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Research Project: STRATEGIES FOR PREDICTING AND CONTROLLING PM10 EMISSIONS FROM AGRICULTURAL SOILS WITHIN THE COLUMBIA PLATEAU

Location: Land Management and Water Conservation Research

Title: SENSITIVITY ANALYSIS OF SOIL AND PM10 LOSS IN WEPS USING LHS-OAT METHOD

Authors
item Feng, Guanglong - WASHINGTON ST UNIVERSITY
item Sharratt, Brenton

Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 2, 2005
Publication Date: July 1, 2005
Repository URL: http://hdl.handle.net/10113/5516
Citation: Feng, G., Sharratt, B.S. 2005. Sensitivity Analysis of Soil and PM10 Loss in WEPS using LHS-OAT Method. Transactions of the ASAE.

Interpretive Summary: The Wind Erosion Prediction System (WEPS) has been developed by the USDA-ARS for the specific application of simulating wind erosion processes from agricultural lands. Although WEPS simulates both the loss of soil and PM10 (particulates <10 microns in diameter that cause air pollution) from agricultural fields, WEPS requires knowledge about 30 or more soil and crop characteristics which are often difficult to measure in the field. Knowing the relative importance of these soil and crop characteristics can help the user in determining which characteristics should be measured with the greatest accuracy in the field to obtain reliable predictions. We found that crop residue cover, soil water content, ridge height, volume of rocks in the soil, soil crust cover, soil aggregate and crust stability, and surface random roughness are the most important soil and crop characteristics affecting erosion processes. On the contrary, bulk density, silt content, and soil aggregate and crust density were the least important soil characteristics affecting erosion. Our results suggest that farmers who use practices which retain residue on the soil surface, conserve soil water, and promote aggregation can effectively reduce soil loss and PM10 emissions from agricultural soils. In addition, reliable estimates of erosion using WEPS (USDA-NRCS field personnel will use WEPS in the near future to determine eligibility of lands for federal farm programs) will hinge upon creating databases with accurate information regarding crop residue cover, soil water content, soil crusting, and soil aggregation.

Technical Abstract: 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 developer and user 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) 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 range of parameters considered in this analysis were obtained from the WEPS Users Manual and determined for the Columbia Plateau region of the United States. 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, 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

   

 
Project Team
Sharratt, Brenton
Kennedy, Ann
Smith, Jeffrey - Jeff
Huggins, David
Gollany, Hero
Long, Daniel - Dan
Williams, John
Wuest, Stewart
 
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Related National Programs
  Air Quality (203)
 
 
Last Modified: 05/21/2013
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