Articles | Volume 11, issue 8
https://doi.org/10.5194/gmd-11-3327-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-3327-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Developing a global operational seasonal hydro-meteorological forecasting system: GloFAS-Seasonal v1.0
Rebecca Emerton
CORRESPONDING AUTHOR
Department of Geography & Environmental Science, University of
Reading, Reading, UK
European Centre for Medium-Range Weather Forecasts (ECMWF),
Reading,UK
Ervin Zsoter
European Centre for Medium-Range Weather Forecasts (ECMWF),
Reading,UK
Department of Geography & Environmental Science, University of
Reading, Reading, UK
Louise Arnal
Department of Geography & Environmental Science, University of
Reading, Reading, UK
European Centre for Medium-Range Weather Forecasts (ECMWF),
Reading,UK
Hannah L. Cloke
Department of Geography & Environmental Science, University of
Reading, Reading, UK
Department of Earth Sciences, Uppsala University, Uppsala, Sweden
Davide Muraro
Image Recognition Integrated Systems (IRIS), Ispra, Italy
Christel Prudhomme
European Centre for Medium-Range Weather Forecasts (ECMWF),
Reading,UK
Centre for Ecology and
Hydrology (CEH), Wallingford, UK
Department of Geography and Environment, University of Loughborough,
Loughborough, UK
Elisabeth M. Stephens
Department of Geography & Environmental Science, University of
Reading, Reading, UK
Peter Salamon
European Commission, Joint Research Centre (JRC), Ispra, Italy
Florian Pappenberger
European Centre for Medium-Range Weather Forecasts (ECMWF),
Reading,UK
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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
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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.
Andrea Betterle and Peter Salamon
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-22, https://doi.org/10.5194/nhess-2024-22, 2024
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Joy Ommer, Jess Neumann, Milan Kalas, Sophie Blackburn, and Hannah L. Cloke
EGUsphere, https://doi.org/10.5194/egusphere-2024-296, https://doi.org/10.5194/egusphere-2024-296, 2024
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What’s the worst that could happen? Recent floods are often claimed that they were 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 forecast and warning need to advance to trigger our imagination of what might be about to happen and start preparing.
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
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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
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Forecasting river flows months in advance is crucial for many water sectors and society. In N. America, snowmelt is a key driver of river flow. This study presents a statistical workflow using snow data to forecast flows months ahead in N. American snow-fed rivers. Variations in predictability across the continent are evident, raising concerns about future river flow predictability amid a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Shahzad Gani, Louise Arnal, Lucy Beattie, John Hillier, Sam Illingworth, Tiziana Lanza, Solmaz Mohadjer, Karoliina Pulkkinen, Heidi Roop, Iain Stewart, Kirsten von Elverfeldt, and Stephanie Zihms
EGUsphere, https://doi.org/10.5194/egusphere-2023-3121, https://doi.org/10.5194/egusphere-2023-3121, 2024
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Science communication in geosciences has societal and scientific value but often operates in "shadowlands." This editorial highlights these issues and proposes potential solutions. Our objective is to create a transparent and responsible geoscience communication landscape, fostering scientific progress, the well-being of scientists, and societal benefits.
Solomon Hailu 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. Discuss., https://doi.org/10.5194/hess-2023-251, https://doi.org/10.5194/hess-2023-251, 2023
Revised manuscript under review for HESS
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Our global precipitation data evaluation for hydrological modelling revealed variations in dataset accuracy. The Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP) followed by ERA5 performed well in some areas but had limitations in others. This informs dataset choice for river discharge modelling and highlights the need for improved global precipitation data quality, especially for daily and extreme values.
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, and Wolfgang Wagner
Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, https://doi.org/10.5194/nhess-23-3305-2023, 2023
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In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
Margarita Choulga, Francesca Moschini, Cinzia Mazzetti, Stefania Grimaldi, Juliana Disperati, Hylke Beck, Peter Salamon, and Christel Prudhomme
EGUsphere, https://doi.org/10.5194/egusphere-2023-1306, https://doi.org/10.5194/egusphere-2023-1306, 2023
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CEMS_SurfaceFields_2022 dataset is a new set of high-resolution maps for land type (e.g. lake, forest), soil properties and population water needs at approximately 2 and 6 km at the Equator covering Europe and Globe (excluding Antarctica) respectively. Paper describes what new high-resolution information and how exactly to use to create the dataset. Paper suggests that dataset can be used as input for river/ weather/ other models, and also for statistical description of the region of interest.
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
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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.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
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
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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.
Kieran M. R. Hunt, Gwyneth R. Matthews, Florian Pappenberger, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 5449–5472, https://doi.org/10.5194/hess-26-5449-2022, https://doi.org/10.5194/hess-26-5449-2022, 2022
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Vera Thiemig, Goncalo N. Gomes, Jon O. Skøien, Markus Ziese, Armin Rauthe-Schöch, Elke Rustemeier, Kira Rehfeldt, Jakub P. Walawender, Christine Kolbe, Damien Pichon, Christoph Schweim, and Peter Salamon
Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, https://doi.org/10.5194/essd-14-3249-2022, 2022
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EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.
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
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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
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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.
Francesco Dottori, Lorenzo Alfieri, Alessandra Bianchi, Jon Skoien, and Peter Salamon
Earth Syst. Sci. Data, 14, 1549–1569, https://doi.org/10.5194/essd-14-1549-2022, https://doi.org/10.5194/essd-14-1549-2022, 2022
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We present a set of hazard maps for river flooding for Europe and the Mediterranean basin. The maps depict inundation extent and depth for flood probabilities for up to 1-in-500-year flood hazards and are based on hydrological and hydrodynamic models driven by observed climatology. The maps can identify two-thirds of the flood extent reported by official flood maps, with increasing skill for higher-magnitude floods. The maps are used for evaluating present and future impacts of river floods.
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
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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.
Vincent Vionnet, Colleen Mortimer, Mike Brady, Louise Arnal, and Ross Brown
Earth Syst. Sci. Data, 13, 4603–4619, https://doi.org/10.5194/essd-13-4603-2021, https://doi.org/10.5194/essd-13-4603-2021, 2021
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Water equivalent of snow cover (SWE) is a key variable for water management, hydrological forecasting and climate monitoring. A new Canadian SWE dataset (CanSWE) is presented in this paper. It compiles data collected by multiple agencies and companies at more than 2500 different locations across Canada over the period 1928–2020. Snow depth and derived bulk snow density are also included when available.
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
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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
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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.
Seán Donegan, Conor Murphy, Shaun Harrigan, Ciaran Broderick, Dáire Foran Quinn, Saeed Golian, Jeff Knight, Tom Matthews, Christel Prudhomme, Adam A. Scaife, Nicky Stringer, and Robert L. Wilby
Hydrol. Earth Syst. Sci., 25, 4159–4183, https://doi.org/10.5194/hess-25-4159-2021, https://doi.org/10.5194/hess-25-4159-2021, 2021
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We benchmarked the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 Irish catchments. We found that ESP is skilful in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. We also conditioned ESP with the winter North Atlantic Oscillation and show that improvements in forecast skill, reliability, and discrimination are possible.
Florian Pappenberger, Florence Rabier, and Fabio Venuti
Nat. Hazards Earth Syst. Sci., 21, 2163–2167, https://doi.org/10.5194/nhess-21-2163-2021, https://doi.org/10.5194/nhess-21-2163-2021, 2021
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The European Centre for Medium-Range Weather Forecasts mission is to deliver high-quality global medium‐range (3–15 d ahead of time) weather forecasts and monitoring of the Earth system. We have published a new strategy, and in this paper we discuss what this means for forecasting and monitoring natural hazards.
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
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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.
Sarah Sparrow, Andrew Bowery, Glenn D. Carver, Marcus O. Köhler, Pirkka Ollinaho, Florian Pappenberger, David Wallom, and Antje Weisheimer
Geosci. Model Dev., 14, 3473–3486, https://doi.org/10.5194/gmd-14-3473-2021, https://doi.org/10.5194/gmd-14-3473-2021, 2021
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This paper describes how the research version of the European Centre for Medium-Range Weather Forecasts’ Integrated Forecast System is combined with climateprediction.net’s public volunteer computing resource to develop OpenIFS@home. Thousands of volunteer personal computers simulated slightly different realizations of Tropical Cyclone Karl to demonstrate the performance of the large-ensemble forecast. OpenIFS@Home offers researchers a new tool to study weather forecasts and related questions.
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
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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
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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
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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.
W. Wagner, V. Freeman, S. Cao, P. Matgen, M. Chini, P. Salamon, N. McCormick, S. Martinis, B. Bauer-Marschallinger, C. Navacchi, M. Schramm, C. Reimer, and C. Briese
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 641–648, https://doi.org/10.5194/isprs-annals-V-3-2020-641-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-641-2020, 2020
Lucy J. Barker, Jamie Hannaford, Simon Parry, Katie A. Smith, Maliko Tanguy, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 23, 4583–4602, https://doi.org/10.5194/hess-23-4583-2019, https://doi.org/10.5194/hess-23-4583-2019, 2019
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It is important to understand historic droughts in order to plan and prepare for possible future events. In this study we use the standardised streamflow index for 1891–2015 to systematically identify, characterise and rank hydrological drought events for 108 near-natural UK catchments. Results show when and where the most severe events occurred and describe events of the early 20th century, providing catchment-scale detail important for both science and planning applications of the future.
Eric Sauquet, Bastien Richard, Alexandre Devers, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 23, 3683–3710, https://doi.org/10.5194/hess-23-3683-2019, https://doi.org/10.5194/hess-23-3683-2019, 2019
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This study aims to identify catchments and the associated water uses vulnerable to climate change. Vulnerability is considered here to be the likelihood of water restrictions which are unacceptable for agricultural uses. This study provides the first regional analysis of the stated water restrictions, highlighting heterogeneous decision-making processes; data from a national system of compensation to farmers for uninsurable damages were used to characterize past failure events.
Katie A. Smith, Lucy J. Barker, Maliko Tanguy, Simon Parry, Shaun Harrigan, Tim P. Legg, Christel Prudhomme, and Jamie Hannaford
Hydrol. Earth Syst. Sci., 23, 3247–3268, https://doi.org/10.5194/hess-23-3247-2019, https://doi.org/10.5194/hess-23-3247-2019, 2019
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This paper describes the multi-objective calibration approach used to create a consistent dataset of reconstructed daily river flow data for 303 catchments in the UK over 1891–2015. The modelled data perform well when compared to observations, including in the timing and the classification of drought events. This method and data will allow for long-term studies of flow trends and past extreme events that have not been previously possible, enabling water managers to better plan for the future.
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
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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.
Jamie Towner, Hannah L. Cloke, Ervin Zsoter, Zachary Flamig, Jannis M. Hoch, Juan Bazo, Erin Coughlan de Perez, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci., 23, 3057–3080, https://doi.org/10.5194/hess-23-3057-2019, https://doi.org/10.5194/hess-23-3057-2019, 2019
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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.
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
Hylke E. Beck, Ming Pan, Tirthankar Roy, Graham P. Weedon, Florian Pappenberger, Albert I. J. M. van Dijk, George J. Huffman, Robert F. Adler, and Eric F. Wood
Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, https://doi.org/10.5194/hess-23-207-2019, 2019
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We conducted a comprehensive evaluation of 26 precipitation datasets for the US using the Stage-IV gauge-radar dataset as a reference. The best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for reporting times. Our findings can be used as a guide to choose the most suitable precipitation dataset for a particular application.
Christophe Lavaysse, Jürgen Vogt, Andrea Toreti, Marco L. Carrera, and Florian Pappenberger
Nat. Hazards Earth Syst. Sci., 18, 3297–3309, https://doi.org/10.5194/nhess-18-3297-2018, https://doi.org/10.5194/nhess-18-3297-2018, 2018
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Forecasting droughts in Europe 1 month in advance would provide valuable information for decision makers. However, these extreme events are still difficult to predict. In this study, we develop forecasts based on predictors using the geopotential anomalies, generally more predictable than precipitation, derived from the ECMWF model. Results show that this approach outperforms the prediction using precipitation, especially in winter and in northern Europe, where 65 % of droughts are predicted.
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
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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.
Lila Collet, Shaun Harrigan, Christel Prudhomme, Giuseppe Formetta, and Lindsay Beevers
Hydrol. Earth Syst. Sci., 22, 5387–5401, https://doi.org/10.5194/hess-22-5387-2018, https://doi.org/10.5194/hess-22-5387-2018, 2018
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Floods and droughts cause significant damages and pose risks to lives worldwide. In a climate change context this work identifies hotspots across Great Britain, i.e. places expected to be impacted by an increase in floods and droughts. By the 2080s the western coast of England and Wales and northeastern Scotland would experience more floods in winter and droughts in autumn, with a higher increase in drought hazard, showing a need to adapt water management policies in light of climate change.
Maliko Tanguy, Christel Prudhomme, Katie Smith, and Jamie Hannaford
Earth Syst. Sci. Data, 10, 951–968, https://doi.org/10.5194/essd-10-951-2018, https://doi.org/10.5194/essd-10-951-2018, 2018
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Potential evapotranspiration (PET) is necessary input data for most hydrological models, used to simulate river flows. To reconstruct PET prior to the 1960s, simplified methods are needed because of lack of climate data required for complex methods. We found that the McGuinness–Bordne PET equation, which only needs temperature as input data, works best for the UK provided it is calibrated for local conditions. This method was used to produce a 5 km gridded PET dataset for the UK for 1891–2015.
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
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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.
Shaun Harrigan, Christel Prudhomme, Simon Parry, Katie Smith, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 22, 2023–2039, https://doi.org/10.5194/hess-22-2023-2018, https://doi.org/10.5194/hess-22-2023-2018, 2018
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We benchmarked when and where ensemble streamflow prediction (ESP) is skilful in the UK across a diverse set of 314 catchments. We found ESP was skilful in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. Results have practical implications for current operational use of the ESP method in the UK.
Hylke E. Beck, Noemi Vergopolan, Ming Pan, Vincenzo Levizzani, Albert I. J. M. van Dijk, Graham P. Weedon, Luca Brocca, Florian Pappenberger, George J. Huffman, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, https://doi.org/10.5194/hess-21-6201-2017, 2017
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This study represents the most comprehensive global-scale precipitation dataset evaluation to date. We evaluated 13 uncorrected precipitation datasets using precipitation observations from 76 086 gauges, and 9 gauge-corrected ones using hydrological modeling for 9053 catchments. Our results highlight large differences in estimation accuracy, and hence, the importance of precipitation dataset selection in both research and operational applications.
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
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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.
Francesco Dottori, Milan Kalas, Peter Salamon, Alessandra Bianchi, Lorenzo Alfieri, and Luc Feyen
Nat. Hazards Earth Syst. Sci., 17, 1111–1126, https://doi.org/10.5194/nhess-17-1111-2017, https://doi.org/10.5194/nhess-17-1111-2017, 2017
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We present a method to use river flow forecasts to estimate the impacts of flood events in terms of flood-prone areas, economic damage, cities and population at risk. We tested our method by simulating the catastrophic floods occurred in May 2014 in Southern Europe. Comparison with observed data shows that our simulations can predict flooded areas and flood impacts well in advance. The method is now being used for real-time risk forecasts to help emergency response and management.
Gregor Laaha, Tobias Gauster, Lena M. Tallaksen, Jean-Philippe Vidal, Kerstin Stahl, Christel Prudhomme, Benedikt Heudorfer, Radek Vlnas, Monica Ionita, Henny A. J. Van Lanen, Mary-Jeanne Adler, Laurie Caillouet, Claire Delus, Miriam Fendekova, Sebastien Gailliez, Jamie Hannaford, Daniel Kingston, Anne F. Van Loon, Luis Mediero, Marzena Osuch, Renata Romanowicz, Eric Sauquet, James H. Stagge, and Wai K. Wong
Hydrol. Earth Syst. Sci., 21, 3001–3024, https://doi.org/10.5194/hess-21-3001-2017, https://doi.org/10.5194/hess-21-3001-2017, 2017
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In 2015 large parts of Europe were affected by a drought. In terms of low flow magnitude, a region around the Czech Republic was most affected, with return periods > 100 yr. In terms of deficit volumes, the drought was particularly severe around S. Germany where the event lasted notably long. Meteorological and hydrological events developed differently in space and time. For an assessment of drought impacts on water resources, hydrological data are required in addition to meteorological indices.
Louise Crochemore, Maria-Helena Ramos, Florian Pappenberger, and Charles Perrin
Hydrol. Earth Syst. Sci., 21, 1573–1591, https://doi.org/10.5194/hess-21-1573-2017, https://doi.org/10.5194/hess-21-1573-2017, 2017
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The use of general circulation model outputs for streamflow forecasting has developed in the last decade. In parallel, traditional streamflow forecasting is commonly based on historical data. This study investigates the impact of conditioning historical data based on circulation model precipitation forecasts on seasonal streamflow forecast quality. Results highlighted a trade-off between the sharpness and reliability of forecasts.
Simon Parry, Robert L. Wilby, Christel Prudhomme, and Paul J. Wood
Hydrol. Earth Syst. Sci., 20, 4265–4281, https://doi.org/10.5194/hess-20-4265-2016, https://doi.org/10.5194/hess-20-4265-2016, 2016
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This paper identifies periods of recovery from drought in 52 river flow records from the UK between 1883 and 2013. The approach detects 459 events that vary in space and time. This large dataset allows individual events to be compared with others in the historical record. The ability to objectively appraise contemporary events against the historical record has not previously been possible, and may allow water managers to prepare for a range of outcomes at the end of a drought.
Louise Crochemore, Maria-Helena Ramos, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, https://doi.org/10.5194/hess-20-3601-2016, 2016
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This study investigates the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. Eight variants of bias correction approaches based on the linear scaling and the distribution mapping methods are applied to the precipitation forecasts prior to generating the streamflow forecasts. One of the main results of the study is that distribution mapping of daily values is successful in improving forecast reliability.
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
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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.
Michalis I. Vousdoukas, Evangelos Voukouvalas, Lorenzo Mentaschi, Francesco Dottori, Alessio Giardino, Dimitrios Bouziotas, Alessandra Bianchi, Peter Salamon, and Luc Feyen
Nat. Hazards Earth Syst. Sci., 16, 1841–1853, https://doi.org/10.5194/nhess-16-1841-2016, https://doi.org/10.5194/nhess-16-1841-2016, 2016
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Coastal flooding has severe socioeconomic impacts that are projected to increase under the changing climate. The present contribution reports on efforts towards a new methodology for mapping coastal flood hazard at European scale, combining the contribution of waves, improved inundation modeling and an open, physics-based framework which can be constantly upgraded whenever new and more accurate data become available.
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
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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
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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.
Lorenzo Alfieri, Luc Feyen, Peter Salamon, Jutta Thielen, Alessandra Bianchi, Francesco Dottori, and Peter Burek
Nat. Hazards Earth Syst. Sci., 16, 1401–1411, https://doi.org/10.5194/nhess-16-1401-2016, https://doi.org/10.5194/nhess-16-1401-2016, 2016
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This work couples recent advances in large scale flood hazard mapping into a pan-European flood risk model to estimate the impact of river floods in a seamless simulation, covering more than two decades.
Results of this research are an important contribution in the reconstruction of a complete dataset of flood impact data. Also, it has direct implications in the area of flood early warning with regard to the rapid risk assessment of flood impacts.
Jon Olav Skøien, Konrad Bogner, Peter Salamon, Paul Smith, and Florian Pappenberger
Proc. IAHS, 373, 109–114, https://doi.org/10.5194/piahs-373-109-2016, https://doi.org/10.5194/piahs-373-109-2016, 2016
V. Thiemig, B. Bisselink, F. Pappenberger, and J. Thielen
Hydrol. Earth Syst. Sci., 19, 3365–3385, https://doi.org/10.5194/hess-19-3365-2015, https://doi.org/10.5194/hess-19-3365-2015, 2015
C. Lavaysse, J. Vogt, and F. Pappenberger
Hydrol. Earth Syst. Sci., 19, 3273–3286, https://doi.org/10.5194/hess-19-3273-2015, https://doi.org/10.5194/hess-19-3273-2015, 2015
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This paper assesses the predictability of meteorological droughts over Europe 1 month in advance using ensemble prediction systems.
It has been shown that, on average and using the most relevant method, 40 % of droughts in Europe are correctly forecasted, with less than 25 % false alarms.
This study is a reference for other studies that are motivated to improving the drought forecasting.
R. D. Field, A. C. Spessa, N. A. Aziz, A. Camia, A. Cantin, R. Carr, W. J. de Groot, A. J. Dowdy, M. D. Flannigan, K. Manomaiphiboon, F. Pappenberger, V. Tanpipat, and X. Wang
Nat. Hazards Earth Syst. Sci., 15, 1407–1423, https://doi.org/10.5194/nhess-15-1407-2015, https://doi.org/10.5194/nhess-15-1407-2015, 2015
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We have developed a global database of daily, gridded Fire Weather Index System calculations beginning in 1980. Input data and two different estimates of precipitation from rain gauges were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications. This data set can be used for analyzing historical relationships between fire weather and fire activity, and in identifying large-scale atmosphere–ocean controls on fire weather.
F. Wetterhall, H. C. Winsemius, E. Dutra, M. Werner, and E. Pappenberger
Hydrol. Earth Syst. Sci., 19, 2577–2586, https://doi.org/10.5194/hess-19-2577-2015, https://doi.org/10.5194/hess-19-2577-2015, 2015
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Dry spells can have a devastating impact on agricuture in areas where irrigation is not available. Forecasting these dry spells could enhance preparedness in sensitive regions and avoid economic loss due to harvest failure. In this study, ECMWF seasonal forecasts are applied in the Limpopo basin in southeastern Africa to forecast dry spells in the seasonal rains. The results indicate skill in the forecast which is further improved by post-processing of the precipitation forecasts.
A. Chiverton, J. Hannaford, I. P. Holman, R. Corstanje, C. Prudhomme, T. M. Hess, and J. P. Bloomfield
Hydrol. Earth Syst. Sci., 19, 2395–2408, https://doi.org/10.5194/hess-19-2395-2015, https://doi.org/10.5194/hess-19-2395-2015, 2015
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Current hydrological change detection methods are subject to a host of limitations. This paper develops a new method, temporally shifting variograms (TSVs), which characterises variability in the river flow regime using several parameters, changes in which can then be attributed to precipitation characteristics. We demonstrate the use of the method through application to 94 UK catchments, showing that periods of extremes as well as more subtle changes can be detected.
I. Giuntoli, J.-P. Vidal, C. Prudhomme, and D. M. Hannah
Earth Syst. Dynam., 6, 267–285, https://doi.org/10.5194/esd-6-267-2015, https://doi.org/10.5194/esd-6-267-2015, 2015
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We assessed future changes in high and low flows globally using runoff projections from global hydrological models (GHMs) driven by global climate models (GCMs) under the RCP8.5 scenario. Further, we quantified the relative size of uncertainty from GHMs and from GCMs using ANOVA. We show that GCMs are the major contributors to uncertainty overall, but GHMs increase their contribution for low flows and can equal or outweigh GCM uncertainty in snow-dominated areas for both high and low flows.
A. C. Spessa, R. D. Field, F. Pappenberger, A. Langner, S. Englhart, U. Weber, T. Stockdale, F. Siegert, J. W. Kaiser, and J. Moore
Nat. Hazards Earth Syst. Sci., 15, 429–442, https://doi.org/10.5194/nhess-15-429-2015, https://doi.org/10.5194/nhess-15-429-2015, 2015
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
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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.
B. Revilla-Romero, J. Thielen, P. Salamon, T. De Groeve, and G. R. Brakenridge
Hydrol. Earth Syst. Sci., 18, 4467–4484, https://doi.org/10.5194/hess-18-4467-2014, https://doi.org/10.5194/hess-18-4467-2014, 2014
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One of the main challenges in global hydrological modelling is the limited availability of observational data for calibration and model verification. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System (GFDS) for converting the flood detection signal into river discharge values. This work also provides a first analysis of the local factors influencing the accuracy of discharge measurement as provided by this system.
P. Trambauer, S. Maskey, M. Werner, F. Pappenberger, L. P. H. van Beek, and S. Uhlenbrook
Hydrol. Earth Syst. Sci., 18, 2925–2942, https://doi.org/10.5194/hess-18-2925-2014, https://doi.org/10.5194/hess-18-2925-2014, 2014
E. Dutra, F. Wetterhall, F. Di Giuseppe, G. Naumann, P. Barbosa, J. Vogt, W. Pozzi, and F. Pappenberger
Hydrol. Earth Syst. Sci., 18, 2657–2667, https://doi.org/10.5194/hess-18-2657-2014, https://doi.org/10.5194/hess-18-2657-2014, 2014
E. Dutra, W. Pozzi, F. Wetterhall, F. Di Giuseppe, L. Magnusson, G. Naumann, P. Barbosa, J. Vogt, and F. Pappenberger
Hydrol. Earth Syst. Sci., 18, 2669–2678, https://doi.org/10.5194/hess-18-2669-2014, https://doi.org/10.5194/hess-18-2669-2014, 2014
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
L. Alfieri, F. Pappenberger, and F. Wetterhall
Nat. Hazards Earth Syst. Sci., 14, 1505–1515, https://doi.org/10.5194/nhess-14-1505-2014, https://doi.org/10.5194/nhess-14-1505-2014, 2014
G. Naumann, E. Dutra, P. Barbosa, F. Pappenberger, F. Wetterhall, and J. V. Vogt
Hydrol. Earth Syst. Sci., 18, 1625–1640, https://doi.org/10.5194/hess-18-1625-2014, https://doi.org/10.5194/hess-18-1625-2014, 2014
H. C. Winsemius, E. Dutra, F. A. Engelbrecht, E. Archer Van Garderen, F. Wetterhall, F. Pappenberger, and M. G. F. Werner
Hydrol. Earth Syst. Sci., 18, 1525–1538, https://doi.org/10.5194/hess-18-1525-2014, https://doi.org/10.5194/hess-18-1525-2014, 2014
E. Mwangi, F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger
Hydrol. Earth Syst. Sci., 18, 611–620, https://doi.org/10.5194/hess-18-611-2014, https://doi.org/10.5194/hess-18-611-2014, 2014
P. Trambauer, E. Dutra, S. Maskey, M. Werner, F. Pappenberger, L. P. H. van Beek, and S. Uhlenbrook
Hydrol. Earth Syst. Sci., 18, 193–212, https://doi.org/10.5194/hess-18-193-2014, https://doi.org/10.5194/hess-18-193-2014, 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
E. Dutra, F. Di Giuseppe, F. Wetterhall, and F. Pappenberger
Hydrol. Earth Syst. Sci., 17, 2359–2373, https://doi.org/10.5194/hess-17-2359-2013, https://doi.org/10.5194/hess-17-2359-2013, 2013
M. H. Ramos, S. J. van Andel, and F. Pappenberger
Hydrol. Earth Syst. Sci., 17, 2219–2232, https://doi.org/10.5194/hess-17-2219-2013, https://doi.org/10.5194/hess-17-2219-2013, 2013
C. Prudhomme and J. Williamson
Hydrol. Earth Syst. Sci., 17, 1365–1377, https://doi.org/10.5194/hess-17-1365-2013, https://doi.org/10.5194/hess-17-1365-2013, 2013
L. Alfieri, P. Burek, E. Dutra, B. Krzeminski, D. Muraro, J. Thielen, and F. Pappenberger
Hydrol. Earth Syst. Sci., 17, 1161–1175, https://doi.org/10.5194/hess-17-1161-2013, https://doi.org/10.5194/hess-17-1161-2013, 2013
C. Prudhomme, T. Haxton, S. Crooks, C. Jackson, A. Barkwith, J. Williamson, J. Kelvin, J. Mackay, L. Wang, A. Young, and G. Watts
Earth Syst. Sci. Data, 5, 101–107, https://doi.org/10.5194/essd-5-101-2013, https://doi.org/10.5194/essd-5-101-2013, 2013
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The eWaterCycle platform for open and FAIR hydrological collaboration
Evaluating the Atibaia River hydrology using JULES6.1
A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
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We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-229, https://doi.org/10.5194/gmd-2023-229, 2023
Revised manuscript accepted for GMD
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model, its large selection of different assimilation algorithms and its modularity with respect to the combination of different types of observations, states and parameters.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-130, https://doi.org/10.5194/gmd-2023-130, 2023
Revised manuscript accepted for GMD
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The Reservoir Assessment Tool (RAT) version 3.0 represents a scalable and customizable software based on hydrologic modeling and satellite remote sensing to monitor the reservoir's dynamic state. The architecture of RAT 3.0 has been designed in such a way that it requires minimal user input with additional flexibility added for the more advanced users. It is more robust and less susceptible to data gaps or instability that satellite remote sensing systems can sometimes experience.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
EGUsphere, https://doi.org/10.5194/egusphere-2023-1590, https://doi.org/10.5194/egusphere-2023-1590, 2023
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Pharmaceuticals and household chemicals released into surface waters through wastewater pose risks to aquatic ecosystems and human health. HydroFATE, a new global model, estimates contaminant concentrations in rivers, helping identify areas of elevated exposure. It predicted concentrations above ecological thresholds of the antibiotic sulfamethoxazole in 390,000 km of rivers worldwide. HydroFATE can guide monitoring and mitigation efforts to safeguard water systems and human well-being.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
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A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
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MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
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Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-182, https://doi.org/10.5194/gmd-2022-182, 2022
Revised manuscript accepted for GMD
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We present the wflow_sbm distributed hydrologic model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast run-time making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
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Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
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With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
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Land surface models allow us to understand and investigate the cause and effect of environmental process changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
Short summary
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
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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.
Global overviews of upcoming flood and drought events are key for many applications from...