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Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments
Author(s) -
Coron L.,
Andréassian V.,
Perrin C.,
Lerat J.,
Vaze J.,
Bourqui M.,
Hendrickx F.
Publication year - 2012
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 - 10.1029/2011wr011721
Subject(s) - environmental science , evapotranspiration , surface runoff , climate change , extrapolation , robustness (evolution) , calibration , snow , climatology , statistics , hydrology (agriculture) , meteorology , mathematics , geology , ecology , biochemistry , oceanography , chemistry , physics , geotechnical engineering , gene , biology
This paper investigates the actual extrapolation capacity of three hydrological models in differing climate conditions. We propose a general testing framework, in which we perform series of split‐sample tests, testing all possible combinations of calibration‐validation periods using a 10 year sliding window. This methodology, which we have called the generalized split‐sample test (GSST), provides insights into the model's transposability over time under various climatic conditions. The three conceptual rainfall‐runoff models yielded similar results over a set of 216 catchments in southeast Australia. First, we assessed the model's efficiency in validation using a criterion combining the root‐mean‐square error and bias. A relation was found between this efficiency and the changes in mean rainfall (P) but not with changes in mean potential evapotranspiration (PE) or air temperature (T). Second, we focused on average runoff volumes and found that simulation biases are greatly affected by changes in P. Calibration over a wetter (drier) climate than the validation climate leads to an overestimation (underestimation) of the mean simulated runoff. We observed different magnitudes of these models deficiencies depending on the catchment considered. Results indicate that the transfer of model parameters in time may introduce a significant level of errors in simulations, meaning increased uncertainty in the various practical applications of these models (flow simulation, forecasting, design, reservoir management, climate change impact assessments, etc.). Testing model robustness with respect to this issue should help better quantify these uncertainties.