Sensitivity of Conceptual and Physically Based Hydrologic Models to Temporal and Spatial Rainfall Sampling

This study investigated the impact of temporal and spatial sampling of rainfall on runoff predictions using a physically based [System Hydrologique European (MIKE SHE)] and conceptual [hydrologic modeling system (HMS)] hydrologic models. The numerical models were applied to Goodwin Creek watershed in northern Mississippi. The drainage area of Goodwin Creek is approximately 21.4 km(2). This study showed that MIKE SHE was more sensitive to both the spatial and the temporal samplings of rainfall than the HMS. The study also showed that errors introduced by coarse sampling scenarios can be significant. Overall, for this particular watershed size, increasing the rain gauge density from 1 to 2 resulted in the most significant improvement for both models. Similarly, a temporal sampling frequency beyond 1 h showed significant deterioration in the quality of the runoff prediction. The combined spatial-temporal sampling experiment showed that increasing the temporal sampling compensates, at least partially, for the loss of rainfall spatial information. It also showed that under poor temporal sampling frequency, the gain in model performance by increasing the spatial sampling density is negligible.