Validating the Kinematic Wave Approach for Rapid Soil Erosion Assessment and Improved Best Management Practice Site Selection to Enhance Training Land Sustainability

RC-200820

Objective

Based on the concept of accumulating overland flow energy, an erosion potential model was developed for Fort Riley, Kansas, under the Strategic Environmental Research and Development Program (SERDP) project Assessing the Impact of Maneuver Training on NPS Pollution and Water Quality (RC-1339). By integrating data from digital elevation models (DEM) and land use/land cover (LULC) classifications in a geographic information system (GIS) environment, the model determines where surface water runoff transitions from overland sheet flow to concentrated flow and, as a result, where the potential for soil erosion and gully formation increases. This model was calibrated and validated with field data from two watersheds in Kansas and proved successful (88% accuracy) in identifying the location of gullies. The objective of this follow-on project is to run the erosion potential model for additional military installations selected from regions with different precipitation regimes, land cover conditions, topographic characteristics, and soil types, as well as to operate the model in a predictive mode to better assess the impact of military training activities on future gully formation.

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Technology Description

The erosion potential model, referred to as the nLS model, developed for Fort Riley is capable of determining areas of high erosion potential and, therefore, the optimal locations for siting erosion-preventing best management practices (BMP). The nLS model is operationalized as a three-layer raster calculation in a GIS using classified LULC data and DEMs. The computed nLS output grid represents a unitless estimate of the energy of surface runoff water as it flows downslope. The relationship between actual gully locations and accumulated energy then are compared to develop a predictive model. Because it requires few inputs, for which easily accessed data sets are available nationwide, data acquisition and preparation times are minimal compared to existing erosion models such as AGNPS and RUSLE. While the nLS model has been shown effective in identifying the location of existing gullies, it also can be operated in a predictive mode to forecast the impact of military training exercises on the formation of new gullies. By incorporating vehicle tracking data from field training or simulator-based exercises, anticipated environmental damage such as vegetation loss and vehicle rutting can be simulated and used as the basis to modify representations of current installation terrain. The validated nLS model then can be run using modified vegetation and topographic input to identify likely sites of gully initiation.

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Benefits

The nLS modeling approach promises to be an effective tool for identifying current and predicting the location of future gully locations on military training lands. Using this approach, military installations will gain the capability to quickly evaluate gully formation potential across their training lands, saving significant time normally spent on ground and air surveys, while making training safer for soldiers and less damaging to equipment. The nLS modeling approach also will assist installation Integrated Training Area Management (ITAM) staff in siting BMPs designed to reduce soil erosion and meet state-enforced total maximum daily loads (TMDL) for streams leaving federal lands. The ability of the nLS model to predict future erosion potential offers several advantages to military installations. Key advantages include providing a sound scientific basis for estimating land rehabilitation and maintenance (LRAM) costs and the ability to estimate and compare environmental impacts associated with realignment and mission change. (Anticipated Project Completion - 2011)

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Symposium & Workshop

Points of Contact

Principal Investigator

Dr. Stacey Hutchinson

Kansas State University

Phone: 785-532-2943

Program Manager

Resource Conservation and Climate Change

SERDP and ESTCP

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