Articles | Volume 28, issue 14
https://doi.org/10.5194/hess-28-3099-2024
© Author(s) 2024. 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-28-3099-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Jul 2024
Research article |
| 17 Jul 2024
| 17 Jul 2024
Global-scale evaluation of precipitation datasets for hydrological modelling
Solomon H. Gebrechorkos
CORRESPONDING AUTHOR
School of Geography and the Environment, University of Oxford, Oxford, UK
School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
Julian Leyland
School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
Simon J. Dadson
School of Geography and the Environment, University of Oxford, Oxford, UK
Sagy Cohen
Department of Geography and the Environment, University of Alabama, Tuscaloosa, AL, USA
Louise Slater
School of Geography and the Environment, University of Oxford, Oxford, UK
Michel Wortmann
School of Geography and the Environment, University of Oxford, Oxford, UK
Philip J. Ashworth
School of Applied Sciences, University of Brighton, Brighton, Sussex, BN2 4AT, UK
Georgina L. Bennett
Department of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4RJ, UK
Richard Boothroyd
School of Geographical & Earth Sciences, University of Glasgow, Glasgow, UK
Hannah Cloke
Department of Geography and Environmental Science, University of Reading, Reading, UK
Department of Meteorology, University of Reading, Reading, UK
Pauline Delorme
Energy and Environment Institute, University of Hull, Hull, UK
Helen Griffith
Department of Geography and Environmental Science, University of Reading, Reading, UK
Richard Hardy
Department of Geography, Durham University, Lower Mountjoy, South Road, Durham, DH1 3LE, UK
Laurence Hawker
School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
Stuart McLelland
Energy and Environment Institute, University of Hull, Hull, UK
Jeffrey Neal
School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
Andrew Nicholas
Department of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4RJ, UK
Andrew J. Tatem
School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
Ellie Vahidi
Department of Geography, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4RJ, UK
Yinxue Liu
School of Geography and the Environment, University of Oxford, Oxford, UK
Justin Sheffield
School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
Daniel R. Parsons
Energy and Environment Institute, University of Hull, Hull, UK
Geography and Environment, Loughborough University, Loughborough, UK
Stephen E. Darby
School of Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK
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Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
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Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Earth Surf. Dynam., 12, 581–599, https://doi.org/10.5194/esurf-12-581-2024, https://doi.org/10.5194/esurf-12-581-2024, 2024
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Bailey J. Anderson, Manuela I. Brunner, Louise J. Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 28, 1567–1583, https://doi.org/10.5194/hess-28-1567-2024, https://doi.org/10.5194/hess-28-1567-2024, 2024
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Elasticityrefers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.
Maximillian Van Wyk de Vries, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Alexander L. Densmore, Tek Bahadur Dong, Alexandre Dunant, Erin L. Harvey, Ganesh K. Jimee, Mark E. Kincey, Katie Oven, Sarmila Paudyal, Dammar Singh Pujara, Anuradha Puri, Ram Shrestha, Nick J. Rosser, and Simon J. Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2024-397, https://doi.org/10.5194/egusphere-2024-397, 2024
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This study focuses on understanding soil moisture, a key factor for evaluating hillslope stability and landsliding. In Nepal, where landslides are common, we used a computer model to better understand how rapidly soil dries out after the monsoon season. We calibrated the model using field data and found that, by adjusting soil properties, we could predict moisture levels more accurately. This helps understand where landslides might occur, even where direct measurements are not possible.
Laurence Hawker, Jeffrey Neal, James Savage, Thomas Kirkpatrick, Rachel Lord, Yanos Zylberberg, Andre Groeger, Truong Dang Thuy, Sean Fox, Felix Agyemang, and Pham Khanh Nam
Nat. Hazards Earth Syst. Sci., 24, 539–566, https://doi.org/10.5194/nhess-24-539-2024, https://doi.org/10.5194/nhess-24-539-2024, 2024
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We present a global flood model built using a new terrain data set and evaluated in the Central Highlands of Vietnam.
Leanne Archer, Jeffrey Neal, Paul Bates, Emily Vosper, Dereka Carroll, Jeison Sosa, and Daniel Mitchell
Nat. Hazards Earth Syst. Sci., 24, 375–396, https://doi.org/10.5194/nhess-24-375-2024, https://doi.org/10.5194/nhess-24-375-2024, 2024
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We model hurricane-rainfall-driven flooding to assess how the number of people exposed to flooding changes in Puerto Rico under the 1.5 and 2 °C Paris Agreement goals. Our analysis suggests 8 %–10 % of the population is currently exposed to flooding on average every 5 years, increasing by 2 %–15 % and 1 %–20 % at 1.5 and 2 °C. This has implications for adaptation to more extreme flooding in Puerto Rico and demonstrates that 1.5 °C climate change carries a significant increase in risk.
Xuxu Wu, Jonathan Malarkey, Roberto Fernández, Jaco H. Baas, Ellen Pollard, and Daniel R. Parsons
Earth Surf. Dynam., 12, 231–247, https://doi.org/10.5194/esurf-12-231-2024, https://doi.org/10.5194/esurf-12-231-2024, 2024
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The seabed changes from flat to rippled in response to the frictional influence of waves and currents. This experimental study has shown that the speed of this change, the size of ripples that result and even whether ripples appear also depend on the amount of sticky mud present. This new classification on the basis of initial mud content should lead to improvements in models of seabed change in present environments by engineers and the interpretation of past environments by geologists.
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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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.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
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This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
Christopher Tomsett and Julian Leyland
Earth Surf. Dynam., 11, 1223–1249, https://doi.org/10.5194/esurf-11-1223-2023, https://doi.org/10.5194/esurf-11-1223-2023, 2023
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Vegetation influences how rivers change through time, yet the way in which we analyse vegetation is limited. Current methods collect detailed data at the individual plant level or determine dominant vegetation types across larger areas. Herein, we use UAVs to collect detailed vegetation datasets for a 1 km length of river and link vegetation properties to channel evolution occurring within the study site, providing a new method for investigating the influence of vegetation on river systems.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
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Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Alessandro Sgarabotto, Irene Manzella, Kyle Roskilly, Miles J. Clark, Georgie L. Bennett, Chunbo Luo, and Aldina M. A. Franco
EGUsphere, https://doi.org/10.5194/egusphere-2023-2596, https://doi.org/10.5194/egusphere-2023-2596, 2023
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Smart sensors have been installed in boulders embedded in landslides to monitor the movements and characterise their hazards. Here, we present laboratory experiments to investigate how to use smart sensors to describe the movements of a cobble down an inclined plane and transmit the recorded motion data via a wireless network. This study contributes to understanding how to make the best use of smart sensors to describe boulder motion and assess the practicalities of their use in field settings.
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.
Melissa Wood, Ivan D. Haigh, Quan Quan Le, Hung Nghia Nguyen, Hoang Ba Tran, Stephen E. Darby, Robert Marsh, Nikolaos Skliris, Joël J.-M. Hirschi, Robert J. Nicholls, and Nadia Bloemendaal
Nat. Hazards Earth Syst. Sci., 23, 2475–2504, https://doi.org/10.5194/nhess-23-2475-2023, https://doi.org/10.5194/nhess-23-2475-2023, 2023
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We used a novel database of simulated tropical cyclone tracks to explore whether typhoon-induced storm surges present a future flood risk to low-lying coastal communities around the South China Sea. We found that future climate change is likely to change tropical cyclone behaviour to an extent that this increases the severity and frequency of storm surges to Vietnam, southern China, and Thailand. Consequently, coastal flood defences need to be reviewed for resilience against this future hazard.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
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A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
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.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
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This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Andrea Gasparotto, Stephen E. Darby, Julian Leyland, and Paul A. Carling
Earth Surf. Dynam., 11, 343–361, https://doi.org/10.5194/esurf-11-343-2023, https://doi.org/10.5194/esurf-11-343-2023, 2023
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In this study the processes leading to bank failures in the hypertidal Severn Estuary are studied employing numerical models and field observations. Results highlight that the periodic fluctuations in water levels drive an imbalance in the resisting (hydrostatic pressure) versus driving (pore water pressure) forces causing a frequent oscillation of bank stability between stable (at high tide) and unstable states (at low tide) both on semidiurnal bases and in the spring–neap transition.
Joshua N. Jones, Georgina L. Bennett, Claudia Abancó, Mark A. M. Matera, and Fibor J. Tan
Nat. Hazards Earth Syst. Sci., 23, 1095–1115, https://doi.org/10.5194/nhess-23-1095-2023, https://doi.org/10.5194/nhess-23-1095-2023, 2023
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We modelled where landslides occur in the Philippines using landslide data from three typhoon events in 2009, 2018, and 2019. These models show where landslides occurred within the landscape. By comparing the different models, we found that the 2019 landslides were occurring all across the landscape, whereas the 2009 and 2018 landslides were mostly occurring at specific slope angles and aspects. This shows that landslide susceptibility must be considered variable through space and time.
Paul D. Bates, James Savage, Oliver Wing, Niall Quinn, Christopher Sampson, Jeffrey Neal, and Andrew Smith
Nat. Hazards Earth Syst. Sci., 23, 891–908, https://doi.org/10.5194/nhess-23-891-2023, https://doi.org/10.5194/nhess-23-891-2023, 2023
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We present and validate a model that simulates current and future flood risk for the UK at high resolution (~ 20–25 m). We show that UK flood losses were ~ 6 % greater in the climate of 2020 compared to recent historical values. The UK can keep any future increase to ~ 8 % if all countries implement their COP26 pledges and net-zero ambitions in full. However, if only the COP26 pledges are fulfilled, then UK flood losses increase by ~ 23 %; and potentially by ~ 37 % in a worst-case scenario.
Yinxue Liu, Paul D. Bates, and Jeffery C. Neal
Nat. Hazards Earth Syst. Sci., 23, 375–391, https://doi.org/10.5194/nhess-23-375-2023, https://doi.org/10.5194/nhess-23-375-2023, 2023
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In this paper, we test two approaches for removing buildings and other above-ground objects from a state-of-the-art satellite photogrammetry topography product, ArcticDEM. Our best technique gives a 70 % reduction in vertical error, with an average difference of 1.02 m from a benchmark lidar for the city of Helsinki, Finland. When used in a simulation of rainfall-driven flooding, the bare-earth version of ArcticDEM yields a significant improvement in predicted inundation extent and water depth.
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.
Elena Bastianon, Julie A. Hope, Robert M. Dorrell, and Daniel R. Parsons
Earth Surf. Dynam., 10, 1115–1140, https://doi.org/10.5194/esurf-10-1115-2022, https://doi.org/10.5194/esurf-10-1115-2022, 2022
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Biological activity in shallow tidal environments significantly influence sediment dynamics and morphology. Here, a bio-morphodynamic model is developed that accounts for hydro-climate variations in biofilm development to estimate the effect of biostabilisation on the bed. Results reveal that key parameters such as growth rate and temperature strongly influence the development of biofilm under a range of disturbance periodicities and intensities, shaping the channel equilibrium profile.
Louise J. Slater, Chris Huntingford, Richard F. Pywell, John W. Redhead, and Elizabeth J. Kendon
Earth Syst. Dynam., 13, 1377–1396, https://doi.org/10.5194/esd-13-1377-2022, https://doi.org/10.5194/esd-13-1377-2022, 2022
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This work considers how wheat yields are affected by weather conditions during the three main wheat growth stages in the UK. Impacts are strongest in years with compound weather extremes across multiple growth stages. Future climate projections are beneficial for wheat yields, on average, but indicate a high risk of unseen weather conditions which farmers may struggle to adapt to and mitigate against.
Chengbin Zou, Paul Carling, Zetao Feng, Daniel Parsons, and Xuanmei Fan
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-119, https://doi.org/10.5194/tc-2022-119, 2022
Manuscript not accepted for further review
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Climate change is causing mountain lakes behind glacier barriers to drain through ice tunnels as catastrophe floods, threatening people and infrastructure downstream. Understanding of how process works can mitigate the impacts by providing advanced warnings. A laboratory study of ice tunnel development improved understanding of how floods evolve. The principles of ice tunnel development were defined numerically and can be used to better model natural floods leading to improved prediction.
Thomas Lees, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens De Bruijn, Reetik Kumar Sahu, Peter Greve, Louise Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, https://doi.org/10.5194/hess-26-3079-2022, 2022
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Despite the accuracy of deep learning rainfall-runoff models, we are currently uncertain of what these models have learned. In this study we explore the internals of one deep learning architecture and demonstrate that the model learns about intermediate hydrological stores of soil moisture and snow water, despite never having seen data about these processes during training. Therefore, we find evidence that the deep learning approach learns a physically realistic mapping from inputs to outputs.
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.
M. G. Ziliani, M. U. Altaf, B. Aragon, R. Houborg, T. E. Franz, Y. Lu, J. Sheffield, I. Hoteit, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1045–1052, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1045-2022, 2022
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
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Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Hong-Yi Li, Zeli Tan, Hongbo Ma, Zhenduo Zhu, Guta Wakbulcho Abeshu, Senlin Zhu, Sagy Cohen, Tian Zhou, Donghui Xu, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 665–688, https://doi.org/10.5194/hess-26-665-2022, https://doi.org/10.5194/hess-26-665-2022, 2022
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We introduce a new multi-process river sediment module for Earth system models. Application and validation over the contiguous US indicate a satisfactory model performance over large river systems, including those heavily regulated by reservoirs. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
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Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang
Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021, https://doi.org/10.5194/hess-25-5981-2021, 2021
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Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.
Thomas Lees, Marcus Buechel, Bailey Anderson, Louise Slater, Steven Reece, Gemma Coxon, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, https://doi.org/10.5194/hess-25-5517-2021, 2021
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We used deep learning (DL) models to simulate the amount of water moving through a river channel (discharge) based on the rainfall, temperature and potential evaporation in the previous days. We tested the DL models on catchments across Great Britain finding that the model can accurately simulate hydrological systems across a variety of catchment conditions. Ultimately, the model struggled most in areas where there is chalky bedrock and where human influence on the catchment is large.
Christopher R. Hackney, Grigorios Vasilopoulos, Sokchhay Heng, Vasudha Darbari, Samuel Walker, and Daniel R. Parsons
Earth Surf. Dynam., 9, 1323–1334, https://doi.org/10.5194/esurf-9-1323-2021, https://doi.org/10.5194/esurf-9-1323-2021, 2021
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Unsustainable sand mining poses a threat to the stability of river channels. We use satellite imagery to estimate volumes of material removed from the Mekong River, Cambodia, over the period 2016–2020. We demonstrate that current rates of extraction now exceed previous estimates for the entire Mekong Basin and significantly exceed the volume of sand naturally transported by the river. Our work highlights the importance of satellite imagery in monitoring sand mining activity over large areas.
Chloe Leach, Tom Coulthard, Andrew Barkwith, Daniel R. Parsons, and Susan Manson
Geosci. Model Dev., 14, 5507–5523, https://doi.org/10.5194/gmd-14-5507-2021, https://doi.org/10.5194/gmd-14-5507-2021, 2021
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Numerical models can be used to understand how coastal systems evolve over time, including likely responses to climate change. However, many existing models are aimed at simulating 10- to 100-year time periods do not represent a vertical dimension and are thus unable to include the effect of sea-level rise. The Coastline Evolution Model 2D (CEM2D) presented in this paper is an advance in this field, with the inclusion of the vertical coastal profile against which the water level can be altered.
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.
Cited articles
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Sci. Data, 5, 170191, https://doi.org/10.1038/sdata.2017.191, 2018.
Acharya, S. C., Nathan, R., Wang, Q. J., Su, C.-H., and Eizenberg, N.: An evaluation of daily precipitation from a regional atmospheric reanalysis over Australia, Hydrol. Earth Syst. Sci., 23, 3387–3403, https://doi.org/10.5194/hess-23-3387-2019, 2019.
Ahmed, K., Shahid, S., Wang, X., Nawaz, N., and Khan, N.: Evaluation of Gridded Precipitation Datasets over Arid Regions of Pakistan, Water, 11, 210, https://doi.org/10.3390/w11020210, 2019.
Alazzy, A. A., Lü, H., Chen, R., Ali, A. B., Zhu, Y., and Su, J.: Evaluation of Satellite Precipitation Products and Their Potential Influence on Hydrological Modeling over the Ganzi River Basin of the Tibetan Plateau, Adv. Meteorol., 2017, e3695285, https://doi.org/10.1155/2017/3695285, 2017.
AL-Falahi, A. H., Saddique, N., Spank, U., Gebrechorkos, S. H., and Bernhofer, C.: Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management, Remote Sens., 12, 2984, https://doi.org/10.3390/rs12182984, 2020.
Araujo Palharini, R. S., Vila, D. A., Rodrigues, D. T., Palharini, R. C., Mattos, E. V., and Pedra, G. U.: Assessment of extreme rainfall estimates from satellite-based: Regional analysis, Remote Sensing Applications: Society and Environment, 23, 100603, https://doi.org/10.1016/j.rsase.2021.100603, 2021.
Bárdossy, A., Kilsby, C., Birkinshaw, S., Wang, N., and Anwar, F.: Is Precipitation Responsible for the Most Hydrological Model Uncertainty?, Front. Water, 4, https://doi.org/10.3389/frwa.2022.836554, 2022.
Bechtold, P., Forbes, R., Sandu, I., Lang, S., and Ahlgrimm, M.: A major moist physics upgrade for the IFS, 24–32, https://www.ecmwf.int/en/newsletter/164/meteorology/major-moist-physics-upgrade-ifs (last access: 19 June 2023), 2020.
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., 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, 2017a.
Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/hess-21-589-2017, 2017b.
Beck, H. E., Pan, M., Roy, T., Weedon, G. P., Pappenberger, F., van Dijk, A. I. J. M., Huffman, G. J., Adler, R. F., and Wood, E. F.: Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS, Hydrol. Earth Syst. Sci., 23, 207–224, https://doi.org/10.5194/hess-23-207-2019, 2019a.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., Dijk, A. I. J. M. van, McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019b.
Brunner, M. I., Slater, L., Tallaksen, L. M., and Clark, M.: Challenges in modeling and predicting floods and droughts: A review, WIREs Water, 8, e1520, https://doi.org/10.1002/wat2.1520, 2021.
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Wayne Higgins, R., and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009132, 2008.
Chen, Y., Hu, D., Duan, X., Zhang, Y., Liu, M., and Shasha, W.: Rainfall-runoff simulation and flood dynamic monitoring based on CHIRPS and MODIS-ET, Int. J. Remote Sens., 41, 4206–4225, https://doi.org/10.1080/01431161.2020.1714779, 2020.
Cohen, S., Kettner, A. J., Syvitski, J. P. M., and Fekete, B. M.: WBMsed, a distributed global-scale riverine sediment flux model: Model description and validation, Comput. Geosci., 53, 80–93, https://doi.org/10.1016/j.cageo.2011.08.011, 2013.
Cohen, S., Kettner, A. J., and Syvitski, J. P. M.: Global suspended sediment and water discharge dynamics between 1960 and 2010: Continental trends and intra-basin sensitivity, Global Planet. Change, 115, 44–58, https://doi.org/10.1016/j.gloplacha.2014.01.011, 2014.
Cohen, S., Syvitski, J., Ashley, T., Lammers, R., Fekete, B., and Li, H.-Y.: Spatial Trends and Drivers of Bedload and Suspended Sediment Fluxes in Global Rivers, Water Resour. Res., 58, e2021WR031583, https://doi.org/10.1029/2021WR031583, 2022.
Day, C. A. and Howarth, D. A.: Modeling Climate Change Impacts on the Water Balance of a Medium-Scale Mixed-Forest Watershed, SE USA, Southeastern Geographer, 59, 110–129, 2019.
Dembélé, M., Schaefli, B., van de Giesen, N., and Mariéthoz, G.: Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa, Hydrol. Earth Syst. Sci., 24, 5379–5406, https://doi.org/10.5194/hess-24-5379-2020, 2020.
Dunn, F. E., Darby, S. E., Nicholls, R. J., Cohen, S., Zarfl, C., and Fekete, B. M.: Projections of declining fluvial sediment delivery to major deltas worldwide in response to climate change and anthropogenic stress, Environ. Res. Lett., 14, 084034, https://doi.org/10.1088/1748-9326/ab304e, 2019.
Eini, M. R., Rahmati, A., and Piniewski, M.: Hydrological application and accuracy evaluation of PERSIANN satellite-based precipitation estimates over a humid continental climate catchment, J. Hydrol., 41, 101109, https://doi.org/10.1016/j.ejrh.2022.101109, 2022.
El Kenawy, A. M., Lopez-Moreno, J. I., McCabe, M. F., and Vicente-Serrano, S. M.: Evaluation of the TMPA-3B42 precipitation product using a high-density rain gauge network over complex terrain in northeastern Iberia, Global Planet. Change, 133, 188–200, https://doi.org/10.1016/j.gloplacha.2015.08.013, 2015.
Fallah, A., Rakhshandehroo, G. R., Berg, P., O, S., and Orth, R.: Evaluation of precipitation datasets against local observations in southwestern Iran, Int. J. Climatol., 40, 4102–4116, https://doi.org/10.1002/joc.6445, 2020.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015.
Gebrechorkos, S. H., Hülsmann, S., and Bernhofer, C.: Evaluation of multiple climate data sources for managing environmental resources in East Africa, Hydrol. Earth Syst. Sci., 22, 4547–4564, https://doi.org/10.5194/hess-22-4547-2018, 2018.
Gebrechorkos, S. H., Bernhofer, C., and Hülsmann, S.: Impacts of projected change in climate on water balance in basins of East Africa, Sci. Total Environ., 682, 160–170, https://doi.org/10.1016/j.scitotenv.2019.05.053, 2019.
Gebrechorkos, S. H., Bernhofer, C., and Hülsmann, S.: Climate change impact assessment on the hydrology of a large river basin in Ethiopia using a local-scale climate modelling approach, Sci. Total Environ., 742, 140504, https://doi.org/10.1016/j.scitotenv.2020.140504, 2020.
Gebrechorkos, S. H., Leyland, J., Darby, S., and Parsons, D.: High-resolution daily global climate dataset of BCCAQ statistically downscaled CMIP6 models for the EVOFLOOD project, NERC EDS Centre for Environmental Data Analysis, https://catalogue.ceda.ac.uk/uuid/c107618f1db34801bb88a1e92 7b82317, 2022a.
Gebrechorkos, S. H., Pan, M., Beck, H. E., and Sheffield, J.: Performance of State-of-the-Art C3S European Seasonal Climate Forecast Models for Mean and Extreme Precipitation Over Africa, Water Resour. Res., 58, e2021WR031480, https://doi.org/10.1029/2021WR031480, 2022b.
Gebrechorkos, S. H., Pan, M., Lin, P., Anghileri, D., Forsythe, N., Pritchard, D. M. W., Fowler, H. J., Obuobie, E., Darko, D., and Sheffield, J.: Variability and changes in hydrological drought in the Volta Basin, West Africa, J. Hydrol., 42, 101143, https://doi.org/10.1016/j.ejrh.2022.101143, 2022c.
Gebrechorkos, S. H., Peng, J., Dyer, E., Miralles, D. G., Vicente-Serrano, S. M., Funk, C., Beck, H. E., Asfaw, D. T., Singer, M. B., and Dadson, S. J.: Global high-resolution drought indices for 1981–2022, Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, 2023.
Geleta, C. D. and Deressa, T. A.: Evaluation of Climate Hazards Group InfraRed Precipitation Station (CHIRPS) satellite-based rainfall estimates over Finchaa and Neshe Watersheds, Ethiopia, Engineering Reports, 3, e12338, https://doi.org/10.1002/eng2.12338, 2021.
GRDC: The Global Runoff Data Centre, 56068 Koblenz, Germany, https://www.bafg.de/GRDC/ (last access: 26 February 2023), 2023.
Grogan, D. S., Zuidema, S., Prusevich, A., Wollheim, W. M., Glidden, S., and Lammers, R. B.: Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality, Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, 2022.
Gu, L., Yin, J., Wang, S., Chen, J., Qin, H., Yan, X., He, S., and Zhao, T.: How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling, J. Hydrol., 617, 128920, https://doi.org/10.1016/j.jhydrol.2022.128920, 2023.
Guo, B., Zhang, J., Xu, T., Croke, B., Jakeman, A., Song, Y., Yang, Q., Lei, X., and Liao, W.: Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models, Water, 10, 1611, https://doi.org/10.3390/w10111611, 2018.
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.
Hafizi, H. and Sorman, A. A.: Assessment of 13 Gridded Precipitation Datasets for Hydrological Modeling in a Mountainous Basin, Atmosphere, 13, 143, https://doi.org/10.3390/atmos13010143, 2022.
Harrigan, S., Zsoter, E., Alfieri, L., Prudhomme, C., Salamon, P., Wetterhall, F., Barnard, C., Cloke, H., and Pappenberger, F.: GloFAS-ERA5 operational global river discharge reanalysis 1979–present, Earth Syst. Sci. Data, 12, 2043–2060, https://doi.org/10.5194/essd-12-2043-2020, 2020.
He, Q., Shen, Z., Wan, M., and Li, L.: Precipitable Water Vapor Converted from GNSS-ZTD and ERA5 Datasets for the Monitoring of Tropical Cyclones, IEEE Access, 8, 87275–87290, https://doi.org/10.1109/ACCESS.2020.2991094, 2020.
Her, Y., Yoo, S.-H., Cho, J., Hwang, S., Jeong, J., and Seong, C.: Uncertainty in hydrological analysis of climate change: multi-parameter vs. multi-GCM ensemble predictions, Sci. Rep., 9, 4974, https://doi.org/10.1038/s41598-019-41334-7, 2019.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hong, Y., Xuan Do, H., Kessler, J., Fry, L., Read, L., Rafieei Nasab, A., Gronewold, A. D., Mason, L., and Anderson, E. J.: Evaluation of gridded precipitation datasets over international basins and large lakes, J. Hydrol., 607, 127507, https://doi.org/10.1016/j.jhydrol.2022.127507, 2022.
Hou, D., Charles, M., Luo, Y., Toth, Z., Zhu, Y., Krzysztofowicz, R., Lin, Y., Xie, P., Seo, D.-J., Pena, M., and Cui, B.: Climatology-Calibrated Precipitation Analysis at Fine Scales: Statistical Adjustment of Stage IV toward CPC Gauge-Based Analysis, J. Hydrometeorol., 15, 2542–2557, https://doi.org/10.1175/JHM-D-11-0140.1, 2014.
Huang, Z., Zhang, Y., Xu, J., Fang, X., and Ma, Z.: Can satellite precipitation estimates capture the magnitude of extreme rainfall Events?, Remote Sens. Lett., 13, 1048–1057, https://doi.org/10.1080/2150704X.2022.2123258, 2022.
Ibrahim, A. H., Molla, D. D., and Lohani, T. K.: Performance evaluation of satellite-based rainfall estimates for hydrological modeling over Bilate river basin, Ethiopia, World Journal of Engineering, ahead-of-print, 21, 1–15, https://doi.org/10.1108/WJE-03-2022-0106, 2022.
Jiang, Q., Li, W., Wen, J., Fan, Z., Chen, Y., Scaioni, M., and Wang, J.: Evaluation of satellite-based products for extreme rainfall estimations in the eastern coastal areas of China, J. Integr. Environ. Sci., 16, 191–207, https://doi.org/10.1080/1943815X.2019.1707233, 2019.
Jiang, S., Wei, L., Ren, L., Zhang, L., Wang, M., and Cui, H.: Evaluation of IMERG, TMPA, ERA5, and CPC precipitation products over mainland China: Spatiotemporal patterns and extremes, Water Science and Engineering, 16, 45–56, https://doi.org/10.1016/j.wse.2022.05.001, 2023.
Jiao, D., Xu, N., Yang, F., and Xu, K.: Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China, Sci. Rep., 11, 17956, https://doi.org/10.1038/s41598-021-97432-y, 2021.
Kidd, C. and Levizzani, V.: Status of satellite precipitation retrievals, Hydrol. Earth Syst. Sci., 15, 1109–1116, https://doi.org/10.5194/hess-15-1109-2011, 2011.
Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P., Skofronick-Jackson, G., and Kirschbaum, D. B.: So, How Much of the Earth's Surface Is Covered by Rain Gauges?, B. Am. Meteorol. Soc., 98, 69–78, https://doi.org/10.1175/BAMS-D-14-00283.1, 2017.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Laiti, L., Mallucci, S., Piccolroaz, S., Bellin, A., Zardi, D., Fiori, A., Nikulin, G., and Majone, B.: Testing the Hydrological Coherence of High-Resolution Gridded Precipitation and Temperature Data Sets, Water Resour. Res., 54, 1999–2016, https://doi.org/10.1002/2017WR021633, 2018.
Lakew, H. B.: Investigating the effectiveness of bias correction and merging MSWEP with gauged rainfall for the hydrological simulation of the upper Blue Nile basin, J. Hydrol., 32, 100741, https://doi.org/10.1016/j.ejrh.2020.100741, 2020.
Lavers, D. A., Harrigan, S., and Prudhomme, C.: Precipitation Biases in the ECMWF Integrated Forecasting System, J. Hydrometeorol., 22, 1187–1198, https://doi.org/10.1175/JHM-D-20-0308.1, 2021.
Lavers, D. A., Simmons, A., Vamborg, F., and Rodwell, M. J.: An evaluation of ERA5 precipitation for climate monitoring, Q. J. Roy. Meteor. Soc., 148, 3152–3165, https://doi.org/10.1002/qj.4351, 2022.
Lehner, B., Verdin, K. L., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, Eos, Transactions, American Geophysical Union, 89, 2, https://doi.org/10.1029/2008EO100001, 2008.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood, E. F.: Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches, Water Resour. Res., 55, 6499–6516, https://doi.org/10.1029/2019WR025287, 2019.
Li, L., Wang, Y., Wang, L., Hu, Q., Zhu, Z., Li, L., and Li, C.: Spatio-temporal accuracy evaluation of MSWEP daily precipitation over the Huaihe River Basin, China: A comparison study with representative satellite- and reanalysis-based products, J. Geogr. Sci., 32, 2271–2290, https://doi.org/10.1007/s11442-022-2047-9, 2022a.
Li, M., Lv, X., Zhu, L., Uchenna Ochege, F., and Guo, H.: Evaluation and Application of MSWEP in Drought Monitoring in Central Asia, Atmosphere, 13, 1053, https://doi.org/10.3390/atmos13071053, 2022b.
López López, P., Sutanudjaja, E. H., Schellekens, J., Sterk, G., and Bierkens, M. F. P.: Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products, Hydrol. Earth Syst. Sci., 21, 3125–3144, https://doi.org/10.5194/hess-21-3125-2017, 2017.
Luo, X., Wu, W., He, D., Li, Y., and Ji, X.: Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin, Chin. Geogr. Sci., 29, 13–25, https://doi.org/10.1007/s11769-019-1014-6, 2019.
Maggioni, V. and Massari, C.: On the performance of satellite precipitation products in riverine flood modeling: A review, J. Hydrol., 558, 214–224, https://doi.org/10.1016/j.jhydrol.2018.01.039, 2018.
Mazzoleni, M., Brandimarte, L., and Amaranto, A.: Evaluating precipitation datasets for large-scale distributed hydrological modelling, J. Hydrol., 578, 124076, https://doi.org/10.1016/j.jhydrol.2019.124076, 2019.
Mehran, A. and AghaKouchak, A.: Capabilities of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations, Hydrol. Process., 28, 2262–2270, https://doi.org/10.1002/hyp.9779, 2014.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An Overview of the Global Historical Climatology Network-Daily Database, J. Atmos. Ocean. Tech., 29, 897–910, https://doi.org/10.1175/JTECH-D-11-00103.1, 2012.
Mianabadi, A., Salari, K., and Pourmohamad, Y.: Drought monitoring using the long-term CHIRPS precipitation over Southeastern Iran, Appl. Water Sci., 12, 183, https://doi.org/10.1007/s13201-022-01705-4, 2022.
Miao, C., Ashouri, H., Hsu, K.-L., Sorooshian, S., and Duan, Q.: Evaluation of the PERSIANN-CDR Daily Rainfall Estimates in Capturing the Behavior of Extreme Precipitation Events over China, J. Hydrometeorol., 16, 1387–1396, https://doi.org/10.1175/JHM-D-14-0174.1, 2015.
Miao, Q., Pan, B., Wang, H., Hsu, K., and Sorooshian, S.: Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network, Water, 11, 977, https://doi.org/10.3390/w11050977, 2019.
Michaelides, S., Levizzani, V., Anagnostou, E., Bauer, P., Kasparis, T., and Lane, J. E.: Precipitation: Measurement, remote sensing, climatology and modeling, Atmos. Res., 94, 512–533, https://doi.org/10.1016/j.atmosres.2009.08.017, 2009.
Moazami, S., Golian, S., Kavianpour, M. R., and Hong, Y.: Comparison of PERSIANN and V7 TRMM Multi-satellite Precipitation Analysis (TMPA) products with rain gauge data over Iran, Int. J. Remote Sens., 34, 8156–8171, https://doi.org/10.1080/01431161.2013.833360, 2013.
Moragoda, N. and Cohen, S.: Climate-induced trends in global riverine water discharge and suspended sediment dynamics in the 21st century, Global Planet. Change, 191, 103199, https://doi.org/10.1016/j.gloplacha.2020.103199, 2020.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Nguyen, P., Thorstensen, A., Sorooshian, S., Hsu, K., Aghakouchak, A., Ashouri, H., Tran, H., and Braithwaite, D.: Global Precipitation Trends across Spatial Scales Using Satellite Observations, B. Am. Meteorol. Soc., 99, 689–697, https://doi.org/10.1175/BAMS-D-17-0065.1, 2018.
Opere, A. O., Waswa, R., and Mutua, F. M.: Assessing the Impacts of Climate Change on Surface Water Resources Using WEAP Model in Narok County, Kenya, Frontiers in Water, 3, https://doi.org/10.3389/frwa.2021.789340, 2022.
Palharini, R. S. A., Vila, D. A., Rodrigues, D. T., Quispe, D. P., Palharini, R. C., de Siqueira, R. A., and de Sousa Afonso, J. M.: Assessment of the Extreme Precipitation by Satellite Estimates over South America, Remote Sens., 12, 2085, https://doi.org/10.3390/rs12132085, 2020.
Parker, W. S.: Reanalyses and Observations: What's the Difference?, B. Am. Meteorol. Soc., 97, 1565–1572, https://doi.org/10.1175/BAMS-D-14-00226.1, 2016.
Peng, J., Dadson, S., Hirpa, F., Dyer, E., Lees, T., Miralles, D. G., Vicente-Serrano, S. M., and Funk, C.: A pan-African high-resolution drought index dataset, Earth Syst. Sci. Data, 12, 753–769, https://doi.org/10.5194/essd-12-753-2020, 2020.
Raimonet, M., Oudin, L., Thieu, V., Silvestre, M., Vautard, R., Rabouille, C., and Moigne, P. L.: Evaluation of Gridded Meteorological Datasets for Hydrological Modeling, J. Hydrometeorol., 18, 3027–3041, https://doi.org/10.1175/JHM-D-17-0018.1, 2017.
Reichle, R. H., Koster, R. D., Lannoy, G. J. M. D., Forman, B. A., Liu, Q., Mahanama, S. P. P., and Touré, A.: Assessment and Enhancement of MERRA Land Surface Hydrology Estimates, J. Climate, 24, 6322–6338, https://doi.org/10.1175/JCLI-D-10-05033.1, 2011.
Reis, A. A. dos, Weerts, A., Ramos, M.-H., Wetterhall, F., and Fernandes, W. dos S.: Hydrological data and modeling to combine and validate precipitation datasets relevant to hydrological applications, J. Hydrol., 44, 101200, https://doi.org/10.1016/j.ejrh.2022.101200, 2022.
Sadeghi, M., Nguyen, P., Naeini, M. R., Hsu, K., Braithwaite, D., and Sorooshian, S.: PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies, Sci. Data, 8, 157, https://doi.org/10.1038/s41597-021-00940-9, 2021.
Salehi, H., Sadeghi, M., Golian, S., Nguyen, P., Murphy, C., and Sorooshian, S.: The Application of PERSIANN Family Datasets for Hydrological Modeling, Remote Sens., 14, 3675, https://doi.org/10.3390/rs14153675, 2022.
Satgé, F., Ruelland, D., Bonnet, M.-P., Molina, J., and Pillco, R.: Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow- hydrological modelling in the Lake Titicaca region, Hydrol. Earth Syst. Sci., 23, 595–619, https://doi.org/10.5194/hess-23-595-2019, 2019.
Seyyedi, H., Anagnostou, E. N., Beighley, E., and McCollum, J.: Hydrologic evaluation of satellite and reanalysis precipitation datasets over a mid-latitude basin, Atmos. Res., 164–165, 37–48, https://doi.org/10.1016/j.atmosres.2015.03.019, 2015.
Shaowei, N., Jie, W., Juliang, J., Xiaoyan, X., Yuliang, Z., Fan, S., and Linlin, Z.: Comprehensive evaluation of satellite-derived precipitation products considering spatial distribution difference of daily precipitation over eastern China, J. Hydrol., 44, 101242, https://doi.org/10.1016/j.ejrh.2022.101242, 2022.
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006.
Sheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat-Capdevila, A., and Verbist, K.: Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data-Poor Regions, Water Resour. Res., 54, 9724–9758, https://doi.org/10.1029/2017WR022437, 2018.
Shen, Y., Xiong, A., Wang, Y., and Xie, P.: Performance of high-resolution satellite precipitation products over China, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2009JD012097, 2010.
Solakian, J., Maggioni, V., and Godrej, A. N.: On the Performance of Satellite-Based Precipitation Products in Simulating Streamflow and Water Quality During Hydrometeorological Extremes, Frontiers in Environmental Science, 8, https://doi.org/10.3389/fenvs.2020.585451, 2020.
Sun, G., Wei, Y., Wang, G., Shi, R., Chen, H., and Mo, C.: Downscaling Correction and Hydrological Applicability of the Three Latest High-Resolution Satellite Precipitation Products (GPM, GSMAP, and MSWEP) in the Pingtang Catchment, China, Adv. Meteorol., 2022, e6507109, https://doi.org/10.1155/2022/6507109, 2022.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons, Rev. Geophys., 56, 79–107, https://doi.org/10.1002/2017RG000574, 2018.
Tang, X., Zhang, J., Gao, C., Ruben, G. B., and Wang, G.: Assessing the Uncertainties of Four Precipitation Products for Swat Modeling in Mekong River Basin, Remote Sens., 11, 304, https://doi.org/10.3390/rs11030304, 2019.
Ursulak, J. and Coulibaly, P.: Integration of hydrological models with entropy and multi-objective optimization based methods for designing specific needs streamflow monitoring networks, J. Hydrol., 593, 125876, https://doi.org/10.1016/j.jhydrol.2020.125876, 2021.
van Huijgevoort, M. H. J., Hazenberg, P., van Lanen, H. A. J., Teuling, A. J., Clark, D. B., Folwell, S., Gosling, S. N., Hanasaki, N., Heinke, J., Koirala, S., Stacke, T., Voss, F., Sheffield, J., and Uijlenhoet, R.: Global Multimodel Analysis of Drought in Runoff for the Second Half of the Twentieth Century, J. Hydrometeorol., 14, 1535–1552, https://doi.org/10.1175/JHM-D-12-0186.1, 2013.
Voisin, N., Wood, A. W., and Lettenmaier, D. P.: Evaluation of Precipitation Products for Global Hydrological Prediction, J. Hydrometeorol., 9, 388–407, https://doi.org/10.1175/2007JHM938.1, 2008.
Wang, M., Rezaie-Balf, M., Naganna, S. R., and Yaseen, Z. M.: Sourcing CHIRPS precipitation data for streamflow forecasting using intrinsic time-scale decomposition based machine learning models, Hydrolog. Sci. J., 66, 1437–1456, https://doi.org/10.1080/02626667.2021.1928138, 2021.
Wang, N., Liu, W., Sun, F., Yao, Z., Wang, H., and Liu, W.: Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China, Atmos. Res., 234, 104746, https://doi.org/10.1016/j.atmosres.2019.104746, 2020.
Wati, T., Hadi, T. W., Sopaheluwakan, A., and Hutasoit, L. M.: Statistics of the Performance of Gridded Precipitation Datasets in Indonesia, Adv. Meteorol., 2022, e7995761, https://doi.org/10.1155/2022/7995761, 2022.
Wisser, D., Fekete, B. M., Vörösmarty, C. J., and Schumann, A. H.: Reconstructing 20th century global hydrography: a contribution to the Global Terrestrial Network- Hydrology (GTN-H), Hydrol. Earth Syst. Sci., 14, 1–24, https://doi.org/10.5194/hess-14-1-2010, 2010.
Wollheim, W. M., Vörösmarty, C. J., Bouwman, A. F., Green, P., Harrison, J., Linder, E., Peterson, B. J., Seitzinger, S. P., and Syvitski, J. P. M.: Global N removal by freshwater aquatic systems using a spatially distributed, within-basin approach, Global Biogeochem. Cy., 22, https://doi.org/10.1029/2007GB002963, 2008.
Wu, Z., Xu, Z., Wang, F., He, H., Zhou, J., Wu, X., and Liu, Z.: Hydrologic Evaluation of Multi-Source Satellite Precipitation Products for the Upper Huaihe River Basin, China, Remote Sens., 10, 840, https://doi.org/10.3390/rs10060840, 2018.
Xiang, Y., Chen, J., Li, L., Peng, T., and Yin, Z.: Evaluation of Eight Global Precipitation Datasets in Hydrological Modeling, Remote Sens., 13, 2831, https://doi.org/10.3390/rs13142831, 2021.
Zambrano-Bigiarini, M., Nauditt, A., Birkel, C., Verbist, K., and Ribbe, L.: Temporal and spatial evaluation of satellite-based rainfall estimates across the complex topographical and climatic gradients of Chile, Hydrol. Earth Syst. Sci., 21, 1295–1320, https://doi.org/10.5194/hess-21-1295-2017, 2017.
Zhu, D., Ilyas, A. M., Wang, G., and Zeng, B.: Long-term hydrological assessment of remote sensing precipitation from multiple sources over the lower Yangtze River basin, China, Meteorol. Appl., 28, e1991, https://doi.org/10.1002/met.1991, 2021.
Zhu, H., Li, Y., Huang, Y., Li, Y., Hou, C., and Shi, X.: Evaluation and hydrological application of satellite-based precipitation datasets in driving hydrological models over the Huifa river basin in Northeast China, Atmos. Res., 207, 28–41, https://doi.org/10.1016/j.atmosres.2018.02.022, 2018.
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.
This study evaluated six high-resolution global precipitation datasets for hydrological...
