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How evaluation of hydrological models influences results of climate impact assessment - an editorial

Urheber*innen
/persons/resource/Valentina.Krysanova

Krysanova,  Valentina
Potsdam Institute for Climate Impact Research;

/persons/resource/Fred.Hattermann

Hattermann,  Fred Fokko
Potsdam Institute for Climate Impact Research;

/persons/resource/zbyszek

Kundzewicz,  Zbigniew W.
Potsdam Institute for Climate Impact Research;

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24710oa.pdf
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Zitation

Krysanova, V., Hattermann, F. F., Kundzewicz, Z. W. (2020): How evaluation of hydrological models influences results of climate impact assessment - an editorial. - Climatic Change, 163, 3, 1121-1141.
https://doi.org/10.1007/s10584-020-02927-8


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_24710
Zusammenfassung
This paper introduces the Special Issue (SI) “How evaluation of hydrological models influences results of climate impact assessment.” The main objectives were as follows: (a) to test a comprehensive model calibration/validation procedure, consisting of five steps, for regional-scale hydrological models; (b) to evaluate performance of global-scale hydrological models; and (c) to reveal whether the calibration/validation methods and the model evaluation results influence climate impacts in terms of the magnitude of the change signal and the uncertainty range. Here, we shortly describe the river basins and large regions used as case studies; the hydrological models, data, and climate scenarios used in the studies; and the applied approaches for model evaluation and for analysis of projections for the future. After that, we summarize the main findings. The following general conclusions could be drawn. After successful comprehensive calibration and validation, the regional-scale models are more robust and their projections for the future differ from those of the model versions after the conventional calibration and validation. Therefore, climate impacts based on the former models are more trustworthy than those simulated by the latter models. Regarding the global-scale models, using only models with satisfactory or good performance on historical data and weighting them based on model evaluation results is a more reliable approach for impact assessment compared to the ensemble mean approach that is commonly used. The former method provides impact results with higher credibility and reduced spreads in comparison to the latter approach. The studies for this SI were performed in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).