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Title of Journal: Stoch Environ Res Risk Assess

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Abbravation: Stochastic Environmental Research and Risk Assessment

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Springer Berlin Heidelberg

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DOI

10.1002/pssa.200306161

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1436-3259

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A method to reduce the computational requirement w

Authors: P Athira K P Sudheer
Publish Date: 2014/10/10
Volume: 29, Issue: 3, Pages: 847-859
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Abstract

The quantification of uncertainty in the simulations from complex physically based distributed hydrologic models is important for developing reliable applications The generalized likelihood uncertainty estimation method GLUE is one of the most commonly used methods in the field of hydrology The GLUE helps reduce the parametric uncertainty by deriving the probability distribution function of parameters and help analyze the uncertainty in model output In the GLUE the uncertainty of model output is analyzed through Monte Carlo simulations which require large number of model runs This induces high computational demand for the GLUE to characterize multidimensional parameter space especially in the case of complex hydrologic models with large number of parameters While there are a lot of variants of GLUE that derive the probability distribution of parameters none of them have addressed the computational requirement in the analysis A method to reduce such computational requirement for GLUE is proposed in this study It is envisaged that conditional sampling while generating ensembles for the GLUE can help reduce the number of model simulations The mutual relationship between the parameters was used for conditional sampling in this study The method is illustrated using a case study of Soil and Water Assessment Tool SWAT model on a watershed in the USA The number of simulations required for the uncertainty analysis was reduced by 90  in the proposed method compared to existing methods The proposed method also resulted in an uncertainty reduction in terms of reduced average band width and high containing ratio

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