| Abstract: | ABSTRACT: Understanding the effects of climate change on water resources requires coupling atmospheric and hydrologic models. With the wide array of hydrologic models, from simple empirical to complex physically based, it is not clear which is preferable to simulate hydrologic variations over long time scales. To address this issue, a black-box artificial neural network (ANN) model was compared to a distributed parameter conceptual Geographic Information System based Hydrologic Modeling System (GIS-HMS). Both models computed daily direct surface runoff in four sub-basins of the West Branch of the Susquehanna River Basin, Pennsylvania and were evaluated with five objective functions. Overall, results were comparable between models. However, the ANN was favored in the larger sub-basins, while GIS-HMS was more accurate in the smaller catchments. Both models were impaired by the poor spatial and temporal resolution of precipitation data and the simplified representation of antecedent soil-moisture conditions. In the context of climate change, where simulations are limited by computing power, results suggest that both models are appropriate. When detailed simulations are essential, GIS-HMS is a preferable model to use. On the other hand, the ANN model is more suitable when multiple scenarios require immediate analysis and the distributed qualities of runoff are not required. |
