Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at Fort Hood

• Published in: Environmental management (New York) Vol. 30; no. 2; pp. 199 - 208
• Main Authors: SHOUFAN FANG, WENTE, Stephen, GERTNER, George Z, GUANGXING WANG, ANDERSON, Alan
• Format: Journal Article
• Language: English
• Published: New York, NY Springer 01.08.2002; Springer Nature B.V
• Subjects: Animal, plant and microbial ecology; Applied ecology; Biological and medical sciences; Conservation of Natural Resources; Conservation, protection and management of environment and wildlife; Disturbance; Ecosystem; Environmental degradation: ecosystems survey and restoration; Environmental impact; Environmental management; Environmental Monitoring; Erosion rates; Forecasting; Fundamental and applied biological sciences. Psychology; Landscape ecology; Military bases; Military training; Models, Theoretical; Motor Vehicles; Poaceae; Reference Values; Regression Analysis; Soil; Soil erosion; Spatial distribution; Traffic; Vegetation; United States; North America; Texas; America; Vegetation map; Uncertainty; Military installation; Landscape; Spatial distribution; Tracked vehicle; Road traffic; Perturbation; Forecast model; Anthropogenic factor
• DOI: 10.1007/s00267-002-2565-2
• ISSN: 0364-152X

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.