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Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology

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Abstract

Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. Different sets of parameters could have equally robust goodness-of-fit indicators, which is known as Equifinality. We assessed the outputs from a lumped conceptual hydrological model to an agricultural watershed in central Chile under strong interannual variability (coefficient of variability of 25%) by using the Equifinality concept and uncertainty bounds. The simulation period ran from January 1999 to December 2006. Equifinality and uncertainty bounds from GLUE methodology (Generalized Likelihood Uncertainty Estimation) were used to identify parameter sets as potential representations of the system. The aim of this paper is to exploit the use of uncertainty bounds to differentiate behavioural parameter sets in a simple hydrological model. Then, we analyze the presence of equifinality in order to improve the identification of relevant hydrological processes. The water balance model for Chillan River exhibits, at a first stage, equifinality. However, it was possible to narrow the range for the parameters and eventually identify a set of parameters representing the behaviour of the watershed (a behavioural model) in agreement with observational and soft data (calculation of areal precipitation over the watershed using an isohyetal map). The mean width of the uncertainty bound around the predicted runoff for the simulation period decreased from 50 to 20 m3s−1 after fixing the parameter controlling the areal precipitation over the watershed. This decrement is equivalent to decreasing the ratio between simulated and observed discharge from 5.2 to 2.5. Despite the criticisms against the GLUE methodology, such as the lack of statistical formality, it is identified as a useful tool assisting the modeller with the identification of critical parameters.

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Acknowledgements

The BFA model was coded in MATLAB by Dr© FulvioRivano and Leonardo Vega. Codes and datasets are available on request. This research was benefited from FONDECYT Grant Nos. 11090032 and 1110298. We thank Dr Claire Baffaut and Dr José Luis Arumí for their comments and suggestions. Thanks to Roto Quezada.

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Authors and Affiliations

  1. Laboratory of Comparative Policy in Water Resources Management, University of Concepcion, CONICYT/FONDAP, 15130015, Concepcion, Chile

    Diego Rivera

  2. Department of Water Resources, Faculty of Agricultural Engineering, University of Concepcion, Concepcion, Chile

    Yessica Rivas

  3. Facultad de Ingeniería, Universidad del Desarroll, Concepcion, Chile

    Alex Godoy

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  1. Diego Rivera
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  2. Yessica Rivas
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  3. Alex Godoy
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Correspondence to Diego Rivera.

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Rivera, D., Rivas, Y. & Godoy, A. Uncertainty in a monthly water balance model using the generalized likelihood uncertainty estimation methodology. J Earth Syst Sci 124, 49–59 (2015). https://doi.org/10.1007/s12040-014-0528-7

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  • DOI: https://doi.org/10.1007/s12040-014-0528-7

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