Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation

International audience Abstract. Recent advancements in catchment hydrology (such as understanding catchment similarity, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cu...

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Bibliographic Details
Published in:Hydrology and Earth System Sciences
Main Authors: Arheimer, Berit, Pimentel, Rafael, Isberg, Kristina, Crochemore, Louise, Andersson, Jafet, Hasan, Abdulghani, Pineda, Luis
Other Authors: Swedish Meteorological and Hydrological Institute (SMHI), Universidad de Córdoba = University of Córdoba Córdoba, Swedish Meteorological and Hydrological Institute (SMHI), Hydrology Research, Norrköping, Hydrosystèmes et Bioprocédés (UR HBAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), RiverLy - Fonctionnement des hydrosystèmes (RiverLy), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Lund University, Yachay Tech University
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2020
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Online Access:https://hal.science/hal-04670900
https://hal.science/hal-04670900/document
https://hal.science/hal-04670900/file/hess-24-535-2020.pdf
https://doi.org/10.5194/hess-24-535-2020
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Summary:International audience Abstract. Recent advancements in catchment hydrology (such as understanding catchment similarity, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques with evaluation against river flow at the global scale for the first time. The modelling procedure was challenging but doable, and even the first model version showed better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for >130 000 catchments (with an average resolution of 1000 km2), delineated to cover the Earth's landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, and the pathways between various compartments. Global parameter values were estimated using a stepwise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily and monthly time series (>10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, the World-Wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best (KGE >0.6) in the eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly ...