Articles | Volume 29, issue 8
https://doi.org/10.5194/hess-29-2023-2025
© 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/hess-29-2023-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2025
Research article |
| 22 Apr 2025
| 22 Apr 2025
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Haoran Hao
CORRESPONDING AUTHOR
State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin, China
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Mingxiang Yang
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
Jianhui Wei
Institute of Meteorology and Climate Research (IMKIFU), Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Shiqin Xu
Hydrology, Agriculture and Land Observation (HALO) Laboratory, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Harald Kunstmann
Institute of Meteorology and Climate Research (IMKIFU), Karlsruhe Institute of Technology, Campus Alpin, Garmisch-Partenkirchen, Germany
Institute of Geography, University of Augsburg, Augsburg, Germany
Centre for Climate Resilience, University of Augsburg, Augsburg, Germany
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Yinchi Zhang, Wanling Xu, Chao Deng, Shao Sun, Miaomiao Ma, Jianhui Wei, Ying Chen, Harald Kunstmann, and Lu Gao
EGUsphere, https://doi.org/10.5194/egusphere-2025-2438, https://doi.org/10.5194/egusphere-2025-2438, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Most studies of compound extremes assume stable climate conditions. We use high-resolution regional climate modeling and non-stationary statistical methods to assess future changes in southeastern China. Our results show that non-stationary models better capture shifts in the risk of compound extremes, highlighting that traditional methods may underestimate future threats.
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.
Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala
Atmos. Meas. Tech., 17, 2165–2182, https://doi.org/10.5194/amt-17-2165-2024, https://doi.org/10.5194/amt-17-2165-2024, 2024
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Commercial microwave links (CMLs) can be used for rainfall retrieval. The detection of rainy periods in their attenuation time series is a crucial processing step. We investigate the usage of rainfall data from MSG SEVIRI for this task, compare this approach with existing methods, and introduce a novel combined approach. The results show certain advantages for SEVIRI-based methods, particularly for CMLs where existing methods perform poorly. Our novel combination yields the best performance.
Patrick Olschewski, Qi Sun, Jianhui Wei, Yu Li, Zhan Tian, Laixiang Sun, Joël Arnault, Tanja C. Schober, Brian Böker, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-95, https://doi.org/10.5194/hess-2024-95, 2024
Revised manuscript under review for HESS
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There are indications that typhoon intensities may increase under global warming. However, further research on these projections and their uncertainties is necessary. We study changes in typhoon intensity under SSP5-8.5 for seven events affecting the Pearl River Delta using Pseudo-Global Warming and a storyline approach based on 16 CMIP6 models. Results show intensified wind speed, sea level pressure drop and precipitation levels for six events with amplified increases for individual storylines.
Patrick Olschewski, Mame Diarra Bousso Dieng, Hassane Moutahir, Brian Böker, Edwin Haas, Harald Kunstmann, and Patrick Laux
Nat. Hazards Earth Syst. Sci., 24, 1099–1134, https://doi.org/10.5194/nhess-24-1099-2024, https://doi.org/10.5194/nhess-24-1099-2024, 2024
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We applied a multivariate and dependency-preserving bias correction method to climate model output for the Greater Mediterranean Region and investigated potential changes in false-spring events (FSEs) and heat–drought compound events (HDCEs). Results project an increase in the frequency of FSEs in middle and late spring as well as increases in frequency, intensity, and duration for HDCEs. This will potentially aggravate the risk of crop loss and failure and negatively impact food security.
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.
Dragan Petrovic, Benjamin Fersch, and Harald Kunstmann
Nat. Hazards Earth Syst. Sci., 24, 265–289, https://doi.org/10.5194/nhess-24-265-2024, https://doi.org/10.5194/nhess-24-265-2024, 2024
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The influence of model resolution and settings on the reproduction of heat waves in Germany between 1980–2009 is analyzed. Outputs from a high-resolution model with settings tailored to the target region are compared to those from coarser-resolution models with more general settings. Neither the increased resolution nor the tailored model settings are found to add significant value to the heat wave simulation. The models exhibit a large spread, indicating that the choice of model can be crucial.
Mohsen Soltani, Bert Hamelers, Abbas Mofidi, Christopher G. Fletcher, Arie Staal, Stefan C. Dekker, Patrick Laux, Joel Arnault, Harald Kunstmann, Ties van der Hoeven, and Maarten Lanters
Earth Syst. Dynam., 14, 931–953, https://doi.org/10.5194/esd-14-931-2023, https://doi.org/10.5194/esd-14-931-2023, 2023
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The temporal changes and spatial patterns in precipitation events do not show a homogeneous tendency across the Sinai Peninsula. Mediterranean cyclones accompanied by the Red Sea and Persian troughs are responsible for the majority of Sinai's extreme rainfall events. Cyclone tracking captures 156 cyclones (rainfall ≥10 mm d-1) either formed within or transferred to the Mediterranean basin precipitating over Sinai.
Efi Rousi, Andreas H. Fink, Lauren S. Andersen, Florian N. Becker, Goratz Beobide-Arsuaga, Marcus Breil, Giacomo Cozzi, Jens Heinke, Lisa Jach, Deborah Niermann, Dragan Petrovic, Andy Richling, Johannes Riebold, Stella Steidl, Laura Suarez-Gutierrez, Jordis S. Tradowsky, Dim Coumou, André Düsterhus, Florian Ellsäßer, Georgios Fragkoulidis, Daniel Gliksman, Dörthe Handorf, Karsten Haustein, Kai Kornhuber, Harald Kunstmann, Joaquim G. Pinto, Kirsten Warrach-Sagi, and Elena Xoplaki
Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, https://doi.org/10.5194/nhess-23-1699-2023, 2023
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The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.
Dragan Petrovic, Benjamin Fersch, and Harald Kunstmann
Nat. Hazards Earth Syst. Sci., 22, 3875–3895, https://doi.org/10.5194/nhess-22-3875-2022, https://doi.org/10.5194/nhess-22-3875-2022, 2022
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The influence of model resolution and settings on drought reproduction in Germany between 1980–2009 is investigated here. Outputs from a high-resolution model with settings tailored to the target region are compared to those from coarser-resolution models with more general settings. Gridded observational data sets serve as reference. Regarding the reproduction of drought characteristics, all models perform on a similar level, while for trends, only the modified model produces reliable outputs.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
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In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
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Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Lu Gao, Haijun Deng, Xiangyong Lei, Jianhui Wei, Yaning Chen, Zhongqin Li, Miaomiao Ma, Xingwei Chen, Ying Chen, Meibing Liu, and Jianyun Gao
The Cryosphere, 15, 5765–5783, https://doi.org/10.5194/tc-15-5765-2021, https://doi.org/10.5194/tc-15-5765-2021, 2021
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There is a widespread controversy on the existence of the elevation-dependent warming (EDW) phenomenon due to the limited observations in high mountains. This study provides new evidence of EDW from the Chinese Tian Shan based on a high-resolution (1 km, 6-hourly) air temperature dataset. The result reveals the significant EDW on a monthly scale. The warming rate of the minimum temperature in winter showed a significant elevation dependence (p < 0.01), especially above 3000 m.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
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In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Christof Lorenz, Tanja C. Portele, Patrick Laux, and Harald Kunstmann
Earth Syst. Sci. Data, 13, 2701–2722, https://doi.org/10.5194/essd-13-2701-2021, https://doi.org/10.5194/essd-13-2701-2021, 2021
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Semi-arid regions depend on the freshwater resources from the rainy seasons as they are crucial for ensuring security for drinking water, food and electricity. Thus, forecasting the conditions for the next season is crucial for proactive water management. We hence present a seasonal forecast product for four semi-arid domains in Iran, Brazil, Sudan/Ethiopia and Ecuador/Peru. It provides a benchmark for seasonal forecasts and, finally, a crucial contribution for improved disaster preparedness.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Julius Polz, Christian Chwala, Maximilian Graf, and Harald Kunstmann
Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020, https://doi.org/10.5194/amt-13-3835-2020, 2020
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Commercial microwave link (CML) networks can be used to estimate path-averaged rain rates. This study evaluates the ability of convolutional neural networks to distinguish between wet and dry periods in CML time series data and the ability to transfer this detection skill to sensors not used for training. Our data set consists of several months of data from 3904 CMLs covering all of Germany. Compared to a previously used detection method, we could show a significant increase in performance.
Maximilian Graf, Christian Chwala, Julius Polz, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, https://doi.org/10.5194/hess-24-2931-2020, 2020
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Commercial microwave links (CMLs), which form large parts of the backhaul from the ubiquitous cellular communication networks, can be used to estimate path-integrated rainfall rates. This study presents the processing and evaluation of the largest CML data set to date, covering the whole of Germany with almost 4000 CMLs. The CML-derived rainfall information compares well to a standard precipitation data set from the German Meteorological Service, which combines radar and rain gauge data.
Benjamin Fersch, Alfonso Senatore, Bianca Adler, Joël Arnault, Matthias Mauder, Katrin Schneider, Ingo Völksch, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 24, 2457–2481, https://doi.org/10.5194/hess-24-2457-2020, https://doi.org/10.5194/hess-24-2457-2020, 2020
Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas Grünewald
The Cryosphere, 13, 1247–1265, https://doi.org/10.5194/tc-13-1247-2019, https://doi.org/10.5194/tc-13-1247-2019, 2019
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The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes, or by suppressed snow ablation driven by micrometeorological effects lowering net radiation and turbulent heat exchange. In this study we discuss the relative contribution of snow accumulation (avalanches) versus micrometeorology (katabatic flow) on the mass balance of the lowest perennial ice field of the Alps, the Ice Chapel.
Maik Renner, Claire Brenner, Kaniska Mallick, Hans-Dieter Wizemann, Luigi Conte, Ivonne Trebs, Jianhui Wei, Volker Wulfmeyer, Karsten Schulz, and Axel Kleidon
Hydrol. Earth Syst. Sci., 23, 515–535, https://doi.org/10.5194/hess-23-515-2019, https://doi.org/10.5194/hess-23-515-2019, 2019
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We estimate the phase lag of surface states and heat fluxes to incoming solar radiation at the sub-daily timescale. While evapotranspiration reveals a minor phase lag, the vapor pressure deficit used as input by Penman–Monteith approaches shows a large phase lag. The surface-to-air temperature gradient used by energy balance residual approaches shows a small phase shift in agreement with the sensible heat flux and thus explains the better correlation of these models at the sub-daily timescale.
Lu Gao, Jianhui Wei, Lingxiao Wang, Matthias Bernhardt, Karsten Schulz, and Xingwei Chen
Earth Syst. Sci. Data, 10, 2097–2114, https://doi.org/10.5194/essd-10-2097-2018, https://doi.org/10.5194/essd-10-2097-2018, 2018
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High-resolution temperature data sets are important for the Chinese Tian Shan, which has a complex ecological environment system. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for this area from 1979 to 2016 based on a robust statistical downscaling framework. The strongest advantage of this method is its independence of local meteorological stations due to a model internal, vertical lapse rate scheme. This method was validated for other mountains.
Dominikus Heinzeller, Diarra Dieng, Gerhard Smiatek, Christiana Olusegun, Cornelia Klein, Ilse Hamann, Seyni Salack, Jan Bliefernicht, and Harald Kunstmann
Earth Syst. Sci. Data, 10, 815–835, https://doi.org/10.5194/essd-10-815-2018, https://doi.org/10.5194/essd-10-815-2018, 2018
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Climate change and population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), an ensemble of high-resolution regional climate change simulations is provided to support adaptation and mitigation measures. This contribution presents the concept and design of the simulations and provides information on the format and dissemination of the available data.
Caroline Brosy, Karina Krampf, Matthias Zeeman, Benjamin Wolf, Wolfgang Junkermann, Klaus Schäfer, Stefan Emeis, and Harald Kunstmann
Atmos. Meas. Tech., 10, 2773–2784, https://doi.org/10.5194/amt-10-2773-2017, https://doi.org/10.5194/amt-10-2773-2017, 2017
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Vertical and horizontal sounding of the planetary boundary layer can be complemented by unmanned aerial vehicles (UAV). Utilizing a multicopter-type UAV spatial sampling of air and simultaneously sensing of meteorological variables is possible for the study of surface exchange processes. During stable atmospheric conditions, vertical methane gradients of about 300 ppb were found. This approach extended the vertical profile height of existing tower-based infrastructure by a factor of five.
Jan Schmieder, Florian Hanzer, Thomas Marke, Jakob Garvelmann, Michael Warscher, Harald Kunstmann, and Ulrich Strasser
Hydrol. Earth Syst. Sci., 20, 5015–5033, https://doi.org/10.5194/hess-20-5015-2016, https://doi.org/10.5194/hess-20-5015-2016, 2016
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We present novel research on the spatiotemporal variability of snowmelt isotopic content in a high-elevation catchment with complex terrain
to improve the isotope-based hydrograph separation method. A modelling approach was used to weight the plot-scale snowmelt isotopic content
with melt rates for the north- and south-facing slope. The investigations showed that it is important to sample at least north- and south-facing slopes,
because of distinct isotopic differences between both slopes.
Bernd Schalge, Jehan Rihani, Gabriele Baroni, Daniel Erdal, Gernot Geppert, Vincent Haefliger, Barbara Haese, Pablo Saavedra, Insa Neuweiler, Harrie-Jan Hendricks Franssen, Felix Ament, Sabine Attinger, Olaf A. Cirpka, Stefan Kollet, Harald Kunstmann, Harry Vereecken, and Clemens Simmer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-557, https://doi.org/10.5194/hess-2016-557, 2016
Manuscript not accepted for further review
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In this work we show how we used a coupled atmosphere-land surface-subsurface model at highest possible resolution to create a testbed for data assimilation. The model was able to capture all important processes and interactions between the compartments as well as showing realistic statistical behavior. This proves that using a model as a virtual truth is possible and it will enable us to develop data assimilation methods where states and parameters are updated across compartment.
Christian Chwala, Felix Keis, and Harald Kunstmann
Atmos. Meas. Tech., 9, 991–999, https://doi.org/10.5194/amt-9-991-2016, https://doi.org/10.5194/amt-9-991-2016, 2016
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Commercial microwave link (CML) networks, like they are used as backbone for the cell phone network, can be used to derive rainfall information. However, data availability is limited due to the lack of suitable data acquisition systems. We have developed and currently operate a custom data acquisition system for CML networks that is able to acquire the required data for a large number of CMLs in real time. This system is the basis for a future countrywide rainfall product derived from CML data.
D. Heinzeller, M. G. Duda, and H. Kunstmann
Geosci. Model Dev., 9, 77–110, https://doi.org/10.5194/gmd-9-77-2016, https://doi.org/10.5194/gmd-9-77-2016, 2016
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We present an in-depth evaluation of the Model for Prediction Across Scales (MPAS) with regards to technical aspects of performing model runs and scalability for medium-size meshes on several HPCs. We also demonstrate the model performance in terms of its capability to reproduce the dynamics of the West African monsoon and its associated precipitation in a pilot study. Finally, we conduct extreme scaling tests on a global 3km mesh with 65,536,002 horizontal grid cells on up to 458,752 cores.
F. Alshawaf, B. Fersch, S. Hinz, H. Kunstmann, M. Mayer, and F. J. Meyer
Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015, https://doi.org/10.5194/hess-19-4747-2015, 2015
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This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.
G. Mao, S. Vogl, P. Laux, S. Wagner, and H. Kunstmann
Hydrol. Earth Syst. Sci., 19, 1787–1806, https://doi.org/10.5194/hess-19-1787-2015, https://doi.org/10.5194/hess-19-1787-2015, 2015
A. Wagner, J. Seltmann, and H. Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-1765-2015, https://doi.org/10.5194/hessd-12-1765-2015, 2015
Manuscript not accepted for further review
R. J. van der Ent, O. A. Tuinenburg, H.-R. Knoche, H. Kunstmann, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 17, 4869–4884, https://doi.org/10.5194/hess-17-4869-2013, https://doi.org/10.5194/hess-17-4869-2013, 2013
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
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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
Kazeem Abiodun Ishola, Gerald Mills, Ankur Prabhat Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci., 29, 2551–2582, https://doi.org/10.5194/hess-29-2551-2025, https://doi.org/10.5194/hess-29-2551-2025, 2025
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Global soil information introduces uncertainty into models that simulate soil hydrothermal changes. Using the Noah with Multiparameterization (Noah-MP) model with two different global soil datasets, we find under-represented soil properties in wet loam, causing a dry bias in soil moisture. This bias is more pronounced and drought categories are more severe in the SoilGrids dataset. We conclude that models should incorporate detailed, region-specific soil information to minimize model uncertainties.
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025, https://doi.org/10.5194/hess-29-2393-2025, 2025
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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 4 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 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
Peyman Abbaszadeh, Fatemeh Gholizadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 29, 2407–2427, https://doi.org/10.5194/hess-29-2407-2025, https://doi.org/10.5194/hess-29-2407-2025, 2025
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The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three southeastern US watersheds during hurricanes, HEAVEN assimilates real-time United States Geological Survey (USGS) streamflow data, boosting forecast accuracy.
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.
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.
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.
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.
Gabriel Rondeau-Genesse, Louis-Philippe Caron, Kristelle Audet, Laurent Da Silva, Daniel Tarte, Rachel Parent, Élise Comeau, and Dominic Matte
EGUsphere, https://doi.org/10.5194/egusphere-2024-2595, https://doi.org/10.5194/egusphere-2024-2595, 2024
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The 2021 drought in Quebec showcased the province’s potential vulnerability. This study uses a storyline approach to explore impacts of future extreme droughts under +2 °C and +3 °C warming scenarios. By combining regional climate and hydrological simulations, it highlights the potential for severe water deficits. Collaboration with water management experts helped project the impacts of those future extreme droughts and their consequences for ecosystems and human activities.
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.
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.
Cited articles
Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., Mount, N. J., See, L. M., Shamseldin, A. Y., Solomatine, D. P., Toth, E., and Wilby, R. L.: Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting, Prog. Phys. Geogr., 36, 480–513, https://doi.org/10.1177/0309133312444943, 2012.
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and Mendoza, P. A.: Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges, Hydrol. Sci. J., 65, 712–725, https://doi.org/10.1080/02626667.2019.1683182, 2020.
Adnan, R. M., Liang, Z., Trajkovic, S., Zounemat-Kermani, M., Li, B., and Kisi, O.: Daily streamflow prediction using optimally pruned extreme learning machine, J. Hydrol., 577, 123981, https://doi.org/10.1016/j.jhydrol.2019.123981, 2019.
Balint, G., Csik, A., Bartha, P., Gauzer, B., and Bonta, I.: Application of meteorological ensembles for Danube flood forecasting and warning, in: Transboundary Floods: Reducing Risks through Flood Management, edited by: Marsalek, J., Stancalie, G., and Balint, G., NATO Sci. Ser., Springer, Dordrecht, 57–68, https://doi.org/10.1007/1-4020-4902-1_6, 2006.
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020.
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections, Clim. Dynam., 57, 2941–2951, https://doi.org/10.1007/s00382-021-05847-0, 2021.
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015.
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023.
Bierkens, M. F. P.: Global hydrology 2015: State, trends, and directions, Water Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173, 2015.
Bonavita, M.: On some limitations of current machine learning weather prediction models, Geophys. Res. Lett., 51, e2023GL107377, https://doi.org/10.1029/2023GL107377, 2024.
Bremnes, J. B.: Ensemble postprocessing using quantile function regression based on neural networks and Bernstein polynomials, Mon. Weather Rev., 148, 403–414, https://doi.org/10.1175/MWR-D-19-0227.1, 2020.
Brotzge, J. A., Berchoff, D., Carlis, D. L., Carr, F. H., Carr, R. H., Gerth, J. J., Gross, B. D., Hamill, T. M., Haupt, S. E., Jacobs, N., McGovern, A., Stensrud, D. J., Szatkowski, G., Szunyogh, I., and Wang, X.: Challenges and opportunities in numerical weather prediction, B. Am. Meteorol. Soc., 104, E698–E705, https://doi.org/10.1175/BAMS-D-22-0172.1, 2023.
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?, J. Climate, 28, 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1, 2015.
Chen, G. and Wang, W.-C.: Short-term precipitation prediction for contiguous United States using deep learning, Geophys. Res. Lett., 49, e2022GL097904, https://doi.org/10.1029/2022GL097904, 2022.
Cho, D., Yoo, C., Im, J., and Cha, D. H.: Comparative assessment of various machine learning-based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in urban areas, Earth Space Sci., 7, e2019EA000740, https://doi.org/10.1029/2019EA000740, 2020.
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009.
Crochemore, L., Ramos, M.-H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016.
de Andrade, F. M., Young, M. P., MacLeod, D., Hirons, L. C., Woolnough, S. J., and Black, E.: Subseasonal precipitation prediction for Africa: Forecast evaluation and sources of predictability, Weather Forecast., 36, 265–284, https://doi.org/10.1175/WAF-D-20-0102.1, 2021.
Dehshiri, S. S. H. and Firoozabadi, B.: A multi-objective framework to select numerical options in air quality prediction models: A case study on dust storm modeling, Sci. Total Environ., 863, 160681, https://doi.org/10.1016/j.scitotenv.2022.160681, 2023.
Di Luca, A., de Elía, R., and Laprise, R.: Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations, Clim. Dynam., 44, 2519–2537, https://doi.org/10.1007/s00382-011-1068-3, 2015.
Dong, N., Wei, J., Yang, M., Yan, D., Yang, C., Gao, H., Arnault, J., Laux, P., Zhang, X., Liu, Y., and Niu, J.: Model estimates of China's terrestrial water storage variation due to reservoir operation, Water Resour. Res., 58, e2021WR031787, https://doi.org/10.1029/2021WR031787, 2022.
Dong, N., Yang, M., Wei, J., Arnault, J., Laux, P., Xu, S., Wang, H., Yu, Z., and Kunstmann, H.: Toward improved parameterizations of reservoir operation in ungauged basins: A synergistic framework coupling satellite remote sensing, hydrologic modeling, and conceptual operation schemes, Water Resour. Res., 59, e2022WR033026, https://doi.org/10.1029/2022WR033026, 2023.
Dong, N., Hao, H., Yang, M., Wei, J., Xu, S., and Kunstmann, H.: Model, Zenodo [code], https://doi.org/10.5281/zenodo.12664798, 2024a.
Dong, N., Hao, H., Yang, M., Wei, J., Xu, S., and Kunstmann, H.: Data, Zenodo [data set], https://doi.org/10.5281/zenodo.12664851, 2024b.
Ebert-Uphoff, I. and Hilburn, K.: Evaluation, tuning and interpretation of neural networks for working with images in meteorological applications, B. Am. Meteorol. Soc., 101, E1654–E1677, https://doi.org/10.1175/BAMS-D-19-0324.1, 2020.
Ferranti, L., Corti, S., and Janousek, M.: Flow-dependent verification of the ECMWF ensemble over the Euro-Atlantic sector, Q. J. Roy. Meteor. Soc., 144, 317–326, https://doi.org/10.1002/qj.3204, 2018.
Gao, S., Huang, D., Du, N., Ren, C., and Yu, H.: WRF ensemble dynamical downscaling of precipitation over China using different cumulus convective schemes, Atmos. Res., 271, 106116, https://doi.org/10.1016/j.atmosres.2022.106116, 2022.
Gassman, P. W., Reyes, M. R., Green, C. H., and Arnold, J. G.: The Soil and Water Assessment Tool: Historical development, applications, and future research directions, Trans. ASABE, 57, 1211–1250, https://doi.org/10.13031/2013.23637, 2014.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
Ham, Y.-G., Kim, J.-H., and Luo, J.-J.: Deep learning for multi-year ENSO forecasts, Nature, 573, 568–572, https://doi.org/10.1038/s41586-019-1559-7, 2019.
Han, L., Chen, M., Chen, K., Chen, H., Zhang, Y., Lu, B., Song, L., and Qin, R.: A deep learning method for bias correction of ECMWF 24–240 h forecasts, Adv. Atmos. Sci., 38, 1444–1459, https://doi.org/10.1007/s00376-021-0434-2, 2021.
Hao, H., Dong, N., Yang, M., Wei, J., Zhang, X., Xu, S., Yan, D., Ren, L., Leng, G., Chen, L., and Zhou, X.: The changing hydrology of an irrigated and dammed Yangtze River: Streamflow, extremes, and lake hydrodynamics, Water Resour. Res., 60, e2024WR037841, https://doi.org/10.1029/2024WR037841, 2024.
Horat, N. and Lerch, S.: Deep Learning for Postprocessing Global Probabilistic Forecasts on Subseasonal Time Scales, Mon. Weather Rev., 152, 667–687, https://doi.org/10.1175/MWR-D-23-0112.1, 2024.
Hu, C. H., Guo, S. L., Xiong, L. H., and Peng, D. Z.: A modified Xin'anjiang model and its application in northern China, Hydrol. Res., 36, 175–192, https://doi.org/10.2166/nh.2005.0013, 2005.
Huang, Z., Zhao, T., Xu, W., Cai, H., Wang, J., Zhang, Y., Liu, Z., Tian, Y., Yan, D., and Chen, X.: A Seven-Parameter Bernoulli-Gamma-Gaussian Model to Calibrate Subseasonal to Seasonal Precipitation Forecasts, J. Hydrol., 610, 127896, https://doi.org/10.1016/j.jhydrol.2022.127896, 2022.
Humphrey, G. B., Gibbs, M. S., Dandy, G. C., and Maier, H. R.: A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network, J. Hydrol., 540, 623–640, https://doi.org/10.1016/j.jhydrol.2016.06.026, 2016.
Jaun, S., Ahrens, B., Walser, A., Ewen, T., and Schär, C.: A probabilistic view on the August 2005 floods in the upper Rhine catchment, Nat. Hazards Earth Syst. Sci., 8, 281–291, https://doi.org/10.5194/nhess-8-281-2008, 2008.
Jiang, M., Weng, B., Chen, J., Huang, T., Ye, F., and You, L.: Transformer-enhanced spatiotemporal neural network for post-processing of precipitation forecasts, J. Hydrol., 630, 130720, https://doi.org/10.1016/j.jhydrol.2024.130720, 2024.
Jiang, Z., Yang, S., Liu, Z., Xu, Y., Xiong, Y., Qi, S., Pang, Q., Xu, J., Liu, F., and Xu, T.: Coupling machine learning and weather forecast to predict farmland flood disaster: A case study in Yangtze River basin, Environ. Model. Softw., 155, 105436, https://doi.org/10.1016/j.envsoft.2022.105436, 2022.
Jin, W., Zhang, W., Hu, J., Weng, B., Huang, T., and Chen, J.: Using the residual network module to correct the sub-seasonal high temperature forecast, Front. Earth Sci., 9, 760766, https://doi.org/10.3389/feart.2021.760766, 2022.
Kim, H., Ham, Y.-G., Joo, Y.-S., and Son, S.-W.: Deep learning for bias correction of MJO prediction, Nat. Commun., 12, 3087, https://doi.org/10.1038/s41467-021-23406-3, 2021.
Kim, T., Yang, T., Zhang, L., and Hong, Y.: Near real-time hurricane rainfall forecasting using convolutional neural network models with Integrated Multi-satellitE Retrievals for GPM (IMERG) product, Atmos. Res., 270, 106037, https://doi.org/10.1016/j.atmosres.2022.106037, 2022.
Kisi, O.: Streamflow forecasting using different artificial neural network algorithms, J. Hydrol. Eng., 12, 532–539, https://doi.org/10.1061/(ASCE)1084-0699(2007)12:5(532), 2007.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Kratzert, F., Klotz, D., Herrnegger, M., Sampson, A. K., Hochreiter, S., and Nearing, G. S.: Toward improved predictions in ungauged basins: Exploiting the power of machine learning, Water Resour. Res., 55, 11344–11354, https://doi.org/10.1029/2019WR026065, 2019.
Kratzert, F., Gauch, M., Nearing, G., and Klotz, D.: NeuralHydrology – A Python library for Deep Learning research in hydrology, J. Open Source Softw., 7, 4050, https://doi.org/10.21105/joss.04050, 2022 (code available at: https://neuralhydrology.readthedocs.io/en/latest/index.html, last access: 1 July 2024).
Lagerquist, R., McGovern, A., and Gagne, D. J., II: Deep learning for spatially explicit prediction of synoptic-scale Fronts, Weather Forecast., 34, 1137–1160, https://doi.org/10.1175/WAF-D-18-0183.1, 2019.
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P.: Learning skillful medium-range global weather forecasting, Science, 382, 1416–1421, https://doi.org/10.1126/science.adi2336, 2023.
Larraondo, P. R., Renzullo, L. J., Van Dijk, A. I., Inza, I., and Lozano, J. A.: Optimization of deep learning precipitation models using categorical binary metrics, J. Adv. Model. Earth Sy., 12, e2019MS001909, https://doi.org/10.1029/2019MS001909, 2020.
Li, J., Li, L., Zhang, T., Xing, H., Shi, Y., Li, Z., Wang, C., and Liu, J.: Flood forecasting based on radar precipitation nowcasting using U-net and its improved models, J. Hydrol., 632, 130871, https://doi.org/10.1016/j.jhydrol.2024.130871, 2024a.
Li, L., Yun, Z., Liu, Y., Wang, Y., Zhao, W., Kang, Y., and Gao, R.: Improving Categorical and Continuous Accuracy of Precipitation Forecasts by Integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian Distribution, Atmos. Res., 298, 107133, https://doi.org/10.1016/j.atmosres.2023.107133, 2024b.
Li, W., Pan, B., Xia, J., and Duan, Q.: Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts, J. Hydrol., 605, 127301, https://doi.org/10.1016/j.jhydrol.2021.127301, 2022.
Li, X., Wu, H., Nanding, N., Chen, S., Hu, Y., and Li, L.: Statistical Bias Correction of Precipitation Forecasts Based on Quantile Mapping on the Sub-Seasonal to Seasonal Scale, Remote Sens., 15, 1743, https://doi.org/10.3390/rs15071743, 2023.
Liang, P., Lin, H., and Ding, Y.: Dominant modes of subseasonal variability of East Asian summertime surface air temperature and their predictions, J. Climate, 31, 2729–2743, https://doi.org/10.1175/JCLI-D-17-0368.1, 2018.
Ling, F., Li, Y., Luo, J.-J., Zhong, X., and Wang, Z.: Two deep learning-based bias-correction pathways improve summer precipitation prediction over China, Environ. Res. Lett., 17, 124025, https://doi.org/10.1088/1748-9326/aca68a, 2022a.
Ling, F., Luo, J.-J., Li, Y., Tang, T., Bai, L., Ouyang, W., and Yamagata, T.: Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole, Nat. Commun., 13, 7681, https://doi.org/10.1038/s41467-022-35412-0, 2022b.
Liu, D., Jiang, W., Mu, L., and Wang, S.: Streamflow prediction using deep learning neural network: case study of Yangtze River, IEEE Access, 8, 90069–90086, https://doi.org/10.1109/ACCESS.2020.2993874, 2020.
Liu, J., Yuan, X., Zeng, J., Jiao, Y., Li, Y., Zhong, L., and Yao, L.: Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning, Hydrol. Earth Syst. Sci., 26, 265–278, https://doi.org/10.5194/hess-26-265-2022, 2022.
Lyu, Y., Zhu, S., Zhi, X., Ji, Y., Fan, Y., and Dong, F.: Improving subseasonal-to-seasonal prediction of summer extreme precipitation over southern China based on a deep learning method, Geophys. Res. Lett., 50, e2023GL106245, https://doi.org/10.1029/2023GL106245, 2023.
Manzanas, R., Lucero, A., Weisheimer, A., and Gutiérrez, J. M.: Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?, Clim. Dynam., 50, 1161–1176, https://doi.org/10.1007/s00382-017-3669-y, 2018.
Mao, G., Vogl, S., Laux, P., Wagner, S., and Kunstmann, H.: Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data, Hydrol. Earth Syst. Sci., 19, 1787–1806, https://doi.org/10.5194/hess-19-1787-2015, 2015.
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I.: Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, https://doi.org/10.1029/2009RG000314, 2010.
Merino, A., García-Ortega, E., Navarro, A., Sánchez, J. L., and Tapiador, F. J.: WRF hourly evaluation for extreme precipitation events, Atmos. Res., 274, 106215, https://doi.org/10.1016/j.atmosres.2022.106215, 2022.
Michalek, A. T., Villarini, G., and Kim, T.: Understanding the impact of precipitation bias-correction and statistical downscaling methods on projected changes in flood extremes, Earth's Future, 12, e2023EF004179, https://doi.org/10.1029/2023EF004179, 2024.
Ni, L., Wang, D., Singh, V. P., Wu, J., Chen, X., Tao, Y., Zhu, X., Jiang, J., and Zeng, X.: Monthly precipitation prediction at regional scale using deep convolutional neural networks, Hydrol. Process., 37, e14954, https://doi.org/10.1002/hyp.14954, 2023.
Nie, Y. and Sun, J.: Improving dynamical-statistical subseasonal precipitation forecasts using deep learning: A case study in Southwest China, Environ. Res. Lett., 19, 044032, https://doi.org/10.1088/1748-9326/ad5370, 2024.
Nooni, I. K., Tan, G., Hongming, Y., Chaibou, A. A. S., Habtemicheal, B. A., Gnitou, G. T., and Lim Kam Sian, K. T. C.: Assessing the performance of WRF Model in simulating heavy precipitation events over East Africa using satellite-based precipitation product, Remote Sens., 14, 1964, https://doi.org/10.3390/rs14091964, 2022.
Peng, T., Zhi, X., Ji, Y., Ji, L., and Tian, Y.: Prediction skill of extended range 2-m maximum air temperature probabilistic forecasts using machine learning postprocessing methods, Atmosphere, 11, 805, https://doi.org/10.3390/atmos11080805, 2020.
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005.
Rasp, S. and Lerch, S.: Neural networks for postprocessing ensemble weather forecasts, Mon. Weather Rev., 146, 3885–3900, https://doi.org/10.1175/MWR-D-18-0187.1, 2018.
Robertson, D. E. and Wang, Q. J.: Seasonal forecasts of unregulated inflows into the Murray River, Australia, Water Resour. Manag., 27, 2747–2769, https://doi.org/10.1007/s11269-013-0313-4, 2013.
Sachindra, D. A., Ahmed, K., Rashid, M. M., Shahid, S., and Perera, B. J. C.: Statistical downscaling of precipitation using machine learning techniques, Atmos. Res., 212, 240–258, https://doi.org/10.1016/j.atmosres.2018.05.022, 2018.
Scheuerer, M. and Hamill, T. M.: Statistical post-processing of ensemble precipitation forecasts by fitting censored, shifted gamma distributions, Mon. Weather Rev., 143, 4578–4596, https://doi.org/10.1175/MWR-D-15-0061.1, 2015.
Shi, X.: Enabling smart dynamical downscaling of extreme precipitation events with machine learning, Geophys. Res. Lett., 47, e2020GL090309, https://doi.org/10.1029/2020GL090309, 2020.
Singhal, A., Jaseem, M., and Jha, S. K.: Spatial connections in extreme precipitation events obtained from NWP forecasts: A complex network approach, Atmos. Res., 282, 106538, https://doi.org/10.1016/j.atmosres.2022.106538, 2023.
Srivastava, A. K., Ullrich, P. A., Rastogi, D., Vahmani, P., Jones, A., and Grotjahn, R.: Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS, Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, 2023.
Sun, L. and Lan, Y.: Statistical downscaling of daily temperature and precipitation over China using deep learning neural models: Localization and comparison with other methods, Int. J. Climatol., 41, 1128–1147, https://doi.org/10.1002/joc.6769, 2021.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K. L.: A review of global precipitation data sets: Data sources, estimation, and intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2014RG000477, 2016.
Tabari, H., Paz, S. M., Buekenhout, D., and Willems, P.: Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought, Hydrol. Earth Syst. Sci., 25, 3493–3517, https://doi.org/10.5194/hess-25-3493-2021, 2021.
Taillardat, M., Mestre, O., Zamo, M., and Naveau, P.: Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics, Mon. Weather Rev., 144, 2375–2393, https://doi.org/10.1175/MWR-D-15-0260.1, 2016.
Tan, Y., Dong, N., Hou, A., and Yan, W.: An improved Xin'anjiang hydrological model for flood simulation coupling snowmelt runoff module in Northwestern China, Water, 15, 3401, https://doi.org/10.3390/w15193401, 2023.
Valdez, E. S., Anctil, F., and Ramos, M.-H.: Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems, Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, 2022.
Vandal, T., Kodra, E., and Ganguly, A. R.: Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation, Theor. Appl. Climatol., 137, 557–570, https://doi.org/10.1007/s00704-018-2613-3, 2019.
Vigaud, N., Tippett, M. K., and Robertson, A. W.: Deterministic skill of subseasonal precipitation forecasts for the East Africa-West Asia sector from September to May, J. Geophys. Res.-Atmos., 124, 11887–11896, https://doi.org/10.1029/2019JD030747, 2019.
Vrac, M. and Friederichs, P.: Multivariate-intervariable, spatial, and temporal bias correction, J. Climate, 28, 218–237, https://doi.org/10.1175/JCLI-D-14-00059.1, 2015.
Wang, R., Zhang, J., Guo, E., Zhao, C., and Cao, T.: Spatial and temporal variations of precipitation concentration and their relationships with large-scale atmospheric circulations across Northeast China, Atmos. Res., 222, 62–73, https://doi.org/10.1016/j.atmosres.2019.02.008, 2019.
Wei, L., Hu, K.-H., and Hu, X.-D.: Rainfall occurrence and its relation to flood damage in China from 2000 to 2015, J. Mt. Sci., 15, 2492–2504, https://doi.org/10.1007/s11629-018-4931-4, 2018.
Weyn, J. A., Durran, D. R., Caruana, R., and Cresswell-Clay, N.: Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models, J. Adv. Model. Earth Sy., 13, e2021MS002502, https://doi.org/10.1029/2021MS002502, 2021.
Xie, J., Hsu, P.-C., Hu, Y., Ye, M., and Yu, J.: Skillful extended-range forecast of rainfall and extreme events in East China based on deep learning, Weather Forecast., 38, 467–486, https://doi.org/10.1175/WAF-D-22-0132.1, 2023.
Xu, Y. P., Gao, X., Zhu, Q., and Zhang, Y.: Coupling a regional climate model and distributed hydrological model to assess future water resources in Jinhua River Basin, East China, J. Hydrol. Eng., 20, 04014054, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001007, 2015.
Yang, S., Yang, D., Chen, J., Santisirisomboon, J., and Zhao, B.: A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data, J. Hydrol., 590, 125206, https://doi.org/10.1016/j.jhydrol.2020.125206, 2020.
You, X. X., Liang, Z. M., Wang, Y. Q., and Zhang, H.: A study on loss function against data imbalance in deep learning correction of precipitation forecasts, Atmos. Res., 281, 106500, https://doi.org/10.1016/j.atmosres.2022.106500, 2023.
Yuan, X., Wood, E. F., Luo, L., and Pan, M.: A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction, Geophys. Res. Lett., 38, L13401, https://doi.org/10.1029/2011GL047792, 2011.
Yuan, X., Ma, F., Wang, L., Zheng, Z., Ma, Z., Ye, A., and Peng, S.: An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions, Hydrol. Earth Syst. Sci., 20, 2437–2451, https://doi.org/10.5194/hess-20-2437-2016, 2016.
Yuan, X., Wang, S., and Hu, Z.-Z.: Do climate change and El Niño increase likelihood of Yangtze River extreme rainfall?, B. Am. Meteorol. Soc., 99, S113–S117, https://doi.org/10.1175/BAMS-D-17-0089.1, 2018.
Zhang, Q., Li, Y. P., Huang, G. H., Wang, H., Li, Y. F., Liu, Y. R., and Shen, Z. Y.: A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region, J. Hydrol., 615, 128730, https://doi.org/10.1016/j.jhydrol.2022.128730, 2022a.
Zhang, T., Liang, Z., Li, W., Wang, J., Hu, Y., and Li, B.: Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks, Hydrol. Earth Syst. Sci., 27, 1945–1960, https://doi.org/10.5194/hess-27-1945-2023, 2023.
Zhang, Y., Ragettli, S., Molnar, P., Fink, O., and Peleg, N.: Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments, J. Hydrol., 614, 128577, https://doi.org/10.1016/j.jhydrol.2022.128577, 2022b.
Zhao, R. J.: The Xin'anjiang model applied in China, J. Hydrol., 135, 371–381, https://doi.org/10.1016/0022-1694(92)90096-E, 1992.
Zhu, E., Yuan, X., and Wood, A.: Benchmark decadal forecast skill for terrestrial water storage estimated by an elasticity framework, Nat. Commun., 10, 1237, https://doi.org/10.1038/s41467-019-09245-3, 2019.
Zhu, S., Remedio, A. R. C., Sein, D. V., Sielmann, F., Ge, F., Xu, J., Peng, T., Jacob, D., Zhi, X., and Fraedrich, K.: Added value of the regionally coupled model ROM in the East Asian summer monsoon modeling, Theor. Appl. Climatol., 140, 375–387, https://doi.org/10.1007/s00704-020-03093-8, 2020.
Short summary
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.
Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme...
