Articles | Volume 28, issue 9
https://doi.org/10.5194/hess-28-2081-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2081-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 May 2024
Research article |
| 08 May 2024
| 08 May 2024
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
Marcus Buechel
CORRESPONDING AUTHOR
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
Louise Slater
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
Simon Dadson
School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
UK Centre for Ecology & Hydrology, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
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Simon Moulds, Louise Slater, Louise Arnal, and Andrew Wood
EGUsphere, https://doi.org/10.31223/X5X405, https://doi.org/10.31223/X5X405, 2024
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Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to four months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to four months ahead in many locations, although in general the skill declines with increasing lead time.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
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This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
Alex Dunant, Tom R. Robinson, Alexander Logan Densmore, Nick J. Rosser, Ragindra Man Rajbhandari, Mark Kincey, Sihan Li, Prem Raj Awasthi, Max Van Wyk de Vries, Ramesh Guragain, Erin Harvey, and Simon Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2024-1374, https://doi.org/10.5194/egusphere-2024-1374, 2024
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Our study introduces a new method using hypergraph theory to assess risks from interconnected natural hazards. Traditional models often overlook how these hazards can interact and worsen each other's effects. By applying our method to the 2015 Nepal earthquake, we successfully demonstrated its ability to predict broad damage patterns, despite slightly overestimating impacts. Being able to anticipate the effects of complex, interconnected hazards is critical for disaster preparedness.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Proc. IAHS, 385, 121–127, https://doi.org/10.5194/piahs-385-121-2024, https://doi.org/10.5194/piahs-385-121-2024, 2024
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This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
Bailey J. Anderson, Manuela I. Brunner, Louise J. Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 28, 1567–1583, https://doi.org/10.5194/hess-28-1567-2024, https://doi.org/10.5194/hess-28-1567-2024, 2024
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Elasticityrefers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.
Maximillian Van Wyk de Vries, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Alexander L. Densmore, Tek Bahadur Dong, Alexandre Dunant, Erin L. Harvey, Ganesh K. Jimee, Mark E. Kincey, Katie Oven, Sarmila Paudyal, Dammar Singh Pujara, Anuradha Puri, Ram Shrestha, Nick J. Rosser, and Simon J. Dadson
EGUsphere, https://doi.org/10.5194/egusphere-2024-397, https://doi.org/10.5194/egusphere-2024-397, 2024
Preprint archived
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This study focuses on understanding soil moisture, a key factor for evaluating hillslope stability and landsliding. In Nepal, where landslides are common, we used a computer model to better understand how rapidly soil dries out after the monsoon season. We calibrated the model using field data and found that, by adjusting soil properties, we could predict moisture levels more accurately. This helps understand where landslides might occur, even where direct measurements are not possible.
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-40, https://doi.org/10.5194/nhess-2024-40, 2024
Revised manuscript under review for NHESS
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Mapping exposure to landslides is necessary to mitigate risk and reduce vulnerability. In this study, we show that there is a poor correlation between building damage and deaths from landslides- such that the deadliest landslides do not always destroy the most buildings and vice versa. This has important implications for our management on landslide risk.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
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This study presents long-term (i.e., 1940–2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). Our proposed GTWS-MLrec performs overall as well as, or is more reliable than, previous TWS datasets.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
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Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Louise J. Slater, Chris Huntingford, Richard F. Pywell, John W. Redhead, and Elizabeth J. Kendon
Earth Syst. Dynam., 13, 1377–1396, https://doi.org/10.5194/esd-13-1377-2022, https://doi.org/10.5194/esd-13-1377-2022, 2022
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This work considers how wheat yields are affected by weather conditions during the three main wheat growth stages in the UK. Impacts are strongest in years with compound weather extremes across multiple growth stages. Future climate projections are beneficial for wheat yields, on average, but indicate a high risk of unseen weather conditions which farmers may struggle to adapt to and mitigate against.
Thomas Lees, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens De Bruijn, Reetik Kumar Sahu, Peter Greve, Louise Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, https://doi.org/10.5194/hess-26-3079-2022, 2022
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Despite the accuracy of deep learning rainfall-runoff models, we are currently uncertain of what these models have learned. In this study we explore the internals of one deep learning architecture and demonstrate that the model learns about intermediate hydrological stores of soil moisture and snow water, despite never having seen data about these processes during training. Therefore, we find evidence that the deep learning approach learns a physically realistic mapping from inputs to outputs.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
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Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
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Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Thomas Lees, Marcus Buechel, Bailey Anderson, Louise Slater, Steven Reece, Gemma Coxon, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, https://doi.org/10.5194/hess-25-5517-2021, 2021
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We used deep learning (DL) models to simulate the amount of water moving through a river channel (discharge) based on the rainfall, temperature and potential evaporation in the previous days. We tested the DL models on catchments across Great Britain finding that the model can accurately simulate hydrological systems across a variety of catchment conditions. Ultimately, the model struggled most in areas where there is chalky bedrock and where human influence on the catchment is large.
Louise J. Slater, Bailey Anderson, Marcus Buechel, Simon Dadson, Shasha Han, Shaun Harrigan, Timo Kelder, Katie Kowal, Thomas Lees, Tom Matthews, Conor Murphy, and Robert L. Wilby
Hydrol. Earth Syst. Sci., 25, 3897–3935, https://doi.org/10.5194/hess-25-3897-2021, https://doi.org/10.5194/hess-25-3897-2021, 2021
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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.
Jian Peng, Simon Dadson, Feyera Hirpa, Ellen Dyer, Thomas Lees, Diego G. Miralles, Sergio M. Vicente-Serrano, and Chris Funk
Earth Syst. Sci. Data, 12, 753–769, https://doi.org/10.5194/essd-12-753-2020, https://doi.org/10.5194/essd-12-753-2020, 2020
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Africa has been severely influenced by intense drought events, which has led to crop failure, food shortages, famine, epidemics and even mass migration. The current study developed a high spatial resolution drought dataset entirely from satellite-based products. The dataset has been comprehensively inter-compared with other drought indicators and may contribute to an improved characterization of drought risk and vulnerability and minimize drought's impact on water and food security in Africa.
Louise J. Slater, Guillaume Thirel, Shaun Harrigan, Olivier Delaigue, Alexander Hurley, Abdou Khouakhi, Ilaria Prosdocimi, Claudia Vitolo, and Katie Smith
Hydrol. Earth Syst. Sci., 23, 2939–2963, https://doi.org/10.5194/hess-23-2939-2019, https://doi.org/10.5194/hess-23-2939-2019, 2019
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This paper explores the benefits and advantages of R's usage in hydrology. We provide an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology as well as a roadmap for R’s future within the discipline.
John K. Hillier, Geoffrey R. Saville, Mike J. Smith, Alister J. Scott, Emma K. Raven, Jonathon Gascoigne, Louise J. Slater, Nevil Quinn, Andreas Tsanakas, Claire Souch, Gregor C. Leckebusch, Neil Macdonald, Alice M. Milner, Jennifer Loxton, Rebecca Wilebore, Alexandra Collins, Colin MacKechnie, Jaqui Tweddle, Sarah Moller, MacKenzie Dove, Harry Langford, and Jim Craig
Geosci. Commun., 2, 1–23, https://doi.org/10.5194/gc-2-1-2019, https://doi.org/10.5194/gc-2-1-2019, 2019
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Worldwide there is intense interest in converting research excellence in universities into commercial success, but there has been scant attention devoted to exactly how individual scientists' workload and incentive structures may be a key barrier to this. Our work reveals the real challenge posed by a time-constrained university culture, better describes how work with business might fit into an academic job, and gives tips on working together in an
user guidefor scientists and (re)insurers.
Stefanie R. Lutz, Andrea Popp, Tim van Emmerik, Tom Gleeson, Liz Kalaugher, Karsten Möbius, Tonie Mudde, Brett Walton, Rolf Hut, Hubert Savenije, Louise J. Slater, Anna Solcerova, Cathelijne R. Stoof, and Matthias Zink
Hydrol. Earth Syst. Sci., 22, 3589–3599, https://doi.org/10.5194/hess-22-3589-2018, https://doi.org/10.5194/hess-22-3589-2018, 2018
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Media play a key role in the communication between scientists and the general public. However, the interaction between scientists and journalists is not always straightforward. In this opinion paper, we present insights from hydrologists and journalists into the benefits, aftermath and potential pitfalls of science–media interaction. We aim to encourage scientists to participate in the diverse and evolving media landscape, and we call on the scientific community to support scientists who do so.
Benoit P. Guillod, Richard G. Jones, Simon J. Dadson, Gemma Coxon, Gianbattista Bussi, James Freer, Alison L. Kay, Neil R. Massey, Sarah N. Sparrow, David C. H. Wallom, Myles R. Allen, and Jim W. Hall
Hydrol. Earth Syst. Sci., 22, 611–634, https://doi.org/10.5194/hess-22-611-2018, https://doi.org/10.5194/hess-22-611-2018, 2018
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Assessing the potential impacts of extreme events such as drought and flood requires large datasets of such events, especially when looking at the most severe and rare events. Using a state-of-the-art climate modelling infrastructure that is simulating large numbers of weather time series on volunteers' computers, we generate such a large dataset for the United Kingdom. The dataset covers the recent past (1900–2006) as well as two future time periods (2030s and 2080s).
Huw W. Lewis, Juan Manuel Castillo Sanchez, Jennifer Graham, Andrew Saulter, Jorge Bornemann, Alex Arnold, Joachim Fallmann, Chris Harris, David Pearson, Steven Ramsdale, Alberto Martínez-de la Torre, Lucy Bricheno, Eleanor Blyth, Victoria A. Bell, Helen Davies, Toby R. Marthews, Clare O'Neill, Heather Rumbold, Enda O'Dea, Ashley Brereton, Karen Guihou, Adrian Hines, Momme Butenschon, Simon J. Dadson, Tamzin Palmer, Jason Holt, Nick Reynard, Martin Best, John Edwards, and John Siddorn
Geosci. Model Dev., 11, 1–42, https://doi.org/10.5194/gmd-11-1-2018, https://doi.org/10.5194/gmd-11-1-2018, 2018
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In the real world the atmosphere, oceans and land surface are closely interconnected, and yet prediction systems tend to treat them in isolation. Those feedbacks are often illustrated in natural hazards, such as when strong winds lead to large waves and coastal damage, or when prolonged rainfall leads to saturated ground and high flowing rivers. For the first time, we have attempted to represent some of the feedbacks between sky, sea and land within a high-resolution forecast system for the UK.
Fiona J. Clubb, Simon M. Mudd, David T. Milodowski, Declan A. Valters, Louise J. Slater, Martin D. Hurst, and Ajay B. Limaye
Earth Surf. Dynam., 5, 369–385, https://doi.org/10.5194/esurf-5-369-2017, https://doi.org/10.5194/esurf-5-369-2017, 2017
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Floodplains and fluvial terraces can provide information about current and past river systems, helping to reveal how channels respond to changes in both climate and tectonics. We present a new method of identifying these features objectively from digital elevation models by analysing their slope and elevation compared to the modern river. We test our method in eight field sites, and find that it provides rapid and reliable extraction of floodplains and terraces across a range of landscapes.
T. R. Marthews, S. J. Dadson, B. Lehner, S. Abele, and N. Gedney
Hydrol. Earth Syst. Sci., 19, 91–104, https://doi.org/10.5194/hess-19-91-2015, https://doi.org/10.5194/hess-19-91-2015, 2015
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Modelling land surface water flow is of critical importance in the context of climate change predictions. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-known topographic index. Here we present new, higher-resolution parameter maps of the topographic index, which are ideal for land surface modelling applications and show important improvements on the previous standard maps from HYDRO1k.
N. C. MacKellar, S. J. Dadson, M. New, and P. Wolski
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-11093-2013, https://doi.org/10.5194/hessd-10-11093-2013, 2013
Revised manuscript not accepted
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations
Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
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Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
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On the use of streamflow transformations for hydrological model calibration
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Catchment response to climatic variability: implications for root zone storage and streamflow predictions
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Projections of streamflow intermittence under climate change in European drying river networks
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China
On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?
Achieving water budget closure through physical hydrological processes modelling: insights from a large-sample study
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Heavy-tailed flood peak distributions: What is the effect of the spatial variability of rainfall and runoff generation?
Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
State updating in the Xin'anjiang Model: Joint assimilating streamflow and multi-source soil moisture data via Asynchronous Ensemble Kalman Filter with enhanced Error Models
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Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA
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Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
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To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025, https://doi.org/10.5194/hess-29-27-2025, 2025
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The influence of watershed disturbances has proved challenging to disentangle from natural streamflow variability. This study evaluates the influence of time-varying hydrologic controls on rainfall–runoff in undisturbed and wildfire-disturbed watersheds using a novel time-series event separation method. Across watersheds, water year type and season influenced rainfall–runoff patterns. Accounting for these controls enabled clearer isolation of wildfire effects.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, https://doi.org/10.5194/hess-28-5511-2024, 2024
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Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
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This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
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Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
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Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
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We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
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Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
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We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
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Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
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We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
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This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
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This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
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A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
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Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
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We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
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Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
Preprint under review for HESS
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Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
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Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
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An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
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The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-230, https://doi.org/10.5194/hess-2024-230, 2024
Revised manuscript accepted for HESS
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Water budget non-closure is a widespread phenomenon among multisource datasets, which undermines the robustness of hydrological inferences. This study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset, and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-181, https://doi.org/10.5194/hess-2024-181, 2024
Revised manuscript accepted for HESS
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Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small compared to large catchments, and that spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show an effect. The results can improve estimations of occurrence probabilities of extreme floods.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-211, https://doi.org/10.5194/hess-2024-211, 2024
Revised manuscript accepted for HESS
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Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping better prepare for and respond to floods.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1464, https://doi.org/10.5194/egusphere-2024-1464, 2024
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Our study conducted a detailed analysis of runoff component and future trend in the Yarlung Tsangpo River basin owing to the existed differences in the published results, and find that the contributions of snowmelt and glacier melt runoff to streamflow were limited, both for ~5 % which were much lower than previous results. The streamflow there will continuously increase in the future, but the overestimated contribution from glacier melt would lead to an underestimation on such increasing trend.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
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Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
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Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
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Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
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A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
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Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
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Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
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Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart Lane, and Francesco Comiti
EGUsphere, https://doi.org/10.5194/egusphere-2024-1687, https://doi.org/10.5194/egusphere-2024-1687, 2024
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In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until an overparametrization limit is reached.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript accepted for HESS
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
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We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
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In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
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Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Revised manuscript accepted for HESS
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This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-118, https://doi.org/10.5194/hess-2024-118, 2024
Revised manuscript accepted for HESS
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This study develops an integrated framework based on the novel Driving index for changes in Precipitation-Runoff Relationships (DPRR) to explore the controls for changes in precipitation-runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation-runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
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It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2024-1030, https://doi.org/10.5194/egusphere-2024-1030, 2024
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Accurate early warning systems are crucial for reducing damages caused by flooding events. In this study, we demonstrate the potential of Long Short-Term Memory Networks for enhancing the forecast accuracy of hydrologic models employed in operational flood forecasting. The presented approach elevated the investigated hydrologic model’s forecast accuracy for further ahead predictions and at flood event runoff.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
EGUsphere, https://doi.org/10.5194/egusphere-2024-993, https://doi.org/10.5194/egusphere-2024-993, 2024
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This study uses deep learning to predict spatially contiguous water runoff in Switzerland from 1962–2023. It outperforms traditional models, requiring less data and computational power. Key findings include increased dry years and summer water scarcity. This method offers significant advancements in water monitoring.
Joško Trošelj and Naota Hanasaki
EGUsphere, https://doi.org/10.5194/egusphere-2024-595, https://doi.org/10.5194/egusphere-2024-595, 2024
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This study presents the first distributed hydrological simulation which confirms the claims raised by historians that the Eastward Diversion Project of the Tone River in Japan was conducted four centuries ago to increase low flows and subsequent travelling possibilities surrounding the Capitol Edo (Tokyo) using inland navigation. We reconstructed six historical river maps and indirectly validated the historical simulations with reachable ancient river ports via increased low-flow water levels.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
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Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Cited articles
Alton, P., Fisher, R., Los, S., and Williams, M.: Simulations of global evapotranspiration using semiempirical and mechanistic schemes of plant hydrology, Global Biogeochem. Cy., 23, 1–12, https://doi.org/10.1029/2009GB003540, 2009.
Anderson, B. J., Slater, L. J., Dadson, S. J., Blum, A. G., and Prosdocimi, I.: Statistical Attribution of the Influence of Urban and Tree Cover Change on Streamflow: A Comparison of Large Sample Statistical Approaches, Water Resour. Res., 58, 1–20, https://doi.org/10.1029/2021wr030742, 2022.
Andréassian, V.: Waters and forests: From historical controversy to scientific debate, J. Hydrol., 291, 1–27, https://doi.org/10.1016/j.jhydrol.2003.12.015, 2004.
Bastin, J.-F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., Zohner, C. M., and Crowther, T. W.: The global tree restoration potential, Science, 365, 76–79, https://doi.org/10.1126/science.aax0848, 2019.
Bathurst, J., Birkinshaw, S., Johnson, H., Kenny, A., Napier, A., Raven, S., Robinson, J., and Stroud, R.: Runoff, flood peaks and proportional response in a combined nested and paired forest plantation/peat grassland catchment, J. Hydrol., 564, 916–927, https://doi.org/10.1016/j.jhydrol.2018.07.039, 2018.
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, https://doi.org/10.1002/hyp.13802, 2020.
Beschta, R. L., Pyles, M. R., Skaugset, A. E., and Surfleet, C. G.: Peakflow responses to forest practices in the western cascades of Oregon, USA, J. Hydrol., 233, 102–120, https://doi.org/10.1016/S0022-1694(00)00231-6, 2000.
Best, M., Essery, R., and Cox, P.: JULES Technical Documentation MOSES 2.2 Technical Documentation, 36 pp., https://jules.jchmr.org/sites/default/files/2023-06/JULES-Technical-documentation-2.pdf (last access: 21 April 2024), 2009.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Betts, R. A., Boucher, O., Collins, M., Cox, P. M., Falloon, P. D., Gedney, N., Hemming, D. L., Huntingford, C., Jones, C. D., Sexton, D. M. H., and Webb, M. J.: Projected increase in continental runoff due to plant responses to increasing carbon dioxide, Nature, 448, 1037–1041, https://doi.org/10.1038/nature06045, 2007.
Beven, K.: Changing ideas in hydrology – The case of physically-based models, J. Hydrol., 105, 157–172, https://doi.org/10.1016/0022-1694(89)90101-7, 1989.
Beven, K.: Towards integrated environmental models of everywhere: Uncertainty, data and modelling as a learning process, Hydrol. Earth Syst. Sci., 11, 460–467, https://doi.org/10.5194/hess-11-460-2007, 2007.
Beven, K. and Lane, S.: On (in)validating environmental models. 1. Principles for formulating a Turing-like Test for determining when a model is fit-for purpose, Hydrol. Process., 36, 1–32, https://doi.org/10.1002/hyp.14704, 2022.
Beven, K., Smith, P. J., and Wood, A.: On the colour and spin of epistemic error (and what we might do about it), Hydrol. Earth Syst. Sci., 15, 3123–3133, https://doi.org/10.5194/hess-15-3123-2011, 2011.
Beven, K. J.: On hypothesis testing in hydrology: Why falsification of models is still a really good idea, Wiley Interdisciplin. Rev. Water, 5, e1278, https://doi.org/10.1002/wat2.1278, 2018.
Beven, K. J. and Cloke, H. L.: Comment on “Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water” by Eric F. Wood et al., Water Resour. Res., 48, 2–4, https://doi.org/10.1029/2011wr010982, 2012.
Birkinshaw, S. J., Bathurst, J. C., and Robinson, M.: 45 years of non-stationary hydrology over a forest plantation growth cycle, Coalburn catchment, Northern England, J. Hydrol., 519, 559–573, https://doi.org/10.1016/j.jhydrol.2014.07.050, 2014.
Blair, G. S., Beven, K., Lamb, R., Bassett, R., Cauwenberghs, K., Hankin, B., Dean, G., Hunter, N., Edwards, L., Nundloll, V., Samreen, F., Simm, W., and Towe, R.: Models of everywhere revisited: A technological perspective, Environ. Model. Softw., 122, 104521, https://doi.org/10.1016/j.envsoft.2019.104521, 2019.
Blöschl, G., Ardoin-Bardin, S., Bonell, M., Dorninger, M., Goodrich, D., Gutknecht, D., Matamoros, D., Merz, B., Shand, P., and Szolgay, J.: At what scales do climate variability and land cover change impact on flooding and low flows?, Hydrol. Process., 21, 1241–1247, https://doi.org/10.1002/hyp.6669, 2007.
Blyth, E. M., Martínez-de la Torre, A., and Robinson, E. L.: Trends in evapotranspiration and its drivers in Great Britain: 1961 to 2015, Prog. Phys. Geogr., 43, 666–693, https://doi.org/10.1177/0309133319841891, 2019.
Blyth, E. M., Arora, V. K., Clark, D. B., Dadson, S. J., De Kauwe, M. G., Lawrence, D. M., Melton, J. R., Pongratz, J., Turton, R. H., Yoshimura, K., and Yuan, H.: Advances in Land Surface Modelling, Curr. Clim. Change Rep., 7, 45–71, https://doi.org/10.1007/s40641-021-00171-5, 2021.
Bonan, G. B.: Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests, Science, 320, 1444–1449, https://doi.org/10.1126/science.1155121, 2008.
Bosch, J. M. and Hewlett, J. D.: A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration, J. Hydrol., 55, 3–23, https://doi.org/10.1016/0022-1694(82)90117-2, 1982.
Bradfer-Lawrence, T., Finch, T., Bradbury, R. B., Buchanan, G. M., and Field, R. H.: Mapping potential areas for woodland creation in the UK, 1–13, https://datastorre.stir.ac.uk/bitstream/11667/179/2/Mapping potential areas for woodland creation.pdf (last access: 30 April 2024), 2014.
Bradshaw, C. J. A., Sodhi, N. S., Peh, K. S.-H., and Brook, B. W.: Global evidence that deforestation amplifies flood risk and severity in the developing world, Glob. Change Biol., 13, 2379–2395, https://doi.org/10.1111/j.1365-2486.2007.01446.x, 2007.
Broadmeadow, S., Thomas, H., Nisbet, T., Valatin, G., Nisbet, T., and Valatin, G.: Valuing flood regulation services of existing forest cover to inform natural capital accounts, The Research Agency of the Forestry Commission, 28 pp., https://cdn.forestresearch.gov.uk/2019/02/final_report_valuing_flood_regulation_services_051218.pdf (last access: 30 April 2024), 2018.
Brown, A. E., Western, A. W., McMahon, T. A., and Zhang, L.: Impact of forest cover changes on annual streamflow and flow duration curves, J. Hydrol., 483, 39–50, https://doi.org/10.1016/j.jhydrol.2012.12.031, 2013.
Brunner, M. I., Slater, L., Tallaksen, L. M., and Clark, M.: Challenges in modeling and predicting floods and droughts: A review, WIREs Water, 8, 1–32, https://doi.org/10.1002/wat2.1520, 2021.
Buechel, M.: Realistic afforestation scenarios in Great Britain at a 1 km scale to run with the land surface model JULES, Zenodo [data set], https://doi.org/10.5281/zenodo.7957084, 2023.
Buechel, M., Slater, L., and Dadson, S.: Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios, Commun. Earth Environ., 3, 1–10, https://doi.org/10.1038/s43247-021-00334-0, 2022.
Burke, T., Rowland, C., Whyatt, J. D., Blackburn, G. A., and Abbatt, J.: Achieving national scale targets for carbon sequestration through afforestation: Geospatial assessment of feasibility and policy implications, Environ. Sci. Policy, 124, 279–292, https://doi.org/10.1016/j.envsci.2021.06.023, 2021.
Carrick, J., Abdul Rahim, M. S. A. Bin, Adjei, C., Ashraa Kalee, H. H. H., Banks, S. J., Bolam, F. C., Campos Luna, I. M., Clark, B., Cowton, J., Domingos, I. F. N., Golicha, D. D., Gupta, G., Grainger, M., Hasanaliyeva, G., Hodgson, D. J., Lopez-Capel, E., Magistrali, A. J., Merrell, I. G., Oikeh, I., Othman, M. S., Ranathunga Mudiyanselage, T. K. R., Samuel, C. W. C., Sufar, E. K. H., Watson, P. A., Zakaria, N. N. A. B., and Stewart, G.: Is planting trees the solution to reducing flood risks?, J. Flood Risk Manage., 12, 1–10, https://doi.org/10.1111/jfr3.12484, 2019.
Clark, D. B. and Gedney, N.: Representing the effects of subgrid variability of soil moisture on runoff generation in a land surface model, J. Geophys. Res., 113, D10111, https://doi.org/10.1029/2007JD008940, 2008.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011.
Clark, M. P., Rupp, D. E., Woods, R. A., Tromp-van Meerveld, H. J., Peters, N. E., and Freer, J. E.: Consistency between hydrological models and field observations: linking processes at the hillslope scale to hydrological responses at the watershed scale, Hydrol. Process., 23, 311–319, 2009.
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301, https://doi.org/10.1029/2010WR009827, 2011.
Clark, M. P., Zolfaghari, R., Green, K. R., Trim, S., Knoben, W. J. M., Bennett, A., Nijssen, B., Ireson, A., and Spiteri, R. J.: The numerical implementation of land models: Problem formulation and laugh tests, J. Hydrometeorol., 22, 1627–1648, https://doi.org/10.1175/JHM-D-20-0175.1, 2021.
Committee on Climate Change: Land use: Reducing emissions and preparing for climate change, 100, 2018.
Committee on Climate Change: Net Zero: The UK's contribution to stopping global warming, Comm. Clim. Change, 277 pp., https://www.theccc.org.uk/wp-content/uploads/2019/05/Net-Zero-The-UKs-contribution-to-stopping-global-warming.pdf (last access: 30 April 2024), 2019a.
Committee on Climate Change: Net Zero Technical Report, 269 pp., https://www.theccc.org.uk/wp-content/uploads/2019/05/Net-Zero-Technical-report-CCC.pdf (last access: 30 April 2024), 2019b.
Cook-Patton, S. C., Leavitt, S. M., Gibbs, D., Harris, N. L., Lister, K., Anderson-Teixeira, K. J., Briggs, R. D., Chazdon, R. L., Crowther, T. W., Ellis, P. W., Griscom, H. P., Herrmann, V., Holl, K. D., Houghton, R. A., Larrosa, C., Lomax, G., Lucas, R., Madsen, P., Malhi, Y., Paquette, A., Parker, J. D., Paul, K., Routh, D., Roxburgh, S., Saatchi, S., van den Hoogen, J., Walker, W. S., Wheeler, C. E., Wood, S. A., Xu, L., and Griscom, B. W.: Mapping carbon accumulation potential from global natural forest regrowth, Nature, 585, 545–550, https://doi.org/10.1038/s41586-020-2686-x, 2020.
Cooper, H. M., Bennett, E., Blake, J., Blyth, E., Boorman, D., Cooper, E., Evans, J., Fry, M., Jenkins, A., Morrison, R., Rylett, D., Stanley, S., Szczykulska, M., Trill, E., Antoniou, V., Askquith-Ellis, A., Ball, L., Brooks, M., Clarke, M. A., Cowan, N., Cumming, A., Farrand, P., Hitt, O., Lord, W., Scarlett, P., Swain, O., Thornton, J., Warwick, A., and Winterbourn, B.: COSMOS-UK: National soil moisture and hydrometeorology data for environmental science research, Earth Syst. Sci. Data, 13, 1737–1757, https://doi.org/10.5194/essd-13-1737-2021, 2021a.
Cooper, M., Patil, S. D., Nisbet, T. R., Thomas, H., Smith, A. R., and McDonald, M. A.: Role of forested land for natural flood management in the UK: A review, WIREs Water, 8, 1–16, https://doi.org/10.1002/wat2.1541, 2021b.
Cox, P. M., Huntingford, C., and Harding, R. J.: A canopy conductance and photosynthesis model for use in a GCM land surface scheme, J. Hydrol., 212–213, 79–94, https://doi.org/10.1016/S0022-1694(98)00203-0, 1998.
Crooks, S. M., Kay, A. L., Davies, H. N., and Bell, V. A.: From catchment to national scale rainfall-runoff modelling: Demonstration of a hydrological modelling framework, Hydrology, 1, 63–88, https://doi.org/10.3390/hydrology1010063, 2014.
Cui, J., Lian, X., Huntingford, C., Gimeno, L., Wang, T., Ding, J., He, M., Xu, H., Chen, A., Gentine, P., and Piao, S.: Global water availability boosted by vegetation-driven changes in atmospheric moisture transport, Nat. Geosci., 15, 982–988, https://doi.org/10.1038/s41561-022-01061-7, 2022.
Cuntz, M., Mai, J., Samaniego, L., Clark, M., Wulfmeyer, V., Branch, O., Attinger, S., and Thober, S.: The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model, J. Geophys. Res.-Atmos., 121, 10676–10700, https://doi.org/10.1002/2016JD025097, 2016.
Dadson, S. J.: Statistical Analysis of Geographical Data, John Wiley & Sons, 252 pp., ISBN 0470977043, 2017.
Dadson, S. J., Bell, V. A., and Jones, R. G.: Evaluation of a grid-based river flow model configured for use in a regional climate model, J. Hydrol., 411, 238–250, 2011.
Dadson, S. J., Hall, J. W., Murgatroyd, A., Acreman, M., Bates, P., Beven, K., Heathwaite, L., Holden, J., Holman, I. P., Lane, S. N., O'Connell, E., Penning-Rowsell, E., Reynard, N., Sear, D., Thorne, C., and Wilby, R.: A restatement of the natural science evidence concerning catchment-based `natural' flood management in the UK, Proc. Roy. Soc. A, 473, 20160706, https://doi.org/10.1098/rspa.2016.0706, 2017.
Davies, H., Rameshwaran, P., and V, B.: Gridded (1 km) physical river characteristics for the UK, UK CEH, https://doi.org/10.5285/6da95899-f3b8-4089-b621-560818aa78ba, 2022.
Denissen, J. M. C., Teuling, A. J., Pitman, A. J., Koirala, S., Migliavacca, M., Li, W., Reichstein, M., Winkler, A. J., Zhan, C., and Orth, R.: Widespread shift from ecosystem energy to water limitation with climate change, Nat. Clim. Change, 12, 677–684, https://doi.org/10.1038/s41558-022-01403-8, 2022.
Dick, J., Miller, J. D., Carruthers-Jones, J., Dobel, A. J., Carver, S., Garbutt, A., Hester, A., Hails, R., Magreehan, V., and Quinn, M.: How are nature based solutions contributing to priority societal challenges surrounding human well-being in the United Kingdom: A systematic map protocol, Environ. Evid., 8, 37, https://doi.org/10.1186/s13750-019-0180-4, 2019.
Do, H. X., Westra, S., and Leonard, M.: A global-scale investigation of trends in annual maximum streamflow, J. Hydrol., 552, 28–43, https://doi.org/10.1016/j.jhydrol.2017.06.015, 2017.
Don, A., Rebmann, C., Kolle, O., Scherer-Lorenzen, M., and Schulze, E. D.: Impact of afforestation-associated management changes on the carbon balance of grassland, Global Change Biol., 15, 1990–2002, https://doi.org/10.1111/j.1365-2486.2009.01873.x, 2009.
Ellison, D., Futter, M. N., and Bishop, K.: On the forest cover-water yield debate: From demand- to supply-side thinking, Global Change Biol., 18, 806–820, https://doi.org/10.1111/j.1365-2486.2011.02589.x, 2012.
Environment Agency: Mapping the potential for Working with Natural Processes-technical report Mapping the potential for Working with Natural Processes-technical report SC150005, 85 pp., https://assets.publishing.service.gov.uk/media/6036c659d3bf7f0ab2f070c1/Working_with_natural_processes_mapping_technical_report.pdf (last access: 30 April 2024), 2018.
Farley, K. A., Jobbágy, E. G., and Jackson, R. B.: Effects of afforestation on water yield: A global synthesis with implications for policy, Global Change Biol., 11, 1565–1576, https://doi.org/10.1111/j.1365-2486.2005.01011.x, 2005.
Feeney, C. J., Cosby, B. J., Robinson, D. A., Thomas, A., Emmett, B. A., and Henrys, P.: Multiple soil map comparison highlights challenges for predicting topsoil organic carbon concentration at national scale, Sci. Rep., 12, 1–13, https://doi.org/10.1038/s41598-022-05476-5, 2022.
Fenner, R.: Spatial evaluation of multiple benefits to encourage multi-functional design of sustainable drainage in Blue-Green cities, Water, 9, 16, https://doi.org/10.3390/w9120953, 2017.
Forest Research: Forestry Statistics 2021 – Chapter 1: Woodland Area and Planting, 60 pp., https://www.forestresearch.gov.uk/tools-and-resources/statistics/forestry-statistics/forestry-statistics-2020/1-woodland-area-and-planting/ (last access: 30 April 2024), 2021a.
Forest Research: Provisional Woodland Statistics, 2021 Edition, 1–33, https://www.forestresearch.gov.uk/tools-and-resources/statistics/statistics-by-topic/woodland-statistics/ (last access: 30 April 2024), 2021b.
Fosser, G., Kendon, E., Chan, S., Lock, A., Roberts, N., and Bush, M.: Optimal configuration and resolution for the first convection-permitting ensemble of climate projections over the United Kingdom, Int. J. Climatol., 40, 3585–3606, https://doi.org/10.1002/joc.6415, 2020.
Fowler, H. J., Lenderink, G., Prein, A. F., Westra, S., Allan, R. P., Ban, N., Barbero, R., Berg, P., Blenkinsop, S., Do, H. X., Guerreiro, S., Haerter, J. O., Kendon, E. J., Lewis, E., Schaer, C., Sharma, A., Villarini, G., Wasko, C., and Zhang, X.: Anthropogenic intensification of short-duration rainfall extremes, Nat. Rev. Earth Environ., 2, 107–122, https://doi.org/10.1038/s43017-020-00128-6, 2021.
Friedlingstein, P., Allen, M., Canadell, J. G., Peters, G. P., and Seneviratne, S. I.: Comment on “The global tree restoration potential,” Science, 366, 76–79, https://doi.org/10.1126/science.aay8060, 2019.
Fuller, R., Smith, G., Sanderson, J., Hill, R. A., Thomson, A. G., Cox, R., Brown, N. J., Clarke, R. T., Rothery, P., and Gerard, F. F.: Land cover map 2000 – final report, UK CEH, https://doi.org/10.5285/f802edfc-86b7-4ab9-b8fa-87e9135237c9, 2002.
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, https://doi.org/10.1016/j.jhydrol.2018.10.039, 2018.
Gedney, N., Cox, P. M., Betts, R. A., Boucher, O., Huntingford, C., and Stott, P. A.: Detection of a direct carbon dioxide effect in continental river runoff records, Nature, 439, 835–838, https://doi.org/10.1038/nature04504, 2006.
Gedney, N., Huntingford, C., Weedon, G. P., Bellouin, N., Boucher, O., and Cox, P. M.: Detection of solar dimming and brightening effects on Northern Hemisphere river flow, Nat. Geosci., 7, 796–800, https://doi.org/10.1038/ngeo2263, 2014.
Grassi, G., House, J., Dentener, F., Federici, S., Den Elzen, M., and Penman, J.: The key role of forests in meeting climate targets requires science for credible mitigation, Nat. Clim. Change, 7, 220–226, https://doi.org/10.1038/nclimate3227, 2017.
Griffin, A., Vesuviano, G., and Stewart, E.: Have trends changed over time? A study of UK peak flow data and sensitivity to observation period, Nat. Hazards Earth Syst. Sci., 19, 2157–2167, https://doi.org/10.5194/nhess-19-2157-2019, 2019.
Griffin, A., Kay, A., Stewart, L., and Spencer, P.: Climate change allowances, non-stationarity and flood frequency analyses, J. Flood Risk Manage., 15, e12783, https://doi.org/10.1111/jfr3.12783, 2022a.
Griffin, A., Kay, A., Sayers, P., Bell, V., Stewart, E., and Carr, S.: Widespread flooding dynamics changing under climate change: characterising floods using UKCP18, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2022-243, in review, 2022b.
Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., Herrero, M., Kiesecker, J., Landis, E., Laestadius, L., Leavitt, S. M., Minnemeyer, S., Polasky, S., Potapov, P., Putz, F. E., Sanderman, J., Silvius, M., Wollenberg, E., and Fargione, J.: Natural climate solutions, P. Natl. Acad. Sci. USA, 114, 11645–11650, https://doi.org/10.1073/pnas.1710465114, 2017.
Gudmundsson, L., Leonard, M., Do, H. X., Westra, S., and Seneviratne, S. I.: Observed Trends in Global Indicators of Mean and Extreme Streamflow, Geophys. Res. Lett., 46, 756–766, https://doi.org/10.1029/2018GL079725, 2019.
Gupta, H. V, Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gush, M. B., Scott, D. F., Jewitt, G. P. W., Schulze, R. E., Hallowes, L. A., and Görgens, A. H. M.: A new approach to modelling streamflow reductions resulting from commercial afforestation in South Africa, S. Afr. Forest. J., 196, 27–36, https://doi.org/10.1080/20702620.2002.10434615, 2002.
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, https://doi.org/10.2166/nh.2021.156, 2021.
Harding, R. J., Weedon, G. P., van Lanen, H. A. J., and Clark, D. B.: The future for global water assessment, J. Hydrol., 518, 186–193, https://doi.org/10.1016/j.jhydrol.2014.05.014, 2014.
Harper, A. B., Cox, P. M., Friedlingstein, P., Wiltshire, A. J., Jones, C. D., Sitch, S., Mercado, L. M., Groenendijk, M., Robertson, E., Kattge, J., Bönisch, G., Atkin, O. K., Bahn, M., Cornelissen, J., Niinemets, Ü., Onipchenko, V., Peñuelas, J., Poorter, L., Reich, P. B., Soudzilovskaia, N. A., and Van Bodegom, P.: Improved representation of plant functional types and physiology in the Joint UK Land Environment Simulator (JULES v4.2) using plant trait information, Geosci. Model Dev., 9, 2415–2440, https://doi.org/10.5194/gmd-9-2415-2016, 2016.
Harper, A. B., Williams, K. E., Mcguire, P. C., Duran Rojas, M. C., Hemming, D., Verhoef, A., Huntingford, C., Rowland, L., Marthews, T., Breder Eller, C., Mathison, C., Nobrega, R. L. B., Gedney, N., Vidale, P. L., Otu-Larbi, F., Pandey, D., Garrigues, S., Wright, A., Slevin, D., De Kauwe, M. G., Blyth, E., Ardö;, J., Black, A., Bonal, D., Buchmann, N., Burban, B., Fuchs, K., De Grandcourt, A., Mammarella, I., Merbold, L., Montagnani, L., Nouvellon, Y., Restrepo-Coupe, N., and Wohlfahrt, G.: Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements, Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, 2021.
Harrigan, S., Hannaford, J., Muchan, K., and Marsh, T. J.: Designation and trend analysis of the updated UK Benchmark Network of river flow stations: The UKBN2 dataset, Hydrol. Res., 49, 552–567, https://doi.org/10.2166/nh.2017.058, 2018.
Hausfather, Z. and Peters, G. P.: Emissions – the `business as usual' story is misleading, Nature, 577, 618–620, https://doi.org/10.1038/d41586-020-00177-3, 2020.
Hawes, M.: Planting carbon storage, Nat. Clim. Change, 8, 556–558, https://doi.org/10.1038/s41558-018-0214-x, 2018.
Helsel, D. R., Hirsch, R. M., Ryberg, K. R., Archfield, S. A., and Gilroy, E. J.: Statistical methods in water resources: US Geological Survey Techniques and Methods, USGS, 458 pp., https://doi.org/10.3133/tm4A3, 2020.
Hoek van Dijke, A. J., Herold, M., Mallick, K., Benedict, I., Machwitz, M., Schlerf, M., Pranindita, A., Theeuwen, J. J. E., Bastin, J. F., and Teuling, A. J.: Shifts in regional water availability due to global tree restoration, Nat. Geosci., 15, 363–368, https://doi.org/10.1038/s41561-022-00935-0, 2022.
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.
Hudson, J. A., Crane, S. B., and Robinson, M.: The impact of the growth of new plantation forestry on evaporation and streamflow in the Llanbrynmair catchments, Hydrol. Earth Syst. Sci., 1, 463–475, https://doi.org/10.5194/hess-1-463-1997, 1997.
Hung, C.-L. J., James, L. A., Carbone, G. J., and Williams, J. M.: Impacts of combined land-use and climate change on streamflow in two nested catchments in the Southeastern United States, Ecol. Eng., 143, 105665, https://doi.org/10.1016/j.ecoleng.2019.105665, 2020.
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, https://doi.org/10.1080/02626667.2017.1366657, 2017.
IPPC: 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, 1542 pp., https://doi.org/10.4337/9781784710644, 2019.
Jules-Lsm: Jules-Lsm.github.io, GitHub [code], https://github.com/jules-lsm/jules-lsm.github.io (last access: 21 April 2024), 2024.
Kay, A. L.: Simulation of river flow in Britain under climate change: Baseline performance and future seasonal changes, Hydrol. Process., 35, 1–10, https://doi.org/10.1002/hyp.14137, 2021.
Kay, A. L., Griffin, A., Rudd, A. C., Chapman, R. M., Bell, V. A., and Arnell, N. W.: Climate change effects on indicators of high and low river flow across Great Britain, Adv. Water Resour., 151, 103909, https://doi.org/10.1016/j.advwatres.2021.103909, 2021.
Kellner, E. and Hubbart, J. A.: Land use impacts on floodplain water table response to precipitation events, Ecohydrology, 11, e1913, https://doi.org/10.1002/eco.1913, 2018.
Kendon, E. J., Fischer, E. M., and Short, C. J.: Variability conceals emerging trend in 100 yr projections of UK local hourly rainfall extremes, Nat. Commun., 14, 1133, https://doi.org/10.1038/s41467-023-36499-9, 2023.
Knoben, W. J. M., Freer, J. E., and Woods, R. A.: Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling–Gupta efficiency scores, Hydrol. Earth Syst. Sci., 23, 4323–4331, https://doi.org/10.5194/hess-23-4323-2019, 2019.
Kundzewicz, Z. W.: Nonstationarity in water resources – Central European perspective, J. Am. Water Resour. Assoc., 47, 550–562, https://doi.org/10.1111/j.1752-1688.2011.00549.x, 2011.
La Follette, P. T., Teuling, A. J., Addor, N., Clark, M., Jansen, K., and Melsen, L. A.: Numerical daemons of hydrological models are summoned by extreme precipitation, Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, 2021.
Lane, R. A., Coxon, G., Freer, J. E., Wagener, T., Johnes, P. J., Bloomfield, J. P., Greene, S., Macleod, C. J. A., and Reaney, S. M.: Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain, Hydrol. Earth Syst. Sci., 23, 4011–4032, https://doi.org/10.5194/hess-23-4011-2019, 2019.
Lane, R. A. and Kay, A. L.: Climate Change Impact on the Magnitude and Timing of Hydrological Extremes Across Great Britain, Front. Water, 3, 1–14, https://doi.org/10.3389/frwa.2021.684982, 2021.
Lane, R. A., Freer, J. E., Coxon, G., and Wagener, T.: Incorporating Uncertainty Into Multiscale Parameter Regionalization to Evaluate the Performance of Nationally Consistent Parameter Fields for a Hydrological Model, Water Resour. Res., 57, 1–19, https://doi.org/10.1029/2020WR028393, 2021.
Lee, D., Min, S. K., Park, I. H., Ahn, J. B., Cha, D. H., Chang, E. C., and Byun, Y. H.: Enhanced Role of Convection in Future Hourly Rainfall Extremes Over South Korea, Geophys. Res. Lett., 49, 1–11, https://doi.org/10.1029/2022GL099727, 2022.
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., and Dadson, S. J.: Benchmarking data-driven rainfall-runoff models in Great Britain: A comparison of long short-term memory (LSTM)-based models with four lumped conceptual models, Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, 2021.
Leung, J. Y. S., Russell, B. D., and Connell, S. D.: Global Warming of 1.5 °C – Summary for Policymakers, Intergov. Panel Clim. Change, 1, 374–381, 2019.
Le Vine, N., Butler, A., McIntyre, N., and Jackson, C.: Diagnosing hydrological limitations of a land surface model: Application of JULES to a deep-groundwater chalk basin, Hydrol. Earth Syst. Sci., 20, 143–159, https://doi.org/10.5194/hess-20-143-2016, 2016.
Lewis, H. W. and Dadson, S. J.: A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom, Hydrol. Process., 35, 1–24, https://doi.org/10.1002/hyp.14438, 2021.
Lewis, S. L., Wheeler, C. E., Mitchard, E. T. A., and Koch, A.: Regenerate natural forests to store carbon, Nature, 568, 25–28, 2019.
Li, Y., Piao, S., Li, L. Z. X., Chen, A., Wang, X., Ciais, P., Huang, L., Lian, X., Peng, S., Zeng, Z., Wang, K., and Zhou, L.: Divergent hydrological response to large-scale afforestation and vegetation greening in China, Sci. Adv., 4, 1–10, https://doi.org/10.1126/sciadv.aar4182, 2018.
Lowe, J. A., Bernie, D., Bett, P., Bricheno, L., Brown, S., Calvert, D., Clark, R., Eagle, K., Edwards, T., Fosser, G., Fung, F., Gohar, L., Good, P., Gregory, J., Harris, G., Howard, T., Kaye, N., Kendon, E., Krijnen, J., Maisey, P., McDonald, R., McInnes, R., McSweeney, C., Mitchell, J. F. B., Murphy, J., Palmer, M., Roberts, C., Rostron, J., Sexton, D., Thornton, H., Tinker, J., Tucker, S., Yamazaki, K., and Belcher, S.: UKCP18 science overview report, Met Office Hadley Centre, Exeter, UK, 1–73, https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP18-Overview-report.pdf (last access: 30 April 2024), 2018.
Mai, J., Craig, J. R., and Tolson, B. A.: Simultaneously determining global sensitivities of model parameters and model structure, Hydrol. Earth Syst. Sci., 24, 5835–5858, https://doi.org/10.5194/hess-24-5835-2020, 2020.
Marc, V. and Robinson, M.: The long-term water balance (1972–2004) of upland forestry and grassland at Plynlimon, mid-Wales, Hydrol. Earth Syst. Sci., 11, 44–60, https://doi.org/10.5194/hess-11-44-2007, 2007.
Martínez-de la Torre, A., Blyth, E. M., and Robinson, E. L.: Water, carbon and energy fluxes simulation for Great Britain using the JULES Land Surface Model and the Climate Hydrology and Ecology research Support System meteorology dataset (1961–2015) [CHESS-land], UK CEH, https://doi.org/10.5285/c76096d6-45d4-4a69-a310-4c67f8dcf096, 2018.
Martínez-de la Torre, A., Blyth, E. M., and Weedon, G. P.: Using observed river flow data to improve the hydrological functioning of the JULES land surface model (vn4.3) used for regional coupled modelling in Great Britain (UKC2), Geosci. Model Dev., 12, 765–784, https://doi.org/10.5194/gmd-12-765-2019, 2019.
Mathison, C., Burke, E., Hartley, A. J., Kelley, D. I., Burton, C., Robertson, E., Gedney, N., Williams, K., Wiltshire, A., Ellis, R. J., Sellar, A. A., and Jones, C. D.: Description and evaluation of the JULES-ES set-up for ISIMIP2b, Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, 2023.
Meier, R., Schwaab, J., Seneviratne, S. I., Sprenger, M., Lewis, E., and Davin, E. L.: Empirical estimate of forestation-induced precipitation changes in Europe, Nat. Geosci., 14, 473–478, https://doi.org/10.1038/s41561-021-00773-6, 2021.
Met Office: UKCP18 Regional Projections on a 12 km grid over the UK for 1980–2080, https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604 (last access: 30 April 2024), 2018.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Climate change: Stationarity is dead: Whither water management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915, 2008.
Moore, R. J.: The PDM rainfall-runoff model, Hydrol. Earth Syst. Sci., 11, 483–499, https://doi.org/10.5194/hess-11-483-2007, 2007.
Murphy, J., Harris, G., Sexton, D., Kendon, E., Bett, P., Clark, R., Eagle, K., Fosser, G., Fung, F., Lowe, J., McDonald, R., McInnes, R., McSweeney, C., Mitchell, J., Rostron, J., Thornton, H., Tucker, S., and Yamazaki, K.: UKCP18 Land report, UKCP18 L. Proj. Sci. Rep. 2018, Met Office, https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP18-Land-report.pdf (last access: 30 April 2024), 2019.
Murphy, T. R., Hanley, M. E., Ellis, J. S., and Lunt, P. H.: Native woodland establishment improves soil hydrological functioning in UK upland pastoral catchments, Land Degrad. Dev., 32, 1034–1045, https://doi.org/10.1002/ldr.3762, 2021.
Newson, M. D. and Calder, I. R.: Forests and water resources: problems of prediction on a regional scale, Philos. T. Roy. Soc. Lond. B, 324, 283–298, https://doi.org/10.1098/rstb.1989.0049, 1989.
Nisbet, T. and Thomas, H.: Trees, woodlands and flooding, Q. J. Forest., 115, 55–63, 2021.
Nisbet, T., Silgram, M., Shah, N., Morrow, K., and Broadmeadow, S.: Woodland for Water: Woodland measures for meeting Water Framework Directive objectives, Forest Research, 156 pp., https://cdn.forestresearch.gov.uk/2022/02/frmg004_woodland4water-2.pdf (last access: 30 April 2024), 2011.
NOAA: Global Monitoring Laboratory – Carbon Cycle Greenhouse Gases: Trends in CO2, https://gml.noaa.gov/ccgg/trends/mlo.html (last access: 31 October 2022), 2022.
O'Briain, R., Shephard, S., Matson, R., Gordon, P., and Kelly, F. L.: The efficacy of riparian tree cover as a climate change adaptation tool is affected by hydromorphological alterations, Hydrol. Process., 34, 2433–2449, https://doi.org/10.1002/hyp.13739, 2020.
Oldfield, E. E., Warren, R. J., Felson, A. J., and Bradford, M. A.: FORUM: Challenges and future directions in urban afforestation, J. Appl. Ecol., 50, 1169–1177, https://doi.org/10.1111/1365-2664.12124, 2013.
Oudin, L., Andréassian, V., Lerat, J., and Michel, C.: Has land cover a significant impact on mean annual streamflow? An international assessment using 1508 catchments, J. Hydrol., 357, 303–316, https://doi.org/10.1016/j.jhydrol.2008.05.021, 2008.
Page, T., Chappell, N. A., Beven, K. J., Hankin, B., and Kretzschmar, A.: Assessing the significance of wet-canopy evaporation from forests during extreme rainfall events for flood mitigation in mountainous regions of the United Kingdom, Hydrol. Process., 34, 4740–4754, https://doi.org/10.1002/hyp.13895, 2020.
Palmer, L.: How trees and forests reduce risks from climate change, Nat. Clim. Change, 11, 374–377, https://doi.org/10.1038/s41558-021-01041-6, 2021.
Pattison, I. and Lane, S. N.: The link between land-use management and fluvial flood risk: A chaotic conception?, Prog. Phys. Geogr., 36, 72–92, https://doi.org/10.1177/0309133311425398, 2012.
Peng, J., Tanguy, M., Robinson, E. L., Pinnington, E., Evans, J., Ellis, R., Cooper, E., Hannaford, J., Blyth, E., and Dadson, S.: Estimation and evaluation of high-resolution soil moisture from merged model and Earth observation data in the Great Britain, Remote Sens. Environ., 264, 112610, https://doi.org/10.1016/j.rse.2021.112610, 2021.
Peskett, L., MacDonald, A., Heal, K., McDonnell, J., Chambers, J., Uhlemann, S., Upton, K., and Black, A.: The impact of across-slope forest strips on hillslope subsurface hydrological dynamics, J. Hydrol., 581, 124427, https://doi.org/10.1016/j.jhydrol.2019.124427, 2020.
Prudhomme, C., Parry, S., Hannaford, J., Clark, D. B., Hagemann, S., and Voss, F.: How well do large-scale models reproduce regional hydrological extremes: In Europe?, J. Hydrometeorol., 12, 1181–1204, https://doi.org/10.1175/2011JHM1387.1, 2011.
Prudhomme, C., Dadson, S., Morris, D., Williamson, J., Goodsell, G., Crooks, S., Boelee, L., Davies, H., Buys, G., Lafon, T., and Watts, G.: Future flows climate: An ensemble of 1-km climate change projections for hydrological application in Great Britain, Earth Syst. Sci. Data, 4, 143–148, https://doi.org/10.5194/essd-4-143-2012, 2012.
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W., Dankers, R., Fekete, B. M., Franssen, W., Gerten, D., Gosling, S. N., Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T., Wada, Y., and Wisser, D.: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, P. Natl. Acad. Sci. USA, 111, 3262–3267, https://doi.org/10.1073/pnas.1222473110, 2014.
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environ. Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.
Ritchie, P. D. L., Harper, A. B., Smith, G. S., Kahana, R., Kendon, E. J., Lewis, H., Fezzi, C., Halleck-Vega, S., Boulton, C. A., Bateman, I. J., and Lenton, T. M.: Large changes in Great Britain's vegetation and agricultural land-use predicted under unmitigated climate change, Environ. Res. Lett., 14, 114012, https://doi.org/10.1088/1748-9326/ab492b, 2019.
Roberts, J. and Rosier, P.: The impact of broadleaved woodland on water resources in lowland UK: I. Soil water changes below beech woodland and grass on chalk sites in Hampshire, Hydrol. Earth Syst. Sci., 9, 596–606, https://doi.org/10.5194/hess-9-596-2005, 2005.
Robinson, E. L., Blyth, E. M., Clark, D. B., Comyn-Platt, E., Finch, J., and Rudd, A. C.: Climate hydrology and ecology research support system meteorology dataset for Great Britain (1961–2015) [CHESS-met] v1.2, NERC Environmental Information Data Centre [data set], https://doi.org/10.5285/b745e7b1-626c-4ccc-ac27-56582e77b900, 2017a.
Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J., and Rudd, A. C.: Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data, Hydrol. Earth Syst. Sci., 21, 1189–1224, https://doi.org/10.5194/hess-21-1189-2017, 2017b.
Robinson, E. L., Huntingford, C., Semeena, V. S., and Bullock, J. M.: CHESS-SCAPE: Future projections of meteorological variables at 1 km resolution for the United Kingdom 1980–2080 derived from UK Climate Projections 2018, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/8194b416cbee482b89e0dfbe17c5786c, 2022.
Roebroek, C. T. J., Melsen, L. A., Hoek van Dijke, A. J., Fan, Y., and Teuling, A. J.: Global distribution of hydrologic controls on forest growth, Hydrol. Earth Syst. Sci., 24, 4625–4639, https://doi.org/10.5194/hess-24-4625-2020, 2020.
Rogger, M., Agnoletti, M., Alaoui, A., Bathurst, J. C., Bodner, G., Borga, M., Chaplot, V., Gallart, F., Glatzel, G., Hall, J., Holden, J., Holko, L., Horn, R., Kiss, A., Kohnová, S., Leitinger, G., Lennartz, B., Parajka, J., Perdigão, R., Peth, S., Plavcová, L., Quinton, J. N., Robinson, M., Salinas, J. L., Santoro, A., Szolgay, J., Tron, S., van den Akker, J. J. H., Viglione, A., and Blöschl, G.: Land use change impacts on floods at the catchment scale: Challenges and opportunities for future research, Water Resour. Res., 53, 5209–5219, https://doi.org/10.1002/2017WR020723, 2017.
ROSE: Rose Documentation, GitHub, https://metomi.github.io/rose/doc/html/index.html (last access: 21 April 2024), 2024.
Schwaab, J., Davin, E. L., Bebi, P., Duguay-Tetzlaff, A., Waser, L. T., Haeni, M., and Meier, R.: Increasing the broad-leaved tree fraction in European forests mitigates hot temperature extremes, Sci. Rep., 10, 1–9, https://doi.org/10.1038/s41598-020-71055-1, 2020.
Seddon, N., Chausson, A., Berry, P., Girardin, C. A. J. J., Smith, A., and Turner, B.: Understanding the value and limits of nature-based solutions to climate change and other global challenges, Philos. T. Roy. Soc. B, 375, 20190120, https://doi.org/10.1098/rstb.2019.0120, 2020.
Seddon, N., Smith, A., Smith, P., Key, I., Chausson, A., Girardin, C., House, J., Srivastava, S., and Turner, B.: Getting the message right on nature-based solutions to climate change, Global Change Biol., 27, 1518–1546, https://doi.org/10.1111/gcb.15513, 2021.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau, J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968.
Shuttleworth, E. L., Evans, M. G., Pilkington, M., Spencer, T., Walker, J., Milledge, D., and Allott, T. E. H.: Restoration of blanket peat moorland delays stormflow from hillslopes and reduces peak discharge, J. Hydrol. X, 2, 100006, https://doi.org/10.1016/j.hydroa.2018.100006, 2019.
Sing, L. and Aitkenhead, M.: Analysis of land suitability for woodland expansion in Scotland: update 2020, Edinburgh Research Archive, https://doi.org/10.7488/era/494, 2020.
Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., Kelder, T., Kowal, K., Lees, T., Matthews, T., Murphy, C., and Wilby, R. L.: Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management, Hydrol. Earth Syst. Sci., 25, 3897–3935, https://doi.org/10.5194/hess-25-3897-2021, 2021b.
Speich, M. J. R., Zappa, M., and Lischke, H.: Sensitivity of forest water balance and physiological drought predictions to soil and vegetation parameters – A model-based study, Environ. Model. Softw., 102, 213–232, https://doi.org/10.1016/j.envsoft.2018.01.016, 2018.
Stratford, C., Miller, J., House, A., Old, G., Acreman, M., Dueñas-Lopez, M. A., Nisbet, T., Newman, J., Burgess-Gamble, L., Chappell, N., Clarke, S., Leeson, L., Monbiot, G., Paterson, J., Robinson, M., Rogers, M., and Tickner, D.: Do Trees in the UK-Relevant River Catchments Influence Fluvial Flood Peaks?, 46 pp., https://core.ac.uk/download/pdf/96704761.pdf (last access: 30 April 2024), 2017.
Teuling, A. J., Taylor, C. M., Meirink, J. F., Melsen, L. A., Miralles, D. G., van Heerwaarden, C. C., Vautard, R., Stegehuis, A. I., Nabuurs, G.-J., and de Arellano, J. V.-G.: Observational evidence for cloud cover enhancement over western European forests, Nat. Commun., 8, 14065, https://doi.org/10.1038/ncomms14065, 2017.
Teuling, A. J., de Badts, E. A. G., Jansen, F. A., Fuchs, R., Buitink, J., Hoek van Dijke, A. J., and Sterling, S. M.: Climate change, reforestation/afforestation, and urbanization impacts on evapotranspiration and streamflow in Europe, Hydrol. Earth Syst. Sci., 23, 3631–3652, https://doi.org/10.5194/hess-23-3631-2019, 2019.
Theil, H.: A Rank-Invariant Method of Linear and Polynomial Regression Analysis, in: Indagationes mathematicae, vol. 12, Springer, 345–381, https://doi.org/10.1007/978-94-011-2546-8_20, 1992.
UKCEH: Climate hydrology and ecology research support system [CHESS], https://catalogue.ceh.ac.uk/documents/7de9790e-66a2-44b5-988e-283d764ef52f (last access: 21 April 2024), 2024.
Van den Hoof, C., Vidale, P. L., Verhoef, A., and Vincke, C.: Improved evaporative flux partitioning and carbon flux in the land surface model JULES: Impact on the simulation of land surface processes in temperate Europe, Agr. Forest Meteorol., 181, 108–124, https://doi.org/10.1016/j.agrformet.2013.07.011, 2013.
van Genuchten, M. T.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
van Kempen, G., van der Wiel, K., and Melsen, L. A.: The impact of hydrological model structure on the simulation of extreme runoff events, Nat. Hazards Earth Syst. Sci., 21, 961–976, https://doi.org/10.5194/nhess-21-961-2021, 2021.
Vereecken, H., Amelung, W., Bauke, S. L., Bogena, H., Brüggemann, N., Montzka, C., Vanderborght, J., Bechtold, M., Blöschl, G., Carminati, A., Javaux, M., Konings, A. G., Kusche, J., Neuweiler, I., Or, D., Steele-Dunne, S., Verhoef, A., Young, M., and Zhang, Y.: Soil hydrology in the Earth system, Nat. Rev. Earth Environ., 3, 573–587, https://doi.org/10.1038/s43017-022-00324-6, 2022.
Villarini, G. and Wasko, C.: Humans, climate and streamflow, Nat. Clim. Change, 11, 725–726, https://doi.org/10.1038/s41558-021-01137-z, 2021.
Vitolo, C., Fry, M., and Buytaert, W.: Rnrfa: An r package to retrieve, filter and visualize data from the uk national river flow archive, R J., 8, 102–116, https://doi.org/10.32614/rj-2016-036, 2016.
Wagener, T., Gleeson, T., Coxon, G., Hartmann, A., Howden, N., Pianosi, F., Rahman, M., Rosolem, R., Stein, L., and Woods, R.: On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation, Wiley Interdisciplin. Rev. Water, 8, 1–17, https://doi.org/10.1002/wat2.1550, 2021.
Wasko, C.: Review: Can temperature be used to inform changes to flood extremes with global warming?, Philos. T. Roy. Soc. A, 379, 20190551, https://doi.org/10.1098/rsta.2019.0551, 2021.
Wasko, C., Sharma, A., and Lettenmaier, D. P.: Increases in temperature do not translate to increased flooding, Nat. Commun., 10, 4–6, https://doi.org/10.1038/s41467-019-13612-5, 2019.
Wasko, C., Nathan, R., Stein, L., and O'Shea, D.: Evidence of shorter more extreme rainfalls and increased flood variability under climate change, J. Hydrol., 603, 126994, https://doi.org/10.1016/j.jhydrol.2021.126994, 2021.
Welsh Government: Woodland Opportunity Map 2021|DataMapWales, https://datamap.gov.wales/maps/woodland-opportunity-map-2021/ (last access: 4 August 2022), 2022.
Wilby, R. L. and Quinn, N. W.: Reconstructing multi-decadal variations in fluvial flood risk using atmospheric circulation patterns, J. Hydrol., 487, 109–121, https://doi.org/10.1016/j.jhydrol.2013.02.038, 2013.
Wilkes, M. A., Bennett, J., Burbi, S., Charlesworth, S., Dehnen-Schmutz, K., Rayns, F., Schmutz, U., Smith, B., Tilzey, M., Trenchard, L., and van de Wiel, M.: Making way for trees? Changes in land-use, habitats and protected areas in Great Britain under “Global tree restoration potential”, Sustainability, 12, 5845, https://doi.org/10.3390/su12145845, 2020.
Xu, R., Li, Y., Teuling, A. J., Zhao, L., Spracklen, D. V., Garcia-Carreras, L., Meier, R., Chen, L., Zheng, Y., Lin, H., and Fu, B.: Contrasting impacts of forests on cloud cover based on satellite observations, Nat. Commun., 13, 670, https://doi.org/10.1038/s41467-022-28161-7, 2022.
Young, P. J., Harper, A. B., Huntingford, C., Paul, N. D., Morgenstern, O., Newman, P. A., Oman, L. D., Madronich, S., and Garcia, R. R.: The Montreal Protocol protects the terrestrial carbon sink, Nature, 596, 384–388, https://doi.org/10.1038/s41586-021-03737-3, 2021.
Zhang, X., Jin, J., Zeng, X., Hawkins, C. P., Neto, A. A. M., and Niu, G.: The Compensatory CO2 Fertilization and Stomatal Closure Effects on Runoff Projection From 2016–2099 in the Western United States, Water Resour. Res., 58, e2021WR030046, https://doi.org/10.1029/2021wr030046, 2022.
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.
Afforestation has been proposed internationally, but the hydrological implications of such large...
