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Calibración / Validación of Watershed Models

National Sedimentation Laboratory. Calibración / Validación of Watershed Models. Ronald L. Bingner, USDA-Agriculture Research Service, Oxford, Mississippi, USA. USDA-ARS-NSL Oxford, Mississippi, USA. USDA-ARS-NSL. Plan de la Presenta ción. Definiciones.

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Calibración / Validación of Watershed Models

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  1. National Sedimentation Laboratory Calibración / Validaciónof Watershed Models Ronald L. Bingner, USDA-Agriculture Research Service, Oxford, Mississippi, USA

  2. USDA-ARS-NSLOxford, Mississippi, USA USDA-ARS-NSL

  3. Plan de la Presentación Definiciones. Calibración / ValidaciónProblemas Soluciones 26 August 2014 3

  4. Definiciones: • Verificación—garantiza que el código cumple la ciencia elegida y sus soluciones matemáticas; es decir, cuando se agregan 1 & 1, recibe 2. • Validación—asegura que si la ciencia elegida da la respuesta "verdadera"; es decir, 2 es correcta. • Calibración—adjusting the input values so that the model output duplicates certain corresponding measured responses.

  5. Validation/Calibration—where input data must match measured data • Meteorology Data • Topographic Data • Landuse Data • Soils Data • Measured Instream Data—water, sediment, & chemicals

  6. Meteorology (24-hour values): • For validation/calibration—must be historical data corresponding to measured instream data: • precipitation • min/max air temperatures • dew point • sky cover • wind speed • For analysis (evaluation)—synthetic (generated) climate data is more useful; e.g., risk analyses, etc.

  7. Cheney Lake Watershed, Kansas

  8. Cheney Lake Watershed with superimposed NEXRAD Weather grid.

  9. Topographic Data: • DEM (digital elevation map)—must have sufficient areal coverage, and horizontal (raster size) & vertical (elevations) resolution to be able to adequately define the watershed’s drainage pattern, stream network, and topographic RUSLE LS-factor. • Hydraulic Geometry—by hydro-geomorphic region • channel width at bankfull • channel hydraulic depth at bankfull • valley width (approximate 100-yr depth)

  10. Shades Creek Watershed, Alabama

  11. EXTRACT THE DEM FOR THE SHADES CREEK WATERSHED ORIGINAL DEM CLIPPED PORTION OF THE DEM OUTLET

  12. Impact of Length & Gradient on Erosion (LS-Factor)

  13. COMPARISON OF THE DIGITIZED STREAM NETWORK WITH GENERATED STREAMS

  14. COMPARE THE DIGITIZED AND GENERATED WATERSHED BOUNDARY GENERATED BOUNDARY DIGITIZED BOUNDARY

  15. Landuse Data—two separate items (homogeneity): • 1st item--digitized landuse map that can be overlaid with the DEM to determine each cells landuse. • 2nd item--database includes properties & parameters that lead to the SCS curve number, daily RUSLE cover C-factor, RUSLE practice P-factor; and agronomic & chemical management operations.

  16. AnnAGNPS Assigned Landuse by Cell 1991 Landuse 2001 Landuse 7% Urban 86% Forest 13% Urban 86% Forest

  17. Soils Data—homogeneity (again): In-situ soil properties that describe the physical & hydraulic properties that allow for daily estimates of the soil’s moisture content, erodibility, sediment particle distribution when erosion occurs, and chemical status (nitrogen cycling, phosphorus content, etc.)

  18. SOILS GIS LAYER STATSGO SOILS SSURGO SOILS

  19. USLE: A = R K LS C PA Relatively Simple Model R - RAINFALL FACTOR K - SOIL ERODIBILITY FACTOR LS - TOPOGRAPHY FACTOR C - COVER MANAGEMENT FACTOR P - SUPPORT PRACTICE FACTOR

  20. Plot StudiesUniform Climate, Slope, Soil, Management

  21. City (Weather) Database R Location K LS Soil C Plant Database Topography P Operations Database Land Use A-RUSLE Soil Loss Estimate General Flow Chart of RUSLE Data files previously defined by user User inputs to describe specific site RUSLE Computations Slide 27

  22. RUSLE2 Area Landscape Overland flow Interrill Rill Ephemeral Gully (Concentrated flow) Erosion Types

  23. Complex Landscapesnon-uniform climate, slopes, soils, management

  24. Watershed Scale Inter-related Processes Complexity of Inter-relationships Basin Watershed Subwatershed Field Plot Scale

  25. AnnAGNPS Identifier Watershed Data Simulation Period Daily Climate Verification Data Global Output Feedlot Management Point Source Gully Feedlot Field Pond Cell Data Reach Data Reach Channel Geometry Reach Nutrient Half-life Management Field Soils Impoundment Non-Crop Tile-Drain Management Schedule Fertilizer Application Pesticides Application Management Operation Runoff Curve Number Crop Strip Crop Contours Irrigation Fertilizer Reference Pesticides Reference Not Used in Single Day Simulation (AGNPS) Required Required if Referenced Optional All Available AnnAGNPS Input Data Sections

  26. Subdivision of Shades Creek Watershed with 620 AnnAGNPS Cells BIRMINGHAM AIRPORT FOR WEATHER DATA OUTLET

  27. DEFINITION OF THE MAIN LOCATIONS IN THE WATERSHED CONCEPTS UPSTREAM BOUNDARY GAGING STATION SHADES CREEK OUTLET CONCEPTS DOWNSTREAM BOUNDARY

  28. ANNUAL RAINFALL, MEASURED & SIMULATED RUNOFF AT THE GAGE

  29. MEASURED & ANNAGNPS SIMULATED ANNUAL SEDIMENT LOAD AT THE GAGE

  30. AnnAGNPS Simulated Runoff by Cell 178.0 – 212.2 212.2 – 286.2 286.2 – 317.6 317.6 – 337.8 337.8 – 362.0 362.0 – 386.5 386.5 – 473.5 473.5 – 583.7 2001 Landuse Scenario, 457 mm/year @ OUTLET Years Simulated - 2004-2028 13% Urban, 86% Forest 2001 Forest to Urban Scenario, 702 mm/year @ OUTLET Years Simulated - 2004-2028 99% Urban, 0% Forest 583.7 – 877.6 877.6 – 1386.0 Millimeters per year

  31. Total Load @ CONCEPTS Downstream Boundary = 18890 T/yr (73 T/yr/km2) Total Load @ Outlet = 21000 T/yr (58 T/yr/km2) Upland Sediment Contributions from AnnAGNPS to the CONCEPTS Main Channel Validation Scenario – 1978-2001 Little Shades Creek (23.4 T/yr/km2)

  32. Goodwin Creek Watershed Mississippi

  33. Goodwin Creek Experimental Watershed GCEW

  34. Characteristicas de la Cuenca • Esta dividida en 14 sub-cuencas con salidas instrumentadas • Areas de drenaje varian de 1.6 from 21.3 km2 • 12 % del area esta cultivada, 27% en pastos, y 50% en bosques. • 30 pluviometros y una estacion central climatologica • Precipitacion media anual es 1460mm • Desague medio anual es 30.000 m3/dia • Produccion media anual de sedimento es 110 ton/dia

  35. Erosion por Flujos Concentrados y Tecnologia de Conservacion • Enfoque en procesos de erosion de terrenos por flujos concentrados que conducen a la formacion de carcavas efimeras y permanentes • Desarrollo de tecnicas efectivas para el control de este tipo de erosion

  36. Goodwin Creek Carcavas de Borde (“Edge of Field Gullies”)

  37. Rainfall & Runoff - 4 Months Totals

  38. Landuse 1982 - 2005

  39. Change in rainfall runoff ratio

  40. Particion de Sedimentos en Suspension Durante una Escorrentia en Abril, 2004

  41. Sediment Trends

  42. ? ? Calibration / Validation Soluciones

  43. Several error indices are commonly used in model evaluation • Mean absolute error (MAE) • Mean square error (MSE) • Root mean square error (RMSE) These indices indicate error in the units (or squared units) of the constituent of interest. RMSE, MAE, and MSE values of 0 indicate a perfect fit. RMSE and MAE values less than half the standard deviation of measured data may be considered low and either is appropriate for model evaluation (Singh et al., 2004).

  44. Nash-Sutcliffe Efficiency (NSE) • Normalized statistic that determines the relative magnitude of the residual variance (“noise”) compared to the measured data variance (“information”) (Nash and Sutcliffe, 1970). • NSE indicates how well the plot of observed versus simulated data fits the 1:1 line. • NSE ranges between -  and 1.0 (1 inclusive), with NSE = 1 being the optimal value. • Values between 0.0 and 1.0 are generally viewed as acceptable levels of performance. • Values <0.0 indicates that the mean observed value is a better predictor than the simulated value, which indicates unacceptable performance.

  45. ≡ Nash-Sutcliffe efficiency coefficient, [nd]; Nash-Sutcliffe is calculated as:

  46. Percent bias(PBIAS) - measures the average tendency of the simulated data to be larger or smaller than their observed counterparts (Gupta et al., 1999). • Optimal value of PBIAS is 0.0. • Low-magnitude values indicate accurate model simulation. • Positive values indicate model underestimation bias • Negative values indicate model overestimation bias.

  47. % Bias, PBIAS, is calculated as:

  48. RMSE- Root mean square error: Lower RMSE values indicates better model performance.

  49. RMSE-observations standard deviation ratio (RSR) (Singh et al., 2004), • Developed to standardize RMSE; • Based on the observation’s standard deviation, combining both an error index and additional information recommended by Legates and McCabe (1999); • RSR is calculated as the ratio of the RMSE and standard deviation of measured data.

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