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July 29-330-Anurag Srivastava

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2019 SWCS International Annual Conference
July 28-31, 2019
Pittsburgh, Pennsylvania

Published in: Environment
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July 29-330-Anurag Srivastava

  1. 1. Anurag Srivastava Research Associate Purdue University, Dept. of Agricultural & Biological Engineering, West Lafayette, IN
  2. 2.  Background  WEPP hillslope validation  WEPP and RUSLE2 study • Part 1 – Climate comparisons • Part 2 – WEPP & RUSLE2 simulations • Part 3 – Results  Summary
  3. 3.  Erosion prediction technologies are often used to assess soil loss rates under current land management practices, and effects of changes to that management.  RUSLE2 (Revised Universal Soil Loss Equation version 2) is the current technology being used by USDA-NRCS for erosion prediction and soil conservation planning.  WEPP (Water Erosion Prediction Project) model is being implemented by USDA-NRCS to replace RUSLE2.  NRCS and others need to understand potential differences when using these 2 different models.
  4. 4.  Zhang et al., 1996. Evaluation of WEPP runoff and soil loss predictions using natural runoff plot data.  WEPP tended to over-predict soil loss for small events with low erosion rates and under-predict soil loss for large events with higher erosion rates.  Means of event and annual soil loss were well predicted.  Tiwari et al., 2000. Evaluation of WEPP and its comparison with USLE and RUSLE.  Compared WEPP average annual soil loss without calibration with USLE and RUSLE.  WEPP performed quite acceptably, at similar levels to both USLE and RUSLE.
  5. 5.  WEPP continuous simulations were conducted using NRCS WEPP for 11 USLE validation sites (1930–1970s).  Bethany, MO; Castana, IA; Geneva, NY; Guthrie, OK; Holly Springs, MS; Madison, SD; Morris, MN; Pendleton, OR; Presque Isle, ME; Tifton, GA; Watkinsville, GA  Climate files were in breakpoint format.  Managements: Tilled-fallow, single crop or crop rotations.  Erodibility, critical shear, and effective hydraulic conductivity values were based on WEPP parameterization equations (NO CALIBRATION).
  6. 6. 1. Srivastava et al., 2017. Comparison of soil loss predictions from RUSLE2 and WEPP in the U.S. under different cropping systems. (21 locations x 3 soil types x 2 managements = 126 runs)
  7. 7.  Cooperative effort by the USDA-ARS National Soil Erosion Research Laboratory (NSERL) and the National Sedimentation Laboratory (NSL).  The study is composed of 3 parts: 1. Climate evaluations for the two models using inputs for each derived from the same observed weather station data. 2. Detailed WEPP and RUSLE2 model simulations at the same locations using the same slope length, slope gradient, soil, and cropping/management inputs. 3. Evaluation of results, including comparisons of long-term average annual soil loss.
  8. 8.  Obtain climate data from weather stations in Iowa with available 15-min or finer resolution precipitation information to create breakpoint precipitation inputs for WEPP.  Monthly EI30 values, EI distribution and average annual R values will be determined, using RUSLE2 rules.  WEPP will be run using base CLIGEN input files and breakpoint input files using various precipitation resolutions.  RUSLE2 will be run for the same locations using the base RUSLE2 climate inputs as well as newly derived values from the 15-min precipitation data.  Unit plot conditions will be used, with a silt loam soil under tilled fallow. Comparisons of each model’s results for different climate inputs, as well as between models will be made.
  9. 9. Average annual soil loss and runoff from WEPP forced by CLIGEN (simulated) and NCDC (observed) data. AREA SOIL LOSS (T/ac-yr) RUNOFF (in/yr) (County) (CLIGEN) (NCDC) (CLIGEN) (NCDC) All Stations 45.0 31.4 9.8 7.4 Adair 48.4 37.0 9.8 8.1 Des Moines 50.0 36.1 11.0 8.7 Hardin 53.0 31.3 11.0 8.3 Jackson 38.9 29.3 10.1 8.2 Plymouth 34.7 32.6 7.3 7.7 WEPP soil loss predictions were reduced by 6% – 69% when using observed breakpoint climate inputs (15- min data)
  10. 10. Average annual EI for Iowa from minimally screened NCDC stations (all storms included) for 1970-2013. 270 260 250 240 230 220200 150 150 RUSLE2 database EI = 150 RUSLE2 EI values derived from the 15-min data were 44% - 60% greater than those in the existing RUSLE2 database
  11. 11.  Detailed evaluations of soil, slope length, slope gradient, and cropping/management effects.  Group 1 simulations (13,320 runs)  5 climate locations in Iowa  7 soils (SiL, L, SiC, S, C, CL, SL)  9 crop management systems  7 slope lengths (30, 50, 72.6, 100, 150, 200, 250 ft)  6 slope gradients (1, 3, 6, 9, 12, 15%)  Constant target crop yields (corn: 120 bu/A soybeans: 35 bu/A)
  12. 12.  Group 2 simulations (4,200 runs)  5 climate locations in Iowa  7 soils (SiL, L, SiC, S, C, CL, SL)  15 crop management systems  1 slope length (150 ft)  1 slope gradient (6%)  Variable target crop yields (corn: 90, 120, 150, 180 bu/A; soybeans: 30, 50, 70, 90 bu/A)  Group 3 simulations (unit plot conditions) (95 runs)  5 climate locations in Iowa  19 soils (3 soils each for 6 textures; 1 clay)  Tilled-fallow management  1 slope length (72.6 ft)  1 slope gradient (9%)
  13. 13. WEPP > RUSLE2 by 24%
  14. 14. WEPP > RUSLE2 by 48%
  15. 15. 52% of simulation runs were between -2 and +2 T/ac/yr differences in soil loss. 80% were within -10 to +10 T/ac/yr.
  16. 16. Both models showed trends of increasing soil loss with increasing slope lengths and slope gradients
  17. 17. Under no-till soybean, RUSLE2 predicted soil loss was higher than WEPP
  18. 18. WEPP > RUSLE2 by 78%
  19. 19. WEPP validation  WEPP validation was performed using NRCS WEPP interface for 11 USLE plots consisting of different landuses.  On an average annual basis:  WEPP predicted runoff and soil loss ~ measured data. WEPP and RUSLE2 comparisons  A study to compare soil erosion predictions by 2 different USDA technologies in Iowa was developed.  The first part of the study on climate inputs to WEPP and RUSLE2 is incomplete.  Preliminary results show that soil loss from:  CLIGEN-generated data > observed 15-min precipitation data  Newly derived RUSLE2 EI > existing RUSLE2 EI  Relative differences in soil loss predictions between WEPP and RUSLE2 increase with increasing model complexity  fallow-tilled < terrain and management < cropping systems
  20. 20.  Using existing climate inputs:  WEPP predictions > RUSLE2 predictions, except for no-till soybean management systems.  For tilled-fallow conditions,  WEPP predicted soil loss values were 24% greater than RUSLE2 predicted soil loss across all climates and soils.  Differences in mean soil loss between WEPP and RUSLE2 increased as slope length and slope gradient.  More work is needed, especially on climate input evaluations and comparisons, and slope effects.
  21. 21.  Part 2 – WEPP & RUSLE2 simulations (Group 2)  WEPP soil loss was 78% higher than RUSLE2 soil loss across all climates, soil textures, and managements.  Both WEPP and RUSLE2 showed trends of decreasing soil loss with increasing crop yields for each soil.  WEPP showed more variability in soil loss with climate for different soil textures compared to RUSLE2.  WEPP soil loss for corn and soybeans with fall plow, fall chisel, spring plow, and spring chisel tillage systems were higher compared to RUSLE2 soil loss.  Under no-till soybean cropping systems, RUSLE2 showed higher soil loss, whereas under no-till corn cropping systems, WEPP and RUSLE2 showed similar ranges of soil loss.
  22. 22.  Part 1 – Climate comparisons  Only part of this work has been completed. We are also still processing finer resolution (1-min) precipitation data, to use in more comparisons.  Generally, results indicate that WEPP model simulations using the breakpoint precipitation inputs (observed 15-min precipitation data from 1970-2013) are less vigorous than those predicted using CLIGEN-generated inputs to WEPP.  WEPP soil loss predictions were reduced by 6% - 69% when using observed breakpoint climate inputs (15-min data).  In terms of computed RUSLE2 EI factors using observed 15-min precipitation data from 1970-2013), values are substantially more vigorous than those in the existing RUSLE2 database.  RUSLE2 EI values derived from the 15-min data were 44% - 60% greater than those in the existing RUSLE2 database.
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  24. 24. 150 50 250 350 450 550 650

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