Sensitivity analysis of soil and PM₁₀ loss in WEPS using the LHS-OAT method.
Authors:
Feng, G. Sharratt, B. USDA, ARS
Source:
Transactions of the ASAE. 2005 July-Aug, v. 48, no. 4, p. 1409-1420.
NALT Subjects:
soil erosion models wind erosion agricultural soils agricultural land mathematical models air quality soil properties Washington Oregon
Other Subjects:
one-factor-at-a-time Columbia Plateau region Wind Erosion Prediction System Latin hypercube sampling
Issue Date:
Jul-2005
Abstract:
The Wind Erosion Prediction System (WEPS) was developed for the specific application of simulating erosion processes from agricultural lands. WEPS is a physically based model, with a moderate to large number of input parameters. Knowledge about model sensitivity is essential to both model developers and users in ascertaining those parameters most influential to modeled object functions. A combined method of Latin hypercube sampling (LHS) and one-factor-at-a-time (OAT) parameter examination was used to assess the sensitivity of parameters in the WEPS erosion submodel in simulating total soil loss, creep/saltation, suspension, and PM10 emission. The ranges of the parameters considered in this analysis were obtained from the WEPS User's Manual and determined for the Columbia Plateau region of the U.S. Overall, the analysis indicated that the model was most sensitive to changes in biomass flat cover, near-surface soil water content, ridge height, wind speed, rock volume, soil wilting-point water content, field length and width, crust cover, aggregate and crust stability, and random roughness. The model was least sensitive to changes in bulk density, silt content, and aggregate and crust density. For the Columbia Plateau, erosion processes were more sensitive to surface soil water content and random roughness in spring than in autumn and more sensitive to residue cover and aggregate mean diameter in autumn than in spring. This sensitivity analysis suggests that residue management, surface soil moisture conservation, aggregation, and field size can effectively influence soil loss and PM10 emission.
