Articles | Volume 28, issue 12
https://doi.org/10.5194/hess-28-2635-2024
© 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-28-2635-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2024
Research article |
| 20 Jun 2024
| 20 Jun 2024
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Alison L. Kay
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Paul Sayers
Sayers and Partners, Watlington, Oxfordshire, OX49 5PY, UK
Victoria Bell
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Elizabeth Stewart
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Sam Carr
Sayers and Partners, Watlington, Oxfordshire, OX49 5PY, UK
Related authors
Adam Griffin, Gianni Vesuviano, and Elizabeth Stewart
Nat. Hazards Earth Syst. Sci., 19, 2157–2167, https://doi.org/10.5194/nhess-19-2157-2019, https://doi.org/10.5194/nhess-19-2157-2019, 2019
Short summary
Short summary
Classical statistical methods for flood frequency estimation assume flooding characteristics do not change over time. Recent focus on climate change has raised questions of the validity of such assumptions. Near-natural catchments are used to focus on climate (not land-use) change, investigating the sensitivity of trend estimates to the period of record. Some key statistics were very sensitive, but conclusive spatial patterns were not found. Smaller floods were most affected by these trends.
Adam Griffin, Luke Shaw, and Elizabeth Stewart
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-325, https://doi.org/10.5194/hess-2018-325, 2018
Revised manuscript not accepted
Short summary
Short summary
To better estimate how big the
1-in-100-yearflood is, historical data such as newspaper archives and bridge markings are used to estimate floods before systematic records began. To incorporate this data, a method involving the use of simulated histories is applied to better estimate relevant statistics in a reliable and dependable way. In this paper, the authors focus on the case study of the Welsh Bridge in Shrewsbury on the Severn in the United Kingdom.
Alison L. Kay, Nick Dunstone, Gillian Kay, Victoria A. Bell, and Jamie Hannaford
Nat. Hazards Earth Syst. Sci., 24, 2953–2970, https://doi.org/10.5194/nhess-24-2953-2024, https://doi.org/10.5194/nhess-24-2953-2024, 2024
Short summary
Short summary
Hydrological hazards affect people and ecosystems, but extremes are not fully understood due to limited observations. A large climate ensemble and simple hydrological model are used to assess unprecedented but plausible floods and droughts. The chain gives extreme flows outside the observed range: summer 2022 ~ 28 % lower and autumn 2023 ~ 42 % higher. Spatial dependence and temporal persistence are analysed. Planning for such events could help water supply resilience and flood risk management.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
Short summary
Short summary
We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Emma L. Robinson, Matthew J. Brown, Alison L. Kay, Rosanna A. Lane, Rhian Chapman, Victoria A. Bell, and Eleanor M. Blyth
Earth Syst. Sci. Data, 15, 4433–4461, https://doi.org/10.5194/essd-15-4433-2023, https://doi.org/10.5194/essd-15-4433-2023, 2023
Short summary
Short summary
This work presents two new Penman–Monteith potential evaporation datasets for the UK, calculated with the same methodology applied to historical climate data (Hydro-PE HadUK-Grid) and an ensemble of future climate projections (Hydro-PE UKCP18 RCM). Both include an optional correction for evaporation of rain that lands on the surface of vegetation. The historical data are consistent with existing PE datasets, and the future projections include effects of rising atmospheric CO2 on vegetation.
Alison L. Kay, Victoria A. Bell, Helen N. Davies, Rosanna A. Lane, and Alison C. Rudd
Earth Syst. Sci. Data, 15, 2533–2546, https://doi.org/10.5194/essd-15-2533-2023, https://doi.org/10.5194/essd-15-2533-2023, 2023
Short summary
Short summary
Climate change will affect the water cycle, including river flows and soil moisture. We have used both observational data (1980–2011) and the latest UK climate projections (1980–2080) to drive a national-scale grid-based hydrological model. The data, covering Great Britain and Northern Ireland, suggest potential future decreases in summer flows, low flows, and summer/autumn soil moisture, and possible future increases in winter and high flows. Society must plan how to adapt to such impacts.
Jamie Hannaford, Jonathan D. Mackay, Matthew Ascott, Victoria A. Bell, Thomas Chitson, Steven Cole, Christian Counsell, Mason Durant, Christopher R. Jackson, Alison L. Kay, Rosanna A. Lane, Majdi Mansour, Robert Moore, Simon Parry, Alison C. Rudd, Michael Simpson, Katie Facer-Childs, Stephen Turner, John R. Wallbank, Steven Wells, and Amy Wilcox
Earth Syst. Sci. Data, 15, 2391–2415, https://doi.org/10.5194/essd-15-2391-2023, https://doi.org/10.5194/essd-15-2391-2023, 2023
Short summary
Short summary
The eFLaG dataset is a nationally consistent set of projections of future climate change impacts on hydrology. eFLaG uses the latest available UK climate projections (UKCP18) run through a series of computer simulation models which enable us to produce future projections of river flows, groundwater levels and groundwater recharge. These simulations are designed for use by water resource planners and managers but could also be used for a wide range of other purposes.
Adam Griffin, Gianni Vesuviano, and Elizabeth Stewart
Nat. Hazards Earth Syst. Sci., 19, 2157–2167, https://doi.org/10.5194/nhess-19-2157-2019, https://doi.org/10.5194/nhess-19-2157-2019, 2019
Short summary
Short summary
Classical statistical methods for flood frequency estimation assume flooding characteristics do not change over time. Recent focus on climate change has raised questions of the validity of such assumptions. Near-natural catchments are used to focus on climate (not land-use) change, investigating the sensitivity of trend estimates to the period of record. Some key statistics were very sensitive, but conclusive spatial patterns were not found. Smaller floods were most affected by these trends.
Adam Griffin, Luke Shaw, and Elizabeth Stewart
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-325, https://doi.org/10.5194/hess-2018-325, 2018
Revised manuscript not accepted
Short summary
Short summary
To better estimate how big the
1-in-100-yearflood is, historical data such as newspaper archives and bridge markings are used to estimate floods before systematic records began. To incorporate this data, a method involving the use of simulated histories is applied to better estimate relevant statistics in a reliable and dependable way. In this paper, the authors focus on the case study of the Welsh Bridge in Shrewsbury on the Severn in the United Kingdom.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Victoria A. Bell, Helen N. Davies, Alison L. Kay, Anca Brookshaw, and Adam A. Scaife
Hydrol. Earth Syst. Sci., 21, 4681–4691, https://doi.org/10.5194/hess-21-4681-2017, https://doi.org/10.5194/hess-21-4681-2017, 2017
Short summary
Short summary
The research presented here provides the first evaluation of the skill of a seasonal hydrological forecast for the UK. The forecast scheme combines rainfall forecasts from the Met Office GloSea5 forecast system with a national-scale hydrological model to provide estimates of river flows 1 to 3 months ahead. The skill in the combined model is assessed for different seasons and regions of Britain, and the analysis indicates that Autumn/Winter flows can be forecast with reasonable confidence.
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Extended-range forecasting of stream water temperature with deep-learning models
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Projections of streamflow intermittence under climate change in European drying river networks
Economic valuation of subsurface water contributions to watershed ecosystem services using a fully integrated groundwater–surface-water model
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric–hydrological model
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Simulating the Tone River eastward diversion project in Japan carried out 4 centuries ago
Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers
Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models
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
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
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
Learning landscape features from streamflow with autoencoders
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
CONCN: A high-resolution, integrated surface water-groundwater ParFlow modeling platform of continental China
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
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
Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis
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?
Assessing the adequacy of traditional hydrological models for climate change impact studies: A case for long-short-term memory (LSTM) neural networks
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
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?
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Assessing the value of high-resolution rainfall and streamflow data for hydrological modeling: An analysis based on 63 catchments in southeast China
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025, https://doi.org/10.5194/hess-29-1703-2025, 2025
Short summary
Short summary
The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025, https://doi.org/10.5194/hess-29-1725-2025, 2025
Short summary
Short summary
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 we observe overparameterization.
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
Hydrol. Earth Syst. Sci., 29, 1685–1702, https://doi.org/10.5194/hess-29-1685-2025, https://doi.org/10.5194/hess-29-1685-2025, 2025
Short summary
Short summary
We generate operational forecasts of daily maximum stream water temperature for 32 consecutive days at 54 stations in Switzerland with our best-performing data-driven model. The average forecast error is 0.38 °C for 1 d ahead and increases to 0.90 °C for 32 d ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025, https://doi.org/10.5194/hess-29-1749-2025, 2025
Short summary
Short summary
Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use inputs of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost.
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., 29, 1615–1636, https://doi.org/10.5194/hess-29-1615-2025, https://doi.org/10.5194/hess-29-1615-2025, 2025
Short summary
Short summary
Our study projects how climate change will affect the drying of river segments and stream networks in Europe, using advanced modelling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent and intense and will start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists for evaluating the ecological health of river ecosystem.
Tariq Aziz, Steven K. Frey, David R. Lapen, Susan Preston, Hazen A. J. Russell, Omar Khader, Andre R. Erler, and Edward A. Sudicky
Hydrol. Earth Syst. Sci., 29, 1549–1568, https://doi.org/10.5194/hess-29-1549-2025, https://doi.org/10.5194/hess-29-1549-2025, 2025
Short summary
Short summary
This study determines the value of subsurface water for ecosystem services' supply in an agricultural watershed in Ontario, Canada. Using a fully integrated water model and an economic valuation approach, the research highlights subsurface water's critical role in maintaining watershed ecosystem services. The study informs on the sustainable use of subsurface water and introduces a new method for managing watershed ecosystem services.
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025, https://doi.org/10.5194/hess-29-1277-2025, 2025
Short summary
Short summary
Data-driven techniques have shown the potential to outperform process-based models in rainfall–runoff simulations. Hybrid models, combining both approaches, aim to enhance accuracy and maintain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025, https://doi.org/10.5194/hess-29-1061-2025, 2025
Short summary
Short summary
This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1033–1060, https://doi.org/10.5194/hess-29-1033-2025, https://doi.org/10.5194/hess-29-1033-2025, 2025
Short summary
Short summary
Owing to differences in the existing published results, we conducted a detailed analysis of the runoff components and future trends in the Yarlung Tsangpo River basin and found that the contributions of snowmelt and glacier melt runoff to streamflow (both ~5 %) are limited and much lower than previous results. The streamflow in this area will continuously increase in the future, but the overestimated contribution of glacier melt could lead to an underestimation of this increasing trend.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025, https://doi.org/10.5194/hess-29-903-2025, 2025
Short summary
Short summary
This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling 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.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci., 29, 785–798, https://doi.org/10.5194/hess-29-785-2025, https://doi.org/10.5194/hess-29-785-2025, 2025
Short summary
Short summary
Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine learning (ML) models are increasingly being applied for flood forecasting. Such models are typically trained on large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets that maximise the spatio-temporal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Joško Trošelj and Naota Hanasaki
Hydrol. Earth Syst. Sci., 29, 753–766, https://doi.org/10.5194/hess-29-753-2025, https://doi.org/10.5194/hess-29-753-2025, 2025
Short summary
Short summary
This study presents the first distributed hydrological simulation which confirms claims raised by historians that the eastward diversion project of the Tone River in Japan was conducted 4 centuries ago to increase low flows and subsequent travelling possibilities surrounding the capital, Edo (Tokyo), using inland navigation. We showed that great steps forward can be made for improving quality of life with small human engineering waterworks and small interventions in the regime of natural flows.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 29, 683–700, https://doi.org/10.5194/hess-29-683-2025, https://doi.org/10.5194/hess-29-683-2025, 2025
Short summary
Short summary
This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci., 29, 627–653, https://doi.org/10.5194/hess-29-627-2025, https://doi.org/10.5194/hess-29-627-2025, 2025
Short summary
Short summary
Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed 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.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
Short summary
Short summary
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 catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci., 29, 335–360, https://doi.org/10.5194/hess-29-335-2025, https://doi.org/10.5194/hess-29-335-2025, 2025
Short summary
Short summary
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 to better prepare for and respond to floods.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-292, https://doi.org/10.5194/hess-2024-292, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We developed the first high-resolution, integrated surface water-groundwater hydrologic model of the entire continental China using ParFlow. The model shows good performance of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279, https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Hydrologic models are needed to provide simulations of water availability, floods and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models, and thus cannot easily be used to select an appropriate model for a specific place.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Fabián Lema, Pablo A. Mendoza, Nicolás A. Vásquez, Naoki Mizukami, Mauricio Zambrano-Bigiarini, and Ximena Vargas
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-221, https://doi.org/10.5194/hess-2024-221, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what it truly reflects in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and time scales for accurate drought characterization and monitoring.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Jean-Luc Martel, François Brissette, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Gabriel Rondeau-Genesse, and Louis-Philippe Caron
EGUsphere, https://doi.org/10.5194/egusphere-2024-2133, https://doi.org/10.5194/egusphere-2024-2133, 2024
Short summary
Short summary
This study compares Long Short-Term Memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrolgical models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
EGUsphere, https://doi.org/10.5194/egusphere-2024-1438, https://doi.org/10.5194/egusphere-2024-1438, 2024
Short summary
Short summary
Common intuition holds that higher input data resolution leads to better results. To assess the benefits of high-resolution data, we conducted simulation experiments using data with various temporal resolutions across multiple catchments, and found that higher resolution data does not always improve model performance, challenging the necessity of pursuing such data. In catchments with small areas or significant flow variability, high-resolution data is more valuable.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Barker, L., Hannaford, J., Muchan, K., Turner, S., and Parry, S.: The winter 2015/2016 floods in the UK – a hydrological appraisal, Weather, 71, 324–333, https://doi.org/10.1002/wea.2822, 2016.
Bell, V. A., Kay, A. L., Jones, R. G., Moore, R. J., and Reynard, N. S.: Use of soil data in a grid-based hydrological model to estimate spatial variation in changing flood risk across the UK, J. Hydrol., 377, 335–350, https://doi.org/10.1016/j.jhydrol.2009.08.031, 2009.
Bell, V. A., Kay, A. L., Cole, S. J., Jones, R. G., Moore, R. J., and Reynard, N. S.: How might climate change affect river flows across the Thames basin? An area-wide analysis using the UKCP09 regional climate model ensemble, J. Hydrol., 442, 89–104, https://doi.org/10.1016/j.jhydrol.2012.04.001, 2012.
Berghuijs, W. R., Harrigan, S., Molnar, P., Slater, L. J., and Kirchner, J. W.: The Relative Importance of Different Flood-Generating Mechanisms Across Europe, Water Resour. Res., 55, 4582–4593, https://doi.org/10.1029/2019WR024841, 2019.
Bevacqua, E., Shepherd, T. G., Watson, P. A. G., Sparrow, S., Wallom, D., and Mitchell, D.: Larger spatial footprint of wintertime total precipitation extremes in a warmer climate, Geophys. Res. Lett., 48, e2020GL091990, https://doi.org/10.1029/2020GL091990, 2021.
Beylich, M., Haberlandt, U., and Reinstorf, F.: Daily vs. hourly simulation for estimating future flood peaks in mesoscale catchments, Hydrol. Res., 52, 821–833, https://doi.org/10.2166/nh.2021.152, 2021.
Blöschl, G., Hall, J., Parajka, J., Perdigão, R. A. P., Merz, B., Arheimer, B., Aronica, G. T., Bilibashi, A., Bonacci, O., Borga, M., Čanjevac, I., Castellarin, A., Chirico, G. B., Claps, P., Fiala, K., Frolova, N., Gorbachova, L., Gül, A., Hannaford, J., Harrigan, S., Kireeva, M., Kiss, A., Kjeldsen, T. R., Kohnová, S., Koskela, J. J., Ledvinka, O., Macdonald, N., Mavrova-Guirguinova, M., Mediero, L., Merz, R., Molnar, P., Montanari, A., Murphy, C., Osuch, M., Ovcharuk, V., Radevski, I., Rogger, M., Salinas, J. L., Sauquet, E., Šraj, M., Szolgay, J., Viglione, A., Volpi, E., Wilson, D., Zaimi, K., and Živković, N.: Changing climate shifts timing of European floods, Science, 357, 588–590, https://doi.org/10.1126/science.aan2506, 2017.
Brunner, M. I., Gilleland, E., Wood, A., Swain, D. L., and Clark, M.: Spatial Dependence of Floods Shaped by Spatiotemporal Variations in Meteorological and Land-Surface Processes, Geophys. Res. Lett., 47, e2020GL088000, https://doi.org/10.1029/2020GL088000, 2020.
Chen, Y., Paschalis, A., Kendon, E., Kim, D., and Onof, C.: Changing spatial structure of summer heavy rainfall, using convection-permitting ensemble, Geophys. Res. Lett., 48, e2020GL090903, https://doi.org/10.1029/2020GL090903, 2021.
Cole, S. J., Moore, R. J., Robson, A. J., Mattingley, P. S., Black, K. B., and Kay, A. L.: Evaluating G2G for use in rapid response catchments: Final Report, R&D Project Report SC110003/R2, Joint Defra/EA Flood and Coastal Erosion Risk Management, Research Contractor: CEH Wallingford, Environment Agency, Bristol, UK, 120 pp., https://nora.nerc.ac.uk/id/eprint/533808 (last access: 1 February 2022), 2013.
Coles, S.: An introduction to statistical modeling of extreme values, Springer, London, https://doi.org/10.1007/978-1-4471-3675-0, 2001.
Collet, L., Harrigan, S., Prudhomme, C., Formetta, G., and Beevers, L.: Future hot-spots for hydro-hazards in Great Britain: a probabilistic assessment, Hydrol. Earth Syst. Sci., 22, 5387–5401, https://doi.org/10.5194/hess-22-5387-2018, 2018.
Cranston, M., Maxey, R., Tavendale, A., Buchanan, P., Motion, A., Cole, S., Robson, A., Moore, R. J., and Minett, A., Countrywide flood forecasting in Scotland: challenges for hydrometeorological model uncertainty and prediction, Weather Radar and Hydrology, edited by: Moore, R. J., Cole, S. J., and Illingworth, A. J., IAHS Press, 538–543, ISBN 9781907161261, 2012.
Davies, H. and Bell, V.: Assessment of methods for extracting low resolution river networks from high resolution digital data, Hydrolog. Sci. J., 54, 17–28, https://doi.org/10.1623/hysj.54.1.17, 2008.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012.
Filipova, V., Lawrence, D., and Skaugen, T.: A stochastic event-based approach for flood estimation in catchments with mixed rainfall and snowmelt flood regimes, Nat. Hazards Earth Syst. Sci., 19, 1–18, https://doi.org/10.5194/nhess-19-1-2019, 2019.
Formetta, G., Prosdocimi, I., Stewart, E. and Bell, V.: Estimating the index flood with continuous hydrological models: an application in Great Britain, Hydrol. Res., 49, 123–133, 2018.
Fuller, R. M., Smith, G. M., Sanderson, J. M., Hill, R. A., and Thomson, A. G.: The UK Land Cover Map 2000: Construction of a parcel-based vector map from satellite images, Cartogr. J., 39, 15–25, https://doi.org/10.1179/caj.2002.39.1.15, 2002.
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., Bell, V., Stewart, E. J., Sayer, P., and Carr, S.: Peak flow and probability of exceedance data for Grid-to-Grid modelled widespread flooding events across mainland GB from 1980–2010 and 2050–2080, NERC EDS Environmental Information Data Centre [data set], https://doi.org/10.5285/26ce15dd-f994-40e0-8a09-5f257cc1f2ab, 2022a.
Griffin, A., Kay A., Stewart, E., Sayers, P., and Carr, S.: Spatially coherent statistical simulation of widespread flooding events under climate change, Hydrol. Res., 53, 1428–1440, https://doi.org/10.2166/nh.2022.069, 2022b.
Grossi, P. and Kunreuther, H.: Catastrophe Modeling: A new approach to managing risk, 25, Springer Science & Business Media, https://doi.org/10.1007/b100669, 2005.
Hough, M. N. and Jones, R. J. A.: The United Kingdom Meteorological Office rainfall and evaporation calculation system: MORECS version 2.0 – an overview, Hydrol. Earth Syst. Sci., 1, 227–239, https://doi.org/10.5194/hess-1-227-1997, 1997.
Irish, J.L., Sleath, A., Cialone, M. A., Knutson, T. R., and Jensen, R. E.: Simulations of Hurricane Katrina (2005) under sea level and climate conditions for 1900, Climatic Change, 122, 635–649, https://doi.org/10.1007/s10584-013-1011-1, 2014.
Jimenez-Cisneros, B.: Responding to the challenges of water security: The eighth phase of the international hydrological programme 2014–2021, P. Int. Assoc. Hydrol. Sci., 366, 10–19, https://doi.org/10.5194/piahs-366-10-2015, 2015.
Kay, A. L.: Simulation of river flow in Britain under climate change: Baseline performance and future seasonal changes, Hydrol. Process., 35, e14137, https://doi.org/10.1002/hyp.14137, 2021.
Kay, A. L.: Differences in hydrological impacts using regional climate model and nested convection-permitting model data, Climatic Change, 173, 11, https://doi.org/10.1007/s10584-022-03405-z, 2022.
Kay, A. L. and Brown, M. J.: Using sub-daily precipitation for grid-based hydrological modelling across Great Britain: assessing model performance and comparing flood impacts under climate change, J. Hydrol.: Reg. Stud., 50, 101588, https://doi.org/10.1016/j.ejrh.2023.101588, 2023.
Kay, A. L., Bell, V. A., Guillod, B. P., Jones, R. G., and Rudd, A. C.: National-scale analysis of low flow frequency: historical trends and potential future changes, Climatic Change, 147, 585–599, https://doi.org/10.1007/s10584-018-2145-y, 2018.
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.
Kay, A. L., Lane, R. A., and Bell, V. A.: Grid-based simulation of soil moisture in the UK: Future changes in extremes and wetting and drying dates, Environ. Res. Lett., 17, 074029, https://doi.org/10.1088/1748-9326/ac7a4e, 2022.
Kay, A. L., Rudd, A. C., and Coulson, J.: Spatial downscaling of precipitation for hydrological modelling: assessing a simple method and its application under climate change in Britain, Hydrol. Process., 37, e14823, https://doi.org/10.1002/hyp.14823, 2023.
Kelder, T., Müller, M., Slater, L. J., Marjoribanks, T. I., Wilby, R. L., Prudhomme, C., Bohlinger, P., Ferranti, L., and Nipen, T.: Using UNSEEN trends to detect decadal changes in 100-year precipitation extremes, npj Clim. Atmos. Sci., 3, 47, https://doi.org/10.1038/s41612-020-00149-4, 2020.
Kendon, E., Short, C., Pope, J., Chan, S., Wilkinson, J., Tucker, S., Bett, P., and Harris, G.: Update to UKCP Local (2.2 km) projections, Met Office Hadley Centre, Exeter, UK, https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/ukcp18_local_update_report_2021.pdf (last access: 1 February 2022), 2021.
Kundzewicz, Z.W., Su, B., Wang, Y., Wang, G., Wang, G., Huang, J., and Jiang, T.: Flood risk in a range of spatial perspectives – from global to local scales, Nat. Hazards Earth Syst. Sci., 19, 1319–1328, https://doi.org/10.5194/nhess-19-1319-2019, 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, 684982, https://doi.org/10.3389/frwa.2021.684982, 2021.
Lavers, D. A. and Villarini, G.: The nexus between atmospheric rivers and extreme precipitation across Europe, Geophys. Res. Lett., 40, 3259–3264, https://doi.org/10.1002/grl.50636, 2013.
Maraun, D., Shephard, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., and Mearns, L. O.: Towards process-informed bias correction of climate change simulations, Nat. Clim. Change, 7, 764–773, https://doi.org/10.1038/nclimate3418, 2017.
Met Office Hadley Centre: UKCP18 Regional Climate Model Projections for the UK, Centre for Environmental Data Analysis, http://catalogue.ceda.ac.uk/uuid/b4d24b3df3754b9d9028447eb3cd89c6 (last access: November 2021), 2018.
Met Office and Hollis, D.: HadUK-Grid Gridded Climate Observations on a 1 km grid over the UK, v1.0.0.0 (1862–2017), Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/2a62652a4fe6412693123dd6328f6dc8, 2019.
Mohr, S., Ehret, U., Kunz, M., Ludwig, P., Caldas-Alvarez, A., Daniell, J. E., Ehmele, F., Feldmann, H., Franca, M. J., Gattke, C., Hundhausen, M., Knippertz, P., Küpfer, K., Mühr, B., Pinto, J. G., Quinting, J., Schäfer, A. M., Scheibel, M., Seidel, F., and Wisotzky, C.: A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 1: Event description and analysis, Nat. Hazards Earth Syst. Sci., 23, 525–551, https://doi.org/10.5194/nhess-23-525-2023, 2023.
Moore, R. J., Cole, S. J., Bell, V. A., and Jones, D. A.: Issues in flood forecasting: ungauged basins, extreme floods and uncertainty, in: Frontiers in Flood Research, IAHS Publ. 305, edited by: Tchiguirinskaia, I., Thein, K. N. N., and Hubert, P., 103–122, ISBN 978-1901502-63-3, 2006.
Moore, R. J., Cole, S. J., and Robson, A. J.: Weather radar and hydrology: a UK operational perspective, in: Weather Radar and Hydrology, IAHS Publ. no. 351, edited by: Moore, R. J., Cole, S. J., and Illingworth, A. J., 429–434, ISBN 9781-907161261, 2012.
Morris, D. G. and Flavin, R. W.: A digital terrain model for hydrology, in: 4th International Symposium on Spatial Data Handling, 23 July 1990, Zurich, 250–262, 1990.
Murphy, J., Harris, G., Sexton, D., Kendon, E., Bett, P., Clark, R., and Yamazaki, K.: UKCP18 land projections: science report, Met Office, Exeter, https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP18-Land-report.pdf (last access: November 2021), 2018.
National River Flow Archive (NRFA): NRFA Peak Flow dataset Version 10, NRFA [data set], https://nrfa.ceh.ac.uk/peak-flow-dataset (last access: September 2021), 2021.
Paz, A. R., Collischonn, W., and Lopes da Silveira, A. L.: Improvements in large-scale drainage networks derived from digital elevation models, Water Resour. Res., 42, W08502, https://doi.org/10.1029/2005WR004544, 2006.
Price, D., Pilling, C., Robbins, G., Lane, A., Boyce, G., Fenwick, K., Moore, R. J., Coles, J., Harrison, T., and Van Dijk, M.: Representing the spatial variability of rainfall for input to the G2G distributed flood forecasting model: operational experience from the Flood Forecasting Centre, Weather Radar and Hydrology, edited by: Moore, R. J., Cole, S. J., and Illingworth, A. J., IAHS Press, 532–537, ISBN 9781907161261, 2012.
Reynard, N. S., Kay, A. L., Anderson, M., Donovan, B., and Duckworth, C.: The evolution of climate change guidance for fluvial flood risk management in England, Prog. Phys. Geogr., 41, 222–237, https://doi.org/10.1177/0309133317702566, 2017.
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafaj, P.: RCP8.5 – A scenario of comparatively high greenhouse gas emissions, Climatic Change, 109, 33–57, https://doi.org/10.1007/s10584-011-0149-y, 2011.
Robinson, E. L., Kay, A. L., Brown, M., Chapman, R., Bell, V., and Blyth, E. M.: Potential evapotranspiration derived from the UK Climate Projections 2018 Regional Climate Model ensemble 1980–2080 (Hydro-PE UKCP18 RCM), NERC Environmental Information Data Centre [data set], https://doi.org/10.5285/eb5d9dc4-13bb-44c7-9bf8-c5980fcf52a4, 2021.
Robinson, E. L., Brown, M. J., Kay, A. L., Lane, R. A., Chapman, R., Bell, V. A. and Blyth, E. M.: Hydro-PE: gridded datasets of historical and future Penman–Monteith potential evaporation for the United Kingdom, Earth Syst. Sci. Data, 15, 4433–4461, https://doi.org/10.5194/essd-15-4433-2023, 2023.
Robson, A. J. and Reed, D. W.: Statistical procedures for flood frequency estimation, Flood Estimation Handbook, Volume 3, Institute of Hydrology, Wallingford, ISBN 9781906698003, 1999.
Rudd, A. C., Bell, V. A., and Kay, A. L.: National-scale analysis of simulated hydrological droughts (1891–2015), J. Hydrol., 550, 368–385, https://doi.org/10.1016/j.jhydrol.2017.05.018, 2017.
Rudd, A. C., Kay, A. L., and Bell, V. A.: National-scale analysis of future river flow and soil moisture droughts: potential changes in drought characteristics, Climatic Change, 156, 323–340, https://doi.org/10.1007/s10584-019-02528-0, 2019.
Rudd, A. C., Kay, A. L., Wells, S. C., Aldridge, T., Cole, S. J., Kendon, E. J. and Stewart, E. J.: Investigating potential future changes in surface water flooding hazard and impact, Hydrol. Process., 34, 139–149, https://doi.org/10.1002/hyp.13572, 2020.
Sayers, P. B., Horritt, M., Carr, S., Kay, A., Mauz, J., Lamb, R., and Penning-Rowsell, E.: Third UK Climate Change Risk Assessment (CCRA3): Future flood risk, Committee on Climate Change, London, https://www.ukclimaterisk.org/wp-content/uploads/2020/07/Future-Flooding-Main-Report-Sayers-1.pdf (last access: 1 September 2021), 2020.
Sayers, P. B., Griffin, A., Lowe, J., Bernie, D., Carr, S., Kay, A., and Stewart, E. J.: Beyond the climate uplift – The importance of accounting for changes in the spatial structure of future fluvial flood events on flood risk in Great Britain, Nat. Hazards, 120, 3773–3798, https://doi.org/10.1007/s11069-023-06350-x, 2024.
Tanguy, M., Dixon, H., Prosdocimi, I., Morris, D. G., and Keller, V. D. J.: Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890–2017) [CEH-GEAR], NERC Environmental Information Data Centre [data set], https://doi.org/10.5285/ee9ab43d-a4fe-4e73-afd5-cd4fc4c82556, 2019.
Tawn, J. A., Towe, R. P., Eastoe, E., and Lamb, R.: Modelling the clustering of extreme events for short-term risk assessment, J. Agr. Biol. Environ. Stat., 25, 32–53, https://doi.org/10.1007/s13253-019-00376-0, 2018a.
Tawn, J. A., Shooter, R., Towe, R. P., and Lamb, R.: Modelling spatial extreme events with environmental applications, Spat. Stat., 28, 39–58, https://doi.org/10.1016/j.spasta.2018.04.007, 2018b.
Thober, S. Kumar, R., Wanders, N., Marx, A., Pan, M., Rakovec, O., Samaniego, L., Sheffield, J., Wood, E. F., and Zink, M.: Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming, Environ. Res. Lett., 13, 014003, https://doi.org/10.1088/1748-9326/aa9e35, 2018.
Vishnu, C. L., Sajinkumar, K. S., Oommen, T., Coffman, R. A., Thrivikramji, K. P., Rani, V. R., and Keerthy, S.: Satellite-based assessment of the August 2018 flood in parts of Kerala, India, Geomat. Nat. Hazards Risk, 10, 758–767, https://doi.org/10.1080/19475705.2018.1543212, 2019.
Wells, S. C., Robson, A. J., Moore, R. J., Cole, S. J., and Rudd, A.: Understanding the national performance of flood forecasting models to guide incident management and investment, FLOODrisk 2016, 3rd European Conference on Flood Risk Management, E3S Web Conf., 7, 18007, https://doi.org/10.1051/e3sconf/20160718007, 2016.
Short summary
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.
Widespread flooding is a major problem in the UK and is greatly affected by climate change and...
