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” (Hy-CoX), 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.

Analysis of High Streamflow Extremes in Climate Change Studies: How Do We Calibrate Hydrological Models? / Majone, Bruno; Avesani, Diego; Zulian, Patrick; Fiori, Aldo; Bellin, Alberto. - In: HYDROLOGY AND EARTH SYSTEM SCIENCES. - ISSN 1607-7938. - 26:14(2022), pp. 3863-3883. [10.5194/hess-26-3863-2022]

Analysis of High Streamflow Extremes in Climate Change Studies: How Do We Calibrate Hydrological Models?

Majone, Bruno;Avesani, Diego;Bellin, Alberto
2022-01-01

Abstract

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” (Hy-CoX), 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.
2022
14
Majone, Bruno; Avesani, Diego; Zulian, Patrick; Fiori, Aldo; Bellin, Alberto
Analysis of High Streamflow Extremes in Climate Change Studies: How Do We Calibrate Hydrological Models? / Majone, Bruno; Avesani, Diego; Zulian, Patrick; Fiori, Aldo; Bellin, Alberto. - In: HYDROLOGY AND EARTH SYSTEM SCIENCES. - ISSN 1607-7938. - 26:14(2022), pp. 3863-3883. [10.5194/hess-26-3863-2022]
File in questo prodotto:
File Dimensione Formato  
2022_HESS_Majone_et_al_HyCoX.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 5.65 MB
Formato Adobe PDF
5.65 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/351400
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 30
  • ???jsp.display-item.citation.isi??? 29
  • OpenAlex ND
social impact