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Distributed Hydrological Models

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Handbook of Hydrometeorological Ensemble Forecasting

Abstract

Physically based distributed hydrological models (PBDHMs), the development of which has been facilitated by advancements in GIS and remote sensing, meteorology, computer science and engineering, and other related science and engineering disciplines, divide the terrain of a basin into fine-resolution cells and calculate the hydrological processes at both the cell and basin scales. Numerous PBDHMs have been proposed. Because PBDHMs can model hydrological processes at a fine resolution and physically derive model parameters from the properties of the terrain, they have the potential to simulate/predict hydrologic processes more effectively, and they can be employed within ungauged basins. Following a brief review of the development of PBDHMs, this chapter introduces the general structures and methodologies of currently utilized PBDHMs. The basin division method, the sources of terrain property data used to construct PBDHMs, and the flow network delineation method are summarized, and the hydrological processes within watersheds, including interception, evapotranspiration, runoff formation and movement, and runoff routing, are discussed. The methodologies most commonly employed by PBDHMs are then introduced, including those used to calculate the interception, evaporation, runoff formation, and runoff routing. Parameter determination methods are discussed, three of which are introduced in detail: the physically based method, the scalar method, and the automated optimization method. Finally, a case study is presented that demonstrates the entire procedure of constructing a Liuxihe model for a river basin flood simulation/prediction to provide the reader with a complete example of the application of a PBDHM to a real-world problem.

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Correspondence to Yangbo Chen .

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Chen, Y. (2018). Distributed Hydrological Models. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40457-3_23-1

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  • DOI: https://doi.org/10.1007/978-3-642-40457-3_23-1

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