Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-5031-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-5031-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A comprehensive uncertainty framework for historical flood frequency analysis: a 500-year-long case study
Mathieu Lucas
UR RiverLy, INRAE, Villeurbanne, France
UR RiverLy, INRAE, Villeurbanne, France
Benjamin Renard
UR RECOVER, Aix Marseille Univ., INRAE, Aix-en-Provence, France
Jérôme Le Coz
UR RiverLy, INRAE, Villeurbanne, France
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We present smash, an open-source framework for high-resolution hydrological modeling and data assimilation. It combines process-based models with neural networks for regionalization, enabling accurate simulations from the catchment scale to the country scale. With an efficient, differentiable solver, smash supports large-scale calibration and parallel computing. Tested on open datasets, it shows strong performance in river flow prediction, making it a valuable tool for research and operational use.
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Understanding and modeling flash-flood-prone areas remains challenging due to limited data and scale-relevant hydrological theory. While machine learning shows promise, its integration with process-based models is difficult. We present an approach incorporating machine learning into a high-resolution hydrological model to correct internal fluxes and transfer parameters between watersheds. Results show improved accuracy, advancing the development of learnable and interpretable process-based models.
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Suspended-sand fluxes in rivers vary with time and space, complicating their measurement. The proposed method captures the vertical and lateral variations of suspended-sand concentration throughout a river cross-section. It merges water samples taken at various positions throughout the cross-section with high-resolution acoustic velocity measurements. This is the first method that includes a fully applicable uncertainty estimation; it can easily be applied to any other study sites.
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Mediterranean floods are causing major damage, and recent studies have shown that, despite the increase in intense rainfall, there has been no increase in river floods. This study reveals that the seasonality of floods changed in the Mediterranean Basin during 1959–2021. There was also an increased frequency of floods linked to short episodes of intense rain, associated with a decrease in soil moisture. These changes need to be taken into consideration to adapt flood warning systems.
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The dataset contains concentrations and fluxes of suspended particle matter (SPM) and several particle-bound contaminants along the Rhône River downstream of Lake Geneva. These data allow us to understand the dynamics and origins. They show the impact of flood events which mainly contribute to a decrease in the contaminant concentrations while fluxes are significant. On the contrary, concentrations are higher during low flow periods probably due to the increase of organic matter.
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Floods in Europe vary on time scales of several decades. Flood-rich and flood-poor periods alternate. Recently floods have again become more frequent. Long time series of peak stream flow, precipitation, and atmospheric variables reveal that until around 1980, these changes were mostly due to changes in atmospheric circulation. However, in recent decades the role of increasing atmospheric moisture due to climate warming has become more important and is now the main driver of flood changes.
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Short summary
The proposed flood frequency model accounts for uncertainty in the perception threshold S and the starting date of the historical period. Using a 500-year-long case study, inclusion of historical floods reduces the uncertainty in flood quantiles, even when only the number of exceedances of S is known. Ignoring threshold uncertainty leads to underestimated flood quantile uncertainty. This underlines the value of using a comprehensive framework for uncertainty estimation.
The proposed flood frequency model accounts for uncertainty in the perception threshold S and...