DEVELOPING GEOGRAPHIC INFORMATION SYSTEM APPLICATIONS IN. ANALYSIS OF ... features, including erosion monitoring and geological materials.
DEVELOPING GEOGRAPHIC INFORMATION SYSTEM APPLICATIONS IN ANALYSIS OF RESPONSES TO LAKE ERIE SHORELINE CHANGES
A Thesis Presented in Partial Fulfillment of the Requirements for The Degree Master of Science in the Graduate School of The Ohio State University
By Jung-kuan Liu, Eng. ****
The Ohio State University 1998
Master’s Examination Committee: Approved by Dr. Rongxing Li, Advisor Advisor Dr. Alan Saalfeld
Graduate Program in Geodetic Science and Surveying
ABSTRACT Geographic Information System (GIS) techniques have been used in numerous natural-resource applications. This thesis presents GIS-based applications in analysis of eroded factors and examines the responses to the shoreline changes. The south shore of Lake Erie from Cranberry Creek to Sheldon Marsh was selected as the study area. Recession modeling of shoreline data span from 1973 to 1990 and cover 10 miles of Lake Erie coast. In study area, some stretches of the landscape has changed dramatically during the past few decades due to statewide interest focusing on the economic losses caused by coastal erosion. Coastal erosion may have a direct impact on the virtual quality of the landscape. Spatial data were collected from 7.5 quadrangles DLG files, aerial photographs of 1973 and 1990, bathymetric data, and tabulated recession rate corresponding to the study area. A spatial model was created based on these data. This research was performed in four major stages: (1) data preparation and pre-processing, (2) spatial data and attribute data creation, (3) database building, and (4) results presentation and analysis. To model the coast, dynamic segmentation was used to implement the linear features, including erosion monitoring and geological materials. The end-point-rate (EPR) model for predicting shoreline positions was employed in this research. Moreover, an alternative way to quantify shoreline changes and related applications was introduced in this thesis.
ii
The model results showed that approximately 17% of the shoreline have become more eroded in the study area, while more than 80% of the shoreline were well protected by structural protection. These results offered some insights for erosion control on the Lake Erie shoreline. From these results, future considerations of erosion were recommended, including an evaluation for launching a new coastal management program and incorporating shore protection. The contribution of this research presented a successful way for directing proper considerations and responses from shoreline changes of Lake Erie based on GIS database. This GIS application, based on widely available and easily developed digital data, provides realistic and valuable information in a short time frame.
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Dedicated to Jessica, my lovely wife. Thank you for your love, support and strength. This is your achievement as well as mine.
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ACKNOWLEDGEMENTS There are a lot of people whose participation made the completion of this thesis possible. First of all, I want to thank Dr. Ron Li, my advisor, whose tireless support and guidance made all the difference. Without his leadership and teamwork, none of this would have been attempted. Thanks to Dr. Alan Saalfeld, who assisted me to create a computing perspective on Geographic Information System (GIS) and supported me with technical guidance for the usage of ArcViewTM. Thanks to the people at the office of Lake Erie Geology Group of Great Lake Center (ONDR), especially to Dr. Scudder Mackey for supplying all coastal-related data and sharing his experiences in the development of coast-related spatial database, and Donald E. Guy, Jr. for his assistance for arranging site visits and providing me with geological knowledge. Thanks to James H. Given Jr., the administrator in Real Estate and Land Management Division of ODNR, who helped me to search for the historical aerial photographs and provided me with the aerial photos of my study area. Thanks to Lisa Taylor, the marine GIS specialist in Marine Geology & Geophysics Division of NOAA/National Geophysical Data Center, for her technical support regarding the data conversion and presentation of bathymetry for Lake Erie.
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Thanks to the people at the Center for Mapping for helping me to solve the problems for the usage of Arc/InfoTM commands. Especially, to Tracy Douglass, the Arc/InfoTM specialist, for her technical and logistic support. Great thanks to my parents and parents-in-law, who always give me strengths and confidence. I love you, and may God bless you all. Special thanks to my wife, Jessica, for your endless emotional support and encouragement; enduring my temper when the pressure went up. I love you too, and may you have many dreams and attain one of them. A very heartfelt THANK YOU to all of you for helping me to achieve this goal!
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VITA June 7, 1967 … … … … … … … … .
Born, Hsinchu, Taiwan
1989 … … … … … … … … … … … .. B.S. Surveying Engineering, Chung-Cheng Institute of Technology, Taoyuan, Taiwan 1989-1996 … … … … … … … … … . Mapping Engineer, Mapping Plant, Topographic Service, Taichung, Taiwan
FIELDS OF STUDY Major Fields: Geodetic Science Concentration: Computer Mapping and Geographic Information System
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TABLE OF CONTENTS Page Abstract … … … … … … … … … … … … … … … … … … … … … … … … … … … .… .…
ii
Dedication … … … … … … … … … … … … … … … … … … … … … … … … … … .… …
iv
Acknowledgments … … … … … … … … … … … … … … … … … … … … … … … … …
v
Vita … … … … … … … … … … … … … … … … … … … … … … … … … … … … .… … ..
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List of Tables … … … … … … … … … … … … … … … … … … … … … … … … … … …
xi
List of Figures … … … … … … … … … … … … … … … … … … … … … … … … … … ...
xii
Chapters: 1.
2.
Introduction … … … … … … … … … … … … … … … … … … … … … … … … …
1
1.1 General … … … … … … … … … … … … … … … … … … … … … … … … … .
1
1.2 Land-Eating Lake Erie … … … … … … … … … … … … … … … … … … … .
2
1.3 How Erosion Works … … … … … … … … … … … … … … … … … … … … .
4
1.3.1 Coastal Geomorphology … … … … … … … … … … … … … … … … ..
5
1.3.2 Waves … … … … … … … … … … … … … … … … … … … … … … … ...
6
1.3.3 Lake Levels and Erosion … … … … … … … … … … … … … … … … ..
7
1.3.4 Littoral Transport and Sand Supply … … … … … … … … … … … … .
10
1.3.5 Erosion and Recession … … … … … … … … … … … … … … … … … .
10
1.4 Study Area … … … … … … … … … … … … … … … … … … … … … … .… ..
12
1.5 Research Objectives … … … … … … … … … … … … … … … … … … … …
13
1.6 Organization … … … … … … … … … … … … … … … … … … … … … … …
16
Data Preparation and Methodology … … … … … … … … … … … … … … .… ..
17
2.1 Introduction … … … … … … … … … … … … … … … … … … … … … … … ..
17
2.2 Data Preparation … … … … … … … … … … … … … … … … … … … … … ..
17
2.2.1 Historical Shoreline Data … … … … … … … … … … … … … … … … viii
18
2.2.2 Bathymetric Data … … … … … … … … … … … … … … … … … … … .
20
2.2.3 Topographic Data … … … … … … … … … … … … … … … … … … …
21
2.2.4 Others … … … … … … … … … … … … … … … … … … … … … … … ..
22
2.3 Methodology … … … … … … … … … … … … … … … … … … … … … … …
3.
4.
24
2.3.1 Data Collection and Manipulation … … … … … … … … … … … .… ..
24
2.3.2 Modeling the Coast – Dynamic Segmentation of the Line … … … .
26
2.3.3 Predicting Shoreline Positions … … … … … … … … … … … … … … .
28
2.3.4 Software Development … … … … … … … … … … … … … … … … …
29
2.3.5 Quantify and Display Shoreline Change – An Alternative Way… ..
31
Implementation … … … … … … … … … … … … … … … … … … … … … … … ..
34
3.1 Introduction … … … … … … … … … … … … … … … … … … … … … … … .
34
3.2 Previous Work … … … … … … … … … … … … … … … … … … … … … … .
34
3.3 Spatial Data Implementation … … … … … … … … … … … … … ..… … … .
36
3.4 Applying Dynamic Segmentation to the Shoreline … … … … … … … … .
37
3.5 Predicting Shoreline Positions … … … … … … … … … … … … … … … … .
40
3.5.1 Buffer Operation for Predicting Future Shoreline … … … … … .… ..
40
3.5.2 Programming for Predicting Future Shoreline … … … … … … … …
41
3.6 Quantify and Display Shoreline Change – An Alternative Way … … … .
43
Presentation and Analysis … … … … … … … … … … … … … … … … … … .… .
47
4.1 Introduction … … … … … … … … … … … … … … … … … … … … … … … ..
47
4.2 Data model … … … … … … … … … … … … … … … … … … … … … … … …
48
4.3 Functional Model … … … … … … … … … … … … … … … … … … … … … ..
51
4.3.1 Geological Materials of Shoreline … .… .… … … … … … … … … … ..
51
4.3.2 Terrain Slope and Bathymetric Slope … … … … … … … … … … … ..
53
4.3.3 Structural Protection and Other Erosion Causes … … .… … … … … .
58
4.4 Applications … … … … … … … … … … … … … … … … … … … … … … … ..
60
4.4.1 Erosion Modeling … … … … … … … … … … … … … … … … … … … .
61
4.4.2 Predicting Shoreline Positions … … … … … … … … … … … … … … .
66
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5.
4.4.3 Hazard Predicting and Coastal Construction Permitting … … … … ..
69
4.4.4 Hot Link … … … … … … … … … … … … … … … … … … … … … … ...
71
4.5 Quantify the Shoreline Change – An alternative Way … … … … … … … .
72
4.6 Summary and Erosion-control Suggestions … … … … … … … … … … … .
78
Conclusions … … … … … … … … … … … … … … … … … … … … … … … … …
80
5.1 General … … … … … … … … … … … … … … … … … … … … … … … … … .
80
5.2 Future Considerations of Erosion … … … … … … … … … … … … … … … .
81
5.3 Recommendations and Future Research … … … … … … … … … … … … ..
83
Bibliography … … … … … … … … … … … … … … … … … … … … … … … … … … …
86
Appendix: Appendix A:
Tabulated Erosion Data … … … … … … … … … … … … … … … … ..
90
Appendix B:
Previous Work – Shore Erosion Study in Study Area … … … … ...
93
Appendix C:
Source Codes for AutoLisp Program … … … … … … … … … … … .
100
Appendix D:
Source Codes for C++ Programs – Predicting the Shoreline Positions … … … … … … … … … … … … … … … … … … … … … … .
102
D.1 GC_L_DAT.C … … … … … … … … … … … … … … … … … … … …
102
D.2 COAST_N.C … … … … … … … … … … … … … … … … … … .… … .
105
D.3 GENSCR.C … … … … … … … … … … … … … … … … … … … … …
108
Appendix E:
Structural Protection Tables … … … … … … … … … … … … … … ...
111
Appendix F:
Inputs and Outputs … … … … … … … … … … … … … … … … … … .
114
F.1 DXF file for 1990 shoreline … … … … … … … … … … … … … … ...
114
F.2 DAT file for 1990 shoreline … … … … … … … … … … … … … … ...
115
F.3 GEN file for 2015 shoreline … … … … … … … … … … … … … … ...
116
F.4 SCR file for 2015 shoreline … … … … … … … … … … … … … … ...
117
F.5 Table for quantifying the shoreline change from 1973 to 1990 .…
118
F.6 Table for eroded direction from 1973 to 1990 … … … … … … … ...
119
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LIST OF TABLES Page
Table 1.1
Ohio Lake Erie erosion statistics by county … … … … … … … … … … … … ...
3
2.1
Aerial photographic search for study area located … … … … … … … … … … .
19
2.2
Recession-rate classes … … … … … … … … … … … … … … … … … … … … …
30
3.1
Summary of recession rate of Huron reaches for previous work … … … … ..
35
3.2
Example of a linear event table … … … … … … … … … … … … … … … … … ..
39
4.1
Themes properties … … … … … … … … … … … … … … … … … … … … … … ..
49
4.2
Event table for shoreline materials … … … … … … … … … … … … … … … … .
52
4.3
Statistics of recession rate of the study area … … … … … … … … … … … … ...
63
4.4
Comparison for long- and short-term recession rate … … … … … … … … .… .
63
4.5
Comparison for long- and short-term area of lost land … … … … … … … … ..
64
4.6
Summary of recession status for different time intervals … … … … … … … ...
64
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LIST OF FIGURES Page
Figure 1.1
Erosion in the mouth of Old Woman Creek … … … … … … … … … … … … ...
4
1.2
Erodibility of Lake Erie shoreline … … … … … … … … … … … … … … … … ..
5
1.3
Beach profile and definition of the near shore … … … … … … … … … … … ...
6
1.4
Lake Erie annual mean water level … … … … … … … … … … … … … … … …
8
1.5
Study area … … … … … … … … … … … … … … … … … … … … … … … … … ...
12
2.1
Bathymetry of Lake Erie … … … … … … … … … … … … … … … … … … … …
20
2.2
Available 1:24,000-scale DLG files … … … … … … … … … … … … … … … ...
22
2.3
Determining the recession rate of Lake Erie … … … … … … … … … … … … ..
26
2.4
Euclidean distance … … … … … … … … … … … … … … … … … … … … … … ..
33
3.1
Comparison of study site between previous and current research … … … … .
35
3.2
Procedures for spatial data generalizations … … … … … … … … … … … … …
36
3.3
Comparison of nodes between shoreline theme and segments of erosion … .
38
3.4
Buffer operation to predict shoreline positions … … … … … … … … … … … ..
40
3.5
Future shoreline prediction based on Y=mx+B equation … … … … … … … ...
42
3.6
The generated distance grid, direction grid, and allocation grid … … … … …
44
3.7
Procedure for quantifying the shoreline changes … … … … … … … … … … ...
46
4.1
Linkage of data model, functional model, and applications … … … … … … ...
47
4.2
Spatial database of the study area … … … … … … … … … … … … … … … … ...
48
4.3
Geological materials of shoreline in the study area … … … … … … … … … …
52
4.4
Recession rate and geological materials of the study area … … … … … … .…
53
4.5
Surface model of the study area … … … … … … … … … ..… … … … … … … ...
54
4.6
Terrain slope of the study area … … … … … … … … … … … … … … … … … ...
55
4.7
Recession rate and terrain slope angle … … … … … … ..… … … … … … … … .
56
4.8
Bathymetry of the study area … … … … … … … … … … … … … … … … … .…
56
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4.9
Recession rate and slope angle derived from bathymetry … … … … … … … ..
57
4.10
Distribution of shoreline protection in the study area … … … … … … … … …
59
4.11
Recession rate of the study area … … … … … .… ...… … … … … … … … … … .
62
4.12
Erosion status of the study area … … … … … … ...… … … … … … … … … … ...
62
4.13
Comparison of recession rate between 3 time intervals … … … … … … … … .
65
4.14
Predicting shoreline positions by using buffer operation … … … … … … … ...
66
4.15
Predicting shoreline positions by using end-point rate model … … … … … …
67
4.16
Procedures to predict future shoreline by using EPR model … … … … … … ..
68
4.17
Comparison for buffer operation and EPR model to predict future shoreline
69
4.18
Hazard prediction – coastal properties affected by coastal erosion … … … ...
70
4.19
Example of presenting hot links function … … … ..… … … … … … … … … … .
72
4.20
Converted 1990 arc coverage into grid format … … … … … … … … … … … ...
73
4.21
Euclidean distance grid … … … … … … … … … … … … … … … … … … … … ...
74
4.22
Recession distance of study area from 1973 to 1990 … … … … … … … … … .
74
4.23
Recession rate based on the mean recession distance from 1973 to 1990 … .
75
4.24
Recession rate from ODNR … … … … … … … … … … … … … … … … … … …
75
4.25
Area of lost land from 1973 to 1990 … … … … ..… … … … … … … … … … …
76
4.26
Direction grid from 1973 to 1990 … … … … … … … … … … … … … … … … ...
77
4.27
Eroded direction from 1973 to 1990 … … … … … … … … … … … … … … … ..
78
5.1
Structural and nonstructural method for protection … … … … … … … … … …
82
5.2
Cooperative and individual approach for implementing protection … … … ...
82
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CHAPTER 1 INTRODUCTION 1.1 General Coastal erosion is a problem throughout the U.S., occurring both on the east and west coasts, the Gulf shore, and the Great Lakes. Many factors act together to influence coastal erosion (Platt, 1998). These include the depth of water bodies, their alignment with prevailing winds, change of water levels, shoreline geology, and the effects of human activities. The shoreline erosion may cause problems when its development approaches (Li, 1997), for example, a residential area. The shape of shoreline may be changed as a result of the shoreline erosion. Moreover, the environment of the coastal zone can be influenced due to the shoreline change. The coastal zone supports a wide range of unique habitats and species (Maslen et al, 1996). The shoreline status is crucial to the human being, wildlife, and properties of these areas. Coastal zone managers, emergency management officials, and coastal property owners need to be aware of the potential risks to coastal property before, during, and after severe storms and hurricanes. As new sensors become available and new technologies are focused on the problems of hazard mitigation in the coastal zone, a wealth of data are being generated which will permit volumetric analyses of recent landform morphology along the coast. These data can provide high spatial and
1
temporal resolution forms for modeling the most recent changes (Dusen, 1997). Geographic Information Systems (GIS) techniques are widely used to analyze different characteristics of the landscape. These applications of landscape can be categorized as planning or management, process modeling, inventory, and assessment. In addition, they offer basic supporting functions for a modern GIS package. Within these categories, a spatial analysis through the use of GIS enables end-users to have information for decision-making (Halls et al, 1996). The modernized coastal erosion management is one of these applications. The coast-related data are both temporally inconsistent and spatially inconsistent. Rather, the data are spatially and temporally dispersed (Dusen, 1997). Data involved in shoreline change are diverse, including spatial data, time series data, social and economic data, and multimedia information (Li, 1997). The spatial data are shoreline positions, topographic data, bathymetric data, parcel data etc. The social economic data are integrated with spatial data to support decision-making, such as census data, city planning map etc. The time series data, for instance, wind and wave observations can be integrated with spatial data through the locations of sensors. GIS can make it possible for the integration of spatial data, time series data, and social economic data to develop a coast-based GIS database. 1.2 Land-Eating Lake Erie Erosion is defined as the gradual wearing away of the earth’s surface by the natural forces of wind and water. For billions of years, oceans have been altering and shaping the earth’s shorelines through erosion. The constant action of winds, waves and ice flows has also affected the shoreline of Lake Erie and the other Great Lakes up to the present day (Ohio Coastal Management Program, 1997). 2
Lake Erie, the great body of fresh water forming Ohio's north coast, is the fourth largest of the five Great Lakes. Lake Erie provides an unlimited water supply to communities along its shore, is an unmatched recreational and sport-fishing area, and provides significant quantities of sand and gravel for construction. On the other hand, Lake Erie is also a dynamic body of water noted for the ferocity of its storm waves and the havoc they wreak along the lakeshore. Waves, currents, shore erosion, and flooding are all problems that must be dealt with in coastal areas (Hansen, 1997).
County
Long-term
Long-term
Short-term
Short-term
Distance(ft.)
Rate (ft./yr.)
Distance (ft.)
Rate (ft./yr.)
Ashtabula
82
0.9
28
1.6
Lake
160
1.7
32
1.9
Cuyahoga
60
0.6
8
0.4
Lorain
80
0.8
12
0.7
Erie (lake)
103
1.6
42
2.5
Ottawa (lake)
208
2.0
27
1.6
Lucas
520
5.4
46
2.7
Erie (bay)
241
2.8
32
1.9
Ottawa (bay)
61
2.0
21
1.2
Long-term: 1877 to 1973
Short-term: 1973 to 1990
Table 1.1: Ohio Lake Erie Erosion Statistics by county (Ohio Geological Survey, 1993)
As illustrated in Table 1.1, over much of this region, recession rates have been less than 1 meter/year (3 feet/year). However, local rates may exceed 2 meters/year (7 3
feet/year) (Highman, 1997). Even where rates are slow, the highly developed nature of the coast makes recession a serious property-damage problem. Besides, about 95% of Ohio's Lake Erie shore (see Figure 1.1) is eroding and nearly 2,500 structures are within 50 feet of destruction. The Ohio Geological Survey estimates that more than 3,200 acres of Ohio’s Lake Erie shore have been lost to erosion since the 1870s. Due to these severe erosion problems, economic losses exceed tens of millions of dollars per year (Ohio Geology Survey, 1993).
A. Old Woman Creek - 1962
B. Old Woman Creek - 1997
Figure 1.1: Erosion in the mouth of Old Woman Creek
1.3 How Erosion Works Primarily, it is the force of waves and gravity that causes erosion. On the coast, the forces of erosion are embodied in waves, currents, and wind. Surface water flow and freeze-thaw cycles may also play a role. Not all of these forces may be present at any particular location. Erosion is a natural process, however, it can be influenced both adversely and beneficially by human activity. Some of the factors that may influence the forces of coastal erosion are discussed below (US Army Corps of Engineers, 1993). 4
1.3.1 Coastal Geomorphology
Figure 1.2: Erodibility of Lake Erie shoreline (Herdendorf et al, 1993)
Geomorphology is the general form of the earth's surface and the changes that occur to it. The coastal geomorphology of Lake Erie region is of glacial origin. Figure 1.2 shows that there are a variety of general shore types in Lake Erie region: high and low rocky bluffs, low flood plains and coastal marshes, high and low sand/till bluffs, sand dunes, and artificial coastlines. Among the eroded shore types, the two most common ones are sand/till bluffs and sand dunes (Herdendorf et al, 1993). Unfortunately, easily eroded banks of glacial till and lacustrine sediments characterize most of Lake Erie’s shores, while fewer reaches are composed of resistant bedrock bluffs (northeastern of Lake Erie coast). 5
Figure 1.3 presents a cross section view of typical beach profile showing significant features and related terms. The erosion process occurs within an area roughly from the bluff crest out into the near shore to a water depth of about 30 feet.
Figure 1.3: Beach profile and definition of the near shore (US Army Corps of Engineers)
1.3.2 Waves Any casual observer of Lake Erie knows that its water is constantly in motion. Forces associated with waves are the primary agencies of erosion on the coastal area, and waves are created by the wind (Trenhaile, 1997). The energy in a wave is related to meteorological factors such as wind speed and duration and is also determined by topographic and hydrographic factors such as distance, or fetch, over which the winds blow, and by the depth of water in the area where the waves are generated. The most dramatic erosion often occurs during storms, partially because the highest energy waves are generated under storm conditions. In addition, storms often 6
produce short-term shifts in lake-levels as water is pushed from one side of a lake to the other, called "setup". The effect of storms is also influenced by wave duration and return frequency. Wave climate describes the wave characteristics that prevail in a particular region. From hour to hour, day to day, and season to season, meteorological, hence wave, conditions can vary significantly. However, when averaged over longer periods of years and decades, both weather and wave climate remain about the same. As a result, since waves are the primary erosion agent, erosion and recession rates will also remain about the same when averaged over longer time periods (US Army Corps of Engineers, 1993). 1.3.3 Lake Levels and Erosion Natural lake-level changes can be divided into three types (CHS, 1996): short term (changes within a few days or less), medium term or seasonal (changes within a year), and long term (changes over a few years or more). Short-term changes are due mostly to wind-stress buildup and barometric pressure changes. These changes can cause lake-level fluctuations on the order of 2 to 3 feet for the west shore of Lake Erie. Medium-term changes are lakewide in effect and are caused primarily by differences in rate of runoff, evaporation, and evapotranspiration (Carter and Guy, 1980). A typical seasonal cycle for Lake Erie shows a high in June or July and a low in January or February; the mean difference in elevation between the high and low water stages is about 1.2 feet. Significant long-term changes are caused largely by major changes in the weather within the Great Lakes basin. Weather changes affect precipitation, which, along with secondary factors such as evaporation and runoff, causes long-term changes in the Lake 7
Erie. As Figure 1.4 illustrates, the record high water levels in Lake Erie of 1952, again during 1972-1973 and once in 1985, have contributed greatly to increased erosion of the shores (Herdendorf et al, 1993).
feet
Lake Erie Annual Mean Water Level (IGLD 1985)
575
574.5 574 573.5 573 572.5 572 571.5 571 570.5 570 569.5 569 568.5
Ye ar 19 20 19 23 19 26 19 29 19 32 19 35 19 38 19 41 19 44 19 47 19 50 19 53 19 56 19 59 19 62 19 65 19 68 19 71 19 74 19 77 19 80 19 83 19 86 19 89 19 92 19 95
568 Av erage Monthly_min Monthly_max
Figure 1.4: Lake Erie annual mean water level (IGLD 1985)
Because water depth partially influences how and where waves will interact with the coastal area, lake-levels may have an influence on coastal erosion, though they do not cause it. Lake-levels naturally fluctuate in response to changing water supplies and weather conditions. The primary factors that effect water level fluctuations are precipitation, evaporation, and runoff. Others include groundwater, ice formation and aquatic plant growth in the outlet rivers, meteorological disturbances and crusted movement. Human activity also has some affect on lake-levels. The diversion of water 8
from the lakes, water consumption, changes in runoff patterns due to urbanization and coastline development, and changes made to the outlet rivers, impacts lake-levels in the long term to a limited extent (US Army Corps of Engineers, 1993). Even though it is feasible to regulate the outflow from the lakes, control of lakelevels is not necessarily possible. It is because the major factors affecting the water supply to Lake Erie, such as precipitation, evaporation and runoff cannot be controlled. Neither can they be accurately predicted or planned for. The effects of the natural fluctuation of water supply to the lakes far exceed any impact human activity has had. On Lake Erie, the lake-level has no significant effect on any of the forces that cause coastal erosion. Variation in lake levels, whether short or long term, has little effect on the creation of waves, which are the primary erosion agents. Most waves are generated far offshore in deep water where such relatively small water level variations are insignificant (US Army Corps of Engineers, 1993). As long as the long-term meteorological and hydrographic factors that determine wave energy remain the same, the long-term erosion rate would remain essentially unchanged. The lake level does, however, have an effect on where wave energy is dissipated on the beach profile, and thus may affect bluff recession rates over short time periods. The lake level is only one of many factors influencing coastal erosion and recession. Up to now, the relative importance of lake-levels compared to the other influencing factors has not been quantified. Observations suggest that along most of the coast, storm duration and return frequency, and sediment supply have much more influence on coastal erosion and recession than lake-levels. 9
1.3.4 Littoral Transport and Sand Supply Erosion is part of a greater process known as littoral transport. Littoral transport is the movement of material by waves and currents on the coastline. The material being transported is primarily sand and gravel, with a small percentage of silt-sized particles and rocks. The source of nearly all the sand that is in the littoral transport system of Lake Erie is from the erosion of the bluffs and dunes (US Army Corps of Engineers, 1993). Very little material is carried by rivers. Littoral transport occurs along the shoreline, as well as into the lake and onto the shore. With respect to sand supply, a given length of coastline may have a surplus, be in balance, or have a deficit in its sand supply budget. A sand supply would be in balance for a particular area if the amount of sand leaving the area was being replaced by an equal amount of sand arriving from adjoining areas. Over a short time period, erosion may occur followed by a build-up of sand (accretion), but over the long term, the area would be in a state of "dynamic equilibrium". A large reduction in the sand supply, or a longterm reduction in supply to an area, creates a deficit in the sand budget that must be balanced, usually by increased erosion. Sand supplies influence erosion by supplying the material that builds and maintains beaches, offshore bars, and the general beach profile. These features dissipate wave energy and the absence of these features, where they would normally be present, and usually results in increased erosion. 1.3.5 Erosion and Recession Erosion and recession are often used interchangeably. However, recession is the landward movement of a feature, such as an elevation contour or the bluff or dune crest, 10
while erosion is the wearing away of land. Recession is expressed as a distance, or a change in distance, while erosion is expressed as a volume, or a change in volume. Recession can be considered as a consequence of erosion. To that extent, it is a reflection of bluff erosion. In addition, bluff recession may be reflective of coastal erosion processes as a result, if averaged over a sufficiently long period of time (US Army Corps of Engineers, 1993). Bluff recession is the most visible aspect of coastal erosion and receives the most attention. However, using only bluff recession as an indicator of erosion rates or erosion trends may be misleading because of the length of time, or lag, that usually occurs between erosion and bluff recession. Coastal erosion occurs over an area roughly from the top of the bluff out into the near shore region to about the 30 foot water depth. As a result, erosion processes (particularly those that occur to the near shore lake bottom) often do not become apparent as bluff erosion or bluff recession until days, weeks, months or even years have passed. In addition, erosion, particularly bluff erosion and recession, does not occur at a constant rate. Over a relatively short period of time, the rate of erosion and recession may vary greatly. It is very common for a reach of coastline to have no bluff recession for months or years and then experience severe bluff recession over a period of days or weeks. This bluff recession may occur during a period of little or no storm activity. At the present time, it is not possible to precisely determine the relationship between any of the erosion forces, or a single factor influencing erosion, and the bluff recession rate, particularly over short time periods. It is only possible to correlate a cause and effect relationship between all the erosion forces and factors influencing erosion, 11
taken as a whole, to the bluff recession rate averaged over a period of many years or decades. The rates of recession thus derived are referred to as "long-term, average, bluff recession rates". 1.4 Study Area
Figure 1.5: Study area
12
As Figure 1.5 shows, the study area for shoreline change analysis extends along the south shore of Lake Erie from Cranberry Creek on the east to Sheldon March State Natural Reserve on the west (mainly the Huron reach). Analysis was completed in all areas where data were deemed by state coastal geologists to be sufficient for realistically estimating long-term shoreline change rates. The reason for choosing this area is that both the tabulated recession rate tables from 1973 to 1990 and the coastal erosion area maps are available. This reach covers nearly 10 miles of shore. Cottage associations and trailer courts occupy the shore zone between Cranberry Creek and Old Woman Creek, and Huron, the only urban area, occupies the remaining of the reach west of Old Woman Creek.
1.5 Research Objectives As it was stated in this chapter, people should pay more attention to their shores, not only focusing on their property, but also managing the problems. Knecht et al. (1996) recently categorized the state coastal management programs into four major fields: 1) protecting coastal resources, 2) managing coastal development, 3) providing public access, and 4) managing hazards. Due to time constrains, this research will focus on the coastal resources protection and hazard management based on the analysis of shoreline change of study area. As Mike Colvin, coastal program manager for the Ohio Department of Natural Resources (ONDR), says, “You don’t prevent erosion, you manage it.” History provides poignant accounts of people whose land and even lives were taken by Lake Erie (Platt, 1998). This research will utilize the Lake Erie-related data collected at ODNR, National 13
Oceanic and Atmospheric Administration (NOAA), GLFS (The Great Lake Forecasting Systems), and other agencies to develop GIS database and analysis applications in order to meet the following three objectives: • Erosion cause analysis and erosion monitoring: GIS applications are now being developed to monitor coastline erosion and accretion by using detailed examinations of historical records (Ligdas, 1996). The analysis of erosion cause will utilize natural process information, such as shoreline deposits, coast material, slope of near-shore zone, bathymetric data model etc. Monitoring of shoreline erosions needs a long-term commitment and is based on the erosion modeling of Lake Erie with GIS database. Objective decisions should be made based on erosion monitoring data that are usually acquired accumulatively during a long period of time (Li, 1996). To update the erosion data more frequently, high-resolution satellite imagery will be used in the future for erosion monitoring. • Building a prediction model: To property owners, the primary advice is “stay out of harm’s way.” Don’t buy or build in areas where the lake will consume property long before children or grandchild can inherit it. Build far from the waste’s edge and consider moving threatened buildings rather than constructing expensive shore protections that must be continually maintained and whose eventual cost may exceed the value of the property itself. Unfortunately, many people make the decision to buy or build by the lake on just such a smiling day as the day you traveled along Lake Erie. Erosion hazards prediction are crucial for those people who own the property or purchase the property in the future along the lake. According to the past long-term erosion statistics and the implementation of erosion monitoring system, the hazard 14
caused by shoreline erosion could be predicted by using the shoreline-prediction model. For example, shoreline of 2015 could be predicted based on the past long-term erosion data, this anticipated shoreline might help people to make the decision for purchasing or building a house along shoreline of Lake Erie. This system might achieve the erosion hazards area estimation by 1) determining existing and potential severe erosion shoreline segments and 2) indicating affected aspects such as parcels, land-use categories, endangered species and others (Li, 1996). • Spatial analysis for decision-making: This system can provide recommendations or scenarios using spatial analysis for decision-making with automated analysis functions. For instance, considering the coastal construction permitting, permitting engineers have used engineering judgement to determine the line of construction by looking at aerial photographs that were developed on a large-scale map. With this system, the engineers approving coastal structure permissions can easily consider all related information such as erosion status/rate, zoning, protection areas, geology, soil type, coastal processes etc (Li, 1996). Furthermore, the evaluation before launching a new coastal protection can be made based on simulation of shoreline model that integrated all related data. The major accomplishment of this research will be the demonstration of GIS spatial analysis capabilities in Lake Erie coastal resource management. The spatial analysis operations will use the spatial data and other environmental and coastal resource data for Lake Erie area collected previously by state and federal government agencies. A successful implementation of this research will make the decision-making process more objective and efficient.
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1.6 Organization This thesis has five chapters. Chapter 1 states a general introduction in current status of shore erosion of Lake Erie, and presents a literature review for those factors that caused erosion. Chapter 2 discusses those data that will be necessary to implement the spatial model and database, and the methodologies that are used in this research. Chapter 3 states all the procedures for spatial data implementation, database design, shoreline position prediction etc. The summary of previous works (Geology Survey, ONDR, 18771937, 1937-1973) regarding the erosion study of study area is also included in this chapter. All the results and demonstration for this research will be presented in Chapter 4, the data analysis and erosion-control suggestions will also be discussed in this chapter. Finally in Chapter 5, the conclusion of this research and recommendations for future research in this field is presented. Shoreline mapping, using the one-meter resolution satellite imagery, (e.g. IKONOS-1) will be taken into account, especially.
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CHAPTER 2 DATA PREPARATION AND METHODOLOGY 2.1 Introduction Spatial data are one of the basic components of GIS, the methodologies that used to implement the spatial data will depend on the spatial data format, scale, coordinate systems, accuracy, resolution (for imagery), and etc. The other basic components of GIS, i.e. attribute data, could be derived from the database files while building up the topology (e.g. in Arc/InfoTM), or be created from database packages (e.g. dBASEIV, Excel, etc.). This chapter discusses all data sources and methodologies that will be used in this research. To avoid the redundancy of literature reviews and to consider the logical structure of writing, more details of methodologies will be stated step by step in Chapter 3 when they are implemented. 2.2 Data Preparation Several sources of data are used in this research. This alone demonstrates one of the well-recognized reasons for using GIS – the integration of disparate data sets (Shaw, et al, 1995). Considering the factors that may influence the forces of coastal erosion and the natural of the applications of GIS, there are four primary sources in this case. Bearing in mind, not all data sets will be used for data analysis, some of them are for meeting the requirements of cartographic designs and map presentation. 17
2.2.1 Historical Shoreline Data Currently, historic linear data provide us with the ability to assess future changes in the shape of the shoreline by reviewing historic snapshots of the shoreline. The longterm rates of change provide mangers and property owners with a clearer picture of the potential hazards confronting coastal development (Dusen, 1997). First of all, the tabulated recession rate data between 1973 to 1990 for study area in digital form was acquired from the Ohio Department of Natural Resources (ODNR). Recession rates were determined by comparing the position of bluff lines shown on 1:10,000-scale U.S. Lake Survey charts and more recent 1:12,000- to 1:4,800-scale aerial photographs from the 1930’s to 1990 (Mackey, 1994). Bluff-line positions from these charts and photographs were transferred to 1:2,400-scale enlargements of aerial photographs taken in 1990. Positions of the transferred lines were then digitized along approximately 7000 shore-normal transect spaced 30 meters apart. Secondly, ODNR also provides Lake Erie Coastal Erosion Area Maps, which are part of the results from Ohio Coastal Management Program. These maps have been revised from the preliminary identification of Lake Erie coastal erosion areas released on September 30, 1996. These maps also show the location of the digital transects and recession lines for 1973 and 1990, respectively. In order to accumulate data for monitoring long term shoreline changes, aerial photographs of the shoreline of Lake Erie should be taken every 5 years (Li, 1995). The photographs should have at least 60% along shore overlap so that the 3D shoreline can be extracted from the photographs by stereo photogrammetric processing.
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Coastal erosion have long employed aerial photography, so historical aerial photographs should be available for deriving the long-term recession rate. An aerial photographic search conducted for the study area located in Erie County of Ohio is shown as Table 2.1. For shoreline segments eroded serially, larger scale aerial photographs should be taken more frequently, for example every 1-3 years. The larger scale photographs may also be used for other purposes such as coastal zone topographic mapping, beach profiling, and erosion interpretation. Besides, for very small sites, total stations and Global Positioning System (GPS) receivers may be used to capture the shoreline periodically and compare the shoreline changes (Li, 1995).
Date
Mission #
Frame Numbers
Scale
4/29/97
9710913
014, 015
1: 19.2K
6/14/73
5258
1: 001, 002
1: 24K
3/28/68
3881
22:575 thru 579
1: 4.8K
23:580 thru 583
1: 4.8K
1/25/62
2207
4: 097 thru 100
1: 9.6K
11/23/60
1740
1: 001 thru 003
1: 9.6k
7/29/58
1104
3: 042 thru 045
1: 9.6K
3/05/49
198v
8: 078 thru 087
1: 4.8K
Table 2.1: Aerial photographic search for study area located in Lake Erie
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2.2.2 Bathymetric Data Bathymetry of Lake Erie (shown as Figure 2.1), exceeding in detail any previous bathymetry, is now available from NGDC (NOAA National Geographical Data Center) with Arc/InfoTM format. The bathymetry was compiled using all good quality historic hydrographic sounding data collected since 1903 from the NOAA National Ocean Service and its predecessor agency for Great Lakes Surveying, the Army Corps of Engineers, and the Canadian Hydrographic Service.
Figure 2.1: Bathymetry of Lake Erie (NGDC, NOAA)
Coordinate system transformation is necessary while integrating other layers since the bathymetric data were saved in geographic coordinate system, i.e. latitude and longitude. Digital sounding data combined with sounding data archived only on paper were used in the compilation. Tracking density ranges from about 125 meters to 500 meters for nearshore areas and 500 meters to 2500 meters for the open lake regions. For the study area, the sounding lines are less than 500 meters apart. 20
The bathymetric data from surveying are stored in survey-sheet archives and bathymetric data bases at the NOAA’s (National Oceanic and Atmospheric Administration) National Ocean Service, the Canadian Hydrographic Service, and the NOAA National Geophysical Data Center (Holcombe et al, 1997). On the other hand, real-time bathymetric data of Lake Erie could derive from the on-line tools of Great Lakes Forecasting System. Hardcopy nautical charts could also provide important bathymetric information. Hydrographic survey of small areas is conducted or contracted by NOAA or USGS (U.S. Geological Survey, Department of the Interior). Bathymetric data are crucial to structure design, hydrological modeling, and shoreline change monitoring. The data acquisition is usually expensive.
2.2.3 Topographic Data Though there is no definitive map base regarding the GIS applications of coastal zone (Maslen et al, 1996), some of the data layers are based on topographic maps, especially the one of 1:24,000-scale, for instance, Lake Erie coastal zone boundary, watersheds, groundwater resources etc. Topographic maps of 1:24,000-scale are maintained by USGS. At this moment, the GIS Support Center of the Ohio Department of Administrative Services will provide access to and distribute the 1:24,000-scale Digital Line Graph (DLG) files based on the 7.5 quadrangles covering the State of Ohio (see Figure 2.2). The layer categories include Boundary, Hydrography, Public Land Survey System (PLSS), Transportation, and Hypsography. All of Huron and part of Vermilion West quadrangles will be used in this research corresponding to the study area.
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These data cover the features on the landside of the shoreline. Digital Elevation Models (DEM) describe the land terrain relief which determines the shoreline shape along with the bathymetric data, the water level and other factors (Li, 1996).
Figure 2.2: Available 1:24,000-scale DLG files 2.2.4 Others • Attribute data: There are two types of data that are used in a GIS environment, i.e. the spatial data and attribute data. Attribute data sets or data views can be associated with the map through links to the spatial data in GIS system. GIS attribute data such as 22
coastal geomorphology, demography, land use, geology, soil type, environment quality etc. should be included for the linkage to spatial data. These data are sometimes necessary in decision making, for example for new structural protection construction, coastal construction permitting, limiting or avoiding environment impacts, and the other purposes. A lot of coastal attribute data can be associated with shoreline segments, including erosion status, structure types, geological materials etc. • Multimedia data: The changing position of the shoreline, the toe of the bluff, and the top of the bluff can be quantified using the image processing software developed for the ‘Remote Video Monitoring System’ (http://coastal.er.usgs.gov/erie/). Multimedia data could provide different aspects of design for coast-based GIS database. For example, terrestrial and aerial photographs are available for shoreline sections of different periods. They are important for interpreting erosion status. Hardcopy photos that were taken during site visits are scanned and saved as Tag Image File Format (TIFF). The scanned images are then related to the desired features and can be displayed by clicking the corresponding segments of shoreline by using the hot link. • Time series data: While implementing shoreline management based on GIS, the time dimension must also be incorporated into GIS data models even in the simplest way by recording when the data were collected and how often it is likely to get updated (Maslen et al, 1996). GIS applications can model cyclical processes such as tidal current movements allowing predictions to be made for the dispersal of pollutants while ‘scenario predictions’ can be computed as a result of spasmodic events such as storm flooding. So, water levels, wave data, wind data, wave surface elevations (tides), river data (daily discharge), and other times series data can describe the 23
processes affecting the shoreline and the other coastal phenomena. Fortunately, these data now can acquire from some real-time systems, e.g. the Great Lake Forecasting System (water levels, wave, wind, temperature, tides etc.), Lake Erie Homepage-US Army Corps of Engineers (water level, storm rise, outflows, weather etc.). A link between the time series data and the spatial data opens a new way of unified database management scheme and an integrated coastal modeling environment (Li, 1996). 2.3 Methodology The GIS based shoreline change analysis described here is initially prepared to combine in an interdisciplinary manner from a variety of coastal information (geological, geomorphological, ecological, meteorological etc.). The main interest is the study of shoreline change and through this the development of a system to assist coastal planning and management issues. 2.3.1 Data Collection and Manipulation The collection of good quality data sets from a large range of organizations is proving extremely time consuming and will continue throughout this research. The data collected were in a wide range of formats from paper documents and maps to photographs and numerous digital data formats. For example, the tabulated recession rate data for study area were in digital form from ODNR. Much work has been involved in sifting through these data to retrieve relevant information. Arc/InfoTM has been used to manipulate and convert much of the data into a suitable format for this research. A general design of functions is presented, which serves as a base for the detailed data model design. Data sets input to system could be metadata of time series, 24
digitized/scanned hard copy maps, existing digital maps, coastal engineering, and scanned aerial photographs. These data are in certain digital formats (Li, 1996). First of all, boundary, hydrography, transportation, and hypsography features were downloaded from 7.5 minutes (i.e. Huron and West Vermilion quads) USGS topographic DLG files distribution center and exported to Arc/InfoTM. The DLG data are digital representations of the information shown on USGS topographic maps. The data contain a full range of attribute codes, have full topological structuring, and have passed a series of quality control checks. Attribute codes are used to describe the characteristics of the feature represented by the DLG node, line, and area elements. Each DLG element has one or more attribute codes composed of a three-digit major code and a four digit minor code (Robinson et al, 1995). These attribute codes will be converted into tables after we build the topology for each layer. Besides, the DLG coordinates are referenced to the Universal Transverse Mercator (UTM) projection system and the intervals for contour lines might be 10 feet or 5 feet depending on the terrain situation. So, the edges might not match for hypsography feature while connecting the adjacent map sheets, thus compiling and generalization will be necessary. Secondly, the 1990 shoreline based on the hydrographic feature was digitized. This line is going to serve as the master line for monitoring shoreline change and the base line for predicting future shoreline. Similarly, the 1973 shoreline from historical shoreline map was digitized and compiled. Next, some buildings within coastal zones of study area were digitized as well because there was no DLG coverage for buildings. These buildings will be the samples for hazard predicting study. Finally, all coverages data sets to build the spatial model were exported to ArcViewTM. 25
2.3.2 Modeling the Coast - Dynamic Segmentation of the Line The historic shoreline data will be segmented for analysis (Dusen, 1997). The criteria used to segment the data were developed within the analysis methods to provide consistent, accurate, and timely temporal shoreline change analysis results. The historic shoreline data will be divided into a lot of segments considering the characteristics of data.
Figure 2.3: Determining the recession rate of Lake Erie (Vincent, 1997)
Traditionally, transects perpendicular to the Lake Erie shorelines are used to estimate coastal erosion losses and derive the recession rate (see Figure 2.3). The tabulated erosion data (Appendix A) from ODNR are also derived by using this traditional method. Fundamentally, erosion study regarding recession rate in this research
26
is based on these tables. For the study area, frame number is from ERI487 to ERI008, and total segments number is 497 with 100 feet (about 30 meters) long for each segment. Once the historic data are segmented into manageable units, transecting and analysis proceed within each of the analysis units. Baselines are constructed on the upland side of all historic shorelines to provide a starting point for the transecting operation. Besides, baselines parallel to the general trend of the historic shorelines will be digitized, so that orthogonally oriented transects originating from the baseline would most closely match transects placed by manual ‘best fit’ methods. The line is segmented or divided into homogenous subunits, by recording the location of a change in attribute, or an event (ESRI, 1992a), in terms of distance along the line from a specified origin or topological node (Bartlett et al, 1997). The basic idea behind dynamic segmentation is that, instead of decomposing the line into homogenous segments, attributes can be defined along a linear feature spanning many arcs or, alternatively, a single line may have multiple attributes assigned to different portions of the same arc (ESRI, 1992a). In the implementation of dynamic segmentation provided by Arc/InfoTM, the term route is given to any linear feature (such as highway, river, shoreline, etc.), regardless of the number of arcs that make up its representation within the database. In a shoreline GIS, the shore can be viewed as a route system consisting of several routes, each representing different parts of the shore. Attributes associated with the route are known as events (ESRI, 1992a), and are referenced in terms of their distances along the route from a defined starting value (Bartlett et al, 1997). 27
2.3.3 Predicting Shoreline Positions Rates of change in shoreline positions are frequently employed to summarize historical shoreline movements and to predict future shoreline positions based on the perceived historical trends (Shao et al, 1998). The method most commonly used, especially by coastal land planners and managers, to predict future shoreline changes is extrapolation of a constant rate-of-change value (Owens, 1985). The popularity of this method is due chiefly to its simplicity (Fenster et al, 1993). As with any empirical technique, no knowledge of or theory regarding the sand transport system is required. Instead, the cumulative effect of all the underlying processes is assumed to be captured in the position history. An assumption which is implicit in this procedure is that the observed historical rate-of-change is the best estimate available for predicting the future. For the purpose of predicting the future of shoreline positions for study area, simple methods or models have been used, such as the End-Point Rate (EPR) method, or Linear Regression (LR). The future shoreline position for a given date is then estimated using the resulting slope and Y-intercept: Shoreline Position = Rate * Date + Intercept This equation shows that EPR model employed a line extracted from the earliest end-point and latest end-point. If we use Y to denote shoreline position, X for date, B for the intercept, and m for rate of shoreline movement, this equation can be simplified as Y=mX+B. The literature reviews and usage of this equation will be stated in section 3.5.2 for implementing the future shoreline.
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2.3.4 Software Development Having standardized the data sets within Arc/InfoTM the data can be exported to ArcViewTM and organized within this system. There is an ‘urgent need to provide simpler access and data manipulation tools that can be used directly and relatively easily by the resource managers and scientists’ themselves (Ricketts, 1992). This is the general perspective for developing software in this research. First of all, for the basic function consideration, this system can be used for viewing data (as a set of predefined views) and giving the user an idea of the kind of data sets that are held (Maslen et al, 1996). This alone allows the user to gain a greater awareness of the area they are interested in. Secondly, users can chose which data sets they want to view and begin to start querying and looking at interactions between various data sets. Tools are available for: adding themes (from a list) to the view, measuring distances and areas of particular features in the view (e.g. distance of a particular length of shoreline), moving around the area of interest easily, buffering, displaying metadata, producing reports, and producing standardized maps (layouts). Most of these functions are based on the basic operation of ArcViewTM. For customizing user environment or designing a graphical user interface, Avenue program might be implemented in the future. For coastal erosion monitoring, shoreline conditions of the study area are stored in the database (dBASEIV format). Current recession situation of shoreline are classified into some categories (shown as Table 2.2) and represented on a map. This is implemented by using the existing results of the Division of Geology Survey of ODNR. Shorelines with different erosion categories are identified with different colors and patterns. Specific 29
information can be queried by clicking a mouse at an interesting shoreline segment when all related attributes are joined or linked. The information will contain recession rate, geological material, adjacent structure, image for hot link, and etc.
Order
1
Recession-rate
< 1 foot/year
Classes
Remark Area of fill and accession are
very slow
included in the very slow class. 2
1 – 3 feet/year
slow
3
3 – 5 feet/year
moderate
4
5 – 7 feet/year
rapid
5
7 – 9 feet/year
very rapid
6
9 – 11 feet/year
extremely rapid
7
> 11 feet/year
Rates greater than “extremely rapid” are given in numerical value ft/yr.
Table 2.2: Recession-rate classes (Carter and Guy, 1980)
At last, the interface includes all the customized functions and most of the standard ArcViewTM buttons and tools that will be implemented in the future. This interface allows the users to further customize the system to their own needs and carry out further analysis. It is envisaged that as users become more familiar with the system, they will end up using this interface for most of their work. 30
2.3.5 Quantify and Display Shoreline Change – An Alternative Way Facing with subtle shoreline change, coastal geologists are often forced to choose between two compromises (Duffy, 1995). They either can use large scale maps that capture the detail but display only a limited portion of the shoreline at one time, or they can average various shoreline segments and display these less detailed data in graphic form next to a map of the coast. An alternative methodology is proposed for preparing and displaying shoreline change data digitized from 1:24,000 hard-copy maps and aerial photographs of the study area. This method will be based on some operations of GRID module in Arc/InfoTM and implemented in ArcViewTM. While the shoreline coverages for a particular beach contained highly detailed positional data, it was difficult to see subtle change in anything other than large scales (Duffy, 1995). To quantify and display shoreline change, we utilize some operations in Arc/InfoTM Grid module and spatial analysis in ArcViewTM. With the shoreline erosion data, we wanted to use the length of the shoreline as the X-axis and the change in shoreline positions between 1973 and 1990 as the Y-axis. The problem is how to get this change information from the two-shoreline arcs. In most vector based GIS software, such as Arc/InfoTM, lines (arcs) and polygons have topology; that is, they have information attached to them which records their starting and ending points, and their left and right sides (for arcs) or their inside and outside (for polygons). Topology makes it possible to assign address to a street arc or to find how many addresses there are within a town polygon (ESRI, 1991). However, topology gives us no information as to what is going on in the empty space around an arc
31
or outside a polygon. Information is only recorded for the coordinates that form the arc or polygon. In the GRID module of Arc/InfoTM, information of grid based coverage is not tied to specific features like arcs or polygons; instead, all data are referenced to a fixed location or cell whose size is defined by the user. Individual cells can be coded with values just as arcs or polygons are; however, there is no empty space in grid coverage, and all cells contain at least the coordinate of their location. Because of this, we can move anywhere in grid coverage and get information such as “How far am I from the nearest cell coded as 1973?” And this is the problem we are concerned with regarding the shoreline change. With simple commands in Arc/InfoTM, grids can be created from arc or polygon coverages and vise versa. Applying one or more statistical and logical functions to another grid can create a new grid. Simply adding two other grids can create the third grid. Grids provide a set of powerful tools for the analysis of geographic data that vary continuously over a region. The EUCDISTANCE command will be employed in this research to generate the third grid, which represents the Euclidean distance from source grid (1973 shoreline). Euclidean distance is calculated from the center of the source cell to the center of each of the surrounding cells (ESRI, 1992b). As shown in Figure 2.4, true Euclidean distance is calculated for each cell in the distance functions. For each cell, the distance is calculated for each source cell by calculating the hypotenuse with the x_max and y_max as the other two legs of triangle. This calculation derives the true Euclidean distance instead of cell distance. The shortest distance to a source is determined and if it is less 32
than the specified maximum distance, the value is assigned to the cell location on the output grid. Next, we can either use Graph (ArcPlot module) command or import the last grid file into ArcViewTM to quantify the shoreline changes.
1
1
True Euclidean Distance
1 Source Cells
x_max y_max
2 SOURCE_GRID Figure 2.4: Euclidean distance
A practical example for quantifying shoreline change by using this alternative way is implemented in section 3.6, and the results and analysis are presented in section 4.5.
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CHAPTER 3 IMPLEMENTATION
3.1 Introduction This chapter discusses all procedures of spatial data implementation, database design, shoreline position prediction, and an alternative way to quantify the shoreline change. Some commercial GIS and Computer Aided Design (CAD) packages were used to implement the spatial data, including Arc/InfoTM Version 7.1, ArcViewTM Version 3.0b, and AutoCadTM Release 13. The Center for Mapping, The Ohio State University (CFM) currently owns the licenses for the first two packages for UNIX system. Considering the database design, Microsoft Excel was used to create new tables, edit existing tables, convert different database format, and import database files for analyzing the results. Moreover, Microsoft C++ and AutoLisp (has to be performed under AutoCadTM) were employed to design program for predicting shoreline positions and spatial data management. 3.2 Previous Work For getting a general review of historical shore erosion regarding the study area, Carter and Guy (1980) investigations’ report could support us with general understandings. The study area of this report is from Cranberry Creek to Sawmill
34
Creek, i.e. Huron reach, which is shorter than the range in this research (see Figure 3.1). Most of aspects concerning erosion study added in this report include nearshore water depths, beach investigation (length, width, and type), land use, shore-protection structures inventory, and recession rates.
Figure 3.1: Comparison of study site between previous and current research
Recession
1877- 1937
1937- 1973
rate
Length (ft)
Percent
Length (ft)
Percent
very slow
19,700
64
23,800
75
slow
9,500
31
6,750
21
moderate
1,650
5
1,000
3
Table 3.1: Summary of recession rate of Huron reaches for previous work
35
The periods for which the recession rates were measured, are 1877-1937 and 1937-1973, respectively. The summary of the erosion study in this area is shown as Table 3.1, and the abstracted document of this report is attached in Appendix B. 3.3 Spatial Data Implementation Figure 3.2 depicts the procedures for creating spatial data model. Basically, data conversion and topology building are implemented under Arc/InfoTM since this software provides the commands to convert most of popular spatial data formats into interchangeable format, such as DLG, DXF, DIME, TIGER etc. For integrating varieties of data source, we use UTM (Universal Transverse Mercator) projection as coordinate system. In ArcViewTM, GIS data are organized according to Themes that are close to layers in Arc/InfoTM. Graphic data of themes could be imported from a drawing file (e.g. shape file), an Arc/InfoTM coverage, and a grid file. Each theme is associated with a GIS data set where additional geometric data, attributes, and topological data are stored (Li, 1995).
DLG Files from USGS
DLGARC (Arc/Info)
THEMES (ArcView)
ARCSHAPE (Arc/Info)
ARCDXF (Arc/Info)
BUILD Topology
Figure 3.2: Procedures for Spatial Data Generalization
36
Compiling (AutoCad)*
DXFARC (Arc/Info)
* There are two reasons for exporting dxf files into AutoCadTM to compile the spatial data instead of editing these files under Arc/InfoTM. 1. We will need to clip the spatial data fitting the range in the study area, and AutoCadTM could easily finish it. 2. The elevation value will be zero after the DLGARC and ARCDXF commands employed for the hypsography (contour) coverage. Re-assign elevation value will perform in AutoCadTM with AutoLispTM program (Appendix C).
Besides, in order to analyze terrain and erosion model, a digital elevation model (DEM) for the study area is necessary. In fact, topographic parameters representing the geometric properties of terrain surface are important for many applications related to environmental model and land use management (Mitasova, 1996). By using hypsographic features (contour) from 7.5 minutes USGS topographic DLG files for the study area and exporting it into Arc/InfoTM, we extracted the DEM with a cell size of 10 meters (about 30 feet). Some 3-D operations, such as deriving slope, deriving aspect, computing hill shading, etc., were base on this DEM. For implementing bathymetry, first, we clipped bathymetric data of Lake Erie to fit in the study area. Then, coordinate system was transferred to UTM from geographic coordinate system. At last, similar to the processes of DEM, we generated 3-D bathymetric model with a cell size of 10 meters. 3.4 Applying Dynamic Segmentation to the Shoreline As we mentioned in chapter 2, each segment of shoreline has a unique value of recession rate. The shoreline is a line theme with arcs and nodes after being digitized from 1:24,000-scale maps. On the other hand, the erosion information should be integrated with shoreline data to indicate the erosion status of the shoreline segments.
37
Obviously, the segments of erosion would not match the arcs from shoreline theme. As illustrated in Figure 3.3, considering the arcs 1-2 and 2-3 in the shoreline theme, they have to split into some new arcs to match the segments with erosion information associated. For solving this problem, dynamic segmentation will be used. It can separate shoreline by using one dimension index of distances along the coastal line (Li, 1995).
Figure 3.3: Comparison of nodes between shoreline theme and segments of erosion
The first stage was to create a route for the study area; that is, the coastal line from Frame No. 487 to Frame No. 008 of Erie County. The actual creation of route was relatively straightforward, though time-consuming and tedious. The basic line was first acquired by digitizing from existing hard-copy 1:24,000-scale topographic map. Next, for each set of events or attributes, the required arcs were selected using the on-screen cursor, and followed by the execution of an Arc command to actually build the route. Arc/InfoTM automatically generated a unique route identifier and calculated the distance of the route 38
from start to end during this process. These distances are stored in their own database table (ESRI, 1992a). Arc/InfoTM supports three types of event, namely, point, line, and continuous, of which the line type was currently used in this research. At this stage, all that has been created is a line, with no extra information related or attached. On the other hand, the route attribute table (RAT) and the section table (SEC) will be generated after we made this route, these features data model together define a route-system. The next step was to create a linear event table (see Table 3.2), which stores details of the events that they are assigned.
ROUTELINK#
FROM
TO
FRAME
TRANS
STD
STR
YRS
1
0.000
29.438
ERI487
487- 1
16.10
0.95
17
1
29.438
58.879
ERI487
487- 2
3.40
0.20
17
1
58.879
88.318
ERI487
487- 3
42.80
2.52
17
1
88.318
117.745
ERI487
487- 4
30.30
1.78
17
Table 3.2: Example of a linear event table
In a linear event database, each entity has a dimension along the coast, measuring in terms of the points along the line where the object or attribute starts and ends. These points are known as the from and the to, respectively. Since each of these start and end points has to be measured on the computer screen by using the cursor, it is obvious that the time taken to create the database will increase in direct relationship to the spatial 39
complexity of the features being mapped (Bartlett et al, 1997). Once completed, the linear table is linked to its associated route by using the Arc command. 3.5 Predicting Shoreline Positions Shoreline position prediction is based on the rate-of-change value method with End-Point Rate (EPR) model, though the data period is only available for 17 years for recession rate data. As we discussed in chapter 2, an assumption in this procedure is that the observed historical rate-of-change is the best estimate available for predicting the future. The methods employed in this research to predict future shoreline positions are buffer operation method and programming method. Both methods predict the shoreline positions for year 2015 of the study area and the processes of implementation will be stated in the following sections. 3.5.1 Buffer Operation for Predicting Future Shoreline
Figure 3.4: Buffer operation to predict shoreline positions
40
In GIS, buffer is defined as an area containing locations within a given range of a given set of features, and is useful for proximity analysis (e.g. find all stream segments within 300 feet of a proposed logging area). Buffers are commonly circular or rectangular around points, and corridors of constant width about linear and areal features (Worboys, 1997). The generation of buffer can be based on either a certain distance (constant-width) or a look-up table (variable-width) in Arc/InfoTM (ESRI, 1997). Then we eliminate the wrong side of buffer zone. Figure 3.4 shows that both lakeward and landward buffering zones will be created along the master shoreline based on the look up table (LUT). Apparently, lakeward side should be removed since rate is positive for recession. First, we need to create a look-up table, and only two columns are needed in this table; that is, key-item and distance from input coverage. The key-item is Trans (i.e. segment number), and distance is derived from annual recession rate (feet/year) multiplied by 25. Next, we performed buffer function along shoreline coverage and removed lakeward buffer zone (i.e. the “right” argument in buffer command). The edge of buffer zone will look like a “line” if the quantity of segments is large enough. This method is easily implemented for predicting shoreline positions, though it is not the best way. 3.5.2 Programming for Predicting Future Shoreline The programming for predicting shoreline positions is based on the end-point rate (EPR) model, assuming the observed historical rate-of-change (the existing erosion data) is the best estimate available for predicting the future. The EPR (Fenster et al, 1993) model utilizes a line extracted from the two end-points, the earliest and latest positions.
41
Using Y to denote shoreline position, X for date, B for the intercept, and m for rate of shoreline movement, then we derive: Y = mX + B ---------------------------------------- (1) As illustrated in Figure 3.5, given N samples, numbered in ascending order by date, the EPR is: mEPR = (Yn-Y1)/(Xn-X1) -------------------------- (2) and the EPR intercept is: BEPR = Y1 – mEPR * X1 = Yn – mEPR * Xn --------(3)
Figure 3.5: Future shoreline prediction based on Y=mX+B equation
Since the end point line can extend beyond the most recent point (X, Y)n, equation (2) can be rewritten to use that position (Yn), and the elapsed time (X-Xn): YEPR = mEPR * (X – Xn) + Yn -------------------------(4) There are three programs designed to predict future shoreline, that is, GC_L_DAT, COAST_N, and GENSCR (source codes are attached in Appendix D). The first program, GC_L_DAT, is used to filter out the X and Y coordinates from DXF file which was exported from 1990 shoreline coverage. COAST_N, the main program, is employed to calculate the new positions for shoreline based on equation (4), and 42
generates a text file with predicting X, Y coordinates. The third program, GENSCR, converts the generated text file into AutoCadTM script file. Next, it imports the script file into AutoCadTM, which is the predicted shoreline. 3.6 Quantify and Display Shoreline Change – An Alternative Way All the methodologies described in previous sections are based on the existing erosion data. The question is: “Is there any other methods that can be used to get the recession rate and calculate the area of lost land?” Here we are going to present an alternative way for quantifying shoreline change based on some operations of GRID module in Arc/InfoTM and implemented in ArcViewTM. The preparation work is to collect sets of circa 1973 and 1990 aerial photographs and 1:24,000-scale hard-copy maps for the study site from ODNR. The study site for this part is located on the right sides of Old Woman Creek about 2000 feet away, which extends 2000 feet along the shoreline. Then we used a high accuracy digitizer to digitize two sets of 1973 and 1990 shoreline data, respectively. Finally, we used the DXFARC command to convert the shoreline data into two Arc/InfoTM coverages. First of all, the LINEGRID command is used to convert both shoreline coverages into grid coverages with one-meter cell size after topology was built up. In the resulting grids, cells which fell at the position of the 1973 or 1990 shoreline were coded as “1973” and “1990”, respectively. All other cells were coded as NODATA (defaults). Secondly, the EUCDISTANCE command is employed to create a new grid from the 1973 shoreline grid. This function calculates the Euclidean distance of every cell in the new grid to the nearest concurrence of a specified value in the original grid (ESRI, 43
1992b). In this case, the specified value was the code “1973” in the 1973 shoreline grid. The resulting grid looks like a buffer around the original 1973 shoreline grid. Unlike a buffer polygon, this buffer was composed of individual cells, each of which contained a distance value. Output Grid 1.0 0.0 0.0 1.0 2.0 3.0 1.4 1.0 0.0 1.0 2.0 3.0 2.2 1.4 1.0 1.4 2.2 3.2 2.0 2.2 2.0 2.2 2.8 3.6 1.0 1.4 2.2 3.2 3.6 4.2 0.0 1.0 2.0 3.0 4.0 5.0
Input Grid
Euc_Distance_Grid
1 1
90
1
0
45 360 27
0
270 270 270
0
270 270 270
45 360 315 297 289
180 27 360 334 315 304 180 225 243 342 327 315
2
0
Source_Grid
270 270 270 270 324
Euc_Direction_Grid 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Value = NODATA
2 1 1 1 1 1 2 2 2 1 1 1 2 2 2 2 2 1 Euc_Allocation_Grid
Figure 3.6: The generated distance grid, direction grid, and allocation grid (ESRI, 1992b)
44
Besides, two additional grid files will be generated while performing the EUCDISTANCE command, i.e. direction grid and allocation grid (see Figure 3.6). The direction grid contains the calculated direction in degrees each cell center is from closet source cell center. The range of values is from 0°to 360°, with ‘0’ being reserved for the source cells. Due east (right) is 90°and the values increase clockwise (180°, south; 270°, west; and 360°, north). On the other hand, the allocation grid identifies the zone of the closest source cell (in Euclidean distance) for each cell (ESRI, 1992b). The intersection between the distance grid and the 1990 shoreline grid created earlier is a new grid. The intersection was done using the conditional statement: “If the shoreline grid cell contains the code “1990”, put the value of the distance grid at that location in the new grid, otherwise put a NODATA value there”. The result of this statement was a grid containing the distance to the nearest 1973 cell at the position of each 1990 cell. Next, we can either use Graph commands (under Arcplot module) or import the last grid file into ArcViewTM to quantify the shoreline change, of which the second method is used in this research. Finally, importing the third grid file into ArcViewTM and performing the SUMMARIZE ZONES function, we derive a table which describes the distance of shoreline change from 1973 to 1990. Although the above procedure may seem complicated, it required only five commands to execute. Figure 3.7 depicts the procedure for quantifying the shoreline change by using this alternative way, and the demonstration of results will be presented in section 4.5.
45
1973 and 1990 shoreline (Arc)
SUMMARIZE ZONES
(ArcView)
1973 and 1990 LINEGRID
Distance grid, direction grid, and allocation grid (Arc/Info)
shoreline (Grid)
EUCDISTANCE (Arc/Info)
Figure 3.7: Procedure for quantifying the shoreline changes
46
CHAPTER 4 PRESENTATION AND ANALYSIS 4.1 Introduction
Data
Functional Model
Application
Identification of priority data sets
Geographic Information System
Erosion Monitoring
Filtering and shifting of data
Geological materials of shoreline
Predicting shoreline positions
Manipulation of data within Arc/Info
Terrain slope and bathymetric slope
Hazard predicting and coastal construction permitting
Incorporation of data within ArcView
Structural Protection
Hot link
and others
Figure 4.1: Linkage of data model, functional model, and applications
In this chapter, we are going to present the results from the implementation of GIS regarding the erosion status in the study area, and analyze the responses of shoreline change. On the other hand, the analysis of results will be based on the operations of basic 48
functions, such as query, select by theme, spatial analysis, network analysis etc. Presenting a general perspective of this research, Figure 4.1 depicts the linkage of data model, functional model (analysis of erosion causes), and applications. The presentation of results and analysis also follows this linkage. On the other hand, an alternative method for quantifying the shoreline change and calculating area of eroded land was also presented in this chapter. Considering the details of demonstration, only about 700 meters of shoreline in the study area was employed for implementing this model. 4.2 Data Model
Figure 4.2: Spatial database of the study area
All coverages were imported into ArcViewTM from Arc/InfoTM with shape files (for Roads, Hydrography, Hypsography, Buildings, and Boundary) or grid file (for DEM) format, then we overlaid all layers (see Figure 4.2). Next, the erosion attribute data, such 48
as Frame Number, Transect Number, Recession Distance, Recession Rate etc., will be linked to the shoreline theme, and this is fulfilled with join or link operation. Since dynamic segments of the shoreline are generated, changes of shoreline related to characteristics such as recession rate, geological materials for shoreline (deposits), structural protection distribution etc. can be implemented by adjusting or redefining the segments with the existing route. Table 4.1 illustrates the properties of themes for spatial data and the sources for joining attribute data in this research. Feature
Theme Roads(shape, Fnode,
Class Line
Data Set rh_rd.shp
Source of
Source of
Spatial Data
Attribute Data
DLG file
After build AAT
A line theme
(Arc
of main roads
Tnode,
Length, Rd_id) Boundary(Shape,
Attribute
Description
Table) table Polygon
rh_bd.shp
DLG file
After
build
PAT
A
polygon
Area, Perimeter,
(Polygon Attribute
theme of city
Bd_id, Ft_name)
Table) table
boundaries
Buildings(Shape,
Polygon
rh_bu.shp
Area, Perimeter,
PAT
polygon
After
1:24,000 maps
(Polygon Attribute
theme
Table) table
the buildings
Bu_id)
build
A
Digitized from
shows
within coastal zone Hydrography( Shape,
Fnode,
Tnode,
Length,
Line
rh_hy.shp
DLG file
After build AAT
A line theme
(Arc
of all water
Attribute
Table) table
bodies
After build AAT
A line theme
pe, Fnode, Tnode,
(Arc
of contours
Dxf_elevation,
Table) table, and
Elevation_m)
join items
Hy_id) Hypsography(Sha
Line
rh_hp.shp
DLG file
Attribute
Continued Table 4.1: Themes properties 49
Table 4.1 (Continued) Feature
Theme
Class
Rhgrid(Count,
Grid
Data Set grid10
Source of
Source of
Spatial Data
Attribute Data
Grid
Elevation value for
Grid file for 10
cells only
meters cell-size
Bathymetry
After build AAT
A
line
theme
from
(Arc
presents
the
NGDC/NOAA
Table) table
contour
of
Elevation_value
generated
)
from
file
Description
Hypsography Bathymetry(Sha pe,
Line
rh_bat.shp
Fnode,
Tnode,
Attribute
bathymetric data
Elevation ) Rhgrid(Count,
Grid
grid10_bat
Elevation value for
Grid file for 10
cells only
meters cell-size
Extracted
After build AAT
A
line
theme
from DLG file
(Arc
presents
the
Grid
Elevation_value
generated
)
from
file
Bathymetry Previous(shape, Fnode,
Line
previous.shp
Tnode,
Length, Pr_id)
Attribute
Table) table
range
of
previous work Frame487_008( Shape,
Line
487_008.shp
Fnode,
Digitized from
After build AAT
A line theme of
1:24,000 maps
(Arc
frame
Line
From,
To,
Frame,
Trans,
Std, Str, Yrs,
from
Table) table
487_008
Dynamic
Route
Attribute
A route theme
487_008 and
Segmentation
Table and Section
for representing
Erosion
implemented
Table
erosion rate in
Tnode, Length) Rate_73_90(
Attribute
Link
the
will
generated
Tables
for
study area
building routes
etc.) Shoreline_mat(
Digitized from
After build AAT
A
geological
(Arc
describes
Seg_no,
map of Lake
Table) table
Category)
Erie
From,
Line
rh_de.shp
To,
Structure( Shape, Str_No, Str_Type,
compiled from
Attribute
Qty,
coastal
and structure table
area
map of 1973
50
the
material Join
Date, Condition)
struc_pt.shp
theme
shoreline
Digitized and
Length,
Point
Attribute
line
PAT
(Point
A point theme
Table)
presents
the
position
of
structures
Once the event table is created, it can be linked explicitly to the shoreline. This allows the database to be queried, viewed, and displayed, using the ArcPlot module of Arc/InfoTM. 4.3 Functional Model In this phase of research, we will focus on the analysis of erosion causes based on the function model of GIS. As we stated in Chapter 1, many factors act together to result in erosion and some of the causes are discussed in the following sections, including geological materials, terrain slope, and effects of human activities. Other erosion factors, especially time series data, were not introduced to this research because the integration of these different scale data is not possible at this moment. 4.3.1 Geological Materials of Shoreline Except for the force of wave, shoreline geology has direct impact to erosion. According to the investigation of ONDR, 74% of lakeshore is composed of easily eroded materials (e.g. sand, till, and clay) in Lake Erie. In the study area, most of reaches of shoreline are composed of sand/till- or clay/till-bluff and are easily eroded. The average bluff height is 25-30 feet from Cranberry Creek to Old Woman Creek, 10-15 feet from Old Woman Creek to Huron River, 6-15 feet from Huron River to Sawmill Creek, respectively. The reaches of Sheldon Marsh are composed of sand beaches. An event table for implementation of geological materials is shown in Table 4.2, of which the data are compiled from “Cross section of shore stratigraphy, Erie and Sandusky Counties” (Carter and Guy, 1980) and “Engineering geology maps of the Ohio
51
ROUTELINK#
FROM(m)
TO(m)
CATEGORY
1
0.0
5500.0
Till
1
5500.0
7628.0
Clay
1
7628.0
11279.0
Till
1
11279.0
11790.0
Clay
1
11790.0
14998.0
Sand
Table 4.2: Event table for shoreline materials
Figure 4.3: Geological materials of shoreline in the study area
Shoreline of Lake Erie” (Pincus, 1960). Figure 4.3 illustrates the categorized geological materials in study area, all of reaches are composed of easily eroded materials (i.e. till, 52
clay, and sand). The shoreline material for Sheldon Marsh is sand, and this could be one of reasons that resulted in severe erosion for this site. Figure 4.4 shows a relationship between geological materials of shoreline and recession rates, as might be expected. Where the shore is composed of till, rates are higher and have a greater range of values than where the shore is composed of clay. Along the sand spit, rates were either less than 1 feet/year or as high at 45 feet/year. Low rates occurred along the protected stable part of the spit, and high rates occurred along an unprotected unstable part of the spit (Sheldon Marsh).
Recession Rate and Geological Matreials 60.00
40.00 30.00 20.00
Rate(ft/yr)
50.00
10.00 0.00 08L-26 07L-31 006-28 005-26 004-24 003-18 002-11 495-14 493-26 492-26 491-12 489-22 488-11 487- 1 ERI08L ERI07L ERI006 ERI005 ERI004 ERI003 ERI002 ERI495 ERI493 ERI492 ERI491 ERI489 ERI488 ERI487 Transect No.
Sand
Clay
Till
Clay
Till
Geological Materials
Figure 4.4: Recession rate and geological materials of the study area 4.3.2 Terrain Slope and Bathymetric Slope Based on the DEM theme that generated from contour lines, some 3-D visualization, for example, slope deriving, shading, and aspect calculating can be easily 53
54
implemented. With 3-D visualization of terrain, as shown in Figure 4.5, we might have general view of terrain in study area and select a study site to find out the relationship between slop and recession rate.
Figure 4.6: Terrain slope of the study area
The slope angles (see Figure 4.6) for terrain can be derived based on the DEM theme by using slope calculation function in ArcViewTM. Derived slope identifies the slope, or maximum rate of change, from each cell to its neighbors. The output slope grid theme represents the degree of slope (e.g., 5-degree slope) for each cell location. Basically, slope angle is a measure of gradient magnitude and influences, for coastal GIS, it can be employed to analyze the relationships between recession rate and nearshore slope angles (see Figure 4.7). Similarly, we can drive slope from bathymetry of study area (see Figure 4.8) and summarize the data in Figure 4.9 55
Slope and Recession Rate 40.0 35.0
Slope(degree)/Rate(ft)
30.0 25.0 20.0 15.0 10.0 5.0 0.0 491- 491- 491- 491- 491- 491- 491- 491- 491- 491- 491- 491- 491- 491- 490- 490- 490- 490- 490- 490- 49014 13 12 11 10 9 8 7 6 5 4 3 2 1 25 24 23 22 21 20 19
Trans No.
Figure 4.7: Recession rate and terrain slope angle
Figure 4.8: Bathymetry of the study area
56
Rate SLOPE
In order to make the analysis reasonable, some parts of the unprotected stretches were implemented to analyze the relationships between recession rate and slope.
Bathy_slope and Recession Rate 6.00
Rate(ft)/Slope(Degree)
5.00
4.00
3.00 Slope
2.00
Rate 1.00
Slope 490-19
490-21
490-23
Trans No.
490-25
491- 2
491- 4
491- 6
491- 8
491-10
491-12
491-14
0.00
Figure 4.9: Recession rate and slope angle derived from bathymetry
Generally, reaches of shoreline with higher slope should have higher recession rate. But, inspecting Figure 4.7 and Figure 4.9, we can not definitely tell whether transects with higher nearshore slope angles have higher recession rate. The possible reasons are, 1) the DEM didn’t support details of terrain slope with 10 meters cell size, 2) low- or high-bluff (usually, they are perpendicular to the water levels in this reach) spreaded nearshore reaches of the study area and it was difficult to represent the terrain by using this DEM, 3) bathymetric data model either didn’t support details of nearshore lakefloor slope with 500-meters sounding data.
57
To solve this problem, firstly, either we can derive high resolution DEM and bathymetric data, or get the slope of beach or bluff (while there is no beach) with field survey. Secondly, coastal terrain model (CTM) with high accuracy (2-3 meters, Li, 1998) might substitute the DEM in this field. Thirdly, it is necessary to merge the DEM or CTM and bathymetric data to derive the slope more reasonable. At last, the bluff should be classified before deriving the slope depending on their relative positions to shoreline. Even though, nearshore slope angle can help us to analyze the relationships between recession rate and geological aspects while adding other factors, e.g. geological materials, bluff height, beach width, beach slope etc. Except for the geological materials of shoreline and slope of nearshore, bedrock elevation, shoreline orientation, beach width, and etc. should also taken into account for analyzing the erosion causes in geological point of view. Furthermore, for the database of 3-D terrain analysis and erosion modeling, there are some applications that can be applied except the slope angles. Aspect angle of terrain, for example, can be employed to represent gradient direction (e.g. direction of flow). Moreover, profile curvature indicates areas of accelerated flow and areas with decreasing flow velocity. 4.3.3 Structural Protection and Other Erosion Causes Considering the effects of human activities, one of the causes resulting in coastal erosion is structural protection, which might be the major contributor to slower or worsen erosion. Knowledgeable professionals can help to ensure successful protection against erosion. Poorly designed or improperly installed devices may be worse than nothing at all. They may actually accelerate erosion, change the ways in which the shoreline can be 58
used. For instance, the groins have produced tiny pocket beaches; seawalls have reduced the amount of sand leaching from the bluff, slowing beach formation. Larger projects, such as Huron jetties and breakwater, have built crescent beaches, but in turn, have started the downdrift shore of sand. So, structural protection should be attempted only after the site has been carefully evaluated and appropriate control measures designed.
Figure 4.10: Distribution of shoreline protection in the study area
For Erie County, structural protection of the shore began in the late 1920’s and early 1930’s, and more than 80% of shoreline is now protected by varieties of structures in the study area. Basically, seven types or compositions of structures were implemented in this site, i.e. groin, groin/seawall, jetty, seawall, breakwater, groin field, and T-groin
59
(see Figure 4.10). Data are compiled from “Structure inventory of Erie and Sandusky Counties” (Carter and Guy, 1980), and are attached in Appendix E. Though protected structures slower the erosion, structures might be damaged or destroyed by natural forces. To update the status of structural protection, investigation should be done every 3-5 years or right after the storm and heavy snow season. Besides, resident or tourist reports are also encouraged. Query can be done after joining the attribute table. System will prompt all attribute data, which include structure number, structure type, quantity, length, constructed date, condition, and remark while clicking the segments. On the other hand, coastal engineers can easily figure out the stretches that are necessary to construct new structures based on some queries. Jetties at Huron are, over time, stripping sand from the natural bar that protects Sheldon Marsh just to the west, one of the few natural areas remaining on the North Coast, as well as from the beaches of Cedar Point. On the other hand, erosion of dikes and barrier beaches increases wetlands loss, stresses wildlife, damages water quality, and limits the environment’s natural ability to absorb floodwaters (Platt, 1998). We didn’t employ related model to analyze other causes of erosion, such as wind, wave, and changing water depth because those time series data are not easy to collect with such a study area. On the other hand, the scale for these data is diverse thus makes the integration of these data difficult. 4.4 Applications This section demonstrates the applications based on this coastal GIS. These applications are some examples that the GIS can support. Besides, most of historical data 60
related to coastal erosion are compiled from Carter and Guy’s report (1980), including tabulated data, aerial photographs, recession-line map and cross-section of bluff. Though the coastal range between the previous work and this research is not completely correspondent, the comparisons can be made after compiling and integrating these data. As we mentioned in previous chapters, this research only concerned the recession of bluff line instead of the “real” shoreline. In fact, the mean high water line has been charted to represent the shoreline on nautical charts and topographic maps of coastal zone (Slama et al, 1980). So, shoreline mapping is much concerned about the dynamic water level. Based on the coastal terrain model (CTM), the predicting water-levels model should be implemented to derive the new positions of shoreline when the water level is going up or down. This model is much better than the linear model that we used in this research while predicting the future shoreline. A 3-D model like this is one of studying topics for our future research. 4.4.1 Erosion Monitoring The erosion status of the shoreline segments is illustrated as Figure 4.11 and Figure 4.12; erosion of shoreline is categorized into seven categories. The summary for erosion status of study area is shown as Table 4.3, and the recession rate was very slow for most of transects because of the implementation of structural protection. As illustrated in Table 4.4, short-term rates are typically higher and show a greater range in values than long-term rates. The higher rates between 1973 and 1990 probably reflect increased erosion during record-high lake levels of 1973 and 1985-1986 (Figure 1.3). Similarly, annual land losses due to coastal erosion did have the same situation (Table 4.5). 61
Figure 4.11: Recession rate of the study area
Huron Reach Recession Rate 60.00 50.00
Rate(ft/yr)
40.00 30.00 20.00 10.00 0.00
08L-22 07L-16 006- 2 004-25 003- 8 001-14 494- 3 492-21 490-21 488-22 487- 1 ERI08L ERI07L ERI006 ERI004 ERI003 ERI001 ERI494 ERI492 ERI490 ERI488 ERI487 Frams No.
Figure 4.12: Erosion status of the study area
62
Order
Category
Class
Length(ft)
Percentage
1
0-1
very slow
33778.19
68.65%
2
1-3
slow
4906.54
9.97%
3
3-5
moderate
2146.02
4.36%
4
5-7
rapid
1214.24
2.47%
5
7-9
very rapid
304.02
0.62%
6
9-11
0
0.00%
7
>11
6857.52
13.94%
49206.53
100.00%
extremely rapid
Total
Table 4.3: Statistics of recession rate of the study area In general, low rates in eastern of study area result from filling and often short-lived shore protection structures. Those transects with the most severely erosion problem were located in Sheldon Marsh State Natural Preserve which had the average rate higher than 45 feet/year. Not only the easily eroded materials (mainly, sand) were composed of Sheldon Marsh, but also no structural protection was implemented in this area. Furthermore, the transects with slow, moderate, or rapid recession rate were located on or close to river mouth delta, for instance, Old Woman Creek. Recession Rates
Erie County
Study Area
Study Area*
Long-term Rate (Mean)
1.6 feet/year
1< x < 3
less than 1.0 feet/year
Short-term Rate (Mean)
2.5 feet/year
7.01 feet/year
0.77 feet/year
(1973 –1990) MAX.
56.32 feet/year
56.32 feet/year
8.07 feet/year
MIN.
0
0
0
(1877 – 1973)
*Excludes Sheldon Marsh Table 4.4: Comparison for long- and short-term recession rate 63
County/Study Site
Area (ha/year)
Volume (m3/year)
Ave. Bluff Height(m)
Erie County (Mean)
0.8
46,548
5.8185
Study Area
3.1372
144,627
4.61
Study Area1
0.301
15204.4
5.05
(1877 – 1990)
1. Excludes Sheldon Marsh, 100 ha = 0.3861021 mile2 = 1 km2 2. For each transect, erosion distance (feet/year) and length of transect can form a rectangle, thus the area can be calculated. Table 4.5: Comparison for long- and short-term area of lost land
Category
Class
Length(ft)
1877-1937
1937-1973
1973-1990*
0-1
Very Slow
19700
63.86%
75.44%
77.88%
1-3
Slow
9500
30.79%
21.39%
12.66%
3-5
Moderate
1650
5.35%
3.17%
5.54%
5-7
rapid
0
0
0
3.13%
7-9
very rapid
0
0
0
0.78%
30850
100.00%
100.00%
99.99%
Total
* Sheldon Marsh State Natural Preserve is excluded in this table. Table 4.6: Summary of erosion status for different time intervals Except for Sheldon Marsh and river mouth delta, some unprotected transects with higher recession rate than before (1877-1973, see Table 4.6) might result from the adjacent structures. Entrapment of sand by the structures (e.g. jetty, groin, seawall etc.) deprived the downdrift shore of sand and led to steeper nearshore slopes and narrow beaches, thus allowing greater wave energy to reach the shore (Carter and Guy, 1980). For example, those transects are located on the right side of Old Woman Creek.
64
For each transect, the comparison of recession rate between different time intervals, i.e. 1877-1937, 1937-1973, and 1973-1990, is not possible. Not only because the position of transects is not the same for each time interval, but also the exact value of recession for time intervals 1877-1937 and 1937-1973 is not available. Instead, we created a table and charts to demonstrate the alternation for these three time intervals (see Table 4.6 and Figure 4.13).
Comparison of Recession Rate 80.00% 70.00% 60.00% 50.00% 40.00% 30.00%
1877-1937
20.00%
1937-1973 1973-1990
10.00% 1973-1990 1937-1973 1877-1937 very rapid 7-9
rapid 5-7
Moderate 3-5
Slow 1-3
Very Slow 0-1
0.00%
Figure 4.13: Comparison of recession rate between 3 time intervals
As Table 4.6 and Figure 4.13 shows, the stretches with very slow rate increases from 1877 to 1990 due to the implementation of more protection structures in past 25 years. To update the erosion status, aerial photographs should be acquired along the shoreline of Lake Erie with a period of five years for implementing the erosion
65
monitoring. Next, to collect data more effectively, high-resolution imagery (e.g. IKONOS-1) will be employed to erosion monitoring and digital shoreline mapping. In this research, dynamic segmentation was implemented for both of erosion monitoring and geological materials themes. Dynamic segmentation brings a number of further advantages to a coastal application. In particular, it presents significant advances over more traditional methods for the incorporation of temporal data within the information system. 4.4.2 Predicting Shoreline Positions
Figure 4.14: Predicting shoreline positions by using buffer operation As we mentioned in Chapter 3, two different methods are employed for predicting shoreline positions. Future shoreline of 25 years was selected because 25 years represents 66
likely conditions within a lifetime and the duration of a typical home mortgage loan (i.e. coastal infrastructure). For the first method (see Figure 4.14), buffer operation to predict future shoreline, the 1990 shoreline in the study area was used as the base shoreline upon which all future extrapolations were made. To determine the 25 years future shoreline scenarios, the recession rate of change along each transect was multiplied by the respective lengths in time (e.g. 2 ft/year * 25 year = 50 ft) to calculate a magnitude of change. These magnitudes of change can form a look up table for buffer operation. Consequently, the 1990 shoreline position at each transect was moved landward or seaward depending on the direction and magnitude of change. This procedure was completed for each transect, and the new 25-year positions were connected with line segments (the edge of buffer zones), thus producing the predicted 2015 shorelines.
Figure 4.15: Predicting shoreline positions by using end-point rate model
67
DAT File (Text File)
COAST_N (C Program)
DXF File from USGS
SHP File* (ArcView)
GC_L_DAT (C Program)
Script File (AutoCad)
GENSCR (C Program)
GEN File (Text File)
* The file need to be converted into DXF file in AutoCad, then imported to Arc/Info for converting it into shape file. Figure 4.16: Procedures to predict future shoreline by using EPR model
For second method, programming for predicting the shoreline positions (see Figure 4.15), the 1990 shoreline was also used as base shoreline and exported to DXF file. Figure 4.16 depicts the procedures to predict the future shoreline positions by using this method, and all input/output files are attached in Appendix F. All temporal files created during these procedures are ASCII files, and are easily processed and can be imported to AutoCadTM by using script command. Figure 4.17 points out that there is no significant difference between these two methods by inspecting the predicting shoreline positions. On the other hand, both results tell us that we will completely lose barriers of Sheldon Marsh State Natural Preserve after 25 years. Both methods for predicting future shoreline positions were based on the linear model, instead of more complex mathematical models, such as sin (or cosine) function model (Shao, 1998). Though future shoreline might not erode in a linear way, the linear model is easily implemented and understood. In the future, dynamic water-levels model will be used to derive the positions of shoreline for predicting shoreline positions.
68
Figure 4.17: Comparison of buffer operation and EPR model to predict future shoreline
4.4.3 Hazard Predicting and Coastal Construction Permitting Based on the shoreline of 2015, certain hazard can be predicted regarding the influence of erosion on environment, for instance, the human properties in coastal zones. Along the coast of Lake Erie, there are some houses located on eroded cliffs. Some owners of houses can not afford the structural protection or just put short-lived structures on the adjacent shores. Thus, they should pay more attention on their coastal properties affected by the coastal erosion. The circle in Figure 4.18 shows those houses are less than 25 meters from the 2015 shoreline. It can be easily implemented by using select by theme function based on coastal GIS database. Owners can easily judge whether their properties are in hazardous 69
situation or not. Furthermore, this function can be used in the processing of coastal construction permit. In places indicated as coastal erosion areas, construction permits are needed, as well as proof that the owner can provide effective erosion protection. In Ohio, the coastal management law requires ODNR to identify the Lake Erie coastal erosion areas and to enforce a permit system governing new construction and development in these areas. Therefore, Ohio’s coastal property owners have a choice: they may build outside coastal erosion areas or they may build inside them if they install effective erosion control measures (Ohio Coastal Management Program, 1997).
Figure 4.18: Hazard prediction – coastal properties affected by coastal erosion Generally, improperly sited and designed construction can destabilize or destroy the beach/dune system, resulting in loss of beaches dunes, and important natural resources (Karunamuni and Leadon, 1997). As a consequence, loss of important 70
functions in storm protection, recreation and environmental habitat may occur. Conventionally, in the review of a permit for construction of a major structure (e.g. a building) on a beach/bluff front property, it is necessary to evaluate the building location relative to the line of construction in the area. In the past, permitting engineers have used aerial photographs to determine the line of construction. Sometimes, buildings with shadows make it difficult to delineate an accurate line of construction. Since the future shoreline positions is easily implemented with GIS application, integrated the construction line and building footprints could help people to make a decision for coastal construction permitting. The GIS solution used to predict hazard and evaluate permit applications has many advantages over the conventional approach. By allowing the user to access the data interactively in the database, time can be saved for the search of data. Data analysis such as overlaying, drawing, and queries can be done instantly. 4.4.4 Hot Link There are two ways to implement the hot link. First, we can create a point event table to link the route for generating the hot link in Arc/InfoTM. The other method utilizes the hot link function in ArcViewTM,, and is employed in this research. In ArcViewTM, hot links let users access virtually data or application directly from a view. Data can be a text file, document, imagery, and project. This function is used to link the image files for shoreline theme and structure sites in this research. The images were scanned from hard-copy photos, which were taken during the site visit of the study area on May 29, 1998. The purpose for implementing this function is to get the basic knowledge regarding the environment of shorelines or structures. An example is shown 71
as Figure 4.19, that the left image is Sheldon Marsh State Natural Preserve and the right image is the hazard house in Oberlin Beach.
Figure 4.19: Example of presenting hot links function GIS is ideally suited to provide up-to-date, accurate information for these applications. For instance, relatively simple-buffering techniques could be used for predicting future shoreline positions or identifying those buildings at the time when erosion are most risk. These applications would also be able to incorporate a greater range of datasets such as cultural and recreational information. 4.5 Quantify the Shoreline Change – An Alternative Way This section will demonstrate an alternative way to calculate the recession rate. As we stated in section 2.3.5 and 3.6, this method is implemented in the GRID module of Arc/InfoTM and spatial analysis function in ArcViewTM.
72
In the first stage of analysis, two grid files were created from arc coverages of 1973 and 1990 shoreline by using LINEGRID command (see Figure 4.20). Next, EUCDISTANCE command is used to calculate the offset from the 1973 shoreline grid to 1990 shoreline grid. The generated grid looks like a buffer around the 1973 shoreline grid. As illustrated in Figure 4.21, each individual cell contained a distance value instead of buffer zones and has been color coded to reflect distance from 1973 shoreline. Moreover, two additional grid files were generated while running EUCDISTANCE command, i.e. direction grid and allocation grid.
Figure 4.20: Converted 1990 arc coverage into grid format
Finally, we imported these three grid files to ArcViewTM and performed the SUMMARIZE ZONES command (spatial analysis functions) with Euclidean distance grid theme active. We derived a table that presented the quantified value for each segment regarding the recession rate (see Appendix F.5). A graph was created based on the table to present the erosion status in the study site (Figure 4.22). Three types of values were employed in Figure 4.22, i.e. minimum, mean, and maximum, because segment might be composed of more than one cell. Next, we extracted the mean value and
73
converted them into feet; thus we can calculate the average recession rate for individual segment of the study site (see Figure 4.23).
Figure 4.21: Euclidean distance grid
Recession Distance 30.0 25.0 OUT_DIS_MI OUT_DIS_ME
15.0
OUT_DIS_MA 10.0 5.0
34
31
28
22
25
Segment No.
19
16
13
10
7
4
0.0 1
Distance(m)
20.0
Figure 4.22: Recession distance of study site from 1973 to 1990
74
Comparison of the results by using this method and the erosion data from ODNR is difficult because the distance of each transect between these two data sets is not correspondent. The dynamic segmentation will be necessary if we are going to compare these two data sets in details. But, we still can have a general idea about the differences by inspecting Figure 4.23 and Figure 4.24.
Recession Rate (ft/yr) 6.00
5.00
Rate(ft/yr)
4.00
3.00
2.00
1.00
0.00 1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Segment No.
Figure 4.23: Recession rate based on the mean recession distance from 1973 to 1990
Recession Rate from ODNR 6.00
Recession_Rate(ft/yr)
5.00 4.00 3.00 2.00 1.00 0.00 49019
49020
49021
49022
49023
49024
49025
4911
4912
4913
4914
4915
4916
4917
4918
4919
Trans No.
Figure 4.24: Recession rate from ODNR
75
49110
49111
49112
49113
49114
The differences might come from different sampling models. For each transect of the ODNR data set, recession rate is derived from the mean value of eroded distance for start- and end-point. But, for grid method, recession rate of each segment is the mean value of cells (usually, more than 2) which are adjacent to the 1973 shoreline.
Area for Eroded Land from 1973 to 1990 60.0 50.0 40.0 30.0 50.0-60.0 40.0-50.0
20.0
30.0-40.0
10.0
20.0-30.0
AREA(m2)
35
33
31
29
27
25
Segment No.
23
21
19
17
15
13
11
9
7
5
0.0-10.0 3
1
10.0-20.0
0.0
Figure 4.25: Area of lost land from 1973 to 1990
Similarly, area data can be derived from the summarized table (Appendix F.5) since the total cells for corresponding segment are available. The area for each cell is 1 m2, consequently, the area of eroded land can be calculated (the total area of eroded land is 700 m2, see Figure 4.25). On the other hand, the volume of lost land can also be calculated if we know the heights of bluff. In this study site, for example, the average of bluff height is 7.62 meters (25 feet). So the estimated volume of lost land is 5,334 m3 (i.e. 0.5334 ha). 76
Furthermore, as we mentioned in section 3.6, the direction grid and allocation grid will be created automatically while performing EUCDISTANCE command. With the similar procedures of the implementation to calculate the recession rate, the eroded direction regarding the 1973 shoreline toward 1990 shoreline can be derived.
Figure 4.26: Direction grid from 1973 to 1990
As illustrated in Figure 4.26 and 4.27, the eroded direction for each segment was presented in minimum-, average-, and maximum-value, respectively (thorough table attached in Appendix F.6). The presentation of eroded direction can be utilized to estimate the shape of historical shoreline and predict the shape of future shoreline, or to build up the model for predicting future shoreline positions while integrating the erosion data. Compared to the traditional method (i.e. the method we discussed in section 2.3.2), this method could support more subtle shoreline change. Besides, the area and volume for lost land can be derived more easily than by the traditional method (see Table 4.3.2). The GRID and Spatial Analysis techniques described above have substantially 77
increased the amount and improved the quality of data being extracted from an arc data set.
Erored direction from 1973 toward 1990 360 330 300 270
Degree
240 210
OUT_DIR_MI
180
OUT_DIR_MA OUT_DIR_ME
150 120 90 60 30 35
33
31
29
27
25
23
21
19
17
15
13
11
9
7
5
3
1
0
Segment No.
Figure 4.27: Eroded direction from 1973 to 1990
4.6 Summary and Erosion-control Suggestions Except the Sheldon Marsh State Natural Reserve area, the shore between Cranberry Creek and Old Woman Creek is now the most rapidly receding stretch not only because beach widths decreases, but also because structural protection is insufficient. With higher bluff coastal zones than average in this stretch, regular structural protection is not easy to construct. So, the combination of well-build seawalls and graded slopes is effective and may be the best means of protecting the shore between Cranberry Creek and Old Woman Creek (Carter and Guy, 1980). Considering the influences to other stretches, an evaluation before constructing a new structure should be made based on the simulation 78
of structural types, length, and width, and on other factors, such as lake-levels, direction of flow, direction of wind etc. Next stretch, between Old Woman Creek and Sawmill Creek, shore-protection structures and sand beaches have reduced the rate of recession along much of the shore. We might pay more attention to monitoring and maintaining the conditions of existing structures than to implementing new structures in this stretch. For example, the effect of the Huron harbor jetties on littoral processes is significant. The lack of sand downdrift of the jetties has reduced beach widths and increased the nearshore slope, allowing greater wave energy to reach the shore, with a consequent increase in shore erosion (Guy, 1998). For Sheldon Marsh, except that the Coastal Barrier Resources System was added (implemented by the Ohio Coastal Management Program), no extra structural protection would be implemented since it is a preserved natural area. But, the influence to wildlife habitat and the plants should be monitored because of the environmental changes. This kind of monitoring can also be implemented based on the coastal GIS database, as we did in this research. On the other hand, we should watch the area changes of coastal wetlands resulted from recession of Sheldon Marsh because the value of coastal wetland is immense. Traditional, wetland consideration efforts have been aimed at protecting waterfowl breeding sites, and to a lesser degree, fish spawning and nursery habitat. More recent efforts toward preservation are based on the knowledge that wetlands provide additional benefits, including flood control, shore erosion protection, nutrient cycling, accumulation of sediments, and the supply of fundamental material for aquatic webs (Herdendorf, 1993). 79
CHAPTER 5 CONCLUSIONS
5.1 General No one can stop erosion. But it can be slowed down and its harmful effects on property can be lessened with correct understanding and proper management. This research successfully presents a GIS solution for management of shoreline change and suggests some aspects for slowing down the erosion. Obviously, erosion is not going to stop people from buying properties or building houses by the Lake Erie shore. This is why ODNR launched the Ohio Coastal Management Program. State policies for reducing the risk of damages and loss due to coastal erosion are essential elements of any effective coastal management strategy. The purposes of this program are to promote proper design of jetties and other erosion-control structures. Furthermore, this program also emphasizes the value of natural barriers and nonstructural methods of stabilizing shorelines that work with, rather than against, natural system. Water is a magnet, and there will always be pressure to develop and redevelop the coast. The challenge is to meet and manage such pressure intelligent: to protect remaining natural areas and to save property owners and the public from avoidable disasters and ruinous erosion-related expenses. Good coastal management requires public awareness. It
80
also demands cooperative partnerships among local, state, and national governments, as well as with individual landowners and developers. With proper update and maintenance of this GIS database, it can help mangers or users to make a good decision in coastal management program. The GIS shown in this research is just a prototype of coastal-based GIS. It is continuously being refined throughout functional model and applications (review Figure 4.1) with the addition of new data source. Likewise, both functional model and applications incorporate user feedback from GIS experts on design issues and GIS novices on requirements specific to their jobs or utilization. So, customization of this GIS will be possible based on this userdriven system. 5.2 Future Considerations of Erosion Though this research only focused on parts of shoreline of Erie County, coastal erosion is now an overall problem for the Lake Erie coastal areas. What can be done to reduce shore erosion along Lake Erie coast, particularly along the most rapidly receding stretches? There are at least two ways of looking at this question, one from a local point of view and the other from a statewide point of view. Before we leap into the next step, it is necessary to create a coastal GIS database, which is similar to the database we created in this research based on a local point of view, or statewide point of view. This database might allow users to retrieve related data interactively, to analyze data, query data, and plot the analyzed data etc. For local considerations, areas with shore lengths of a few hundred feet or less can probably be most easily protected by manmade structures, particularly if the stretch is between protected areas. Implementing a GIS database might not be possible for local 81
consideration, but property owners can utilize the statewide (or nationwide) coastal GIS database. The necessity of structure or type of protection will depend on the analysis of all related factors.
A. structural method - groin
B. Nonstructural method – beach fill
Figure 5.1: Structural and nonstructural method for protection (US Army Corps of Eng.)
Figure 5.2: Cooperative and individual approach for implementing protection
82
For the statewide point of view, stretches with shore lengths of several hundred feet or more could be protected by 1) a structural method (see Figure 5.1.A), 2) a nonstructural method such as increasing the supply of sand along the stretch (see Figure 5.1.B), or 3) a combination nonstructural and structural method such as increasing the supply of sand as well as building structures to retain it. In which way to implement the protection will depend on the results of analysis. Simulation by using GIS database might be necessary while adding a new structural or nonstructural protection. When the simulation for building new protection (structural or nonstructural) is done, in many cases, we will need to give some thought to the possible effects that erosion control measures on our property will have on the property of others. As discussed earlier, many of the measures that can be taken to protect our property may result in increased erosion of the neighboring shoreline. As several property owners share a waterfront, it often makes good economic sense to cooperate in building a single device to retard or arrest erosion, such as a filled or perched beach, breakwater, groin etc (see Figure 5.2). A cooperative measure may well cost substantially less than the sum of the expenses of individual protection. Considering the countrywide or statewide point of view, sometimes, it may be wise for entire communities to cooperate in the erosion control. 5.3 Recommendations and Future Research 1. To monitor the shoreline change, aerial photographs should be acquired along the shoreline of Lake Erie with a period of five years. For getting the real-time digital data from aerial photographs, GPS might be used to acquire the coordinates of ground
83
control points (GCPs) and softcopy photogrammetry should be employed for mapping the shoreline. 2.
For the next step of this research, one-meter resolution satellite (e.g. IKONOS-1) images will be used for digital shoreline mapping. We didn’t take into account the accuracy of digital shoreline databases in this research. But, according to the National Standard for Digital Shoreline Databases, the accuracy of Coastal Terrain Model (CTM) is expected to 2-3 meters. The water level accuracy is to be submeters (Li, 1998). The stereo image strips can be used to determine the CTM, and the accuracy of the CTM is expected to be 2 meters horizontally and 3 meters vertically with GCPs. Consequently, using the high-resolution images to generate the digital shoreline is possible. Moreover, the short revisit period (1-4 days, depending on the satellites and latitude) makes it possible to map an area frequently without special flight planning and scheduling as required in aerial photogrammetric data acquisition. Besides, according to Gonzalez’s (1998) recent research, map products at 1:4,000scale could be routinely obtained if control points are being used during postprocessing. So, the high-resolution imagery has high potential for shoreline mapping and erosion monitoring.
3. For enhancing the functional model to analyze erosion causes, it is necessary to merge the DEM and bathymetry, for example, sand transport system analysis, wave current analysis, tidal current analysis etc. The different datum between DEM and bathymetry should be resolved before merging them. For instance, the datum for bathymetry of Lake Erie is IGLD 1985 (International Great Lake Datum), but for DEM of the study area is using National Geodetic Vertical Datum of 1926. 84
4. Based on 3-D analysis and erosion models, to evaluate a new structural protection is possible for future research. For example, the evaluation will be analyzed based on the simulation for implementing different combinations of structural types, length, and width, and other factors, such as high or low lake-levels, direction of flows, the energy of wave, direction of wind, and the existing structures. This kind of simulation can support engineers to make decisions in adding a protected structure, and decrease the possibilities for making a mistake. 5. Many aspects can be applied in the future based on this system. For example, the spatial analysis techniques developed in this research can be modified and used for other Lake Erie research and applications. Some related topics were proposed or implemented recently, such as preserving wildlife habitat, air and water quality monitoring, environmental changes resulted from changes of coastal wetland, site selection for a park etc (Hart et al, 1997). Moreover, we can study some successful real-time systems related to Lake Erie from Internet, such as Great Lakes Forecasting System (The Ohio State University), Shoreline Recession Rates for Lake Erie and Lake Ontario (US Army Corps of Engineers, http://hank.ncb.usace.army.mil/ GreatLakes/recession), Great Lake Environmental Research Laboratory (GLERL), etc. Embracing the trend of the Internet might be the next step of this research. This GIS database can be put on the Internet for providing real-time information regarding the shoreline erosion, hazards predicting and spatial analysis, etc.
85
BIBLIOGRAPHY Bartlett, D., R. Devoy, S. Mccall, and I. O’Connor (1997), A Dynamically Segmented Linear Data Model of the Coast, Marine Geodesy, Vol. 20, pp.137-151. Carter, Charles H. and Guy, Donald E. Jr. (1980), Lake Erie Shore Erosion and Flooding, Erie and Sandusky Counties, OHIO: Setting, Process, and Recession Rates From 1877 to 1973, pp.1-129. CHS, (1996), Fluctuations in Lake Erie – Types, Department of Fisheries and Oceans, Canadian Hydrographic Service, http://chswww.bur.dfo.ca/danp/fluctuations.html. Duffy, William and Dickson, Stephen M (1995), Using Grid and Graph to Quantify and Display Shoreline Change, ESRI’95 User Conference proceedings, ESRI, http://www.esri.com/base/common/userconf/proc95/proc95/home.htm Dusen, Charles Van (1997), Vector Based Shoreline Change Analysis, ESRI’97 User Conference Proceedings, ESRI, http: //www.esri.com /base /common /userconf /proc97 /proc97 /home.htm ESRI, (1991), Arc/InfoTM Use’s Guide V. 6.0, Arc/InfoTM Data Model, Concept, & Key Terms. ESRI, (1992a), Arc/InfoTM Use’s Guide V. 6.0, Dynamic Segmentation, Modeling Linear Features. ESRI, (1992b), Arc/InfoTM Use’s Guide V. 6.1, GRID Command References. ESRI, (1997), Understanding GIS The Arc/InfoTM Method, Self-Study Workbook, Version 7.1 for Unix and Windows NT. Fenster, M. S., R. Dolan, and J. F. Elder (1993), A New Method for Predicting Shoreline Positions from Historical Data, Journal of Coastal Research, Vol. 9, pp. 147-171. Gonzalea, Alexander (1998), Horizontal Accuracy Assessment of The New Generation of High Resolution Satellite Imagery for Mapping Purposes, Master’s Thesis, The Ohio State University, pp. 67-70. Great Lake Environmental Research Laboratory (GLERL), http://www.glerl.noaa.gov/ Great Lakes Forecasting System (GLFS), http://superior.eng.ohio-state.edu/ 86
Guy, Donald E. Jr. (1998), Personal Communication, during the site visit of study area on May 29, 1998. Halls, Joanne, Hayes, M. O., Michel, J. and Zengel, S. (1996), Natural Resource Mapping Using GIS: Coastal and Watershed Applications, ESRI’96 User Conference Proceedings, ESRI, http: //www.esri.com /base /common /userconf /proc96 /proc96 /home.htm Hansen, Michael C. (1997), The History of Lake Erie, http: //www.dnr.ohio.gov /odnr /geo_survey /lakeerie /lefact1.htm. Hart, David, Allen Miller, Bernard Memann, and Stephen Ventura (1997), Developing GIS Applications for Coastal Management in Wisconsin Local Government, ESRI’97 User Conference proceedings, ESRI, http: //www.esri.com /base /common /userconf /proc97 /proc97 /home.htm Herdendorf, Charles E. and Bolsenga, Stanley J. (1993), Lake Erie and Lake St. Clair Handbook, Wayne State University Press, Detroit, pp. 11-91 and pp. 355-380. Highman, Thomas A. (1997), A Study of Soil Joints in Relation to Bluff Erosion Along Lake Erie Shoreline, Northeast Ohio, Kent State University, pp. 3-12. Holcombe, Troy L., John S. Warren, Lisa A. Taylor, David F. Reid, and Charles E. Herdendorf (1997), Lakefloor Geomorphology of Western Lake Erie, Journal of Great Lake Research, Vol.23, No.2, pp. 190-200. Karunamuni, Anura and Leadon, M. E. (1997), An ArcViewTM Application on Coastal Construction Permitting, ESRI’97 User Conference Proceedings, ESRI, http: //www.esri.com /base /common /userconf /proc97 /proc97 /home.htm Knecht, R.W., B. Cicin-Sain and G. Fisk (1996), Perceptions of the Performance of the State Coastal Zone Management Programs in the United States, Coastal Management, Vol. 24, pp. 141-163. Li, Ron (1995), Institutional Strengthening for Shoreline Management, Second report by the GIS Specialist, AGRA Earth & Environmental Limited, Calgary, Alberta. Li, Ron (1996), Institutional Strengthening for Shoreline Management, Third report by the GIS Specialist, AGRA Earth & Environmental Limited, Calgary, Alberta. Li, Ron (1997), Geographical Information Systems for Shoreline Management - A Malaysian Experience, 1997 GIS/LIS Proceedings, pp. 322-329. Li, Ron (1998), Potential of High-Resolution Satellite Imagery for National Mapping Products, PE&RS (not been published, in queues). Ligdas, C. Nadia (1996), Menus in Arc/InfoTM: Lessons Learned from the Development of a menu-based GIS for Coastal Management. ESRI’96 (European) User Conference 87
Proceedings, ESRI, http: //www.esri.com /base /common /userconf /europroc96 /toplvl /title1.htm Mackey, Scudder D. and Guy, Donald E. Jr. (1994), Comparison of Long- And Shortterm Recession Rate Along Ohio’s Central Basin Shore of Lake Erie, 2ND Annual Lake Erie Coastal Erosion Study Workshop, USGS. Maslen, John, Jonathan Peltenburg, and Stephen Atkins (1996), Focusing in on the Firths: Can GIS fulfil its potential for Coastal Zone Management? ESRI’96 (European) User Conference Proceedings, ESRI, http: //www.esri.com /base /common /userconf /europroc96 /toplvl /title1.htm Mitasova, Helena (1996), Terrain Analysis and Erosion Modeling, US Army CERL, http://www.cecer.army.mil/grass/viz/erosion.html Ohio Coastal Management Program (1997), Coastal Erosion Area Management, ODNR, http://www.dnr.state.oh.us/odnr/relm/coastal/erosion.htm Ohio Geologic Survey (1993), The Lake Erie Coastal Erosion Problem in Ohio, http://www.dnr.ohio.gov/odnr/geo_survey/lakeerie/ Owens, D. W. (1985), Coastal Management in North Carolina, APA Journal, pp.322-329. Pincus, Howard J. (1960), Engineering Geology of the Ohio Shore Line of Lake Erie, Maps, Ohio Department of Natural Resource. Platt, Carolyn V. (1998), Land-Eating Lake, Erosion Along Lake Erie, Timeline, January – February, Ohio Historical Society, pp. 42-53. Ricketts, P. J. (1992), Current Approaches in GIS for Coastal Management, Marine Pollution Bulletin, Canada, Vol 25 1-4, pp. 82-87. Robinson, A. H., Morrison, J. C., Muehrcke, P.C., Kimerling, A. J., and Guptill, S. C. (1995), Elements of Cartography, 6th Edition, pp. 225-228, 230-231. Shao, Guofan, Young, D. R., Porter, J. H., and Hayden, B. P. (1998), An Integration of Remote Sensing and GIS to Examine the Responses of Shrub Ticket Distributions to Shoreline Change on Virginia Barrier Islands, Journal of Coastal Research, Vol.14 No.1, pp. 299-307. Shaw, Barbara and Allen, James R. (1995), Analysis of a Dynamic Shoreline at Sandy Hook, New Jersey Using a Geographic Information System, 1995 ACSM/ASPRS Annual Convention & Exposition Technical Papers Vol.2, pp.382-391. Slama, C. C., Theurer, C., and Henriksen, S. W. (1980), Manual of Photogrammetry, American Society of Photogrammetry, VA. 88
Trenhaile, A.S. (1997), Coastal Dynamics and Landforms, Clarendon Press, Oxford, pp. 13-86. US Army Corps of Engineers (1993), Great Lakes Erosion Fact Sheet, Great Lakes Hydraulics and Hydrology Branch, http://sparky.nce.usace.army.mil/hes/erosfact.html; Low Cost Shore Protection, http://sparky.nce.usace.army.mil/shore.protection /lcshmpg.html Vincent, Robert K. (1997), Fundamentals of Geological and Environmental Remote Sensing, pp. 274-281. Worboys, Michael F. (1997), GIS – A Computing Perspective, pp. 343-352.
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APPENDIX A TABULATED EROSION DATA The elapsed time is 17 years (1973-1990) for these tables, and the original files are from ODNR with dBASE IV format. Only some parts of tables are attached here. FRAME ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI487 ERI488 ERI488 ERI488 ERI488 ERI488
TRANS 487- 1 487- 2 487- 3 487- 4 487- 5 487- 6 487- 7 487- 8 487- 9 487-10 487-11 487-12 487-13 487-14 487-15 487-16 487-17 487-18 487-19 487-20 487-21 487-22 487-23 487-24 487-25 487-26 487-27 487-28 488- 1 488- 2 488- 3 488- 4 488- 5
STD 16.10 3.40 42.80 30.30 34.10 31.80 9.00 14.80 0.00 17.80 18.80 25.80 14.00 9.90 12.60 8.00 5.00 0.00 5.60 0.00 0.00 0.00 0.00 0.00 26.00 5.20 7.90 11.00 4.80 0.00 5.20 6.30 17.00
STR 0.95 0.20 2.52 1.78 2.00 1.87 0.53 0.87 0.00 1.05 1.11 1.52 0.83 0.58 0.74 0.47 0.29 0.00 0.33 0.00 0.00 0.00 0.00 0.00 1.53 0.31 0.47 0.65 0.28 0.00 0.31 0.37 1.00
YRS 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 90
FRAME ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI488 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI489 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490
TRANS 488-12 488-13 488-14 488-15 488-16 488-17 488-18 488-19 488-20 488-21 488-22 488-23 488-24 488-25 488-26 488-27 489- 1 489- 2 489- 3 489- 4 489- 5 489- 6 489- 7 489- 8 489- 9 489-10 489-11 489-12 489-13 489-14 489-15 489-16 489-17 489-18 489-19 489-20 489-21 489-22 489-23 490- 1 490- 2 490- 3 490- 4 490- 5 490- 6 490- 7 490- 8 490- 9 490-10
STD 0.00 0.00 13.90 46.50 32.00 25.10 32.30 41.00 24.90 13.40 14.00 0.00 14.00 1.60 17.20 11.00 14.60 34.20 27.30 32.80 16.20 19.10 16.30 25.80 25.80 8.20 25.00 10.90 16.40 29.20 31.90 20.30 8.60 15.10 6.90 7.00 12.90 15.40 15.00 14.20 6.80 6.40 7.80 0.00 0.00 0.00 0.00 12.10 11.80
STR 0.00 0.00 0.82 2.73 1.88 1.48 1.90 2.41 1.46 0.79 0.82 0.00 0.83 0.09 1.01 0.65 0.86 2.01 1.61 1.93 0.95 1.12 0.96 1.52 1.52 0.48 1.47 0.64 0.96 1.72 1.87 1.19 0.51 0.89 0.40 0.41 0.76 0.91 0.88 0.84 0.40 0.38 0.46 0.00 0.00 0.00 0.00 0.71 0.69
YRS 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 91
FRAME ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI490 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491 ERI491
TRANS 490-12 490-13 490-14 490-15 490-16 490-17 490-18 490-19 490-20 490-21 490-22 490-23 490-24 490-25 491- 1 491- 2 491- 3 491- 4 491- 5 491- 6 491- 7 491- 8 491- 9 491-10 491-11 491-12 491-13 491-14 491-15 491-16 491-17 491-18
STD
STR
YRS
0.00 0.00 5.20 0.00 0.00 0.00 0.00 15.20 8.60 64.40 93.60 75.10 90.10 79.20 91.20 83.90 78.20 81.30 68.10 59.10 74.60 86.40 55.70 34.90 22.40 8.40 9.50 0.00 0.00 0.40 2.40 9.00
0.00 0.00 0.31 0.00 0.00 0.00 0.00 0.90 0.51 3.79 5.50 4.42 5.30 4.66 5.37 4.94 4.60 4.78 4.01 3.47 4.39 5.08 3.28 2.05 1.32 0.50 0.56 0.00 0.00 0.02 0.14 0.53
17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
FRAME:
Frame number of aerial photograph
TRANS:
Transect number
STD:
Recession distance (feet) between 1973 and 1990
STR:
Recession rate (feet/year) between 1973 and 1990
YRS:
Elapsed years
92
APPENDIX B Previous Work - Shore Erosion Study in Study Area Extracted from Carter and Guy (1980), Lake Erie Shore Erosion and Flooding, Erie and Sandusky Counties, OHIO: Setting, Process, and Recession Rates From 1877 to 1973. Huron reach This reach covers nearly 7 miles of shore between Cranberry Creek and Sawmill Creek (pl. 1). Cottage associations and trailer courts occupy the shore zone between Cranberry Creek and Old Woman Creek, and Huron, the only urban area, occupies the remainder of the reach west of Old Woman Creek. Huron is a Great Lakes port with three important industries: the Huron Lime Co., the Pillsbury Co., and the New York Central and St. Louis Railroad iron docks. Long (1,350 and 3,000 ft) jetties protect the Huron Harbor entrance, and a diked disposal site is attached to the west jetty. A city park just west of the harbor jetties provides the only public access to the shoreline, and the first national estuarine sanctuary on the Great Lakes is located at Old Woman Creek. The stretch from Cranberry Creek to the Huron River is fronted by discontinuous beaches bordered by sand nearshore; the stretch from the Huron River to Sawmill Creek is fronted by pocket beaches bordered by shale nearshore. Numerous groins and seawalls are found along the shoreline, especially west of the Huron River. The shore in general is composed of till capped by glaciolacustrine sediments. Recession rates range from very slow to moderate. Physical setting, circa 1973 Nearshore zone. The Cranberry Creek to Huron River stretch is characterized by 1 to 10 ft of sand covering till of shale; this sand is thickest near the Huron 93
east jetty. Along this stretch, water depths 500 ft offshore range from 7.0 to 9.5 ft. A sand bar lies across each of ranges 16, 17, 19, and 20, and two bars lie across range 21. The Huron River to Sawmill Creek stretch is characterized by shale; however, from range 23 to range 26 the shale (or till in places) is covered by very fine to fine sand near the shore. Water depths 500 ft offshore range from 4.5 to 10.5 ft. A nearshore bar was mapped across ranges 24 and 25. Beach zone. From Cranberry Creek to the Huron River, narrow to medium beaches are separated by stretches without beaches. A large beach, 50 – 150 ft wide by 4,950 ft long, is trapped just east of the Huron jetties. From the Huron River to Sawmill Creek, beaches are small or nonexistent; the existing beaches are in embayments. Shoreline beach thickness along this reach ranges from 2.5 to > 3.5 ft. Shore zone. The shore is a rolling upland plain that slopes gently lakeward to intersect bluffs or slopes. The lakeshore relief decreases gradually from east to west, with bluffs and slopes 30 ft high along the eastern section of the reach and banks no more than 15 ft high along the western section of the reach. From Cranberry Creek to Old Woman Creek, ill makes up the lower half of the shore and glaciolacustrine sediments the upper half (pl. 2). However, as the contact between the till and glaciolacustrine sediments dips to the west, the proportion of glaciolacustrine material in the shore increases. From the Huron River the Sawmill Creek the easily eroded glaciolacustrine clay is the principal shore deposit. Physical processes, circa 1973 Rotational slumps are the main result of wave erosion along the shore from Cranberry Creek to just west of Old Woman Creek (range 20). However, debris flows and block falls are locally important in the glaciolacustrine sediments just east of Oberlin Beach (range 18). West of the Huron River, extensive shore protection has essentially eliminated the mass wasting of shore deposits. Historic perspective: 1877, 1937, 1973 Land use. The shore zone has changed from primarily agricultural land use to urban land use since 1877. Earliest urban development began west of the Huron River and has progressed eastward along the reach. 94
Shore-protection structures. The only stickout structures shown on the 1877 maps were the two Huron jetties. These jetties, the largest structures built along this reach, were constructed between 1827 and 1831. Major modification of the jetty complex in 1908 included construction of a new 1,800–ft east jetty, deepening of the channel to 19 ft below Low Water Datum, and extension of the west jetty by 240 ft.. Between 1933 and 1935, further modification of the harbor complex included a 1,200–ft extension of the west jetty and deepening of the channel to 25 ft below Low Water Datum (U.S. Army, corps of Engineers, 1946, pl11, table II). Structural protection of the shore began in the late 1920’s and early 1930’s. At least 50 stickout structures had been built by 1937; these structures were concentrated just west of Cranberry Creek and between the Huron River and Sawmill Creek. Of the 106 structures inventoried (U.S. Army, Corps of Engineers, 1953a, pl. 1) in 1949, one was built before 1900, 52 between 1900 and 1937, and 46 after 1937. The date of construction of 7 structures was not indicated. In 1937, 3 percent of the shore had dense structural protection and 32 percent had moderate structural protection. Between 1937 and 1973 the number of stickout structures increased to 79. The amount of shore with dense structural protection rose to 36 percent and the amount of shore with moderate structural protection decreased to 28 percent. Each end of the shore between Cranberry Creek and Old Woman Creek is protected by groins and seawalls, with 5,050 ft of the intervening 7,450 ft of shore unprotected. From Old Woman Creek to Sawmill Creek, seawalls are the most common structure and front 58 percent of this stretch. Beaches. A comparison of 1877 and 1958 beaches shows a major change in beach distribution as well as a decrease in beach with. Since construction of the Huron jetties, sand has accreted along about 5,000 ft of the shore just to the east. In 1877, a nearly continuous narrow to wide beach extended from Cranberry Creek to Sawmill Creek. In 1937, a continuous beach extended from Cranberry Creek to the Huron River, but from the Huron River to Sawmill Creek beach widths had decreased, and there were stretches without beaches. By 1968 and 1973, the once-continuous beach between Cranberry Creek and the Huron River was broken by stretches without beaches, and the 95
remaining beaches had decrease in width. From the Huron River west to Sawmill Creek the length of shore without beaches increased, and 79 percent of this stretch had no beach in 1973. Shoreline shape. There have been no major changes in the shoreline shape since 1877 because recession has paralleled the earlier shoreline shape (pl. 3, sheet 1). During the 1877-1937 period, the shoreline became more deeply embayed east and west of the shale headland west of the Huron River. During the 1937-1973 period, several small embayments developed along the shoreline between Cranberry Creek and Old Woman Creek because of differential erosion adjacent to long groins; the largest embayment developed downdrift of structure no. 94. Recession History The modal recession rate for this reach was very slow for both the 1877-1937 period and the 1937-1973 period. The range in rates, very slow to moderate, was also the same or both periods. However, the shoreline distribution of these rates changed between the periods. 1877-1937 The modal recession rate was very slow, and the range in rates was very slow to moderate. In general, the stretches with lower recession rates were between Cranberry Creek and the Huron River, and the stretches with higher rates were between the Huron River and Sawmill Creek. There were three stretches west of the Huron jetties which receded at a moderate rate: a 450-ft stretch just west of Huron City Park, a 600-ft stretch west of range 30, and a 600-ft stretch adjacent to Sawmill Creek (pl, 3, sheet 1). The stretch west of Huron city Park was structurally unprotected throughout the period. In 1922, two T-groins (part of structure no. 133) were built along the stretch west of range 30, but recession probably continued, as the groins appear to have been outflanked and subsequently repaired. Before 1937, four more groins and a seawall (structure no. 133) were built along this stretch, but there is evidence of erosion behind the wall. The stretch adjacent to Sawmill Creek was also unprotected until 1922, at which time two groins (structure nos. 137, 138c) were built. Recession appears to have continued despite the groins, as the shore is embayed between the groins, and the groins have been outflanked and repaired. 96
1937-1973 The modal recession rate was very slow and the range in rates was very slow to moderate. During this period, recession rates increased along the shore between Cranberry Creek and Old Woman Creek, remained the same between Old Woman Creek and the Huron River, and decreased west of the Huron River. Moderate rates of recession were mapped along four stretches: a 100-ft stretch west of range 15; a 150-ft stretch between range 17 and 18; and two stretches, 100 ft and 650 ft long, between ranges 24 and 25. The stretch west of range 15 was just downdrift of the most westerly of 15 groins (structure nos. 91-94) fronting the shore between Cranberry Creek and range 15. The receding shore had no structural protection and no beach. The stretch between range 17 and 18 had a narrow beach but was otherwise unprotected and lay downdrift of a series of groins and groin fields (structure nos. 91-99) along the shore between Cranberry Creek and this site. The stretches between ranges 24 and 15, which were downdrift of the Huron jetties, had narrow beaches but lacked structural protection. Interpretation of recession 1877 – 1937 The Huron jetties were probably the cause of recession at moderate rates along the three stretches downdrift of the Huron jetties. Entrapment of sand by the east jetty deprived the downdrift shore of sand and led to steeper nearshore slopes and narrower beaches, thus allowing greater wave energy to reach the shore. The narrow beach along the unprotected stretch west of Huron City Park did not provide enough protection to prevent shore erosion, and the groins built in 1922 at the stretch west of range 30 (part of structure no. I33) and at the stretch adjacent to Sawmill Creek (structure nos. 137, 138c) did not provide effective protection because of insufficient sand in the littoral system. The low recession rates from Cranberry Creek to the Huron River during this period appear to be the result of an ample sand supply – a continuous sand beach extended from Ceylon Junction (range 16) to the Huron River as late as 1946 (U.S. Army, Corps of Engineers, 1946, p.15)–as well as increased structural protection in the 1920’s and 1930’s. The sand trapped by the Huron east jetty had a profound effect in reducing the recession rates along about 5,000 ft of shore to the east (see discussion of 97
beach changes). The stretches between the Huron River and Sawmill Creek with low recession rates were generally structurally protected for most of this period. 1937-1973 This moderate rates of recession along four stretches of shore resulted primarily from the disruption of the littoral sand transport by groins and jetties. Sand was trapped by these structures or was deflected too far offshore to protect the adjacent downdrift shore. The general increase in recession rates between Cranberry Creek and Old Woman Creek was due to narrower beaches and inadequate manmade shore protection. As in the 1877-1937 period, about 5,000 ft of shore just east of the Huron jetties was protected by a medium to wide sand beach. The general decrease in rates between the Huron River and Sawmill Creek was due to greatly increased shore protection. Recession forecast (2010 A. D.) Between Cranberry Creek and Old Woman Creek, recession threatens several cottage associations and a trailer court; continued recession even at very slow of slow rates will result in the loss of buildings within 5 to 15 year. From Old Woman Creek to Sawmill Creek the shore is reasonably well protected, and shore recession is not anticipated as long as the structures are maintained. Summary and erosion – control suggestions The shore between Cranberry Creek and Old woman Creek is now the most rapidly receding stretch because of decreased beach widths and moderate structural protection. Between Old Woman Creek and Sawmill Creek shore-protection structures and sand beaches have reduced the rate of recession along much of the shore. The combination of well-built seawall and graded slopes is effective and may be the best means of protecting the shore between Cranberry Creek and Old Woman Creek. The effect of the Huron harbor jetties on littoral processes is significant. The jetties have blocked the movement of sand, forming a large protective beach along the updrift shore and starving the downdrift beaches. The lack of sand downdrift of the jetties has reduces beach widths and increased the nearshore slope, allowing greater wave energy to reach the shore with a consequent increase in shore erosion. Although the most 98
pronounced effects of the jetties are seen adjacent to the structures, the jetties probably influence the shoreline for at least 1-mile updrift and for at least 4 to 5 miles downdrift. The influence of the Huron jetties may extend as far as the Cedar Point jetty.
99
APPENDIX C Source Codes for AutoLisp Program – Assign the elevation value for contours ;******************************************************* ;** This program is used to change contour elevation.
**
;**
Designed by Jung-Kuan Liu
**
;**
The last update date:02-21-1998
**
;******************************************************* (defun c:chele () (setq X (ssget)) (setq ID 0) (repeat (sslength X) (setq IT (entget (ssname X ID))) (redraw (ssname X ID) 3) (cond ((assoc 38 IT) (setq EL(getreal "\nInput the elevation :")) (if EL (entmod (subst (cons 38 EL) (assoc 38 IT) IT)) );END OF IF (redraw (ssname X ID) 4) );END OF CONTOUR FOUND (t (setq EL(getreal "\nInput the elevation :")) (if EL (entmod (reverse (cons (cons 38 EL) (reverse (entget (ssname X ID)))))) );END OF IF 100
(redraw (ssname X ID) 4)) );END OF COND (cond ((assoc 62 IT) (princ "0=BLOCK 256=BYLAYER 1=R 2=Y 3=G 4=C 5=B 6=V 7=W 10=BROWN") (princ (strcat "\nCHOOSE NEW COLOR : " ) ) (setq NUM (getint)) (if NUM (entmod (subst (cons 62 NUM) (assoc 62 IT) IT)) );END OF IF (redraw (ssname X id) 4) );END OF COLOR FOUND (t (princ "0=BLOCK 256=LAYER 1=R 2=Y 3=G 4=C 5=B 6=V 7=W 10=BROWN") (princ "\nCHOOSE NEW COLOR : ") (setq NUM (getint)) (if NUM (entmod (reverse (cons (cons 62 NUM) (reverse (entget (ssname X id)))))) );END OF IF (redraw (ssname X id) 4) );END OF NO COLOR FOUND );END OF COND (setq ID(1+ ID)) );END OF REPEAT (print) );END OF PROGRAM 101
APPENDIX D Sources Codes for C++ Program – Predicting the Shoreline Positions D.1 GC_L_DAT.C – To generate a text file (extracts X, Y value) from DXF file /* TO generate a DAT file from AutoCad DXF file. Issued by Jung-kuan liu Last updated on Apr.20,’98 */ #include #include #include #define maxnum 600 #define maxlen 120 typedef struct { double x; double y; } vertex; struct polyline { vertex vetx[maxnum]; int vetxnum; int flag; int type; } pline[maxnum]; void main() { char ch_x[20], ch_y[20]; char infnm[20], outfnm[20], s[maxlen]; int num=-1; int vetxnum; int i, j; int tag; int flag; FILE *fp, *fq; 102
printf("\nInput DXF_file- Output DAT_file:\n"); scanf("%s %s",infnm,outfnm); printf("\nRunning............\n"); if ( (!(fp = fopen(infnm, "r"))) || (!(fq = fopen(outfnm,"w")))) { printf ( "Can't open file: %s or %s\n", infnm, outfnm); exit(2); } fseek(fp, 0, 0); fseek(fq, 0, 0); fgets(s,maxlen,fp); while (strstr(s, "EOF") == NULL) { vetxnum = 0; flag = 0; tag = 0; num++; pline[num].flag = 0; pline[num].type = 0; fgets(s, maxlen,fp); while ( !strstr(s, "SEQEND")) { if (!flag) if (atoi(s) == 70) pline[num].flag = 1; if (strstr(s, "LINE")) tag = 1; if (tag == 1) if (strstr(s, "ROAD")) pline[num].type =1; if (strstr(s, "VERTEX")) { flag = 1; /* get x */ fgets(s, maxlen, fp); while (atoi(s) != 10) { fgets(s, maxlen, fp); } fgets(s, maxlen, fp); sscanf(s, "%s", ch_x); 103
pline[num].vetx[vetxnum].x = atof(ch_x); /* get y */ fgets(s, maxlen, fp); fgets(s, maxlen, fp); sscanf(s, "%s", ch_y); pline[num].vetx[vetxnum].y = atof(ch_y); vetxnum++; } if (strstr(s, "EOF")) { break; } fgets(s, maxlen, fp); } pline[num].vetxnum = vetxnum; } fprintf(fq, "Input file: %s\n", infnm); fprintf(fq, "Output file: %s\n", outfnm); fprintf(fq, "Number of polylines: %d\n\n", num); for (i = 0; i < num; i++) { if (pline[i].type == 1) { if (pline[i].flag == 1) fprintf (fq,"\nId: %d Road %d Status: Close\n", i+1, pline[i].vetxnum); else fprintf (fq,"\nId: %d Road %d Status: Open\n", i+1, pline[i].vetxnum); } else { if (pline[i].flag ==1) fprintf (fq,"\nId: %d Contour %d Status: Close\n", i+1, pline[i].vetxnum); else fprintf (fq,"\nId: %d Contour %d Status: Open\n", i+1, pline[i].vetxnum); } fprintf(fq, " X Y\n"); for (j = 0; j < pline[i].vetxnum; j++) fprintf (fq,"%lf %lf\n", pline[i].vetx[j].x, pline[i].vetx[j].y); } fclose(fp); fclose(fq); } 104
D.2 COAST_N.C – To calculate the shoreline positions based on the EPR model /* To generalize the coast line based on the End-Point Rate Issued by Jung-kuan Liu Last updated on Apr. 28, 1998 */ #include #include #include #include #define maxnum 575 #define maxlen 60 typedef struct { double x; double y; double str; } vertex; struct polyline { vertex vetx[maxnum]; int vnum; char status[20]; } pl, newpl; int plnum, yrs; FILE *fp, *fq; int flag(double k); void generalize(int i); void main() { char ch_x[20], ch_y[20], ch_str[20],status[20], tmp[20], ch_num[20], type[20]; char infnm[20], outfnm[20], s[maxlen]; int num = 0; int i, j, year; printf("\nInput DAT_file(.dat)- Output Dat_file(.gen)- \n"); scanf("%s %s", infnm, outfnm); printf("\nPlease input the year for new coast line (e.g. 2015)\n"); scanf("%d",&year); printf("\nRunning........\n"); 105
if ( (!(fp = fopen(infnm, "r"))) || (!(fq = fopen(outfnm, "w")))) { printf ( " Can't open file: %s or %s\n", infnm, outfnm); exit(2); } fseek(fp, 0, 0); fseek(fq, 0, 0); yrs = year-1990; fgets(s, maxlen, fp); while (!strstr(s, "Number of polylines")) { fgets(s, maxlen, fp); } sscanf(s, "%s %s %s %s", tmp, tmp, tmp, ch_num ); plnum = atoi(ch_num); i = 0; fprintf(fq, "Input file: %s\n", infnm); fprintf(fq, "output file: %s\n", outfnm); fprintf(fq, "# of polylines before generalizetion: %d\n\n", plnum); while (fgets(s, maxlen, fp) != NULL) { if (strstr(s, "Id")) { sscanf(s, "%s %s %s %s %s %s", tmp, tmp, type, ch_num, tmp, pl.status); pl.vnum = atoi(ch_num); fgets(s, maxlen, fp); for (j = 0 ; j < pl.vnum; j++) { fgets(s, maxlen, fp); sscanf(s, "%s %s %s", ch_x, ch_y, ch_str); pl.vetx[j].x = atof(ch_x); pl.vetx[j].y = atof(ch_y); pl.vetx[j].str = atof(ch_str); } generalize(i); i++; } } fclose(fp); fclose(fq); } 106
/***********************************************************/ void generalize(int id) { double x1, x2, y1, y2, t; int num = 0; int j; num = 0; strcpy(newpl.status, pl.status); newpl.vetx[num].x = pl.vetx[0].x; newpl.vetx[num++].y = pl.vetx[0].y; for (j = 1; j 0) return 1; else if (det == 0) return 0; else return -1; }
107
D.3 GENSCR.C – To convert the generalized file into AutoCadTM script file /* To generate the AutoCad script file from GEN_file. Issued by Jung-kuan Liu Last updated on Apr.28,’98 */ #include #include #include #define maxnum 600 #define maxlen 150 typedef struct { double x; double y; } point; struct polyline { point vetx[maxnum]; int vnum; char status[20]; } pl; int plnum; double minx, miny, maxx, maxy; FILE *fp, *fq; void compare(double x, double y); void main() { char ch_x[20], ch_y[20], status[20], tmp[20], ch_num[20]; char infnm[20], outfnm[20], s[maxlen]; int num = 0; int j; int flag = 0; double x, y; double dx, dy; printf("\nCreate AutoCad Script file for input DATA file\n"); printf("\nInput DAT_file and Output SCR_file\n"); scanf("%s %s", infnm, outfnm); 108
printf("\nRunning....\n"); if ( (!(fp = fopen(infnm, "r"))) || (!(fq = fopen(outfnm, "w")))) { printf (" Can't open file: %s or %s\n", infnm, outfnm); exit(2); } fseek(fp, 0, 0); fseek(fq, 0, 0); while (fgets(s, maxlen, fp) != NULL) { if (strstr(s, "Id")) { sscanf(s, "%s %s %s %s %s %s", tmp, tmp, tmp, ch_num, tmp, pl.status); pl.vnum = atoi(ch_num); fprintf(fq, "PLINE\n"); fgets(s, maxlen, fp); for (j = 0; j < pl.vnum; j++) { fgets(s, maxlen, fp); sscanf(s, "%s %s", ch_x, ch_y); x = atof(ch_x); y = atof(ch_y); if (!flag){ minx = x; maxx = x; miny = y; maxy = y; flag = 1; } else compare(x,y); fprintf(fq, "%lf,%lf\n", x, y); } if (!strcmp(pl.status, "Close")) fprintf(fq, "C\n"); else fprintf(fq, "\n"); } } dx = maxx - minx; dy = maxy - miny; fprintf(fq, "ZOOM W %lf,%lf %lf,%lf\n", minx,miny, maxx,maxy); fclose(fp); fclose(fq); } void compare(double x, double y) 109
{ if (x < minx) minx = x; else if (x > maxx) maxx = x; if (y < miny) miny = y; else if (y > maxy) maxy = y; }
110
APPENDIX E STRUCTURAL PROTECTION TABLES STR_NO
STR_TYPE
QTY LENGTH
CONS_DATE
90.0 pre-1949 280.0 1918,post-1973 pre-1949 930.0 1918,post-1973 350.0 1918
CONDITION
86 87 88 89 90
Groin Groin/Seawall Groin Groin/Seawall Jetty
1 2 2 4 2
good poor-fair poor fair-good fair-good
91
Groin/Seawall
7
1000.0 1918
92
Groin
3
200.0 1918
92a 92b 93 93a 93b 94
Groin Groin Groin/Seawall Groin Groin Groin/Seawall
1 1 2 1 1 1
1937 1937 350.0 1918,post-1973 fair-good 1937 1937 50.0 1918,post-1973 fair
94a 95
Groin Groin/Seawall
2 3
96
Groin/Seawall
1
96a 97
Groin Groin/Seawall
1 21
1937 1950.0 1930,1945-48
poor-good
98 99 100 101
Groin/Seawall Groin Seawall Groin/Seawall
5 1 1 9
850.0 post-1956 15.0 post-1956 80.0 post-1968 1450.0 1948-49,1974
fair-good poor poor fair-good
102 103
Seawall Seawall
1 1
170.0 post-1973 690.0 post-1949
104
Groin
3
15.0 post-1956
poor
105 106
Seawall Groin
1 1
450.0 post-1968 10.0
poor poor
poor-good fair
1937 1050.0 1918
poor-good
550.0 post-1956
111
poor-good
good poor-good
REMARK
groin has been repaired Seawall built after 1973 Submerged Seawall built after 1973 structures hav e been maintained three groins submerged or in ruins 70-ft seawall extends west from middle groin
Seawall built after 1973
new concrete cap on 80-ft long groin one groin maintained; others in ruins groin in ruins; seawall in good condition groins 25-60ft;much seawall new since 1973 groins 80 ft long flanked and submerged groins submerged, once there were 23 groins fronted by old breakwater center section of wall damaged eastern two groins cov ered by str_ no 102
STR_NO
STR_TYPE
QTY LENGTH
CONS_DATE
CONDITION
107 108
Breakwater Seawall
1 1
250.0 340.0 1975
poor good
109 110 111
Seawall Groin/Seawall Seawall
1 9 1
270.0 1975 630.0 pre-1973 430.0 pre-1973
good good good
112
Groin
1
113 114
Seawall Seawall
1 1
115 116 117
Seawall Seawall Jetty
1 1 2
118 119
Seawall Seawall
1 1
200.0 pre-1949 300.0 1933
fair-good good
119a Groin 120 Seawall 121 Breakwater
1 1 1
1937 400.0 1973 450.0 1929
fair good
122 123
Seawall Seawall
1 1
330.0 post-1968 530.0 1929;1945-46
fair-good fair-good
124
Seawall
1
500.0 post-1973
fair
125
Groin field
4
1975
fair
126 127 127a 128 129 130
Groin/Seawall Seawall Groin Seawall Groin/Seawall Seawall
8 1 1 1 8 1
840.0 1910-1949 670.0 1929;1946 1937 1900.0 post-1949 880.0 1948-49,1974 100.0 pre-1949
131
Groin
3
1945
132
Seawall
1
1150.0 1923
133
T-groin
6
75.0 1923
134 135
Groin Seawall
1 1
75.0 1947 1650.0 1945-47
135a Groin 136 Groin 137 Groin
1 1 1
210.0 post-1956,pre1968 300.0 1973 1650.0 post-1956,pre1968 1600.0 pre-1949 300.0 1975 1827-1932
fair good good fair-good good good
fair-good fair-good poor-fair fair good poor poor-good poor-fair good poor-good
1937 60.0 pre-1937 150.0 pre-1937 112
good poor
REMARK
submerged structure cov ers part of str_no 109 groins 15 ft long concrete wall built abov e and behind seawall storm drain outfall; stru. repaired post-1973 Seawall built of concrete east of str_no 112
East jetty 1350 ft long; relocated in 1908 concrete apron back of seawall
may hav e been a seawall at one time stone rubble used as backfill seawall constructed of earth, stone, and rubble temporary str to be replace with a breakwater groins 50-100 ft long
rebuilt since 1973 groins up to 120 ft long concrete poured down bank groins completely submerged sections repaired since 1973 groins partially submerged; "T" 50 ft long sections of seawall repaired since 1973 new concrete cap in 1975 sections of groin repaired since 1968
STR_NO
STR_TYPE
QTY LENGTH
CONS_DATE
138
Seawall
1
1850.0 pre-1949
138a 138b 138c 139 140 140a 141
Breakwater Groin Groin Seawall Seawall Jetty Seawall
1 1 1 1 1 1 1
1937 1937 1937 180.0 1973 70.0 pre-1973 1937 3100.0 1973
142 143 144
Seawall Seawall Groin/Seawall
1 1 5
900.0 1941;1973 510.0 1973 400.0 1947-48
CONDITION
poor-good
REMARK
sections of seawall repaired since 1973
fair fair good good fair fair
sev eral groins extend lakeward from seawall NASA water intake groins 10-30 ft long; two groins submerge
* Extracted from Carter and Guy’s report (1980), Appendix A. – Structure Inventory of Erie and Sandusky Counties.
113
APPENDIX F INPUTS AND OUTPUTS F.1 DXF file for 1990 shoreline, exported from shoreline theme (parts of it) 0 SECTION 2 ENTITIES 0 POLYLINE 8 MASTER_LINE 66 1 10 0.0 20 0.0 30 0.0 0 VERTEX 8 MASTER_LINE 10 377372.915 20 4582280.7929 30 0.0 0 VERTEX 8 MASTER_LINE 10 377345.9867 20 4582266.8464 30 0.0 0 VERTEX 8
VERTEX 8 MASTER_LINE 10 377260.7009 20 4582238.3442 30 0.0 0 VERTEX 8 MASTER_LINE 10 377231.1446 20 4582233.0544 30 0.0 0 VERTEX 8 MASTER_LINE 10 377201.3256 20 4582230.0 30 0.0 0 VERTEX 8 MASTER_LINE 10 377171.4207 20 4582227.5924 30 0.0 114
30 0.0 0 VERTEX 8 MASTER_LINE 10 377085.4446 20 4582201.1303 30 0.0 0 VERTEX 8 MASTER_LINE 10 377055.8946 20 4582196.1769 30 0.0 0 VERTEX 8 MASTER_LINE 10 377029.0534 20 4582183.1475 30 0.0 0 VERTEX 8 MASTER_LINE 10 376999.5564 20
MASTER_LINE 10 377318. 1539 20 4582255.5557 30 0.0 0 VERTEX 8 MASTER_LINE 10 377289.8645 20 4582245.4786 30 0.0 0
0 VERTEX 8 MASTER_LINE 10 377142.8319 20 4582218.7984 30 0.0 0 VERTEX 8 MASTER_LINE 10 377114.327 20 4582209.3677
4582178.1054 30 0.0 0 VERTEX 8 MASTER_LINE 10 376969.8641 20 4582174.9215 30 0.0 0 VERTEX 8 MASTER_LINE 10
F.2 DAT file for 1990 shoreline, converted from DXF file (1990 shoreline, parts of it) after GC_L_DAT.C was performed. X Y STR 377372.915000 4582280.792900 0.95 377345.986700 4582266.846400 0.20 377318.153900 4582255.555700 2.52 377289.864500 4582245.478600 1.78 377260.700900 4582238.344200 2.00 377231.144600 4582233.054400 1.87 377201.325600 4582230.000000 0.53 377171.420700 4582227.592400 0.87 377142.831900 4582218.798400 0.00 377114.327000 4582209.367700 1.05 377085.444600 4582201.130300 1.11 377055.894600 4582196.176900 1.52 377029.053400 4582183.147500 0.83 376999.556400 4582178.105400 0.58 376969.864100 4582174.921500 0.74 376941.305900 4582165.793600 0.47 376914.939900 4582151.490200 0.29 376893.441100 4582130.860600 0.00 376867.073500 4582119.360500 0.33 376837.952400 4582115.409300 0.00 376808.344800 4582110.366100 0.00 376778.947200 4582104.285700 0.00 376750.372900 4582095.141400 115
X Y STR 376722.028500 4582085.230000 0.00 376693.899900 4582074.752400 1.53 376665.718000 4582064.744400 0.31 376636.380200 4582058.385200 0.47 376608.470600 4582047.390500 0.65 376581.368400 4582035.204200 0.28 376555.577500 4582026.664900 0.00 376529.693400 4582019.265200 0.31 376503.730400 4582012.020100 0.37 376477.330100 4582006.632500 1.00 376450.972500 4582001.034000 0.00 376426.912500 4581989.120200 1.01 376401.017300 4581982.779800 1.71 376374.655200 4581977.732000 0.00 376348.538300 4581971.342400 1.15 376321.673300 4581969.132100 0.71 376295.901000 4581962.664900 0.00 376269.962500 4581955.677500 0.00 376243.263400 4581952.378600 0.82 376216.368900 4581952.455400 2.73 376190.324500 4581946.596200 1.88 376163.876300 4581943.212200 1.48 376138.435300 4581934.345700 1.90
0.00 F.3 GEN file for 2015 shoreline, generated from DAT file (1990 shoreline, parts of it) after COAST_N.C was performed. X 377372.915000 377346.687577 377325.372256 377294.415913 377264.322329 377233.654979 377201.737126 377171.952702 377142.831900 377116.840120 377087.764415 377057.809416 377031.815313 377000.301068 376970.465301 376942.396262 376915.993633 376893.441100 376868.078777 376837.952400 376808.344800 376778.947200 376750.372900 376722.028500 376697.969449 376666.508502 376637.138878 376610.285997 376582.243375 376555.577500 376530.342690 376504.488214 376478.853737 376450.972500 376430.327672 376404.116186 376374.655200 376350.620783
X 376269.962500 376244.029616 376216.309496 376193.468740 376165.307581 376143.199989 376113.679129 376085.836118 376058.955151 376032.097274 376004.963100 375978.666566 375951.178731 375924.051560 375899.075593 375873.989494 375836.852381 375801.025875 375766.400426 375732.208520 375701.434864 375667.657803 375630.356100 375596.416929 375562.168696 375534.148752 375496.663869 375460.244551 375424.663158 375390.678360 375356.791673 375322.338057 375286.986020 375254.191070 375220.328936 375185.863666 375150.070045 375116.249090
Y 4582280.792900 4582265.493127 4582237.761670 4582232.701437 4582223.540726 4582219.027875 4582225.982422 4582220.984381 4582218.798400 4582201.771633 4582192.996444 4582184.753876 4582177.457825 4582173.748987 4582169.314841 4582162.382217 4582149.547814 4582130.860600 4582117.055585 4582115.409300 4582110.366100 4582104.285700 4582095.141400 4582085.230000 4582063.827121 4582062.518396 4582054.885081 4582042.782188 4582033.258264 4582026.664900 4582016.993986 4582009.304454 4581999.166381 4582001.034000 4581982.223237 4581970.123457 4581977.732000 4581962.830442 116
Y 4581955.677500 4581946.177357 4581931.652885 4581932.619914 4581932.025793 4581920.674189 4581913.185694 4581917.965531 4581919.562507 4581916.324981 4581917.333400 4581908.670934 4581914.174206 4581907.776073 4581904.941948 4581897.336931 4581888.758595 4581895.233544 4581894.881441 4581902.929689 4581892.120694 4581884.166663 4581882.398430 4581883.575289 4581896.994291 4581877.921757 4581877.992687 4581880.958128 4581881.698150 4581884.494082 4581892.141207 4581898.782481 4581902.793919 4581917.280727 4581916.327883 4581910.925278 4581913.558168 4581927.309918
376322.116922 376295.901000
4581963.740119 4581962.664900
375084.361423 375051.213220
4581926.498434 4581934.129257
F.4 SCR file for 2015 shoreline, generated from GEN file (2015 shoreline, parts of it) after GENSCR.C was performed. PLINE 377372.915000,4582280.792900 377346.687577,4582265.493127 377325.372256,4582237.761670 377294.415913,4582232.701437 377264.322329,4582223.540726 377233.654979,4582219.027875 377201.737126,4582225.982422 377171.952702,4582220.984381 377142.831900,4582218.798400 377116.840120,4582201.771633 377087.764415,4582192.996444 377057.809416,4582184.753876 377031.815313,4582177.457825 377000.301068,4582173.748987 376970.465301,4582169.314841 376942.396262,4582162.382217 376915.993633,4582149.547814 376893.441100,4582130.860600 376868.078777,4582117.055585 376837.952400,4582115.409300 376808.344800,4582110.366100 376778.947200,4582104.285700 376750.372900,4582095.141400 376722.028500,4582085.230000 376697.969449,4582063.827121 376666.508502,4582062.518396 376637.138878,4582054.885081 376610.285997,4582042.782188 376582.243375,4582033.258264 376555.577500,4582026.664900 376530.342690,4582016.993986 376504.488214,4582009.304454 376478.853737,4581999.166381 376450.972500,4582001.034000 376430.327672,4581982.223237 376404.116186,4581970.123457
376269.962500,4581955.677500 376244.029616,4581946.177357 376216.309496,4581931.652885 376193.468740,4581932.619914 376165.307581,4581932.025793 376143.199989,4581920.674189 376113.679129,4581913.185694 376085.836118,4581917.965531 376058.955151,4581919.562507 376032.097274,4581916.324981 376004.963100,4581917.333400 375978.666566,4581908.670934 375951.178731,4581914.174206 375924.051560,4581907.776073 375899.075593,4581904.941948 375873.989494,4581897.336931 375836.852381,4581888.758595 375801.025875,4581895.233544 375766.400426,4581894.881441 375732.208520,4581902.929689 375701.434864,4581892.120694 375667.657803,4581884.166663 375630.356100,4581882.398430 375596.416929,4581883.575289 375562.168696,4581896.994291 375534.148752,4581877.921757 375496.663869,4581877.992687 375460.244551,4581880.958128 375424.663158,4581881.698150 375390.678360,4581884.494082 375356.791673,4581892.141207 375322.338057,4581898.782481 375286.986020,4581902.793919 375254.191070,4581917.280727 375220.328936,4581916.327883 375185.863666,4581910.925278 375150.070045,4581913.558168 117
375116.249090,4581927.309918 375084.361423,4581926.498434 375051.213220,4581934.129257 375018.798501,4581936.773746
376374.655200,4581977.732000 376350.620783,4581962.830442 376322.116922,4581963.740119 376295.901000,4581962.664900
F.5 Table for quantifying the shoreline change from 1973 to 1990 ZONE1_90_ COUNT AREA
OUT_DIS_RA OUT_DIS_ME
OUT_DIS_MI
OUT_DIS_ST
UT_DIS_ MA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
13 58 38 25 37 14 9 4 9 11 10 8 18 16 18 23 33 28 16 16 12 18 27 33 17 24 33 36 33 3 4 19 19 16 2
13.0 58.0 38.0 25.0 37.0 14.0 9.0 4.0 9.0 11.0 10.0 8.0 18.0 16.0 18.0 23.0 33.0 28.0 16.0 16.0 12.0 18.0 27.0 33.0 17.0 24.0 33.0 36.0 33.0 3.0 4.0 19.0 19.0 16.0 2.0
0.0000 0.0000 2.0000 7.0000 10.0000 17.0000 17.7200 18.6815 20.8806 19.2094 19.7990 19.2354 17.2627 17.4642 19.0000 19.6469 22.0907 22.4722 24.5153 25.3180 26.0192 24.0416 23.4094 25.0000 22.0000 22.0000 20.6155 3.6056 2.0000 4.4721 4.0000 2.8284 0.0000 0.0000 0.0000
1.0000 3.1623 7.0000 10.1980 17.0000 18.1108 18.9737 21.2132 22.2036 21.1896 20.5183 20.5183 19.2354 19.0000 20.0000 22.5610 24.3311 25.0000 26.0192 26.4764 27.8568 27.2947 25.1794 27.5136 25.0799 27.8568 28.2843 20.6155 5.0000 5.0000 4.4721 4.1231 3.0000 1.0000 0.0000 118
1.0000 3.1623 5.0000 3.1980 7.0000 1.1108 1.2536 2.5317 1.3230 1.9802 0.7193 1.2829 1.9727 1.5358 1.0000 2.9141 2.2403 2.5278 1.5039 1.1584 1.8376 3.2531 1.7700 2.5136 3.0799 5.8568 7.6687 17.0100 3.0000 0.5279 0.4721 1.2947 3.0000 1.0000 0.0000
0.2308 1.7690 4.2438 8.5336 13.6740 17.6133 18.2446 19.9268 21.6410 20.0402 19.9410 19.7095 17.9982 18.2232 19.5000 21.0647 23.3464 23.6101 25.1309 25.9660 27.0201 25.5133 24.1223 26.1104 23.1084 24.1378 25.2639 12.2071 3.1090 4.8240 4.1488 3.5924 1.0778 0.5000 0.0000
0.4213 1.0296 1.5864 1.0283 2.0518 0.4494 0.3682 0.9600 0.4736 0.5479 0.2368 0.4022 0.5352 0.4772 0.5000 0.8450 0.5548 0.6769 0.4309 0.2812 0.5747 1.0354 0.4350 0.7209 0.9415 1.7778 2.3042 5.2667 1.1286 0.2488 0.1933 0.4474 0.9981 0.5000 0.0000
*
OUT_DIS_MI:
Minimum
distance,
OUT_DIS_MA:
Maximum
distance,
OUT_DIS_RA: Data range, OUT_DIS_ME: Mean value, OUT_DIS_ST: Standard deviation; cell size = 1meter. F.6 Table for eroded direction from 1973 toward 1990 ZONE1 COUNT OUT_DIR_MI
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
13 58 38 25 37 14 9 4 9 11 10 8 18 16 18 23 33 28 16 16 12 18 27 33 17 24 33 36 33 3 4 19 19 16 2
0 0 10 6 4 4 4 9 8 45 33 24 4 4 360 3 3 8 3 3 3 17 5 5 3 339 7 15 19 27 15 15 0 0 0
OUT_DIR_M A 270 360 360 360 360 360 360 16 88 71 45 44 360 360 360 360 360 21 19 360 29 36 25 37 360 360 354 45 360 360 360 360 360 270 0
OUT_DIR_RA OUT_DIR_ME OUT_DIR_ST
270 360 350 354 356 356 356 7 80 26 12 20 356 356 0 357 357 13 16 357 26 19 20 32 357 21 347 30 341 333 345 345 360 270 0 119
55.3846 197.1035 253.7895 208.8000 303.4054 235.2143 89.5556 12.2500 63.8889 57.8182 42.5000 33.5000 38.1111 250.7500 360.0000 117.0435 28.8788 14.6071 12.0000 75.5625 14.9167 26.8333 15.0741 20.9697 35.7647 352.5417 47.4545 30.8889 295.9394 249.0000 190.5000 219.9474 171.9474 95.6250 0.0000
103.1515 169.1135 156.4261 170.6386 128.6612 167.4409 144.6291 2.4875 30.0239 8.5047 4.0311 6.5000 78.4481 162.0650 0.0000 160.7467 58.9385 3.3095 4.5415 136.6912 7.9839 4.3493 4.7056 8.2810 81.2509 8.2764 95.3139 7.9365 124.6034 156.9777 169.5531 164.3553 170.5605 97.9138 0.0000
* OUT_DIR_MI: Minimum angle (degree), OUT_DIR_MA: Maximum angle (degree), OUT_DIR_RA: Data range, OUT_DIR_ME: Mean value, OUT_DIR_ST: Standard deviation; cell size = 1meter.
120
