1 empirical models based on the universal soil loss equation fail to predict discharges from...

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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

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Presentation Overview

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1.Abstract2.Background Information3.Methods

1. Location2. Water Quality Data3. Spatial Data4. Data Analysis

4.Results/Discussion5.Conclusion6.My Comments7.Open Discussion

Presentation Overview

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1. Abstract

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1. Abstract

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1. Abstract

Goal: Accurately predict sediment loads/yields in un-gauged 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.

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2. Background Information

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A = R K LS CPThe Universal Soil Loss Equation

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 rainfallerosivity (Mg ha h ha−1 MJ−1 mm−1)CP = characterizes land cover and conservation management practices (dimensionless).

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The Revised Universal Soil Loss Equation 2

incorporate a broader set of land coverclasses and attempt to capture deposition in complex terrains

More sub-factors

Daily time step

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Universal

Soil

Loss

Equation

= Edge of Field


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Universal

Soil

Loss

Equation

= Catchment Scale


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Sediment

Delivery

Ratios


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Sediment

Delivery

Ratios


1. Estimate from calibration dataor2. Use complex spatial algorithms

Yagow 1998SEDMOD

Exported fromField

Observed atWQ site

Transport

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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).

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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

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3. Methods

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3. Methods

Water Quality Data

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3. Methods

SERC DATAcontinuous monitoring stations in 78 basins within 166,000 km2

Chesapeake Bay watershed

5-91,126 ha

across physiographic regionsCoastal Plain = 45Piedmont = 10Mesozoic Lowland = 7Appalachian Mountain = 9Appalachian Plateau = 7

selected across a range of land cover proportions

no reservoirs, no point sources

Water Quality Data

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0 20 40 60 80 100

forest

agriculture

residential/commercial

percent impervious

0-82%

2-100%

0-39%

0-40%

3. Methods

SERC DATA

Water Quality Data

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3. Methods

USGSS DATAcontinuous monitoring stations in 23 additional basins within 166,000 km2 Chesapeake Bay watershed

101-90,530 ha

no reservoirs

Water Quality Data

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0 20 40 60 80 100

forest

agriculture

residential/commercial 0-30%

5-100%

0-40%

USGS DATA

3. Methods Water Quality Data

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SERC WATER QUALITY DATA

3. Methods

•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

Water Quality Data

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3. Methods Regional Spatial Data

Source Year ResolutionConverted 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 30 m

RESAC Dataset (% impervious) 2003 30 m

Spatial Climate Analysis Service 2002 1:250,000

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3. Methods Regional Spatial Data

USLE Analysis

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3. Methods USLE AnalysisGRID-BASED USLE ANALYSIS

RKLSCP RKLSCP RKLSCP



R (rainfall erosivity) =

Derivded from linear interpolationof national iso-erodent map

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3. MethodsGRID-BASED USLE ANALYSIS




K (surface soil erodibility)=

STATSGO resampled to 30m

USLE Analysis

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L (slope length) =

NED DEM resampled to 30 m

USLE Analysis

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S (slope steepness)=

NED DEM resampled to 30 m

USLE Analysis

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C (cover management)=

Consolidated NLCD 30m




***no differentiation of erosion control practices

USLE Analysis

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P (support practice factor) =

1

***no differentiation of erosion control practices

USLE Analysis

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3. Methods

Revised-USLE2 Analysis

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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)

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3. Methods

SDR’s

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3. Methods SDR’s

3 LUMPED-PARAMETER

2 SPATIALLY EXPLICIT

“life is a box, but spatial relationships matter”

“where life is a box and space is only considered in terms of area”

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3. Methods

Runoff

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3. Methods Runoff

CN (curve number) method used to estimate runoff potential and annual runoff

STATSGO hydrosoilgrp + LC = CN

Monthly time step annual value

(for multiple linear regression)

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3. Methods

Multiple Linear Regression

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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)

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4. Results/Discussion

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4. Results

USLE vs RUSLE2

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USLE vs RUSLE 24. Results

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USGS Data

Pearson r = 0.95, p<0.001

USLE vs RUSLE 24. Results

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4. Results

USLE & RUSLE2 (SDR) vs SERC & USGS

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USLE & RUSLE2 (SDR) vs SERC & USGS4. Results

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Negative spearmen r =


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All p values = not statistically significant

Negative spearmen r =


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4. Results

USLE Parameters vs USLE

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USLE parameters vs USLE4. Results

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4. Results

Univariate Regressions

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4. Results Univariate Regressions

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4. Results Univariate Regressions

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4. Results

Best Subsets Multiple Regression Analysis

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4. Results Best Subsets Multiple Regression Analysis

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(dead sheep)

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4. Results

Other Multiple Linear Regressions

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4. Results Other Multiple Linear Regressions

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LC

LC

4. Results Other Multiple Linear Regressions

PC

PC

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5. Conclusion

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5. Conclusion

Widespread misuse of USLE and derivatives

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5. Conclusion


Multiple linear regression fails

fierceromance.blogspot.com

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5. Conclusion



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


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5. Conclusion



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.”


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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).

USE WITH CAUTION (DON’T USE)

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5. Conclusion

1. Identify predictor variables that conceptually link landscape and stream characteristics to flow velocity, stream power, and the ability to transport sediment

In order to accurately predict sediment discharges in ungauged drainage basins, scientists need to:

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5. Conclusion


2. Incorporate metrics to indicate potential sediment sources within streams, including bank erosion and legacy sediments


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5. Conclusion



3. Develop predictions for temporal scales finer than the long-term annual average time frame


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5. Conclusion



3. Develop predictions for temporal scales finer than the long-term annual average time frame


WE NEED CONSISTENT AND VERIFIABLE RESULTS!!!

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6. My Comments

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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).

6. My Comments

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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”)

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6. My Comments

Weakest Points




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6. My Comments

Weakest Points




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6. My Comments

Weakest Points




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1 empirical models based on the universal soil loss equation fail to predict discharges from...

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units of soil loss

soil loss ha1 yr1r

background informationa

background informationthe

background informationdanger

background informationmodels

background information2

background information1