Performance of Loss Models for Predicting Flood Hydrographs in a Semiarid Watershed with Limited Observations Using Deterministic and Probabilistic Hydrologic Models

Prediction of flood hydrographs in semiarid regions is a complex task due to limited rainfall-runoff observations. The application of complex loss models that have intensive input data requirements can be impractical for such regions. The performance of three loss models, namely, initial and constant rate (IC), Soil Conservation Service (SCS), and constant fraction (CF), in prediction of flood events in a 37.2-km2 semiarid watershed was evaluated using deterministic and probabilistic hydrologic models. We quantified the performance in terms of bias, error, and correlation via relative error (RE), Nash-Sutcliff efficiency (NSE), and percent bias (PBIAS) for 14 events with dry prestorm conditions and a range of rainfall properties (duration, depth, and temporal pattern) and runoff characteristics (peak, volume, and time to peak). The NSE values of the deterministic model ranged from 0.61 to 0.90 and -0.50 to 0.63 for calibration and validation, respectively, in the best model (IC). The results suggest that the performance of loss models was inconsistent in terms of hydrograph attributes. The IC model was best in terms of peak flow according to both deterministic and probabilistic models and best in terms of volume according to the deterministic model, but similar to SCS and better than the CF based on the probabilistic model. The CF model mostly underestimated the runoff volume and peak flow. The performances of the loss models were almost identical in the prediction of the time to peak. These results suggested that deterministic models may be insufficient for selecting the best loss models. Probabilistic models, incorporating the parametric uncertainty, are needed to further evaluate the performance of loss models. There was no correlation between the performance of models and the size of events. Rainfall temporal pattern was found to be an effective factor in the accuracy of flood hydrology predictions. The results can guide the selection of loss models in semiarid watersheds.