PT  - JOURNAL ARTICLE
AU  - A.R. Buda
AU  - P.J.A. Kleinman
AU  - G.W. Feyereisen
AU  - D.A. Miller
AU  - P.G. Knight
AU  - P.J. Drohan
AU  - R.B. Bryant
TI  - Forecasting runoff from Pennsylvania landscapes
AID  - 10.2489/jswc.68.3.185
DP  - 2013 May 01
TA  - Journal of Soil and Water Conservation
PG  - 185--198
VI  - 68
IP  - 3
4099  - http://www.jswconline.org/content/68/3/185.short
4100  - http://www.jswconline.org/content/68/3/185.full
AB  - 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.