Empirical Models Based on the
Universal Soil Loss Equation
Fail to Predict Discharges from
Chesapeake Bay Catchments
Boomer, Kathleen B.
Weller, Donald E.
Jordan, Thomas E.
of the Smithsonian Environmental Research Center
Journal of Environmental Quality, 2008
1
Presentation Overview
2
Presentation Overview
1. Abstract
2. Background Information
3. Methods
1.
2.
3.
4.
Location
Water Quality Data
Spatial Data
Data Analysis
4. Results/Discussion
5. Conclusion
6. My Comments
7. Open Discussion
3
1. Abstract
4
1. Abstract
5
1. Abstract
Goal: Accurately predict sediment loads/yields in ungauged basins.
Methods: Test the most widely used equation, USLE with
accurate water quality data significant number of catchments
from 2 different agencies. Also attempt a multiple linear
regression approach.
Results: The USLE and all its derivatives perform very
poorly, even using SDR’s. So does the multiple linear
regression.
Conclusion: USLE & multiple linear regression are not
advised.
6
2. Background Information
7
2. Background Information
The Universal Soil Loss Equation
A = R K LS CP
A = estimated long-term annual soil loss (Mg soil loss ha−1 yr−1)
R = rainfall and runoff factor representing the summed erosive potential
of all rainfall events in a year
(MJ mm ha−1 h−1 yr−1)
L = slope length (dimensionless)
S = slope steepness (dimensionless)
K = soil erodibility factor representing units of soil loss per unit of rainfall
erosivity (Mg ha h ha−1 MJ−1 mm−1)
CP = characterizes land cover and conservation management practices
(dimensionless).
8
2. Background Information
The Revised Universal Soil Loss Equation 2
incorporate a broader set of land cover
classes and attempt to capture deposition in complex terrains
More sub-factors
Daily time step
9
2. Background Information
Universal
Soil =
Loss
Equation
Edge of Field
10
2. Background Information
Universal
Soil =
Loss
Equation
Catchment Scale
11
2. Background Information
Sediment
Delivery
Ratios
12
2. Background Information
Sediment
Delivery
Ratios
1. Estimate from calibration data
or
2. Use complex spatial algorithms
Yagow 1998
SEDMOD
Transport
Exported from
Field
Observed at
WQ site
13
2. Background Information
Models that rely on USLE for calibration
GWLF (Generalized Watershed Loading Function; Haith and Shoemaker, 1987)
AGNPS (AGricultural Non-Point Source; Young et al., 1989)
SWAT (Soil & Water Assessment Tool; Arnold and Allen, 1992)
HSPF (Hydrological Simulation Program-Fortran; Bicknell et al., 1993)
SEDD (Sediment Delivery Distributed model; Ferro and Porto, 2000).
14
2. Background Information
DANGER!!!
GROSS EROSION VS. SEDIMENT TRANSPORT
FIELD OBSERVATIONS VS. REGIONAL SPATIAL DATA
Van Rompaey et al., 2003 – 98 catchments in europe, poor results
Wischmeier and Smith 1978; Risse et al., 1993; Kinnell, 2004a) – Not for Catchment
15
3. Methods
16
3. Methods
Water Quality Data
17
3. Methods
Water Quality Data
SERC DATA
continuous monitoring stations in 78 basins within 166,000 km2
Chesapeake Bay watershed
5-91,126 ha
across physiographic regions
Coastal Plain
Piedmont
Mesozoic Lowland
Appalachian Mountain
Appalachian Plateau
= 45
= 10
=7
=9
=7
selected across a range of land cover proportions
no reservoirs, no point sources
18
3. Methods
Water Quality Data
SERC DATA
0-40%
percent impervious
residential/commercial
0-82%
agriculture
0-39%
forest
2-100%
0
20
40
60
80
100
19
3. Methods
Water Quality Data
USGSS DATA
continuous monitoring stations in 23 additional basins within 166,000 km2
Chesapeake Bay watershed
101-90,530 ha
no reservoirs
20
3. Methods
Water Quality Data
USGS DATA
residential/commercial
0-30%
agriculture
0-40%
forest
5-100%
0
20
40
60
80
100
21
3. Methods
Water Quality Data
SERC WATER QUALITY DATA
•Automated samplers, continuous stage,
•flow-weighted water samples composited weekly
•<= 1 year, 1974-2004
•Annual mean flow rates * flow-weighted mean conc = annual avg loads
•Yield=load/area
USGS WATER QUALITY DATA
•Samples collected daily or determined by ESTIMATOR model
22
3. Methods
Regional Spatial Data
Source
Year
Resolution
Converted
Resolution
USGS National Elevation Dataset
1999
27.78 m
30 m
USGS National Landcover Database
1992
30 m
USDA-NRCS STATSGO Soils Database
1995
1:250,000
RESAC Dataset (% impervious)
2003
30 m
Spatial Climate Analysis Service
2002
1:250,000
30 m
23
3. Methods
Regional Spatial Data
USLE Analysis
24
3. Methods
USLE Analysis
GRID-BASED USLE ANALYSIS
RKLSCP RKLSCP RKLSCP
R (rainfall erosivity) =
RKLSCP RKLSCP RKLSCP
Derivded from linear interpolation
of national iso-erodent map
RKLSCP RKLSCP RKLSCP
25
3. Methods
USLE Analysis
GRID-BASED USLE ANALYSIS
RKLSCP RKLSCP RKLSCP
K (surface soil erodibility)=
RKLSCP RKLSCP RKLSCP
STATSGO resampled to 30m
RKLSCP RKLSCP RKLSCP
26
3. Methods
USLE Analysis
GRID-BASED USLE ANALYSIS
RKLSCP RKLSCP RKLSCP
L (slope length) =
RKLSCP RKLSCP RKLSCP
NED DEM resampled to 30 m
RKLSCP RKLSCP RKLSCP
27
3. Methods
USLE Analysis
GRID-BASED USLE ANALYSIS
RKLSCP RKLSCP RKLSCP
S (slope steepness)=
RKLSCP RKLSCP RKLSCP
NED DEM resampled to 30 m
RKLSCP RKLSCP RKLSCP
28
3. Methods
USLE Analysis
GRID-BASED USLE ANALYSIS
RKLSCP RKLSCP RKLSCP
C (cover management)=
RKLSCP RKLSCP RKLSCP
Consolidated NLCD 30m
RKLSCP RKLSCP RKLSCP
***no differentiation of erosion control practices
29
3. Methods
USLE Analysis
GRID-BASED USLE ANALYSIS
RKLSCP RKLSCP RKLSCP
P (support practice factor) =
RKLSCP RKLSCP RKLSCP
RKLSCP
1
RKLSCP RKLSCP
***no differentiation of erosion control practices
30
3. Methods
Revised-USLE2 Analysis
31
3. Methods
RUSLE-2
•Automated
•identifies potential sediment transport routes using raster grid cumulation
and max downhill slope methods
•Identifies depositional zones
•L= surface overland flow distance from origin to deposition or stream
•CP (cover and practice) calculated from the RUSLE database (wider range
of land cover characteristics)
32
3. Methods
SDR’s
33
3. Methods
SDR’s
3 LUMPED-PARAMETER
“where life is a box and space is only considered in terms of area”
2 SPATIALLY EXPLICIT
“life is a box, but spatial relationships matter”
34
3. Methods
Runoff
35
3. Methods
Runoff
(for multiple linear regression)
CN (curve number) method used to estimate runoff potential and annual runoff
STATSGO hydrosoilgrp + LC = CN
Monthly time step annual value
36
3. Methods
Multiple Linear Regression
37
3. Methods
Multiple Linear Regression
Considered additional parameters
•Physiographic province
•Watershed size
•Variation in terrain complexity
•Topographic relief ratio
•Land cover proportions
•Percent impervious area
•Runoff potential
•Annual average runoff (CN method)
38
4. Results/Discussion
39
4. Results
USLE vs RUSLE2
40
4. Results
USLE vs RUSLE 2
41
4. Results
USLE vs RUSLE 2
USGS Data
Pearson r = 0.95, p<0.001
42
4. Results
USLE & RUSLE2 (SDR)
vs SERC & USGS
43
4. Results
USLE & RUSLE2 (SDR) vs SERC & USGS
44
4. Results
USLE & RUSLE2 (SDR) vs SERC & USGS
Negative spearmen r =
45
4. Results
USLE & RUSLE2 (SDR) vs SERC & USGS
Negative spearmen r =
All p values = not statistically significant
46
4. Results
USLE Parameters vs USLE
47
4. Results
USLE parameters vs USLE
48
4. Results
Univariate Regressions
49
4. Results
Univariate Regressions
50
4. Results
Univariate Regressions
51
4. Results
Best Subsets
Multiple Regression Analysis
52
4. Results
Best Subsets Multiple Regression Analysis
53
4. Results
Best Subsets Multiple Regression Analysis
54
4. Results
Best Subsets Multiple Regression Analysis
(dead sheep)
55
4. Results
Other Multiple Linear Regressions
56
4. Results
Other Multiple Linear Regressions
57
4. Results
Other Multiple Linear Regressions
PC
PC
LC
LC
58
5. Conclusion
59
5. Conclusion
Widespread misuse of USLE and derivatives
60
5. Conclusion
fierceromance.blogspot.com
Widespread misuse of USLE and derivatives
Multiple linear regression fails
61
5. Conclusion
fierceromance.blogspot.com
Widespread misuse of USLE and derivatives
Multiple linear regression fails
elevated sediment loads  short term events
Static models do not represent dynamic interactions among
parameters, which change on a small time step
questionable spatial data
62
5. Conclusion
fierceromance.blogspot.com
Widespread misuse of USLE and derivatives
Multiple linear regression fails
elevated sediment loads  short term events
Static models do not represent dynamic interactions among
parameters, which change on a small time step
questionable spatial data
“…trends collectively suggest scientists […] have not captured the
linkages between the catchment landscape setting and the physical
mechanisms that regulate erosion and sediment transport
processes.”
63
5. Conclusion
Models that rely on USLE for calibration
GWLF (Generalized Watershed Loading Function; Haith and Shoemaker, 1987)
AGNPS (AGricultural Non-Point Source; Young et al., 1989)
SWAT (Soil & Water Assessment Tool; Arnold and Allen, 1992)
HSPF (Hydrological Simulation Program-Fortran; Bicknell et al., 1993)
SEDD (Sediment Delivery Distributed model; Ferro and Porto, 2000).
64
5. Conclusion
In order to accurately predict sediment discharges in ungauged drainage basins,
scientists need to:
1. Identify predictor variables that conceptually link landscape and
stream characteristics to flow velocity, stream power, and the ability
to transport sediment
65
5. Conclusion
In order to accurately predict sediment discharges in ungauged drainage basins,
scientists need to:
1. Identify predictor variables that conceptually link landscape and
stream characteristics to flow velocity, stream power, and the ability
to transport sediment
2. Incorporate metrics to indicate potential sediment sources within
streams, including bank erosion and legacy sediments
66
5. Conclusion
In order to accurately predict sediment discharges in ungauged drainage basins,
scientists need to:
1. Identify predictor variables that conceptually link landscape and
stream characteristics to flow velocity, stream power, and the ability
to transport sediment
2. Incorporate metrics to indicate potential sediment sources within
streams, including bank erosion and legacy sediments
3. Develop predictions for temporal scales finer than the long-term
annual average time frame
67
5. Conclusion
In order to accurately predict sediment discharges in ungauged drainage basins,
scientists need to:
1. Identify predictor variables that conceptually link landscape and
stream characteristics to flow velocity, stream power, and the ability
to transport sediment
2. Incorporate metrics to indicate potential sediment sources within
streams, including bank erosion and legacy sediments
3. Develop predictions for temporal scales finer than the long-term
annual average time frame
WE NEED CONSISTENT AND VERIFIABLE RESULTS!!!
68
6. My Comments
69
6. My Comments
I would also emphasize not only a need for stronger scientific theories but
finer resolution spatial data. The closer raster based data (and any other
spatial data for that matter) becomes to the actual landscape, the greater
chance there is of describing the processes that control sediment yields (in
additional to other “contaminants”) at the catchment scale. The stronger the
GIS database, the greater potential for success (however, it remains to bee
seen exactly what level of spatial and temporal detail is needed to optimize
results and minimize costs).
70
6. My Comments
Weakest Points
Violates a fundamental principle of spatial analysis
No spatially explicit terms in multiple linear regression
Long term based model applied to tiny temporal window
(observed data have low probability of representing
“average conditions”)
71
6. My Comments
Weakest Points
Violates a fundamental principle of spatial analysis
No spatially explicit terms in multiple linear regression
Long term based model applied to tiny temporal window
(observed data have low probability of representing
“average conditions”)
72
6. My Comments
Weakest Points
Violates a fundamental principle of spatial analysis
No spatially explicit terms in multiple linear regression
Long term based model applied to tiny temporal window
(observed data have low probability of representing
“average conditions”)
73
6. My Comments
Weakest Points
Violates a fundamental principle of spatial analysis
No spatially explicit terms in multiple linear regression
Long term based model applied to tiny temporal window
(observed data have low probability of representing
“average conditions”)
74
OPEN
DISCUSSION
75
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