Sediment Delivery Estimates in Water Quality Models Altered by Resolution and Source of Topographic Data

  • Beeson P
  • Sadeghi A
  • Lang M
  • et al.
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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), use the Universal Soil Loss Equation (USLE) and Modified USLE to estimate sediment loss. The slope length and steepness factor, a critical parameter in USLE, significantly affects sediment loss estimates. Depending on slope range, a twofold difference in slope estimation potentially results in as little as 50% change or as much as 250% change in the LS factor and subsequent sediment estimation. 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 fine spatial resolution DEMs are usually significantly higher than slopes derived from coarser DEMs. This increased slope results in considerable variability in modeled sediment 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) and sources. Overall, we observed over a 2.5-fold increase in slope when using a 3-m instead of 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 alter modeled sediment loss. © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America.

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Beeson, P. C., Sadeghi, A. M., Lang, M. W., Tomer, M. D., & Daughtry, C. S. T. (2014). Sediment Delivery Estimates in Water Quality Models Altered by Resolution and Source of Topographic Data. Journal of Environmental Quality, 43(1), 26–36. https://doi.org/10.2134/jeq2012.0148

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