaotic. This means that the effects of minor variations in initial conditions, or of small perturbations, are exponentially magnified over time. Butterfly effects imply elaborations of both Bailey Effects and ‘‘bones’’ connections. Chaos (= dynamical instability) produces the possibility of Bailey effects disproportionately large and long-lived relative to the initial

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stimulus, and causal webs more subtle and complex than leg bones connected to knee bones connected to thigh bones suggests. Butterfly effects also make possible historical connectedness via ‘‘memory,’’ in the sense that the effects of variations or disturbances that are no longer detectable are manifest in the current state of the system.





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THE DOMINANT CONTROLS CONCEPT Posted 21 March 2016

Axioms of the Dominant Controls Concept The “dominant processes concept” in hydrological modeling argues, in essence, that there are too many potentially relevant hydrological processes to feasibly or efficiently include them all in a single model. However, in any given watershed a handful of processes dominate the hydrological response, and an effective model may be developed based on those. This argues for adapting models to local conditions and needs, rather than attempting to construct “one size fits all” models designed to handle any watershed, anytime, anywhere. Grayson & Blöschl (2000a) are credited with initiating the DPC; I encountered it through Bellie Sivakumar (2004, 2008). In an article on avulsions a few years ago, I argued that the dominant processes concept can be generalized to a “dominant controls concept” (DCC) in geomorphology, and probably Earth and environmental sciences more generally. The DCC implies that, while there may exist a very large number of factors and processes that can influence a given phenomenon (in that case, avulsions) in any given geomorphic system some will be irrelevant and others of comparatively negligible influence, leaving a few dominant controls to deal with.

According to the DCC, structure & dynamics of this New Zealand shore platform—like any other Earth surface system—is dominated by a few key controls.

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Not long after, I wrote down some axioms of the DCC/DPC, intending to eventually flesh them out in an article. That looks increasingly unlikely, but as I think they might be useful, I am laying them out here. The DCC is intended to guide not only predictive modeling, but also any effort to understand, analyze, or represent Earth Surface systems. The axioms: 1. For any Earth surface system (ESS) a few controls account for the great majority of variation. This includes variations in system response and outputs, and also variability of key components and fluxes within the system. My experience with geomorphic, hydrologic, and pedologic systems indicates that the number of dominant controls is generally a half-dozen or less. Note that this refers to specific ESS, such as the Shawnee Run watershed, Kentucky, or the Otter Creek marshes, North Carolina—not to generic ESS such as fluviokarst watersheds or brackish marshes in general. 2. ESS responses to change are dominated by a few critical or essential behaviors or phenomenologies. This is a corollary to axiom 1. 3. Dominant controls & responses vary: •Geographically between ESS •Synoptically within ESS •With spatial & temporal scales. This axiom recognizes the fundamental, irreducible role of geographical, historical, and scale contingency in ESS—which was the primary motivation for developing the DPC and later the DCC in the first place. 4. Appropriate models or representations include dominant controls and exclude or minimize others. This follows fairly obviously from 1 and 2 above. But since axioms are self-evident truths that require no proof, that’s OK . . . . 5. Appropriate models or representations thus vary geographically, temporally, and with scale. To me, and I think many others, both personal and collective experience support numbers 1 and 3 above as axiomatic, and if those are accepted, 2, 4, 5 must also be true. --------------------------------------------------------------------------- Grayson RB, Blöschl G. 2000a. Summary of pattern comparison and concluding remarks. In Spatial Patterns in Catchment Hydrology: Observations and Modeling, Grayson RB, Blöschl G (eds). Cambridge University Press: Cambridge, UK; 355–367. Sivakumar, B., 2004. Dominant processes concept in hydrology: moving forward. Hydrological Processes 18, 234–235. Sivakumar, B., 2008. Dominant processes concept, model simplification and classification framework in catchment hydrology. Stochastic Environmental Research and Risk Assessment 22, 737–748.



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TIPPING POINTS & OTHER METAPHORS Posted 6 April 2016

From 2010 through the first two-thirds of 2015, at least 211 scientific articles with the term “tipping point” and 109 with “regime shift” in the title were published (according to the Web of Science database, as of 23 November 2015). These span a broad range of science, technology, and engineering, but the geosciences are well represented. In recent years the concept of tipping points in the global environment related to climate change, regime shifts, ecosystem collapse and other phenomena has garnered a great deal of both scientific and public attention. “Tipping point” is often used in public (and sometimes scientific) discourse to refer to impending doom, or at least major environmental changes with uncertain and potentially negative impacts. However, tipping points are not necessarily associated with negative impacts on humans. Nor are they inevitably associated with direct or indirect human agency, as Earth history is marked by numerous tipping points and regime shifts. Tipping points are a type of threshold phenomenon. In systems theory (and Earth and environmental sciences) a threshold is a boundary separating different behaviors or states (qualitatively different conditions) of a system. Tipping points are thresholds (but not all thresholds are tipping points) that result in rapid or abrupt state changes relative to the time scale under consideration. Regime shifts are threshold-driven state changes that may be gradual or abrupt. Regime shifts are a subset of thresholds that are generally understood to apply at a broad landscape or ecosystem scale. Understanding scientific concepts is heavily dependent on the metaphors we use to visualize, analyze, and communicate them. Recognizing that tipping point itself is a metaphor, I got to wondering about what other metaphors might be useful in exploring these abrupt shifts in Earth systems. Balance The tipping point notion is at least implicitly based on a metaphor analogous to a balance or scale. In fluvial geomorphology this analogy is often used with respect to aggradation or degradation with a diagram or conceptual model like the one below:



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(www.fgmorph.com/fg_2_9.php) My interpretation of this, by the way, is quite different from the traditional one. Some assume (on the basis of precious little evidence) that fluvial systems seek to equalize sediment supply and transport capacity and keep the scale balanced. My view is that the scale is usually tipped to one side or the other, but increases or decreases in sediment supply or transport capacity can cause it to tip in the other direction. Seesaw Since the idea of a balance as a weighing device is indeed to achieve balance, I prefer the seesaw metaphor for the situation described above. This is a pretty good metaphor for a fairly common situation in Earth systems, where an unstable equilibrium separates two alternative stable states. The Cliff This analogy is firmly related to a doom-based view of tipping points. The TP occurs when the system is pushed to the edge, and a precipitous decline. Dominoes In this case a line of dominoes is the analog of a complex, interlinked environmental system with many components. Tipping one domino (a local tipping point) will cause some or all of the others to fall as well (a global or regional TP).



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Any of these metaphors, and no doubt others, have their advantages in communicating certain ideas and analyzing certain problems. The dominoes analogy seems to me to have particular promise. Suppose each domino is designated xi, with i = 1, 2, . . . , n total dominoes. The dominoes are all assumed to be adjacent to at least one other domino. We further assume that a domino tipping either left or right will knock over exactly one other domino (except for the first and last, x1and xn, which could tip in one direction without disturbing other dominoes). This characterizes (or caricatures) a situation where a local tipping point anywhere in the chain of dominoes has a unique effect. The two end dominoes are all-ornothing: If they tip one way, nothing else happens. The other way, and every other domino goes down. For all the others, the number of dominoes that fall also depend on which way an individual tips. Using a simple left-to-right description the line of dominoes (i.e., x1 is the first, left-most and xn the last, right-most domino), the number of fallen dominoes is n – i + 1 if it tips right, and dominoes xi through xn fall. A left tip, and dominoes x1 through xi go over, with the total number tipped equal to i. Any domino is highly sensitive to any left-tips for of any dominoes to its right, and insensitive to any left-tips to its left, and vice-versa. We can also look at probabilities. If every piece has an equal probability of being disturbed, and falling left or right is equally probable, then p(xi, R) = p(xi, L) = 1/2n—that is, the probability of any given domino tipping left or right is 1/2n. The probability of a tipover at domino j, given the falling or domino i, is as follows: p(xj:xi) = 0 if j > i and i tips left.

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p(xj:xi) = 0 if j < i and i tips right. p(xj:xi) = 1 if j < i and i tips left. p(xj:xi) = 1 if j < i and i tips right. This seems to me to convey the idea that tipping points, like pretty much everything else in the geosciences are difficult to generalize about without geographical and historical context!



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GEOMORPHOLOGICAL FLICKERING Posted 26 April 2016

As environmental systems approach critical thresholds or tipping points, they may experience increased variability, which in the literature on critical environmental state transitions has been referred to as “flickering” (e.g., Lenton, 2011; Scheffer et al., 2012; Dakos et al., 2013). This is primarily the case for noisy, stochastic systems, which is not the case for many lab and mathematical models, but is emphatically so for most real-world environmental systems. As Dakos et al. (2013) put it: Most work on generic early warning signals for critical transitions focuses on indicators of the phenomenon of critical slowing down that precedes a range of catastrophic bifurcation points. However, in highly stochastic environments, systems will tend to shift to alternative basins of attraction already far from such bifurcation points. In fact, strong perturbations (noise) may cause the system to “flicker” between the basins of attraction of the system’s alternative states. As a result, under such noisy conditions, critical slowing down is not relevant, and one would expect its related generic leading indicators to fail, signaling an impending transition. My Kentucky colleague Daehyun Kim has led the way in expanding this sort of reasoning and analysis to the spatial domain (Kim and Arthur, 2014; Kim and Shin, 2016). In a geomorphology context, it is not always intuitively clear to me how or why increased variability would occur near a tipping point, at least in terms of geomorphic phenomena. Thus I set out to work out how this could happen, both for my own benefit and so that I could hopefully explain it to students. I see five general ways this could happen. For each I give a brief explanation, an intuitive example of the phenomenon, and a geomorphological example. 1. Approach to Threshold. As a geomorphic system approaches critical threshold, occasional large events or disturbances occasionally exceed the threshold, increasing variability. This is closest to the standard explanations in the critical transitions literature. Intuitive example: A bucket of water getting near full. Large events (water into the bucket) will cause spillover, but for a while between these events evaporation or maybe a slow leak bring the level back down again until the bucket is so full (or infill events so frequent) that it overflows constantly. Until then, measurements of water level in the bucket or outflow from it will be highly variable. Geomorphological example: Sedimentary basin approaching capacity. Autocompaction or minor erosion slows approach to complete filling. Sediment bypasses the basin during large events, but small events add more material.



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2. Increased disturbance frequency. Force-resistance thresholds may be exceeded due to increased force magnitudes, declining resistance, or a combination. They may also be exceeded due to increased frequency of disturbances (“force events”) that tax the ability of the system to recover to full resistance between events. Thus resistance is more variable and effects become more variable. Intuitive example: Vegetation community disturbed often, but irregularly, so that succession can never proceed to a late successional community. Thus measurements of, e.g., biomass, NPP, community composition, richness, etc. will be highly variable. Geomorphological example: A hillslope (e.g., on a lakeshore) that is undercut frequently enough to trigger new mass movements, so that the slope never becomes restabilized. Thu, slope morphology becomes more variable. 3. Rate changes in linked processes. Thresholds may be related to relative rates of linked processes (e.g., glacial accumulation vs. ablation), and could be exceeded due to rate changes in either or both processes. When the linked processes are both changing, but at an unsteady pace or variable rates, the location of the threshold itself may fluctuate, creating variability. Intuitive example: A bank balance where income is increasing (or decreasing), but at variable pace, and so are expenses. Thus the break-even threshold varies from month to month in an unpredictable way. Geomorphological example: Both weathering and erosion rates are increased or decreased (e.g., by climate change). However, the relative rates & pace of change vary. Thus the threshold between weathering- and transport-limitation varies, along with denudation rates. 4. Approach to storage capacity. As storage capacity is approached, additional storage may become more unsteady as the trap or sink becomes less effective. Thus, for instance, small events may continue to add to storage, while large events result in a net decrease in storage. Intuitive example: Moisture storage in a potted plant. When it is kept near saturation, large events (waterings) mostly splash out or drain right through, while small pourings are absorbed. Measurements of moisture output thus appear to begin “flickering” as the occasional splashouts and drainouts are superimposed on the more regular evapotranspiration losses. Geomorphological example: A nebkha or field-edge dune begins to approach the maximum height dictated by vegetation. Small wind events add to sand storage in the dune, but large events result in net erosion. Thus measurements of dune sand storage or flux increase in variability.

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5. Approach to use or transformation capacity. Phenomenology similar to item 4 above, but in this case availability of a resource approaches maximum capacity of use by the system. Small inputs can still be fully utilized, but larger inputs cannot. Intuitive example: Background nutrient levels in an aquatic ecosystem approach the maximum potential rate of uptake. Thus small inputs can be still be processed, but large inputs cannot be fully processed and have toxic effects. Measurements of, e.g., nutrient concentrations & fluxes, dissolved oxygen, community composition, etc. will become more variable. Geomorphological example: Relatively slow inputs of deposited sediment to soil can be transformed by pedogenesis and incorporated into the soil, resulting in a cumulic (upbuilding) soil. More rapid inputs that exceed the rate of pedogenic transformation bury the soil, and pedogenesis is initiated in the deposited material, resulting in buried soil profiles or bisequal (or multisequal) soils. If sediment inputs are near the threshold, then small changes in deposition or pedogenesis rates (or small local spatial variations) can lead to divergent pedogenesis. ________________________________________________________________ Dakos, V., et al., 2013. Flickering as an early warning signal. Theoretical Ecology 6: 309-317. Kim, D., Arthur, M.A. 2014, Changes in community structure and species–landform relationship after repeated fire disturbance in an oak-dominated temperate forest. Ecological Research 29: 661–671. Kim, D., Shin, Y.H., 2016. Spatial autocorrelation potentially indicates the degree of changes in the predictive power of environmental factors for plant diversity. Ecological Indicators 60: 1130-1141. Lenton, T.M., 2011. Early warning of climate tipping points. Nature Climate Change 1: 201-209. Scheffer, M., et al. 2012. Anticipating critical transitions. Science 338: 344-348.



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THE PERPETUAL QUEST FOR EFFICIENCY AND STABILITY IN EARTH SURFACE SYSTEMS Posted 24 August 2016

Fluxes of mass and energy in hydrological and geomorphological processes, and in environmental systems in general, preferentially select and reinforce the most efficient pathways. In doing so, they also tend to selectively preserve the most stable and resistant materials and structures, and remove the weaker and unstable ones. This suggests that Earth surface systems should generally evolve toward more efficient flux paths and networks, and a prevalence of stable and resistant forms. The purpose of this essay is to explore why the attractor condition of maximum efficiency and stability is not fully attained. Numerous theories, hypotheses, and conceptual frameworks exist in geosciences that predict or seek to explain the development of flow paths in Earth surface systems (ESS). These include so-called “extremal” principles and the least action principle in hydrology and fluvial geomorphology, principles of preferential flow in hydrology, constructal theory, and various optimality principles in geophysics and ecology. Extremal principles related to hydraulic geometry (interrelationships between fluvial channels and the flows within them) are consistent with respect to their fundamental hydrological and geomorphological implications, and Huang and Nanson (2000; Nanson and Huang, 2008; 2016) argue that all can be subsumed under a more general principle of least action (i.e., geomorphic work is performed with the minimum possible energy). Phillips (2010) generalized this even further, contending that water flows will be more prevalent along more efficient rather than less efficient pathways, and that emergent feedbacks cause these paths to be preferentially preserved and enhanced.

Guadalupe River, Texas

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The least action principle (LAP) in physics states that the motion between any two points in a conservative dynamical system is such that the action has a minimum value with respect to all paths between the points that correspond to the same energy--in essence, that nature always finds the most efficient path. The general applicability and utility of the LAP in physics is not contested, though debate persists as whether the LAP is a true physical law. In ESS, the LAP is manifested by accomplishing work (e.g., fluvial sediment transport, ecosystem productivity, productivity, heat flux in fluids) with as little energy as possible. With a given energy input, conservation laws coupled with maximum efficiency in accomplishing work dictates that energy dissipation via entropy must be maximized (Maximum Entropy Production; MEP). Thus there exists a general consistency among optimality principles based on energy, power, and entropy. This phenomenon also clarifies the superficial contradiction between extrema based on minimization and maximization, as minimization of energy to perform work implies maximization of dissipation and entropy. Note also that work itself is not necessarily minimized; only the energy deployed to perform that work. In stream channels, for instance, extremal principles do not suggest that sediment transport is minimized, but rather minimization of the energy used to accomplish a given amount of transport.

Fluvial transport of glacially-derived silt (rock flour), South Island, New Zealand The concept of preferential flow has been principally associated with soil hydrology and physics, but Uhlenbrook (2006) noted that preferential flow applies to all hydrological phenomena at all scales. Preferential flow may be predetermined or influenced by preexisting structures and spatial variability, but even in homogeneous materials preferential flows develop due to dynamical instabilities, with reinforcement of incipient preferential paths (Liu et al., 1994). Hunt (2016) linked subsurface water and solute flows to nutrient

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uptake and plant growth using critical path analysis and percolation theory, showing that similar phenomenologies exist among these processes. Woldenberg (1969) developed a theory explaining how and why channelized flow systems evolve toward more efficient networks. A similar general principle was formalized by Bejan (2007) as the “constructal law:” “For a flow system to persist in time (to survive) it must evolve in such a way that it provides easier and easier access to the currents that flow through it.” Prevalence of preferential flow at a broad range of scales was reviewed by Lin (2010), who discussed the tendency for these to either develop into, or be controlled by, morphological features in soils and landscapes. Constructal-type behavior arises from traditional path-of-least-resistance reasoning if selection results in persistence of the preferring flow paths, though, contrary to constructal theory, flow patterns in ESS are often strongly influenced by factors other than the flow itself. Next: Gradient & Morphological Selection --------------------------------------------------References: Bejan, A., 2007. Constructal theory of pattern formation. Hydrology and Earth System Sciences 11, 753–768. Huang HQ, Nanson GC (2000) Hydraulic geometry and maximum flow efficiency as products of the principle of least action. Earth Surface Processes and Landforms 25: 1-16. Hunt AG (2016) Spatio-temporal scaling of vegetation growth and soil formation from percolation theory. Vadose Zone Journal 15: DOI: 10.2136/vzj2015.01.0013. Lin, H., 2010. Linking principles of soil formation and flow regimes. Journal of Hydrology 393, 3–19. Liu Y, Steenhuis TS, Parlange J-Y (1994) Formation and persistence of fingered flow fields in coarse-grained soils under different moisture contents. Journal of Hydrology 159: 187-195. Nanson, G. C., & Huang, H. Q. (2008). Least action principle, equilibrium states, iterative adjustment and the stability of alluvial channels. Earth Surface Processes and Landforms, 33(6), 923-942. Nanson, G.C., Huang, H.Q., 2016. A philosophy of rivers: equilibrium states, channel evolution, teleomatics and the least action principle. Geomorphology doi:10.1016/j.geomorph.2016.07.024. Phillips, J.D. 2010. The job of the river. Earth Surface Processes and Landforms 35: 305-313. Uhlenbrook S. 2006. Catchment hydrology--a science in which all processes are preferential. Hydrological Processes 20: 3581-3586. Woldenberg MJ. 1969. Spatial order in fluvial systems:Horton's laws derived from mixed hexagonal hierarchies of drainage basin areas. Geological Society of America Bulletin 80: 97– 112.



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THE PERPETUAL QUEST FOR EFFICIENCY PART 2—GRADIENT & MORPHOLOGICAL SELECTION Posted 24 August 2016

Gradient Selection Preferential flow phenomena are specific cases of what Phillips (2010, 2011) called the principle of gradient selection: the most efficient flux gradients are preferentially utilized, preserved, and replicated. Gradient selection is based on the twofold notion that (1) the most efficient potential flow paths are preferentially selected; and (2) use of or flow along these paths further enhances their efficiency and/or contributes to their preservation. While Phillips (2010) was concerned with hydrologic flows and geomorphic processes, the evolution of preferential flow paths by gradient selection has broader applicability. Selection of more efficient paths is not perfect. This selection sometimes occurs deliberately, even intentionally, as when root growth seeks to optimize access to water and nutrients, or a human (or other large fauna) seeks to find the easiest path through thick brush. In other cases, potential transport paths of varying efficiency are encountered by chance (e.g., by sheet flow on a hillslope, or diffuse infiltration into soil). In both cases the mechanisms for route selection are imperfect, and the “decisions” are highly local—in the examples, neither the animal, root, or flowing water can “see” beyond its immediate surroundings.



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Karst conduits exposed in a pocket valley, central Kentucky The strongest forms of gradient selection involve positive feedback, where use of a pathway enhances its flux efficiency—for instance, when karst fracture flow enlarges the conduit by dissolution, or concentrated surface runoff incises a channel. A weaker version is when flow or use simply does not degrade the efficiency of a route. Sometimes this does happen, as for example when material transported by percolating water results in clogging of pores, or when overuse of an unpaved road or path renders it rutted and muddy.



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Feedback and efficiency selection in surface runoff. The positive feedbacks generally involve creating or enhancing features that are relatively resistant and tend to persist, such as gullies, stream channels, macropores, ground water conduits, and well-trampled footpaths. However, this does not always occur, and may be limited, for example when a conduit enlarges to the point of collapse or a trail becomes severely eroded and more difficult to traverse. Morphological selection In geomorphology there also exists morphological selection via resistance (Phillips, 2011). Weathering and erosion preferentially or more rapidly remove weaker, less resistant, less stable, and more exposed materials and features, thus preferentially preserving more resistant and stable ones. Sometimes positive feedback accelerates resistance selection via gradient selection, as mass and energy fluxes become concentrated along channels and other pathways cut into weaker materials.



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Resistant sandstone, Three Sisters formation, Blue Mountains, New South Wales But this does not always, inexorably lead toward increasing domination by the more resistant forms. In part this is because of the role of disturbances and changing boundary conditions. But we must also consider differential resistance of the same materials to different processes and in different contexts. Quartz sand, for instance is a strong, stable, low-solubility material that is highly resistant to modification by chemical weathering. However, sandy soils or sediments may be highly vulnerable to wind or water erosion, though even that will vary according to topographic settings, hydrological context, and vegetation cover. Similarly, many limestones have a high degree of physical strength and are resistant to mechanical erosion, but are soluble and have low resistance to dissolution. Resistance may also change over time, sometimes due to the opposing forces or processes. Aging, senescence, death and decay of organisms and organic structures decreases resistance over time, for instance, and undercutting of slopes increases their propensity for failure. Erosion and weathering may eventually result in increased surficial resistance due to armoring, crusting or sealing, or case hardening. Rock weathering reduces rock mass strength, but also produces resistant residuals and secondary minerals.



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Turtle-turbation on a sand bar, Sabine River, Texas/Louisiana. Quartz sand is highly resistant to chemical weathering, and more resistant to transport than silt, but otherwise relatively easy to move around by water, wind, and turtles.

Finally, denudational forces are often applied to layered regoliths, soils, and sedimentary rocks, and to materials and surfaces that are heterogeneous in three dimensions. Thus it is not unusual for the removal of weaker materials to expose more resistant ones. Next: Barriers to Continuous Increases in Efficiency -------------------------------------------------------------------References: Phillips, J.D. 2010. The job of the river. Earth Surface Processes and Landforms 35: 305313. Phillips, J.D. 2011. Emergence and pseudo-equilibrium in geomorphology. Geomorphology 132: 319-326.



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THE PERPETUAL QUEST FOR EFFICIENCY PART 3—WHY ISN’T EVERYTHING ALWAYS BECOMING MORE EFFICIENT? Posted 24 August 2016

The principle of gradient selection, along with a variety of “optimality” principles in geomorphology, geophysics, hydrology, and ecology (e.g., Patten, 1995; Fath et al., 2001; Lapenis, 2002; Ozawa et al., 2003; Kleidon et al., 2010; Quijano and Lin, 2014), is in essence a particular case of a broader principle of efficiency selection. Given this common behavior in many types of Earth surface systems, why do we not observe a general global trend toward ever more efficient routes and networks of flows? First, note that gradient and efficiency selection are tendencies that (like natural selection in biological evolution) apply in the aggregate, and not to individual cases. Also recall from part 2 that gradient selection is imperfect even where it operates. Second, the least action principle means that any work is done using the least amount of energy. That means that any excess energy must be dissipated, and this dissipation may directly (or indirectly via its morphological effects) modify the most efficient flow paths. This is best established for turbulent fluid flows, and has been most clearly illustrated by Nanson and Huang (2016) in their work on least action principles in stream channels. A third factor mitigating against steady progress toward maximum efficiency is the highly local nature of gradient selection. The wanderer through the brush cannot see the clearing beyond the immediate field of vision, and by choosing the immediate easiest path may miss it entirely. Thus local variations in resistance can result in paths much different from what would occur to achieve maximum efficiency at the broader scale (see, e.g., Hunt’s (2016) discussion of this phenomenon with respect to subsurface fluxes). This is related to the phenomenon of canalization. The term is used most commonly in its literal sense, with respect to anthropic channelization of river channels or canal construction, or channel incision, and in evolutionary biology. In evolutionary genetics canalization refers to the shaping and constraint of evolutionary pathways by selection (Waddington, 1957). The concept also applies to development of ecosystems and biosphere evolution (Levchenko, 1997). Once local selections are made, in either the evolutionary sense or with respect to flow paths, this constrains or influences future paths. A fourth barrier to attainment of maximum efficiency is the inherent dynamism of the planet and its environmental systems. Boundary conditions are variable due to climate change, tectonics, sea-level change, and other factors, and ESS are more or less constantly adjusting to those changes. Disturbances also modify or destroy flow paths, both accidentally and deliberately (through anthropic impacts and ecosystem engineering).

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Fluviokarst stream, central Kentucky Finally, we must bear in mind that many different forms of matter and energy are in flux in ESS, and many different entities or phenomena may direct or influence those fluxes. Thus, for example, even though in a fluviokarst system water will always locally prefer the most efficient route, the “competition” between surface fluvial and subsurface karst flow paths can result in either or both of the fluvial and ground water networks being globally suboptimal. Beavers or humans seeking to optimize water storage and flux for their own needs may dam streams and deliberately disrupt the optimal hydrologic flow path. And, there are opportunistic legacy effects—joint and fracture patterns in rock, unrelated to flow dynamics, become preferential pathways for root growth, moisture flux, weathering, and dissolution. Similarly, paths selected by burrowing fauna or roots as most efficient for their needs may not be the most efficient for subsequent water flow, and surface flows utilize inherited valleys or channels created by other geomorphic processes. Thus the tendencies toward maximum efficiency are often unrealized, or incompletely realized.



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References: Fath BD, Patten BC, Choi JS (2001) Complementarity of ecological goal functions. Journal of Theoretical Biology 208: 493-506. Hunt AG (2016) Spatio-temporal scaling of vegetation growth and soil formation from percolation theory. Vadose Zone Journal 15: DOI: 10.2136/vzj2015.01.0013. Kleidon A, Malhi Y, Cox PM (2010) Maximum entropy production in environmental and ecological systems. Philosophical Transactions of the Royal Society B 365: 1297-1302. Lapenis AG (2002) Directed evolution of the biosphere: biogeochemical selection or Gaia? Professional Geographer 54: 379-391. Levchenko VF (1999) Evolution of life as improvement of management by energy flows. International Journal of Computing Anticipatory Systems 5: 199-220. Nanson, G.C., Huang, H.Q., 2016. A philosophy of rivers: equilibrium states, channel evolution, teleomatics and the least action principle. Geomorphology doi:10.1016/j.geomorph.2016.07.024. Ozawa H, Ohmura A, Lorenz RD, Pujol T (2003) The second law of thermodynamics and the global climate system: a review of the maximum entropy production principle. Reviews of Geophysics 41: 1018, doi:10.1029/2002RG000113. Patten BC (1995) Network integration of ecological extremal principles: exergy, emergy, power, ascendency, and indirect effects. Ecological Modelling 79: 75-84. Quijano J, Lin H (2014) Entropy in the critical zone: a comprehensive review. Entropy 16: 3482-3536. Waddington CH (1957). The Strategy Of The Genes. George Allen & Unwin.





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PROVIDERS OF PREDICTABILITY Posted 7 January 2017

From my student days onward, the aspects of nature that interested me most were the apparent anomalies--the things that were uncertain and unpredicted; that weren't like they were supposed to be. Nature contains both regular, ordered, predictable aspects, and irregular, disordered and unpredictable facets. As scientists we are taught to focus on the former and eliminate, ignore, or circumvent the latter. But anyone who spends much time in the field knows that our planet is a source of infinite variety and ever-increasing uncertainty (because the more you learn, the more you realize that you don't know). But what always fascinated me was not that (for instance) the soils or streams or eastern North Carolina or central Kentucky fit, and can be predicted by, some broad pattern. It was the fact that you can often auger the ground at two spots less than a meter apart and find completely different soils, or walk or canoe a stream channel and easily find features not explained or predicted by the conventional scientific wisdom. Thus my career has focused on spatial and temporal variability in nature; on sources of irregularity and uncertainty. I have come to view the world primarily through the lenses of spatial variability, historical contingency, nonlinear complexity, etc. So much so, in fact, that I figured it was time to stop and remind myself (and whatever readers of this blog are out there) of the aspects of nature that DO facilitate predictability and create order.

First and foremost is the fact that Earth surface systems are determined by a triad of

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factors: laws, place, and history. While place and history factors are geographically and historically contingent, and have to be evaluated on a case-by-case basis, the law factors are general, if not universal, and to the extent we understand them, we can use them to predict. Second, some phenomena are strongly influenced by predictable cycles. The best known in a geoscience context are those controlled by Earth-sun-moon relationships--diurnal and seasonal cycles and lunar tides. Other celestial mechanics (i.e., Croll-Milankovitch cycles) can also introduce a degree of predictability. Cycles also exist in some biological phenomena (cicadas and other insects; mast production in walnut and other trees, for instance). Teleconnection patterns in ocean-atmosphere interactions such as the El Nino-Southern Oscillation, Madden-Julian Oscillation, Pacific Decadal Oscillation, etc. provide some degree of predictability in climate and oceanographic phenomena, though these effects are still being teased out. Note that while the patterns themselves are not always predictable, their impacts on climate and ocean phenomena can be, as well as subsidiary impacts on, e.g., hydrological, ecological, and geomorphological processes. A fourth source of predictability in Earth systems are circumstances where a single dominant, overriding factor comes into play. A rather obvious example is landscape and topographic change for a site being mined or landscaped for construction. Here the effects and forcings of weather, climate, hydrology, biota, geomorphic processes etc. almost cease to matter--it is the plans and activities of humans that determine the outcome. A meteorological example in the southeastern U.S. is development and strengthening of the Bermuda High. When this feature is in play, the weather is easily predictable (and reliably, nastily hot, humid, hazy, and still).



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Rock weathering--an inexorable, unidirectional, irreversible process. Slowly varying, irreversible, directional processes provide another aspect of predictability. These are basically inevitable trends that proceed irreversibly in only one direction. For example, when rocks are exposed at the surface they began to weather, and continue to do so until they are broken down as far as they can be or something (e.g., deep burial) shuts down the process. General denudation and downwasting is another example--it continues inexorably until complete or the geomorphic clock is reset, even if offset or opposed by uplift. Finally, even where multiple causality is involved and laws, place and history factors are all relevant, sometimes particular combinations of circumstances provide signatures or diagnostics that can predict or explain. This is the basic of synoptic meteorology and climatology, still a basic tool of forecasting. A comparable approach to hydrology and geomorphology has been applied by a number of researchers, often under the name of event-based prediction or explanation.



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OCCAM’S SELECTION Posted 16 January 2017

Generalized Darwinism holds, in essence, that the principles of variation, selection, and retention (preservation) and replication that are the cornerstone of Darwinian biological evolution are applicable to development and evolution of (to exaggerate only slightly) damn near anything. This perspective, most actively debated in evolutionary economics, is detectable (though sometimes without the specific label) in many science and social science fields. Generalized Darwinism has many critics, but most critiques I've seen fall into two categories (to simplify and generalize wildly): (1) a lack of fidelity to biological evolution; or (2) an inability to solve every problem in evolutionary economics, system theory, etc. Those criticisms are accurate, but not valid (in my estimation), as the "generalized" clearly implies a move beyond biological evolution, and no conceptual or analytical framework is ever the answer to everything, even in a relatively small subdiscipline. Like some others, rather than invoke Darwin's name and all the associated baggage, I prefer to point out the importance of selection (of which Darwinian natural selection is one example) in a variety of environmental systems (see these previous posts: A, B, C). The logic I propound (which is hardly unique or original to me) is that (1) variations occur; (2) some variations are more durable, stable, efficient, or otherwise favorable; and (3) those are more likely than others to be preserved, enhanced, and replicated (i.e, selected). Pretty consistent with GD, in other words. So I have no quarrel with GD in terms of its axioms and worldview, though I hardly endorse (or even know about) all its claims or applications. The problem I have is when selection is interpreted as a goal function or a purported law of nature rather than an emergent property. The fact that some phenomenon recurs repeatedly, and can be interpreted in terms of selection, does not mean that nature has adopted this outcome as a goal. Does nature prefer sandstone ridgetops? Maybe, but a simpler explanation is that sandstones are more weathering resistant and physically durable than the other sedimentary rocks they often occur with. Thus they are preferentially preserved (selected) as denudation proceeds, and thus plateaus and tablelands in sedimentary rocks around the world often have sandstone ridgetops.



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My first published arguments along these lines--in the context of ecosystem and biosphere evolution--are here. I subsequently made similar arguments for geomorphic and hydrological systems. Because variations occur and selection happens in all sorts of phenomena, temptation is strong to propose natural laws or goal functions. For example, this paper expresses it as the "persistence principle," which is stated as "nature seeks persistent forms." Mostly the authors are not wrong, but the "nature seeking" part is troublesome. A more accurate, if self-evident, way to put it, is "persistent forms persist." Thus they are more common and last longer than other forms. They are selected for, probabilistically (as almost all selection occurs), and their common occurrence is an emergent property of this selection, not a goal function of Mother Nature. The emergent approach is simpler (and thus preferred by Occam's Razor) than trying to ascertain and explain why rivers, atmospheric energy transfers, biogeochemical cycles, or chemical reactions (for instance), should seek or prefer anything.



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METANARRATIVES, EXTREMAL PRINCIPLES, & ANOTHER REJECTION Posted 14 April 2017

The attached paper (In Defense of Metanarratives:Extremal Principles, Optimality and Selection in Earth Surface Systems) was originally written in early 2015 and revised in April 2015, as an invited paper for a special issue of a geography journal. By mutual agreement with the guest editors, I withdrew the paper after deciding that I was unwilling/unable to satisfy some of the major recommendations of reviewers. The major, but by no means only, issues were that referees and guest editors felt I should more fully address history and philosophy of science issues and parse the definitions of principles, theories, narratives, etc. I felt that I could say what I was trying to say without getting into that stuff, which would have taken a lot of work on my part that would have seriously inhibited my studies on the (to me) far more interesting and important topics of how Earth surface systems actually work. After sitting on it for two years, and publishing bits and pieces of the ideas on optimality and selection in other contexts (but not the metanarratives part) I concluded that I am unlikely to ever resubmit it anywhere. But I did put a lot of work into writing the damn thing, so I am posting it online, for what it is worth. I have not changed it, other than a bit of formatting (embedding figures and tables in the document and an added note or two) and correcting a few errors I missed the first time around.



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In Defense of Metanarratives: Extremal Principles, Optimality and Selection in Earth Surface Systems Jonathan Phillips Tobacco Road Research Team, Department of Geography, University of Kentucky April, 2017 Preface This paper was originally written in early 2015 and revised in April 2015, as an invited paper for a special issue of a geography journal. By mutual agreement with the guest editors, I withdrew the paper after deciding that I was unwilling/unable to satisfy the demands of one reviewer. The major, but by no means only, issues were that the referee and guest editors felt I should more fully address history and philosophy of science issues and parse the definitions of principles, theories, narratives, etc. I felt that I could say what I was trying to say without getting into that stuff, which would have taken a lot of work on my part that would have seriously inhibited my studies on the (to me) far more interesting and important topics of how Earth surface systems actually work. After sitting on it for two years, and publishing bits and pieces of the ideas on optimality and selection in other contexts (but not the metanarratives part) I concluded that I am unlikely to ever resubmit it anywhere. But I did put a lot of work into writing the damn thing, so I am posting it online, for what it is worth. I have not changed it, other than a bit of formatting (embedding figures and tables in the document and an added note or two) and correcting a few errors I missed the first time around.

Abstract Metanarratives are critiqued and even rejected by many geographers and geoscientists. Yet, despite the inescapable role of geographical and historical contingency in physical geography, metanarratives are helpful, perhaps even necessary, in part because equifinality is common in Earth surface systems (ESS). Similarity of forms and patterns implies a possible single underlying cause. However, by definition the similar outcomes of equifinality are not the result of the same underlying processes, indicating that any encompassing construct must be in the form of a metanarrative. An effective metanarrative need not be strictly true, but should be useful in explanation, and its implications subject to empirical verification. Metanarratives should also be simplifying rather than complexifying. An example proposed here is the principle of efficiency selection: the most efficient pathways and modes of mass and energy flux are preferentially preserved and enhanced. This explains and unifies optimality principles proposed for a variety of ESS. Efficiency selection is testable based on observations and simplifying in that it encompasses a number of situations with a single concise proposition. According to the principle of efficiency selection, apparent optimality in ESS is neither teleological nor deterministically inevitable, but rather an emergent property. Keywords: metanarrative, equifinality, extremal principles, optimality, Earth surface systems, efficiency selection



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Introduction Physical geography and geosciences have, sometimes grudgingly, accepted that no matter how much data and detail we achieve, explanation cannot be reduced to universal laws of physics and chemistry. We have also recognized the flaws and hazards of overarching “theories of everything”, and accepted the irreducible geographical and historical contingency in Earth surface systems (ESS). Conversely, there remains a need to synthesize, contextualize, compare, and contrast case studies. Though global laws and generalities can, in combination with local and contingent factors, explain ESS, we need conceptual frameworks that tie together phenomena and patterns, not just process mechanics--that is, we need metanarratives. This paper argues for the utility of metanarratives, via an example based on optimality principles.

Metanarratives A narrative is an account or story of events, experiences, or observations. A metanarrative is, essentially, a narrative about narratives. More complex, specific, and nuanced definitions of metanarrative are deployed in various social science and humanities fields (e.g., Nunning, 2001). Here I use the most general of the two definitions from the Oxford dictionary: an overaching account or interpretation of events and circumstances that provides a pattern or structure for people’s beliefs and gives meaning to their experiences (substitute “conceptual frameworks” and “observations” to make the definition more geoscience-friendly). A narrative about stream channel morphology or ecosystem structure, for example, might be based on principles of energy dissipation. A metanarrative might encompass energy dissipation, and also other narratives/principles based on, e.g., least work, maximum efficiency, minimum entropy, etc. I use the term metanarrative here because I focus on the role and importance of overarching, integrative explanatory or interpretive frameworks. Because these may conceivably take the form of theories, hypotheses, conceptual models, principles, laws, or paradigms, metanarrative is used here as a broad, general term that may include all of these forms. This paper will not parse the definitions of theories, paradigms, etc., or seek to classify explanatory frameworks--partly due to space limitations, but also because such categories are overlapping and contested (note: this was my attempt to bypass what the editors wanted me to do. They didn't buy it). Nor does space allow exploration of the philosophical implications touched upon here. Following a general discussion of the role of metanarratives, the paper turns to the phenomenon of equifinality, a key motivation for seeking overarching explanations. It then proposes a metanarrative to encompass the phenomenological equifinality associated with a broad class of “optimality” theories, and proceeds to a discussion of the characteristics of effective metanarratives in geoscience. Some scientists have perpetually sought all-encompassing theories that explain, well, everything. “Everything” may be confined to a domain, such as landscape evolution; sometimes the goal is to explain all of nature. Geoscientists have become cynical with respect to theories of everything, partly due to recognition that explanation in the fieldbased sciences has irreducible elements of geographical and historical contingency, and thus of local idiosyncrasy (see, e.g., Turner et al., 2013; Wilcock et al., 2013; Furlani and Ninfo, 2015; Cullum et al., 2016; Van Dyke, 2016). A somewhat jaded view of grand theory also results from the fact that constructs promoted as universally applicable have



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fallen well short, be they domain-specific theories such as the cycle of erosion or plate tectonics, or broader constructs such as self-organized criticality, chaos theory, constructal laws, or catastrophe theory (these notions have not been shown to be incorrect; just incomplete). Because metanarratives include or may resemble grand theories of everything, geoscientists may be skeptical of metanarratives in general. Many social critics and postmodernists are strongly critical of metanarratives. In social sciences and humanities, metanarratives are represented as claiming to be above local or “ordinary” accounts. These metanarratives, typically designated as such by their critics rather than their proponents (Marxist political economy is an often-cited example), are characterized as claiming to capture universal properties of human experience and thereby supposedly superior to more idiosyncratic, grounded accounts. Postmodern social critics have argued for rejection of metanarratives in favor of the local, and acknowledgement of the social and political nature of all narratives. Pednyowsky (2003), for example, shows how critical scholars have constructed a metanarrative of science to contrast with alternative social construction of nature narratives. Ironically, Pednyowsky (2003) also reveals how treating science as a single metanarrative obscures the great variety of scientific practices. Though the term is applied almost exclusively in social sciences and humanities, the concept of metanarrative is applicable to the geosciences. Evolution by means of natural selection, ecological succession, plate tectonics, Gaia theory, Milankovitch cycles, steady-state equilibrium, and others are examples of overarching constructs that have influenced physical geography and can legitimately be termed metanarratives. Succession, for instance, subsumes more specific narratives or theories based on, e.g., facilitation, niche-assembly, and cycles, and is thus a metanarrative by the broad definition used here. Critiques of metanarratives as a class are generally based on: (1) A tendency to obscure or distort important local factors (not necessarily just details); and (2) Failure to be universally applicable within their domains. The first critique is often true, though this does not necessarily invalidate the metanarratives. The second, even when true, is not a good reason (by itself) for rejecting explanations. For example, the conservation laws for energy and mass are universally operable, but even in problems of say, sediment transport or fluid dynamics they do not always explain everything and are not applicable, in a practical sense, to all problems. That does not invalidate the use of the conservation laws. Even sound, useful metanarratives can impede scholarship if used uncritically, and allowed to indeed obscure local factors. However, we cannot possibly observe all ESS, or make sense of them, without some simplifying framework. We must organize a potentially infinite amount of information, but we also need to tell stories, and to leverage what we learn from the cases observed to the many more that we cannot. Some kind of broader construct is required. Metanarratives need not be universal deterministic laws. They may be probabilistic rather than deterministic--not necessarily in a strict statistical sense, but describing likelihoods and tendencies rather than inexorable outcomes. Effective metanarratives need not be reductionist (they do not have to be atomistic or applicable bottom-up); they can operate at any relevant spatial and temporal scale, or a range of scales. And, they should simplify interpretation of case studies.



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One reason that metanarratives are called for is the presence of equifinality, explored below.

Equifinality Equifinality occurs when different processes, environmental controls, or histories lead to similar outcomes. This causal convergence is common in Earth system and geographic phenomena (e.g., Haines-Young and Petch, 1983; Culling, 1987; Beven, 2006; Savenjie, 2001; Schulz et al., 2001; Bunting and Middleton, 2009; Cruslock et al., 2010; Nicholas and Quine, 2010; Paik and Kumar, 2010; Patterson and Hoalst-Pullen, 2011; Tett et al., 2013). Equifinality is both a real-world phenomenon (e.g., a variety of processes in varied settings create vertical texture-contrast soils, Phillips and Lorz, 2008), and a property of some classes of models, whereby models based on different processes, assumptions, or theories produce similar results (c.f. Beven, 2006). In some cases both forms of equifinality exist—for example, channel networks formed in quite different environmental settings with different dominant processes often show topological and statistical similarities nonetheless, and these structures have been successfully reproduced using models based on quite different assumptions (e.g., Abrahams, 1984; Zanardo et al., 2013). Equifinality is directly linked to metanarratives for two reasons. First, the similarity of forms, patterns and behaviors implies the possibility of some common cause. Second, by definition the similar outcomes of equifinality are not the result of the same fundamental processes, indicating that any common cause is best described by an overarching construct that subsumes multiple underlying processes. The role of metanarratives will be further explored below via an exploration of optimality in ESS, one manifestation of phenomenological equifinality in physical geography.

Optimality principles Environmental sciences abound with explanations based on principles positing that development of ESS is governed or characterized by a tendency to maximize or minimize some aspect of energy or mass flux or organizational characteristics. Because these are often thought to enhance the function of ESS by increasing efficiency or stability, and for brevity, these are referred to here as optimal principles. And because they either propose or seek to explain similar phenomena arising in a variety of systems, and may be based on different models or assumptions, optimality-based explanations are an example of equifinality. Case studies and field observations provide empirical support, to varying degrees, for optimality principles discussed here—all, in some senses, “work.” But ESS have no intentionality, and no one has ever explained why an ESS should maximize, minimize, or optimize anything (see, e.g., Phillips, 2011; Quijano and Lin, 2014). Without intent or governing laws, how do we explain the widespread (though hardly universal) success of optimality principles in describing, predicting, and modeling ESS? Is there a potential metanarrative that can explain this? If so, it could not only address the question of why the principles (often) work, but also potentially tie them together in a way that facilitates expansion to new phenomena and new cases.



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ESS are controlled and influenced by a set of general factors—laws, principles, relationships that apply to any air mass, ecosystem, karst landscape, etc., any time, anywhere. They are also controlled and influenced by geographically and historically contingent factors that are not necessarily applicable in all cases, and are sometimes unique. Metanarratives can be a tool for identifying commonalities among ESS influenced by different sets of general factors, or when those global controls are inadequate for explanation. Here a metanarrative is developed that identifies commonalities among optimality principles derived from different mechanistic underpinnings, and for different ESS phenomena. Optimal principles have been proposed in ecology, geomorphology, climatology, and fluid dynamics (see supplemental material). While these use different terminology and methods, and stress different aspects of ESS, many are consistent with each other, if not equivalent. For instance, optimal principles of ecosystem development based on exergy, emergy, power, and ascendency are formally related, and the metrics highly correlated (Patten 1995; Ulanowicz et al. 2006). Fath et al. (2001) and Yen et al. (2014) showed that in the context of ecological networks, most optimal principles are mutually consistent. Ozawa et al. (2003) showed the equivalency of optimal principles related to atmospheric heat flux, global climate, fluid convection, and turbulent dissipation. Extremal principles related to hydraulic geometry (interrelationships between fluvial channels and the flows within them) have been shown to be consistent with respect to their fundamental hydrological and geomorphological implications, and Huang and Nanson (2000; Nanson and Huang, 2008) indicate that all can be subsumed under a more general principle of least action (i.e., geomorphic work is performed with the minimum possible energy). The least action principle (LAP) in physics states that the motion between any two points in a conservative dynamical system is such that the action has a minimum value with respect to all paths between the points that correspond to the same energy. In essence, the LAP suggests that nature always finds the most efficient path. In ESS, this means accomplishing work (e.g., productivity in ecosystems, heat flux in fluids, sediment transport in rivers) with as little energy as possible (Levchenko, 1999; Huang and Nanson, 2000). For a given input of energy, maximum efficiency in accomplishing work, coupled with conservation laws, dictates maximization of energy dissipation via entropy (Maximum Entropy Production; MEP)—thus the general consistency of optimality principles based on energy, power, and entropy. Confusion sometimes arises as to exactly what is being optimized—extremal principles applied to fluvial channels do not, for instance, propose that sediment transport is minimized, but rather that the energy used per unit of sediment transport is minimized. Some optimal principles (see supplemental material) are directly linked to the LAP, by proposing maximum efficiency in energy use and/or mass fluxes. Others are either directly based on MEP, or propose maximum energy throughput, which also implies MEP (Fath et al. 2001; Ozawa et al. 2003; Dewar, 2005; Kleidon et al. 2010). A third group is based on preferential utilization, preservation, or replication of the most efficient flux gradients, and is thus based on a principle of gradient selection (GS; Phillips 2010a; 2011). Table 1 lists optimal principles according to their framing with respect to LAP, MEP, or GS.



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Table 1. Optimal principles (see supplemental material) linked to overarching principles of least action (LAP), maximum entropy production (MEP), and gradient selection (GS). Source references are given in the supplemental material.

Principle Maximum energy efficiency Maximum power Maximum generation of available potential energy Minimum stream power Maximum energy cycling Minimum entropy exchange Maximum exergy storage; maximum emergy Maximum energy residence time Maximum flow efficiency Increasing ascendency Maximum energy dissipation Minimum empower/exergy ratio Increasing energy flow Least action Maximum energy flux Biogeochemical selection Maximum entropy production (MEP) MEP MEP Gradient selection MEP Maximum power MEP

Source Kropotkin 1902 Lotka 1922; Odum 1991 Lorenz 1960

Principle LAP MEP MEP

e.g., Brebner & Wilson 1967; Yang 1971 Morowitz 1968 Paltridge, 1975 Jørgensen & Mejer 1979; Odum 1991; Jørgensen 1997 Cheslak & Lamarra 1981 e.g. Davies & Sutherland, 1980; Yang et al., 1981; Jia 1990 Ulanowicz 1986; 1997 Schneider & Kay 1994 Bastianoni & Marchettini 1997 Levchenko, 1999; Levchenko et al., 2012 Huang & Nanson 2000; Nanson & Huang 2008 Eagleson 2002 Lapenis, 2002 Ozawa et al., 2003; Dewar 2005

LAP, GS

Dewar 2010 Kleidon et al. 2010 Phillips 2010; 2011 del Jesus et al. 2012 Kleidon et al. 2013 Lin, 2015

MEP MEP GS MEP MEP MEP

MEP MEP MEP MEP LAP, GS LAP MEP GS MEP; LAP LAP, GS MEP GS MEP

Equivalence of optimal principles does not imply redundancy. With different domains of origin and application, and various metrics and criteria, most are of interest independently of their commonalities. The key question is why these principles seem to work. ESS cannot plan or desire any particular pathway or outcome. At least three possible explanations exist for this phenomenological equifinality that do not require goal functions: Pathways and outcomes associated with the LAP, MEP, and GS are more probable than other outcomes; positive feedbacks reinforce LAP/MEP/GS trends; and/or features and evolutionary pathways associated with LAP/MEP/GS are preferentially



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preserved and enhanced by selection processes. As shown below, all three apply and are interrelated.

Probability and Feedback Many extremal principles are based on optimal outcomes as tendencies or probabilities and make no claims of determinism or inevitability (e.g., Smith 1986; Lapenis 2002; Nanson and Huang, 2008; Dewar 2010; Kleidon et al., 2010; 2013; Lin 2015). The mechanisms, however, are generally based on feedbacks that reinforce some outcomes and/or inhibit others. Optimal behavior is related to feedbacks when they either reinforce optimal phenomena that happen to occur, or mitigate against suboptimal trends. For example, when more rapid or efficient material use or cycling in ecological systems confers a survival or reproductive advantage, this positively reinforces the trend toward maximizing cycling rates (e.g. Kropotkin 1902; Lapenis 2002; del Jesus et al. 2012). Ozawa et al. (2003) proposed feedback mechanisms as an explanation for MEP in fluid dynamics and climate. With respect to morphologies resulting from turbulent flows, Nanson and Huang (2008) considered feedbacks of slope in river channels. These feedback effects, through a series of iterative adjustments, nudge the fluvial system toward a steady state defined by transport capacity ≈ imposed water and sediment load. These configurations are more stable (and thus optimal in a loose sense) than alternatives, and thus tend to persist (Nanson and Huang 2008). Nanson and Huang (2008) used the term “survival of the most stable” to describe the iterative adjustments, and others have also invoked a process of hydraulic selection (more efficient flow paths are preferentially formed and enhanced) in the formation of fluvial channels (Leopold 1994; Twidale 2004; Phillips 2010a). Ulanowicz (1997) presented similar arguments (i.e., stability is positively related to persistence) for ecological systems. To the extent optimal patterns are based on probability or feedback considerations, both imply selection in the sense that the optimal patterns are more likely be preserved, reinforced, or replicated. With respect to how optimal pathways and configurations arise, probability implies feedbacks and feedbacks imply selection.

Selection Feedbacks increase the probability of optimal configurations, and these are manifest via selection. In ecological systems, for example, all that is necessary to produce a trend toward maximum mass and energy fluxes or entropy is that ecological systems become saturated (all niches become occupied; all resource space is ultimately used), and that higher productivity rates confer advantages to the organisms involved and are thus selected for (Lapenis 2002; Phillips, 2008). The general logic applies to other hypotheses regarding ecological systems in Table 1. These hypotheses can all be related to the notion that phenomena that increase, e.g., energy or mass flux or storage, or otherwise nudge the system toward the optimum involve advantages in survival, competitive abilities, mutualism, or reproduction. Gradient Selection. The principle of gradient selection in geomorphology and hydrology is simply that the most efficient flow paths are dominant, and that these tend to persist and grow over time (Phillips 2011). For the specific case of stream channels, Huang and



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Nanson (2000; 2007; Nanson and Huang 2008; Huang et al. 2014) showed that the principle of maximum flow efficiency is a product of the LAP. Phillips (2010a) proposed a similar but more general principle of hydraulic selection, and Smith (2010) invoked selection of the most efficient pathways, and positive reinforcement of these, in his theory for the emergence of channelized drainage. Using surface water flow (Q) as an example, standard flow resistance relationships (in this case the D’Arcy-Weisbach equation) give Q = A (8g R S/f )0.5

(1)

where A is cross-sectional area (product of width w and mean depth d), g is the gravity constant, R is hydraulic radius, S is energy grade slope, and f a friction factor. The relative flow of two competing flow paths 1, 2 is given by Q1/Q2 = (w1/w2) (d1/d2) (R1/R2)0.5 (S1/S2)0.5(f1/f2)-0.5

(2)

For sheet flows, and most channel flows where w >> d, hydraulic radius is approximated by mean depth (R ≈ d). Substituting d for R in eq. (1), Q ∝ (w1, d1.5, S0.5, f-0.5).

(3)

Thus pathways allowing for deeper flow are the single most important influence on the efficiency of alternative pathways. An increase in S, however, or an opportunity (e.g., via a river cutoff or avulsion) to access a steeper path with no decrease in Q, d, or velocity (V) (or decreases that are proportionately less than the increased slope) results in increased mean boundary shear stress (τ), cross-sectional stream power (Ω), and stream power per unit weight of water (ψ):

τ=γRS≈γdS

(4)

Ω=γQS

(5)

ψ=VS

(6)

where γ is specific gravity of water. The increased shear stress and stream power may result in channel erosion, thus increasing A, R, d. This creates a more efficient flow path (⇑Q). This may have further positive feedbacks to shear stress and stream power, up to the point where water availability and structural limits on slope gradients or channel size become limiting. This sequence, visualized in Figure 1, is consistent with the LAP, and maximizes energy dissipation and entropy. The underlying process mechanisms, including adjustments of channel and flow geometry, result in a configuration that happens to produce characteristics of the system conforming to optimality. Optimality conditions are the result, not the cause, of the of the system characteristics. The same phenomena occur when an established channel is able to access a more efficient route, as in the case of an avulsion or cutoff (Figure 2).



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Figure 1. Relationships between positive feedback, gradient selection, and energy dissipation in surface runoff.

Figure 2. Relationships between gradient selection, positive feedback, and energy dissipation for the case of stream flow access to a steeper flow path.



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Similar reasoning applies to subsurface flows, or a combination of potential surface and subsurface flow paths, at least if subsurface fluxes exhibit positive feedback whereby favored flow paths are self-enhancing due to effects of saturation on hydraulic conductivity, pipe or solutional erosion, or other factors. A number of studies indicate that this is indeed often the case (e.g. Liu et al. 1994; Price 1994; Gabrovsek and Dreybrodt 2001; Filipponi et al. 2009).

Principle of Efficiency Selection The metanarrative emerging from this analysis is efficiency selection: Pathways and configurations that are most efficient in obtaining and using energy are selected for, in the sense of being more likely to occur and persist. Like biological selection, efficiency selection is not deterministic -- not all of the fittest individuals or more efficient features survive, grow, and replicate, but they do so in greater proportion than less fit or efficient ones. Thus there is a tendency over time toward configurations with greater efficiency. These are not dictated by any law; nor do they require any goal function within an ESS; they are emergent phenomena. Specific examples of more efficient configurations being more likely to occur and/or be preserved or perpetuated are given by, e.g., Ozawa et al. (2003) for climate, Lapenis (2002) for biosphere development, Smith (2010) for surface hydrologic flows, and Hunt (2016) for subsurface flows. In general, however, more efficient configurations are selected for because (1) they are more likely to occur in the first place; (2) positive feedbacks often reinforce them; and (3) in many cases they are more stable and thus more likely to persist.

In Defense of Metanarratives Useful metanarratives need not be reductionist, or “grand theories of everything.” Good metanarratives simplify disparate phenomena, and are subject to empirical evaluation. These criteria are discussed below, both in general terms and with respect to efficiency selection.

Truth and pragmatism Acceptance of metanarratives because they work is consistent with Baker’s (1996) view that geoscientists are philosophically pragmatic. Despite occasional nods to logical positivism, critical realism, etc., and common philosophical apathy, geoscientists in general are receptive to whatever approaches achieve research goals. A model or metanarrative does not have to be strictly true to be pragmatically useful. Many effective metanarratives, even more so than scientific theories in general, are based on probabilities and tendencies (as is efficiency selection), and are thereby tacitly acknowledged to occasionally be false. Moreover, a metanarrative may be useful even when known to be false. For instance, the assumption of steady-state soil thickness often employed in soil and landscape evolution models and underlying some dating methods is often violated, and is not an accurate representation of soil and regolith processes and evolution. However, the fact that steady-state thickness is not a truth statement about soils or regolith may have little or no effect on the efficacy and utility of some models and methods based on the assumption (Phillips, 2010b).



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Similarly, subsurface water flux is often satisfactorily described and modeled based on assumptions of flow through a porous medium described by D’Arcy’s law. This is done even in some cases where flow is known to be non-Darcian and characterized by preferential flowpaths, because in many cases the preferential pathways are numerous and scattered enough so that, in the aggregate, moisture fluxes approximate flow through a porous medium (e.g., Weyman, 1973; Beven and Germann, 1982). The status of Darcian flow and steady-state soil thickness as metanarratives can be debated, but they show that an explanatory construct need not be strictly correct to be useful.

Testability and evaluation Efficiency selection, like Darwinian natural selection, is not testable for individual cases. As selection is non-deterministic, falsification in individual cases does not falsify the principle. Because testing must involve numerous cases, selection principles apply in the aggregate, not to individual ESS. However, a candidate metanarrative must be shown to be true (with respect to its implications for ESS) in at least some cases, and an accepted one should be verified in the majority of the cases empirically examined. It should also meet the criterion of parsimony (Occam’s Razor); it must be simpler than competing narratives that also conform to empirical observations. Thus, while a physical geography metanarrative need not be experimentally falsifiable, its implications about ESS (as opposed to its internal assumptions) do have to be ground-truthed.

Simplification W.M. Davis’s cycle of erosion was a dominant metanarrative in geology and physical geography from the late 19th through the mid-20th century. This construct was an effective metanarrative for a number of reasons, one of which was that it simplified interpretations of landscapes and landforms. The supplanting of the Cycle by other paradigms is a well-known story in geomorphology (Chorley et al., 1973; Orme, 2007), but it can be argued that one reason for its fall from grace was that it was complexified, at least with respect to explanation of individual landscapes. When the original cyclic theory could not be applied to, e.g., karst or arid landscapes, new versions of the cycle were proposed. When key assumptions such as episodic uplift followed by long tectonic quiescence were questioned or refuted, the cycle could have been simplified as a construct applying to specific situations of dominantly fluvially-eroded landscapes where uplift is followed by a period of tectonic stability hold. In such cases, Davis’ original model indeed accurately describes landscape evolution. However, adherents of the cycle instead developed more complicated stories to attempt to fit field evidence into the cyclic theory. The steady-state and Darcian flow concepts mentioned earlier have in common that they are convenient fictions, but also that they are simplifications. This is a hallmark of a good metanarrative. The Cycle of Erosion fell out of favor because the way it was deployed ultimately complicated rather than simplified the study of landscape evolution. Other metanarratives based on comparing rates or intensity of “competing” processes (uplift vs. denudation; force vs. resistance; etc.) simplified things (Orme, 2007). A counter example is the soil-landscape paradigm or so-called “clorpt” model, describing geographical variations in soils as the result of the combined influences of climate (cl), biota or organisms (o), topography or relief (r), geology or parent material (p), and time (t). Thus Soil = f(cl, o, r, p, t). This approach has also been generalized to ESS of all



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types (Johnson and Hole, 1994; Huggett, 1995). The soil factor framework comes originally from Dokuchaev (1883); the “clorpt” form was popularized by Jenny (1941). Though the factorial model has been critiqued, the general soil-landscape paradigm remains the chief metanarrative underlying pedology, soil geography, and practical soil surveying and mapping. One reason for the longstanding vitality of this metanarrative is that it simplifies soil geography. The basic premise is quite simple: soils are products of the environment. Second, it provides a handy tool; a checklist of environmental factors. Third, it is general enough (and also acknowledges the possibility of locally important environmental controls) to accommodate observed soils without having to complicate the concept or models based on it. On account of these traits, it cannot be falsified, only (in)validated based on its utility in explaining soils. The mass and energy flux dynamics underlying many of the optimality principles proposed for ESS are sometimes quite complex, and differ between, say, fluid convection, biogeochemical cycles, and sediment transport. However, a simplifying construct such as efficiency selection is able to explain and unify the optimality notions.

Goal functions and teleology (Note: reviews were also particularly critical of this section). An appropriate geoscience metanarrative should not imply intentionality of nonliving entities or the necessity of an external designer. With respect to optimality, for instance, it has never been clear why ESS would maximize or minimize any particular quantity or flux. This form of teleological implication (noting that teleology is broadly and variously defined and does not always imply intentionality or a guiding hand) are one reason that an increasing number of physical geographers are skeptical of metanarratives based on “balance of nature” ideas whereby ESS are supposed to seek some form of balance or equilibrium (e.g., Gibson and Brown, 1985; Perry, 2002; Nanson and Huang, 2008; Smith, 2010). These explanations become much more attractive when they can be framed in terms of, e.g., emergent behavior rather than purported goals of environmental systems, as emergence is independent of any teleological implications and is simpler than postulating goal functions. Rather than a universal physical or geographical principle dictating optimality, efficiency selection is a simplifying metanarrative that identifies a common phenomenology that is emergent and probabilistic rather than deterministic. Higher probabilities of optimal behavior are associated with positive feedbacks, and these optimal developmental pathways are manifest via selection, whereby the more efficient structures, relationships, and interactions are more likely to be preserved and replicated than other possibilities. Therefore optimal-like behavior in ESS does not require, or necessarily imply, any goal functions. It also does not require that LAP, MEP, or GS have status as deterministic laws (Kleidon et al., 2010; 2013). Rather, all that is required is that the maximization or minimization involved increases the likelihood of survival and replication of the responsible entity. Optimality in ESS is therefore neither teleological, nor deterministically inevitable. Rather, it is an emergent property arising from selection.



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Concluding comments Metanarratives are useful, but they need not be “theories of everything,” atomistic, reductionist, or teleological. Effective metanarratives need not even be strictly true, though they must either reveal or reflect empirically verifiable truths, or serve a pragmatic role in doing so. Useful metanarratives in geosciences must be empirically verifiable, and lead to simplification rather than complexification of interpretations. The principle of efficiency selection is proposed as an example of a useful metanarrative, and suggests that others based on emergent properties may also be useful. References Abrahams AD (1984) Channel networks: a geomorphological perspective. Water Resources Research 20: 161-168. Beven K (2006) A manifesto for the equifinality thesis. Journal of Hydrology 320: 18-36. Beven K, Germann P (1982) Macropores and water flow in soils. Water Resources Research 18: 13111325. Bunting MJ, Middleton R (2009) Equifinality and uncertainty in the interpretation of pollen data: the Multiple Scenario Approach to reconstruction of past vegetation mosaics. The Holocene 19: 799-803. Chorley RJ, Beckinsale RP, Dunn AJ (1973). The History of the Study of Landforms or the Development of Geomorphology. Vol. 2. The Life and Work of William Morris Davis. Methuen, London, 874 p. Cruslock EM, Naylor LA, Foote YL, Swantesson JOH (2010) Geomorphologic equifinality: A comparison between shore platforms in Hoga Kusten and Faro, Sweden and the Vale of Glamorgan, South Wales, UK. Geomorphology 114: 78-88. Culling WEH (1987) Equifinality: modern approaches to dynamical systems and their potential for geographical thought. Transactions of the Institute of British Geographers 12: 52–72. Cullum C, Rogers KH, Brierley G, Witkowski ETF (2016). Ecological classification and mapping for landscape management and science: Foundations for the description of patterns and processes. Progress in Physical Geography 40: 38-65. Dewar R.C. (2005) Maximum entropy production and non-equilibrium statistical mechanics. In Kleidon, A., Lorenz, R.D. (eds), Non-Equilibrium Thermodynamics and the Production of Entropy. Understanding Complex Systems. Berlin: Springer, p. 41-55. Dokuchaev VV (1883) Russian Chernozem. Selected Works of V.V. Dokuchaev, Vol. 1, p. 14–419. Moscow, 1948. Israel Program for Scientific Translations Ltd. (for USDA-NSF), S. Monson, Jerusalem, 1967. (Translated from Russian into English by N. Kaner). Fath BD, Patten BC, Choi JS (2001) Complementarity of ecological goal functions. Journal of Theoretical Biology 208: 493-506. Filipponi M, Jeannin P-Y, Tacher L. (2009) Evidence of inception horizons in karst conduit networks. Geomorphology 106: 86-99. Furlani S, Ninfo A (2015) Is the present the key to the future? Earth-Science Reviews 142: 38-46. Gabrovsek F, Dreybrodt W (2001) A model of the early evolution of karst aquifers in limestone in the dimensions of length and depth. Journal of Hydrology 240: 206-224. Gibson CWD, Brown VK (1985) Plant succession: theory and applications. Progress in Physical Geography 9: 473-493.



158

Haines-Young RH, Petch JR (1983) Multiple working hypotheses—equifinality and the study of landforms. Transactions of the Institute of British Geographers 8: 458-466 Huang HQ, Deng C, Nanson GC, Fan B, Liu X, Liu T, Ma Y (2014) A test of equilibrium theory and a demonstration of its practical application for predicting the morphodynamics of the Yangtze River. Earth Surface Processes and Landforms 39: 669-675. Huang HQ, Nanson GC (2000) Hydraulic geometry and maximum flow efficiency as products of the principle of least action. Earth Surface Processes and Landforms 25: 1-16. Huang HQ, Nanson GC (2007) Why some alluvial rivers develop an anabranching pattern. Water Resources Research 43: W07441, doi:10.1029/2006WR005223. Huggett RJ (1995) Geoecology. Routledge, London. Hunt AG (2016) Spatio-temporal scaling of vegetation growth and soil formation from percolation theory. Vadose Zone Journal 15: DOI: 10.2136/vzj2015.01.0013. Jenny HA (1941) The Factors of Soil Formation. McGraw-Hill, New York. Jesus M del, Foti R, Rinaldo A, Rodriguez-Iturbe I (2012) Maximum entropy production, carbon assimilation, and the spatial organization of vegetation in river basins. Proceedings National Academy of Sciences (USA) 109: 20837-20841. Johnson DL, Hole FD (1994) Soil formation theory: a summary of its principal impacts on geography, geomorphology, soil-geomorphology, Quaternary geology, and paleopedology. In Factors of Soil Formation: A Fiftieth Anniversary Retrospective. Soil Science Society of America Special Publication 33, p. 111-126. Jørgensen SE (1997) Integration of Ecosystem Theories: A Pattern (2

nd

ed). Kluwer: Dordrecht.

Kleidon A, Malhi Y, Cox PM (2010) Maximum entropy production in environmental and ecological systems. Philosophical Transactions of the Royal Society B 365: 1297-1302. Kleidon A, Zehe E, Ehret U, Scherer U (2013) Thermodynamics, maximum power, and the dynamics of preferential river flow structures at the continental scale. Hydrology and Earth System Sciences 17: 225-251. Kropotkin PA (1902) Mutual Aid: A Factor of Evolution (ed. P. Avrich). New York University Press. Lapenis AG (2002) Directed evolution of the biosphere: biogeochemical selection or Gaia? Professional Geographer 54: 379-391. Levchenko VF (1999) Evolution of life as improvement of management by energy flows. International Journal of Computing Anticipatory Systems 5: 199-220. Lin H (2015) Themodynamic entropy fluxes reflect ecosystem characteristics and succession. Ecological Modelling 298: 75-86. Liu Y, Steenhuis TS, Parlange J-Y (1994) Formation and persistence of fingered flow fields in coarsegrained soils under different moisture contents. Journal of Hydrology 159: 187-195. Nanson GC, Huang HQ (2008) Least action principle, equilibrium states, iterative adjustment and the stability of alluvial channels. Earth Surface Processes and Landforms 33: 923-942. Nicholas AP, Quine TA (2010) Quantitative assessment of landform equifinality and palaeoenvironmental reconstruction using geomorphic models. Geomorphology 121: 167-183. Nunning A (2001) Metanarration as a gap in narrative theory: Definition, typology and outline of a practical history of metanarrative narrator's commentaries. AAA-Arbeiten Aus Anglistik und Amerikanistik 26: 125164. Orme AR (2007) The rise and fall of the Davisian cycle of erosion: Prelude, fugue, coda, and sequel. Physical Geography 28: 474-506.



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Ozawa H, Ohmura A, Lorenz RD, Pujol T (2003) The second law of thermodynamics and the global climate system: a review of the maximum entropy production principle. Reviews of Geophysics 41: 1018, doi:10.1029/2002RG000113. Paik K, Kumar P (2010) Optimality approaches to describe characteristic fluvial patterns on landscapes. Philosophical Transactions of the Royal Society B 365: 1387-1395. Patterson MW, Hoalst-Pullen N (2011) Dynamic equifinality: the case of south-central Chile’s evolving forest landscape. Applied Geography 31: 641-649. Patten BC (1995) Network integration of ecological extremal principles: exergy, emergy, power, ascendency, and indirect effects. Ecological Modelling 79: 75-84. Pedynowski D (2003) Science(s)—which, when and whose? Probing the metanarrative of scientific knowledge in the social construction of nature. Progress in Human Geography 27: 735-752. Perry GLW (2002) Landscapes, space and equilibrium: shifting viewpoints. Progress in Physical Geography 26: 339-359. Phillips JD (2008) Goal functions in ecosystem and biosphere evolution. Progress in Physical Geography 32: 51-64. Phillips JD (2010a) The job of the river. Earth Surface Processes and Landforms 35: 305-313. Phillips JD (2010b) The convenient fiction of steady-state soil thickness. Geoderma 156: 389-398. Phillips JD (2011) Emergence and pseudo-equilibrium in geomorphology. Geomorphology 132: 319-326. Phillips JD, Lorz C (2008) Origins and implications of soil layering. Earth-Science Reviews 89: 144-155. Price AG (1994) Measurement and variability of physical properties and soil water distribution in a forest podzol. Journal of Hydrology 161: 347-364. Quijano J, Lin H (2014) Entropy in the critical zone: a comprehensive review. Entropy 16: 3482-3536. Savenjie HHG (2001) Equifinality: a blessing in disguise? Hydrological Processes 15: 2835-2838. Schulz K, Jarvis A, Beven K, Soegaard H (2001) The predictive uncertainty of land surface fluxes in response to increasing ambient carbon dioxide. Journal of Climate 14: 2551-2562. Smith CH (1986) A contribution to the geographic interpretation of biological change. Acta Biotheoretica 35: 229-278. Smith TR (2010) A theory for the emergence of channelized drainage. Journal of Geophysical ResearchEarth Surface 115: F02023, doi:10.1029/2008FJ001114. Tett SFB, Mineter MJ, Cartis C, Rowlands DJ, Liu P (2013) Can Top-of-Atmosphere Radiation Measurements Constrain Climate Predictions? Part I: Tuning. Journal of Climate 26: 9348-9366. Turner MG, Donato DC, Romme WH (2013) Consequences of spatial heterogeneity for ecosystem services in changing forest landscapes: priorities for future research. Landscape Ecology 28: 1081-1097. Ulanowicz RE (1997) Ecology, the Ascendent Perspective. Columbia University Press, New York. Ulanowicz RE, Jørgensen SE, Fath BD (2006) Exergy, information and aggradation: An ecosystems reconciliation. Ecological Modelling 198: 520-524. Van Dyke C (2016) Nature's complex flume - Using a diagnostic state-and-transition framework to understand post-restoration channel adjustment of the Clark Fork River, Montana. Geomorphology 254: 115.



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Yen JD, Paganin DM, Thomson JR, MacNally R (2014) Thermodynamic extremization principles and their relevance to ecology. Austral Ecology 39: 619-632. Weyman DR (1973) Measurements of the downslope flow of water in a soil. Journal of Hydrology 20: 267288. Wilcock D, Brierley G, Howitt R (2013) Ethnogeomorphology. Progress in Physical Geography 37: 573-600. Zanardo S, Zaliapin I, Foufoula-Georgiou E (2013). Are American rivers Tokunaga self-similar? New results on fluvial network topology and its climatic dependence. Journal of Geophysical Research-Earth Surface 18: 166-183, doi:10.1029/2012JF002392

SUPPLEMENTAL MATERIAL Table S1. Examples of extremal (optimal) principles in Earth surface systems.

Principle

Domain

Summary

Maximum energy efficiency

Biological systems

Evolution selects for most energy efficient combination of organisms Systems organize to maximize energy throughput

Maximum power

Maximum generation of available potential energy Minimum stream power

Focuses on mutual aid in evolution rather than competition Ecological Equivalent to systems maximization of energy throughflow Atmosphere Atmosphere heat flux Equivalent to operates to maximize maximum rate of potential entropy energy production production Fluvial channels Channels adjust so as Phrased in to transport various forms sediment with minimum possible expenditure of work

Maximum energy cycling

Biological systems

Minimum entropy exchange

Climate

Maximum exergy storage; maximum emergy

Ecological systems

Maximum energy residence time

Ecological systems



Comments

Systems organize to maximize mass & energy cycling Ocean-atmosphere heat flux minimizes entropy exchange with external environment Systems maximize storage of useful energy (emergy)

Systems organize to maximize energy residence time

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Equivalent to maximum entropy export to external environment Accumulation of mass & energy; exergy = maximum possible useful work Equivalent to maximum exergy storage &

Source Kropotkin 1902

Lotka 1922; Odum 1991 Lorenz 1960

e.g., Brebner & Wilson 1967; Yang 1971; reviews: Griffiths 1984; Paik & Kumar, 2010 Morowitz 1968 Paltridge, 1975

Jørgensen & Mejer 1979; Odum 1991; Jørgensen 1997 Cheslak & Lamarra 1981

Maximum flow efficiency

Fluvial channels Channels adjust to maximize flow efficiency & minimize energy expenditure

Increasing ascendency

Ecosystems

Maximum energy dissipation

Biological systems

Minimize empower/exergy ratio

Ecological systems

Increasing energy flow

Biosphere evolution

Least action

Fluvial channels Channels tend to adjust so as to transport sediment with the minimum possible work Vegetation Natural selection favors maximum energy flux Biosphere, Selection favors ecosystems faster & more efficient energy & nutrient cycling Fluid At steady state, convection convection maximizes heat flux and thus entropy export Turbulent flows At steady state, entropy export maximized by turbulent energy dissipation

Maximum energy flux Biogeochemical selection Maximum entropy production (MEP)

MEP



Ecosystem development characterized by increasing ascendency Systems increase order at the expense of disorder (entropy) in surrounding systems Efficiency enhanced by maximizing empower relative to exergy Biosphere has evolved by maximizing energy flow

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maximum emergy Phrased in various forms

Ascendency = f(total mass/energy flux, specificity of each flow) Generally equivalent to maximum entropy production Empower = rate of emergy acquisition; Self-organizing mechanisms promote maximum energy efficiency Consistent with maximum flow efficiency

e.g., Davies & Sutherland, 1980; Yang et al., 1981; Jia 1990 reviews: Molnar & Ramirez 1998; Paik & Kumar, 2010 Ulanowicz 1980; 1997

Schneider & Kay 1994

Bastianoni & Marchettini 1997 Levchenko 1999; Levchenko et al. 2012 Huang & Nanson 2000; Nanson & Huang 2008

Maximum Eagleson 2002 equated with optimum Tendency toward Lapenis, 2002 maximum productivity & recycling Ozawa et al., 2003



Ozawa et al., 2003; Dewar 2005

MEP

Plant physiology

MEP

Environmental & ecological systems

Gradient selection

Geomorphic systems

MEP

Vegetation & carbon assimilation

Maximum power

Drainage basin evolution

MEP

Ecological succession

Optimization theories unified by MEP Nonequilibrium thermodynamic systems organized in steady state such that entropy production is maximized Steeper, more efficient flux paths tend to persist & grow Vegetation evolves toward maximum productivity, associated with MEP Maximization of sediment transport to deplete topographic gradients Rate of entropy production increases during succession

“Survival of the likeliest”

Dewar 2010



Kleidon et al. 2010

Also: resistance selection— preferential preservation of more resistant features

Phillips 2010; 2011

del Jesus et al. 2012

Based on Kleidon et al. tendency of ESS 2013 to deplete driving gradients as rapidly as possible Ecosystem net Lin, 2015 energy budget must export entropy

References for Table 1 (text) and Table S1 (above). Bastianoni S, Marchettini N (1997) Emergy/exergy ratio as a measure of the level of organization of systems. Ecological Modelling 99: 33-40.

Brebner A, Wilson KC (1967) Derivation of the regime equations from relationships for pressurized flow by use of the principle of minimum energy-degradation rate. Proceedings, Institute of Civil Engineers 36: 47-62. Cheslak EF, Lamarra VA (1981) The residence time of energy as a measure of ecological organization. In Mitsch WJ, Bossermann RW, Klopatek JM (eds), Energy and Ecological Modeling. Elsevier, Amsterdam, p. 591-600. Davies TRH, Sutherland AJ (1980) Resistance to flow past deformable boundaries. Earth Surface Processes and Landforms 5: 175-179. Dewar RC (2010) Maximum entropy production and plant optimization theories. Philosophical Transactions of the Royal Society B 365: 1429-1435. Eagleson PS (2002) Ecohydrology: Darwinian Expression of Vegetation Form and Function. Cambridge Univ Press, New York. Griffiths GA (1984) Extremal hypotheses for river regime: an illusion of progress. Water Resources Research 20: 113-118.



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Jia Y (1990) Minimum Froude number and the equilibrium of sand bed rivers. Earth Surface Processes and Landforms 15: 199-209. Jørgensen SE, Mejer HF (1979) A holistic approach to ecological modeling. Ecological Modelling 7: 169189. Kleidon A, Malhi Y, Cox PM (2010) Maximum entropy production in environmental and ecological systems. Philosophical Transactions of the Royal Society B 365: 1297-1302. Kleidon A, Zehe E, Ehret U, Scherer U (2013) Thermodynamics, maximum power, and the dynamics of preferential river flow structures at the continental scale. Hydrology and Earth System Sciences 17: 225-251. Kropotkin PA (1902) Mutual Aid: A Factor of Evolution (ed. P. Avrich). New York University Press. Levchenko VF (1999) Evolution of life as improvement of management by energy flows. International Journal of Computing Anticipatory Systems 5: 199-220. Levchenko VF, Kazansky AB, Sabirov MA, Semenova EM (2012). Early biosphere: origin and evolution. In Ishwaran N (ed.), The Biosphere. Rijeka, Croatia: InTech, p. 1-32.

Lorenz EN (1960) Generation of available potential energy and the intensity of the general circulation. In Pfeffer RF (ed), Dynamics of Climate. Permagon, Tarrytown, NY, p. 86-92.

Lotka AJ (1922) Contributions to the energetics of evolution. Proceedings, National Academy of Sciences (USA) 8: 147-151.

Molnar P, Ramirez JA (1998) Energy dissipation theories and optimal characteristics of river networks. Water Resources Research 34: 1809-1818.

Morowitz HJ (1968) Energy Flow in Biology. Biological Organization as a Problem in Thermal Physics. Academic Press, New York.

Odum HT (1991) Emergy and biogeochemical cycles. In Rossi C, Tiezzi E (eds), Ecological Physical Chemistry. Elsevier, Amsterdam, p. 25-65.

Paltridge GW (1975) Global dynamics and climate—a system of minimum entropy exchange. Quarterly Journal of the Royal Meteorological Society 101: 475-484.

Schneider ED, Kay JJ (1994) Life as a manifestation of the second law of thermodynamics. Mathematical and Computer Modeling 19: 25-48. Ulanowicz RE (1980) An hypothesis on the development of natural communities. Journal of Theoretical Biology 85: 223-245.

Yang CT (1971) Potential energy and stream morphology. Water Resources Research 7: 311-322. Yang CT, Song CCS, Woldenberg MJ (1981) Hydraulic geometry and minimum rate of energy dissipation. Water Resources Research 17: 1014-1018.





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See also: Entry title Infinite Sand Gators

Section





How It’s Done

The Perfect Floods of Texas





Rivers and Streams

The Dialectics of Geomorphic Complexity

How It’s Done

Axioms of Geomorphology

Geomorphology







Western Desert, Egypt



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Forest Biogeomorphology

Outer western Carpathians, Czech Republic



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SYCAMORES & HILLSLOPES Posted 4 June 2014

Below are some recent photographs of sycamore trees (Platanus occidentalis) in limestone bedrock at Herrington Lake, Kentucky (about37.78o N, 84.71o W). As you can see, the tree roots and trunks exploit joints in the rock, and accelerate weathering both by physically displacing limestone slabs and widening joints by root growth, and by facilitating biochemical weathering along both live and dead roots.

These are some nice examples of root/bedrock interaction, and the general phenomena are not uncommon, though usually much more difficult to see. The Herrington Lake shores also appear to illustrate a process by which the sycamores accelerate weathering and mass movements (other trees are also involved, but Platanus occidentalis seems to be the most common and effective): 1. Plants colonize the exposed bedrock, with roots exploiting bedrock joints. 2. Tree roots accelerate weathering and loosen joint blocks. 3. While the tree is still alive, root growth envelopes rock fragments and the trees provide a physical barrier to downslope transport. 4. When the tree dies, the rock fragments are released downslope. Field evidence of all these steps is rather obvious and abundant. However, it remains to be demonstrated that the sycamores and related trees result in faster weathering and hillslope degradation than would otherwise occur.



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Herrington Lake was formed by damming (in the 1920s) the Dix River gorge. As a deep, steep-sided, bedrock controlled gorge, the lakeside hillslopes are characterized by exposed bedrock and thin soils. The creation of the lake may have provided an unusual opportunity for Platanus occidentalis colonization. Sycamores are highly shade intolerant and require direct sunlight for establishment. The lake setting provides that. The tree also requires moist conditions (it is quite common on river banks and in bottomlands in the region). The presence of the lake ensures that along the lower slopes, roots penetrating joints will soon encounter water. Finally, sycamore is native to the region (though usually more scattered than is the case along Herrington Lake) along streams, and seeds are dispersed by water (as well as wind). Thus the Dix River and other lake tributaries provided a seed source. Beyond being a good example of root-rock interactions that are important in weathering, pedogenesis, and forest geomorphology, the Herrington Lake sycamores raise two other interesting issues: 1. The role of vegetation in destabilizing rather than stabilizing hillslopes. This has been noted before in other contexts, but usually with respect to hydrological rather than rock weathering effects.



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2. The role of relatively benign (compared to say, deforestation or alien invasive species) ecological change in producing profound geomorphic change.



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Information about Platanus occidentalis was taken from: Sullivan, Janet. 1994. Platanus occidentalis. In: Fire Effects Information System, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fire Sciences Laboratory. Available: http://www.fs.fed.us/database/feis/ [2014, June 4]. My own published work on tree root effects includes: Phillips, J.D., Marion, D.A., Turkington, A.V. 2008. Pedologic and geomorphic impacts of a tornado blowdown event in a mixed pine-hardwood forest. Catena 75: 278-287. Phillips, J.D., Turkington, A.V., Marion, D.A. 2008. Weathering and vegetation effects in early stages of soil formation. Catena 72: 21-28. Phillips, J.D. 2008. Soil system modeling and generation of field hypotheses. Geoderma 145: 419-425. Added August 2017: Pawlik, L., Phillips, J.D., Samonil, P., 2016. Roots, rock, and regolith: biomechanical and biochemical weathering by trees and its impact on hillslopes - A critical literature review. Earth-Science Reviews 159: 142-159. Phillips, J.D., 2016. Biogeomorphology and contingent ecosystem engineering in karst landscapes. Progress in Physical Geography 40: 503-526. DOI: 10.1177/0309133315624641 Phillips, J.D., Marion, D.A.,Yocum, C., Mehlhope, S.H., Olson, J.W. 2015. Geomorphological impacts of a tornado disturbance in a subtropical forest. Catena 125, 111-119. Shouse, M.L., Phillips, J.D., 2016. Soil deepening by trees and the effects of parent material. Geomorphology 269: 1-7.



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TREES BEHAVING BADLY Posted 26 June 2015

I recently submitted a manuscript to Catena, entitled Hillslope Degradation by Trees in Central Kentucky. The reviews came back generally positive, and requesting minor to moderate revisions. I took care of those revisions, and resubmitted. The paper was then sent to a third referee, who pretty thoroughly trashed it. Catena's editor then rejected it (with option to resubmit). However, I am at an age & stage where I have to pick my battles, and this is not one I choose to fight. But I still think the paper has some worthwhile stuff in it, so I have posted it online. You can get it here. The abstract is below, but be forwarned that the third reviewer deemed it "quite poorly written", "hard to follow," and a "mishmash of various statements." I don't think it's that bad . . . .



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GEOMORPHIC IMPACTS OF TREES Posted 11 June 2016

I've just spent a couple of excellent weeks working on a project investigating biogeomorphic impacts of trees, particularly in old-growth forests. With Pavel Samonil (Forest Ecology Dept., Sylva Tarouc Inst., Brno) and his PhD student Pavel Danek, we visited a number of sites in the Czech Republic. There is much to be done--some of the impacts we identified have never been studied before; others have been studied enough to reveal some complex questions and uncertainties. A sampling of what we saw follows.



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HURRICANE MATTHEW & FOREST BIOGEOMORPHOLOGY Posted 10 October 2016

Hurricane Matthew devastated Haiti and other Caribbean areas, and did tremendous damage in Florida and South Carolina (I rode out the storm in Myrtle Beach, SC with my son Nate, his wife Morgan, and my delightful 2-year-old granddaughter Caroline). By the time it got to North Carolina, winds were down to gale force, but rain was ferocious (15 to 40 cm) in much of eastern N.C. Where I am at the moment, in Croatan, there was "only" about 10 cm of rain, and only gale force winds. However, that was enough, as it usually is, to get some geomorphic work done in the forest. Below are some photos of trees uprooted by the storm in Croatan National Forest in the Flanner Beach area. Uprooting not only does significant soil mixing, but the pit-mound topography left behind significantly influences hillslope and soil processes for decades (and occasionally longer) thereafter.



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Another example from a cemetery near Maysville, N.C.



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Despite the wet ground (which facilitates uprooting), there was a lot of breakage at the Croatan/Flanner Beach sites. This is common for pines in the coastal plain, which have a deep taproot and are well-anchored. Hardwoods such as oak and beech, in this setting, tend to have shallower root systems and are usually more likely to uproot. Today, though, I saw a number of broken hardwoods:



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The geomorphic impacts of broken trees are not as immediate as those of uprooting, beyond the sudden addition of a lot of biomass to the surface at one time. However, as the stumps decay, they leave behind depressions (stumpholes) and root channels that strongly influence both surface and subsurface water and sediment fluxes. Below is a stumphole from an older event at the same site; it's more than a meter across.

This area is prone to tropical cyclones and midlatitude cyclones (northeasters), so events that cause a number of tree uproots and breakages come along every few years. The



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Flanner Beach site includes several generations of uproots, for example. How can you tell the old ones from the recent ones? Actually it's pretty easy now; the hardwoods still have their leaves here, and the understory is still green. So the trees uprooted in Matthew still have green leaves, and have crushed green plants below them. In this subtropical environment plants grow fast, and even the soil on a rootwad quickly gets a vegetation cover. Below is a tree that was uprooted several years ago, and cut because it fell across a trail. The rootwad ended up almost upside down above the ground surface, and is now home to a mini-forest of approximately 5-year-old loblolly pines.

Forest biogeomorphology involves the reciprocal interactions between biological effects of trees on geomorphology, and geomorphic effects of topography, soil formation, drainage, etc. on trees. This Matthew-uprooted tree shows the flattening or spreading of roots that often occurs here due to high water tables--similar phenomena occur in other environments when roots encounter bedrock.



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MORE FOREST BIOGEOMORPHOLOGY & GEOECOLOGY Posted 14 October 2016

Imagine exploring and mapping a newly discovered cave opening. At this point, there is only one set of questions--how long, deep, tall, wide, etc. is the passage, and where does it go? But as you begin to map it, more often than not, other passages and fissures will be discovered (and many of them will lead to others, and so on). This opens up a whole new set of questions. Some of the passages can be mapped, assuming someone can get the time and resources. Others can't be no matter how skilled the spelunker; they are too small. But even these can possibly be explored later, perhaps with remote control or AI tiny robots or probes; or with imaging techniques that can see through rock. This is a pretty good metaphor, I think, for research in general. The more you learn, the more you discover you don't know, and more potential pathways for research appear-some possible now, some awaiting new techniques. I was thinking about that recently, assessing the impacts of Hurricane/Tropical Storm Matthew on a forest in North Carolina (see this previous post). Despite all the work that I and (mostly) others have done on the geomorphological, pedological, hydrological, and ecological effects of strong wind events in forests (due to, e.g., tree uprooting and breakage), every time I look it seems more questions are raised. What follows are basically observations, questions, and suggestions, with limited answers or insight. All photos are from the Croatan National Forest in Craven County, N.C. I already talked about wind-driven uprooting vs. breakage, but in my further forays into the forest I noticed more partially uprooted trees. In the cases I observed, this happened because the tipped-over trunk is blocked by other trees. Thus, you'd think denser vegetation and closer tree spacing would favor this phenomenon. However, in some cases uprooting of one tree can cause a chain reaction, knocking others over (either directly, or in concert with the wind). I observed this also, but didn't get any really good pictures.



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Even among the partial uproots, we see variations in the extent of root exposure & breakage on the uptilted side--compare the photos below to those above.



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At least some of the trees simply continued growing in their tilted position, reorienting their trunks, and/or sending up new vertical branches from the tilted trunk.



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Speaking of tree spacing, check out the picnic area below, where there was no uprooting or breakage at all, despite the greatest exposure to winds off the Neuse River estuary of any site examined. Possible reasons--removal of unhealthy or dead trees by the forest service, and better drainage than many of the other sites I looked at.



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Breakage vs. uprooting also depends on the type of tree and its root system:



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For dead trees, or living trees with unhealthy root systems, shallow uprooting may occur-that is, the rootwad will consist of just a thin layer of soil. This results in a much smaller microtopographic feature (smaller mound, shallower pit) than uprooting of a live tree with healthy roots.

Also observed some trunkwash, a phenomenon my colleagues and I recently recognized in this article. The net effect of trunks and branches lying on the ground seems to be bioprotection and upslope sediment retention, but sometimes the funneling of runoff results in some slopewash on the downslope side.



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Since 2004, I've been harping on the idea that trees preferentially reoccupy the same microsites, with implications for weathering and soil formation. Those who've spent a lot of time in the forest have noted plenty of stump-sprouting (new shoots and ultimately in some cases trunks) from stumps. In some cases, however, a different species can sprout from a stump, or trees of two different types may essentially share a microsite--this may happen when animals (e.g., squirrels) bury a nut or seed beneath roots of living trees.



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But what determines whether the rotted stumps host new trees before a depression can be formed, vs. forming a stumphole, like the one below? And we still don't know much about the extent to which (or circumstances under which) stumpholes infill via slumping of the surrounding soil, deposition of transported sediment, or organic litter.



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Finally, it used to be thought that there was little or no water erosion in the flat coastal plain. However, that was disproven for good back in the 1990s (see, e.g., this and this). At least some of the water's gotta move, and when it does it takes some sediment with it, as the rill below shows.



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STAGES OF BIOGEOMORPHIC EFFECTS Posted 29 May 2017

The biogeomorphic impacts of organisms may differ at different stages in the development of landforms, ecosystems, or the individual organisms. I was thinking about this recently here along the shoreline bluffs of the Neuse River estuary, North Carolina, where I have been both looking at some soil profiles and enjoying the coastline. There are at least five distinctly different biogeomorphic roles trees play along this shoreline--many more if you wanted to get more specific within these categories. The specifics are probably of only limited applicability elsewhere, but the general principle-multiple effects, which vary at different stages of both landform and vegetation development--is widely valid.

Trees and other vegetation grow thick and fast in this moist subtropical climate. Stage 1A Surface Bioprotection Trees (including canopy, roots, and litter) protect the ground surface from erosion and add organic matter to soil.



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This spot where a tree was recently removed shows the local deepening of the soil (compare to sedimentary layering preserved adjacent) associated with a mature tree. 1B Regolith and Soil Formation Tree roots penetrate substrate, displacing mass and creating local density differences. Moisture is funneled along trunk and roots. CO2 production by root, rhizosphere, and associated microbial communities facilitate weathering. Organic acids formed by water in contact with roots and litter, facilitating weathering and translocation.

Toppled trees along the Neuse River estuary shoreline. Stage 2 Bioturbation, Erosion, and Mass Wasting Cliff/bluff shoreline retreat undermines trees, or exposes them to outward (toward estuary) windthrow. Uprooting removes material from bluff top, reinforcing shoreline

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retreat.

Toeslope debris--woody and sedimentary.

Waves during high water must remove toeslope debris before undercutting the bluffs. Stage 3 Shoreline Bioprotection Toppled trees and large woody debris protect bluffs, absorb wave energy, and trap sediment from both bluff and beach sources. This toeslope sediment and debris must be removed during storms to reinitiate shoreline retreat.



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Long term observation of these shorelines shows that tree trunks and woody debris are both added and removed by wave and current processes, as well as added by erosion of adjacent bluffs. Stage 4 Deposition Dead trees and woody debris transported and redeposited along shore or at river bottom. Wood may have local effects in deposition or scour, as well as surface roughness.



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BIOGEOMORPHIC NICHE CONSTRUCTION BY UPROOTING Posted 22 June 2017

Tree uprooting in forests has all sorts of ecological, pedological, and geomorphological impacts. Those are not just related to disturbance--because of the time it takes uprooted trees to decompose, and the distinctive pit-mound topography created, those impacts may last decades to centuries (and sometimes even longer). One discussion I've often had with colleagues who study this sort of thing has to do with ecosystem engineering and niche construction. Obviously uprooting is a major biogeomorphic process. Obviously it has important impacts on habitat. But do these impacts favor either the engineer species (i.e., the tipped over tree) or some species? Or are they more or less neutral, in the sense of modifying habitat but not necessarily in such a way as to systematically favor any given species?

Uprooted Norway spruce. Some of my friends and colleagues in the Blue Cats Research Group in the Czech Republic have shown in old-growth forests of central Europe that the mounds eventually resulting from uprooting are favorable sites for new trees to grow, due to favorable soil conditions, and perhaps also freedom from immediately adjacent competition for light, water, etc. (Šebkova et al., 2012). But sometimes the new tree is the same species as the old one that uprooted, sometimes not (often one cannot tell, but they have awesome historical tree census records at their sites). The new mound-trees were often European beech, suggesting that the ecosystem engineering effects might be positive or negative, depending on whether the uprooted tree was beech or not. However, we could not dismiss the notion that, since there is a thriving population of beech around, that it could simply

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be seed source effects--that is, with plenty of beech nuts around in a mature latesuccessional forest, maybe odds are that any favorable opportunity for new tree growth favors Fagus sylvatica.

Mature Fagus sylvatica on old tree throw mound. Now comes another study out of central Europe, lead-authored by another friend and colleague (Pawlik et al., 2017) that pretty much nails it down, at least for their sites in Poland's Sudetes Mountains. The short version of the story is that the forest was originally a mature mixed forest, including beech, spruce, and other species. That was replaced, as is often the case in that part of the world, by a Norway spruce-dominated forest in the 19th and early 20th century (spruce is favored by the timber industry). Spruce has a much shallower, spreading root system than beech, and is therefore more prone to uprooting (though Fagus certainly does get uprooted). A windstorm in 1933 caused widespread uprooting in the forest among the Picea abies, with the usual pit and mound microtopography eventually resulting. The mounds (rather than the pits) are overwhelmingly the favorite sites for tree regeneration since, and the new trees are mainly beech. Because beech were few and scattered, this cannot simply be a seed-source phenomenon.



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Mature Fagus sylvatica on old tree throw mound. Thus, at least there, uprooting of Picea constitutes passive ecosystem engineering (i.e., does not necessarily favor the engineer species) and negative niche construction (favoring organisms other than the engineer). In addition to the effects on uprooting, differences in root architecture also influence interaction with bedrock and thus weathering and moisture flow and other aspects of soil and regolith development (see review by Pawlik et al., 2016). Further, beech and spruce litter differs greatly in composition, leading to substantial differences in soil chemistry and microbiology.



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Uprooted Norway Spruce, above, and root mound of uprooted beech. Note the difference in thickness. Thus, the biogeomorphic effects of uprooting not influence species composition, forest succession, and local soil properties, but the entire (bio)geomorphic regime of a hillslope or forest stand (see, e.g., Phillips et al., 2017). -------------------------------------------------------------Note: photos are from my collection, taken in forests in the Czech Republic similar to those discussed above, but are not related to those specific studies and not originally intended to illustrate the phenomena involved.



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Responses to and Effects of Climate and Sea-Level Rise

Otter Creek, Craven County, North Carolina



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AMPLIFIERS & FILTERS Posted 29 July 2014

A big problem with predicting responses to global climate change (or other environmental changes) is that they are nonlinear and thus disproportionate. Sometimes large changes can have relatively small responses, while in other cases small changes can have disproportionately large impacts. Responses to environmental change are sometimes characterized by amplifiers— phenomena that reinforce or exaggerate the effects of the change. For example, if coastal land is subsiding, this amplifies the effects of sea level rise. Or, when warming results in permafrost thawing, this releases methane, a heat-trapping greenhouse gas, this leads to further warming. However, there are also filters—phenomena that resist, offset, or diminish the effects of the change. For instance, if coastal land is tectonically or isostatically uplifting, this can offset or even eliminate effects of sea level rise with respect to coastal submergence. Or, if warming results in increased cloud cover, which reflects more radiation, this counteracts the warming.

Marshes in the Trinity River delta, Texas, part of the Galveston Bay complex (Google Earth image). Here subsidence due to a combination of natural compaction and human activity (extraction of oil, gas, and water) is resulting in subsidence of the wetlands, which amplifies effects of rising sea level.



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I was reminded of this by today’s news (http://www.sciencedaily.com/releases/2014/07/140728153933.htm) of confirmation of an amplifier effect of global warming—water vapor increase in the upper troposphere. Because H2O is a greenhouse gas, and cloud formation is not an issue in the upper troposphere, this is a clear amplifier effect. The summary of the news story: “A new study . . . confirms rising levels of water vapor in the upper troposphere -- a key amplifier of global warming -- will intensify climate change impacts over the next decades. The new study is the first to show that increased water vapor concentrations in the atmosphere are a direct result of human activities.” The responses to the (proven and undebatable) increase in the global concentration of carbon dioxide and other greenhouse gases include both amplifier and filter effects, as we talk about quite a bit in my GEO 130 (Earth’s Physical Environment) class. If it were not for some of the filter effects (i.e., if the physics of greenhouse gases were the only thing affecting temperature), it would already be hotter than it is (and it is getting hotter, globally). On the other hand, right now the weight of the evidence suggests that the amplifier effects, such as the tropospheric water vapor, and the fact that land and ocean surfaces bared by melting ice absorb more radiation than ice, are winning. The amplifier and filter concept also applies to geomorphological, ecological, and other (including economic and political) responses to climate change. The climate changes themselves can be amplified or filtered, and the responses of, for instance, landforms or vegetation to the net climate change can themselves be amplified or filtered. Thus you could have several layers of amplification ratcheting up the effects of change, several layers of filtering diminishing or obscuring those effects, or some combination of amplifiers and filters. I examined this in the context of how Kentucky rivers have responded to climate change in the last couple of million years (Phillips, 2010). Another piece discusses amplifiers and filters in more detail with respect to how landforms and landscapes respond to all kinds of disturbances (Phillips, 2009). Phillips, J.D. 2010. Amplifiers, filters, and the response of Kentucky rivers to climate change. Climatic Change 103: 571-595. Phillips, J.D. 2009. Changes, perturbations, and responses in geomorphic systems. Progress in Physical Geography 33: 17-30.



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CLIMATE CHANGE & ENVIRONMENTAL MANAGEMENT Posted 11 October 2014

Climate change is here, it’s real, and it won’t be easy for humans to deal with. But few things are all good or all bad, and so it may be for climate change, at least with respect to environmental science and management. A vast literature has accumulated in the past two or three decades in geosciences, environmental sciences, and ecology acknowledging the pervasive—and to some extent irreducible—roles of uncertainty and contingency. This does not make prediction impossible or unfeasible, but does change the context of prediction. We are obliged to not only acknowledge uncertainty, but also to frame prediction in terms of ranges or envelopes of probabilities and possibilities rather than single predicted outcomes. Think of hurricane track forecasts, which acknowledge a range of possible pathways, and that the uncertainty increases into the future.

Forecast track for Hurricane Lili, September 30, 2002. The range of possible tracks and the increasing uncertainty over time are clear. Source: National Hurricane Center. Applied environmental science and environmental management has traditionally been based on what I think of as a medical-style model—if a patient is presenting certain symptoms, then this is the treatment. The environmental analogy, be it forest or range

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management, agricultural policy, stream restoration, etc., has been similar—if this is the problem or the situation, then that is what you do. Sometimes this works; sometimes it doesn’t. An approach more consistent with the way environmental systems actually work is based on flexibility, adaptability, and identifying ranges of options. This is the case with or without climate change, but perhaps dealing with the uncertainties, path dependence, complex interconnections, and inevitable surprises we know are associated with ongoing and near-future climate change, we can change the culture of environmental and natural resource policy and management. Rather than (at least implicitly) pretending certainty in forecasts, we acknowledge uncertainty, contingency, and possible surprises. Rather than single prescribed management options, we develop and present ranges of options, with multiple objectives and multiple potential pathways. And rather than implementing a policy, restoration scheme, etc., and “riding it until it crashes,” we build in flexibility and adaptive capacity.



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TELECONNECTIVITY Posted 1 November 2014

Last month the climatologist Justin Maxwell from Indiana University gave an interesting talk at our department about drought-busting tropical cyclones. In his talk, and in conversations before and after with our physical geography crew, he had some interesting things to say about climate teleconnections involving mainly sea surface temperature and pressure patterns such as ENSO, NAO, etc. If teleconnections and the various acronyms are unfamiliar, check out the National Climatic Data Center’s teleconnections page: http://www.ncdc.noaa.gov/teleconnections/ In the northern hemisphere there are at least 10 of these that influence our weather and climate. So after talking and listening to Justin I got the idea to apply some algebraic graph theory methods I’ve been using to assess complexity and to identify patterns in geomorphology and pedology to these teleconnections. The idea was to set up a network (graph) where each of these teleconnection patterns is a node. These nodes would be considered positively connected if their phases are synchronized—that is, they both tend to be in positive or negative phases at the same time. A negative connection would exist if they tend to be in opposite phases, and no link if their phases are apparently unrelated. I got the time series of monthly teleconnection pattern indices here: http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml. I then used these time series (1950 to present) to try to identify the connections. Unfortunately, the plots all look more or less like this:

Plot of values of the North Atlantic Oscillation (NAO) index (vertical axis) vs. the East Atlantic (EA) index (horizontal axis). Once could interpret this either of two ways. One is that the indices are all unrelated to each other, producing a meaningless graph/network of completely unconnected nodes. Another interpretation is that ALL connections are possible—that is, a positive or negative phase of any teleconnection pattern may be associated (synchronous) with a

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positive or negative phase of any other. This would result in mathematical graph of the maximum possible complexity for the number of nodes. Climatologists are increasingly able to explain more and more phenomena based on these teleconnection patterns, and that trend will continue. But my little foray into that work shows that as all of these global teleconnections operate contemporaneously, with their weather and climate impacts superimposed on each other, the system is extremely and inherently complex—consistent with earlier determinations of chaotic dynamics of the atmosphere. As Yogi Berra is supposed to have said: “Prediction is difficult; especially the future.”



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SOIL EROSION & CLIMATE CHANGE Posted 17 April, 2015

A lot of us in the geoscience business are concerned these days with interpreting ongoing and past, and predicting future, responses of landforms, soils, and ecosystems to climate change. As one of my interests is rivers, I have noted over the years that in a lot of the literature on paleohydrology the major changes, such as major influxes of sediment, seem to occur at climate transitions, rather than after climate changes or shifts have had a chance to settle in and exert their impacts for awhile. A related issue is the relationship between precipitation, temperature, runoff, erosion, and vegetation. As climate changes both temperature and precipitation regimes change. And as every physical geography student knows, moisture availability is not just about precipitation, but the balance between precipitation and evapotranspiration (ET). So, if both temperature and precipitation are increasing (as is the case on average on much of the planet now), whether available moisture increases or decreases depends on the relative increases of precipitation and ET.

Soil erosion on cropland. Increases in available moisture, also called effective precipitation, would tend to promote both runoff and soil erosion on the one hand, and vegetation cover on the other. Since vegetation reduces erosion, we have another case of the result hinging on the net effects of “competing” processes. So, how does this all play out? The two best ways to find out, in my view, are case studies of actual responses, and complex system models that directly address the networks of interrelationships. The former, as I mentioned, typically show major changes in rivers, and influxes of

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sediment, at climate transitions. For the latter, previous work (including some of my own, but mostly by others) suggests dynamical instability—that is, the network of interrelationships is such that changes to any part of the system (e.g., accelerated erosion, or change in vegetation cover) tend to persist and grow (within limits) over time, rather than a return to the pre-disturbance condition. I decided to take another look, using a model slightly more complex than the vegetation-erosion interaction models (these predict that the system tends to “tip” to either a maximum vegetation/minimum erosion or a maximum erosion/minimum vegetation state, with intermediate states unstable). The model below, though, is less complicated than more elaborate models (again, including some of my own) that also include soil hydrologic properties, nutrients, and other factors. Those also typically indicate dynamical instabilities under many circumstances.

Interrelationships among soil erosion, runoff, and vegetation under conditions of changing effective preciptation. Green arrows indicate positive links, indicating that an increase or decrease in one component produces a change in the other in the same direction. Red arrows are negative links, characterizing relationships where a change in one component produces a change in the other in the opposite direction. Many of the links are fairly self-evident—other things being equal, effective precipitation is positively related to both runoff and vegetation cover, vegetation reduces erosion, and vice-versa (due to loss of topsoil, nutrients, etc.). Erosion also tends to increase runoff, due to reduced soil moisture storage capacity and exposure of low-permeability subsoils.



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I actually prefer these qualitative (links are simply positive or negative) models, as they are more general than quantitative ones. For example, the quantitative effect of runoff on soil erosion varies with topography, soil factors, etc., and is more or less unique for a given situation. The qualitative relationship, however—runoff goes down, soil erosion goes down, and vice-versa—is more or less universal. Anyway, the dynamical stability of models such as this can be analyzed mathematically (remember, qualitative ≠ non-mathematical) using the RouthHurwitz criteria. I’ll spare you the details (if you are interested, shoot me an e-mail), but the results are that the network of relationships are dynamically unstable. From the perspective of climate change, that means that if a change in effective precipitation results in a change in vegetation cover or surface runoff, the effects of that change are likely to persist and grow (perhaps disproportionately large compared to the original change). This explains why paleohydrological studies typically show major changes or regime shifts during climate transition periods. Those changes in an unstable system generate disproportionately large responses in sediment dynamics. There’s an obvious warning here with respect to ongoing and future climate change—relatively minor climate-driven disturbances could result in disproportionately severe erosion and land degradation. But there’s also opportunity—in some situations relatively minor climate-driven disturbances in areas already experiencing erosion or degradation could be tipped into a minimum erosion, non-degrading state. And even in the former case, opportunities exist in such unstable systems to initiate relatively large desirable changes with relatively small “disturbances” such as, e.g., vegetation plantings or erosion control measures.

Eroded and restored gullies in Ethiopia (https://pcwoolner.files.wordpress.com/2013/02/mscfso.jpg).

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CLIMATE AND HISTORY: GEOGRAPHY MATTERS Posted 17 June 2015

Just finished John Brooke’s Climate Change and the Course of Global History: A Rough Journey (Cambridge University Press, 2014). If nothing else, the book is a remarkable achievement with respect to the breadth and depth of literature and ideas brought to bear, including history, geography, geology, anthropology, economics, climatology, ecology, and archaeology. Brooke also makes a compelling case for a significant role for environmental change in general, and climate change in particular, in influencing human affairs and history (and, of course, vice-versa). Brooks does not ignore, or even downplay, effects of culture, politics, power and social relations, economics, and technology. Yet he will almost certainly be accused, if not already, of being an environmental determinist. Environmental or geographical determinism is a set of ideas positing that physical geography is a (and in its extreme forms, the) major factor in determining human culture and societal development. Environmental determinist is used a perjorative in academic circles. Late 19th and early 20th century environmental determinism was used to support and justify racism and imperialism. This, plus its inherent scientific/intellectual flaws (some unique to ED, some shared by any explanatory frameworks that claim that it’s all about one set of factors), caused ED to not only fall out of favor, but to become a put-down applied to anyone or anything suggesting a major role for environmental and geographic factors in the course of human affairs. Few (hopefully, no one) would deny that the physical environment poses both opportunities/advantages and constraints/disadvantages for various human affairs and activities in different locations and situations. Few would argue (and as far as I know, no scholar has in the late 20th and early 21st century has) that environmental factors influence humans independently of society, culture, politics, economics, and individual actions and decisions. Some human phenomena are very strongly influenced by physical geography; some hardly at all. ED was practiced and espoused by anthropologists and economists, but most of all by geographers. The rejection of ED and the (understandable) revulsion toward its racist/imperialist past have caused some geographers to swing too far in the other direction—to seriously underestimate the role of the environment in some cases. Even more commonly, the tendency is to label scholars who choose to focus on environmental or geographic factors as environmental determinists and thereby dismiss their work as clueless, racist, or both (see, e.g., the vitriol aimed at Jared Diamond). Arguments that projects such as Brooks’ do not do full justice to non-environmental factors are true enough, I suppose. But equally true are arguments that the work of, e.g., political ecologists, political economists, feminists, geneticists, etc. does not do full justice to factors other than their primary focus. Except perhaps for work of a very limited geographical and historical scope, I doubt that anyone can do full justice



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to all relevant explanatory factors. Yet few are ever accused of being political, genetic, or gender determinists. I close with two points: It is never “all about” anything. Whether we are dealing with the human condition, physical landscapes, ecosystems, or anything else, no single (set of) factors— climate, geology, topography, economics, politics, culture, etc.—tells the whole story and explains all that needs explaining. Geography matters. Human survival and well-being affects, and is affected by, the non-human environment. Location, both absolute and relative, confers advantages and disadvantages. Natural resources provide the capital that underlie economies. As much as we might like to think that the human spirit and ingenuity can negate these facts, they can’t. And as much as we might like to place all the on blame humans and our institutions when things go wrong, (because we can potentially fix these, while we cannot fix insolation or tsunamis), we can’t always do so.



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POLARIZATION Posted 19 June 2015

A paper recently published in Polar Geography gives the depressing, but unfortunately not unexpected, confirmation that people’s political orientation affects their perceptions and knowledge on environmental sciences. Lawrence Hamilton surveyed New Hampshire residents from 2011-2015 on some basic facts about Earth’s poles. Five key questions: 1. Which of the following three statements do you think is more accurate? Over the past few years, the ice on the Arctic Ocean in late summer— A. covers less area than it did 30 years ago B. declined but then recovered to about the same area it had 30 years ago C. covers more area than it did 30 years ago 2. If the Arctic region becomes warmer in the future, do you think that will have-- A. no effects on the weather where you live B. minor effects on the weather where you live C. major effects on the weather where you live? 3. Which of the following possible changes would, if it happened, do the most to raise sea levels? A. melting of land ice in Greenland and the Antarctic B. melting of glaciers in the Himalaya and Alaska C. melting of sea ice on the Arctic Ocean 4. Which of these best describes the North Pole? A. ice a few feet or yards thick, floating over a deep ocean B. ice more than a mile thick, over land C. a mainly rocky, mountainous landscape. 5. Which of these best describes the South Pole? A. ice a few feet or yards thick, floating over a deep ocean B. ice more than a mile thick, over land C. a mainly rocky, mountainous landscape The correct answers, familiar to anyone who has had freshman-level physical geography, are A, C, A, A, B. All of these are basic facts, not subject to debate or uncertainty, except perhaps question 2. But even this is non-controversial for New Hampshire and New England. First of all, as we’ve come to expect in any test of U.S. knowledge of anything—science, geography, math, etc.—a majority of people got the questions wrong. What’s interesting, however, is that responses to factual questions such as whether Arctic sea ice has declined, were politicized as if the researchers were asking for climate change opinions. That is, right-wingers (my term, not Hamilton’s) were more likely to either be unaware of, or refuse to acknowledge, sea ice declines. Hamilton also tested the combination of actual and self-assessed knowledge (i.e., how much you know vs. how much you think you know). About 41 to 47 percent of Democrats, Republicans, and Independents showed a combination of low knowledge but an opinion that they have moderate or high levels of understanding. However, this goes to 61 percent among self-identified Tea Party supporters!



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Political arch-conservatives (apparently) can't read this graph. Hamilton concludes that variations in the combination of self-confidence and actual knowledge suggests that different strategies are needed to reach different groups. Simply providing more information is only likely to work for some people. Confirmation of what we have long suspected, and experienced anecdotally, with respect to right-wingers and science, is certainly depressing with respect to action on climate change and other issues. Polluters and deniers not only have pretty much unlimited resources to promote their political views, but those receptive or vulnerable to those views seem to develop a certain immunity to data, facts and reason. It is also depressing to those of us who teach. How many of our students are resistant to ideas that conflict with their political views—even ideas that are unequivocably fact-based and verifiable? A news article on Hamilton’s findings is here: http://www.sciencedaily.com/releases/2015/06/150616102132.htm ------------------------------------------------------------------------------- Hamilton, L.C. Polar facts in the age of polarization. Polar Geography, 2015; 1 DOI: 10.1080/1088937X.2015.1051158



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MAKIN’ IT RAIN Posted 1 July 2015

No, not like this. Dang, it’s been raining a lot lately. Today, for instance, here in Mercer County, Kentucky, Herrington Lake’s level has risen almost 2 m in the last two weeks. Discharge of the Dix River has topped 1000 cfs (28.3 cms) twice in the past week, where the mean flow for early July is about 40 cfs (1.1 cms) over the past 71 years. We had another gullywasher, frog-choker rainstorm this morning. If it seems to have been an unusually wet summer here in Kentucky, you’re right. The graphic below shows departures from normal (mean) rainfall totals for June. That pinkish blob in north-central Kentucky, showing 8 inches (203 mm) above normal is the Lexington area.



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This follows a wet spring hereabouts. Below is the departure-from-normal precipitation map for April, 2015, a month in which one-day precipitation records were set in Lexington, Frankfort, and Louisville.

So is there a point, other than griping about a wet spring and summer? Some recently-published work confirms some earlier studies in showing that ongoing global warming results in more heavy precipitation events. In this case, a team from Germany’s Potsdam Institute for Climate Impact Research (PIK) looked specifically at record-breaking rainfall events. According to a PIK summary: “Heavy rainfall events setting ever new records have been increasing strikingly in the past thirty years. While before 1980, multi-decadal fluctuations in extreme rainfall events are explained by natural variability, a team of scientists detected a clear upward trend in the past few decades towards more unprecedented daily rainfall events . . . An advanced statistical analysis of rainfall data from the years 1901 to 2010 derived from thousands of weather stations around the globe shows that over 1980-2010 there were 12 percent more of these events than expected in a stationary climate, a scenario without global warming.” Climatologists have long suspected, and then known, that a warmer climate accelerates the hydrological cycle. Evaporation and transpiration go faster at higher temperatures, recycling water vapor into the atmosphere faster, where it is eventually returned as precipitation. Warmer air can hold more water vapor, and vapor-laden air masses are a key in developing intense precipitation events.



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The latest downpour here in Mercer County, or the big rainfalls of April, like any individual meteorological event or episode, are neither proof nor refutation of a climate trend (in the June map above, for instance, you can see parts of Kentucky that had below average rainfall). But it seems clear that from here on out we can expect more frequent and intense heavy rainfall events, and more days like today1 (another thunderstorm rain just started outside). That means more runoff (other things being equal, the faster rain falls, the greater the proportion of surface runoff), more soil erosion, and more floods. So this is about much more than my dog tracking mud all over the house. We should be on the lookout for possible related changes in dominant runoff mechanisms, flood frequencies, soil erosion rates, and nonpoint source water pollution. The reference for the PIK study is below; the press release is here. Jascha Lehmann, Dim Coumou, Katja Frieler. Increased record-breaking precipitation events under global warming. Climatic Change, 2015; DOI: 10.1007/s10584-015-1434-y



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DROWNING THE COAST Posted 22 July 2015

A new review of decades of research on the historic effects of melting polar ice on global sea levels found that sea level has risen at least 6 m above present levels on multiple occasions over the past 3 million years. That’s relevant to us because polar ice is melting now. Even more concerning is that 6 m of sea level rise was caused by an increase of only 1 to 2 oC in global mean temperatures. A rise of that much is all but certain in the next few decades (2014 set yet another mark as the warmest year on record), and an increase of up to 6 oC is possible by 2100. We know that many areas on our planet are less than 6 m above current sea level. Just for the heck of it, I checked on the elevations of some familiar locations on the U.S. Atlantic and Gulf Coasts. Boardwalk, Myrtle Beach, SC: 4 m above sea level Boardwalk, Atlantic City, NJ: 2 m Miami, FL: mean elevation < 2 m French Quarter, New Orleans, LA Bourbon St.: 3 m Louis Armstrong Park: -1 m Houston Ship Channel, Dupont facility: 1 to 15 m Lake Charles, LA, petrochemical complex: 4 m Swan Quarter, NC (mainland opposite the Outer Banks): 1 m New York city, Battery Park: 5 m



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Tom Allen of East Carolina University produced this map of vulnerability based on sea level rise of 1 supercritical or rapid flow. But variations in Fr within the subcritical range (where it typically falls) can be significantly related to, e.g., geomorphic units and habitats within channels.

Shawnee Run, Kentucky But what about other potential indicators? The Reynolds number (Re) is a measure

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of flow turbulence, shear stress (t) measures force exerted against channel boundary, and stream power indicates the rate of work or energy expenditure of flow. All of these, along with velocity or discharge, would seem to be as good or better indicators. So why is Fr so commonly employed? If you take a look at the equations below, you can see that Fr varies directly and proportionately with velocity, and as the negative 0.5 power of depth. Reynolds number varies directly with V and d, and shear stress directly with d and slope. The stream power measures vary directly and proportionally with S and either V or Q. Thus, as flow conditions vary from wetter to drier periods and low to high flows, the Froude number is likely to be less variable than the other parameters. Thus, we can hypothesize that Fr is a preferred habitat indicator because it is more consistent.

My fluvial geomorphology class this semester tested this idea using field measurement data from U.S. Geological Survey gaging stations. These data include measurements of Q, V, channel width, and cross-sectional area. From these mean depth and the Froude number can be derived. They compared the coefficient of variation (mean divided by standard deviation) of Fr to that of discharge (related to cross-sectional stream power), V (related to Re and unit stream power as well as Fr) and d (related to Re and shear stress). A higher coefficient of variation (CV) for

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Froude number supports the notion that Fr is a preferred indicator because it is more consistent. The students chose gaging stations representing a variety of fluvial settings, including bedrock-controlled, fluviokarst and alluvial channels; and high-gradient mountain and low-gradient coastal plain streams. Here’s what they found: South Elkhorn Creek, Kentucky: The CV of Fr was greater than that for Q, d, and V for eight of the rating curves included in the data, with mixed results for three others. Elkhorn Creek, Kentucky: : The CV of Fr was greater than that for Q, d, and V for all rating curves. Dix River, Kentucky: The CV of Fr was greater than that for Q, d, and V for all rating curves. Upper Cumberland River, Kentucky: The CV of Fr was greater than that for Q, but less than the CV of V and d. Clear Fork, Kentucky: The CV of Fr was greater than that for Q, but less than the CV of V and d. Cheat River, West Virginia: The CV of Fr was greater than that for Q, d, and V for all rating curves. Lower Waccamaw River, South Carolina: The CV of Fr was greater than that for Q, and V and less than for d, for all rating curves. This site is tidally influenced, and d is almost constant as a consequence (less than 1 m variation for the entire data set). Savannah River, Georgia/South Carolina (two stations near Augusta, GA): The CV of Fr was greater than that for Q, d, and V for six rating curves, and lower than Q, d, V for one rating curve. Lower Colorado River, Texas (Bastrop): CV of Fr was greater than that for Q, d, but less than that of V for all rating curves. Lower Colorado River, Texas (Bay City): The CV of Fr was greater than that for Q, d, and V for all rating curves. Overall, results support the hypothesis that Froude number is less variable at a given location than other hydraulic indicators. Clifford et al. (2006) noted that different combinations of V and d could produce the same Froude number, and this was evident at many of the sites above. Many sites show bimodal relationships (i.e., two distinct trends) in relationships between Fr and V, Q, or d.

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The data were also mostly low Fr. No value higher than Fr = 0.7 was recorded at any time at any station, and most values were less than 0.4 (many much less). Thus, sampling at smaller, steeper sites during high flows would be advisable for any future studies along these lines. The students in the class who did the analyses are: Kornelia Wielisczko, Wisam Muttashar, Marielle Manning, Wei Ji, Jeremy Eddy, Sidney Dobson, and Darion Carden. --------- Clifford, N.J., Harmar, O.P., Harvey, G., Petts, G.E., 2006. Physical habitat, ecohydraulics and river design: a review and re-evaluation of some popular concepts and methods. Aquatic Conservation: Marine & Freshwater Ecosystems 16: 389-408.



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THE PERFECT FLOODS OF TEXAS Posted late 25 May, 2015

As I write, there is flooding in central Texas, and more to come. The focus is rivers and creeks in the San Antonio and Guadalupe River systems in the Balcones Escarpment area along the San Antonio-Austin Corridor, with effects beginning to felt downstream.

Destroyed trees along banks of the Blanco River, Wimberly, TX, after the flood of 24 May, 2015 (photo by Jay Janner, Associated Press). This area is no stranger to large floods. In the vicinity of the Balcones escarpment, the Guadalupe and other rivers that cross the escarpment are prone to highmagnitude flooding—the incidence of such flooding is higher than any other area of the U.S. Caran and Baker (1986) identified a combination of climatological and runoff-response factors contributing to the high flooding potential. The region lies within a zone of convergence of polar air masses and easterly waves or tropical cyclones. A well-developed easterly wave approaching a lobe of high pressure, such as those often associated with a polar surge into middle latitudes, may produce strongly instability and heavy rains, as was the case, for example in the extreme floods that occurred on the Guadalupe River in 2002. Orographic effects associated with the escarpment topography can also enhance these rains (Caran and Baker, 1986). Nielsen-Gammon et al.’s (2005) climatological analysis shows that in general, exreme rainfall events in central Texas are associated with a northern deflection of the northeasterly trade winds into Texas, with deep southerly winds extending into the troposphere. This pumps abundant tropical moisture into the region with high potential for instability. Precipitation events producing more than 20 in (500 mm) of rain occur several times per decade in Texas (Earl & Dixon, 2005; Neilson

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Gammon et al., 2005).

Cars transported by the Guadalupe River in Gruene, TX, during a June, 2010 flood (NWS photo). The topography and surface conditions result in a large and rapid runoff response to heavy rainfall events. Steep slopes, narrow valleys, thin soils (many with low infiltration capacity) over limestone bedrock, and relatively sparse vegetation cover result in high runoff and consequent stream discharges. Several other studies have confirmed the atypical flood regime of this area, and the general causal factors identified above (Baker, 1977; Patton and Baker, 1977; Alfinowicz et al., 2005; Curran et al., 2005). Recent work suggests a general pattern of increasing base flow discharge in Hill Country rivers such as the upper Guadalupe as grasslands formerly degraded by overgrazing convert to woodlands (Wilcox and Huang, 2010). Unusually high roughness or flow resistance values in rivers of the Edwards Plateau and Balcones escarpment such as the upper Guadalupe may contribute to flooding by reducing channel conveyance capacity (Conyers and Fonstad, 2005). The thin soils of the Edwards Plateau are believed to be a legacy of Quaternary climate change. A combination of temperature, precipitation, and vegetation changes led to soil degradation, according to the reconstruction of Toomey et al. (1993). During the late glacial maximum and the latter stages of the most recent glacial period (about 20-10 Ka), the uplands had thick, reddish, clay-rich soils under open savanna vegetation. The transition to the Holocene climate resulted in reduced vegetation cover, and initiated soil erosion and truncation. The general phenomena identified by Toomey et al. (1993) have been confirmed by other studies in the region (see review in Ricklis, 2004).

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So what we have in this region is a confluence of factors (a perfect storm, if you will)—climatology, topography at both a broad (escarpment) and local scale (steep slopes), soils, vegetation, and land use—that combine to produce a propensity for high-magnitude floods. In this the San Antonio/Guadalupe River area is unique— there are certainly other areas particularly prone to flooding, but not for exactly the same reasons. But the area is also typical, in that all places and environmental systems have a unique combination of environmental factors and historical legacies (e.g., the Quaternary legacy of thin soils) that result in elements of uniqueness or idiosyncrasy, overprinted on the general laws and principles that apply to hydrology and flooding everywhere and always. Sometimes, from the human perspective, these “perfect landscapes” have obvious and negative aspects (e.g., extreme floods in Texas), sometimes obvious and positive traits (e.g., some famous wine-producing regions), but more often “perfection” that is more subtle. ----------------------------------------------------------------------- Afinowicz, J.D., Munster, C.L., Wilcox, B.P. 2005. Modeling effects of brush management on the rangeland water budget: Edwards Plateau, Texas. Journal of the American Water Resources Association 41, 181-193. Baker, V.R., 1977. Stream channel response to floods, with examples from Texas. Geological Society of America Bulletin 88, 1057-1071. Caran, S.C., Baker, V.R., 1986. Flooding along the Balcones Escarpment, central Texas. In:The Balcones escarpment-geology, hydrology, ecology and social development in central Texas, Geological Society of America p. 1-14. Conyers, M.M., Fonstad, M.A. 2005. The unusual channel resistance of the Texas Hill Country and its effect on flood flow predictions. Physical Geography 26, 379-395. Curran, J.C., Bryan, D., Jennings, M. 2005. A comparison of modeled flood characteristics to measurements of the 2002 flood on the Guadalupe River, Texas. Physical Geography 26, 396-408. Earl, R.A., Dixon, R.W. 2005. Reassessment of storm and flood probabilities in south-central Texas. Physical Geography 26, 365-378. Nielsen-Gammon, J.W., Zhang, F., Odins, A.M., Myoung, B. 2005. Extreme rainfall in Texas: patterns and predictability. Physical Geography 26, 340-364. Patton, P.C., Baker, V.G., 1977. Geomorphic response of central Texas stream channels to catastrophic rainfall and runoff. In: D.O. Doehring, Editor, Geomorphology In Arid Regions, State University of New York, Binghamton, NY, pp. 189–217. Ricklis, R.A. 2004. The archaeology of the native American occupation of southeast Texas. In The Prehistory of Texas, Pertulla, T.K. (ed.). College Station: Texas A&M University Anthropology Series, No. 9; 181-197. Toomey, R.S. III, Blum, M.D., Valastro, S., Jr. 1993. Late Quaternary climates and environments of the Edwards Plateau, Texas. Global and Planetary Change 7, 299-320.



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POLLY’S BEND—INITIAL CONDITIONS Posted 2 August 2015

South of Lexington and north of Danville, Kentucky, the Kentucky River makes a major turn from a generally SW to NW direction. Shortly downstream, there is a compound “gooseneck” meander bend called Polly’s Bend.

Google EarthTM image of Polly’s Bend. The maximum width from tip to tip is ~ 5 km.; minimum width of the neck is ~ 350 - 400 m. While not the norm, such tight bends are not uncommon in winding alluvial rivers, and will eventually be cut off during a flood, when the channel cuts across the narrow neck. Polly’s Bend, however, is entrenched in bedrock. The narrow neck (and the rest of the bend) has more than 100 m of solid limestone bedrock to cut through. So a classic meander cutoff, with flow going overbank across the neck and cutting a new channel; that ain’t gonna happen.



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Shaded relief map of the Kentucky River gorge area in central Kentucky, from an earlier report on evolution of meander bends in this area. Bends 13-16 comprise Polly’s Bend. Entrenched bedrock meanders are not unique to the Kentucky River, by any means. The San Juan River, Utah, for example, is famous for its entrenched gooseneck meanders. “Normal” alluvial meander bends are formed by laterally migrating streams capable of eroding their banks fairly readily. That’s not the case with Polly’s Bend or other entrenched bedrock meanders. The conventional wisdom—at least partly true—is that the river had developed the meander bends when it began downcutting, and the channel incision basically locked the bends into the bedrock. This, however, implies that the bends (at least with respect to their planform geometry) are pretty much static—that is, even as the channel incises, it does not move laterally. That is not the case, however. While they are nowhere near as dynamic as alluvial meanders, these entrenched meanders can be dynamic, extending even as the river downcuts. W.M. (Drew) Andrews of the Kentucky Geological Survey worked out the geomorphic evolution of the Kentucky River in his 2004 dissertation. Basically, the downcutting that created the Kentucky River gorge started 1.3 to 1.8 Ma, triggered by glacial rearrangement of the ancestral Ohio River system. Sedimentological and topographic evidence shows where some of the pre-incision channel existed (e.g., Quaternary fluvial terrace deposits), so it can be shown that some of the meanders have extended, or even developed, during the downcutting (see figure below).



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Kentucky River meander bend near Winchester, KY, chosen because high- level Quaternary terrace deposits here allow the approximate local pre-incision course to be identified. As the meander extends as shown, the direction of runoff from location 1 to the river is reversed, and the distance increases. At location 2, the distance to the river is decreased and the local slope steepened. Background tones on the image indicate the karst potential of the mapped geological formations, with darker colors indicating greater potential (Figure 2 from this report). An alluvial meander grows or extends due to erosion on the outside of the bend (cutbank) coupled with accretion on the inside (point bar). In an entrenched bedrock meander, there is erosion (much of it due to slope processes) on the outer bend, though far more slowly than the bank erosion in alluvial channels. On the inside of the bend, there is a so-called slip-off slope, as the ground is beveled off by the lateral movement of the channel. You can see the slip-off slope on the inside of two of the Polly’s Bend loops in the figure below.



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Topographic cross-sections across the apex of two of the Polly’s Bend meanders. Polly’s Bend seems to have been growing during the incision of the Kentucky River gorge. In the next installment I’ll take a look at additional evidence of the growth of the bend, and exactly how much it seems to have grown.



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THE CURIOUS EXPANSION OF POLLY’S BEND Posted 6 August 2015

Though the meander bends in the Kentucky River gorge area are considered to be mostly inherited (i.e., they were there before the river began downcutting about 1.5 million years ago), they are not static features. This continues a previous post looking at Polly’s Bend.

Geologic map of Polly’s Bend (from Kentucky Geological Survey’s Geologic Mapping Service). Ollr, Oto, Ocn are all Ordovician limestones. Qal is Quaternary alluvium, and the stippled pattern with the red + is Quaternary fluvial terrace deposits. Polly’s Bend is about 5 km in maximum width. The bedrock into which the Kentucky River is cut is overwhelmingly limestone and dolomite—certainly it does not contain any quartzites or sandstones. However, the upper portions of the Kentucky River watershed does contain those rock types. Therefore rounded pebbles of quartzite and sandstone cannot be derived from the local or underlying rock; they can only have been transported by water from elsewhere. The presence of such rounded gravels is what was used to map the Quaternary high fluvial terrace deposits (high relative to the modern river) such as the one shown in the figure above. However, these exotic, fluvially-transported gravels well above the modern river level are a lot more common than the geologic maps suggest. The photo below shows such gravels

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about 70 m above the river in the downstream (NW) corner of Polly’s Bend, where no fluvial terrace deposits are mapped. Throughout this area you do not have to look very hard to find them, either.

Rounded sandstone & quartzite gravels on upper slopes near the northwest corner of Polly’s Bend. I also noticed a lot of sand in the soils. You don’t get sand from limestone or any of the local rocks; that also has to have been transported in. That sent me to looking at the soil maps for the area (through the USDA’s Web Soil Survey). Sure enough, many of the soils on the uplands within Polly’s Bend are part of map units that include the Chenault series. This soil type is described as forming on old alluvium deposited on soils derived from weathering of underlying limestone. What I observed is sandier than you’d expect in the Chenault, but entirely consistent with the concept of a soil derived from alluvial sediments over limestone. The soil mappers most likely used the rounded gravels as an indicator.



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Soil map of Polly’s Bend. All the map unit symbols beginning with “C” are dominated by the Chenault series. Hu, ErB, AsB, and No symbols indicate younger alluvial terrace and floodplain soils. Taking into account the old alluvium represented by rounded gravels and the Chenault soils, the slip-off slope topography (see the previous post) and the location of the more modern alluvial and terrace soils, one can estimate the former channel position preincision:

Thick blue line shows estimated pre-incision Kentucky River channel. The former and current channel positions indicate that some portions of the channel have migrated laterally by as much as 1.5 to 2 km! This seems like a lot for a bedrock channel, but only requires a mean rate of roughly a centimeter a year, so it is entirely plausible. But what caused a fairly typical bend for this stretch of river (in terms of its geometry) to develop into an increasingly tight, complex, gooseneck meander? Geomorphic processes



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rarely operate at a steady rate, either. Most likely the migration has occurred in fits and starts, with a few episodes of relatively rapid movement separated by periods of little or no migration. What kind of events might these have been, and what triggered them? And what do all those dolines (sinkholes) tell us?

Surface relief of NW Polly’s Bend—note the small depressions, which are karst sinkholes (dolines; indicated by diamond symbols on the soil maps). I’ll explore these later on, as time allows. Could be a few days or a few months, depending on whatever else comes up to suck up my time or sidetrack my easilydistracted attention.



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FLUVIODIVERSITY Posted 14 August 2015

One of the classic principles/relationships in biogeography is called the species-area curve, relating the number of different species found (usually of some particular taxonomic group; e.g., birds or plants) to the area sampled. These curves are usually well fit by an exponential relationship: S=cAb where S is the number of species, A is area, c is a constant representing the number of species in the smallest area sampled, and b represents the rate of increase of species with area. While b could be greater than 1 if major biogeographical boundaries are transgressed (so that whole new sets of species are encountered), otherwise b < 1, and usually much less; 0.25 is a fairly common value. Juanjo Ibanez and I (in separate studies) found that similar trends apply to soil diversity, with S in this case indicating number of different soil types (e.g., soil series). In his very broad scale analyses, Juanjo also found b » 0.25, while in my landscape-scale studies b was in the range of 0.6. Syntheses of this work are found in the book Pedodiversity (CRC Press, 2013) edited by Ibanez and James Bockheim. The flattening out of the curves (i.e., b < 1) reflects the fact that as more area is sampled, you do find new soils or species, but increasingly you encounter types that have already been enumerated. Does the same general pattern hold for diversity of geomorphology along rivers? I have done geomorphic zonation studies for four different rivers in Texas in support of the Texas Instream Flow Program, along lengths of river ranging from about 210 to 700 km (Sabine, Trinity, Brazos, and Guadalupe Rivers). From this, a relationship between river or valley length sampled and geomorphic diversity can be determined. The geomorphic zones were identified using the River Styles approach pioneered by Kirstie Fryirs and Gary Brierley (see their book Geomorphology and River Management. Applications of the River Styles Framework, Oxford UP, 2005). Geomorphic zones or river styles were identified based on similarities of geological setting, hydrologic regime, valley confinement, dominant substrate types, morphometric properties such as slope and sinuosity, planform, and other properties relevant to specific rivers such as presence or absence of sandy point bars, avulsed reaches, and dam effects. Using these maps, for each river I sampled every 10 km in the upstream-downstream direction, counting the total number of geomorphic zones or river styles encountered. Rather than power functions of the type found in soil richness and species-area curves, the relationships are all linear—the more channel you sample, the more styles (geomorphic diversity) you encounter, with the geomorphic diversity increasing in direct proportion to length.

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Results for Guadalupe River In some cases—particularly the Guadalupe River—the studied section passes through several different ecoregions and physiographic provinces. However, in the Trinity example (basically the river from Dallas downstream) this external variability is much more limited, and the Sabine (downstream from Toledo Bend) is entirely within the coastal plain. In all four cases, the study areas extended to the mouth of the river at the coast. Thus, in the lower reaches it was inevitable that new river styles associated with deltaic environments and coastal backwater effects would be found—thus the curves could not flatten out at the very end. But even if this is discounted—for instance, if you chopped off the lower end--the relationships would still be linear. The relationships are given below, where RS = number of river styles and L = river length or distance downstream. Sabine: Trinity: Brazos:

RS = 0.0272 L + 0.6668

R2 = 0.96

RS = 0.0292 L + 2.5175

R2 = 0.98

RS = 0.0559 L – 3.0732

R2 = 0.98

Guadalupe: RS = 0.0161 L – 0.0552

R2 = 0.97

I need to think about this some more, but right now two potential (and not mutually exclusive) explanations come to mind. First is the inherent variety of rivers and landforms. As I have argued before in many contexts, any geomorphic system represents a specific combination of environmental

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controls and history unlikely to be duplicated elsewhere. Thus, even within a single river system, as you move down (or up) stream you inevitably encounter new, unique combinations and thus new geomorphic zones. The nature of rivers is such that, even where the external environmental controls are constant, along the channel discharge is systematically changing, new tributaries are encountered, and local disturbances occur. Second is the way geomorphic zones or river styles are identified. It is not a classification system in the sense of pre-existing categories (such as biological species or soil taxa) that river segments are placed into (there are such classification systems; it would be interesting to do a similar analysis using a pigeon-hole type classification). Given that one is not obliged to find a best-fit category, this reduces the likelihood that duplicate categories will arise. It would be interesting do a similar analysis for more detailed levels of geomorphic classification, such as geomorphic and hydraulic units. In the lower Sabine, geomorphic units (GU) and hydraulic units (HU) have been identified, and linked to the geomorphic zones, but not mapped in such a way as to enable a length vs. diversity analysis. The six river styles of the lower Sabine contain 35 GUs and 82 HUs within the channel. Some GUs occur throughout; others are restricted to particular river styles. Some HUs are widespread, but none occur in every river style. Thus, while I cannot show you a quantitative analysis, I can say with confidence that, at least in the Sabine, a similar plot of GUs and HUs would show a steady, probably linear, increase with length or distance. Also, what about a species-length curve along a river, examining the rate of increase in the number of fish, diatom, or aquatic macrophyte species along the channel? Since geomorphic categories are closely related to habitats, would the trends be similar? Or are species habitat preferences more general (e.g., a particular critter or microbe may need or prefer a muddy pool, but beyond that the other geomorphic aspects of the setting may not matter)? Lots to be done here! The technical reports containing the geomorphic zonations can be obtained here. The relevant titles are: Hydraulic Units of the Lower Sabine River (2011). Flow Modifications and Geomorphic Thresholds in the Lower Brazos River (2013) Geomorphic Processes, Controls, and Transition Zones in the Middle and Lower Trinity River (2008) Field Data Collection in Support of Geomorphic Classification of the Lower Brazos and Navasota Rivers (2007) Geomorphic Processes, Controls, and Transition Zones in the Guadalupe River (2011)



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BANK FULL OF IT Posted 26 August 2015

Fluvial geomorphologists, along with hydrologists and river engineers, have long been concerned with the flows or discharges that are primarily responsible for forming and shaping river channels. In the mid-20th century it was suggested that this flow is associated with bankfull stage—the stage right at the threshold of overflowing the channel—and that this occurs, on average, about every year or two in humid-climate perennial streams. If you have to choose just one flow to fixate on—and sometimes you do, for various management, design, and assessment purposes—and have no other a priori information about the river, bankfull is indeed the best choice. But, of course, nature is not that simple. Some streams have more than one (range of) discharge(s) that are critical in forming or maintaining the channel. Some channels and some discharge regimes are in the process of changing or adjusting to new environmental constraints, such that the whole idea of a single formative discharge is a moving target. Some streams undergo cycles—or perhaps episodes is a better word—of channel infilling and excavation. Sometimes, even within humid temperate climates, the bankfull flow does not correspond with a 1- to 2-year recurrence interval. And where it does, it is typically so only when you calculate it using the annual maximum discharge, not using partial duration, daily, or other series.

Banks of the Kentucky River Without even going into streams in other climate regimes, or bank geometry that makes it difficult in some cases to define exactly where the bank tops are, you have compound channels. Here major incision events or episodes create a large macrochannel, with inset floodplains or benches defining a smaller channel within (no doubt there other scenarios for compound channels, too). The relationship between bankfull flow and the year-or-two mean recurrence interval has become so entrenched that there exist techniques designed to identify a “bankfull” level within incised channels where the expected recurrence interval discharge does not correspond with the bank tops (incidentally, that’s why I use the term banktop flow to

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avoid confusion). Anyway, a couple years ago I did a study looking at threshold discharges along a 681 km reach of the lower Brazos River, Texas, for thalweg connectivity (to maintain continuous downstream flow), bed inundation (the entire river bed is inundated), high but subbanktop flows, channel-floodplain connectivity stages (where water is exchanged between the channel and floodplain, and overbank flow. I also estimated thresholds for transport of sand bed forms and medium gravel, and for cohesive bank erosion. The article based on that study just came out in Hydrological Sciences Journal. I’ve pasted in the abstract below, but the headline is that no single flow is dominant either hydrologically or geomorphically, and the one to two-year flood has no special significance. Also, due to backwater flooding of tributaries, high-water subchannels that are activated by sub-banktop stages, and occasional gaps in the natural levee, channelfloodplain connectivity occurs at much lower discharges than overbank flooding. I know from my own studies and experience that these phenomena also occur in other rivers of the region, and from the literature that there are many streams where no single flow is dominant. It will be interesting to see where future studies of reference, critical, threshold, or channel-forming flows take us.



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BEDROCK CHANNEL EROSION Posted 21 May 2016

There are four main mechanisms of bedrock channel erosion—abrasion, dissolution, cavitation, and weathering-and-plucking. The latter occurs when weathering along joints and bedding planes of the bedrock loosens slabs or clasts, which are then entrained (plucked) during high flows. Cavitation is difficult to observe or prove in the field, but likely occurs in the stream I visited this week, Raven Run (near Lexington, KY).1 The other mechanisms all clearly exist. Weathering and plucking is the dominant erosion mechanism of the bedrock streams hereabouts—the photo shows the flat surfaces and angular features that result from weathering along the horizontal bedding planes of the limestone and the frequent vertical joints, and subsequent removal of the resulting slabs.

Raven Run, Kentucky. In this reach of Raven Run, however, there are numerous potholes and cavities that result from a combination of dissolution (this is a fluviokarst area, after all) and abrasion. Abrasion is not dominant, because it requires “tools” (gravel to abraid the bedrock), and there isn’t much here, and few are rounded, as generally occurs with abrasional “grinders.” However, the near-circular shape of some of the potholes and presence of grinders in some of them indicates that some abrasion is going on. In addition to the potholes, there are lower-relief sculpted forms on the channel bed indicating dissolution. In the photo below, check out the difference between the moss coverage on the left side of the photo on the shaded north slope, compared to the slightly less shaded south slope. What is the relationship between the moss cover and channel processes? The moss probably facilitates weathering during lower flows by holding moisture, adding CO2 and organic acids to it, and facilitating microbial activity. During higher flows, does it provide significant protection against abrasion? What kind of hydraulic conditions or



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abrasive bedload transport does it take to remove the moss cover? Danged if I know, but there’s a thesis there for somebody.

Raven Run, Kentucky. 1Note added August, 2017: A recent article raises serious doubt as to whether

cavitation is actually a significant process in natural channels: Carling, P.A. et al. 2017. The bubble bursts for cavitation in natural rivers: laboratory experiments reveal minor role in bedrock erosion rates. Earth Surface Processes & Landforms 42: 1308-1316.



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LESSONS FROM ICICLE BEND Posted 26 January 2017

Last year, my fluvial geomorphology class investigated the origin of an unusual overhanging bedrock stream bank on Shawnee Run, Kentucky. Because on our first field visit the cliff above was festooned with large (up to 2 m long) icicles, we named it Icicle Bend. In the course of our fieldwork, we discovered what we eventually determined to be higher level paleovalley of Shawnee Run nearby. Stream channel changes by cutoffs, avulsions, and capture happen all the time. But, invariably, the new, "winning" channel represents a more efficient (i.e., more direct and steeper) path. In this case, however, the opposite appeared to have happened--a shorter, steeper path represented by the paleovalley was abandoned during general downcutting of the stream for the modern path via Icicle Bend. PhD student Tasnuba Jerin and I decided to further investigate this anomaly. To make a long story short, we found that the abandonment of the old channel was associated with capture of streamflow by a subsurface karst conduit, which was later uncovered. Beyond being an interesting field problem, the case indicates (or reinforces) that the laws governing flow paths operate on a very local scale--thus achieving maximum efficiency at a given point (in this case, where the conduit opened in the stream channel) may not lead to a more efficient path at the broader scale.

Inferred evolution of the Icicle Bend area, with the modern topography as a background, adapted from Figure 9 of the article. A is pre-capture, with a presumed groundwater flow following general trends of major joints. In B, flow is diverted into the subsurface route, with the surface route drying up. At stage C, the former surface route has been



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abandoned, with groundwater flow connecting the upstream and downstream portions of Shawnee Run. Subsequent exhumation or collapse of the karst conduits produces the contemporary situation (D). The article based on this work was recently published in Geomorphology. The abstract is below: Development of fluvial systems is often described and modeled in terms of principles related to maxima, minima, or optima of various hydraulic or energy parameters that can generally be encompassed by a principle of efficiency selection (more efficient flow routes tend to be preferentially selected and enhanced). However, efficiency selection is highly localized, and the cumulative effects of these local events may or may not produce more efficient pathways at a broader scale. This is illustrated by the case of Icicle Bend on Shawnee Run, a limestone bedrock stream in central Kentucky. Field evidence indicates that a paleochannel was abandoned during downcutting of the stream, and the relocation was analyzed using a flow partitioning model. The bend represents abandonment of a steeper, straighter, more efficient channel at the reach scale in favor of a longer, currently less steep and less efficient flow path. This apparently occurred due to capture of Shawnee Run flow by a subsurface karst flow path that was subsequently exhumed. The development of Icicle Bend illustrates the local nature of efficiency selection, and the role of historical contingency in geomorphic evolution.



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See also: Entry Dynamic Equilibrium(?) in Rivers Changing Lanes The Principle of Gradient Selection Δ Deltas Why Them? Why There?









Section







Earth Surface System Theory







Earth Surface System Theory







Earth Surface System Theory







Environmental Management & Applied Geoscience







Environmental Management & Applied Geoscience

Herbert River, Queensland, Australia





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Envrionmental Management and Applied Geoscience

Waipaoa River, North Island, New Zealand



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THE SEMANTICS OF RESILIENCE Posted 10 October 2014

Resistance of environmental systems is their capacity to withstand or absorb force or disturbance with minimal change. In many cases we can measure it based on, e.g., strength or absorptive capacity. Resilience is the ability of a system to recover after a disturbance or applied force to (or toward) its pre-disturbance condition—in many cases a function of dynamical stability. In my classes I illustrate the difference by comparing a steel bar and a rubber band. The steel bar has high resistance and low resilience—you have to apply a great deal of force to bend it, but once bent it stays bent. A rubber band has low resistance and high resilience—it is easily broken, but after any application of force short of the breaking point, it snaps back to its original state. These definitions are broadly consistent in use in Earth sciences, systems theory, and engineering. Unfortunately (at least for interdisciplinary communication), the term is used differently in ecology. Ecologists often use resilience in a way that overlaps with resistance as defined above, defining it as the amount of disturbance that an ecosystem could withstand without changing self-organized processes and structures (defined as alternative stable states). This follows C.H. Holling, who in 1973 popularized ecological resilience (though apparently Elton used the term in ecology in 1958), and termed resilience as I defined it above “engineering resilience”. It didn’t help that Holling defined stability as something separate from resilience, when by some definitions dynamical stability is an indicator of resilience. It gets even more confusing when you consider that a few ecologists use something like my definition of resilience above, and more confusing yet when you account for the varied way the term is used in the literature of, e.g., environmental management and policy, natural hazards, political ecology, sustainability studies, etc. In fact the term seems to generate a lot of debate in those fields, though more about implications and interpretations of resilience than its definition. My introduction to resilience concepts was via geosciences and systems theory. Thus it came as a surprise to me when some geomorphology colleagues reported that, when using the term resilience as dynamical stability, they ran afoul of ecologists in the audience, who argued that they were using the term incorrectly. They weren’t, of course—they just weren’t using it the same way that ecologists often do. There is unlikely to be any agreement across the sciences on a single, unified definition. Thus, the best we can do is define explicitly what we mean when we use the term.



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THE INHERENT EPHEMERALITY OF WETLANDS Posted 22 December 2014

As a citizen, an environmentalist, and a scientist, I am absolutely committed to the conservation and preservation of wetlands. The ecosystem services provided by wetlands are immense; their hydrologic, ecologic, economic, and aesthetic values are long since beyond serious question. However, as we strive to protect these inarguably valuable resources, we need to keep one thing in mind—marshes, swamps, bogs, and other wetlands are inherently and irreducibly subject to change. First, many of them are geologically ephemeral. They are recently formed and very young in geological terms, and under no circumstances would they be expected to remain static—geomorphically, hydrologically, ecologically, or locationally—for very long. The estuaries of the Gulf coast of the U.S., for example—and their associated tidal flats, salt and freshwater marshes, mangrove swamps, freshwater swamps, etc.—were established in approximately their current locations only about 3000 years ago. That’s nothing in geological time. Even at that, both the external boundaries and internal dynamics have been anything but static in that time, and change is ongoing. This kind of youth and dynamism is the rule, not the exception, for wetlands around the world.

A seasonally-flooded wetland (and a favorite fishing spots) in Mercer County, KY. Second, the creation and maintenance of a particular type or form of wetland depends on a specific set of interactions among hydrologic, geomorphic, and ecological processes. All of those can change as sea-levels rise and fall, as climates get wetter or drier, as rivers migrate and shift their channels, as plants and animals disperse—not to mention any number of human modifications! All of those can also change due to the inherent, internal evolutionary dynamics of ground water flow systems, channels, soils, landforms, and biotic communities. And when any factor (topography, hydrology, vegetation, etc.) changes, it affects all the others.



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Inventories, maps, or estimates of wetlands should recognize their dynamism—that is, these are inevitably snapshots (stills from a continuous movie, if you will). Detection of change in and of itself is not a cause for concern. The known or potential drivers of those changes, and net losses of wetlands (globally or locally) are a cause for concern. We can and should expect to continue to have wetlands, but we should not expect them to remain constant for long periods. Protecting and conserving wetlands requires that we allow them to move. They have to be able to respond to these environmental changes geographically—sometimes by expanding or contracting; sometimes by shifting positions. Protecting and conserving wetlands requires that we allow them to change. Some wetland changes wrought by human actions are avoidable and deleterious (certain wetland destruction is). However, we should be aware that they are gonna change, with or without humans. So we need to recognize that change per se—e.g., a shift in vegetation communities, the infilling of an oxbow lake, fringe erosion of a salt marsh—is not a cause for concern. Protecting and conserving wetlands requires that we protect and conserve wetlands. Their inherent ephemerality and dynamism should never be used as an excuse to let miners, developers, etc. destroy and degrade wetlands. This may seem self-evident, and no more likely than someone arguing that it should be OK to kill protected species because new ones will be born. However, we’ve seen many arguments that we need do nothing about human impacts on climate because climate can change without human agency, so I thought I should make this point clear.



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DUST BOWL DYNAMICS Posted 23 December 2014

A conversation with other scientists about severe, dust-bowl type wind erosion and erosion risks got me to thinking about the key interrelationships involved. The severe erosion and land degradation in the U.S. Great Plains in the 1930s was a combination of a particular confluence of environmental factors that set up aeolian erosion risk (climate, periodic low soil moisture, topography), a prolonged drought, and human factors (replacing natural grassland vegetation with crops that left fields bare part of the year). In other areas where the environmental risk factors are present, how stable or resilient is the landscape to severe wind erosion?

Archival photo from Kansas showing cropland degraded by wind erosion in the 1930s. They key factors, as I seem them, are wind erosion, soil moisture, and vegetation cover. The key interactions are shown below. Negative links in this case mean that a change in one component results in a change in the other in the opposite direction. Thus, for instance, a decrease in soil moisture, other things being equal, leads to an increase in wind erosion, and increased soil moisture to reduced erosion. A positive link indicates that a change in one component leads to a change in the other in the same direction. Thus the only positive link in the model below shows that increases or decreases in soil moisture lead to corresponding increases or declines in vegetation cover. The dotted lines in the figure are undetermined. The vegetation to soil moisture link could be positive or negative. Denser vegetation could increase soil moisture in some cases due to shade effects and the role of soil organic matter in promoting moisture storage. On the other hand, plants use water, and denser vegetation could draw down soil moisture. The erosion to soil moisture link could be negative (due to loss of soil moisture storage capacity as

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topsoil is lost) or negligible (i.e., no arrow), if topsoil or soil thickness is not limiting with respect to water storage. The self-limiting negative links reflect the fact that soil moisture, wind, and vegetation all may be limited by factors other than each other, such as climate, nutrients, geomorphic processes, etc.

The dynamical stability (resilience) of the system can be determined using the RouthHurwitz criteria. This analysis shows that instability is possible when the vegetation cover to soil moisture link is positive, and the aeolian erosion to soil moisture link is negative. Dynamical instability indicates that the system is vulnerable to small changes and disturbances (i.e., changes in soil moisture, wind erosion, or vegetation). If the vegetation to moisture link is negative, and the effects of erosion on soil moisture are unimportant, the system is stable and therefore resilient to non-catastrophic changes. So what does this all mean? First, it may provide a framework for assessing vulnerability to land degradation by wind erosion. Second, it highlights key research needs to make such assessments—specifically, how soil moisture responds to soil loss from aeolian erosion and to changes in vegetation cover, which no doubt vary in specific environments. A final note: instability is not always a bad thing. If the system is already degraded or threatened, dynamical instability suggests that measures such as erosion control or vegetation establishment can “flip” the system to a more preferable state.



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(Archival photo)



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Δ DELTAS Posted 23 January 2015

Several studies have noted the temporal coincidence between shoreline erosion around some major deltas (e.g., Nile, Mississippi, Ebro), and the reduction of stream sediment loads due to reforestation, soil conservation practices, and trapping of river sediment behind dams. There are, of course, excellent reasons to suspect a causal link, but the link itself has not, in my view, been fully established. First, in many cases it has not been established that reduction of soil erosion within river basins and sediment trapping behind dams has reduced sediment delivery to the coastal zone. In some cases, particularly where rivers cross a coastal plain, there are sediment “bottlenecks” that limit sediment delivery to the coast. While the upstream changes do indeed reduce fluvial sediment loads in parts of the river, and may well reduce input to the bottlenecks upstream of the deltas/estuaries, it may be that even with the reduced loads there is still as much sediment as the river can carry in its lower reaches. Lower reaches of coastal plain rivers are often characterized by extensive sediment storage space, frequent overbank flow that delivers sediment to these storage zones, and very low stream power. It is often difficult to assess changes in sediment actually getting to deltas and estuaries because the downstream-most gaging station is well upstream of the coast, and of any lower-river transport bottlenecks (for more extensive discussions and case study evidence, see this, this, this, this, this, and that). Some studies also don’t pay attention to other factors that influence delta erosion and deterioration, such as (accelerated) sea-level rise, subsidence, compaction, and human modifications that either limit sedimentation (e.g., flood control levees), prevent avulsions that may be important in deltaic sediment distribution (e.g., channel stabilization), or contribute to delta wetland loss (dredging, canals, etc.). The point is that in some cases deltas could be experiencing net erosion even where river sediment supply is not decreasing.



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False-color LANDSAT image of the Mississippi River delta. It is well established that human settlement and expansion dramatically increased soil erosion and river sediment loads in many cases due to deforestation, agriculture, mining, etc. To the extent reforestation, soil conservation and sediment controls reduce erosion, and dams trap sediment, this could be viewed as returning the sediment regime to its premodern situation. Thus, if we want to say that choking off this excess anthropic sediment is starving deltas, shouldn’t we also observe that the excess anthropic sediment was resulting in delta progradation before? I did a quick-and-dirty literature review on this. I was not thorough enough to draw any conclusions, but I did see enough to know this: the evidence is mixed and varied. There are some cases where accelerated soil erosion can be shown to have led to delta growth, some cases where this is unclear, and some where no evident delta growth occurred. Resolving this question means evaluating each river and delta on a case-by-case basis, accounting for the possibility of sediment bottlenecks, and considering the other factors that influence delta growth or erosion.



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WHY THEM? WHY THERE? Posted July 16 2015

In Johnson County, Kentucky, today, lots of people along Patterson Creek are wondering “why me?” A flash flood Monday (July 13) tore through that eastern Kentucky community, leaving three people dead, a dozen missing at one point, and destroying about 150 homes and who knows how many cars, barns, etc. (news story). As a Kentuckian, and as a veteran of a couple of hurricanes back in 1996 in North Carolina, I sympathize with wondering why you, or your community, got hit while others didn’t. As a geomorphologist and hydrologist who was worked on flash flooding in the southern Appalachians, I also wonder about the scientific aspects—why the severe flood event in this particular location? Make no mistake—the area around Flat Gap is not the only one in Kentucky that has gotten a lot of rain recently, and high water, runoff, soil erosion, and filled-up sinkholes are common lately throughout eastern and central Kentucky. But why the much more severe flooding at Patterson Creek? Did they get more rain? The eastern Kentucky climate division that includes Johnson County was recorded as having received 2.28 inches of rain July 13-15, and 3.21 in over the July 9-15 period (1 inch = 25.4 mm, the preferred SI unit for precipitation). This is about 2 inches over what would be considered normal for the period. The University of Kentucky Agricultural Weather Center’s (http://wwwagwx.ca.uky.edu) modeled precipitation maps based on observed rainfall shows the entire eastern Kentucky region with 3 to more than 4 times normal precipitation for the week ending at 19:00 (EDT) July 15. But note that some areas (Louisville vicinity, southern Ohio) are even higher:



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National weather service radar-based precipitation for 08:00 am July 12 to 08:00 July 15 shows a splotch of red, indicating precipitation of 6 inches or more, exactly where the Patterson Creek flooding occurred. The 7-day radar precipitation map also shows a big red streak that includes the NW corner of Johnson County. But also note that precipitation was equally high in a number of other sites in eastern and central Kentucky that experienced no more than nuisance flooding. It is also worth noting that creeks immediately adjacent to Patterson Creek did not experience catastrophic flash flooding.



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Topography and soils? In eastern Kentucky and the southern Appalachians flash flooding is relatively common. The regional climate is such that intense frontal or thunderstorm precipitation occurs now

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and then. Slopes are steep and soils are generally thin, both of which tend to increase runoff and the rapidity of runoff response. Thus the region is prone to occasional flash floods, which of course can be exacerbated when surface mining, logging, and other land uses which tend to increase runoff occur. But the Patterson Creek watershed does not appear to be atypical of the region with respect to topography or soils. Land use In the Google Earth image below, Patterson Creek valley is in the middle. The unforested areas you see on the ridgetops are former surface mine sites. The effects of surface mining on runoff and flooding in eastern Kentucky depend quite a bit on local details of soil, topography, vegetation, mining and reclamation practices, and local hydrology. However, the most common impact is an increase in the amount and timing of runoff, which contributes directly to flash flooding.

Patterson Creek is, alas, hardly unique within the region with respect to having been strip-mined for coal. So while the mined lands are a reasonable suspect in the recent flood, their presence alone does not explain why Patterson Creek flooded while others did not. Patterson Creek is also not unique in having residents along the valley bottom and thus vulnerable to flooding. Due to the steep slopes of the region, valley-bottom settlement is common in the region, and often the only practical option for farming or homesteading.



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A perfect storm? Likely the explanation is a combination of heavy localized precipitation (the spatial variation of which often occurs on scales too fine to show up in radar measurements) falling on already-soaked ground in an area predisposed to flash flooding, flowing into a creek which was probably running high to start with. Only detailed onsite work could even hope to settle the extent to which meteorological, topographic, soil, and land use factors contributed. Due to a relatively sparse network of rain gages, the coarse resolution of radar-based precipitation estimates, and no stream gages on Patterson Creek or any other smaller stream in the region, thus would be difficult to solve. However, incidents such as this illustrate a broader point of the combined, interacting effect of environmental factors and history (from precipitation the previous few days to the legacy of land use) at a particular place and time to produce unexpected results. For risk and hazard management, perhaps this suggests the need to try to identify and map combinations of risk factors (such as active or reclaimed surface mines in low-order watersheds).



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SOIL EROSION RISES AGAIN! Posted 18 March 2016

In the 1930s, the Dust Bowl and the legacy of massive post-Civil War cut-out-andget-out logging and, particularly in the south, of what amounts to shifting cultivation brought a soil erosion crisis to the attention of the USA and the world. In the 1980s, a realization that problematic erosion persists despite great improvements in soil conservation and a heightened concerned with nonpoint source pollution from agriculture brought renewed attention to erosion, this time focused particularly on off-site impacts. On-site impacts of soil erosion are the environmental degradation and lost productivity due to soil loss, while off-site impacts are related to pollution and costs associated with where the soil ends up. Now, we are at it again, with another wave of attention to soil erosion.

Eroded farmland in Alabama, 1930s (WPA photo by Arthur Rothstein). A recent meta-analysis of global soil erosion rates by a group of Spanish geomorphologists and soil scientists in Geomorphology reveals a surprising amount of variability in measured and estimated erosion rates that has generated a great deal of discussion. And physical geographer Frans Kwaad (University of Amsterdam) has reinvigorated discussion of the relative onsite and offsite impacts of erosion. Finally, loss of peat soils to fire and drainage has led Indonesia to offer a $1 million prize for developing a method to quickly and accurately map the



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country’s peat soils.

Eroded field in the Netherlands (F. Kwaad photo). The meta-analysis by Garcia-Ruiz et al. (2015) considered published data from >4000 sites worldwide, finding that “there is extraordinarily high variability in erosion rates, with almost any rate apparently possible irrespective of slope, climate, scale, land use/land cover and other environmental characteristics.” In general, results do not refute the known relationships of water erosion with precipitation, topography, and land use, but the amount of scatter is enormous. More tellingly, Garcia-Ruiz et al. found that apparent rates vary with the method used to measure or estimate soil loss, size of the study area, study duration, and time scale considered. From their abstract: “ . . . the data suggest that only order of magnitude approximations of erosion rates are possible, and these retain a very large degree of uncertainty. Consequently, for practical purposes such as calculation of global sediment budgets, empirical equations are not a substitute for direct measurements. Our results also show that a large proportion of the experiments have been short-term (less than 3 years), which reduces dramatically the reliability of the estimated erosion rates, given the highly non-normal behavior of soil erosion (time-dependency). Despite the efforts already made, more long-term measurement experiments need to be performed, especially in regions of the world that are under-represented in global datasets.”



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Erosion rates vs. annual precipitation. Check out the scatter! Figure from Garcia-Ruiz et al. (2015). The paper contains the usual call for more data, and the equally usual plea for standardized measurement and reporting procedures. However, in this case there is a better-than-usual case for such calls. But let me add a few caveats. First, while Garcia-Ruiz et al. (2015) cast plenty of legitimate doubt on our globalscale understanding of soil erosion rates, that does not necessarily question or invalidate individual studies in context. For instance, you can (and should) question whether erosion-plot results from a cornfield in Ohio measured over two years are comparable to profile truncation estimates covering a 100-year period in North Carolina. But if the studies in question are competently conducted, then you have good data on plot-scale soil loss from a cornfield in a particular 2-year period, and on net soil surface lowering over a century. Second, don’t let the results make you think we don’t understand erosion causes and processes. We do. There is, of course, always more to be learned, but the basic mechanisms are well understood, and can generally be worked out pretty well for any given situation. The problem is that those situations are extraordinarily variable, and it is thus difficult to generalize from one site to another.

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Third, the results underscore the geomorphic folk wisdom that the answer to every question is “it depends on the scale.” With respect to time scales and study durations, we have long known (for a variety of geomorphic processes, not just soil erosion) that longer periods generally yield lower mean rates. This is because the longer the time period, the more likely it is to include periods where nothing much is happening. With respect to area, and spatial scale, it has also been known that soil loss is dominated by hotspots of high erosion, and that within a watershed a small proportion of the drainage area produces a large proportion of the sediment. If you are measuring a hotspot you’ll get high rates, and otherwise you’ll get lower rates. If you sample or measure a large area, the cumulative results will underestimate the hotspots and overestimate the rest. Also, the larger the area the more opportunity there is for temporary storage of eroded sediment, and thus the measurement technique comes into play—measurements of local removal may overestimate the amount of soil leaving the area, while measurements of output from the entire area will underestimate local soil loss. Finally, uncertainty over quantitative rates of erosion in no way invalidates solid qualitative evidence of erosion problems. If topsoil is removed or thinning, there’s an erosion problem.* If infertile or less fertile subsoils are being exposed, there’s an erosion problem. If eroded sediment is clogging ditches or streams, or piling up along fences or field edges, there’s an erosion problem. If rills appear after a rainstorm, there’s an erosion problem. If you see exposed tree roots or gullies or lag deposits or erosion pavements, there’s an erosion problem.

No matter what technique you use, or what numbers you get, or how certain you are of those numbers, this area of the Rif Mountains, Morocco, has an erosion problem (F. Kwaad photo).



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*”Problem” assumes that the erosion is detrimental to human interests and/or ecosystem services. Certainly erosion occurs naturally, and sometimes with no net adverse impacts. From a purely scientific perspective, you could substitute “accelerated erosion” for “an erosion problem,” with accelerated indicating removal rates well in excess of soil formation or upbuilding rates. NEXT: onsite vs. offsite costs. ________________________________________________________________ Garcia-Ruiz JM, Begueria S, Nadal-Romero E, Gonzalez-Hildago JC, Lana-Renault N, Sanjuan Y. 2015. A meta-analysis of soil erosion rates across the world. Geomorphology 239: 160-173.



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SOIL EROSION: COUNTING THE COSTS Posted 18 March 2016

In my previous post (Soil Erosion Rises Again!) I noted a recent spike in interest in erosion and soil conservation, following previous ones in the 1930s and 1980s. One manifestation is the work of Frans Kwaad, a Dutch physical geographer, who has reinvigorated discussion of the relative onsite and offsite costs of erosion. Onsite (economic) costs are generally related to declines in crop or grazing productivity, or the loss or degradation of economically productive land. Offsite costs are associated with pollution and infrastructure damage associated with the deposition or delivery of eroded sediment, habitat damage or destruction, nuisance costs of removal of deposited sediment, etc. Kwaad’s work-in-progress synthesizes a number of studies and data sources, and on-balance indicates that off-site costs are greater.

Cleaning up eroded soil after a storm in the Netherlands (F. Kwaad photo). A key message is that no matter who does the accounting and how they do it, on- and offsite costs of erosion are truly massive, and easily justify any imaginable expenditures on soil conservation, erosion control, and stormwater management. But, as Kwaad and many of the sources he cites acknowledge, such estimates are problematic. Soil quality is only one of many factors that influences crop productivity, and productivity is only one of several factors that influences monetary values of crops. Similarly, off-site costs of erosion are often entangled with other factors such as, e.g., pesticides, metals and nitrates sorbed to the eroded soil. Second, most of the studies deal with intensive, mechanized agriculture. In such systems, productivity declines associated with reduced soil fertility is often masked

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with additional inputs of synthetic fertilizers, fossil fuels, and other inputs. There are also many technical issues associated with the monetization of environmental impacts.

Sediment-choked channel during a storm in northern Morocco (F. Kwaad photo). Maybe most problematically, even if monetization was an exact science, it is only one way of tallying the score, and many would argue that it is not the best or most important. Loss of crop productivity in a subsistence agriculture system, for example, is far more devastating than in industrial agriculture, even though the monetized value may be less. And when ecosystem services, or a favorite fishing spot, are ruined essentially forever, how can you put a value on that? We live in a society that fetishizes money and numbers, and it is not only inevitable that these will be used to assess impacts, but necessary to address power brokers in language they understand and accept. They key points, to me, are that soil erosion has costs, monetary and otherwise, onsite and offsite. Those costs are fairly easily minimized, as soil conservation and erosion control techniques and technologies are well developed. If monetizing the costs gets attention, and helps justify taking those measures, then so be it. But we should recognize that soil is the natural capital on which the rest of the economy (and most important things) depends!



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Soil erosion is a food security issue in India, among other places (http://www.indianbureaucracy.com/loss-of-fertile-soils-a-food-security-r...)



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THE NITROGEN BOMB Posted 2 July 2017

I just finished reading Paul Bogard's The Ground Beneath Us, (I recommend it), which among other things warns us yet again about the serious issues--environmental, economic, public health, food security--associated with over-reliance on chemical and fossil-fuel intensive industrial agriculture. It's a good 40-years-later follow-up to Wendell Berry's classic Unsettling of America: Culture and Agriculture (Sierra Club Books, 1977). It also reminded me of a much more technical and difficult book I read a few years back, Jozef Visser's Down to Earth, subtitled "A Historical and Sociological Analysis of the Rise of 'Industrial' Agriculture and the Prospects for the Re-rooting of Agriculture in the Local Farmer and Ecology. Visser, who has graduate degrees in chemistry and a long career in agricultural chemistry, returned to graduate school later in life to produce this book, which is his dissertation from the University of Waginengen (Netherlands). A pdf is available free at the link above, and I recommend it.

Down to Earth has much to say on a complex, multi-faceted topic, but one thing it says

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that I can't recall seeing anywhere else is that the rise of synthetic nitrogen fertilizer is not only directly linked to the munitions industry, but promoted by collusion between the munitions/agrochemical industry, government, and government-supported scientists. In the early 20th century a couple of German scientists/engineers developed a method for converting N2 gas (a form of N not usable by plants or for making explosives) to nitrate and ammonia. It's called the Haber-Bosch process; Fritz Haber and Carl Bosch were later named the most influential chemical engineers of the 20th century. During World War II the industrial capacity for Haber-Bosch-ing skyrocketed to produce explosives. After the war, even though many of the European facilities had been destroyed, the bomb makers had lost the explosives market, and turned their attention to producing NO3 and NH4 for synthetic fertilizers. Synthetic N production and artificial (as opposed to organic) fertilizer use skyrocketed. Even in sources focused mainly on the problems associated with synthetic N, this turn of events is reported as basically a happenstance. In agrochemical-friendly accounts, it is presented as a heroic tale. What Visser shows, however, is that the rise of synthetic N fertilizer involved collusion (conspiracy might not be too strong a word) between the chemical companies, government entities, and some academics supported by the chemical industry and government agencies. And since the U.S. had far more N-making capacity (our factories didn't get blown up in the war), much of this collusion took place in the U.S. (though the German chemical giant BASF, the first to implement the HaberBosch process, is also a major player). You can't really blame the manufacturers for trying to find new markets (well, you can, but you can't really expect any other behavior). And you can't be too surprised to see government agencies helping to bail out an industry. But Visser makes a strong case that the U.S. Department of Agriculture not only favored research on synthetic fertilizers, but actually suppressed science questioning the need for them or supporting the superiority of organic N. For all but perhaps the agrochemical industry, it is recognized that agricultural in the U.S. and other industrialized countries has an over-reliance on synthetic N fertilizer, and that in terms of plant nutrition and ecological values, organic N is superior. There is also no real disputing that the overuse of synthetic N is taking a serious toll on natural resources. And, as mentioned above, even the agrochemical industry does not dispute that the manufacture of synthetic N fertilizer evolved from the munitions industry. What Visser makes clear, and that I wish other scholars would follow up on, is the deliberate collusion between industry and government to make it all happen. It's a lesson even more worth considering in the current environment where heads of U.S. government agencies (Scott Pruitt at EPA being the obvious example) are getting their advice from and having their agendas set by the very entities they are supposed to be overseeing.



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See also: Entry

Section

Climate Change and Environmental Management Drowning the Coast Soil Erosion & Climate Change

Responses to and Effects of Climate Change & Sea Level Rise

River Restoration and Rehabilitation

Rivers and Streams

Manly Beach, New South Wales, Australia



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Geomorphology Theories & Notions of Landscape Evolution; Visions of the Practice of Geomorphology

Flinders Ranges, South Australia



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THE CYCLE OF EROSION Posted 11 January 2015

Out on the trails of Shaker Village at Pleasant Hill, Kentucky, this morning, I got to thinking about William Morris Davis’ “cycle of erosion” conceptual model (also called the geographical or geomorphological cycle). The drive-by, oversimplified version is that landscape evolution starts with uplift of a more-or-less planar, low relief surface. Weathering and erosion goes to work, and results in an initial stage of increasing relief as streams carve valleys, and slope processes operate on the slopes thereby created. Eventually, however, as the streams begin to approach base level, a new stage of decreasing relief begins as hilltops and drainage divides are lowered and valleys infilled. This continues until the entire landscape is about as close to baselevel as the geophysics of mass transport will allow, creating a low-relief, almost-planar surface called a peneplain. At some point a new episode of uplift occurs and the cycle begins anew. I was thinking of this because many landscapes in the world, like the one I was viewing this morning, do give the impression of a dissected plateau or a low-relief surface into which denudational processes have cut.

Google Earth image of part of the landscape at Shaker Village, KY. As historians of Earth science—as well as almost anyone who’s had a geomorphology course—is aware, Davis’s cyclical concepts dominated geomorphology (as well as those aspects of historical and structural geology that overlap with geomorphology) for much of the 20th century. The theory is flawed, and a backlash began in the 1950s, and it is very much out of fashion, though it persists in areas of Earth science other than geomorphology. Why was the Cycle of Erosion so successful, and so dominant, for so long? There are a

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number of historical, sociological, and political reasons that figure into this, but there are good scientific ones as well. From a theoretical perspective, under the conditions Davis specified (an episode of uplift followed by a long period of no further uplift, erosion dominated by fluvial processes, and no major sea-level, climate or tectonic change), the postulated cycle of erosion is exactly what would happen, as physical models testify. Of course, those conditions have rarely (perhaps never) been met in Earth history for a long enough period to allow the cycle to run its course, but the reasoning was not at all unsound. From the perspective of a landscape observer—a geographer, geologist, or layperson, and in the field or on maps and images—the cycle does provide a plausible explanation for the widespread occurrence of eroded landscapes where the ridges, peaks, and interfluves (the high bits) are, or at least appear to be, accordant (i.e., at approximately the same elevation). There are indeed many landscapes on our planet where the high parts appear to be part of a surface that has been dissected from the top down rather than the stickyuppy parts having been raised from the bottom up. I think some of the ideas in the Earth and environmental sciences about steady-state equilibrium are analogous to the Cycle of Erosion in several ways. First of all, they are (in many cases) seriously flawed and give misleading impressions about how nature works. Second, however, the reasoning behind them is often sound, though even more often based on highly simplified assumptions rarely met in nature. Third, they may provide at least superficially plausible explanations for observations. This is the case, for instance, with the steady-state model of graded stream profiles. My ideas on the latter point are laid out in Phillips (2010; 2011), and my only contribution to debates on peneplains and such in Phillips (2002). There are many discussions and critiques of Davis’s cyclical ideas; several that I especially like are in Rhoads and Thorn’s (1996) edited volume The Scientific Nature of Geomorphology.



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STRAT-AND-TRANSITION MODELS Posted 8 March 2015

The reconstruction of past environmental change is more important than ever. First, we look for precedents, principles, and lessons from the past as we try to understand and predict ongoing and future environmental change based on the fundamental wisdom that “if it did happen, it can happen.” Second, all kinds of new ideas on the coevolution of life, landforms, climate, and Earth itself need testing, verification—and maybe most importantly—hypothesis generation from the historical record. The most important historical records for all but the past couple of centuries are stratigraphic. Environmental change is recorded in the sedimentary rock record, in geologically modern sedimentary deposits, and in soil layers. However, geoscientists have long realized that the stratigraphic record is incomplete—“more gap than record,” Derek Ager famously pointed out, with the preserved events equally famously termed “frozen accidents.” The current state of affairs is well summarized in and recently published volume titled Strata and Time: Probing the Gaps in Our Understanding (Smith et al., 2015).

Stacked paleosols overlain by limestone in the Flint Hills of Kansas (image source: http://www.scifaithkansas.net/guide/FlintHillsGuide4.html) As the book notes, a number of statistical and analytical methods have been developed to confront the fact that stratigraphic evidence often indicates apparently adjacent (in the stratigraphic column) events that are actually separated by long periods where little or nothing happened, and any number of events or episodes that were not preserved or were erased by erosion.

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I’ve been working the past few years with state-and-transition models (see this and that). These are essentially box-and-arrow models that show the state, stage, or condition of an environmental system (e.g., landforms or soil types; vegetation communities; modes of landscape evolution) and the possible transitions among them. Could this approach be applied to the stratigraphic record? I think it is worth a try, and I will flesh this out a bit in my next post.

State-and-transition model for geomorphic environments in the San Antonio River Delta, Texas (from Phillips, 2014).



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STRAT-AND-TRANSITION MODELS II Posted 9 March 2014

This is a continuation of my earlier post on applying state-and-transition models (STM) to stratigraphic information, to account for the missing bits.

Barrell’s (1917) explanation of how oscillatory variations in base level control the timing of deposition. Sedimentation can only occur when base level is actively rising. These short intervals are indicated by the black bars in the top diagram. The resulting stratigraphic column, shown at the left, is full of disconformities, but appears to be the result of continuous sedimentation. Noted sedimentologist Andrew Miall has used this example in several articles to illustrate the problems of gaps in sedimentary & stratigraphic records. A stratigraphic record of paleoenvironments, for instance, indicates a number of different states. For example, a transgressive coastal sequence might have facies indicating nearshore, beach, dune, back-barrier, estuary, and freshwater swamp environments. These would represent the states in the STM, which are considered connected if one state

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can transition directly to another, with no intermediate states. Assuming for the moment a high degree of confidence in the STM, suppose one was faced with a sequence of beach sand overlying freshwater swamp peat. The STM could tell you whether such a transition was possible or likely, or whether intermediate states such as backbarrier marsh or openwater estuary must have existed, but are not preserved in the record. How does one build stratigraphic STMs? STMs applied to problems in contemporary environmental change are based on a combination of monitoring, observation, historical reconstruction, modeling, and theory. For stratigraphic STMs, once reasonable states are identified, we might use a hierarchy of evidence to establish whether transitions can occur from state A to state B. 1. Theoretical plausibility. Given our knowledge of applicable laws and principles, is it (a) plausible, and (b) likely, that the AàB transition could occur. In many cases this can be explored via simulation models. 2. Stratigraphic evidence. If AàB can happen, then there should exist stratigraphic evidence (ideally in multiple samples) that this has occurred. 3. Observational evidence (modern analogs). The strongest form of support is field measurements or observations that the transition has occurred. A good STM not only records what transitions are possible, but what causes or drives them. In the example below, for instance, specific geomorphic and pedologic processes are identified that drive the soil changes depicted.

Soil geomorphic state transition model for an agricultural landscape in the N.C. coastal plain (from Phillips, 2014).

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OK, now suppose you have a sedimentary sequence that shows low marsh overlying alluvial crevasse splays, and your interpretation is guided by the STM shown below (which I have high confidence in, because I developed it!). This shows that in this environment, you cannot transition directly from crevasse splay to low marsh, and indicates that at least one intermediate stage is missing. The STM also indicates that, because of the various, interactive drivers of state changes in this system, the sequence one would expect if, say, only sea-level rise or increased sediment inputs were driving the system may not exist. But the absence of the sequence doesn’t necessarily mean the driver is absent!

STM for geomorphic environments in the San Antonio River Delta, Texas (from Phillips, 2014). The STM framework is not a revolutionary technique for solving stratigraphic puzzles. Rather, it is a way of thinking about and analyzing environmental change that may be helpful to stratigraphers. In addition, this conceptual model is amenable to the analytical tools of graph and network theory, which could provide paleoenvironmental analysis with a new set of quantitative tools (see this previous post). -------------------------------------------Barrell, J. 1917. Rhythms and the measurement of geologic time. Geological Society of America Bulletin, 28: 745-904.



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PLENTY OF PENEPLAINS? Posted 21 May 2015

In the late 19th and early 20th century, William Morris Davis popularized the concept of the peneplain, an extensive low-relief erosion surface graded to sea level. Peneplains were strongly associated with Davis’ cyclical model of landscape evolution, which fell out of favor with most geomorphologists decades ago. By association, the discussion and study of peneplains also fell out of favor. But peneplains are making a comeback. This is best illustrated by a report from the Geological Survey of Denmark and Greenland (Green et al., 2013), though the ideas and evidence are also laid out in a number of journal articles by the various coauthors. The report is concerned with development of elevated passive continental margins (think of, e.g., the Great Escarpment of Africa, the eastern Australian highlands, or the main subject of the report, west Greenland). The arguments are strongly dependent on the identification and interpretation of planation surfaces. As these planation surfaces are low-relief, regionally extensive, and are eroded across geological materials of varying resistance, and because the authors present evidence that they were originally graded to sea-level (they were subsequently uplifted), they can be legitimately referred to as peneplains.





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The report also includes a nice review and synthesis of the peneplain concept and its utility in reconstructing landscape evolution. One of the papers they cite is my 2002 paper: Erosion, isostatic response, and the missing peneplains. On the few occasions this paper is cited, it is normally, and not completely unfairly, cited as representing the anti-peneplain viewpoint. The main point of that paper, however, is to attempt an explanation as to why so few (some would argue no) examples of contemporary peneplains exist. The paper acknowledges existence of old planation surfaces that fit all definitions of peneplain other than their role in a Davisian cycle, and that the theory of peneplain formation (i.e., what would happen with long periods of denudation in the absence of substantial changes in tectonics or base level) is sound. This being the case, and with the amount of effort geomorphologists and other geologists have put into searching for peneplains, where are they? Note that this refers to peneplains graded to Quaternary sea-levels, not the elevated planation surfaces identified by Green et al. (2013) and others. My proposed explanation was based on the dynamical instability of the interrelationships among denudation, deposition, elevation, isostatic uplift, and isostatic subsidence. This implies that no particular state of landscape evolution (peneplanation or otherwise) would persist over long periods in the face of perturbations associated with tectonics, climate, sea-level, or other factors affecting topographic evolution over long time periods and broad areas. If this is true, then: (1) There should be few, if any, examples of landscapes (such as peneplains) that require the operation of a single mode of topographic evolution over time periods longer than those at which fluctuations of climate, sea level, and tectonic activity occur. (2) Evidence should exist that changes in climate, sea level, and tectonic activity result in changes in the fundamental mode of landscape evolution rather than just fluctuations in the rates of geomorphic processes. (3) Ancient landscapes should not show evidence of (or their existence should not require) the continual existence of any particular mode or state of landscape evolution throughout their history. In the 2002 article, I tentatively accepted all three, and still stick with (1) and (2). Evidence presented by Green et al. (2013), both primary and in their review of other work, brings point (3) into serious doubt. The question is, is this because my model is wrong (nnnnooooooo . . . . .!), or because (as others have argued) the late Cenozoic has been more active than Earth’s geological norm with respect to tectonics and climate change? This could explain the presence of ancient, uplifted peneplains and the absence of geologically contemporary ones. Green et al. (2013) do, for instance, point out that 6 millon years is apparently not enough time for a peneplain-type planation surface to develop; 26 Ma being more like it. Overall, their work is an excellent example of integrating topographic, stratigraphic, thermochronological, and age-dating evidence, and of arguments where observational evidence (rather than theoretical predispositions or prevailing orthodoxy) are paramount. It also poses some interesting challenges to the tectonicgeological orthodoxy regarding mountain building, in the tradition (in my opinion, Green et al. do not cite it) of Ollier and Pain’s The Origin of Mountains (Cambridge

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Univ. Press, 2000). It also shows the utility of elevated planation surfaces in reconstructing landscape evolution, regardless of how uncool it may be to call them peneplains. Finally, a minor, maybe even trivial, quibble: Green et al. (2013) cite my 2005 paper on weathering and landscape evolution as supporting a view that base level is unimportant with respect to weathering. This is off-base (pun, as always, intended); my paper includes the statement “ . . . denudation and weathering are limited by base level . . . . “ So there. ------------------------------------------- Green, P.F., Lidmar-Bergstrom, K., Japsen, P., Bonow, J.M., Chalmers, J.A., 2013. Stratigraphic Landscape Analysis, Thermochronology, and the Episodic Development of Passive Continental Margins. Geological Survey of Denmark and Greenland Bulletin 30, 150 p. URL: http://www.geus.dk/publications/bull/nr30/index-uk.htm



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ROMANTIC GEOMORPHOLOGY Posted 14 July 2015

In common parlance, romantic typically refers to the pursuit of love and affection, or to an idealistic, unrealistic outlook. The definitions of romantic as idealistic often includes synonyms such as dreamy, starry-eyed, impractical, and Quixotic, and may list realistic as an antonym. However, Romanticism (typically indicated with the capital R to distinguish it from other usages) as a movement of the late 18th and early 19th century applied to science as well as to art and literature. Lately I’ve stumbled across a few things that made me want to play with the idea of what a Romantic geomorphologist would be like. One was Daniel Gade’s book, Curiosity, Inquiry, and the Geographical Imagination (Peter Lang publishers, 2011). Among many other things, he draws a direct link between Romanticism and curiosity-driven inquiry, and proposed 14 tenets of the Romantic imagination as it relates to research. While focused on Gade’s specialty, cultural and historical geography, many of the tenets describe characteristics of the geoscience and geoscientists I most admire. A little further investigation indicated that some historical scientific giants I admire—Humboldt, Darwin, Gauss—are linked with the Romantic tradition, as well as some whose contributions span science and the creative arts (e.g., Goethe).

Johann Wolfgang von Goethe (1749-1832). Best known for his poetry & plays, he thought his scientific work would be recognized as his greatest contributions. This included work in geology, meteorology, & fluid dynamics. According to Cunningham and Jardine’s introduction to their edited volume on



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Romanticism and the Sciences (Cambridge U.P., 1990), Romanticism in arts and literature had four basic principles: original unity of humans and nature in a “golden age”; later separation of humans from nature and the fragmentation of human faculties; interpretability of the universe in human, spiritual terms; and the possibility of salvation through the contemplation of nature. OK, this is not much of a blueprint for geomorphology or other sciences, but as a general background attitude for science, Romanticism promoted several themes that are relevant: Holism (admittedly in the form of anti-reductionism rather than in addition to reductionism), a sense of human-nature connection and recursive influences, and a valorization of creativity and experience. It began to seem as though there was some overlap with my notion of Badass Geomorphology. Two of the tenets seem to apply more to the individual Romantic in than their approach to science and scholarship: an individualistic approach to research, and opposition to “officialism” (bureaucrasies and the status quo). These are right in line with my Badass Geomorphology archetype. So is the notion of historical perspective (“historicist vision”). While this certainly does not characterize all geoscientists, history is an important and irreducible part of our overall program. Gade’s tenets of “interest in landscape form and content,” “affinity for the organic” (i.e., attention to self-organized wholes rather than parts), and “idea of process” (i.e., ongoing change), described in terms meant to contrast the Romantic with other approaches to cultural geography, are easily translatable to much geomorphological work. The scholar imbued with the Romantic imagination also unapologetically “stands on the shoulders” of previous observers, though this seems to me a hallmark of good scholarship from any perspective. Romantics also reject utilitarianism. This does not mean they are unconcerned with the implications of their work for society or the planet, or that they reject pragmatism with respect to methods and techniques employed in research. It means that inquiry is driven strictly, or at least primarily, by a need or desire to know. The practical problem-solving value is secondary, and need not be apparent at the outset. I believe this characterizes many of us when we are first drawn to geomorphology, though the exigencies of thesis and dissertation proposals; research funding; and demonstrating “relevance” to governing boards and administrators often beats it out of us over time. Five other tenets, termed by Gade as a search for the exotic, focus on particulars, depreciation of the obvious, a quest for authenticity, and diversity for its own sake, are a bit harder to reconcile with geomorphology. I’ll take these up in a future post.



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ROMANTIC GEOMORPHOLOGY—PART 2 Posted 16 July 2015

This continues my previous post, toying with the notion of what a Romantic geomorphology would be like. This is based on the Romantic movement in art, literature, and science, rather than the more common meanings related to amourness and love, or to unrealistic idealism. Though, come to think of it, maybe Romantic geomorphology in those terms is also worth thinking about . . . . Anyway, in the earlier post I noted that Daniel Gade’s book, Curiosity, Inquiry, and the Geographical Imagination (Peter Lang publishers, 2011) proposed 14 tenets of the Romantic imagination as it relates to research. Eight of them, in my view, apply readily to geomorphology and geosciences in general, though certainly not all practitioners display or even aspire to all of these traits. Six others need a bit more dissection. Search for the Exotic Romantic scholars are drawn to anomalies and dissimilitudes. “Extreme, bizzare or grotesque patterns of the world held an uncanny attraction for the Romantic mind,” Gade writes. We can assume he refers to something beyond the interest in oddities and extremes that pretty much all humans share. He writes of research that purposefully seeks out exotic locations and situations. Certainly this describes much of geoscience. We are inordinately drawn to the spectacular, active landscapes of, e.g., New Zealand or the Himalayas. We spend a lot of time trying to explain mysteries such as the sliding rocks of Death Valley, the fairy circles of Namibia, Uluru, and the like. We are drawn to spewing geysers, the deepest caves, the highest cliffs. What curious scientist could not wonder, and seek to learn about such things?

Fairy circles in the Namibian desert (www.express.co.uk).



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However, we also have a mandate to deal with our planet as a whole, as it presents itself to us. We cannot do that by restricting our research to the places that are most scenic, fun, or exotic. In fact, those that do the (literal and figurative) dirty work of research in urban landscapes, surface mined areas, and other active and important sites that do not generally appeal to the Romantic imagination deserve a great deal of credit. Some of us (well, me, anyway) are also coming around to a somewhat different view of exotica. The perfect landscape concept I’m always on about is based on the idea that any Earth surface system, whether the Geysers of Yellowstone, the Atacama desert, wheat fields of Ukraine, or an urban brownfield, represents a specific combination of environmental controls and a specific chain of historical events. The probability of these specific combinations occurring in any given time and place are vanishingly small. Thus to some extent they are all perfect, unique, and idiosyncratic, whether or not they are exotic.

A perfect Ukrainian wheat landscape (www. express.be). Focus on the Particular Romantic affinities lean toward the “particularity of the material” and prefer empiricism to theory and the local to the global. In this sense many geoscientists share the Romantic sensibility, and many do not. However, in recent years it is increasingly recognized, even among those who lean toward theory and generality, that Earth systems have irreducible elements of geographical and historical contingency (i.e., perfection in the sense above). The more Romantic geomorphologists delight in this; others may only grudgingly acknowledge it.



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Characterization of Place Much of Gade’s discussion on this point is a tirade against the straw man of positivism. Positivist was a label attached to quantitative geographers and geoscientists during the “quantitative revolution” in the mid-20th century. It’s one of those things like “neoliberal” or “secular humanist” that are used by critics to “other” those they criticize. Very few self-identify as positivists, though many accepted the label, as it was often bandied about as characterizing anyone who is interested in measurement and not averse to mathematical and statistical methods. Few geomorphologists or quantitative geographers care much about –isms, so there was never much pushback on this label. However, if you read the literature on the scientific nature of physical geography and Earth sciences, you realize that even among the quantifiers you could classify at least as many as critical realists, pragmatists, or phenomenologists as positivists. Anyway, though, an interest in place fits neatly with the (renewed) attention to historical and geographical contingency in geomorphology, and the Romantic geomorphologist would be right at home with it (pun intended). Depreciation of the Obvious Here the Romantic inquisitor is not happy with pedestrian conclusions or well-worn topics. Unexpected findings are what excite her. Again, we can assume that this means something more than the excitement over new findings and groundbreaking results that characterize any good scholar, Romantic or otherwise. The Romantic as idealized by Gade is not just pleased to happen upon such things, but is constantly pushing for them. In particular, this involves deliberately countermanding prevailing viewpoints and conventional wisdoms. Some very interesting geomorphology has been based exactly on this approach. Some of it is highly esoteric, but some very practical—for example the notion that in some environments soil erosion on steep slopes should actually be encouraged, to develop arable valley-bottom soils. Quest for authenticity The examples here apply to cultural landscapes and are not readily applicable to geomorphology unless you resort to some notion of naturalness unaffected by human agency, which is difficult to justify here in the Anthropocene. The Romantic prefers the real to the fake; the idiosyncratic to the standardized; the local café to the fast food chain. In terms of personal preference (as opposed to research practice), most geomorphologists are pretty earthy folks (again, pun intended), and would agree with this perspective. Diversity for its own sake In this section Gade focuses on cultural diversity, and the Romantic appreciation for the value and uniqueness of each and every culture. In geomorphology a perfect landscape perspective, along with the burgeoning interests in geodiversity, pedodiversity, and biodiversity, is right in line with this.



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The Romantic Geomorphologist Taking into account all 14 tenets of the Romantic scholar’s imagination, and recognizing that few individuals, be they geomorphologists, cultural-historical geographers, or anything else, are likely to exemplify all of them, I think it can be said that there exists a strong tradition and a strong contemporary strand of Romantic geomorphology.

Romancing a fluviokarst stream in central Kentucky



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LANDSCAPE EVOLUTION ENERGY Posted 14 January 2016

Geoscientists modeling landscape evolution overwhelmingly (not exclusively, but indeed overwhelmingly) emphasize geophysical aspects, mainly tectonic uplift and erosion. Erosion is typically modeled based on some form of the stream power law, where erosion rates are a power-law function of stream discharge and slope. Discharge is itself often assumed to be a function of drainage area. There’s nothing wrong with studying the interactions of uplift and denudation without paying much heed to climate, biota, and other factors; I’ve dabbled in this myself. In a 2009 article, I argued (showed, I like to think) that on a global scale, the biological energy “subsidy” to landscape evolution is comparable to, and even exceeds, the geophysical component. I also introduced a “landscape evolution space” (LES) concept (see also this) attempting to incorporate climatic, biotic, and geophysical contributions. While there is more attention than ever to biotic influences on geomorphology, the LES concept hasn’t gotten much traction, and landscape modeling is still overwhelmingly geophysical. In that’s context, let’s take a look at energy inputs to the Earth’s surface. The total mechanical power driving global tectonics is 4.8 X 107 watts (W), which translates to 0.297 megajoules (MJ) per square meter per year. By contrast, total solar radiation absorbed the atmosphere and land surface is 240 W m-2, or 7574 MJ m-2 yr-1. On average 5680 MJ m-2 yr-1 is absorbed at the surface, and drives biological processes, moisture fluxes, air movements, heat fluxes, etc. (more than 19,000 times the tectonic power). Global mean gross primary productivity (energy used to produce biomass, plus respiration) is estimated at 315 to 420 MJ m-2 yr-1. The mean elevation of the continents is 874 m. Given the density of crustal rocks, the potential energy associated with moving all mass above sea level to below base level averages 22.698 MJ m-2 . Even if this were entirely accomplished in a century, the rate of potential energy conversion would be only 0.227 MJ m-2 yr-1; 2.27 X 10-5 if we give it a million years, which is still unrealistically fast. Before any of that mass can be moved, however, it must be converted to transportable material. Estimating the energy required to break down rock is fiendishly difficult, but in the past I’ve used tensile strength (resistance to a force tending to tear a mass apart) as a rough index. Typical tensile strength of rocks ranges from about 4 to 30 megapascals (MPa = 1 MJ m-3). With a mean of 847 m3 of rock per m2 of the land surface, this implies about 3500 to 26,220 MJ m-2 to get the job done. In the unlikely event it all got done in a century, we’re talking 35 to 262 MJ m-2 yr-1. If we give it a million years, then 0.026 to 0.0045 MJ m-2 yr-1. Of course, that’s assuming that a cumulative energy input can eventually add up to the threshold tensile strength; we don’t really know if that’s true. A graphical comparison looks like this:



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Energy inputs associated with total solar radiation absorption, ground absorption, gross primary productivity, tectonics, rock breakdown, and potential energy (PE) conversion rates associated with reducing continents to sea level. The breakdown and PE figures assume that this is accomplished within 1 Ma, an unrealistically rapid rate. But wait—the graphic above has a logarithmic scale; otherwise the last four would not be discernible. Below is the same figure with an arithmetic scale, showing that even though the tectonic power is an order of magnitude or more greater than the (unrealistically high) breakdown and PE rates, it is still invisible compared to the solar and biological inputs.



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So what does all this mean? First, some caveats—these are rough, back-of-the-envelope calculations based on global estimates of processes that vary wildly geographically and temporally. But still, allowing for error and variability, the implications are pretty clear: •Solar and biological energy inputs to Earth surface systems are far higher than any energy associated with tectonics or mechanics of mass transfers. Even if only a microscopic fraction of this solar and biological input is geomorphologically significant, it is still a helluva lot! •Energy inputs required to break down rock are (at least) two to three orders of magnitude higher than the rate of potential energy conversion associated with moving mass to base-level. •Energy inputs of tectonic uplift are, globally, more than sufficient to “replenish” losses from denudation. The most important implication, however, is that we have a lot to learn out the energetics of landscape evolution. ______________________________________________________ The basis for the quantitative estimates above comes from: Phillips, J.D. 2009. Biological energy in landscape evolution. American Journal of Science 309: 271-290. Smil, V. 2008. Energy in Nature and Society. MIT Press.



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RE-ENCHANTMENT REVISITED Posted 19 February 2016



25 years ago, Vic Baker and Rowl Twidale published an article in Geomorphology called “The Reenchantment of Geomorphology.” At the time, I found their essay interesting and provocative, but annoying, and I disagreed with much of their message and with their overall tone. Over the years, however, I have come to have a much different perspective—overall, I have largely come around to Baker and Twidale’s view. Here’s the abstract of their paper: Much of modern Geomorphology lacks the enchantment that the science possessed a century ago. Practical and philosophical impediments are thwarting modern attempts to achieve a satisfying understanding of landforms and their genesis. In recent years, even the security of geomorphologists' academic bases has been threatened within the cognate disciplines of Geography and Geology. During the 1960s these fields experienced so-called “scientific revolutions,” which many geomorphologists either uncritically embraced or assumed to be irrelevant. While commendable in spirit, progressive initiatives to establish research traditions in landscape evolution, climatic geomorphology, and process studies all encountered fundamental limitations as unifying themes. More disturbing are ideological impositions that advocate geomorphological concentration on timeless, theoretical, or utilitarian problems. While facilitating precision of explanation and prediction, various geoideological bandwagons may stifle creativity, insight, and intellectual satisfaction. Most insidious is the substitution of elegantly structured methodology and theory for spontaneity, serendipity, and common sense. Hope for the reenchantment of Geomorphology lies in a new connectedness to nature that will facilitate the identification of anomalies and the formulation of outrageous hypotheses of causation. In the words of William Morris Davis, “…violence must be done to many of our accepted principles.” Examples of such ideas may be found in fringe areas of the discipline, including planetary geomorphology, tectonic geomorphology, and denudation chronology with emphasis on ancient paleosurfaces. Geomorphologists should consider inverting their belief that they are achieving progressive (timebound) understanding of invariant (timeless) laws in nature. Rather, they may choose a geophysiological view in which the richness of natural history is revealed in a timeless conversation with the Earth.



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An enchanted ventifact, White Desert, Egypt

Regular readers of this blog (both of you!) may find it surprising that I ever objected to much of this rhetoric, so let me provide a bit of context. I got my PhD in 1985, and most geomorphologists of my generation were trained, even (benignly and informally) indoctrinated, in an approach that eschewed regional and historical approaches (especially Davisian ones!), valorized quantification and modeling, and sought to eliminate geography and history in favor of universal laws. In 1991 I was not only just a few years past this training, but also deep into quantitative models, and had not yet recognized the critical connections between geographical and historical contingency and the complex nonlinear dynamics I was studying. Stuff I wrote in 1992, for example, was concerned with linking nonlinear dynamical systems to the ahistorical approaches of Strahler, Chorley, Leopold, Scheidegger, et al., not to ideas about contingency that I subsequently developed/discovered. So here come Twidale and Baker, with some critical questioning and sharp critiques of my worldview. At the time I had heard of and read other work by both men, who were then and now widely respected as giants in the field. I had not met either, though I did subsequently, and was fortunate enough to spend several days in the field with Rowl in South Australia in 2005. In retrospect, I suspect some of my adverse reaction was not just that I disagreed with their take, but because I knew intuitively there was some truth to their critique. And despite my allegiance at the time to a non-reenchantment methodology, their comments about the role of intuition, a sense of wonder, and “conversations with



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the Earth” resonated with me at some level. Like many of us in the discipline, I got into the study of landforms and surface processes because of a love of and fascination with rivers, beaches, marshes, and such, not due to a fascination with steady-state mass balances, sediment transport equations, or roughness coefficients. In the past 2.5 decades, I’ve become pretty good at merging my scientist self with the part of me that encounters landforms while holding a fishing rod, a water bottle, a cold beer, or the hand of my wife or child (or, most recently, my granddaughter!). Plenty of encounters with the laser level, soil auger, and other instruments in hand eventually taught me, first, that geography and history always matter, and that we cannot eliminate their influence no matter how much (at least as scientists) we want to. Then I stopped wanting to. One of these days I hope to write a post (or maybe even an article) titled (based on a variation on the subtitle of Stanley Kubrick’s film Dr. Strangelove) called How I Stopped Worrying and Learned to Love Contingency.







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Since reenchantment of geomorphology was published, I think a lot more of us have, at least implicitly, come to embrace, or at least accept, Baker and Twidale’s viewpoint. Historical geomorphology is reinvigorated (not least because of new dating methods and technologies), most of us acknowledge (and a significant minority embrace) historical and geographical contingency, and narratives are increasingly valorized by geoscientists rather than being dismissed as “mere” storytelling. On the other hand, a nomothetic, mathematical model-based approach is still dominant, and (too) many of us still fetishize quantification for its own sake. Anyway, Rowl and Vic, thanks for writing this back in the day! Sorry I didn’t appreciate it then.

Rowl Twidale in the Flinders Range, South Australia, 2005.



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AXIOMS OF GEOMORPHOLOGY Posted 12 December 2016

Axiomatic approaches to science and mathematics depend on an underlying set of statements, principles, or propositions that apply to all situations within the domain of study. The axioms run the gamut from undisputed universal laws to widely or even universally accepted but unproved or unprovable generalizations, to propositional stipulations adopted for analytical convenience or because they raise interesting questions. Examples abound in mathematics and formal logic, and in science, engineering and technological applications of math and logic. Although it is only occasionally referred to as such, the laws of stratigraphy (details in any geology textbook) form an axiomatic approach to sedimentology, sedimentary geology, and related palaeoenvironmental studies. The laws of original horizontality, lateral continuity, superposition, and crosscutting relationships are assumed in this approach to apply to all sedimentary deposits, and therefore form an axiomatic system for interpretation. Thinking about these matters inspired me to make a preliminary stab at a set of axioms for geomorphology, which are laid out below. There exist at least three different definitions or concepts of axiom: (1) a self-evident truth that requires no proof; (2) a universally accepted principle or rule; and (3) a proposition that is assumed without proof for the sake of studying the consequences that follow from it. I make no effort to distinguish the axioms below with respect to these categories, or to get into the semantics of axioms, laws, principles, maxims, propositions, guidelines, etc. I do not deny that these differences might be significant, but for me personally that sort of parsing bores me to tears. Let me also note that there have been previous efforts to lay out a list of key general concepts or principles of geomorphology, admirably summarized by Gregory & Lewin (2014), and including my own 11 principles of Earth surface systems (Phillips, 1999), focusing on nonlinear dynamics. (Proposed) Axioms of Geomorphology These are based on the assumption that the ultimate goal of geomorphology is to explain the origin, evolution, and changes in landscapes. I acknowledge that some axioms are irrelevant to specific research problems (e.g., principles related to environmental and historical context are not directly relevant to laboratory experiments). 1. Landscape forms and patterns are indicators (clues) of formative processes and history. This has always been an underlying implicit or explicit assumption of our field, and in many cases is a necessary one, as the long timescales involved prevent direct observation. 2. Different aspects of the landscape are inherently no more or less important as clues or

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indicators. Key indicators and the ability to interpret them, however, will vary in different situations. This is intended to highlight that we cannot assume that “it’s all about” , for instance, topography or geochemistry in all situations, and that we cannot assume a priori that factors such as vegetation, soil type, grain size, etc. either are or are not key to the story to be told. 3. History matters. Landscapes cannot be fully explained and interpreted without considering their history, including inheritance, path dependence, event timing and sequence, and changes in environmental controls. 4. Geography matters. Landscapes make little sense outside their geographic (place and ecological) contexts.

We can learn a lot from abstract theory, models, and lab work. However, understanding landscapes ultimately requires boots on the ground. 5. Landscape processes, forms and patterns are underpinned and constrained by general laws and principles. These include laws per se and generally applicable principles of (at least) physics, chemistry, biology, geology, and geography. The most important, general, and inviolable are laws of conservation of matter and energy. Thus Landscape systems can be described and understood based on flows, transformations, and storage of energy



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and matter. I can’t decide if this should be a separate axiom, or a corollary. 6. Laws, Place, History: landscapes can be understood as representing the combined, interacting effects of generally applicable rules or principles, location- or region-specific environmental influences and controls, and age, time or path-dependent factors (follows from 3, 4, 5). An essay on this, discussing this axiom and providing some further explanation and justification for items 3-6, is available in online-first form here. 7. Scale matters. The relationships between, and the factors governing, landscape forms and processes vary with spatial and temporal scale or resolution. 8. Selection happens. More durable, resistant, resilient, stable, and efficient forms, patterns, and behaviors are preferentially preserved, and may be reinforced and replicated, relative to less durable, resistant, resilient, stable, and efficient entities. Previous posts on this theme are here, here, here, and there. 9. Selection is non-deterministic. Selection is an aggregate phenomenon, expressed in tendencies and probabilities. It does not always apply or occur in individual cases. 10. Dominant controls. While many different law, place, and history factors may influence landscapes, for any particular landscape, a relatively small subset of these control landscape evolution and responses. The dominant controls concept holds that while there may exist a very large number of factors and processes that can influence a given phenomenon, in any given geomorphic system some will be irrelevant and others of comparatively negligible influence, leaving a few dominant controls to deal with. In another post I laid out five axioms underlying the DCC, and acknowledging its inspiration from the dominant processes concept in hydrology.



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See also: Entry

Section

Δ Deltas Soil Erosion Rises Again!

Environmental Management & Applied Geoscience

The Dialectics of Geomorphic Complexity Geomorphological Flickering

Earth Surface System Theory

Lockyer Creek bed, Queensland, Australia



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Soils, Regolith, and Karst

Soil profile in Queensland, Australia (top); limestone root grooves in central Kentucky.



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CONNECTING THE DOT FACTORS Posted 26 October 2015

The standard conceptual model for pedology, soil geomorphology, and soil geography is often called the “clorpt” model, for the way it was portrayed in Hans Jenny’s famous 1941 book The Factors of Soil Formation: S = f(cl, o, r, p, t) . . . . This equation states that soil types or soil properties (S) are a function of climate (cl), biotic effects (o for organisms), topography (r for relief), parent material (p), and t for time, conceived as the age of the surface the soils are formed on, or the time period the soil has been developing under a given broad set of environmental controls. This factorial approach, considering soils as a function of the combined, interacting influences of environmental factors such as geology, climate, and biota, was originated by V.V. Dokuchaev in Russia in the 1880s, popularized in English by C.F. Marbut in the 1920s and 1930s, and developed by Jenny into the familiar clorpt form. Much has been written about the state factor model, including a 50th anniversary retrospective of Jenny’s book that addresses not only the model’s history and impacts on soil science, but also its impacts on geomorphology, geoarchaeology, geography, paleoenvironmental reconstructions, and ecology. My own take (from the 1990s) on the factorial model as a nonlinear dynamical system is here and here. Note that the clorpt equation, when correctly displayed, always has some trailing dots. This was Jenny’s way of showing that, in any landscape, one should consider climate, organisms, topography, parent material and time factors, but that there are other factors (the “dot factors”) that are critical in some locations and situations, but not everywhere. I’ve always thought the state factor model was a good general representation of geographical explanation, where multiple causality is the rule. That is, the phenomenon of interest is determined by multiple environmental (or in other contexts, perhaps economic, political, etc.) controls that may themselves interact. The representation with the dot factors is also dead on, in my view, because it reflects the fact that any Earth surface system is influenced by a set of general or universal factors that apply everywhere and always, and by a set of local or contingent factors that are specific to place and time. The dot factors can be thought of as representing the latter. In days of old (i.e., when I was a student), aeolian deposition was often cited as a dot factor—significant in some areas but not everywhere. Now I think aeolian inputs are more significant over much of the planet than we once imagined, but that’s another story. I wanted to yap a bit about dot factors not only because of the intrinsic geoscience significance, but because with ongoing climate and other environmental changes we are likely to see dot factors in many environments change—wax or wane in relative importance; perhaps emerge or fade away.



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For example, in the coastal zone salinity and sea level change are crucial soil-forming factors (I’ll stick to this term for brevity’s sake, but keep in mind that these are also critical geomorphic and ecological factors, too). As sea level rises, the effects of these dot factors will increase in importance over a broader area inland (and decrease seaward, as, e.g., salt marshes are eroded and drowned).

Wind erosion & deposition in the United Kingdom (www.geograph.org.uk). Sediment redistribution by erosion and deposition is an important soil-forming factor, and in most landscapes is accounted for by “r”, as these are generally topographically driven. However, in some arid and semi-arid environments (and coastal sand dunes) wind is the major erosion and sediment transport agent, and of course aeolian processes, while influenced by topography, are driven by pressure gradients, not gravity. Climate change may well produce situations where such processes become both more and less active.



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A Canadian cryosol with subsurface ice body (Agriculture Canada photo). Cryosols in periglacial and tundra environments are affected by subsurface ice bodies. As permafrost thaws during global warming, these ice lenses will disappear. There is also strong evidence that climate change is influencing fire regimes (for instance, increasing fire frequency in the western US). Some impacts of fire could perhaps be incorporated in the organic factor of the clorpt equation, but fire has impacts on soils independently of its effects on vegetation and organic matter.

Sycamore tree penetrating and displacing bedrock in central Kentucky.



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The biotic or organic factor in the soil factor equation was originally viewed as primarily reflecting general correlations between soil type and vegetation cover, though the importance of other organisms is more widely recognized now. A common subject of my own research in recent years—and that of many others—is the effects of individual trees on soils, which can be considerable. It could be argued (I will not do so here) that this could, or could not, be incorporated into the “o” factor. However, in some cases these effects (e.g., uprooting) are closely related to disturbance events such as tornadoes, other windstorms, and ice storms; and pest infestations or other tree-killing events. These are all likely to be influenced by climate change, and perhaps best considered as local dot factors. Other pedologists could no doubt add other examples. The main point is this—as climate changes, it is not just the cl factor in the form of temperature, precipitation, etc. that will change—in many cases the dot factors will change, too.



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MAKING PEACE WITH MACROPORES Posted 16 January 2017

The journal Hydrological Processes has recently been publishing a series of articles and commentaries in tribute to the estimable Keith Beven, the recently retired hydrologist from the University of Lancaster. One of his many fundamental contributions has consisted of drawing attention to the importance of, and making fundamental insight into, the phenomenon of macropores and preferential flows. One of those commentaries, by Markus Weiler, addressed these contributions as well as unresolved issues in understanding and simulating preferential flow. No hillslope hydrologist, geomorphologist or pedologist would dispute the existence or frequent occurrence of preferential water flux in soils, or its importance in many cases at the scales of soil physics to hillslopes. However, Weiler points out that the observed differences in flow pathways at the pedon or hillslope scale are not necessarily detectable at the watershed scale. Does macropore flow matter at the catchment scale? Weiler's answer is yes, though he points out that many scientists believe otherwise.

Uprooted tree at Zofinksy, Czech Republic. The soil in the rootwad shows roots, large pores or pipes associated with root channels of decayed roots, and soil cracks. These and other less obvious features in most soils are avenues for preferential flow. Weiler's commentary got me to thinking about a long time interest of mine--scale linkage. That is, how do we link processes and phenomena that operate at widely varying spatial (and temporal) scales. After all, in hydrology, geomorphology, and pedology we deal with scales from the molecular to the planetary. You can't explain



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the hydrology of the Ohio River based on soil water tension in an earthworm burrow; nor can you gain insight into macropore (or for that matter matrix) flow based on global hydrology. Yet the processes are indeed related across those scales.

Stemwash reflecting a key form of preferential flow, stemflow (Razula, Czech Republic). What I've found via several different approaches (most recent publication here) is that processes and relationships operating at fundamentally different scales (e.g., more than two levels apart in a scale hierarchy) are essentially independent, in the same sense that macropore vs. matrix flow at the scale of soil physics is not detectable in stream discharge. However, as Weiler points out, preferential flow is detectable in stream and watershed chemical properties. The diagram below comes from an analysis of fluviokarst systems in Kentucky reported here, but except for the top one the hierarchical levels (if not the specific nodes and links) are applicable to hydrogeomorphic systems in general. Watershed runoff is several levels away from the soil physics or the plot/patch level, and thus should be independent of processes restricted to those lower levels. When it is not, this suggests that macropores and preferential flows are manifest at the intermediate, hillslope scale. That in turn supports Weiler's inference that connectivity of preferential flow paths is key (where they are strongly connected, then preferential flow should be manifest at the hillslope level).



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To add a bit to the questions and research agenda laid out by Weiler, a potential complication could be different preferential flow paths at different, overlapping scales--for example, macropores at a soil physics scale, stem and root flow at a small plot scale, and biogeomorphically-induced microtopography at a hillslope scale. Further, the "selection" of individual flow paths is highly localized and may or may not be reflected in broader-scale patterns.

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KARSTIFICATION AT BOWMAN’S BEND Posted 7 February 2017

As the Kentucky River in central Kentucky continues to downcut through Ordovician limestones in the Kentucky River gorge area, entrenched meanders grow. On the outside of these bends tributary streams are truncated, their slopes accordingly steepen, and fluvial dissection becomes more dominant. On the inner part of the bends, slip-off slopes develop that are far less steep than valley walls on the outer bends, but steeper on average than the adjacent uplands. These inner bend areas are hotspots for karstification, and are pockmarked with numerous dolines (karst sinkholes). Streams are few and small; larger tributary streams are apparently diverted away from the inner bends; in any case no tributaries with a surface drainage area of more than a few km2 join the river on the inside of a meander (at least in the gorge area). This article includes a section on the "Badass Bends" and their diverging karst vs. fluvial dynamics. This previous post discusses the expansion of the Polly's Bend compound meander.

The interior of Bowman's Bend on the Kentucky River, from 1.5 m LiDAR data. The numerous pockmarks are karst sinkholes (dolines). While it is relatively easy to explain what the inner bends are karsty-er and the outer bends fluvial-er, and vice versa, it is not so easy to explain why the inner bend areas are such karst hotspots, even compared to adjacent uplands. It seems to be a combination of three factors. First, Kentucky River incision is, at least over the last 1.5 million years or so, the major driving factor in landform evolution for both karst and fluvial processes. Areas closer to the river are more profoundly affected, so any karstification going on there is likely to be faster and more intense, on average, than on the interfluves. Second, in some cases groundwater flow takes a shortcut across the bends, forming and widening karst conduits. Some surface expression of this is to be expected as collapses occasionally occur into the underlying conduits and cavities. Third, the erosional bevelling of the



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slipoff slopes may promote karst development by exposing underlying joints, etc. These dynamics are evident on the ground, especially during winter, when our lush vegetation does not obscure them so much. Last weekend I was out hiking at Bowman's Bend, and got a few pictures--just the tip of the iceberg, so to speak.



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Solutional features are often focused where joints intersect. The white lines added to this photo show the trend of the two joints that intersect at the center of this sink.



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Interaction of fluvial and karst--a swallet connected to a subsurface conduit in an ephemeral channel.



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REGOLITH MOBILITY Posted 31 March 2017

Some recent theoretical and modeling work on regolith and the so-called critical zone draws a distinction between the entire thickness of weathered material H and the mobile thickness h that is potentially (re)movable by erosion and mass wasting. As H > h this implies that in many cases there exists a subsurface immobile layer. This distinction between a potentially laterally mobile and a fixed layer of weathered material is no doubt useful as a model assumption. It is also probably true, or close enough, for some very thick weathered mantles. And of course, the mobile:immobile distinction is self-evidently true during periods when a portion, but not all, of the regolith thickness is being transported.

Regolith in the flysch Carpathians, Czech Republic. Lighter upper layer is a landslide deposit. Lower material (the black layer is charcoal, apparently from a fire at some point) is weathered in place. It is all potentially mobile.



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It is a mistake, however, to assume that a mobile upper and a fixed lower layer of regolith is a good general conceptual model of how regoliths and hillslopes work. As with many other such concepts (e.g., various steady-state assumptions; distinctions between solum and the "rest" of the soil; random disturbances, etc.) situational accuracy and conceptual and/or modeling utility do not necessarily add up to generally applicable truths. First, consider phenomena such as deep-seated mass movements, gully incision, and regolith stripping. All may involve movement of the entire regolith, even in some cases where the weathered mantle is pretty thick. Second, the H vs. h concept is based on notions of surface removal, emphasizing mechanical processes. We have to keep in mind the importance in some situations of mass transport in solution, and of vertical transport (e.g., leaching and vertical translocation).

Soil and weathered mantle, Sumava Mountains, Czech Republic. The assumption that mobility decreases with depth, or of a movable:fixed threshold also overlooks a great deal of empirical evidence and conceptual models involving lateral material movement on the subsurface. Soil piping, pipe/macropore/conduit erosion, and lateral translocation are all examples (see, e.g., Sommer et al., 2000; 2001; Uchida et al., 2001; Fox and Wilson, 2010; Jones, 2010). The importance of such processes is explicitly recognized in 3-layer models of regolith, hillslope, and landscape evolution, which recognize not only the potentially erodible surface at the top and the bedrock weathering

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front at the bottom of the regolith, but also an intermediate surface where contrasts (in e.g., permeability, bulk density, biological effects) associated with regolith layering direct fluxes laterally. Don Johnson's dynamic denudation model is not only a good example, but his papers review other concepts along these lines, too (Johnson, 1993; Johnson et al., 2005). Sometimes the rooting depth of plants (especially trees) or the biologically active layer or biomantle is equated with the mobile layer. I wonder if this association, where it occurs, is inevitable. In most environments exposed bedrock (and other subsoil material) is quickly colonized and modified by plants, animals (especially insects), and microbes. They play a key role in weathering and in accumulation of both organic and inorganic material. Biota also play important direct and indirect roles in mass fluxes. Thus, to the extent biomantles and mobile surface layers are coincident, it is more likely to be because biota created the layers than to be a matter of organisms colonizing the mobile layer.

Weathering profile with thin soil but thick regolith, Czech Republic.



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Being of the opinion that the entire weathered mantle is potentially movable under all circumstances except perhaps thick sedimentary layers on low-relief terrain, in my own work I distinguish between the entire weathered thickness (basically the same as H above) and the portion of H that has been converted to soil S (that is, has acquired pedogenic properties not inherited from the parent material). I argued that for a noneroding landscape in steady-state, H/S approaches 1, or H ≈ S. Thus a significant layer of weathered material that has not been converted to soil (e.g., saprolite, saprolith, Cr horizons, loose rock) indicates non-steady state (which is very common)--but not necessarily immobility of the lower layer. My arguments in this regard are here.

---------------------------------------------------------------------------------References: Fox, G.A., Wilson, G.V. 2010. The role of subsurface flow in hillslope and stream bank erosion: a review. Soil Sci. Soc. Am. J. 74: 717-733. Johnson, D.L. 1993. Dynamic denudation evolution of tropical, subtropical and temperate landscapes with three tiered soils: toward a general theory of landscape evolution. Quat. Internat. 17: 67-78. Johnson, D.L., et al. 2005. Reflections on the nature of soil and its biomantle. Ann. Assoc. Am. Geogr. 95: 11-31. Jones, J.A.A. 2010. Soil piping and catchment response. Hydrol. Proc. 24: 1548-1566. Sommer, M., et al. 2000. Lateral podzolization in a granite landscape. Soil Sci. Soc. Am. J. 64: 1434-1442. Sommer, M., et al. 2001. Lateral podzolization in a sandstone landscape. Geoderma 103: 231-247. Uchida, T., et al. 2001. Effects of pipeflow on hydrological process and its relation to landslide: a review of pipeflow studies in forested headwater catchments. Hydrol. Proc. 15: 2151-2174.



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A CHURNING URN OF BURNING FUNK Posted 23 May 2017

In studies of soil formation and landscape evolution, we often think in terms of a (over-) simplified "conveyor belt" model, where bedrock is weathered to create the raw material for soil formation at the base. Further up toward the ground surface, this weathered rock is progressively modified into soil. Thus, as you go from the base of the soil or weathering profile, material gets progressively more modified, and (in terms of soil rather than rock), older. Anyone who's spent time in more than a few soil pits or road cuts knows that the conveyor belt is, at best, a loose approximation and often hardly applicable at all. Variations in properties of the rock or parent material, dynamical instabilities and positive feedbacks in weathering and other pedogenetic processes work in many cases to create increasingly variable and heterogeneous (both vertically and horizontally) regoliths over time. Critical processes operate in all directions (not just vertically), and moisture fluxes and biological activity follow preferential, self-reinforcing paths. Further, mass is added not just from weathering, but from deposition and organic matter, and removed by erosion, leaching, fire, and decomposition.

Weathering profile in Union County, South Carolina, formed in granitoid rocks. At the top, mostly out of view, is an Ultisol. At and next to the rock hammer are minimally weathered granite corestones. The lines are highly weathered veins from the rock, where



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preferential water flow and root penetration are concentrated. Within the area of the photo are intact rock, weathered rock (saprolite), near-final weathering products (iron and aluminum oxides and kaolinites) and everything in between. The solid bedrock weathering front is >10 m below the section pictured. In fact, the mechanical metaphor I prefer is not a conveyor belt, but (to borrow a phrase from James Taylor's "Streamroller Blues"), a Churning Urn of Burning Funk. If you want to stick with a conveyor belt analog, you should realized that in a given landscape there are actually multiple conveyor belts, operating at different rates, moving in different directions, and some of them re leaky or prone to breakdown.

Elvis Presley also recorded James Taylor’s “Steamroller Blues.” The song, intended as a parody of inauthentic blues songs performed by many rock bands in the late 1960s and early 1970s, includes the line: “I’m a cement mixer for you baby, a churnin’ urn of burnin’ funk.” It is not known whether Taylor or Presley grasped the pedological implications.



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