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.
Review article
| Highlight paper
| 
07 Jul 2021
Review article | Highlight paper |
| 07 Jul 2021
| 07 Jul 2021
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 W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025, https://doi.org/10.5194/hess-29-2393-2025, 2025
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 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
This article is included in the Encyclopedia of Geosciences
Emma Ford, Manuela I. Brunner, Hannah Christensen, and Louise Slater
EGUsphere, https://doi.org/10.5194/egusphere-2025-1493, https://doi.org/10.5194/egusphere-2025-1493, 2025
Short summary
Short summary
This study aims to improve prediction and understanding of extreme flood events in UK near-natural catchments. We develop a machine learning framework to assess the contribution of different features to flood magnitude estimation. We find weather patterns are weak predictors and stress the importance of evaluating model performance across and within catchments.
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
Short summary
Short summary
This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Lisa Claire Orme, Francis Ludlow, Natasha Langton, Jenny Kristina Sjӧstrӧm, Malin Kylander, Conor Murphy, Sean Pyne-O'Donnell, Jonathan Turner, Nannan Li, Sarah Jessica Davies, Fraser Mitchell, and John Alphonsus Matthews
EGUsphere, https://doi.org/10.5194/egusphere-2025-3737, https://doi.org/10.5194/egusphere-2025-3737, 2025
This preprint is open for discussion and under review for Climate of the Past (CP).
Short summary
Short summary
The link between storms and volcanic eruptions in northwest Europe is investigated. Past storminess was reconstructed using sand deposits in a coastal peatbog from western Ireland. Together with similar records from northwest Europe this shows six periods of high storminess in the last 2500 years, coinciding roughly with the largest eruptions of this time. A record of past windiness for Ireland (600–1616CE) created from the “Irish Annals” supports enhanced storminess for 3 years after eruptions.
This article is included in the Encyclopedia of Geosciences
Annie Y.-Y. Chang, Shaun Harrigan, Maria-Helena Ramos, Massimiliano Zappa, Christian M. Grams, Daniela I. V. Domeisen, and Konrad Bogner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3411, https://doi.org/10.5194/egusphere-2025-3411, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study presents a machine learning-aided hybrid forecasting framework to improve early warnings of low flows in the European Alps. It combines weather regime information, streamflow observations, and model simulations (EFAS). Even using only weather regime data improves predictions over climatology, while integrating different data sources yields the best result, emphasizing the value of integrating diverse data sources.
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., 25, 1937–1942, https://doi.org/10.5194/nhess-25-1937-2025, https://doi.org/10.5194/nhess-25-1937-2025, 2025
Short summary
Short summary
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 of landslide risk.
This article is included in the Encyclopedia of Geosciences
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025, https://doi.org/10.5194/hess-29-2393-2025, 2025
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 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
This article is included in the Encyclopedia of Geosciences
Emma Ford, Manuela I. Brunner, Hannah Christensen, and Louise Slater
EGUsphere, https://doi.org/10.5194/egusphere-2025-1493, https://doi.org/10.5194/egusphere-2025-1493, 2025
Short summary
Short summary
This study aims to improve prediction and understanding of extreme flood events in UK near-natural catchments. We develop a machine learning framework to assess the contribution of different features to flood magnitude estimation. We find weather patterns are weak predictors and stress the importance of evaluating model performance across and within catchments.
This article is included in the Encyclopedia of Geosciences
Bailey J. Anderson, Eduardo Muñoz-Castro, Lena M. Tallaksen, Alessia Matano, Jonas Götte, Rachael Armitage, Eugene Magee, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-1391, https://doi.org/10.5194/egusphere-2025-1391, 2025
Short summary
Short summary
When flood happen during, or shortly after, droughts, the impacts of can be magnified. In hydrological research, defining these events can be challenging. Here we have tried to address some of the challenges defining these events using real-world examples. We show how different methodological approaches differ in their results, make suggestions on when to use which approach, and outline some pitfalls of which researchers should be aware.
This article is included in the Encyclopedia of Geosciences
Eduardo Muñoz-Castro, Bailey J. Anderson, Paul C. Astagneau, Daniel L. Swain, Pablo A. Mendoza, and Manuela I. Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-781, https://doi.org/10.5194/egusphere-2025-781, 2025
Short summary
Short summary
Flood impacts can be enhanced when they occur after droughts, yet the effectiveness of hydrological models in simulating these events remains unclear. Here, we calibrated four conceptual hydrological models across 63 catchments in Chile and Switzerland to assess their ability to detect streamflow extremes and their transitions. We show that drought-to-flood transitions are more difficult to capture in semi-arid high-mountain catchments than in humid low-elevation catchments.
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
Hydrol. Earth Syst. Sci., 29, 1319–1333, https://doi.org/10.5194/hess-29-1319-2025, https://doi.org/10.5194/hess-29-1319-2025, 2025
Short summary
Short summary
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 data sets to show that annual memory is typically absent in precipitation but strong in terrestrial water stores and also present in evaporation and streamflow (including low flows and floods). Our experiments show that hysteretic models provide behaviour that is consistent with these observed memory behaviours.
This article is included in the Encyclopedia of Geosciences
Alexandre Dunant, Tom R. Robinson, Alexander L. 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
Nat. Hazards Earth Syst. Sci., 25, 267–285, https://doi.org/10.5194/nhess-25-267-2025, https://doi.org/10.5194/nhess-25-267-2025, 2025
Short summary
Short summary
Natural hazards like earthquakes often trigger other disasters, such as landslides, creating complex chains of impacts. We developed a risk model using a mathematical approach called hypergraphs to efficiently measure the impact of interconnected hazards. We showed that it can predict broad patterns of damage to buildings and roads from the 2015 Nepal earthquake. The model's efficiency allows it to generate multiple disaster scenarios, even at a national scale, to support preparedness plans.
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
Short summary
Short summary
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
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
Short summary
Short summary
This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
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
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
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
Short summary
Short summary
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
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
Short summary
Short summary
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
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...
