Understanding watershed suspended sediment transport

14 downloads 98 Views 268KB Size Report
2001; Nicholas, 2003). The achievement of all these aims primarily .... 1981; Cordova and Gonzalez, 1997) and the magnitude-frequency approach (eg, Stow.
Progress in Physical Geography http://ppg.sagepub.com

Understanding watershed suspended sediment transport Peng Gao Progress in Physical Geography 2008; 32; 243 DOI: 10.1177/0309133308094849 The online version of this article can be found at: http://ppg.sagepub.com/cgi/content/abstract/32/3/243

Published by: http://www.sagepublications.com

Additional services and information for Progress in Physical Geography can be found at: Email Alerts: http://ppg.sagepub.com/cgi/alerts Subscriptions: http://ppg.sagepub.com/subscriptions Reprints: http://www.sagepub.com/journalsReprints.nav Permissions: http://www.sagepub.co.uk/journalsPermissions.nav Citations http://ppg.sagepub.com/cgi/content/refs/32/3/243

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Progress in Physical Geography 32(3) (2008) pp. 243–263

Ý Understanding watershed suspended sediment transport Peng Gao* Department of Geography, Syracuse University, Syracuse, NY 13244, USA Abstract: Suspended sediment at the watershed scale has played a critical role in sediment pollution, water-quality degradation, and the impairment of riparian ecosystems, and thus has been widely studied in many disciplines. This paper synthesizes a variety of methods adopted in suspended sediment monitoring, estimation and modelling for understanding sediment transport processes and determining the suspended sediment load. Methods for sediment monitoring are described in terms of direct and indirect approaches. Estimation of suspended sediment load is commonly achieved by establishing a sediment rating curve. Different approaches toward the establishment of a sediment rating curve are examined thoroughly. Techniques of sediment modelling are summarized via depiction of various hydrological and sediment models at the watershed scale. The paper ends with the discussion of future developments in suspended sediment studies at the watershed scale. Key words: sediment monitoring, sediment rating curve, suspended sediment, watershed modelling.

I Introduction River restoration, because of continuous degradation of river ecosystems and loss of aquatic biodiversity, has been recognized by governments at all levels and various stakeholders as an essential means for better natural resource management (Wohl et al., 2005). Almost all river restoration projects involve sediment and sediment transport because ‘each component of the channel morphology is connected by the sediment system of supply-transport-storage’ (Sear et al., 1994). Directly, sediment, especially total suspended sediment (TSS), has been identified as the leading cause of river impairments (US EPA, 2000). Indirectly, suspended sediment often

acts as a transporting machine for carrying nutrients, trace metals, semi-volatile organic compounds, and pesticides (US EPA, 2000), and thus affects physical, chemical, and biological properties of aquatic ecosystems (Lloyd et al., 1987; Kirk, 1988; Dahlgren et al., 2004). Therefore, determination of TSS loads in rivers is critical for establishing science-based criteria for the maximum daily amount of sediment that aquatic vegetation can tolerate (ie, total maximum daily loads, TMDLs) (Fitzgerald et al., 2001; Kuhnle and Wren, 2005), for the development of erosion management and pollution control strategies (Gao et al., 2007), and for the design and operation of irrigation systems and river

*Email: [email protected] © 2008 SAGE Publications

DOI: 10.1177/0309133308094849

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

244 Progress in Physical Geography 32(3) regulation facilities (Mizumura, 1989). This has resulted in increasing numbers of projects on sediment-related river restoration at the watershed scale (Kondolf, 1998; Ward et al., 2001; Randle et al., 2003; Pennisi, 2004). However, many of them have failed (Williams et al., 1997; Kondolf et al., 2001). Among various reasons, two are fundamental: (1) these projects were performed with the minimum scientific context; and (2) these projects merely focus on a single, isolated reach of rivers (Wohl et al., 2005). Recent scientific advance on physical processes of sediment transport, erosion, and deposition, and the corresponding response in channel forms and aquatic ecosystems (eg, Leopold et al., 1995; Jarvie et al., 2002; Perry and Taylor, 2007) indicates that successful watershed restoration relies on the understanding of key sediment transport processes and their linkages beyond the channel reach (such as hillslope lands and upstream channels, floodplain areas and downstream channels) as sediment transport varies both spatially and temporally at the watershed scale. Accordingly, good sediment-related river restoration projects require thorough understanding of various sediment transport processes and their interactions, which can be primarily achieved by identifying the provenance of suspended sediment and determining suspended sediment load based on sediment monitoring and modelling. Methods for and associated problems in recognizing suspended sediment sources have been discussed in depth in a recent review paper (Collins and Walling, 2004). This paper, however, summarizes a variety of methods that have been employed to determine suspended sediment load in a watershed. Because of the intrinsic connection between sediment sources and sediment transport, some methods have been used for both sediment source identification and sediment load determination. This paper focuses on methods and problems related to suspended sediment transport in channels within a watershed. The paper synthesizes

the methods commonly used for measurement of suspended sediment, determination of sediment load, and prediction of sediment load and its spatial distribution at the watershed scale using hydrological and sediment models. In the end, it discusses the possible directions of future work on suspended sediment transport. II Sediment measurement Sediment measurement, which refers to the determination of suspended sediment load (Qs) by in situ sediment sampling or monitoring in the main stream or at the outlet of the interested watershed, has been carried out for a long time (eg, Campbell and Bauder, 1940; Miller, 1951; Douglas, 1967; Walling and Kleo, 1979; Walling and Webb, 1981; Reid and Dunne, 1984; Lopes and Lane, 1988; Rondeau et al., 2000; Walling et al., 2001; Hasholt, 2005; Hotta et al., 2007; Salles et al., 2008). The purposes of sediment measurement, however, are considerably diverse. Among them a few are for determining the degree of soil erosion or performing sediment budget (Walling, 1974; 1988; Paustain and Beschta, 1979; Rowe, 2001; Peart et al., 2005; Sui et al., 2005), for investigating sediment dynamics (Rieger et al., 1988; Hudson, 1989; Huggitt and Lu, 2000; Krishnaswamy et al., 2001; Old et al., 2005), for exploring the effect of a particular sediment source, such as bank erosion, landslide, or volcanic eruption on sediment load (Skau et al., 1980; Prestegaard, 1988; Laubel et al., 2000; Major, 2001), for understanding the transport of contaminants and nutrients (Milhous, 1982; Thomas, 1988; Conrad and Saunderson, 2000; Langlois et al., 2005; Neal et al., 2006), and for investigating the effect of riparian zone or vegetation on in-channel sedimentation (Ghadiri et al., 2000; Steiger et al., 2001; Nicholas, 2003). The achievement of all these aims primarily depends on the accurate estimation of Qs (Walling, 1977), which is fundamentally controlled by the quality of the collected sediment data. While it is theoretically necessary to undertake

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport measurements for obtaining representative data at a range of spatial locations and over various temporal scales within a watershed, developing a sampling/monitoring strategy that includes all possible sediment sources is often impractical and unrealistic (Collins and Walling, 2004). Consequently, a variety of techniques have been developed to obtain suspended sediment data with different intensities (Wren et al., 2000). These techniques may be generally divided into two categories: direct and indirect measurement. 1 Direct measurement In as much as TSS is driven by water discharge, high values of suspended sediment concentration (C) are often associated with high flow rates (Larson et al., 1997; ValeroGarces et al., 1999; Lewis and Eads, 2001), which typically occur in a short period of one year (Walling and Webb, 1987). The typical infrequent regular sediment sampling strategies (such as once a month), therefore, can miss significant sediment concentrations and thus cause the inaccurate estimation of sediment load (Dickinson, 1981; Olive and Rieger, 1988; Walling et al., 1992). Consequently, the estimation of sediment yield, which is the total sediment load per year for a given watershed, will be inaccurate if the sediment sampling strategy is not appropriately designed to capture the high sediment concentrations (Beschta, 1978; Rieger et al., 1988; Thomas, 1988; Ollesch et al., 2005). A commonly used alternative approach is deploying suspended sediment sampling using automated pumping (AP) samplers (Bogen, 1988; Johnson, 1992; Russell et al., 1998; Lecce et al., 2006; Herman et al., 2008). The AP sampler uses an integrated pump to draw suspended sediment from stream flow by an intake system through which samples are drawn from a point of the sampled cross-section. Thus the AP sampler is essentially a point sampler. It creates a velocity in the intake tube greater than the setting velocity of suspended particles to pull them laterally from their streamline and

245

accelerate them in the direction of the intake (Edwards and Glysson, 1999). The sample distribution system can divert correctly pumped samples to bottles in sequence and an AP sampler can automatically take as many as 48 samples (Hicks and Gomez, 2003). Sampling is usually triggered and stopped by a predetermined threshold value of stage, flow velocity, or turbidity (Lewis, 1996; 2003; Harmel et al., 2003). With an appropriate preset time interval between samples, the AP sampler is capable of capturing peak sediment concentrations during storms and hence effectively avoiding the trend of underestimation of TSS loads caused by infrequent monthly sampling (Walling and Webb, 1981; Ferguson, 1986; Foster et al., 1992). The automatic nature of the AP samplers practically reduces the manpower for frequent site visits and intensive sample transformation while collecting relatively intense sediment samples. The commonlymentioned disadvantages of the AP samplers are (1) limited sample bottles and pumping head, (2) possible mechanical breakdown, and (3) bias toward fine particles (typically sand) due to the failure of achieving isokinetic sampling condition (Edwards and Glysson, 1999; Wren et al., 2000; Hicks and Gomez, 2003). The first problem merely limits the usage of the AP samples in large rivers. In many small or upland watersheds where suspended sediment is a typical environment issue due to agriculture and urbanization, employment of an AP sampler may be costeffective. The second and third problems typically occur in gravel-bed rivers with high flow rates. Given that an AP sampler is a point sampler, it is critical to verify whether the collected point sediment concentration is representative of average concentration of the whole cross-section. The assumption that suspended sediment of fine particles is well mixed throughout the water column is widely used and has been approved in several individual rivers and agricultural drains (Walling and Teed, 1971; Carling, 1984;

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

246 Progress in Physical Geography 32(3) Kuhnle et al., 2000; Gao et al., 2007). However, the vertical concentration gradient described by the sediment theory has also been widely observed (Zhao and Niu, 1982; Umeyaina, 1992; Wren et al., 1999). Therefore, the assumption should not be automatically accepted without verification. In addition, setting the appropriate sampling time period is essential for achieving accurate sediment samples. For the AP samplers triggered by stage or flow velocity, the threshold value of either stage or flow velocity needs to be predetermined. In practice, however, setting up the threshold value requires consideration of the duration of the hydrograph at the sampling site in order to ensure the collected samples cover the entire period of the event. This is unfortunately a trial-anderror process. Determination of the triggering value and sampling interval requires knowledge of the possible rainfall duration, the physiographic condition of the studied watershed, and the location of the sampling site. Therefore, a preliminary study should be conducted prior to the formal sediment sampling. 2 Indirect measurement The AP samplers are good at capturing high sediment concentrations during rainfall events, but the number of sediment samples collected is limited by the number of bottles included. Therefore there is no assurance that an AP sampler with a predetermined sampling period can capture the entire variation of sediment load during one rainfall event unless more sediment samples are possibly taken. This can be achieved by using turbidity sensors (Jansson, 1992; Sun et al., 2001; Dahlgren et al., 2004; Lawler et al., 2006). Turbidity describes the degree at which light intensity is attenuated by particles in water. A typical type of turbidity sensor is the optical backscatter sensor (OBS) that receives the reflected light from the sediment-laden flow. Instead of directly obtaining the suspended sediment concentration, a turbidity sensor measures the turbidity of flow caused by

suspended sediment. Therefore, sediment sampling by turbidity sensors is an indirect means by which turbidity serves as a surrogate for suspended sediment concentration (eg, Brasington and Richards, 2000; Melis et al., 2003; Pavanelli and Bigi, 2005; Presto et al., 2006). The turbidity data are usually stored in a battery-powered data logger that can automatically control the sampling frequency in terms of a preset time interval. Because the data logger can store a large amount of data, the sampling interval for a turbidity sensor may be set at will and a data series with short (ie, ‘continuous’) time interval (ie, one or five minutes) is readily obtained. This technique provides a costeffective way of in situ monitoring continuous variation of suspended sediment concentration, which greatly improves the accuracy of determining sediment yields compared with the traditional infrequent sediment sampling. Because turbidity can be sensed accurately at the low values, using the turbidity sensor for low concentration samples also reduces effectively the errors involved in the gravimetric analysis, the laboratory method of obtaining suspended sediment concentration. Consequently, turbidity sensors have been widely used to measure continuous suspended sediment concentration in marine environments (Downing et al., 1981; Jaffe et al., 1984; Kineke and Sternberg, 1992), estuaries (Sternberg, 1989), and continental shelves (Hanes and Huntley, 1986; Wright et al., 1994). They have also been increasingly adopted by researchers for sediment yield estimation in surface water (Jansson, 1992; Lewis, 1996; Schoellhamer and Wright, 2003; Orwin and Smart, 2004; Old et al., 2005) and for understanding the process of sediment transport in agricultural drain channels (Gao et al., 2008). The key to the success of this method is to assume that there exists a clear and unique relationship between measured turbidity values and the associated sediment concentration. Nonetheless, turbidity is very sensitive to particle sizes and sediment

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport composition. Although many studies have shown that turbidity varies linearly with sediment concentrations of homogeneous size (Green and Boon, 1993; Black and Rosenberg, 1994; Lewis, 1996), it has been discovered that particles of different sizes have different effect on turbidity given the same sediment concentration (Lewis, 1996) and those ranging from 0.2 to 5 µm for mineral sediment and from 1 to 20 µm for organic grains are most sensitive to turbidity (Kirk, 1988; Davies-Colley et al., 1992; Gippel, 1995). In both natural streams and artificial channels, suspended sediment particles are typically heterogeneous. For instance, the component of particle sizes in suspended sediment varies rapidly with flow rates in small upland watersheds (Johnson, 1992) and widely in different lowland British rivers (Walling and Woodward, 2000). Therefore, if two samples have the same sediment concentration but one has finer particles than the other, then the sample of finer particles will have a higher value of turbidity than the other (eg, Foster et al., 1992; Lenzi and Marchi, 2000; Pavanelli and Pagliarani, 2002; Teixeira and Caliari, 2005). In such cases, there is no unique relationship between turbidity values and the associated suspended sediment concentrations, and the obtained turbidity is not an appropriate surrogate for suspended sediment concentration (Riley, 1998). It is interesting to note, however, that the clear and unique turbidity-concentration relationships have been identified in many natural streams and agricultural drain channels that consist of suspended sediment of heterogeneous sizes, though the relationships may be linear or non-linear (Lewis, 1996; Sun et al., 2001; Gao et al., 2008). This may be due to the fact that variable components of particle sizes in sediment samples may offset the multiplicity of turbidity values for the same concentration. In addition to the particle size, many other factors such as water colour, bio-fouling, physical disturbance, and hydrodynamic

247

spike may generate lots of noise in turbidity time series recorded by real-time turbidity sensors (eg, Gippel, 1995; Hatcher et al., 2000; Sutherland et al., 2000; Wagner et al., 2000; Sadar, 2002; Gao et al., 2008). Therefore, whether suspended sediment concentration can be replaced by turbidity or not needs to be carefully examined before the initiation of a sediment sampling campaign based on turbidity sensors (Teixeira and Caliari, 2005). III Sediment estimation 1 Sediment rating curve Although different methods such as the flowduration approach (eg, Walling and Webb, 1981; Cordova and Gonzalez, 1997) and the magnitude-frequency approach (eg, Stow and Chang, 1987) have been used for determining Qs, the most commonly used method is developing a sediment rating curve (SRC) using a limited set of data and calculating Qs, based on the established SRC, by Qs = CQ where C is TSS concentration (mgL-1) and Q is mean water discharge (m3s-1) in channels (Campbell and Bauder, 1940; Miller, 1951; Tanaka et al., 1983; Reid and Dunne, 1984; Park, 1992; Horowitz et al., 2001; Lewis, 2003). A SRC is a mathematical relationship between C (or Qs) and Q that can be generally represented by: M = aQb

(1)

where M is either Qs or C; a and b are rating curve parameters determined by regression analysis using obtained data. The SRC method reduces sediment sampling intensity and hence allows for cost-effective sediment studies in cases where the watersheds lack financial and labour resources. It also permits the precise prediction of Qs by only measuring Q, which is very useful in the watersheds of flashy hydrological responses (Walling, 1977). Furthermore, the parameters a and b can be used to infer the relationships

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

248 Progress in Physical Geography 32(3) between various sediment transport processes and sources (Asselman, 2000; Syvitski et al., 2000). For example, the variation of b values has been interpreted in terms of (1) different storm events (Tanaka et al., 1983) or the difference between rising and falling stages of hydrographs (Park, 1992), and (2) variable erodibility in different regions such as dry and wet areas, different stream powers in different rivers, or different extent of new sediment sources (Walling, 1974; Mimikou, 1982; Kesel, 1989; Morgan, 1995; Bramblett and Fuhrer, 1999; Ebbert et al., 2003). It is, therefore, reasonable to state that the SRC method is one of the most widely used approaches to quantifying Qs in both natural streams and artificial channels. 2 Problems However, together with its wide application is a series of criticisms on the SRC method. The most salient problem is that many established relationships between Q s or C and water discharge exhibited significant scatter (Abrahams and Kellerhals, 1973; Walling, 1977; Walling and Webb, 1981; Tanaka et al., 1983; Jansson, 1996; Kronvang et al., 1997; Gao et al., 2007). The fundamental hydraulic reason causing the scatter is that suspended sediment load in natural rivers is essentially a non-capacity load (Walling, 1977). Consequently, for a given water discharge, Qs varies with not only Q but also upstream sediment supply that is related to geological and geomorphological conditions, soil types, and land use and land cover. Another problem of the SRC is that the estimated values of Qs using SRCs for one year (ie, sediment yields), may substantially differ from the actual ones by –80% to 900% (eg, Walling, 1977; Dickinson, 1981; Geary, 1984; Ferguson, 1986; Singh and Durgunoglu, 1989; Walling et al., 1992; Asselman, 2000). This inaccurate nature of estimating sediment load has been attributed to potential pitfalls in logarithmic transformation of the original data and in matching C to Q for different time intervals. The SRC developed using least-square

linear regression analysis with logarithmic transformed data inherently contains a statistical bias that leads to the underestimation of sediment load, the degree of which increases with Q (Ferguson, 1986; Singh and Durgunoglu, 1989; Asselman, 2000). Although several bias-corrected methods have been developed (Ferguson, 1986; 1987; Koch and Smille, 1986; Cohn et al., 1992), many studies revealed the overestimation of sediment yields by these methods (eg, Walling and Webb, 1988) suggesting the log-transformation bias is not the only culprit for the inaccurate estimation of the sediment load. It should be noticed that the SRC is commonly developed using discrete samples, but the sediment yield is a cumulative value of Qs over a continuous period of one year. Therefore, many possible approaches have been developed in which the established SRC is used to calculate C either for the flow duration shorter than the sampling interval (eg, the discharge-weighted mean concentration method) or for C in the new flow duration (Walling and Webb, 1981; Ferguson, 1987).Applying different methods to the same data set (Walling and Webb, 1981; Clarke, 1990a) showed that calculated sediment yields are unfortunately highly variable, suggesting that such approaches are generally not stable. The instability may be caused by the fact that the SRC does not consider the inherent temporal variation in concentration and discharge during a storm event (Rieger et al., 1988; Walling and Webb, 1988). 3 Solutions The problems involved in the SRC method indicate the complexity of suspended sediment transport. Continuous effort of investigation has led to a variety of solutions for such problems. a Stochastic approach: Different empirical models based on time-dependent stochastic processes (eg, Sickle, 1981; Caroni et al., 1984) were created to improve sediment yield or load estimation. Several statistical methods

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport based on either error estimates (Clarke, 1990a; 1990b; Gilroy et al., 1990; Crawford, 1991), or stratified probability sampling or stratified logged mean (Thomas, 1985; Thomas and Lewis, 1993; 1995; Jansson, 1996), have emerged to increase the accuracy of sediment yield estimation. These methods demonstrate the improvement on certain statistical properties that lead to higher precision and accuracy of sediment load estimation (eg, Crawford, 1991). However, the improvement is often at the cost of more stringent requirements on the original data, which greatly limits their application. For example, the assumption that C and Q have the same distribution in some methods (eg, Clarke, 1990b) and the need to update input data for the stochastic models (Sickle, 1981; Caroni et al., 1984) suggest that C and Q data have to be stationary, while the non-stationary character of the data has been consistently reported (Walling, 1977; Chappell et al., 1998; Horowitz, 2002). Therefore, the accuracy of sediment estimation is dependent on the time interval (eg, monthly or hourly intervals) selected for arranging the data (Horowitz, 2002). As another example, the long data series implied in many of the proposed methods suggests that these methods are merely appropriate for estimation of longterm sediment yields in watersheds with well-established monitoring stations. b Statistical approach: An alternative solution could concentrate on improving the predictive ability of the SRC by accounting for the complicated influence on sediment concentration other than Q. This was achieved by introducing the multilinear regression analysis in which new variables representing additional influences such as land use, topography (eg, relief ratio), catchment area, and rainfall were added in a SRC as independent variables to estimate Q s (Walling, 1977; Dunne, 1979; Collins, 1981; Kronvang et al., 1997; Verstraeten and Poesen, 2001). Although the improved estimation of sediment

249

load is demonstrated, the methods require additional effort to obtain more input data, which undermines the cost-effective advantage of the SRC in the first place. Furthermore, the application of these relationships to the watersheds different from those where the relationships are developed (ie, the extrapolation of a SRC) is often problematic (de Vente et al., 2006). Theoretically, establishing a SRC implies the acceptance of the assumption that sediment concentration responds instantaneously to water discharge. However, the below-capacity nature of suspended sediment transport suggests that sediment concentration is not only controlled by flow hydraulics, but also influenced by sediment supply, which is broadly related to topographic conditions, soil types, land use, and land cover. Therefore, sediment concentration at the sampling site does not have to respond instantaneously to water discharge and a wellfitted SRC may be merely a ‘coincidence’ of multiple processes of sediment transport within a watershed occurring in the sampling period. There is no guarantee in theory that such coincidence persists, which means the SRC method should be used with the awareness of its limitations. In contrast to the SCR, the ‘not in phase’ relation is often referred to as a hysteresis loop expressed in the plot of C against Q (Peart and Walling, 1982; Park, 1992; Asselman, 1999; Brasington and Richards, 2000; Hudson, 2003). The analysis of the orientation of the loop (ie, hysteresis analysis) may expose the link between in-stream sediment transport and sediment sources. A clock-wise hysteresis loop forms (1) when Cpeak precedes Qpeak (eg, Peart and Walling, 1982; Walling and Kane, 1984) due to the process in which sediment is scoured from channel beds and sediment transport is controlled by stream power, or (2) when C decreases considerably during the falling limb of the hydrograph (ie, sediment depletion) because of the decreasing flow velocity

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

250 Progress in Physical Geography 32(3) (eg, Walling and Webb, 1981; Asselman, 1999). An anticlockwise hysteresis loop prevails (1) when sediment is derived from finedgrained hillslope soils and flow velocity in the upstream area is less than that in the channel (Klein, 1984), (2) when transport is limited by sediment availability (Walling and Webb, 1982; Park, 1992), (3) when flow velocity associated with a given suspended sediment load is less than flow wave celerity, which determines Qpeak (Brasington and Richards, 2000), or (4) when higher C in the falling limb than in the rising limb is caused by (i) an additional local source of fine sediment due to bank-caving events (Asselman, 1999) and (ii) the supply of additional silt/clay from runoff-derived sources (Williams, 1989). However, patterns of hysteresis loops may be much more complicated than the typical five patterns described by Williams (1989) so that sources of sediment are hard to distinguish (Gao and Pasternack, 2007). IV Sediment modelling At the watershed scale, a significant fraction of suspended sediment is derived from hillslope. This proportion of sediment load in streams depends on both erosion rate on hillslope and delivery rate to the channel. Neither of these two processes is necessarily related to water discharge (Pickup, 1988). Therefore, sediment estimation based on the oversimplified sediment rating curve method is not accurate due to the inability of describing the dynamics of suspended sediment transport within a watershed unless the watershed is so small that sediment deposition can be ignored. With the emergence of computers and the increase in numerical processing power, a logical alternative for estimating watershed sediment load is to describe various physical processes controlling sediment movement by mathematical equations and to simulate the dynamic processes of sediment transport at the watershed scale using computers. This gives rise to a promising and dynamic branch for the study of sediment transport: sediment modelling.

1 Development of sediment modelling Accounting for the comprehensive interaction of various soil erosion processes on hillslopes led to a suite of complex response models that can be embodied by the dynamic basin (DB) sediment yield model (Moore, 1984). The DB model incorporates three mathematical equations to describe the physical processes of sediment availability, removal, and transport. The degree of predicting accuracy depends on the richness of information on the spatial distribution of rainfall and runoff within the watershed. The major limit of such response models lies in their incapability of identifying spatial variation of soil erosion within a watershed. One of the earliest models overcoming this limit is Universal Soil Loss Equation (USLE) or Revised USLE (RUSLE) that describes the potential of soil erosion in a watershed based on five parameters representing comprehensive effects of hydrology, soil, topography, land use and land cover, and precipitation on sediment transport (Wischmeier and Smith, 1978; Renard et al., 1991). The USLE-type models are essentially rainfallbased index models in which the degree of soil erosion due to rainfall is indirectly represented by a combination of several indices standing for various factors such as topography, soil, land use, and land cover that control soil erosion. The effort of trying to estimate soil erosion more precisely gave rise to models using such indexes as Soil Conservation Service Curve-Number-based runoff estimations (such as CREAMS, Chemicals, Runoff and Erosion from Agricultural Management Systems; Knizel and Nicks, 1980). In the index models, hydrological and sediment transport processes are essentially described in an indirect and qualitative manner. Using governing equations to describe various watershed processes and solving these equations in both spatial and temporal domains result in the process-based models – such as KINEROS (Smith et al., 1995) and WEPP (Flanagan et al., 2001). The USLE-type, Curve-Numberbased, and process-based models indeed represent three different methods employed

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport in sediment modelling to characterize the physical processes of sediment movement and resultant soil erosion at the watershed scale. Although the process-based method has apparently been more reliable and accurate because it relies on mathematical equations to describe various hydrological and sediment processes both on hillslopes and in channels (eg, de Aragao et al., 2005), the other two methods, especially the USLEtype method, are still widely adopted mainly because of their easy implementation (Rompaey et al., 2001; Asselman et al., 2003; Sivertun and Prange, 2003; Yang et al., 2003; Vigiak et al., 2005). 2 Lumped and distributed models At the watershed scale, the processes of sediment transport on hillslope and in channel are integrated with each other (Michaelides and Wainwright, 2002). Ignoring the in-channel process will result in the inaccurate estimation of suspended sediment load because of inchannel deposition and re-suspension, and bank erosion, while overlooking the hillslope process will lose the track of sediment sources. Therefore, sediment modelling at the watershed scale needs to include both processes. This has been achieved by the attempt of mathematically characterizing and modelling physical processes controlling soil erosion in uplands and sediment transport in channels at the watershed scale (Singh, 1995; Jakeman et al., 1999; Singh and Frevert, 2002), which leads to a still fast-growing research area: process-based watershed modelling (Singh and Frevert, 2006). In terms of the spatial discretion and the linkage between the discrete elements, two general types can be identified among abundant watershed models: the lumped model and the distributed model. In the lumped models, a watershed is normally divided into limited, relatively coarser areas, assuming that variations of various physical processes within each area are negligible. However, there are no rules that can be used to determine the size of the areas.

251

Accordingly, different models adopt areas with different methods. Examples of such models are DWSM, MMF, and SWAT. DWSM, which stands for Dynamic Watershed Simulation Model, was developed by the Illinois State Water Survey (Borah, 2002). In DWSM, watersheds comprise channel segments and hillslopes. The channel segments are connected to each other in terms of the channel network structure. Hillslopes are assumed uniform areas that have to join to their neighbouring channel branches. Therefore, the lumped area is defined based on the alignment of the stream network within the watershed. MMF (Morgan, Morgan and Finney) is an empirical model developed to estimate mean annual soil loss from field-sized areas on hillslopes (Morgan et al., 1984); the element (lumped) areas are, depending on the availability of the data, either clearly defined as fields that have homogeneous land use and soil type (Vigiak et al., 2006) or roughly regarded as portions of hillslope homogeneous per slope direction and gradient (Vigiak et al, 2005). So, the size of the lumped area is more or less determined subjectively. SWAT (Soil and Water Assessment Tool) is a watershed model for predicting the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land-use and management conditions over long periods of time (Arnold et al., 1993). In SWAT, a watershed is divided into several sub-watersheds, each of which is further divided into several hydrological response units (HRUs) (van Liew and Garbrecht, 2003; Neitsch et al., 2005). Each HRU represents a lumped land area within the sub-watershed that consists of unique land cover, soil, and management combinations (Flugel, 1995). However, HRUs may be delineated in different ways, each of which may give rise to different model outputs (Bongartz, 2003). Therefore, generation of the lumped areas in SWAT is inconsistent and instable. Another feature of the lumped models is that the connections between the lumped areas

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

252 Progress in Physical Geography 32(3) and channel segments (or the paths for water and sediment movement) have to be predetermined and provided as part of the input file. The determination of these connections is primarily based on the topography and the structure of stream network within the studied watershed. The distributed models cover the studied watershed by a grid with regular sizes and apply values representing physiographic conditions of a watershed to each cell. Examples in the large pool of the distributed models are LIZEM (de Roo et al., 1996), TOPMODEL (Beven et al., 1995; Beven and Freer, 2001), MIKE-SHE (Refsgaard and Storm, 1995), and AGNPS (Young et al., 1987). The first three were developed in Europe, while the last one, the Agricultural NonPoint Source Pollution model, was developed by the US Department of Agriculture, Agricultural Research Service (USDA-ARS) to estimate soil erosion in farmlands, pastures, and urban areas, and stream discharge and sediment routing in channels. It has been recently upgraded to AnnAGNPS (Bingner and Theurer, 2001). The connection between cells in the distributed models is determined by flow direction which is commonly calculated by the D8 method, also termed the eight-direction pour point method (Maidment, 2002). This method assigns the flow direction of a cell to one of its neighbours that has the steepest slope (Douglas, 1986; O’Callaghan and Mark, 1984; Fairfield and Leymarie, 1991; Martz and Garbrecht, 1992). Although several different methods of determining flow direction have been developed (Quinn et al., 1991; Costa-Cabral and Burges, 1994; Tarboton, 1997), the D8 method is still widely used (de Jong et al., 1999; Vigiak et al., 2006). The distributed models have been broadly used to estimate TSS load from watersheds and develop best management practices (BMPs) for sediment reduction (Ferro et al., 1998; Perrone and Madramootoo, 1999; Muttiah and Wurbs, 2002; Trauth and Adams, 2004; Renschler and Lee, 2005; Bracmort et al., 2006) because they incorporate most, if not all, physical

processes occurring both on hillslopes and in channels. 3 Sediment load prediction While the lumped models enjoy the advantages of fast processing, with simple and less data input, they are explicitly or implicitly criticized mainly due to the fact that only single values are assigned to the lumped areas to represent hydrological and sediment processes, which ignores the significant spatial variations with the areas. The prevalence of the distributed models is assured by the belief that these models are capable of producing more accurate predictions of sediment load because they successfully represent the spatial variations of various physical processes. However, several problems arose. First, many models such as ANSWERS (Beasley et al., 1980) and TOPMODEL are used to estimate discharge and sediment load only at the outlet of the watershed (Jetten et al., 2003; de Vente et al., 2006). Consequently, the predicted sediment load and discharge may be correct, but the distributions of sediment sources are unknown. This problem is often referred to as predicting the correct result for the wrong reasons (Takken et al., 1999; FavisMortlook et al., 2001; Jetten et al., 2003). Second, accurate prediction of erosion patterns in the watershed is still far away. One reason is that the distributed models often fail to include sediment load produced from point sources such as channel beds and banks, and riparian areas (Trimble, 1997; Jansson, 2002; Poesen et al., 2003; Nagle and Ritchie, 2004). Another reason is that there are still no theoretical solutions to accurately describe in mathematical form the complex interactions among various hydraulic and erosion processes. For example, many models use the universal soil loss equation to estimate sediment loads on uplands (or subwatersheds), which involves at least two theoretical pitfalls: (1) the use of slope length factor in USLE implies that sediment yield increases with watershed area, which is contradictary to the commonly accepted inverse

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport relationship between the two (Walling, 1983); and (2) USLE fails to characterize soils being eroded but not being able to move out of the modelled (subwatershed) area (Beach, 1994; Parsons et al., 2006). A third reason is that many distributed models require a large amount of input data (for instance, AnnAGNPS requires 27 input parameters). Unfortunately, many of these input variables are intrinsically stochastic (Brazier et al., 2000), and often unknown. Measuring or modelling these values adds considerable uncertainty to the model outcomes (Jetten et al., 2003). In addition, it has been noticed that the more simple lumped models may perform equally well as the more complex distributed models (Jetten et al., 2003; White, 2005), because the uncertainty and additional errors resulting from introducing more parameters in the distributed models often outweighs the potential improvement in prediction due to a better process description (Jetten et al., 2003). Therefore, the distributed models still have limited abilities of predicting spatial and temporal variations of sediment movement. V Future development 1 Sediment measurement and calculation Suspended sediment monitoring and estimation are closely related to each other. The accuracy of sediment load calculation is significantly affected by sampling frequency and the length of the sampled data. The limitations and disadvantages of various methods described previously suggest the process of suspended sediment transport is complex and the quantification of suspended sediment load is difficult. Therefore, instead of seeking a general, uniform method for suspended sediment monitoring and estimation, one should adopt different methods for different purposes of sediment study because no sediment study requires the knowledge of all aspects of sediment transport. Two general directions of sediment study for future development can be laid out in

253

terms of purpose. The first concerns longterm trend of sediment load variation such as predicting the future sedimentation of reservoirs for the determination of their lifespan. In this direction, data sets are often very long (more than five years). Therefore, the accuracy of the adopted sampling method is not critical as sampling errors caused by the short-term variations of sediment load are unlikely to affect the long-term sediment load estimation. In fact, Horowitz (2003) revealed that data with low frequency (eg, weekly or monthly data as oppose to hourly, daily data) can lead to better predicted results. The focus of sediment study in this direction should be on exploring the appropriate regression technique that may provide the best sediment load estimation. This focus is exemplified by the detailed statistical analysis of a recent study (Crowder et al., 2007) showing that different sediment-discharge curves (eg, concave or convex) need different non-linear regression methods for best sediment load estimation. It should be noted that, no matter how accurate the developed relationships are, they possess an implied assumption that the driving processes (such as climate and land-cover conditions) are stable (White, 2005). Nonetheless, the fact that significant human impacts on suspended sediment load in rivers have been observed worldwide over the last century (Tiffen et al., 1994; Chen et al., 2001; Poulos et al., 1996; de Boer, 1997) suggests that this assumption is often unrealistic. Therefore, the extrapolation of such relationships to the future for predictions such as the useful reservoir life due to sedimentation should take the uncertainty of the output into consideration (White, 2005). The second deals with short-term sediment dynamics that may be linked to various physical- or human-induced environmental changes such as excess sediment load because of bank erosion or upland landslide, and sediment pollution due to agriculture and urbanization. In this direction, designing a good sampling strategy for capturing spatial and temporal variations of sediment load

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

254 Progress in Physical Geography 32(3) outweighs the choice of different regression methods because, for short-term sediment estimation, missing sediment variation in the time interval shorter than sampling interval can cause significant errors. In a sedimenttransport study (Gao et al., 2007), sediment analysis based on weekly sampled data predicts that the erosion process dominates the studied agricultural drain channels while the field reconnaissance demonstrated that these drain channels are indeed dominated by the deposition process. This wrong prediction is caused by the fact that many sediment spikes occurring in a period of much less than one week are missed by the weekly sampling. Therefore, the key for sediment study in this direction is to understand to what (spatial and temporal) degree one needs to capture sediment dynamics for the purpose of the study. The appropriateness of an adopted sampling method or technique should thus be judged by whether the collected data can represent the sediment dynamics that matter to the original goals. This has been endorsed by several newly developed sampling methods. Phillips et al. (2000) developed a simple inexpensive sampler to efficiently collect timeintegrated samples to include all sizes of sediment transported during one storm. Pavanelli and Bigi (2005) verified a new indirect method of estimating suspended sediment load based on the free settable solids and Imhoff cone analysis, though it has not been tested in other rivers. 2 Sediment modelling Essentially, watershed modelling is the approach of attempting to estimate water discharge and sediment load at larger scales (eg, subwatersheds or watersheds) based on empirical rules (such as USLE-types) or governing equations of various relevant physical processes (such as process-based models) at the small scale (eg, fields, cells, or control volume). However, hydrological and sediment processes at the watershed scale are quite complicated. For lumped models, this complexity may lead to good predictions for

the wrong reasons (Beven, 2002) due to the lack of information at the smaller scales. For distributed models, this complexity promotes the inclusion of more equations to account for the additional processes involved, which inevitably increases the number of unknown parameters and thus adds additional uncertainty to the models (Beven, 2002). The increased uncertainty could render the models unverifiable (Bloschl, 2001). Therefore, if a watershed is viewed as a system, watershed modelling is basically still a grey-box (if not black-box) approach. Consequently, both lumped and distributed models may provide the same degree of prediction accuracy. This can be very well represented by Beven’s equifinality thesis, which refers to the potential for multiple acceptable models as representations of hydrological and other environmental systems (Beven, 2006). In this regard, future development in watershed modelling should concentrate on more creative use of existing models than on generating new models. A typical trend in the evolution of watershed modelling is the development of new models via the combination of several existing models such as BASINS and IWAN. BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) is a comprehensive environmental analysis model developed by the US Environmental Protection Agency to assess water quality at the watershed scale (US EPA, 2000). BASINS adopts the ArcViewGIS platform and allows the user to choose different internally coupled models such as SWAT (the Soil and Water Assessment Tool developed by USDA-ARS; Arnold et al., 1993) and HSPF (Hydrological Simulation Program-Fortran; Bicknell et al., 1993). Each of these two incorporated models is a standalone model and can be used individually. IWAN (the integrated winter erosion and nutrient load model) is a model that characterizes the dynamics of source areas of sediment and sedimentassociated nutrient transport (Ollesch et al., 2005). It couples four standalone models:

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport WaSim-ETH (Water balance Simulation model ETH; Ollesch et al., 2005), which simulates the hydrology of watersheds spatially on a cell base; SMEM (Snowmelt Erosion Model, Sukhanovski et al., 2004), which is a cell-based distributed model for estimating soil detachment by concentrated flow in rills during snowmelt; AGNPS; and Extended ANIMO (Agricultural Nitrogen Model; Groenendijk and Kroes, 1999) with a phosphate module. This development absorbs the advantages of the existing models without reinventing the wheel, while avoiding their disadvantages. Another trend is incorporating geographic information system (GIS) into various watershed models in which GIS serves as input data source, output display platform, or simulation interface (Brooks and McDonnell, 2000; de Roo et al, 2000; Ogden et al., 2001; Svorin, 2003; Jain et al., 2005; Tetzlaff et al., 2007; Ozcan et al., 2008). The fast advance of GIS techniques thus will continuously improve sediment modelling. References Abrahams, A.D. and Kellerhals, R. 1973: Correlations between water discharge and concentration of suspended solids for five large prairie rivers. In Fluvial processes and sedimentation, Proceedings of Hydrology Symposium 9, National Research Council of Canada, Edmonton: University of Alberta, 96–113. Arnold, J.G., Allen, P.M. and Bernhardt, G. 1993: A comprehensive surface-groundwater flow model. Journal of Hydrology 142, 47–69. Asselman, N.E.M. 1999: Suspended sediment dynamics in a large drainage basin: the River Rhine. Hydrological Processes 13, 1437–50. — 2000: Fitting and interpretation of sediment rating curves. Journal of Hydrology 234, 228–48. Asselman, N.E.M., Middelkoop, H. and van Dijk, P.M. 2003: The impact of changes in climate and land use on soil erosion, transport and deposition of suspended sediment in the River Rhine. Hydrological Processes 17, 3225–44. Beach, T. 1994: The fate of eroded soil: sediment sinks and sediment budgets of agrarian landscapes in southern Minnesota, 1851–1988. Annals of the Association of American Geographers 84, 5–28. Beasley, D.B., Huggins, L.F. and Monke, J.M. 1980: ANSWERS: a model for watershed planning.

255

Transactions of the ASAE (American Society of Agricultural Engineers) 23, 938–44. Beschta, R.L. 1978: Long-term patterns of sediment production following rod construction and logging in the Oregon coast range. Water Resources Research 14, 1011–16. Beven, K. 2002: Towards an alternative blueprint for a physically based digitally simulated hydrological response modelling system. Hydrological Processes 16, 189–206. — 2006: A manifesto for the equifinality thesis. Journal of Hydrology 320, 18–36. Beven, K.J. and Freer, J. 2001: A dynamic TOPMODEL. Hydrological Processes 15, 1993–2011. Beven, K.J., Lamb, R., Quinn, P., Romanowicz, R. and Freer, J. 1995: ‘TOPMODEL’. In Singh, V.P., editor, Computer models of watershed hydrology, Highlands Ranch, CO: Water Resources Publications, 627–68. Bicknell, B.R., Imhoff, J.C., Kittle, J.L., Donigian, A.S. Jr and Johanson, R.C. 1993: Hydrologic Simulation Program – FORTRAN (HSPF): user’s Manual for Release 10. Report EPA/600/R-93/174. Athens, GA: US EPA Environmental Research Laboratory. Bingner, R.L. and Theurer, F.D. 2001: AnnAGNPS technical processes: documentation, version 2. Oxford, MS: National Sedimentation Laboratory. Black, K.P. and Rosenberg, M.A. 1994: Suspended sand measurements in a turbulent environment: field comparison of optical and pump sampling techniques. Coastal Engineering 24, 137–50. Bloschl, G. 2001: Scaling in hydrology. Hydrological Processes 15, 709–11. Bogen, J. 1988: A monitoring programme of sediment transport in Norwegian rivers. In IAHS Publication 174, Wallingford: International Association of Hydrological Sciences, 149–59. Bongartz, K. 2003: Applying different spatial distribution and modelling concepts in three nested mesoscale catchments of Germany. Physics and Chemistry of the Earth 28, 1343–49. Borah, D.K. 2002: Watershed scale nonpoint source pollution models: mathematical bases. St Joseph, MI: American Society of Agricultural Engineers (ASAE), meeting paper 022091. Bracmort, K.S., Arabi, M., Frankenberger, J.R., Engel, B.A. and Arnold, J.G. 2006: Modelling long-term water quality impact of structural BMPs. Transactions of the ASABE (American Society of Agricultural and Biological Engineers) 49, 367–74. Bramblett, K.L. and Fuhrer, G.J. 1999: Suspended sediment and turbidity. In Morace, J.L., Fuhrer, G.J., Rinella, J.F., McKenzie, S.W. and others, editors, Surface-water quality assessment of the Yakima River Basin in Washington: overview and findings, 1987–91, US Geological Survey WaterResources Investigations Report 98-4113, 28–41.

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

256 Progress in Physical Geography 32(3) Brasington, J. and Richards, K. 2000: Turbidity and suspended sediment dynamics in small catchments in the Nepal Middle Hills. Hydrological Processes 14, 2559–74. Brazier, R.E., Beven, K.J., Freer, J. and Rowan, J.S. 2000: Equifinality and uncertainty in physicallybased soil erosion models: application of the GLUE methodology to WEPP – the Water Erosion Prediction Project for sites in the UK and US. Earth Surface Processes and Landforms 25, 825–45. Brooks, S.M. and McDonnell, R.A. 2000: Research advances in geocomputation for hydrological and geomorphological modelling towards the twentyfirst century. Hydrological Processes 14, 1899–907. Campbell, F.B. and Bauder, H.A. 1940: A ratingcurve method for determining silt discharge of streams. Transactions of the American Geophysical Union 21, 603–607. Carling, P.A. 1984: Comparison of suspended sediment rating curves obtained using two sampling methods. Catena 55, 43–49. Caroni, E., Singh, V.P. and Ubertini, L. 1984: Rainfall-runoff sediment yield relation by stochastic modelling. Hydrological Sciences 29, 203–18. Chappell, N.A., McKenna, P., Bidin, K., Douglas, I. and Walsh, R.P.D. 1998: Upscaling suspendedsediment flows in disturbed rainforest terrain: role of localized new sources. In Proceedings of the 3rd International Conference on GeoComputation, University of Bristol, United Kingdom, 17–19 September 1998, Bristol: GeoComputation CD-ROM. Chen, Z., Li, J., Shen, H. and Wang, Z. 2001: Yangtze River of China: historical analysis of discharge variability and sediment flux. Geomorphology 41, 77–91. Clarke, R.T. 1990a: Bias and variance of some estimates of suspended sediment load. Journal of Hydrological Science 35, 253–61. — 1990b: Statistical characteristics of some estimators of sediment and nutrient loadings. Water Resources Research, 26, 2229–33. Cohn, T.A., Caulder, D.L., Gilroy, E.J., Zynjuk, L.D. and Summers, R.M. 1992: The validity of a simple statistical model for estimating fluvial constituent loads: an empirical study involving nutrient loads entering Chesapeake Bay. Water Resources Research 28, 2353–63. Collins, A.L. and Walling, D.E. 2004: Documenting catchment suspended sediment sources: problems, approaches and prospects. Progress in Physical Geography 28, 159–96. Collins, M.B. 1981: Sediment yield studies of headwater catchments in Sussex, S.E. England. Earth Surface Processes and Landforms 6, 517–39. Conrad, C. and Saunderson, H. 2000: Temporal and spatial patterns of suspended sediment yields for selected rivers in the eastern United States:

implications for nutrient and contaminant transfer. In IAHS Publication 263, Wallingford: International Association of Hydrological Sciences, 37–46. Cordova, J.R. and Gonzalez, M. 1997: Sediment yield estimation in small watersheds based on streamflow and suspended sediment discharge measurements. Soil Technology 11, 57–69. Costa-Cabral, M. and Burges, S.J. 1994: Digital Elevation Model Networks (DEMON): a model of flow over hillslopes for computation of contributing and dispersal areas. Water Resources Research 30, 1681–92. Crawford, C.G. 1991: Estimation of suspendedsediment rating curves and mean suspendedsediment loads. Journal of Hydrology 129, 331–48. Crowder, D.W., Demissie, M. and Markus, M. 2007: The accuracy of sediment loads when logtransformation produces nonlinear sediment loaddischarge relationships. Journal of Hydrology 336, 250–68. Dahlgren, R., van Nieuwenhuyse, E. and Litton, G. 2004: Transparency tube provides reliable waterquality measurements. California Agriculture 58, 149–53. Davies-Colley, R.J., Vant, W.N. and Smith, D.G. 1992: Colour and clarity of natural waters. New York: Ellis Horwood. de Aragao, R., Srinivasan, V.S., Suzuki, K., Kadota, A., Oguro, M. and Sakata, Y. 2005: Evaluation of a physically-based model to simulate the runoff and erosion processes in a semiarid region in Brazil. In IAHS Publication 292, Wallingford: International Association of Hydrological Sciences, 85–93. de Boer, D.H. 1997: Changing contribution of suspended sediment sources in small basins resulting from European settlement on the Canadian prairies. Earth Surface Processes and Landforms 22, 623–39. de Jong, S.M., Paracchini, M.L., Bertolo, F., Folving, S., Megier, J. and de Roo, A.P.J. 1999: Regional assessment of soil erosion using the distributed model SEMMED and remotely sensed data. Catena 37, 291–308. de Roo, A.P.J., Wesseling, C.G. and Ritsema, C.J. 1996: A single-event physically based hydrological and soil erosion model for drainage basins. I: Theory, input and output. Hydrological Processes 10, 1107–17. de Roo, A.P.J., Wesseling, C.G. and van Deursen, W.P.A. 2000: Physically based river basin modelling within a GIS: the LISFLOOD model. Hydrological Processes 14, 1981–92. de Vente, J., Poesen, J., Bazzoffi, P., Rompaey, A.V. and Verstraeten, G. 2006: Predicting catchment sediment yield in Mediterranean environments: the importance of sediment sources and connectivity in Italian drainage basins. Earth Surface Processes and Landforms 31, 1017–34.

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport Dickinson, W.T. 1981: Accuracy and precision of suspended loads. In IAHS Publication 133, Wallingford: International Association of Hydrological Sciences, 195–202. Douglas, D.H. 1986: Experiments to locate ridges and channels to create a new type of digital elevation Model. Cartographica 23, 29–61. Douglas, I. 1967: Man, vegetation and the sediment yield of rivers. Nature 215, 925–28. Downing, J.P., Sternberg, R.W. and Lister, C.R.B. 1981: New instrumentation for the investigation of sediment suspension processes in the shallow marine environment. Marine Geology 42, 19–34. Dunne, T. 1979: Sediment yield and land use in tropical catchments. Journal of Hydrology 42, 281–300. Ebbert, J.C., Embrey, S.S. and Kelley, J.A. 2003: Concentrations and loads of suspended sediment and nutrients in surface water of the Yakima river basin, Washington, 1999–2000 – with an analysis of trend in concentrations. US Geological Survey WaterResources Investigations Report 03-4026. Edwards, T.K. and Glysson, G.D. 1999: Field methods for measurements of fluvial sediment. In Techniques of Water-Resources Investigations of the US Geological Survey, Book 3: Application of hydraulics, Chapter 2, 89 pp. Fairfield, J. and Leymarie, P. 1991: Drainage networks from Grid Digital Elevation Models. Water Resources Research 27, 29–61. F a v i s - M o r t l o c k , D . T . , B o a r d m a n , J . and MacMillan, V.J. 2001: The limits of erosion modelling: why we should proceed with care. In Harmon, R.S. and Doe, W.W., editors, Landscape erosion and evolution modelling, New York: Kluwer Academic, 477–516. Ferguson, R.I. 1986: River loads underestimated by rating curves. Water Resources Research 22, 74–76. — 1987: Accuracy and precision of methods for estimating river loads. Earth Surface Processes and Landforms 12, 95–104. Ferro, V., Porto, P. and Tusa, G. 1998: Testing a distributed approach for modelling sediment delivery. Journal of Hydrological Sciences 43, 425–42. Fitzgerald, J., Borden, C. and McNamara, J.P. 2001: Use of direct fluvial sediment monitoring to develop a total maximum daily load for the middle fork payette river subbasin in central Idaho. In Proceedings of the Seventh Federal Interagency Sedimentation Conference, Reno, Nevada VII, 18–25. Flanagan, D.C., Ascough, J.C., Nearing, M.A. and Laflen, J.M. 2001: The Water Erosion Predict Project (WEPP) model. In Harmon, R.S. and Doe, W.W., editors, Landscape erosion and evolution modelling, New York: Kluwer Academic, 145–99. Flugel, W.A. 1995: Delineating hydrological response units (HRUs) by GIS analysis regional hydrological

257

modelling using PRMS/MMS in the drainage basin of the river Brol, Germany. Hydrological Processes 9, 423–36. Foster, I.D.L., Millington, R. and Grew, R.G. 1992: The impact of particle size controls on stream turidity measurement; some implications for suspended sediment yield estimation. In IAHS Publication 210, Wallingford: International Association of Hydrological Sciences, 51–62. Gao, P. and Pasternack, G. 2007: Dynamics of suspended sediment transport at field-scale drain channels of irrigation-dominated watersheds in the Sonoran Desert, southeastern California. Hydrological Processes 21, 2081–92. Gao, P., Pasternack, G.B., Bali, K.M. and Wallender, W.W. 2007: Suspended-sediment transport in an intensively cultivated watershed in southeastern California. Catena 69, 239–52. — 2008: Estimating suspended sediment concentration using turbidity in an irrigation-dominated southeastern California watershed. Journal of Irrigation and Drainage Engineering, in press. Geary, P.M. 1984: Sediment and solute transport in a representative basin. Australian Geographical Studies 161–75. Gilroy, E.J., Hirsch, R.M. and Cohn, T.A. 1990: Mean square error of regression-based constituent transport estimates. Water Resources Research 26, 2069–77. Ghadiri, H., Hogarth, B. and Rose, C. 2000: The effectiveness of grass strips for the control of sediment and associated pollutant transport in runoff. In IAHS Publication 263, Wallingford: International Association of Hydrological Sciences, 83–91. Gippel, C.J. 1995: Potential of turbidity monitoring for measuring the transport of suspended solids in streams. Hydrological Processes 9, 83–97. Green, M.O. and Boon, J.D. III 1993: The measurement of constituent concentrations in nonhomogeneous sediment suspensions using optical backscatter sensors. Marine Geology 110, 73–81. Groenendijk, P. and Kroes, J.G. 1999: Modelling nitrogen and phosphorus leaching to groundwater and surface water with ANIMO 3.5. Report 144. Wageningen: Winand Staring Centre. Hanes, D.M. and Huntley, D.A. 1986: Continuous measurements of suspended sand concentration in a wave dominated nearshore environment. Continental Shelf Research 6, 585–96. Harmel, R.D., King, K.W. and Slade, R.M. 2003: Automated storm water sampling on small watersheds. Applied Engineering in Agriculture 19, 667–74. Hasholt, B. 2005: The sediment budgets of arctic drainage basins. In IAHS Publication 292, Wallingford: International Association of Hydrological Sciences, 48–57.

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

258 Progress in Physical Geography 32(3) Hatcher, A., Hill, P., Grant, J. and Macpherson, P. 2000: Spectral optical backscatter of sand in suspension: effects of particle size, composition and colour. Marine Geology 168, 115–28. Herman, E.K., Toran, L. and White, W.B. 2008: Threshold events in spring discharge: evidence from sediment and continuous water level measurement. Journal of Hydrology 351, 98–106. Hicks, D.M. and Gomez, B. 2003: Sediment transport. In Kondolf, G.M. and Piegay, H., editors, Tools in fluvial geomorphology, Chichester: Wiley, 425–61. Horowitz, A.J. 2002: The use of rating (transport) curves to predict suspended sediment concentration: a matter of temporal resolution. In Turbidity and Other Sediment Surrogates Workshop, 30 April– 2 May, Reno, Nevada. — 2003: An evaluation of sediment rating curves for estimating suspended sediment concentrations for subsequent flux calculations. Hydrological Processes 17, 3387–409. Horowitz, A.J., Elrick, K.A. and Smith, J.J. 2001: Estimating suspended sediment and trace element fluxes in large river basins: methodological considerations as applied to the NASQAN program. Hydrological Processes 15, 1107–32. Hotta, N., Kayama, T. and Suzuki, M. 2007: Analysis of suspended sediment yields after low impact forest harvesting. Hydrological Processes 21, 3565–75. Hudson, H.R. 1989: A case study of approaches for determing diffuse suspended sediment sources and processes. In IAHS Publication 184, Wallingford: International Association of Hydrological Sciences, 85–94. Hudson, P.F. 2003: Event sequence and sediment exhaustion in the lower Panuco Basin, Mexico. Catena 52, 57–76. Huggitt, D.L. and Lu, X. 2000: Determining variations in sediment yield in large rivers: an example of the Upper Yangtze. In IAHS Publication 263, Wallingford: International Association of Hydrological Sciences, 19–28. Jaffe, B.E., Sternberg, R.W. and Sallenger, A.H. 1984: The role of suspended sediment in shorenormal beach profile changes. In Proceedings of the 19th International Conference on Coastal Engineering, American Society of Civil Engineers, Houston, 1983–1996. Jain, M.K., Kothyari, U.C. and Ranga Raju, K.G. 2005: GIS based distributed model for soil erosion and rate of sediment outflow from catchments. Journal of Hydraulic Engineering 131, 755–69. Jakeman, A.J., Green, T.R., Beavis, S.G., Zhang, Li, Dietrich, C.R. and Vrapper, P.F. 1999: Modelling upland and instream transport in a large catchment. Hydrological Processes 13, 745–52.

Jansson, M.B. 1992: Turbidimeter measurements in a tropical river, Costa Rica. In IAHS Publication 210, Wallingford: International Association of Hydrological Sciences, 71–78. — 1996: Estimating a sediment rating curve of the Reventazon river at Palomo using logged mean loads within discharge classes. Journal of Hydrology 183, 227–41. — 2002: Determining sediment source areas in a tropical river basin, Costa Rica. Catena 47, 63–84. Jarvie, H.P., Oguchi, T. and Neal, C. 2002: Exploring the linkages between river water chemistry and watershed characteristics using GISbased catchment and locality analyses. Regional Environmental Change 3, 36–50. Jetten, V., Govers, G. and Hessel, R. 2003: Erosion models: quality of spatial predictions. Hydrological Processes 17, 887–900. Johnson, R.C. 1992: Towards the design of a strategy for sampling suspended sediments in small headwater catchment. In IAHS Publication 210, Wallingford: International Association of Hydrological Sciences, 225–32. Kesel, R.H. 1989: The role of the Mississippi river in wetland loss in southeastern Louisiana. US Environmental Geology and Water Sciences 13, 183–93. Kineke, G.C. and Sternberg, R.W. 1992: Measurements of high concentration suspended sediments using the optical backscatterance sensor. Marine Geology 108, 253–58. Kirk, J.T.O. 1988: Optical water quality – what does it mean and how should we measure it? Journal of the Water Pollution Control Federation 60, 194–97. Klein, M. 1984: Anti clockwise hysteresis in suspended sediment concentration during individual storms. Catena 11, 251–57. Koch, R.W. and Smillie, G.M. 1986: Comment on ‘River loads underestimated by rating curves’ by R.I. Ferguson. Water Resources Research 22, 2121–22. Kondolf, G.M. 1998: Lessons learned from river restoration projects in California. Aquatic Conservation: Marine and Freshwater 8, 39–52. Kondolf, G.M., Smeltzer, M.W. and Railsback, S. 2001: Design and performance of a channel reconstruction project in a coastal California gravelbed stream. Environmental Management 28, 761–76. Knizel, W.G. and Nicks, A.D. 1980: CREAMS: a fieldscale model for chemical, runoff and erosion from agricultural management systems. Conservation Research Report 26. Washington, DC: US Department of Agriculture, Science and Education Administration. Krishnaswamy, J., Richter, D.D., Halpin, P.N. and Hofmockel, M.S. 2001: Spatial patterns of suspended sediment yields in a humid tropical watershed in Costa Rica. Hydrological Processes 15, 2237–57.

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport Kronvang, B., Laubel, A. and Grant, R. 1997: Suspended sediment and particulate phosphorus transport and delivery pathways in an arable catchment, Gelbaek stream, Denmark. Hydrological Processes 11, 627–42. Kuhnle, R.A. and Wren, D.G. 2005: Cross-stream variations in suspended sediment transport over dunes, implications for sampling. In Gray, J.R., editor, Proceedings of the Federal Interagency Sediment Monitoring Instrument and Analysis Research Workshop, Flagstaff, AZ: US Geological Survey Circular 1276, Appendix 4. Kuhnle, R.A., Bennett, S.J., Alonso, C.V., Binger, R.L. and Langendoen, E. 2000: Sediment transport processes in agricultural watersheds. International Journal of Sediment Research 15, 182–97. Langlois, J.L., Johnson, D.W. and Mehuys, G.R. 2005: Suspended sediment dynamics associated with snowmelt runoff in a small mountain stream of Lake Tahoe (Nevada). Hydrological Processes 19, 3569–80. Larson, W.E., Lindstrom, M.J. and Schumacher, T.E. 1997: The role of severe storms in soil erosion: a problem needing consideration. Journal of Soil and Water Conservation 52, 90–95. Laubel, A.R., Kronvang, B., Larsen, S.L., Pedersen, M.L. and Svendsen, L.M. 2000: Bank erosion as a source of sediment and phosphorus delivery to small Danish streams. In IAHS Publication 263, Wallingford: International Association of Hydrological Sciences, 75–82. Lawler, D.M., Petts, G.E., Foster, I.D.L. and Harper, S. 2006: Turbidity dynamics during spring storm events in an urban headwater river system: the Upper Tame, West Midlands. UK. Science of the Total Environment 360, 109–26. Lecce, S.A., Pease, P.P., Gares, P.A. and Wang, J. 2006: Seasonal controls on sediment delivery in a small coastal plain watershed, North Carolina, USA. Geomorphology 73, 246–60. Lenzi, M.A. and Marchi, L. 2000: Suspended sediment load during floods in a small stream of the Dolomites (northeastern Italy). Catena 39, 267–82. Leopold, L.B., Wolman, M.G. and Miller, J.P. 1995: Fluvial processes in geomorphology. New York: Dover Publications. Lewis, J. 1996: Turbidity-controlled suspended sediment sampling for runoff-event load estimation. Water Resources Research 32, 2299–310. — 2003: Turbidity-controlled sampling for suspended sediment load estimation. In IAHS Publication 283, Wallingford: International Association of Hydrological Sciences, 13–20. Lewis, J. and Eads, R. 2001: Turbidity threshold sampling for suspended load estimation. In Proceedings of the Seventh Federal Interagency Sedimentation Conference, Reno, Nevada III, 110–17.

259

Lloyd, D.S., Koenings, J.P. and LaPerriere, J.D. 1987: Effects of turbidity in fresh waters of Alaska. North American Journal of Fisheries Management 7, 18–33. Lopes, V.L. and Lane, L.J. 1988: Modelling sedimentation processes in small watersheds. IAHS Publication 174, Wallingford: International Association of Hydrological Sciences, 497–508. Maidment, D.R. 2002: Arc Hydro: GIS for water resources. Redlands, CA: ESRI Press. Major, J.J. 2001: Evaluation and timing of suspendedsediment transport following the 1980 Mount St Helens eruption. In Proceedings of the Seventh Federal Interagency Sedimentation Conference, Reno, Nevada I, 13–44. Martz, L.W. and Garbrecht, J. 1992: Numerical definition of drainage network and subcatchment areas from digital elevation models. Computers and Geosciences 18, 747–61. Melis, T.S., Topping, D.J. and Rubin, D.M. 2003: Testing laser-based sensors for continuous in situ monitoring of suspended sediment in the Colorado River, Arizona. In IAHS Publication 283, Wallingford: International Association of Hydrological Sciences, 21–27. Michaelides, K. and Wainwright, J. 2002: Modelling the effects of hillslope–channel coupling on catchment hydrological response. Earth Surface Processes and Landforms 27, 1441–57. Miller, C.R. 1951: Analysis of flow duration/sediment rating curve method of computing sediment yield. Denver, CO: US Bureau of Reclamation. Milhous, R.T. 1982: Effect of sediment transport and flow regulation on the ecology of gravel-bed rivers. Chichester: Wiley, 819–27. Mimikou, M. 1982: An investigation of suspended sediment rating curves in western and northern Greece. Hydrological Sciences 27, 369–83. Mizumura, K. 1989: Hydrological approach to prediction of sediment yield. Journal of Hydraulic Engineering 115, 529–35. Moore, R.J. 1984: A dynamic model of basin sediment yield. Water Resources Research 20, 89–103. Morgan, R.P.C. 1995: Soil erosion and conservation (second edition). London: Longman. Morgan, R.P.C., Morgan, D.D.V. and Finney, H.J. 1984: A predictive model for the assessment of soil erosion risk. Journal of Agricultural Engineering Research 30, 245–53. Muttiah, R.S. and Wurbs, R.A. 2002: Scaledependent soil and climate variability effects on watershed water balance of the SWAT model. Journal of Hydrology 256, 264–85. Nagle, G.N. and Ritchie, J.C. 2004: Wheat field erosion rates and channel bottom sediment sources in an intensively cropped northeastern Oregon drainage basin. Land Degradation and Development 15, 15–26.

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

260 Progress in Physical Geography 32(3) Neal, C., Neal, M., Leeks, G.J.L., Old, G., Hill, L. and Wickham, H. 2006: Suspended sediment and particulate phosphorus in surface waters of the upper Thames Basin, UK. Journal of Hydrology 330, 142–54. Neitsch, S.L., Arnold, J.G., Kiniry, J.R. and Williams, J.R. 2005: Soil and water assessment tool theoretical documentation, version 2005. Temple, TX: USDA-ARS Grassland, Soil And Water Research Laboratory. Nicholas, A.P. 2003: Modelling and monitoring flow and suspended sediment transport in lowland river flood plain environments. In IAHS Publication 283, Wallingford: International Association of Hydrological Sciences, 45–54. O’Callaghan, J.F. and Mark, D.M. 1984: The extraction of drainage networks from digital elevation data. Computer Vision, Graphics and Image Processing 28, 328–44. Ogden, F.L., Garbrecht, J., DeBarry, P.A. and Johnson, L.E. 2001: GIS and distributed watershed models. II: Modules, interfaces, and models. Journal of Hydrological Engineering 6, 515–23. Old, G.H., Lawler, D.M. and Snorrason, Á. 2005: Discharge and suspended sediment dynamics during two jökulhlaups in the Skaftá river, Iceland. Earth Surface Processes and Landforms 30, 1441–60. Olive, L.J. and Rieger, W.A. 1988: An examination of the role of sampling strategies in the study of suspended sediment transport. In IAHS Publication 174, Wallingford: International Association of Hydrological Sciences, 259–67. Ollesch, G., Kistner, I., Sukhanovski, Y. and Rode, M. 2005: Dynamic and modelling of sediment associated nutrients in a low mountain environment. In IAHS Publication 292, Wallingford: International Association of Hydrological Sciences, 171–78. Ollesch, G., Sukhanovski, Y., Kistner, I., Rode, M. and Meißner, R. 2005: Characterization and modelling of the spatial heterogeneity of snowmelt erosion. Earth Surface Processes and Landforms 30, 197–211. Orwin, J.F. and Smart, C.C. 2004: Short-term spatial and temporal patterns of suspended sediment transfer in proglacial channels, Small River Glacier, Canada. Hydrological Processes 18, 1521–42. Ozcan, A.U., Erpul, G., Basaran, M. and Erdogan, H.E. 2008: Use of USLE/GIS technology integrated with geostatistics to assess soil erosion risk in different land uses of Indagi Mountain Pass – Cankiri, Turkey. Environmental Geology 53, 1731–41. Park, J. 1992: Suspended sediment transport in a mountainous catchment. Science Reports of the Institute of Geoscience, University of Tsukuba A13, 137–97.

Parsons, A.J., Wainwright, J., Brazier, R.E. and Powell, D.M. 2006: Is sediment delivery a fallacy? Earth Surface Processes and Landforms 31, 1325–28. Paustain, S.J. and Beschta, R.L. 1979: The suspended sediment regime of an Oregon Coast. Range stream. Water Resources Bulletin 15, 144–54. Pavanelli, D. and Bigi, A. 2005: A new indirect method to estimate suspended sediment concentration in a river monitoring programme. Biosystems Engineering 92, 513–20. Pavanelli, D. and Pagliarani, A. 2002: Monitoring water flow, turbidity and suspended sediment load, from an Apennine catchment basin, Italy. Biosystems Engineering 83, 463–68. Peart, M.R. and Walling, D.E. 1982: Particle size characteristics of fluvial suspended sediment. In IAHS Publication 137, Wallingford: International Association of Hydrological Sciences, 397–407. Peart, M.R., King, J.P. and Ruse, M.E. 2005: Sediment production by landslides in Hong Kong: two case studies. In IAHS Publication 291, Wallingford: International Association of Hydrological Sciences, 29–36. Pennisi, E. 2004: The grand (canyon) experiment. Science 306, 1884–86. Perrone, J. and Madramootoo, C.A. 1999: Sediment yield prediction using AGNPS. Journal of Soil and Water Conservation 54, 415–19. Perry, C. and Taylor, K., editors 2007: Environmental sedimentology. Oxford: Blackwell. Phillips, J.M., Russell, M.A. and Walling, D.E. 2000: Time-integrated sampling of fluvial suspended sediment: a simple methodology for small catchments. Hydrological Processes 14, 2589–602. Pickup, G. 1988: Hydrology and sediment models. In Anderson, M.G., editor, Modelling geomorphology systems, New York: Wiley, 153–215. Poesen, J., Nachtergaele, J., Verstraeten, G. and Valentin, C. 2003: Gully erosion and environmental change: importance and research needs. Catena 50, 91–133. Poulos, S.E., Collins, M. and Evans, G. 1996: Water-sediment fluxes of Greek rivers, southeastern Alpine Europe: annual yields, seasonal variability, delta formation and human impact. Zeitschrift für Geomorphologie 40, 243–61. Prestegaard, K.L. 1988: Morphological controls on sediment delivery pathways. In IAHS Publication 174, Wallingford: International Association of Hydrological Sciences, 533–40. Presto, M.K., Ogston, A.S., Storlazzi, C.D. and Field, M.E. 2006: Temporal and spatial variability in the flow and dispersal of suspended-sediment on a fringing reef flat, Molokai, Hawaii. Estusrine, Coastal and Shelf Science 67, 67–81.

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport Quinn, P., Beven, K., Chevallier, P. and Planchon, O. 1991: The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models. Hydrological Processes 5, 59–80. Randle, T.J., Bountry, J., Jackson, B. and Smillie, G. 2003: Elwha River Restoration Draft Sediment Monitoring and Management Plan. In Recommendations of the Elwha River Physical Processes Monitoring Workshop 13–17 August 2001, Port Angeles, WA: US Bureau of Reclamation, 56 pp. ??–??. Refsgaard, K.G. and Storm, J.C. 1995: MIKE-SHE. In Singh, V.P., editor, Computer models of watershed hydrology, Highlands Ranch, CO: Water Resources Publications. Reid, L.M. and Dunne, T. 1984: Sediment production from forest and road surfaces. Water Resources Research 20, 1753–61. Renard, K.G., Foster, G.R., Weesies, G.A. and Porter, J.P. 1991: RUSLE: revised universal soil loss equation. Journal of Soil and Water Conservation 46, 30–33. Renschler, C.S. and Lee, T. 2005: Spatially distributed assessment of short- and long-term impacts of multiple best management practices in agricultural watersheds. Journal of Soil and Water Conservation 60, 446–56. Rieger, W.A., Olive, L.J. and Gippel, C.J. 1988: Channel sediment behavior as a basis for modelling delivery processes. In IAHS Publication 174, Wallingford: International Association of Hydrological Sciences, 541–48. Riley, S.J. 1998: The sediment concentration-turbidity relation: its value in monitoring at Ranger Uranium mine, Northern territory, Australia. Catena 32, 1–14. Rompaey, A.J.J.V., Verstraeten, G., Oost, K.V., Govers, G. and Poesen, J. 2001: Modelling mean annual sediment yield using a distributed approach. Earth Surface Processes and Landforms 26, 1221–36. Rondeau, B., Cossa, D., Gagnon, P. and Bilodeau, L. 2000: Budget and sources of suspended sediment transported in the St Lawrence River, Canada. Hydrological Processes 14, 21–36. Rowe, T.G. 2001: Loads and yields of suspended sediment for selected watersheds in the lake Tahoe basin, California and Nevada. In Proceedings of the Seventh Federal Interagency Sedimentation Conference, Reno, Nevada III, 18–23. Russell, M.A., Walling, D.E., Webb, B.W. and Bearne, R. 1998: The composition of nutrient fluxes from contrasting UK river basins. Hydrological Processes 12, 1461–82. Sadar, M. 2002: Turbidity instrumentation – an overview of today’s available technology. In Turbidity and Other Sediment Surrogates Workshop, 30 April–2

261

May, Reno, Nevada. Retrieved 2 June 2008 from http://water.USGS.gov/osw/techniques/TSS/ listofabstracts.htm Salles, C., Tournoud, M.G. and Chu, Y. 2008: Estimating nutrient and sediment flood loads in a small Mediterranean river. Hydrological Processes 22, 242–53. Schoellhamer, D.H. and Wright, S.A. 2003: Continuous measurement of suspended-sediment discharge in rivers by use of optical backscatterance sensors. In IAHS Publication 283, Wallingford: International Association of Hydrological Sciences, 28–36. Sear, D.A., Thorne, C.R. and Brookes, A. 1994: Geomorphological approach to stream stabilization and restoration: case study of the Mimmshall Brook, Hertfordshire, UK. Regulated Rivers 9, 205–23. Sickle, J.V. 1981: Long-term distributions of annual sediment yields from small watersheds. Water Resources Research 17, 659–63. Singh, K.P. and Durgunoglu, A. 1989: Developing accurate and reliable stream sediment yields. In IAHS Publication 184, Wallingford: International Association of Hydrological Sciences, 193–99. Singh, V.P., editor 1995: Computer models of watershed hydrology. CO: Water Resources Publications. Singh, V.P. and Frevert, D., editors 2002: Mathematical models of small watershed hydrology and applications. Highlands Ranch, CO: Water Resources Publications. —, editors 2006: Watershed models. Florida: Taylor and Francis Group. Sivertun, A. and Prange, L. 2003: Non-point source critical area analysis in the Gisselö watershed using GIS. Environmental Modelling and Software 18, 887–98. Smith, R.E., Goodrich, D.C. and Quinton, J.N. 1995: Dynamic, distributed simulation of watershed erosion: the KINEROS2 and EUROSEM models. Journal of Soil Erosion and Water Conservation 50, 517–20. Skau, C.M., Brown, J.C. and Nadolski, J.A. 1980: Snowmelt sediment from Sierra Nevada headwater. In Symposium on Watershed Management 1980, Boise, Idaho, volume 1, Reston, VA: American Society of Civil Engineers, 418–29. Steiger, J., Gurnell, A.M., Ergenzinger, P. and Snelder, D. 2001: Sedimentation in the riparian zone of an incising river. Earth Surface Processes and Landforms 26, 91–108. Sternberg, R.W. 1989: Instrumentation for estuarine research. Journal of Geophysical Research 94, 14289–301. Stow, D.W. and Chang, H.H. 1987: Magnitudefrequency relationship of coastal sand delivery by a southern California stream. Geo-Marine Letters 7, 217–22.

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

262 Progress in Physical Geography 32(3) Sui, J., Jackson, P. and Fang, D. 2005: Investigation of the sediment budget of a reach of the Yellow River in the Loess Plateau. In IAHS Publication 291, Wallingford: International Association of Hydrological Sciences, 172–81. Sun, H., Cornish, P.S. and Daniell, T.M. 2001: Turbidity-based erosion estimation in a catchment in South Australia. Journal of Hydrology 253, 227–38. Sukhanovski, Y.P., Demidov, V.D. and Ollesch, G. 2004: A model of rill erosion by snowmelt. In IAHS Publication 288, Wallingford: International Association of Hydrological Sciences, 354–60. Sutherland, T.F., Lane, P.M., Amos, C.L. and Downing, J. 2000: The calibration of optical backscatter sensors for suspended sediment of varying darkness levels. Marine Geology 162, 587–97. Svorin, J. 2003: A test of three soil erosion models incorporated into a geographical information system. Hydrological Processes 17, 967–77. Syvitski, J.P., Morehead, M.D., Bahr, D.B. and Mulder, T. 2000: Estimating fluvial sediment transport: the rating parameters. Water Resources Research 36, 2747–60. Takken, I., Beuselinck, L., Nachtergaele, J., Govers, G., Poesen, J. and Degraer, G. 1999: Spatial evaluation of a physically based distributed erosion model (LIZEM). Catena 37, 431–47. Tanaka, T., Marui, A., Yasuhara, M. and Takayama, S. 1983: Reconnaissance study on suspended sediment discharge during a storm event. Annual Report of the Institute of Geoscience, University of Tsukuba 9, 32–35. Tarboton, D.G. 1997: A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research 33, 309–19. Teixeira, E.C. and Caliari, P.C. 2005: Estimation of the concentration of suspended solids in rivers from turbidity measurement: error assessment. In IAHS Publication 291, Wallingford: International Association of Hydrological Sciences, 151–60. Tetzlaff, D., Soulsby, C., Waldron, S., Malcolm, I.A., Bacon, P.J., Dunn, S.M., Lilly, A. and Youngson, A.F. 2007: Conceptualization of runoff processes using a geographical information system and tracers in a nested mesoscale catchment. Hydrological Processes 21, 1289–307. Thomas, R.B. 1985: Estimating total suspended sediment yield with probability sampling. Water Resources Research 21, 1381–88. — 1988: Monitoring baseline suspended sediment in forested basin: the effect of sampling on suspended sediment rating curves. Journal of Hydrological Science 33, 499–514. Thomas, R.B. and Lewis, J. 1993: A comparison of selection at list time and time-stratified sampling

for estimating suspended sediment loads. Water Resources Research 29, 1247–56. — 1995: An evaluation of flow-stratified sampling for estimating suspended sediment loads. Journal of Hydrology 170, 27–45. Tiffen, M., Mortimore, M. and Gichuki, F. 1994: More people, less erosion: environmental recovery in Kenya. New York: Wiley. Trauth, K.M. and Adams, D.S. 2004: Watershbased modelling with AGNPS for storm water management. Journal of Water Resources Planning and Management 130, 206–14. Trimble, S.W. 1997: Contribution of stream channel erosion to sediment yield from an urbanizing watershed. Science 278, 1442–44. Umeyaina, M. 1992: Vertical distribution of suspended sediment in uniform open-channel flow. Journal of Hydraulic Engineering 118, 936–41. US Environmental Protection Agency (EPA) 2000: The quality of our nation’s waters: a summary of the National Water Quality Inventory 1998 Report to Congress. EPA 841-S-00-001. Washington, DC: Office of Water. Valero-Garces, B.L., Navas, A., Machin, J. and Walling, D. 1999: Sediment sources and siltation in mountain reservoirs: a case study from the Central Spanish Pyrenees. Geomorphology 28, 23–41. van Liew, M.W. and Garbrecht, J. 2003: Hydrological simulation of the Little Washita river experimental watershed using SWAT. Journal of the American Water Resources Association 39, 413–26. Verstraeten, G. and Poesen, J. 2001: Factors controlling sediment yield from small intensively cultivated catchments in a temperate humid climate. Geomorphology 40, 123–44. V i g i a k , O . , O k o b a , B . O . , S t e r k , G . and Groenenberg, S. 2005: Modelling catchment-scale erosion patterns in the East African Highlands. Earth Surface Processes and Landforms 30, 183–96. Vigiak, O., Sterk, G., Romanowicz, R.J. and Beven, K.J. 2006: A semi-empirical model to assess uncertainty of spatial patterns of erosion. Catena 66, 198–210. Wagner, R.J., Mattraw, H.C., Ritz, G.F. and Smith, B.A. 2000: Guidelines and standard procedures for continuous water-quality monitors: site selection, field operation, calibration, record computation, and reporting. US Geological Survey WaterResources Investigations Report 00-4252, 27 pp. Walling, D.E. 1974: Suspended sediment and solute yields from a small catchment prior to urbanization. In Walling, G.K.J and Walling, D.E., editors, Fluvial processes in instrumented watersheds, Institute of British Geographers Special Publication 6, 169–92. — 1977: Limitations of the rating curve technique for estimating suspended sediment loads, with particular

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008

Peng Gao: Understanding watershed suspended sediment transport reference to British rivers. In IAHS Publication 122, Wallingford: International Association of Hydrological Sciences, 34–47. — 1983: The sediment delivery problem. Journal of Hydrology 65, 209–37. — 1988: Erosion and sediment yield research-some recent perspectives. Journal of Hydrology 100, 113–41. Walling, D.E. and Kane, P. 1984: Suspended sediment properties and their geomorphological significance. In Burt, T.P. and Walling, D.E., editors, Catchment experiments in fluvial geomorphology. Norwich: Geo Books, 399–415. Walling, D.E. and Kleo, A.H.A. 1979: Sediment yields of rivers in areas of low precipitation: a global view. In IAHS Publication 128, Wallingford: International Association of Hydrological Sciences, 479–93. Walling, D.E. and Teed, A. 1971: A simple pumping sampler for research into suspended sediment transport in small catchments. Journal of Hydrology 13, 325–37. Walling, D.E. and Webb, B.W. 1981: The reliability of suspended sediment load data. In IAHS Publication 133, Wallingford: International Association of Hydrological Sciences, 177–94. — 1982: Sediment availability and the prediction of storm-period sediment yields. In IAHS Publication 137, Wallingford: International Association of Hydrological Sciences, 327–37. — 1987: Suspended load in gravel-bed rivers: UK experience. In Thorne, C.R., Bathurst, J.C. and Hey, R.D., editors, Sediment transport in gravel rivers, Chichester: Wiley. — 1988: The reliability of rating curve estimates of suspended sediment yield: some further comments. In IAHS Publication 174, Wallingford: International Association of Hydrological Sciences, 337–50. Walling, D.E. and Woodward, J.C. 2000: Effective particle size characteristics of fluvial suspended sediment transported by lowland British rivers. In IAHS Publication 263, Wallingford: International Association of Hydrological Sciences, 129–39. Walling, D.E., Collins, A.L., Sichingabula, H.M. and Leeks, G.J.L. 2001: Integrated assessment of catchment suspended sediment budgets: a Zambian example. Land Degradation and Development 12, 387–415. Walling, D.E., Webb, B.W. and Woodward, J.C. 1992: Some sampling considerations in the design of effective strategies for monitoring sediment-associated transport. In IAHS Publication 210, Wallingford: International Association of Hydrological Sciences, 279–88.

263

Ward, J.V., Tockner, K., Uehlinger, U. and Malard, F. 2001: Understanding natural patterns and processes in river corridors as the basis for effective river restoration. Regulated Rivers: Research and Management 17, 709–19. White, S. 2005: Sediment yield prediction and modelling. Hydrological Processes 19, 3053–57. Williams, G.P. 1989: Sediment concentration versus water discharge during single hydrological events in rivers. Journal of Hydrology 111, 89–106. Williams, J.E., Wood, C.A. and Dombeck, M.P. 1997: Understanding watershed-scale restoration. In Williams, J.E., Wood, C.A. and Dombeck, M.P. editors, Watershed restoration: principles and practices, Bethesda, MD: American Fisheries Society, 1–16. Wischmeier, W.H. and Smith, D.D. 1978: Predicting rainfall erosion losses – a guide to conservation planning. Agriculture Handbook 537. Washington, DC: US Department of Agriculture. Wohl, E., Angermeier, P.L., Bledsoe, B., Kondolf, G.M., MacDonnell, L., Merritt, D.M., Palmer, M.A. and Poff, N.L. 2005: River restoration. Water Resources Research 41, W10301, DOI: 10.1029/2005WR003985. Wren, D.G., Barkdoll, B.D., Kuhnle, R.A., and Derrow, R.W. 2000: Field techniques for suspended-sediment measurement. Journal of Hydraulic Engineering 126, 97–104. Wren, D.G., Bennett, S.J., Barkdoll, B.D. and Kuhnle, R.A. 1999: Distribution of suspended sediment over a flat, mobile sand bed. In Proceedings of the Water Resources Engineering 99 Conference of the American Society of Civil Engineers, Seattle, WA, Reston, VA: ASCE. Wright, L.D., Xu, J.P. and Madsen, O.S. 1994: Across-shelf benthic transports on the inner shelf of the Middle Atlantic Bight during the ‘Halloween storm’ of 1991. Marine Geology 118, 61–77. Yang, D., Kanae, S., Oki, T., Koike, T. and Musiake, K. 2003: Global potential soil erosion with reference to land use and climate changes. Hydrological Processes 17, 2913–28. Young, R.A., Onstad, C.A., Bosch, D.D. and Anderson, W.P. 1987: AGNPS, agricultural nonpoint source pollution model: a watershed analytical tool. US Department of Agriculture Conservation Research Report 35. Zhao, B. and Niu, Z. 1982: Analysis of the vertical distribution of high sediment concentrations in the Yellow River and studies of methods of observation. In IAHS Publication 137, Wallingford: International Association of Hydrological Sciences, 421–30.

Downloaded from http://ppg.sagepub.com at SYRACUSE UNIV LIBRARY on October 30, 2008