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Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: The effect of DEM resolution on slope estimation and sediment predictions

Authors

Submitted to: Annual International SWAT Conference
Publication Type: Abstract Only
Publication Acceptance Date: April 25, 2011
Publication Date: June 15, 2011
Citation: Sadeghi, A.M., Beeson, P.C., Lang, M.W., Tomer, M.D. 2011. The effect of DEM resolution on slope estimation and sediment predictions [abstract]. 2011 International SWAT Conference. 2011 CDROM.

Technical Abstract: Moderate resolution (30 m) digital elevation models (DEMs) are normally 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 (SWAT), utilize the Modified Universal Soil Loss Equation (MUSLE) to estimate sediment loss from the land. It relies on a slope length and steepness LS factor which a fourfold increase in slope values results in a six-times increase in the LS factor and subsequent sediment estimation. Recently, the availability of much finer resolution (~2-3 m) DEMs derived from Light Detection and Ranging (LiDAR) data have increased and water quality modelers are eager to take advantage of these finer resolution data. However, the use of these finer resolution data are not always appropriate, since slope values derived from fine spatial resolution DEMs are usually significantly higher than those estimated from coarser DEMs resulting in considerable variability in model output. This paper addresses the implications of parameterizing models using slope values calculated from DEMs with different spatial resolutions (90, 30, 10, and 3 m). Here, we see a 250% and 420% increase in slope from the 90m to 3m DEMs depending on the region, which has a 380% and 630% increase in sediment loss estimate from the MUSLE. The results of a comparison among different slope calculations and associated sediment model predictions on well-monitored watersheds are presented and discussed.

   

 
Project Team
Crow, Wade
Cosh, Michael
Kustas, William - Bill
Alfieri, Joseph
McCarty, Gregory
Sadeghi, Ali
Gish, Timothy
Jackson, Thomas
Anderson, Martha
 
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  Water Availability and Water Management (211)
 
 
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