Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological Response

Abstract The robust estimation of flood peak discharge values is critical for designing mitigation measures and increasing preparedness to natural hazards. Traditional flood estimation methods are, however, severely limited by data series shorter than the return period of interest, as they only use...

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Main Authors: Pietro Devò, Stefano Basso, Marco Marani
Format: Article
Language:English
Published: Wiley 2025-05-01
Series:Water Resources Research
Subjects:
Online Access:https://doi.org/10.1029/2024WR038667
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author Pietro Devò
Stefano Basso
Marco Marani
author_facet Pietro Devò
Stefano Basso
Marco Marani
author_sort Pietro Devò
collection DOAJ
description Abstract The robust estimation of flood peak discharge values is critical for designing mitigation measures and increasing preparedness to natural hazards. Traditional flood estimation methods are, however, severely limited by data series shorter than the return period of interest, as they only use annual maxima or a few values above a high threshold. Here we couple two recent advances in flood estimation from short data samples, namely the Metastatistical Extreme Value Distribution (MEVD) and a conceptual model of flood generation processes, the Physically based Extreme Value (PhEV) distribution of river flows. The result is a methodology, defined through a few hydrologic attributes describing runoff generation and catchment response, to estimate extreme discharge in poorly gauged basins, which we test on data from 178 catchments in Germany. We find that extremes are best estimated when PhEV runoff‐generation parameters are set using the long‐term mean discharge and precipitation depth, while catchment response parameters are estimated by statistical fitting to observed peak streamflow values. This estimation method interestingly outperforms a methodology in which all parameters are tuned to optimize the reproduction of the statistics of observed peak streamflow values. Our results show that the median relative error associated with MEVD‐PhEV, across the large data set explored, consistently remains between −50% and +50%. Hence, MEVD‐PhEV yields useful estimates of extreme flows with limited observational information and with no need of preselecting a suitable distribution for ordinary peak discharge values, a step that is substituted by the inclusion of catchment hydrologic information.
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spelling doaj-art-b95fdda7b79544b3828929edd6e16f5a2025-08-20T03:22:20ZengWileyWater Resources Research0043-13971944-79732025-05-01615n/an/a10.1029/2024WR038667Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological ResponsePietro Devò0Stefano Basso1Marco Marani2Department of Civil, Environmental and Architectural Engineering University of Padua Padua ItalyDepartment of Catchment Hydrology Helmholtz Center for Environmental Research ‐ UFZ Halle (Saale) GermanyDepartment of Civil, Environmental and Architectural Engineering University of Padua Padua ItalyAbstract The robust estimation of flood peak discharge values is critical for designing mitigation measures and increasing preparedness to natural hazards. Traditional flood estimation methods are, however, severely limited by data series shorter than the return period of interest, as they only use annual maxima or a few values above a high threshold. Here we couple two recent advances in flood estimation from short data samples, namely the Metastatistical Extreme Value Distribution (MEVD) and a conceptual model of flood generation processes, the Physically based Extreme Value (PhEV) distribution of river flows. The result is a methodology, defined through a few hydrologic attributes describing runoff generation and catchment response, to estimate extreme discharge in poorly gauged basins, which we test on data from 178 catchments in Germany. We find that extremes are best estimated when PhEV runoff‐generation parameters are set using the long‐term mean discharge and precipitation depth, while catchment response parameters are estimated by statistical fitting to observed peak streamflow values. This estimation method interestingly outperforms a methodology in which all parameters are tuned to optimize the reproduction of the statistics of observed peak streamflow values. Our results show that the median relative error associated with MEVD‐PhEV, across the large data set explored, consistently remains between −50% and +50%. Hence, MEVD‐PhEV yields useful estimates of extreme flows with limited observational information and with no need of preselecting a suitable distribution for ordinary peak discharge values, a step that is substituted by the inclusion of catchment hydrologic information.https://doi.org/10.1029/2024WR038667hydrologycatchment hydrologyextreme streamflow/discharge
spellingShingle Pietro Devò
Stefano Basso
Marco Marani
Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological Response
Water Resources Research
hydrology
catchment hydrology
extreme streamflow/discharge
title Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological Response
title_full Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological Response
title_fullStr Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological Response
title_full_unstemmed Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological Response
title_short Estimation of Extreme Floods Using a Statistical and Conceptual Model of the Hydrological Response
title_sort estimation of extreme floods using a statistical and conceptual model of the hydrological response
topic hydrology
catchment hydrology
extreme streamflow/discharge
url https://doi.org/10.1029/2024WR038667
work_keys_str_mv AT pietrodevo estimationofextremefloodsusingastatisticalandconceptualmodelofthehydrologicalresponse
AT stefanobasso estimationofextremefloodsusingastatisticalandconceptualmodelofthehydrologicalresponse
AT marcomarani estimationofextremefloodsusingastatisticalandconceptualmodelofthehydrologicalresponse