Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-247-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-247-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jan 2025
Research article |
| 16 Jan 2025
| 16 Jan 2025
The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context
Maria Staudinger
CORRESPONDING AUTHOR
Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
Martina Kauzlaric
Mobiliar Lab for Natural Risks, University of Bern, 3012 Bern, Switzerland
Alexandre Mas
Univ. Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, Grenoble, 38000, France
Guillaume Evin
Univ. Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, Grenoble, 38000, France
Benoit Hingray
Univ. Grenoble Alpes, INRAE, CNRS, IRD, Grenoble INP, IGE, Grenoble, 38000, France
Daniel Viviroli
Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
Related authors
Daniel Viviroli, Martin Jury, Maria Staudinger, Martina Kauzlaric, Heimo Truhez, and Douglas Maraun
EGUsphere, https://doi.org/10.5194/egusphere-2025-1920, https://doi.org/10.5194/egusphere-2025-1920, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Estimating the frequency and magnitude of floods is challenging due to the limited length of streamflow records. Here, we explore whether an extensive archive of meteorological forecasts run over past dates can assist in this context. After processing and concatenating these data for use as input to a hydrological model, we derive flood statistics from simulated streamflow. Results are promising for the larger catchments studied, providing a valuable complementary perspective on rare floods.
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2025-1076, https://doi.org/10.5194/egusphere-2025-1076, 2025
Short summary
Short summary
Four process-based and four data-driven hydrological models are compared using different training data. We found process-based models to perform better with small data sets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Daniel Viviroli, Anna E. Sikorska-Senoner, Guillaume Evin, Maria Staudinger, Martina Kauzlaric, Jérémy Chardon, Anne-Catherine Favre, Benoit Hingray, Gilles Nicolet, Damien Raynaud, Jan Seibert, Rolf Weingartner, and Calvin Whealton
Nat. Hazards Earth Syst. Sci., 22, 2891–2920, https://doi.org/10.5194/nhess-22-2891-2022, https://doi.org/10.5194/nhess-22-2891-2022, 2022
Short summary
Short summary
Estimating the magnitude of rare to very rare floods is a challenging task due to a lack of sufficiently long observations. The challenge is even greater in large river basins, where precipitation patterns and amounts differ considerably between individual events and floods from different parts of the basin coincide. We show that a hydrometeorological model chain can provide plausible estimates in this setting and can thus inform flood risk and safety assessments for critical infrastructure.
Maria Staudinger, Stefan Seeger, Barbara Herbstritt, Michael Stoelzle, Jan Seibert, Kerstin Stahl, and Markus Weiler
Earth Syst. Sci. Data, 12, 3057–3066, https://doi.org/10.5194/essd-12-3057-2020, https://doi.org/10.5194/essd-12-3057-2020, 2020
Short summary
Short summary
The data set CH-IRP provides isotope composition in precipitation and streamflow from 23 Swiss catchments, being unique regarding its long-term multi-catchment coverage along an alpine–pre-alpine gradient. CH-IRP contains fortnightly time series of stable water isotopes from streamflow grab samples complemented by time series in precipitation. Sampling conditions, catchment and climate information, lab standards and errors are provided together with areal precipitation and catchment boundaries.
Michael Stoelzle, Maria Staudinger, Kerstin Stahl, and Markus Weiler
Proc. IAHS, 383, 43–50, https://doi.org/10.5194/piahs-383-43-2020, https://doi.org/10.5194/piahs-383-43-2020, 2020
Short summary
Short summary
The role of recharge and catchment storage is crucial to understand streamflow drought sensitivity. Here we introduce a model experiment with recharge stress tests as complement to climate scenarios to quantify the streamflow drought sensitivities of catchments in Switzerland. We identified a pre-drought period of 12 months as maximum storage-memory for the study catchments. From stress testing, we found up to 200 days longer summer streamflow droughts and minimum flow reductions of 50 %–80 %.
Guillaume Evin, Benoit Hingray, Guillaume Thirel, Agnès Ducharne, Laurent Strohmenger, Lola Corre, Yves Tramblay, Jean-Philippe Vidal, Jérémie Bonneau, François Colleoni, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Peng Huang, Matthieu Le Lay, Claire Magand, Paola Marson, Céline Monteil, Simon Munier, Alix Reverdy, Jean-Michel Soubeyroux, Yoann Robin, Jean-Pierre Vergnes, Mathieu Vrac, and Eric Sauquet
EGUsphere, https://doi.org/10.5194/egusphere-2025-2727, https://doi.org/10.5194/egusphere-2025-2727, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Explore2 provides hydrological projections for 1,735 French catchments. Using QUALYPSO, this study assesses uncertainties, including internal variability. By the end of the century, low flows are projected to decline in southern France under high emissions, while other indicators remain uncertain. Emission scenarios and regional climate models are key uncertainty sources. Internal variability is often as large as climate-driven changes.
Daniel Viviroli, Martin Jury, Maria Staudinger, Martina Kauzlaric, Heimo Truhez, and Douglas Maraun
EGUsphere, https://doi.org/10.5194/egusphere-2025-1920, https://doi.org/10.5194/egusphere-2025-1920, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Estimating the frequency and magnitude of floods is challenging due to the limited length of streamflow records. Here, we explore whether an extensive archive of meteorological forecasts run over past dates can assist in this context. After processing and concatenating these data for use as input to a hydrological model, we derive flood statistics from simulated streamflow. Results are promising for the larger catchments studied, providing a valuable complementary perspective on rare floods.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
EGUsphere, https://doi.org/10.5194/egusphere-2025-1779, https://doi.org/10.5194/egusphere-2025-1779, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Traditional precipitation analyses often misrepresent intense rainfall's spatial variability. This study evaluates different spatial covariances to capture this variability in a geostatistical framework. The best covariance includes anisotropy derived from daily climate model simulations, offering a reliable alternative to anisotropy estimation using rain gauges. These findings highlight the importance of including anisotropy when generating precipitation inputs for hydrological modeling.
Elisa Kamir, Samuel Morin, Guillaume Evin, Penelope Gehring, Bodo Wichura, and Ali Nadir Arslan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-225, https://doi.org/10.5194/essd-2025-225, 2025
Preprint under review for ESSD
Short summary
Short summary
This article describes a dataset of annual snow depth maximum across Europe, from 1961 to 2015, based on a regional reanalysis. It evaluates the performance of the dataset, against in-situ snow depth observations. This dataset is found to perform well in most environments, with challenges at high elevation and some coastal areas. Assessing the quality of this dataset is necessary in order to use it as a baseline to infer future changes of extreme snow loads under climate change.
Eric Sauquet, Guillaume Evin, Sonia Siauve, Ryma Aissat, Patrick Arnaud, Maud Bérel, Jérémie Bonneau, Flora Branger, Yvan Caballero, François Colléoni, Agnès Ducharne, Joël Gailhard, Florence Habets, Frédéric Hendrickx, Louis Héraut, Benoît Hingray, Peng Huang, Tristan Jaouen, Alexis Jeantet, Sandra Lanini, Matthieu Le Lay, Claire Magand, Louise Mimeau, Céline Monteil, Simon Munier, Charles Perrin, Olivier Robelin, Fabienne Rousset, Jean-Michel Soubeyroux, Laurent Strohmenger, Guillaume Thirel, Flore Tocquer, Yves Tramblay, Jean-Pierre Vergnes, and Jean-Philippe Vidal
EGUsphere, https://doi.org/10.5194/egusphere-2025-1788, https://doi.org/10.5194/egusphere-2025-1788, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
The Explore2 project has provided an unprecedented set of hydrological projections in terms of the number of hydrological models used and the spatial and temporal resolution. The results have been made available through various media. Under the high-emission scenario, the hydrological models mostly agree on the decrease in seasonal flows in the south of France, confirming its hotspot status, and on the decrease in summer flows throughout France, with the exception of the northern part of France.
Yves Tramblay, Guillaume Thirel, Laurent Strohmenger, Guillaume Evin, Lola Corre, Louis Heraut, and Eric Sauquet
EGUsphere, https://doi.org/10.5194/egusphere-2025-1635, https://doi.org/10.5194/egusphere-2025-1635, 2025
Short summary
Short summary
How climate change impacts floods in France? Using simulations for 3000 rivers in climate projections, results show that flood trends vary depending on the region. In the north, floods may become more severe, but in many other areas, the trends are mixed. Floods from intense rainfall are becoming more frequent, while snowmelt floods are strongly decreasing. Overall, the study shows that understanding what causes floods is key to predicting how they are likely to change with the climate.
Carlo Destouches, Arona Diedhiou, Sandrine Anquetin, Benoit Hingray, Armand Pierre, Dominique Boisson, and Adermus Joseph
Earth Syst. Dynam., 16, 497–512, https://doi.org/10.5194/esd-16-497-2025, https://doi.org/10.5194/esd-16-497-2025, 2025
Short summary
Short summary
This work provides a relevant analysis of changes in extreme precipitation over the Caribbean and their link with warming in different ocean basins. It also improves our understanding of the impact of warming on extreme precipitation events, which can cause devastating damage to economic sectors such as agriculture, biodiversity, health, and energy.
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
EGUsphere, https://doi.org/10.5194/egusphere-2025-1076, https://doi.org/10.5194/egusphere-2025-1076, 2025
Short summary
Short summary
Four process-based and four data-driven hydrological models are compared using different training data. We found process-based models to perform better with small data sets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Markus Mosimann, Martina Kauzlaric, Olivia Martius, and Andreas Paul Zischg
Abstr. Int. Cartogr. Assoc., 9, 26, https://doi.org/10.5194/ica-abs-9-26-2025, https://doi.org/10.5194/ica-abs-9-26-2025, 2025
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
Short summary
Short summary
This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
Hydrol. Earth Syst. Sci., 28, 2579–2601, https://doi.org/10.5194/hess-28-2579-2024, https://doi.org/10.5194/hess-28-2579-2024, 2024
Short summary
Short summary
The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
Short summary
Short summary
Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
Guillaume Evin, Matthieu Le Lay, Catherine Fouchier, David Penot, Francois Colleoni, Alexandre Mas, Pierre-André Garambois, and Olivier Laurantin
Hydrol. Earth Syst. Sci., 28, 261–281, https://doi.org/10.5194/hess-28-261-2024, https://doi.org/10.5194/hess-28-261-2024, 2024
Short summary
Short summary
Hydrological modelling of mountainous catchments is challenging for many reasons, the main one being the temporal and spatial representation of precipitation forcings. This study presents an evaluation of the hydrological modelling of 55 small mountainous catchments of the northern French Alps, focusing on the influence of the type of precipitation reanalyses used as inputs. These evaluations emphasize the added value of radar measurements, in particular for the reproduction of flood events.
Marvin Höge, Martina Kauzlaric, Rosi Siber, Ursula Schönenberger, Pascal Horton, Jan Schwanbeck, Marius Günter Floriancic, Daniel Viviroli, Sibylle Wilhelm, Anna E. Sikorska-Senoner, Nans Addor, Manuela Brunner, Sandra Pool, Massimiliano Zappa, and Fabrizio Fenicia
Earth Syst. Sci. Data, 15, 5755–5784, https://doi.org/10.5194/essd-15-5755-2023, https://doi.org/10.5194/essd-15-5755-2023, 2023
Short summary
Short summary
CAMELS-CH is an open large-sample hydro-meteorological data set that covers 331 catchments in hydrologic Switzerland from 1 January 1981 to 31 December 2020. It comprises (a) daily data of river discharge and water level as well as meteorologic variables like precipitation and temperature; (b) yearly glacier and land cover data; (c) static attributes of, e.g, topography or human impact; and (d) catchment delineations. CAMELS-CH enables water and climate research and modeling at catchment level.
Isabelle Ousset, Guillaume Evin, Damien Raynaud, and Thierry Faug
Nat. Hazards Earth Syst. Sci., 23, 3509–3523, https://doi.org/10.5194/nhess-23-3509-2023, https://doi.org/10.5194/nhess-23-3509-2023, 2023
Short summary
Short summary
This paper deals with an exceptional snow and rain event in a Mediterranean region of France which is usually not prone to heavy snowfall and its consequences on a particular building that collapsed completely. Independent analyses of the meteorological episode are carried out, and the response of the building to different snow and rain loads is confronted to identify the main critical factors that led to the collapse.
Erwan Le Roux, Guillaume Evin, Raphaëlle Samacoïts, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 17, 4691–4704, https://doi.org/10.5194/tc-17-4691-2023, https://doi.org/10.5194/tc-17-4691-2023, 2023
Short summary
Short summary
We assess projected changes in snowfall extremes in the French Alps as a function of elevation and global warming level for a high-emission scenario. On average, heavy snowfall is projected to decrease below 3000 m and increase above 3600 m, while extreme snowfall is projected to decrease below 2400 m and increase above 3300 m. At elevations in between, an increase is projected until +3 °C of global warming and then a decrease. These results have implications for the management of risks.
Kaltrina Maloku, Benoit Hingray, and Guillaume Evin
Hydrol. Earth Syst. Sci., 27, 3643–3661, https://doi.org/10.5194/hess-27-3643-2023, https://doi.org/10.5194/hess-27-3643-2023, 2023
Short summary
Short summary
High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
Juliette Blanchet, Alix Reverdy, Antoine Blanc, Jean-Dominique Creutin, Périne Kiennemann, and Guillaume Evin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-197, https://doi.org/10.5194/hess-2023-197, 2023
Revised manuscript not accepted
Short summary
Short summary
The Alpine region is strongly affected by torrential floods, sometimes leading to severe negative impacts on society, economy, and the environment. Understanding such natural hazards and their drivers is essential to mitigate related risks. In this article we study the atmospheric conditions at the origin of damaging torrential events in the Northern French Alps over the long run, using a database of reported occurrence of damaging torrential flooding in the Grenoble conurbation since 1851.
Laurent Strohmenger, Eric Sauquet, Claire Bernard, Jérémie Bonneau, Flora Branger, Amélie Bresson, Pierre Brigode, Rémy Buzier, Olivier Delaigue, Alexandre Devers, Guillaume Evin, Maïté Fournier, Shu-Chen Hsu, Sandra Lanini, Alban de Lavenne, Thibault Lemaitre-Basset, Claire Magand, Guilherme Mendoza Guimarães, Max Mentha, Simon Munier, Charles Perrin, Tristan Podechard, Léo Rouchy, Malak Sadki, Myriam Soutif-Bellenger, François Tilmant, Yves Tramblay, Anne-Lise Véron, Jean-Philippe Vidal, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023, https://doi.org/10.5194/hess-27-3375-2023, 2023
Short summary
Short summary
We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
Maxime Morel, Guillaume Piton, Damien Kuss, Guillaume Evin, and Caroline Le Bouteiller
Nat. Hazards Earth Syst. Sci., 23, 1769–1787, https://doi.org/10.5194/nhess-23-1769-2023, https://doi.org/10.5194/nhess-23-1769-2023, 2023
Short summary
Short summary
In mountain catchments, damage during floods is generally primarily driven by the supply of a massive amount of sediment. Predicting how much sediment can be delivered by frequent and infrequent events is thus important in hazard studies. This paper uses data gathered during the maintenance operation of about 100 debris retention basins to build simple equations aiming at predicting sediment supply from simple parameters describing the upstream catchment.
Cécile Duvillier, Nicolas Eckert, Guillaume Evin, and Michael Deschâtres
Nat. Hazards Earth Syst. Sci., 23, 1383–1408, https://doi.org/10.5194/nhess-23-1383-2023, https://doi.org/10.5194/nhess-23-1383-2023, 2023
Short summary
Short summary
This study develops a method that identifies individual potential release areas (PRAs) of snow avalanches based on terrain analysis and watershed delineation and demonstrates its efficiency in the French Alps context using an extensive cadastre of past avalanche limits. Results may contribute to better understanding local avalanche hazard. The work may also foster the development of more efficient PRA detection methods based on a rigorous evaluation scheme.
Sarah Shannon, Anthony Payne, Jim Freer, Gemma Coxon, Martina Kauzlaric, David Kriegel, and Stephan Harrison
Hydrol. Earth Syst. Sci., 27, 453–480, https://doi.org/10.5194/hess-27-453-2023, https://doi.org/10.5194/hess-27-453-2023, 2023
Short summary
Short summary
Climate change poses a potential threat to water supply in glaciated river catchments. In this study, we added a snowmelt and glacier melt model to the Dynamic fluxEs and ConnectIvity for Predictions of HydRology model (DECIPHeR). The model is applied to the Naryn River catchment in central Asia and is found to reproduce past change discharge and the spatial extent of seasonal snow cover well.
Juliette Blanchet, Alix Reverdy, Antoine Blanc, Jean-Dominique Creutin, Périne Kiennemann, and Guillaume Evin
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-276, https://doi.org/10.5194/nhess-2022-276, 2023
Manuscript not accepted for further review
Short summary
Short summary
We study the atmospheric conditions at the origin of damaging torrential events in the Northern French Alps over the long run. We consider seven atmospheric variables that describe the nature of the air masses involved and the possible triggers of precipitation and we try to isolate the most discriminating variables. The results show that humidity and particularly humidity transport plays the greatest role under westerly flows while instability potential is mostly at play under southerly flows.
Daniel Viviroli, Anna E. Sikorska-Senoner, Guillaume Evin, Maria Staudinger, Martina Kauzlaric, Jérémy Chardon, Anne-Catherine Favre, Benoit Hingray, Gilles Nicolet, Damien Raynaud, Jan Seibert, Rolf Weingartner, and Calvin Whealton
Nat. Hazards Earth Syst. Sci., 22, 2891–2920, https://doi.org/10.5194/nhess-22-2891-2022, https://doi.org/10.5194/nhess-22-2891-2022, 2022
Short summary
Short summary
Estimating the magnitude of rare to very rare floods is a challenging task due to a lack of sufficiently long observations. The challenge is even greater in large river basins, where precipitation patterns and amounts differ considerably between individual events and floods from different parts of the basin coincide. We show that a hydrometeorological model chain can provide plausible estimates in this setting and can thus inform flood risk and safety assessments for critical infrastructure.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
Earth Syst. Dynam., 13, 1059–1075, https://doi.org/10.5194/esd-13-1059-2022, https://doi.org/10.5194/esd-13-1059-2022, 2022
Short summary
Short summary
Anticipating risks related to climate extremes is critical for societal adaptation to climate change. In this study, we propose a statistical method in order to estimate future climate extremes from past observations and an ensemble of climate change simulations. We apply this approach to snow load data available in the French Alps at 1500 m elevation and find that extreme snow load is projected to decrease by −2.9 kN m−2 (−50 %) between 1986–2005 and 2080–2099 for a high-emission scenario.
Guillaume Evin, Samuel Somot, and Benoit Hingray
Earth Syst. Dynam., 12, 1543–1569, https://doi.org/10.5194/esd-12-1543-2021, https://doi.org/10.5194/esd-12-1543-2021, 2021
Short summary
Short summary
This research paper proposes an assessment of mean climate change responses and related uncertainties over Europe for mean seasonal temperature and total seasonal precipitation. An advanced statistical approach is applied to a large ensemble of 87 high-resolution EURO-CORDEX projections. For the first time, we provide a comprehensive estimation of the relative contribution of GCMs and RCMs, RCP scenarios, and internal variability to the total variance of a very large ensemble.
Guillaume Evin, Matthieu Lafaysse, Maxime Taillardat, and Michaël Zamo
Nonlin. Processes Geophys., 28, 467–480, https://doi.org/10.5194/npg-28-467-2021, https://doi.org/10.5194/npg-28-467-2021, 2021
Short summary
Short summary
Forecasting the height of new snow is essential for avalanche hazard surveys, road and ski resort management, tourism attractiveness, etc. Météo-France operates a probabilistic forecasting system using a numerical weather prediction system and a snowpack model. It provides better forecasts than direct diagnostics but exhibits significant biases. Post-processing methods can be applied to provide automatic forecasting products from this system.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
The Cryosphere, 15, 4335–4356, https://doi.org/10.5194/tc-15-4335-2021, https://doi.org/10.5194/tc-15-4335-2021, 2021
Short summary
Short summary
Extreme snowfall can cause major natural hazards (avalanches, winter storms) that can generate casualties and economic damage. In the French Alps, we show that between 1959 and 2019 extreme snowfall mainly decreased below 2000 m of elevation and increased above 2000 m. At 2500 m, we find a contrasting pattern: extreme snowfall decreased in the north, while it increased in the south. This pattern might be related to increasing trends in extreme snowfall observed near the Mediterranean Sea.
Maria Staudinger, Stefan Seeger, Barbara Herbstritt, Michael Stoelzle, Jan Seibert, Kerstin Stahl, and Markus Weiler
Earth Syst. Sci. Data, 12, 3057–3066, https://doi.org/10.5194/essd-12-3057-2020, https://doi.org/10.5194/essd-12-3057-2020, 2020
Short summary
Short summary
The data set CH-IRP provides isotope composition in precipitation and streamflow from 23 Swiss catchments, being unique regarding its long-term multi-catchment coverage along an alpine–pre-alpine gradient. CH-IRP contains fortnightly time series of stable water isotopes from streamflow grab samples complemented by time series in precipitation. Sampling conditions, catchment and climate information, lab standards and errors are provided together with areal precipitation and catchment boundaries.
Erwan Le Roux, Guillaume Evin, Nicolas Eckert, Juliette Blanchet, and Samuel Morin
Nat. Hazards Earth Syst. Sci., 20, 2961–2977, https://doi.org/10.5194/nhess-20-2961-2020, https://doi.org/10.5194/nhess-20-2961-2020, 2020
Short summary
Short summary
To minimize the risk of structure collapse due to extreme snow loads, structure standards rely on 50-year return levels of ground snow load (GSL), i.e. levels exceeded once every 50 years on average, that do not account for climate change. We study GSL data in the French Alps massifs from 1959 and 2019 and find that these 50-year return levels are decreasing with time between 900 and 4800 m of altitude, but they still exceed return levels of structure standards for half of the massifs at 1800 m.
Michael Stoelzle, Maria Staudinger, Kerstin Stahl, and Markus Weiler
Proc. IAHS, 383, 43–50, https://doi.org/10.5194/piahs-383-43-2020, https://doi.org/10.5194/piahs-383-43-2020, 2020
Short summary
Short summary
The role of recharge and catchment storage is crucial to understand streamflow drought sensitivity. Here we introduce a model experiment with recharge stress tests as complement to climate scenarios to quantify the streamflow drought sensitivities of catchments in Switzerland. We identified a pre-drought period of 12 months as maximum storage-memory for the study catchments. From stress testing, we found up to 200 days longer summer streamflow droughts and minimum flow reductions of 50 %–80 %.
Cited articles
Bennett, B., Leonard, M., Deng, Y., and Westra, S.: An empirical investigation into the effect of antecedent precipitation on flood volume, J. Hydrol., 567, 435–445, https://doi.org/10.1016/j.jhydrol.2018.10.025, 2018. a, b
Bergström, S.: Computer Models of Watershed Hydrology, edited by: Singh, V. P., Water Resources Publications, Colorado, USA, 1995. a
Berthet, L., Andréassian, V., Perrin, C., and Javelle, P.: How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments, Hydrol. Earth Syst. Sci., 13, 819–831, https://doi.org/10.5194/hess-13-819-2009, 2009. a
Beven, K.: Towards the use of catchment geomorphology in flood frequency predictions, Earth Surf. Proc. Land., 12, 69–82, https://doi.org/10.1002/esp.3290120109, 1987. a
Blazkova, S. and Beven, K.: Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the Czech Republic, J. Hydrol., 292, 153–172, https://doi.org/10.1016/j.jhydrol.2003.12.025, 2004. a, b
Blume, T. and van Meerveld, H. I.: From hillslope to stream: methods to investigate subsurface connectivity, WIREs Water, 2, 177–198, https://doi.org/10.1002/wat2.1071, 2015. a
Brocca, L., Melone, F., and Moramarco, T.: On the estimation of antecedent wetness conditions in rainfall–runoff modelling, Hydrol. Process., 22, 629–642, https://doi.org/10.1002/hyp.6629, 2008. a
Brunner, M. I., Seibert, J., and Favre, A.: Bivariate return periods and their importance for flood peak and volume estimation, WIREs Water, 3, 819–833, https://doi.org/10.1002/wat2.1173, 2016. a
Brunner, M. I., Viviroli, D., Sikorska, A. E., Vannier, O., Favre, A., and Seibert, J.: Flood type specific construction of synthetic design hydrographs, Water Resour. Res., 53, 1390–1406, https://doi.org/10.1002/2016WR019535, 2017. a, b
Brunner, M. I., Swain, D. L., Wood, R. R., Willkofer, F., Done, J. M., Gilleland, E., and Ludwig, R.: An extremeness threshold determines the regional response of floods to changes in rainfall extremes, Communications Earth & Environment, 2, 173, https://doi.org/10.1038/s43247-021-00248-x, 2021. a, b
Calver, A. and Lamb, R.: Flood frequency estimation using continuous rainfall-runoff modelling, Phys. Chem. Earth, 20, 479–483, https://doi.org/10.1016/S0079-1946(96)00010-9, 1995. a
Cameron, D., Beven, K., Tawn, J., Blazkova, S., and Naden, P.: Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty), J. Hydrol., 219, 169–187, https://doi.org/10.1016/S0022-1694(99)00057-8, 1999. a
Castellarin, A., Kohnová, S., Gaál, L., Fleig, A., Salinas, J. L., Toumazis, A., Kjeldsen, T., and Macdonald, N.: A review of applied-statistical methods for flood-frequency analysis in Europe, Tech. rep., The Centre for Ecology & Hydrology (FloodFreq COST Action ES0901), http://nora.nerc.ac.uk/id/eprint/19286/ (last access: 20 March 2024), 2012. a
Do, H. X., Mei, Y., and Gronewold, A. D.: To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes?, Geophys. Res. Lett., 47, e2020GL088684, https://doi.org/10.1029/2020GL088684, 2020. a
Eckhardt, K.: How to construct recursive digital filters for baseflow separation, Hydrol. Process., 19, 507–515, https://doi.org/10.1002/hyp.5675, 2005. a, b
Evin, G., Favre, A.-C., and Hingray, B.: Stochastic generation of multi-site daily precipitation focusing on extreme events, Hydrol. Earth Syst. Sci., 22, 655–672, https://doi.org/10.5194/hess-22-655-2018, 2018. a, b
Evin, G., Favre, A.-C., and Hingray, B.: Stochastic generators of multi-site daily temperature: comparison of performances in various applications, Theor. Appl. Climatol., 135, 811–824, https://doi.org/10.1007/s00704-018-2404-x, 2019. a
Falter, D., Schröter, K., Dung, N. V., Vorogushyn, S., Kreibich, H., Hundecha, Y., Apel, H., and Merz, B.: Spatially coherent flood risk assessment based on long-term continuous simulation with a coupled model chain, J. Hydrol., 524, 182–193, https://doi.org/10.1016/j.jhydrol.2015.02.021, 2015. a, b, c, d
Fischer, S.: A seasonal mixed-POT model to estimate high flood quantiles from different event types and seasons, J. Appl. Stat., 45, 2831–2847, https://doi.org/10.1080/02664763.2018.1441385, 2018. a, b
Fischer, S. and Schumann, A. H.: Multivariate Flood Frequency Analysis in Large River Basins Considering Tributary Impacts and Flood Types, Water Resour. Res., 57, e2020WR029029, https://doi.org/10.1029/2020WR029029, 2021. a, b
Fischer, S., Schumann, A., and Bühler, P.: Timescale-based flood typing to estimate temporal changes in flood frequencies, Hydrolog. Sci. J., 64, 1867–1892, https://doi.org/10.1080/02626667.2019.1679376, 2019. a
Froidevaux, P., Schwanbeck, J., Weingartner, R., Chevalier, C., and Martius, O.: Flood triggering in Switzerland: the role of daily to monthly preceding precipitation, Hydrol. Earth Syst. Sci., 19, 3903–3924, https://doi.org/10.5194/hess-19-3903-2015, 2015. a
García Hernández, J., Foehn, A., Fluixá-Sanmartín, J., Roquier, B., Brauchli, T., and Paredes Arquiola, J.: RS MINERVE – Technical manual, v2.25, Tech. rep., CREALP, Sion, https://crealp.ch/wp-content/uploads/2021/09/rsminerve_technical_manual_v2.25.pdf (last access: 20 March 2024), 2020. a
Genuer, R., Poggi, J.-M., and Tuleau, C.: Random Forests: some methodological insights, arXiv [preprint], https://doi.org/10.48550/arXiv.0811.3619, 21 November 2008. a
Grimaldi, S., Petroselli, A., and Serinaldi, F.: Design hydrograph estimation in small and ungauged watersheds: continuous simulation method versus event-based approach, Hydrol. Process., 26, 3124–3134, https://doi.org/10.1002/hyp.8384, 2012. a
Guillot, P. and Duband, D.: La méthode du gradex pour le calcul de la probabilité des crues à partir des pluies, in: Floods and their Computation: Proceedings of the Leningrad Symposium, August 1967, 84, edited by: International Association of Hydrological Sciences and World Meteorological Organization, Gentbrugge, Paris, 560–569, https://unesdoc.unesco.org/ark:/48223/pf0000014318 (last access: 20 March 2024), 1969. a
Gupta, A. and Govindaraju, R. S.: Uncertainty quantification in watershed hydrology: Which method to use?, J. Hydrol., 616, 128749, https://doi.org/10.1016/j.jhydrol.2022.128749, 2023. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hingray, B., Schaefli, B., Mezghani, A., and Hamdi, Y.: Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments, Hydrolog. Sci. J., 55, 1002–1016, https://doi.org/10.1080/02626667.2010.505572, 2010. a
Horton, P., Schaefli, B., and Kauzlaric, M.: Why do we have so many different hydrological models? A review based on the case of Switzerland, WIREs Water, 9, e1574, https://doi.org/10.1002/wat2.1574, 2022. a
Hundecha, Y. and Merz, B.: Exploring the relationship between changes in climate and floods using a model-based analysis, Water Resour. Res., 48, W04512, https://doi.org/10.1029/2011WR010527, 2012. a
Isotta, F. A., Begert, M., and Frei, C.: Long-Term Consistent Monthly Temperature and Precipitation Grid Data Sets for Switzerland Over the Past 150 Years, J. Geophys. Res.-Atmos., 124, 3783–3799, https://doi.org/10.1029/2018JD029910, 2019. a
Keller, L., Rössler, O., Martius, O., and Weingartner, R.: Delineation of flood generating processes and their hydrological response, Hydrol. Process., 32, 228–240, https://doi.org/10.1002/hyp.11407, 2018. a
Klemeš, V.: Tall Tales about Tails of Hydrological Distributions. I, J. Hydrol. Eng., 5, 227–231, https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(227), 2000a. a, b
Klemeš, V.: Tall Tales about Tails of Hydrological Distributions. II, J. Hydrol. Eng., 5, 232–239, https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(232), 2000b. a, b
Köplin, N., Schädler, B., Viviroli, D., and Weingartner, R.: The importance of glacier and forest change in hydrological climate-impact studies, Hydrol. Earth Syst. Sci., 17, 619–635, https://doi.org/10.5194/hess-17-619-2013, 2013. a
Köplin, N., Schädler, B., Viviroli, D., and Weingartner, R.: Seasonality and magnitude of floods in Switzerland under future climate change, Hydrol. Process., 28, 2567–2578, https://doi.org/10.1002/hyp.9757, 2014. a
Lafaysse, M., Hingray, B., Mezghani, A., Gailhard, J., and Terray, L.: Internal variability and model uncertainty components in future hydrometeorological projections: The Alpine Durance basin, Water Resour. Res., 50, 3317–3341, https://doi.org/10.1002/2013WR014897, 2014. a
Lamb, R., Faulkner, D., Wass, P., and Cameron, D.: Have applications of continuous rainfall–runoff simulation realized the vision for process-based flood frequency analysis?, Hydrol. Process., 30, 2463–2481, https://doi.org/10.1002/hyp.10882, 2016. a, b
Lang, M., Arnaud, P., Carreau, J., Deaux, N., Dezileau, L., Garavaglia, F., Latapie, A., Neppel, L., Paquet, E., Renard, B., Soubeyroux, J.-M., Terrier, B., Veysseire, J.-M., Aubert, Y., Auffray, A., Borchi, F., Bernardara, P., Carre, J.-C., Chambon, D., Cipriani, T., Delgado, J.-L., Doumenc, H., Fantin, R., Jourdain, S., Kochanek, K., Paquier, A., Sauquet, E., and Tramblay, Y.: Résultats du projet ExtraFlo (ANR 2009–2013) sur l'estimation des pluies et crues extrêmes, Houille Blanche, 5–13, https://doi.org/10.1051/lhb/2014010, 2014. a
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997. a
Lobligeois, F., Andréassian, V., Perrin, C., Tabary, P., and Loumagne, C.: When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events, Hydrol. Earth Syst. Sci., 18, 575–594, https://doi.org/10.5194/hess-18-575-2014, 2014. a
Makkonen, L.: Plotting Positions in Extreme Value Analysis, J. Appl. Meteorol. Clim., 45, 334–340, https://doi.org/10.1175/JAM2349.1, 2006. a
Mathevet, T. and Garçon, R.: Tall tales from the hydrological crypt: are models monsters?, Hydrolog. Sci. J., 55, 857–871, https://doi.org/10.1080/02626667.2010.503934, 2010. a
Merz, B., Basso, S., Fischer, S., Lun, D., Blöschl, G., Merz, R., Guse, B., Viglione, A., Vorogushyn, S., Macdonald, E., Wietzke, L., and Schumann, A.: Understanding Heavy Tails of Flood Peak Distributions, Water Resour. Res., 58, e2021WR030506, https://doi.org/10.1029/2021WR030506, 2022. a
Merz, R. and Blöschl, G.: Flood frequency hydrology: 1. Temporal, spatial, and causal expansion of information, Water Resour. Res., 44, W08432, https://doi.org/10.1029/2007WR006744, 2008. a, b
Merz, R. and Blöschl, G.: Process controls on the statistical flood moments – a data based analysis, Hydrol. Process., 23, 675–696, https://doi.org/10.1002/hyp.7168, 2009. a
Michele, C. and Salvadori, G.: On the derived flood frequency distribution: analytical formulation and the influence of antecedent soil moisture condition, J. Hydrol., 262, 245–258, https://doi.org/10.1016/S0022-1694(02)00025-2, 2002. a
Naghettini, M., Potter, K. W., and Illangasekare, T.: Estimating the upper tail of flood-peak frequency distributions using hydrometeorological information, Water Resour. Res., 32, 1729–1740, https://doi.org/10.1029/96WR00200, 1996. a, b, c
Nied, M., Hundecha, Y., and Merz, B.: Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin, Hydrol. Earth Syst. Sci., 17, 1401–1414, https://doi.org/10.5194/hess-17-1401-2013, 2013. a, b
Nied, M., Schröter, K., Lüdtke, S., Nguyen, V. D., and Merz, B.: What are the hydro-meteorological controls on flood characteristics?, J. Hydrol., 545, 310–326, https://doi.org/10.1016/j.jhydrol.2016.12.003, 2017. a, b, c, d
Obled, C., Zin, I., and Hingray, B.: Choix des pas de temps et d’espace pour des modélisations parcimonieuses en hydrologie des crues, La Houille Blanche, 95, 81–87, https://doi.org/10.1051/lhb/2009059, 2009. a
Okoli, K., Mazzoleni, M., Breinl, K., and Di Baldassarre, G.: A systematic comparison of statistical and hydrological methods for design flood estimation, Hydrol. Res., 50, 1665–1678, https://doi.org/10.2166/nh.2019.188, 2019. a
Pathiraja, S., Westra, S., and Sharma, A.: Why continuous simulation? the role of antecedent moisture in design flood estimation, Water Resour. Res., 48, W06534, https://doi.org/10.1029/2011WR010997, 2012. a
Rogger, M., Kohl, B., Pirkl, H., Viglione, A., Komma, J., Kirnbauer, R., Merz, R., and Blöschl, G.: Runoff models and flood frequency statistics for design flood estimation in Austria – Do they tell a consistent story?, J. Hydrol., 456–457, 30–43, https://doi.org/10.1016/j.jhydrol.2012.05.068, 2012. a
Schulte, L., Wetter, O., Wilhelm, B., Peña, J. C., Amann, B., Wirth, S. B., Carvalho, F., and Gómez-Bolea, A.: Integration of multi-archive datasets for the development of a four-dimensional paleoflood model of alpine catchments, Global Planet. Change, 180, 66–88, https://doi.org/10.1016/j.gloplacha.2019.05.011, 2019. a
Seibert, J.: On the need for benchmarks in hydrological modelling, Hydrol. Process., 15, 1063–1064, https://doi.org/10.1002/hyp.446, 2001. a
Seibert, J. and Vis, M. J. P.: Teaching hydrological modeling with a user-friendly catchment-runoff-model software package, Hydrol. Earth Syst. Sci., 16, 3315–3325, https://doi.org/10.5194/hess-16-3315-2012, 2012. a
Sikorska, A. E., Viviroli, D., and Seibert, J.: Flood-type classification in mountainous catchments using crisp and fuzzy decision trees, Water Resour. Res., 51, 7959–7976, https://doi.org/10.1002/2015WR017326, 2015. a, b
Sikorska-Senoner, A. E. and Seibert, J.: Flood-type trend analysis for alpine catchments, Hydrolog. Sci. J., 65, 1281–1299, https://doi.org/10.1080/02626667.2020.1749761, 2020. a
Sikorska-Senoner, A. E., Schaefli, B., and Seibert, J.: Downsizing parameter ensembles for simulations of rare floods, Nat. Hazards Earth Syst. Sci., 20, 3521–3549, https://doi.org/10.5194/nhess-20-3521-2020, 2020. a
Staudinger, M., Stoelzle, M., Cochand, F., Seibert, J., Weiler, M., and Hunkeler, D.: Your work is my boundary condition! Challenges and approaches for a closer collaboration between hydrologists and hydrogeologists, J. Hydrol., 571, 235–243, https://doi.org/10.1016/j.jhydrol.2019.01.058, 2019. a
Swisstopo: DHM25, https://www.swisstopo.admin.ch/en/height-model-dhm25 (last access: 1 January 2025), 2005. a
Tarasova, L., Merz, R., Kiss, A., Basso, S., Blöschl, G., Merz, B., Viglione, A., Plötner, S., Guse, B., Schumann, A., Fischer, S., Ahrens, B., Anwar, F., Bárdossy, A., Bühler, P., Haberlandt, U., Kreibich, H., Krug, A., Lun, D., Müller-Thomy, H., Pidoto, R., Primo, C., Seidel, J., Vorogushyn, S., and Wietzke, L.: Causative classification of river flood events, WIREs Water, 6, e1353, https://doi.org/10.1002/wat2.1353, 2019. a, b, c
Tarasova, L., Basso, S., and Merz, R.: Transformation of Generation Processes From Small Runoff Events to Large Floods, Geophys. Res. Lett., 47, e2020GL090547, https://doi.org/10.1029/2020GL090547, 2020. a, b
Thébault, C., Perrin, C., Andréassian, V., Thirel, G., Legrand, S., and Delaigue, O.: Multi-model approach in a variable spatial framework for streamflow simulation, Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, 2024. a
Todini, E.: Role and Treatment of Uncertainty in Real-Time Flood Forecasting, Hydrol. Process., 18, 2743–2746, https://doi.org/10.1002/hyp.5687, 2004. a
Tramblay, Y., Mimeau, L., Neppel, L., Vinet, F., and Sauquet, E.: Detection and attribution of flood trends in Mediterranean basins, Hydrol. Earth Syst. Sci., 23, 4419–4431, https://doi.org/10.5194/hess-23-4419-2019, 2019. a
Vance, T. R., Kiem, A. S., Jong, L. M., Roberts, J. L., Plummer, C. T., Moy, A. D., Curran, M. A., and van Ommen, T. D.: Pacific decadal variability over the last 2000 years and implications for climatic risk, Communications Earth & Environment, 3, 33, https://doi.org/10.1038/s43247-022-00359-z, 2022. a
Viviroli, D., Sikorska-Senoner, A. E., Evin, G., Staudinger, M., Kauzlaric, M., Chardon, J., Favre, A.-C., Hingray, B., Nicolet, G., Raynaud, D., Seibert, J., Weingartner, R., and Whealton, C.: Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin, Nat. Hazards Earth Syst. Sci., 22, 2891–2920, https://doi.org/10.5194/nhess-22-2891-2022, 2022. a, b, c, d, e, f, g
Wasko, C. and Nathan, R.: Influence of changes in rainfall and soil moisture on trends in flooding, J. Hydrol., 575, 432–441, https://doi.org/10.1016/j.jhydrol.2019.05.054, 2019. a
Weingartner, R. and Aschwanden, H.: Discharge Regime – the Basis for the Estimation of Average Flows, Hydrological Atlas of Switzerland, Plate 5.2, FOEN – Federal Office for the Environment, Bern, ISBN 3952026204, 1992. a
Westerberg, I., Guerrero, J.-L., Seibert, J., Beven, K. J., and Halldin, S.: Stage-discharge uncertainty derived with a non-stationary rating curve in the Choluteca River, Honduras, Hydrol. Process., 25, 603–613, https://doi.org/10.1002/hyp.7848, 2011. a
Westerberg, I. K., Sikorska-Senoner, A. E., Viviroli, D., Vis, M., and Seibert, J.: Hydrological model calibration with uncertain discharge data, Hydrolog. Sci. J., 67, 2441–2456, https://doi.org/10.1080/02626667.2020.1735638, 2020. a
Wilhelm, B., Rapuc, W., Amann, B., Anselmetti, F. S., Arnaud, F., Blanchet, J., Brauer, A., Czymzik, M., Giguet-Covex, C., Gilli, A., Glur, L., Grosjean, M., Irmler, R., Nicolle, M., Sabatier, P., Swierczynski, T., and Wirth, S. B.: Impact of warmer climate periods on flood hazard in the European Alps, Nat. Geosci., 15, 118–123, https://doi.org/10.1038/s41561-021-00878-y, 2022. a
Wilks, D. S. and Wilby, R. L.: The weather generation game: a review of stochastic weather models, Progress in Physical Geography: Earth and Environment, 23, 329–357, https://doi.org/10.1177/030913339902300302, 1999. a
Winter, B., Schneeberger, K., Dung, N., Huttenlau, M., Achleitner, S., Stötter, J., Merz, B., and Vorogushyn, S.: A continuous modelling approach for design flood estimation on sub-daily time scale, Hydrolog. Sci. J., 64, 539–554, https://doi.org/10.1080/02626667.2019.1593419, 2019. a
Woldemeskel, F. and Sharma, A.: Should flood regimes change in a warming climate? The role of antecedent moisture conditions, Geophys. Res. Lett., 43, 7556–7563, https://doi.org/10.1002/2016GL069448, 2016. a
Wood, A. W. and Lettenmaier, D. P.: An ensemble approach for attribution of hydrologic prediction uncertainty, Geophys. Res. Lett., 35, L14401, https://doi.org/10.1029/2008GL034648, 2008. a
Short summary
Various combinations of antecedent conditions and precipitation result in floods of varying degrees. Antecedent conditions played a crucial role in generating even large ones. The key predictors and spatial patterns of antecedent conditions leading to flooding at the basin's outlet were distinct. Precipitation and soil moisture from almost all sub-catchments were important for more frequent floods. For rarer events, only the predictors of specific sub-catchments were important.
Various combinations of antecedent conditions and precipitation result in floods of varying...
Altmetrics
Final-revised paper
Preprint