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Conference Paper

Flood tailored regional rainfall-runoff modelling with stochastic discharge ensemble generation

Authors

Lombardo,  Luca
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Pesce,  Matteo
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Cafiero,  Luigi
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Terasova,  Larisa
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Claps,  Pierluigi
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Vogel,  Richard
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Papalexiou,  Simon
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Merz,  Ralf
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Viglione,  Alberto
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Lombardo, L., Pesce, M., Cafiero, L., Terasova, L., Claps, P., Vogel, R., Papalexiou, S., Merz, R., Viglione, A. (2023): Flood tailored regional rainfall-runoff modelling with stochastic discharge ensemble generation, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3619


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020923
Abstract
As our climate system climbs through its current warming path, temperature and precipitation are greatly affected also in their extremes. There is a general concern that climate change may affect also the magnitude and frequency of river floods and that existing and planned hydraulic structures and flood defenses may become inadequate to provide the required protection level in the future. At the same time, land-use changes and river training also have affected, are affecting, and will affect river floods in the future. Deterministic rainfall-runoff models are used in nearly all hydrologic planning, design, and management activities, and in theory can account for changes in forcing and processes due to climate and environmental change. Yet they are not easily transferred to ungauged locations, and they cannot properly reproduce extreme design quantiles obtained from commonly accepted statistical methods. We aim at coupling a newly developed methodology for regional model parameter calibration based on machine learning techniques, which allow for tuning models in a distributed way over large areas, to a post-processing approach that adds a stochastic error to the deterministic model predictions and reduces the bias in the estimation of extreme discharges associated to low exceedance probabilities. The research questions are:How well can the stochastic post-processing of the outputs of rainfall-runoff models, designed to reproduce everyday river flow dynamics, reproduce the statistics of extreme events obtainable through flood frequency analysis?How well regionally distributed rainfall-runoff models with post-processed outputs compare to regional flood frequency analysis outcomes?Preliminary results will be discussed during the presentation.