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

Shoufan Fang, Stephen Wente, George Z. Gertner, Guangxing Wang, Alan Anderson

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)199-208
Number of pages10
JournalEnvironmental Management
Issue number2
StatePublished - Aug 1 2002


  • Disturbance
  • Error budget
  • Off-road vehicle traffic
  • Support practice factor
  • Uncertainty analysis

ASJC Scopus subject areas

  • Global and Planetary Change
  • Ecology
  • Pollution


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