Journal
HYDROLOGY AND EARTH SYSTEM SCIENCES
Volume 26, Issue 14, Pages 3863-3883Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/hess-26-3863-2022
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Funding
- Ministero dell'Istruzione, dell'Universita e della Ricerca, Department of Excellence [L.232/2016]
- European Commission, Horizon 2020 framework program EoCoE-II [824158]
- Provincia autonoma di Bolzano - Alto Adige (project SHE)
- European Union - FSE-REACT-EU, PON Research and Innovation 2014-2020 [DM1062/2021]
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In this study, a new method called Hydrological Calibration of eXtremes (HyCoX) is introduced, which calibrates hydrological models by maximizing the probability that the modeled and observed high streamflow extremes belong to the same statistical population. The application in the Adige River catchment using a distributed hydrological model showed that this procedure preserves statistical coherence and produces reliable quantiles of the annual maximum streamflow for assessment studies.
Climate change impact studies on hydrological extremes often rely on hydrological models with parameters inferred through calibration procedures using observed meteorological data as input forcing. We show that this procedure can lead to a biased evaluation of the probability distribution of high streamflow extremes when climate models are used. As an alternative approach, we introduce a methodology, coined Hydrological Calibration of eXtremes (HyCoX), in which the calibration of the hydrological model, as driven by climate model output, is carried out by maximizing the probability that the modeled and observed high streamflow extremes belong to the same statistical population. The application to the Adige River catchment (southeastern Alps, Italy) by means of HYPERstreamHS, a distributed hydrological model, showed that this procedure preserves statistical coherence and produces reliable quantiles of the annual maximum streamflow to be used in assessment studies.
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