Articles | Volume 22, issue 6
https://doi.org/10.5194/hess-22-3175-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-3175-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2018
Research article |
| 07 Jun 2018
| 07 Jun 2018
Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL,
CNRS), Centre d'Etudes de Saclay, Orme des Merisiers, 91190
Gif-sur-Yvette, France
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Duncan Pappert, Alexandre Tuel, Dim Coumou, Mathieu Vrac, and Olivia Martius
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This study examines the mechanisms that characterise long-lasting (persistent) and short hot spells in Europe in a comparative framework. By analysing weather data, we found that long spells in Southwestern Europe are typically preceded by dry soil conditions and driven by multiple persistence-inducing mechanisms. In contrast, short spells occur in a more transient atmospheric situation and exhibit fewer drivers. Understanding persistent heat extremes can help improve their prediction.
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-102, https://doi.org/10.5194/hess-2024-102, 2024
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Atmospheric variables from climate models often present biases relative to the past. In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. We tested several Multivariate Bias Correction Methods (MBCMs) for 5 physical variables that are input variables for 4 process models. We provide recommendations regarding the use of MBCMs when multivariate and time dependent processes are involved.
Davide Faranda, Gabriele Messori, Erika Coppola, Tommaso Alberti, Mathieu Vrac, Flavio Pons, Pascal Yiou, Marion Saint Lu, Andreia N. S. Hisi, Patrick Brockmann, Stavros Dafis, Gianmarco Mengaldo, and Robert Vautard
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We aim to combine multiple global climate models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α pooling", aggregating the cumulative distribution functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments with European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
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Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
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Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
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Anna Denvil-Sommer, Marion Gehlen, and Mathieu Vrac
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Mathieu Vrac and Soulivanh Thao
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We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations).
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Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
Emanuele Bevacqua, Michalis I. Vousdoukas, Theodore G. Shepherd, and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 20, 1765–1782, https://doi.org/10.5194/nhess-20-1765-2020, https://doi.org/10.5194/nhess-20-1765-2020, 2020
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Coastal compound flooding (CF), caused by interacting storm surges and high water runoff, is typically studied based on concurring storm surge extremes with either precipitation or river discharge extremes. Globally, these two approaches show similar CF spatial patterns, especially where the CF potential is the highest. Deviations between the two approaches increase with the catchment size. The precipitation-based analysis allows for considering
local-rainfall-driven CF and CF in small rivers.
Bastien François, Mathieu Vrac, Alex J. Cannon, Yoann Robin, and Denis Allard
Earth Syst. Dynam., 11, 537–562, https://doi.org/10.5194/esd-11-537-2020, https://doi.org/10.5194/esd-11-537-2020, 2020
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Recently, multivariate bias correction (MBC) methods designed to adjust climate simulations have been proposed. However, they use different approaches, leading potentially to different results. Therefore, this study intends to intercompare four existing MBC methods to provide end users with aid in choosing such methods for their applications. To do so, a wide range of evaluation criteria have been used to assess the ability of MBC methods to correct statistical properties of climate models.
Eric Pohl, Christophe Grenier, Mathieu Vrac, and Masa Kageyama
Hydrol. Earth Syst. Sci., 24, 2817–2839, https://doi.org/10.5194/hess-24-2817-2020, https://doi.org/10.5194/hess-24-2817-2020, 2020
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Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, https://doi.org/10.5194/hess-24-2841-2020, 2020
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At subdaily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.
Florentin Breton, Mathieu Vrac, Pascal Yiou, Pradeebane Vaittinada Ayar, and Aglaé Jézéquel
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2020-26, https://doi.org/10.5194/esd-2020-26, 2020
Revised manuscript not accepted
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We investigate North Atlantic weather seasonality over 1979–2100 by classifying year-round fields of 500 hPa geopotential height from one reanalysis dataset and 12 climate models. Generally, models have seasonal structures similar to the reanalyses. Historical winter (summer) conditions decrease (increase), due to uniform Z500 increase (i.e. uniform warming). However, relative to the increasing Z500 seasonal cycle, future seasonality (spatial patterns, seasonal cycle) appears almost stationary.
Giulia Carella, Mathieu Vrac, Hélène Brogniez, Pascal Yiou, and Hélène Chepfer
Earth Syst. Sci. Data, 12, 1–20, https://doi.org/10.5194/essd-12-1-2020, https://doi.org/10.5194/essd-12-1-2020, 2020
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Observations of relative humidity for ice clouds over the tropical oceans from a passive microwave sounder are downscaled by incorporating the high-resolution variability derived from simultaneous co-located cloud profiles from a lidar. By providing a method to generate pseudo-observations of relative humidity at high spatial resolution, this work will help revisit some of the current key barriers in atmospheric science.
Anna Denvil-Sommer, Marion Gehlen, Mathieu Vrac, and Carlos Mejia
Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019, https://doi.org/10.5194/gmd-12-2091-2019, 2019
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This work is dedicated to a new model that reconstructs the surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean on a monthly 1°×1° grid. The model is based on a feed-forward neural network and represents the nonlinear relationships between pCO2 and the ocean drivers. Reconstructed pCO2 has a satisfying accuracy compared to independent observational data and shows a good agreement in seasonal and interannual variability with three existing mapping methods.
Yoann Robin, Mathieu Vrac, Philippe Naveau, and Pascal Yiou
Hydrol. Earth Syst. Sci., 23, 773–786, https://doi.org/10.5194/hess-23-773-2019, https://doi.org/10.5194/hess-23-773-2019, 2019
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Bias correction methods are used to calibrate climate model outputs with respect to observations. In this article, a non-stationary, multivariate and stochastic bias correction method is developed based on optimal transport, accounting for inter-site and inter-variable correlations. Optimal transport allows us to construct a joint distribution that minimizes energy spent in bias correction. Our methodology is tested on precipitation and temperatures over 12 locations in southern France.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, https://doi.org/10.5194/hess-22-5919-2018, 2018
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We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.
Claire Waelbroeck, Sylvain Pichat, Evelyn Böhm, Bryan C. Lougheed, Davide Faranda, Mathieu Vrac, Lise Missiaen, Natalia Vazquez Riveiros, Pierre Burckel, Jörg Lippold, Helge W. Arz, Trond Dokken, François Thil, and Arnaud Dapoigny
Clim. Past, 14, 1315–1330, https://doi.org/10.5194/cp-14-1315-2018, https://doi.org/10.5194/cp-14-1315-2018, 2018
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Recording the precise timing and sequence of events is essential for understanding rapid climate changes and improving climate model predictive skills. Here, we precisely assess the relative timing between ocean and atmospheric changes, both recorded in the same deep-sea core over the last 45 kyr. We show that decreased mid-depth water mass transport in the western equatorial Atlantic preceded increased rainfall over the adjacent continent by 120 to 980 yr, depending on the type of climate event.
Guillaume Latombe, Ariane Burke, Mathieu Vrac, Guillaume Levavasseur, Christophe Dumas, Masa Kageyama, and Gilles Ramstein
Geosci. Model Dev., 11, 2563–2579, https://doi.org/10.5194/gmd-11-2563-2018, https://doi.org/10.5194/gmd-11-2563-2018, 2018
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It is still unclear how climate conditions, and especially climate variability, influenced the spatial distribution of past human populations. Global climate models (GCMs) cannot simulate climate at sufficiently fine scale for this purpose. We propose a statistical method to obtain fine-scale climate projections for 15 000 years ago from coarse-scale GCM outputs. Our method agrees with local reconstructions from fossil and pollen data, and generates sensible climate variability maps over Europe.
Adjoua Moise Famien, Serge Janicot, Abe Delfin Ochou, Mathieu Vrac, Dimitri Defrance, Benjamin Sultan, and Thomas Noël
Earth Syst. Dynam., 9, 313–338, https://doi.org/10.5194/esd-9-313-2018, https://doi.org/10.5194/esd-9-313-2018, 2018
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This study uses the cumulative distribution function transform (CDF-t) method to provide bias-corrected data over Africa using WFDEI as a reference dataset. It is shown that CDF-t is very effective in removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets, particularly for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields.
Emanuele Bevacqua, Douglas Maraun, Ingrid Hobæk Haff, Martin Widmann, and Mathieu Vrac
Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, https://doi.org/10.5194/hess-21-2701-2017, 2017
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We develop a conceptual model to quantify the risk of compound events (CEs), i.e. extreme impacts to society which are driven by statistically dependent climatic variables. Based on this model we study compound floods, i.e. joint storm surge and high river level, in Ravenna (Italy). The model includes meteorological predictors which (1) provide insight into the physical processes underlying CEs, as well as into the temporal variability, and (2) allow us to statistically downscale CEs.
Pascal Yiou, Aglaé Jézéquel, Philippe Naveau, Frederike E. L. Otto, Robert Vautard, and Mathieu Vrac
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 17–31, https://doi.org/10.5194/ascmo-3-17-2017, https://doi.org/10.5194/ascmo-3-17-2017, 2017
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The attribution of classes of extreme events, such as heavy precipitation or heatwaves, relies on the estimate of small probabilities (with and without climate change). Such events are connected to the large-scale atmospheric circulation. This paper links such probabilities with properties of the atmospheric circulation by using a Bayesian decomposition. We test this decomposition on a case of extreme precipitation in the UK, in January 2014.
Claudia Volosciuk, Douglas Maraun, Mathieu Vrac, and Martin Widmann
Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, https://doi.org/10.5194/hess-21-1693-2017, 2017
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For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.
Jérôme Pernin, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 115–136, https://doi.org/10.5194/ascmo-2-115-2016, https://doi.org/10.5194/ascmo-2-115-2016, 2016
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Here, we propose a classification methodology of various space-time atmospheric datasets into discrete air mass groups homogeneous in temperature and humidity through a probabilistic point of view: both the classification process and the data are probabilistic. Unlike conventional classification algorithms, this methodology provides the probability of belonging to each class as well as the corresponding uncertainty, which can be used in various applications.
Benjamin Grouillet, Denis Ruelland, Pradeebane Vaittinada Ayar, and Mathieu Vrac
Hydrol. Earth Syst. Sci., 20, 1031–1047, https://doi.org/10.5194/hess-20-1031-2016, https://doi.org/10.5194/hess-20-1031-2016, 2016
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This original paper provides a guideline to select statistical downscaling methods (SDMs) in climate change impact studies (CCIS) to minimize uncertainty from downscaling. Three SDMs were applied to NCEP reanalysis and 2 GCM data values. We then analyzed the sensitivity of the hydrological model to the various downscaled data via 5 hydrological indicators representing the main features of the hydrograph. Our results enable selection of the appropriate SDMs to be used to build climate scenarios.
P. Yiou, M. Boichu, R. Vautard, M. Vrac, S. Jourdain, E. Garnier, F. Fluteau, and L. Menut
Clim. Past, 10, 797–809, https://doi.org/10.5194/cp-10-797-2014, https://doi.org/10.5194/cp-10-797-2014, 2014
Ségolène Crossouard, Soulivanh Thao, Thomas Dubos, Masa Kageyama, Mathieu Vrac, and Yann Meurdesoif
EGUsphere, https://doi.org/10.5194/egusphere-2025-1418, https://doi.org/10.5194/egusphere-2025-1418, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Current atmospheric models are limited by the computational time required for physical processes, known as physical parameterizations. To address this, we developed neural network-based emulators to replace these parameterizations in the IPSL climate model, using a simplified aquaplanet setup. We found that incorporating some physical knowledge, such as latent variables, into the learning process can improve predictions.
Yoann Robin, Mathieu Vrac, Aurélien Ribes, Occitane Barbaux, and Philippe Naveau
EGUsphere, https://doi.org/10.5194/egusphere-2025-1121, https://doi.org/10.5194/egusphere-2025-1121, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We describe an improved method and the associated free licensed package ANKIALE (ANalysis of Klimate with bayesian Inference: AppLication to extreme Events) for estimating the statistics of temperature extremes. This method uses climate model simulations (including multiple scenarios simultaneously) to provide a prior of the real-world changes, constrained by the observations. The method and the tool are illustrated via an application to temperature over Europe until 2100, for four scenarios.
Pradeebane Vaittinada Ayar, Stella Bourdin, Davide Faranda, and Mathieu Vrac
EGUsphere, https://doi.org/10.5194/egusphere-2025-252, https://doi.org/10.5194/egusphere-2025-252, 2025
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The tracking of Tropical cyclones (TCs) remains a matter of interest for the investigation of observed and simulated tropical cyclones. In this study, Random Forest (RF), a machine learning approach, is considered to track TCs. RF associates TC occurrence or absence to different atmospheric configurations. Compared to trackers found in the literature, it shows similar performance for tracking TCs, better control over false alarm, more flexibility and reveal key variables allowing to detect TCs.
Paul C. Astagneau, Raul R. Wood, Mathieu Vrac, Sven Kotlarski, Pradeebane Vaittinada Ayar, Bastien François, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-3966, https://doi.org/10.5194/egusphere-2024-3966, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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To study floods and droughts are likely to change in the future, we use climate projections from climate models. However, we first need to adjust the systematic biases of these projections at the catchment scale before using them in hydrological models. Our study compares statistical methods that can adjust these biases, but specifically for climate projections that enable a quantification of internal climate variability. We provide recommendations on the most appropriate methods.
Joséphine Schmutz, Mathieu Vrac, Bastien François, and Burak Bulut
EGUsphere, https://doi.org/10.5194/egusphere-2025-461, https://doi.org/10.5194/egusphere-2025-461, 2025
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In recent years, Europe has faced severe hot and dry events affecting biodiversity, agriculture, and health. Understanding past significant variation in their occurrence is key for adaptation. This paper identifies emerging hotspots in Europe and North Africa. Since the 1970s, the Iberian Peninsula, Maghreb, and Central Europe have seen more frequent events, driven by rising temperature maxima, while Eastern Europe has experienced a decline due to changes in drought.
Robin Noyelle, Davide Faranda, Yoann Robin, Mathieu Vrac, and Pascal Yiou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3167, https://doi.org/10.5194/egusphere-2024-3167, 2024
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Extreme meteorological and climatological events properties are changing under human caused climate change. Extreme events attribution methods seek to estimate the contribution of global warming in the probability and intensity changes of extreme events. Here we propose a procedure to estimate these quantities for the flow analogues method which compare the observed event to similar events in the past.
Germain Bénard, Marion Gehlen, and Mathieu Vrac
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-31, https://doi.org/10.5194/esd-2024-31, 2024
Revised manuscript accepted for ESD
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We introduce a novel approach to compare Earth System Model output using a causality-based approach. The analysis of interactions between atmospheric, oceanic, and biogeochemical variables in the North Atlantic Subpolar Gyre highlights the dynamics of each model. This method reveals potential underlying causes of model differences, offering a tool for enhanced model evaluation and improved understanding of complex Earth system dynamics under past and future climates.
Duncan Pappert, Alexandre Tuel, Dim Coumou, Mathieu Vrac, and Olivia Martius
EGUsphere, https://doi.org/10.5194/egusphere-2024-2980, https://doi.org/10.5194/egusphere-2024-2980, 2024
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This study examines the mechanisms that characterise long-lasting (persistent) and short hot spells in Europe in a comparative framework. By analysing weather data, we found that long spells in Southwestern Europe are typically preceded by dry soil conditions and driven by multiple persistence-inducing mechanisms. In contrast, short spells occur in a more transient atmospheric situation and exhibit fewer drivers. Understanding persistent heat extremes can help improve their prediction.
Denis Allard, Mathieu Vrac, Bastien François, and Iñaki García de Cortázar-Atauri
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-102, https://doi.org/10.5194/hess-2024-102, 2024
Preprint under review for HESS
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Atmospheric variables from climate models often present biases relative to the past. In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. We tested several Multivariate Bias Correction Methods (MBCMs) for 5 physical variables that are input variables for 4 process models. We provide recommendations regarding the use of MBCMs when multivariate and time dependent processes are involved.
Davide Faranda, Gabriele Messori, Erika Coppola, Tommaso Alberti, Mathieu Vrac, Flavio Pons, Pascal Yiou, Marion Saint Lu, Andreia N. S. Hisi, Patrick Brockmann, Stavros Dafis, Gianmarco Mengaldo, and Robert Vautard
Weather Clim. Dynam., 5, 959–983, https://doi.org/10.5194/wcd-5-959-2024, https://doi.org/10.5194/wcd-5-959-2024, 2024
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We introduce ClimaMeter, a tool offering real-time insights into extreme-weather events. Our tool unveils how climate change and natural variability affect these events, affecting communities worldwide. Our research equips policymakers and the public with essential knowledge, fostering informed decisions and enhancing climate resilience. We analysed two distinct events, showcasing ClimaMeter's global relevance.
Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz
Earth Syst. Dynam., 15, 735–762, https://doi.org/10.5194/esd-15-735-2024, https://doi.org/10.5194/esd-15-735-2024, 2024
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We aim to combine multiple global climate models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α pooling", aggregating the cumulative distribution functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments with European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Proc. IAHS, 385, 121–127, https://doi.org/10.5194/piahs-385-121-2024, https://doi.org/10.5194/piahs-385-121-2024, 2024
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This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
Cedric Gacial Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, and Cyrille Flamant
Nat. Hazards Earth Syst. Sci., 23, 1313–1333, https://doi.org/10.5194/nhess-23-1313-2023, https://doi.org/10.5194/nhess-23-1313-2023, 2023
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Heat waves (HWs) are climatic hazards that affect the planet. We assess here uncertainties encountered in the process of HW detection and analyse their recent trends in West Africa using reanalysis data. Three types of uncertainty have been investigated. We identified 6 years with higher frequency of HWs, possibly due to higher sea surface temperatures in the equatorial Atlantic. We noticed an increase in HW characteristics during the last decade, which could be a consequence of climate change.
Bastien François and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 23, 21–44, https://doi.org/10.5194/nhess-23-21-2023, https://doi.org/10.5194/nhess-23-21-2023, 2023
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Compound events (CEs) result from a combination of several climate phenomena. In this study, we propose a new methodology to assess the time of emergence of CE probabilities and to quantify the contribution of marginal and dependence properties of climate phenomena to the overall CE probability changes. By applying our methodology to two case studies, we show the importance of considering changes in both marginal and dependence properties for future risk assessments related to CEs.
Antoine Grisart, Mathieu Casado, Vasileios Gkinis, Bo Vinther, Philippe Naveau, Mathieu Vrac, Thomas Laepple, Bénédicte Minster, Frederic Prié, Barbara Stenni, Elise Fourré, Hans Christian Steen-Larsen, Jean Jouzel, Martin Werner, Katy Pol, Valérie Masson-Delmotte, Maria Hoerhold, Trevor Popp, and Amaelle Landais
Clim. Past, 18, 2289–2301, https://doi.org/10.5194/cp-18-2289-2022, https://doi.org/10.5194/cp-18-2289-2022, 2022
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This paper presents a compilation of high-resolution (11 cm) water isotopic records, including published and new measurements, for the last 800 000 years from the EPICA Dome C ice core, Antarctica. Using this new combined water isotopes (δ18O and δD) dataset, we study the variability and possible influence of diffusion at the multi-decadal to multi-centennial scale. We observe a stronger variability at the onset of the interglacial interval corresponding to a warm period.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
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Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Yoann Robin and Mathieu Vrac
Earth Syst. Dynam., 12, 1253–1273, https://doi.org/10.5194/esd-12-1253-2021, https://doi.org/10.5194/esd-12-1253-2021, 2021
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We propose a new multivariate downscaling and bias correction approach called
time-shifted multivariate bias correction, which aims to correct temporal dependencies in addition to inter-variable and spatial ones. Our method is evaluated in a
perfect model experimentcontext where simulations are used as pseudo-observations. The results show a large reduction of the biases in the temporal properties, while inter-variable and spatial dependence structures are still correctly adjusted.
Cedric G. Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, Philippe Peyrillé, and Cyrille Flamant
Weather Clim. Dynam., 2, 893–912, https://doi.org/10.5194/wcd-2-893-2021, https://doi.org/10.5194/wcd-2-893-2021, 2021
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This work assesses the forecast of the temperature over the Sahara, a key driver of the West African Monsoon, at a seasonal timescale. The seasonal models are able to reproduce the climatological state and some characteristics of the temperature during the rainy season in the Sahel. But, because of errors in the timing, the forecast skill scores are significant only for the first 4 weeks.
Anna Denvil-Sommer, Marion Gehlen, and Mathieu Vrac
Ocean Sci., 17, 1011–1030, https://doi.org/10.5194/os-17-1011-2021, https://doi.org/10.5194/os-17-1011-2021, 2021
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In this work we explored design options for a future Atlantic-scale observational network enabling the release of carbon system estimates by combining data streams from various platforms. We used outputs of a physical–biogeochemical global ocean model at sites of real-world observations to reconstruct surface ocean pCO2 by applying a non-linear feed-forward neural network. The results provide important information for future BGC-Argo deployment, i.e. important regions and the number of floats.
Mathieu Vrac and Soulivanh Thao
Geosci. Model Dev., 13, 5367–5387, https://doi.org/10.5194/gmd-13-5367-2020, https://doi.org/10.5194/gmd-13-5367-2020, 2020
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We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations).
It consists on a method reordering the ranks of the time series according to their multivariate distance to a reference time series.
Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
Emanuele Bevacqua, Michalis I. Vousdoukas, Theodore G. Shepherd, and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 20, 1765–1782, https://doi.org/10.5194/nhess-20-1765-2020, https://doi.org/10.5194/nhess-20-1765-2020, 2020
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Coastal compound flooding (CF), caused by interacting storm surges and high water runoff, is typically studied based on concurring storm surge extremes with either precipitation or river discharge extremes. Globally, these two approaches show similar CF spatial patterns, especially where the CF potential is the highest. Deviations between the two approaches increase with the catchment size. The precipitation-based analysis allows for considering
local-rainfall-driven CF and CF in small rivers.
Bastien François, Mathieu Vrac, Alex J. Cannon, Yoann Robin, and Denis Allard
Earth Syst. Dynam., 11, 537–562, https://doi.org/10.5194/esd-11-537-2020, https://doi.org/10.5194/esd-11-537-2020, 2020
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Recently, multivariate bias correction (MBC) methods designed to adjust climate simulations have been proposed. However, they use different approaches, leading potentially to different results. Therefore, this study intends to intercompare four existing MBC methods to provide end users with aid in choosing such methods for their applications. To do so, a wide range of evaluation criteria have been used to assess the ability of MBC methods to correct statistical properties of climate models.
Eric Pohl, Christophe Grenier, Mathieu Vrac, and Masa Kageyama
Hydrol. Earth Syst. Sci., 24, 2817–2839, https://doi.org/10.5194/hess-24-2817-2020, https://doi.org/10.5194/hess-24-2817-2020, 2020
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Existing approaches to quantify the emergence of climate change require several user choices that make these approaches less objective. We present an approach that uses a minimum number of choices and showcase its application in the extremely sensitive, permafrost-dominated region of eastern Siberia. Designed as a Python toolbox, it allows for incorporating climate model, reanalysis, and in situ data to make use of numerous existing data sources and reduce uncertainties in obtained estimates.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, https://doi.org/10.5194/hess-24-2841-2020, 2020
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At subdaily resolution, rain intensity exhibits a strong variability in space and time due to the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection). In this paper we explore a new method to simulate rain type time series conditional to meteorological covariates. Afterwards, we apply stochastic rain type simulation to the downscaling of precipitation of a regional climate model.
Florentin Breton, Mathieu Vrac, Pascal Yiou, Pradeebane Vaittinada Ayar, and Aglaé Jézéquel
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2020-26, https://doi.org/10.5194/esd-2020-26, 2020
Revised manuscript not accepted
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We investigate North Atlantic weather seasonality over 1979–2100 by classifying year-round fields of 500 hPa geopotential height from one reanalysis dataset and 12 climate models. Generally, models have seasonal structures similar to the reanalyses. Historical winter (summer) conditions decrease (increase), due to uniform Z500 increase (i.e. uniform warming). However, relative to the increasing Z500 seasonal cycle, future seasonality (spatial patterns, seasonal cycle) appears almost stationary.
Giulia Carella, Mathieu Vrac, Hélène Brogniez, Pascal Yiou, and Hélène Chepfer
Earth Syst. Sci. Data, 12, 1–20, https://doi.org/10.5194/essd-12-1-2020, https://doi.org/10.5194/essd-12-1-2020, 2020
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Observations of relative humidity for ice clouds over the tropical oceans from a passive microwave sounder are downscaled by incorporating the high-resolution variability derived from simultaneous co-located cloud profiles from a lidar. By providing a method to generate pseudo-observations of relative humidity at high spatial resolution, this work will help revisit some of the current key barriers in atmospheric science.
Anna Denvil-Sommer, Marion Gehlen, Mathieu Vrac, and Carlos Mejia
Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019, https://doi.org/10.5194/gmd-12-2091-2019, 2019
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This work is dedicated to a new model that reconstructs the surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean on a monthly 1°×1° grid. The model is based on a feed-forward neural network and represents the nonlinear relationships between pCO2 and the ocean drivers. Reconstructed pCO2 has a satisfying accuracy compared to independent observational data and shows a good agreement in seasonal and interannual variability with three existing mapping methods.
Yoann Robin, Mathieu Vrac, Philippe Naveau, and Pascal Yiou
Hydrol. Earth Syst. Sci., 23, 773–786, https://doi.org/10.5194/hess-23-773-2019, https://doi.org/10.5194/hess-23-773-2019, 2019
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Bias correction methods are used to calibrate climate model outputs with respect to observations. In this article, a non-stationary, multivariate and stochastic bias correction method is developed based on optimal transport, accounting for inter-site and inter-variable correlations. Optimal transport allows us to construct a joint distribution that minimizes energy spent in bias correction. Our methodology is tested on precipitation and temperatures over 12 locations in southern France.
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, https://doi.org/10.5194/hess-22-5919-2018, 2018
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We propose a method for unsupervised classification of the space–time–intensity structure of weather radar images. The resulting classes are interpreted as rain types, i.e. pools of rain fields with homogeneous statistical properties. Rain types can in turn be used to define stationary periods for further stochastic rainfall modelling. The application of rain typing to real data indicates that non-stationarity can be significant within meteorological seasons, and even within a single storm.
Claire Waelbroeck, Sylvain Pichat, Evelyn Böhm, Bryan C. Lougheed, Davide Faranda, Mathieu Vrac, Lise Missiaen, Natalia Vazquez Riveiros, Pierre Burckel, Jörg Lippold, Helge W. Arz, Trond Dokken, François Thil, and Arnaud Dapoigny
Clim. Past, 14, 1315–1330, https://doi.org/10.5194/cp-14-1315-2018, https://doi.org/10.5194/cp-14-1315-2018, 2018
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Recording the precise timing and sequence of events is essential for understanding rapid climate changes and improving climate model predictive skills. Here, we precisely assess the relative timing between ocean and atmospheric changes, both recorded in the same deep-sea core over the last 45 kyr. We show that decreased mid-depth water mass transport in the western equatorial Atlantic preceded increased rainfall over the adjacent continent by 120 to 980 yr, depending on the type of climate event.
Guillaume Latombe, Ariane Burke, Mathieu Vrac, Guillaume Levavasseur, Christophe Dumas, Masa Kageyama, and Gilles Ramstein
Geosci. Model Dev., 11, 2563–2579, https://doi.org/10.5194/gmd-11-2563-2018, https://doi.org/10.5194/gmd-11-2563-2018, 2018
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It is still unclear how climate conditions, and especially climate variability, influenced the spatial distribution of past human populations. Global climate models (GCMs) cannot simulate climate at sufficiently fine scale for this purpose. We propose a statistical method to obtain fine-scale climate projections for 15 000 years ago from coarse-scale GCM outputs. Our method agrees with local reconstructions from fossil and pollen data, and generates sensible climate variability maps over Europe.
Adjoua Moise Famien, Serge Janicot, Abe Delfin Ochou, Mathieu Vrac, Dimitri Defrance, Benjamin Sultan, and Thomas Noël
Earth Syst. Dynam., 9, 313–338, https://doi.org/10.5194/esd-9-313-2018, https://doi.org/10.5194/esd-9-313-2018, 2018
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This study uses the cumulative distribution function transform (CDF-t) method to provide bias-corrected data over Africa using WFDEI as a reference dataset. It is shown that CDF-t is very effective in removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets, particularly for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields.
Emanuele Bevacqua, Douglas Maraun, Ingrid Hobæk Haff, Martin Widmann, and Mathieu Vrac
Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, https://doi.org/10.5194/hess-21-2701-2017, 2017
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We develop a conceptual model to quantify the risk of compound events (CEs), i.e. extreme impacts to society which are driven by statistically dependent climatic variables. Based on this model we study compound floods, i.e. joint storm surge and high river level, in Ravenna (Italy). The model includes meteorological predictors which (1) provide insight into the physical processes underlying CEs, as well as into the temporal variability, and (2) allow us to statistically downscale CEs.
Pascal Yiou, Aglaé Jézéquel, Philippe Naveau, Frederike E. L. Otto, Robert Vautard, and Mathieu Vrac
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 17–31, https://doi.org/10.5194/ascmo-3-17-2017, https://doi.org/10.5194/ascmo-3-17-2017, 2017
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The attribution of classes of extreme events, such as heavy precipitation or heatwaves, relies on the estimate of small probabilities (with and without climate change). Such events are connected to the large-scale atmospheric circulation. This paper links such probabilities with properties of the atmospheric circulation by using a Bayesian decomposition. We test this decomposition on a case of extreme precipitation in the UK, in January 2014.
Claudia Volosciuk, Douglas Maraun, Mathieu Vrac, and Martin Widmann
Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, https://doi.org/10.5194/hess-21-1693-2017, 2017
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For impact modeling, infrastructure design, or adaptation strategy planning, high-quality climate data on the point scale are often demanded. Due to the scale gap between gridbox and point scale and biases in climate models, we combine a statistical bias correction and a stochastic downscaling model and apply it to climate model-simulated precipitation. The method performs better in summer than in winter and in winter best for mild winter climate (Mediterranean) and worst for continental winter.
Jérôme Pernin, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 115–136, https://doi.org/10.5194/ascmo-2-115-2016, https://doi.org/10.5194/ascmo-2-115-2016, 2016
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Here, we propose a classification methodology of various space-time atmospheric datasets into discrete air mass groups homogeneous in temperature and humidity through a probabilistic point of view: both the classification process and the data are probabilistic. Unlike conventional classification algorithms, this methodology provides the probability of belonging to each class as well as the corresponding uncertainty, which can be used in various applications.
Benjamin Grouillet, Denis Ruelland, Pradeebane Vaittinada Ayar, and Mathieu Vrac
Hydrol. Earth Syst. Sci., 20, 1031–1047, https://doi.org/10.5194/hess-20-1031-2016, https://doi.org/10.5194/hess-20-1031-2016, 2016
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This original paper provides a guideline to select statistical downscaling methods (SDMs) in climate change impact studies (CCIS) to minimize uncertainty from downscaling. Three SDMs were applied to NCEP reanalysis and 2 GCM data values. We then analyzed the sensitivity of the hydrological model to the various downscaled data via 5 hydrological indicators representing the main features of the hydrograph. Our results enable selection of the appropriate SDMs to be used to build climate scenarios.
P. Yiou, M. Boichu, R. Vautard, M. Vrac, S. Jourdain, E. Garnier, F. Fluteau, and L. Menut
Clim. Past, 10, 797–809, https://doi.org/10.5194/cp-10-797-2014, https://doi.org/10.5194/cp-10-797-2014, 2014
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Enhanced evaluation of hourly and daily extreme precipitation in Norway from convection-permitting models at regional and local scales
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
High-resolution land surface modelling over Africa: the role of uncertain soil properties in combination with forcing temporal resolution
Investigating the global and regional response of drought to idealized deforestation using multiple global climate models
Distribution, trends, and drivers of flash droughts in the United Kingdom
Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?
Leveraging a radar-based disdrometer network to develop a probabilistic precipitation phase model in eastern Canada
Assessment of seasonal soil moisture forecasts over the Central Mediterranean
Do land models miss key soil hydrological processes controlling soil moisture memory?
Observation-driven model for calculating water-harvesting potential from advective fog in (semi-)arid coastal regions
Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets
Barriers of urban hydro-meteorological simulation: a review
Downscaling the probability of heavy rainfall over the Nordic countries
Modelling convective cell life cycles with a copula-based approach
Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5
Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model
Assessing rainfall radar errors with an inverse stochastic modelling framework
Towards a Robust Hydrologic Data Assimilation System for Hurricane-induced River Flow Forecasting
Enhanced hydrological modelling with the WRF-Hydro lake/reservoir module at Convection-Permitting scale: a case study of the Tana River basin in East Africa
Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution
Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Karakoram Himalaya
Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau
Skilful probabilistic predictions of UK floods months ahead using machine learning models trained on multimodel ensemble climate forecasts
FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
The role of land-atmosphere coupling in subseasonal surface air temperature prediction
On the combined use of rain gauges and GPM IMERG satellite rainfall products for hydrological modelling: impact assessment of the cellular-automata-based methodology in the Tanaro River basin in Italy
An increase in the spatial extent of European floods over the last 70 years
140-year daily ensemble streamflow reconstructions over 661 catchments in France
The agricultural expansion in South America's Dry Chaco: regional hydroclimate effects
Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Improving runoff simulation in the Western United States with Noah-MP and VIC models
Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France
Global catalog of soil moisture droughts over the past four decades
Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)
Global total precipitable water variations and trends over the period 1958–2021
Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends
Identification of compound drought and heatwave events on a daily scale and across four seasons
Implementation of global soil databases in NOAH-MP model and the effects on simulated mean and extreme soil hydrothermal changes
Potential for historically unprecedented Australian droughts from natural variability and climate change
Flood risk assessment for Indian sub-continental river basins
Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities
Divergent future drought projections in UK river flows and groundwater levels
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
Hydroclimatic processes as the primary drivers of the Early Khvalynian transgression of the Caspian Sea: new developments
Accounting for hydroclimatic properties in flood frequency analysis procedures
Understanding the influence of “hot” models in climate impact studies: a hydrological perspective
A semi-parametric hourly space–time weather generator
A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics
Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025, https://doi.org/10.5194/hess-29-2133-2025, 2025
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We compared hourly and daily extreme precipitation across Norway from HARMONIE Climate models at convection-permitting 3 km (HCLIM3) and 12 km (HCLIM12) resolutions. HCLIM3 more accurately captures the extremes in most regions and seasons (except in summer). Its advantages are more pronounced for hourly extremes than for daily extremes. The results highlight the value of convection-permitting models in improving extreme-precipitation predictions and in helping the local society brace for extreme weather.
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025, https://doi.org/10.5194/hess-29-2023-2025, 2025
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Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.
Bamidele Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 29, 1659–1683, https://doi.org/10.5194/hess-29-1659-2025, https://doi.org/10.5194/hess-29-1659-2025, 2025
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We studied how soil and weather data affect land model simulations over Africa. By combining soil data processed in different ways with weather data of varying time intervals, we found that weather inputs had a greater impact on water processes than soil data type. However, the way soil data were processed became crucial when paired with high-frequency weather inputs, showing that detailed weather data can improve local and regional predictions of how water moves and interacts with the land.
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025, https://doi.org/10.5194/hess-29-1637-2025, 2025
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Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 29, 1295–1317, https://doi.org/10.5194/hess-29-1295-2025, https://doi.org/10.5194/hess-29-1295-2025, 2025
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The study provides a detailed characterisation of flash drought in the UK for 1969–2021. The spatio-temporal distribution and trends of flash droughts are highly variable, with important regional and seasonal contrasts. In the UK, flash drought development responds primarily to precipitation variability, while the atmospheric evaporative demand plays a secondary role. We also found that the North Atlantic Oscillation is the main circulation pattern controlling flash drought development.
Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley J. Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025, https://doi.org/10.5194/hess-29-1201-2025, 2025
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Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and the Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025, https://doi.org/10.5194/hess-29-1135-2025, 2025
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Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Lorenzo Silvestri, Miriam Saraceni, Bruno Brunone, Silvia Meniconi, Giulia Passadore, and Paolina Bongioannini Cerlini
Hydrol. Earth Syst. Sci., 29, 925–946, https://doi.org/10.5194/hess-29-925-2025, https://doi.org/10.5194/hess-29-925-2025, 2025
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This work demonstrates that seasonal forecasts of soil moisture are a valuable resource for groundwater management in the areas of the Central Mediterranean where longer memory timescales are found. In particular, they show significant correlation coefficients and forecast skill for the deepest soil moisture at 289 cm depth. Wet and dry events can be predicted 6 months in advance, and, in general, dry events are better captured than wet events.
Mohammad A. Farmani, Ali Behrangi, Aniket Gupta, Ahmad Tavakoly, Matthew Geheran, and Guo-Yue Niu
Hydrol. Earth Syst. Sci., 29, 547–566, https://doi.org/10.5194/hess-29-547-2025, https://doi.org/10.5194/hess-29-547-2025, 2025
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Soil moisture memory (SMM) shows how long soil stays moist after rain, impacting climate and ecosystems. Current models often overestimate SMM, causing inaccuracies in evaporation predictions. We enhanced a land model, Noah-MP, to include better water flow and ponding processes, and we tested it against satellite and field data. This improved model reduced overestimations and enhanced short-term predictions, helping create more accurate climate and weather forecasts.
Felipe Lobos-Roco, Jordi Vilà-Guerau de Arellano, and Camilo del Río
Hydrol. Earth Syst. Sci., 29, 109–125, https://doi.org/10.5194/hess-29-109-2025, https://doi.org/10.5194/hess-29-109-2025, 2025
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Water resources are fundamental for the social, economic, and natural development of (semi-)arid regions. Precipitation decreases due to climate change obligate us to find new water resources. Fog harvesting (FH) emerges as a complementary resource in regions where it is abundant but untapped. This research proposes a model to estimate FH potential in coastal (semi-)arid regions. This model could have broader applicability worldwide in regions where FH could be a viable water source.
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard
Hydrol. Earth Syst. Sci., 29, 85–108, https://doi.org/10.5194/hess-29-85-2025, https://doi.org/10.5194/hess-29-85-2025, 2025
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We assess 63 gridded ground (G), satellite (S), and reanalysis (R) climate datasets. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; however, R outperformed G when underlying data had low station density. G outperformed S or R datasets, although better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.
Xuan Chen, Job Augustijn van der Werf, Arjan Droste, Miriam Coenders-Gerrits, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-3988, https://doi.org/10.5194/egusphere-2024-3988, 2025
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Our research highlights the need to integrate urban land surface and hydrological models to better predict and manage compound climate disasters in cities. We find that inadequate representation of water surfaces, hydraulic systems, and detailed building representations are key areas for improvement in future models. Coupled models show promise but face challenges at regional and neighbourhood scales. Interdisciplinary communication is crucial to enhance urban hydrometeorological simulations.
Rasmus E. Benestad, Kajsa M. Parding, and Andreas Dobler
Hydrol. Earth Syst. Sci., 29, 45–65, https://doi.org/10.5194/hess-29-45-2025, https://doi.org/10.5194/hess-29-45-2025, 2025
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We present a new method to calculate the chance of heavy downpour and the maximum rainfall expected over a 25-year period. It is designed to analyse global climate models' reproduction of past and future climates. For the Nordic countries, it projects a wetter climate in the future with increased intensity but not necessarily more wet days. The analysis also shows that rainfall intensity is sensitive to future greenhouse gas emissions, while the number of wet days appears to be less affected.
Chien-Yu Tseng, Li-Pen Wang, and Christian Onof
Hydrol. Earth Syst. Sci., 29, 1–25, https://doi.org/10.5194/hess-29-1-2025, https://doi.org/10.5194/hess-29-1-2025, 2025
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This study presents a new algorithm to model convective storms. We used advanced tracking methods to analyse 165 storm events in Birmingham (UK) and reconstruct storm cell life cycles. We found that cell properties like intensity and size are interrelated and vary over time. The new algorithm, based on vine copulas, accurately simulates these properties and their evolution. It also integrates an exponential shape function for realistic rainfall patterns, enhancing its hydrological applicability.
Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
Hydrol. Earth Syst. Sci., 28, 4903–4925, https://doi.org/10.5194/hess-28-4903-2024, https://doi.org/10.5194/hess-28-4903-2024, 2024
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This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.
Peter E. Levy and the COSMOS-UK team
Hydrol. Earth Syst. Sci., 28, 4819–4836, https://doi.org/10.5194/hess-28-4819-2024, https://doi.org/10.5194/hess-28-4819-2024, 2024
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Having accurate up-to-date maps of soil moisture is important for many purposes. However, current modelled and remotely sensed maps are rather coarse and not very accurate. Here, we demonstrate a simple but accurate approach that is closely linked to direct measurements of soil moisture at a network sites across the UK, to the water balance (precipitation minus drainage and evaporation) measured at a large number of catchments (1212) and to remotely sensed satellite estimates.
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
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Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Peyman Abbaszadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-209, https://doi.org/10.5194/hess-2024-209, 2024
Revised manuscript accepted for HESS
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The Hybrid Ensemble and Variational Data Assimilation framework for Environmental System (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three Southeastern U.S. watersheds during hurricanes, HEAVEN assimilates real-time USGS streamflow data, boosting forecast accuracy.
Ling Zhang, Lu Li, Zhongshi Zhang, Joël Arnault, Stefan Sobolowski, Anthony Musili Mwanthi, Pratik Kad, Mohammed Abdullahi Hassan, Tanja Portele, and Harald Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-278, https://doi.org/10.5194/hess-2024-278, 2024
Revised manuscript accepted for HESS
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To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
Hydrol. Earth Syst. Sci., 28, 4455–4476, https://doi.org/10.5194/hess-28-4455-2024, https://doi.org/10.5194/hess-28-4455-2024, 2024
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We conducted a high-resolution hydrological simulation from 1959 to 2020 across France. We used a simple trial-and-error calibration to reduce the biases of the simulated water budget compared to observations. The selected simulation satisfactorily reproduces water fluxes, including their spatial contrasts and temporal trends. This work offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.
Marc Girona-Mata, Andrew Orr, Martin Widmann, Daniel Bannister, Ghulam Hussain Dars, Scott Hosking, Jesse Norris, David Ocio, Tony Phillips, Jakob Steiner, and Richard E. Turner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2805, https://doi.org/10.5194/egusphere-2024-2805, 2024
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We introduce a novel method for improving daily precipitation maps in mountain regions and pilot it across three basins in the Hindu Kush Karakoram Himalaya (HKH). The approach leverages climate model and weather station data, along with statistical / machine learning techniques. Our results show this approach outperforms traditional methods, especially in remote, ungauged areas, suggesting it could be used to improve precipitation maps across much of the HKH, as well as other mountain regions.
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, https://doi.org/10.5194/hess-28-4361-2024, 2024
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Our findings show that runoff in the Yarlung Zangbo (YZ) basin is primarily driven by rainfall, with the largest glacier runoff contribution in the downstream sub-basin. Annual runoff increased in the upper stream but decreased downstream due to varying precipitation patterns. It is expected to rise throughout the 21st century, mainly driven by increased rainfall.
Simon Moulds, Louise Slater, Louise Arnal, and Andrew Wood
EGUsphere, https://doi.org/10.31223/X5X405, https://doi.org/10.31223/X5X405, 2024
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Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to four months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to four months ahead in many locations, although in general the skill declines with increasing lead time.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
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Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Yuna Lim, Andrea M. Molod, Randal D. Koster, and Joseph A. Santanello
EGUsphere, https://doi.org/10.5194/egusphere-2024-2312, https://doi.org/10.5194/egusphere-2024-2312, 2024
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To better utilize a given set of predictions, identifying “forecasts of opportunity” has great value. It can help anticipate when prediction skill will be higher. This study reveals that when strong L-A coupling is detected 3–4 weeks into a forecast, the prediction skill for surface air temperature at this lead increases across the Midwest and northern Great Plains. Regions experiencing strong L-A coupling exhibit warm and dry anomalies, leading to improved predictions of abnormally warm events.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
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The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
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We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci., 28, 3457–3474, https://doi.org/10.5194/hess-28-3457-2024, https://doi.org/10.5194/hess-28-3457-2024, 2024
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Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.
María Agostina Bracalenti, Omar V. Müller, Miguel A. Lovino, and Ernesto Hugo Berbery
Hydrol. Earth Syst. Sci., 28, 3281–3303, https://doi.org/10.5194/hess-28-3281-2024, https://doi.org/10.5194/hess-28-3281-2024, 2024
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The Gran Chaco is a large, dry forest in South America that has been heavily deforested, particularly in the dry Chaco subregion. This deforestation, mainly driven by the expansion of the agricultural frontier, has changed the land's characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
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We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
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
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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.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, and Miroslav Trnka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1434, https://doi.org/10.5194/egusphere-2024-1434, 2024
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We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, which was clustered into 775/630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
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
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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.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
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Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Kyungmin Sung, Max C. A. Torbenson, and James H. Stagge
Hydrol. Earth Syst. Sci., 28, 2047–2063, https://doi.org/10.5194/hess-28-2047-2024, https://doi.org/10.5194/hess-28-2047-2024, 2024
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This study examines centuries of nonstationary trends in meteorological drought and pluvial climatology. A novel approach merges tree-ring proxy data (North American Seasonal Precipitation Atlas – NASPA) with instrumental precipitation datasets by temporally downscaling proxy data, correcting biases, and analyzing shared trends in normal and extreme precipitation anomalies. We identify regions experiencing recent unprecedented shifts towards drier or wetter conditions and shifts in seasonality.
Baoying Shan, Niko E. C. Verhoest, and Bernard De Baets
Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, https://doi.org/10.5194/hess-28-2065-2024, 2024
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This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves (CDHW) across four seasons. Using this method, we could establish the start and/or end dates of drought (or heatwave) events. We found an increase in the frequency of heatwaves and CDHW events in Belgium caused by climate change. We also found that different months have different chances of CDHW events.
Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-304, https://doi.org/10.5194/hess-2023-304, 2024
Revised manuscript accepted for HESS
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The global soil information contributes to uncertainty in many models that monitor soil hydrothermal changes. Using the NOAH-MP model with two different global soil information, we show under-represented soil properties in wet loam soil, leading to dry bias in soil moisture. The dry bias is higher and drought categories are more severe in SOILGRIDS. We conclude that models should consider using detailed regionally-derived soil information, to reduce model uncertainties.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
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Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Urmin Vegad, Yadu Pokhrel, and Vimal Mishra
Hydrol. Earth Syst. Sci., 28, 1107–1126, https://doi.org/10.5194/hess-28-1107-2024, https://doi.org/10.5194/hess-28-1107-2024, 2024
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A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive damage of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
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Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
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We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
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We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Alexander Gelfan, Andrey Panin, Andrey Kalugin, Polina Morozova, Vladimir Semenov, Alexey Sidorchuk, Vadim Ukraintsev, and Konstantin Ushakov
Hydrol. Earth Syst. Sci., 28, 241–259, https://doi.org/10.5194/hess-28-241-2024, https://doi.org/10.5194/hess-28-241-2024, 2024
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Paleogeographical data show that 17–13 ka BP, the Caspian Sea level was 80 m above the current level. There are large disagreements on the genesis of this “Great” Khvalynian transgression of the sea, and we tried to shed light on this issue. Using climate and hydrological models as well as the paleo-reconstructions, we proved that the transgression could be initiated solely by hydroclimatic factors within the deglaciation period in the absence of the glacial meltwater effect.
Joeri B. Reinders and Samuel E. Munoz
Hydrol. Earth Syst. Sci., 28, 217–227, https://doi.org/10.5194/hess-28-217-2024, https://doi.org/10.5194/hess-28-217-2024, 2024
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Flooding presents a major hazard for people and infrastructure along waterways; however, it is challenging to study the likelihood of a flood magnitude occurring regionally due to a lack of long discharge records. We show that hydroclimatic variables like Köppen climate regions and precipitation intensity explain part of the variance in flood frequency distributions and thus reduce the uncertainty of flood probability estimates. This gives water managers a tool to locally improve flood analysis.
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault
Hydrol. Earth Syst. Sci., 27, 4355–4367, https://doi.org/10.5194/hess-27-4355-2023, https://doi.org/10.5194/hess-27-4355-2023, 2023
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Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
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Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
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Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
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
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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.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
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In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
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Short summary
This study presents a multivariate bias correction method named R2D2 to adjust both the 1d-distributions and inter-variable/site dependence structures of climate simulations in a high-dimensional context, while providing some stochasticity. R2D2 is tested on temperature and precipitation reanalyses and illustrated on future simulations. In both cases, R2D2 is able to correct the spatial and physical dependence, opening proper use of climate simulations for impact (e.g. hydrological) models.
This study presents a multivariate bias correction method named R2D2 to adjust both the...
