Addressing Spatial Heterogeneity in the Discrete Generalized Nash Model for Flood Routing

Abstract

River flood routing is one of the key components of hydrologic modeling and the topographic heterogeneity of rivers has great effects on it. It is beneficial to take into consideration such spatial heterogeneity, especially for hydrologic routing models. The discrete generalized Nash model (DGNM) based on the Nash cascade model has the potential to address spatial heterogeneity by replacing the equal linear reservoirs into unequal ones. However, it seems impossible to obtain the solution of this complex high order differential equation directly. Alternatively, the strict mathematical derivation is combined with the deeper conceptual interpretation of the DGNM to obtain the heterogeneous DGNM (HDGNM). In this work, the HDGNM is explicitly expressed as a linear combination of the inflows and outflows, whose weight coefficients are calculated by the heterogeneous S curve. Parameters in HDGNM can be obtained in two different ways: optimization by intelligent algorithm or estimation based on physical characteristics, thus available to perform well in both gauged and ungauged basins. The HDGNM expands the application scope, and becomes more applicable, especially in river reaches where the river slopes and cross-sections change greatly. Moreover, most traditional routing models are lumped, whereas the HDGNM can be developed to be semidistributed. The middle Hanjiang River in China is selected as a case study to test the model performance. The results show that the HDGNM outperforms the DGNM in terms of model efficiency and smaller relative errors and can be used also for ungauged basins.