Uncertainty in a monthly water balance model using

Authors: Diego Rivera Yessica Rivas Alex Godoy

Publish Date: 2015/02/10

Volume: 124, Issue: 1, Pages: 49-59

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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 goodnessoffit 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 52 to 25 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 parametersThe 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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