AN ABSTRACT OF THE THESIS OF
Franziska Whelan for the degree of Doctor of Philosophy in Geography
presented on April 6, 2000. Title: Analysis of Land Use / Land Cover and the
Frequency of Bankfull Flow in Selected Salmon Habitat Recovery Streams in
the Pacific Northwest Using GIS.
Abstract approved:
Bankfull discharge is an important indicator of streamfiow and affects
physical instream habitat. Geographic Information Systems (G IS), hydrologic
modeling, and statistical analysis were utilized to assess the relationship of
bankfull discharge recurrence intervals and land use / land cover watershed
wide and for stream adjacent buffers and bands of varying widths. Land use I
land cover was determined for watersheds and stream adjacent zones along
salmon habitat recovery streams in 71 large Pacific Northwest watersheds.
Watersheds were defined using an integrated methodology for watershed
delineation relying on the ArcView Spatial Analyst Hydrologic Modeling
extension.
Databases include field collected data, gaging station data, USGS land
use / land cover data, USGS digital elevation models, a stream network
coverage, and supplemental data. Data were analyzed using ArcView,
ArcView Spatial Analyst with the Hydrologic Modeling extension, Arclnfo, and
S-PLUS.
Land use alterations at the watershed scale were found to affect the
frequency of critical streamfiow events. Bankfull flow occurs more frequently
in watersheds with high percentages of agricultural or urban land use. This
study recommends land use / land cover assessment at the watershed scale
to be an important consideration in river restoration and other stream
management efforts.
Stream adjacent land use / land cover is significantly correlated to
bankfull discharge recurrence interval and corresponding flood risk. This
dissertation, therefore, proposes two new analytical techniques that evaluate
spatial patterns of land use / land cover and their influences on flood risk for
large watersheds. The Critical Zone Management Model presented in this
study is designed to aid land managers in the assessment of flood risk for
large watersheds. A Land Use Runoff index (LUR-index) suggests stream
corridor sensitive zones should be larger than presently delineated in land and
watershed planning. Land use I land cover within this sensitive zone is
strongly correlated to streamfiow. This research may further be used for future
watershed management considerations, and flood risk prediction studies.
© Copyright by Franziska Whelan
April 6, 2000
All Rights Reserved
ANALYSIS OF LAND USE / LAND COVER AND THE
FREQUENCY OF BANKFULL FLOW IN SELECTED SALMON
HABITAT RECOVERY STREAMS IN THE PACIFIC NORTHWEST
USING GIS
by
Franziska Whelan
A DISSERTATION
Submitted to
Oregon State University
in partial fulfillment of
the requirement for the
degree of
Doctor of Philosophy
Presented April 6, 2000
Commencement June 2000
Doctor of Philosophy thesis of Franziska Whelan presented on April 6, 2000.
APPROVED:
Major Profesr, repr e jg Geography
Chair of Department of
Dean of Grad
!-'
G?ce
hool
I understand that my thesis will become part of the permanent collection of
Oregon State University libraries. My signature below authorizes release of
my thesis to any reader upon request.
Franziska Whelan, Author
ACKNOWLEDGEMENTS
During the past three years I have received great support and
encouragement from so many people that it is impossible to name them all
here. I express my deepest thanks to all of them for their time and thoughts.
I
would like to thank several people who have supported and assisted me in this
research.
I would like to thank all my committee members for providing guidance
and encouragement. Dr. Philip Jackson deserves my greatest gratitude for his
support as my advisor. Phil always offered advice and critique based on his
expert knowledge. A special thanks goes to my friend and committee member
Dr. Dawn Wright for her friendship, and for her excellent comments and
recommendations. I also thank Dr. Charles Rosenfeld for his support during
my years at Oregon State University. I would also like to thank Dr. John
Buckhouse for his field trips that motivated me to focus on watershed based
research.
A special thanks goes to Dr. Janine Castro for her friendship and her
excellent comments on my manuscripts. My dear friends Stephanie Moret and
Engelene Chrysostom provided motivation and support during the past five
years of graduate school. I also thank Alan and Sonya Leigh for their
encouragement and friendship. I would like to express my gratitude to my
dearest German friends Angelika Israel and Raif Sonntag for their
encouragement and moral support throughout the nine years of my university
education in Germany and the United States.
I would like to express my gratitude to my family who has provided
encouragement and endless support. My deepest gratitude is reserved for my
husband, Nathaniel Whelan. Thank you Nate, for your great commitment and
love. Your encouragement and patience were invaluable. My parents in
Germany always encouraged me to be self-confident and definite. They are
wonderful parents and reserve my sincerest thanks. Their support during the
past few years in an "American graduate school" was invaluable. My brother
Florian deserves my sincerest thanks for his love. I would also like to thank
my grandmother for her wisdom.
Finally, I would like to thank God for the wisdom and strength I received
to complete this research. I am grateful for the opportunities in life that were
given to me.
TABLE OF CONTENTS
Page
CHAPTER I. THE SIGNIFICANCE OF THE FREQUENCY OF BANKFULL
FLOW FOR SALMON HABITAT STREAM RECOVERY AND
FLOOD RISK PREDICTION
1.1. INTRODUCTION
2
1.2. PURPOSE
3
1.3. FORMAT
4
1.4. JUSTIFICATION
5
I.5.STUDYAREA
8
1.6. LITERATURE REVIEW
1.6.1. Bankfull Discharge
1.6.2. Bankfull Discharge Recurrence Interval
1.6.3. Flood Risk Analysis
1.6.4. Land Use/Land Cover
1.6.5. Riparian Buffers
1.6.5.1. Definition and Overview
1.6.5.2. Influence of Riparian Buffers on
Aquatic Ecosystems
1.6.6. Geographic Information Systems (GIS) Analysis
and Scale
10
10
11
12
13
15
15
15
17
1.7. TERMINOLOGY
18
1.8. DESCRIPTION OF DATA
20
1.8.1. USGS Gaging Station Database
1.8.2. Digital Elevation Models
1.8.3. Land Use / Land Cover
1.8.4. Field Stream Gage Database
20
20
20
21
TABLE OF CONTENTS (Continued)
Page
1.8.5. EPA River Reach File
1.8.6. Climate Data
21
22
1.9. REFERENCES
23
1.10. WEB AND FTP REFERENCES
29
CHAPTER II. AS ESSMENT OF GIS HYDROLOGIC MODELING FOR THE
DELINEATION OF SELECTED SALMON HABITAT
30
WATERSHEDS IN THE PACIFIC NORTHWEST
II.1.ABSTRACT
31
11.2. INTRODUCTION
31
11.3. OBJECTIVES
32
11.4. BACKGROUND
33
11.5. STUDYAREA
34
11.6. DATABASES
35
11.7. METHODOLOGY
35
11.7.1. DEM Acquisition and Manipulation
11.7.2. Pour Point Determination and Watershed
Delineation
11.7.3. Quality Control and Spatial Editing
11.8. ANALYSIS
11.8.1. Suitability of Spatial Analyst Hydrologic Modeling
for Watershed Delineation
11.8.2. The Influence of Watershed Attributes on
Watershed Delineation Using Spatial Analyst
Hydrologic Modeling
36
39
42
44
45
48
TABLE OF CONTENTS (Continued)
Page
11.8.3. Supplemental Methods Required to Improve
Delineation Accuracy
41
11.9. CONCLUSIONS
42
11.10. REFERENCES
55
11.11. WEB AND FTP REFERENCES
58
11.12. APPENDIX
59
CHAPTER III. VARIABILITY IN THE FREQUENCY OF BANKFULL
FLOW WITH DIFFERENT LAND USE I LAND COVER
TYPES IN PACIFIC NORTHWEST WATERSHEDS
64
III.1.ABSTRACT
65
111.2. INTRODUCTION
66
111.3. OBJECTIVES
67
111.4. JUSTIFICATION
67
111.5. STUDY AREA AND DATA SOURCES
69
111.5.1. USGS Gaging Station Database
111.5.2. Digital Elevation Models
111.5.3. Land Use / Land Cover
111.5.4. Field Stream Gage Database
111.5.4.1 Bankfull Discharge Recurrence Interval
111.5.4.2 Slope
111.5.4.3 Bed Material Type
111.5.5. Climate
70
71
71
73
73
73
73
74
TABLE OF CONTENTS (Continued)
Page
111.6. METHODOLOGY
111.6.1. Watershed Delineation Through Hydrologic
Modeling Using GIS
111.6.2. Determination of Dominating Climate
111.6.3. Geoprocessing and Determination of
Dominating Land Use I Land Cover
111.6.4. Calculation of the Human Use Index
111.6.5. Statistical Tests
111.6.5.1 Pearson Correlation Tests
111.6.5.2 Linear Regression
111.7. STATISTICAL ANALYSIS
111.7.1. Summary Statistics for Land Use Variables and
Bankfull Discharge Recurrence Interval
111.7.2. Correlation and Regression Tests for Watersheds,
No Segmentation
111.7.3. Correlation Tests for Bankfull Discharge
Recurrence Interval Versus Land Use / Land
Cover for Data Segmented by Climate Type
111.7.3.1 Summary Statistics
111.7.3.2 Statistical Tests
111.7.4. Correlation Tests for Bankfull Discharge
Recurrence Interval Versus Land Use I Land
Cover for Data Segmented by Bed Material Type
111.7.4.1 Summary Statistics
111.7.4.2 Statistical Tests
111.7.5. Correlation Tests for Bankfull Discharge
Recurrence Interval Versus Land Use I Land
Cover for Data Segmented by Slope
111.7.5.1 Summary Statistics
75
75
77
78
80
81
81
82
82
83
85
87
87
88
92
92
93
95
96
TABLE OF CONTENTS (Continued)
Page
111.7.5.2 Statistical Tests
111.7.6. Summary of Correlation Tests and Regression
Equations
97
100
111.8. CONCLUSIONS
103
111.9. REFERENCES
109
111.10. WEB AND FTP REFERENCES
112
lll.11.APPENDIX
113
CHAPTER IV. THE RELATIONSHIP OF STREAM ADJACENT
LAND USE I LAND COVER TO THE FREQUENCY OF
BANKFULL FLOW: DEVELOPMENT OF A CRITICAL
ZONE MANAGEMENT MODEL FOR FLOOD RISK
ASSESSMENT IN LARGE PACIFIC NORTHWEST
WATERSHEDS
IV.1.ABSTRACT
INTRODUCTION
BACKGROUND
IV.3.1. Flood Risk Analysis
IV.3.1.1. Overview
IV.3.1.2. Flood Records
IV.3.1 .3. Runoff Equations
lV.3.1.4. Current Limitations
lV.3.2. Riparian Buffers
IV.3.2.1. Overview
lV.3.2.2. Riparian Buffer Influence on Runoff!
Discharge
124
125
125
127
127
127
128
128
129
129
129
130
TABLE OF CONTENTS (Continued)
Page
IV.3.2.3. Riparian Buffer Influence on Filtering,
Habitat, Erosion, and Stream
Temperature
IV.3.2.4. Variable Versus Fixed Width Buffers
131
134
OBJECTIVES
135
JUSTIFICATION
136
STUDY AREA AND DATA SOURCES
138
IV.6.1. USGS Gaging Station Database
lV.6.2. Digital Elevation Models
IV.6.3. Land Use I Land Cover
IV.6.4. Field Stream Gage Database
IV.6.5. Stream Network - River Reach File
140
140
140
141
143
TERMINOLOGY
143
METHODOLOGY
145
IV.8.1. Watershed Delineation Through Hydrologic
Modeling Using GIS
IV.8.2. Stream Network Geoprocessing
IV.8.3. Creation of Stream Adjacent Buffers
IV.8.4. Geoprocessing and Determination of
Dominant Land Use I Land Cover
IV.8.5. Buffer Land Use / Land Cover Data Manipulation
and Band Creation
IV.8.6. Determination of the Human Use Index
IV.8.7. Determination of the Land Use Runoff Index
STATISTICAL ANALYSIS
IV.9.1. Summary Statistics
IV.9.1 .1. Summary Statistics for Bankfull
Discharge Recurrence Intervals
145
147
147
148
149
151
151
155
156
156
TABLE OF CONTENTS (Continued)
Page
IV.9.1 .2. Summary Statistics for Land Use I
Land Cover Within Buffer Areas
IV.9.1 .3. Summary Statistics for Land Use /
Land Cover Within Bands
IV.9.2. Correlation Tests for a Relationship of Land
Use I Land Cover Data Versus Bankfull
Discharge Recurrence Interval
IV.9.2.1. Correlation Tests for Land Use / Land
Cover for Buffer Areas
IV.9.2.2. Correlation Tests for Land Use / Land
Coverfor Bands
157
162
166
166
167
IV.9.3. Correlation Tests for a Relationship of the
U-Index Versus Bankfull Discharge Recurrence
Interval for Buffers and Bands
168
IV.9.4. Correlation Tests for a Relationship of the
LUR-Index Versus Bankfull Discharge
Recurrence Interval for Buffers and Bands
171
IV.1O. CRITICAL ZONE MANAGEMENT MODEL
173
lV.11. CONCLUSIONS
179
REFERENCES
184
WEB AND FTP REFERENCES
190
APPENDIX
191
CHAPTER V. SUMMARY AND CONCLUSIONS
V.1. SUMMARY STATEMENT
199
200
TABLE OF CONTENTS (Continued)
Page
ASSESSMENT AND SUITABILITY OF GIS
HYDROLOGIC MODELING FOR DIGITAL WATERSHED
DELINEATION
201
VARIABILITY IN BANKFULL DISCHARGE RECURRENCE
INTERVALS WITH DIFFERENT LAND USE I LAND COVER
TYPES IN PACIFIC NORTHWEST WATERSHEDS
203
FLOOD RISK ASSESSMENT FOR LARGE PACIFIC
NORTHWEST WATERSHEDS: THE RELATIONSHIP OF
STREAM ADJACENT LAND USE I LAND COVER TO THE
FREQUENCY OF BANKFULL FLOW
207
GIS IN WATERSHED STUDIES
211
REFERENCES
213
BIBLIOGRAPHY
214
WEB AND FTP REFERENCES
223
LIST OF FIGURES
Figure
Page
1.1.
Study Area (Oregon, Washington, Idaho).
11.1.
Study Area (Oregon Washington, Idaho).
34
11.2.
Flow Direction Grid.
37
11.3
Flow Accumulation Grid.
38
11.4.
DEM Mosaic with Delineated Watershed for Stream Gage
#14157500, Coast Fork Willamette, Oregon.
41
11.5.
Delineated Watersheds
44
11.6.
Area Differences in Square Miles After Initial and Final
Watershed Delineation Compared to USGS Reference Areas.
49
111.1.
Study Area (Oregon Washington, Idaho).
70
111.2.
Pacific Northwest Land Use / Land Cover and Stream Gaging
Stations.
72
111.3
Study Area Watersheds and Koppen Climate Regions.
75
111.4.
DEM Mosaic with Delineated Watershed for Stream Gage
#14157500, Coast Fork Willamette, Oregon.
76
111.5.
Digitally Delineated Study Area Watersheds.
77
111.6.
Land Use Land Cover (LULC) in Study Area Watersheds.
79
111.7.
Land Use / Land Cover for Coast Fork Willamette Watershed,
USGS Gaging Station # 14157500.
80
111.8.
Summary of Land Use / Land Cover for Delineated Watersheds
84
111.9.
Study Area Watersheds Classified by the Human Use Index
(U-Index).
85
9
LIST OF FIGURES (Continued)
Figure
Page
111.10. Bankfull Discharge Recurrence Intervals (Classified by Natural
Breaks) and Dominating Land Use! Land Cover by Watershed.
IV. 1.
104
Range of Recommended Buffer Widths by Buffer Function;
Adapted from Castelle et al. (1994).
132
Study Area (Oregon, Washington, Idaho).
139
Pacific Northwest Land Use / Land Cover and Stream Gaging
Stations.
142
DEM Mosaic with Delineated Watershed for Stream Gage
#14157500, Coast Fork Willamette, Oregon.
146
Digitally Delineated Study Area Watersheds.
147
Land Use / Land Cover Buffer Outlines for Bear Creek at
Medford Watershed, USGS Gaging Station # 14357500.
150
Study Area Watersheds Rated by the Land Use Runoff Index.
155
Summary Statistics for Proportional Land Use I Land Cover
Located Within Selected Buffer Widths and Compared to Mean
Land Use / Land Cover for Total Watershed.
157
Summary Statistics for Proportional U-Index for Selected
Buffers in Comparison to Mean U-Index for Watersheds.
162
Summary Statistics for Proportional Land Use! Land Cover
Located Within 200 Meter Bands and Compared to Mean
Land Use I Land Cover for Total Watershed Area.
164
Summary Statistics for Proportional U-Index for 200 Meter
Bands in Comparison to Mean U-Index for Total Watershed.
165
Critical Zone Management Model.
177
LIST OF FIGURES (Continued)
Figure
V.1.
Page
Flow Chart of the Relationships Investigated With This
Research. Flow Regime is Critical for The Ecological
Integrity of Aquatic Streams (Modified, After Poff et al., 1997).
Land Use I Land Cover Alters Bankfull Discharge Recurrence
Interval and Affects Salmon Habitat and Flood Risk.
201
LIST OF TABLES
Table
Page
Two-sample t-test for Watersheds Delineated Using Spatial
Analyst Hydrologic Modeling and USGS Reference Areas.
45
Ten Maximum Area Differences for Initially Delineated
Watersheds Using Spatial Analyst Hydrologic Modeling as the
Only Method Compared to USGS Reference Areas.
46
11.3
Outlier Watersheds that Exceed Area Difference Limits.
47
111.1.
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover,
68 Watersheds.
86
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover
for 8 Watersheds Characterized by B-Climate.
89
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover
for 25 Watersheds Characterized by C-Climate.
90
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover
for 35 Watersheds Characterized by D-Climate.
91
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover
for 30 Cobble-Bed Watersheds.
94
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover
for 38 Gravel-Bed Watersheds.
95
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover
for 25 High-Slope Watersheds.
98
11.1.
11.2.
111.2.
111.3.
111.4.
111.5.
111.6.
111.7.
LIST OF TABLES (Continued)
Table
111.8.
111.9.
Page
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use / Land Cover
for 27 Medium-Slope Watersheds.
99
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use / Land Cover
for 16 Low Slope Watersheds.
100
111.10. Summary Table for Pearson Correlation Tests.
101
111.11. Regression Equations for Bankfull Discharge Recurrence
Intervals Determined by Land Use I Land Cover Variables
in Pacific Northwest Watersheds.
102
lV.5.
Riparian Buffer Functions, Width Recommendations, and
Ecological Responses.
133
R Factor by Land Use / Land Cover Class.
153
Summary Statistics for Proportions of Total Land Use I Land
Cover and U-Index Located Within Respective Buffer Zones.
159
Summary Statistics for Proportions of Total Land Use / Land
Cover and U-Index Located Within Respective Bands.
163
Summary of Pearson Correlation Coefficients for Bankfull
Discharge Recurrence Interval Versus U-Index for Buffers,
68 Watersheds.
169
Summary of Pearson Correlation Coefficients for Bankfull
Discharge Recurrence Interval Versus U-Index for Bands,
68 Watersheds.
170
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus LUR-Index for Buffers,
68 Watersheds.
172
LIST OF TABLES (Continued)
Table
lV.8.
IV.9.
Page
Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus LUR-Index for Bands,
68 Watersheds.
173
Regression Equations for Bankfull Discharge Recurrence
Interval Determined by U-Index and LUR-Index for Selected
Management Zones. Equations are Based on 68 Data Sets.
178
LIST OF APPENDICES
Appendix
ll.A
Page
Watershed Delineation Script With Changed Value For
Pour Point Snap Distance.
60
Projection Files for the Conversion from Geographic
Coordinates to the Albers Conic Equal Area Projection
System.
63
lll.A
Critical Values of the Pearson Correlation Coefficient r.
114
lll.B
Summary Statistics for Land Use I Land Cover Variables and
Bankfull Discharge Recurrence Interval for 68 Watersheds.
115
Summary Statistics for Land Use I Land Cover and Bankfull
Discharge Recurrence Intervals Based on Slope Categories.
116
Summary Statistics for Land Use I Land Cover and Bankfull
Discharge Recurrence Intervals Based on Climate Categories.
117
Summary Statistics for Land Use I Land Cover and Bankfull
Discharge Recurrence Intervals Based on Bed-Material
Classes.
118
lll.F
Watersheds Ranked by U-Index.
119
lII.G
Land Use / Land Cover in Percent and U-Index Data by
Watershed.
120
lll.H
Land Use / Land Cover Data in Acres for All Watersheds.
122
IV.A
Summary Statistics for Bankfull Discharge Recurrence
Intervals Based on 68 Observations.
192
Summary Statistics for Land Use I Land Cover for Different
Buffer Widths.
193
Summary of Pearson Correlation Coefficient r for Land Use I
Land Cover Versus Bankfull Discharge Recurrence Interval for
Varying Buffer Widths.
194
Il.B
lll.0
lIl.D
lll.E
IV.B
IV.0
LIST OF APPENDICES (Continued)
Appendix
IV.D
IV.E
Page
Summary of Pearson Correlation Coefficients for Land Use I
Land Cover Versus Bankfull Discharge Recurrence Interval for
Bands.
196
Watersheds Rated by the LUR-Index Based on Land Use I
Land Cover Within the 600 Meter Buffer Area.
198
To my loving parents Rita and Günter, my brother Florian, and my dearest
grandmother Omi.
2
1.1. INTRODUCTION
The frequency of recurrence of bankfull flow plays an important role
from a hydrologic and geomorphologic point of view (Petit and Pauquet, 1997).
At bankfull discharge, the self-formed channel is filled to the level of the active
floodplain. Bankfull discharge is generally considered to have a 1.5-year
recurrence interval (Leopold et al., 1964; Dury, 1977; Williams, 1978).
However, recent research conducted by Petit and Pauquet (1997) and by
Castro (1997) detected variability in the recurrence intervals for bankfull flow.
There is presently a lack of information of the influence of drainage basin
alterations, specifically land use I land cover, on the frequency of bankfull flow,
and related flood risk.
Bankfull discharge is one of the most important indictors of streamflow.
Poff et al. (1997, p.769) call streamfiow a "master variable that limits the
distribution and abundance of riverine species and regulates the ecological
integrity of flowing water systems". Streamfiow is strongly correlated to many
physicochemical characteristics of rivers, such as water temperature, channel
geomorphology, and habitat diversity. Many rivers no longer sustain healthy
ecosystems that provide habitat to anadromous fish. The degradation of
salmonid habitat is a critical environmental issue. Bankfull discharge is the
critical channel forming discharge and responsible for the creation and
maintenance of salmonid habitat. Human induced alterations of flow regime
can change established patterns of hydrologic variation causing alterations in
habitat dynamics, and creating new conditions to which native fish species and
other biota may be poorly adapted. Land use activities, including livestock
grazing, agriculture, and urbanization, are known to cause altered flow
regimes (Poff et al., 1997). This research uses the frequency of bankfull flow
as a streamfiow indicator and determines the relationship between bankfull
discharge recurrence interval and land use I land cover at the watershed scale
for selected salmon habitat recovery streams in the Pacific Northwest
3
(Oregon, Washington, and Idaho). This research further determines whether
land use I land cover within close approximation to the stream network enacts
a stronger influence on the frequency of bankfull flow and associated flood risk
compared to watershed wide land use / land cover. The identification of areas
within watersheds that strongly influence streamfiow is crucial for the effective
management of large watersheds. Study area watersheds range in size from
approximately 12,000 to 9 million acres.
Current flood risk prediction methods have limited application to large
watersheds. Commonly used methods, including the estimation of storm
runoff volume or the rational runoff method, were either designed for small
catchments or require a detail of information on watershed characteristics that
is not feasible to determine for large watersheds. There is a need for a
simplistic yet scientifically sound flood risk prediction model that allows land
managers and planners to evaluate flood risk based on land use / land cover
assessments within large watersheds. Poff et al. (1997) identified the need for
the incorporation of critical flow events such as bankfull flow into a broader
framework of ecosystem management at the watershed scale.
The Critical Zone Management Model presented with this research
provides a conceptual model to determine which watershed areas and land
use / land cover classes are most influential on increasing flood risk. This
model incorporates the Land Use Runoff index to predict the frequency of
bankfull flow. The index was calculated based on the relationships between
surface runoff and land use / land cover for stream adjacent zones of varying
widths.
1.2. PURPOSE
This research uses an integrated GIS hydrologic modeling approach to
determine the relationship between bankfull discharge recurrence interval and
4
land use / land cover types, including urban, agriculture, forest, and range
land. The relationship between the frequency of bankfull flow and the human
use index (U-index), a parameter indicating the extent of anthropogenic land
use / land cover within watersheds, is also to be determined. Assessment of
land use / land cover was conducted for total watersheds and for stream
adjacent areas for selected Pacific Northwest watersheds.
Purpose of this study is to educate watershed and land mangers on (1)
the suitability and suggested modifications of GIS hydrologic modeling
technical tools; (2) the influence of land use I land cover on streamfiow; and
(3) a breakpoint for buffer widths in large watersheds after which the influence
of land use / land cover on streamfiow is no longer significant. This research
further provides a model to aid in land use / land cover dependant flood risk
prediction for large watersheds
1.3. FORMAT
This dissertation is presented in manuscript format. Chapters two,
three, and four are presented as individual manuscripts following journal
submission guidelines. Chapters one and five introduce and summarize the
research conducted for this dissertation.
The first manuscript tests and evaluates an integrated GIS hydrologic
modeling methodology for the delineation of selected salmon habitat recovery
watersheds in the Pacific Northwest (Oregon, Washington, and Idaho).
Watershed delineation relies on the ArcView Spatial Analyst Hydrologic
Modeling extension and is applied and evaluated for 71 watersheds.
Manuscript one demonstrates and evaluates the suitability of the ArcView
Spatial Analyst Hydrologic modeling extension for accurate watershed
delineation. Watershed attributes that have the greatest impact on watershed
delineation accuracy are determined. The chapter further suggests
5
modifications and supplemental methods required for the improvement of
delineation accuracy. The digitally delineated watersheds presented in the
first manuscript were utilized for the following two chapters.
The second manuscript assesses land use I land cover at the
watershed scale and its correlation to the frequency of bankfull flow.
Dominating land use I land cover categories include forest, agriculture, urban,
and range land. The relationship between bankfull discharge recurrence
interval and the human use index (U-index), a parameter designed to indicate
the extent of anthropogenic land use I land cover, is also determined for the
watershed scale.
The third manuscript determines the relationship between land use I
land cover in stream adjacent zones of varying widths and bankfull discharge
recurrence interval. The manuscript develops an indicator for land use - runoff
relationships and presents a model for flood risk prediction titled the Critical
Zone Management Model. The model was specifically designed for large
watersheds and is based on relationships between bankfull discharge
recurrence interval and land use I land cover.
1.4. JUSTIFICATION
The correlation of hydrologic and geomorphic parameters to watershed
characteristics, in particular land use I land cover, is important for regional
planning, river preservation, flow studies, and flood hazard studies. GIS aids
in analyzing this correlation through hydrologic modeling and spatial analysis
of watershed parameters, including land use I land cover.
The determination of bankfull discharge is vital for the analysis of river
regimes. Bankfull discharge plays a significant role in hydrology and river
morphology, since it forms and maintains channel geometry. Stream
management and restoration efforts should operate within an understanding of
6
regional watershed characteristics, including human induced land cover
alterations. Land use I land cover may alter the hydrologic characteristics of a
watershed including the recurrence interval of channel shaping flows such as
bankfull discharge. Previously reviewed studies incorporated many factors,
including climate, terrain, vegetation, and soils, into their research on the
geographic variation in streamfiow characteristics. However, human induced
alterations on drainage basins had not yet been considered. Therefore, this
study investigates empirical relationships between land use I land cover at the
watershed scale and bankfull discharge recurrence intervals. This research
examines the relative sensitivity of stream flow to land use and land cover
within entire watersheds and stream adjacent buffers of varying widths.
Reviewed studies suggest that management recommendations for the
implementation of riparian buffers at the watershed scale is scarce (Muscutt at
al., 1993; Castelle et al., 1994). This may be due to the 'idealized' buffer
approach of traditional riparian studies, which would be costly and time
consuming to implement at the catchment scale. Muscutt at al. (1993)
identified the need for research on the impact of buffers at the catchment
scale. This study assesses the relationship between bankfull discharge
recurrence interval and land use I land cover for buffer zones of different width
at the catchment scale of large Pacific Northwest watersheds. Reviewed
riparian buffer studies focused on riparian buffer functions other than the
recurrence of a streamflow event. Traditional riparian buffer studies assessed
specifically designed or selected buffer types, such as forested or grass cover
buffers. None of the reviewed studies have analyzed how streamfiow is
affected by land use I land cover within 'real world' stream adjacent areas.
Since discharges above bankfull cause flooding, the frequency of
occurrence of bankfull flow indicates the risk of flooding and is therefore to be
considered in regional planning (Leopold, 1996). Land managers increasingly
face the challenges of flood risk prediction. Traditional flood risk prediction
methods are limited by the availability of historic flood records, the quantity of
7
data required for flood risk calculation, and their lack of applicability to large
watersheds. There is a need for a simplistic yet scientifically sound
conceptual model for flood risk prediction that allows land managers and
planners to evaluate flood risk based on land use / land cover assessments
within large watersheds. This research develops the Critical Zone
Management Model which could become a valuable component of flood-risk
related decision making processes for land managers. The model integrates
the frequency of bankfull flow and the land use runoff index (LUR-index),
which was developed for this study and incorporates land use I land cover surface runoff relationships for stream adjacent zones of varying widths.
The frequency of bankfull discharge is a valuable indicator of
streamfiow health. Changes in the frequency of bankfull flow reflect
alterations of streamflow and fish habitat. All bankfull discharge data complied
for this research were collected on designated salmon habitat recovery
streams (USDA-SCS, 1994; Castro, 1997). Salmonids have declined
significantly in the Pacific Northwest in past decades, caused in part by habitat
degradation through anthropogenic alterations of streams and watersheds.
The degradation of salmonid populations and their habitat is a critical
environmental issue in the Pacific Northwest and has been addressed by the
Northwest Marine Fisheries Service listing of salmon, steelhead, and cutthroat
as threatened or endangered species (Northwest Marine Fisheries Service,
1998). Bankfull discharge plays an important role in the creation and
maintenance of physical instream habitat since bankfull is the critical channel
forming discharge. The timing or predictability of flow events, such as bankfull
discharge, is critical ecologically because the lifecycles of many aquatic and
riparian species are timed to either avoid or exploit flows of variable
magnitudes. For example, the natural timing of high or low flows provides
cues for life cycle transitions for fish species, such as spawning, egg hatching,
rearing, reproduction, or migration up- or downstream (Poff et al., 1997).
Human induced alterations of flow regime can change established patterns of
8
hydrologic variation causing alterations in habitat dynamics, and creating new
conditions to which native fish species and other biota may be poorly adapted
(Poff et al., 1997). Land use activities, including livestock grazing, agriculture,
and urbanization, are known to cause altered flow regimes (Poff et al., 1997).
Recent advances in GIS technology and regional digital data availability
promoted the GIS approach for this multi-watershed regional scale study. This
study utilizes streamfiow data collected at the reach scale of stream systems
and various landscape scale data, including land use / land cover data and
digital elevation models. Streamfiow and other data on aquatic ecosystems
are typically collected at the centimeter to meter scale, represented by plots or
transects. Landscape scale datasets are generally collected at a smaller scale
often using remote sensing techniques. Ecosystem models derived from large
scale field data may predict instream habitat characteristics for a particular
plot, while landscape processes, such as floods or anthropogenic land uses,
occurring at the watershed scale, may alter reach scale processes (Pastor and
Johnston, 1992). This study combines both the reach scale and the
landscape scale to assess the influences of land use / land cover on the
frequency of a streamfiow event. The public availability of digital land use I
land cover data sets for the United States made this research on hydrologic
variables and their responses to anthropogenic landscape alterations possible
for this large study area. The GIS approach was the only practical method for
organizing and analyzing the voluminous datasets required for this study. The
relationships developed are specific to the Pacific Northwest due to the
physical characteristics of this region.
1.5. STUDY AREA
The study area (Figure 1.1) includes 71 designated salmon habitat
recovery streams in the Pacific Northwest, including Oregon, Washington, and
Idaho (USDA-SCS, 1994). Selected streams are located in watersheds that
contain critical habitat for anadromous salmonids. Streams were defined by
the Natural Resources Conservation Service (NRCS) as Salmon Initiative
streams. Salmon Initiative streams are characterized by (1) critical salmonid
habitat; (2) a public interest in the fishery and ecological watershed condition;
and (3) private ownership of significant portions of the watershed (Castro,
1997). Study area watersheds range in area from approximately 12,000 acres
to 9 million acres and comprise a total land area of 53,860,169 acres
(84,156.5 square miles).
Saiir
teat
jeaS
Jters Equ
ee Conic Prrjon
Figure 1.1: Study Area (Oregon Washington, Idaho).
b
5
MLES
100 ILCMEFEF
10
1.6. LITERATURE REVIEW
1.6.1. Bankfull Discharge
Bankfull discharge marks the condition of incipient flooding, since it is
the flow that fills the channel to the top of the banks, also referred to as the
floodplain level. Dunne and Leopold (1996) define bankfull discharge as the
stage corresponding to the discharge at which channel maintenance is most
effective. Bankfull is the discharge that creates and maintains the average
morphological characteristics of channels. At bankfull discharge sediment is
moved, bars are formed or removed, and bends and meanders are formed or
changed. Bankfull discharge is the flow rate of a river when the water surface
is at floodplain level. Bankfull discharge plays a critical role in engineering,
planning, land management, and geomorphology, since it has an important
impact on channel shape and cross section characteristics. Technical
literature in diverse applications and fields, including fisheries inventories,
engineering studies, riparian surveys, and sediment studies, references
bankfull measurements (Castro, 1997).
Williams (1987) identified ten different definitions for bankfull flow used
in earlier studies. Each definition could result in different quantities of bankfull
discharge. Small differences in stage can have significant influences on the
value for bankfull discharge especially for wide rivers. Bankfull stage or
elevation has been defined in several different ways. The definition of bankfull
stage for this research is based on definitions of bankfull by Wolman and
Leopold (1957), Leopold and Skibitzke (1967), and Emmett (1975). Forthis
research, bankfull stage is defined as the water surface elevation that fills the
self-formed channel to the level of the active floodplain. This definition is
utilized by the USDA Forest Service (USDA-FS, 1995).
Bankfull stage refers to the water level where flooding begins.
Floodplains inundate when streamflow in the channel exceeds bankfull stage.
11
Topographically and geologically, a floodplain is defined as the relative flat
surface occupying much of the valley bottom and normally being underlain by
unconsolidated sediment (Ritter et al., 1995). Inactive floodplains are called
terraces, and are rarely inundated. Active floodplains are periodically
inundated and shaped by the river. The active floodplain is the relatively flat
surface adjacent to the alluvial channel. Geomorphologists today increasingly
use the term floodplain to refer to the active floodplain.
Several criteria have been set to determine the bankfull stage in the
field. Field indicators for bankfull include the tops of point bars, changes in
vegetation, topographic breaks at bankfull, a change in size distributions, and
changes in debris deposited (Leopold, 1996). Scour lines, vegetation limits,
changes between bed and bank materials, the presence of flood deposited silt
or abrupt changes in slope are also field indicators for bankfull elevation
(Gordon et al., 1994).
1.6.2. Bankfull Discharge Recurrence Interval
Bankfull discharge is generally assumed to have a constant recurrence
interval of 1.5 years in the annual flood series (Leopold, 1996). Once bankfull
discharge is known, its recurrence interval can be determined from flood
frequency curves based on gaging station records (Leopold, 1996).
Williams (1978) determined bankfull recurrence intervals ranging from
1.01 to 32 years for 36 gaging stations. Williams (1987) did not find a
common frequency of occurrence of ban kfull discharge among rivers, but
determined a mode or peak recurrence interval of about 1.5 years on the
annual maximum series. Williams (1987) concluded that recurrence intervals
of bankfull discharge vary with certain channel and basin characteristics
(Bowen, 1959; Wolf, 1959; Kilpatrick and Barnes, 1964).
12
Dury et al. (1963) determined a recurrence interval of 1.6 years for
bankfull discharge. He suggested that analysis of bankfull discharge in terms
of frequency or duration was more rewarding than in terms of stage. Dury et
al. (1963) determined a relationship of q= ri = 1.6 (annual series) between
bankfull discharge recurrence interval (ri) and bankfull discharge (qbf).
Petit and Pauquet (1997) studied bankfull discharge recurrence
intervals in gravel-bed rivers in Belgium. The recurrence intervals for Ardenne
rivers were dependent on drainage area and largely deviated from the widely
accepted average 1.5-year period. Petit and Pauquet (1997) determined
recurrence intervals between 0.4 to 0.7 years for Ardenne rivers with
catchments smaller than 250 km2, and recurrence intervals ranging from 1.5 to
2 years for larger drainage basins.
Castro (1997) determined a mean bankfull discharge recurrence
interval of 1.4 years for the Pacific Northwest, including Oregon, Washington,
and Idaho. Castro (1997) further determined an association between bankfull
discharge recurrence interval and ecoregion. An average recurrence interval
of 1.2 years was determined for ecoreg ions in the more humid areas of
western Oregon and Washington.
1.6.3. Flood Risk Analysis
Watershed managers need to increasingly make decisions about flood
risk when budget limits full-scale studies by hired hydrologic consultants.
Rough flood risk assessment is also necessary to determine whether the
problem is important enough to demand a more sophisticated analysis
(Leopold, 1996).
Commonly used information to predict flood risk include historical flood
records published by several government and state agencies including the
U.S. Department of Agriculture (USDA), United State Geological Service
13
(USGS), and the U.S. Forest Service (USFS). Probability analysis of flood
records is frequently applied and uses the annual maximum series or the
partial duration series for flood frequency analysis (Dunne and Leopold, 1996;
Brooks et al., 1993). A statement of the probability of floods greater than their
average frequency of occurrence is required for engineering design, floodinsurance planning, and land-use zoning for flood prone areas (Dunne and
Leopold, 1996).
Other frequently used methods to predict flood risk include hydrograph
separation and the estimation of storm runoff volume or peak runoff (Marsh,
1991; Dunne and Leopold, 1996; Brooks et al., 1993). Storm runoff estimates
incorporate knowledge of rainfall - runoff relationships, and detailed watershed
information including cover types (e.g. row crops, small grain, rotational
meadow), treatment or practices (e.g. straight row, contoured), hydrologic
condition, hydrologic soil group, antecedent moisture condition, land use, and
percent of impervious surface. The calculation of flood peak discharges,
including peak runoff, is a commonly used method for flood risk prediction.
The rational runoff method predicts peak runoff rates from data on rainfall
intensity and drainage basin characteristics such as drainage area, soils, and
detailed land use I land cover information. The rational runoff method is
recommended for catchments up to 200 acres only, but commonly applied to
basins up to one square mile in size.
1.6.4. Land Use I Land Cover
Land use refers to human's activities which are directly related to the
land, while land cover describes the vegetation and artificial construction
covering the land surface (Osborne and Wiley, 1988). Limited methodologies
are available for addressing land use I land cover effects on stream flow.
According to Osborne and Wiley (1988), methodologies for watershed land
14
use effects on stream ecosystems are not well established. Stream
ecosystems are closely related to their watersheds and affected by
anthropogenic uses (Osborne and Wiley, 1988).
Stream ecosystems can be adversely affected by human alteration of
surrounding land cover. Physical and biological relationships between
streams and terrestrial landscapes are affected when natural vegetated
landscapes are converted to urban or agricultural land uses. Anthropogenic
land use / land cover alterations in Pacific Northwest watersheds have been
accompanied by a decrease in water quality of surface waters, especially in
urban and agricultural catchments. Human induced alterations in Pacific
Northwest watersheds include increased levels of light, stream temperature,
non-point source pollutants, runoff, invasive species, decreased channel
stability, changes in streamflow, instream habitat degradation, and reduced
habitat diversity due to the reduction of riparian ecotones (Correll, 1991;
Naiman et al., 1992; Gregory et al., 1991). The extent of anthropogenic land
use / land cover, specifically agricultural and urban land use, is expressed
through the human use index (U-index) which is also used for this research
(Jones et al., 1997).
Land use I land cover influences streamfiow and habitat quality.
Catchments with a higher percentage of natural vegetation in catchments rank
higher in habitat quality than sites with largely agricultural land use (Roth et al.,
1996). According to Roth et al. (1996), natural vegetation can moderate
streamfiow and extremes during normal flood and drought cycles. Removal of
native vegetation increases the potential for overland flow and channel erosion
(Berkman and Rabeni, 1987). Naturally vegetated watershed do not show
excess overland flow (Roth et al., 1996). Natural vegetation therefore
regulates the frequency of bankfull discharge and flood events.
15
1.6.5.
Riparian Buffers
1.6.5.1. Definition and Overview
Human induced impacts on aquatic resources are reflected in stream
adjacent land use I land cover. Traditionally established buffers attempt to
minimize anthropogenic effects on streams and are typically comprised of
forested or grass land riparian zones (Brazier and Brown, 1973; Morning,
1975; Burns 1970; Karr and Schlosser 1976). Riparian vegetation performs a
variety of functions in the protection of aquatic resources, as reviewed in
chapter four. According to Castelle et al. (1994), specific riparian functions
should determine the dimensions of buffers.
1.6.5.2. Influence of Riparian Buffers on Aquatic Ecosystems
Riparian vegetation has been documented to have a positive influence
on runoff, discharge, filtering, habitat diversity, erosion, and stream
temperature. Chapter four presents a detailed literature review for these
riparian buffer functions. Hydrologic processes are mediated by riparian
vegetation and channel morphology (Schlosser and Karr, 1981 a; Cooper et
al., 1987). The riparian buffer function of moderating stormwater runoff has
been documented (Castelle et at., 1994; Schlosser and Karr, 1981a). Two
mechanisms indicate the influence of riparian zones on runoff; (1) reduced
surface runoff due to increased infiltration in vegetated buffer strips (Muscutt et
al., 1993; Castelle et al., 1994), and (2) reduced surface flow velocities due to
increased hydraulic roughness in buffer strips (Muscutt et al., 1993; Schlosser
and Karr, 1981a). Muscutt et al. (1993) concluded that infiltration is greater in
healthy soils associated with permanent vegetation and high root density,
compared to compacted agricultural soils. An increase in infiltration rates
16
reduces the surface runoff and the corresponding streamfiow response
(Muscutt et al. 1993).
Changes in discharge and increases in stream temperature may alter
biotic communities and alter fish habitat (Baltz et al., 1987; Hicks et al., 1991;
Schlosser, 1982). Barton et al. (1985) concluded that the required length for
buffer strips in order to achieve significant improvements in stream quality are
longer for discharge compared to stream temperature control. Riparian forest
cover may have a moderating effect on discharge through increasing bank
storage (Freeze and Cherry, 1979) or reducing overland flow. Schlosser and
Karr (1981 a) documented that rapid transport of runoff is characteristic for
stream sections with no riparian vegetation.
Castelle et al. (1994) reviewed buffer widths required to perform certain
riparian buffer functions, including filtering, habitat, erosion, and stream
temperature. Chapter four contains a detailed literature review on these
riparian buffer functions and associated buffer width recommendations.
Filtering is the main documented riparian buffer function. Riparian buffers are
widely used to remove sediments, nutrients, and pollutants, contained in
surface runoff (Castelle et al., 1994). Riparian buffer zones have a positive
effect on loads of sediment and phosphorus in surface runoff. The take-up of
nutrients through riparian zones benefits water quality of aquatic resources in
agricultural catchments (Castelle et at., 1994; Dillaha et al., 1989). Riparian
vegetation controls sediment and erosion (Cooper et al., 1987; Young et al.,
1980; Dillaha et al., 1989). Riparian vegetation shades streams and has been
directly correlated to stream temperatures and fish habitat (Beschta and
Taylor, 1988; Brazier and Brown, 1973). Riparian buffers are ecotones and
provide habitat diversity. An understanding of riparian zones serves as a
framework for an understanding of fluvial ecosystems with their organization,
diversity, and ecology (Naiman et al., 1992).
17
1.6.6. Geographic Information System (GIS) Analysis and Scale
GIS allows natural resource and land managers the ability to conduct
spatial analysis on water and land resources. Watershed related studies
utilizing GIS include Kompare's (1998) analysis of near-stream vegetative
cover and instream biotic integrity using digital land cover data for the state of
Illinois. Osborne and Wiley (1988) conducted a GIS buffer analysis to study
the influence of riparian vegetation on instream nutrient concentrations.
Youberg et al. (1998) used GIS to develop a method for evaluating streamwater relationships from watershed parameters derived from a digital elevation
model (DEM) and field collected data. Fels and Matson (1998) used a GIS
analysis of DEMs to develop a hydrogeomorphic land classification.
Studying the characteristics of stream systems requires a range of
spatial scales ranging from microhabitat to the entire stream network (Frissell
et al., 1986; Sedell et at., 1990). Rivers are hierarchical systems. Large
watersheds are comprised of smaller tributaries and their catchments, many
stream reaches, pools, riffles, and other habitat units.
GIS based analysis
conducted for this study utilizes field data collected at the reach scale and land
use I land cover data acquired at the landscape scale. According to Roth et
at. (1996), the functional benefits of natural vegetation within the watershed
are likely to be scale dependent. At the local scale of a few hundred meters,
riparian vegetation influences stream morphology by providing inputs of litter
and woody debris, and aid in maintaining local bank and channel stability.
However, the extent of riparian vegetation over a larger spatial scale
influences overall stream energy sources and flow regime. At the broader
scale of the watershed or entire stream, land cover patterns have been related
to instream conditions utilizing GIS (Roth et al., 1996).
Roth et al. (1996) investigated stream condition, indicated by the Index
of Biological Integrity (IBI) and the Habitat Index (HI), as a function of land use
/ cover in southeastern Michigan. Land use and cover was quantified at the
18
local (1:1), reach (1:5,000), and catchment scale (1:24,000) to determine if
measurement scale influenced the effectiveness of vegetation measures as
predictors of instream ecological status. Roth et al. (1996) found that land use
variables at smaller spatial scales were the most effective predictors of site to
site variation in stream condition. Measures of local riparian vegetation at site
and reach scales typically were ineffective predictors. Roth et al. (1996) found
that measures of land use and riparian vegetation at smaller spatial scales are
better predictors of instream condition than are local measures.
1.7. TERMINOLOGY
Many technical terms used throughout this dissertation are unique to
the field of fluvial geomorphology. In addition to this terminology, a variety of
definitions is commonly applied to riparian buffer studies. Terminology
referring to stream adjacent areas throughout this research may differ from
previous studies. This research focuses on an analysis of 'real world' stream
adjacent areas - with their anthropogenic alterations and land uses, whereas
traditional riparian studies concentrate on specifically designed or selected
buffers such as forest or grass land riparian zones. The following definitions
will be used throughout this study:
Bankfull Discharge: The discharge that fills the channel to the level of the
active floodplain. This discharge is a critical channel forming discharge.
Bankfull discharge is the most efficient discharge. At bankfull
discharge, a maximum amount of sediment and water is transported
with the least amount of energy.
Bankfull Discharge Recurrence Interval: The mean interval of recurrence or
the frequency of bankfull flow. Bankfull discharge recurrence interval
expresses the probability that bankfull discharge will occur in any given
year. Frequency of occurrence is inversely related to the magnitude of
19
flow. For example, the probability of occurrence of a 2 year event is 1/2
= 50% for any given year.
Buffer: Stream adjacent area of a specified width.
Band: Area of a specified width that parallels the stream network and may
either be directly adjacent to the stream network or offset by a specified
distance.
Critical Zone Management Model: A management model presented in this
study. The model identifies stream adjacent land use I land cover
areas within a watershed that play a critical role in streamfiow
alterations. Stream adjacent zones that are important for flood risk
assessment are delineated.
Land Use Runoff Index (LUR-index): An indicator variable presented in this
study. The LUR-index is based on land use / land cover and
associated runoff values. The index ranges from I to 5, where I
indicates low flood risk and a naturally forested watersheds, while 5
indicates high flood risk due to increased surface runoff in a heavily
urbanized environment.
Human Use Index (U-index): An indicator variable measuring the degree of
anthropogenic land use I land cover within a watershed. The U-index
expresses the agricultural and urban area within a watershed in
percent.
Proportional Human Use Index: The proportion of all agricultural and urban
land use I land cover of the watershed located within a band or buffer
area.
Proportional Land Use I Land Cover: The proportion of a particular land use /
land cover of the watershed located within a band or a buffer area.
20
1.8. DESCRIPTION OF DATA
The primary databases compiled for this research include: (I) a USGS
gaging station database; (2) DEMs for the entire study area; (3) a land use I
land cover (LULC) database, (4) a field stream gage database containing
bankfull discharge data; (5) a stream network coverage; and (6) supplemental
data, such as a Koppen-Geiger climate classification for the Pacific Northwest.
1.8.1. USGS Gaging Station Database
The USGS stream gaging station database includes locational data for
71 gaging stations in the study area. The locational data includes gaging
station coordinates and large scale maps detailing the station's location
relative to natural and manmade features (USGS, 1999d).
1.8.2. Digital Elevation Models
One degree DEMs available from the USGS were used for hydrologic
modeling (USGS, 1999c). USGS DEM grids are in geographic coordinates
(ground units: arc-seconds, surface units: meters). A DEM mosaic was
created for the Pacific Northwest to allow for accurate delineation of
watersheds extending over grid boundaries.
1.8.3. Land Use / Land Cover
Land use characteristics of the Pacific Northwest were analyzed using
digital LULC data files developed and distributed by the USGS and the United
21
States Environmental Protection Agency (USEPA) (USEPA, 1999a). USGS
LULC data, digitized from NASA and USGS aerial photography, were initially
produced by the National Mapping Program at 1:250,000. The data are
available in digital format as 1:250,000-scale USGS base maps for the entire
United States. The scale of the LULC coverages is consistent with the scale
of one-degree DEMs, and makes a reasonable assessment of the Pacific
Northwest and its regional conditions possible. To assure consistent
interpretation of land use I land cover, the standard criteria used for USGS
classification were applied to the entire study area. The interpretations were
based on a land use I land cover system developed for use with remotely
sensed data. The USGS LULC classification is based on Anderson's et al.
(1976) land use classification.
1.8.4. Field Stream Gage Database
Bankfull stage was determined from field observations at active USGS
gaging stations (Castro, 1997) using guidelines defined by Dunne and Leopold
(1996). Bankfull discharge recurrence intervals were calculated based on
annual maximum flow frequency curves representing 50 years of data. Gaged
streams were selected since long-term streamfiow records are necessary in
order to determine the recurrence intervals for bankfull stage.
1.8.5. EPA River Reach File
The USEPA Reach File Version 1.0 (RFI) for the conterminous United
States was used as the database for the stream network. The file RFI was
developed by the USEPA in 1982 and is available as an ARC/INFO coverage
on the USEPA web site (USEPA, 1999b). RFI was prepared by the USEPA
22
from stable base color separates of National Oceanographic and Aeronautical
Administration (NOAA) aeronautical charts. According to the USEPA, these
charts provided the best nationwide hyrographic coverages (USEPA, 1986).
They include all hydrography shown on USGS 1:250,000 scale maps.
The file is a vector database of streams and is commonly used by the
U.S. Fish and Wildlife Service, USGS, USEPA, and other natural resources
agencies. The USEPA uses RFI extensively for water quality modeling on
river basins.
1.8.6. Climate Data
Pacific Northwest watersheds were categorized by climate regions
based on the Koppen-Geiger climate classification system. Three broad
Koppen climate regions were delineated and mapped from the classification of
72 weather stations in the Pacific Northwest (Lucas and Jackson, 1995).
Chapter three describes the three major climate regions.
23
1.9. REFERENCES
Anderson, JR., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A Land
Use and Cover Classification System for Use With Remote Sensor
Data. U.S. Geological Sui'vey Professional Paper 964.
Baltz, D.M, B.Bondracek, L.R. Brown, and P.B. Moyle. 1987. Influence of
Temperature on Microhabitat Choice by Fishes in a California Stream.
Transactions of the American Fisheries Society, 116:12-20.
Barton, D.R., W.D. Taylor, and R.M. Biette. 1985. Dimensions of Riparian
Buffer Strips Required to Maintain Trout Habitat in Southern Ontario
Streams. North American Journal of Fisheries Managment, 5:364-378.
Berkman, H.E. and C.F. Rabeni. 1987. Effect of Siltation on Stream Fish
Communities. Envir. Biol. Fishes, 18:285-294.
Beschta, R.L., and R.L. Taylor. 1988. Stream Temperature Increases and
Land Use in a Forested Oregon Watershed. Water Resources Bulletin,
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Bowen, H.C. 1959. Discussion of "A Study of the Bankfull Discharges of
Rivers in England and Wales", by M. Nixon, Proc. Inst. Civil Eng.
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Brazier, J.R., and G.W. Brown. 1973. Buffer Strips for Stream Temperature
Control. Forest Research Laboratory, Oregon State University,
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Brooks, K.N., P.F. Ffolliott, H.M. Gregersen, and J.L. Thames. 1993.
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Burns, J.E. 1970. The Importance of Streamside Vegetation to Trout and
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Castelle, A.J., A.W. Johnson, and C. Conolly. 1994. Wetland and Stream
Buffer Size Requirements - A Review. Journal of Environmental Quality,
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24
Castro, J. 1997. Bankfull Flow Recurrence Intervals: Patterns in the Pacific
Northwest. Submitted to Water Resources Research.
Cooper, J.R., J.W. Gilliam, R.B. Daniels, and W.P. Robarge. 1987. Riparian
Areas as Filters for Agricultural Sediment. Soil Sc!. Soc. Am., 51:416420.
Correll, D.L. 1991. Human Impact on the Functioning of Landscape
Boundaries. In Ecotones, The Role of Landscape Boundaries in the
Management and Restoration of Changing Environments by Holland,
M.M., P.G. Risser, and R.J. Naiman (ed.). Chapman and Hall, New
York, NY.
Craig, N.J., and E. Kuenzler. 1983. Land Use, Nutrient Yield, and
Eutrophication in the Chowan River Basin. University of North Carolina,
Water Resources Institute. Report No. 205.
Dillaha, T.A., R.B. Reneau, S. Mostaghimi, and D. Lee. 1989. Vegetative
Filter Strips for Agricultural Nonpoint Source Pollution Control.
Transactions of the A SAE, 32:513-519.
Dunne, T., and L.B. Leopold. 1996. Water in Environmental Planning. W.H.
Freeman and Co., San Francisco, CA.
Dury, G.H., J.R. Hails, and H.B. Robbie. 1963. Bankfull Discharge and the
Magnitude Frequency Series. Australian Journal of Science, 26:123124.
Dury, G.H. 1977. Underfit Streams: Retrospect, Perspect, and Prospect. In
River Channel Changes by Gregory, K.J. (ed.), pp. 282-293.
Emmett, W.W. 1975. The Channels and Waters of the Upper Salmon River
Area, Idaho. U.S. Geological Survey Professional Paper 870-A.
Fels, J.E., and K.C. Matson. 1998. A Cognitively-Based Approach for
Hydrogeomorphic Land Classification Using Digital Terrain Models.
Proceedings of the 1998 ESRI User Conference by the Environmental
Systems Research Institute, Redlands, CA.
Freeze, R.A., and J.A. Cherry. 1979. Groundwater. Prentice-Hall, Inc.,
Englewood Cliffs, NJ.
25
Frissel, C.A., W.J. Liss, C.E. Warren, and M.D. Hurley. 1986. A Hierarchical
Framework for Stream Habitat Classification: Viewing Streams in a
Watershed Context. Environ. Mgmt., 10:199-214.
Gordon, N.D., T.A. McMahon, and B.L. Finlayson. 1994. Stream Hydrology,
An Introduction for Ecologists. John Wiley & Sons, New York, NY.
Gregory, S.V., F.J. Swanson, W.A. McKee, and K.W. Cummins. 1991. An
Ecosystem Perspective of Riparian Zones - Focus on Links Between
Land and Water. BioScience, 41:540-551.
Hicks, B.J., J.D. Hall, P.A. Bisson, and J.R. Sedell. 1991. Responses of
Salmonids to Habitat Changes. American Fisheries Society Special,
15:483-518.
Jones, K.B., K.H. Riitters, J.D. Wickham, R.D. Tankersley Jr., R.V. O'Neill,
D.J. Chaloud, E.R. Smith, and A.C. Neale. 1997. An Ecological
Assessment of the United States Mid-Atlantic Region: A Landscape
Atlas. U.S. Environmental Protection Agency. Office of Research and
Development, Washington, D.C., EPA/600/R-97/1 30.
Karr, J.R., and l.J.Schlosser. 1976. Impact of Nearstream Vegetation and
Stream Morphology on Water Quality and Stream Biota. U.S.
Environmental Protection Agency, Washington, D.C., EPA-600/3-77097.
Kilpatrick, F.A., and H.H. Barnes. 1964. Channel Geometry of Piedmont
Streams as Related to Frequency Floods. U.S. Geological Suivey
Professional Paper 422-E.
Kompare, T.N. 1998. A Preliminary Study of Near-Stream Vegetative Cover
and In-Stream Biological Integrity in the Lower Fox River, Illinois.
Proceedings of the 1998 ESRI User Conference by the Environmental
Systems Research Institute, Redlands, CA.
Leopold, L.B. 1996. A View of the River. Harvard University Press,
Cambridge, MA.
Leopold, L.B., and H.E. Skibitzke. 1967. Observations on Unmeasured
Rivers. Geogr. Ann., 49:247-255.
Leopold, L.B., M.G. Wolman, and J.D. Miller. 1964. Fluvial Processes in
Geomorphology. Freeman, San Francisco, CA.
26
Lucas, B. and P.L. Jackson. 1995. Koppen Climate Classification of the
Pacific Northwest (1961 to 1990 Temperature and Precipitation Data).
Unpublished map (scale 1:4,000,000).
Marsh, W.M. 1991. Landscape Planning, EnvironmentalApplications. John
Wiley & Sons, Inc., New York, NY.
Morning, J.R. 1975. The Alsea Watershed Study: Effects of Logging on
Three Headwater Streams of the Aisea River, Oregon. Part ill Discussion and Recommendations. Oregon Department of Fisheries
and Wildlife, Corvallis, OR, Fisheries Research Report 9.
Muscutt, A.D., G.L. Harris, S.W. Bailey, and D.B. Davies. 1993. Buffer
Zones to Improve Water Quality: A Review of Their Potential Use in UK
Agriculture. Agriculture, Ecosystems and Environment, 45:59-77.
Naiman, R.J., H. Decamps, and M. Pollock. 1992. The Role of Riparian
Corridors in Maintaining Regional Biod iversity. Ecological Applications,
3:209-212.
Olsen, D.S., A.C. Whitaker, and D.F. Potts. 1997. Assessing Stream
Channel Stability Thresholds Using Flow Competence Estimates At
Bankfull Stage. Journal of the American Water Resources Association,
33(6):1 197-1207.
Omernik, J.M. 1976. The Influence of Land Use on Stream Nutrient Levels.
U.S. Environmental Protection Agency, EPA-600/3-73-014.
Osborne, L.L., and M.J. Wiley. 1988. Empirical Relationships Between Land
Use/Cover and Stream Water Quality in an Agricultural Watershed.
Journal of Environmental Management, 26:9-27.
Pastor, J. and C.A. Johnston. 1992. Using Simulation Models and
Geographic Information Systems to Integrate Ecosystem and
Landscape Ecology. In Watershed Management, Balancing
Sustainability and Environmental Change by Naiman, R.J. (ed.). 1992.
Springer Verlag, New York, NY, pp. 324-346.
Petit, F., and A. Pauquet. 1997. Bankfull Discharge Recurrence Interval in
Gravel-Bed Rivers. Earth Surface Processes and Landforms, q
22:685-693.
27
Poff, N.L., J.D. Allan, M.B. Bain, J.R. Karr, K.L. Prestegard, B.D. Richter,
R.E. Sparks, and J.0 Stromberg. 1997. The Natural Flow Regime, A
Paradigm for River Conservation and Restoration. BioScience, 47(11):
769-784.
Ritter, D.F., R.C. Kochel, and J.R. Miller. 1995. Process Geomorphology.
(3rded.) Wm. C. Brown Publishers, Dubuque, IA.
Roth, N.E., J.D. Allan, and D.L. Erickson. 1996. Landscape Influences on
Stream Biotic Integrity at Multiple Spatial Scales. Landscape Ecology,
I 1(3):141-156.
Schlosser, l.J., and J.R. Karr. 1981a. Riparian Vegetation and Channel
Morphology Impact on Spatial Patterns of Water Quality in Agricultural
Watersheds. Environmental Management, 5:233-243.
Schiosser, l.J., and J.R. Karr. 1981b. Water Quality in Agricultural
Watersheds: Impact of Riparian Vegetation During Base Flow. Water
Resources Bulletin, 17:233-240.
Schlosser, l.J. 1982. Trophic Structure, Reproductive Success and Growth
Rate of Fishes in a Natural and Modified Headwater Stream. Canadian
Journal of Fisheries and Aquatic Sciences, 39:968-978.
Sedell, J.R., G.H. Reeves, F.R. Hauer, J.A. Stanford, and C.P. Hawkins.
1990. Role of Refugia in Recovery from Disturbances: Modern
Fragmented and Disconnected River Systems. Environ. Mgmt., 14:711724.
Stream Systems Technology Center. 1993. Would the Real Bankfull Please
Stand Up. Stream Notes, April, 1993.
USDA-FS. 1995. A Guide to Field Identification of Bankfull Stage in the
Western United States (Video). U.S. Department of Agriculture, Forest
Service, Rocky Mountain Forest and Range Experimental Station,
Stream Systems Technology Center, Fort Collins, CO.
USDA -FS. 1992. Integrated Riparian Evaluation Guide. U.S. Department of
Agriculture, Forest Service, Intermountain Region, Ogden, UT.
USDA-SCS. 1994. Salmon Recovery Initiative Draft. United States
Department of Agriculture, Soil Conservation Service, West National
Technical Center, Portland, OR.
28
USGS. 1990. Land Use and Land Cover Digital Data From 1:250,000- and
1:100,000-Scale Maps--Data Users Guide 4. U.S. Geological Survey,
Reston, VA.
Williams, G.P. 1978. Bank-Full Discharge of Rivers. Water Resources
Research, 14(6):1 141-1154.
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Rivers in England and Wales", by M. Nixon, Proc. Inst. Civil Eng., 14:
400-402.
Wolman and Leopold. 1957. River Flood Plains: Some Observations on
Their Formation. U.S. Geological Survey Professional Paper, 282-C,
pp. 86-1 09.
Youberg, A.D., D.P. Guertin, and G.L. Ball. 1998. Developing StreamWatershed Relationships for Selecting Reference Site Characteristics
Using ARC/INFO. Proceedings of the 1998 ESRI User Conference by
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Vegetated Buffer Strips in Controlling Pollution From Feedlot Runoff: J.
Environ. Qual., 9:483-487.
29
1.10. WEB AND FTP REFERENCES
Northwest Marine Fisheries Service. 1998. Northwest Fisheries Science
Center. http:I/research . nwfsc. noaa . gov.
USEPA. I 999a. Geographic In formation Retrieval Analysis System
(GIRAS) Directoty. U.S. Environmental Protection Agency,
ftp://ftp.epa .gov/pu b/spd ata/EPAG I RAS.
USEPA. I 999b. USEPA Reach File Version 1.0 (RFI) for the Conterminous
United States (CONUS). U.S. Environmental Protection Agency,
http://www.epa .gov/ng ispg m3/nsd i/projects/ill _meta. html.
USEPA. 1986. Meatadata for RFI - Reach File Manual, Draft of June 30
1986. U.S. Environmental Protection Agency, C.Robert Horn,
http://www.epa.gov/ngispg m3/nsd i/projects/ill _meta.html.
USGS. 1999a. USGS - Water Resources of the United States: Background
Data Sets for Water Resources: HUC and Streams (Digital Line
Graphs). U.S. Geological Survey,
http://water.usgs.gov/G IS/background. html.
USGS. 1999b. Land Use/Land Cover Data (1:250,000). U.S. Geological
Survey,
http://edcwww.cr. usgs .gov/glis/hyper/guide/l _250_lulcfig/states. html.
USGS. I 999c. Global Land In formation System (GLIS): 1-Degree-Digital
Elevation Models. U.S. Geological Survey,
http://edcwww.cr.usgs.gov/glis/hyper/guide/1 dgr_demfig/states.html
USGS. 1999d. United States Water Resources Page: National Water
Information System (NWIS). U.S. Geological Survey,
http//waterdata. usgs.gov/nwis-w.
30
CHAPTER II
ASSESSMENT OF GIS HYDROLOGIC MODELING FOR THE
DELINEATION OF SELECTED SALMON HABITAT WATERSHEDS IN THE
PACIFIC NORTHWEST
Franziska Whelan
Submit to The Professional Geographer
31
11.1. ABSTRACT
This study utilizes geographic information systems (GIS) for watershed
delineation of 71 Pacific Northwest (Oregon, Washington, and Idaho)
watersheds, and evaluates an integrated methodology for watershed
delineation relying on the ArcView Spatial Analyst Hydrologic Modeling
extension. Watershed delineation was an initial step for data acquisition for a
larger study about the correlation of land use I land cover and streamfiow
variables. Gaging station data for 71 streams, USGS land use I land cover
data, and USGS digital elevation models were analyzed using ArcView,
ArcView Spatial Analyst with the Hydrologic Modeling extension, and Arclnfo.
The major findings of this research are (1) exact pour point location
determination integrated with modifications to the ArcView watershed
delineation script may dramatically increase watershed boundary accuracy, (2)
low gradient and I or dense urban areas surrounding pour point negatively
impact delineation, and (3) consistent quality control and use of supplemental
delineation methods is recommended for all digitally delineated watersheds.
11.2. INTRODUCTION
This chapter overviews the development of a watershed delineation
methodology relying on the functionality of the ArcView Spatial Analyst
Hydrologic Modeling extension. The study evaluates this methodology and
provides a basis for future studies that intend to utilize Spatial Analyst for
hydrologic or geomorphic research.
Watershed delineation for 71 Pacific Northwest watersheds was
required for a larger study on the correlation between land use and streamflow
variables (Whelan, 2000a). Presently, there is a lack of information on the
influence of drainage basin alterations, specifically land use, on a variety of
32
hydrologic and geomorphic parameters. This research compiles land use I
land cover data (LULC) and digital elevation models (DEMs) for the Pacific
Northwest states of Oregon, Idaho, and Washington. The compiled database
will be useful in future watershed related studies and research projects in the
Pacific Northwest.
Suitable tools for digital watershed delineation in ArcView GIS, ArcView
Spatial Analyst, Arclnfo, and the statistical analysis program S-PLUS were
utilized for this study.
11.3. OBJECTIVES
The purpose of this study is to develop and evaluate a methodology
based on the ArcView Spatial Analyst Hydrologic Modeling extension for
watershed delineation of selected Pacific Northwest watersheds. This study
seeks: (1) to demonstrate the suitability of the Spatial Analyst Hydrologic
Modeling extension for accurate watershed delineation; (2) to describe
watershed attributes that have the greatest impact on accurate digital
watershed delineation; and (3) to determine modifications and supplemental
methods required to improve delineation accuracy.
Helpful GIS tools for land management and for geomorphic and
hydrologic studies include the hydrologic functions of Spatial Analyst, which
aid in watershed delineation and the definition of stream networks across a
surface. Applications of this functionality include management units
identification based on watersheds, land use analysis within a selected
watershed, water reserves identification, stream network determination across
a surface for the design of riparian buffers based on stream order, or water
runoff estimation for the purpose of flood control.
33
11.4. BACKGROUND
GIS allows natural resource and land managers the ability to conduct
spatial analysis on water resources. Watershed related studies utilizing GIS
include Kompare's (1998) analysis of near-stream vegetative cover and instream biotic integrity using digital land cover data for the state of Illinois.
Osborne and Wiley (1988) conducted a GIS buffer analysis to study the
influence of riparian vegetation on in-stream nutrient concentrations. Youberg
et al. (1998) used GIS to develop a method for deriving stream-water
relationships for the selection of reference site characteristics from watershed
parameters derived from a DEM and field collected data. Fels and Matson
(1998) used a GIS analysis of DEMs to conduct a hydrogeomorphic land
classification.
Few studies have been completed utilizing and evaluating GIS
hydrologic modeling tools. Horton (1932), Strahler (1957), and Leopold and
Maddock (1953) have described watershed geometry. Hydrologic modeling
using GIS is largely based on these geometric relationships between drainage
basins, stream networks, and channels (Youberg et al., 1998). There is
presently a lack of information on the suitability of GIS modeling tools, in
particular the Spatial Analyst Hydrologic Modeling extension, for watershed
delineation.
The correlation of hydrologic and geomorphic parameters to watershed
characteristics, in particular land use and cover, is vital for regional planning
efforts, river preservation, and flow studies, as well as studies on flood
hazards. GIS aids in analyzing this correlation through hydrologic modeling
and spatial analysis of watershed parameters, including land use.
Watershed delineation for this study was conducted at designated
salmon habitat recovery streams in the Pacific Northwest (USDA-SCS, 1994).
The decline of salmonids in the Pacific Northwest in the past decades has
been caused in part by habitat degradation through anthropogenic alterations
of streams and watersheds. The degradation of salmonid habitat is a critical
environmental issue throughout the Pacific Northwest and requires watershed
related research (Castro, 1997).
11.5. STUDY AREA
The study area includes 71 designated salmon habitat recovery streams in
the Pacific Northwest, including Oregon, Idaho, and Washington (Figure 11.1).
Castro (1997) has determined geomorphic indicators at the reach scale of
these streams. The hydrologic modeling and land use assessment was
conducted at the watershed scale.
StieaSarir9 9teat
Stia,
D Se ,uicies
ty
kea S
7
AJtes Euo Ari Caic Fkejiai
Figure 11.1: Study Area (Oregon Washington, Idaho).
o
MLES
1OOKiLavIEr
35
11.6. DATABASES
The primary databases compiled for this study include a United States
Geological Survey (USGS) gaging station database (USGS, 1999d) for pour
point determination, and one-degree DEMs (USGS, 1999c) for surface
modeling. Other coverages included a LULC database (USEPA, 1999a)
hydrologic unit coverages (HUCs) (USGS, 1999a), and digital line graphs
(DLGs) (USGS, 1999a) for the Pacific Northwest. The HUC and DLG data
were provided by the USGS as background data sets for water resources.
HUCs are hydrologic areas based on surface topography, containing the
drainage area of major rivers and surface drainage basins (Seaber et al.,
1987). HUC units at the fourth level, the most detailed level of classification
available, were utilized for this study. This study also used state boundary
coverages for Oregon, Washington, and Idaho (USGS, 1999a). The
databases for this study are the primary regional scale coverages available for
the study area and are in wide use by public and private GIS research
organizations.
11.7. METHODOLOGY
The topography and relative relief of an area determines the flow of surface
water. Spatial Analyst is an extension for ArcView that allows users to analyze
and model raster GIS data, such as a DEM. The Hydrologic Modeling
extension is also an ArcView extension, and requires the Spatial Analyst
extension to be loaded. The Hydrologic Modeling extension provides
functions that use DEM values to model the flow of surface water. This
extension, in conjunction with supplemental methods, was utilized for
watershed delineation for 71 stream sampling points. The following
methodology focuses on (1) the acquisition and manipulation of DEMs; (2) the
36
determination of pour points and the delineation of watersheds; and (3) quality
control and spatial editing of the delineated watersheds.
11.7.1. DEM Acquisition and Manipulation
Ninety-seven one degree DEMs were downloaded in compressed format
from the USGS web sites for Oregon, Washington, and Idaho, and were
converted to grids. The grids have a scale of 1:250,000, which is consistent
with the scale of the LULC data layer. USGS DEM grids are expressed in arcseconds (geographic). Hydrologic modeling was conducted using the original
coordinate system and projection. All delineated watersheds required
conversion to the Albers Conic Equal Area projection after hydrologic
modeling was completed.
DEMs were mosaicked for the entire study area to allow for accurate
delineation of watersheds extending over grid boundaries. The grid
transformation tools in Spatial Analyst provide the mosaic function for grids.
The following hydrologic modeling functions were utilized for DEM
manipulation: FillSinks, FlowDirection, and FlowAccumulation. These
functions use the mosaicked DEMs as input grids, model the surface
characteristics, and create new grids. The new grid output files were then
used to model watershed characteristics.
FillSinks "corrects" the DEM grid by identifying any existing sinks.
Sinks are incorrect values on the DEM that are of lower elevation compared to
all surrounding values. Water flowing into sinks would not be able to flow out
and therefore cause inaccuracies in flow path and drainage mapping as well
as in watershed delineation. The FillSinks request fills these artificial
depressions and creates a new grid. The new output filled grid served as the
base grid for all subsequent hydrologic modeling.
37
Next, FlowDirection was used to determine the directions in which
water would flow out of each cell. This in turn created yet another output grid
WI
(Figure 11.2).
Row cIreica:
1 (east)
2 (southeast)
4 (south)
-
8(souUest)
16(st)
32 (nothst)
64 (north)
128 (northeast)
ia Ws
Figure 11.2: Flow Direction Grid.
FlowAccumulation was then used to create a stream network.
FlowAccumulation calculates the number of upslope cells flowing into a
location based on the flow direction grid. The resulting flow accumulation grid
displays the major drainage network (Figure 11.3).
38
Flow Acwmu!aticri
NixterdLpslcpecIs)
o - 499
-
9999
-
O-49999
L.
6JO
0
11ho1d
J0
1200
Iue: 500
rWers
Figure 11.3: Flow Accumulation Grid.
Data processing and management issues need to be considered when
working with large mosaicked grid files. The largest mosaicked grids in terms
of data size were produced from the FlowAccumulation function, averaging
150 Mb per state and over 500 Mb for the entire Pacific Northwest. Hardware
available to the user will determine if modeling is possible at the state or
regional scale. During the initial phase of the project, a 333 MHz PC with 128
Mb RAM was used and was incapable of calculating the FillSinks,
FlowDirection, FlowAccumulation, and FlowPath requests on mosaics of DEM
grids for the individual states or the entire Pacific Northwest. During the later
phase of the project, a 433 MHz PC with 128 MB RAM was used and found
capable of executing these requests on large mosaics. Mosaics are required if
watersheds extend over several individual grids. Also, working with mosaics
39
saves time. All hydrologic modeling functions need to take place on the
original mosaicked DEM. Watersheds will not delineate accurately if grids are
not mosaicked until the various hydrologic functions are executed on the
individual grids.
11.7.2. Pour Point Determination and Watershed Delineation
A stream station database including 71 USGS gaging stations along
designated salmon habitat recovery streams in the Pacific Northwest was
compiled for this study (Castro, 1997). Streams were selected based on
watersheds that contain critical habitat for salmonids. The selected stream
gages monitor watersheds ranging from approximately 12,000 acres to 9
million acres in size.
Before watershed delineation, the exact location of each gaging station
had to be determined. Latitude and longitude data allowed for an approximate
location of stream stations. However, for correct watershed delineation, it is
crucial to locate the exact sampling point on the stream. Minor deviations from
the stream or flow path will initiate the delineation of small side watersheds
rather than the target watersheds.
The digital stream sampling point layer indicated the approximate
location of stream stations. USGS gage station location maps, avaiabIe
through the USGS water data web site (USGS, 1999d) at scales of 1:12,808,
1:25,617, 1:51,234, 1:102,468, and 1:205,265were utilized as supplemental
information. Hardcopy topographic maps at scales of 1:150,000, and
1:300,000 for Oregon (DeLorme, 1996), 1:150,000 for Washington (DeLorme,
1998), and 1:250,000 for Idaho (DeLorme, 1998), as well as selected USGS
topographic maps at scales of 1:250,000, were referenced to determine the
exact location for each stream sampling point.
40
Once the location of the stream sampling point was determined, the
pour point had to be placed exactly onto the stream network, or onto the flow
path. Flow path delineation utilized the filled grid, FtowDirection grid, and
FlowAccumulation grid. Hydrologic properties need to be set to define the
correct input FlowDirection and FlowAccumulation grids. The filled grid should
be the active grid in the view of an ArcView session. The filled grid allows for
good visualization of the topography in the area.
The flow accumulation layer displays the stream network. Flow paths
of medium-sized and large streams overlain on the stream network are
defined by the FlowAccumulation grid. The view of the FlowAccumulation grid
was manipulated by changing the value and label in the legend editor of an
ArcView view (legend type: graduated color, color ramps: red monochromatic).
The stream network was clearly displayed when the value and label of the first
legend entry, which was the entry with the smallest value range and lightest
color, was changed to a value range of 1-500. This change caused values of
500 and larger to be displayed in dark colors. The stream network was then
clearly displayed in dark colors and black on a light colored background.
Classifying values by their standard deviations and displaying the first
category in a light color, and the following categories in dark colors also
displayed the stream network well.
Once the pour point was defined, the watershed request was used to
delineate the watershed based on that pour point. Since the described
methodology allowed for accurate determination of stream sampling points,
the Watershedlool script for watershed delineation was modified (script see
Appendix Il.A). By default, the WatershedTool script uses a snap distance of
240 cells around the pour point for watershed delineation. Delineated
watersheds will therefore include cells that are downstream or at level of the
actual watershed. The snap distance of 240 cells assumes that pour points
could not be located accurately on the grid. However, since the described
methodology allowed for accurate pour point location, the default 240 cell
41
snap distance introduces an unnecessary error to the watershed delineation.
Therefore, the grid cell accumulation value for a snapped pour point was
changed from 240 to 0 cells in the following script line:
'caic watershed
theWater - theFlowDir.Watershed(theScrGnd.SnapPourPoint(theAccum,O))
This script change caused a significant increase in delineation accuracy.
Watershed delineation was now based on the determined pour point only.
Users should only change the script if the exact location of the pour point has
been determined.
Delineated watersheds are displayed in the view as temporary grids
called "a watershed" and need to be converted to shapefiles. The DEM
delineated watersheds are in the geographic projection (Figure 11.4). A
conversion from the geographic projection to Albers Conic Equal Area was
conducted to allow for future overlays with the LULC data (Appendix ll.B).
Arclnfo was used for this conversion, since ArcView (version 3.1) does not
offer projection conversions.
fILLU
0
0-377
378-755
755-11a3
1134-1510
1-
1511- 1868
0W
s
Figure 11.4: OEM Mosaic with Delineated Watershed for Stream Gage #
14157500, Coast Fork Willamette.
42
11.7.3. Quality Control and Spatial Editing
To determine the accuracy of the digitally delineated watersheds,
quality control was conducted that included reference checks using a digital
stream layer, the LULC data layer and topographic maps, flow path
comparison, HUC references, and area check. The delineated watersheds
were compared to features displayed on these reference maps and
coverages.
Land features of concern included reservoirs that could potentially alter
the flow paths on the DEM5 and therefore contribute to incorrect watershed
delineation. When large bodies of water or reservoirs were located within or
upstream of the delineated watersheds, flow path delineation was conducted
to check for potential flow path interruption or reversion. The corresponding
original watersheds in geographic coordinates and the filled grid,
FlowDirection grid, and FlowAccumulation grids were used for this check.
The LULC layer and the digital stream layer were used to confirm the
location of land use features, including streams, lakes, reservoirs, urban
areas, and roads, within or outside of the delineated watersheds. Topographic
maps were referenced to confirm the location of these land features relative to
the delineated watershed boundaries.
Quality control was also conducted through area comparisons for all
watersheds. A database of USGS determined drainage areas for stream
gaging stations was compiled and used as a control for all watersheds. USGS
drainage areas were determined through manual watershed delineation on
1:24,000 USGS topographic maps. USGS drainage areas are listed for all
gaging stations on the individual gaging station's web site. Areas of the
digitally delineated watersheds were calculated after the conversion of all
watersheds to Albers Conic Equal Area projection. A merged shapefile
containing all delineated watersheds was created to allow for an efficient area
calculation using the table of that merged shapefile. Watershed areas were
43
calculated using the calculator function and the request
[shape].ReturnArea/(4046.95*640). The units of the Albers Equal Area
projection were set in meters. Dividing the area by (4046.95*640) converts the
area to square miles. Differences between USGS defined areas and
delineated watersheds were determined in both absolute and relative values
(square miles and percent).
Watersheds were re-delineated when watershed areas exceeded set
difference limits from the USGS reference areas. The process of redelineation revealed the underlying problems causing inaccurate delineation.
Following successful re-delineation, all quality control checks were conducted
again. When re-delineation confirmed delineation problems due to faulty DEM
grids or other watershed characteristics, supplemental methods including
HUC5 and digitizing were used to correct or adjust the delineated areas.
HUC5 were referenced when area differences were detected for
relatively medium and large area watersheds, and were used to identify
missing sub-watersheds for delineated watersheds that were too small
compared to the USGS drainage area. For watersheds larger than the USGS
reference value, HUG were referenced to identify areas that should not have
been included in the delineated watersheds. Small area differences were
eliminated by spatially editing the boundaries of the digitally delineated
watersheds to correspond with HUG boundaries. Figure 11.5 displays all
delineated watersheds.
Sited
D Ste Bwiies
y_-._ QQ
e
edWthedB
w+I
971
MLES
o
1XKlLDP.ET
Figure 11.5: Delineated Watersheds.
11.8. ANALYSIS
Statistical methods used to determine the reliability of Spatial Analyst
Hydrologic Modeling extension for watershed delineation included t-tests,
ANOVA F-tests, and descriptive statistics. The evaluation of the described
methodology is based on one degree DEMs used for this study. Spatial
Analyst Hydrologic Modeling may perform at different levels of accuracy with
different data sets.
45
11.8.1. Suitability of Spatial Analyst Hydrologic Modeling for Watershed
Delineation
A two-sample t-test for means was used to determine whether Spatial
Analyst Hydrologic Modeling extension was suitable for watershed delineation
with no supplemental methods used. The analysis data set did not include ten
watersheds that were delineated using topographic maps and hydrologic units
only. These ten watersheds could not be delineated using Spatial Analyst
Hydrologic Modeling due to watershed pour points located in areas of no
gradient, or watersheds located in metropolitan areas. The one degree DEMs
were unsuitable for watershed delineation in dense urban areas because of
human altered topography and hydrologic features, such as storm water
management infrastructure, detention basins, interbasin flow and diversions.
Accuracy of the initially delineated watersheds using Spatial Analyst
was determined through comparing watershed areas of the digitally delineated
watersheds to USGS defined drainage areas. Statistically, there was no
significant difference (two-sided p-value = 0.63) for watersheds delineated
using Spatial Analyst Hydrologic Modeling as the only method and USGS
reference areas (Table 11.1).
Mean
Variance
Observations
Hypothesized Mean Difference
df
Stat
P(T<=t) two-tail
INITIAL DELINEATED AREA
(SQUARE MILES)
USGS REFERENCE AREA
(SQUARE MILES)
971.1476
1903047.295
949.1426
1969923.307
61
61
0
60
0.4732
0.6378
Table 11.1: Two-sample t-test for Watersheds Delineated Using Spatial Analyst
Hydrologic Modeling and USGS Reference Areas.
46
Despite no significant difference for a group of 61 watersheds, area
differences were quite large for some individual watersheds. Table 11.2 lists
the ten maximum area differences for watersheds delineated using Spatial
Analyst Hydrologic Modeling as the only method compared to USGS reference
areas. The mean initial difference for 61 delineated watersheds was 229
square miles with a standard deviation of 1288 square miles. Area differences
varied from 0 square miles up to 2679 square miles.
GAGE
NUMBER
13235000
14359000
14048000
12027500
12205000
14033500
14207500
12031000
12414900
13336500
INITIALLY
DELINEATED
AREA
(SQUARE MILES)
3135
1486
7106
574
360
2107
601
1201
365
1825
USGS
REFERENCE
AREA
(SQUARE MILES)
456
2053
7580
895
INITIAL
DIFFERENCE
(SQUARE MILES)
INITIAL
DIFFERENCE (%)
2679
567
474
588
28
321
36
105
2290
706
1294
275
1910
255
243
183
8
105
93
90
85
15
7
6
33
4
Table 11.2: Ten Maximum Area Differences for Initially Delineated Watersheds
Using Spatial Analyst Hydrologic Modeling as the Only Method
Compared to USGS Reference Areas.
Due to the large area differences for some individual watersheds,
difference limits were arbitrarily set despite the statistically insignificant
difference between the two groups. Difference limits were set to be either>
4%, or >10 square miles, whichever was the least. Watersheds exceeding
area difference limits were re-delineated or corrected using the supplemental
methods described. Spatial editing was applied to 46 out of 61 delineated
watersheds.
47
Six outlier watersheds exceeded the difference limits (Table 11.3). These
watersheds were initially spatially edited, however the final delineated
watershed areas still exceeded the set difference limits. Based on referenced
topographic maps, these six watersheds were judged to be accurate, and
repeated spatial editing was avoided as this could lead to data manipulation in
terms of "matching" the data with the required target areas.
For example, one
outlier watershed exceeded the limit for the 4% area difference. The
exceptionally small watershed was initially delineated with an area of 25
square miles, spatial editing was applied and resulted in a final watershed
area of 20 square miles. A two square mile difference amounted to 12.9%,
compared to the USGS reference area of 17.7 square miles. The other five
outlier watersheds exceeded the square mile difference limit, however, due to
the extremely large size of these watersheds, deviations from the USGS
reference areas amounted to less than 2% of the total watershed area.
GAGE
NUMBER
13346800
13296500
13302005
14312000
13333000
13266000
INITIALLY
DELINEATED
AREA
(SQUARE
MILES)
25
791
834
1657
3297
1382
INITIAL
USGS
REFERENCE
DIFFERENCE
AREA
(%) I (SQ MILES)
(SQUARE
MILES)
18
41/7
802
845
1670
3275
1460
1/11
1/11
1/13
1/22
5/78
FINAL
DIFFERENCE
(%) I (SQ MILES)
13/2
2/15
1/11
0.7/13
0.5/16
1/17
Table 11.3: Outlier Watersheds that Exceed Area Difference Limits.
Watershed accuracy improved after applying spatial editing and
supplemental methods, including reference checks using a digital stream
layer, the LULC data layer, topographic maps, and HUC references. Figure
11.6 illustrates the increase in delineation accuracy. Drainage areas of
48
watersheds delineated using Spatial Analyst as the only method was
compared to final delineated watersheds and USGS reference areas.
11.8.2. The Influence of Watershed Attributes on Watershed Delineation Using
Spatial Analyst Hydrologic Modeling
Watershed attributes that influenced the delineation accuracy of Spatial
Analyst hydrologic modeled watersheds were specific to one degree DEM
data. Watershed attributes of concern included large metropolitan areas, and
areas with little or no gradient within the watershed. Other watershed
attributes that may influence watershed delineation accuracy include land use
features, such as reservoirs or lakes. Delineation problems due to the above
became evident immediately for ten watersheds. These ten problem
watersheds were delineated through digitizing or through correlation with
established hydrologic units. These watersheds were not included in the
following analysis, since delineation problems were not caused by the
malfunctioning of the software but by data problems.
Watershed delineation using the Spatial Analyst Hydrologic Modeling
extension was impossible for watersheds with large metropolitan areas. This
study included the following three mainly metropolitan watersheds; USGS
gaging stations 14202000 (Pudding River at Aurora), 14203500 (Tualatin
River near Dilley), and 14207500 (Tualatin River near West Linn). Delineation
attempts resulted in nonsensical shapefiles of several grid cells in one long
row and were not remotely suitable for statistical testing. These watersheds
were manually delineated using 1:250,000 USGS topographic maps and
digitized.
1600
p.
1400
p
1200
1000
800
600
2400
liii] I iIII:iIi-I
i000
II
USGS Gaging Station
8000
)
I
p
6000
5000
4000
Legend
3000
2000
D elineated Area Using Spatial Analyst Only
F inal Area Using Supplemental Methods
1000
U SGS Reference Area
0
Figure 11.6: Area Differences in Square Miles After Initial and Final Watershed Delineation Compared to USGS
Refer ence Areas
50
Watershed pour points in areas with little to no gradient also influenced
delineation accuracy. Faulty delineations were immediately evident for these
extreme cases. Watersheds with little or no gradient were also digitized after
manual watershed delineation on 1:250,000 USGS topographic maps. Some
of these watersheds were identical to the metropolitan watersheds.
Watershed delineation with the Spatial Analyst Hydrologic Modeling
extension may be inaccurate when lakes or reservoirs are located within or
near watersheds. Occasionally, reservoirs and lakes were inaccurately
delineated as a watershed boundary. To avoid these types of inaccuracies,
topographic maps and LULC coverages were referenced for quality control.
Two-sample t-tests assuming unequal variances were conducted to
determine if delineated drainage area was correlated to specific delineation
methods. Watersheds delineated using HUG as a supplemental method were
significantly larger than watersheds successfully delineated using Spatial
Analyst Hydrologic Modeling only (t-test, two-sided p-value <0.05). This
difference is partly accountable to the relatively small scale of the fourth order
HUC data. There is suggestive but inconclusive evidence that digitized
watersheds are smaller than watersheds that were delineated using Spatial
Analyst Hydrologic Modeling only (t-test, two-sided p-value = 0.09). The
sample set for the digitized watersheds also included the smallest watershed
(18 square miles) of this study. Mean area for watersheds delineated using
HUG, as a supplemental method was 1868 square miles, compared to a mean
watershed size of 878 square miles for watersheds successfully delineated
with Spatial Analyst Hydrologic Modeling only, and a mean drainage area of
429 square miles for delineated watersheds supplemented by digitizing.
51
11.8.3.
Supplemental Methods Required to Improve Delineation Accuracy
Supplemental spatial editing was required for 46 of 61 watersheds
exceeding the defined difference limits. HUC data, topographic maps, and
LULC were utilized as reference data layers for all spatial editing.
One-way ANOVA F-tests were conducted to determine whether the
differences between final delineated area and USGS reference area were
significant for various supplemental delineation methods. Delineation
accuracy, in percent watershed area, differed significantly between the
supplemental methods (one-way AN OVA F-test, two-sided p-value < 0.01),
however, the differences for absolute areas (in square miles) were not
significant. Mean final area difference for Spatial Analyst delineated
watersheds was 1.2% (standard error 0.1%), with a maximum of 3.8%. Mean
difference for delineated watersheds supplemented by digitizing was 4.1%
(standard error 3.0%), including one outlier of 12.9%. Mean area difference
for watersheds supplemented by HUC was 0.7% (standard error 0.3%) with a
maximum of 1.6%. The tested data set included only the 61 watersheds that
were initially delineated with Spatial Analyst Hydrologic Modeling. The ten
problem watersheds mentioned earlier were not included, since the initial
watershed area could not be delineated using Spatial Analyst Hydrologic
Modeling. The areas delineated initially with Spatial Analyst delineated areas
were compared to the final areas improved through supplemental methods.
A two-sample t-test was conducted to determine whether area
differences between delineated watershed areas and USGS reference areas
were reduced by utilizing the supplemental methods described. There was
moderate but inconclusive evidence for a decrease in differences from the
initially delineated watersheds using Spatial Analyst Hydrologic Modeling only
and the final delineated watersheds (two-sided t-test, p-value = 0.07).
Supplemental methods increased the accuracy of watershed delineation.
Initially, watershed areas differed 19%, or 89 square miles, from their
52
reference areas before the utilization of supplemental methods. Mean area
differences were reduced to only 1%, or 5 square miles, after supplemental
methods were used. There is no significant difference in the increase in area
accuracy (two-sample t-test, two-sided p-value> 0.05) between the
supplemental methods used.
11.9. CONCLUSIONS
This study developed and evaluated an integrated methodology for
watershed delineation relying on the Spatial Analyst Hydrologic Modeling
extension. It does not present a "cookbook" for watershed delineation, since
accurate watershed delineation should be conducted on case-by-case basis.
Project planning is a crucial component of GIS based hydrologic research.
Proper identification of appropriate data layers and initial data preparation
before any modeling is conducted are often overlooked. Development and
evaluation of the described methodology were based on one degree DEM
data. Spatial Analyst Hydrologic Modeling may perform at different levels of
accuraOy with different DEM data sets. The major findings of this research are
(1) Spatial Analyst Hydrologic Modeling extension is suitable for digital
watershed delineation if supplemental methods are utilized; (2) accurate pour
point location determination may dramatically increase watershed delineation
accuracy; (3)10w gradient and I or dense urban areas surrounding the pour
point have the greatest negative impact on digital watershed delineation; and
(4) consistent quality control and use of supplemental delineation methods,
including HUC reference and area checks, is recommended for all digitally
delineated watersheds.
Successful watershed delineation requires accurate determination of
pour point location. Pour points can be accurately located by referencing
supplemental large scale topographic maps displaying the pour point in
53
combination with DEM flow path delineation. Accurate pour point location will
allow the user to edit the WatershedTool script in ArcView by reducing the grid
cell accumulation value for a snapped pour point from 240 to 0. Digital
watershed delineation using the edited script increases watershed area and
boundary accuracy.
Initial watershed areas delineated using Spatial Analyst only and USGS
reference watershed areas were not significantly different (two sided p-value =
0.63). Despite no significant difference, numerous watersheds had quite large
area differences, ranging up to 2,679 square miles. (This data set of 61
watersheds did not include ten watersheds with characteristics unsuitable for
automated delineation.) Approximately 75% of the watersheds in this study
required supplemental spatial editing based on set difference limits.
Regardless of area difference, boundary checks are recommended for all
watersheds as a quality assurance measure.
Spatial Analyst hydrologic delineation supplemented by accepted
delineation methods, such as hydrologic unit maps and manual digitizing of
topographic maps, provided the most accurate watersheds. The utilization of
specific supplemental methods is a case-by-case study based on watershed
characteristics, including watershed size, land use, and large hydrologic
features. Typically, hydrologic units were the stronger reference for relative
large watersheds. Watershed features, such as little to no gradient or dense
urban areas surrounding the pour point, had the strongest negative impacts on
delineation. To a lesser degree, delineation problems caused by lakes and
reservoirs were often overcome by referencing topographic and LULC maps.
The Spatial Analyst Hydrologic Modeling extension is accurate and
useful for watershed delineation when quality control is conducted.
Recommended quality control steps include reference checks using a digital
stream layer, a LULC data layer, and topographic maps, flow path
comparison, HUC references, and area check. This study recommends
consistent employment of these quality control steps.
54
The described methodology provides a basis for future studies that
intend to utilize Spatial Analyst Hydrologic Modeling for hydrologic,
geomorphic, or other watershed related research requiring less detail than
field scale attribute measurement. This method represents one solution
among a spectrum of possible solutions to the mapping and delineation of
watersheds. Traditional solutions include hand delineation and manual
digitizing. The Spatial Analyst Hydrologic Modeling extension was chosen
primarily for its efficiency and objectivity of watershed delineation, and for an
ease of digital mapping and later spatial analysis or overlays with other data.
Recommended future research includes an analysis of watershed delineation
accuracy using Spatial Analyst Hydrologic Modeling for DEM5 with higher
resolution.
In addition to its primary objective of developing and evaluating a
methodology for digital watershed delineation, this study has compiled a land
use and cover map for the Pacific Northwest. Beyond watershed related
applications, this information might also serve as an important component in
the study and management of other natural resources in the Pacific Northwest
and comparable landscapes.
55
11.10. REFERENCES
Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A Land Use
and Cover Classification System for Use With Remote Sensor Data.
U.S. Geological Suivey Professional Paper 964.
Castro, J. 1997. Bankfull Flow Recurrence Intervals: Patterns in the Pacific
Northwest. Submitted to Water Resources Research.
Delorme. 1998. Idaho Atlas and Gazetteer. 2' ed. Yarmouth, ME.
Delorme. 1998. Washington Atlas and Gazetteer. 4th ed. Yarmouth, ME.
Delorme. 1996. Oregon Atlas and Gazetteer. 2nd ed. Freeport, ME.
Environmental Systems Research Institute. 1996. Advanced Spatial Analysis
Using Raster and Vector Data. In Using the Arc View SpatialAnalyst.
Redlands, CA.
Fels, J.E., and K.C. Matson. 1998. A Cognitively-Based Approach for
Hydrogeomorphic Land Classification Using Digital Terrain Models. In
Proceedings of the 1998 ESRI User Conference by the Environmental
Systems Research Institute, Redlands, CA.
Gordon, N.D., l.A. McMahon, and B.L. Finlayson. 1994. Stream Hydrology:
An Introduction for Ecologists. John Wiley & Sons, New York.
Guttenberg, A.Z. 1965. New Directions in Land Use Classification. Bureau of
Community Planning, University of Illinois, Urbane, IL.
Horton, R.E. 1932. Drainage Basin Characteristics. Transactions of the
American Geophysical Union, 13:350-361.
Johnston, C.A., N.E. Detenbeck, J.P. Bonde, and G.J. Niemi. 1988.
Geographic Information Systems for Cumulative Impact Analysis.
Photo grammetric Engineering and Remote Sensing, 54(11): 1609-1615.
Kilpatrick, F.A., and H.H. Barnes. 1964. Channel Geometry of Piedmont
Streams as Related to Frequency Floods. U.S. Geological Suivey
Professional Paper 422-E.
56
Kompare, T.N. 1998. A Preliminary Study of Near-Stream Vegetative Cover
and In-Stream Biological Integrity in the Lower Fox River, Illinois.
Proceedings of the 1998 ESRI User Conference by the Environmental
Systems Research Institute, Redlands, CA.
Leopold, L.B. 1996. A View of the River. Harvard University Press, Cambridge,
MA.
Leopold, L.B., M.G. Wolman, and J.D. Miller. 1964. Fluvial Processes in
Geomorphology. Freeman, San Francisco, CA.
Leopold, L.B., and T. Maddock. 1953. The Hydraulic Geometry of Stream
Channels and Some Physiographic Implications. U.S. Geological
Suivey Professional Paper 252.
Loelkes, G.L. Jr., G.E. Howard, Jr., E.L. Schwertz, Jr., P.D. Lampert, and S.W.
Miller. 1983. Land Use/Land Cover and Environmental
Photointerpretation Keys. U.S. Geological Suivey Bulletin 1600.
Mitchell, W.B., S.C. Guptill, K.E. Anderson, R.G. Fegeas, and C.A. Hallam.
1977. GIRAS--A Geographic Information and Analysis System for
Handling Land Use and Land Cover Data. U.S. Geological Suivey,
Professional Paper 1059.
Osborne, L.L., and M.J. Wiley. 1988. Empirical Relationships Between Land
Use/Cover and Stream Water Quality in an Agricultural Watershed.
Journal of Environmental Management, 26:9-27.
Richards, K. 1982. Rivers, Form and Processes in Alluvial Channels.
Methuen, MA.
Seaber, P.R., F.P. Kapinos, and G.L. Knapp. 1987. Hydrologic Unit Maps.
U.S. Geological Suivey Water-Supply Paper 2294.
Strahier, A. N. 1957. Quantitative Analysis of Watershed Geomorphology.
Transactions of the American Geophysical Union, 38:913-920.
USDA-SCS. 1994. Salmon Recovery Initiative Draft. United States
Department of Agriculture, Soil Conservation Service, West National
Technical Center, Portland, OR.
USGS. 1991. Land Use and Land Cover and Associated Maps Factsheet.
U.S. Geological Survey, Reston, VA.
57
USGS 1990. Land Use and Land Cover Digital Data From 1:250,000- and
1:100,000-Scale Maps -Data Users Guide 4. U.S. Geological Survey
Reston, VA.
Whelan, F. 2000a. Variability in the Frequency of Bankfull Flow with Different
Land Use / Land Cover Types in Pacific Northwest Watersheds. To be
submitted to Journal of the American Water Resources Association.
Youberg, A.D., D.P. Guertin, and G.L. BaIl. 1998. Developing StreamWatershed Relationships for Selecting Reference Site Characteristics
Using ARC/INFO. Proceedings of the 1998 ESRI User Conference by
the Environmental Systems Research Institute, Redlands, CA.
58
11.11. WEB AND FTP REFERENCES
USEPA. I 999a. Geographic In formation Retrieval Analysis System (GIRAS)
Dire ctoiy. U.S. Environmental Protection Agency,
ftp://ftp.epa .gov/pu b/spdata/E PAG I RAS.
USGS. I 999a. USGS - Water Resources of the United States: Background
Data Sets for Water Resources.' HUG and Streams (Digital Line
Graphs). U.S. Geological Survey,
http:Ilwater. usgs.gov/GlS/background . html.
USGS. 1999b. Land Use/Land Cover Data (1:250,000). U.S. Geological
Survey,
http://edcwww.cr.usgs.gov/glis/hyper/guide/1 _250_lulcfig/states. html.
USGS. I 999c. Global Land Information System (GLIS): 1-Degree-Digital
Elevation Models. U.S. Geological Survey,
http://edcwww.cr.usgs.gov/glis/hyper/guide/1 _dgr_demfig/states. html.
USGS. 1999d. United States Water Resources Page: National Water
In formation System (NWIS). U.S. Geological Survey,
http//waterdata. usgs.gov/nwis-w.
59
60
Appendix lI.A: Watershed Delineation Script with Changed Value for Pour
Point Snap Distance.
Watershed delineation utilizes the aGrid.Watershed request, which
determines the contributing area above a set of cells in a grid. The total area
flowing to a given pour point is the output of the aGrid Watershed request. The
pour point is the lowest point along the watershed boundary. Therefore, the
DEM should be free of artificial sinks. The following script was used for this
study. The default value of 240 cells for the snap-to-pour-point-distance was
changed to 0 cells.
WatershedTool script
ay. UseWaitCursor
theView = av.GetActiveDoc
theDisplay = theView.' 3D.AddToTl N
theView = av.GetActiveDoc
thePrj = theView.GetProjection
get the surface
theSurfaceTheme = theView.GetEditableTheme
if (theSurfaceTheme = NIL) then
return NIL
end
theTin = theSurfaceTheme.Get5urface
Get collection of feature themes to pass to tin builder dialog
themeList = {}
for each t in theView.GetActiveThen-ies
if (t.ls(FTheme)) then
theFTab = t.GetFTab
if (theFTab.GetShapeClass.lsSubclassOf(Point) or
(theFTab.GetShapeClass.lsSubclassOf(MultiPoint) and
(theFTab.GetShapeClass.GetClassName <> "MultiPatch"))) then
themeList.Add(t)
end
end
end
tinBuildList = tinBuilderDialog.Show(themeList)
if (tinBuildList.Count 0) then
61
return NIL
end
'call TinBuilder script to do work
success = ay. Run("3D.TinBu ilder",{thelin ,tinBuild List,TRUE,"Add Features to
TI N",thePrj})
if (success.Not) then
MsgBox.Error("Error adding features to" ++ theSurfaceTheme.GetName +
".","Add Features to TIN")
return NIL
end
theSurfaceTheme. lnvalidate(TRU E)GetDisplay
theGridTheme = theView.GetActiveThemes.Get(0)
p = theDisplay.ReturnUserPoint
theGrid = theGridTheme.GetGrjd
mPoint = MultiPoint.Make({p})
theSrcGrid = theGrid .ExtractByPoints(mPoint, Prj.MakeNull, FALSE)
'get flow dir and acc from extension preferences
hydroExt = Extension. Find("Hydrologic Modeling (sample)")
if (hydroExt = NIL) then
MsgBox. Error("Cannot find extension !",'Watershed Tool")
return NIL
end
flowDirGThemeName = hydroExt.GetPreferences.Get("Flow Direction
Property")
theFlowDirGTheme = theView. FindTheme(flowDirGThemeName)
if (theFlowDirGTheme = NIL) then
MsgBox.Error("Cannot find flow direction theme in view!",'Watershed Tool")
return NIL
end
theFlowDir = theFlowDirGTheme.GetGrid
flowAccGThemeName = hydroExt.GetPreferences.Get("Flow Accumulation
Property")
theFlowAccGTheme = theView.FindTheme(flowAccGThemeName)
if (theFlowAccGTheme = NIL) then
MsgBox.Error("Cannot find flow accumulation theme in view!","Watershed
Tool")
return NIL
end
theAccum = theFlowAccGTheme.GetGrid
'calc watershed
theWater = theFlowDir.Watershed(theSrcGrid .SnapPourPoint(theAccum,O))
62
rename data set
aFN = av.GetProject.GetWorkDir. MakeTmp("watt", "")
theWater. Rename(aFN)
check if output is ok
if (theWater.HasError) then return NIL end
create a theme
gthm = GTheme.Make(theWater)
'set name of theme
gthm.SetName("A Watershed")
add theme to the specified View
theView.AddTheme(gthm)
63
Appendix lI.B: Projection Files for the Conversion from Geographic
Coordinates to the Albers Conic Equal Area Projection System.
Conversion from geographic to Albers Equal Area projection system
input
projection geographic
units ds
datum wgs72 seven
parameters
output
projection albers
units meters
spheroid clarke 1866
datum nar d three
parameters
29 30 0.00
45 30 0.00
-9600.00
23 0 0.00
0.00000
0.00000
end
Conversion from Albers Equal Area to geographic coordinates
input
projection albers
units meters
spheroid clarke 1866
datum nar d three
parameters
29 30 0.00
45 30 0.00
-96 0 0.00
23 0 0.00
0.00000
0.00000
output
projection geographic
units ds
datum wgs72 seven
parameters
end
64
CHAPTER III
VARIABILITY IN THE FREQUENCY OF BANKFULL FLOW WITH
DIFFERENT LAND USE I LAND COVER TYPES IN PACIFIC NORTHWEST
WATERSHEDS
Franziska Whelan
Submit to Journal of the American Water Resources Association
65
111.1. ABSTRACT
Geographic information systems (GIS), hydrologic modeling, and
statistical analysis were utilized to assess land use / land cover at the
watershed scale and its correlation to the frequency of bankfull flow for 71
designated salmon habitat recovery streams in the Pacific Northwest. The
regional approach and availability of applicable databases permitted the
reliance on GIS as the primary data collection and analysis tool for this study.
Bankfull discharge is an important indicator of streamfiow. At bankfull
flow, the active channel is filled to the top of the banks. The frequency of
occurrence of bankfull flow indicates the risk of flooding.
Land use alterations at the watershed scale were found to affect the
frequency of critical streamfiow events. Significant relationships were
detected between bankfull discharge recurrence intervals and agricultural land
use as well as the human use index (U-index). Bankfull flow occurs more
frequently in watersheds with high percentages of agricultural or urban land
use. The U-index is largely determined by agricultural land use in the Pacific
Northwest. Forest cover is negatively correlated to the bankfull discharge
recurrence interval.
The frequency of bankfull flow is also affected by climate. Watersheds
were classified into Köppen climate regions. Mean bankfull discharge
recurrence interval was significantly higher in D-climate watersheds, compared
to B-and C-climate watersheds. This study recommends land use assessment
at the watershed scale to be an important consideration in river restoration and
other stream management efforts. This research may further be used for
future watershed management considerations, and flood risk prediction
studies.
66
111.2. INTRODUCTION
Bankfull discharge is one of the most important indicators of
streamfiow. At bankfull discharge, the self-formed channel is filled to the level
of the active floodplain. The frequency of recurrence of bankfull flow plays an
important role from a hydrologic and geomorphologic point of view (Petit,
1997). Bankfull discharge recurrence interval also is a good indicator for the
risk of flooding.
Bankfull discharge recurrence interval was first established to be 1.5
years (Leopold et al., 1964). Dury (1977) determined a bankfull discharge
recurrence interval of 1.58 years. Williams (1978) confirmed Leopold's et al.
(1964) estimate of 1.5 years. Petit (1997) analyzed bankfull discharge
recurrence intervals in gravel-bed rivers in Belgium and determined recurrence
intervals in the order of 0.4 to 0.7 years for Ardenne rivers with catchments
smaller than 250 km2. However, recurrence intervals in larger drainage basins
were determined to be 1.5 to 2 years (Petit, 1997). Research by Castro
(1997) detected a regional variability of bankfull discharge recurrence intervals
in the Pacific Northwest (Oregon, Washington, and Idaho), ranging from a low
of a 1.0-year bankfull discharge recurrence interval to a high of 3.11 years.
Castro (1997) determined a mean bankfull recurrence interval of 1.2 years for
ecoregions in the more humid areas, including western Oregon and
Washington.
There is presently a lack of information on the influence of drainage
basin alterations, specifically land use, on bankfull discharge recurrence
intervals. The purpose of this study is to assess the relationship between
bankfull discharge recurrence intervals and land use / land cover in selected
Pacific Northwest watersheds.
67
111.3. OBJECTIVES
This study uses an integrated GIS approach to hydrologic modeling to
determine whether there is a correlation between bankfull discharge
recurrence intervals in the Pacific Northwest (Oregon, Washington, and Idaho)
and their associated variability with dominating land use / land cover types,
including forest, agriculture, urban, and range land. This paper also
investigates the relationship of bankfull discharge recurrence intervals with the
human use index (U-index), a parameter designed to indicate the extent of
anthropogenic land use / land cover in watersheds. Important management,
hydrology and geomorphology questions considered, include: (1) Is there a
quantitative relationship between land use / land cover type and bankfull
discharge recurrence interval? (2) Can the relationship be modeled with any
degree of certainty? (3) Are specific land use / land cover types associated
with certain trends in the mean bankfull discharge recurrence interval?
111.4. JUSTIFICATION
The determination of bankfull discharge is vital for the analysis of river
regimes, for river preservation and instream flow studies. Bankfull discharge
plays a significant role in hydrology and river morphology, since it forms and
maintains channel geometry. Because discharges above bankfull cause
flooding, the frequency of occurrence of bankfull flow indicates the risk of
flooding and is therefore to be considered in regional planning (Leopold,
1996). There is a need for watershed managers to predict flood risk based on
a watershed's land use / land cover characteristics. Information on bankfull
discharge recurrence intervals also plays an important role in stream
restoration and management. River restoration efforts should operate within
an understanding of regional watershed characteristics, including human
68
induced land cover alterations. Land use may alter the hydrologic
characteristics of a watershed including the recurrence interval of channel
shaping flows such as bankfull discharge. Osborne (1988) indicates that land
use and land cover have significant effects on stream water quality.
This research examines the relative sensitivity of stream flow,
specifically bankfull discharge recurrence intervals, to land use at the
watershed scale. The previously reviewed studies incorporated many factors,
including climate, terrain, vegetation, and soils, into their research on the
geographic variation in streamfiow characteristics. However, human induced
alterations on drainage basins had not yet been considered. Therefore, this
study investigates empirical relationships between land use I land cover at the
watershed scale and bankfull discharge recurrence intervals.
Recent advances in GIS technology and regional digital data availability
promoted the GIS approach for this multi-watershed regional scale study. This
study utilizes streamfiow data collected at the reach scale of stream systems
and various landscape scale data, including land use I land cover data and
digital elevation models. Streamfiow and other data on aquatic ecosystems
are typically collected at the centimeter to meter scale, represented by plots or
transects. Landscape scale datasets are generally collected at a smaller scale
often using remote sensing techniques. Ecosystem models derived from large
scale field data may predict instream habitat characteristics for a particular
plot, while landscape processes, such as floods or anthropogenic land uses,
occurring at the watershed scale may alter reach scale processes (Pastor and
Johnston, 1992). This study combines both the reach scale and the
landscape scale to assess the influences of land use / land cover on the
frequency of a streamflow event. The public availability of digital land use /
land cover data sets for the United States made this research on hydrologic
variables and their responses to anthropogenic landscape alterations possible
for this large study area. The relationships developed are specific to the
Pacific Northwest, due to the physical geographic characteristics of this region.
69
Bankfull discharge recurrence intervals have been determined at
designated salmon habitat recovery streams in the Pacific Northwest (USDA-
SCS, 1994; Castro, 1997). In the past decades, salmonids have declined
significantly in the Pacific Northwest, caused in part by habitat degradation
through anthropogenic alterations of streams and watersheds. The
degradation of salmonid habitat is a critical environmental issue throughout the
Pacific Northwest and is presently being addressed by the Northwest Marine
Fisheries Service listing of salmon, steelhead and cutthroat trout as threatened
or endangered species (Northwest Marine Fisheries Service, 1998). Bankfull
is the critical channel forming discharge and plays an important role in the
creation and maintenance of physical instream habitat.
111.5. STUDY AREA AND DATA SOURCES
The study area (Figure 111.1) includes 71 designated salmon habitat
recovery streams in the Pacific Northwest, including Oregon, Idaho, and
Washington (USDA-SCS, 1994). Streams were selected based on
watersheds that contain critical habitat for anadromous salmonids. The
Natural Resources Conservation Service (NRCS) defined these streams as
Salmon Initiative streams. These streams are characterized by a public
interest in the fishery and ecological watershed condition. Another NRCS
selection criterion was private ownership of significant portions of the
watersheds (Castro, 1997). Study area watersheds range in size from
approximately 12,000 acres to 9 million acres.
70
Sn Swyflrg Ste
Se Bmies
PficS
Pjbecs Eqtfl N cr,iic Prc4n
Q71 PALES
O
100 ILCMETEI
Figure 111.1: Study Area (Oregon Washington, Idaho).
The primary databases compiled for this study include: (1) a United
States Geological Survey (USGS) gaging station database; (2) one degree
digital elevation models (DEMs) for the entire study area; (3) a land use / land
cover (LULC) database, (4) a field stream gage database containing bankfull
discharge data, and (5) a Koppen-Geiger climate classification for the Pacific
Northwest.
111.5.1. USGS Gaging Station Database
The USGS stream gaging station database includes locational data for
71 gaging stations in the study area. The locational data include gaging
71
station coordinates and large scale maps detailing the location relative to
natural and manmade features (USGS, 1999d).
111.5.2. Digital Elevation Models
Surface topography of the study area was analyzed using a mosaic of
one degree DEMs downloaded from the USGS (USGS, 1999c). The DEM5
correspond to a scale of 1:250,000 and were converted to grids for analysis in
ArcView (Whelan, 2000b).
111.5.3. Land Use / Land Cover
Land use characteristics of the Pacific Northwest were analyzed
using digital Land Use / Land Cover (LULC) data files developed by and
available from the United States Geological Survey (USGS) and the United
States Environmental Protection Agency (USEPA) (USEPA, 1999a). USGS
LULC data, digitized from NASA and USGS aerial photography, were initially
produced by the National Mapping Program at 1:250,000. The data are
available in digital format as 1:250,000-scale USGS base maps for the entire
United States. The scale of the LULC coverages is consistent with the scale
of one-degree DEMs, and makes a reasonable assessment of the Pacific
Northwest and its regional conditions possible (Figure 111.2).
To assure consistent interpretation of land use / land cover, the
standard criteria used for USGS classification were applied to the entire study
area. The interpretations were based on a land use / land cover system
developed for use with remotely sensed data. The USGS LULC classification
is based on Anderson's (1976) land use classification.
Legend
Stream Sampling Site at
A
Gaging Station
EJ State Boundaries
Use / Land Cover Categorèes
Urban or Built-Up Land
Agricultural Land
Range land
Forest
Water Areas
Wetlands
Barren Land
Tundra
Perennial Snow or Ice
3
Albers Equ Area Conic Projection
9
0
55
75 120
MILES
50 1O KILOM ETERS
Figure 111.2: Pacific Northwest Land Use I Land Cover and Stream Gaging Stations.
73
111.5.4. Field Stream Gage Database
111.5.4.1 Bankfull Discharge Recurrence Interval
Bankfull stage was determined from field observations at active USGS
gaging stations (Castro, 1997) using guidelines defined by Dunne and Leopold
(1996). The frequency of bankfull flow was calculated based on annual
maximum flow frequency curves representing 50 years of data. Gaged
streams were selected since long-term streamfiow records are necessary in
order to determine the recurrence intervals for bankfull stage.
111.5.4.2 Slope
Channel slope was measured from topographic maps for all streams.
The database was complemented by field measurements for wadeable
streams conducted by Castro (1997). The longitudinal channel slope is used
to approximate the water surface slope at bankfull flow.
111.5.4.3 Bed Material Type
Bed material size is the dominant particle size of the stream bed
material (D50) and was divided into six size classes, including bedrock,
boulder, cobble, gravel, sand, and fines. The field database for bed material
type was established by Castro (1997). For the purpose of statistical analysis,
bed material was categorized into two distinctive groups, cobble-bed rivers,
including bedrock, boulder, and cobble; and gravel-bed rivers, including
gravel, sand, and fines.
74
111.5.5. Climate
Climate may alter the hydrologic regime and has therefore been
considered in this study. Pacific Northwest watersheds were categorized by
climate regions based on the Koppen-Geiger climate classification system.
Three broad Koppen climate regions have been delineated and mapped from
the classification of 72 weather stations in the Pacific Northwest (Lucas and
Jackson, 1995). The three major climate regions include dry climates (Bclimates), mesothermal climates (C-climates), and microthermal climates (D-
climates). C- and D-climates may also be described as low middle-latitude
climates and high middle latitude climates, respectively (Conte, D.J. et al.,
1997). Figure 111.3 displays this climate classification and the study
watersheds.
In the study area, the dry B climates are characterized by semiarid
short-grass prairie, or steppe, which is a transitional climate that lies in-
between the more humid C- and D-climates. In this region, potential
evapotranspiration and transpiration exceed precipitation.
The C-climates in the study area include the Mediterranean climate
type (Cs) as well as the humid climates with moist winters (CO. Annual
precipitation exceeds evapotranspiration. Subdivisions s and f are based on
precipitation; s characterizing dry summer s, and f characterizing moist climate
all year. The C-climates are found west of the Cascade mountains, where
maritime influence moderates climate and contributes to mild winters and
summers.
D-climates are found in the eastern Cascades and in the eastern part of
the study area. D-climates surround the dry B-climates. The microthermal
climates (D) are characterized by continental climate with summers that are
dry (Ds) or where precipitation occurs in all months (Df).
75
STifl
te
se
SkdykeaWth
(ty Oirr1es
c-aurr
-EJB-ame5
(POItEcTTI Qt)
- alTd
(Matjttn a>
9
E k caicPmjeai
0'
i
75 190 ML
KILc*iE1B
Figure 111.3: Study Area Watersheds and KOppen Climate Regions.
111.6. METHODOLOGY
111.6.1. Watershed Delineation Through Hydrologic Modeling Using GIS
Hydrologic modeling utilizing the ArcView Hydrologic Modeling
Extension and supplemental methods was conducted on 97 one-degree DEMs
for Oregon, Idaho, and Washington (Whelan, 2000b). Surface characteristics
were modeled using the following hydrologic modeling functions in ArcView
Spatial Analyst : FillSinks, FlowDirection, and FlowAccumulation. Output files
of these hydrologic modeling functions include flow direction and flow
accumulation grids displaying the drainage network.
76
A digital stream sample point layer including locations of all 71 gaging
stations was compiled and used for flow path delineation together with USGS
gage station location maps, and USGS topographic maps. Flow paths were
delineated using the hydrologic modeling function FlowPath.
Watersheds were digitally delineated using the WatershedTool script
provided by the Hydrologic Modeling extension. The script was modified for a
significant increase in delineation accuracy (Whelan, 2000b). Figure 111.4
displays a digitally delineated study watershed and its flowpath.
Irç P
Sbeun
rlad
&
LktVies
0-377
LJ
. 113
1134.1510
1511- iaes
18-
___
0
Figure 111.4: OEM Mosaic with Delineated Watershed for Stream Gage #
14157500, Coast Fork Willamette, Oregon.
Quality control was conducted through reference checks using a digital
stream layer, the LULC database, topographic maps, hydrologic unit
coverages, USGS area reference data, and flow path comparisons. Figure
111.5 displays all 71 digitally delineated watersheds.
77
Stie
Srqlrç Site at
cng Statim
=1Statewxies
- Ine
tIy OiredWidB
O151OO PALES
o
1)KILCtETB
Figure 111.5: Digitally Delineated Study Area Watersheds.
111.6.2. Determination of Dominating Climate
Watershed climate type was determined based on dominance of one
climate type within a watershed (Figure 111.3). Digital climate data were clipped
by delineated watersheds using geoprocessing functions in ArcView. For this
study, watershed climate was determined by dominance, and therefore
characterizes the climate that influences the hydrologic regime, rather than the
climate that is representative at the gaging station. Streamfiow characteristics
at gaging stations located within a certain climate type are often determined by
streamfiow patterns from another climate type upstream. Climate
categorization conducted for this study avoided the problem of exotic streams.
The hydrologic regime of exotic streams is determined by headwater climate
types that are different from the climates of the lower watershed region or the
region surrounding the gaging station.
78
111.6.3. Geoprocessing and Determination of Dominant Land Use / Land Cover
The digitally delineated watersheds were overlain with the LULC data
layer. LULC data were then clipped out for each watershed using the
geoprocessing function in ArcView. The clipped polygons were stratified into
LULC categories. The total area was determined for nine major LULC
categories: urban or built-up land, agricultural, range land, forest, water areas,
wetland, barren land, tundra, and perennial snow or ice (Figure 111.6, Figure
111.7). For the purpose of this study, four dominating land use I land cover
classes, including forest, urban, agriculture, and range land, were utilized for
statistical analysis, since there was a very limited representation of the
categories: water, wetland, barren land, tundra, and perennial snow or ice.
The four major land use I land cover categories include multiple minor land
use categories: (1) urban or built-up land, including residential areas,
commercial services, industrial areas, transportation, communications,
industrial and commercial, mixed urban or built-up land, and other urban or
built-up land; (2) agricultural land, including cropland and pasture, orchards,
groves, vineyards, nurseries, confined feeding operations, and other
agricultural land; (3) range land, including herbaceous range land, shrub and
brush range land, and mixed range land; and (4) forest land, including the
subcategories deciduous forest land, evergreen forest land, and mixed forest
land. The area occupied by these land use / land cover categories was
determined in proportion to total watershed area.
Legend
Stream Sampling Site at
Gaging Station
LJ State Boundaries
I
I Watershed Boundaries
Land Use! Land Cover Categories
Urban or Built-Up Land
Agricultural Land
Range Land
Forest
Water Areas
Wetlands
Barren Land
Tundra
Perennial Snow o r Ice
Albers Equal Area Conic Projection
Figure 111.6: Land Use I Land Cover (LULC) in Study Area Watersheds.
9
0
5 F;P 7i5 1O
MILES
O ióo KILOMETERS
80
-
U..I Ldc.r
Uti
iIt-Lk, L
fe
nd
Tiri&a
8i
0
5
Plbeis E
I
5
lOMles
kes oric Pi4eciai
Figure 1117: Land Use / Land Cover for Coast Fork Willamette Watershed,
USGS Gaging Station # 14157500.
111.6.4. Calculation of the Human Use Index
The human use index (U-index) is an indicator variable measuring the
degree of anthropogenic impacts based on land use / land cover (Jones et al.,
1997). For this study, the index was calculated by determining the percentage
of total watershed area that is characterized by urban or agricultural land use I
land cover. The index is calculated as follows:
U-index = 100 * (agriculture area + urban area) / total watershed area
The U-index calculation does not weight agriculture or urban land use I land
cover in respect to their anthropogenic impacts. The U-index provides a
ranking of watersheds based on the degree of human land change (Appendix
lll.F). Regional patterns of the U-index at the watershed scale help identify
81
areas that have experienced the greatest land cover conversion from the
natural vegetation (Jones et al., 1997).
The U-index is a watershed indicator primarily concerned with soil
erosion and runoff processes (Jones et al., 1997). Significant hydrologic
altering events, such as absence of forest overstory, increased artificial
drainages, decreased infiltration rates, and increased surface runoff,
characterize agricultural and urban land use / land cover. The U-index
provides a profound indicator for these anthropogenic land uses hypothesized
to influence the frequency of bankfull flow
111.6.5. Statistical Tests
111.6.5.1 Pearson Correlation Tests
Pearson correlation analysis was utilized to measure the relationship
between bankfull discharge recurrence interval and land use / land cover. The
Null hypothesis tests for no correlation (test for H0: p = 0 against Hi: p
0)
and may be rejected if the absolute value of r is greater than the critical
Pearson correlation coefficient. The critical values for r are based on the
number of observations and are listed in Appendix lll.A.
The Pearson correlation coefficient is calculated by the population
correlation calculation that returns the covariance of two data sets divided by
the product of their standard deviations. The Pearson product moment
correlation coefficient, r, is a dimensionless index that ranges from -1.0 to 1.0
inclusive and reflects the extent of a linear relationship between two data sets.
Positive correlation means large values of the bankfull discharge recurrence
interval data are associated with large values of a particular land use variable.
Negative correlation means small values of the bankfull discharge recurrence
82
interval data are associated with large values of a land use variable. The
correlation is near zero if values in both data sets are unrelated.
111.6.5.2. Linear Regression
If the Pearson correlation test confirmed a significant relationship, linear
regression analysis was performed to further test and to quantify this
relationship. The "least squares" method was used to fit a line through the
bankfull discharge recurrence interval data. This analysis allowed for
determining how bankfull discharge recurrence interval as the single
dependent variable was affected by one or several land use variables. Land
use variables were the independent variables. Results of linear regression
analysis may be used for future predictions of bankfull discharge recurrence
intervals based on land use I land cover.
The resulting regression equations are y = mx + b, where y is the
independent variable bankfull discharge recurrence interval, x is a land use
variable, such as percent agricultural land cover, b is the y intercept, and m is
the slope or coefficient for x.
111.7. STATISTICAL ANALYSIS
Statistical analysis tested for a relationship between bankfull recurrence
interval and LULC after accounting for the possibly confounding physical
variables climate, slope, and bed material. The sample size of 71 streams
provided a sufficient data set for the statistical analysis of the four dominating
LULC categories. Land use I land cover variables and the U-index were used
as indicator variables. Bankfull discharge recurrence intervals were compared
for different land use I land cover categories.
83
Statistical tests performed included Pearson correlation tests, simple
and multiple linear regression, ANOVA F-tests, and t-tests. Statistical tests
were conducted on untransformed data and bankfull discharge recurrence
interval data at logarithmic and at the reciprocal scale. The interpretation of
the tests using the logarithmic scale refer to log-linear relationships or the
median bankfull discharge recurrence interval, rather than the mean.
The following three out of 71 data sets were excluded from statistical
analysis: gaging stations 13302005 (Pahsimeroi River at Ellis), 13342450
(Lapwai Creek near Lapwai), and 14033500 (Umatilla River near Umatilla).
These three stations displayed extremely large bankfull discharge recurrence
intervals (7.7, 18, and 5.05 years, respectively). The gaging station at the
Pashimeroi River at Ellis was recently relocated, therefore the field
measurements conducted at the new location did not correspond to the USGS
data used to generate the flow frequency curve for determining bankfull
discharge recurrence interval. The calculated bankfull discharge recurrence
interval for the Lapwai Creek near Lapwai gaging station is a result of USGS
gage data that contradicted with field measurements. The USGS recorded
discharge did not correlate with the field measured discharge at the time of
field data collection. The Umatilla River near Umatilla gaging station data was
removed due to a lack of bankfull indicators at the time of field data collection
(Castro, 1997). All parametric statistical tests were performed on 68 data sets.
111.7.1. Summary Statistics For Land Use Variables and Bankfull Discharge
Recurrence Intervals
Figure 111.8 illustrates a summary for the land use statistics by
watershed. Land use I land cover in study area watersheds is dominated by
forest, followed by agriculture and range land (Appendix lll.B). Forest occupies
76.4% on average, and ranges from 10.8% to 100%. Agriculture occupies
between 0% and 76.7% of study area watersheds, with a mean of 10.4%.
84
Mean watershed area occupied by range land is 10.0% and varies from 0% to
60.0%.
Iid inh&i
J
I
..
.
'T. '
S
-C
C
rN
S
cw
SØSe
OTh1OO
PJtes Eaá Nee CorE Rt4fln
O
IALES
1KIL*.T
Figure 111.8: Summary of Land Use I Land Cover for Delineated Watersheds.
Figure 111.9 presents a summary of U-indices for all watersheds. The U-
index (11.6%) is largely determined by the percentage of agriculture within
watersheds and ranges from 0% to 89.2%. The majority of watersheds have a
U-index of equal to or less than 5%. Urban land occupies the smallest portion
of study area watersheds (1.17%) (Appendices lll.G and III.H).
For the 68 study area watersheds, mean bankfull discharge recurrence
interval is 1.48 years (standard error = 0.08 years), the median is 1.25 years
(standard deviation = 0.63 years). Minimum bankfull discharge recurrence
interval is 1 year, maximum bankfull discharge recurrence interval is 4.27 years
(Appendix lll.B).
85
Leg.nd
State Bouncfles
-
U-Index Rated Watersheds
1%
1-5%
5-125%
125-25%
25-50%
'50%
40
35
30
25
20
15
z
9
a
10
5 50 75190 MILES
50 'iOo KILOMETERS
5
0
6
srV.d U.Ind.x In P,rc.,I
Albers Equal Area Conic Projection
Figure 111.9: Study Area Watersheds Classified by the Human Use Index
(U-Index).
111.7.2. Correlation and ReQression Tests for Watersheds. No Seqmentation
The critical values of the Pearson correlation coefficient r for 70
observations (n = 68) are 0.236 for a 95% confidence level (a = 0.05), and r =
0.305 for a 99% confidence level (a = 0.01). Appendix lll.A lists all critical
values for the Pearson correlation coefficient for the correlation tests and
different sample sizes applied in this study.
Table 111.1 summarizes statistically significant correlation between
bankfull discharge recurrence intervals and percent land use category within
watersheds at the 99% and 95% confidence level. Land use categories were
86
determined as percent cover per watershed and include urban, range land,
agriculture, and forest. Pearson correlation tests were also run for the human
use index (U-index) versus bankfull discharge recurrence interval.
N =68
URBAN
URBAN
AGRICULTURE
RANGE
FOREST
U-INDEX
1
GRICULTURE
RANGE
rFOREST
U-INDEX
3ANKFULL
RECURRENCE
N(BANKFULL
RECURRENCE
1/(BANKFULL
RECURRENCE
0.4959
-0.1495
-0.3905
0.5884
-0.0602
0.0438
-0.8195
0.9939
-0.2178
-0.5813
0.0218
0.0378
-0.8125
0.1276
-0.2105
-0.066
-c).2389
0.0710
0.1193
-0.2308
)2482
0.1016
0.1053
-0.2405
-0.0743
1
-C
1
1
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
SUGGESTIVE
ALPHA = 0.01
ALPHA = 0.05
r0.125
r = 0.305
r = 0.236
Table 111.1: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use / Land Cover, 68
Watersheds.
A statistically significant negative correlation between agricultural land
use and median bankfull discharge recurrence intervals was determined with a
95% level of confidence (r = -0.239). The negative correlation provides
evidence for shorter bankfull discharge recurrence intervals with larger
percentages of agricultural land use.
Linear regression was performed to quantify this relationship (two-sided
p-value = 0.04). The regression equation is:
LN (bankfull recurrence) = 0.3767 - 0.0045 * percent agricultural cover
The 95% confidence interval for the coefficient for agricultural land use ranges
from 0.009 to - 4.8032*10-6.
87
There is statistically significant evidence for a negative correlation
between bankfull discharge recurrence interval at the reciprocal scale and the
U-index (r = -0.241, 95% confidence level). This relationship is driven by the
negative correlation between agricultural land use and bankfull discharge
recurrence interval. The U-index is largely determined by agricultural rather
than urban land use and cover in the study watersheds (r = 0.993, 99%
confidence level). Moderate evidence for a negative correlation between the
U-index and bankfull discharge recurrence intervals suggests an increase in
the frequency of bankfull flow with an increase in urban and agricultural land
use. Linear regression confirmed this relationship (two-sided p-value = 0.048).
The regression equation is:
1/(bankfull recurrence) = 0.725 - 0.0027 * U-index
The 95% confidence interval for the coefficient for U-index ranges from 0.0053
to 2.1839*105. See Table 111.11 for a summary of regression statistics.
There is also suggestive but inconclusive evidence for a positive
correlation between bankfull discharge recurrence interval and forest cover (r =
0.127).
111.7.3. Correlation Tests for Bankfull Discharge Recurrence Interval Versus
Land Use / Land Cover for Data Segmen ted by Climate T ype
111.7.3.1. Summary Statistics
The classification of study area watersheds by climate type resulted in 8
watersheds with a B-climate, 25 watersheds with a C-climate, and 35
watersheds with a D-climate. Mean bankfull discharge recurrence intervals for
B- and C-climate watersheds are comparable (1.38 years with a standard error
of 0.18 years, and 1.4 years with a standard error of 0.15 years, respectively).
Median bankfull discharge recurrence intervals are also comparable for B- and
C-type rivers (1.21 and 1.02 years, respectively).
88
Watersheds in the D-climate region display a higher mean bankfull
discharge recurrence interval compared to C- and B-climate rivers (Appendix
lll.D). Mean bankfull discharge recurrence interval for D-climate rivers is 1.56
years (standard error = 0.09 years). The median bankfull discharge recurrence
interval is 1.36 years.
Land use I land cover in watersheds in the B-climate region is
dominated by forest (38.6%) and agriculture (36.2%). The dominance of
agricultural land use is reflected in the U-index (37.1%). Range land occupies
23.9% of B-climate watersheds. Forest cover (87.9%), followed by agriculture
(7.6%) dominates land use in C-climate watersheds. The mean U-index for
these watersheds is 9.3%. Urban land use occupies 1.7% of C-climate
watersheds. Land use in D-climate watersheds is dominated by forest
(76.9%), followed by range land (13%). Agricultural land use occupies 6.6%
and accounts for the U-index (7.4%). Urban land use occupies less than one
percent in D-climate watersheds (Appendix lll.D).
111.7.3.2 Statistical Tests
Correlation tests were conducted for eight watersheds characterized by
climate type B (Table 111.2). Range land is negatively correlated to the
reciprocal of bankfull discharge recurrence interval (r = -0.715). Linear
regression analysis confirmed this relationship (two-sided p-value = 0.04).
Statistical analysis provides significant evidence that range land may contribute
to a decrease in the frequency of bankfull flow in B-climate watersheds. The
regression equation is:
1/(bankfull recurrence) = 1.0497 - 0.0111 * percent range land
The 95% confidence interval for the coefficient of range land ranges from 0.0218 to -0.0002. See Table 111.11 for a summary of regression statistics.
89
There is suggestive but inconclusive evidence that agricultural land use
is negatively correlated to bankfull discharge recurrence interval at the
logarithm scale (r = -0.557). Suggestive but inconclusive evidence was also
found for a negative correlation of the U-index and of urban land use to
bankfull discharge recurrence interval at the logarithm scale (r = -0.578 and r =
-0.624, respectively). Range land and forest cover are positively correlated to
bankfull discharge recurrence interval (r = 0.677 and 0.357, respectively).
CLIMATE B
URBAN
(N =8)
URBAN
AGRICULTURE
RANGE
FOREST
J-INDEX
iANKI-ULL
RECURRENCE
N(BANKFULL
RECURRENCE)
1/(BANKFULL
RECURRENCE)
AGRICULTURE
RANGE
FOREST
U-INDEX
1
-0.0550
-0.1489
0.1423
-0.0233
-0.6185
-0.8630
-0.8947
0.9995
-0.5251
0.5507
-0.8688
0.6388
-0.8913
0.3569
-0.5453
-0.6236
-0.5575
0.6775
0.3760
-0.5779
-0.6168
-0.5925
0.7153
0.3992
-0.6128
1
1
1
1
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
SUGGESTIVE
r=0.35
Table 111.2: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use / Land Cover for 8
Watersheds Characterized by B-Climate.
Correlation tests were conducted for 25 watersheds characterized by
climate type C (Table 111.3). Range land is positively correlated to median
bankfull discharge recurrence interval for C-climate watersheds (r = 0.419,
95% level of confidence). Linear regression analysis confirmed this
relationship (two-sided p-value = 0.03). The regression equation is:
90
LN(bankfull recurrence) = 0.1517+ 0.0754 * percent range land
The 95% confidence interval for the coefficient of range land ranges from 0.005
to 0.1458. See Table 111.11 for a summary of regression statistics.
Suggestive yet statistically inconclusive relationships between bankfull
discharge recurrence intervals and land use variables forest, agriculture, and
urban, were also determined for C-climate watersheds. Suggestive but
inconclusive evidence for a negative correlation between agricultural land use
and between urban land use and median bankfull discharge recurrence interval
was found (r = -0.270 and r = -0.212, respectively). The Pearson correlation
coefficient for the U-index (r = -0.271) also implies a negative correlation
between bankfull discharge recurrence interval at the logarithm scale and the
U-index. There is suggestive but inconclusive evidence for a positive
correlation between percent forest land and median bankfull discharge
recurrence interval (r = 0.178).
CLIMATE C
(N = 25)
URBAN
URBAN
RANGE
AGRICULTURE
FOREST
U-INDEX
BANKFULL
RECURRENCE
LN(BANKFULL
RECURRENCE)
-1/(BANKFULL
RECURRENCE)
I
0.7346
-0.0174
-0.7700
0.8100
-0.1986
AGRICULTURE
RANGE
FOREST
U-INDEX
-0.9404
0.1615
-0.2367
0.1781
-0.2705
0.1871
-0.2961
1
-0.1460
-u.i2u
0.9929
-0.2336
-0.2121
-0.2701
-0.2198
-0.2980
1
-0.0732
-0.1292
0.4173
1
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
iU((iES I IVh
r=0.17
Table 111.3: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover for 25
Watersheds Characterized by C-Climate.
91
Correlation tests were run for 35 watersheds characterized by D-climate
(Table 111.4). Suggestive but inconclusive evidence was found for a negative
correlation between agriculture and median bankfull discharge recurrence
interval (r = -0.144). There is also suggestive evidence that urban land use is
positively correlated to bankfull discharge recurrence interval for D-climate
watersheds (r = 0.165).
There is suggestive but inconclusive evidence for a positive relationship
between forest cover and median bankfull discharge recurrence interval (r
0.157) and a negative relationship between range land and median bankfull
discharge recurrence interval (r = -0.169).
CLIMATE D
(N = 35)
JRBAN
GRICULTURE
RANGE
OREST
J-INDEX
3ANKFULL
RECURRENCE
N(BANKFULL
ECURRENCE
1/(BANKFULL
RECURRENCE
URBAN
AGRICULTURE
RANGE
FOREST
U-INDEX
1
0.6697
-0.1211
-0.5299
0.7462
0.1650
-0.1011
-0.7239
0.9941
-0.1301
-0.5906
-0.1083
-0.1577
-0.7262
0.1392
-0.0925
0.1754
-0.1442
-0.1689
0.1569
-0.1037
0.1837
-0.1525
-0.1778
0.1702
-0.1099
1
1
1
1
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
ALPHA = 0.01
SUGGESTIVE
ALPHA = 005
r = 0.43
r =0.130
r = 0.335
Table 111.4: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover for 35
Watersheds Characterized by D-Climate.
The correlation tests for bankfull discharge recurrence intervals versus
land use I land cover data segmented by climate type displayed similar
correlations for B- and C-climate type watersheds. In contrast, D-climate
92
watersheds displayed different patterns in their relationships between bankfull
discharge recurrence intervals and land use I land cover. In particular, urban
and range land displayed correlations which were opposite to those detected
for B- and C-climate watersheds (Table 111.11).
This analysis may conflict with Castro (1997) who found a significant
relationship difference in bankfull discharge recurrence intervals in C-climates
compared to B- and D-climates. Climate classification by Castro (1997) was
based on the climate region the stream gage was located in. Exotic streams
may bias climate classification based on gaging station climate.
111.7.4. Correlation Tests for Bankfull Discharge Recurrence Interval Versus
Land Use / Land Cover for Data Segmented by Bed Material Type
All 68 watersheds were segmented into two categories according to bed
material. Gravel-bed rivers included fines, sand, and gravel as prevailing bed
material. Cobble-bed rivers included cobble, boulder, and bedrock as
prevailing bed material. The effect of land use on bankfull discharge
recurrence interval was analyzed for a total of 30 cobble-bed river and 38
gravel-bed river watersheds.
111.7.4.1. Summary Statistics
Mean bankfull discharge recurrence interval for cobble-bed rivers is 1.55
years (standard error = 0.13 years). Median bankfull discharge recurrence
interval is 1.36. Mean bankfull discharge recurrence interval in gravel-bed
rivers is slightly smaller (1.43 years, standard error = 0.09 years). Median
bankfull discharge recurrence interval is 1.17 years (Appendix lll.E).
Watersheds with cobble-bed rivers are largely dominated by forest
cover (82.1%). Range land and agricultural land share the remaining portion of
93
these watersheds (9.1% and 6.0%, respectively). The U-index is dominated by
agricultural land use and amounts to 6.2%. Urban land use occupies less than
one percent in cobble-bed river watersheds. Land use in gravel-bed
watersheds is also dominated by forest (72%), however, the area covered by
forest is smaller than the area in cobble-bed river watersheds. Agricultural
land use occupies 13.9%, and range land occupies 10.7% of watersheds in the
gravel-bed category. The U-index is 15.9%. Urban land use occupies 1.9% in
gavel-bed watersheds (Appendix Ill. E).
111.7.4.2. Statistical Tests
The Pearson correlation coefficient r was calculated to determine
whether there is a correlation between bankfull discharge recurrence intervals
segmented by bed material type and watershed land use variables.
Correlation tests (Table 111.5) for cobble-bed rivers (n = 30) detected a positive
correlation between the bankfull discharge recurrence interval at the reciprocal
scale and forest cover (r = 0.3613, 95 % confidence level). Linear regression
for percent forest cover within watersheds versus reciprocal bankfull discharge
recurrence interval was statistically significant (two-sided p-value = 0.04). The
regression equation determined for this significant relationship is:
1/(bankfull recurrence) = 1.0316 - 0.0038 * percent forest
The 95 % confidence interval for the coefficient for percent forest land ranges
from -0.0075 to 3.34*106. This positive correlation between reciprocal
bankfull discharge recurrence interval and forest provides strong statistical
evidence for a decrease in the frequency of bankfull flow with an increase in
forest land
94
COBBLE-BED
(N = 30)
URBAN
AGRICULTURE
RANGE
FOREST
U-INDEX
BANKFULL
RECURRENCE
LN(BANKFULL
RECURRENCE)
-1/(BANKFULL
RECURRENCE)
URBAN
AGRICULTURE
RANGE
FOREST
U-INDEX
I
U.b(U1
-0.0426
-0.4044
0.5909
-0.1844
I
0.1140
-0.8068
0.9997
-0.2295
-0.6297
0.1107
-0.1949
-0.8048
0.2829
-0.2311
-0.2019
-0.2816
-0.2225
0.3300
-0.2828
-0.2049
-0.3216
-0.2385
1
1
1
-0.3222
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
SUGGESTIVE
'HA 0.01
= 0.463
r0.2
Table 111.5: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use I Land Cover for 30 CobbleBed Watersheds.
For cobble-bed rivers, suggestive but inconclusive evidence was found
for a negative correlation between bankfull discharge recurrence interval at the
logarithm scale and urban, agricultural, and range land cover (r = -0.202, r = -
0.282, and r = -0.222, respectively). Correlation tests for the U-index
confirmed this suggestive negative relationship (r = -0.283).
Correlation tests were conducted for 38 watersheds characterized by
gravel-bed rivers (Table 111.6). Suggestive but inconclusive evidence was
found for a negative correlation between bankfull discharge recurrence interval
and agriculture (r = -0.191)as well as for the U-index (r = -0.178). There also
was suggestive but inconclusive evidence for a positive relationship for range
land and bankfull discharge recurrence interval at the logarithm scale (r =
0.210).
95
GRAVEL
(N = 38)
JRBAN
AGRICULTURE
RANGE
OREST
J-INDEX
3ANKFULL
RECURRENCE
N(BANNKFULL
RECURRENCE)
.1/(BANNKFULL
ECURRENCE)
URBAN
AGRICULTURE
RANGE
FOREST
U-INDEX
1
0.5077
-0.2146
-0.3836
1
0.61 14
-0.0171
-0.0059
-0.8118
0.9922
-0.1912
-0.5630
-0.0366
0.2105
-0.8013
-0.0131
-0.1781
-0.0153
-0.1862
0.2548
-0.0454
-0.1733
-0.0157
-0.1729
0.2970
-0.0818
-0.1611
1
1
1
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
(N =35/N =40)
SUGGESTIVE
r0.1410.14
ALPHA = 005
= 0.335 I 0.312
ALPHA = 001
r = 0.430 I 0.402
Table 111.6: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use / Land Cover for 38 GravelBed Watersheds.
111.7.5. Correlation Tests for Bankfull Discha rg e Recurrence Interval Versus
Land Use / Land Cover for Data Segmented by Slope
In order to test for the effect of land use variables on bankfull discharge
recurrence intervals, bankfull discharge recurrence data were segmented into
three groups based on slope. Bankfull discharge recurrence interval is
positively correlated to slope (r = 0.243, 95% confidence level). Correlation
tests for 68 watersheds and for individual segmentation groups confirmed this
relationship. This correlation was accounted for by testing for a relationship
between bankfull discharge recurrence interval and land use / land cover
based on slope segmentation groups. Segmentation was based on natural
breaks in the slope data. Watersheds with low slope rivers are characterized
by slopes ranging from 0 to 0.2 percent with a mean of 0.1 percent(n = 16).
Medium slope rivers include 0.2 to 0.5 percent slope with a mean of 0.3
96
percent (n = 27), and high slope rivers are characterized by slopes larger than
0.5 with a mean of 1.1 percent (n = 25). This segmentation allows for a test of
the effect of land use I land cover variables by removing the variation in
bankfull discharge recurrence intervals that is based on slope.
111.7.5.1. Summary Statistics
Mean bankfull discharge recurrence interval for high slope rivers is 1.69
years (standard errors = 0.15 years). Median bankfull discharge recurrence
interval is 1.41 for high slope rivers. Low slope rivers display a mean bankfull
discharge recurrence interval of 1.24 years (standard error = 0.13 years). The
median bankfull discharge recurrence interval is only 1.02 years. Median slope
rivers display a bankfull discharge recurrence interval of 1.43 years (standard
error = 0.09 years). Median bankfull discharge recurrence interval is 1.25
years (Appendix lll.C).
Watersheds with high slope rivers show a dominance of forest cover
(73.3%), and a relatively large portion of range land (13.2%). The amount of
agricultural land (8.7%) drives the U-index (10.3%). Urban land use accounts
for only 1.7%. Land use in watersheds with low slope rivers is dominated by
forest (75%), followed by agriculture (18%). Range land occupies 4.4%. The
U-index (19.8%) is largely driven by agricultural land use. Urban land use
accounts for 1.8%. Land use in medium slope watersheds is dominated by
forest (80.2%, followed by range land (10.3%) and agricultural land (7.5%).
The U-index (7.9%) is driven by agricultural land use. Urban land use
occupies less than one percent in medium slope watersheds (Appendix lIl.C).
97
111.7.5.2. Statistical Tests
Bankfull discharge recurrence intervals were significantly different for
low versus high slope rivers (t-test, two-sided p-value
0.04). Low slope rivers
have a lower mean bankfull discharge recurrence interval (1.24 years) than
high slope rivers (1.69 years). The mean bankfull discharge recurrence
interval for high slope rivers is significantly higher than the mean bankfull
discharge recurrence intervals for both, low and medium slope rivers (t-test,
two-sided p-value = 0.03)
Correlation tests were conducted for 25 watersheds characterized by
high slope (Table 111.7). There is suggestive but inconclusive evidence for a
positive correlation between bankfull discharge recurrence interval and forest
cover for high slope rivers (r = 0.186). For high slope rivers, suggestive but
inconclusive evidence was also found for a negative correlation between
bankfull discharge recurrence interval at the logarithm scale and agricultural
land use (r = -0.173). The U-index reflects this relationship with a correlation
coefficient of -0.172.
98
HIGH SLOPE
(N = 25)
URBAN
URBAN
AGRICULTURE
RANGE
FOREST
U-INDEX
1
GRICULTURE
RANGE
FOREST
J-INDEX
3ANKFULL
RECURRENCE
N(BANKFULL
RECURRENCE)
1/(BANKFULL
RECURRENCE)
0.7222
-0.2435
-0.5091
0.8016
1
-u.it,
-0.0318
-0.7929
0.9925
-0.1728
-0.5393
-0.0707
-0.0343
-0.7757
0.1857
-0.1716
-u.izu
-0.1634
0.0024
0.1490
-0.1629
-0.1198
-0.1420
0.0327
0.1056
-0.1440
1
1
1
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
SUGGESTIVE
ALPHA = 0.01
r0.17
r 0.505
Table 111.7: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use / Land Cover for 25 HighSlope Watersheds.
Correlation tests were conducted on 27 watersheds characterized by
rivers with medium slope (Table 111.8). There is suggestive but inconclusive
evidence for a positive correlation for forest versus bankfull discharge
recurrence interval (r = 0.187). In contrast to low slope rivers, there is
suggestive but inconclusive evidence for a negative correlation for range land
versus bankfull discharge recurrence interval (r = -0.186). There is
inconclusive evidence for a positive relationship between urban land use and
bankfull discharge recurrence interval (r = 0.242). Evidence is moderate yet
inconclusive for a negative correlation for both agricultural land use and the Uindex versus bankfull discharge recurrence interval (r
respectively).
-0.227 and r = -0.215,
99
MEDIUM SLOPE
(N = 27)
URBAN
URBAN
AGRICULTURE
RANGE
FOREST
U-INDEX
1
GRICULTURE
RANGE
0.3800
0.2303
OREST
J-INDEX
-0.41 97
0.4121
3ANFQ-ULL
RECURRENCE
N(BANKFULL
RECURRENCE)
1
0.2424
0.3269
-0.8247
0.9994
-0.2273
-0.7949
0.3307
-0.1858
-0.8282
0.1871
-0.2147
0.1957
-0.2129
-0.1618
0.1673
-0.2022
1
1
1/(BANKFULL
0.1646
-0.1869
-0.1305
0.1356
-0.1778
RECURRENCE)
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
(for N = 25. N=30
SUGGESTIVE
r=0.17
Table 111.8: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use / Land Cover for 27 MediumSlope Watersheds.
Correlation tests were conducted for 16 watersheds with low slope
rivers (Table 111.9). There is suggestive but inconclusive evidence for a
negative correlation between bankfull discharge recurrence interval at the
logarithm scale and agricultural land use and the U-index (r = -0.256 and 0.261, respectively).
A positive correlation is suggested between bankfull discharge recurrence
intervals and range land (r = 0.318).
100
LOW SLOPE
URBAN
(N=16)
JRBAN
GRICULTURE
RANGE
r OREST
J-INDEX
F3ANKFULL
RECURRENCE
N(BANKFULL
RECURRENCE)
1/(BANKFULL
RECURRENCE)
AGRICULTURE
RANGE
FOREST
U-INDEX
1
0.2249
-0.0832
-0.2340
0.2881
1
-0.4354
0.0753
-u.uu
0.0823
-0.9301
0.9979
-0.1890
-0.1289
-0.1850
1
1
0.31 86
-0.9298
0.0392
-0.1906
-0.2563
0.3210
0.0999
-0.2606
-0.3201
0.3207
0.1589
-0.3270
1
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
SUGGESTIVE
ALPHA 0.01
ALPHA 0.05
r = 0.25
rO623
r=O.497
Table 111.9: Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus Land Use / Land Cover for 16 Low
Slope Watersheds.
111.7.6. Summary of Correlation Tests and Regression Equations
Pearson correlation tests were run for 68 watersheds, and for
watersheds segmented according to climate, bed material, and slope. Table
111.10 summarizes all detected statistically significant and suggestive
correlations between bankfull discharge recurrence interval, land use variables,
and the U-index. Negative relationships in table 111.10 indicate a decrease of
the bankfull discharge recurrence interval with an increase of a particular
variable. A decrease in the recurrence interval means that bankfull discharge
occurs more frequently. Clear trends in the negative relationships between
bankfull discharge recurrence intervals versus urban and agricultural land use,
and the U-index are displayed. Positive relationships displayed in table 111.10
indicate an increase of the bankfull discharge recurrence interval with an
increase of a particular variable. An increase in the recurrence interval means
bankfull discharge occurs less frequently, indicating a decrease in the risk of
101
flooding. Forest cover tends to be positively correlated to bankfull discharge
recurrence intervals. There is no clear trend of the relationship between range
land and the frequency of bankfull flow.
SEGMENTATION
NONE
CLIMATE
BED
MATERIAL
SLOPE
GROUPS
N
None
B Climate
C Climate
D Climate
Gravel
Cobble
68
Hiah
25
URBAN
AGRICULTURE
U-INDEX
8
25
35
-++
38
30
++
Medium
27
Low
16
+ positive correlation
- negative correlation
++ suggestive but inconclusive evidence for positive correlation
-- suqqestive but inconclusive evidence for neQative correlation
FOREST
RANGE
++
++
++
++
+
+
--
+
++
++
-++
Table 111.10: Summary Table for Pearson Correlation Tests.
Table 111.1 1 summarizes regression equations for bankfull discharge
recurrence intervals as the dependent variable and different land use
categories as independent variables. Regression analysis was conducted only
for the correlations which were statistically significant with a confidence level of
at least 95%. The coefficients for the land use variables or the U-index
indicate the direction of the relationship between these variable and bankfull
discharge recurrence intervals. Positive (negative) relationships indicate an
increase (decrease) in bankfull discharge recurrence interval with an increase
in a particular land use variable.
EQUATION
VALID FOR
ALL
WATERSHEDS
ALL
WATERSHEDS
B-CLIMATE
WATERS H ED S
- -.
--
BASED
ON N
DATA
SETS
68
68
8
U-ULIMAI E
WATERSHEDS
25
COBBLE-BED
RIVERS
30
Table 111.11:
Y
LN(bankfull
discharge
recurrence
interval)
1/(bankfull
discharge
recurrence
interval)
1/(bankfull
discharge
recurrence
interval)
LN(bankfull
discharge
recurrence
interval)
1/(bankfull
discharge
recurrence
interval)
LOWER 95%
CONFIDENCE
LEVEL
UPPER 95 %
CONFIDEN CE
LEVEL
R
SIDED
P-VALUE
0.0045
0.0
00091
_48032*10
23.9
0 7250
0 0027
0 04
2 1839*10
0 0053
24 1
Range land
(percent)
1.0497
-00111
0.04
-00218
-0.0002
71.5
Range land
(percent)
0.1517
0.0754
0 03
0.005
0.1458
41 9
Forest
(percent)
1.0316
0 0038
0.04
0 0075
_3.34*100
362
INTERCEPT
COEFFICIENT
Agriculture
(percent)
0.3767
U-index
(percent)
X
Two-
Regression Equations for BankfuJ Disch arge Recurrence Intervals Determined by Land Use I Land Cover
Variables in Pacific NorthwestWatershe ds
103
111.8. CONCLUSIONS
This research addresses fundamental issues of fluvial geomorphology
including streamfiow changes. Land use changes can greatly alter the
quantity, seasonal distribution, and relative proportion of water and sediment
supplied to the stream channels. Alterations in water and sediment supply
commonly cause changes in the stream channel, floodplain, and riparian
areas (Andrews, 1995). These changes may affect downstream aquatic and
riparian resources, including fish habitat. The detected relationships between
anthropogenic land use, particularly urban and agricultural land use, and
bankfull discharge recurrence intervals indicate an increasing risk of flooding
with an increase in anthropogenic land use / land cover at the watershed level
(Figure 111.10). Higher frequency of flooding caused by reduced channel size
or floodplain storage, as well as loss of ecological in-stream habitat is often a
consequence of substantial modifications to natural streamfiows. The loss of
physical instream habitat may negatively impact salmonids along designated
salmon habitat recovery streams.
Streamfiow changes are reflected in variations in the frequency of
bankfull flow. Bankfull discharge recurrence interval is influenced by human
drainage basin alterations, which are reflected in land use and cover. Clear
trends on the effects of urban, agricultural, and forest cover, and the U-index
on bankfull discharge recurrence intervals were detected.
The human use index (U-index) is a measure of human use, and
combines the proportion of a watershed that is urbanized or agriculturally
used. The U-index is a watershed indicator primarily concerned with soil
erosion and runoff processes. In Pacific Northwest watersheds, the U-index is
largely driven by agricultural rather than urban land use and cover. A
statistically significant negative correlation was detected for the U-index and
bankfull discharge recurrence interval. Bankfull flow occurs more frequently in
watersheds with a high U-index. The negative relationship between bankfull.
Legend
i:i
State Boundanes
Study Area Watersheds Classifled
by Banktull Discharge Recurrence
Intervals (Natural Breaks)
1 -1.24 years
1.24- 1.72 years
1.72-2.63 years
2.63-4.27 years
5.05- 18 years (Outliers)
Land U se/Land Cover
Urban Land
I
I
Agricultural Land
Range Land
Forest
Albers Equal Area Conic Projection
9
6
Z5
o
07
190 MILES
KILOMETERS
Figure 111.10: Bankfull Discharge Recurrence Intervals (Classified by Natural Breaks) and Dominating Land Use!
Land Cover by Watershed.
-
105
discharge recurrence interval and the U-index was confirmed by all statistical
tests run on watersheds segmented by climate, bed-material, and slope
Agricultural land use tends to increase the frequency of bankfull flow
and the risk of flooding. A statistically significant negative log-linear
relationship was detected for agricultural land use and bankfull discharge
recurrence interval. This relationship provides statistical evidence for shorter
bankfull discharge recurrence intervals with increasing agricultural land use
within a watershed. The negative relationship between agricultural land use
and bankfull discharge recurrence interval was confirmed by all other
statistical tests. Agricultural land is often characterized by shallow rooted
vegetation and minimal overstory canopy, effectively decreasing infiltration
rates and contributing to an increase in the frequency of bankfull flow.
At this scale of analysis, urban land use plays a minor role in affecting
bankfull discharge recurrence intervals. This may be due to the low
proportions of watershed area that is urbanized within the salmon habitat
recovery watersheds in the study area (1.2% on average). Study area
streams are designated salmon habitat recovery streams and may not be
representative of urban influenced watersheds. Urban land use I land cover
may play a major role in affecting the frequency of bankfull flow in watersheds
with large metropolitan areas. Suggestive evidence was found for a negative
correlation between urban land use and the frequency of bankfull flow for all
watersheds, B- and C-climate groups, and the cobble-bed river group.
According to early studies by Leopold et al. (1964), 50% of urbanized area is
characterized by impervious surfaces. Impervious surfaces increase surface
flow. This explains why these proportionally small urbanized areas have an
impact on streamfiow variables.
Forest cover decreases the frequency of bankfull flow and the risk of
flooding. This negative relationship was statistically significant for cobble-bed
rivers. Suggestive yet inconclusive evidence for this positive relationship
between the frequency of bankfull flow and forest cover exists consistently
106
through the majority of data sets, including all watersheds, all watersheds
segmented by climate, and high and medium slope watersheds. Higher
infiltration rates in a forested environment may account for a decrease in
surface flow.
The relationship between range land and bankfull discharge recurrence
interval varies. Range land decreases the risk of flooding in B- and C-climate
watersheds. A statistically significant positive relationship between bankfull
discharge recurrence interval and range land was detected for these climate
categories. This relationship was also supported for gravel-bed and low slope
rivers. Cobble-bed rivers, medium slope rivers, and D-climate watersheds,
however, experience an inverse relationship. This negative relationship
between bankfull discharge recurrence intervals and range land indicated an
increase of the risk of flooding with an increase in range land. Vegetation
type, grazing intensity, and land management, may contribute to the variations
in the relationship between range land and the frequency of bankfull flow. For
example, water yield increases have been documented after vegetation on a
watershed was converted from deep-rooted to shallow-rooted species.
(Shrubs are often deep-rooted, whereas grasses tend to be shallow-rooted
species). Water yield increases have also been documented after vegetative
cover was changed from plant species with high interception capacities to
species with lower interception capacities (Brooks, K.N. et al., 1993). The
effects of range land on the frequency of bankfull flow may differ for range land
along stream channels compared to range land located in upland watersheds.
Further research is recommended on streamfiow variations in range land
dominated watersheds.
Bankfull discharge recurrence intervals are affected by climate. Mean
bankfull discharge recurrence interval is significantly lower in B-and C-climate
watersheds (1.38 years, and 1.4 years, respectively) compared to D-climate
watersheds (1.56 years). Also, B- and C-climate watersheds displayed
different patterns in their relationships between bankfull discharge recurrence
107
interval and land use I land cover. In particular, urban and range land
displayed correlations (negative and positive, respectively) for B- and Cclimate watersheds that were opposite to those detected for D-climate
watersheds.
Statistical evidence for the important influence of land use on the
hydrologic regime of a watershed has been presented. Negative relationships
were detected between bankfull discharge recurrence interval and agricultural
land use and the U-index. Urban and agricultural land uses contribute to soil
compaction and the increase in impermeable surfaces. Surface flow
increases and transports water faster to channels. This may explain the
increase in the frequency of bankfull flow in watersheds dominated by these
land uses. Forest, however, displays a positive correlation to bankfull
discharge recurrence intervals. The frequency of bankfull flow decreases with
an increase in forest cover. Infiltration rates and subsurface flow are
maximized in forests. Future research is needed on the moderating effect of
riparian buffers on hydrologic regimes altered by land use. There is also a
need for future research on the buffering effect of riparian forest on bankfull
flow in watersheds dominated by urban and agricultural land use.
This study recommends land use assessment at the watershed scale to
be an important consideration in river restoration and other stream
management efforts. Findings and equations provided in this study may be
used by watershed managers to aid in the determination of flood risk based on
land use I land cover at the watershed scale. This research demonstrates that
successful stream management efforts need to consider the entire upland
watershed, and may be used for future watershed management
considerations and flood risk prediction studies. Efforts concentrating on one
particular stream reach or on the river only, and not considering land use I
land cover, may prove unsuccessful.
The GIS approach is a strong asset to fluvial geomorphology studies.
GIS provided a powerful tool for this complex watershed data management
108
and hydrologic modeling projects. In contrast to a traditional field based
approach, data resolution and scale limit the detail of GIS analysis. Field data
collection is required for accurate reach scale data compilation. Field data
may be complemented by landscape scale GIS databases for extensive
regional analysis. Scale and resolution requirements within a hydrologic
management research project's objectives should dictate the proper approach.
This project's objectives dictated an integrated traditional and GIS approach.
The comprehensive database compiled for this study may be beneficial for
further studies related to natural resources and human impacts on Pacific
Northwest watersheds.
109
111.9. REFERENCES
Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A Land Use
and Cover Classification System for Use With Remote Sensor Data.
U. S. Geological Suivey Professional Paper 964.
Andrews, E.D. 1995. Effective Discharge and the Design of Channel
Maintenance Flows for Gravel-Bed Rivers. In Natural and
Anthropogenic InfluenOes in Fluvial Geomorphology - The Wolman
Volume by Costa, J.E., A.J. Miller, J.W. Potter, and P.R. Wilcock (ed.).
1995. American Geophysical Union, Washington, D.C., pp. 151-164.
Bowen, H.C. 1959. Discussion of"A Study of the Bankfull Discharges of
Rivers in England and Wales, 'by M. Nixon. Proc. Inst. Civil Eng.
14:396-397.
Brooks, K.N., P.F. Ffolliott, H.M. Gregersen, and J.L. Thames. 1993.
Hydrology and the Management of Watersheds. Iowa State University
Press, Ames, IA.
Castro, J. 1997. Bankfull Flow Recurrence Intervals: Patterns in the Pacific
Northwest. Submitted to Water Resources Research.
Conte, D.J., D.J. Thompson, and L.L. Moses. 1997. Earth Science. An
Integrated Perspective. 2nd ed.. Wm. C. Brown Publishers, Dubuque,
IA.
Dunne, T., and L.B. Leopold. 1996. Water in Environmental Planning. W.H.
Freeman and Co., San Francisco, CA.
Dury, G.H. 1977. Underfit Streams: Retrospect, Perspect, and Prospect. In
River Channel Changes by Gregory, K.J. (ed.), pp. 282-293.
Gordon, N.D., T.A. McMahon, and B.L. Finlayson. 1994. Stream Hydrology.
An Introduction for Ecologists. John Wiley & Sons, New York, NY.
Jackson, P.L. 1992. Climate of the Pacific Northwest. In Atlas of the Pacific
Northwest, 8th ed, by A.J. Kimerling and P.L. Jackson (eds.). Oregon
State University Press, Corvallis, OR.
Johnston, C.A., N.E. Detenbeck, J.P. Bonde, and G.J. Niemi. 1988.
Geographic Information Systems for Cumulative Impact Analysis.
Photogrammetric Engineering and Remote Sensing, 54(11 ):1609-161 5.
110
Jones, K.B., K.H. Riiters, J.D. Wickham, R.D. Tankersley Jr., R.V. O'Neill, D.J.
Chaloud, E.R. Smith, and A.C. Neale. 1997. An EcologicalAssessment
of the United States Mid-Atlantic Region: A Landscape Atlas. U.S.
Environmental Protection Agency. Office of Research and
Development, Washington, D.C., EPA/600/R-97/130.
Leopold, L.B. 1996. A View of the River. Harvard University Press, Cambridge,
MA.
Leopold, L.B., M.G. Wolman, and J.D. Miller. 1964. Fluvial Processes in
Geomorphology. Freeman, San Francisco, CA.
Lucas, B. and P.L. Jackson. 1995. Koppen Climate Classification of the Pacific
Northwest (1961 to 1990 temperature and precipitation data).
Unpublished map (scale 1:4,000,000).
Osborne, L.L., and M.J. Wiley. 1988. Empirical Relationships Between Land
Use/Cover and Stream Water Quality in an Agricultural Watershed.
Journal of Environmental Management, 26:9-27.
Pastor, J. and C.A. Johnston. 1992. Using Simulation Models and Geographic
Information Systems to Integrate Ecosystem and Landscape Ecology.
In Watershed Management, Balancing Sustainability and Environmental
Change, by Naiman, R.J. (ed.). 1992. Springer Verlag, New York, NY,
pp. 324-346.
Petit, F., and A. Pauquet. 1997. Bankfull Discharge Recurrence Interval in
Gravel-Bed Rivers. Earth Surface Processes and Landforms, (22):685693.
USDA-SCS. 1994. Salmon Recoveiy Initiative Draft. United States
Department of Agriculture, Soil Conservation Service, West National
Technical Center, Portland, OR.
USGS. 1991. Land Use and Land Cover and Associated Maps Factsheet.
U.S. Geological Survey, Reston, VA.
USGS. 1990. Land Use and Land Cover Digital Data from 1:250,000- and
1:100,000-Scale Maps -Data Users Guide 4. U.S. Geological Survey
Reston, VA.
Whelan, F. 2000b. Assessment of GIS Hydrologic Modeling for the Delineation
of Selected Salmon Habitat Watersheds in the Pacific Northwest. To be
submitted to The Professional Geographer.
111
Williams, G.P. 1978. Bank-Full Discharge of Rivers. Water Resources
Research, 14(6):1 141-1154.
Wolf, P.O. 1959. Discussion of" 'A Study of the Bankfull Discharges of Rivers
in England and Wales,' by M. Nixon". Proc. Inst. Civil Eng., 14:400-402.
112
111.10. WEB AND FTP REFERENCES
Northwest Marine Fisheries Service. 1998. Northwest Fisheries Science
Center. http://research.nwfsc.noaa.gov.
USEPA. I 999a. Geographic In formation Retrieval Analysis System (GIRAS)
Directo,y. U.S. Environmental Protection Agency.
ftp://ftp.epa .gov/pub/spdata/EPAG I RAS.
USGS. I 999a. USGS - Water Resources of the United States: Background
Data Sets for Water Resources: HUC and Streams (Digital Line
Graphs). U.S. Geological Survey,
http://water.usgs.gov/GIS/background . html.
USGS. 1999b. Land Use/Land Cover Data (1:250,000). U.S. Geological
Survey,
http://edcwww.cr.usgs.gov/glis/hyper/guide/1 _250_lulcfig/states. html.
USGS. I 999c. Global Land Information System (GLIS): 1-Degree-Digital
Elevation Models. U.S. Geological Survey,
http://edcwww.cr.usgs.gov/glis/hyper/gu ide/i _dgr_demfig/states. html.
USGS. 1999d. United States Water Resources Page: National Water
Information System (NWIS). U.S. Geological Survey,
http//waterdata. usgs.gov/nwis-w.
USGS. 1998. Global Land Information System (GLIS). U.S. Geological
Survey, http:/Iedcwww.cr. usgs.gov.glis. html.
113
Appendix lILA: Critical Values of the Pearson Correlation Coefficient r.
ALL
WATERSH EDS
CATEGORIES
N=68
CLIMATE TYPE
B
C
D
LOW
N=8
N=25
N=35
N=16
NUMBER OF
OBSERVATIONS
LEVEL OF
CONFIDENCE
I-'IAKSON N
ALPHA = 0.01
0.305
0.834
ALPHA = 0.05
0.236
0.707
0.35
SU(3(.hS I IVE
EVIDENCE
BED MATERIAL TYPE
SLOPE
MEDIUM
HIGH
N=27 N=25
GRAVEL
COBBLE
N=38
N=30
CRITICAL VALUES OF THE PEARSON CORRELATION COEFFICIENT R
N10
0.125
N=8
N=25
N=35
N=16
N=25
N=25
N=35
N=40
N=30
0.505
0.430
0.623
0.505
0.505
0.430
0.402
0.463
0.396
0.335
0.497
0.396
0.396
0.335
0.170
0.130
0.250
0.170
0.170
0.140
Reject H0 if the absolute r is greater than the critical value.
0.361
0.140
0.200
Appendix III.B: Summary Statistics for Land Use I Land Cover Variables and Bankfull Discharge Recurrence Interval
for 68 Watersheds.
N =68
URBAN
(in percent)
AGRICULTURE
(in percent)
RANGE
(in percent)
FOREST
(in percent)
U-INDEX
(in percent)
BANKFUL L
R ECU RREN CE
AEAN
1.1751
10.4197
9.9916
Jö.4207
11.5947
(in years)
1.4834
3TANDARD
0.2952
2.1702
1.5634
2.7728
2.3307
0.0760
U.2Z(4
1.9307
4.4901
4.2392
2.994
1.255
2.4347
17.8957
12.8923
22.8651
19.2198
0.6269
3.4828
2.3189
1.7411
-1.2137
2.3673
2.1651
12.5456
76.6982
60.0151
89.2437
89.2437
3 27
AINIMUM
0
0
0
10.7563
0
1
MAXIMUM
12.5456
Ib.69S2
60.0151
100
89.2437
4.27
3UM
79.9044
708.5365
679.4254
5196.608
788.4409
100.87
0.5893
4.3317
3.1206
5.5345
4.6522
0.1517
ERROR
MbDIAN
STANDARD
DEVIATION
SKIWNbSS
RANGE
CONFIDENCE
LEVEL (95.0%)
Appendix lII.C: Summary Statistics for Land Use / Land Cover and Bankfull Discharge Recurrence Interval
Based on Slope Categories.
SLOPE
(in percent)
CD
w
w
FOREST
(in percent)
U-INDEX
(in percent)
BANKFULL
RECURRENCE
(in years)
Mean
11
16668
86877
13.2161
73.2722
10.3545
1.6916
Standard Error
0.15800
0.7352
3.5828
2.9263
4 7321
4.1451
0 1513
Median
O.S2
00898
0.1468
7 1055
83.2531
1.5116
1.4100
Standard Deviation
0.790021
3 67616
17.91409
14.63171
23 66047
20.72554
0.75657
Minimum
0.51
0
0
0
10.7563
0
1.0100
Maximum
43
12.5453
76.6982
60.0151
99.0608
89 2437
4.2700
Mean
0.3396
0.3542
7.5384
10.3328
80.1895
7.8926
1.4304
Standard Error
0.01701
0.1033
2 6816
4.0837
2.7225
0.0959
Medan
0.32
0.1876
08729
24014
39118
883625
1.3970
1.2500
Standard Deviation
0.08838
0 5368
13 9341
12.4782
21 2196
14.1467
0.4983
Mm mum
0.21
0
0
0
31.79715
0
1
Maxmum
049
20343
550128
486466
100.0000
55.6187
2.9500
Mean
0.11813
17920
179879
4.3774
74.9805
19.7799
1.2475
Standard Error
0.01096
0 3818
5.5870
2.21 96
6.1985
5.6851
0.1305
Medan
0.115
15209
101139
07870
86.2914
12.4056
10250
Standard Devaton
0.04385
1 5273
223481
88785
24.7940
227403
05220
Mnmum
005
019
0
0
0
16.8225
0
1
52754
755667
342248
9.bUb5
76.9763
3.1100
ci
Maxmum
Appendix 111.0: Summary Statistics for Land Use I Land Cover and Bankfull Discharqe Recurrence Interval Based on
Climate Categories.
AGRICULTURE
(in percent)
Mean
09764
Standard Error
Median
Standard Deviation
Skewness
Ranae
Minimum
Maximum
0.2476
0.9570
0.7002
0.0700
2.0343
Mean
Standard Error
Median
Standard Deviation
Skewness
Ranae
Minimum
Maximum
Mean
Standard Error
Median
Standard Dev ation
Skewness
Ranqe
Minimum
Max mum
12.4991
36.1601
7.8199
36.7912
22.1179
0.5839
64.6413
10.9255
75.5667
7.5935
2.7360
2.0097
13.6799
2 8525
59.0351
23.9014
4.2212
26.8689
11.9395
-0.5046
33.6378
6.1764
39.8142
1.2604
0.4259
0.3326
2.1294
2.0246
7.1056
0
0
0
12.4991
59.0351
6 5548
2 5136
14.8704
7 1056
13.0487
2.2915
10.6602
13.5568
37644
766982
18118
600151
0
2.0343
1.6874
0.5540
0.3977
2.7701
2.7855
0 8546
0.4090
00898
24197
41157
125456
06716
0
0
0
125456
766982
600151
FOREST
(n percent)
U-INDEX
(in percent)
385868
37.1365
7.8102
37.7483
22.0905
0.6224
65.7270
11.2493
76.9763
9.2809
3.1654
2.8802
15.8268
2.6267
64.3105
4.5179
39.1173
12.7784
-0.1090
42.3254
168225
59.1480
87.9220
3.1780
92.9690
15.8900
-2.2270
64.6681
35.3319
100
76.8533
3.3129
832531
195995
-18483
86.6743
10.75628
97.4306
BANKFULL
RECURRENCE
(in years)
1.3775
0.1845
1 2050
0.5219
2.5109
1.6000
1.0300
2.6300
1.3976
0.1521
1.0200
0.7606
2.8060
3.2700
0
1
64.3105
7.4093
2.8040
1.5116
16.5886
4.0650
89.2437
4.2700
1.5689
0.0917
1.3600
0 5426
1.3991
2.1100
0
89.2437
3.1100
Appendix lll.E: Summary Statistics for Land Use / Land Cover and Bankfull Discharqe Recurrence Interval Based on
Bed-Material Classes.
AGRICULTURE
(in percent)
FOREST
(in percent)
U-INDEX
(in percent)
L
CE
R
Mean
0.2430
5.9614
9.1427
82.0804
6.2044
1 5490
Standard Error
0 0870
2 7545
1 9586
3.5404
2.8050
0.1256
Median
0.0894
0.6694
6.0846
87.4023
0.8410
I 3600
Standard Deviation
04767
15.0867
10.7276
19.3916
15.3635
0.6882
Skewness
3.3876
3.9018
1.9998
-1.9405
3.8823
2.60S3
Range
2.2726
75 5667
48.6466
83.0795
76.9763
3.2700
Minimum
0
0
0
16.82252
0
1
Maximum
2.2726
75 5667
48 6466
99.9020
76 9763
4.2700
Mean
1.9109
139393
106617
71.9525
15.8502
1.4316
Standard Error
0.49458
3 13171
2.34932
3.99179
3.40956
0.09378
Median
0.7670
6.9247
2.3461
80.4962
8.1228
1.1700
Standard Deviation
3.0488
19.3052
14.4822
246071
21 .0179
0.5781
Skewness
2.5439
1.7657
1.5845
-0.8566
1 8918
1.6087
Range
125456
766982
60.0151
892437
89.2437
2.1100
Mnimum
0
0
0
1075628
0
1
Maxmum
125456
76.6982
60.0151
100.0000
89.2437
31100
Sum
726146
5296945
405.1440
2734.1958
6023091
54.4000
LU
WI'
Wz
0
>
w
0
119
Appendix III.F: Watersheds Ranked by U-Index.
GAGE
NUMBER
U-INDEX
(in percent)
BANKFULL
RECURRENCE
(in years)
12010000
13240000
12205000
12209000
13311000
13313000
13186000
13185000
13336500
13340600
12414500
14328000
13310700
13337000
14050000
14159000
14308000
13235000
12452800
13200000
14222500
14308600
14337600
12449500
14325000
14339000
13239000
13297355
12449950
14305500
13296500
14046500
12479500
14306500
12414900
12167000
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
1.01
1.41
1.42
1.85
1.72
0.01
1.16
1.07
1.24
1.16
1.20
2.95
0.01
1.71
0.01
0.01
0.01
0.01
0.02
0.04
0.06
0.06
0.07
0.13
0.23
0.25
0.58
0.85
1.10
1.14
1.40
1.44
1.45
1.51
1.54
1.72
1.95
2.56
2.88
2.94
(ii'\(iE
NUMBER
14312000
13316500
14372300
14038530
13258500
13297330
13302005*
1.61
14048000
13339500
12013500
13305000
14359000
14157500
13334700
13266000
12500450
12027500
13333000
12031000
12484500
14357500
13338500
14203500
12510500
14021000
14026000
14033500*
1.30
1.36
1.00
13344500
12422950
13342450*
1.36
1.84
14207500
14017000
14202000
14018500
13346800
1.86
2.44
4.27
1.09
2.60
1.25
1.34
1.02
1.06
2.20
2.01
1.00
1.52
2.95
1.00
1.32
1.45
Bankfull Discharge Recurrence Interval Outliers.
U-INDEX
bANKJ-ULL
(in percent) RECURRENCE
(in years)
4.63
1.02
4.98
1.16
6.25
6.65
6.96
7.41
7.47
7.54
7.80
8.71
8.73
1.18
1.12
1.04
2.20
7.70*
1.13
1.56
1.00
1.81
1.37
10.46
11.25
12.18
12.96
14.35
14.57
15.14
15.31
16.02
18.12
20.89
22.77
36.85
38.65
41.33
42.02
44.66
47.59
50.49
55.62
64.31
76.98
89.24
1.01
2.63
1.09
1.25
1.01
1.39
1.00
3.11
1.9b
1.b4
1.00
1.16
1.09
1.26
5.05*
1.45
1.00
18.00*
1.01
1.15
1.00
1.03
1.40
120
Appendix III.G: Land Use I Land Cover in Percent and U-Index Data by
Watershed.
GAGE
NUMBER
12010000
12013500
12027500
12031000
12167000
12205000
12209000
12414500
12414900
12422950
12449500
12449950
12452800
12479500
12484500
12500450
12510500
13185000
13186000
13200000
13235000
13239000
13240000
13258500
13266000
13296500
13297330
13297355
13302005k
13305000
13310700
13311000
13313000
13316500
13333000
13334700
13336500
13337000
BANKFULL
U-INDEX
FOREST
URBAN
AGRIRANGE
(in percent) (in percent) RECURRENCE
(in percent) CULTURE
LAND
(in years)
(in percent) (in percent)
0.00
0.40
2.15
3.23
0.93
0.00
0.00
0.01
0.23
0.08
0.23
0.28
0.00
1.89
1.98
2.03
1.62
0.01
0.00
0.08
0.07
1.40
0.00
0.21
0.19
0.25
7.41
1.4
0.01
0.06
0.01
0.00
0.00
0.18
0.54
0.00
0.01
0.02
0.00
8.31
12.20
11.92
2.01
0.00
0.00
0.00
2.69
44.57
0.87
1.23
0.13
0.67
13.33
10.93
21.15
0.00
0.00
0.15
0.00
0.04
0.00
6.75
11.99
1.47
0.00
0.00
7.47
8.66
0.00
0.00
0.00
4.81
14.03
1.25
0.00
0.00
0.00
0.00
0.03
0.99
0.10
3.50
0.00
100.00
91.29
85.07
82.64
93.30
73.37
7.01
92.68
96.67
53.85
88.39
84.23
91.37
88.36
67.72
59.15
41.60
87.20
76.84
84.25
87.29
89.69
83.25
67.48
39.00
72.09
0.17
1.49
6.22
10.66
3.10
2.55
14.76
26.55
34.22
8.62
19.46
15.42
6.24
2.14
10.87
25.43
48.65
14.35
18.09
24.53
66.84
60.02
1.29
1.26
2.39
5.99
11.07
39.81
13.36
16.20
98.61
0.00
8.71
14.35
15.14
2.94
0.00
0.00
0.01
.9Z
44.66
1.10
1.51
1.01
1.00
1.45
1.41
1.42
1.20
1.32
1.00
2.01
1.36
0.13
2.56
1.25
15.31
3.11
1.25
.16
1.07
.16
12.96
22.77
0.01
0.00
0.23
0.07
1.44
0.00
6.96
12.18
1.72
72.21
7.41
73.17
14.37
25.18
97.43
95.68
97.10
1.45
7.47
8.73
0.01
0.00
0.00
88.61
4.98
73.14
48.94
14.57
11.25
0.01
0.02
82.41
80.71
1.00
1.00
2.95
1.34
2.60
1.61
1.01
1.04
1.09
1.36
2.20
1.30
7.70w
1.81
1.71
1.85
1.72
1.16
1.39
2.63
1.24
1.86
121
Appendix III.G (Continued).
GAGE
NUMBER
13338500
13339500
13340600
13342450*
13344500
13346800
14017000
14018500
14021000
14026000
14033500*
14038530
14046500
14048000
14050000
14157500
14159000
14202000
14203500
14207500
14222500
14305500
14306500
14308000
14308600
14312000
14325000
14328000
14337600
14339000
14357500
14359000
14372300
URBAN
AGRIFOREST
U-INDEX
BANKFULL
RANGE
(in percent) CULTURE
LAND
(in percent) (in percent) RECURRENCE
(in percent) (in percent)
(in years)
0.27
0.02
0.01
0.42
0.23
12.55
0.61
1.41
0.79
1.13
1.30
0.Z4
0.09
0.09
0.04
4.54
0.06
5.28
1.16
12.50
0.22
0.24
0.09
0.01
0.11
1.42
0.43
0.01
0.13
0.25
4.57
2.27
2.20
17.85
7.78
0.00
47.17
41.79
76.70
55.01
75.57
36.06
37.52
40.03
6.40
1.85
7.45
0.00
5.92
0.00
59.04
19.73
37.99
0.03
1.30
2.79
0.Ub
0.47
3.21
0.71
0.00
0.72
1.15
11.45
6.81
4.04
3.91
1.95
77.53
90.05
14.39
14.38
84.01
27.19
0.00
s'.i
6.18
20.15
29.39
38.81
25.35
25.64
32.08
2.02
0.23
5.07
u.00
0.38
0.08
6.97
0.00
0.07
0.00
0.33
u.bo
0.22
0.70
37.79
30.77
bib
36.63
16.82
42.99
31.80
19.74
67.99
72.33
60.32
90.88
88.39
92.97
35.33
78.35
48.97
92.78
98.15
0.04
10.46
0.06
64.31
20.89
50.49
0.25
1.54
99.06
u.
94.61
98.51
4.63
2.62
1.36
87.51
91.91
Bankfull discharge recurrence interval outliers
1.b4
2.88
o.ub
7.11
0.b4
47.59
42.02
89.24
55.62
76.98
36.85
38.65
41.33
6.65
1.95
97.01
190
99.06
97.97
97.40
75.87
o.
18.12
7.80
0.01
1.14
0.01
1.54
1.56
1.16
18.00*
1.45
1.40
1.15
1.03
1.09
1.26
5.05*
.12
1.84
.13
2.44
1.01
4.27
1.00
1.00
1.01
1.02
1.00
1.00
1.09
1.06
1.02
1.00
0.85
2.95
2.20
1.40
16.02
9.08
6.25
1.52
1.95
1.37
1.18
122
Appendix III.H: Land Use! Land Cover Data in Acres for All Watersheds.
GAGE
NUMBER
12010000
12013500
12027500
12031000
12167000
12205000
12209000
12414500
12414900
12422950
12449500
12449950
12452800
12479500
12484500
12500450
12510500
13185000
13186000
13200000
13235000
13239000
13240000
13258500
13266000
13296500
13297330
13297355
13302005*
13305000
13310700
13311000
13313000
13316500
13333000
13334700
13336500
13337000
13338500
URBAN
(in acres)
0.00
329.18
12451.32
26785.46
1563.07
0.00
0.00
72.32
393.59
69.10
1911.90
3218.22
0.00
5956.46
20282.93
45282.33
62354.45
30.51
3.59
214.28
197.19
1258.33
0.00
806.33
1732.45
1294.57
1375.90
714.95
34.10
370.55
28.48
0.00
1.79
655.13
11456.59
0.00
88.89
130.86
2025.12
AGRICULTURE
(in acres)
RANGE
LAND
(in acres)
FOREST
(in acres)
0.00
6880.22
70638.70
98951.58
3384.45
0.00
0.00
0.00
4634.91
36288.76
7248.69
13969.88
165.82
2114.87
136663.34
243191.66
0.00
0.00
157.82
8227.51
173.13
2366.55
0.00
46033.77
285.73
1215.20
51676.10
121218.21
3884.63
8021.65
151349.34
34864.14
75569.98
492439.67
686280.16
157116.34
49565.42
66804.69
bUö(4.9b
166565.55
43843.76
733982.25
WATERSHED
AREA
(in acres)
34864.14
82779.37
578848.21
830396.86
168407.79
67558.44
67749.48
656313.24
172294.64
81416.82
830428.28
957760.84 1137105.59
114395.30
278257.15
125203.72
314904.03
694433.37 1025417.26
1316585.34 2225918.26
813881.20 1316813.10 1600665.47 3847546.17
0.00
45342.83 458488.13 bZu.1
10.57
79305.75 313134.51 407525.79
374.66
39344.54 214964.30 255149.15
0.00
18557.60 259583.53 297366.64
33.86
1926.30
80551.51
89806.40
0.00
31292.96
3401.12
26052.35
25828.13
97329.94 258267.61 382728.95
110726.08 449301.92 360200.41 923604.84
7694.35
3,'b9o.1u bLö14.91
75031.12
0.00
13413.65
18576.53
3361.23
0.00
36099.24
49339.28
12101.48
39850.05 356637.86 76666.37 533595.55
49736.85 344539.23 144547.85 574087.47
0.00
2703.93
204226.36 209612.08
0.00
12218.52
12770.66
161.07
0.00
136485.76 140568.51
3363.32
17999.32
331622.95 374255.53
22428.71
295322.97 233049.03 1539982.44 2105595.26
11933.94
bi114.,'j 106086.06
42237.36
0.00
163781.43 1009967.78 1225585.12
0.00
121599.96 605632.78 750419.95
132349.98 29008.45 574905.35 741558.60
BANKFULL
RE-
CURRENCE
(in years)
1.00
1.00
1.01
1.00
1.45
1.41
1.42
1.20
1.32
1.00
2.01
1.36
1.25
2.95
3.11
1.25
1.16
1.07
.16
1.34
2.60
1.61
1.01
1.U4
1.U9
1.36
2.20
1.30
7.70*
1.81
1.71
1.85
1.72
1.16
1.39
2.63
1.24
1.86
1.54
123
Appendix III.H (Continued).
GAGE
NUMBER
URBAN
(in acres)
13339500
13340600
13342450*
35.07
79.65
659.90
13344500
13346800
14017000
14018500
14021000
14026000
14033500*
658.31
1560.34
1786.53
23424.57
3251.45
9272.15
19149.19
591.27
3094.32
4354.77
33.20
18385.88
127.65
16283.92
933.74
56978.43
169.78
306.98
194.88
28.68
465.31
15074.06
477.47
24.37
784.35
1913.92
8411.66
29867.53
218464.35
14038530
14046500
14048000
14050000
14157500
14159000
14202000
14203500
14207500
14222500
14305500
14306500
14308000
14308600
14312000
14325000
14328000
14337600
14339000
14357500
14359000
14372300
AGRICULTURE
(in acres)
1<AN(iE
LAND
(in acres)
BAN KFULL
SHED
REAREA
CURRENCE
(in acres)
(in years)
141243.01 156849.14
1.56
727858.62
bbi91 .14
1.16
18.00*
59123.75
156450.12
89479.35 290797.46
1.45
1337.80
12437.37
1.40
108008.81 294845.90
1.15
279558.71 1661811.97
1.03
177141.15 412094.62
1.09
1.26
262053.95 824143.03
5.05*
290464.80 1471699.31
F-01<ES I
VVAI bJ-
(in acres)
12205.50
iub1.
0.00
1Z4bb.4
73799.34
22502.37
121527.39
79063.12
9539.24
0.00
162203.03 22762.85
1255776.61 102640.18
148599.34 83020.97
309242.80 242226.33
589054.97 571103.87
165012.33 242684.48
15538.94
61510.83
60416.37 836619.93 2359865.84 3262641.82
360971.09 1555074.37 2924237.62 4847517.49
0.00
74323.61
81779.32
1654.43
358216.99 405272.26
23993.03
929.42
11151.02 204545.64 220014.81
0.00
182229.49
109062.27 308679.69
15913.79
308.01
63188.70
80651.83
173185.66
386.07
223241.49 455859.76
22.51
72692.25
78346.36
5461.81
1687.56
1
129467.53
0.00
(Ub9.b
5840.13
203279.00 209535.02
141.91
139.78
285257.21 285537.06
0.00
1916.42
407537.86 411400.95
1368.20
34032.10
6186.95 1003520.09 1060734.21
793.71
109817.79 111473.59
247.82
0.00
1404.93
199987.57 201883.58
4302.46
587081.60 599245.29
3289.83
8902.80
754169.79 774264.54
4167.96
21062.68
13075.78 139626.71 184022.22
89469.29
34417.17 11 bUU19.9 1314220.39
401187.98 14oi.
Bankfufl Discharge Recurrence Interval Outliers.
9119930.74 9J144.b1
1.12
1.84
1.13
2.44
1.01
4.27
1.00
1.00
1.01
1.02
1.00
1.00
1.09
1.06
1.02
1.00
2.95
2.20
1.52
1.95
1.37
.18
124
CHAPTER IV
THE RELATIONSHIP OF STREAM ADJACENT LAND USE I LAND COVER
TO THE FREQUENCY OF BANKFULL FLOW:
DEVELOPMENT OF A CRITICAL ZONE MANAGEMENT MODEL FOR
FLOOD RISK ASSESSMENT IN LARGE PACIFIC NORTHWEST
WATERSHEDS
Franziska Whelan
Submit to Journal of Environmental Management
125
IV.1. ABSTRACT
Geographic Information Systems (GIS), hydrologic modeling, and
statistical analysis were utilized to assess the relationship of bankfull
discharge recurrence intervals and land use / land cover for stream adjacent
buffers and bands of varying widths. Land use / land cover was determined
for stream adjacent zones along salmon habitat recovery streams in 71 large
Pacific Northwest watersheds. Stream adjacent land use / land cover is
significantly correlated to bankfull discharge recurrence interval. The
frequency of bankfull flow indicates the risk of flooding. Watershed managers
face challenges due to increases in flood hazard caused by land use / land
cover changes. There is a need for a simple, yet scientifically sound model
that focuses on spatial patterns of land use / land cover and their influences on
flood risk for large watersheds. The Critical Zone Management Model
presented in this study aids land managers in the assessment of flood risk for
large watersheds. Extensive GIS based analysis and the development of a
Land Use Runoff index (LUR-index) determined a 600 meter sensitive zone.
Land use / land cover within this zone is strongly correlated to streamfiow.
This study provides land managers and planners with a conceptual model
determining which watershed areas and land use I land cover types are most
influential on increasing flood risk.
IV.2. INTRODUCTION
Bankfull discharge recurrence interval is one of the most important
indicators of streamfiow and flood risk. At bankfull discharge, the channel is
filled to the top of its banks. Discharges above bankfull cause flooding
(Leopold, 1996).
126
Bankfull discharge recurrence interval was first established to be 1 .5
years (Leopold et al., 1964). Williams (1978) confirmed Leopold's et al. (1964)
estimate. Dury (1977) calculated a bankfull discharge recurrence interval of
1.58 years. Recent research conducted by Petit and Pauquet (1997) and
Castro (1997) suggest variability in bankfull discharge recurrence intervals.
Petit and Pauquet (1997) analyzed bankfull discharge recurrence intervals in
gravel-bed rivers in Belgium and determined recurrence intervals ranging from
0.4 to 0.7 years for Ardenne rivers with catch ments smaller than 250 km2.
Petit and Pauquet (1997) further determined recurrence intervals from 1.5 to 2
years for larger drainage basins. Research by Castro (1997) detected a
regional variability of bankfull discharge recurrence intervals in the Pacific
Northwest (Oregon, Washington, and Idaho), ranging from 1 year to 3.11
years. Castro (1997) determined a mean bankfull discharge recurrence
interval of 1.2 years for ecoregions in the more humid areas of western
Oregon and Washington.
Whelan (2000a) studied bankfull discharge recurrence intervals in
Pacific Northwest watersheds and determined an increase in the frequency of
bankfull flow with an increase of anthropogenic land uses, including
agricultural and urban land use I land cover, at the watershed scale. There is
presently a lack of information on the influence of stream adjacent land use I
land cover on the frequency of bankfull flow and on flood risk for large
watersheds. Study area watersheds range in size from approximately 12,000
to 9 million acres. For these large watersheds, catchment-wide land use may
be less influential on the frequency of bankfull flow than land use within close
approximation to the stream network. The purpose of this study is to assess
the relationship between bankfull discharge recurrence intervals and stream
adjacent land use I land cover for buffers of varying widths in large Pacific
Northwest watersheds.
Bankfull discharge recurrence interval indicates the risk of flooding.
Flood risk prediction methods that incorporate land use variables, including the
127
estimation of storm runoff volume or the rational runoff method were either
designed for small catchments or require a detail of information on watershed
characteristics that are not feasible to determine for large watersheds. There
is a need for a simplistic yet scientifically sound flood risk prediction model that
allows land managers and planners to evaluate flood risk based on land use I
land cover assessments within large watersheds. This study provides a
conceptual model to determine which watershed areas and land use I land
cover classes are most influential on increasing flood risk. The Critical Zone
Management Model presented in this paper incorporates the land use runoff
index to predict the frequency of bankfull flow, and should aid planners,
developers, and land owners to better understand streamflow influencing land
use / land cover factors within different watershed zones. The land use runoff
index was calculated based on the relationships between surface runoff and
land use I land cover for stream adjacent zones of varying widths.
IV.3. BACKGROUND
IV.3.1. Flood Risk Analysis
IV.3.1.1. Overview
Land managers face watershed management challenges due to
increases in flood hazard caused by land use changes. Watershed managers
need to increasingly make decisions about flood risk when full-scale studies by
hired hydrologic consultants are limited by budget constraints. Rough flood
risk assessment is also necessary to determine whether the problem is
important enough to demand a more sophisticated analysis (Leopold, 1996).
Popular, currently used methods for flood risk prediction include historical
flood records and various runoff equations.
128
IV.3.1 .2. Flood Records
Commonly used information to predict flood risk include historical flood
records published by several government and state agencies including the
U.S. Department of Agriculture (USDA), United State Geological Service
(USGS), and the U.S. Forest Service. Probability analysis of flood records is
frequently applied and uses the annual maximum series or the partial duration
series for flood frequency analysis (Dunne and Leopold, 1996; Brooks et al.,
1993). A statement of the probability of floods greater than their average
frequency of occurrence is required for engineering design, flood-insurance
planning, and land-use zoning for flood prone areas (Dunne and Leopold,
1996).
IV.3.1 .3. Runoff Equations
Frequently used methods to predict flood risk include hydrograph
separation and the estimation of storm runoff volume or peak runoff (Marsh,
1991; Dunne and Leopold, 1996; Brooks etal., 1993). Storm runoff estimates
incorporate knowledge of rainfall - runoff relationships, and detailed watershed
information including cover types (e.g., row crops, small grain, rotational
meadow), treatment or practices (e.g., straight row, contoured), hydrologic
condition, hydrologic soil group, antecedent moisture condition, land use, and
percent of impervious surface. The calculation of flood peak discharges,
including peak runoff, is a commonly used method for flood risk prediction.
The rational runoff method predicts peak runoff rates from data on rainfall
intensity and drainage basin characteristics such as drainage area, soils, and
detailed land use / land cover information. The rational runoff method is
recommended for catchments up to 200 acres, but is commonly applied to
basins up to one square mile in size.
129
IV.3.1 .4. Current Limitations
Current flood risk prediction methods have serious limitations in their
applicability to large drainage basins (> one square mile). Further limitations
include the lack of availability of historic flood records; land use / land cover
alterations that may have changed flood probabilities; and theoretical
frequency distributions that may not always fit to sample records; and the
costly amount of data and information required for risk calculations. There is a
need for a simple, yet scientifically sound model that focuses on spatial
patterns of land use / land cover and their influences on flood risk for large
watersheds. The model should allow land owners, managers, and planners to
assess flood risk in large watersheds in a cost-effective way. This study
provides land owners and planners with a conceptual model to determine
which watershed areas and land use / land cover types are most influential on
flood risk. As in other conceptual models, the model presented in this study
has limitations and is based on assumptions. The model was developed for
large watersheds in the Pacific Northwest.
IV.3.2. Riparian Buffers
IV.3.2.1. Overview
Anthropogenic land cover alterations in Pacific Northwest watersheds
have been accompanied by a decrease in water quality of surface waters,
especially in urban and agricultural catchments. Aquatic resources are subject
to human induced disturbances that can alter the biological, physical, and
chemical characteristics of stream ecosystems. Human induced alterations in
Pacific Northwest watersheds include increased levels of light, stream
temperature, non-point source pollutants, runoff, invasive species, decreased
130
channel stability, changes in streamfiow, in-stream habitat degradation, and
reduced habitat diversity due to the reduction of riparian ecotones (Correll,
1991; Naiman et al., 1992; Gregory et al., 1991). Multiple water and land
users may not notice these anthropogenic stream alterations and the
increasing degradation of aquatic resources until fish diversity and number
significantly decrease. The decrease of salmonids is a critical environmental
issue in the Pacific Northwest and has been correlated to salmon habitat
degradation (Gillis, 1995).
Human induced impacts on aquatic resources are reflected in stream
adjacent land use. Traditionally established buffers are documented to
minimize anthropogenic effects on streams and are typically comprised of
forested or grass land riparian zones (Brazier and Brown, 1973; Morning,
1975; Burns 1970; Karr and Schlosser 1976). Riparian vegetation performs a
variety of functions in the protection of aquatic resources. According to
Castelle et al. (1994), specific riparian functions should determine the
dimensions of buffers. Castelle et al. (1994) and Barton et al. (1985) criticize
that established buffer size requirements have largely been based on political
acceptability rather than on scientific studies.
IV.3.2.2. Riparian Buffer Influence on Runoff I Discharge
Hydrologic processes are mediated by riparian vegetation and channel
morphology (Schlosser and Karr, 1981a; Cooper et al., 1987). The riparian
buffer function of moderating stormwater runoff has been documented
(Castelle et al., 1994; Schlosser and Karr, 1981a). Two mechanisms show the
influence of riparian zones on runoff; (1) reduced surface runoff due to
increased infiltration in vegetated buffer strips (Muscutt et al., 1993; Castelle et
al., 1994), and (2) reduced surface flow velocities due to increased hydraulic
roughness in buffer strips (Muscutt et al., 1993; Schlosser and Karr, 1981 a).
131
Muscutt et al. (1993) conclude that infiltration is greater in healthy soils
associated with permanent vegetation and high root density, compared to
compacted agricultural soils. The more precipitation and surface runoff
infiltrates into the soil the smaller the surface runoff and the slower the
streamfiow response. Surface runoff occurs on disturbed or compacted soils
with reduced infiltration capacity, and may be induced by heavy precipitation
events causing surface soils to be saturated (Muscutt et al., 1993).
Both changes in discharge and increases in stream temperature may
alter biotic communities and change fish habitat (Baltz et al., 1987; Hicks et
al., 1991; Schlosser, 1982) Barton et al. (1 985) concluded that the required
length for buffer strips in order to achieve significant improvements in stream
quality are longer for discharge compared to stream temperature control.
Riparian forest cover may have a moderating effect on discharge through
increasing bank storage (Freeze and Cherry, 1979) or reducing overland flow.
Schlosser and Karr (1981 a) documented that rapid transport of runoff is
characteristic for stream sections with no riparian vegetation. Riparian
vegetation causes slower release of runoff to the stream and higher substrate
stability (Schiosser and Karr, 1981a).
lV.3.2.3. Riparian Buffer Influence on Filtering, Habitat, Erosion, and Stream
Temperature
Castelle et al. (1994) reviewed buffer widths required to perform certain
riparian buffer functions. Recommended widths for buffer strips vary largely
based on the goals of buffering (Figure IV.1). Castelle et al. (1992)
established criteria to evaluate the adequacy of buffer size for the protection of
aquatic resources. Criteria include the functional value of the aquatic
resource, the intensity of adjacent land use, buffer characteristics, and
required buffer functions.
132
Species Diversity
Nutrient Removal
U.
Sediment Removal
Water Temperattxe Moderation
0
20
40
60
80
100
120
Buffer Width (meters)
Figure IV.1: Range of Recommended Buffer Widths By Buffer Function;
Adapted from Castelle et al. (1994).
Castelle et al. (1994) summarize that the minimum buffer sizes on the
low end of the recommended range of widths for a particular buffer function
provide for the maintenance of the physical and chemical properties of
streams. Buffer sizes on the high end of buffer width ranges for a particular
buffer function are the minimum necessary for the maintenance of the
biological components of many wetlands and streams.
A summary of representative studies and their findings on riparian
functions and recommended buffer widths was compiled for this study and is
presented in Table IV. 1. The main documented riparian buffer function is the
filtering function. Riparian buffers are widely used to remove sediments,
nutrients, and pollutants, contained in surface runoff (Castelle et al., 1994).
These dissolved substances enter a vegetated stream corridor and are
restricted from entering the channel by friction, root absorption, clay, and soil
organic matter (The Federal Interagency Stream Restoration Working Group,
1998). It has been documented that the effectiveness of buffer zones varies
with the transport mechanism of the pollutants (Muscutt et aI., 1993). Riparian
buffer zones have a positive effect on loads of sediment and phosphorus in
surface runoff. The take-up of nutrients is an important riparian buffer
function, especially in agricultural catchments (Castelle et al., 1994; Dillaha et
133
al., 1989). This buffer function is directly correlated to sediment control since
a large volume of nutrients is carried by sediment. Riparian vegetation
controls sediment and erosion by slowing down surface flow, trapping
sediment, stabilizing stream banks, and increasing infiltration (Cooper et al.,
1987; Young et al., 1980; Dillaha et al., 1989). Roots hold back erodible soil
and maintain soil structure, therefore increasing infiltration. Vegetation further
resists the formation of channels and gullies by slowing down sheet flow
(Castelle et al., 1994).
FUNCTION
I
WATER
MI-'NA I UI'(L
ULUt.ItAL
PROBLEM
Temperature
Temperature
WIDTH
(METERS)
20-30
Mm of 30
Temperature
15-50
Temperature
30.5
Temperature
Temperature
Sediment
Sediment
Pollutants
FILTERING
HABITAT
10
Variable
10-60
Variable
Variable
Nutrients
Nutrients
5-90
Nutrients
15-80, 60
Nutrients
Variable
Pollutants
5, 8,30
Nutrients
species
diversity
Species
decline
Uflannel
erosion
50
BUFFER EFFECT
REFERENCE(S)
Maintain temperature. shade
Maintain temperature, shade
Castelle et al.. 1994
escnta ana I aylor, 1 eo;
Lynch et al.. 1985
Phillips, 1989
Maintain temperature, effect is
based on Dhvsical condition of area
Improve Cutthroat trout habitat
Improve Trout habitat
Improve Salmonid habitat
Filter sediment
Filter sediment
Filter seaiment ana pnospnorus,
Based on transport mechanisms
Nutrient interception
Nutrient dynamics
Hickman and Raleigh,
1982
Barton etal.. 1985
Theurer et al.. 1955
Castelle et al.. 1994
Uooer et al.. 19W
uscurt et al., iii
Castelle et al., 1994
Peterjohn and Correll,
1984
21.4. 21.3
3-106.7
Control non-point source pollution,
dependent on soil - landform veaetation comolex
Nutrient interception from croo land
eauce pollutants, correiatea to
sediment control, retention of
soluble pollutants
Nutrient uptake
Increase habitat diversity
Castelle et al., 1994
Variable
Improve salmonid habitat
Theurer et al., 1985
Slow down surface flow, trap
sediment, stabilize stream banks,
increase infiltration
vvolman, i; ooper et
Variable
EROSION
Water Quality
Variable
increase oan staolllty,
reduce sediment input
Water Quality
0, 4.6. 9.1
Control non-point source pollution
Phillips, 1989
Jordan etal., 1993
Muscutt et al., 1993
Younqetai, 1980
al., 1987; Young etal.,
1980; Dillaha etal., 1989
Schlosser and Karr, issia;
Smith et aL, 1989:
Barton etal., 1985
Dillaha et aI.. 1989
Table lV.1: Riparian Buffer Functions, Width Recommendations, and
Ecological Responses.
134
Riparian vegetation shades streams and has been directly correlated to
stream temperatures and fish habitat (Beschta and Taylor, 1988; Brazier and
Brown, 1973). Elevated stream temperatures due to the absence of riparian
vegetation cause fish mortality due to increases in the metabolism, respiration,
and oxygen demand of salmonids, as well as the higher susceptibility of fish to
diseases (Armour, 1994; Beschta and Taylor, 1988; Hostetler, 1991).
Increased stream temperatures further decrease the solubility of dissolved
oxygen, increase the rate of organic decomposition which also uses oxygen,
and intensify the toxicity of many substances (Armour, 1994; McSwain, 1987;
Theurer et al., 1985; Hicks et al., 1991).
Riparian buffers are ecotones and provide habitat diversity. Ecotones
form transition zones between the upland watersheds and the streams
(Gregory et al., 1991). The creation of unique edge habitat is important for
species diversity and abundance. Naiman et al. (1992) believe that an
understanding of riparian zones serves as a framework for an understanding
of fluvial ecosystems with their organization, diversity, and ecology.
IV.3.2.4. Variable Versus Fixed Width Buffers
Recommendations for riparian buffer widths may be based on fixed
widths or variable widths based on a hydrologic or landscape attributes.
Variable width buffers require an assessment of the ecological condition for
each stream reach within a watershed. This assessment involves trained
personnel and may prove overly time- and cost-intensive. Variable width
buffers are also less predictive for land use planning (Castelle et al., 1994).
Fixed width buffers are based on a single parameter such as buffer function.
These buffer widths are more easily enforced than variable width buffers since
they do not require regulatory personnel with specialized knowledge on
ecological principles. Fixed width buffers also allow for greater regulatory
135
predictability, and require smaller expenditures of both time and money to
administer (Castelle et al., 1994).
IV.4. OBJECTIVES
This study uses a GIS approach to determine whether there is a
correlation between bankfull discharge recurrence interval and land use / land
cover within stream adjacent zones in large Pacific Northwest watersheds
(Oregon, Washington, and Idaho). Land use / land cover categories
investigated include urban, agriculture, forest, and range land. This study also
investigates the relationship between bankfull discharge recurrence interval
and two indices: the human use index (U-index), a parameter designed to
indicate the extent of anthropogenic land use / land cover; and the land use
runoff index (LUR-index), an indicator estimating surface runoff and flood risk
based on land use / land cover. Objectives for this study include: (1) to
provide an overview of land use / land cover patterns in stream adjacent areas
of different widths in Pacific Northwest salmon habitat recovery watersheds;
(2) to determine whether there is a relationship between bankfull discharge
recurrence interval and riparian land use / land cover variables and indices for
different buffer widths; (3) to develop a land use runoff indicator that rates
runoff potential based on land use / land cover characteristics for different
buffer widths; (4) to evaluate the suitability of the U-index and LUR-index for
the prediction of bankfull discharge recurrence intervals for different buffer
widths; (5) to provide regression equations for the prediction of the frequency
of bankfull flow based on the indicator and the U-index for different buffer
widths; and (6) to develop a Critical Zone Management Model for large Pacific
Northwest watersheds to aid watershed land use decision makers in the
assessment of flood risk and to determine at what distance from the stream
network anthropogenic land use will no longer significantly impact flood risk.
136
This study presents a conceptual model determining which watershed areas
and land use / land cover classes are most influential on increasing flood risk.
In contrast to current flood risk assessment methods, the Critical Zone
Management Model addresses large watersheds and is not limited by costly
and time-consuming data requirements for risk calculations.
This study extends previously reviewed research and focuses on the
riparian buffer function of streamfiow moderation and the decrease of flood
risk. Due to the assumption that streamfiow influencing buffers are much
larger than traditional buffers (Barton et al., 1985), stream adjacent areas to be
assessed in this study extend much further out than common buffers. This
research suggests buffer widths based on the characteristics of stream
adjacent areas, appropriate for flood risk management and restoration efforts
that target streamflow related problems, including physical instream habitat.
IV.5. JUSTIFICATION
Bankfull discharge plays a significant role in hydrology and
geomorphology, since it forms and maintains channel geometry. The
frequency of bankfull discharge indicates the risk of flooding since discharges
above bankfull cause flooding (Leopold, 1996). Land use/land cover has
been documented to alter the frequency of bankfull flow at the watershed
scale (Whelan, 2000a). Riparian characteristics may be more important than
gross watershed characteristics due to the hydrologic impact of riparian
vegetation and its interaction with channel morphology (Karr and Schlosser,
1981a). Riparian land use / land cover may alter the hydrologic characteristics
of a watershed including the recurrence interval of bankfull discharge.
This study assesses empirical relationships between the frequency of
bankfull flow and land use I land cover in varying riparian buffer widths.
Previous studies incorporated factors such as climate, terrain, vegetation, and
137
soils in their research on the geographic variation of streamfiow
characteristics, however, land use / land cover has not been considered. The
reviewed riparian buffer studies focused on riparian buffer functions other than
the recurrence of a streamfiow event. Traditional riparian buffer studies
assessed specifically designed or selected riparian buffer types, such as
forested or grass cover buffers. None of the reviewed studies have analyzed
how streamfiow is affected by land use / land cover within 'real world' stream
adjacent areas.
Muscutt et al. (1993) summarizes that there are currently no widely
accepted procedures for designing buffer zones. According to Castelle et al.
(1994), there is a lack of scientifically sound buffer size requirements.
Resource agencies need to base buffer requirements on scientifically based
criteria in order to balance watershed development with the protection of
natural resources. Muscutt et al. (1993) identified that "knowledge on the
potential effects of buffer zones appears to be limited". According to Muscutt
et al. (1993), further research on buffer zones is necessary to maximize
potential water quality and habitat improvements caused by riparian buffer
zones.
Present research suggests that management recommendations for the
implementation of riparian buffers at the watershed scale are scarce. This
may be due to the 'idealized' buffer approach of traditional riparian studies,
which would be costly and time consuming to implement at the catchment
scale. Muscutt at al. (1993) identified the need for research on the impact of
buffers at the catchment scale. This study assesses the relationship between
bankfull discharge recurrence interval and land use / land cover for buffer
zones of different width at the catch ment scale of large Pacific Northwest
watersheds.
The frequency of bankfull discharge is a valuable indicator of
streamfiow health. Changes in the frequency of bankfull flow reflect
alterations of streamfiow and fish habitat. Bankfull discharge recurrence
138
intervals were determined for designated salmon habitat recovery streams in
the Pacific Northwest (USDA-SCS, 1994). Salmonids have declined
significantly in the Pacific Northwest in past decades, caused in part by habitat
degradation through anthropogenic alterations of streams and watersheds.
The degradation of salmonid populations and their habitat is a critical
environmental issue in the Pacific Northwest and has been addressed by the
Northwest Marine Fisheries Service listing of salmon, steelhead, and cutthroat
as threatened or endangered species (Northwest Marine Fisheries Service,
1998). Bankfull discharge plays an important role in the creation and
maintenance of physical instream habitat since bankfull is the critical channel
forming discharge.
GIS was used to assess riparian buffers at varying widths for
designated salmon habitat recovery streams within 71 Pacific Northwest
watersheds. This research develops a methodology that could become a
valuable component of flood-risk related decision making processes for land
managers. The GIS approach was the only practical method for organizing
and analyzing the voluminous datasets required for this study. Streamfiow
data collected at the reach scale were used in conjunction with various
landscape scale datasets, including land use / land cover and digital elevation
models. The multi-watershed scale of this study would not have been possible
in the past due to the lack of accurate, regional digital data availability and
without recent advances in GIS technology. The relationships developed are
specific to the Pacific Northwest due to the physical characteristics of this
region.
IV.6. STUDY AREA AND DATA SOURCES
The study area (Figure IV.2) includes 71 designated salmon habitat
recovery streams in the Pacific Northwest, including Oregon, Washington, and
139
Idaho (USDA-SCS, 1994). Selected streams are located in watersheds that
contain critical habitat for anadromous salmonids. Streams were defined by
the Natural Resources Conservation Service (NRCS) as Salmon Initiative
streams. Salmon Initiative streams are characterized by (1) critical salmonid
habitat; (2) a public interest in the fishery and ecological watershed condition;
and (3) private ownership of significant portions of the watershed (Castro,
1997). Study area watershed range in area from approximately 12,000 acres
to 9 million acres and comprise a total land area of 53,860,169 acres
(84,156.5 square miles).
-y-
Sai*ig 9te
D
Eg N Caiic Pqon
e uithies
MLES
o,
1tKILalErERs
Figure IV.2: Study Area (Oregon Washington, Idaho).
The primary databases compiled for this study include: (1) a USGS gaging
station database; (2) one degree digital elevation models (DEM5); (3) a land use
I land cover (LULC) database, (4) a field derived stream gage database
140
containing bankfull discharge data, and (5) an EPA River Reach File consisting
of a stream network coverage for the study area. All landscape scale coverages,
including DEMs, LULC, and stream network, have a scale of 1:250,000, which
makes a reasonable assessment of the Pacific Northwest and its regional
conditions possible.
IV.6.1. USGS Gaging Station Database
The USGS stream gaging station database includes locational data for 71
gaging stations in the study area. The locational data includes gaging station
coordinates and large scale maps detailing the location relative to natural and
manmade features (USGS, 1999d).
lV.6.2. Digital Elevation Models
One degree DEMs available from the USGS were used for hydrologic
modeling (USGS, 1 999c). USGS DEM grids are in geographic coordinates
(ground units: arc-seconds, surface units: meters). A DEM mosaic was created
for the Pacific Northwest to allow for accurate delineation of watersheds
extending over grid boundaries.
IV.6.3. Land Use I Land Cover
Land use / land cover characteristics of the Pacific Northwest were
analyzed using digital Land Use I Land Cover (LULC) data files developed by the
United States Geological Survey (USGS) and available by the USGS and the
United States Environmental Protection Agency (USEPA) (USEPA, 1999a).
USGS LULC data were digitized from NASA and USGS aerial photography, and
141
were initially produced by the National Mapping Program at a scale of 1:250,000
for the United States. An LULC mosaic was created for the Pacific Northwest to
allow for accurate land use I land cover assessment of watersheds extending
over grid boundaries (Figure IV.3).
The standard criteria used for USGS classification were applied for the
entire study area to ensure consistent interpretation of land use I land cover.
Interpretations were based on a land use / land cover system developed for use
with remotely sensed data. The USGS LULC classification is based on the
Anderson et al. (1976) land use classification.
lV.6.4. Field Stream Gage Database
Bankfull stage was determined from field observations at active USGS
gaging stations (Castro, 1997) using guidelines defined by Dunne and Leopold
(1996). Bankfull discharge recurrence intervals were calculated based on annual
maximum flow frequency curves representing 50 years of data. Gaged streams
were selected since long-term streamfiow records are necessary in order to
determine the recurrence intervals for bankfull stage.
Legend
Stream Sampling Ste at
Gaging Station
II State Boundaries
Use / Land Cover Cateqor
Urban or Bufit-Up Land
Agricultural Land
Range land
Forest
Water Areas
Wetlands
S Barren Land
I
Tundra
Perennial Snow or Ice
4
p
Aibers Equa Area Con c Projection
Figure IV.3: Pacific Northwest Land Use I Land Cover and Stream Gaging Stations.
a
s
ip 75 120
o ibo
MILES
METERS
KILO
143
IV.6.5. Stream Network - River Reach File
The US EPA Reach File Version 1.0 (RFI) for the conterminous United
States was used as the database for the stream network. The file RFI was
developed by the USEPA in 1982 and is available as an ARC/INFO coverage
on the USEPA web site (USEPA, 1 999b). The file is a vector database of
streams and is commonly used by the U.S. Fish and Wildlife Service, USGS,
USEPA, and other natural resources agencies. The USEPA uses RFI
extensively for water quality modeling on river basins. The coverage is
intended for general water resources applications within the GIS user
community.
RFI was prepared by the USEPA from stable base color separates of
National Oceanographic and Aeronautical Administration (NOAA) aeronautical
charts. According to the USEPA, these charts provided the best nationwide
hyrographic coverages (USEPA, 1986). They included all hydrography shown
on USGS 1:250,000 scale maps. All hydrographic features on the NOAA
charts were optically scanned using a scanner resolution finer than the feature
line width. The reach file is a line coverage in Albers Equal Area projection
(USEPA, 1986).
lV.7. TERMINOLOGY
This study focuses on an analysis of the 'real world' stream adjacent
areas, with their anthropogenic alterations and land uses, and analyzes their
impact on streamfiow. Terminology used for these stream adjacent areas may
differ from previous studies that applied the term "buffer". Traditionally, buffers
have been defined as vegetated zones located between natural resources and
adjacent areas subject to human alteration (Castelle et al., 1994). Buffers are
also often referred to as vegetated filter strips. Traditional studies on riparian
144
buffers focus on specifically designed or selected buffers such as forest or
grass land riparian zones. The following definitions will be used throughout
this study:
Bankfull Discharge: The discharge that fills the channel to the level of the
active floodplain. This discharge is a critical channel forming discharge.
Bankfull discharge is the most efficient discharge. At bankfull
discharge, a maximum amount of sediment and water is transported
with the least amount of energy (Leopold et al., 1994).
Bankfull Discharge Recurrence Interval: The mean interval of recurrence of
bankfull flow. Bankfull discharge recurrence interval expresses the
probability that bankfull discharge will occur in any given year. For
example, the probability of occurrence of a 2 year event is 1/2 = 50%
for any given year.
Buffer: Stream adjacent area of a specified width.
Band: Area of a specified width that parallels the stream network and may
either be directly adjacent to the stream network or offset by a specified
distance.
Critical Zone Management Model: A management model presented in this
study. The model identifies stream adjacent land use / land cover
areas within a watershed that play a critical role in streamfiow
alterations. Stream adjacent zones that are important for flood risk
assessment are delineated.
Land Use Runoff Index (LUR-index): An indicator variable presented in this
study. The LUR-index is based on land use / land cover and
associated runoff values. The index ranges from I to 5, where I
indicates low flood risk and a naturally forested watersheds, while 5
indicates high flood risk due to increased surface runoff in a heavily
urbanized environment.
Human Use Index (U-index): An indicator variable measuring the degree of
anthropogenic land use! land cover within a watershed. The U-index
145
expresses the agricultural and urban area within a watershed in
percent.
Proportional Human Use Index: Indicates what proportion of all agricultural
and urban land use I land cover of the watershed is located within a
band or buffer area.
Proportional Land Use / Land Cover: The proportion of a particular land use /
land cover of the watershed that is located within a band or a buffer
area.
IV.8. METHODOLOGY
IV.8.1. Watershed Delineation Through Hydrologic Modeling Using GIS
Ninety-seven USGS one-degree DEMs were converted to grids and
mosaicked to create a complete coverage for the Pacific Northwest, including
Oregon, Idaho, and Washington. The DEM mosaic allowed for accurate
hydrologic modeling and delineation of watersheds extending over grid
boundaries. Surface characteristics of the DEM mosaic were modeled using
the following ArcView Spatial Analyst Hydrologic Modeling extension
functions: FillSinks, Flow Direction, and FlowAccumulation. The flow direction
and flow accumulation output grids display surface flow patterns and drainage
networks. These output grid files were used to model hydrologic watershed
characteristics.
A digital stream sample point layer including locations of all 71 gaging
stations was compiled and used for flow path delineation together with USGS
gage station location maps, and USGS topographic maps. Flow paths were
delineated using the hydrologic modeling function FlowPath. Watersheds
were delineated using the WatershedTool script provided by the ArcView
Spatial Analyst hydrologic modeling extension. The script was modified for a
146
significant increase in delineation accuracy (Whelan, 2000b). Figure IV.4
displays a digitally delineated watershed and its delineated flow path.
0-377
318-7
75.11
1134-1510
1511 -18
1
44.31
33a
0
ui
Figure IV.4: DEM Mosaic with Delineated Watershed for Stream Gage #
14157500, Coast Fork Willamette, Oregon.
Quality control was conducted to ensure that watersheds were
accurately delineated. Quality control included reference checks using the
USEPA reach file for a comparison of stream networks and flow paths, the.
LULC data layer, hydrologic unit coverages, USGS topographic maps, and
USGS reference areas for all watersheds. All delineated watersheds were
converted to Albers Conic Equal Area projection to allow for future overlays
with the LULC and stream network data layers. Figure lV.5 displays all
digitally delineated watersheds.
147
Jbs
k Cac Pit
ion
9751 MLES
a
1OOKILD1EJ
Figure IV.5: Digitally Delineated Study Area Watersheds.
IV.8.2. Stream Network Geoprocessing
The USEPA reach file was overlain with the digitally delineated
watersheds. Stream network was then clipped out for each watershed using
the geoprocessing function in ArcView. The new stream coverage contained
all stream networks located within study area watersheds.
IV.8.3. Creation of Stream Adjacent Buffers
Study area stream network was buffered using the Create
Buffers function in ArcView GIS. Buffer distances were set to 30, 60, 90, 110,
200, 400, 600, 800, 1000, 1200, 1400, and 1600 meters. Computer
processing time and memory decreased with increasing buffer width. The
148
stream network data layer for all study area watersheds is a relatively large
vector dataset and required more system resources than is minimally
recommended for running ArcView GIS. For this project, although a 433 MHz
PC with 128 MB RAM was used, it was not capable of buffering the stream
network as one entity. To accomplish all buffering successfully, the amount of
virtual memory was increased, and the stream network data were broken into
subsets. The larger the buffer distance, the fewer subsets were required to
accomplish the buffering.
lV.8.4. Geoprocessing and Determination of Dominant Land Use I Land Cover
The digitally delineated watersheds and buffers were overlain with the
LULC data layer. LULC data were then clipped out for each watershed and all
buffer widths using the geoprocessing function provided by ArcView GIS. The
clipped watersheds and buffers were stratified into nine LULC categories:
urban or built-up land, agricultural land, range land, forest, water areas,
wetland areas, barren land, tundra, and perennial snow or ice (Figure IV.6).
For this study, four dominating land use I land cover classes, including urban
land, agricultural land, range land, and forest, were used for statistical
analyses, since there was a very limited representation of the categories water
area, wetlands, barren land, tundra, and perennial snow and ice. The four
major land use I land cover categories include multiple minor land use
categories: (1) urban or built-up land, including residential areas, commercial
services, industrial areas, transportation, communications, industrial and
commercial, and mixed urban, and other urban or built-up land; (2) agricultural
land, including cropland and pasture, orchards, groves, vineyards, nurseries,
confined feeding operations, and other agricultural land; (3) range land,
including herbaceous range land, shrub and brush range land, and mixed
149
range land; and (4) forest land, including the subcategories deciduous forest
land, evergreen forest land, and mixed forest land.
IV.8.5. Buffer Land Use / Land Cover Data Manipulation and Band Creation
Total land use I land cover area in acres was determined for all nine
major LULC categories for both watershed area and buffers for all 71
watersheds. The area data for all LULC categories were then converted to
proportions relative to both watershed area and buffer area for the different
buffer widths. Data was further processed by determining the proportions for
each LULC category within bands of increasing distance from the stream
network. These bands include 0-30 meters, 30-60 meters, 60-90 meters, 90110 meters, 110-200 meters, and 0-200 meters, 200-400 meters, 400-600
meters, 600-800 meters, 800-1000 meters, 1000-1200 meters, 1200-1400
meters, and 1400-1600 meters. The calculations of both land use / land cover
categories and proportional land use I land cover categories for bands allowed
for stronger statistical analysis compared to buffers, since land use / land
cover data for bands are not cumulative.
000
0
1000
aO
3DOO
Scale 1 350000
A bers Equal Area Conic Project on
Legend
Steam Network
0 Meter Buffer
- Urban Land
Agricultural Land
J
Range Land
Water Areas
Forest
Wetlands
Figure IV.6: Land Use / Land Cover Buffer Outlines for Bear Creek at Medford Watershed, USGS Gaging Station #
14357500.
151
IV.8.6. Determination of the Human Use Index
The human use index (U-index) is an indicator variable measuring the
degree of anthropogenic impacts based on land use / land cover (Jones et al.,
1997). For this study, the U-index was calculated by determining the
percentages of total watershed area, total buffer area, and total band area, that is
characterized by urban or agricultural land use / land cover. The index was
calculated separately for watersheds, buffers, and bands, using the following
equations:
U-indexwafershed = 100 * (agricultural area + urban area) / watershed area
U-indexbuffer
= 100 * (agricultural area + urban area) / buffer area
U-indexbafld
= 100 * (agricultural area + urban area) /band area
The U-index calculation does not weight agricultural or urban land use / land
cover in respect to their potential impacts on streamfiow. The watershed U-index
rating provides a ranking based on the degree of human land change. Regional
patterns of the U-index at the watershed scale help identify areas that have
experienced the greatest land cover conversion from the natural vegetation
(Jones et al., 1997). The U-index provides an indicator for the anthropogenic
land uses urban and agriculture hypothesized to influence the frequency of
bankfull flow. Agricultural and urban land are characterized by significant
hydrologic altering events, such as absence of forest overstory, increased
artificial drainages, decreased infiltration rates, and increased surface runoff.
lV.8.7. Determination of the Land Use Runoff Index
This study developed a land use runoff index (LUR-index) based on land
use / land cover and associated runoff and infiltration capacity parameters. Land
use I land cover was derived from the generated land use / land cover data for
watersheds, buffers, and bands. Infiltration capacity parameters are land use /
land cover dependent and were derived from values for the rational runoff
152
coefficient, C (Leopold, 1996; American Society of Civil Engineers, 1969). The
rational runoff coefficient, C, is a coefficient used for the rational runoff method,
which predicts peak runoff rates. Peak runoff rates are calculated from data on
rainfall intensity, drainage area, and other drainage basin characteristics such as
land cover and soils. Values for the coefficient, C, reflect soil type, surface
roughness, vegetation, and land use (Leopold, 1996).
Two factors justify the development of the LUR-index. First, study area
watersheds range from approximately 12,000 to 9 million acres in size, making
the application of the rational runoff method, which is recommended for
catchments up to 200 acres, unreasonable. Second, an assessment of soil type,
surface roughness, and other drainage basin characteristics necessary to
determine the rational runoff coefficient, C, seems unreasonable for the size of
the study area watersheds. Such an assessment would be time and cost
prohibitive for these large watersheds. The LUR-index was especially developed
for large watersheds and indicates the relationship between land use and surface
runoff. The LUR-index does not account for rainfall intensity because it is not
intended to estimate runoff volume.
The LUR-index expresses the potential effect of land use I land cover oh
streamfiow and the risk of flooding. Land use - runoff relationships incorporated
in the LUR-index are based on mean estimates for the C value, and are
expressed on a rating scale. The index indicates the potential effects of land use
I land cover on streamfiow on a I to 5 rating scale, where I indicates low flood
risk, and 5 indicates high flood risk and elevated runoff.
The LUR-index weights the potential impact that the major land use I land
cover categories typically have on streamfiow. Proportional watershed areas
with urban, agriculture, range, and forest land use I land cover were determined
and multiplied with the runoff factor, R. The R factor is specific for each land use
I land cover type and indicates the potential to increase surface runoff (Table
IV.2).
153
LAND USE/LAND COVER
RUNOFF FACTORR
Urban
5
Aariculture
Range
Forest
4
3
1
Table lV.2: R Factor by Land Use / Land Cover Class.
The R factor was derived from the rational runoff coefficient, C. Mean
values for the rational runoff coefficient are 0 for forest, 0.15 for range land, 0.40
for agriculture, and 0.75 for urban land use (adapted from Leopold, 1996). The
mean C values were transformed from 0,0.15,0.40, and 0.75, to 1, 1.15, 1.40,
and 1.75, respectively to generate the R factor on a I to 5 scale. The R factor
was then calculated for the four dominating land use I land cover types. To
establish the I to 5 rating scale, R was set to be I for forest and 5 for urban land
use I land cover. The R factor for agricultural and range land use I land cover
was calculated as follows:
Rrange land
= 5 * 1.15/1.75
Ragricuiture
5 * 1.40/1.75
=3.28=3
=4
The R factor is required to generate the LUR-index. The LUR-index
represents a weighted indicator for the potential impacts of land use / land cover
on streamfiow and is calculated as follows:
LUR-index = (5 * percent urban + 4 * percent agriculture
+ 3 * percent range +
* percent forest) /100
The LUR-index ranges from I to 5 and is solely based on the four dominating
land use I land cover types, including urban, agriculture, range land, and forest.
The LUR-index could fall slightly below I if land use / land cover categories, such
as water areas, wetland, barren land, tundra, or perennial snow and ice, occupy
portions of the buffer area. These land uses occupy insignificantly small areas in
the study area watersheds.
154
A LUR-index of I characterizes a forested landscape and correlates to low
flood risk. The LUR-index increases with an increase in anthropogenic land use I
land cover. If the LUR-index equals 2 or 3, flood risk is slight to moderate,
respectively. Flood risk is high if the LUR-index is 4, and dominating land use /
land cover is agriculture and urban land use. Flood risk is severe if the LURindex equals 5, which would occur in fully urbanized watersheds.
The LUR-index was applied to buffers, bands, and study area watersheds
to evaluate potential streamfiow changes caused by land use I land cover (Figure
IV.7). The LUR-index was designed for the determination of land use I land
cover - streamfiow relationships, and may not be applied to assess water quality
condition in terms of sediment transport, chemical pollutants, or biological
condition.
155
by LL.b
WR4ndeir'es)
-
WPUIfl Dflfl ° 00. Sal!!
Sal,'
<1.1
1.1-1.5
1.5-2.0
zo-ao
30-5.0
N
3
WIZBbAd
Figure IV.7: Study Area Watersheds Rated by the Land Use Runoff Index.
IV.9. STATISTICAL ANALYSIS
Statistical analysis tested for a relationship between bankfull discharge
recurrence interval and land use I land cover data at the watershed level, for
buffer zones, and for bands. The sample size of 71 watersheds was sufficient for
the statistical analysis of bankfull discharge recurrence intervals and their
relationship to four dominating land use I land cover categories, to the U-index,
and to the LUR-index.
Statistical tests performed included Pearson correlation tests, simple and
multiple linear regression, ANOVA F-tests, and t-tests. Statistical tests were
conducted on untransformed data, and on bankfull discharge recurrence interval
156
data at the natural logarithm scale and at the reciprocal scale. The interpretation
of tests using data transformed to the natural logarithm refers to median rather
than mean values.
The following three out of 71 data sets were excluded from statistical
analysis: gaging stations 13302005 (Pahsimeroi River at Ellis), 13342450
(Lapwai Creek near Lapwai), and 14033500 (Umatilla River near Umatilla).
These three stations displayed extremely large bankfull discharge recurrence
intervals (7.7, 18, and 5.05 years, respectively). Outliers were due to
inconsistencies in gaging station location, USGS streamfiow records, or the
absence of bankfull indicators in the field. The gaging station at the Pashimeroi
River at Ellis was recently relocated, therefore the field measurements conducted
at the new location did not correspond to the USGS data used to generate the
flow frequency curve for determining bankfull discharge recurrence interval. The
calculated bankfull discharge recurrence interval for the Lapwai Creek near
Lapwai gaging station is a result of USGS gage data that contradicted with field
measurements. The USGS recorded discharge did not correlate to the field
measured discharge at the time of field data collection. The data for the Umatilla
River near Umatilla gaging station was removed due to a lack of bankfull
indicator at the time of field data collection (Castro, 1997). All statistical tests
were performed on 68 data sets.
IV.9.1. Summary Statistics
lV.9.1 .1. Summary Statistics for Bankfull Discharge Recurrence Intervals
Mean bankfull discharge recurrence interval for the 68 study area
watersheds is 1.48 years (standard error = 0.08 years), and the median is 1.25
years (standard deviation = 0.63 years). Minimum bankfull discharge recurrence
interval is 1 year, maximum bankfull discharge recurrence interval is 4.27 years
(Appendix lV.A).
157
IV.9.1 .2. Summary Statistics for Land Use / Land Cover Within Buffer Areas
Land use / land cover in study area watersheds is dominated by forest
(76.4%), followed by agriculture (10.4%) and range land (10.0%). Urban land
use occupies the smallest portion of study area watersheds (1.2%). The U-index
(11.6%) is largely determined by the percentage of agriculture within a watershed
(Appendix IV.B). Land use I land cover within buffers is characterized by higher
percentages of urban and agricultural land compared to watershed-wide land use
I land cover (Figure IV.8). For example, urban land use within buffers declines
with increasing buffer width and ranges from 1.6% for the 200 meter buffer to
1.3% for the 1600 meter buffer. Mean agricultural cover decreases from 13.3%
to 11.8% for the 200 to 1600 meter buffers. Forest and range land increase with
increasing buffer width. On average, 71% of buffer area is occupied by forest,
and 10% by range land. Appendix IV.B summarizes land use I land cover for all
delineated buffers.
80.00
70.00
60.00
Co.
50.00
40.00
c'3
3000
Urban Land
. Agrictitur Land
I Range Land
20.00
I.
Foi
IL.
0
0
0
3
BufVsr Widths In Metsrs
Figure IV.8: Summary Statistics for Proportional Land Use I Land Cover Located
Within Selected Buffer Widths and Compared to Mean Land
Use I Land Cover for Total Watershed.
158
Figure IV.8 summarizes the proportions of watershed wide dominant land
use / land cover types that are located within buffer areas. The 200, 400, 600,
800, 1000, and 1200 meter buffer, occupy 8.1%, 15.8%, 23.0%, 30.0%, 36.6%,
and 42.8% of total watershed area, respectively. Figure IV.8 illustrates the large
proportion of anthropogenic land use / land cover within buffer zones compared
to their proportion within the total watershed.
SUMMARY
STATISTICS
(PROPORTIONAL)
Butter Area
Mean
Standard Error
Skewness
c
IL
60
90
110
124
247
35
3.28
0.56
6 53
9.69
1 06
1.51
323
3.13
0.00
44 57
300
0 00
62 72
4 50
11.69
1 75
2.83
0.00
72.76
759
1117
1346
1.15
1 59
1.84
765
276
256
0.00
23 58
2.80
0.55
5 40
0.00
28.67
0 00
46.66
0.00
63.02
8 35
2.35
0 00
0.00
23.58
3.77
0.57
Mm mum
Maximum
Mean
Standard Error
Skewness
M nimum
Maximum
Mean
Standard Error
Skewness
Minimum
Maximum
Mean
Standard Error
Skewness
Mm mum
Maximum
Mean
Standard Error
Skewness
Minimum
Max mum
1.14
0.12
1.83
5.60
1.10
5.45
0.00
57.44
2.27
023
1.59
5.37
0.00
83.09
3.40
0.34
182
170
000
522
000
1036
0.00
121
241
004
008
017
0.18
033
2.19
co
cci)
Table IV.3:
30
0.66
4.36
1502
360
013
016
100
650
7276
10.07
1.81
5.11
0.00
9379
4 16
041
1.59
0 00
17.83
4.39
0.15
0.16
1.23
7.92
BUFFER WIDTH (in meters)
400
600
200
8 14
19.86
2.44
1 99
0 00
98.52
23.20
2 67
1 60
0.00
9852
1690
2 18
3 07
0.00
100.00
7.64
0.71
1.24
0 00
2886
7 92
0 27
0 12
2 28
14 19
15.75
3594
3.34
0.86
0.00
100.00
41 77
3.47
0.51
0 00
100.00
30.47
2.99
1.07
0.00
100.00
1541
1 39
115
0 00
55.88
15.38
0.52
0.09
4.47
27.36
800
1000
1200
2302
4728
2997
42.77
66.09
3.70
0 36
0.00
100.00
55.65
3 75
-0.07
0.00
100.00
41.47
3.45
0.47
0.00
100 00
22.63
1.89
1 22
0 00
85.76
377
-008
000
36.56
61.94
3.74
56.37
100.00
65.93
3.76
-0.57
0.00
100.00
51.43
3.64
-0.03
0.00
100.00
29 55
2.24
083
0 00
2249
9841
29.30
0 73
0 04
6 53
092
-003
864
3955
50.83
-035
000
10000
71 88
3.74
-0.87
0.00
100.00
58.12
3.73
-0.28
0.00
100.00
36.36
2.51
0.33
0.00
100.00
35.82
1.10
-0.11
10.66
61.01
363
-053
1.44
100.00
75 36
3.61
-111
0.00
100.00
63.31
369
-0.48
0.00
100.00
42 74
2.78
-0.04
0.00
100.00
42.02
1.24
-022
1265
69 90
Statistics for Proportions of Total Land Use / Land Cover and U-Index Located Within Respective
es.
160
Table IV.3. includes the proportion of total watershed area these buffer
zones occupy. This proportion may be compared to the percentages of dominant
land use / land cover categories located within the buffer areas.
Urban land use occupies larger portions of stream adjacent areas,
compared to total watershed area. On average, 23.2%, 41.8%, 55.7%, 65.9%,
71.9%, and 75.4% of all urban land use within a watershed is located within the
200, 400, 600, 800, 1000, and 1200 meter buffer, respectively. Approximately
one quarter of all urban land use in a watershed is located within the 200 meter
buffer, or within 8.1% of total watershed area. The largest proportion of urban
land use within the 200 meter buffer is 98.5% (gaging station 13340600, North
Fork Clearwater River), followed by 92.6% (gaging station 14308000, South
Umpqua River at Tiller). Over half (55.7%) of all urban land use within a
watershed is located within the 600 meter buffer, which occupies less than one
quarter (23.0%) of the total watershed area. Over 80% of all urban land use
within the watershed is located within the 600 meter buffers for 20 of 71
watersheds. The 1200 meter buffer occupies less than half (42.8%) of the total
watershed area, yet over three quarters (75.4%) of all urban land-use are located
within 1200 meters from study area streams. Over 80% of all urban land use
within the watershed is located within the 1200 meter buffer for 36 out of 71
watersheds.
Agricultural land use is also dominant in stream adjacent areas (Table
4.3). On average, 16.9%, 30.5%, 41.5%, 51.4%, 58.1%, and 63.3% of all
agricultural area within study area watersheds lies within the 200, 400, 600, 800,
1000, and 1200 meter buffer, respectively. On average, over half (51.4%) of all
agricultural land use lies within the 800 meter buffer, which occupies less than
one third (30.0%) of the total watershed area. Approximately two-thirds of all
agricultural land use within the watershed lie within the 1000 meter buffer, which
occupies only 36.6% of total watershed area. All agricultural land use within the
South Fork Boise River watershed (gaging station 1318600) was located within
the 200 meter buffer. Over 80% of agricultural area is located within the 1200
meter buffer for 18 of 71 watersheds.
161
The distribution of range land within buffer zones is proportionate to buffer
area. Of total range land within watersheds, 7.6%, 15.4%, 22.6%, 29.6%, 36.4%,
and 42.7% is located within the 200, 400, 600, 800, 1000, and 1200 meter
buffers. The distribution for forest land is comparable to range land. Of total
forest land within watersheds, 7.9%, 15.4%, 22.5%, 29.3%, 35.8%, and 42.0% is
located within the 200, 400, 600, 800, 1000, and 1200 meter buffer. These
summary statistics shows that range and forest land are relatively evenly
distributed across watersheds, while urban and agricultural land use concentrate
on stream adjacent areas.
The U-index decreases with increasing buffer width from 14.9% for the
200 meter buffer to 11.6% watershed wide. A watershed wide U-index includes
and may be biased by agricultural and urban land use located close to the
stream network. Therefore, a watershed-wide U-index may not be the most
suitable index for the degree of human use for studies that focus on stream
systems and large watersheds.
The proportion of watershed anthropogenic land use I land cover that is
located within buffer areas is expressed by the proportional U-index (Table IV.3).
Figure IV.9 illustrates the proportional U-index for all buffer areas and compares
this summary statistics to mean watershed data. The figure illustrates that most
urban and agricultural land use I land cover is located within close approximation
to the stream network.
162
7000-
C
00.00'
S
S
a.
C
50.00-v
aSC 40O0
-F
S
30.00
C
C
rC
2000
a
a.
10.00-
5
WkJth i M.tei-
Figure IV.9: Summary Statistics for Proportional U-Index for Selected Buffers in
Comparison to Mean U-Index for Watersheds.
lV.9.1 .3. Summary Statistics for Land Use / Land Cover Within Bands
Land use I land cover was calculated for bands of 0-30 meters, 30-60
meters, 60-90 meters, 90-110 meters, 110-200 meters, and for 200 meter fixed
width bands ranging from 0 to 1600 meters. In contrast to cumulative buffer
data, data for bands are separate entities and allow for statistical comparisons
between selected bands. An average 200 meter fixed width band consists of
1.5% urban, 12% agriculture, 10% range land, and 74% forest cover.
Analysis of proportional land use / land cover data allow for a statistical
comparison of the concentration of selected land use I land cover types within
fixed width bands (Table IV.4). Figure IV. 10 illustrates the proportion of the four
dominant land use I land cover categories that is located within band areas, and
compares this summary to mean watershed data.
LAND USE/LAND
COVER SUMMARY
x
a)
C
Co
I.-
a)
IC)
I.-
a)
CC
Coca
STATI STICS
Band Area
Mean
30-60
60-90
90-110
110200
1.23
1 22
0.81
325
200
363
817
Standard Error
Skewness
Minimum
Maximum
Mean
Standard Error
Skewness
Minimum
Maximum
0.00
27.38
3.16
0.46
2.68
0 00
19.14
3.58
0.45
2.04
0.00
18.15
Mean
280
275
172
Standard Error
0.55
5.49
0 00
28.77
1.13
0.49
5.16
0.00
25.65
0 11
1 81
0.11
1.46
0.23
3.23
0.00
10 69
0.76
0.07
0 00
0.00
4.66
1.19
0.04
0.15
0 00
0.32
1.17
0.00
281
1165
0 79
0 03
1) 34
023
3.53
0.12
0.07
1 03
214
1.42
628
SkwnRss
Minimum
Maximum
Mean
Standard Error
Skewness
M n mum
Max mum
M
0 51
3 03
0.00
21 47
3 82
0.59
308
514
Li-Minimum
1 20
0 04
0.16
1) 34
Maximum
2.17
U)
0
n
Standard Error
Skewness
113
2 29
0.26
0.85
1.32
0 00
27.85
9.74
0.95
1.38
067
0.00
10.03
0.00
27 85
6.83
0 66
111
0.00
23.85
0 25
1.90
0.00
1003
117
013
348
BAND WIDTH (in meters)
0-200
2U0400600AOl)
400
600
8 14
7.62
6.94
7.27
1986 16.08 1135
908
2 44
0 91
1 49
0 95
1 99
-1.13
1.41
5.66
0.00
0 00
0.00
0.00
9852
1 59
46.39 94.85
23.20 18 57 13.89 10.28
2.67
0.80
1 61
1 36
1.60
-110
1.58
471
0 00
0.00
0.00
0.00
98 52
1.59
55.98 94.&5
16.90
13.57
11.00
996
2.18
0 81
1.48
0.81
307
-201
4.87
0.28
0.00
0 00
0.00
0.00
100.03 0 28
11 26
7.64
7 77
7.22
6 92
0.71
068
050
0.62
1.24
-0.12
-0.09
1.32
0.00
0.00
0.00
0.00
28.86 27.01 2988 1639
7.46
1.92
6.81
7.10
0.27
0.24
0 20
0 22
0.12
-0.03
000
-013
2.28
2.19
2.10
2 06
14.19
1317 1219 1128
8i
800-
1000-
1000
1200
6.20
659
sbo
052
1.07
0.00
23.12
595
0.72
1 27
0.00
25.79
6.69
0 53
1.07
0.00
19.01
6 81
054
065
0 00
21.25
6 52
018
-0 32
WATERSHED AREA
(in acres)
758593.93
96526 23
25299.86
0.70
14001600
5.39
5 16
0 55
-0.02
1 49
0.41
3 67
000
000
11.43
3.48
32.72
0.00
15.67
2.36
0.39
0 00
1279201 18
9013 15
3340.86
6.25
416
034
12001400
434
605
289
047
051
1.60
0.00
19.46
5.19
0.40
0.75
0.00
15.30
6.38
0.51
2.70
0.00
26.16
3.16
151
0.00
26.26
6.20
0.16
202
-0.53
1.99
10.17
9.81
0.00
1 33
0.00
11.89
2.93
0.30
0.37
0.00
10.74
4.17
0.33
-0.20
0.00
2220
1108
5.81
5.40
0.11
-1 65
1 99
6 82
0.32
0.09
0.00
8.14
5.35
0.49
112
0.13
-1.05
1.97
8.45
1)01)
218464 35
87513.08
23703.83
3.97
0.00
1255776.61
117893.94
32588.37
3.74
0.00
155507437
534383 78
137731 65
6.14
1337 80
9119930.74
Table IV.4: Summary Statistics for Proportions of Total Land Use I Land Cover and U-Index Located Within Respective
Bands.
164
Urban and agricultural land use I land cover decrease with increasing
distance from the stream network. For example, 23.2% of all urban land use
within a watershed is located within the 0-200 meter band, 10.3% is located in
the 600-800 meter band, and 3.5% is located within the 1000-1200 meter band.
Less than one quarter of all urban land use within a watershed is located more
than 1200 meters away from the stream network. Agricultural land use displays
a similar pattern, where 16.9% of all agricultural land of a watershed is located
within the first 200 meters from the stream network, 10.0% is located within the
600-800 meter band, and 5.2% is located within the 1000-12000 meter band.
Agricultural land occupies 36.7% beyond the 1200 meter zone, which occupies
57.2% of the total watershed area.
Forest land and range land display a different pattern. Both land uses
appear evenly distributed across the bands and the watershed (Figure IV.10).
Large portions of upland areas within watersheds are forested or used as range
land (Table IV.4).
3
Land
U Aguicultural Land
. Inge Land
U Foreet
200.400
400-800
800-800
800.1000
1000-1200
etehed
Band Width in Msri
Figure IV. 10: Summary Statistics for Proportional Land Use / Land Cover
Located Within 200 Meter Bands and Compared to Mean Land
Use I Land Cover for Total Watershed Area.
165
The U-index for the 200 meter fixed width bands decreases steadily from
14.3% for the 0-200 meter band to 13.7% for the 200-400 meter band to 11.3%
for the 400-600 meter band to 11.0% for the 1000-1200 meter band. Mean Uindex for the area beyond 1200 meters is 10.8%. The watershed average lies at
11.6%. These summary statistics suggest that land use / land cover within the
bands in close proximation to the stream network drives the U-index. The
proportional U-index for all bands is illustrated in figure IV.11 and was calculated
based on the proportion of watershed anthropogenic land use that is located
within selected fixed width bands. As reflected in the U-index, urban and
agricultural land use / land cover within bands decreases with increasing
distance from the stream network. Figure IV. 11 compares the proportional Uindex for 200 meter bands to the mean watershed wide U-index.
20.00
C
V
U
C
V
C
V
C
18.00
16.00
14.00
12,00
10.00
8.00
t0
6.00
2
2.00
0
0.
a-
4.00
0.00
0-200
200-400 400-600 600-800 800-1000
1000- warshed
1200
Band Width In M.tsi
Figure IV. 11: Summary Statistics for Proportional U-Index for 200 Meter Bands
in Comparison to Mean U-Index for Total Watershed.
166
The proportional U-index for the 200 meter bands decreases with
increasing distance from the stream channel. The 200 meter band directly
adjacent to the stream network has the highest U-index of all bands and
incorporates 19.9% of all anthropogenic land use / land cover within the
watersheds. This 0-200 meter band U-index is about twice the U-index for the
600-800 meter band which contains only 9.1 % of all anthropogenic land use /
land cover. Only 4.2% of all urban and agricultural land use / land cover is
located within the 1000-1200 meter band.
IV.9.2. Correlation Tests for a Relationship of Land Use / Land Cover Data
Versus Bankfull Discharge Recurrence Interval
Pearson correlation tests were conducted to test for a relationship
between bankfull discharge recurrence interval, land use / land cover variables,
the U-index, and the LUR-index for different buffer and band areas and total
watershed area. The Pearson correlation coefficient r was calculated to
determine whether there is a correlation between the variables of interest. The
Null hypothesis tests for no correlation (test for H0: p = 0 against Hi: p
0) and
may be rejected if the absolute value of r is greater than the critical Pearson
correlation coefficient. The critical values of the Pearson correlation coefficient r
for 70 observations (n = 68) are 0.236 for a 95% confidence level (oc = 0.05), and
r = 0.305 for a 99% confidence level (a = 0.01).
IV.9.2.1. Correlation Tests for Land Use / Land Cover for Buffer Areas
A statistically significant negative correlation between bankfull discharge
recurrence intervals and agricultural land use was detected for all buffer widths
and at the watershed scale (Appendix IV.C). The negative relationship between
agriculture and bankfull discharge recurrence interval transformed to the
167
reciprocal was detected with a 99% level of confidence for all buffers up to 1000
meters. Level of confidence decreases to 95% for buffers larger than 1000
meters and the entire watershed. All detected statistically significant correlations
between bankfull discharge recurrence interval and agricultural land use are
negative, and indicate that the frequency of bankfull flow increases with
increasing agricultural land use.
Suggestive yet inconclusive evidence exists for a positive relationship
between forest and bankfull discharge recurrence interval. Suggestive evidence
suggests that bankfull discharge recurrence interval increases with an increase in
forest cover. No statistically significant relationships were determined for bankfull
discharge recurrence intervals versus urban or range land.
IV.9.2.2. Correlation Tests for Land Use / Land Cover for Bands
Statistical analysis of land use / land cover in buffers was conducted, yet it
does not allow for detailed analysis of break points in the relationship between
land use I land cover and the hydrologic variable, since buffers are cumulative.
Therefore, statistical analysis tested for a correlation between land use variables
and bankfull discharge recurrence interval by band width. Band widths include 030 meters, 30-60 meters, 60-90 meters, 90-110 meters, 110-200 meters, and
fixed width 200 meter bands ranging from 0 to 1600 meters. Agricultural land
use was most influential on bankfull discharge recurrence interval in all bands up
to 600 meters from the stream network (99% level of confidence for a negative
correlation). The level of confidence for this statistically significant negative
relationship between agricultural land use and bankfull discharge recurrence
interval decreases to 95% for bands located within a 600 to 1200 meter distance
from the stream network. Bands beyond the 1000-1 200 meter width show no
correlation between agriculture and bankfull discharge recurrence interval
(Appendix IV.D).
168
Suggestive yet inconclusive evidence was found for a positive relationship
between bankfull discharge recurrence interval and forest for all bands. This
relationship suggests that the frequency of bankfull flow decreases with an
increase in forest land. No statistically significant relationships were determined
for bankfull discharge recurrence intervals versus urban or range land.
IV.9.3. Correlation Tests for a Relationship of the U-Index Versus Bankfull
Discharge Recurrence Interval for Buffers and Bands
The U-index was calculated for all delineated buffers, bands, and
watersheds. A statistically significant negative correlation between bankfull
discharge recurrence intervals and U-index was detected for all buffer widths and
for the watershed scale (Table lV.5).
169
BUFFER WIDTH
(in meters)
30
60
90
110
200
400
600
800
1000
1200
1400
1600
WATERSHED
PEARSON CORRELATION COEFFICENTS FOR U-INDEX VERSUS
BANKFULL RECURRENCE
BANKFULL
RECURRENCE
-0.2862
-0.2861
-0.2855
-0.2851
-0.2855
-0.2794
-0.2734
-0.2670
-0.2605
-0.2540
-0.2475
-0.2407
-0.2105
LN(BANKFULL
RECURRENCE)
-0.3176
-0.3175
-0.3170
-0.3166
-0.3168
-0.3110
-0.3043
-0.2896
-0.2822
-0.2747
-0.2668
-0.2308
-1 /(BANKFULL
RECURRENCE)
-0.3361
-0.3360
-0.3355
-0.3352
-0.3351
-0.3297
-0.3224
-0.3143
-0.3061
-0.2897
0.28 1 1
-0.2405
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
ALPHA = 0.01
ALPHA = 0.05
r = 0.305
r = 0.236
Table IV.5: Summary of Pearson Correlation Coefficients for Bankfull
Discharge Recurrence Interval Versus U-Index for Buffers, 68
Watersheds.
The Pearson correlation coefficient decreases with increasing buffer width.
The detected relationship shows statistical significance with a 99% level of
confidence for all buffers up to the 400 meters at the logarithm scale and for all
buffers up to 1000 meters at the reciprocal scale. Level of confidence decreases
to 95% for buffers larger than 1000 meters and for the whole watershed.
Correlation analysis of U-index versus bankfull discharge recurrence
interval for bands allowed for a more detailed analysis compared to buffers since
bands are not cumulative. Statistically significant relationships between the Uindex and bankfull discharge recurrence interval were detected for all bands up
to a 1200 meter distance from the stream network (Table V.6). Overall
watershed data also displayed this relationship. Statistical significance with the
170
highest level of confidence (99%) was detected for all bands up to the 200-400
meter band. This finding presents evidence that urban and agricultural land use
have the greatest influence on ban kfull discharge recurrence interval when
located within 400 meters of the stream network. The level of confidence for the
detected relationships decreases to 95% for bands that are located more than
400 meters away from the stream network. Beyond the 1000 meters, only the
reciprocal value of bankfull discharge recurrence interval shows a statistically
significant relationship with the U-index at a 95% level of confidence. No
statistically significant relationship could be detected beyond 1200 meters.
BAND WIDTH
(in meters)
30-60
60-90
90-110
110-200
200-400
400-600
600-800
800-1000
1000-1200
1200-1400
1400-1600
WATERSHED
PEARSON CORRELATION COEFFICENTS FOR U-INDEX VERSUS
BANKFULL RECURRENCE
BANKFULL
RECURRENCE
-0.2737
-0.2711
-O.2S.35
-0.2853
-0.2706
-0.2579
-02417
-0.2265
-0.2106
-0.1937
-0.1718
-0.2105
LN(BANKFULL
RECURRENCE
-0.3034
-0.3151
-0.3164
-0.3026
-0.2871
-0.2686
1 ;j
-0.2329
-0.2136
-0.1881
-0.2308
-1 /(BANKFULL
RECURRENCE
-0.3210
-0.3184
-0.3338
-0.3345
-02i7
-U3U3 (
-02831
-0.2642
-0.2442
-0.2235
-0.1964
-0.2405
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
ALPHA = 0.05
ALPHA = 0.01
r = 0.236
r = 0.305
Table lV.6: Summary of Pearson Correlation Coefficients for Bankfull Discharge
Recurrence Interval Versus U-Index for Bands, 68 Watersheds.
Overall watershed data show a significant correlation at a 95% level of
confidence for the reciprocal of bankfull discharge recurrence interval. The
watershed data encompass all bands and buffers zones including the bands up
171
to 400 meters that showed a high level of confidence (99%) in their statistically
significant relationship between bankfull discharge recurrence interval and the Uindex.
IV.9.4. Correlation Tests for a Relationship of the LUR-Index Versus Bankfull
Discharge Recurrence Interval for Buffers and Bands
Pearson correlation analysis was conducted for the LUR-index versus
bankfull discharge recurrence interval for all buffers, bands, and watersheds. All
detected statistically significant relationships were negative and indicate that
watersheds with a larger LUR-index exhibit lower bankfull discharge recurrence
intervals and a higher risk of flooding.
Statistically significant evidence was determined with a 95% level of
confidence for a negative correlation between the LUR-Index and bankfull
discharge recurrence interval for all buffers (Table IV.7). No statistically
significant relationship was detected for the LUR-index at the watershed scale.
172
BUFFER WIDTH
(in meters)
30
60
PEARSON CORRELATION COEFFICENTS FOR LUR-INDEX VERSUS
BANKFULL RECURRENCE
BANKFULL
RECURRENCE
-0.2708
LN(BANKFULL
RECURRENCE)
-0.2818
-1/(BANKFULL
RECURRENCE)
-0.2823
-02819
-02806
-02703
-02813
90
110
-0.2691
200
400
600
800
1000
1200
-0.2716
-0.2571
-0.2510
-0.2455
-0.2405
-0.2355
-0.2304
-0.2443
-0.1816
-0.2801
-0.2792
-0.2816
-0.2668
-0.2599
-0.2539
1400
1600
watershed
-U.2i34
-U.24ö(
-U.Z44
-0.2379
-0.2769
-0.1889
-u
f:J?3
-0.281 1
0.2665
-O.2böf
-0.2525
-0.2472
-0.2416
-0.2359
-0.2967
-0.1875
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
ALPHA = 0.05
r 0.236
Table IV.7: Pearson Correlation Coefficients for Bankfull Discharge Recurrence
Interval Versus LUR-Index for Buffers, 68 Watersheds.
Correlation results at the buffer level suggest that analysis based on buffer
data is not strong enough due to the cumulative nature of the data. Therefore,
correlation analysis based on the LUR-index determined for bands was
conducted (Table IV.8).
Statistically significant negative relationships between the LUR-index and
bankfull discharge recurrence interval were determined for all fixed width 200
meter bands up to 600 meters from the stream network (95% level of
confidence). The LUR-index for the 600 meter buffer ranges from 0.92 to 3.88
with a mean of 1.65 (standard error = 0.08). The correlation between the LURindex and bankfull discharge recurrence interval is always negative and indicates
that urban and agricultural land use increase the risk of flooding (and the LUR-
index) while forest decreases the risk. The Pearson correlation coefficient
173
declines steadily for bands beyond 600 meters. No relationship was found
between the LUR-index and bankfull discharge recurrence interval for watershedwide analysis.
BAND WIDTH
(in meters)
0-200
200-400
400-600
600-800
800-1000
1000-1200
1200-1400
1400-1600
WATERSHED
PEARSON CORRELATION COEFFICENTS FOR LUR-INDEX VERSUS
BANKFULL RECURRENCE
BANKFULL
RECURRENCE
-0.2716
-0.2365
-0.2359
-0.2237
-0.2134
-0.2016
-0.1918
-0.1700
-0.18 16
LN(BANKFULL
RECURRENCE)
-0.2816
-1/(BANKFULL
RECURRENCE)
-0.2308
-0.2205
-0.2074
-0.2018
-0.1777
-0.1889
-0.2284
-0.2182
-0.2042
-0.2031
-0.1787
-0.1875
CRITICAL VALUES FOR THE PEARSON CORRELATION COEFFICIENT
Table IV.8: Pearson Correlation Coefficient for Bankfull Discharge Recurrence
Interval Versus LUR-Index for Bands, 68 Watersheds.
IV.10. CRITICAL ZONE MANAGEMENT MODEL
Results of the analysis of the relationship between the LUR-index and the
frequency of bankfull flow aided in the development of the Critical Zone
Management Model (Figure IV.12). The model was generated for large
watersheds with a minimum catchment size of 12,000 acres. The model
identifies stream adjacent zones that play a critical role in flood risk assessment
and streamfiow changes of large watersheds. The model was developed to aid
watershed land managers and decision makers in flood risk assessment and
174
streamfiow related watershed management of large watersheds. Zone
delineation was based on scientific findings rather than on political acceptability.
The model delineates two zones, the functional zone (0-110 meters) and
the sensitive zone (0-600 meters). The delineation of the functional zone is
largely based on reviewed studies on riparian buffer functions and associated
buffer width recommendations. The four primary riparian buffer functions that
characterize the functional zone include species diversity (3-110 meters), stream
temperature maintenance (20-30 meters), sediment removal (10-60 meters), and
nutrient removal (5-90 meters). The sensitive zone includes these buffer zones
but adds an additional width to encompass the requirement for streamfiow
maintenance (600 meters). The delineation of the sensitive zone is based on
findings from this study including the correlation between the LUR-index and
bankfull discharge recurrence interval for different bands and buffers.
The functional zone has been delineated based on riparian zone
management studies on stream temperature, sediment and nutrient removal, and
habitat diversity. Traditional riparian buffer studies concentrate on these stream
adjacent areas and typically do not extend further out. Land use / land cover
within the functional zone is critical not only for the frequency of bankfull
discharge, but also for other physical and ecological stream characteristics.
Land use I land cover in the functional zone plays an important role in
determining the frequency of streamfiow events and is highly correlated to flood
risk indicators, including the LUR-index.
Functional
Sensitive
Zone
Zone
80
Water Temperature
Sediment Removal
Nutrient Removal
Species Diversity
U-index for Bands
LUR- index for Bands
U-index for Buffers
LUR- index for Buffers
200
40()
Iurban -
600
110
1000
Meters in Stream from Distance
0
Agriculture
Range Land
Forest
12(X)
Model. Management Zone Critical IV.12: Figure
176
The delineation of the sensitive zone was based on the analysis of flood
risk indicators including the LUR-index and its correlation to the frequency of
bankfull flow. The sensitive zone includes the functional zone and was
delineated based on high significance in the relationships between LUR-index,
U-index, and bankfull discharge recurrence interval, as detected in this study.
The extent of the sensitive zone presents a cutoff for watershed managers to
assess land use I land cover impacts on streamfiow (600 meters). Increases
in urbanization and agricultural land use I land cover in the sensitive zone are
highly correlated to increases in flood risk, as indicated by the negative
relationship between the U-index and the frequency of bankfull flow (99%
confidence level), and between the LUR-index and bankfull discharge
recurrence interval (95% confidence level).
The Critical Zone Management Model presents new recommendations
for required management areas for stream restoration. Stream restoration
projects in salmon habitat recovery watersheds should account for streamfiow
including bankfull discharge. Bankfull flows are critical channel shaping flows
and are necessary for the maintenance of physical instream habitat, therefore,
stream restoration efforts in large watersheds should not be limited to the
traditionally delineated riparian buffers of 110 meters or less, but should
include the 600 meter sensitive zone. The frequency of bankfull flow also
indicates the risk of flooding. Land use I land cover in stream adjacent areas
up to 600 meters is highly correlated to bankfull flows. Urban and agricultural
cover within the sensitive zone increases the risk of flooding.
Regression analysis was conducted to predict bankfull discharge
recurrence interval based on land use variables as indicated by the U-index or
the LUR-index within the functional and sensitive zones (Table IV.9).
Regression equations are listed based on zone width. Presented equations
may be used by land managers to calculate the expected frequency of bankfull
flow based on a land use assessment within a respective buffer zone. The
177
selection of a specific buffer width depends on required buffer function and
watershed management goals.
MANAGEMENT
ZONE1
FUNCTIONAL
ZONE
MAXIMUM
ZONE
WIDTH
(in meters)
110
SENSITIVE ZONE
600
FUNCTIONAL
ZONE
110
SENSITIVE ZONE
600
FUNCTIONAL
ZONE
110
SENSITIVE ZONE
600
Y
LN(bankfull
discharge
recurrence
interval)
LN(bankfull
discharge
recurrence
interval)
1/(bankfull
discharge
recurrence
interval)
1/(bankfull
discharge
recurrence
interval)
1/(bankfull
discharge
recurrence
interval)
1/(bankfull
discharge
recurrence
interval)
X
b
LOWER 95%
CONFIDENCE
LEVEL
UPPER 95 %
CONFIDENCE
LEVEL
R
SIDED
P-VALUE
0.5562
-0.1405
0.0211
-0.2593
-0.0218
27.9
LURINDEX
0.5414
-0.1320
0.0323
-0.2526
-0.0115
26.0
LURINDEX
0.6135
0.0882
0.0208
0.0138
0.1627
28.0
LURINDEX
0.6236
0.0824
0.0331
0.0068
0.1580
25.9
U-INDEX
0.7043
0.0036
0.0052
0.0011
0.0060
33.5
U-INDEX
0.7079
0.0035
0.0073
0.0010
0.0060
32.2
INTERCEPT
a
COEFFICIENT
LURINDEX
1VVO-
Table IV.9: Regression Equations for Bankfull Discharge Recurrence Interval Determined by U-Index and LUR-Index
for Selected Management Zones. Equations are Based on 68 Data Sets. (1 Management Zone at Maximum
Width of Buffer.)
179
IV.1 1. CONCLUSIONS
Changes in streamfiow characteristics due to land use / land cover
alterations as addressed in this study are a fundamental issue in fluvial
geomorphology. Land use I land cover can greatly alter the quality, seasonal
distribution, and relative proportion of water and sediment supplied to the
stream network. Alterations in water supply commonly cause changes in the
stream channel, floodplain, and riparian areas (Andrews, 1995). These
changes are known to affect downstream aquatic resources, including critical
salmonid habitat. This study identifies critical land uses and areas within large
watersheds in respect to their effects on the frequency of streamfiow events.
The identification of these land uses and areas within watersheds will assist in
improving streamfiow and fish habitat, and offers significant insight for
evaluating the potential impact of alternative management strategies on
aquatic resources.
This study successfully used the GIS approach to provide a regional
characterization of land use I/and cover for critical salmon habitat recovery
watersheds in the Pacific Northwest (Oregon, Washington, and Idaho).
Landscape scale GIS databases coupled with accurate reach scale field
collected data provided an integrated data set. Recent advances in GIS
technology also permit the complex data management, analyses and modeling
previously unavailable. The comprehensive database compiled for this study
may be beneficial for further studies related to natural resources and human
impacts on Pacific Northwest watersheds.
GIS based analyses determined the relationship between bankfull
discharge recurrence interval and land use / land cover within stream adjacent
zones of varying widths in the 71 large watersheds in the study area. Land
use I land cover categories investigated include urban, agriculture, forest, and
range land. Land use I land cover was determined for buffers and bands.
While the determination of land use / land cover for buffers establishes
180
cumulative land use values, the analysis of land use / land cover within bands
determines the "additional" effect land use has in a particular band. Bands
allow for detailed analysis of break points in the relationship between land use
I land cover and streamfiow.
Land use I land cover within the study area watersheds is dominated by
forest cover (76.4%), followed by agriculture (10.4%), and range land (10.0%).
Over half (55.7%) of all urban land use within these watersheds is located
within 600 meters of the stream network, despite the 600 meter buffer
encompasses less than one quarter (23.0%) of the total watershed area. Over
40% of all agricultural area within the watersheds is also located within the 600
meter buffer.
Land use hand cover is correlated to the frequency of bankfull flow.
Statistically significant correlations were determined for agricultural land use
and bankfull discharge recurrence interval for all buffer widths, total watershed
area, and bands up to 1600 meters from the stream network. The negative
correlation between agricultural land use and bankfull discharge recurrence
interval indicates that the frequency of bankfull flow and the risk of flooding
increase with an increase in agricultural land. No statistically significant
relationships between other individual land use / land cover variables and
bankfull discharge recurrence interval were determined. Throughout the data
sets, there is suggestive yet inconclusive evidence for a positive relationship
between bankfull discharge recurrence interval and forest cover indicating that
forest decreases flood risk.
GIS was also used successfully to investigate the relationship between
bankfull discharge recurrence interval and the human use index (U-index), a
parameter designed to indicate the extent of anthropogenic land use / land
cover. The suitability of the U-index as a predictor for bankfull discharge
recurrence intervals was evaluated for varying buffer widths and for bands
paralleling the stream network. The U-index for buffers up to 1000 meters is
highly correlated to bankfull discharge recurrence intervals (99% level of
181
confidence). All detected correlations to bankfull discharge recurrence
intervals are negative and reflect the determination of the U-index by
agricultural land use. The decrease in the strength of this relationship
between U-index and the frequency of bankfull flow is associated with a
decrease in urban land use I land cover beyond 1000 meters. Early studies by
Leopold et al. (1964) characterized 50% of urbanized area as impervious
surfaces. Impervious surfaces contribute to an increase in surface flow and
higher flood risk.
This study developed a Land Use Runoff Index (LUR-index) based on
land use / land cover information to aid in flood risk assessment for large
watersheds. The LUR-index proved to be a useful indicator in determining the
spatial extent of land use I land cover influence on flood risk. A negative
correlation between the LUR-index and bankfull discharge recurrence interval
was determined and indicates that urban and agricultural land use increase
the risk of flooding while forest decreases the risk. This relationship was
determined with statistical significance for all buffers up to 1600 meters but not
for the whole watershed. The LUR-index is also significantly correlated to
each 200 meter band extending from the stream to a distance of 600 meters.
This study recognizes the 600 meter boundary as a primary delineation
component of the Critical Zone Management Model.
A Critical Zone Management Model was developed to aid land
managers and decision-makers in watershed management and flood risk
assessment of large watersheds. The Critical Zone Management Model
identifies stream adjacent zones that play a critical role in flood risk
assessment. This model provides a cut-off for land managers to determine
what distance from a stream urbanization, and agriculture, will no longer
significantly impact flood risk. Management zones include the functional zone
(0-110 meters) and the sensitive zone (0-600 meters). While the delineation
of the functional zone is based on previously reviewed studies focusing on
riparian buffer functions, the sensitive zone is based on flood risk indicators.
182
Traditional riparian buffer studies concentrate on the functional zone and
typically do not extend further out. Land use / land cover within this zone is
critical not only for the frequency of bankfull discharge, but also for several
other physical and ecological stream characteristics. Land use / land cover in
the functional zone plays an important role in determining the frequency of
streamfiow events and is highly correlated to flood risk indicators, including the
LUR-index. The sensitive zone includes the functional zone and was
delineated based on high significance in the relationships between the LUR-
index, U-index, and bankfull discharge recurrence interval. All potential land
use / land cover alterations within this 600 meter zone should anticipate
streamfiow changes. Increases in urbanization and agricultural land use I land
cover in the sensitive zone are highly correlated to increases in flood risk, as
indicated by the negative relationship between the U-index and the frequency
of bankfull flow (99% confidence level). Since bankfull discharge is a channel
shaping and maintaining flow, stream restoration efforts in large watersheds
should not be limited to the traditionally delineated riparian buffers of 110
meters or less, but should include the entire 600 meter sensitive zone.
A new recommendation for riparian widths for streamflow assessment
or restoration is provided with this study. Stream restoration projects in
salmon habitat recovery watersheds should consider streamfiow
characteristics including the frequency of bankfull discharge. Land use / land
cover in stream adjacent areas up to 600 meters is highly correlated to
bankfull flows. The recommended base buffer width for watershed
management efforts targeting the frequency of bankfull flow and related
instream habitat characteristics is 600 meters. Regression equations based
on zones presented in the Critical Zone Management Model help predict
bankfull discharge recurrence interval based on land use variables as
indicated by the U-index or the LUR-index.
Establishing specific recommendations for streamfiow prediction is
challenging because our understanding of the interactions of hydrologic
183
components with geomorphic and ecological processes is incomplete (Poff et
al., 1997). However, the results of this GIS based analyses indicate significant
relationships between land use I land cover and streamfiow. An
understanding of the complex, real-world effects of land use / land cover on
streamfiow provides scientifically sound data to aid watershed managers.
184
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191
IV.14. APPENDIX
192
Appendix IV.A: Summary Statistics for Bankfull Discharge Recurrence
Intervals Based on 68 Observations.
SUMMARY STATISTICS FOR BANKFULL
DISCHARGE RECURRENCE INTERVALS
(N=68)
Mean
Standard Error
Median
Standard Deviation
Skewness
Minimum
Maximum
Confidence Level (95.0%)
BANKFULL
RECURRENCE
(in years)
1.48
0.08
1.26
0.63
2.17
I
4.27
0.15
Appendix IV.B: Summary Statistics for Land Use / Land Cover for Different Buffer Widths.
*SUMMARY
STATISTICS
FOR N=71
Mean
Standard Error
Skewness
Minimum
Maximum
Mean
,
w
Standard Error
Skewness
Minimum
Maximum
Mean
Standard Error
Skewness
Minimum
Maximum
Mean
Standard Error
Skewness
'Minimum
Maximum
BUFFER WIDTH
(in meters)
30
1.65
0.33
2.72
0.00
12.60
13.45
2.19
1.74
0.00
82.07
10.84
1.74
1.64
0.00
64.06
71.59
2.92
-0.83
5.34
100.00
60
1.65
0.33
2.71
0.00
12.58
13.44
2.19
1.74
0.00
81.94
10.83
1.74
1.64
0.00
64.07
71.63
2.92
-0.83
5.49
100.00
15.10
2.36
1.77
0.00
94.51
90
1.65
0.33
2.70
0.00
12.56
13.43
2.19
1.74
0.00
81.86
10.84
1.74
1.65
0.00
64.19
71.66
2.92
-0.83
5.64
100.00
15.08
2.36
1.77
0.00
94.36
110
1.65
0.33
2.70
0.00
12.55
13.42
2.19
1.75
0.00
81.87
10.84
1.74
1.66
0.00
64.26
71.67
2.92
-0.83
5.74
100.00
15.06
2.36
200
400
1.61
1.60
0.32
2.70
0.00
12.45
13.33
2.18
1.77
0.00
81.90
10.86
1.73
1.68
0.00
64.59
71.57
0.32
2.73
0.00
12.72
13.08
2.16
2.91
-0.83
6.26
100.00
14.94
2.35
1.82
0.00
81.32
11.15
1.76
1.68
0.00
65.18
71.93
2.92
-0.88
7.28
100.00
14.68
2.33
600
1.58
0.32
2.87
0.00
13.13
12.75
WATER
-SHED
800
1.50
0.32
3.04
0.00
13.31
2.13
12.47
2.13
1.86
1.91
0.00
0.00
78.93
11.73
79.81
11.46
1.80
1.66
0.00
65.80
72.16
2.94
-0.92
7.93
100.00
14.33
1.83
1.66
0.00
66.54
72.39
2.96
-0.96
8.42
100.00
13.98
2.30
1000
1.42
0.31
3.22
0.00
13.37
12.23
2.13
1.95
0.00
78.27
11.89
1.85
1.67
0.00
67.36
72.63
2.97
-0.98
8.81
1200
1.36
0.30
3.43
0.00
13.40
12.04
2.13
1.98
0.00
77.39
11.94
1.85
1.69
0.00
68.16
72.87
2.98
-1.00
9.21
1400
1.32
0.30
3.59
0.00
13.79
11.90
2.14
2.00
0.00
76.74
11.93
1.86
1.71
0.00
68.68
73.07
2.98
-1.02
9.47
100.00
13.22
100.00 100.00
13.65
13.40
2.31
2.30
2.30
2.31
Standard Error
2.36
1.77
1.79
1.82
1.98
2.02
2.05
1.88
1.94
Skewness
1.77
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Minimum
94.26
93.74
92.72
92.07
91.19
90.79
91.58
90.53
Maximum
94.67
*Summary statistics expresses what proportion of buffer area is occupied by a particular land use I land cover category.
Mean
15.11
1600
1.29
0.30
3.69
0.00
13.92
11.80
2.15
2.02
0.00
76.65
11.87
1.85
1.73
0.00
68.87
73.24
2.99
-1.03
9.42
100.00
13.10
2.32
2.07
0.00
90.58
1.17
0.30
3.48
0.00
12.55
10.42
2.17
2.31
0.00
76.70
9.99
1.56
1.74
0.00
60.01
76.42
2.77
-1.21
10.76
100.00
11.59
2.33
2.36
0.00
89.24
194
Appendix IV.C: Summary of Pearson Correlation Coefficient r for Land Use /
Land Cover Versus Bankfull Discharge Recurrence Interval for
Varying Buffer Widths.
BUFFER
WIDTH
30
METERS
60
METERS
Iiivi
90
I
rc
110
Ivit I
200
It
gr'trnr'
uvui
rco
I
400
600
METERS
800
METERS
1000
a
ivi Artrnr.
rc
I
PEARSON CORRELATION COEFFICENTS FOR LAND USE I LAND COVER
VERSUS BANKFULL RECURRENCE
LAND USE I LAND
RESPONSE VARIABLE
COVER
BANKFULL
LN(BANKFULL
-1 /(BANKFULL
RECURRENCE
RECURRENCE)
RECURRENCE)
URBAN
-0.1030
-0.0981
-0.0921
AGRICULTURE
-0.3282
-0.3492
-0.2936
RANGE LAND
0.1059
0.0143
0.0630
FOREST
0.1766
0.1912
0.1864
URBAN
-0.0994
-0.0939
-0.1036
AGRICULTURE
-0.3279
-0.3488
-0.2933
RANGE LAND
0.0142
0.0630
0.1060
FOREST
0.1868
0.1769
0.1918
URBAN
-0.0979
-0.0931
-0.1015
AGRICULTURE
-0.2931
-0.3277
-0.3485
RANGE LAND
0.0637
0.1069
0.0146
FOREST
0.1919
0.1767
0.1868
URBAN
-0.0922
-0.0999
-0.0966
AGRICULTURE
-0.2930
-0.3275
-0.3482
0.0643
0.10(1
RANGE LAND
0.0149
FOREST
0.1866
0.1764
0.1919
URBAN
-0.1016
-0.0989
-0.1029
AGRICULTURE
-0.2930
-0.3472
RANGE LAND
0.0682
0.1126
0.0176
FOREST
0.1613
0.1757
0.1705
URBAN
-0.1110
-0.1070
-0.1016
AGRICULTURE
-0.2873
-0,3207
-0.3403
0.1144
RANGE LAND
0.0164
0.0683
FOREST
0.1848
0.1721
0.1924
URBAN
-0.1125
-0.1190
-0.1045
AGRICULTURE
-0.31 35
-0.3322
RANGE LAND
0.1078
0.0113
0.0622
FOREST
0.1687
0.1824
0.1909
URBAN
-U.124(
-0.1167
-0.1068
AGRICULTURE
-0.3227
-0.3051
-0.2740
RANGE LAND
0.0992
0.0553
0.0064
FOREST
0.1801
0.1658
0.1888
URBAN
-0.1289
-0.1088
-u.1uu
AGRICULTURE
-0.2966
-0.2667
-0.3132
RANGE LAND
0.0916
0.0026
0.0495
FOREST
0.1626
0.1858
0.1770
195
Appendix IV.0 (Continued).
BUFFER
WIDTH
1200
MArTrne.
I
1400
ivi
I
1600
ivi
I
rc
WATERSHED
PEARSON CORRELATION COEFFICENTS FOR LAND USE / LAND COVER
VERSUS BANKFULL RECURRENCE
LAND USE/LAND
RESPONSE VARIABLE
COVER
bANKIULL
LN(bANlcJ-ULL
-1 /(BANKFULL
RECURRENCE
RECURRENCE)
RECURRENCE)
URBAN
-0.1190
-0.1211
-0.1078
AGRICULTURE
-0.3044
-0.2597
-0.2886
RANGE LAND
0.0449
0.0859
-0.0006
FOREST
0.1833
0.1743
0.1595
URBAN
-0.1142
-0.1226
-0.1034
AGRICULTURE
-0.2531
0.0811
RANGE LAND
-0.0037
0.0408
FOREST
0.1813
0.1119
0.1 bb5
URBAN
0.1129
-0.1049
-0.0953
AGRICULTURE
-02735
-0.2879
-O24( 56
RANGE LAND
0.0369
0.0767
-0.0069
0.169 1
FOREST
0.1789
0.1534
URBAN
-0.0743
-0.0660
-0.0602
AGRICULTURE
-0.2179
-0.2389
0.0710
RANGE LAND
0.0378
0.1016
FOREST
0.1193
0.1053
0.1276
CRITICAL VALUES FOR THE PEARSON CORRELATION
COEFFICIENT
ALPHA = 0.05
ALPHA = 0.01
r = 0.305
r = 0.236
196
Appendix IV.D: Summary of Pearson Correlation Coefficients for Land Use I
Land Cover Versus Bankfull Discharge Recurrence Interval for
Bands.
BAND
WIDTH
30-60
60-90
90-110
110-200
200-400
400-600
600-800
800-1000
1000-1200
PEARSON CORRELATION COEFFICENTS FOR LAND USE / LAND COVER
VERSUS BANKFULL RECURRENCE
LAND USE I LAND
RESPONSE VARIABLE
COVER
BANKFULL
-1 /(BANKFULL
LN(BANKFULL
RECURRENCE
RECURRENCE)
RECURRENCE)
URBAN
-0.0987
-0.0937
-0.1026
AGRICULTURE
-0.31 33
-0.3333
-0.2806
RANGE
0.0423
0.0969
0.1435
FOREST
0.1960
0.1930
0.1844
URBAN
-0.0928
-0.0891
-0.0953
AGRICULTURE
-0.3115
-0.2790
-0.3312
RANGE
0.1014
0.1487
0.0459
FOREST
0.1837
0.1957
0.1925
URBAN
-0.0913
-0.0890
-0.0925
AGRICULTURE
-0.3472
-0.2923
-0.3266
RANGE
FOREST
URBAN
AGRICULTURE
RANGE
FOREST
URBAN
AGRICULTURE
RANGE
FOREST
URBAN
AGRICULTURE
RANGE
FOREST
URBAN
AGRICULTURE
RANGE
FOREST
URBAN
AGRICULTURE
RANGE
FOREST
URBAN
AGRICULTURE
RANGE
FOREST
0.0164
0.1918
-0.1052
-0.2926
0.0204
0.1602
-0.0933
-0.2795
0.0146
0.2164
-0.1100
-0.2643
0.0011
0.1863
-0.1134
-0.2453
-0.0069
0.1788
-0.1151
-0.2286
-0.0106
0.1682
-0.0953
-0.2134
-0.0165
0.1639
0.0669
0.1860
-0.1065
-0.3259
0.0723
0.1548
-0.1063
-0.3122
0.0677
0.2059
-0.1238
-0.2940
0.0500
0.1765
-0.1307
-0.2719
0.0359
U.lb9b
-0.1336
-0.2528
0.0280
0.1593
-0.1067
-0.2359
0.0212
0.1542
0.1115
0.1752
-0.1062
-0.3454
0.1179
0.1462
-0.1184
-0.3311
0.1152
0.1890
-0.1355
-0.3103
0.0944
0.1607
-0.1448
-0.2856
0.0745
0.1542
-0.1475
-0.2648
0.0627
0.1444
-0.1139
-02410
0.0559
0.1382
197
Appendix IV.D (Continued).
BAND
WIDTH
1200-1400
1400-1600
WAI hF(SHED
PEARSON CORRELATION COEFFICENTS FOR LAND USE I LAND COVER
VERSUS BANKFULL RECURRENCE
LANDUSE/LAND
RESPONSE VARIABLE
COVER
BANKFULL
LN(BANKFULL
-1/(BANKFULL
RECURRENCE
RECURRENCE)
RECURRENCE)
URBAN
-0.0703
-0.0634
-0.0760
AG RICU LTURE
-0.2195
-0.2294
-0.1992
RANGE
0.0103
0.0454
-0.0266
FOREST
0.1508
0.1334
0.16 19
URBAN
-0.0048
-0.0067
-0.0085
AGRICULTURE
-0.2001
-0.2085
-0.1824
RANGE
-0.0049
0.0300
-0.0400
FOREST
0.1400
0.1208
0.1536
URBAN
-0.0743
-0.0602
-0.0660
AGRICULTURE
-0.2179
RANGE
0.0710
0.1016
0.0378
FOREST
0.1053
0.1276
0.1193
CRITICAL VALUES FOR THE PEARSON CORRELATION
COEFFICIENT
ALPHA =0.01
ALPHA = 0.05
= 0236
r = 0.305
198
Appendix IV.E: Watersheds Rated by the LUR-Index Based on Land Use /
Land Cover Within the 600 Meter Buffer Area.
WATERSHEDS RATED BY LUR-INDEX BASED ON THE 600 METER
BUFFER
GAGE
LUR-INDEX
LUR-INDEX
GAGE
NUMBER
NUMBER
14050000
0.92
1.44
14359000
13239000
13316500
1.44
0.99
14159000
1.45
13296500
0.99
12209000
1.46
1.00
13297330
12010000
1.61
1.00
13338500
14328000
1.62
1.00
12484500
14308000
1.00
1.64
14046500
12205000
13297355
1.66
1.01
13310700
1.01
13333000
1.71
14222500
14157500
1.71
1.03
14337600
12500450
1.77
1.04
14308600
1.77
12013500
1.04
1.83
13240000
14203500
1.05
1.93
12479500
13334700
1.06
13339500
1.06
1.95
14048000
14339000
1.97
1.07
12031000
12452800
1.07
13258500
1.98
2.00
13235000
12027500
1.07
2.01
13311000
12510500
1.08
12414500
2.05
14038530
1.09
2.17
13313000
14357500
1.10
2.27
14325000
14021000
1.12
2.51
14306500
13266000
1.15
13336500
13342450
2.64
1.17
14305500
2.68
1.17
14017000
13185000
1.18
14202000
2.71
12449500
2.73
1.21
14026000
13340600
2.75
1.22
13305000
12414900
2.77
1.23
13302005
13337000
2.87
1.26
14207500
12167000
1.27
2.88
13344500
14312000
1.28
2.90
14033500
13186000
3.04
1.33
12422950
14372300
1.34
14018500
3.26
12449950
1.36
13346800
3.88
13200000
1.43
199
CHAPTER V
SUMMARY AND CONCLUSIONS
Franziska Whelan
200
V.1. SUMMARY STATEMENT
This research addresses fundamental issues of fluvial geomorphology
including streamfiow changes. Land use changes can greatly alter the quantity,
seasonal distribution, and relative proportion of water and sediment supplied to
the stream channels. Alterations in water and sediment supply commonly cause
changes in the stream channel, floodplain, and riparian areas (Andrews, 1995).
These changes may affect downstream aquatic and riparian resources, including
fish habitat. The frequency of bankfull flow indicates the risk of flooding. This
research analyzed relationships between bankfull discharge recurrence intervals
and land use I land cover for total watershed area and for stream adjacent areas
of different widths. Figure V.1 summarizes the relationships investigated with
this research. Land use I land cover influences the frequency of bankfull flow.
This research focuses on relationships between land use I land cover and
bankfull discharge recurrence intervals in selected salmon habitat recovery
streams. Bankfull discharge plays a critical role in the maintenance and creation
of salmon habitat. The natural flow regime is important for sustaining biodiversity
and ecosystem integrity in aquatic systems (Poff et al., 1997).
201
Land Use/Land Cover
Flow Regime
(Indicated by Bankfull Discharge
Recurrence Interval)
Magnitude
Frequency
V
Water
Quality
Physical
Habitat
Energy
Sources
Biotic
Interactions
Salmonid Habitat
Flood
Risk
V
Ecological Integrity
Figure V.1: Flow Chart of the Relationships Investigated With This Research. Flow Regime is
Critical for The Ecological Integrity of Aquatic Streams (Modified, After Poff et al.,
1997). Land Use / Land Cover Alters Bankfull Discharge Recurrence Interval and
Affects Salmon Habitat and Flood Risk.
V.2. ASSESSMENT AND SUITABILITY OF GIS HYDROLOGIC MODELING
FOR DIGITAL WATERSHED DELINEATION
This research developed and evaluated an integrated methodology for
watershed delineation relying on the Spatial Analyst Hydrologic Modeling
extension. Development and evaluation of the described methodology was
based on one degree digital elevation model (DEM) data. Spatial Analyst
Hydrologic Modeling may perform at different levels of accuracy with different
DEM data sets.
The major findings of this research are (1) Spatial Analyst Hydrologic
Modeling extension is suitable for digital watershed delineation if supplemental
202
methods are utilized; (2) accurate pour point location determination may
dramatically increase watershed delineation accuracy; (3) low gradient and I or
dense urban areas surrounding the pour point have the greatest negative impact
on digital watershed delineation; and (4) consistent quality control and use of
supplemental delineation methods, including hydrologic unit coverage (HUC)
reference and area checks, is recommended for all digitally delineated
watersheds.
Project planning is a crucial component of geographic information system
(GIS) based hydrologic research. Proper identification of appropriate data layers
and initial data preparation before any modeling is conducted are often
overlooked. Successful watershed delineation requires accurate determination
of pour point location. Pour points can be accurately located by referencing
supplemental large scale topographic maps displaying the pour point in
combination with DEM flow path delineation. Accurate pour point location will
allow the user to edit the WatershedTool script in ArcView by reducing the grid
cell accumulation value for a snapped pour point from 240 to 0. Digital
watershed delineation using the edited script increases watershed area and
boundary accuracy.
Initial watershed areas delineated using Spatial Analyst only and United
States Geological Service (USGS) reference watershed areas were not
significantly different (two sided p-value = 0.63). Despite no significant
difference, numerous watersheds had quite large area differences, ranging up to
2,679 square miles. Approximately 75% of the watersheds in this study required
supplemental spatial editing based on set difference limits. Regardless of area
difference, boundary checks are recommended for all watersheds as a quality
assurance measure.
Spatial Analyst hydrologic delineation supplemented by accepted
delineation methods, such as hydrologic unit maps and manual digitizing of
topographic maps, provided the most accurate watersheds. The utilization of
specific supplemental methods is a case-by-case study based on watershed
characteristics, including watershed size, land use, and large hydrologic features.
203
Watershed features, such as little to no gradient or dense urban areas
surrounding the pour point, had the strongest negative impacts on delineation.
To a lesser degree, delineation problems caused by lakes and reservoirs were
often overcome by referencing topographic and land use / land cover (LULC)
maps.
The Spatial Analyst Hydrologic Modeling extension is accurate and useful
for watershed delineation when quality control is conducted. Recommended
quality control steps include reference checks using a digital stream layer, a
LULC data layer, and topographic maps, flow path comparison, HUC references,
and area check. This study recommends consistent employment of these quality
control steps.
The presented method represents one solution among a spectrum of
possible solutions to the mapping and delineation of watersheds. Traditional
solutions include hand delineation and manual digitizing. The Spatial Analyst
Hydrologic Modeling extension was chosen primarily for its efficiency and
objectivity of watershed delineation, and for an ease of digital mapping and later
spatial analysis or overlays with other data. Recommended future research
includes an analysis of watershed delineation accuracy using Spatial Analyst
Hydrologic Modeling for DEM5 with higher resolution.
V.3. VARIABILITY IN BANKFULL DISCHARGE RECURRENCE INTERVALS
WITH DIFFERENT LAND USE / LAND COVER TYPES IN PACIFIC
NORTHWEST WATERSHEDS
The frequency of bankfull flow is correlated to anthropogenic land use I
land cover at the watershed scale. The detected relationships between
anthropogenic land use, particularly urban and agricultural land use, and
bankfull discharge recurrence intervals indicate an increased risk of flooding
with an increase in anthropogenic land use / land cover. Higher frequency of
flooding caused by reduced channel size or floodplain storage, as well as loss
of ecological in-stream habitat is often a consequence of substantial
204
modifications to natural streamfiow. The loss of physical instream habitat may
negatively impact salmonids along designated salmon habitat recovery
streams.
Streamfiow changes are reflected in variations in the frequency of
bankfull flow. Bankfull discharge recurrence interval is influenced by human
drainage basin alterations, which are reflected in land use and cover. Clear
trends on the effects of urban, agricultural, and forest cover, and the U-index
on bankfull discharge recurrence intervals were detected.
The human use index (U-index) is a measure of human use, and
combines the proportion of a watershed that is urbanized or agriculturally
used. The U-index is a watershed indicator primarily concerned with soil
erosion and runoff processes. In Pacific Northwest watersheds, the U-index is
largely driven by agricultural rather than urban land use and cover. A
statistically significant negative correlation was detected for the U-index and
bankfull discharge recurrence interval. Bankfull flow occurs more frequently in
watersheds with a high U-index. The negative relationship between bankfull
discharge recurrence interval and the U-index was confirmed by all statistical
tests run on watersheds segmented by climate, bed-material, and slope.
Agricultural land use tends to increase the frequency of bankfull flow
and the risk of flooding. A statistically significant negative log-linear
relationship was detected for agricultural land use and bankfull discharge
recurrence interval. This relationship provides statistical evidence for shorter
bankfull discharge recurrence intervals with increasing agricultural land use
within a watershed. The negative relationship between agricultural land use
and bankfull discharge recurrence interval was confirmed by all other
statistical tests. Agricultural land is often characterized by shallow rooted
vegetation and minimal overstory canopy, effectively decreasing infiltration
rates and contributing to an increase in the frequency of bankfull flow.
At this scale of analysis, urban land use plays a minor role in affecting
bankfull discharge recurrence intervals. This may be due to the low
205
proportions of watershed area that is urbanized within the salmon habitat
recovery watersheds in the study area (1.2% on average). Watersheds
containing designated salmon habitat recovery streams may not be
representative of urban influenced watersheds. Urban land use / land cover
may play a major role in affecting the frequency of bankfull flow in watersheds
with large metropolitan areas. Suggestive evidence was found for a negative
correlation between urban land use and the frequency of bankfull flow for all
watersheds, B- and C-climate groups, and the cobble-bed river group.
According to early studies by Leopold et al. (1964), 50% of urbanized area is
characterized by impervious surfaces. Impervious surfaces increase surface
flow. This explains why these proportionally small urbanized areas have an
impact on streamfiow variables.
Forest cover decreases the frequency of bankfull flow and the risk of
flooding. This negative relationship was statistically significant for cobble-bed
rivers. Suggestive yet inconclusive evidence for this positive relationship
between the frequency of bankfull flow and forest cover exists consistently
through the majority of data sets, including all watersheds, all watersheds
segmented by climate, and high and medium slope watersheds. Higher
infiltration rates in a forested environment may account for a decrease in
surface flow.
The relationship between range land and bankfull discharge recurrence
interval varies. Range land decreases the risk of flooding in B- and C-climate
watersheds. A statistically significant positive relationship between bankfull
discharge recurrence interval and range land was detected for these climate
categories. This relationship was also supported for gravel-bed and low slope
rivers. Cobble-bed rivers, medium slope rivers, and D-climate watersheds,
however, experience an inverse relationship. This negative relationship
between bankfull discharge recurrence intervals and range land indicated an
increase of the risk of flooding with an increase in range land. Vegetation
type, grazing intensity, and land management, may contribute to the variations
206
in the relationship between range land and the frequency of bankfull flow. For
example, water yield increases have been documented after vegetation on a
watershed was converted from deep-rooted to shallow-rooted species.
(Shrubs are often deep-rooted, whereas grasses tend to be shallow-rooted
species). Water yield increases have also been documented after vegetative
cover was changed from plant species with high interception capacities to
species with lower interception capacities (Brooks et al., 1993). The effects of
range land on the frequency of bankfull flow may differ for range land along
stream channels compared to range land located in upland watersheds.
Further research is recommended on streamfiow variations in range land
dominated watersheds.
Bankfull discharge recurrence intervals are affected by climate. Mean
bankfull discharge recurrence interval is significantly lower in B-and C-climate
watersheds (1.38 years, and 1.4 years, respectively) compared to D-climate
watersheds (1.56 years). Also, B- and C-climate watersheds displayed
different patterns in their relationships between bankfull discharge recurrence
interval and land use I land cover. In particular, urban and range land
displayed correlations (negative and positive, respectively) for B- and Cclimate watersheds that were opposite to those detected for D-climate
watersheds.
Statistical evidence for the important influence of land use on the
hydrologic regime of a watershed has been presented. Negative relationships
were detected between bankfull discharge recurrence interval and agricultural
land use and the U-index. Urban and agricultural land uses contribute to soil
compaction and the increase in impermeable surfaces. Surface flow
increases and transports water faster to channels. This may explain the
increase in the frequency of bankfull flow in watersheds dominated by these
land uses. Forest, however, displays a positive correlation to bankfull
discharge recurrence intervals. The frequency of bankfull flow decreases with
an increase in forest cover. Infiltration rates and subsurface flow are
207
maximized in forests. Future research is needed on the moderating effect of
riparian buffers on hydrologic regimes altered by land use. There is also a
need for future research on the buffering effect of riparian forest on bankfull
flow in watersheds dominated by urban and agricultural land use.
This study recommends land use assessment at the watershed scale to
be an important consideration in river restoration and other stream
management efforts. This research demonstrates that successful stream
management efforts need to consider the entire upland watershed. Efforts
concentrating on one particular stream reach or on the river only, and not
considering land use / land cover, may prove unsuccessful.
V.4. FLOOD RISK ASSESSMENT FOR LARGE PACIFIC NORTHWEST
WATERSHEDS: THE RELATIONSHIP OF STREAM ADJACENT LAND USE I
LAND COVER TO THE FREQUENCY OF BANKFULL FLOW
Changes in streamfiow characteristics due to land use / land cover
alterations as addressed in this study are a fundamental issue in fluvial
geomorphology. This research determined the critical role land use I land
cover plays in stream adjacent areas of large watersheds and the effects of
land use / land cover in these areas on the frequency of streamfiow events.
Land use I land cover can greatly alter the quality, seasonal distribution, and
relative proportion of water and sediment supplied to the stream network.
Alterations in water supply commonly cause changes in the stream channel,
floodplain, and riparian areas (Andrews, 1995). These changes are known to
affect downstream aquatic resources, including critical salmonid habitat. This
study identifies critical land uses and areas within large watersheds in respect
to their effects on streamfiow. The identification of these land uses and areas
within watersheds will assist in improving streamfiow and fish habitat, and be
significant for evaluating the potential impact of alternative management
strategies on aquatic resources.
208
This study adapted the GIS approach to provide a regional
characterization of land use I/and cover for critical salmon habitat recovery
watersheds in the Pacific Northwest (Oregon, Washington, and Idaho).
Landscape scale GIS databases coupled with accurate reach scale field
collected data provided an integrated data set. GIS based analyses
determined the relationship between bankfull discharge recurrence interval
and land use / land cover within stream adjacent zones of varying widths in the
71 large watersheds in the study area. Land use I land cover categories
investigated include urban, agriculture, forest, and range land. Land use I
land cover was determined for buffers and bands. While the determination of
land use / land cover for buffers establishes cumulative land use values, the
analysis of land use I land cover within bands determines the "additional"
effect land use has in a particular band. Bands allow for detailed analysis of
break points in the relationship between land use I land cover and streamfiow.
Land use / land cover within the study area watersheds is dominated by
forest cover (76.4%), followed by agriculture (10.4%), and range land (10.0%).
Over half (55.7%) of all urban land use within these watersheds is located
within 600 meters of the stream network, despite the fact that the 600 meter
buffer encompasses less than one quarter (23.0%) of the total watershed
area. Over 40% of all agricultural area within the watersheds is also located
within the 600 meter buffer.
Land use I/and cover is correlated to the frequency of bankfull flow.
Statistically significant correlations were determined for agricultural land use
and bankfull discharge recurrence interval for all buffer widths, total watershed
area, and bands up to 1600 meters from the stream network. The negative
correlation between agricultural land use and bankfull discharge recurrence
interval indicates that the frequency of bankfull flow and the risk of flooding
increase with an increase in agricultural land.
GIS was also used successfully to investigate the relationship between
bankfull discharge recurrence interval and the human use index (U-index), a
209
parameter designed to indicate the extent of anthropogenic land use I land
cover. The suitability of the U-index as a predictor for bankfull discharge
recurrence intervals was evaluated for varying buffer widths and for bands
paralleling the stream network. The U-index for buffers up to 1000 meters is
highly correlated to bankfull discharge recurrence intervals (99% level of
confidence). All detected correlations to bankfull discharge recurrence
intervals are negative and reflect the determination of the U-index by
agricultural land use. The decrease in the strength of this relationship
between U-index and the frequency of bankfull flow is associated with a
decrease in urban land use / land cover beyond 1000 meters. Early studies by
Leopold et al. (1964) characterized 50% of urbanized area as impervious
surfaces. Impervious surfaces contribute to an increase in surface flow and
higher flood risk.
This study developed a Land Use Runoff Index (LUR-index) based on
land use / land cover information to aid in flood risk assessment for large
watersheds. The LUR-index proved to be a useful indicator in determining the
spatial extent of land use I land cover influence on flood risk. A negative
correlation between the LUR-index and bankfull discharge recurrence interval
was determined and indicates that urban and agricultural land use increase
the risk of flooding while forest decreases the risk. This relationship was
determined with statistical significance for all buffers up to 1600 meters but not
for the whole watershed. The LUR-index is also significantly correlated to
each 200 meter band extending from the stream to a distance of 600 meters.
This study recognizes the 600 meter boundary as a primary delineation
component of the Critical Zone Management Model.
This research developed a Critical Zone Management Model to aid land
managers and decision-makers in watershed management and flood risk
assessment of large watersheds. The Critical Zone Management Model
identifies stream adjacent zones that play a critical role in flood risk
assessment. This model provides a cut-off for land managers to determine
210
what distance from a stream urbanization, and agriculture, will no longer
significantly impact flood risk. Management zones include the functional zone
(0-1 10 meters) and the sensitive zone (0-600 meters). While the delineation
of the functional zone is based on previously reviewed studies focusing on
riparian buffer functions, the sensitive zone is based on flood risk indicators.
Traditional riparian buffer studies concentrate on the functional zone and
typically do not extend further out. Land use I land cover within this zone is
critical not only for the frequency of bankfull discharge, but also for several
other physical and ecological stream characteristics. Land use I land cover in
the functional zone plays an important role in determining the frequency of
streamfiow events and is highly correlated to flood risk indicators, including the
LUR-index. The sensitive zone includes the functional zone and was
delineated based on high significance in the relationships between the LUR-
index, U-index, and bankfull discharge recurrence interval. All potential land
use I land cover alterations within this 600 meter zone should anticipate
streamfiow changes. Increases in urbanization and agricultural land use / land
cover in the sensitive zone are highly correlated to increases in flood risk, as
indicated by the negative relationship between the U-index and the frequency
of bankfull flow (99% confidence level). Since bankfull discharge is a channel
shaping and maintaining flow, stream restoration efforts in large watersheds
should not be limited to the traditionally delineated riparian buffers of 110
meters or less, but should include the entire 600 meter sensitive zone.
This research provides a new recommendation for riparian widths for
streamflow assessment or restoration. Stream restoration projects in salmon
habitat recovery watersheds should consider streamfiow characteristics
including the frequency of bankfull discharge. Land use I land cover in stream
adjacent areas up to 600 meters is highly correlated to bankfull flows. The
recommended base buffer width for watershed management efforts targeting
the frequency of bankfull flow and related instream habitat characteristics is
600 meters. Regression equations based on zones presented in the Critical
211
Zone Management Model help predict bankfull discharge recurrence interval
based on land use variables as indicated by the U-index or the LUR-index.
Establishing specific recommendations for streamfiow prediction is
challenging because our understanding of the interactions of hydrologic
components with geomorphic and ecological processes is incomplete (Poff et
al., 1997.). However, the results of these GIS based analyses indicate
significant relationships between land use I land cover and streamfiow. An
understanding of the complex, real-world effects of land use / land cover on
streamfiow provides scientifically sound data to aid watershed managers.
V.5. GIS IN WATERSHED STUDIES
The GIS approach is a strong asset to fluvial geomorphology studies.
GIS provided a powerful tool for this complex watershed data management
and hydrologic modeling project. This study successfully used the GIS
approach to provide a regional characterization of land use / land cover for
critical salmon habitat recovery watersheds in the Pacific Northwest (Oregon,
Washington, and Idaho). Landscape scale GIS databases coupled with
accurate reach scale field collected data provided an integrated data set. In
contrast to a traditional field based approach, data resolution and scale limit
the detail of GIS analysis. Field data collection is required for accurate reach
scale data compilation. This research demonstrates how field data can be
complemented by landscape scale GIS databases for extensive regional
analysis.
Scale and resolution requirements within a hydrologic management
research project's objectives should dictate the proper approach. This
project's objectives dictated an integrated traditional and GIS approach. The
comprehensive database compiled for this study may be beneficial for further
212
studies related to natural resources and human impacts on Pacific Northwest
watersheds.
213
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