Maneuver training of mechanized infantry and armor units requires large areas of high-quality training lands. Training restrictions due to noise, threatened and endangered species habitat protection, water quality requirements, etc. have reduced available training lands. These restrictions result in heavier usage of the remaining maneuver areas and place greater stress on the ecological system. It may not be possible to repair and revegetate areas before they must be used again. These restrictions do not consider the day-to-day weather patterns. Intense rainfall or long periods of saturated soil coupled with regulations concerning non-point source (NPS) pollution and requirements to meet total maximum daily loads (TMDLs) may result in unforeseen and considerable delays in maneuver training.
The objectives of this project were to identify sources of NPS pollution resulting from military activities, to assess the impact of this pollution on surface water quality, and to provide information for training land managers to lessen the impact of training on water quality. Specific objectives included: (1) evaluating the most effective means to cross streams during maneuvers based on frequency, intensity, and stream stage; (2) developing a Geographical Information System (GIS) for use with NPS models to model the contribution of NPS pollution on a representative watershed at Fort Riley; and (3) developing improved field-portable technologies to provide timely monitoring of NPS runoff to provide managers options during a maneuver exercise to lessen impact on water quality without canceling training.
A comprehensive analysis of military activities, climatic factors, and environmental responses was undertaken. A watershed water quality model was used in conjunction with remotely sensed information and GIS to assess the impact of training on water quality, specifically on the amount of soil erosion. Field measured soil moisture data were used to validate satellite estimates. Weather, vegetation stage, and training activities were linked to water quality. A matrix of training intensity and weather was created to provide a tool for assessing the environmental cost of training maneuvers. Three buffer sites were instrumented with runoff samplers to determine the effect of vegetated buffers on controlling NPS pollution. A complete survey to characterize the buffer, including vegetation and soil characteristics, was conducted at each field site. The Riparian Ecosystem Management Model (REMM) was adapted to each site to determine the optimal buffer width to control soil erosion caused by military maneuvers. New real-time data collection systems were developed and installed at Low Water Stream Crossings (LWSC) to assess the impact of vehicle crossings on stream water quality and erosion dynamics.
This study documented strong evidence of a difference in vegetation condition between areas subjected to differing intensities of military training. The combined use of Landsat TM and MODIS normalized difference vegetation index (NDVI) imagery in this project was able to measure a significant difference in greenness between the training area and impact area control study sites.
Each of the Revised Universal Soil Loss Equation (RUSLE) factors were combined with a training intensity factor layer in local raster operation to estimate annual soil loss. Incorporation of a training intensity factor resulted in a difference in estimated annual soil losses of between 0.00 and 5.40 tons/acre/year as compared to the unmodified RUSLE. Adding the training intensity factor increased the maximum soil loss to over 247 tons/acre year. A difference map was created by subtracting the original RUSLE results grid from the modified RUSLE output to determine the difference in soil loss estimates between the two models and to help better visualize where those differences were located. The difference map shows areas of increased estimated soil losses on grasslands within training and maneuver areas.
A calibrated and validated GIS-enabled kinematic wave model (nLS) as developed and can be delivered to end users as an ArcGIS model for use in desktop ESRI GIS software. This project transitioned to a follow-on Environmental Security Technology Certification Program (ESTCP) project: Validating the Kinematic Wave Approach for Rapid Soil Erosion Assessment and Improved Best Management Practice Site Selection to Enhance Training Land Sustainability (RC-200820). The ESTCP project is designed to demonstrate the use of this nLs model as a fully functional ArcGIS Server application with geoprocessing, data extraction, and geodatabase editing capability.
Methods to estimate annual soil erosion losses, and the potential impact of military training activities on soil loss, are useful to military land managers. This study illustrates the applicability of the RUSLE model for military training lands and how GIS and remote sensing techniques can save time and resources in determining rates of erosion for large spatial extents. Currently, locating gully points is a time-consuming and potentially dangerous task if conducted on the ground and expensive if completed via air survey. The application of the methods embodied in the user-friendly tools developed in this project will permit land managers to reduce the size of and prioritize their search area by focusing their attention on sites most likely to develop gullies in accordance with the biophysical characteristics of the training area. The tangible dollar value is time saved in locating gullies. The unknown savings comes in reduction of injuries to soldiers and repairs to damaged equipment.
The nLS modeling approach also promises to be the premier tool for siting best management practices designed to reduce soil erosion, thereby assisting installations in meeting current or future sediment TMDLs for streams leaving federal lands. The ability to predict future erosion potential with the nLS model offers several significant advantages to military installations, including: (1) the ability to assess training land impacts from scheduled training exercises given current environmental conditions, (2) providing a sound scientific basis to estimate Land Rehabilitation and Maintenance (LRAM) program costs to repair and prevent gully erosion on current, future, or rental training lands, and (3) the ability to estimate and compare environmental impacts due to training events associated with installation realignment or mission change. Financial considerations can be expanded from analyzing the expected expense of only one exercise to predicting annual and/or reoccurring costs by simulating multiple training exercises.