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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #283132
Title: Use of fine resolution terrain data in soil loss equations

Author
item Beeson, Peter
item Sadeghi, Ali
item LANG, M - Us Forest Service (FS)
item Tomer, Mark
item Daughtry, Craig

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/5/2012
Publication Date: 10/21/2012
Citation: Beeson, P.C., Sadeghi, A.M., Lang, M.W., Tomer, M.D., Daughtry, C.S. 2012. Use of fine resolution terrain data in soil loss equations [abstract]. ASA-CSSA-SSSA Annual Meeting Abstracts. 2012 CDROM.

Interpretive Summary:

Technical Abstract: The Dust Bowl of the 1930's focused US attention on soil erosion and land conservation. The Universal Soil Loss Equation (USLE) was the result of this effort and has remained one of the most widely used equations for soil erosion prediction world-wide. This empirical relationship has been incorporated into many computer-based water quality models critical for solving environmental and land management problems. However, the sources of information needed to solve the equation have changed during the last 70 years. For example, the derivation of slope, the most critical topographic value used in the equation, has changed dramatically since the development of the original model. Moderate resolution (30 m) digital elevation models (DEMs) have traditionally been used to estimate slope for the parameterization of non-point source, process-based water quality models. These models, such as the Soil and Water Assessment Tool, utilize USLE and Modified USLE to estimate sediment loss. Recently, the availability of much finer resolution (~3 m) DEMs derived from Light Detection and Ranging (LiDAR) data has increased. However, the use of these data may not always be appropriate because slope values derived from finer spatial resolution DEMs are usually significantly higher than slope derived from coarser resolution DEMs. This increase results in considerable variability in modeled sediment output estimations based on different resolution DEMs. This study addresses the implications of parameterizing models using slope values calculated from DEMs with different spatial resolutions (90, 30, 10, and 3 m) and sources. Overall, we observed over a 2.5 fold increase in slope when using a 3 m versus a 90 m DEM, which increased modeled soil loss using the USLE calculation by 130%. Care should be taken when using LiDAR-derived DEMs to parameterize water quality models because doing so can result in significantly higher slopes, which considerably alters modeled sediment loss. USDA is an equal opportunity provider and employer