Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments
Identifieur interne : 002F95 ( Istex/Corpus ); précédent : 002F94; suivant : 002F96Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments
Auteurs : L. Coron ; V. Andréassian ; C. Perrin ; J. Lerat ; J. Vaze ; M. Bourqui ; F. HendrickxSource :
- Water Resources Research [ 0043-1397 ] ; 2012-05.
English descriptors
- KwdEn :
- Andreassian, Average conditions, Bias, Calibration, Calibration performance, Calibration period, Calibration periods, Catchment, Catchment scale, Chiew, Climate, Climate change, Climate change impact studies, Climate change impacts, Climate characteristics, Climate conditions, Climate difference, Climate differences, Climate variables, Climatic, Climatic characteristics, Climatic conditions, Climatic differences, Conceptual models, Coron, Csiro land, Daily data, Earth syst, Entire catchment, Free parameters, Further research, Generalized test, Gr4j, Gsst, Gsst procedure, Hydrol, Hydrological, Hydrological models, Interannual, Interannual variability, Large number, Mathevet, Median, Median bias, Merz, Methodology, Model calibration, Model error, Model parameters, Model performance, Model robustness, Mordor6, Ndings, Objective function, Optimized, Oudin, Overview, Parameter, Parameter dependency, Parameter sets, Parameter transfer, Parameter transferability, Parameter transfers, Parameter values, Percentile, Performance loss, Performance losses, Potential evapotranspiration, Rainfall, Resour, Rmse, Robustness, Robustness loss, Runoff, Simhyd, Simulation, Simulation bias, Subperiods, Teng, Testing hydrological models, Time series, Transferability, Validation, Validation bias, Validation periods, Variability, Various conditions, Vaze, Water budget, Water resour, Wetter, Wide range, Year periods.
- Teeft :
- Andreassian, Average conditions, Bias, Calibration, Calibration performance, Calibration period, Calibration periods, Catchment, Catchment scale, Chiew, Climate, Climate change, Climate change impact studies, Climate change impacts, Climate characteristics, Climate conditions, Climate difference, Climate differences, Climate variables, Climatic, Climatic characteristics, Climatic conditions, Climatic differences, Conceptual models, Coron, Csiro land, Daily data, Earth syst, Entire catchment, Free parameters, Further research, Generalized test, Gr4j, Gsst, Gsst procedure, Hydrol, Hydrological, Hydrological models, Interannual, Interannual variability, Large number, Mathevet, Median, Median bias, Merz, Methodology, Model calibration, Model error, Model parameters, Model performance, Model robustness, Mordor6, Ndings, Objective function, Optimized, Oudin, Overview, Parameter, Parameter dependency, Parameter sets, Parameter transfer, Parameter transferability, Parameter transfers, Parameter values, Percentile, Performance loss, Performance losses, Potential evapotranspiration, Rainfall, Resour, Rmse, Robustness, Robustness loss, Runoff, Simhyd, Simulation, Simulation bias, Subperiods, Teng, Testing hydrological models, Time series, Transferability, Validation, Validation bias, Validation periods, Variability, Various conditions, Vaze, Water budget, Water resour, Wetter, Wide range, Year periods.
Abstract
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.
Url:
DOI: 10.1029/2011WR011721
Links to Exploration step
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<front><div type="abstract">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.</div>
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<abstractGroup><abstract type="main"><p xml:id="wrcr13439-para-0001" label="1">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.</p>
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<affiliation>Hydrosystems and Bioprocesses Research Unit,Irstea, Antony,France</affiliation>
<affiliation>E-mail: laurent.coron@edf.fr</affiliation>
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<abstract>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.</abstract>
<abstract type="short">Proposing methods to analyse model robustness in contrasted climate conditions Quantifying robustness losses due to climate change in parameters transfer Estimating homogeneity of results over a large number of catchments</abstract>
<note type="additional physical form">Tab‐delimited Table 1.Tab‐delimited Table 2.</note>
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<topic authorityURI="http://psi.agu.org/taxonomy5/1846">Hydrology: Model calibration</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/3333">Atmospheric Processes: Model calibration</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/1847">Hydrology: Modeling</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/1952">Informatics: Modeling</topic>
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<identifier type="ISSN">0043-1397</identifier>
<identifier type="eISSN">1944-7973</identifier>
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<part><date>2012</date>
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