H13C-1551
Beyond the SCS curve number: A new stochastic spatial runoff approach

Monday, 14 December 2015
Poster Hall (Moscone South)
Mark S Bartlett Jr, Duke University, Civil and Environmental Engineering, Durham, NC, United States, Anthony Parolari, Duke University, Durham, NC, United States, Jeffrey McDonnell, University of Saskatchewan, Saskatoon, SK, Canada and Amilcare M Porporato, Duke Univ, Durham, NC, United States
Abstract:
The Soil Conservation Service curve number (SCS-CN) method is the standard approach in practice for predicting a storm event runoff response. It is popular because its low parametric complexity and ease of use. However, the SCS-CN method does not describe the spatial variability of runoff and is restricted to certain geographic regions and land use types. Here we present a general theory for extending the SCS-CN method. Our new theory accommodates different event based models derived from alternative rainfall-runoff mechanisms or distributions of watershed variables, which are the basis of different semi-distributed models such as VIC, PDM, and TOPMODEL. We introduce a parsimonious but flexible description where runoff is initiated by a pure threshold, i.e., saturation excess, that is complemented by fill and spill runoff behavior from areas of partial saturation. To facilitate event based runoff prediction, we derive simple equations for the fraction of the runoff source areas, the probability density function (PDF) describing runoff variability, and the corresponding average runoff value (a runoff curve analogous to the SCS-CN). The benefit of the theory is that it unites the SCS-CN method, VIC, PDM, and TOPMODEL as the same model type but with different assumptions for the spatial distribution of variables and the runoff mechanism. The new multiple runoff mechanism description for the SCS-CN enables runoff prediction in geographic regions and site runoff types previously misrepresented by the traditional SCS-CN method. In addition, we show that the VIC, PDM, and TOPMODEL runoff curves may be more suitable than the SCS-CN for different conditions. Lastly, we explore predictions of sediment and nutrient transport by applying the PDF describing runoff variability within our new framework.