A Comparison of Rainfall-Runoff Modeling Techniques on Small Upland Catchments

TLDR: In this article, a set of model performance calculations for three event-based rainfall-runoff models on three data sets involving 269 events from small upland catchments are reported.

Abstract: This paper reports a set of model performance calculations for three event-based rainfall-runoff models on three data sets involving 269 events from small upland catchments. The models include a regression model, a unit hydrograph model, and a quasi-physically based model. The catchments are from the Washita River Experimental Watershed, Oklahoma; the Mahantango Creek Experimental Watershed, Pennsylvania; and the Hubbard Brook Experimental Forest, New Hampshire. Model performance was assessed for a verification period that is carefully distinguished from the calibration period. Performance assessment was carried out both in forecasting mode and in prediction mode. The results show surprisingly poor forecasting efficiencies for all models on all data sets. The unit hydrograph model and the quasi-physically based model have little forecasting power; the regression model is marginally better. The performance of the models in prediction mode is better. The regression model and the unit hydrograph model showed acceptable predictive power, but the quasi-physically based model produced acceptable predictions on only one of the three catchments. We believe that the primary barrier to the successful application of physically based models in the field lies in the scale problems that are associated with the unmeasurable spatial variability of rainfall and soil hydraulic properties. The fact that simpler, less data intensive models provided as good or better predictions than a physically based model is food for thought.