Identifying sites prone to surface runoff has been a cornerstone of conservation and nutrient management programs, relying upon site assessment tools that support strategic, as opposed to operational, decision making. We sought to develop simple, empirical models to represent two highly different mechanisms of surface runoff generation-saturation excess runoff and infiltration excess runoff-using variables available from short-term weather forecasts. Logistic regression models were developed from runoff monitoring studies in Pennsylvania, fitting saturation excess runoff potential to rainfall depth, rainfall intensity, and soil moisture, and infiltration excess runoff potential to rainfall depth and intensity. Testing of the models in daily hindcasting mode over periods of time and at sites separate from where they were developed confirmed a high degree of skill, with Brier Skill Scores ranging from 0.61 to 0.65 and Gilbert Skill Scores ranging from 0.39 to 0.59. These skill scores are as good as models used in weather forecasting. Results point to the capability to forecast site-specific surface runoff potential for diverse soil conditions, with advances in weather forecasting likely to further improve the predictive ability of runoff models of this type.
All Science Journal Classification (ASJC) codes
- Agronomy and Crop Science
- Water Science and Technology
- Soil Science
- Nature and Landscape Conservation