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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #365494

Research Project: Uncertainty of Future Water Availability Due to Climate Change and Impacts on the Long Term Sustainability and Resilience of Agricultural Lands in the Southern Great Plains

Location: Agroclimate and Natural Resources Research

Title: Effects of different DEM resolutions and sources on soil erosion: A case study using the CSLE model

Author
item LI, AO - Beijing Normal University
item Zhang, Xunchang

Submitted to: Soil Science Society of America Annual Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 6/15/2019
Publication Date: 11/11/2019
Citation: Li, A., Zhang, X.J. 2019. Effects of different DEM resolutions and sources on soil erosion: A case study using the CSLE model [abstract]. Soil Science Society of America Annual Meeting. Available at: https://scisoc.confex.com/scisoc/2019am/meetingapp.cgi/Paper/118904.

Interpretive Summary: Abstract only.

Technical Abstract: Slope gradient (S) and slope length (L) are the two important topographic factors used in the Universal Soil Loss Equation (USLE) for soil erosion prediction. These factors are usually extracted the digital elevation models (DEM). We hypothesize that different DEM resolutions and sources influence extracted topographic factors and therefore soil loss estimation. The objective of this study is to quantify the effect of different DEM resolutions on the topographic factor calculation and therefore soil loss prediction on different geomorphological types in China and United States. We selected three landforms of plains, hills and mountains in both countries. We used opensource DEMs of 3m, 10m, 30m and 90m, and a LS software tool to calculate topographic factors. The Chinese Soil Loss Equation (CSLE) was used to calculate average annual soil loss. The results showed that different DEM resolutions had different effects on the extracted topographic factors. The L factor was overestimated as DEM resolutions coarsened, and the trend of the overestimation in different landforms was the same. The S factor was underestimated as DEM resolution decreased, and the trend of the underestimation was the same. The LS factor values had the same trend as the predicted soil erosion, with the smallest difference between DEM resolutions in the plains, the largest in the mountains, and intermediate in the hills. The potential uncertainties introduced by different DEM resolutions and sources in soil erosion prediction are presented, and appropriate selection of DEM resolutions are discussed. A simple and effective correction method would be developed to improve the accuracy of LS factor estimation for use with low DEM resolutions, so as to predict soil erosion more accurately.