Articles | Volume 25, issue 9
https://doi.org/10.5194/nhess-25-3161-2025
© 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-25-3161-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Sep 2025
Research article |
| 09 Sep 2025
| 09 Sep 2025
Climate change impacts on floods in West Africa: new insight from two large-scale hydrological models
Serigne Bassirou Diop
CORRESPONDING AUTHOR
Laboratoire Leïdi “Dynamique des Territoires et Développement”, Univ. Gaston Berger, Saint-Louis, Senegal
Job Ekolu
Centre for Agroecology, Water and Resilience, Coventry University, Coventry, UK
Water for Production Department, Ministry of Water and Environment, Uganda
Yves Tramblay
Espace-Dev, Univ. Montpellier, IRD, Montpellier, France
Bastien Dieppois
Centre for Agroecology, Water and Resilience, Coventry University, Coventry, UK
Stefania Grimaldi
European Commission, Joint Research Centre (JRC), Ispra, Italy
Ansoumana Bodian
Laboratoire Leïdi “Dynamique des Territoires et Développement”, Univ. Gaston Berger, Saint-Louis, Senegal
Juliette Blanchet
Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
Ponnambalam Rameshwaran
UK Centre for Ecology & Hydrology, Wallingford, UK
Peter Salamon
European Commission, Joint Research Centre (JRC), Ispra, Italy
Benjamin Sultan
Espace-Dev, Univ. Montpellier, IRD, Montpellier, France
Related authors
No articles found.
Gabrielle Sorini, Juliette Blanchet, Gérémy Panthou, Théo Vischel, and Yves Tramblay
EGUsphere, https://doi.org/10.5194/egusphere-2026-1176, https://doi.org/10.5194/egusphere-2026-1176, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study investigates regional flood trends across West Africa using time-varying extreme-value distributions. While most catchments show declining flood magnitudes up to the 1970s–1990s, recent trends diverge, ranging from decrease to intensification, revealing an original typology of regional hydrosystems. These results refine the narrative of hydrological changes in West Africa and provide clues to their attribution.
Abubakar Haruna, Juliette Blanchet, Guillaume Evin, and Emmanuel Paquet
Adv. Stat. Clim. Meteorol. Oceanogr., 12, 87–109, https://doi.org/10.5194/ascmo-12-87-2026, https://doi.org/10.5194/ascmo-12-87-2026, 2026
Short summary
Short summary
This study advances nonstationary precipitation modeling by using single, flexible distributions to analyze trends across the full daily spectrum. We demonstrate that evolving shape parameters are critical for accurately capturing observed differential changes in low, medium, and extreme quantiles. This method ensures statistical consistency, providing reliable trend assessments over the two-component framework common in climate impact analysis.
Nicolas Decoopman, Juliette Blanchet, Antoine Blanc, and Cécile Caillaud
EGUsphere, https://doi.org/10.5194/egusphere-2026-1202, https://doi.org/10.5194/egusphere-2026-1202, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study evaluates the ability of the convection-permitting regional climate model AROME to reproduce precipitation extremes and their trends, at daily and hourly scales in France, over 1959–2022. Overall, the results highlight the added value of the explicit-convection model for extreme precipitation studies while underscoring its limitations for convective extremes.
Peter Salamon, Tim Sperzel, Goncalo Ramos Gomes, Marco Radke-Fretz, Carina-Denise Lemke, Carlo Russo, Christoph Schweim, Ervin Zsoter, Alessandro Dosio, Mariapina Vomero, Markus Ziese, and Stefania Grimaldi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-723, https://doi.org/10.5194/essd-2025-723, 2026
Preprint under review for ESSD
Short summary
Short summary
We introduce the European Meteorological Observations -1 (EMO-1) dataset developed to support the European Flood Awareness System. EMO-1 provides maps with interpolated meteorological observations across Europe at daily and 6-hourly time steps and with a pixel size of ~1.5 km for the period 1990–2024. The dataset uses observations from thousands of meteorological stations from 47 different data providers for interpolation. It is publicly available at the Joint Research Centre Data Catalogue.
Jesús Casado-Rodríguez, Juliana Disperati, Stefania Grimaldi, and Peter Salamon
EGUsphere, https://doi.org/10.5194/egusphere-2026-904, https://doi.org/10.5194/egusphere-2026-904, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Dams significantly change river flows, yet they are difficult to represent in global hydrological models. We tested four different modeling methods using data from 164 reservoirs to find the most effective approach. We found that tracking water storage is better than tracking outflow for predicting reservoir behavior. This discovery enables the use of satellite information for better water management. These improvements are being implemented in the European and Global Flood Awareness Systems.
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
Hydrol. Earth Syst. Sci., 30, 1023–1051, https://doi.org/10.5194/hess-30-1023-2026, https://doi.org/10.5194/hess-30-1023-2026, 2026
Short summary
Short summary
Explore2 provides hydrological projections for 1,735 French catchments. Using QUALYPSO (Quasi-Ergodic Analysis of Climate Projections Using Data Augmentation), 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.
Andrea Ficchì, Davide Bavera, Stefania Grimaldi, Francesca Moschini, Alberto Pistocchi, Carlo Russo, Peter Salamon, and Andrea Toreti
EGUsphere, https://doi.org/10.5194/egusphere-2026-43, https://doi.org/10.5194/egusphere-2026-43, 2026
Short summary
Short summary
This study introduces a new metric for hydrological model calibration combining traditional model efficiency measures with a new component to better assess similarity between observed and simulated river flows. Tests on two hydrological models and large catchment samples show improved low-flow simulations without degrading high-flow accuracy. The new metric can support more reliable modeling for droughts and floods and is already being adopted in operational monitoring and forecasting systems.
Camille Crapart, Sandrine Anquetin, Juliette Blanchet, and Arona Diedhiou
Hydrol. Earth Syst. Sci., 30, 163–181, https://doi.org/10.5194/hess-30-163-2026, https://doi.org/10.5194/hess-30-163-2026, 2026
Short summary
Short summary
Our study investigates global dryland dynamics and aridification under future climate scenarios. By employing the Food and Agriculture Organisation Aridity Index and an ensemble of 13 models from the 6th Coupled Model Intercomparison Project, we provide projections for dryland distribution and aridity index across three shared socio-economic pathways (2-4.5, 3-7.0, and 5-8.5) for the near-term (2030–2059) and for the long-term (2070–2099) future.
Ian Castellanos, Martin Ménégoz, Juliette Blanchet, Julien Beaumet, Hubert Gallée, Eduardo Moreno-Chamarro, Chantal Staquet, and Xavier Fettweis
EGUsphere, https://doi.org/10.5194/egusphere-2025-6211, https://doi.org/10.5194/egusphere-2025-6211, 2025
Short summary
Short summary
The Alps host glaciers, distinct ecosystems, socio-economic interests and water resources that are being impacted by climate change. In this study, we aim at understanding how warming occurs in the Alps in projected scenarios and what physical processes drive it. We find under these scenarios that elevations around the snowline will warm faster than elsewhere, because snow retreats to higher elevations. Indeed, snow slows down warming due to its high albedo and the energy consumed to melt it.
Yves Tramblay, Guillaume Thirel, Laurent Strohmenger, Guillaume Evin, Lola Corre, Louis Heraut, and Eric Sauquet
Hydrol. Earth Syst. Sci., 29, 7023–7039, https://doi.org/10.5194/hess-29-7023-2025, https://doi.org/10.5194/hess-29-7023-2025, 2025
Short summary
Short summary
How does climate change impact floods in France? Using simulations for 3727 rivers with climate projections, results show that flood trends vary depending on the region. In the north, floods may become more severe, but in the south, 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.
Gerhard Krinner, Aude Champouillon, Juliette Blanchet, and Frédérique Chéruy
EGUsphere, https://doi.org/10.5194/egusphere-2025-3553, https://doi.org/10.5194/egusphere-2025-3553, 2025
Short summary
Short summary
Although the scientific community has made much progress over the last decades, climate models still do not perfectly simulate the present climate. Therefore, the model outputs are usually corrected for these errors. This article presents a method to apply successive stages of repeated error correction that lead to a better simulation of the present climate than in previous studies, in which the same correction method had been applied only once.
John Robotham, Emily Trill, James Blake, Ponnambalam Rameshwaran, Peter Scarlett, Gareth Old, and Joanna Clark
Earth Syst. Sci. Data, 17, 4277–4291, https://doi.org/10.5194/essd-17-4277-2025, https://doi.org/10.5194/essd-17-4277-2025, 2025
Short summary
Short summary
There is currently limited evidence about how land-based “natural flood management” measures affect soil properties. We therefore measured soil physical and hydraulic properties (n = 1300) at seven field sites (Thames catchment, UK). The sites cover a range of geologies, land use, and management. Dataset applications include hydrological and land surface modelling and validation of remote sensing observations. The dataset also provides a baseline against which future soil changes may be compared.
Sebastian Berghald, Juliette Blanchet, Antoine Blanc, and David Penot
EGUsphere, https://doi.org/10.5194/egusphere-2025-3073, https://doi.org/10.5194/egusphere-2025-3073, 2025
Short summary
Short summary
Our study analyses extreme precipitation in the French Alps using extreme value theory on long-term observations. We compare daily and hourly observations and find regionally and seasonally different trends. On annual resolution, daily extremes show positive trends in the south and negative trends in the north, while trends in hourly extremes are noisier with an appearing east-west divide between increases in the high Alps and decreases in the pre-Alps.
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
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.
Laurent Pascal Malang Diémé, Christophe Bouvier, Ansoumana Bodian, and Alpha Sidibé
Nat. Hazards Earth Syst. Sci., 25, 1095–1112, https://doi.org/10.5194/nhess-25-1095-2025, https://doi.org/10.5194/nhess-25-1095-2025, 2025
Short summary
Short summary
We propose a decision support tool that detect the occurrence of flooding by drainage overflow, with sufficiently short calculation times. The simulations are based on a drainage topology on 5 m grids, incorporating changes to surface flows induced by urbanization. The method can be used for flood mapping in project mode and in real time. It applies to the present situation as well as to any scenario involving climate change or urban growth.
Sivarama Krishna Reddy Chidepudi, Nicolas Massei, Abderrahim Jardani, Bastien Dieppois, Abel Henriot, and Matthieu Fournier
Hydrol. Earth Syst. Sci., 29, 841–861, https://doi.org/10.5194/hess-29-841-2025, https://doi.org/10.5194/hess-29-841-2025, 2025
Short summary
Short summary
This study explores how deep learning can improve our understanding of groundwater levels, using an approach that combines climate data and physical characteristics of aquifers. By focusing on different types of groundwater levels and employing techniques like clustering and wavelet transform, the study highlights the importance of targeting relevant information. This research not only advances groundwater simulation but also emphasizes the benefits of different modelling approaches.
Aloïs Tilloy, Dominik Paprotny, Stefania Grimaldi, Goncalo Gomes, Alessandra Bianchi, Stefan Lange, Hylke Beck, Cinzia Mazzetti, and Luc Feyen
Earth Syst. Sci. Data, 17, 293–316, https://doi.org/10.5194/essd-17-293-2025, https://doi.org/10.5194/essd-17-293-2025, 2025
Short summary
Short summary
This article presents a reanalysis of Europe's river streamflow for the period 1951–2020. Streamflow is estimated through a state-of-the-art hydrological simulation framework benefitting from detailed information about the landscape, climate, and human activities. The resulting Hydrological European ReAnalysis (HERA) can be a valuable tool for studying hydrological dynamics, including the impacts of climate change and human activities on European water resources and flood and drought risks.
Ather Abbas, Yuan Yang, Ming Pan, Yves Tramblay, Chaopeng Shen, Haoyu Ji, Solomon H. Gebrechorkos, Florian Pappenberger, Jong Cheol Pyo, Dapeng Feng, George Huffman, Phu Nguyen, Christian Massari, Luca Brocca, Tan Jackson, and Hylke E. Beck
EGUsphere, https://doi.org/10.5194/egusphere-2024-4194, https://doi.org/10.5194/egusphere-2024-4194, 2025
Short summary
Short summary
Our study evaluated 23 precipitation datasets using a hydrological model at global scale to assess their suitability and accuracy. We found that MSWEP V2.8 excels due to its ability to integrate data from multiple sources, while others, such as IMERG and JRA-3Q, demonstrated strong regional performances. This research assists in selecting the appropriate dataset for applications in water resource management, hazard assessment, agriculture, and environmental monitoring.
Andrea Betterle and Peter Salamon
Nat. Hazards Earth Syst. Sci., 24, 2817–2836, https://doi.org/10.5194/nhess-24-2817-2024, https://doi.org/10.5194/nhess-24-2817-2024, 2024
Short summary
Short summary
The study proposes a new framework, named FLEXTH, to estimate flood water depth and improve satellite-based flood monitoring using topographical data. FLEXTH is readily available as a computer code, offering a practical and scalable solution for estimating flood depth quickly and systematically over large areas. The methodology can reduce the impacts of floods and enhance emergency response efforts, particularly where resources are limited.
Margarita Choulga, Francesca Moschini, Cinzia Mazzetti, Stefania Grimaldi, Juliana Disperati, Hylke Beck, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, https://doi.org/10.5194/hess-28-2991-2024, 2024
Short summary
Short summary
CEMS_SurfaceFields_2022 dataset is a new set of high-resolution maps for land type (e.g. lake, forest), soil properties and population water needs at approximately 2 and 6 km at the Equator, covering Europe and the globe (excluding Antarctica). We describe what and how new high-resolution information can be used to create the dataset. The paper suggests that the dataset can be used as input for river, weather or other models, as well as for statistical descriptions of the region of interest.
Ansoumana Bodian, Papa Malick Ndiaye, Serigne Bassirou Diop, Lamine Diop, Alain Dezetter, Andrew Ogilvie, and Koffi Djaman
Proc. IAHS, 385, 415–421, https://doi.org/10.5194/piahs-385-415-2024, https://doi.org/10.5194/piahs-385-415-2024, 2024
Short summary
Short summary
Reference evapotranspiration (ET0) is an essential parameter for hydrological modeling, irrigation planning and for studying the impacts of climate change on water resources. This work evaluate 20 alternative methods of estimating ET0 in order to adapt them to the climatic context of the 3 mains basins of Senegal where very little climate data is available. The methods of Valiantzas 1, Doorenboss & Pruitt and Penman are the most robust for the estimation of ET0 in this context.
Papa Malick Ndiaye, Ansoumana Bodian, Serigne Bassirou Diop, Lamine Diop, Alain Dezetter, Andrew Ogilvie, and Koffi Djaman
Proc. IAHS, 385, 305–311, https://doi.org/10.5194/piahs-385-305-2024, https://doi.org/10.5194/piahs-385-305-2024, 2024
Short summary
Short summary
The analyze of the trends of ET0 at the scale of the Senegal, Gambia and Casamance river basins using reanalyze data of NASA/POWER over 1984–2019 shows that ET0 increases significantly in 32% of the Senegal basin and decreases in less than 1% of it. In the Casamance and Gambia basins, the annual ET0 drops by 65% and 18%, respectively. Temperature and relative humidity show an increasing trend over all basins while wind speed and radiation decrease, confirming the so-called "evaporation paradox".
Laurent Pascal Diémé, Christophe Bouvier, Ansoumana Bodian, and Alpha Sidibé
Proc. IAHS, 385, 175–180, https://doi.org/10.5194/piahs-385-175-2024, https://doi.org/10.5194/piahs-385-175-2024, 2024
Short summary
Short summary
This study aims at proposing a modelling of flows and overflows of structures at fine resolution (5 m) for rainfall intensities of different return periods. The overflow points of the network are identified by the difference between the maximum flow and the capacity of the network to evacuate floods. The results of the simulations show that the drainage network appears to be overflowing for rare frequency rainfall events (100 years). The method seems adaptable to different contexts.
Nils Poncet, Philippe Lucas-Picher, Yves Tramblay, Guillaume Thirel, Humberto Vergara, Jonathan Gourley, and Antoinette Alias
Nat. Hazards Earth Syst. Sci., 24, 1163–1183, https://doi.org/10.5194/nhess-24-1163-2024, https://doi.org/10.5194/nhess-24-1163-2024, 2024
Short summary
Short summary
High-resolution convection-permitting climate models (CPMs) are now available to better simulate rainstorm events leading to flash floods. In this study, two hydrological models are compared to simulate floods in a Mediterranean basin, showing a better ability of the CPM to reproduce flood peaks compared to coarser-resolution climate models. Future projections are also different, with a projected increase for the most severe floods and a potential decrease for the most frequent events.
Lorenzo Alfieri, Andrea Libertino, Lorenzo Campo, Francesco Dottori, Simone Gabellani, Tatiana Ghizzoni, Alessandro Masoero, Lauro Rossi, Roberto Rudari, Nicola Testa, Eva Trasforini, Ahmed Amdihun, Jully Ouma, Luca Rossi, Yves Tramblay, Huan Wu, and Marco Massabò
Nat. Hazards Earth Syst. Sci., 24, 199–224, https://doi.org/10.5194/nhess-24-199-2024, https://doi.org/10.5194/nhess-24-199-2024, 2024
Short summary
Short summary
This work describes Flood-PROOFS East Africa, an impact-based flood forecasting system for the Greater Horn of Africa. It is based on hydrological simulations, inundation mapping, and estimation of population and assets exposed to upcoming river floods. The system supports duty officers in African institutions in the daily monitoring of hydro-meteorological disasters. A first evaluation shows the system performance for the catastrophic floods in the Nile River basin in summer 2020.
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.
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, and Wolfgang Wagner
Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, https://doi.org/10.5194/nhess-23-3305-2023, 2023
Short summary
Short summary
In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
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.
Yves Tramblay, Patrick Arnaud, Guillaume Artigue, Michel Lang, Emmanuel Paquet, Luc Neppel, and Eric Sauquet
Hydrol. Earth Syst. Sci., 27, 2973–2987, https://doi.org/10.5194/hess-27-2973-2023, https://doi.org/10.5194/hess-27-2973-2023, 2023
Short summary
Short summary
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.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
Short summary
Short summary
This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
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.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Vera Thiemig, Goncalo N. Gomes, Jon O. Skøien, Markus Ziese, Armin Rauthe-Schöch, Elke Rustemeier, Kira Rehfeldt, Jakub P. Walawender, Christine Kolbe, Damien Pichon, Christoph Schweim, and Peter Salamon
Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, https://doi.org/10.5194/essd-14-3249-2022, 2022
Short summary
Short summary
EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.
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.
Abubakar Haruna, Juliette Blanchet, and Anne-Catherine Favre
Hydrol. Earth Syst. Sci., 26, 2797–2811, https://doi.org/10.5194/hess-26-2797-2022, https://doi.org/10.5194/hess-26-2797-2022, 2022
Short summary
Short summary
Reliable prediction of floods depends on the quality of the input data such as precipitation. However, estimation of precipitation from the local measurements is known to be difficult, especially for extremes. Regionalization improves the estimates by increasing the quantity of data available for estimation. Here, we compare three regionalization methods based on their robustness and reliability. We apply the comparison to a dense network of daily stations within and outside Switzerland.
Yves Tramblay and Pere Quintana Seguí
Nat. Hazards Earth Syst. Sci., 22, 1325–1334, https://doi.org/10.5194/nhess-22-1325-2022, https://doi.org/10.5194/nhess-22-1325-2022, 2022
Short summary
Short summary
Monitoring soil moisture is important during droughts, but very few measurements are available. Consequently, land-surface models are essential tools for reproducing soil moisture dynamics. In this study, a hybrid approach allowed for regionalizing soil water content using a machine learning method. This approach proved to be efficient, compared to the use of soil property maps, to run a simple soil moisture accounting model, and therefore it can be applied in various regions.
Francesco Dottori, Lorenzo Alfieri, Alessandra Bianchi, Jon Skoien, and Peter Salamon
Earth Syst. Sci. Data, 14, 1549–1569, https://doi.org/10.5194/essd-14-1549-2022, https://doi.org/10.5194/essd-14-1549-2022, 2022
Short summary
Short summary
We present a set of hazard maps for river flooding for Europe and the Mediterranean basin. The maps depict inundation extent and depth for flood probabilities for up to 1-in-500-year flood hazards and are based on hydrological and hydrodynamic models driven by observed climatology. The maps can identify two-thirds of the flood extent reported by official flood maps, with increasing skill for higher-magnitude floods. The maps are used for evaluating present and future impacts of river floods.
Antoine Blanc, Juliette Blanchet, and Jean-Dominique Creutin
Weather Clim. Dynam., 3, 231–250, https://doi.org/10.5194/wcd-3-231-2022, https://doi.org/10.5194/wcd-3-231-2022, 2022
Short summary
Short summary
Precipitation variability and extremes in the northern French Alps are governed by the atmospheric circulation over western Europe. In this work, we study the past evolution of western Europe large-scale circulation using atmospheric descriptors. We show some discrepancies in the trends obtained from different reanalyses before 1950. After 1950, we find trends in Mediterranean circulations that appear to be linked with trends in seasonal and extreme precipitation in the northern French Alps.
Ernest Amoussou, Gil Mahe, Oula Amrouni, Ansoumana Bodian, Christophe Cudennec, Stephan Dietrich, Domiho Japhet Kodja, and Expédit Wilfrid Vissin
Proc. IAHS, 384, 1–4, https://doi.org/10.5194/piahs-384-1-2021, https://doi.org/10.5194/piahs-384-1-2021, 2021
Short summary
Short summary
This short paper is the preface of the PIAHS volume of the IAHS/UNESCO FRIEND-Water conference of Cotonou in November 2021.
Manuel Fossa, Bastien Dieppois, Nicolas Massei, Matthieu Fournier, Benoit Laignel, and Jean-Philippe Vidal
Hydrol. Earth Syst. Sci., 25, 5683–5702, https://doi.org/10.5194/hess-25-5683-2021, https://doi.org/10.5194/hess-25-5683-2021, 2021
Short summary
Short summary
Hydro-climate observations (such as precipitation, temperature, and river discharge time series) reveal very complex behavior inherited from complex interactions among the physical processes that drive hydro-climate viability. This study shows how even small perturbations of a physical process can have large consequences on some others. Those interactions vary spatially, thus showing the importance of both temporal and spatial dimensions in better understanding hydro-climate variability.
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.
Yves Tramblay, Nathalie Rouché, Jean-Emmanuel Paturel, Gil Mahé, Jean-François Boyer, Ernest Amoussou, Ansoumana Bodian, Honoré Dacosta, Hamouda Dakhlaoui, Alain Dezetter, Denis Hughes, Lahoucine Hanich, Christophe Peugeot, Raphael Tshimanga, and Patrick Lachassagne
Earth Syst. Sci. Data, 13, 1547–1560, https://doi.org/10.5194/essd-13-1547-2021, https://doi.org/10.5194/essd-13-1547-2021, 2021
Short summary
Short summary
This dataset provides a set of hydrometric indices for about 1500 stations across Africa with daily discharge data. These indices represent mean flow characteristics and extremes (low flows and floods), allowing us to study the long-term evolution of hydrology in Africa and support the modeling efforts that aim at reducing the vulnerability of African countries to hydro-climatic variability.
Cited articles
Agoungbome, S. M. D., Seidou, O., and Thiam, M.: Evaluation and update of two regional methods (ORSTOM and CIEH) for estimations of flow used in structural design in West Africa, in: Innovations and Interdisciplinary Solutions for Underserved Areas, edited by: Kebe, C. M. F., Gueye, A., Ndiaye, A., and Garba, A., Springer Int. Publ., https://doi.org/10.1007/978-3-319-98878-8_15, 153–162, 2018.
Aich, V., Liersch, S., Vetter, T., Fournet, S., Andersson, J. C. M., Calmanti, S., van Weert, F. H. A., Hattermann, F. F., and Paton, E. N.: Flood projections within the Niger River Basin under future land use and climate change, Sci. Total Environ., 562, 666–677, https://doi.org/10.1016/j.scitotenv.2016.04.021, 2016.
Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, https://doi.org/10.1109/TAC.1974.1100705, 1974.
Almazroui, M., Saeed, F., Saeed, S., Nazrul Islam, M., Ismail, M., Klutse, N. A. B., and Siddiqui, M. H.: Projected change in temperature and precipitation over Africa from CMIP6, Earth Syst. Environ., 4, 455–475, https://doi.org/10.1007/s41748-020-00161-x, 2020.
Andersson, J., Pechlivanidis, I., Gustafsson, D., Donnelly, C., and Arheimer, B.: Key factors for improving large-scale hydrological model performance, Eur. Water, 49, 77–88, 2015.
Arnell, N. W. and Gosling, S. N.: The impacts of climate change on river flood risk at the global scale, Climatic Change, 134, 387–401, https://doi.org/10.1007/s10584-014-1084-5, 2016.
Awotwi, A., Annor, T., Anornu, G. K., Quaye-Ballard, J. A., Agyekum, J., Ampadu, B., Nti, I. K., Gyampo, M. A., and Boakye, E.: Climate change impact on streamflow in a tropical basin of Ghana, West Africa, J. Hydrol. Reg. Stud., 34, 100805, https://doi.org/10.1016/j.ejrh.2021.100805, 2021.
Babaousmail, H., Ayugi, B. O., Ojara, M., Ngoma, H., Oduro, C., Mumo, R., and Ongoma, V.: Evaluation of CMIP6 models for simulations of diurnal temperature range over Africa, J. Afr. Earth Sci., 202, 104944, https://doi.org/10.1016/j.jafrearsci.2023.104944, 2023.
Bell, V. A., Kay, A. L., Jones, R. G., and Moore, R. J.: Development of a high resolution grid-based river flow model for use with regional climate model output, Hydrol. Earth Syst. Sci., 11, 532–549, https://doi.org/10.5194/hess-11-532-2007, 2007.
Berger, V. W. and Zhou, Y.: Kolmogorov–Smirnov test: overview, in: Wiley StatsRef: Statistics Reference Online, 1st edn., edited by: Kenett, R. S., Longford, N. T., Piegorsch, W. W., and Ruggeri, F., Wiley, https://doi.org/10.1002/9781118445112.stat06558, 2014.
Biaou, S., Gouwakinnou, G. N., Noulèkoun, F., Salako, K. V., Houndjo Kpoviwanou, J. M. R., Houehanou, T. D., and Biaou, H. S. S.: Incorporating intraspecific variation into species distribution models improves climate change analyses of a widespread West African tree species (Pterocarpus erinaceus Poir, Fabaceae), Glob. Ecol. Conserv., 45, e02538, https://doi.org/10.1016/j.gecco.2023.e02538, 2023.
Bichet, A., Diedhiou, A., Hingray, B., Evin, G., Touré, N. E., Browne, K. N. A., and Kouadio, K.: Assessing uncertainties in the regional projections of precipitation in CORDEX-AFRICA, Climatic Change, 162, 583–601, https://doi.org/10.1007/s10584-020-02833-z, 2020.
Blanchet, J., Molinié, G., and Touati, J.: Spatial analysis of trend in extreme daily rainfall in southern France, Clim. Dyn., 51, 799–812, https://doi.org/10.1007/s00382-016-3122-7, 2018.
Bodian, A., Dezetter, A., Deme, A., and Diop, L.: Hydrological evaluation of TRMM rainfall over the Upper Senegal River Basin, Hydrology, 3, 15, https://doi.org/10.3390/hydrology3020015, 2016.
Bodian, A., Dezetter, A., Diop, L., Deme, A., Djaman, K., and Diop, A.: Future climate change impacts on streamflows of two main West Africa river basins: Senegal and Gambia, Hydrology, 5, 21, https://doi.org/10.3390/hydrology5010021, 2018.
Bodian, A., Diop, L., Panthou, G., Dacosta, H., Deme, A., Dezetter, A., Ndiaye, P. M., Diouf, I., and Vischel, T.: Recent trend in hydroclimatic conditions in the Senegal River Basin, Water, 12, 436, https://doi.org/10.3390/w12020436, 2020.
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J., Guenet, B., Guez, L., E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model, J Adv Model Earth Syst, 12, https://doi.org/10.1029/2019ms002010, 2020.
Bruneau, P., Gascuel-Odoux, C., Robin, P., Merot, Ph., and Beven, K.: Sensitivity to space and time resolution of a hydrological model using digital elevation data, Hydrol. Process., 9, 69–81, https://doi.org/10.1002/hyp.3360090107, 1995.
Brunner, M. I., Slater, L., Tallaksen, L. M., and Clark, M.: Challenges in modeling and predicting floods and droughts: A review, WIREs Water, 8, e1520, https://doi.org/10.1002/wat2.1520, 2021.
Calton, B., Schellekens, J., and Martinez-de la Torre, A.: Water Resource Reanalysis v1: Data access and model verification results (Version v1.02), Zenodo [software], https://doi.org/10.5281/zenodo.57760, 2016.
Chagnaud, G., Panthou, G., Vischel, T., and Lebel, T.: A synthetic view of rainfall intensification in the West African Sahel, Environ. Res. Lett., 17, 044005, https://doi.org/10.1088/1748-9326/ac4a9c, 2022.
Chagnaud, G., Panthou, G., Vischel, T., and Lebel, T.: Capturing and attributing the rainfall regime intensification in the West African Sahel with CMIP6 models, J. Climate, 36, 1823–1843, https://doi.org/10.1175/jcli-d-22-0412.1, 2023.
Choulga, M., Moschini, F., Mazzetti, C., Grimaldi, S., Disperati, J., Beck, H., Salamon, P., and Prudhomme, C.: Technical note: Surface fields for global environmental modelling, Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, 2024.
Chu, H., Lin, Y., Huang, C., Hsu, C., and Chen, H.: Modelling the hydrologic effects of dynamic land-use change using a distributed hydrologic model and a spatial land-use allocation model, Hydrol. Process., 24, 2538–2554, https://doi.org/10.1002/hyp.7667, 2010.
Coles , G.S.: An introduction to statistical modeling of extreme value, Springer-Verlag, Heidelberg, 2001.
CRED: 2021 Disasters in numbers, CRED, Brussels, 2022.
Davie, J. C. S., Falloon, P. D., Kahana, R., Dankers, R., Betts, R., Portmann, F. T., Wisser, D., Clark, D. B., Ito, A., Masaki, Y., Nishina, K., Fekete, B., Tessler, Z., Wada, Y., Liu, X., Tang, Q., Hagemann, S., Stacke, T., Pavlick, R., Schaphoff, S., Gosling, S. N., Franssen, W., and Arnell, N.: Comparing projections of future changes in runoff from hydrological and biome models in ISI-MIP, Earth Syst. Dynam., 4, 359–374, https://doi.org/10.5194/esd-4-359-2013, 2013.
Dawson, C. W., Abrahart, R. J., Shamseldin, A. Y., Wilby, R. L., and See, L. M.: Neural network modelling of the 20-year flood event for catchments across the UK, in: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, Montreal, QC, Canada, 31 July 2005 - 04 August 2005, 4, 2637–2642, https://doi.org/10.1109/IJCNN.2005.1556319, 2005.
De Longueville, F., Ozer, P., Gemenne, F., Henry, S., Mertz, O., and Nielsen, J. Ø.: Comparing climate change perceptions and meteorological data in rural West Africa to improve the understanding of household decisions to migrate, Climatic Change, 160, 123–141, https://doi.org/10.1007/s10584-020-02704-7, 2020.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Descroix, L., Guichard, F., Grippa, M., Lambert, L. A., Panthou, G., Mahé, G., Gal, L., Dardel, C., Quantin, G., Kergoat, L., Bouaïta, Y., Hiernaux, P., Vischel, T., Pellarin, T., Faty, B., Wilcox, C., Malam Abdou, M., Mamadou, I., Vandervaere, J.-P., Diongue-Niang, A., Ndiaye, O., Sané, Y., Dacosta, H., Gosset, M., Cassé, C., Sultan, B., Barry, A., Amogu, O., Nka Nnomo, B., Barry, A., and Paturel, J.-E.: Evolution of surface hydrology in the Sahelo-Sudanian strip: an updated review, Water, 10, 748, https://doi.org/10.3390/w10060748, 2018.
Diallo, A., Donkor, E., and Owusu, V.: Climate change adaptation strategies, productivity and sustainable food security in southern Mali, Climatic Change, 159, 309–327, https://doi.org/10.1007/s10584-020-02684-8, 2020.
Diop, S. B., Tramblay, Y., Bodian, A., Ekolu, J., Rouché, N., and Dieppois, B.: Flood frequency analysis in West Africa, J. Flood Risk Manag., 18, e70001, https://doi.org/10.1111/jfr3.70001, 2025.
Dosio, A., Jones, R. G., Jack, C., Lennard, C., Nikulin, G., and Hewitson, B.: What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models, Clim. Dyn., 53, 5833–5858, https://doi.org/10.1007/s00382-019-04900-3, 2019.
Dosio, A., Jury, M. W., Almazroui, M., Ashfaq, M., Diallo, I., Engelbrecht, F. A., Klutse, N. A. B., Lennard, C., Pinto, I., Sylla, M. B., and Tamoffo, A. T.: Projected future daily characteristics of African precipitation based on global (CMIP5, CMIP6) and regional (CORDEX, CORDEX-CORE) climate models, Clim. Dyn., 57, 3135–3158, https://doi.org/10.1007/s00382-021-05859-w, 2021.
Dotse, S.-Q., Larbi, I., Limantol, A. M., Asare-Nuamah, P., Frimpong, L. K., Alhassan, A.-R. M., Sarpong, S., Angmor, E., and Ayisi-Addo, A. K.: Rainfall projections from Coupled Model Intercomparison Project Phase 6 in the Volta River Basin: implications on achieving sustainable development, Sustainability, 15, 1472, https://doi.org/10.3390/su15021472, 2023.
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E., Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C., Durachta, J., Dussin, R., Gauthier, P. P. G., Griffies, S. M., Guo, H., Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R., Milly, P. C. D., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A., Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T., Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B., Zeng, Y., and Zhao, M.: The GFDL Earth System Model Version 4.1 (GFDL‐ESM 4.1): Overall Coupled Model Description and Simulation Characteristics, J Adv Model Earth Syst, 12, https://doi.org/10.1029/2019ms002015, 2020.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012.
Ekolu, J., Dieppois, B., Tramblay, Y., Villarini, G., Slater, L. J., Mahé, G., Paturel, J.-E., Eden, J. M., Moulds, S., Sidibe, M., Camberlin, P., Pohl, B., and van de Wiel, M.: Variability in flood frequency in sub-Saharan Africa: The role of large-scale climate modes of variability and their future impacts, J. Hydrol., 640, 131679, https://doi.org/10.1016/j.jhydrol.2024.131679, 2024.
Ekolu, J., Dieppois, B., Diop, S. B., Bodian, A., Grimaldi, S., Salamon, P., Villarini, G., Eden, J. M., Monerie, P.-A., van de Wiel, M., and Tramblay, Y.: How could climate change affect the magnitude, duration and frequency of hydrological droughts and floods in West Africa during the 21st century? A storyline approach, J. Hydrol., 660, 133482, https://doi.org/10.1016/j.jhydrol.2025.133482, 2025.
El Adlouni, S., Ouarda, T. B. M. J., Zhang, X., Roy, R., and Bobée, B.: Generalized maximum likelihood estimators for the nonstationary generalized extreme value model, Water Resour. Res., 43, W03410, https://doi.org/10.1029/2005WR004545, 2007.
Elagib, N. A., Zayed, I. S. A., Saad, S. A. G., Mahmood, M. I., Basheer, M., and Fink, A. H.: Debilitating floods in the Sahel are becoming frequent, J. Hydrol., 599, 126362, https://doi.org/10.1016/j.jhydrol.2021.126362, 2021.
EM-DAT: The OFDA/CRED International Disaster Database, Centre for Research on the Epidemiology of Disasters (CRED), Université catholique de Louvain, http://www.emdat.be (last access: 9 July 2024), 2015.
Engmann, S. and Cousineau, D.: Comparing distributions: The two-sample Anderson–Darling test as an alternative to the Kolmogorov–Smirnov test, J. Appl. Quant. Methods, 6, 1–17, 2011.
Famien, A. M., Janicot, S., Ochou, A. D., Vrac, M., Defrance, D., Sultan, B., and Noël, T.: A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies, Earth Syst. Dynam., 9, 313–338, https://doi.org/10.5194/esd-9-313-2018, 2018.
Farris, S., Deidda, R., Viola, F., and Mascaro, G.: On the role of serial correlation and field significance in detecting changes in extreme precipitation frequency, Water Resour. Res., 57, e2021WR030172, https://doi.org/10.1029/2021WR030172, 2021.
Feaster, T. D., Gotvald, A. J., Musser, J. W., Weaver, J. C., Kolb, K. R., Veilleux, A. G., and Wagner, D. M.: Magnitude and frequency of floods for rural streams in Georgia, South Carolina, and North Carolina, 2017—Results, U. S. Geological Survey Scientific Investigations Report, 2023–5006, 75 pp., U.S. Geological Survey, https://doi.org/10.3133/sir20235006, 2023.
Fisher, R. A.: Statistical Methods for Research Workers, in: Breakthroughs in Statistics, edited by: Kotz, S. and Johnson, N. L., Springer Series in Statistics, Springer, New York, NY, https://doi.org/10.1007/978-1-4612-4380-9_6, 66–70, 1992.
Flaounas, E., Drobinski, P., Vrac, M., Bastin, S., Lebeaupin-Brossier, C., Stéfanon, M., Borga, M., and Calvet, J.-C.: Precipitation and temperature space–time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods, Clim. Dyn., 40, 2687–2705, https://doi.org/10.1007/s00382-012-1558-y, 2013.
Fortin, F.-A., De Rainville, F.-M., Gardner, M.-A. G., Parizeau, M., and Gagné, C.: DEAP: Evolutionary algorithms made easy, J. Mach. Learn. Res., 13, 2171–2175, 2012.
Fréchet, M.: Sur la loi de probabilité de l'écart maximum, Annales Soc. Polon. Math., 6, 93–116, 1927.
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K., Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F., Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts of 1.5 +°C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, 2017.
Gebremeskel, S., Liu, Y. B., De Smedt, F., Hoffmann, L., and Pfister, L.: Assessing the hydrological effects of landuse changes using distributed hydrological modelling and GIS, Int. J. River Basin Manag., 3, 261–271, https://doi.org/10.1080/15715124.2005.9635266, 2005.
Gilleland, E. and Katz, R. W.: extRemes 2.0: An Extreme Value Analysis Package in R, Journal of Statistical Software, 72, 1–39, https://doi.org/10.18637/jss.v072.i08, 2016.
Gosling, S. N., Zaherpour, J., Mount, N. J., Hattermann, F. F., Dankers, R., Arheimer, B., Breuer, L., Ding, J., Haddeland, I., Kumar, R., Kundu, D., Liu, J., Van Griensven, A., Veldkamp, T. I. E., Vetter, T., Wang, X., and Zhang, X.: A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C, Climatic Change, 141, 577–595, https://doi.org/10.1007/s10584-016-1773-3, 2017.
Gudmundsson, L., Wagener, T., Tallaksen, L. M., and Engeland, K.: Evaluation of nine large-scale hydrological models with respect to the seasonal runoff climatology in Europe, Water Resour. Res., 48, W11504, https://doi.org/10.1029/2011WR010911, 2012.
Gumbel, E. J.: Statistics of Extremes, Columbia University Press, New York, Chichester, West Sussex, https://doi.org/10.7312/gumb92958, 1958.
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.
Haddeland, I., Matheussen, B. V., and Lettenmaier, D. P.: Influence of spatial resolution on simulated streamflow in a macroscale hydrologic model, Water Resour. Res., 38, 29–1–29-10, https://doi.org/10.1029/2001WR000854, 2002.
Hamdi, Y., Duluc, C.-M., and Rebour, V.: Temperature extremes: estimation of non-stationary return levels and associated uncertainties, Atmosphere, 9, 129, https://doi.org/10.3390/atmos9040129, 2018.
Hamed, K. H. and Rao, A. R.: A modified Mann-Kendall trend test for autocorrelated data, J. Hydrol., 204, 182–196, https://doi.org/10.1016/S0022-1694(97)00125-X, 1998.
Han, X., Mehrotra, R., Sharma, A., and Rahman, A.: Incorporating nonstationarity in regional flood frequency analysis procedures to account for climate change impact, J. Hydrol., 612, 128235, https://doi.org/10.1016/j.jhydrol.2022.128235, 2022.
Hansen, J., Ruedy, R., Sato, M., and Lo, K.: Global surface temperature change, Rev. Geophys., 48, RG4004, https://doi.org/10.1029/2010RG000345, 2010.
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional climate predictions, B. Am. Meteorol. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009.
Hawkins, E. and Sutton, R.: Time of emergence of climate signals, Geophys. Res. Lett., 39, L01702, https://doi.org/10.1029/2011GL050087, 2012.
Heinicke, S., Volkholz, J., Schewe, J., Gosling, S. N., Müller Schmied, H., Zimmermann, S., Mengel, M., Sauer, I. J., Burek, P., Chang, J., Kou-Giesbrecht, S., Grillakis, M., Guillaumot, L., Hanasaki, N., Koutroulis, A., Otta, K., Qi, W., Satoh, Y., Stacke, T., Yokohata, T., and Frieler, K.: Global hydrological models continue to overestimate river discharge, Environ. Res. Lett., 19, 074005, https://doi.org/10.1088/1748-9326/ad52b0, 2024.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hochberg, Y. and Benjamini, Y.: Controlling the false discovery rate: A practical and powerful approach to multiple testing, J. R. Stat. Soc. B, 57, 289–300, 1995.
Hosking, J. R. M.: L-moments: Analysis and estimation of distributions using linear combinations of order statistics, J. R. Stat. Soc. B, 52, 105–124, https://doi.org/10.1111/j.2517-6161.1990.tb01775.x, 1990.
Hossain, A., Mathias, C., and Blanton, R.: Remote sensing of turbidity in the Tennessee River using Landsat 8 satellite, Remote Sens.-Basel, 13, 3785, https://doi.org/10.3390/rs13183785, 2021.
Houghton, J. T., Ding, Y., Griggs, D., Noguer, M., van der Linden, P., Dai, X., Maskell, M., and Johnson, C.: Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, 881 pp., 2001.
Huang, X., Yin, J., Slater, L. J., Kang, S., He, S., and Liu, P.: Global projection of flood risk with a bivariate framework under 1.5–3.0 °C warming levels, Earths Future, 12, e2023EF004312, https://doi.org/10.1029/2023EF004312, 2024.
IPCC: Climate Change 2021: The Physical Science Basis, Sixth Assessment Report (AR6), Cambridge University Press, 2391 pp., 2021.
Jajarmizad, M., Harun, S., and Salarpour, M.: A review on theoretical consideration and types of models in hydrology, J. Environ. Sci. Technol., 5, 249–261, https://doi.org/10.3923/jest.2012.249.261, 2012.
Jayaweera, L., Wasko, C., and Nathan, R.: Modelling non-stationarity in extreme rainfall using large-scale climate drivers, J. Hydrol., 636, 131309, https://doi.org/10.1016/j.jhydrol.2024.131309, 2024.
Katz, R. W.: Statistical methods for nonstationary extremes, in: Extremes in a Changing Climate, Water Sci. Technol. Libr., vol. 65, edited by: AghaKouchak, A., Easterling, D., Hsu, K., Schubert, S., and Sorooshian, S., Springer, Dordrecht, https://doi.org/10.1007/978-94-007-4479-0_2, 15–37, 2013.
Kauffeldt, A., Halldin, S., Rodhe, A., Xu, C.-Y., and Westerberg, I. K.: Disinformative data in large-scale hydrological modelling, Hydrol. Earth Syst. Sci., 17, 2845–2857, https://doi.org/10.5194/hess-17-2845-2013, 2013.
Kendall, M. G.: Rank correlation methods, 4th Edn., 2nd impression, Griffin, London, ISBN 0852641990, 1975.
Khaliq, M. N., Ouarda, T. B. M. J., Gachon, P., Sushama, L., and St-Hilaire, A.: Identification of hydrological trends in the presence of serial and cross correlations: A review of selected methods and their application to annual flow regimes of Canadian rivers, J. Hydrol., 368, 117–130, https://doi.org/10.1016/j.jhydrol.2009.01.035, 2009.
Klutse, N. A. B., Quagraine, K. A., Nkrumah, F., Quagraine, K. T., Berkoh-Oforiwaa, R., Dzrobi, J. F., and Sylla, M. B.: The climatic analysis of summer monsoon extreme precipitation events over West Africa in CMIP6 simulations, Earth Syst. Environ., 5, 25–41, https://doi.org/10.1007/s41748-021-00203-y, 2021.
Koubodana, H. D., Atchonouglo, K., Adounkpe, J. G., Amoussou, E., Kodja, D. J., Koungbanane, D., Afoudji, K. Y., Lombo, Y., and Kpemoua, K. E.: Surface runoff prediction and comparison using IHACRES and GR4J lumped models in the Mono catchment, West Africa, Proc. IAHS, 384, 63–68, https://doi.org/10.5194/piahs-384-63-2021, 2021.
Krishnamurthy, P. K., Lewis, K., and Choularton, R. K.: Climate impacts on food security and nutrition—A review of existing knowledge, Met Office and WFP's Office for Climate Change, Environment and Disaster Risk Reduction, Exeter, UK, 2012.
Kwakye, S. O. and Bárdossy, A.: Hydrological modelling in data-scarce catchments: Black Volta basin in West Africa, SN Appl. Sci., 2, 628, https://doi.org/10.1007/s42452-020-2454-4, 2020.
Lajaunie, M.-L., Bonzanigo, L., Fraval, P., and Scheierling, S. M.: World Bank engagement in transboundary waters in West Africa: retrospective and lessons learned, World Bank Group, Washington, D. C., https://documents.worldbank.org/curated/en/652141636371253908 (last access: 4 September 2025), 2021.
Lalou, R., Sultan, B., Muller, B., and Ndonky, A.: Does climate opportunity facilitate smallholder farmers' adaptive capacity in the Sahel?, Palgrave Commun., 5, 81, https://doi.org/10.1057/s41599-019-0288-8, 2019.
Lange, S.: Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset, Earth Syst. Dynam., 9, 627–645, https://doi.org/10.5194/esd-9-627-2018, 2018.
Lange, S.: EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI) [data set], Version 1.1, GFZ Data Services, https://doi.org/10.5880/PIK.2019.004, 2019.
Lawrence, D.: Uncertainty introduced by flood frequency analysis in projections for changes in flood magnitudes under a future climate in Norway, J. Hydrol. Reg. Stud., 28, 100675, https://doi.org/10.1016/j.ejrh.2020.100675, 2020.
Lee, H., Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P., Trisos, C., Romero, J., Aldunce, P., Barret, K., and others: IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, Lee, H. and Romero, J. (eds.), IPCC, Geneva, Switzerland, 2023.
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187n, 1945.
Martins, E. S. and Stedinger, J. R.: Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data, Water Resour. Res., 36, 737–744, https://doi.org/10.1029/1999WR900330, 2000.
Masson-Delmotte, V. P., Zhai, P., Pirani, S. L., Connors, C., Péan, S., Berger, N., Caud, Y., Chen, L., Goldfarb, M. I., and Scheel Monteiro, P. M.: IPCC, 2021: Summary for Policymakers, in: Climate Change 2021: The Physical Science Basis, Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2021.
Matthew, O. A., Owolabi, O. A., Osabohien, R., Urhie, E., Ogunbiyi, T., Olawande, T. I., Edafe, O. D., and Daramola, P. J.: Carbon emissions, agricultural output and life expectancy in West Africa, Int. J. Energy Econ. Policy, 10, 489–496, https://doi.org/10.32479/ijeep.9177, 2020.
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S., Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann, S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz‐de‐la‐Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S., Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens, B., von Storch, J., Tian, F., Voigt, A., Vrese, P., Wieners, K., Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J Adv Model Earth Syst, 11, 998–1038, https://doi.org/10.1029/2018ms001400, 2019.
Michelangeli, P.-A., Vrac, M., and Loukos, H.: Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophys. Res. Lett., 36, L11701, https://doi.org/10.1029/2009GL038401, 2009.
Monerie, P.-A., Dittus, A. J., Wilcox, L. J., and Turner, A. G.: Uncertainty in simulating twentieth century West African precipitation trends: The role of anthropogenic aerosol emissions, Earths Future, 11, e2022EF002995, https://doi.org/10.1029/2022EF002995, 2023.
Mudge, J. F., Baker, L. F., Edge, C. B., and Houlahan, J. E.: Setting an optimal α that minimizes errors in null hypothesis significance tests, PLoS ONE, 7, e32734, https://doi.org/10.1371/journal.pone.0032734, 2012.
Mulcahy, J. P., Johnson, C., Jones, C. G., Povey, A. C., Scott, C. E., Sellar, A., Turnock, S. T., Woodhouse, M. T., Abraham, N. L., Andrews, M. B., Bellouin, N., Browse, J., Carslaw, K. S., Dalvi, M., Folberth, G. A., Glover, M., Grosvenor, D. P., Hardacre, C., Hill, R., Johnson, B., Jones, A., Kipling, Z., Mann, G., Mollard, J., O'Connor, F. M., Palmiéri, J., Reddington, C., Rumbold, S. T., Richardson, M., Schutgens, N. A. J., Stier, P., Stringer, M., Tang, Y., Walton, J., Woodward, S., and Yool, A.: Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations, Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, 2020.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Ndehedehe, C. E.: The water resources of tropical West Africa: problems, progress, and prospects, Acta Geophys., 67, 621–649, https://doi.org/10.1007/s11600-019-00260-y, 2019.
Nicholson, S. E.: Climate of the Sahel and West Africa, Oxford Res. Encycl. Clim. Sci., Oxford University Press, https://doi.org/10.1093/acrefore/9780190228620.013.510, 2018.
Niel, H., Paturel, J.-E., and Servat, E.: Study of parameter stability of a lumped hydrologic model in a context of climatic variability, J. Hydrol., 278, 213–230, https://doi.org/10.1016/S0022-1694(03)00158-6, 2003.
Nka, B. N., Oudin, L., Karambiri, H., Paturel, J. E., and Ribstein, P.: Trends in floods in West Africa: analysis based on 11 catchments in the region, Hydrol. Earth Syst. Sci., 19, 4707–4719, https://doi.org/10.5194/hess-19-4707-2015, 2015.
Noël, T., Loukos, H., Defrance, D., Vrac, M., and Levavasseur, G.: Extending the global high-resolution downscaled projections dataset to include CMIP6 projections at increased resolution coherent with the ERA5-Land reanalysis, Data Brief, 45, 108669, https://doi.org/10.1016/j.dib.2022.108669, 2022.
Nooni, I. K., Ogou, F. K., Chaibou, A. A. S., Nakoty, F. M., Gnitou, G. T., and Lu, J.: Evaluating CMIP6 historical mean precipitation over Africa and the Arabian Peninsula against satellite-based observation, Atmosphere, 14, 607, https://doi.org/10.3390/atmos14030607, 2023.
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., and Solecki, W.: The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century, Global Environ. Chang., 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017.
Orange, D.: Hydroclimatologie du Fouta Djalon et dynamique actuelle d'un vieux paysage latéritique (Afrique de l'Ouest), PhD thesis, Université de Strasbourg 1, Strasbourg, France, 220 pp., 1990.
Panthou, G., Vischel, T., Lebel, T., Quantin, G., Pugin, A.-C. F., Blanchet, J., and Ali, A.: From pointwise testing to a regional vision: An integrated statistical approach to detect nonstationarity in extreme daily rainfall. Application to the Sahelian region, J. Geophys. Res.-Atmos., 118, 8222–8237, https://doi.org/10.1002/jgrd.50340, 2013.
Papalexiou, S. M. and Koutsoyiannis, D.: Battle of extreme value distributions: A global survey on extreme daily rainfall, Water Resour. Res., 49, 187–201, https://doi.org/10.1029/2012WR012557, 2013.
Pechlivanidis, I. G., Arheimer, B., Donnelly, C., Hundecha, Y., Huang, S., Aich, V., Samaniego, L., Eisner, S., and Shi, P.: Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions, Climatic Change, 141, 467–481, https://doi.org/10.1007/s10584-016-1723-0, 2017.
Pielke, R. and Ritchie, J.: How Climate Scenarios Lost Touch With Reality, Issues Sci. Technol., 37, 74–83, 2021.
Pokhrel, P., Gupta, H. V., and Wagener, T.: A spatial regularization approach to parameter estimation for a distributed watershed model, Water Resour. Res., 44, W12407, https://doi.org/10.1029/2007WR006615, 2008.
Pospichal, B., Karam, D. B., Crewell, S., Flamant, C., Hünerbein, A., Bock, O., and Saïd, F.: Diurnal cycle of the intertropical discontinuity over West Africa analysed by remote sensing and mesoscale modelling, Q. J. Roy. Meteor. Soc., 136, 92–106, https://doi.org/10.1002/qj.435, 2010.
Prosdocimi, I. and Kjeldsen, T.: Parametrisation of change-permitting extreme value models and its impact on the description of change, Stoch. Env. Res. Risk A., 35, 307–324, https://doi.org/10.1007/s00477-020-01940-8, 2021.
Prudhomme, C., Zsótér, E., Matthews, G., Melet, A., Grimaldi, S., Zuo, H., Hansford, E., Harrigan, S., Mazzetti, C., de Boisseson, E., Salamon, P., and Garric, G.: Global hydrological reanalyses: The value of river discharge information for world-wide downstream applications – The example of the Global Flood Awareness System GloFAS, Meteorol. Appl., 31, e2192, https://doi.org/10.1002/met.2192, 2024.
Rai, S., Hoffman, A., Lahiri, S., Nychka, D. W., Sain, S. R., and Bandyopadhyay, S.: Fast parameter estimation of generalized extreme value distribution using neural networks, Environmetrics, 35, e2845, https://doi.org/10.1002/env.2845, 2024.
Rameshwaran, P., Bell, V. A., Davies, H. N., and Kay, A. L.: How might climate change affect river flows across West Africa?, Climatic Change, 169, 21, https://doi.org/10.1007/s10584-021-03256-0, 2021.
Rameshwaran, P., Bell, V. A., Brown, M. J., Davies, H. N., Kay, A. L., Rudd, A. C., and Sefton, C.: Use of abstraction and discharge data to improve the performance of a national-scale hydrological model, Water Resour. Res., 58, e2021WR029787, https://doi.org/10.1029/2021WR029787, 2022.
Reed, S., Koren, V., Smith, M., Zhang, Z., Moreda, F., Seo, D.-J., and Dmip Participants, A.: Overall distributed model intercomparison project results, J. Hydrol., 298, 27–60, https://doi.org/10.1016/j.jhydrol.2004.03.031, 2004.
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Crespo Cuaresma, J., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Aleluia Da Silva, L., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environ. Chang., 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.
Rodríguez-Fonseca, B., Mohino, E., Mechoso, C. R., Caminade, C., Biasutti, M., Gaetani, M., Garcia-Serrano, J., Vizy, E. K., Cook, K., Xue, Y., Polo, I., Losada, T., Druyan, L., Fontaine, B., Bader, J., Doblas-Reyes, F. J., Goddard, L., Janicot, S., Arribas, A., Lau, W., Colman, A., Vellinga, M., Rowell, D. P., Kucharski, F., and Voldoire, A.: Variability and predictability of West African droughts: A review on the role of sea surface temperature anomalies, J. Climate, 28, 4034–4060, https://doi.org/10.1175/JCLI-D-14-00130.1, 2015.
Roudier, P., Sultan, B., Quirion, P., and Berg, A.: The impact of future climate change on West African crop yields: What does the recent literature say?, Global Environ. Chang., 21, 1073–1083, https://doi.org/10.1016/j.gloenvcha.2011.04.007, 2011.
Salamon, P., Grimaldi, S., Disperati, J., Prudhomme, C., Choulga, M., Moschini, F., and Mazzetti, C.: LISFLOOD static and parameter maps for GloFAS, European Commission, JRC132801, 2023.
Santer, B. D., Bonfils, C. J. W., Fu, Q., Fyfe, J. C., Hegerl, G. C., Mears, C., Painter, J. F., Po-Chedley, S., Wentz, F. J., Zelinka, M. D., and Zou, C.-Z.: Celebrating the anniversary of three key events in climate change science, Nat. Clim. Change, 9, 180–182, https://doi.org/10.1038/s41558-019-0424-x, 2019.
Sauer, I. J., Reese, R., Otto, C., Geiger, T., Willner, S. N., Guillod, B. P., Bresch, D. N., and Frieler, K.: Climate signals in river flood damages emerge under sound regional disaggregation, Nat. Commun., 12, 2128, https://doi.org/10.1038/s41467-021-22153-9, 2021.
Scholz, F. W. and Stephens, M. A.: K-sample Anderson-Darling tests of fit, for continuous and discrete cases, Technical Report No. 81, University of Washington, Seattle, 22 pp., 1986.
Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., and Bronaugh, D.: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate, J. Geophys. Res.-Atmos., 118, 1716–1733, https://doi.org/10.1002/jgrd.50203, 2013.
Song, J.-H., Her, Y., and Kang, M.-S.: Estimating reservoir inflow and outflow from water level observations using expert knowledge: Dealing with an ill-posed water balance equation in reservoir management, Water Resour. Res., 58, e2020WR028183, https://doi.org/10.1029/2020WR028183, 2022.
Stackhouse Jr., P. W., Gupta, S. K., Cox, S. J., Mikovitz, C., Zhang, T., and Hinkelman, L. M.: The NASA/GEWEX Surface Radiation Budget Release 3.0: 24.5-year dataset, GEWEX News, 21, 10–12, 2011.
Stedinger, J. R., and Griffis, V. W.: Getting From Here to Where? Flood Frequency Analysis and Climate, J. Am. Water Resour. As., 47, 506–513, https://doi.org/10.1111/j.1752-1688.2011.00545.x, 2011.
Sule, I. and Odekunle, M. O.: Landscapes of West Africa: A Window on a Changing World, CILSS, U. S. Geological Survey EROS, Garretson, SD, USA, 2016.
Sultan, B. and Gaetani, M.: Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation, Front. Plant Sci., 7, 1262, https://doi.org/10.3389/fpls.2016.01262, 2016.
Tang, Q., Oki, T., Kanae, S., and Hu, H.: The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation, J. Hydrometeorol., 8, 499–512, https://doi.org/10.1175/JHM589.1, 2007.
Taylor, C. M., Belušić, D., Guichard, F., Parker, D. J., Vischel, T., Bock, O., Harris, P. P., Janicot, S., Klein, C., and Panthou, G.: Frequency of extreme Sahelian storms tripled since 1982 in satellite observations, Nature, 544, 475–478, https://doi.org/10.1038/nature22069, 2017.
Tarpanelli, A., Paris, A., Sichangi, A. W., O'Loughlin, F., and Papa, F.: Water resources in Africa: the role of Earth observation data and hydrodynamic modeling to derive river discharge, Surv. Geophys., 44, 97–122, https://doi.org/10.1007/s10712-022-09744-x, 2023.
Tian, C., Huang, G., Lu, C., Song, T., Wu, Y., and Duan, R.: Northward shifts of the Sahara Desert in response to twenty-first-century climate change, J. Climate, 36, 3417–3435, https://doi.org/10.1175/JCLI-D-22-0169.1, 2023.
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125–140, https://doi.org/10.5194/hess-13-125-2009, 2009.
Totin, E., Padgham, J., Ayivor, J., Dietrich, K., Fosu-Mensah, B., Gordon, C., Habtezion, S., Tweneboah Lawson, E., Mensah, A., Nukpezah, D., Ofori, B., Piltz, S., Sidibé, A., Sissoko, M., Traore, P., Dazé, A., and Echeverría, D.: Vulnerability and Adaptation to Climate Change in Semi-Arid Areas in West Africa, International Development Research Center, Canada, https://doi.org/10.13140/RG.2.2.15263.87202, 2016.
Tramblay, Y. and Somot, S.: Future evolution of extreme precipitation in the Mediterranean, Climatic Change, 151, 289–302, https://doi.org/10.1007/s10584-018-2300-5, 2018.
Tramblay, Y., Villarini, G., and Wei, Z.: Observed changes in flood hazard in Africa, Environ. Res. Lett., 15, 104005, https://doi.org/10.1088/1748-9326/abb90b, 2020.
Tramblay, Y., Rouché, N., Paturel, J.-E., Mahé, G., Boyer, J.-F., Amoussou, E., Bodian, A., Dacosta, H., Dakhlaoui, H., Dezetter, A., Hughes, D., Hanich, L., Peugeot, C., Tshimanga, R., and Lachassagne, P.: ADHI: the African Database of Hydrometric Indices (1950–2018), Earth Syst. Sci. Data, 13, 1547–1560, https://doi.org/10.5194/essd-13-1547-2021, 2021.
Tramblay, Y., El Khalki, E. M., Khedimallah, A., Sadaoui, M., Benaabidate, L., Boulmaiz, T., Boutaghane, H., Dakhlaoui, H., Hanich, L., Ludwig, W., Meddi, M., Saidi, M. E., and Mahé, G.: Regional flood frequency analysis in North Africa, J. Hydrol., 630, 130678, https://doi.org/10.1016/j.jhydrol.2024.130678, 2024.
Tran, Q. Q., De Niel, J., and Willems, P.: Spatially distributed conceptual hydrological model building: A generic top-down approach starting from lumped models, Water Resour. Res., 54, 8064–8085, https://doi.org/10.1029/2018WR023566, 2018.
UNDRR: Annual Report 2023, United Nations Office for Disaster Risk Reduction, 44 pp., 2023.
UNEP: Adaptation Gap Report 2020, United Nations Environment Programme, Nairobi, Kenya, 120 pp., 2021.
Van der Knijff, J. M., Younis, J., and De Roo, A. P. J.: LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood simulation, Int. J. Geogr. Inf. Sci., 24, 189–212, https://doi.org/10.1080/13658810802549154, 2010.
van der Land, V., Romankiewicz, C., and van der Geest, K.: Environmental change and migration: A review of West African case studies, in: Routledge Handbook of Environmental Displacement and Migration, edited by: McLeman, R. and Gemenne, F., Routledge, London and New York, ISBN 9781315638843, 163–177, 2018.
Vintrou, E.: Cartographie et caractérisation des systèmes agricoles au Mali par télédétection à moyenne résolution spatiale, PhD thesis, AgroParisTech, Montpellier, France, 2012.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo, P.: The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data, Water Resour. Res., 50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014.
Weibull, W.: A statistical distribution function of wide applicability, J. Appl. Mech., 18, 293–297, https://doi.org/10.1115/1.4010337, 1951.
Wasko, C., Westra, S., Nathan, R., Orr, H. G., Villarini, G., Villalobos Herrera, R., and Fowler, H. J.: Incorporating climate change in flood estimation guidance, Philos. T. R. Soc. A, 379, 20190548, https://doi.org/10.1098/rsta.2019.0548, 2021.
Wasko, C., Guo, D., Ho, M., Nathan, R., and Vogel, E.: Diverging projections for flood and rainfall frequency curves, J. Hydrol., 620, 129403, https://doi.org/10.1016/j.jhydrol.2023.129403, 2023.
Wilcox, C., Vischel, T., Panthou, G., Bodian, A., Blanchet, J., Descroix, L., Quantin, G., Cassé, C., Tanimoun, B., and Koné, S.: Trends in hydrological extremes in the Senegal and Niger Rivers, J. Hydrol., 566, 531–545, https://doi.org/10.1016/j.jhydrol.2018.07.063, 2018.
Wilks, D. S.: On “Field Significance” and the False Discovery Rate, J. Appl. Meteorol. Clim., 45, 1181–1189, https://doi.org/10.1175/JAM2404.1, 2006.
Wilks, D. S.: “The stippling shows statistically significant grid points”: How research results are routinely overstated and overinterpreted, and what to do about it, B. Am. Meteorol. Soc., 97, 2263–2273, https://doi.org/10.1175/BAMS-D-15-00267.1, 2016.
Wilson, C. B., Valdes, J. B., and Rodriguez-Iturbe, I.: On the influence of the spatial distribution of rainfall on storm runoff, Water Resour. Res., 15, 321–328, https://doi.org/10.1029/WR015i002p00321, 1979.
Wolock, D. M. and Price, C. V.: Effects of digital elevation model map scale and data resolution on a topography-based watershed model, Water Resour. Res., 30, 3041–3052, https://doi.org/10.1029/94WR01971, 1994.
Yukimoto, S., Kawai, H., Koshino, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, J. Meteorol. Soc. Jpn., 97, 931–965, https://doi.org/10.2151/jmsj.2019-051, 2019.
Zhao, F., Nie, N., Liu, Y., Yi, C., Guillaumot, L., Wada, Y., Burek, P., Smilovic, M., Frieler, K., Buechner, M., Schewe, J., and Gosling, S. N.: Benefits of calibrating a global hydrological model for regional analyses of flood and drought projections: a case study of the Yangtze River Basin, Water Resour. Res., 61, e2024WR037153, https://doi.org/10.1029/2024WR037153, 2025.
Short summary
West Africa is very vulnerable to river floods. Current flood hazards are poorly understood due to limited data. This study is filling this knowledge gap using recent databases and two regional hydrological models to analyze changes in flood risk under two climate scenarios. Results show that most areas will see more frequent and severe floods, with some increasing by over 45 %. These findings stress the urgent need for climate-resilient strategies to protect communities and infrastructure.
West Africa is very vulnerable to river floods. Current flood hazards are poorly understood due...
Altmetrics
Final-revised paper
Preprint