Abstract
Modeling rainfall-runoff at watershed scale is important for water resources management, safe yield computation, and design of flood control structures. Frequent droughts and floods of 1993 in Kansas and other midwestern U.S. are testimonies to the need for good predictive models at the watershed scale. The response of a watershed to precipitation is complicated by various hydrologic components that are distributed within it in a heterogeneous manner. Watershed runoff depends on geomorphologic properties (such as topology, vegetation, soil type) of the watershed and other climatic factors (precipitation, temperature, etc.) of the region. The influence of all these factors is not understood clearly. As a consequence, there exists some skepticism in the use of physically-based models for predicting watershed runoff (Grayson et al., 1992).
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Zhang, B., Govindaraju, R.S. (2000). Modular Neural Networks for Watershed Runoff. In: Govindaraju, R.S., Rao, A.R. (eds) Artificial Neural Networks in Hydrology. Water Science and Technology Library, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9341-0_5
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DOI: https://doi.org/10.1007/978-94-015-9341-0_5
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