Identifying dominant runoff mechanisms and their lumped modeling: a data-based modeling approach

Abstract

The authors developed a methodology for identifying dominant runoff mechanisms of a watershed and their lumped modeling as a data-based modeling approach with precipitation and runoff data which would contribute to the reduction of uncertainties in both the model structure and the model parameter. We firstly separated a hydrograph into several runoff components by a recession analysis of runoff data and a filter separation method. Secondly, we estimated storage as a function of runoff for each component. Finally, we constructed a single Tank model for each component, where both the runoff component and the estimated storage were used as constraint conditions in identifying coefficients of runoff and infiltration. By applying this approach, we found that (1) the constructed Tank model perfectly traced the runoff components separated by the filter separation method, (2) there are almost no uncertainties in the model structure and the parameter if the result of filter separation can be assumed to be reliable, and (3) we can even estimate effective rainfall with our approach. These results imply our methodology allows identifying and modeling dominant rainfall-storage-runoff mechanisms with minimal uncertainties in model structure and parameter, using hourly precipitation and runoff data alone.