Articles | Volume 23, issue 7
https://doi.org/10.5194/hess-23-3057-2019
© Author(s) 2019. 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-23-3057-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jul 2019
Research article |
| 18 Jul 2019
| 18 Jul 2019
Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin
Department of Geography & Environmental Science, University of
Reading, Reading, RG6 6AB, UK
Hannah L. Cloke
Department of Geography & Environmental Science, University of
Reading, Reading, RG6 6AB, UK
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
Department of Earth Sciences, Uppsala University, Uppsala, 752 36,
Sweden
Centre of Natural Hazards and Disaster Science, CNDS, Uppsala, 752 36, Sweden
Ervin Zsoter
European Centre for Medium-Range Weather Forecasts, Shinfield Park,
Reading, RG6 9AX, UK
Department of Geography & Environmental Science, University of
Reading, Reading, RG6 6AB, UK
Zachary Flamig
University of Chicago Center for Data Intensive Science, Chicago, USA
Jannis M. Hoch
Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht, the Netherlands
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
Juan Bazo
Red Cross Red Crescent Climate Centre, 2521 CV The Hague, the
Netherlands
Universidad Tecnológica del Perú (UTP), Lima, Peru
Erin Coughlan de Perez
International Research Institute for Climate and Society, Columbia
University, Palisades, NY 10964, USA
Red Cross Red Crescent Climate Centre, 2521 CV The Hague, the
Netherlands
Elisabeth M. Stephens
Department of Geography & Environmental Science, University of
Reading, Reading, RG6 6AB, UK
Related authors
Jamie Towner, Andrea Ficchí, Hannah L. Cloke, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 25, 3875–3895, https://doi.org/10.5194/hess-25-3875-2021, https://doi.org/10.5194/hess-25-3875-2021, 2021
Short summary
Short summary
We examine whether several climate indices alter the magnitude, timing and duration of floods in the Amazon. We find significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative SST years in the central Pacific Ocean. This response is not repeated when the negative anomaly is positioned further east. These results have important implications for both social and physical sectors working towards the improvement of flood early warning systems.
Gwyneth Matthews, Hannah L. Cloke, Sarah L. Dance, and Christel Prudhomme
EGUsphere, https://doi.org/10.5194/hess-2024-3989, https://doi.org/10.5194/hess-2024-3989, 2025
Short summary
Short summary
Forecasts provide information crucial for managing floods and for water resource planning, but they often have errors. “Post-processing” reduces these errors but is usually only applied at river gauges, leaving areas without gauges uncorrected. We developed a new method that uses spatial information contained within the forecast to spread information about the errors from gauged locations to ungauged areas. Our results show that the method successfully makes river forecasts more accurate.
Barry van Jaarsveld, Niko Wanders, Edwin H. Sutanudjaja, Jannis Hoch, Bram Droppers, Joren Janzing, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Earth Syst. Dynam., 16, 29–54, https://doi.org/10.5194/esd-16-29-2025, https://doi.org/10.5194/esd-16-29-2025, 2025
Short summary
Short summary
Policy makers use global hydrological models to develop water management strategies and policies. However, it would be better if these models provided information at higher resolution. We present a first-of-its-kind, truly global hyper-resolution model and show that hyper-resolution brings about better estimates of river discharge, and this is especially true for smaller catchments. Our results also suggest that future hyper-resolution models need to include more detailed land cover information.
Jerom P. M. Aerts, Jannis M. Hoch, Gemma Coxon, Nick C. van de Giesen, and Rolf W. Hut
Hydrol. Earth Syst. Sci., 28, 5011–5030, https://doi.org/10.5194/hess-28-5011-2024, https://doi.org/10.5194/hess-28-5011-2024, 2024
Short summary
Short summary
For users of hydrological models, model suitability often hinges on how well simulated outputs match observed discharge. This study highlights the importance of including discharge observation uncertainty in hydrological model performance assessment. We highlight the need to account for this uncertainty in model comparisons and introduce a practical method suitable for any observational time series with available uncertainty estimates.
Safaa Naffaa, Frances F. E. Dunne, Jannis Hoch, Geert Sterk, Steven S. M. de Jong, and Rens L. P. H. van Beek
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-255, https://doi.org/10.5194/hess-2024-255, 2024
Preprint under review for HESS
Short summary
Short summary
This paper introduces the RDSM model. Human impacts such as climate change, land cover change and reservoir construction can be explicitly modelled and evaluated. We applied RDSM to the Amazon. We also validated the model and we conclude that RDSM effectively represents the patterns of monthly and annual variations of discharge and sediment transport across the Amazon Basin and to the ocean. Our findings are relevant to the research community working on the Amazon Basin and on similar topics.
Joy Ommer, Jessica Neumann, Milan Kalas, Sophie Blackburn, and Hannah L. Cloke
Nat. Hazards Earth Syst. Sci., 24, 2633–2646, https://doi.org/10.5194/nhess-24-2633-2024, https://doi.org/10.5194/nhess-24-2633-2024, 2024
Short summary
Short summary
What’s the worst that could happen? Recent floods are often claimed to be beyond our imagination. Imagination is the picturing of a situation in our mind and the emotions that we connect with this situation. But why is this important for disasters? This survey found that when we cannot imagine a devastating flood, we are not preparing in advance. Severe-weather forecasts and warnings need to advance in order to trigger our imagination of what might happen and enable us to start preparing.
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
Short summary
Short summary
This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
Joy Ommer, Milan Kalas, Jessica Neumann, Sophie Blackburn, and Hannah L. Cloke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1186, https://doi.org/10.5194/egusphere-2024-1186, 2024
Short summary
Short summary
What do we regret about our disaster preparedness? This study showed that we regret most not having taken any actions! Also, we only regret actions which end up threatening our life! What we don't regret is helping others! The findings of this study suggest that the no-regrets approach could be a suitable framework for moving towards longer term disaster preparedness.
Clare Lewis, Tim Smyth, Jess Neumann, and Hannah Cloke
Nat. Hazards Earth Syst. Sci., 24, 121–131, https://doi.org/10.5194/nhess-24-121-2024, https://doi.org/10.5194/nhess-24-121-2024, 2024
Short summary
Short summary
Meteotsunami are the result of atmospheric disturbances and can impact coastlines causing injury, loss of life, and damage to assets. This paper introduces a novel intensity index to allow for the quantification of these events at the shoreline. This has the potential to assist in the field of natural hazard assessment. It was trialled in the UK but designed for global applicability and to become a widely accepted standard in coastal planning, meteotsunami forecasting, and early warning systems.
Clare Lewis, Tim Smyth, David Williams, Jess Neumann, and Hannah Cloke
Nat. Hazards Earth Syst. Sci., 23, 2531–2546, https://doi.org/10.5194/nhess-23-2531-2023, https://doi.org/10.5194/nhess-23-2531-2023, 2023
Short summary
Short summary
Meteotsunami are globally occurring water waves initiated by atmospheric disturbances. Previous research has suggested that in the UK, meteotsunami are a rare phenomenon and tend to occur in the summer months. This article presents a revised and updated catalogue of 98 meteotsunami that occurred between 1750 and 2022. Results also demonstrate a larger percentage of winter events and a geographical pattern highlighting the
hotspotregions that experience these events.
Jannis M. Hoch, Edwin H. Sutanudjaja, Niko Wanders, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 27, 1383–1401, https://doi.org/10.5194/hess-27-1383-2023, https://doi.org/10.5194/hess-27-1383-2023, 2023
Short summary
Short summary
To facilitate locally relevant simulations over large areas, global hydrological models (GHMs) have moved towards ever finer spatial resolutions. After a decade-long quest for hyper-resolution (i.e. equal to or smaller than 1 km), the presented work is a first application of a GHM at 1 km resolution over Europe. This not only shows that hyper-resolution can be achieved but also allows for a thorough evaluation of model results at unprecedented detail and the formulation of future research.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Bin Cao, Gabriele Arduini, and Ervin Zsoter
The Cryosphere, 16, 2701–2708, https://doi.org/10.5194/tc-16-2701-2022, https://doi.org/10.5194/tc-16-2701-2022, 2022
Short summary
Short summary
We implemented a new multi-layer snow scheme in the land surface scheme of ERA5-Land with revised snow densification parameterizations. The revised HTESSEL improved the representation of soil temperature in permafrost regions compared to ERA5-Land; in particular, warm bias in winter was significantly reduced, and the resulting modeled near-surface permafrost extent was improved.
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, https://doi.org/10.5194/hess-26-2939-2022, 2022
Short summary
Short summary
The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used does not represent the flow in the rivers correctly. We found that, by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected, thus becoming more useful.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
Short summary
Short summary
We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
Joaquín Muñoz-Sabater, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Margarita Choulga, Shaun Harrigan, Hans Hersbach, Brecht Martens, Diego G. Miralles, María Piles, Nemesio J. Rodríguez-Fernández, Ervin Zsoter, Carlo Buontempo, and Jean-Noël Thépaut
Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, https://doi.org/10.5194/essd-13-4349-2021, 2021
Short summary
Short summary
The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
Chloe Brimicombe, Claudia Di Napoli, Rosalind Cornforth, Florian Pappenberger, Celia Petty, and Hannah L. Cloke
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-242, https://doi.org/10.5194/nhess-2021-242, 2021
Revised manuscript not accepted
Short summary
Short summary
Heatwaves are an increasing risk to African communities. This hazard can have a negative impact on peoples lives and in some cases results in their death. This study shows new information about heatwave characteristics through a list of heatwave events that have been reported for the African continent from 1980 until 2020. Case studies are useful helps to inform the development of early warning systems and forecasting, which is an urgent priority and needs significant improvement.
Colin Keating, Donghoon Lee, Juan Bazo, and Paul Block
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, https://doi.org/10.5194/nhess-21-2215-2021, https://doi.org/10.5194/nhess-21-2215-2021, 2021
Short summary
Short summary
Disaster planning has historically underallocated resources for flood preparedness, but evidence supports reduced vulnerability via early actions. We evaluate the ability of multiple season-ahead streamflow prediction models to appropriately trigger early actions for the flood-prone Marañón River and Piura River in Peru. Our findings suggest that locally tailored statistical models may offer improved performance compared to operational physically based global models in low-data environments.
Jamie Towner, Andrea Ficchí, Hannah L. Cloke, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 25, 3875–3895, https://doi.org/10.5194/hess-25-3875-2021, https://doi.org/10.5194/hess-25-3875-2021, 2021
Short summary
Short summary
We examine whether several climate indices alter the magnitude, timing and duration of floods in the Amazon. We find significant changes in both flood magnitude and duration, particularly in the north-eastern Amazon for negative SST years in the central Pacific Ocean. This response is not repeated when the negative anomaly is positioned further east. These results have important implications for both social and physical sectors working towards the improvement of flood early warning systems.
Sazzad Hossain, Hannah L. Cloke, Andrea Ficchì, Andrew G. Turner, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-97, https://doi.org/10.5194/hess-2021-97, 2021
Manuscript not accepted for further review
Short summary
Short summary
Hydrometeorological drivers are investigated to study three different flood types: long duration, rapid rise and high water level of the Brahmaputra river basin in Bangladesh. Our results reveal that long duration floods have been driven by basin-wide rainfall whereas rapid rate of rise due to more localized rainfall. We find that recent record high water levels are not coincident with extreme river flows. Understanding these drivers is key for flood forecasting and early warning.
Shaun Harrigan, Ervin Zsoter, Lorenzo Alfieri, Christel Prudhomme, Peter Salamon, Fredrik Wetterhall, Christopher Barnard, Hannah Cloke, and Florian Pappenberger
Earth Syst. Sci. Data, 12, 2043–2060, https://doi.org/10.5194/essd-12-2043-2020, https://doi.org/10.5194/essd-12-2043-2020, 2020
Short summary
Short summary
A new river discharge reanalysis dataset is produced operationally by coupling ECMWF's latest global atmospheric reanalysis, ERA5, with the hydrological modelling component of the Global Flood Awareness System (GloFAS). The GloFAS-ERA5 reanalysis is a global gridded dataset with a horizontal resolution of 0.1° at a daily time step and is freely available from 1979 until near real time. The evaluation against observations shows that the GloFAS-ERA5 reanalysis was skilful in 86 % of catchments.
Louise Arnal, Liz Anspoks, Susan Manson, Jessica Neumann, Tim Norton, Elisabeth Stephens, Louise Wolfenden, and Hannah Louise Cloke
Geosci. Commun., 3, 203–232, https://doi.org/10.5194/gc-3-203-2020, https://doi.org/10.5194/gc-3-203-2020, 2020
Short summary
Short summary
The Environment Agency (EA), responsible for flood risk management in England, is moving towards the use of probabilistic river flood forecasts. By showing the likelihood of future floods, they can allow earlier anticipation. But making decisions on probabilistic information is complex and interviews with EA decision-makers highlight the practical challenges and opportunities of this transition. We make recommendations to support a successful transition for flood early warning in England.
Jannis M. Hoch, Dirk Eilander, Hiroaki Ikeuchi, Fedor Baart, and Hessel C. Winsemius
Nat. Hazards Earth Syst. Sci., 19, 1723–1735, https://doi.org/10.5194/nhess-19-1723-2019, https://doi.org/10.5194/nhess-19-1723-2019, 2019
Short summary
Short summary
Flood events are often complex in their origin and dynamics. The choice of computer model to simulate can hence determine which level of complexity can be represented. We here compare different models varying in complexity (hydrology with routing, 1-D routing, 1D/2D hydrodynamics) and assess how model choice influences the accuracy of results. This was achieved by using GLOFRIM, a model coupling framework. Results show that accuracy depends on the model choice and the output variable considered.
Elisabeth M. Stephens, David J. Spiegelhalter, Ken Mylne, and Mark Harrison
Geosci. Commun., 2, 101–116, https://doi.org/10.5194/gc-2-101-2019, https://doi.org/10.5194/gc-2-101-2019, 2019
Short summary
Short summary
The UK Met Office ran an online game to highlight the best methods of communicating uncertainty in their online forecasts and to widen engagement in probabilistic weather forecasting. The game used a randomized design to test different methods of presenting uncertainty and to enable participants to experience being
luckyor
unluckywhen the most likely scenario did not occur. Over 8000 people played the game; we found players made better decisions when provided with forecast uncertainty.
Sazzad Hossain, Hannah L. Cloke, Andrea Ficchì, Andrew G. Turner, and Elisabeth Stephens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-286, https://doi.org/10.5194/hess-2019-286, 2019
Manuscript not accepted for further review
Jessica L. Neumann, Louise Arnal, Rebecca E. Emerton, Helen Griffith, Stuart Hyslop, Sofia Theofanidi, and Hannah L. Cloke
Geosci. Commun., 1, 35–57, https://doi.org/10.5194/gc-1-35-2018, https://doi.org/10.5194/gc-1-35-2018, 2018
Short summary
Short summary
Seasonal hydrological forecasts (SHF) can predict floods, droughts, and water use in the coming months, but little is known about how SHF are used for decision-making. We asked 11 water sector participants what decisions they would make when faced with a possible flood event in 6 weeks' time. Flood forecasters and groundwater hydrologists responded to the flood risk more than water supply managers. SHF need to be tailored for use and communicated more clearly if they are to aid decision-making.
Rebecca Emerton, Ervin Zsoter, Louise Arnal, Hannah L. Cloke, Davide Muraro, Christel Prudhomme, Elisabeth M. Stephens, Peter Salamon, and Florian Pappenberger
Geosci. Model Dev., 11, 3327–3346, https://doi.org/10.5194/gmd-11-3327-2018, https://doi.org/10.5194/gmd-11-3327-2018, 2018
Short summary
Short summary
Global overviews of upcoming flood and drought events are key for many applications from agriculture to disaster risk reduction. Seasonal forecasts are designed to provide early indications of such events weeks or even months in advance. This paper introduces GloFAS-Seasonal, the first operational global-scale seasonal hydro-meteorological forecasting system producing openly available forecasts of high and low river flow out to 4 months ahead.
Edwin H. Sutanudjaja, Rens van Beek, Niko Wanders, Yoshihide Wada, Joyce H. C. Bosmans, Niels Drost, Ruud J. van der Ent, Inge E. M. de Graaf, Jannis M. Hoch, Kor de Jong, Derek Karssenberg, Patricia López López, Stefanie Peßenteiner, Oliver Schmitz, Menno W. Straatsma, Ekkamol Vannametee, Dominik Wisser, and Marc F. P. Bierkens
Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, https://doi.org/10.5194/gmd-11-2429-2018, 2018
Short summary
Short summary
PCR-GLOBWB 2 is an integrated hydrology and water resource model that fully integrates water use simulation and consolidates all features that have been developed since PCR-GLOBWB 1 was introduced. PCR-GLOBWB 2 can have a global coverage at 5 arcmin resolution and supersedes PCR-GLOBWB 1, which has a resolution of 30 arcmin only. Comparing the 5 arcmin with 30 arcmin simulations using discharge data, we clearly find improvement in the model performance of the higher-resolution model.
Louise Arnal, Hannah L. Cloke, Elisabeth Stephens, Fredrik Wetterhall, Christel Prudhomme, Jessica Neumann, Blazej Krzeminski, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 22, 2057–2072, https://doi.org/10.5194/hess-22-2057-2018, https://doi.org/10.5194/hess-22-2057-2018, 2018
Short summary
Short summary
This paper presents a new operational forecasting system (driven by atmospheric forecasts), predicting river flow in European rivers for the next 7 months. For the first month only, these river flow forecasts are, on average, better than predictions that do not make use of atmospheric forecasts. Overall, this forecasting system can predict whether abnormally high or low river flows will occur in the next 7 months in many parts of Europe, and could be valuable for various applications.
Konstantinos Bischiniotis, Bart van den Hurk, Brenden Jongman, Erin Coughlan de Perez, Ted Veldkamp, Hans de Moel, and Jeroen Aerts
Nat. Hazards Earth Syst. Sci., 18, 271–285, https://doi.org/10.5194/nhess-18-271-2018, https://doi.org/10.5194/nhess-18-271-2018, 2018
Short summary
Short summary
Preparedness activities and flood forecasting have received increasing attention and have led towards new science-based early warning systems. Understanding the flood triggering mechanisms will result in increasing warning lead times, providing sufficient time for early action. Findings of this study indicate that the consideration of short- and long-term antecedent conditions can be used by humanitarian organizations and decision makers for improved flood risk management.
Jannis M. Hoch, Jeffrey C. Neal, Fedor Baart, Rens van Beek, Hessel C. Winsemius, Paul D. Bates, and Marc F. P. Bierkens
Geosci. Model Dev., 10, 3913–3929, https://doi.org/10.5194/gmd-10-3913-2017, https://doi.org/10.5194/gmd-10-3913-2017, 2017
Short summary
Short summary
To improve flood hazard assessments, it is vital to model all relevant processes. We here present GLOFRIM, a framework for coupling hydrologic and hydrodynamic models to increase the number of physical processes represented in hazard computations. GLOFRIM is openly available, versatile, and extensible with more models. Results also underpin its added value for model benchmarking, showing that not only model forcing but also grid properties and the numerical scheme influence output accuracy.
Erin Coughlan de Perez, Elisabeth Stephens, Konstantinos Bischiniotis, Maarten van Aalst, Bart van den Hurk, Simon Mason, Hannah Nissan, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 21, 4517–4524, https://doi.org/10.5194/hess-21-4517-2017, https://doi.org/10.5194/hess-21-4517-2017, 2017
Short summary
Short summary
Disaster managers would like to use seasonal forecasts to anticipate flooding months in advance. However, current seasonal forecasts give information on rainfall instead of flooding. Here, we find that the number of extreme events, rather than total rainfall, is most related to flooding in different regions of Africa. We recommend several forecast adjustments and research opportunities that would improve flood information at the seasonal timescale in different regions.
Jannis M. Hoch, Arjen V. Haag, Arthur van Dam, Hessel C. Winsemius, Ludovicus P. H. van Beek, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 21, 117–132, https://doi.org/10.5194/hess-21-117-2017, https://doi.org/10.5194/hess-21-117-2017, 2017
Short summary
Short summary
Modelling inundations is pivotal to assess current and future flood hazard, and to define sound measures and policies. Yet, many models focus on the hydrologic or hydrodynamic aspect of floods only. We combined both by spatially coupling a hydrologic with a hydrodynamic model. This way we are able to balance the weaknesses of each model with the strengths of the other. We found that model coupling can indeed strongly improve discharge simulation, and see big potential in our approach.
Erin Coughlan de Perez, Bart van den Hurk, Maarten K. van Aalst, Irene Amuron, Deus Bamanya, Tristan Hauser, Brenden Jongma, Ana Lopez, Simon Mason, Janot Mendler de Suarez, Florian Pappenberger, Alexandra Rueth, Elisabeth Stephens, Pablo Suarez, Jurjen Wagemaker, and Ervin Zsoter
Hydrol. Earth Syst. Sci., 20, 3549–3560, https://doi.org/10.5194/hess-20-3549-2016, https://doi.org/10.5194/hess-20-3549-2016, 2016
Short summary
Short summary
Many flood disaster impacts could be avoided by preventative action; however, early action is not guaranteed. This article demonstrates the design of a new system of forecast-based financing, which automatically triggers action when a flood forecast arrives, before a potential disaster. We establish "action triggers" for northern Uganda based on a global flood forecasting system, verifying these forecasts and assessing the uncertainties inherent in setting a trigger in a data-scarce location.
Louise Arnal, Maria-Helena Ramos, Erin Coughlan de Perez, Hannah Louise Cloke, Elisabeth Stephens, Fredrik Wetterhall, Schalk Jan van Andel, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3109–3128, https://doi.org/10.5194/hess-20-3109-2016, https://doi.org/10.5194/hess-20-3109-2016, 2016
Short summary
Short summary
Forecasts are produced as probabilities of occurrence of specific events, which is both an added value and a challenge for users. This paper presents a game on flood protection, "How much are you prepared to pay for a forecast?", which investigated how users perceive the value of forecasts and are willing to pay for them when making decisions. It shows that users are mainly influenced by the perceived quality of the forecasts, their need for the information and their degree of risk tolerance.
Dave MacLeod, Hannah Cloke, Florian Pappenberger, and Antje Weisheimer
Hydrol. Earth Syst. Sci., 20, 2737–2743, https://doi.org/10.5194/hess-20-2737-2016, https://doi.org/10.5194/hess-20-2737-2016, 2016
Short summary
Short summary
Soil moisture memory is a key aspect of seasonal climate predictions, through feedback between the land surface and the atmosphere. Estimates have been made of the length of soil moisture memory; however, we show here how estimates of memory show large variation with uncertain model parameters. Explicit representation of model uncertainty may then improve the realism of simulations and seasonal climate forecasts.
G. Balsamo, C. Albergel, A. Beljaars, S. Boussetta, E. Brun, H. Cloke, D. Dee, E. Dutra, J. Muñoz-Sabater, F. Pappenberger, P. de Rosnay, T. Stockdale, and F. Vitart
Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, https://doi.org/10.5194/hess-19-389-2015, 2015
Short summary
Short summary
ERA-Interim/Land is a global land surface reanalysis covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim and a precipitation bias correction based on GPCP. A selection of verification results show the added value in representing the terrestrial water cycle and its main land surface storages and fluxes.
C. C. Sampson, T. J. Fewtrell, F. O'Loughlin, F. Pappenberger, P. B. Bates, J. E. Freer, and H. L. Cloke
Hydrol. Earth Syst. Sci., 18, 2305–2324, https://doi.org/10.5194/hess-18-2305-2014, https://doi.org/10.5194/hess-18-2305-2014, 2014
F. Wetterhall, F. Pappenberger, L. Alfieri, H. L. Cloke, J. Thielen-del Pozo, S. Balabanova, J. Daňhelka, A. Vogelbacher, P. Salamon, I. Carrasco, A. J. Cabrera-Tordera, M. Corzo-Toscano, M. Garcia-Padilla, R. J. Garcia-Sanchez, C. Ardilouze, S. Jurela, B. Terek, A. Csik, J. Casey, G. Stankūnavičius, V. Ceres, E. Sprokkereef, J. Stam, E. Anghel, D. Vladikovic, C. Alionte Eklund, N. Hjerdt, H. Djerv, F. Holmberg, J. Nilsson, K. Nyström, M. Sušnik, M. Hazlinger, and M. Holubecka
Hydrol. Earth Syst. Sci., 17, 4389–4399, https://doi.org/10.5194/hess-17-4389-2013, https://doi.org/10.5194/hess-17-4389-2013, 2013
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
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
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
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
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
Deep learning based sub-seasonal precipitation and streamflow forecasting over the source region of the Yangtze River
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
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
Enhanced Evaluation of Sub-daily and Daily Extreme Precipitation in Norway from Convection-Permitting Models at Regional and Local Scales
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
A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates
Investigating the response of land–atmosphere interactions and feedbacks to spatial representation of irrigation in a coupled modeling framework
Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia
Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-212, https://doi.org/10.5194/hess-2024-212, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather impacts, yet current long-term forecast products are often embedded with uncertainties. We develop a deep learning based modelling framework to improve 30-day rainfall and streamflow forecasts by combining advanced neural networks and outputs from physical models. With the forecast error reduced by up to 32%, the framework has the potential to enhance water management and disaster preparedness.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
Short summary
Short summary
Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Gokturk
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-68, https://doi.org/10.5194/hess-2024-68, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We compared extreme precipitations in Norway from convection-permitting models at 3 km resolution (HCLIM3) and regional climate model at 12 km (HCLIM12) and show that the HCLIM3 is more accurate than HCLIM12 in predicting the intense rainfalls that can lead to floods, especially at local scales. This is more clear in hourly extremes than daily. Our research suggests using more detailed climate models could improve forecasts, helping the local society brace for the impacts of extreme weather.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
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
Short summary
Short summary
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.
Yuanhong You, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu
Hydrol. Earth Syst. Sci., 27, 2919–2933, https://doi.org/10.5194/hess-27-2919-2023, https://doi.org/10.5194/hess-27-2919-2023, 2023
Short summary
Short summary
This study aims to investigate the performance of a genetic particle filter which was used as a snow data assimilation scheme across different snow climates. The results demonstrated that the genetic algorithm can effectively solve the problem of particle degeneration and impoverishment in a particle filter algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground snow depth measurement.
Patricia Lawston-Parker, Joseph A. Santanello Jr., and Nathaniel W. Chaney
Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
Short summary
Short summary
Irrigation has been shown to impact weather and climate, but it has only recently been considered in prediction models. Prescribing where (globally) irrigation takes place is important to accurately simulate its impacts on temperature, humidity, and precipitation. Here, we evaluated three different irrigation maps in a weather model and found that the extent and intensity of irrigated areas and their boundaries are important drivers of weather impacts resulting from human practices.
Marcos Julien Alexopoulos, Hannes Müller-Thomy, Patrick Nistahl, Mojca Šraj, and Nejc Bezak
Hydrol. Earth Syst. Sci., 27, 2559–2578, https://doi.org/10.5194/hess-27-2559-2023, https://doi.org/10.5194/hess-27-2559-2023, 2023
Short summary
Short summary
For rainfall-runoff simulation of a certain area, hydrological models are used, which requires precipitation data and temperature data as input. Since these are often not available as observations, we have tested simulation results from atmospheric models. ERA5-Land and COSMO-REA6 were tested for Slovenian catchments. Both lead to good simulations results. Their usage enables the use of rainfall-runoff simulation in unobserved catchments as a requisite for, e.g., flood protection measures.
Tuantuan Zhang, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu, and Binquan Li
Hydrol. Earth Syst. Sci., 27, 1945–1960, https://doi.org/10.5194/hess-27-1945-2023, https://doi.org/10.5194/hess-27-1945-2023, 2023
Short summary
Short summary
We use circulation classifications and spatiotemporal deep neural networks to correct raw daily forecast precipitation by combining large-scale circulation patterns with local spatiotemporal information. We find that the method not only captures the westward and northward movement of the western Pacific subtropical high but also shows substantially higher bias-correction capabilities than existing standard methods in terms of spatial scale, timescale, and intensity.
Cited articles
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J.,
and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175,
https://doi.org/10.5194/hess-17-1161-2013, 2013.
Alfieri, L., Cohen, S., Galantowicz, J., Schumann, G. J., Trigg, M. A., Zsoter, E., Prudhomme, C., Kruczkiewicz, A., Coughlan de Perez, E., Flamig, Z., Rudari., R., Wu, H., Adler, R. F., Brakenbridge, R. G., Kettner, A.,
Weerts, A., Matgen, P., Islam, S. A. K. M., and Salamon, P.: A global network for operational flood risk reduction, Environ. Sci. Policy, 84, 149–158, https://doi.org/10.1016/j.envsci.2018.03.014, 2018.
Adreadis, K. M., Schumann, G. J.-P., Stampoulis, D., Bates, P. D., Brakenridge, G. R., and Kettner, A. J.: Can atmospheric reanalysis datasets be used to reproduce flooding over large scales?, Geophys. Res. Lett., 44, 10369–10377, https://doi.org/10.1002/2017GL075502, 2017.
Andreadis, K. M., Schumann, G. J. P., Stampoulis, D., Bates, P. D., Brakenridge, G. R., and Kettner, A. J.: Can Atmospheric Reanalysis Data Sets
Be Used to Reproduce Flooding Over Large Scales?, Geophys. Res. Lett., 44,
10369–10377, https://doi.org/10.1002/2017GL075502, 2017.
Arnell, N. W. and Gosling, S. N.: The impacts of climate change on river flood risk at the global scale, Climatic Change, 134, 387–401,
https://doi.org/10.1007/s10584-014-1084-5, 2016.
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi, M., and Betts, A. K.: A revised hydrology for the ECMWF model:
Verification from field site to terrestrial water storage and impact in the
Integrated Forecast System, J. Hydrometeorol., 10, 623–643,
https://doi.org/10.1175/2008JHM1068.1, 2009.
Balsamo, G., Pappenberger, F., Dutra, E., Viterbo, P., and Van den Hurk, B.
J. J. M.: A revised land hydrology in the ECMWF model: a step towards daily
water flux prediction in a fully-closed water cycle, Hydrol. Process., 25,
1046–1054, https://doi.org/10.1002/hyp.7808, 2010.
Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke,
H., Dee, H., Dutra, D., Muñoz-Sabater, J., Pappenberger, F., de Rosnay, P., Stockdale. T., and Vitart, F.: ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015, 2015.
Balsamo, G., Agusti-Panareda, A., Albergel, C., Arduini, G., Beljaars, A.,
Bidlot, J., Bousserez, N., Boussetta, S., Brown, A., Buizza, R., Buontempo, C., Chevallier, F., Choulga, M., Cloke, H., Cronin, M. F., Dahoui, M., De
Rosnay, P., Dirmeyer, P. A., Drusch, M., Dutra, E., Ek, M. B., Gentine, P.,
Hewitt, H., Keeley, S. P. E., Kerr, Y., Kumar, S., Lupu, C., Mahfouf, J. F.,
McNorton, J., Mecklenburg, S., Mogensen, K., Muñoz-Sabater, J., Orth, R., Rabier, F., Reichle, R., Ruston, B, Pappenberger, F., Sandu, I., Seneviratne, S. I., Tietsche, S., Trigo, I. F., Uijlenhoet, R., Wedi, N., Woolway, R. L., and Zeng, X.: Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review, Remote Sens., 10, 2038, https://doi.org/10.3390/rs10122038, 2018.
Beck, H. E., van Dijk, A. I., de Roo, A., Dutra, E., Fink, G., Orth, R., and
Schellekens, J.: Global evaluation of runoff from ten state-of-the-art
hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903,
https://doi.org/10.5194/hess-21-2881-2017, 2017a.
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017b.
Bergström, S.: The HBV model, in: Comput. Model. Watershed Hydrol.,
chap. The HBV mo, edited by: Singh, V., Water Resouces Publications, Highlands Ranch Co., Colorado, USA, 1995.
Beven, K. J.: Rainfall-Runoff Modelling: The Primer, 2nd Edn., Wiley-Blackwell, Chichester, UK, 2012.
Bierkens, M. F.: Global hydrology 2015: State, trends, and directions, Water
Resour. Res., 51, 4923–4947, https://doi.org/10.1002/2015WR017173, 2015.
Bierkens, M. F., Bell, V. A., Burek, P., Chaney, N., Condon, L. E., David, C. H., de Roo, A., Döll, P., Drost, N., Famigiletti, J. S., Flörke, M., Gochis, D. J., Houser, P., Hut, R., Keune, J., Kollet, S., Maxwell, R. M., Reager, J. T., Samaniego, L., Sudicky, E., Sutanudiaia, E. H., van de Giesen, N., Winsemius, H., and Wood, E. F.: Hyper-resolution global hydrological modelling: what is next? “Everywhere and locally relevant”, Hydrol. Process., 29, 310–320, https://doi.org/10.1002/hyp.10391, 2015.
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W.,
Gerten, D., Lotze-Campen, H., Muller, C., Reichstein, M., and Smith, B.: Modelling the role of agriculture for the 20th century global terrestrial carbon balance, Global Change Biol., 13, 679–706, https://doi.org/10.1111/j.1365-2486.2006.01305.x, 2007.
Butts, M. B., Payne, J. T., Kristensen, M., and Madsen, H.: An evaluation of
the impact of model structure on hydrological modelling uncertainty for
streamflow simulation, J. Hydrol., 298, 242–266, https://doi.org/10.1016/j.jhydrol.2004.03.042, 2004.
Clark III, R. A., Flamig, Z. L., Vergara, H., Hong, Y., Gourley, J. J., Mandl, D. J., Frye, S., Handy, M., and Patterson, M.: Hydrological modeling
and capacity building in the Republic of Namibia, B. Am. Meteorol. Soc., 98, 1697–1715, https://doi.org/10.1175/BAMS-D-15-00130.1, 2016.
Correa, S. W., de Paiva, R. C. D., Espinoza, J. C., and Collischonn, W.:
Multi-decadal Hydrological Retrospective: Case study of Amazon floods and
droughts, J. Hydrol., 549, 667–684, https://doi.org/10.1016/j.jhydrol.2017.04.019, 2017.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Belijaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, S., Hólm, E .V., Isaksen, L., Kallberg, P., Köhler, M., Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thèpaut, J. N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
De Groeve, T., Thielen-del Pozo, J., Brakenridge, R., Adler, R., Alfieri, L., Kull, D., Lindsay, F., Imperiali, O., Pappenberger, F., Rudari, R., Salamon, P., Villars, N., and Wyjad, K.: Joining forces in a global flood partnership, B. Am. Meteorol. Soc., 96, ES97–ES100, https://doi.org/10.1175/BAMS-D-14-00147.1, 2015.
Devia, G. K., Ganasri, B. P., and Dwarakish, G. S.: A review on hydrological models, Aquat. Proced., 4, 1001–1007, https://doi.org/10.1016/j.aqpro.2015.02.126, 2015.
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for
deriving water availability indicators: model tuning and validation, J. Hydrol., 270, 105–134, https://doi.org/10.1016/S0022-1694(02)00283-4, 2003.
ECMWF: A brief description of reforecasts, available at:
https://confluence.ecmwf.int/display/S2S/A+brief+description+of+reforecasts
(last access: 25 September 2018), 2016.
ECMWF: What are the changes from ERA-Interim to ERA5?, available at:
https://confluence.ecmwf.int//pages/viewpage.action?pageId=74764925, (last access: 31 August 2018), 2017.
ECMWF: What is ERA-5?, available at:
https://confluence.ecmwf.int/display/CKB/What+is+ERA5, last access: 8 October 2018.
Emerton, R. E., Stephens, E. M., Pappenberger, F., Pagano, T. C., Weerts, A.
H., Wood, A. W., Salamon, P., Brown, J. D., Hierdt, N., Donnelly, C., Baughc,
C. A., and Cloke, H. L.: Continental and global scale flood forecasting systems, Wiley Interdiscip. Rev. Water, 3, 391–418, https://doi.org/10.1002/wat2.1137, 2016.
Emerton, R., Zsoter, E., Arnal, L., Cloke, H. L., Muraro, D., Prudhomme, C.,
Stephens, E. M., Salamon, P., and Pappenberger, F.: Developing a global
operational seasonal hydro-meteorological forecasting system: GloFAS v2.2
Seasonal v1.0, Geosci. Model Dev., 11, 3327–3346, https://doi.org/10.5194/gmd-11-3327-2018, 2018.
Espinoza, J. C., Ronchail, J., Guyot, J. L., Cochonneau, G., Naziano, F.,
Lavado, W., De Oliveria, E., Pombosa, R., and Vauchel, P.: Spatio-temporal
rainfall variability in the Amazon basin countries (Brazil, Peru, Bolivia,
Colombia, and Ecuador), Int. J. Climatol., 29, 1574–1594, https://doi.org/10.1002/joc.1791, 2009.
Espinoza, J. C., Ronchail, J., Frappart, F., Lavado, W., Santini, W., and
Guyot, J. L.: The major floods in the Amazonas River and tributaries (western Amazon basin) during the 1970–2012 period: A focus on the 2012 flood, J. Hydrometeorol., 14, 1000–1008, https://doi.org/10.1175/JHM-D-12-0100.1, 2013.
Espinoza, J. C., Marengo, J. A., Ronchail, J., Carpio, J. M., Flores, L. N.,
and Guyot, J. L.: The extreme 2014 flood in south-western Amazon basin: the
role of tropical-subtropical South Atlantic SST gradient, Environ. Res. Lett., 9, 124007, https://doi.org/10.1088/1748-9326/9/12/124007, 2014.
Filizola, N. and Guyot, J. L.: Suspended sediment yields in the Amazon basin: an assessment using the Brazilian national data set, Hydrol. Process., 23, 3207–3215, https://doi.org/10.1002/hyp.7394, 2009.
Forbes, R., Haiden, T., and Magnusson, L.: Improvements in IFS forecasts of
heavy precipitation, Meteorology section of ECMWF newsletter No. 144, ECMWF,
Reading, UK, 21–26, https://doi.org/10.21957/jxtonky0, 2015.
Gilleland, M. E.: Package `verification', available at:
http://cran.utstat.utoronto.ca/web/packages/verification/verification.pdf
(last access: 25 September 2018), 2015.
Gloor, M. R. J. W., Brienen, R. J., Galbraith, D., Feldpausch, T. R.,
Schöngart, J., Guyot, J. L., Espinoza, J. C., Llyod, J., and Phillips, O. L.: Intensification of the Amazon hydrological cycle over the last two decades, Geophys. Res. Lett., 40, 1729–1733, https://doi.org/10.1002/grl.50377, 2013.
Gosling, S. N. and Arnell, N. W.: Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis, Hydrol. Process., 25, 1129–1145, https://doi.org/10.1002/hyp.7727, 2011.
Gourley, J. J., Flamig, Z. L., Vergara, H., Kirstetter, P. E., Clark III, R.
A., Argyle, E., Arthur, A., Martinaitis, S., Terti, G., Erlingis, J. M., Hong, Y., and Howard, K. W.: The FLASH Project: Improving the tools for flash
flood monitoring and prediction across the United States, B. Am. Meteorol. Soc., 98, 361–372, https://doi.org/10.1175/BAMS-D-15-00247.1, 2017.
Greuell, J. W., Andersson, J., Donnelly, C., Feyen, L., Gerten, D., Ludwig, F., Pisacane, G., Roudier, P., and Schaphoff, S.: Evaluation of five
hydrological models across Europe and their suitability for making projections under climate change Hydrol. Earth Syst. Sci. Discuss., 12,
10289–10330, https://doi.org/10.5194/hessd-12-10289-2015, 2015.
Gudmundsson, L., Wagener, T., Tallaksen, L. M., and Engeland, K.: Evaluation
of nine large-scale hydrological models with respect to the seasonal runoff climatology in Europe, Water Resour. Res., 48, W11504, https://doi.org/10.1029/2011WR010911, 2012.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Guse, B., Pfannerstill, M., Gafurov, A., Kiesel, J., Lehr, C., and Fohrer, N.: Identifying the connective strength between model parameters and performance criteria, Hydrol. Earth Syst. Sci., 21, 5663–5679,
https://doi.org/10.5194/hess-21-5663-2017, 2017.
Haddeland, I., Clark, D. B., Franssen, W., Ludwig, F., VOß, F., Arnell, N. W., Bertrand, N., Best, M., Folwell, S., Gerten, D., Gomes, S., Gosling,
S. N., Hagemann, S., Hanasaki, N., Harding, R., Heinke, J., Kabat, P.,
Koirala, S., Oki, T., Polcher, J., Stacke, T., Viterbo, P., Weedon, G. P., and
Yeh, P.: Multimodel estimate of the global terrestrial water balance: Setup
and first results, J. Hydrometeorol., 12, 869–884, https://doi.org/10.1175/2011JHM1324.1, 2011.
Hattermann, F. F., Krysanova, V., Gosling, S. N., Dankers, R., Daggupati, P., Donnelly, C., Flörke, M., Huang, S., Motovilov, Y., Buda, S., Yang, T., Müller, C., Leng, G., Tang, Q., Portmann, F. T., Hagemann, S., Gerten, D., Wada, Y., Masaki, Y., Alemayehu, T., Satoh, Y., and Samaniego, L.: Cross-scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins, J. Clim. Change, 141, 561–576, https://doi.org/10.1007/s10584-016-1829-4, 2017.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D.,
Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Clim. Change, 3, 816, https://doi.org/10.1038/nclimate1911, 2013.
Hirpa, F. A., Salamon, P., Beck, H. E., Lorini, V., Alfieri, L., Zsoter, E.,
and Dadson, S. J.: Calibration of the Global Flood Awareness System (GloFAS)
using daily streamflow data, J. Hydrol., 566, 595–606,
https://doi.org/10.1016/j.jhydrol.2018.09.052, 2018.
Hoch, J. M., Haag, A. V., Dam, A. V., Winsemius, H. C., van Beek, L. P., and
Bierkens, M. F.: Assessing the impact of hydrodynamics on large-scale flood
wave propagation–a case study for the Amazon Basin, Hydrol. Earth Syst. Sci., 21, 117–132, https://doi.org/10.5194/hess-21-117-2017, 2017a.
Hoch, J. M., Neal, J., Baart, F., van Beek, L. P. H., Winsemius, H., Bates, P., and Bierkens, M. F.: GLOFRIM v1.0 – A globally applicable computational
framework for integrated hydrological–hydrodynamic modelling, Geosci. Model
Dev., 10, 3913–3929, https://doi.org/10.5194/gmd-10-3913-2017, 2017b.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM multisatellite
precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor
precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/JHM560.1, 2007.
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Gu, G.: Improving the global precipitation record: GPCP version 2.1, Geophys. Res. Lett., 36, L17808, https://doi.org/10.1029/2009GL040000, 2009.
IFRC: Disaster Relief Fund (DREF) Peru: Floods, available at:
https://reliefweb.int/sites/reliefweb.int/files/resources/MDRPE005du1.pdf
(last access: 25 September 2018), 2013.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Latrubesse, E. M., Arima, E. Y., Dunne, T., Park, E., Baker, V. R., d'Horta, F. M., Wight, C., Wittmann, F., Zuanon, J., Baker, P. A., Ribas, C. C.,
Norgaard, R. B., Filizola, N., Ansar, A., Flyvbjerg, B., and Stevaux, J. C.:
Damming the rivers of the Amazon basin, Nature, 546, 363–369, https://doi.org/10.1038/nature22333, 2017.
Legates, D. R. and McCabe, G. J.: Evaluating the use of “goodness-of-fit”
measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233–241, https://doi.org/10.1029/1998WR900018, 1999.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from
spaceborne elevation data, Eos Trans. Am. Geophys. Union, 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Magilligan, F. J. and Nislow, K. H.: Changes in hydrologic regime by dams, Geomorphology, 71, 61–78, https://doi.org/10.1016/j.geomorph.2004.08.017, 2005.
Marengo, J. A. and Espinoza, J. C.: Extreme seasonal droughts and floods in
Amazonia: causes, trends and impacts, Int. J. Climatol., 36, 1033–1050,
https://doi.org/10.1002/joc.4420, 2016.
Marengo, J. A., Tomasella, J., Soares, W. R., Alves, L. M., and Nobre, C. A.: Extreme climatic events in the Amazon basin, Theor. Appl. Climatol., 107, 73–85, https://doi.org/10.1007/s00704-011-0465-1, 2012.
Marengo, J. A., Alves, L. M., Soares, W. R., Rodriguez, D. A., Camargo, H.,
Riveros, M. P., and Pabló, A. D.: Two contrasting severe seasonal extremes in tropical South America in 2012: flood in Amazonia and drought in
northeast Brazil, J. Climate, 26, 9137–9154, https://doi.org/10.1175/JCLI-D-12-00642.1, 2013.
Meade, R. H., Rayol, J. M., Da Conceicão, S. C., and Natividade, J. R.:
Backwater effects in the Amazon River basin of Brazil, Environ. Geol. Water
Sci., 18, 105–114, https://doi.org/10.1007/BF01704664, 1991.
Meigh, J. R., McKenzie, A. A., and Sene, K. J.: A grid-based approach to water scarcity estimates for eastern and southern Africa, Water Resour. Mange., 13, 85–115, https://doi.org/10.1023/A:1008025703712, 1999.
Meng, J., Li, L., Hao, Z., Wang, J., and Shao, Q.: Suitability of TRMM
satellite rainfall in driving a distributed hydrological model in the source
region of Yellow River, J. Hydrol., 509, 320–332, https://doi.org/10.1016/j.jhydrol.2013.11.049, 2013.
Mittermaier, M., Roberts, N., and Thompson, S. A.: A long-term assessment of
precipitation forecast skill using the Fractions Skill Score, Meterol. Appl., 20, 176–186, https://doi.org/10.1002/met.296, 2013.
Mizukami, N., Rakovec, O., Newman, A. J., Clark, M. P., Wood, A. W., Gupta, H. V., and Kumar, R.: On the choice of calibration metrics for “high-flow” estimation using hydrologic models, Hydrol. Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019, 2019.
Mundial Grupo Banco: Lidando com Perdas: opções de Proteção Financeira contra Desastres no Brasil (Dealing with losses: options of financial protection against disasters in Brazil), available at:
http://bibliotecadigital.planejamento.gov.br/xmlui/bitstream/handle/iditem/658/Banco Mundial_opcoes_de prote\%C3\%A7\%C3\%A3o financeira contra desastres no Brasil.pdf?sequence=1 (last access: 25 September 2018), 2014.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Novak, D. R., Bailey, C., Brill, K. F., Burke, P., Hogsett, W. A., Rausch, R., and Schichtel, M.: Precipitation and temperature forecast performance at
the Weather Prediction Center, Weather Forecast., 29, 489–504,
https://doi.org/10.1175/WAF-D-13-00066.1, 2014.
Ovando, A., Tomasella, J., Rodriguez, D. A., Martinez, J. M., Siqueira-Junior, J. L., Pinto, G. L. N., Passy, P., Vauchel, P., Noriega, L.,
and von Randow, C.: Extreme flood events in the Bolivian Amazon wetlands, J. Hydrol.: Reg. Stud., 5, 293–308, https://doi.org/10.1016/j.ejrh.2015.11.004, 2016.
Paiva, R. C. D., Collischonn, W., Bonnet, M. P., and De Gonçalves, L. G. G.: On the sources of hydrological prediction uncertainty in the Amazon, Hydrol. Earth Syst. Sci., 16, 3127–3137, https://doi.org/10.5194/hess-16-3127-2012, 2012.
Paiva, R. C. D., Buarque, D. C., Collischonn, W., Bonnet, M. P., Frappart, F., Calmant, S., and Mendes, C. A. B.: Large-scale hydrologic and hydrodynamic modeling of the Amazon River basin, Water Resour. Res., 49,
1226–1243, https://doi.org/10.1002/wrcr.20067, 2013.
Pappenberger, F., Dutra, E., Wetterhall, F., and Cloke, H. L.: Deriving global flood hazard maps of fluvial floods through a physical model cascade,
Hydrol. Earth Syst. Sci., 16, 4143–4156, https://doi.org/10.5194/hess-16-4143-2012, 2012.
Revilla-Romero, B., Beck, H. E., Burek, P., Salamon, P., de Roo, A., and
Thielen, J.: Filling the gaps: Calibrating a rainfall-runoff model using
satellite-derived surface water extent, Remote Sens. Environ., 171, 118–131,
https://doi.org/10.1016/j.rse.2015.10.022, 2015.
Ronchail, J., Cochonneau, G., Molinier, M., Guyot, J. L., Chaves, A. G. D. M., Guimarães, V., and De Oliveira, E.: Interannual rainfall variability
in the Amazon basin and sea-surface temperatures in the equatorial Pacific and the tropical Atlantic Oceans, Int. J. Climatol., 22, 1663–1686,
https://doi.org/10.1002/joc.815, 2002.
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and Freer, J. E.: A high-resolution global flood hazard model, Water Resour. Res., 51, 7358–7381, https://doi.org/10.1002/2015WR016954, 2015.
Schenk, C. J., Roland, J., Viger, R. J., and Anderson, C. P.: Maps showing geology, oil and gas fields, and geologic provinces of the South America Region, USGS open-file report 97-470D, US Department of the Interior, Denver, USA, https://doi.org/10.3133/ofr97470D, 1999.
Schöngart, J. and Junk, W. J.: Forecasting the flood-pulse in Central
Amazonia by ENSO-indices, J. Hydrol., 335, 124–132, https://doi.org/10.1016/j.jhydrol.2006.11.005, 2007.
Smith, P., Pappenberger, F., Wetterhall, F., Thielen, J., Krzeminski, B.,
Salamon, P., Muraro, D., Kalas, M., and Baugh, C.: On the operational
implementation of the European Flood Awareness System (EFAS), ECMWF
Tech. Memorandum 778, ECMWF, Reading, UK., 1–34, 2016.
Sood, A. and Smakhtin, V.: Global hydrological models: a review, Hydrolog. Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Sutanudjaja, E. H., Beek, R. V., Wanders, N., Wada, Y., Bosmans, J. H., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K.,
Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O.,
Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P. N.:
PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model,
Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018.
Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., and De Roo, A.: Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin, J. Hydrol., 499, 324–338, https://doi.org/10.1016/j.jhydrol.2013.07.012, 2013.
Timpe, K. and Kaplan, D.: The changing hydrology of a dammed Amazon, Sci. Adv., 3, e1700611, https://doi.org/10.1126/sciadv.1700611, 2017.
Tomasella, J., Borma, L. S., Marengo, J. A., Rodriguez, D. A., Cuartas, L. A., Nobre, C., and Prado, M. C.: The droughts of 1996–1997 and 2004–2005 in Amazonia: hydrological response in the river main-stem, Hydrol. Process., 25, 1228–1242, https://doi.org/10.1002/hyp.7889, 2010.
Trigg, M. A., Birch, C. E., Neal, J. C., Bates, P. D., Smith, A., Sampson, C. C., Yamazaki, D., Hirabayashi, Y., Pappenberger, F., Dutra, E., Ward, P. J., Winsemius, H. C., Salamon, P., Dottorri, F., Rudari, R., Kappes, M. S., Simpson, A. L., Hadzilacos, G., and Fewtrell, T. J.: The credibility challenge for global fluvial flood risk analysis, Environ. Res. Lett., 11,
094014, https://doi.org/10.1088/1748-9326/11/9/094014, 2016.
US Geological Survey.: Global 30 Arc-Second Elevation (GTOPO30), US Geological Survey, Center for Earth Resources Observation and Science (EROS), available at: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-30-arc-second
(last access: 6 June 2019), 1996.
van Beek, L. P. H. and Bierkens, M. F. P.: The Global Hydrological Model
PCR-GLOBWB: Conceptualization, Parameterization and Verification, available
at: http://vanbeek.geo.uu.nl/suppinfo/vanbeekbierkens2009.pdf (last access: 3 September 2018), 2008.
van Beek, L. P. H., Wada, Y., and Bierkens, M. F. P.: Global monthly water
stress: 1. Water balance and water availability, Water Resour. Res., 47,
W07517, https://doi.org/10.1029/2010WR009791, 2011.
van den Hurk, B. J. and Viterbo, P.: The Torne-Kalix PILPS 2 (e) experiment as a test bed for modifications to the ECMWF land surface scheme, Global
Planet. Change, 38, 165–173, https://doi.org/10.1016/S0921-8181(03)00027-4, 2003.
van den Hurk, B. J. J. M., Viterbo, P., Beljaars, A. C. M., and Betts, A. K.: Offline validation of the ERA40 surface scheme, ECMWF TechMemo 295, ECMWF,
Reading, UK, 2000.
van Der Knijff, J. M., Younis, J., and De Roo, A. P. J.: LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood
simulation, Int. J. Geogr. Inf. Sci., 24, 189–212, https://doi.org/10.1080/13658810802549154, 2010.
van Huijgevoort, M. H. J., Van Lanen, H. A. J., Teuling, A. J., and Uijlenhoet, R.: Identification of changes in hydrological drought
characteristics from a multi-GCM driven ensemble constrained by observed
discharge, J. Hydrol., 512, 421–434, https://doi.org/10.1016/j.jhydrol.2014.02.060, 2014.
Wang, J., Hong, Y., Li, L., Gourley, J. J., Khan, S. I., Yilmaz, K. K., Adler, R. F., Policelli, F. S., Habib, S., Irwn, D., Limaye, A. S., Korme, T.,
and Okello, L.: The coupled routing and excess storage (CREST) distributed
hydrological model, Hydrolog. Sci. J., 56, 84–98, https://doi.org/10.1080/02626667.2010.543087, 2011.
Ward, P. J., Jongman, B., Salamon, P., Simpson, A., Bates, P., De Groeve, T., Muis, S., Coughlan de Perez, E., Rudari, R., Trigg, M. A., and Winsemius, H. C.: Usefulness and limitations of global flood risk models, Nat. Clim. Change, 5, 712–715, https://doi.org/10.1038/nclimate2742, 2015.
Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., van den Akker, O., and Heynert, K.: The Delft-FEWS flow forecasting system, Environ. Model.
Softw., 40, 65–77, https://doi.org/10.1016/j.envsoft.2012.07.010, 2013.
Wood, E. F., Roundy, J. K., Troy, T. J., Van Beek, L. P. H., Bierkens, M. F., Blyth, E., Roo, D. A., Döll, P., Ek, M., Famigiletti, J., Gochish, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011.
Xue, X., Hong, Y., Limaye, A., Gourley, J., Huffman, G., Khan, S., Dorji, C., and Chen, S.: Statistical and hydrological evaluation of TRMM-based
Multi-Satellite Precipitation Analysis over the Wangchu Basin of Bhutan: Are
the latest satellite precipitation products 3B42V7 ready for use in ungauged
basins?, J. Hydrol., 499, 91–99, https://doi.org/10.1016/j.jhydrol.2013.06.042, 2013.
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based description of floodplain inundation dynamics in a global river routing model, Water Resour. Res., 47, W04501, https://doi.org/10.1029/2010WR009726, 2011.
Yamazaki, D., Lee, H., Alsdorf, D. E., Dutra, E., Kim, H., Kanae, S., and Oki, T.: Analysis of the water level dynamics simulated by a global river
model: A case study in the Amazon River, Water Resour. Res., 48, W09508, https://doi.org/10.1029/2012WR011869, 2012.
Yamazaki, D., O'Loughlin, F., Trigg, M. A., Miller, Z. F., Pavelsky, T. M.,
and Bates, P. D.: Development of the global width database for large rivers,
Water Resour. Res., 50, 3467–3480, https://doi.org/10.1002/2013WR014664, 2014a.
Yamazaki, D., Sato, T., Kanae, S., Hirabayashi, Y., and Bates, P. D.: Regional flood dynamics in a bifurcating mega delta simulated in a global
river model, Geophys. Res. Lett., 41, 3127–3135, https://doi.org/10.1002/2014GL059744, 2014b.
Zajac, Z., Revilla-Romero, B., Salamon, P., Burek, P., Hirpa, F. A., and Beck, H.: The impact of lake and reservoir parameterization on global streamflow simulation, J. Hydrol., 548, 552–568, https://doi.org/10.1016/j.jhydrol.2017.03.022, 2017.
Zambrano-Bigiarini, M.: Package hydroGOF. Goodness-of-fit Functions for
Comparison of Simulated and Observed Hydrological Time Series, available at:
http://www.rforge.net/hydroGOF/ (last access: 25 September 2018), 2017.
Zhang, Y., Hong, Y., Wang, X., Gourley, J. J., Xue, X., Saharia, M., Ni, G.,
Wang, G., Huang, Y., Chen, S., and Tang, G.: Hydrometeorological analysis and remote sensing of extremes: Was the July 2012 Beijing flood event detectable and predictable by global satellite observing and global weather modeling systems?, J. Hydrometeorol., 16, 381–395, https://doi.org/10.1175/JHM-D-14-0048.1, 2015.
Zhang, Y., Zheng, H., Chiew, F. H., Arancibia, J. P., and Zhou, X.: Evaluating regional and global hydrological models against streamflow and
evapotranspiration measurements, J. Hydrometeorol., 17, 995–1010,
https://doi.org/10.1175/JHM-D-15-0107.1, 2016.
Zhao, F., Veldkamp, T. I., Frieler, K., Schewe, J., Ostberg, S., Willner, S., Schauberger, B., Gosling, S. N., Schmied, H. M., Portmann, F. T., Leng, G., Huang, M., Liu, X., Tang, Q., Hanasaki, N., Biemans, H., Gerten, D., Satoh, Y., Pkhrel, Y., Stacke, T., Ciais, P., Chang, J., Ducharne, A., Guimberteau, M., Wada, Y, Kim, H., and Yamazaki, D.: The critical role of the routing scheme in simulating peak river discharge in global hydrological models, Environ. Res. Lett., 12, 075003, https://doi.org/10.1088/1748-9326/aa7250, 2017.
Zsoter, E., Cloke, H., Stephens, E., de Rosnay, P., Muñoz-Sabater, J.,
Prudhomme, C., and Pappenberger, F.: How well do operational Numerical Weather Prediction setups represent hydrology?, J. Hydrometeorol., 14,
https://doi.org/10.1175/JHM-D-18-0086.1, in press, 2019.
Short summary
This study presents an intercomparison analysis of eight global hydrological models (GHMs), assessing their ability to simulate peak river flows in the Amazon basin. Results indicate that the meteorological input is the most influential component of the hydrological modelling chain, with the recent ERA-5 reanalysis dataset significantly improving the ability to simulate flood peaks in the Peruvian Amazon. In contrast, calibration of the Lisflood routing model was found to have no impact.
This study presents an intercomparison analysis of eight global hydrological models (GHMs),...
