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Using spatially distributed parameters and multi‐response objective functions to solve parameterization of complex applications of semi‐distributed hydrological models
Author(s) -
Marcé Rafael,
Ruiz Carlos E.,
Armengol Joan
Publication year - 2008
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2006wr005785
Subject(s) - temporal discretization , discretization , calibration , watershed , computer science , inversion (geology) , surface runoff , routing (electronic design automation) , function (biology) , distributed element model , mathematical optimization , hydrology (agriculture) , environmental science , mathematics , geology , statistics , structural basin , machine learning , geomorphology , geotechnical engineering , mathematical analysis , ecology , computer network , physics , evolutionary biology , quantum mechanics , biology
Application of semi‐distributed hydrological models to large, heterogeneous watersheds deals with several problems. On one hand, the spatial and temporal variability in catchment features should be adequately represented in the model parameterization, while maintaining the model complexity in an acceptable level to take advantage of state‐of‐the‐art calibration techniques. On the other hand, model complexity enhances uncertainty in adjusted model parameter values, therefore increasing uncertainty in the water routing across the watershed. This is critical for water quality applications, where not only streamflow, but also a reliable estimation of the surface versus subsurface contributions to the runoff is needed. In this study, we show how a regularized inversion procedure combined with a multiobjective function calibration strategy successfully solves the parameterization of a complex application of a water quality‐oriented hydrological model. The final value of several optimized parameters showed significant and consistent differences across geological and landscape features. Although the number of optimized parameters was significantly increased by the spatial and temporal discretization of adjustable parameters, the uncertainty in water routing results remained at reasonable values. In addition, a stepwise numerical analysis showed that the effects on calibration performance due to inclusion of different data types in the objective function could be inextricably linked. Thus caution should be taken when adding or removing data from an aggregated objective function.