Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-3897-2021
© Author(s) 2021. 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-25-3897-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management
Louise J. Slater
CORRESPONDING AUTHOR
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Bailey Anderson
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Marcus Buechel
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Simon Dadson
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
U.K. Centre for Ecology and Hydrology, Maclean Building, Crowmarsh Gifford, Wallingford OX10 8BB, UK
Shasha Han
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Shaun Harrigan
Forecast Department, European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK
Timo Kelder
Geography and Environment, Loughborough University, Loughborough, UK
Katie Kowal
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Thomas Lees
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
Tom Matthews
Geography and Environment, Loughborough University, Loughborough, UK
Conor Murphy
Irish Climate Analysis and Research UnitS (ICARUS), Department of Geography, Maynooth University, Maynooth, Co. Kildare, Ireland
Robert L. Wilby
Geography and Environment, Loughborough University, Loughborough, UK
Related authors
Simon Moulds, Louise Slater, Louise Arnal, and Andrew Wood
EGUsphere, https://doi.org/10.31223/X5X405, https://doi.org/10.31223/X5X405, 2024
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Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to four months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to four months ahead in many locations, although in general the skill declines with increasing lead time.
This article is included in the Encyclopedia of Geosciences
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
This article is included in the Encyclopedia of Geosciences
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
Short summary
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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 article is included in the Encyclopedia of Geosciences
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
Short summary
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
This article is included in the Encyclopedia of Geosciences
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
Short summary
Short summary
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.
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
Louise J. Slater, Guillaume Thirel, Shaun Harrigan, Olivier Delaigue, Alexander Hurley, Abdou Khouakhi, Ilaria Prosdocimi, Claudia Vitolo, and Katie Smith
Hydrol. Earth Syst. Sci., 23, 2939–2963, https://doi.org/10.5194/hess-23-2939-2019, https://doi.org/10.5194/hess-23-2939-2019, 2019
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This paper explores the benefits and advantages of R's usage in hydrology. We provide an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology as well as a roadmap for R’s future within the discipline.
This article is included in the Encyclopedia of Geosciences
John K. Hillier, Geoffrey R. Saville, Mike J. Smith, Alister J. Scott, Emma K. Raven, Jonathon Gascoigne, Louise J. Slater, Nevil Quinn, Andreas Tsanakas, Claire Souch, Gregor C. Leckebusch, Neil Macdonald, Alice M. Milner, Jennifer Loxton, Rebecca Wilebore, Alexandra Collins, Colin MacKechnie, Jaqui Tweddle, Sarah Moller, MacKenzie Dove, Harry Langford, and Jim Craig
Geosci. Commun., 2, 1–23, https://doi.org/10.5194/gc-2-1-2019, https://doi.org/10.5194/gc-2-1-2019, 2019
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Worldwide there is intense interest in converting research excellence in universities into commercial success, but there has been scant attention devoted to exactly how individual scientists' workload and incentive structures may be a key barrier to this. Our work reveals the real challenge posed by a time-constrained university culture, better describes how work with business might fit into an academic job, and gives tips on working together in an
This article is included in the Encyclopedia of Geosciences
user guidefor scientists and (re)insurers.
Stefanie R. Lutz, Andrea Popp, Tim van Emmerik, Tom Gleeson, Liz Kalaugher, Karsten Möbius, Tonie Mudde, Brett Walton, Rolf Hut, Hubert Savenije, Louise J. Slater, Anna Solcerova, Cathelijne R. Stoof, and Matthias Zink
Hydrol. Earth Syst. Sci., 22, 3589–3599, https://doi.org/10.5194/hess-22-3589-2018, https://doi.org/10.5194/hess-22-3589-2018, 2018
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Media play a key role in the communication between scientists and the general public. However, the interaction between scientists and journalists is not always straightforward. In this opinion paper, we present insights from hydrologists and journalists into the benefits, aftermath and potential pitfalls of science–media interaction. We aim to encourage scientists to participate in the diverse and evolving media landscape, and we call on the scientific community to support scientists who do so.
This article is included in the Encyclopedia of Geosciences
Fiona J. Clubb, Simon M. Mudd, David T. Milodowski, Declan A. Valters, Louise J. Slater, Martin D. Hurst, and Ajay B. Limaye
Earth Surf. Dynam., 5, 369–385, https://doi.org/10.5194/esurf-5-369-2017, https://doi.org/10.5194/esurf-5-369-2017, 2017
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Floodplains and fluvial terraces can provide information about current and past river systems, helping to reveal how channels respond to changes in both climate and tectonics. We present a new method of identifying these features objectively from digital elevation models by analysing their slope and elevation compared to the modern river. We test our method in eight field sites, and find that it provides rapid and reliable extraction of floodplains and terraces across a range of landscapes.
This article is included in the Encyclopedia of Geosciences
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
This article is included in the Encyclopedia of Geosciences
Wouter R. Berghuijs, Ross A. Woods, Bailey J. Anderson, Anna Luisa Hemshorn de Sánchez, and Markus Hrachowitz
EGUsphere, https://doi.org/10.5194/egusphere-2024-2954, https://doi.org/10.5194/egusphere-2024-2954, 2024
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Water balances of catchments will often strongly depend on their state in the recent past but such memory effects may persist at annual timescales. We use global datasets to show that annual memory is typically absent in precipitation but strong in terrestrial water stores and also present evaporation and streamflow (including low flows and floods). Our experiments show that hysteretic models provide behavior that is consistent with these observed memory behaviors.
This article is included in the Encyclopedia of Geosciences
Simon Moulds, Louise Slater, Louise Arnal, and Andrew Wood
EGUsphere, https://doi.org/10.31223/X5X405, https://doi.org/10.31223/X5X405, 2024
Short summary
Short summary
Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to four months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to four months ahead in many locations, although in general the skill declines with increasing lead time.
This article is included in the Encyclopedia of Geosciences
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
Short summary
Short summary
Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
This article is included in the Encyclopedia of Geosciences
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
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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 article is included in the Encyclopedia of Geosciences
Alex Dunant, Tom R. Robinson, Alexander Logan Densmore, Nick J. Rosser, Ragindra Man Rajbhandari, Mark Kincey, Sihan Li, Prem Raj Awasthi, Max Van Wyk de Vries, Ramesh Guragain, Erin Harvey, and Simon Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1374, https://doi.org/10.5194/egusphere-2024-1374, 2024
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Our study introduces a new method using hypergraph theory to assess risks from interconnected natural hazards. Traditional models often overlook how these hazards can interact and worsen each other's effects. By applying our method to the 2015 Nepal earthquake, we successfully demonstrated its ability to predict broad damage patterns, despite slightly overestimating impacts. Being able to anticipate the effects of complex, interconnected hazards is critical for disaster preparedness.
This article is included in the Encyclopedia of Geosciences
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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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
This article is included in the Encyclopedia of Geosciences
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Proc. IAHS, 385, 121–127, https://doi.org/10.5194/piahs-385-121-2024, https://doi.org/10.5194/piahs-385-121-2024, 2024
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This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
This article is included in the Encyclopedia of Geosciences
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
Short summary
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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
Preprint archived
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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.
This article is included in the Encyclopedia of Geosciences
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-40, https://doi.org/10.5194/nhess-2024-40, 2024
Revised manuscript under review for NHESS
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Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides- such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management on landslide risk.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
Katherine Dooley, Ciaran Kelly, Natascha Seifert, Therese Myslinski, Sophie O'Kelly, Rushna Siraj, Ciara Crosby, Jack Kevin Dunne, Kate McCauley, James Donoghue, Eoin Gaddren, Daniel Conway, Jordan Cooney, Niamh McCarthy, Eoin Cullen, Simon Noone, Conor Murphy, and Peter Thorne
Clim. Past, 19, 1–22, https://doi.org/10.5194/cp-19-1-2023, https://doi.org/10.5194/cp-19-1-2023, 2023
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The highest currently recognised air temperature (33.3 °C) ever recorded in the Republic of Ireland was logged at Kilkenny Castle in 1887. This paper reassesses the plausibility of the record using various methods such as inter-station reassessment and 20CRv3 reanalysis. As a result, Boora 1976 at 32.5 °C is presented as a more reliable high-temperature record for the Republic of Ireland. The final decision however rests with the national meteorological service, Met Éireann.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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
Short summary
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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.
This article is included in the Encyclopedia of Geosciences
Samuel O. Awe, Martin Mahony, Edley Michaud, Conor Murphy, Simon J. Noone, Victor K. C. Venema, Thomas G. Thorne, and Peter W. Thorne
Clim. Past, 18, 793–820, https://doi.org/10.5194/cp-18-793-2022, https://doi.org/10.5194/cp-18-793-2022, 2022
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We unearth and analyse 2 decades of highly valuable measurements made on Mauritius at the Royal Alfred Observatory, where several distinct thermometer combinations were in use and compared, at the turn of the 20th century. This series provides unique insights into biases in early instrumental temperature records. Differences are substantial and for some instruments exhibit strong seasonality. This reinforces the critical importance of understanding early instrumental series biases.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
Hadush Meresa, Conor Murphy, Rowan Fealy, and Saeed Golian
Hydrol. Earth Syst. Sci., 25, 5237–5257, https://doi.org/10.5194/hess-25-5237-2021, https://doi.org/10.5194/hess-25-5237-2021, 2021
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The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
Jian Peng, Simon Dadson, Feyera Hirpa, Ellen Dyer, Thomas Lees, Diego G. Miralles, Sergio M. Vicente-Serrano, and Chris Funk
Earth Syst. Sci. Data, 12, 753–769, https://doi.org/10.5194/essd-12-753-2020, https://doi.org/10.5194/essd-12-753-2020, 2020
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Africa has been severely influenced by intense drought events, which has led to crop failure, food shortages, famine, epidemics and even mass migration. The current study developed a high spatial resolution drought dataset entirely from satellite-based products. The dataset has been comprehensively inter-compared with other drought indicators and may contribute to an improved characterization of drought risk and vulnerability and minimize drought's impact on water and food security in Africa.
This article is included in the Encyclopedia of Geosciences
Paolo De Luca, Gabriele Messori, Robert L. Wilby, Maurizio Mazzoleni, and Giuliano Di Baldassarre
Earth Syst. Dynam., 11, 251–266, https://doi.org/10.5194/esd-11-251-2020, https://doi.org/10.5194/esd-11-251-2020, 2020
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We show that floods and droughts can co-occur in time across remote regions on the globe and introduce metrics that can help in quantifying concurrent wet and dry hydrological extremes. We then link wet–dry extremes to major modes of climate variability (i.e. ENSO, PDO, and AMO) and provide their spatial patterns. Such concurrent extreme hydrological events may pose risks to regional hydropower production and agricultural yields.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
Louise J. Slater, Guillaume Thirel, Shaun Harrigan, Olivier Delaigue, Alexander Hurley, Abdou Khouakhi, Ilaria Prosdocimi, Claudia Vitolo, and Katie Smith
Hydrol. Earth Syst. Sci., 23, 2939–2963, https://doi.org/10.5194/hess-23-2939-2019, https://doi.org/10.5194/hess-23-2939-2019, 2019
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This paper explores the benefits and advantages of R's usage in hydrology. We provide an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology as well as a roadmap for R’s future within the discipline.
This article is included in the Encyclopedia of Geosciences
John K. Hillier, Geoffrey R. Saville, Mike J. Smith, Alister J. Scott, Emma K. Raven, Jonathon Gascoigne, Louise J. Slater, Nevil Quinn, Andreas Tsanakas, Claire Souch, Gregor C. Leckebusch, Neil Macdonald, Alice M. Milner, Jennifer Loxton, Rebecca Wilebore, Alexandra Collins, Colin MacKechnie, Jaqui Tweddle, Sarah Moller, MacKenzie Dove, Harry Langford, and Jim Craig
Geosci. Commun., 2, 1–23, https://doi.org/10.5194/gc-2-1-2019, https://doi.org/10.5194/gc-2-1-2019, 2019
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Worldwide there is intense interest in converting research excellence in universities into commercial success, but there has been scant attention devoted to exactly how individual scientists' workload and incentive structures may be a key barrier to this. Our work reveals the real challenge posed by a time-constrained university culture, better describes how work with business might fit into an academic job, and gives tips on working together in an
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user guidefor scientists and (re)insurers.
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.
This article is included in the Encyclopedia of Geosciences
Stefanie R. Lutz, Andrea Popp, Tim van Emmerik, Tom Gleeson, Liz Kalaugher, Karsten Möbius, Tonie Mudde, Brett Walton, Rolf Hut, Hubert Savenije, Louise J. Slater, Anna Solcerova, Cathelijne R. Stoof, and Matthias Zink
Hydrol. Earth Syst. Sci., 22, 3589–3599, https://doi.org/10.5194/hess-22-3589-2018, https://doi.org/10.5194/hess-22-3589-2018, 2018
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Media play a key role in the communication between scientists and the general public. However, the interaction between scientists and journalists is not always straightforward. In this opinion paper, we present insights from hydrologists and journalists into the benefits, aftermath and potential pitfalls of science–media interaction. We aim to encourage scientists to participate in the diverse and evolving media landscape, and we call on the scientific community to support scientists who do so.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
Conor Murphy, Ciaran Broderick, Timothy P. Burt, Mary Curley, Catriona Duffy, Julia Hall, Shaun Harrigan, Tom K. R. Matthews, Neil Macdonald, Gerard McCarthy, Mark P. McCarthy, Donal Mullan, Simon Noone, Timothy J. Osborn, Ciara Ryan, John Sweeney, Peter W. Thorne, Seamus Walsh, and Robert L. Wilby
Clim. Past, 14, 413–440, https://doi.org/10.5194/cp-14-413-2018, https://doi.org/10.5194/cp-14-413-2018, 2018
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This work reconstructs a continuous 305-year rainfall record for Ireland. The series reveals remarkable variability in decadal rainfall – far in excess of the typical period of digitised data. Notably, the series sheds light on exceptionally wet winters in the 1730s and wet summers in the 1750s. The derived record, one of the longest continuous series in Europe, offers a firm basis for benchmarking other long-term records and reconstructions of past climate both locally and across Europe.
This article is included in the Encyclopedia of Geosciences
Benoit P. Guillod, Richard G. Jones, Simon J. Dadson, Gemma Coxon, Gianbattista Bussi, James Freer, Alison L. Kay, Neil R. Massey, Sarah N. Sparrow, David C. H. Wallom, Myles R. Allen, and Jim W. Hall
Hydrol. Earth Syst. Sci., 22, 611–634, https://doi.org/10.5194/hess-22-611-2018, https://doi.org/10.5194/hess-22-611-2018, 2018
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Assessing the potential impacts of extreme events such as drought and flood requires large datasets of such events, especially when looking at the most severe and rare events. Using a state-of-the-art climate modelling infrastructure that is simulating large numbers of weather time series on volunteers' computers, we generate such a large dataset for the United Kingdom. The dataset covers the recent past (1900–2006) as well as two future time periods (2030s and 2080s).
This article is included in the Encyclopedia of Geosciences
Huw W. Lewis, Juan Manuel Castillo Sanchez, Jennifer Graham, Andrew Saulter, Jorge Bornemann, Alex Arnold, Joachim Fallmann, Chris Harris, David Pearson, Steven Ramsdale, Alberto Martínez-de la Torre, Lucy Bricheno, Eleanor Blyth, Victoria A. Bell, Helen Davies, Toby R. Marthews, Clare O'Neill, Heather Rumbold, Enda O'Dea, Ashley Brereton, Karen Guihou, Adrian Hines, Momme Butenschon, Simon J. Dadson, Tamzin Palmer, Jason Holt, Nick Reynard, Martin Best, John Edwards, and John Siddorn
Geosci. Model Dev., 11, 1–42, https://doi.org/10.5194/gmd-11-1-2018, https://doi.org/10.5194/gmd-11-1-2018, 2018
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In the real world the atmosphere, oceans and land surface are closely interconnected, and yet prediction systems tend to treat them in isolation. Those feedbacks are often illustrated in natural hazards, such as when strong winds lead to large waves and coastal damage, or when prolonged rainfall leads to saturated ground and high flowing rivers. For the first time, we have attempted to represent some of the feedbacks between sky, sea and land within a high-resolution forecast system for the UK.
This article is included in the Encyclopedia of Geosciences
Fiona J. Clubb, Simon M. Mudd, David T. Milodowski, Declan A. Valters, Louise J. Slater, Martin D. Hurst, and Ajay B. Limaye
Earth Surf. Dynam., 5, 369–385, https://doi.org/10.5194/esurf-5-369-2017, https://doi.org/10.5194/esurf-5-369-2017, 2017
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Floodplains and fluvial terraces can provide information about current and past river systems, helping to reveal how channels respond to changes in both climate and tectonics. We present a new method of identifying these features objectively from digital elevation models by analysing their slope and elevation compared to the modern river. We test our method in eight field sites, and find that it provides rapid and reliable extraction of floodplains and terraces across a range of landscapes.
This article is included in the Encyclopedia of Geosciences
Daniel Green, Dapeng Yu, Ian Pattison, Robert Wilby, Lee Bosher, Ramila Patel, Philip Thompson, Keith Trowell, Julia Draycon, Martin Halse, Lili Yang, and Tim Ryley
Nat. Hazards Earth Syst. Sci., 17, 1–16, https://doi.org/10.5194/nhess-17-1-2017, https://doi.org/10.5194/nhess-17-1-2017, 2017
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This paper demonstrates a novel method of evaluating emergency responder accessibility at the city scale during fluvial and surface water flood events of varying magnitudes. Results suggest that surface water flood events within the city of Leicester, UK, may cause more disruption to emergency responders when compared to fluvial flood events of the same magnitude. This study provides evidence to guide strategic planning for decision makers prior to and during flood events.
This article is included in the Encyclopedia of Geosciences
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.
This article is included in the Encyclopedia of Geosciences
J. Armstrong, R. Wilby, and R. J. Nicholls
Nat. Hazards Earth Syst. Sci., 15, 2511–2524, https://doi.org/10.5194/nhess-15-2511-2015, https://doi.org/10.5194/nhess-15-2511-2015, 2015
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A criterion to categorise climate change adaptation frameworks is presented denoting characteristics of three key frameworks established in the literature: scenario–led, decision-centric and vulnerability–led. Applying the criterion, the usability of frameworks is examined in coastal Suffolk. Results indicate adaptation frameworks established in the literature are not utilised in isolation in everyday practice. In reality, hybrid approaches are utilised to overcome aspects of framework weakness.
This article is included in the Encyclopedia of Geosciences
T. R. Marthews, S. J. Dadson, B. Lehner, S. Abele, and N. Gedney
Hydrol. Earth Syst. Sci., 19, 91–104, https://doi.org/10.5194/hess-19-91-2015, https://doi.org/10.5194/hess-19-91-2015, 2015
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Modelling land surface water flow is of critical importance in the context of climate change predictions. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-known topographic index. Here we present new, higher-resolution parameter maps of the topographic index, which are ideal for land surface modelling applications and show important improvements on the previous standard maps from HYDRO1k.
This article is included in the Encyclopedia of Geosciences
J. Crossman, M. N. Futter, P. G. Whitehead, E. Stainsby, H. M. Baulch, L. Jin, S. K. Oni, R. L. Wilby, and P. J. Dillon
Hydrol. Earth Syst. Sci., 18, 5125–5148, https://doi.org/10.5194/hess-18-5125-2014, https://doi.org/10.5194/hess-18-5125-2014, 2014
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We projected potential hydrochemical responses in four neighbouring catchments to a range of future climates. The highly variable responses in streamflow and total phosphorus (TP) were governed by geology and flow pathways, where larger catchment responses were proportional to greater soil clay content. This suggests clay content might be used as an indicator of catchment sensitivity to climate change, and highlights the need for catchment-specific management plans.
This article is included in the Encyclopedia of Geosciences
B. Merz, J. Aerts, K. Arnbjerg-Nielsen, M. Baldi, A. Becker, A. Bichet, G. Blöschl, L. M. Bouwer, A. Brauer, F. Cioffi, J. M. Delgado, M. Gocht, F. Guzzetti, S. Harrigan, K. Hirschboeck, C. Kilsby, W. Kron, H.-H. Kwon, U. Lall, R. Merz, K. Nissen, P. Salvatti, T. Swierczynski, U. Ulbrich, A. Viglione, P. J. Ward, M. Weiler, B. Wilhelm, and M. Nied
Nat. Hazards Earth Syst. Sci., 14, 1921–1942, https://doi.org/10.5194/nhess-14-1921-2014, https://doi.org/10.5194/nhess-14-1921-2014, 2014
S. Harrigan, C. Murphy, J. Hall, R. L. Wilby, and J. Sweeney
Hydrol. Earth Syst. Sci., 18, 1935–1952, https://doi.org/10.5194/hess-18-1935-2014, https://doi.org/10.5194/hess-18-1935-2014, 2014
R. L. Wilby and D. Yu
Hydrol. Earth Syst. Sci., 17, 3937–3955, https://doi.org/10.5194/hess-17-3937-2013, https://doi.org/10.5194/hess-17-3937-2013, 2013
N. C. MacKellar, S. J. Dadson, M. New, and P. Wolski
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-11093-2013, https://doi.org/10.5194/hessd-10-11093-2013, 2013
Revised manuscript not accepted
Related subject area
Subject: Global hydrology | Techniques and Approaches: Modelling approaches
Drivers of global irrigation expansion: the role of discrete global grid choice
Changes in mean evapotranspiration dominate groundwater recharge in semi-arid regions
Merging modelled and reported flood impacts in Europe in a combined flood event catalogue for 1950–2020
Global-scale evaluation of precipitation datasets for hydrological modelling
Influence of irrigation on root zone storage capacity estimation
River flow in the near future: a global perspective in the context of a high-emission climate change scenario
A high-resolution perspective of extreme rainfall and river flow under extreme climate change in Southeast Asia
Unveiling hydrological dynamics in data-scarce regions: experiences from the Ethiopian Rift Valley Lakes Basin
Technical note: Comparing three different methods for allocating river points to coarse-resolution hydrological modelling grid cells
Representing farmer irrigated crop area adaptation in a large-scale hydrological model
The effect of climate change on the simulated streamflow of six Canadian rivers based on the CanRCM4 regional climate model
Combined impacts of climate and land-use change on future water resources in Africa
Deep learning for quality control of surface physiographic fields using satellite Earth observations
Global dryland aridity changes indicated by atmospheric, hydrological, and vegetation observations at meteorological stations
Root zone soil moisture in over 25 % of global land permanently beyond pre-industrial variability as early as 2050 without climate policy
The benefits and trade-offs of multi-variable calibration of WGHM in the Ganges and Brahmaputra basins
Assessment of pluri-annual and decadal changes in terrestrial water storage predicted by global hydrological models in comparison with the GRACE satellite gravity mission
Improving the quantification of climate change hazards by hydrological models: a simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration
Towards reducing the high cost of parameter sensitivity analysis in hydrologic modeling: a regional parameter sensitivity analysis approach
Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables
Methodology for constructing a flood-hazard map for a future climate
Diagnosing modeling errors in global terrestrial water storage interannual variability
Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1 km over the European continent
Poor correlation between large-scale environmental flow violations and freshwater biodiversity: implications for water resource management and the freshwater planetary boundary
Accuracy of five ground heat flux empirical simulation methods in the surface-energy-balance-based remote-sensing evapotranspiration models
Coupling a global glacier model to a global hydrological model prevents underestimation of glacier runoff
Revisiting large-scale interception patterns constrained by a synthesis of global experimental data
Investigating coastal backwater effects and flooding in the coastal zone using a global river transport model on an unstructured mesh
Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States
Quantifying overlapping and differing information of global precipitation for GCM forecasts and El Niño–Southern Oscillation
Globally widespread and increasing violations of environmental flow envelopes
Inundation prediction in tropical wetlands from JULES-CaMa-Flood global land surface simulations
Soil moisture estimation in South Asia via assimilation of SMAP retrievals
Toward hyper-resolution global hydrological models including human activities: application to Kyushu island, Japan
Towards hybrid modeling of the global hydrological cycle
The importance of vegetation in understanding terrestrial water storage variations
Large-scale sensitivities of groundwater and surface water to groundwater withdrawal
A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models
A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods
Bright and blind spots of water research in Latin America and the Caribbean
Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters
Robust historical evapotranspiration trends across climate regimes
A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling
Global scenarios of irrigation water abstractions for bioenergy production: a systematic review
Coordination and control – limits in standard representations of multi-reservoir operations in hydrological modeling
Uncertainty of simulated groundwater recharge at different global warming levels: a global-scale multi-model ensemble study
Ubiquitous increases in flood magnitude in the Columbia River basin under climate change
Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors
The role of household adaptation measures in reducing vulnerability to flooding: a coupled agent-based and flood modelling approach
Assessing global water mass transfers from continents to oceans over the period 1948–2016
Sophie Wagner, Fabian Stenzel, Tobias Krueger, and Jana de Wiljes
Hydrol. Earth Syst. Sci., 28, 5049–5068, https://doi.org/10.5194/hess-28-5049-2024, https://doi.org/10.5194/hess-28-5049-2024, 2024
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Statistical models that explain global irrigation rely on location-referenced data. Traditionally, a system based on longitude and latitude lines is chosen. However, this introduces bias to the analysis due to the Earth's curvature. We propose using a system based on hexagonal grid cells that allows for distortion-free representation of the data. We show that this increases the model's accuracy by 28 % and identify biophysical and socioeconomic drivers of historical global irrigation expansion.
This article is included in the Encyclopedia of Geosciences
Tuvia Turkeltaub and Golan Bel
Hydrol. Earth Syst. Sci., 28, 4263–4274, https://doi.org/10.5194/hess-28-4263-2024, https://doi.org/10.5194/hess-28-4263-2024, 2024
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Future climate projections suggest that climate change will impact groundwater recharge, with its exact effects being uncertain due to incomplete understanding of rainfall, evapotranspiration, and recharge relations. We studied the effects of changes in the average, spread, and frequency of extreme events of rainfall and evapotranspiration on groundwater recharge. We found that increasing or decreasing the potential evaporation has the most dominant effect on groundwater recharge.
This article is included in the Encyclopedia of Geosciences
Dominik Paprotny, Belinda Rhein, Michalis I. Vousdoukas, Paweł Terefenko, Francesco Dottori, Simon Treu, Jakub Śledziowski, Luc Feyen, and Heidi Kreibich
Hydrol. Earth Syst. Sci., 28, 3983–4010, https://doi.org/10.5194/hess-28-3983-2024, https://doi.org/10.5194/hess-28-3983-2024, 2024
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Long-term trends in flood losses are regulated by multiple factors, including climate variation, population and economic growth, land-use transitions, reservoir construction, and flood risk reduction measures. Here, we reconstruct the factual circumstances in which almost 15 000 potential riverine, coastal and compound floods in Europe occurred between 1950 and 2020. About 10 % of those events are reported to have caused significant socioeconomic impacts.
This article is included in the Encyclopedia of Geosciences
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
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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 article is included in the Encyclopedia of Geosciences
Fransje van Oorschot, Ruud J. van der Ent, Andrea Alessandri, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 2313–2328, https://doi.org/10.5194/hess-28-2313-2024, https://doi.org/10.5194/hess-28-2313-2024, 2024
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Vegetation plays a crucial role in regulating the water cycle by transporting water from the subsurface to the atmosphere via roots; this transport depends on the extent of the root system. In this study, we quantified the effect of irrigation on roots at a global scale. Our results emphasize the importance of accounting for irrigation in estimating the vegetation root extent, which is essential to adequately represent the water cycle in hydrological and climate models.
This article is included in the Encyclopedia of Geosciences
Omar V. Müller, Patrick C. McGuire, Pier Luigi Vidale, and Ed Hawkins
Hydrol. Earth Syst. Sci., 28, 2179–2201, https://doi.org/10.5194/hess-28-2179-2024, https://doi.org/10.5194/hess-28-2179-2024, 2024
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This work evaluates how rivers are projected to change in the near future compared to the recent past in the context of a warming world. We show that important rivers of the world will notably change their flows, mainly during peaks, exceeding the variations that rivers used to exhibit. Such large changes may produce more frequent floods, alter hydropower generation, and potentially affect the ocean's circulation.
This article is included in the Encyclopedia of Geosciences
Mugni Hadi Hariadi, Gerard van der Schrier, Gert-Jan Steeneveld, Samuel J. Sutanto, Edwin Sutanudjaja, Dian Nur Ratri, Ardhasena Sopaheluwakan, and Albert Klein Tank
Hydrol. Earth Syst. Sci., 28, 1935–1956, https://doi.org/10.5194/hess-28-1935-2024, https://doi.org/10.5194/hess-28-1935-2024, 2024
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We utilize the high-resolution CMIP6 for extreme rainfall and streamflow projection over Southeast Asia. This region will experience an increase in both dry and wet extremes in the near future. We found a more extreme low flow and high flow, along with an increasing probability of low-flow and high-flow events. We reveal that the changes in low-flow events and their probabilities are not only influenced by extremely dry climates but also by the catchment characteristics.
This article is included in the Encyclopedia of Geosciences
Ayenew D. Ayalew, Paul D. Wagner, Dejene Sahlu, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 28, 1853–1872, https://doi.org/10.5194/hess-28-1853-2024, https://doi.org/10.5194/hess-28-1853-2024, 2024
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The study presents a pioneering comprehensive integrated approach to unravel hydrological complexities in data-scarce regions. By integrating diverse data sources and advanced analytics, we offer a holistic understanding of water systems, unveiling hidden patterns and driving factors. This innovative method holds immense promise for informed decision-making and sustainable water resource management, addressing a critical need in hydrological science.
This article is included in the Encyclopedia of Geosciences
Juliette Godet, Eric Gaume, Pierre Javelle, Pierre Nicolle, and Olivier Payrastre
Hydrol. Earth Syst. Sci., 28, 1403–1413, https://doi.org/10.5194/hess-28-1403-2024, https://doi.org/10.5194/hess-28-1403-2024, 2024
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This work was performed in order to precisely address a point that is often neglected by hydrologists: the allocation of points located on a river network to grid cells, which is often a mandatory step for hydrological modelling.
This article is included in the Encyclopedia of Geosciences
Jim Yoon, Nathalie Voisin, Christian Klassert, Travis Thurber, and Wenwei Xu
Hydrol. Earth Syst. Sci., 28, 899–916, https://doi.org/10.5194/hess-28-899-2024, https://doi.org/10.5194/hess-28-899-2024, 2024
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Global and regional models used to evaluate water shortages typically neglect the possibility that irrigated crop areas may change in response to future hydrological conditions, such as the fallowing of crops in response to drought. Here, we enhance a model used for water shortage analysis with farmer agents that dynamically adapt their irrigated crop areas based on simulated hydrological conditions. Results indicate that such cropping adaptation can strongly alter simulated water shortages.
This article is included in the Encyclopedia of Geosciences
Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha
EGUsphere, https://doi.org/10.5194/egusphere-2024-182, https://doi.org/10.5194/egusphere-2024-182, 2024
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This study is likely the first Canada-wide assessment of climate change impact on the hydro-climatology of its major river basins. It finds that the precipitation, runoff, and temperature are all expected to increase over Canada in the future. The northerly Mackenzie and Yukon Rivers are relatively less affected by climate change compared to the southerly Fraser and Columbia Rivers which are located in the milder Pacific north-western region.
This article is included in the Encyclopedia of Geosciences
Celray James Chawanda, Albert Nkwasa, Wim Thiery, and Ann van Griensven
Hydrol. Earth Syst. Sci., 28, 117–138, https://doi.org/10.5194/hess-28-117-2024, https://doi.org/10.5194/hess-28-117-2024, 2024
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Africa's water resources are being negatively impacted by climate change and land-use change. The SWAT+ hydrological model was used to simulate the hydrological cycle in Africa, and results show likely decreases in river flows in the Zambezi and Congo rivers and highest flows in the Niger River basins due to climate change. Land cover change had the biggest impact in the Congo River basin, emphasizing the importance of including land-use change in studies.
This article is included in the Encyclopedia of Geosciences
Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, and Tim Palmer
Hydrol. Earth Syst. Sci., 27, 4661–4685, https://doi.org/10.5194/hess-27-4661-2023, https://doi.org/10.5194/hess-27-4661-2023, 2023
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Lakes play an important role when we try to explain and predict the weather. More accurate and up-to-date description of lakes all around the world for numerical models is a continuous task. However, it is difficult to assess the impact of updated lake description within a weather prediction system. In this work, we develop a method to quickly and automatically define how, where, and when updated lake description affects weather prediction.
This article is included in the Encyclopedia of Geosciences
Haiyang Shi, Geping Luo, Olaf Hellwich, Xiufeng He, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Hydrol. Earth Syst. Sci., 27, 4551–4562, https://doi.org/10.5194/hess-27-4551-2023, https://doi.org/10.5194/hess-27-4551-2023, 2023
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Using evidence from meteorological stations, this study assessed the climatic, hydrological, and ecological aridity changes in global drylands and their associated mechanisms. A decoupling between atmospheric, hydrological, and vegetation aridity was found. This highlights the added value of using station-scale data to assess dryland change as a complement to results based on coarse-resolution reanalysis data and land surface models.
This article is included in the Encyclopedia of Geosciences
En Ning Lai, Lan Wang-Erlandsson, Vili Virkki, Miina Porkka, and Ruud J. van der Ent
Hydrol. Earth Syst. Sci., 27, 3999–4018, https://doi.org/10.5194/hess-27-3999-2023, https://doi.org/10.5194/hess-27-3999-2023, 2023
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This research scrutinized predicted changes in root zone soil moisture dynamics across different climate scenarios and different climate regions globally between 2021 and 2100. The Mediterranean and most of South America stood out as regions that will likely experience permanently drier conditions, with greater severity observed in the no-climate-policy scenarios. These findings underscore the impact that possible future climates can have on green water resources.
This article is included in the Encyclopedia of Geosciences
H. M. Mehedi Hasan, Petra Döll, Seyed-Mohammad Hosseini-Moghari, Fabrice Papa, and Andreas Güntner
EGUsphere, https://doi.org/10.5194/egusphere-2023-2324, https://doi.org/10.5194/egusphere-2023-2324, 2023
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We calibrate a global hydrological model using multiple observations to analyse the benefits and trade-offs of multi-variable calibration. We found such an approach to be very important for understanding the real-world system. However, some observations are very essential to the system, in particular streamflow. We also showed uncertainties in the calibration results, which is often useful for making informed decisions. We emphasis to consider observation uncertainty in model calibration.
This article is included in the Encyclopedia of Geosciences
Julia Pfeffer, Anny Cazenave, Alejandro Blazquez, Bertrand Decharme, Simon Munier, and Anne Barnoud
Hydrol. Earth Syst. Sci., 27, 3743–3768, https://doi.org/10.5194/hess-27-3743-2023, https://doi.org/10.5194/hess-27-3743-2023, 2023
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The GRACE (Gravity Recovery And Climate Experiment) satellite mission enabled the quantification of water mass redistributions from 2002 to 2017. The analysis of GRACE satellite data shows here that slow changes in terrestrial water storage occurring over a few years to a decade are severely underestimated by global hydrological models. Several sources of errors may explain such biases, likely including the inaccurate representation of groundwater storage changes.
This article is included in the Encyclopedia of Geosciences
Thedini Asali Peiris and Petra Döll
Hydrol. Earth Syst. Sci., 27, 3663–3686, https://doi.org/10.5194/hess-27-3663-2023, https://doi.org/10.5194/hess-27-3663-2023, 2023
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Hydrological models often overlook vegetation's response to CO2 and climate, impairing their ability to forecast impacts on evapotranspiration and water resources. To address this, we suggest involving two model variants: (1) the standard method and (2) a modified approach (proposed here) based on the Priestley–Taylor equation (PT-MA). While not universally applicable, a dual approach helps consider uncertainties related to vegetation responses to climate change, enhancing model representation.
This article is included in the Encyclopedia of Geosciences
Samah Larabi, Juliane Mai, Markus Schnorbus, Bryan A. Tolson, and Francis Zwiers
Hydrol. Earth Syst. Sci., 27, 3241–3263, https://doi.org/10.5194/hess-27-3241-2023, https://doi.org/10.5194/hess-27-3241-2023, 2023
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The computational cost of sensitivity analysis (SA) becomes prohibitive for large hydrologic modeling domains. Here, using a large-scale Variable Infiltration Capacity (VIC) deployment, we show that watershed classification helps identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. Findings reveal the opportunity to leverage climate and land cover attributes to reduce the cost of SA and facilitate more rapid deployment of large-scale land surface models.
This article is included in the Encyclopedia of Geosciences
Tanja Denager, Torben O. Sonnenborg, Majken C. Looms, Heye Bogena, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 27, 2827–2845, https://doi.org/10.5194/hess-27-2827-2023, https://doi.org/10.5194/hess-27-2827-2023, 2023
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This study contributes to improvements in the model characterization of water and energy fluxes. The results show that multi-objective autocalibration in combination with mathematical regularization is a powerful tool to improve land surface models. Using the direct measurement of turbulent fluxes as the target variable, parameter optimization matches simulations and observations of latent heat, whereas sensible heat is clearly biased.
This article is included in the Encyclopedia of Geosciences
Yuki Kimura, Yukiko Hirabayashi, Yuki Kita, Xudong Zhou, and Dai Yamazaki
Hydrol. Earth Syst. Sci., 27, 1627–1644, https://doi.org/10.5194/hess-27-1627-2023, https://doi.org/10.5194/hess-27-1627-2023, 2023
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Since both the frequency and magnitude of flood will increase by climate change, information on spatial distributions of potential inundation depths (i.e., flood-hazard map) is required. We developed a method for constructing realistic future flood-hazard maps which addresses issues due to biases in climate models. A larger population is estimated to face risk in the future flood-hazard map, suggesting that only focusing on flood-frequency change could cause underestimation of future risk.
This article is included in the Encyclopedia of Geosciences
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
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We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
This article is included in the Encyclopedia of Geosciences
Jannis M. Hoch, Edwin H. Sutanudjaja, Niko Wanders, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 27, 1383–1401, https://doi.org/10.5194/hess-27-1383-2023, https://doi.org/10.5194/hess-27-1383-2023, 2023
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To facilitate locally relevant simulations over large areas, global hydrological models (GHMs) have moved towards ever finer spatial resolutions. After a decade-long quest for hyper-resolution (i.e. equal to or smaller than 1 km), the presented work is a first application of a GHM at 1 km resolution over Europe. This not only shows that hyper-resolution can be achieved but also allows for a thorough evaluation of model results at unprecedented detail and the formulation of future research.
This article is included in the Encyclopedia of Geosciences
Chinchu Mohan, Tom Gleeson, James S. Famiglietti, Vili Virkki, Matti Kummu, Miina Porkka, Lan Wang-Erlandsson, Xander Huggins, Dieter Gerten, and Sonja C. Jähnig
Hydrol. Earth Syst. Sci., 26, 6247–6262, https://doi.org/10.5194/hess-26-6247-2022, https://doi.org/10.5194/hess-26-6247-2022, 2022
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The relationship between environmental flow violations and freshwater biodiversity at a large scale is not well explored. This study intended to carry out an exploratory evaluation of this relationship at a large scale. While our results suggest that streamflow and EF may not be the only determinants of freshwater biodiversity at large scales, they do not preclude the existence of relationships at smaller scales or with more holistic EF methods or with other biodiversity data or metrics.
This article is included in the Encyclopedia of Geosciences
Zhaofei Liu
Hydrol. Earth Syst. Sci., 26, 6207–6226, https://doi.org/10.5194/hess-26-6207-2022, https://doi.org/10.5194/hess-26-6207-2022, 2022
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Ground heat flux (G) accounts for a significant fraction of the surface energy balance (SEB), but there is insufficient research on these models compared with other flux. The accuracy of G simulation methods in the SEB-based remote sensing evapotranspiration models is evaluated. Results show that the accuracy of each method varied significantly at different sites and at half-hour intervals. Further improvement of G simulations is recommended for the remote sensing evapotranspiration modelers.
This article is included in the Encyclopedia of Geosciences
Pau Wiersma, Jerom Aerts, Harry Zekollari, Markus Hrachowitz, Niels Drost, Matthias Huss, Edwin H. Sutanudjaja, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 5971–5986, https://doi.org/10.5194/hess-26-5971-2022, https://doi.org/10.5194/hess-26-5971-2022, 2022
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We test whether coupling a global glacier model (GloGEM) with a global hydrological model (PCR-GLOBWB 2) leads to a more realistic glacier representation and to improved basin runoff simulations across 25 large-scale basins. The coupling does lead to improved glacier representation, mainly by accounting for glacier flow and net glacier mass loss, and to improved basin runoff simulations, mostly in strongly glacier-influenced basins, which is where the coupling has the most impact.
This article is included in the Encyclopedia of Geosciences
Feng Zhong, Shanhu Jiang, Albert I. J. M. van Dijk, Liliang Ren, Jaap Schellekens, and Diego G. Miralles
Hydrol. Earth Syst. Sci., 26, 5647–5667, https://doi.org/10.5194/hess-26-5647-2022, https://doi.org/10.5194/hess-26-5647-2022, 2022
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A synthesis of rainfall interception data from past field campaigns is performed, including 166 forests and 17 agricultural plots distributed worldwide. These site data are used to constrain and validate an interception model that considers sub-grid heterogeneity and vegetation dynamics. A global, 40-year (1980–2019) interception dataset is generated at a daily temporal and 0.1° spatial resolution. This dataset will serve as a benchmark for future investigations of the global hydrological cycle.
This article is included in the Encyclopedia of Geosciences
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
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Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
This article is included in the Encyclopedia of Geosciences
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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In this study, we use three models to forecast river streamflow operationally for 13 months (September 2020 to October 2021) at 10 gauges in the western US. The first model is a state-of-the-art physics-based streamflow model (GloFAS). The second applies a bias-correction technique to GloFAS. The third is a type of neural network (an LSTM). We find that all three are capable of producing skilful forecasts but that the LSTM performs the best, with skilful 5 d forecasts at nine stations.
This article is included in the Encyclopedia of Geosciences
Tongtiegang Zhao, Haoling Chen, Yu Tian, Denghua Yan, Weixin Xu, Huayang Cai, Jiabiao Wang, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 26, 4233–4249, https://doi.org/10.5194/hess-26-4233-2022, https://doi.org/10.5194/hess-26-4233-2022, 2022
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This paper develops a novel set operations of coefficients of determination (SOCD) method to explicitly quantify the overlapping and differing information for GCM forecasts and ENSO teleconnection. Specifically, the intersection operation of the coefficient of determination derives the overlapping information for GCM forecasts and the Niño3.4 index, and then the difference operation determines the differing information in GCM forecasts (Niño3.4 index) from the Niño3.4 index (GCM forecasts).
This article is included in the Encyclopedia of Geosciences
Vili Virkki, Elina Alanärä, Miina Porkka, Lauri Ahopelto, Tom Gleeson, Chinchu Mohan, Lan Wang-Erlandsson, Martina Flörke, Dieter Gerten, Simon N. Gosling, Naota Hanasaki, Hannes Müller Schmied, Niko Wanders, and Matti Kummu
Hydrol. Earth Syst. Sci., 26, 3315–3336, https://doi.org/10.5194/hess-26-3315-2022, https://doi.org/10.5194/hess-26-3315-2022, 2022
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Direct and indirect human actions have altered streamflow across the world since pre-industrial times. Here, we apply a method of environmental flow envelopes (EFEs) that develops the existing global environmental flow assessments by methodological advances and better consideration of uncertainty. By assessing the violations of the EFE, we comprehensively quantify the frequency, severity, and trends of flow alteration during the past decades, illustrating anthropogenic effects on streamflow.
This article is included in the Encyclopedia of Geosciences
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
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Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
This article is included in the Encyclopedia of Geosciences
Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar
Hydrol. Earth Syst. Sci., 26, 2221–2243, https://doi.org/10.5194/hess-26-2221-2022, https://doi.org/10.5194/hess-26-2221-2022, 2022
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Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.
This article is included in the Encyclopedia of Geosciences
Naota Hanasaki, Hikari Matsuda, Masashi Fujiwara, Yukiko Hirabayashi, Shinta Seto, Shinjiro Kanae, and Taikan Oki
Hydrol. Earth Syst. Sci., 26, 1953–1975, https://doi.org/10.5194/hess-26-1953-2022, https://doi.org/10.5194/hess-26-1953-2022, 2022
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Global hydrological models (GHMs) are usually applied with a spatial resolution of about 50 km, but this time we applied the H08 model, one of the most advanced GHMs, with a high resolution of 2 km to Kyushu island, Japan. Since the model was not accurate as it was, we incorporated local information and improved the model, which revealed detailed water stress in subregions that were not visible with the previous resolution.
This article is included in the Encyclopedia of Geosciences
Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein
Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, https://doi.org/10.5194/hess-26-1579-2022, 2022
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We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
This article is included in the Encyclopedia of Geosciences
Tina Trautmann, Sujan Koirala, Nuno Carvalhais, Andreas Güntner, and Martin Jung
Hydrol. Earth Syst. Sci., 26, 1089–1109, https://doi.org/10.5194/hess-26-1089-2022, https://doi.org/10.5194/hess-26-1089-2022, 2022
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We assess the effect of how vegetation is defined in a global hydrological model on the composition of total water storage (TWS). We compare two experiments, one with globally uniform and one with vegetation parameters that vary in space and time. While both experiments are constrained against observational data, we found a drastic change in the partitioning of TWS, highlighting the important role of the interaction between groundwater–soil moisture–vegetation in understanding TWS variations.
This article is included in the Encyclopedia of Geosciences
Marc F. P. Bierkens, Edwin H. Sutanudjaja, and Niko Wanders
Hydrol. Earth Syst. Sci., 25, 5859–5878, https://doi.org/10.5194/hess-25-5859-2021, https://doi.org/10.5194/hess-25-5859-2021, 2021
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We introduce a simple analytical framework that allows us to estimate to what extent large-scale groundwater withdrawal affects groundwater levels and streamflow. It also calculates which part of the groundwater withdrawal comes out of groundwater storage and which part from a reduction in streamflow. Global depletion rates obtained with the framework are compared with estimates from satellites, from global- and continental-scale groundwater models, and from in situ datasets.
This article is included in the Encyclopedia of Geosciences
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
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Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
This article is included in the Encyclopedia of Geosciences
Jérôme Kopp, Pauline Rivoire, S. Mubashshir Ali, Yannick Barton, and Olivia Martius
Hydrol. Earth Syst. Sci., 25, 5153–5174, https://doi.org/10.5194/hess-25-5153-2021, https://doi.org/10.5194/hess-25-5153-2021, 2021
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Episodes of extreme rainfall events happening in close temporal succession can lead to floods with dramatic impacts. We developed a novel method to individually identify those episodes and deduced the regions where they occur frequently and where their impact is substantial. Those regions are the east and northeast of the Asian continent, central Canada and the south of California, Afghanistan, Pakistan, the southwest of the Iberian Peninsula, and north of Argentina and south of Bolivia.
This article is included in the Encyclopedia of Geosciences
Alyssa J. DeVincentis, Hervé Guillon, Romina Díaz Gómez, Noelle K. Patterson, Francine van den Brandeler, Arthur Koehl, J. Pablo Ortiz-Partida, Laura E. Garza-Díaz, Jennifer Gamez-Rodríguez, Erfan Goharian, and Samuel Sandoval Solis
Hydrol. Earth Syst. Sci., 25, 4631–4650, https://doi.org/10.5194/hess-25-4631-2021, https://doi.org/10.5194/hess-25-4631-2021, 2021
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Latin America and the Caribbean face many water-related stresses which are expected to worsen with climate change. To assess the vulnerability, we reviewed over 20 000 multilingual research articles using machine learning and an understanding of the regional landscape. Results reveal that the region’s inherent vulnerability is compounded by research blind spots in niche topics (reservoirs and risk assessment) and subregions (Caribbean nations), as well as by its reliance on one country (Brazil).
This article is included in the Encyclopedia of Geosciences
Michiel Maertens, Gabriëlle J. M. De Lannoy, Sebastian Apers, Sujay V. Kumar, and Sarith P. P. Mahanama
Hydrol. Earth Syst. Sci., 25, 4099–4125, https://doi.org/10.5194/hess-25-4099-2021, https://doi.org/10.5194/hess-25-4099-2021, 2021
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In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.
This article is included in the Encyclopedia of Geosciences
Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
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Evapotranspiration (ET) links the water, energy and carbon cycle on land. Reliable ET estimates are key to understand droughts and flooding. We develop a new ET dataset, DOLCE V3, by merging multiple global ET datasets, and we show that it matches ET observations better and hence is more reliable than its parent datasets. Next, we use DOLCE V3 to examine recent changes in ET and find that ET has increased over most of the land, decreased in some regions, and has not changed in some other regions
This article is included in the Encyclopedia of Geosciences
Frederik Kratzert, Daniel Klotz, Sepp Hochreiter, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 25, 2685–2703, https://doi.org/10.5194/hess-25-2685-2021, https://doi.org/10.5194/hess-25-2685-2021, 2021
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We investigate how deep learning models use different meteorological data sets in the task of (regional) rainfall–runoff modeling. We show that performance can be significantly improved when using different data products as input and further show how the model learns to combine those meteorological input differently across time and space. The results are carefully benchmarked against classical approaches, showing the supremacy of the presented approach.
This article is included in the Encyclopedia of Geosciences
Fabian Stenzel, Dieter Gerten, and Naota Hanasaki
Hydrol. Earth Syst. Sci., 25, 1711–1726, https://doi.org/10.5194/hess-25-1711-2021, https://doi.org/10.5194/hess-25-1711-2021, 2021
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Ideas to mitigate climate change include the large-scale cultivation of fast-growing plants to capture atmospheric CO2 in biomass. To maximize the productivity of these plants, they will likely be irrigated. However, there is strong disagreement in the literature on how much irrigation water is needed globally, potentially inducing water stress. We provide a comprehensive overview of global irrigation demand studies for biomass production and discuss the diverse underlying study assumptions.
This article is included in the Encyclopedia of Geosciences
Charles Rougé, Patrick M. Reed, Danielle S. Grogan, Shan Zuidema, Alexander Prusevich, Stanley Glidden, Jonathan R. Lamontagne, and Richard B. Lammers
Hydrol. Earth Syst. Sci., 25, 1365–1388, https://doi.org/10.5194/hess-25-1365-2021, https://doi.org/10.5194/hess-25-1365-2021, 2021
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Amid growing interest in using large-scale hydrological models for flood and drought monitoring and forecasting, it is important to evaluate common assumptions these models make. We investigated the representation of reservoirs as separate (non-coordinated) infrastructure. We found that not appropriately representing coordination and control processes can lead a hydrological model to simulate flood and drought events that would not occur given the coordinated emergency response in the basin.
This article is included in the Encyclopedia of Geosciences
Robert Reinecke, Hannes Müller Schmied, Tim Trautmann, Lauren Seaby Andersen, Peter Burek, Martina Flörke, Simon N. Gosling, Manolis Grillakis, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Wim Thiery, Yoshihide Wada, Satoh Yusuke, and Petra Döll
Hydrol. Earth Syst. Sci., 25, 787–810, https://doi.org/10.5194/hess-25-787-2021, https://doi.org/10.5194/hess-25-787-2021, 2021
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Billions of people rely on groundwater as an accessible source of drinking water and for irrigation, especially in times of drought. Groundwater recharge is the primary process of regenerating groundwater resources. We find that groundwater recharge will increase in northern Europe by about 19 % and decrease by 10 % in the Amazon with 3 °C global warming. In the Mediterranean, a 2 °C warming has already lead to a reduction in recharge by 38 %. However, these model predictions are uncertain.
This article is included in the Encyclopedia of Geosciences
Laura E. Queen, Philip W. Mote, David E. Rupp, Oriana Chegwidden, and Bart Nijssen
Hydrol. Earth Syst. Sci., 25, 257–272, https://doi.org/10.5194/hess-25-257-2021, https://doi.org/10.5194/hess-25-257-2021, 2021
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Using a large ensemble of simulated flows throughout the northwestern USA, we compare daily flood statistics in the past (1950–1999) and future (2050–1999) periods and find that nearly all locations will experience an increase in flood magnitudes. The flood season expands significantly in many currently snow-dominant rivers, moving from only spring to both winter and spring. These results, properly extended, may help inform flood risk management and negotiations of the Columbia River Treaty.
This article is included in the Encyclopedia of Geosciences
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
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We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
This article is included in the Encyclopedia of Geosciences
Yared Abayneh Abebe, Amineh Ghorbani, Igor Nikolic, Natasa Manojlovic, Angelika Gruhn, and Zoran Vojinovic
Hydrol. Earth Syst. Sci., 24, 5329–5354, https://doi.org/10.5194/hess-24-5329-2020, https://doi.org/10.5194/hess-24-5329-2020, 2020
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The paper presents a coupled agent-based and flood model for Hamburg, Germany. It explores residents’ adaptation behaviour in relation to flood event scenarios, economic incentives and shared and individual strategies. We found that unique trajectories of adaptation behaviour emerge from different flood event series. Providing subsidies improves adaptation behaviour in the long run. The coupled modelling technique allows the role of individual measures in flood risk management to be examined.
This article is included in the Encyclopedia of Geosciences
Denise Cáceres, Ben Marzeion, Jan Hendrik Malles, Benjamin Daniel Gutknecht, Hannes Müller Schmied, and Petra Döll
Hydrol. Earth Syst. Sci., 24, 4831–4851, https://doi.org/10.5194/hess-24-4831-2020, https://doi.org/10.5194/hess-24-4831-2020, 2020
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We analysed how and to which extent changes in water storage on continents had an effect on global ocean mass over the period 1948–2016. Continents lost water to oceans at an accelerated rate, inducing sea level rise. Shrinking glaciers explain 81 % of the long-term continental water mass loss, while declining groundwater levels, mainly due to sustained groundwater pumping for irrigation, is the second major driver. This long-term decline was partly offset by the impoundment of water in dams.
This article is included in the Encyclopedia of Geosciences
Cited articles
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and Mendoza,
P. A.: Large-sample hydrology: recent progress, guidelines for new datasets
and grand challenges, Hydrolog. Sci. J., 65, 712–725, 2020. a
AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I.,
Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M., Ragno, E., and Sadegh, M.:
Climate Extremes and Compound Hazards in a Warming World,
Annu. Rev. Earth Pl. Sc., 48, 519–548,
https://doi.org/10.1146/annurev-earth-071719-055228, 2020. a, b, c
Aguilar, E., Auer, I., Brunet, M., Peterson, T. C., and Wieringa, J.: Guidance
on metadata and homogenization, Wmo Td, 1186, 1–53, 2003. a
Aguilar, E., Peterson, T. C., Obando, P. R., Frutos, R., Retana, J. A., Solera, M., Soley, J., García, I. G., Araujo, R. M., Santos, A. R., and Valle, V. E: Changes
in precipitation and temperature extremes in Central America and northern
South America, 1961–2003, J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2005JD006119, 2005. a
Aguilera, H., Guardiola-Albert, C., Naranjo-Fernández, N., and Kohfahl, C.:
Towards flexible groundwater-level prediction for adaptive water management:
using Facebook’s Prophet forecasting approach, Hydrolog. Sci. J., 64, 1504–1518, https://doi.org/10.1080/02626667.2019.1651933, 2019. a
Ali, H., Fowler, H. J., and Mishra, V.: Global observational evidence of strong
linkage between dew point temperature and precipitation extremes, Geophys. Res. Lett., 45, 12–320, 2018. a
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018. a
Anagnostopoulou, C. and Tolika, K.: Extreme precipitation in Europe:
statistical threshold selection based on climatological criteria, Theor. Appl. Climatol., 107, 479–489, 2012. a
Andreadis, K. M. and Lettenmaier, D. P.: Trends in 20th century drought over
the continental United States, Geophys. Res. Lett., 33, https://doi.org/10.1029/2006GL025711, 2006. a
Archfield, S. A., Hirsch, R. M., Viglione, A., and Blöschl, G.: Fragmented
patterns of flood change across the United States, Geophys. Res. Lett., 43, 10–232, 2016. a
Asian Development Bank: Climate change adjustments for detailed engineering
design of roads: Experience from Viet Nam, Knowledge Product, Asian Development Bank, Mandaluyong City 1550, Philippines, https://doi.org/10.22617/TIM200148-2, 2020. a
Avissar, R. and Werth, D.: Global hydroclimatological teleconnections resulting
from tropical deforestation, J. Hydrometeorol., 6, 134–145, 2005. a
Bao, J., Sherwood, S. C., Alexander, L. V., and Evans, J. P.: Future increases
in extreme precipitation exceed observed scaling rates,
Nat. Clim. Change, 7, 128–132, 2017. a
Barbosa, S. M., Scotto, M. G., and Alonso, A. M.: Summarising changes in air temperature over Central Europe by quantile regression and clustering, Nat. Hazards Earth Syst. Sci., 11, 3227–3233, https://doi.org/10.5194/nhess-11-3227-2011, 2011. a
Barkhordarian, A., von Storch, H., and Bhend, J.: The expectation of future
precipitation change over the Mediterranean region is different from what we
observe, Clim. Dynam., 40, 225–244, 2013. a
Bathurst, J. C., Fahey, B., Iroumé, A., and Jones, J.: Forests and floods:
using field evidence to reconcile analysis methods, Hydrol. Process., 34, 3295–3310, 2020. a
Bazrafshan, J. and Hejabi, S.: A non-stationary reconnaissance drought index
(NRDI) for drought monitoring in a changing climate,
Water Resour. Manage., 32, 2611–2624, 2018. a
Befort, D. J., Wild, S., Kruschke, T., Ulbrich, U., and Leckebusch, G. C.:
Different long-term trends of extra-tropical cyclones and windstorms in
ERA-20C and NOAA-20CR reanalyses, Atmos. Sci. Lett., 17, 586–595,
https://doi.org/10.1002/asl.694, 2016. a
Bell, V., Davies, H., Kay, A., Marsh, T., Brookshaw, A., and Jenkins, A.:
Developing a large-scale water-balance approach to seasonal forecasting:
application to the 2012 drought in Britain, Hydrol. Process., 27,
3003–3012, 2013. a
Benoit, L., Vrac, M., and Mariethoz, G.: Dealing with non-stationarity in sub-daily stochastic rainfall models, Hydrol. Earth Syst. Sci., 22, 5919–5933, https://doi.org/10.5194/hess-22-5919-2018, 2018. a
Benoit, L., Vrac, M., and Mariethoz, G.: Nonstationary stochastic rain type generation: accounting for climate drivers, Hydrol. Earth Syst. Sci., 24, 2841–2854, https://doi.org/10.5194/hess-24-2841-2020, 2020. a
Berg, A., Findell, K., Lintner, B., Giannini, A., Seneviratne, S. I., Van Den Hurk, B., Lorenz, R., Pitman, A., Hagemann, S., Meier, A., and Cheruy, F:
Land–atmosphere feedbacks amplify aridity increase over land under global
warming, Nat. Clim. Change, 6, 869–874, 2016. a
Berg, P., Moseley, C., and Haerter, J. O.: Strong increase in convective
precipitation in response to higher temperatures, Nat. Geosci., 6,
181–185, 2013. a
Berghuijs, W., Woods, R., and Hrachowitz, M.: A precipitation shift from snow
towards rain leads to a decrease in streamflow, Nat. Clim. Change, 4,
583–586, 2014. a
Berghuijs, W. R., Aalbers, E. E., Larsen, J. R., Trancoso, R., and Woods,
R. A.: Recent changes in extreme floods across multiple continents,
Environ. Res. Lett., 12, 114035, https://doi.org/10.1088/1748-9326/aa8847, 2017. a
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J. W.:
The relative importance of different flood-generating mechanisms across
Europe, Water Resour. Res., 55, 4582–4593, 2019b. a
Bertola, M., Viglione, A., Vorogushyn, S., Lun, D., Merz, B., and Blöschl, G.: Do small and large floods have the same drivers of change? A regional attribution analysis in Europe, Hydrol. Earth Syst. Sci., 25, 1347–1364, https://doi.org/10.5194/hess-25-1347-2021, 2021. a
Bhattarai, K. and O'Connor, K.: The effects over time of an arterial drainage
scheme on the rainfall-runoff transformation in the Brosna catchment, Phys. Chem. Earth Pt. A/B/C, 29, 787–794, 2004. a
Blöschl, G., Hall, J., Viglione, A., Perdigão, R. A., Parajka, J., Merz, B., Lun, D., Arheimer, B., Aronica, G. T., Bilibashi, A., and Boháč, M:
Changing climate both increases and decreases European river floods, Nature,
573, 108–111, 2019. a
Blöschl, G., Kiss, A., Viglione, A., Barriendos, M., Böhm, O., Brázdil, R., Coeur, D., Demarée, G., Llasat, M. C., Macdonald, N., and Retsö, D: Current European flood-rich period exceptional compared with past 500
years, Nature, 583, 560–566, 2020. a
Bonnet, R., Boé, J., and Habets, F.: Influence of multidecadal variability on high and low flows: the case of the Seine basin, Hydrol. Earth Syst. Sci., 24, 1611–1631, https://doi.org/10.5194/hess-24-1611-2020, 2020. a
Broderick, C., Matthews, T., Wilby, R. L., Bastola, S., and Murphy, C.:
Transferability of hydrological models and ensemble averaging methods between
contrasting climatic periods, Water Resour. Res., 52, 8343–8373, 2016. a
Broderick, C., Murphy, C., Wilby, R. L., Matthews, T., Prudhomme, C., and
Adamson, M.: Using a scenario-neutral framework to avoid potential
maladaptation to future flood risk, Water Resour. Res., 55, 1079–1104,
2019. a
Brönnimann, S., Rajczak, J., Fischer, E. M., Raible, C. C., Rohrer, M., and Schär, C.: Changing seasonality of moderate and extreme precipitation events in the Alps, Nat. Hazards Earth Syst. Sci., 18, 2047–2056, https://doi.org/10.5194/nhess-18-2047-2018, 2018. a, b
Brown, C. and Wilby, R. L.: An alternate approach to assessing climate risks,
Eos Trans. AGU, 93, 401–402, 2012. a
Brunner, M. I. and Gilleland, E.: Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach, Hydrol. Earth Syst. Sci., 24, 3967–3982, https://doi.org/10.5194/hess-24-3967-2020, 2020. a
Brunner, M. I., Furrer, R., Sikorska, A. E., Viviroli, D., Seibert, J., and
Favre, A.-C.: Synthetic design hydrographs for ungauged catchments: a
comparison of regionalization methods,
Stoch. Env. Res. Risk A., 32, 1993–2023, 2018. a
Brunner, M. I., Hingray, B., Zappa, M., and Favre, A.-C.: Future Trends in the
Interdependence Between Flood Peaks and Volumes: Hydro-Climatological Drivers
and Uncertainty, Water Resour. Res., 55, 4745–4759, 2019. a
Brunner, M. I., Gilleland, E., Wood, A., Swain, D. L., and Clark, M.: Spatial
dependence of floods shaped by spatiotemporal variations in meteorological
and land-surface processes, Geophys. Res. Lett., 47, e2020GL088000, https://doi.org/10.1029/2020GL088000, 2020. a
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. a
Burnham, K. P. and Anderson, D. R.: Multimodel inference: understanding AIC and
BIC in model selection, Sociol. Method. Res., 33, 261–304, 2004. a
Buzan, J. R. and Huber, M.: Moist heat stress on a hotter Earth,
Annu. Rev. Earth Pl. Sc., 48, 623–655, https://doi.org/10.1146/annurev-earth-053018-060100, 2020. a
Caeiro, F. and Gomes, M. I.: Threshold selection in extreme value analysis,
Extreme value modeling and risk analysis: Methods and applications, 1,
69–86, Taylor & Francis Group, Chapman and Hall/CRC, New York, 2016. a
Camargo, S. J. and Sobel, A. H.: Western North Pacific tropical cyclone
intensity and ENSO, J. Climate, 18, 2996–3006, 2005. a
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020. a
Champion, A. J., Blenkinsop, S., Li, X.-F., and Fowler, H. J.: Synoptic-scale
precursors of extreme UK summer 3-hourly rainfall, J. Geophys. Res.-Atmos., 124, 4477–4489, 2019. a
Cheng, L. and AghaKouchak, A.: Nonstationary precipitation
intensity-duration-frequency curves for infrastructure design in a changing
climate, Sci. Rep., 4, 7093,
https://doi.org/10.1038/srep07093, 2014. a
Chiew, F. and McMahon, T.: Detection of trend or change in annual flow of
Australian rivers, Int. J. Climatol., 13, 643–653, 1993. a
Cid-Serrano, L., Ramírez, S. M., Alfaro, E. J., and Enfield, D. B.:
Analysis of the Latin American west coast rainfall predictability using an
ENSO index, Atmósfera, 28, 191–203, 2015. a
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple
working hypotheses for hydrological modeling, Water Resour. Res., 47, https://doi.org/10.1029/2010WR009827, 2011. a
Clark, M. P., Wilby, R. L., Gutmann, E. D., Vano, J. A., Gangopadhyay, S.,
Wood, A. W., Fowler, H. J., Prudhomme, C., Arnold, J. R., and Brekke, L. D.:
Characterizing uncertainty of the hydrologic impacts of climate change,
Current Climate Change Reports, 2, 55–64, 2016. a
Coccolo, S., Kämpf, J., Mauree, D., and Scartezzini, J.-L.: Cooling
potential of greening in the urban environment, a step further towards
practice, Sustain. Cities Soc., 38, 543–559, 2018. a
Collins, M. J.: River flood seasonality in the Northeast United States:
Characterization and trends, Hydrol. Process., 33, 687–698, 2019. a
Corella, J. P., Valero-Garcés, B. L., Vicente-Serrano, S. M., Brauer, A.,
and Benito, G.: Three millennia of heavy rainfalls in Western Mediterranean:
frequency, seasonality and atmospheric drivers, Sci. Rep., 6, 1–11,
2016. a
Court, A.: Measures of streamflow timing, J. Geophys. Res., 67,
4335–4339, 1962. a
Courty, L. G., Wilby, R. L., Hillier, J. K., and Slater, L. J.:
Intensity-duration-frequency curves at the global scale,
Environ. Res. Lett., 14, 084045, https://doi.org/10.1088/1748-9326/ab370a, 2019. a
Covey, C., Gleckler, P. J., Phillips, T. J., and Bader, D. C.: Secular trends
and climate drift in coupled ocean-atmosphere general circulation models,
J. Geophys. Res.-Atmos., 111, https://doi.org/10.1029/2005JD006009, 2006. a
Cowan, T., Undorf, S., Hegerl, G. C., Harrington, L. J., and Otto, F. E.:
Present-day greenhouse gases could cause more frequent and longer Dust Bowl
heatwaves, Nat. Clim. Change, 10, 505–510, 2020. a
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020. a
Crooks, S. and Kay, A.: Simulation of river flow in the Thames over 120 years:
Evidence of change in rainfall-runoff response?, J. Hydrol., 4, 172–195, 2015. a
Cunderlik, J. M. and Ouarda, T. B.: Trends in the timing and magnitude of
floods in Canada, J. Hydrol., 375, 471–480, 2009. a
Cunderlik, J. M., Ouarda, T. B., and Bobée, B.: Determination of flood
seasonality from hydrological records/Détermination de la
saisonnalité des crues à partir de séries hydrologiques,
Hydrolog. Sci. J., 49, https://doi.org/10.1623/hysj.49.3.511.54351, 2004. a
Dadson, S. J., Hall, J. W., Murgatroyd, A., Acreman, M., Bates, P., Beven, K., Heathwaite, L., Holden, J., Holman, I. P., Lane, S. N., and O'Connell, E.: A restatement
of the natural science evidence concerning catchment-based “natural” flood
management in the UK, P. Roy. Soc. A-Math. Phy., 473, 20160706, https://doi.org/10.1098/rspa.2016.0706, 2017. a
De Luca, P., Messori, G., Wilby, R. L., Mazzoleni, M., and Di Baldassarre, G.: Concurrent wet and dry hydrological extremes at the global scale, Earth Syst. Dynam., 11, 251–266, https://doi.org/10.5194/esd-11-251-2020, 2020. a
De Niel, J. and Willems, P.: Climate or land cover variations: what is driving observed changes in river peak flows? A data-based attribution study, Hydrol. Earth Syst. Sci., 23, 871–882, https://doi.org/10.5194/hess-23-871-2019, 2019. a
de Ruiter, M. C., Couasnon, A., van den Homberg, M. J., Daniell, J. E., Gill,
J. C., and Ward, P. J.: Why we can no longer ignore consecutive disasters,
Earth's Future, 8, e2019EF001425, https://doi.org/10.1029/2019EF001425, 2019. a
Dhakal, N., Jain, S., Gray, A., Dandy, M., and Stancioff, E.: Nonstationarity
in seasonality of extreme precipitation: A nonparametric circular statistical
approach and its application, Water Resour. Res., 51, 4499–4515, 2015. a
Dickson, R. R., Meincke, J., Malmberg, S.-A., and Lee, A. J.: The “great
salinity anomaly” in the northern North Atlantic 1968–1982,
Prog. Oceanogr., 20, 103–151, 1988. a
Donat, M., Renggli, D., Wild, S., Alexander, L., Leckebusch, G., and Ulbrich,
U.: Reanalysis suggests long-term upward trends in European storminess since
1871, Geophys. Res. Lett., 38, https://doi.org/10.1029/2011GL047995, 2011. a, b
Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R., Dunn, R. J. H., Willett, K. M., Aguilar, E., Brunet, M., Caesar, J., and Hewitson, B.: Updated analyses of temperature
and precipitation extreme indices since the beginning of the twentieth
century: The HadEX2 dataset, J. Geophys. Res.-Atmos.,
118, 2098–2118, 2013. a, b, c, d, e
Donat, M. G., Lowry, A. L., Alexander, L. V., O’Gorman, P. A., and Maher, N.:
More extreme precipitation in the world’s dry and wet regions, Nat. Clim. Change, 6, 508–513, https://doi.org/10.1038/nclimate2941, 2016. a
Du, T., Xiong, L., Xu, C.-Y., Gippel, C. J., Guo, S., and Liu, P.: Return
period and risk analysis of nonstationary low-flow series under climate
change, J. Hydrol., 527, 234–250, 2015. a
Dudley, R. W., Hodgkins, G. A., McHale, M., Kolian, M. J., and Renard, B.:
Trends in snowmelt-related streamflow timing in the conterminous United
States, J. Hydrol., 547, 208–221, 2017. a
Easterling, D. R., Kunkel, K. E., Wehner, M. F., and Sun, L.: Detection and
attribution of climate extremes in the observed record, Weather and Climate
Extremes, 11, 17–27, 2016. a
ECMWF: C3S Climate projections, available at: https://confluence.ecmwf.int/display/CKB/C3S+Climate+projections (last access: 6 July 2021),
2020. a
Ekström, M., Gutmann, E. D., Wilby, R. L., Tye, M. R., and Kirono, D. G.:
Robustness of hydroclimate metrics for climate change impact research, WIRES Water, 5, e1288, https://doi.org/10.1002/wat2.1288, 2018. a
Emanuel, K. and Center, L.: Response of Global Tropical Cyclone Activity to
Increasing CO2: Results from Downscaling CMIP6 Models, J. Climate, 34, 1–54, 2020. a
Enfield, D. B. and Mayer, D. A.: Tropical Atlantic sea surface temperature
variability and its relation to El Niño-Southern Oscillation, J. Geophys. Res.-Oceans, 102, 929–945, 1997. a
Engmann, S. and Cousineau, D.: Comparing distributions: the two-sample
Anderson-Darling test as an alternative to the Kolmogorov-Smirnoff test,
Journal of applied quantitative methods, 6, 1–17, 2011. a
Environment Agency, U.: Adapting to Climate Change: Advice for Flood and
Coastal Erosion Risk Management Authorities,
available at: https://www.gov.uk/government/publications/adapting-to-climate-change-for-risk-management-authorities (last access: 6 July 2021),
2016. a
Erdman, C. and Emerson, J. W.: A fast Bayesian change point analysis for the
segmentation of microarray data, Bioinformatics, 24, 2143–2148, 2008. a
Faulkner, D., Luxford, F., and Sharkey, P.: Rapid Evidence Assessment of
Non-Stationarity in Sources of UK Flooding, Tech. rep., Environment Agency, Environment Agency, Horizon House, Bristol, 2020. a
Ferguson, C. R. and Villarini, G.: An evaluation of the statistical homogeneity
of the Twentieth Century Reanalysis, Clim. Dynam., 42, 2841–2866, 2014. a
Fernando, D. N., Chakraborty, S., Fu, R., and Mace, R. E.: A process-based
statistical seasonal prediction of May–July rainfall anomalies over Texas
and the Southern Great Plains of the United States, Climate Services, 16,
100133, https://doi.org/10.1016/j.cliser.2019.100133, 2019. a
Ferreira, S. and Ghimire, R.: Forest cover, socioeconomics, and reported flood
frequency in developing countries, Water Resour. Res., 48, https://doi.org/10.1029/2011WR011701, 2012. a
Fowler, H. J., Ali, H., Allan, R. P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Cabi, N. S., Chan, S., Dale, M., and Dunn, R. J.: Towards advancing
scientific knowledge of climate change impacts on short-duration rainfall
extremes, Philos. T. Roy. Soc. A, 379, 20190542, https://doi.org/10.1098/rsta.2019.0542, 2021a. a
Fowler, K., Knoben, W., Peel, M., Peterson, T., Ryu, D., Saft, M., Seo, K.-W.,
and Western, A.: Many commonly used rainfall-runoff models lack long, slow
dynamics: implications for runoff projections, Water Resour. Res., 56,
e2019WR025286, https://doi.org/10.1029/2019WR025286, 2020. a
Fowler, K. J., Peel, M. C., Western, A. W., Zhang, L., and Peterson, T. J.:
Simulating runoff under changing climatic conditions: Revisiting an apparent
deficiency of conceptual rainfall-runoff models, Water Resour. Res.,
52, 1820–1846, 2016. a
Fowler, K. J. A., Acharya, S. C., Addor, N., Chou, C., and Peel, M. C.: CAMELS-AUS: Hydrometeorological time series and landscape attributes for 222 catchments in Australia, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2020-228, in review, 2021b. a
Freund, M., Henley, B. J., Karoly, D. J., Allen, K. J., and Baker, P. J.: Multi-century cool- and warm-season rainfall reconstructions for Australia's major climatic regions, Clim. Past, 13, 1751–1770, https://doi.org/10.5194/cp-13-1751-2017, 2017. a
Frich, P., Alexander, L. V., Della-Marta, P., Gleason, B., Haylock, M., Tank,
A. K., and Peterson, T.: Observed coherent changes in climatic extremes
during the second half of the twentieth century, Clim. Res., 19,
193–212, 2002. a
Fryzlewicz, P.: Wild binary segmentation for multiple change-point
detection, Ann. Stat., 42, 2243–2281, 2014. a
Ganguli, P. and Coulibaly, P.: Does nonstationarity in rainfall require nonstationary intensity–duration–frequency curves?, Hydrol. Earth Syst. Sci., 21, 6461–6483, https://doi.org/10.5194/hess-21-6461-2017, 2017. a
Gao, J., Kirkby, M., and Holden, J.: The effect of interactions between
rainfall patterns and land-cover change on flood peaks in upland peatlands,
J. Hydrol., 567, 546–559, 2018. a
Gao, M., Mo, D., and Wu, X.: Nonstationary modeling of extreme precipitation in
China, Atmos. Res., 182, 1–9, 2016. a
Gibbs, W. and Maher, J.: Rainfall deciles as drought indicators, Bureau of
Meteorology Bulletin, Commonwealth of Australia, Melbourne, no. 48, 29, 1967. a
Gilliland, J. M. and Keim, B. D.: Surface wind speed: trend and climatology of
Brazil from 1980–2014, Int. J. Climatol., 38, 1060–1073,
2018. a
Gleick, P. H. and Palaniappan, M.: Peak water limits to freshwater withdrawal
and use, P. Natl. Acad. Sci. USA, 107,
11155–11162, 2010. a
Grill, G., Lehner, B., Lumsdon, A. E., MacDonald, G. K., Zarfl, C., and
Liermann, C. R.: An index-based framework for assessing patterns and trends
in river fragmentation and flow regulation by global dams at multiple scales,
Environ. Res. Lett., 10, 015001, https://doi.org/10.1088/1748-9326/10/1/015001, 2015. a
Gudmundsson, L. and Seneviratne, S. I.: Anthropogenic climate change affects
meteorological drought risk in Europe, Environ. Res. Lett., 11,
044005, https://doi.org/10.1088/1748-9326/11/4/044005, 2016. a, b
Gudmundsson, L., Boulange, J., Do, H. X., Gosling, S. N., Grillakis, M. G., Koutroulis, A. G., Leonard, M., Liu, J., Schmied, H. M., Papadimitriou, L., and Pokhrel, Y: Globally observed trends in mean and extreme river flow attributed to
climate change, Science, 371, 1159–1162, 2021. a
Guillod, B. P., Jones, R. G., Bowery, A., Haustein, K., Massey, N. R., Mitchell, D. M., Otto, F. E. L., Sparrow, S. N., Uhe, P., Wallom, D. C. H., Wilson, S., and Allen, M. R.: weather@home 2: validation of an improved global–regional climate modelling system, Geosci. Model Dev., 10, 1849–1872, https://doi.org/10.5194/gmd-10-1849-2017, 2017. a
Hänsel, S., Schucknecht, A., and Matschullat, J.: The Modified Rainfall
Anomaly Index (mRAI) – is this an alternative to the Standardised
Precipitation Index (SPI) in evaluating future extreme precipitation
characteristics?, Theor. Appl. Climatol., 123, 827–844, 2016. a
Hall, J. and Blöschl, G.: Spatial patterns and characteristics of flood seasonality in Europe, Hydrol. Earth Syst. Sci., 22, 3883–3901, https://doi.org/10.5194/hess-22-3883-2018, 2018. a
Hall, J. and Perdigão, R. A.: Who is stirring the waters?, Science, 371,
1096–1097, 2021. a
Hall, J., Arheimer, B., Borga, M., Brázdil, R., Claps, P., Kiss, A., Kjeldsen, T. R., Kriaučiūnienė, J., Kundzewicz, Z. W., Lang, M., Llasat, M. C., Macdonald, N., McIntyre, N., Mediero, L., Merz, B., Merz, R., Molnar, P., Montanari, A., Neuhold, C., Parajka, J., Perdigão, R. A. P., Plavcová, L., Rogger, M., Salinas, J. L., Sauquet, E., Schär, C., Szolgay, J., Viglione, A., and Blöschl, G.: Understanding flood regime changes in Europe: a state-of-the-art assessment, Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, 2014. a
Hamed, K.: Enhancing the effectiveness of prewhitening in trend analysis of
hydrologic data, J. Hydrol., 368, 143–155, 2009a. a
Hamed, K.: Exact distribution of the Mann–Kendall trend test statistic for
persistent data, J. Hydrol., 365, 86–94, 2009b. a
Hamlet, A. F., Mote, P. W., Clark, M. P., and Lettenmaier, D. P.: Effects of
temperature and precipitation variability on snowpack trends in the western
United States, J. Climate, 18, 4545–4561, 2005. a
Han, S. and Coulibaly, P.: Probabilistic flood forecasting using hydrologic
uncertainty processor with ensemble weather forecasts, J. Hydrometeorol., 20, 1379–1398, 2019. a
Hannaford, J. and Marsh, T. J.: High-flow and flood trends in a network of
undisturbed catchments in the UK, Int. J. Climatol., 28, 1325–1338, 2008. a
Hannaford, J., Mastrantonas, N., Vesuviano, G., and Turner, S.: An updated
national-scale assessment of trends in UK peak river flow data: how robust
are observed increases in flooding?, Hydrol. Res., 52, 699–718, 2021 a
Hao, Z., Singh, V. P., and Xia, Y.: Seasonal drought prediction: advances,
challenges, and future prospects, Rev. Geophys., 56, 108–141, 2018. a
Harrigan, S., Murphy, C., Hall, J., Wilby, R. L., and Sweeney, J.: Attribution of detected changes in streamflow using multiple working hypotheses, Hydrol. Earth Syst. Sci., 18, 1935–1952, https://doi.org/10.5194/hess-18-1935-2014, 2014. a, b, c, d
Harrigan, S., Cloke, H., and Pappenberger, F.: Innovating global hydrological
prediction through an Earth system approach, WMO Bulletin, 69, World Meteorological Organisation, 2020. a
Harrison, P. A., Dunford, R. W., Holman, I. P., Cojocaru, G., Madsen, M. S.,
Chen, P.-Y., Pedde, S., and Sandars, D.: Differences between low-end and
high-end climate change impacts in Europe across multiple sectors, Reg. Environ. Change, 19, 695–709, 2019. a
Hart, N. C., Gray, S. L., and Clark, P. A.: Sting-jet windstorms over the North
Atlantic: climatology and contribution to extreme wind risk, J. Climate, 30, 5455–5471, 2017. a
Hartmann, D., Klein Tank, A., Rusticucci, M., Alexander, L., Bronnimann, S.,
Charabi, Y., Dentener, F., Dlugokencky, E., Easterling, D., Kaplan, A.,
Soden, B., Thorne, P., Wild, M., and Zhai, P.: Observations: Atmosphere and
Surface, book section 2, 159–254, Cambridge University Press, Cambridge,
United Kingdom, New York, NY, USA, https://doi.org/10.1017/CBO9781107415324.008,
2013. a
Harvey, B., Shaffrey, L., and Woollings, T.: Equator-to-pole temperature
differences and the extra-tropical storm track responses of the CMIP5 climate
models, Clim. Dynam., 43, 1171–1182, 2014. a
Hastie, T. and Tibshirani, R.: Generalized additive models: some applications,
J. Am. Stat. Assoc., 82, 371–386, 1987. a
Hausfather, Z., Menne, M. J., Williams, C. N., Masters, T., Broberg, R., and
Jones, D.: Quantifying the effect of urbanization on US Historical
Climatology Network temperature records, J. Geophys. Res.-Atmos., 118, 481–494, 2013. a
Haynes, K., Fearnhead, P., and Eckley, I. A.: A computationally efficient
nonparametric approach for changepoint detection, Stat. Comput.,
27, 1293–1305, 2017. a
Hazeleger, W., van den Hurk, B. J., Min, E., van Oldenborgh, G. J., Petersen,
A. C., Stainforth, D. A., Vasileiadou, E., and Smith, L. A.: Tales of future
weather, Nat. Clim. Change, 5, 107–113, 2015. a
Hecht, J. S. and Vogel, R. M.: Updating urban design floods for changes in
central tendency and variability using regression, Adv. Water Res., 136, 103484, https://doi.org/10.1016/j.advwatres.2019.103484, 2020. a, b
Hegerl, G. C., Hoegh-Guldberg, O., Casassa, G., Hoerling, M. P., Kovats, R. S., Parmesan, C., Pierce, D. W., and Stott, P. A: Good practice guidance
paper on detection and attribution related to anthropogenic climate change,
in: Meeting report of the intergovernmental panel on climate change expert
meeting on detection and attribution of anthropogenic climate change, IPCC
Working Group I Technical Support Unit, University of Bern, Bern,
Switzerland, 2010. a
Held, I. M. and Soden, B. J.: Robust responses of the hydrological cycle to
global warming, J. Climate, 19, 5686–5699, 2006. a
Helsel, D., Hirsch, R., Ryberg, K., Archfield, S., and Gilroy, E.: Statistical
methods in water resources, U.S. Geological Survey Techniques and Methods,
Elsevier, book 4, chapter A3, version
1.1, Reston, VA, USA, https://doi.org/10.3133/tm4a3, 2020. a, b, c, d
Hermanson, L., Ren, H. L., Vellinga, M., Dunstone, N. D., Hyder, P., Ineson, S., Scaife, A. A., Smith, D. M., Thompson, V., Tian, B., and Williams, K. D: Different types of
drifts in two seasonal forecast systems and their dependence on ENSO, Clim. Dynam., 51, 1411–1426, 2018. a
Hillier, J. K., Matthews, T., Wilby, R. L., and Murphy, C.: Multi-hazard
dependencies can increase or decrease risk, Nat. Clim. Change, 10, 595–598, 2020. a
Hipel, K. W. and McLeod, A. I.: Time series modelling of water resources and
environmental systems, Elsevier, Amsterdam, London, New York, Tokyo, 1994. a
Hirschboeck, K. K.: Flood hydroclimatology, Flood geomorphology, 27, 27–49, 1988. a
Hodgkins, G., Dudley, R., Archfield, S. A., and Renard, B.: Effects of climate,
regulation, and urbanization on historical flood trends in the United States,
J. Hydrol., 573, 697–709, 2019. a
Hodgkins, G. A. and Dudley, R. W.: Changes in the timing of winter–spring
streamflows in eastern North America, 1913–2002, Geophys. Res. Lett., 33, https://doi.org/10.1029/2005GL025593, 2006. a
Hoerling, M., Eischeid, J., Perlwitz, J., Quan, X., Zhang, T., and Pegion, P.:
On the increased frequency of Mediterranean drought, J. Climate, 25,
2146–2161, 2012. a
Holgate, C., Van Dijk, A., Evans, J., and Pitman, A.: The Importance of the
One-Dimensional Assumption in Soil Moisture-Rainfall Depth Correlation at
Varying Spatial Scales, J. Geophys. Res.-Atmos., 124,
2964–2975, 2019. a
Hrachowitz, M. and Clark, M. P.: HESS Opinions: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017. a
Hulme, M.: Attributing weather extremes to “climate change”: A review,
Prog. Phys. Geog., 38, 499–511, 2014. a
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M.,
Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback
dominates land carbon uptake variability, Nature, 592, 65–69, 2021. a
Hundecha, Y., St-Hilaire, A., Ouarda, T., El Adlouni, S., and Gachon, P.: A
nonstationary extreme value analysis for the assessment of changes in extreme
annual wind speed over the Gulf of St. Lawrence, Canada,
J. Appl. Meteorol. Clim., 47, 2745–2759, 2008. a
Iacob, O., Brown, I., and Rowan, J.: Natural flood management, land use and
climate change trade-offs: the case of Tarland catchment, Scotland,
Hydrolog. Sci. J., 62, 1931–1948, 2017. a
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., and Emmer, A.: Importance and
vulnerability of the world's water towers, Nature, 577, 364–369, 2020. a
International Hydropower Association: Hydropower Sector Climate
Resilience Guide, London, UK, available at: https://www.hydropower.org/publications/hydropower-sector-climate-resilience-guide (last access: 6 July 2021),
2019. a
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 1535 pp., 2013. a
IPCC: Summary for Policymakers, in: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land
management, food security, and greenhouse gas fluxes in terrestrial
ecosystems, edited by: Shukla, P. R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D. C., Zhai, P., Slade, R., Connors, S., van Diemen, R., Ferrat, M., Haughey, E., Luz, S., Neogi, S., Pathak, M., Petzold, J., Portugal Pereira, J., Vyas, P., Huntley, E., Kissick, K., Belkacemi, M., and Malley, J., in press, 2019. a
James, L. A.: Incision and morphologic evolution of an alluvial channel
recovering from hydraulic mining sediment, Geol. Soc. Am. Bull., 103, 723–736, 1991. a
Jones, P. D., Harpham, C., and Lister, D.: Long-term trends in gale days and
storminess for the Falkland Islands, Int. J. Climatol.,
36, 1413–1427, 2016. a
Jovanovic, T., Mejía, A., Gall, H., and Gironás, J.: Effect of
urbanization on the long-term persistence of streamflow records, Physica A, 447, 208–221, 2016. a
Karaseva, M. O., Prakash, S., and Gairola, R.: Validation of high-resolution
TRMM-3B43 precipitation product using rain gauge measurements over
Kyrgyzstan, Theor. Appl. Climatol., 108, 147–157, 2012. a
Karl, T. R., Meehl, G. A., Miller, C. D., Hassol, S. J., Waple, A. M., and
Murray, W. L.: Weather and climate extremes in a changing climate, Tech.
rep., US Climate Change Science Program, U.S. Climate Change Science Program, 2008. a
Kelder, T., Muller, M., Slater, L., Marjoribanks, T., Wilby, R. L., Prudhomme,
C., Bohlinger, P., Ferranti, L., and Nipen, T.: Using UNSEEN trends to detect
decadal changes in 100-year precipitation extremes, npj Clim. Atmos. Sci., 3, 47, https://doi.org/10.1038/s41612-020-00149-4,
2020. a, b
Kemter, M., Merz, B., Marwan, N., Vorogushyn, S., and Blöschl, G.: Joint
trends in flood magnitudes and spatial extents across Europe, Geophys. Res. Lett., 47, e2020GL087464, https://doi.org/10.1029/2020GL087464, 2020. a
Kendall, M.: Rank Correlation Methods, Charles Griffin, London, England, 1975. a
Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., and
Senior, C. A.: Heavier summer downpours with climate change revealed by
weather forecast resolution model, Nat. Clim. Change, 4, 570–576, 2014. a
Kettner, A. J., Cohen, S., Overeem, I., Fekete, B. M., Brakenridge, G. R., and
Syvitski, J. P.: Estimating Change in Flooding for the 21st Century Under a
Conservative RCP Forcing: A Global Hydrological Modeling Assessment, book
section 9, 157–167, Wiley Online Library,
https://doi.org/10.1002/9781119217886.ch9, 2018. a
Kharin, V. V., Zwiers, F., Zhang, X., and Wehner, M.: Changes in temperature
and precipitation extremes in the CMIP5 ensemble, Climatic change, 119,
345–357, 2013. a
Khouakhi, A., Villarini, G., Zhang, W., and Slater, L. J.: Seasonal
predictability of high sea level frequency using ENSO patterns along the US
West Coast, Adv. Water Resour., 131, 103377, https://doi.org/10.1016/j.advwatres.2019.07.007, 2019. a
Killick, R., Fearnhead, P., and Eckley, I. A.: Optimal detection of
changepoints with a linear computational cost, J. Am. Stat. Assoc., 107, 1590–1598, 2012. a
Kim, H.-M. and Webster, P. J.: Extended-range seasonal hurricane forecasts for
the North Atlantic with a hybrid dynamical-statistical model, Geophys. Res. Lett., 37, https://doi.org/10.1029/2010GL044792, 2010. a
Kirchmeier-Young, M. C. and Zhang, X.: Human influence has intensified extreme
precipitation in North America, P. Natl. Acad. Sci. USA, 117, 13308–13313, 2020. a
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment
characterization, rainfall-runoff modeling, and doing hydrology backward,
Water Resour. Res., 45, https://doi.org/10.1029/2008WR006912, 2009. a
Kjellström, E., Bärring, L., Jacob, D., Jones, R., Lenderink, G., and
Schär, C.: Modelling daily temperature extremes: recent climate and
future changes over Europe, Climatic Change, 81, 249–265, 2007. a
Knutson, T. R. and Ploshay, J. J.: Detection of anthropogenic influence on a
summertime heat stress index, Climatic Change, 138, 25–39, 2016. a
Kornhuber, K., Coumou, D., Vogel, E., Lesk, C., Donges, J. F., Lehmann, J., and
Horton, R. M.: Amplified Rossby waves enhance risk of concurrent heatwaves in
major breadbasket regions, Nat. Clim. Change, 10, 48–53, 2020. a
Kossin, J. P.: A global slowdown of tropical-cyclone translation speed, Nature,
558, 104–107, 2018. a
Koutsoyiannis, D.: Nonstationarity versus scaling in hydrology, J. Hydrol., 324, 239–254, 2006. a
Koutsoyiannis, D. and Montanari, A.: Negligent killing of scientific concepts:
the stationarity case, Hydrolog. Sci. J., 60, 1174–1183, 2015. a
Krishnan, A. and Bhaskaran, P. K.: Skill assessment of global climate model
wind speed from CMIP5 and CMIP6 and evaluation of projections for the Bay of
Bengal, Clim. Dynam., 55, 2667–2687, 2020. a
Kundzewicz, Z. W. and Stakhiv, E. Z.: Are climate models “ready for prime
time” in water resources management applications, or is more research
needed?, Hydrolog. Sci. J.,
55, 1085–1089, 2010. a
Kunkel, K. E., Karl, T. R., Easterling, D. R., Redmond, K., Young, J., Yin, X.,
and Hennon, P.: Probable maximum precipitation and climate change,
Geophys. Res. Lett., 40, 1402–1408, 2013. a
Lackmann, G. M.: Hurricane Sandy before 1900 and after 2100, B. Am. Meteorol. Soc., 96, 547–560, 2015. a
Lang, M., Ouarda, T., and Bobée, B.: Towards operational guidelines for
over-threshold modeling, J. Hydrol., 225, 103–117, 1999. a
Lavers, D. A., Allan, R. P., Wood, E. F., Villarini, G., Brayshaw, D. J., and
Wade, A. J.: Winter floods in Britain are connected to atmospheric rivers,
Geophys. Res. Lett., 38, https://doi.org/10.1029/2011GL049783, 2011. a
Lavers, D. A., Villarini, G., Allan, R. P., Wood, E. F., and Wade, A. J.: The
detection of atmospheric rivers in atmospheric reanalyses and their links to
British winter floods and the large-scale climatic circulation, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2012JD018027, 2012. a
Leckebusch, G. C., Renggli, D., and Ulbrich, U.: Development and application of
an objective storm severity measure for the Northeast Atlantic region,
Meteorol. Z., 17, 575–587, 2008. a
Lee, K. and Singh, V. P.: Analysis of Uncertainty and Non-stationarity in
Probable Maximum Precipitation in Brazos River Basin, J. Hydrol., 590,
125526, https://doi.org/10.1016/j.jhydrol.2020.125526, 2020. a
Lee, O., Sim, I., and Kim, S.: Application of the non-stationary
peak-over-threshold methods for deriving rainfall extremes from temperature
projections, J. Hydrol., 585, 124318, https://doi.org/10.1016/j.jhydrol.2019.124318, 2020. a, b
Leelaruban, N. and Padmanabhan, G.: Drought occurrences and their
characteristics across selected spatial scales in the Contiguous United
States, Geosciences, 7, 59, https://doi.org/10.3390/geosciences7030059, 2017. a
Lenderink, G. and Van Meijgaard, E.: Increase in hourly precipitation extremes
beyond expectations from temperature changes, Nat. Geosci., 1, 511–514,
2008. a
Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S.,
and Schellnhuber, H. J.: Tipping elements in the Earth's climate system,
P. Natl. Acad. Sci. USA, 105, 1786–1793, 2008. a
Levy, M., Lopes, A., Cohn, A., Larsen, L., and Thompson, S.: Land use change
increases streamflow across the arc of deforestation in Brazil, Geophys. Res. Lett., 45, 3520–3530, 2018. a
Li, W., Jiang, Z., Xu, J., and Li, L.: Extreme precipitation indices over China
in CMIP5 models. Part II: probabilistic projection, J. Climate, 29,
8989–9004, 2016. a
Li, Y., Fowler, H. J., Argüeso, D., Blenkinsop, S., Evans, J. P.,
Lenderink, G., Yan, X., Guerreiro, S. B., Lewis, E., and Li, X.-F.: Strong
intensification of hourly rainfall extremes by urbanization, Geophys. Res. Lett., 47, e2020GL088758, https://doi.org/10.1029/2020GL088758, 2019. a
Li, Y., Wright, D. B., and Byrne, P. K.: The Influence of Tropical Cyclones on
the Evolution of River Conveyance Capacity in Puerto Rico, Water Resour. Res., 56, e2020WR027971, https://doi.org/10.1029/2020WR027971, 2020. a
Liepert, B. G. and Lo, F.: CMIP5 update of “Inter-model variability and
biases of the global water cycle in CMIP3 coupled climate models”,
Environ. Res. Lett., 8, 029401, https://doi.org/10.1088/1748-9326/8/2/029401, 2013. a
Lomas, K. J. and Giridharan, R.: Thermal comfort standards, measured internal
temperatures and thermal resilience to climate change of free-running
buildings: A case-study of hospital wards, Build. Environ., 55,
57–72, 2012. a
Longobardi, A. and Villani, P.: Trend analysis of annual and seasonal rainfall
time series in the Mediterranean area, Int. J. Climatol.,
30, 1538–1546, 2010. a
Lorenz, R., Stalhandske, Z., and Fischer, E. M.: Detection of a climate change
signal in extreme heat, heat stress, and cold in Europe from observations,
Geophys. Res. Lett., 46, 8363–8374, 2019. a
Lorenzo-Lacruz, J., Vicente-Serrano, S. M., López-Moreno, J. I.,
Morán-Tejeda, E., and Zabalza, J.: Recent trends in Iberian streamflows
(1945–2005), J. Hydrol., 414, 463–475, 2012. a
Ma, S., Zhou, T., Angélil, O., and Shiogama, H.: Increased chances of
drought in southeastern periphery of the Tibetan plateau induced by
anthropogenic warming, J. Climate, 30, 6543–6560, 2017. a
Macdonald, N., Werritty, A., Black, A., and McEwen, L.: Historical and pooled
flood frequency analysis for the River Tay at Perth, Scotland, Area, 38,
34–46, 2006. a
Madsen, H., Lawrence, D., Lang, M., Martinkova, M., and Kjeldsen, T.: Review of
trend analysis and climate change projections of extreme precipitation and
floods in Europe, J. Hydrol., 519, 3634–3650, 2014. a
Maher, N., Matei, D., Milinski, S., and Marotzke, J.: ENSO change in climate
projections: Forced response or internal variability?, Geophys. Res. Lett., 45, 11–390, 2018. a
Maher, N., Lehner, F., and Marotzke, J.: Quantifying the role of internal
variability in the temperature we expect to observe in the coming decades,
Environ. Res. Lett., 15, 054014, https://doi.org/10.1088/1748-9326/ab7d02, 2020. a
Mahmood, R., Pielke Sr, R. A., Hubbard, K. G., Niyogi, D., Dirmeyer, P. A., McAlpine, C., Carleton, A. M., Hale, R., Gameda, S., Beltrán‐Przekurat, A., and Baker, B: Land cover changes and their biogeophysical effects on climate,
Int. J. Climatol., 34, 929–953, 2014. a
Mallakpour, I. and Villarini, G.: The changing nature of flooding across the
central United States, Nat. Clim. Change, 5, 250–254, 2015. a
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, 1945. a
Mann, H. B. and Whitney, D. R.: On a test of whether one of two random
variables is stochastically larger than the other, Ann. Math. Stat., 50–60, 1947. a
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., and Venema, V. K. C.: Precipitation
downscaling under climate change: Recent developments to bridge the gap
between dynamical models and the end user, Rev. Geophys., 48, https://doi.org/10.1029/2009RG000314, 2010. a
Markonis, Y., Papalexiou, S., Martinkova, M., and Hanel, M.: Assessment of
water cycle intensification over land using a multisource global gridded
precipitation dataset, J. Geophys. Res.-Atmos., 124,
11175–11187, 2019. a
Martínez-Alvarado, O., Gray, S. L., Hart, N. C., Clark, P. A., Hodges, K.,
and Roberts, M. J.: Increased wind risk from sting-jet windstorms with
climate change, Environ. Res. Lett., 13, 044002, https://doi.org/10.1088/1748-9326/aaae3a, 2018. a
Masys, A. J., Yee, E., and Vallerand, A.: “Black Swans”,“Dragon Kings”
and Beyond: Towards Predictability and Suppression of Extreme All-Hazards
Events Through Modeling and Simulation, in: Applications of Systems Thinking
and Soft Operations Research in Managing Complexity, 131–141, Springer, Cham, 2016. a, b
McCarthy, G. D., Gleeson, E., and Walsh, S.: The influence of ocean variations
on the climate of Ireland, Weather, 70, 242–245, 2015. a
McIntosh, B. S., Ascough II, J. C., Twery, M., Chew, J., Elmahdi, A., Haase, D., Harou, J. J., Hepting, D., Cuddy, S., Jakeman, A. J., and Chen, S.:
Environmental decision support systems (EDSS) development–Challenges and
best practices, Environ. Modell. Softw., 26, 1389–1402, 2011. a
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought
frequency and duration to time scales, in: Proceedings of the 8th Conference
on Applied Climatology, 22, 179–183, Boston, 17–22 January 1993. a
McSweeney, C., Jones, R., Lee, R. W., and Rowell, D.: Selecting CMIP5 GCMs for
downscaling over multiple regions, Clim. Dynam., 44, 3237–3260, 2015. a
Mediero, L., Santillán, D., Garrote, L., and Granados, A.: Detection and
attribution of trends in magnitude, frequency and timing of floods in Spain,
J. Hydrol., 517, 1072–1088, 2014. a
Mei, W., Xie, S.-P., Primeau, F., McWilliams, J. C., and Pasquero, C.:
Northwestern Pacific typhoon intensity controlled by changes in ocean
temperatures, Sci. Adv., 1, e1500014, https://doi.org/10.1126/sciadv.1500014, 2015. a
Mekonen, A. A., Berlie, A. B., and Ferede, M. B.: Spatial and temporal drought
incidence analysis in the northeastern highlands of Ethiopia,
Geoenvironmental Disasters, 7, 1–17, 2020. a
Mestre, O., Domonkos, P., Picard, F., Auer, I., Robin, S., Lebarbier, E., Böhm, R., Aguilar, E., Guijarro, J. A., Vertacnik, G., and Klancar, M.: HOMER: a
homogenization software–methods and applications, Quarterly Journal of the Hungarian Meteorological Service, 117, 47–67, 2013. a
Milly, P. C. D., Wetherald, R. T., Dunne, K., and Delworth, T. L.: Increasing
risk of great floods in a changing climate, Nature, 415, 514–517, 2002. a
Min, S.-K., Zhang, X., Zwiers, F. W., and Hegerl, G. C.: Human contribution to
more-intense precipitation extremes, Nature, 470, 378–381, 2011. a
Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C., and De Arellano, J.
V.-G.: Mega-heatwave temperatures due to combined soil desiccation and
atmospheric heat accumulation, Nat. Geosci., 7, 345–349, 2014. a
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.:
Land–atmospheric feedbacks during droughts and heatwaves: state of the
science and current challenges, Ann. NY Acad. Sci.,
1436, 19–35, https://doi.org/10.1111/nyas.13912, 2019. a
Mishra, A. K. and Singh, V. P.: A review of drought concepts, J. Hydrol., 391, 202–216, 2010. a
Mitchell, D., Heaviside, C., Vardoulakis, S., Huntingford, C., Masato, G.,
Guillod, B. P., Frumhoff, P., Bowery, A., Wallom, D., and Allen, M.:
Attributing human mortality during extreme heat waves to anthropogenic
climate change, Environ. Res. Lett., 11, 074006, https://doi.org/10.1088/1748-9326/11/7/074006, 2016. a
Mood, A. M.: On the asymptotic efficiency of certain nonparametric two-sample
tests, Ann. Math. Stat., 25, 514–522, 1954. a
Mora, C., Dousset, B., Caldwell, I. R., Powell, F. E., Geronimo, R. C., Bielecki, C. R., Counsell, C. W., Dietrich, B. S., Johnston, E. T., Louis, L. V., and Lucas, M. P.: Global risk of deadly heat, Nat. Clim. Change, 7,
501–506, 2017. a
Mote, P. W., Hamlet, A. F., Clark, M. P., and Lettenmaier, D. P.: Declining
mountain snowpack in western North America, B. Am. Meteorol. Soc., 86, 39–50, 2005. a
Murphy, C., Wilby, R. L., Matthews, T., Horvath, C., Crampsie, A., Ludlow, F., Noone, S., Brannigan, J., Hannaford, J., McLeman, R., and Jobbova, E.: The forgotten
drought of 1765–1768: Reconstructing and re-evaluating historical droughts
in the British and Irish Isles, Int. J. Climatol., 40, 5329–5351, https://doi.org/10.1002/joc.6521, 2020a. a
Murphy, C., Wilby, R. L., Matthews, T. K., Thorne, P., Broderick, C., Fealy, R., Hall, J., Harrigan, S., Jones, P., McCarthy, G., and MacDonald, N.: Multi-century
trends to wetter winters and drier summers in the England and Wales
precipitation series explained by observational and sampling bias in early
records, Int. J. Climatol., 40, 610–619,
2020b. a, b
Murray, R. J. and Simmonds, I.: A numerical scheme for tracking cyclone centres
from digital data. Part I: Development and operation of the scheme, Aust.
Meteor. Mag, 39, 155–166, 1991. a
Mwagona, P. C., Yao, Y., Shan, Y., Yu, H., and Zhang, Y.: Trend and Abrupt
Regime Shift of Temperature Extreme in Northeast China, 1957–2015, Adv. Meteorol., 2018, 2315372, https://doi.org/10.1155/2018/2315372, 2018. a
Myhre, G., Alterskjær, K., Stjern, C. W., Hodnebrog, Ø., Marelle, L., Samset, B.H., Sillmann, J., Schaller, N., Fischer, E., Schulz, M., and Stohl, A.:
Frequency of extreme precipitation increases extensively with event rareness
under global warming, Sci. Rep., 9, 1–10, 2019. a
Nathan, R., McMahon, T., Peel, M., and Horne, A.: Assessing the degree of
hydrologic stress due to climate change, Climatic Change, 156, 87–104, 2019. a
Naveau, P., Hannart, A., and Ribes, A.: Statistical methods for extreme event
attribution in climate science, Annu. Rev. Stat. Appl., 7, 89–110, 2020. a
Neri, A., Villarini, G., Slater, L. J., and Napolitano, F.: On the statistical
attribution of the frequency of flood events across the US Midwest, Adv. Water Resour., 127, 225–236, https://doi.org/10.1016/j.advwatres.2019.03.019, 2019. a
Ng, C. H. J. and Vecchi, G. A.: Large-scale environmental controls on the
seasonal statistics of rapidly intensifying North Atlantic tropical cyclones,
Clim. Dynam., 54, 3907–3925, 2020. a
Niu, X., Wang, S., Tang, J., Lee, D. K., Gutowski, W., Dairaku, K., McGregor, J., Katzfey, J., Gao, X., Wu, J., and Hong, S. Y.: Ensemble evaluation and projection
of climate extremes in China using RMIP models, Int. J. Climatol., 38, 2039–2055, 2018. a
Noone, S., Murphy, C., Coll, J., Matthews, T., Mullan, D., Wilby, R. L., and
Walsh, S.: Homogenization and analysis of an expanded long-term monthly
rainfall network for the Island of Ireland (1850–2010),
Int. J. Climatol., 36, 2837–2853, 2016. a
O'Connor, P., Murphy, C., Matthews, T., and Wilby, R.: Reconstructed monthly
river flows for Irish catchments 1766–2010, Geosciences Data Journal, 8, 34–54, 2020. a
Oliver, E. C., Donat, M. G., Burrows, M. T., Moore, P. J., Smale, D. A., Alexander, L. V., Benthuysen, J. A., Feng, M., Gupta, A. S., Hobday, A. J., and Holbrook, N. J.: Longer and more frequent marine heatwaves over the past century,
Nat. Commun., 9, 1–12, 2018. a
Ouarda, T. B. and Charron, C.: Nonstationary Temperature-Duration-Frequency
curves, Sci. Rep., 8, 1–8, 2018. a
O’Reilly, C. H., Zanna, L., and Woollings, T.: Assessing External and
Internal Sources of Atlantic Multidecadal Variability Using Models, Proxy
Data, and Early Instrumental Indices, J. Climate, 32, 7727–7745,
2019. a
Palmer, W. C.: Meteorological drought, Research paper no. 45, US Weather
Bureau, Washington, DC, 58 pp., 1965. a
Paltan, H., Waliser, D., Lim, W. H., Guan, B., Yamazaki, D., Pant, R., and
Dadson, S.: Global floods and water availability driven by atmospheric
rivers, Geophys. Res. Lett., 44, 10–387, 2017. a
Papacharalampous, G. and Tyralis, H.: Hydrological time series forecasting
using simple combinations: Big data testing and investigations on one-year
ahead river flow predictability, J. Hydrol., 590, 125205,
https://doi.org/10.1016/j.jhydrol.2020.125205, 2020. a
Park, I.-H. and Min, S.-K.: Role of convective precipitation in the
relationship between subdaily extreme precipitation and temperature, J. Climate, 30, 9527–9537, 2017. a
Peña-Angulo, D., Vicente-Serrano, S. M., Domínguez-Castro, F., Murphy, C., Reig, F., Tramblay, Y., Trigo, R. M., Luna, M. Y., Turco, M., Noguera, I., and Aznárez-Balta, M.: Long-term precipitation in Southwestern Europe reveals no clear
trend attributable to anthropogenic forcing, Environ. Res. Lett., 15, 094070, https://doi.org/10.1088/1748-9326/ab9c4f, 2020. a
Perkins, S. E. and Alexander, L. V.: On the measurement of heat waves, J. Climate, 26, 4500–4517, 2013. a
Peterson, T. C., Willett, K. M., and Thorne, P. W.: Observed changes in surface
atmospheric energy over land, Geophys. Res. Lett., 38, L16707, https://doi.org/10.1029/2011GL048442, 2011. a, b
Pettitt, A.: A non-parametric approach to the change-point problem, J. Roy. Stat. Soc. C-App., 28, 126–135,
1979. a
Pielke Sr., R. A. and Wilby, R. L.: Regional climate downscaling: What's the
point?, Eos Trans. AGU, 93, 52–53, 2012. a
Pinter, N., Ickes, B. S., Wlosinski, J. H., and Van der Ploeg, R. R.: Trends in
flood stages: Contrasting results from the Mississippi and Rhine River
systems, J. Hydrol., 331, 554–566, 2006. a
Pinter, N., Jemberie, A. A., Remo, J. W., Heine, R. A., and Ickes, B. S.: Flood
trends and river engineering on the Mississippi River system, Geophys. Res. Lett., 35, L23404, https://doi.org/10.1029/2008GL035987, 2008. a
Poff, N. L., Brown, C. M., Grantham, T. E., Matthews, J. H., Palmer, M. A., Spence, C. M., Wilby, R. L., Haasnoot, M., Mendoza, G. F., Dominique, K. C., and Baeza, A.: Sustainable water management under future uncertainty with
eco-engineering decision scaling, Nat. Clim. Change, 6, 25–34, 2016. a
Poschlod, B., Ludwig, R., and Sillmann, J.: Ten-year return levels of sub-daily extreme precipitation over Europe, Earth Syst. Sci. Data, 13, 983–1003, https://doi.org/10.5194/essd-13-983-2021, 2021. a
Prosdocimi, I., Kjeldsen, T. R., and Svensson, C.: Non-stationarity in annual and seasonal series of peak flow and precipitation in the UK, Nat. Hazards Earth Syst. Sci., 14, 1125–1144, https://doi.org/10.5194/nhess-14-1125-2014, 2014. a
Prudhomme, C., Wilby, R. L., Crooks, S., Kay, A. L., and Reynard, N. S.:
Scenario-neutral approach to climate change impact studies: application to
flood risk, J. Hydrol., 390, 198–209, 2010. a
Pryor, S., Conrick, R., Miller, C., Tytell, J., and Barthelmie, R.: Intense and
extreme wind speeds observed by anemometer and seismic networks: An eastern
US case study, J. Appl. Meteorol. Clim., 53,
2417–2429, 2014. a
Raymond, C., Matthews, T., and Horton, R. M.: The emergence of heat and
humidity too severe for human tolerance, Sci. Adv., 6, eaaw1838, https://doi.org/10.1126/sciadv.aaw1838, 2020b. a, b
Read, L. K. and Vogel, R. M.: Reliability, return periods, and risk under
nonstationarity, Water Resour. Res., 51, 6381–6398, 2015. a
Reggiani, P., Renner, M., Weerts, A., and Van Gelder, P.: Uncertainty
assessment via Bayesian revision of ensemble streamflow predictions in the
operational river Rhine forecasting system, Water Resour. Res., 45, W02428, https://doi.org/10.1029/2007WR006758, 2009. a
Requena, A. I., Burn, D. H., and Coulibaly, P.: Estimates of gridded relative
changes in 24 h extreme rainfall intensities based on pooled frequency
analysis, J. Hydrol., 577, 123940, https://doi.org/10.1016/j.jhydrol.2019.123940, 2019. a
Restrepo-Posada, P. J. and Eagleson, P. S.: Identification of independent
rainstorms, J. Hydrol., 55, 303–319, 1982. a
Ribeiro, S., Caineta, J., and Costa, A. C.: Review and discussion of
homogenisation methods for climate data, Phys. Chem. Earth Pt. A/B/C, 94, 167–179, 2016. a
Rootzén, H. and Katz, R. W.: Design life level: quantifying risk in a
changing climate, Water Resour. Res., 49, 5964–5972, 2013. a
Ross, G. J., Tasoulis, D. K., and Adams, N. M.: Nonparametric monitoring of
data streams for changes in location and scale, Technometrics, 53, 379–389,
2011. a
Ryberg, K. R., Hodgkins, G. A., and Dudley, R. W.: Change points in annual peak
streamflows: Method comparisons and historical change points in the United
States, J. Hydrol., 583, 124307, https://doi.org/10.1016/j.jhydrol.2019.124307, 2019. a, b
Saeed, F., Hagemann, S., and Jacob, D.: Impact of irrigation on the South Asian
summer monsoon, Geophys. Res. Lett., 36, L20711, https://doi.org/10.1029/2009GL040625, 2009. a
Salas, J. D. and Obeysekera, J.: Revisiting the concepts of return period and
risk for nonstationary hydrologic extreme events, J. Hydrol. Eng., 19, 554–568, 2014. a
Salas, J. D., Anderson, M. L., Papalexiou, S. M., and Frances, F.: PMP and
Climate Variability and Change: A Review, J. Hydrol. Eng.,
25, 03120002, https://doi.org/10.1061/(ASCE)HE.1943-5584.0002003, 2020. a
Schaller, N., Kay, A. L., Lamb, R., Massey, N. R., Van Oldenborgh, G. J., Otto, F. E., Sparrow, S. N., Vautard, R., Yiou, P., Ashpole, I., and Bowery, A.: Human
influence on climate in the 2014 southern England winter floods and their
impacts, Nat. Clim. Change, 6, 627–634, 2016. a
Schaller, N., Sillmann, J., Anstey, J., Fischer, E. M., Grams, C. M., and
Russo, S.: Influence of blocking on Northern European and Western Russian
heatwaves in large climate model ensembles, Environ. Res. Lett.,
13, 054015, https://doi.org/10.1088/1748-9326/aaba55, 2018. a
Scherrer, S. C., Fischer, E. M., Posselt, R., Liniger, M. A., Croci-Maspoli,
M., and Knutti, R.: Emerging trends in heavy precipitation and hot
temperature extremes in Switzerland, J. Geophys. Res.-Atmos., 121, 2626–2637, 2016. a
Schlef, K. E., Moradkhani, H., and Lall, U.: Atmospheric circulation patterns
associated with extreme United States floods identified via machine learning,
Sci. Rep., 9, 1–12, 2019. a
Schott, F. A., Xie, S.-P., and McCreary Jr., J. P.: Indian Ocean circulation and
climate variability, Rev. Geophys., 47, RG1002, https://doi.org/10.1029/2007RG000245, 2009. a
Scott, A. J. and Knott, M.: A cluster analysis method for grouping means in the
analysis of variance, Biometrics, 30, 507–512, 1974. a
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau,
J. Am. Stat. Assoc., 63, 1379–1389, 1968. a
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B.,
Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil
moisture–climate interactions in a changing climate: A review, Earth-Sci.
Rev., 99, 125–161, 2010. a
Serinaldi, F., Kilsby, C. G., and Lombardo, F.: Untenable nonstationarity: An
assessment of the fitness for purpose of trend tests in hydrology, Adv. Water Resour., 111, 132–155, 2018. a
Sharma, A., Wasko, C., and Lettenmaier, D. P.: If precipitation extremes are
increasing, why aren't floods?, Water Resour. Res., 54, 8545–8551,
2018. a
Shaw, T. A., Baldwin, M., Barnes, E. A., Caballero, R., Garfinkel, C. I., Hwang, Y. T., Li, C., O'gorman, P. A., Rivière, G., Simpson, I. R., and Voigt, A.: Storm
track processes and the opposing influences of climate change, Nat.
Geosci., 9, 656–664, 2016. a
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West, I. M., Fowler, H. J., James, R., Maraun, D., Martius, O., and Senior, C. A.: Storylines:
an alternative approach to representing uncertainty in physical aspects of
climate change, Climatic change, 151, 555–571, 2018. a, b
Sherwood, S. C. and Huber, M.: An adaptability limit to climate change due to
heat stress, P. Natl. Acad. Sci. USA, 107,
9552–9555, 2010. a
Shkolnik, I., Pavlova, T., Efimov, S., and Zhuravlev, S.: Future changes in
peak river flows across northern Eurasia as inferred from an ensemble of
regional climate projections under the IPCC RCP8. 5 scenario, Clim.
Dynam., 50, 215–230, 2018. a
Sillmann, J., Croci-Maspoli, M., Kallache, M., and Katz, R. W.: Extreme cold
winter temperatures in Europe under the influence of North Atlantic
atmospheric blocking, J. Climate, 24, 5899–5913, 2011. a
Sillmann, J., Shepherd, T. G., van den Hurk, B., Hazeleger, W., Martius, O.,
Slingo, J., and Zscheischler, J.: Event-based storylines to address climate
risk, Earth's Future, 9, e2020EF001783, https://doi.org/10.1029/2020EF001783, 2021. a
Slater, L., Villarini, G., Archfield, S., Faulkner, D., Lamb, R., Khouakhi, A.,
and Yin, J.: Global Changes in 20-year, 50-year and 100-year River Floods,
Geophys. Res. Lett., 48, e2020GL091824, https://doi.org/10.1029/2020GL091824, 2021. a, b, c, d
Slater, L. J. and Villarini, G.: Recent trends in US flood risk, Geophys. Res. Lett., 43, 12–428, 2016. a
Slater, L. J. and Villarini, G.: Evaluating the drivers of seasonal streamflow
in the US Midwest, Water, 9, 695, https://doi.org/10.3390/w9090695, 2017b. a
Slutzky, E.: The summation of random causes as the source of cyclic processes,
Econometrica, 5, 105–146, 1937. a
Smelser, M. and Schmidt, J.: An assessment methodology for determining
historical changes in mountain streams, USDA Department of Agriculture Forest
Service, Rocky Mountain Research Station, Tech. rep., General Technical
report RMRS-GTS-6, U.S. Department of Agriculture, Forest
Service, Rocky Mountain Research Station, Fort Collins, CO, 29 pp., 1998. a
Smith, D. M., Scaife, A. A., Eade, R., Athanasiadis, P., Bellucci, A., Bethke, I., Bilbao, R., Borchert, L. F., Caron, L. P., Counillon, F., and Danabasoglu, G.: North
Atlantic climate far more predictable than models imply, Nature, 583,
796–800, 2020. a
Smith, K. A., Hannaford, J., Bloomfield, J., McCarthy, M., Parry, S., Barker, L. J., Svensson, C., Tanguy, M., Marchant, B., McKenzie, A., and Legg, T.: The
hydroclimatology of UK droughts: evidence from newly recovered and
reconstructed datasets from the late 19th century to present, AGU Fall Meeting Abstracts, December 2017,
H11O–02, 2017. a
Sornette, D. and Ouillon, G.: Dragon-kings: mechanisms, statistical methods and
empirical evidence, The European Physical Journal Special Topics, 205, 1–26,
2012. a
Soulsby, C., Dick, J., Scheliga, B., and Tetzlaff, D.: Taming the flood-How far
can we go with trees?, Hydrol. Process., 31, 3122–3126, 2017. a
Spinoni, J., Naumann, G., and Vogt, J. V.: Pan-European seasonal trends and
recent changes of drought frequency and severity, Global Planet.
Change, 148, 113–130, 2017. a
Sterl, A.: On the (in) homogeneity of reanalysis products, J. Climate,
17, 3866–3873, 2004. a
Stott, P. A., Christidis, N., Otto, F. E., Sun, Y., Vanderlinden, J. P., van Oldenborgh, G. J., Vautard, R., von Storch, H., Walton, P., Yiou, P., and Zwiers, F. W.:
Attribution of extreme weather and climate-related events, WIRES Clim. Change, 7, 23–41, 2016. a
Strazzo, S., Collins, D. C., Schepen, A., Wang, Q., Becker, E., and Jia, L.:
Application of a hybrid statistical–dynamical system to seasonal prediction
of North American temperature and precipitation, Mon. Weather Rev., 147,
607–625, 2019. a
Sun, X., Li, Z., and Tian, Q.: Assessment of hydrological drought based on
nonstationary runoff data, Hydrol. Res., 51, 894–910, https://doi.org/10.2166/nh.2020.029, 2020b. a
Sutcliffe, J. and Parks, Y.: The hydrology of the Nile, IAHS Special
Publication, no. 5, IAHS Press, Institute of hydrology, Wallingford,
Oxfordshire, 1999. a
Sutton, R. T. and Dong, B.: Atlantic Ocean influence on a shift in European
climate in the 1990s, Nat. Geosci., 5, 788–792, 2012. a
Tallaksen, L. M. and Van Lanen, H. A.: Hydrological drought: processes and
estimation methods for streamflow and groundwater, vol. 48, Elsevier, Amsterdam, 2004. a
Tan, X. and Shao, D.: Precipitation trends and teleconnections identified using
quantile regressions over Xinjiang, China, Int. J. Climatol., 37, 1510–1525, 2017. a
Taylor, S. J. and Letham, B.: Forecasting at Scale, Am. Stat.,
72, 37–45, https://doi.org/10.1080/00031305.2017.1380080, 2018. a
Theil, H.: A Rank-Invariant Method of Linear and Polynomial Regression Analysis, in: Henri Theil's Contributions to Economics and Econometrics, edited by: Raj, B. and Koerts, J., Advanced Studies in Theoretical and Applied Econometrics, vol. 23, Springer, Dordrecht, https://doi.org/10.1007/978-94-011-2546-8_20, 1992. a
Thirumalai, K., DiNezio, P. N., Okumura, Y., and Deser, C.: Extreme
temperatures in Southeast Asia caused by El Niño and worsened by global
warming, Nat. Commun., 8, 1–8, 2017. a
Thorne, P. W., Allan, R. J., Ashcroft, L., Brohan, P., Dunn, R. H., Menne, M. J., Pearce, P. R., Picas, J., Willett, K. M., Benoy, M., and Bronnimann, S.: Toward an integrated
set of surface meteorological observations for climate science and
applications, B. Am. Meteorol. Soc., 98,
2689–2702, 2017. a
Thyer, M., Frost, A. J., and Kuczera, G.: Parameter estimation and model
identification for stochastic models of annual hydrological data: Is the
observed record long enough?, J. Hydrol., 330, 313–328, 2006. a
Torralba, V., Doblas-Reyes, F. J., and Gonzalez-Reviriego, N.: Uncertainty in
recent near-surface wind speed trends: a global reanalysis intercomparison,
Environ. Res. Lett., 12, 114019, https://doi.org/10.1088/1748-9326/aa8a58, 2017. a, b, c, d
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The changing
character of precipitation, B. Am. Meteorol. Soc.,
84, 1205–1218, 2003. a
Trenberth, K. E., Fasullo, J. T., and Shepherd, T. G.: Attribution of climate
extreme events, Nat. Clim. Change, 5, 725–730, 2015. a
Uhlemann, S., Thieken, A. H., and Merz, B.: A consistent set of trans-basin floods in Germany between 1952–2002, Hydrol. Earth Syst. Sci., 14, 1277–1295, https://doi.org/10.5194/hess-14-1277-2010, 2010. a
Ukkola, A. M., Prentice, I. C., Keenan, T. F., Van Dijk, A. I., Viney, N. R.,
Myneni, R. B., and Bi, J.: Reduced streamflow in water-stressed climates
consistent with CO2 effects on vegetation, Nat. Clim. Change, 6, 75–78,
2016. a
Ummenhofer, C. C. and Meehl, G. A.: Extreme weather and climate events with
ecological relevance: a review, Philos. T. Roy. Soc. B, 372, 20160135, https://doi.org/10.1098/rstb.2016.0135, 2017. a
United States Army Corps of Engineers: Incorporating sea level change in
civil works ER 1110-2-8162, Sea Level Change Calculator,
available at: https://www.usace.army.mil/corpsclimate/Climate_Preparedness_and_Resilience/App_Flood_Risk_Reduct_Sandy_Rebuild/SL_change_curve_calc/ (last access: 6 July 2021),
2019. a
Van den Brink, H., Können, G., Opsteegh, J., Van Oldenborgh, G., and
Burgers, G.: Estimating return periods of extreme events from ECMWF seasonal
forecast ensembles, Int. J. Climatol., 25, 1345–1354, 2005. a
Vecchi, G. A., Zhao, M., Wang, H., Villarini, G., Rosati, A., Kumar, A., Held,
I. M., and Gudgel, R.: Statistical–dynamical predictions of seasonal North
Atlantic hurricane activity, Mon. Weather Rev., 139, 1070–1082, 2011. a
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A
multiscalar drought index sensitive to global warming: the standardized
precipitation evapotranspiration index, J. Climate, 23, 1696–1718,
2010. a
Vicente‐Serrano, S. M., Peña‐Gallardo, M., Hannaford, J., Murphy, C., Lorenzo‐Lacruz, J., Dominguez‐Castro, F., López‐Moreno, J. I., Beguería, S., Noguera, I., Harrigan, S., and Vidal, J. P.: Climate, irrigation,
and land cover change explain streamflow trends in countries bordering the
northeast Atlantic, Geophys. Res. Lett., 46, 10821–10833, 2019. a, b
Vicente‐Serrano, S. M., Domínguez‐Castro, F., Murphy, C., Hannaford, J., Reig, F., Peña‐Angulo, D., Tramblay, Y., Trigo, R. M., Mac Donald, N., Luna, M. Y., and Mc Carthy, M.: Long-term variability and trends in meteorological
droughts in Western Europe (1851–2018), Int. J. Climatol., 41, E690–E717, 2021. a
Villarini, G. and Serinaldi, F.: Development of statistical models for at-site
probabilistic seasonal rainfall forecast, Int. J. Climatol., 32, 2197–2212, 2012. a
Villarini, G. and Slater, L. J.: Examination of changes in annual maximum gauge
height in the continental United States using quantile regression, J. Hydrol. Eng., 23, 06017010, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001620, 2018. a
Villarini, G. and Zhang, W.: Projected changes in flooding: a continental US
perspective, Ann. NY Acad. Sci., 1–9, https://doi.org/10.1111/nyas.14359, 2020. a
Villarini, G., Serinaldi, F., Smith, J. A., and Krajewski, W. F.: On the
stationarity of annual flood peaks in the continental United States during
the 20th century, Water Resour. Res., 45, 2009a. a
Villarini, G., Smith, J. A., Serinaldi, F., Bales, J., Bates, P. D., and
Krajewski, W. F.: Flood frequency analysis for nonstationary annual peak
records in an urban drainage basin, Adv. Water Resour., 32,
1255–1266, 2009b. a
Villarini, G., Taylor S., Wobus, C., Vogel, R., Hecht, J., White, K. D., Baker, B., Gilroy, K., Olsen, J. R., and Raff, D.: Floods and Nonstationarity: A Review, CWTS 2018-01, U.S. Army Corps of Engineers, Washington, DC, 2018. a
Vogel, R. M., Rosner, A., and Kirshen, P. H.: Brief Communication: Likelihood of societal preparedness for global change: trend detection, Nat. Hazards Earth Syst. Sci., 13, 1773–1778, https://doi.org/10.5194/nhess-13-1773-2013, 2013. a
Volpi, E., Fiori, A., Grimaldi, S., Lombardo, F., and Koutsoyiannis, D.: One
hundred years of return period: Strengths and limitations, Water Resour.
Res., 51, 8570–8585, 2015. a
Von Storch, H. and Zwiers, F. W.: Statistical analysis in climate research,
Cambridge university press, Cambridge, 2001. a
Wagenmakers, E.-J. and Farrell, S.: AIC model selection using Akaike weights,
Psychon. B. Rev., 11, 192–196, 2004. a
Walsh, K. J., McBride, J. L., Klotzbach, P. J., Balachandran, S., Camargo, S. J., Holland, G., Knutson, T. R., Kossin, J. P., Lee, T. C., Sobel, A., and Sugi, M.: Tropical cyclones and climate change, WIRES Clim. Change, 7, 65–89, 2016. a
Walz, M. A., Befort, D. J., Kirchner-Bossi, N. O., Ulbrich, U., and Leckebusch,
G. C.: Modelling serial clustering and inter-annual variability of European
winter windstorms based on large-scale drivers, Int. J. Climatol., 38, 3044–3057, https://doi.org/10.1002/joc.5481, 2018. a
Wang, Q., Schepen, A., and Robertson, D. E.: Merging seasonal rainfall
forecasts from multiple statistical models through Bayesian model averaging,
J. Climate, 25, 5524–5537, 2012. a
Wang, S. S., Zhao, L., Yoon, J.-H., Klotzbach, P., and Gillies, R. R.:
Quantitative attribution of climate effects on Hurricane Harvey's extreme
rainfall in Texas, Environ. Res. Lett., 13, 054014, https://doi.org/10.1088/1748-9326/aabb85, 2018. a
Wang, X. L., Zwiers, F. W., Swail, V. R., and Feng, Y.: Trends and variability
of storminess in the Northeast Atlantic region, 1874–2007, Clim. Dynam.,
33, 1179, 2009. a
Ward, K., Lauf, S., Kleinschmit, B., and Endlicher, W.: Heat waves and urban
heat islands in Europe: A review of relevant drivers, Sci. Total Environ., 569, 527–539, 2016. a
Warner, R. F.: Influence of climate change and climatic variability on the hydrologic regime and water resources. International symposium, International union of geodesy and geophysics, General assembly, 19, Vancouver, 1987, 168, 327–338, 1987. a
Wasko, C. and Sharma, A.: Quantile regression for investigating scaling of
extreme precipitation with temperature, Water Resour. Res., 50,
3608–3614, 2014. a
Wasko, C. and Sharma, A.: Steeper temporal distribution of rain intensity at
higher temperatures within Australian storms, Nat. Geosci., 8, 527–529,
2015. a
Wasko, C., Sharma, A., and Lettenmaier, D. P.: Increases in temperature do not
translate to increased flooding, Nat. Commun., 10, 1–3, 2019. a
Wasko, C., Nathan, R., and Peel, M. C.: Changes in antecedent soil moisture
modulate flood seasonality in a changing climate, Water Resour. Res.,
56, e2019WR026300, https://doi.org/10.1029/2019WR026300, 2020a. a, b
Wasko, C., Nathan, R., and Peel, M. C.: Trends in global flood and streamflow
timing based on local water year, Water Resour. Res., 56,
e2020WR027233, https://doi.org/10.1029/2020WR027233, 2020b. a, b, c, d
Wasko, C., Westra, S., Nathan, R., Orr, H. G., Villarini, G.,
Villalobos Herrera, R., and Fowler, H. J.: Incorporating climate change in
flood estimation guidance, Philos. T. Roy. Soc. A,
379, 20190548, https://doi.org/10.1098/rsta.2019.0548, 2021. a, b
Weber, H. and Wunderle, S.: Drifting Effects of NOAA Satellites on Long-Term
Active Fire Records of Europe, Remote Sens., 11, 467, https://doi.org/10.3390/rs11040467, 2019. a
Weisheimer, A., Schaller, N., O'Reilly, C., MacLeod, D. A., and Palmer, T.:
Atmospheric seasonal forecasts of the twentieth century: multi-decadal
variability in predictive skill of the winter North Atlantic Oscillation
(NAO) and their potential value for extreme event attribution, Q. J. Roy. Meteorol. Soc., 143, 917–926, 2017. a
Weisheimer, A., Befort, D. J., MacLeod, D., Palmer, T., O’Reilly, C., and
Strømmen, K.: Seasonal Forecasts of the Twentieth Century, B. Am. Meteorol. Soc., 101, E1413–E1426, 2020. a
Wenzel, S., Cox, P. M., Eyring, V., and Friedlingstein, P.: Emergent
constraints on climate-carbon cycle feedbacks in the CMIP5 Earth system
models, J. Geophys. Res.-Biogeo., 119, 794–807, 2014. a
Wester, P., Mishra, A., Mukherji, A., and Shrestha, A. B.: The Hindu Kush
Himalaya assessment: mountains, climate change, sustainability and people,
Springer Nature, Cham, 2019. a
Westra, S. and Sisson, S. A.: Detection of non-stationarity in precipitation
extremes using a max-stable process model, J. Hydrol., 406,
119–128, 2011. a
Westra, S., Alexander, L. V., and Zwiers, F. W.: Global increasing trends in
annual maximum daily precipitation, J. Climate, 26, 3904–3918,
https://doi.org/10.1175/JCLI-D-12-00502.1, 2013. a, b
Whitfield, P. H., Burn, D. H., Hannaford, J., Higgins, H., Hodgkins, G. A.,
Marsh, T., and Looser, U.: Reference hydrologic networks I. The status and
potential future directions of national reference hydrologic networks for
detecting trends, Hydrolog. Sci. J., 57, 1562–1579, 2012. a
Wilby, R.: When and where might climate change be detectable in UK river
flows?, Geophys. Res. Lett., 33, L19407, https://doi.org/10.1029/2006GL027552, 2006. a
Wilby, R. and Murphy, C.: Decision-making by water managers despite climate
uncertainty, in: The Oxford Handbook of Planning for Climate Change Hazards, Oxford University Press, Oxford, 2019. a
Wilby, R. L. and Quinn, N. W.: Reconstructing multi-decadal variations in
fluvial flood risk using atmospheric circulation patterns, J. Hydrol., 487, 109–121, 2013. a
Wilby, R. L., Clifford, N. J., De Luca, P., Harrigan, S., Hillier, J. K., Hodgkins, R., Johnson, M. F., Matthews, T. K., Murphy, C., Noone, S. J., and Parry, S.: The “dirty dozen” of freshwater science: detecting then
reconciling hydrological data biases and errors, WIRES Water, 4, e1209, https://doi.org/10.1002/wat2.1209, 2017. a
Wilcock, D. and Wilcock, F.: Modelling the hydrological impacts of
channelization on streamflow characteristics in a Northern Ireland catchment,
IAHS-AISH P., 231, 41–48, 1995. a
Wild, S., Befort, D. J., and Leckebusch, G. C.: Was the extreme storm season in
winter 2013/14 over the North Atlantic and the United Kingdom triggered by
changes in the West Pacific warm pool?, B. Am. Meteorol. Soc., 96, S29–S34, 2015. a
Wilhite, D. A.: Droughts: a global assesment, Routledge, Taylor & Francis, United Kingdom, 2016. a
Wine, M. L.: Under non-stationarity securitization contributes to uncertainty
and Tragedy of the Commons, J. Hydrol., 568, 716–721, 2019. a
WMO: Handbook of Drought Indicators and Indices, World Meteorological
Organization (WMO) and Global Water Partnership (GWP), Geneva, Switzerland, 2016. a
Woo, G.: Downward Counterfactual Search for Extreme Events, Front. Earth
Sci., 7, 340, 2019. a
Woollings, T. and Blackburn, M.: The North Atlantic jet stream under climate
change and its relation to the NAO and EA patterns, J. Climate, 25,
886–902, 2012. a
Woollings, T., Gregory, J. M., Pinto, J. G., Reyers, M., and Brayshaw, D. J.:
Response of the North Atlantic storm track to climate change shaped by
ocean–atmosphere coupling, Nat. Geosci., 5, 313–317, 2012. a
Wu, C., Yeh, P. J.-F., Chen, Y.-Y., Hu, B. X., and Huang, G.: Future
Precipitation-Driven Meteorological Drought Changes in the CMIP5 Multimodel
Ensembles under 1.5 ∘C and 2 ∘C Global Warming, J. Hydrometeorol., 21, 2177–2196, 2020a. a
Wu, J., Han, Z., Xu, Y., Zhou, B., and Gao, X.: Changes in Extreme Climate
Events in China Under 1.5 ∘C–4 ∘C Global Warming Targets: Projections
Using an Ensemble of Regional Climate Model Simulations, J. Geophys. Res.-Atmos., 125, e2019JD031057, https://doi.org/10.1029/2019JD031057, 2020b. a
Wunsch, C.: The interpretation of short climate records, with comments on the
North Atlantic and Southern Oscillations, B. Am. Meteorol. Soc., 80, 245–256, 1999. a
Xu, S., Wu, C., Wang, L., Gonsamo, A., Shen, Y., and Niu, Z.: A new
satellite-based monthly precipitation downscaling algorithm with
non-stationary relationship between precipitation and land surface
characteristics, Remote Sens. Environ., 162, 119–140, 2015. a
Xu, Z., FitzGerald, G., Guo, Y., Jalaludin, B., and Tong, S.: Impact of
heatwave on mortality under different heatwave definitions: a systematic
review and meta-analysis, Environment Int., 89, 193–203, 2016. a
Yan, L., Xiong, L., Guo, S., Xu, C.-Y., Xia, J., and Du, T.: Comparison of four
nonstationary hydrologic design methods for changing environment, J. Hydrol., 551, 132–150, 2017. a
Yosef, Y., Aguilar, E., and Alpert, P.: Changes in extreme temperature and
precipitation indices: Using an innovative daily homogenized database in
Israel, Int. J. Climatol., 39, 5022–5045, 2019. a
Young, I. R. and Ribal, A.: Multiplatform evaluation of global trends in wind
speed and wave height, Science, 364, 548–552,
https://doi.org/10.1126/science.aav9527, 2019. a
Yuan, X., Wang, L., Wu, P., Ji, P., Sheffield, J., and Zhang, M.: Anthropogenic
shift towards higher risk of flash drought over China, Nat. Commun.,
10, 1–8, 2019. a
Yue, S., Ouarda, T. B., Bobée, B., Legendre, P., and Bruneau, P.: Approach
for describing statistical properties of flood hydrograph, J. Hydrol. Eng., 7, 147–153, 2002a. a
Yue, S., Pilon, P., and Cavadias, G.: Power of the Mann–Kendall and Spearman's
rho tests for detecting monotonic trends in hydrological series, J. Hydrol., 259, 254–271, 2002b. a
Yule, G. U.: Why do we sometimes get nonsense-correlations between
Time-Series?–a study in sampling and the nature of time-series, J. R. Stat. Soc., 89, 1–63, 1926. a
Zhai, A. R. and Jiang, J. H.: Dependence of US hurricane economic loss on
maximum wind speed and storm size, Environ. Res. Lett., 9,
064019, https://doi.org/10.1088/1748-9326/9/6/064019, 2014. a
Zhai, P., Zhou, B., and Chen, Y.: A review of climate change attribution
studies, J. Meteorol. Res., 32, 671–692, 2018. a
Zhan, W., He, X., Sheffield, J., and Wood, E. F.: Projected seasonal changes in
large-scale global precipitation and temperature extremes based on the CMIP5
ensemble, J. Climate, 33, 5651–5671, 2020. a
Zhang, W., Villarini, G., Vecchi, G. A., and Smith, J. A.: Urbanization
exacerbated the rainfall and flooding caused by hurricane Harvey in Houston,
Nature, 563, 384–388, 2018. a
Zhang, X., Hegerl, G., Zwiers, F. W., and Kenyon, J.: Avoiding inhomogeneity in
percentile-based indices of temperature extremes, J. Climate, 18,
1641–1651, 2005. a
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson,
T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in
extremes based on daily temperature and precipitation data, WIRES Clim. Change, 2, 851–870, 2011. a
Zhang, Y. and Fueglistaler, S.: How tropical convection couples high moist
static energy over land and ocean, Geophys. Res. Lett., 47,
e2019GL086387, https://doi.org/10.1029/2019GL086387, 2020. a
Zhao, T., Bennett, J. C., Wang, Q., Schepen, A., Wood, A. W., Robertson, D. E.,
and Ramos, M.-H.: How suitable is quantile mapping for postprocessing GCM
precipitation forecasts?, J. Climate, 30, 3185–3196, 2017. a
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., and Maraun, D.: A typology of compound weather and climate events, Nature
reviews earth & environment, 1, 333–347, 2020. a
Zulkafli, Z., Perez, K., Vitolo, C., Buytaert, W., Karpouzoglou, T., Dewulf,
A., De Bievre, B., Clark, J., Hannah, D. M., and Shaheed, S.: User-driven
design of decision support systems for polycentric environmental resources
management, Environ. Modell. Softw., 88, 58–73, 2017. a
Short summary
Weather and water extremes have devastating effects each year. One of the principal challenges for society is understanding how extremes are likely to evolve under the influence of changes in climate, land cover, and other human impacts. This paper provides a review of the methods and challenges associated with the detection, attribution, management, and projection of nonstationary weather and water extremes.
Weather and water extremes have devastating effects each year. One of the principal challenges...