Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment |
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| Authors: | Haykel Sellami Sihem Benabdallah Isabelle La Jeunesse Marnik Vanclooster |
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| Institution: | 1. Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgiumhaysellami@yahoo.fr;3. Centre de Recherches et des Technologies des Eaux, Technopole Borj Cedria, Soliman, Tunisia;4. UMR 6173 CITERES, Université de Tours, Tours, France;5. Faculty of Sciences, LETG-Angers LEESA UMR 6554 CNRS, Université de Angers, Angers, France;6. Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium |
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| Abstract: | ABSTRACTClimate models and hydrological parameter uncertainties were quantified and compared while assessing climate change impacts on monthly runoff and daily flow duration curve (FDC) in a Mediterranean catchment. Simulations of the Soil and Water Assessment Tool (SWAT) model using an ensemble of behavioural parameter sets derived from the Generalized Likelihood Uncertainty Estimation (GLUE) method were approximated by feed-forward artificial neural networks (FF-NN). Then, outputs of climate models were used as inputs to the FF-NN models. Subsequently, projected changes in runoff and FDC were calculated and their associated uncertainty was partitioned into climate model and hydrological parameter uncertainties. Runoff and daily discharge of the Chiba catchment were expected to decrease in response to drier and warmer climatic conditions in the 2050s. For both hydrological indicators, uncertainty magnitude increased when moving from dry to wet periods. The decomposition of uncertainty demonstrated that climate model uncertainty dominated hydrological parameter uncertainty in wet periods, whereas in dry periods hydrological parametric uncertainty became more important. Editor M.C. Acreman; Associate editor S. Kanae |
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| Keywords: | model parameter SWAT climate models uncertainty climate change impacts artificial neural network |
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