1. 1 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

2. 2 Presentation Overview

3. 3 1.Abstract 2.Background Information 3.Methods 1.Location 2.Water Quality Data 3.Spatial Data 4.Data Analysis 4.Results/Discussion 5.Conclusion 6.My Comments 7.Open Discussion Presentation Overview

4. 4 1. Abstract

5. 5

6. 6 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.

7. 7 2. Background Information

8. 8 A = R K LS CP The 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 rainfall erosivity (Mg ha h ha−1 MJ−1 mm−1) CP = characterizes land cover and conservation management practices (dimensionless).

9. 9 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

10. 10 U niversal S oil L oss E quation = Edge of Field 2. Background Information

11. 11 U niversal S oil L oss E quation = Catchment Scale 2. Background Information

12. 12 S ediment D elivery R atios 2. Background Information

13. 13 S ediment D elivery R atios 2. Background Information 1. Estimate from calibration data or 2. Use complex spatial algorithms Yagow 1998 SEDMOD Exported from Field Observed at WQ site Transport

14. 14 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).

15. 15 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

16. 16 3. Methods

17. 17 3. Methods Water Quality Data

18. 18 3. Methods SERC DATA continuous monitoring stations in 78 basins within 166,000 km2 Chesapeake Bay watershed 5-91,126 ha across physiographic regions Coastal Plain = 45 Piedmont = 10 Mesozoic Lowland = 7 Appalachian Mountain = 9 Appalachian Plateau = 7 selected across a range of land cover proportions no reservoirs, no point sources Water Quality Data

19. 19 0-82% 2-100% 0-39% 0-40% 3. Methods SERC DATA Water Quality Data

20. 20 3. Methods USGSS DATA continuous monitoring stations in 23 additional basins within 166,000 km2 Chesapeake Bay watershed 101-90,530 ha no reservoirs Water Quality Data

21. 21 0-30% 5-100% 0-40% USGS DATA 3. Methods Water Quality Data

22. 22 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

23. 23 3. Methods Regional Spatial Data SourceYearResolution Converted Resolution USGS National Elevation Dataset199927.78 m30 m USGS National Landcover Database199230 m USDA-NRCS STATSGO Soils Database19951:250,00030 m RESAC Dataset (% impervious)200330 m Spatial Climate Analysis Service20021:250,000

24. 24 3. Methods Regional Spatial Data USLE Analysis

25. 25 3. Methods USLE Analysis GRID-BASED USLE ANALYSIS R KLSCP R (rainfall erosivity) = Derivded from linear interpolation of national iso-erodent map

26. 26 3. Methods GRID-BASED USLE ANALYSIS R K LSCP K (surface soil erodibility)= STATSGO resampled to 30m USLE Analysis

27. 27 3. Methods GRID-BASED USLE ANALYSIS RK L SCP L (slope length) = NED DEM resampled to 30 m USLE Analysis

28. 28 3. Methods GRID-BASED USLE ANALYSIS RKL S CP S (slope steepness)= NED DEM resampled to 30 m USLE Analysis

29. 29 3. Methods GRID-BASED USLE ANALYSIS C (cover management)= Consolidated NLCD 30m RKLS C P ***no differentiation of erosion control practices USLE Analysis

30. 30 3. Methods GRID-BASED USLE ANALYSIS RKLSC P P (support practice factor) = 1 ***no differentiation of erosion control practices USLE Analysis

31. 31 3. Methods Revised-USLE2 Analysis

32. 32 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)

33. 33 3. Methods SDR’s

34. 34 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”

35. 35 3. Methods Runoff

36. 36 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)

37. 37 3. Methods Multiple Linear Regression

38. 38 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)

39. 39 4. Results/Discussion

40. 40 4. Results USLE vs RUSLE2

41. 41 USLE vs RUSLE 2 4. Results

42. 42 USGS Data Pearson r = 0.95, p<0.001 USLE vs RUSLE 2 4. Results

43. 43 4. Results USLE & RUSLE2 (SDR) vs SERC & USGS

44. 44 USLE & RUSLE2 (SDR) vs SERC & USGS 4. Results

45. 45 Negative spearmen r = USLE & RUSLE2 (SDR) vs SERC & USGS 4. Results

46. 46 All p values = not statistically significant Negative spearmen r = USLE & RUSLE2 (SDR) vs SERC & USGS 4. Results

47. 47 4. Results USLE Parameters vs USLE

48. 48 USLE parameters vs USLE 4. Results

49. 49 4. Results Univariate Regressions

50. 50 4. Results Univariate Regressions

51. 51 4. Results Univariate Regressions

52. 52 4. Results Best Subsets Multiple Regression Analysis

53. 53 4. Results Best Subsets Multiple Regression Analysis

54. 54 4. Results Best Subsets Multiple Regression Analysis

55. 55 4. Results Best Subsets Multiple Regression Analysis (dead sheep)

56. 56 4. Results Other Multiple Linear Regressions

57. 57 4. Results Other Multiple Linear Regressions

58. 58 LC 4. Results Other Multiple Linear Regressions PC

59. 59 5. Conclusion

60. 60 5. Conclusion Widespread misuse of USLE and derivatives

61. 61 5. Conclusion Widespread misuse of USLE and derivatives Multiple linear regression fails fierceromance.blogspot.com

62. 62 5. Conclusion 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 fierceromance.blogspot.com

63. 63 5. Conclusion 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.” fierceromance.blogspot.com

64. 64 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)

65. 65 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:

66. 66 5. Conclusion 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 In order to accurately predict sediment discharges in ungauged drainage basins, scientists need to:

67. 67 5. Conclusion 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 In order to accurately predict sediment discharges in ungauged drainage basins, scientists need to:

68. 68 5. Conclusion 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 In order to accurately predict sediment discharges in ungauged drainage basins, scientists need to: WE NEED CONSISTENT AND VERIFIABLE RESULTS!!!

69. 69 6. My Comments

70. 70 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

71. 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. 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. 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. 74 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”)

75. 75 www.jqjacobs.net OPEN DISCUSSION