Articles | Volume 29, issue 1
https://doi.org/10.5194/hess-29-1-2025
© Author(s) 2025. 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-29-1-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jan 2025
Research article |
| 03 Jan 2025
| 03 Jan 2025
Modelling convective cell life cycles with a copula-based approach
Chien-Yu Tseng
Department of Civil Engineering, National Taiwan University, Taipei, 106319, Taiwan
Department of Civil Engineering, National Taiwan University, Taipei, 106319, Taiwan
Imperial College London, London, SW7 2AZ, United Kingdom
Christian Onof
Imperial College London, London, SW7 2AZ, United Kingdom
Related authors
No articles found.
Chi-Ling Wei, Pei-Chun Chen, Chien-Yu Tseng, Ting-Yu Dai, Yun-Ting Ho, Ching-Chun Chou, Christian Onof, and Li-Pen Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1918, https://doi.org/10.5194/egusphere-2024-1918, 2024
Short summary
Short summary
pyBL is an open-source package for generating realistic rainfall time series based on the Bartlett-Lewis (BL) model. It can preserve not only standard but also extreme rainfall statistics across various timescales. Notably, compared to traditional frequency analysis methods, the BL model requires only half the record length (or even shorter) to achieve similar consistency in estimating sub-hourly rainfall extremes. This makes it a valuable tool for modelling rainfall extremes with short records.
Abrar Habib, Athanasios Paschalis, Adrian P. Butler, Christian Onof, John P. Bloomfield, and James P. R. Sorensen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-27, https://doi.org/10.5194/hess-2023-27, 2023
Preprint withdrawn
Short summary
Short summary
Components of the hydrological cycle exhibit a “memory” in their behaviour which quantifies how long a variable would stay at high/low values. Being able to model and understand what affects it is vital for an accurate representation of the hydrological elements. In the current work, it is found that rainfall affects the fractal behaviour of groundwater levels, which implies that changes to rainfall due to climate change will change the periods of flood and drought in groundwater-fed catchments.
Y. K. Chen, Y. T. Lin, H. Y. Yen, N. H. Chang, H. M. Lin, K. H. Yang, C. S. Chen, L. P. Wang, H. K. Cheng, H. H. Wu, and J. Y. Han
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1091–1096, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1091-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1091-2022, 2022
Christian Onof and Li-Pen Wang
Hydrol. Earth Syst. Sci., 24, 2791–2815, https://doi.org/10.5194/hess-24-2791-2020, https://doi.org/10.5194/hess-24-2791-2020, 2020
Short summary
Short summary
The randomised Bartlett–Lewis (RBL) model is widely used to synthesise rainfall time series with realistic statistical features. However, it tended to underestimate rainfall extremes at sub-hourly and hourly timescales. In this paper, we revisit the derivation of equations that represent rainfall properties and compare statistical estimation methods that impact model calibration. These changes effectively improved the RBL model's capacity to reproduce sub-hourly and hourly rainfall extremes.
Jeongha Park, Christian Onof, and Dongkyun Kim
Hydrol. Earth Syst. Sci., 23, 989–1014, https://doi.org/10.5194/hess-23-989-2019, https://doi.org/10.5194/hess-23-989-2019, 2019
Short summary
Short summary
Rainfall data are often unavailable for the analysis of water-related problems such as floods and droughts. In such cases, researchers use rainfall generators to produce synthetic rainfall data. However, data from most rainfall generators can serve only one specific purpose; i.e. one rainfall generator cannot be applied to analyse both floods and droughts. To overcome this issue, we invented a multipurpose rainfall generator that can be applied to analyse most water-related problems.
David Cross, Christian Onof, Hugo Winter, and Pietro Bernardara
Hydrol. Earth Syst. Sci., 22, 727–756, https://doi.org/10.5194/hess-22-727-2018, https://doi.org/10.5194/hess-22-727-2018, 2018
Short summary
Short summary
Extreme rainfall is one of the most significant natural hazards. However, estimating very large events is highly uncertain. We present a new approach to construct intense rainfall using the structure of rainfall generation in clouds. The method is particularly effective at estimating short-duration extremes, which can be the most damaging. This is expected to have immediate impact for the estimation of very rare downpours, with the potential to improve climate resilience and hazard preparedness.
Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Susana Ochoa-Rodriguez, Patrick Willems, Abdellah Ichiba, Li-Pen Wang, Rui Pina, Johan Van Assel, Guendalina Bruni, Damian Murla Tuyls, and Marie-Claire ten Veldhuis
Hydrol. Earth Syst. Sci., 21, 2361–2375, https://doi.org/10.5194/hess-21-2361-2017, https://doi.org/10.5194/hess-21-2361-2017, 2017
Short summary
Short summary
Data from 10 urban or peri-urban catchments located in five EU countries are used to analyze the imperviousness distribution and sewer network geometry. Consistent scale invariant features are retrieved for both (fractal dimensions can be defined), which enables to define a level of urbanization. Imperviousness representation in operational model is also found to exhibit scale-invariant features (even multifractality). The research was carried out as part of the UE INTERREG IV RainGain project.
L.-P. Wang, S. Ochoa-Rodríguez, C. Onof, and P. Willems
Hydrol. Earth Syst. Sci., 19, 4001–4021, https://doi.org/10.5194/hess-19-4001-2015, https://doi.org/10.5194/hess-19-4001-2015, 2015
Short summary
Short summary
A new methodology is proposed in this paper, focusing on improving the applicability of the operational weather radar data to urban hydrology with rain gauge data. The proposed methodology employed a simple yet effective technique to extract additional information (called local singularity structure) from radar data, which was generally ignored in related works. The associated improvement can be particularly seen in capturing storm peak magnitudes, which is critical for urban applications.
Z. Zulkafli, W. Buytaert, C. Onof, W. Lavado, and J. L. Guyot
Hydrol. Earth Syst. Sci., 17, 1113–1132, https://doi.org/10.5194/hess-17-1113-2013, https://doi.org/10.5194/hess-17-1113-2013, 2013
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Downscaling the probability of heavy rainfall over the Nordic countries
Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5
Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model
Assessing rainfall radar errors with an inverse stochastic modelling framework
Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution
Spatiotemporal responses of runoff to climate change in the southern Tibetan Plateau
FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
On the combined use of rain gauges and GPM IMERG satellite rainfall products for hydrological modelling: impact assessment of the cellular-automata-based methodology in the Tanaro River basin in Italy
An increase in the spatial extent of European floods over the last 70 years
140-year daily ensemble streamflow reconstructions over 661 catchments in France
The agricultural expansion in South America's Dry Chaco: regional hydroclimate effects
Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Improving runoff simulation in the Western United States with Noah-MP and variable infiltration capacity
Distribution, trends and drivers of flash droughts in the United Kingdom
Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?
Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France
What Are the Key Soil Hydrological Processes to Control Soil Moisture Memory?
Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)
Global total precipitable water variations and trends over the period 1958–2021
Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends
Identification of compound drought and heatwave events on a daily scale and across four seasons
Leveraging a Disdrometer Network to Develop a Probabilistic Precipitation Phase Model in Eastern Canada
Observation-driven model for calculating water harvesting potential from advective fog in (semi-)arid coastal regions
Assessment of seasonal soil moisture forecasts over Central Mediterranean toward groundwater management
Potential for historically unprecedented Australian droughts from natural variability and climate change
Review of Gridded Climate Products and Their Use in Hydrological Analyses Reveals Overlaps, Gaps, and Need for More Objective Approach to Model Forcings
Flood risk assessment for Indian sub-continental river basins
Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities
Divergent future drought projections in UK river flows and groundwater levels
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
Hydroclimatic processes as the primary drivers of the Early Khvalynian transgression of the Caspian Sea: new developments
Accounting for hydroclimatic properties in flood frequency analysis procedures
Understanding the influence of “hot” models in climate impact studies: a hydrological perspective
A semi-parametric hourly space–time weather generator
A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics
Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates
Investigating the response of land–atmosphere interactions and feedbacks to spatial representation of irrigation in a coupled modeling framework
Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia
Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks
Sensitivity of the pseudo-global warming method under flood conditions: a case study from the northeastern US
Hybrid forecasting: blending climate predictions with AI models
Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: a case study in the Upper Colorado River basin
Local moisture recycling across the globe
How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?
Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany
The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting
Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System
Rasmus E. Benestad, Kajsa M. Parding, and Andreas Dobler
Hydrol. Earth Syst. Sci., 29, 45–65, https://doi.org/10.5194/hess-29-45-2025, https://doi.org/10.5194/hess-29-45-2025, 2025
Short summary
Short summary
We present a new method to calculate the chance of heavy downpour and the maximum rainfall expected over a 25-year period. It is designed to analyse global climate models' reproduction of past and future climates. For the Nordic countries, it projects a wetter climate in the future with increased intensity but not necessarily more wet days. The analysis also shows that rainfall intensity is sensitive to future greenhouse gas emissions, while the number of wet days appears to be less affected.
Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
Hydrol. Earth Syst. Sci., 28, 4903–4925, https://doi.org/10.5194/hess-28-4903-2024, https://doi.org/10.5194/hess-28-4903-2024, 2024
Short summary
Short summary
This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.
Peter E. Levy and the COSMOS-UK team
Hydrol. Earth Syst. Sci., 28, 4819–4836, https://doi.org/10.5194/hess-28-4819-2024, https://doi.org/10.5194/hess-28-4819-2024, 2024
Short summary
Short summary
Having accurate up-to-date maps of soil moisture is important for many purposes. However, current modelled and remotely sensed maps are rather coarse and not very accurate. Here, we demonstrate a simple but accurate approach that is closely linked to direct measurements of soil moisture at a network sites across the UK, to the water balance (precipitation minus drainage and evaporation) measured at a large number of catchments (1212) and to remotely sensed satellite estimates.
Amy C. Green, Chris Kilsby, and András Bárdossy
Hydrol. Earth Syst. Sci., 28, 4539–4558, https://doi.org/10.5194/hess-28-4539-2024, https://doi.org/10.5194/hess-28-4539-2024, 2024
Short summary
Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
Hydrol. Earth Syst. Sci., 28, 4455–4476, https://doi.org/10.5194/hess-28-4455-2024, https://doi.org/10.5194/hess-28-4455-2024, 2024
Short summary
Short summary
We conducted a high-resolution hydrological simulation from 1959 to 2020 across France. We used a simple trial-and-error calibration to reduce the biases of the simulated water budget compared to observations. The selected simulation satisfactorily reproduces water fluxes, including their spatial contrasts and temporal trends. This work offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci., 28, 4361–4381, https://doi.org/10.5194/hess-28-4361-2024, https://doi.org/10.5194/hess-28-4361-2024, 2024
Short summary
Short summary
Our findings show that runoff in the Yarlung Zangbo (YZ) basin is primarily driven by rainfall, with the largest glacier runoff contribution in the downstream sub-basin. Annual runoff increased in the upper stream but decreased downstream due to varying precipitation patterns. It is expected to rise throughout the 21st century, mainly driven by increased rainfall.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
Hydrol. Earth Syst. Sci., 28, 4127–4155, https://doi.org/10.5194/hess-28-4127-2024, https://doi.org/10.5194/hess-28-4127-2024, 2024
Short summary
Short summary
Forecasting river flow months in advance is crucial for water sectors and society. In North America, snowmelt is a key driver of flow. This study presents a statistical workflow using snow data to forecast flow months ahead in North American snow-fed rivers. Variations in the river flow predictability across the continent are evident, raising concerns about future predictability in a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
Short summary
Short summary
The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
Short summary
Short summary
We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci., 28, 3457–3474, https://doi.org/10.5194/hess-28-3457-2024, https://doi.org/10.5194/hess-28-3457-2024, 2024
Short summary
Short summary
Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.
María Agostina Bracalenti, Omar V. Müller, Miguel A. Lovino, and Ernesto Hugo Berbery
Hydrol. Earth Syst. Sci., 28, 3281–3303, https://doi.org/10.5194/hess-28-3281-2024, https://doi.org/10.5194/hess-28-3281-2024, 2024
Short summary
Short summary
The Gran Chaco is a large, dry forest in South America that has been heavily deforested, particularly in the dry Chaco subregion. This deforestation, mainly driven by the expansion of the agricultural frontier, has changed the land's characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
Short summary
Short summary
Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
Short summary
Short summary
We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
EGUsphere, https://doi.org/10.5194/egusphere-2024-1969, https://doi.org/10.5194/egusphere-2024-1969, 2024
Short summary
Short summary
In this study, we present a detailed characterisation of flash drought in United Kingdom over the period 1969–2021.The spatiotemporal distribution of flash droughts is highly variable, with important regional and seasonal contrasts. In the UK, flash drought occurrence responds primarily to precipitation variability, although the atmospheric evaporative demand (AED) is important as a secondary driver. The atmospheric and oceanic conditions during flash droughts development were also analyzed.
Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-132, https://doi.org/10.5194/hess-2024-132, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and The Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
Hydrol. Earth Syst. Sci., 28, 2579–2601, https://doi.org/10.5194/hess-28-2579-2024, https://doi.org/10.5194/hess-28-2579-2024, 2024
Short summary
Short summary
The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Mohammad Ali Farmani, Ali Behrangi, Aniket Gupta, Ahmad Tavakoly, Matthew Geheran, and Guo-Yue Niu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1256, https://doi.org/10.5194/egusphere-2024-1256, 2024
Short summary
Short summary
This study investigates how key hydrological processes enhance soil water retention and release in land surface models, crucial for accurate weather and climate forecasting. Experiments show that soil hydraulics effectively sustain soil moisture. Additionally, allowing surface water ponding and improving soil permeability through macropores both enhance soil moisture persistency in the models.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
Short summary
Short summary
Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
Short summary
Short summary
Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Kyungmin Sung, Max C. A. Torbenson, and James H. Stagge
Hydrol. Earth Syst. Sci., 28, 2047–2063, https://doi.org/10.5194/hess-28-2047-2024, https://doi.org/10.5194/hess-28-2047-2024, 2024
Short summary
Short summary
This study examines centuries of nonstationary trends in meteorological drought and pluvial climatology. A novel approach merges tree-ring proxy data (North American Seasonal Precipitation Atlas – NASPA) with instrumental precipitation datasets by temporally downscaling proxy data, correcting biases, and analyzing shared trends in normal and extreme precipitation anomalies. We identify regions experiencing recent unprecedented shifts towards drier or wetter conditions and shifts in seasonality.
Baoying Shan, Niko E. C. Verhoest, and Bernard De Baets
Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, https://doi.org/10.5194/hess-28-2065-2024, 2024
Short summary
Short summary
This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves (CDHW) across four seasons. Using this method, we could establish the start and/or end dates of drought (or heatwave) events. We found an increase in the frequency of heatwaves and CDHW events in Belgium caused by climate change. We also found that different months have different chances of CDHW events.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-78, https://doi.org/10.5194/hess-2024-78, 2024
Preprint under review for HESS
Short summary
Short summary
Observations from a study site network in eastern Canada showed a temperature interval the overlapping probabilities for rain, snow or a mix of both. Models using random forest algorithms were developed to classify the precipitation phase using meteorological data to evaluate operational applications. They showed significantly improved phase classification compared to benchmarks, but misclassification led to costlier errors. However, accurate prediction of mixed phase remains a challenge.
Felipe Lobos-Roco, Jordi Vilà-Guerau de Arellano, and Camilo de Rio
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-110, https://doi.org/10.5194/hess-2024-110, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Water resources are fundamental for social, economic, and natural development of (semi-)arid regions. Precipitation decreases due to climate change obligates us to find new water resources. Fog harvesting emerges as a complementary one in regions where it is abundant but untapped. This research proposes a model to estimate fog harvesting potential in coastal (semi-)arid regions. This model could have broader applicability worldwide in regions where fog harvesting could be a viable water source.
Lorenzo Silvestri, Miriam Saraceni, Giulia Passadore, and Paolina Bongioannini Cerlini
EGUsphere, https://doi.org/10.5194/egusphere-2024-889, https://doi.org/10.5194/egusphere-2024-889, 2024
Short summary
Short summary
This work demonstrates that seasonal forecasts of soil moisture are a valuable resource for groundwater management in certain areas of the Central Mediterranean. In particular, they show significant correlation coefficients and forecast skill for the deepest soil moisture at 289 cm depth. Wet and dry events can be predicted 6 months in advance and, in general, dry events are better captured than wet events.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
Short summary
Short summary
Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-58, https://doi.org/10.5194/hess-2024-58, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
We assess 60 gridded climate datasets [ground- (G), satellite- (S), reanalysis-based (R)]. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; but R outperformed G when underlying data had low station density. G outperformed S or R datasets, though better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.
Urmin Vegad, Yadu Pokhrel, and Vimal Mishra
Hydrol. Earth Syst. Sci., 28, 1107–1126, https://doi.org/10.5194/hess-28-1107-2024, https://doi.org/10.5194/hess-28-1107-2024, 2024
Short summary
Short summary
A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive damage of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
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.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
Short summary
Short summary
We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Alexander Gelfan, Andrey Panin, Andrey Kalugin, Polina Morozova, Vladimir Semenov, Alexey Sidorchuk, Vadim Ukraintsev, and Konstantin Ushakov
Hydrol. Earth Syst. Sci., 28, 241–259, https://doi.org/10.5194/hess-28-241-2024, https://doi.org/10.5194/hess-28-241-2024, 2024
Short summary
Short summary
Paleogeographical data show that 17–13 ka BP, the Caspian Sea level was 80 m above the current level. There are large disagreements on the genesis of this “Great” Khvalynian transgression of the sea, and we tried to shed light on this issue. Using climate and hydrological models as well as the paleo-reconstructions, we proved that the transgression could be initiated solely by hydroclimatic factors within the deglaciation period in the absence of the glacial meltwater effect.
Joeri B. Reinders and Samuel E. Munoz
Hydrol. Earth Syst. Sci., 28, 217–227, https://doi.org/10.5194/hess-28-217-2024, https://doi.org/10.5194/hess-28-217-2024, 2024
Short summary
Short summary
Flooding presents a major hazard for people and infrastructure along waterways; however, it is challenging to study the likelihood of a flood magnitude occurring regionally due to a lack of long discharge records. We show that hydroclimatic variables like Köppen climate regions and precipitation intensity explain part of the variance in flood frequency distributions and thus reduce the uncertainty of flood probability estimates. This gives water managers a tool to locally improve flood analysis.
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault
Hydrol. Earth Syst. Sci., 27, 4355–4367, https://doi.org/10.5194/hess-27-4355-2023, https://doi.org/10.5194/hess-27-4355-2023, 2023
Short summary
Short summary
Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
Short summary
Short summary
Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
Short summary
Short summary
Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
Kaltrina Maloku, Benoit Hingray, and Guillaume Evin
Hydrol. Earth Syst. Sci., 27, 3643–3661, https://doi.org/10.5194/hess-27-3643-2023, https://doi.org/10.5194/hess-27-3643-2023, 2023
Short summary
Short summary
High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
Short summary
Short summary
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
Yuanhong You, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu
Hydrol. Earth Syst. Sci., 27, 2919–2933, https://doi.org/10.5194/hess-27-2919-2023, https://doi.org/10.5194/hess-27-2919-2023, 2023
Short summary
Short summary
This study aims to investigate the performance of a genetic particle filter which was used as a snow data assimilation scheme across different snow climates. The results demonstrated that the genetic algorithm can effectively solve the problem of particle degeneration and impoverishment in a particle filter algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground snow depth measurement.
Patricia Lawston-Parker, Joseph A. Santanello Jr., and Nathaniel W. Chaney
Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
Short summary
Short summary
Irrigation has been shown to impact weather and climate, but it has only recently been considered in prediction models. Prescribing where (globally) irrigation takes place is important to accurately simulate its impacts on temperature, humidity, and precipitation. Here, we evaluated three different irrigation maps in a weather model and found that the extent and intensity of irrigated areas and their boundaries are important drivers of weather impacts resulting from human practices.
Marcos Julien Alexopoulos, Hannes Müller-Thomy, Patrick Nistahl, Mojca Šraj, and Nejc Bezak
Hydrol. Earth Syst. Sci., 27, 2559–2578, https://doi.org/10.5194/hess-27-2559-2023, https://doi.org/10.5194/hess-27-2559-2023, 2023
Short summary
Short summary
For rainfall-runoff simulation of a certain area, hydrological models are used, which requires precipitation data and temperature data as input. Since these are often not available as observations, we have tested simulation results from atmospheric models. ERA5-Land and COSMO-REA6 were tested for Slovenian catchments. Both lead to good simulations results. Their usage enables the use of rainfall-runoff simulation in unobserved catchments as a requisite for, e.g., flood protection measures.
Tuantuan Zhang, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu, and Binquan Li
Hydrol. Earth Syst. Sci., 27, 1945–1960, https://doi.org/10.5194/hess-27-1945-2023, https://doi.org/10.5194/hess-27-1945-2023, 2023
Short summary
Short summary
We use circulation classifications and spatiotemporal deep neural networks to correct raw daily forecast precipitation by combining large-scale circulation patterns with local spatiotemporal information. We find that the method not only captures the westward and northward movement of the western Pacific subtropical high but also shows substantially higher bias-correction capabilities than existing standard methods in terms of spatial scale, timescale, and intensity.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
Short summary
Short summary
We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
Short summary
Short summary
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Zexuan Xu, Erica R. Siirila-Woodburn, Alan M. Rhoades, and Daniel Feldman
Hydrol. Earth Syst. Sci., 27, 1771–1789, https://doi.org/10.5194/hess-27-1771-2023, https://doi.org/10.5194/hess-27-1771-2023, 2023
Short summary
Short summary
The goal of this study is to understand the uncertainties of different modeling configurations for simulating hydroclimate responses in the mountainous watershed. We run a group of climate models with various configurations and evaluate them against various reference datasets. This paper integrates a climate model and a hydrology model to have a full understanding of the atmospheric-through-bedrock hydrological processes.
Jolanda J. E. Theeuwen, Arie Staal, Obbe A. Tuinenburg, Bert V. M. Hamelers, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 27, 1457–1476, https://doi.org/10.5194/hess-27-1457-2023, https://doi.org/10.5194/hess-27-1457-2023, 2023
Short summary
Short summary
Evaporation changes over land affect rainfall over land via moisture recycling. We calculated the local moisture recycling ratio globally, which describes the fraction of evaporated moisture that rains out within approx. 50 km of its source location. This recycling peaks in summer as well as over wet and elevated regions. Local moisture recycling provides insight into the local impacts of evaporation changes and can be used to study the influence of regreening on local rainfall.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
Short summary
Short summary
Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 1109–1132, https://doi.org/10.5194/hess-27-1109-2023, https://doi.org/10.5194/hess-27-1109-2023, 2023
Short summary
Short summary
Rainfall volumes at varying duration and frequencies are required for many engineering water works. These design volumes have been provided by KOSTRA-DWD in Germany. However, a revision of the KOSTRA-DWD is required, in order to consider the recent state-of-the-art and additional data. For this purpose, in our study, we investigate different methods and data available to achieve the best procedure that will serve as a basis for the development of the new KOSTRA-DWD product.
Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders
Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, https://doi.org/10.5194/hess-27-501-2023, 2023
Short summary
Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai
Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, https://doi.org/10.5194/hess-27-139-2023, 2023
Short summary
Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Cited articles
Aas, K., Nagler, T., Jullum, M., and Løland, A.: Explaining Predictive Models Using Shapley Values and Non-Parametric Vine Copulas, Depend. Model., 9, 62–81, https://doi.org/10.1515/demo-2021-0103, 2021. a
Ahn, K.-H.: Coupled Annual and Daily Multivariate and Multisite Stochastic Weather Generator to Preserve Low- and High-Frequency Variability to Assess Climate Vulnerability, J. Hydrol., 581, 124443, https://doi.org/10.1016/j.jhydrol.2019.124443, 2020. a
Archer, L., Hatchard, S., Devitt, L., Neal, J. C., Coxon, G., Bates, P. D., Kendon, E. J., and Savage, J.: Future Change in Urban Flooding Using New Convection-Permitting Climate Projections, Water Resour. Res., 60, e2023WR035533, https://doi.org/10.1029/2023WR035533, 2024. a
Benoit, L., Allard, D., and Mariethoz, G.: Stochastic Rainfall Modeling at Sub-kilometer Scale, Water Resour. Res., 54, 4108–4130, https://doi.org/10.1029/2018WR022817, 2018. a
Birmingham City Council: Birmingham Surface Water Management Plan, Final Report, https://www.birmingham.gov.uk/downloads/file/2561/surface_water_management_plan_for_birmingham_-_final_report (last access: 17 February 2024), 2015. a
Burton, A., Kilsby, C., Fowler, H., Cowpertwait, P., and O'Connell, P.: RainSim: A Spatial–Temporal Stochastic Rainfall Modelling System, Environ. Model. Softw., 23, 1356–1369, https://doi.org/10.1016/j.envsoft.2008.04.003. a
Capsoni, C. and Luini, L.: Analysis of spatial and temporal properties of rain cells for rainfall modeling purposes, Italian Journal of Remote Sensing/Rivista Italiana di Telerilevamento, 41, 51–62, https://doi.org/10.5721/ItJRS20094134, 2009. a
Capsoni, C., Fedi, F., Magistroni, C., Paraboni, A., and Pawlina, A.: Data and theory for a new model of the horizontal structure of rain cells for propagation applications, Radio Sci., 22, 395–404, https://doi.org/10.1029/RS022i003p00395, 1987. a
CIWEN: Rainfall Modelling Guide 2016, CIWEN, London, https://www.ciwem.org/assets/pdf/Special Interest Groups/Urban Drainage Group/CIWEM-UDG-Rainfall-Guide-2016.pdf (last access: 30 January 2024), 2016. a
Cooke, R. M.: Markov and entropy properties of tree-and vine-dependent variables, in: Proceedings of the ASA section of Bayesian statistical science, vol. 27, American Statistical Association Alexandria, VA, USA, https://rogermcooke.net/rogermcooke_files/Markov-tree-vine-dependence.PDF (last access: 15 January 2024), 1997. a
Coppola, E., Sobolowski, S., Pichelli, E., Raffaele, F., Ahrens, B., Anders, I., Ban, N., Bastin, S., Belda, M., Belusic, D., Caldas-Alvarez, A., Cardoso, R. M., Davolio, S., Dobler, A., Fernandez, J., Fita, L., Fumiere, Q., Giorgi, F., Goergen, K., Güttler, I., Halenka, T., Heinzeller, D., Hodnebrog, Ø., Jacob, D., Kartsios, S., Katragkou, E., Kendon, E., Khodayar, S., Kunstmann, H., Knist, S., Lavín-Gullón, A., Lind, P., Lorenz, T., Maraun, D., Marelle, L., van Meijgaard, E., Milovac, J., Myhre, G., Panitz, H.-J., Piazza, M., Raffa, M., Raub, T., Rockel, B., Schär, C., Sieck, K., Soares, P. M. M., Somot, S., Srnec, L., Stocchi, P., Tölle, M. H., Truhetz, H., Vautard, R., de Vries, H., and Warrach-Sagi, K.: A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean, Clim. Dynam., 55, 3–34, 2020. a
Cowpertwait, P. S. P.: A generalized point process model for rainfall, P. Roy. Soc. Lond. A, 447, 23–37, https://doi.org/10.1098/rspa.1994.0126, 1994. a
Czado, C.: Analyzing Dependent Data with Vine Copulas: A Practical Guide With R, in: vol. 222 of Lecture Notes in Statistics, Springer International Publishing, ISBN 978-3-030-13784-7, ISBN 978-3-030-13785-4, https://doi.org/10.1007/978-3-030-13785-4, 2019. a
Czado, C. and Nagler, T.: Vine Copula Based Modeling, Annu. Rev. Stat. Appl., 9, 453–477, https://doi.org/10.1146/annurev-statistics-040220-101153, 2022. a, b
Darlington, T., Edwards, M. R. A., Lissaman, V. A., Riley, R., Sugier, J., Kitchen, M., Adams, D., Cox, R., Freeman, N., Norman, K., O'Boyle, R., Sloan, C., and Smees, M.: Designing an Operational C-Band Radar to Realise the Benefits of Dual-Polarization, Met Office, Exeter, UK, https://digital.nmla.metoffice.gov.uk/SO_e19216fe-01f0-4187-84dd-f14707407eb8/ (last access: 30 December 2023), 2016. a
del Moral, A., Rigo, T., and Llasat, M. C.: A radar-based centroid tracking algorithm for severe weather surveillance: identifying split/merge processes in convective systems, Atmos. Res., 213, 110–120, https://doi.org/10.1016/j.atmosres.2018.05.030, 2018. a
Diederen, D. and Liu, Y.: Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes, Stoch. Environ. Res. Risk A., 34, 1369–1383, 2020. a
Dijkstra, E. W.: A Note on Two Problems in Connexion with Graphs, Numer. Math., 1, 269–271, https://doi.org/10.1007/BF01386390, 1959. a
Dixon, M. and Wiener, G.: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting – A Radar-Based Methodology, J. Atmos. Ocean. Tech., 10, 785–797, https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2, 1993. a
Eggimann, S., Mutzner, L., Wani, O., Schneider, M. Y., Spuhler, D., Moy de Vitry, M., Beutler, P., and Maurer, M.: The potential of knowing more: A review of data-driven urban water management, Environ. Sci. Technol., 51, 2538–2553, 2017. a
Fatichi, S., Ivanov, V. Y., and Caporali, E.: Simulation of future climate scenarios with a weather generator, Adv. Water Resour., 34, 448–467, https://doi.org/10.1016/j.advwatres.2010.12.013, 2011. a
Féral, L., Sauvageot, H., Castanet, L., and Lemorton, J.: HYCELL – A new hybrid model of the rain horizontal distribution for propagation studies: 1. Modeling of the rain cell, Radio Sci., 38, 1056, https://doi.org/10.1029/2002RS002802, 2003. a, b, c
Ferraris, L., Gabellani, S., Rebora, N., and Provenzale, A.: A Comparison of Stochastic Models for Spatial Rainfall Downscaling, Water Resour. Res., 39, 2003WR002504, https://doi.org/10.1029/2003WR002504, 2003. a
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, 2021a. a
Fowler, H. J., Wasko, C., and Prein, A. F.: Intensification of short-duration rainfall extremes and implications for flood risk: current state of the art and future directions, Philos. T. Roy. Soc. A, 379, 20190541, https://doi.org/10.1098/rsta.2019.0541, 2021b. a
Genest, C. and Favre, A.-C.: Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask, J. Hydrol. Eng., 12, 347–368, https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(347), 2007. a
Ghirardin, S., Capsoni, C., and Luini, L.: Time Evolution of Synthetic Rain Cells for the Synthesis of Attenuation Time Series, in: IEEE 2016 10th European Conference on Antennas and Propagation (EuCAP), 10–15 April 2016, Davos, Switzerland, 1–5, ISBN 978-88-907018-6-3, https://doi.org/10.1109/EuCAP.2016.7481928, 2016. a, b
Gires, A., Tchiguirinskaia, I., and Schertzer, D.: Blunt extension of discrete universal multifractal cascades: development and application to downscaling, Hydrolog. Sci. J., 65, 1204–1220, 2020. a
Golding, B. W.: Nimrod: A System for Generating Automated Very Short Range Forecasts, Meteorol. Appl., 5, 1–16, https://doi.org/10.1017/S1350482798000577, 1998. a
Gooch, M. N.: Use of Rainfall Data from Flow Surveys, WaPUG User Note No. 6, CIWEM – Chartered Institute of Water and Environmental Management, https://www.ciwem.org/assets/pdf/Special Interest Groups/Urban Drainage Group/WAPUG_User_Note_06.pdf (last access: 20 January 2024), 2009. a
Green, A. C., Kilsby, C., and Bárdossy, A.: A framework for space–time modelling of rainfall events for hydrological applications of weather radar, J. Hydrol., 630, 130630, https://doi.org/10.1016/j.jhydrol.2024.130630, 2024. a
Guerreiro, S. B., Fowler, H. J., Barbero, R., Westra, S., Lenderink, G., Blenkinsop, S., Lewis, E., and Li, X.-F.: Detection of continental-scale intensification of hourly rainfall extremes, Nat. Clim. Change, 8, 803–807, https://doi.org/10.1038/s41558-018-0245-3, 2018. a
Guhathakurta, P., Sreejith, O. P., and Menon, P. A.: Impact of climate change on extreme rainfall events and flood risk in India, J. Earth Syst. Sci., 120, 359–373, https://doi.org/10.1007/s12040-011-0082-5, 2011. a
Gyasi-Agyei, Y. and Melching, C. S.: Modelling the Dependence and Internal Structure of Storm Events for Continuous Rainfall Simulation, J. Hydrol., 464–465, 249–261, https://doi.org/10.1016/j.jhydrol.2012.07.014. a
Halladay, K., Kahana, R., Johnson, B., Still, C., Fosser, G., and Alves, L.: Convection-permitting climate simulations for South America with the Met Office Unified Model, Clim. Dynam., 61, 5247–5269, 2023. a
Handwerker, J.: Cell tracking with TRACE3D – a new algorithm, Atmos. Res., 61, 15–34, https://doi.org/10.1016/S0169-8095(01)00100-4, 2002. a
Harrison, D. L., Driscoll, S. J., and Kitchen, M.: Improving Precipitation Estimates from Weather Radar Using Quality Control and Correction Techniques, Meteorol. Appl., 7, 135–144, https://doi.org/10.1017/S1350482700001468, 2000. a
Harrison, D. L., Scovell, R. W., and Kitchen, M.: High-Resolution Precipitation Estimates for Hydrological Uses, Proc. Inst. Civ. Eng.-Water Manage., 162, 125–135, https://doi.org/10.1680/wama.2009.162.2.125, 2009. a
Ivanov, V. Y., Bras, R. L., and Curtis, D. C.: A weather generator for hydrological, ecological, and agricultural applications, Water Resour. Res., 43, W10406, https://doi.org/10.1029/2006WR005364, 2007. a
Jaworski, P., Durante, F., and Härdle, W. K. (Eds.): Copulae in Mathematical and Quantitative Finance: Proceedings of the Workshop Held in Cracow, 10–11 July 2012, in: vol. 213 of Lecture Notes in Statistics, Springer, Berlin, Heidelberg, ISBN 978-3-642-35406-9, ISBN 978-3-642-35407-6, https://doi.org/10.1007/978-3-642-35407-6, 2013. a
Joe, H.: Multivariate Extreme-Value Distributions with Applications to Environmental Data, The Canadian Journal of Statistics/La Revue Canadienne de Statistique, 22, 47–64, 1994. a
Jones, P., Kilsby, C., Harpham, C., Glenis, V., and Burton, A.: UK Climate Projections science report: Projections of future daily climate for the UK from the Weather Generator, UK Climate Projections science report, https://core.ac.uk/download/pdf/299302528.pdf (last access: 10 February 2024), 2010. a
Kaczmarska, J., Isham, V., and Onof, C.: Point process models for fine-resolution rainfall, Hydrolog. Sci. J., 59, 1972–1991, https://doi.org/10.1080/02626667.2014.925558, 2014. a, b, c
Kendon, E., Prein, A., Senior, C., and Stirling, A.: Challenges and outlook for convection-permitting climate modelling, Philos. T. Roy. Soc. A, 379, 20190547, https://doi.org/10.1098/rsta.2019.0547, 2021. a, b
Kim, D.-S., Maki, M., Shimizu, S., and Lee, D.-I.: X-band dual-polarization radar observations of precipitation core development and structure in a multi-cellular storm over Zoshigaya, Japan, on August 5, 2008, J. Meteorol. Soc. Jpn. Ser. II, 90, 701–719, https://doi.org/10.2151/jmsj.2012-509, 2012. a, b
Koutsoyiannis, D., Onof, C., and Wheater, H. S.: Multivariate rainfall disaggregation at a fine timescale, Water Resour. Res., 39, 1173, https://doi.org/10.1029/2002WR001600, 2003. a
Latif, S. and Simonovic, S. P.: Parametric Vine Copula Framework in the Trivariate Probability Analysis of Compound Flooding Events, Water, 14, 2214, https://doi.org/10.3390/w14142214. a
Lenderink, G., de Vries, H., Fowler, H. J., Barbero, R., van Ulft, B., and van Meijgaard, E.: Scaling and responses of extreme hourly precipitation in three climate experiments with a convection-permitting model, Philos. T. Roy. Soc. A, 379, 20190544, https://doi.org/10.1098/rsta.2019.0544, 2021. a
Liu, S. C., Fu, C., Shiu, C.-J., Chen, J.-P., and Wu, F.: Temperature dependence of global precipitation extremes, Geophys. Res. Lett., 36, L17702, https://doi.org/10.1029/2009GL040218, 2009. a
Liu, W., Li, X., and Rahn, D. A.: Storm Event Representation and Analysis Based on a Directed Spatiotemporal Graph Model, Int. J. Geogr. Inf. Sci., 30, 948–969, https://doi.org/10.1080/13658816.2015.1081910, 2016. a, b
Lucas-Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P., Lemonsu, A., Kotlarski, S., and Caillaud, C.: Convection-permitting modeling with regional climate models: Latest developments and next steps, Wiley Interdisciplin. Rev.: Clim. Change, 12, e731, https://doi.org/10.1002/wcc.731, 2021a. a
Lucas-Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P., Lemonsu, A., Kotlarski, S., and Caillaud, C.: Convection-permitting modeling with regional climate models: Latest developments and next steps, WIREs Clim. Change, 12, e731, https://doi.org/10.1002/wcc.731, 2021b. a
Marshall, J. S. and Palmer, W. M. K.: The Distribution Of Raindrops With Size, J. Atmos. Sci., 5, 165–166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2, 1948. a
Met Office: Met Office Rain Radar Data from the NIMROD System, NCAS British Atmospheric Data Centre [code], http://catalogue.ceda.ac.uk/uuid/82adec1f896af6169112d09cc1174499/ (last access: 30 November 2023), 2003. a
Muñoz Lopez, C., Wang, L.-P., and Willems, P.: Statistical characterization of rainfall fields based upon a 12-year high-resolution radar archive of Belgium, Atmos. Res., 283, 106544, https://doi.org/10.1016/j.atmosres.2022.106544, 2023. a, b, c, d
Nagler, T. and Vatter, T.: Pyvinecopulib, Zenodo [code], https://doi.org/10.5281/zenodo.10435751, 2023. a
Ochoa-Rodriguez, S., Wang, L.-P., Gires, A., Pina, R. D., Reinoso-Rondinel, R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., van Assel, J., et al.: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation, J. Hydrol., 531, 389–407, 2015. a
Onof, C. and Wang, L.-P.: Modelling rainfall with a Bartlett–Lewis process: new developments, Hydrol. Earth Syst. Sci., 24, 2791–2815, https://doi.org/10.5194/hess-24-2791-2020, 2020. a, b, c
Onof, C. and Wheater, H. S.: Modelling of British rainfall using a random parameter Bartlett-Lewis Rectangular Pulse Model, J. Hydrol., 149, 67–95, https://doi.org/10.1016/0022-1694(93)90100-N, 1993. a
Onof, C. and Wheater, H. S.: Improvements to the modelling of British rainfall using a modified Random Parameter Bartlett-Lewis Rectangular Pulse Model, J. Hydrol., 157, 177–195, https://doi.org/10.1016/0022-1694(94)90104-X, 1994. a
Papalexiou, S. M.: Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency, Adv. Water Resour., 115, 234–252, 2018. a
Papalexiou, S. M., Serinaldi, F., and Porcu, E.: Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond, Water Resour. Res., 57, e2020WR029466, https://doi.org/10.1029/2020WR029466, 2021. a
Paschalis, A., Molnar, P., Fatichi, S., and Burlando, P.: A Stochastic Model for High-Resolution Space-Time Precipitation Simulation: A Stochastic Model for Space-Time Precipitation, A Stochastic Model for High-Resolution Space-Time Precipitation Simulation, Water Resour. Res., 49, 8400–8417, https://doi.org/10.1002/2013WR014437, 2013. a, b
Peak, J. E. and Tag, P. M.: Segmentation of Satellite Imagery Using Hierarchical Thresholding and Neural Networks, J. Appl. Meteorol., 33, 605–616, https://doi.org/10.1175/1520-0450(1994)033<0605:SOSIUH>2.0.CO;2, 1994. a
Peleg, N., Fatichi, S., Paschalis, A., Molnar, P., and Burlando, P.: An advanced stochastic weather generator for simulating 2-D high-resolution climate variables, J. Adv. Model. Earth Syst., 9, 1595–1627, https://doi.org/10.1002/2016MS000854, 2017. a
Perkins, S. E., Pitman, A. J., Holbrook, N. J., and McAneney, J.: Evaluation of the AR4 Climate Models' Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions, J. Climate, 20, 4356–4376, https://doi.org/10.1175/JCLI4253.1. a
Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P., and Holland, G. J.: The future intensification of hourly precipitation extremes, Nature Clim. Change, 7, 48–52, 2017. a
Prein, A. F., Liu, C., Ikeda, K., Bullock, R., Rasmussen, R. M., Holland, G. J., and Clark, M.: Simulating North American mesoscale convective systems with a convection-permitting climate model, Clim. Dynam., 55, 95–110, 2020. a
Rebora, N. and Ferraris, L.: The structure of convective rain cells at mid-latitudes, Adv. Geosci., 7, 31–35, https://doi.org/10.5194/adgeo-7-31-2006, 2006. a
Rodriguez-Iturbe, I., Cox, D. R., and Isham, V.: Some models for rainfall based on stochastic point processes, P. Roy. Soc. Lond. A, 410, 269–288, https://doi.org/10.1098/rspa.1987.0039, 1987. a
Rodriguez-Iturbe, I., Cox, D. R., and Isham, V.: A point process model for rainfall: further developments, P. Roy. Soc. Lond. A:, 417, 283–298, https://doi.org/10.1098/rspa.1988.0061, 1988. a
Rosenblatt, M.: Remarks on a Multivariate Transformation, Ann. Math. Stat., 23, 470–472, 1952. a
Salvadori, G. and De Michele, C.: Frequency analysis via copulas: Theoretical aspects and applications to hydrological events, Water Resour. Res., 40, W12511, https://doi.org/10.1029/2004WR003133, 2004. a
Salvadori, G. and De Michele, C.: Statistical characterization of temporal structure of storms, Adv. Water Resour., 29, 827–842, https://doi.org/10.1016/j.advwatres.2005.07.013, 2006. a
Sandford, C.: Correcting for wind drift in high resolution radar rainfall products: a feasibility study, J. Hydrol., 531, 284–295, https://doi.org/10.1016/j.jhydrol.2015.03.023, 2015. a
Schertzer, D. and Lovejoy, S.: Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes, J. Geophys. Res.-Atmos., 92, 9693–9714, https://doi.org/10.1029/JD092iD08p09693, 1987. a
Segond, M.-L. and Onof, C.: Modelling of space–time rainfall for three UK regions, in: Proceedings of the Institution of Civil Engineers – Water Management, vol. 162, Thomas Telford Ltd, 147–158, https://doi.org/10.1680/wama.2009.162.2.147, 2009. a
Shehu, B. and Haberlandt, U.: Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics, Hydrol. Earth Syst. Sci., 26, 1631–1658, https://doi.org/10.5194/hess-26-1631-2022, 2022. a
Shusse, Y., Maki, M., Shimizu, S., Iwanami, K., Maesaka, T., Suzuki, S.-I., Sakurai, N., and Misumi, R.: Relationship between precipitation core behavior in cumulonimbus clouds and surface rainfall intensity on 18 August 2011 in the Kanto Region, Japan, J. Meteorol. Soc. Jpn. Ser. II, 93, 215–228, https://doi.org/10.2151/jmsj.2015-012, 2015. a
Simões, N. E., Ochoa-Rodríguez, S., Wang, L.-P., Pina, R. D., Marques, A. S., Onof, C., and Leitão, J. P.: Stochastic Urban Pluvial Flood Hazard Maps Based upon a Spatial-Temporal Rainfall Generator, Water, 7, 3396–3406, https://doi.org/10.3390/w7073396, 2015. a
Sparks, N. J., Hardwick, S. R., Schmid, M., and Toumi, R.: IMAGE: A Multivariate Multi-Site Stochastic Weather Generator for European Weather and Climate, Stoch. Environ. Res. Risk A., 32, 771–784, https://doi.org/10.1007/s00477-017-1433-9. a
Štrumbelj, E. and Kononenko, I.: An Efficient Explanation of Individual Classifications using Game Theory, J. Mach. Learn. Res., 11, 1–18, 2010. a
Thorndahl, S. and Andersen, C. B.: CLIMACS: A method for stochastic generation of continuous climate projected point rainfall for urban drainage design, J. Hydrol., 602, 126776, https://doi.org/10.1016/j.jhydrol.2021.126776, 2021. a
Tootoonchi, F., Sadegh, M., Haerter, J. O., Räty, O., Grabs, T., and Teutschbein, C.: Copulas for Hydroclimatic Analysis: A Practice-oriented Overview, WIREs Water, 9, e1579, https://doi.org/10.1002/wat2.1579, 2022. a
Tosunoglu, F., Gürbüz, F., and İspirli, M. N.: Multivariate Modeling of Flood Characteristics Using Vine Copulas, Environ. Earth Sci., 79, 459, https://doi.org/10.1007/s12665-020-09199-6. a
Trapp, R. J., Hoogewind, K. A., and Lasher-Trapp, S.: Future Changes in Hail Occurrence in the United States Determined through Convection-Permitting Dynamical Downscaling, J. Climate, 32, 5493–5509, https://doi.org/10.1175/JCLI-D-18-0740.1, 2019. a
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The Changing Character of Precipitation, B. Am. Meteorol. Soc., 84, 1205–1218, https://doi.org/10.1175/BAMS-84-9-1205, 2003. a
Van De Velde, J., Demuzere, M., De Baets, B., and Verhoest, N.: Future Multivariate Weather Generation by Combining Bartlett-Lewis and Vine Copula Models, Hydrolog. Sci. J., 68, 1–15, https://doi.org/10.1080/02626667.2022.2144322, 2023. a
von Hardenberg, J., Ferraris, L., and Provenzale, A.: The shape of convective rain cells, Geophys. Res. Lett., 30, 2280, https://doi.org/10.1029/2003GL018539, 2003. a
Wang, L.-P., Ochoa-Rodríguez, S., Van Assel, J., Pina, R. D., Pessemier, M., Kroll, S., Willems, P., and Onof, C.: Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment, J. Hydrol., 531, 408–426, https://doi.org/10.1016/j.jhydrol.2015.05.049, 2015. a
Wheater, H. S., Isham, V. S., Cox, D. R., Chandler, R. E., Kakou, A., Northrop, P. J., Oh, L., Onof, C., and Rodriguez-Iturbe, I.: Spatial-temporal rainfall fields: modelling and statistical aspects, Hydrol. Earth Syst. Sci., 4, 581–601, https://doi.org/10.5194/hess-4-581-2000, 2000. a
Wilcox, C., Aly, C., Vischel, T., Panthou, G., Blanchet, J., Quantin, G., and Lebel, T.: Stochastorm: A Stochastic Rainfall Simulator for Convective Storms, J. Hydrometeorol., 22, 387–404, https://doi.org/10.1175/JHM-D-20-0017.1, 2021. a, b, c, d
Wilks, D. S. and Wilby, R. L.: The weather generation game: a review of stochastic weather models, Prog. Phys. Geogr., 23, 329–357, 1999. a
Willems, P.: A spatial rainfall generator for small spatial scales, J. Hydrol., 252, 126–144, https://doi.org/10.1016/S0022-1694(01)00446-2, 2001. a, b, c, d
Wright, D. B., Yu, G., and England, J. F.: Six decades of rainfall and flood frequency analysis using stochastic storm transposition: Review, progress, and prospects, J. Hydrol., 585, 124816, https://doi.org/10.1016/j.jhydrol.2020.124816, 2020. a
Yiou, P.: AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531–543, https://doi.org/10.5194/gmd-7-531-2014, 2014. a
Zhao, Y., Nearing, M. A., and Guertin, D. P.: A Daily Spatially Explicit Stochastic Rainfall Generator for a Semi-Arid Climate, J. Hydrol., 574, 181–192, https://doi.org/10.1016/j.jhydrol.2019.04.006, 2019. a
Short summary
This study presents a new algorithm to model convective storms. We used advanced tracking methods to analyse 165 storm events in Birmingham (UK) and reconstruct storm cell life cycles. We found that cell properties like intensity and size are interrelated and vary over time. The new algorithm, based on vine copulas, accurately simulates these properties and their evolution. It also integrates an exponential shape function for realistic rainfall patterns, enhancing its hydrological applicability.
This study presents a new algorithm to model convective storms. We used advanced tracking...
