Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-4903-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-28-4903-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2024
Research article |
| 18 Nov 2024
| 18 Nov 2024
Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5
British Antarctic Survey, Cambridge, UK
Department of Engineering, University of Cambridge, Cambridge, UK
Andrew Orr
British Antarctic Survey, Cambridge, UK
Javier Hernandez-González
Microsoft Research, Cambridge, UK
Scott Hosking
British Antarctic Survey, Cambridge, UK
The Alan Turing Institute, London, UK
Richard E. Turner
Department of Engineering, University of Cambridge, Cambridge, UK
Publisher's note: the paper was adjusted on 23 April 2025. The adjustments included the typo "chloropeth", the incorrect formatting of the matrix K, the text of the caption of table C2, as well as incorrect years given in the captions of tables 4, C1, and C2.
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Marc Girona-Mata, Andrew Orr, Martin Widmann, Daniel Bannister, Ghulam Hussain Dars, Scott Hosking, Jesse Norris, David Ocio, Tony Phillips, Jakob Steiner, and Richard E. Turner
Hydrol. Earth Syst. Sci., 29, 3073–3100, https://doi.org/10.5194/hess-29-3073-2025, https://doi.org/10.5194/hess-29-3073-2025, 2025
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We introduce a novel method for improving daily precipitation maps in mountain regions and pilot it across three basins in the Hindu Kush Himalaya (HKH). The approach leverages climate model and weather station data, along with statistical or machine learning techniques. Our results show that this approach outperforms traditional methods, especially in remote ungauged areas, suggesting that it could be used to improve precipitation maps across much of the HKH, as well as other mountain regions.
Ella Gilbert, Denis Pishniak, José Abraham Torres, Andrew Orr, Michelle Maclennan, Nander Wever, and Kristiina Verro
The Cryosphere, 19, 597–618, https://doi.org/10.5194/tc-19-597-2025, https://doi.org/10.5194/tc-19-597-2025, 2025
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We use three sophisticated climate models to examine extreme precipitation in a critical region of West Antarctica. We found that rainfall probably occurred during the two cases we examined and that it was generated by the interaction of air with steep topography. Our results show that kilometre-scale models are useful tools for exploring extreme precipitation in this region and that more observations of rainfall are needed.
Xavier J. Levine, Ryan S. Williams, Gareth Marshall, Andrew Orr, Lise Seland Graff, Dörthe Handorf, Alexey Karpechko, Raphael Köhler, René R. Wijngaard, Nadine Johnston, Hanna Lee, Lars Nieradzik, and Priscilla A. Mooney
Earth Syst. Dynam., 15, 1161–1177, https://doi.org/10.5194/esd-15-1161-2024, https://doi.org/10.5194/esd-15-1161-2024, 2024
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While the most recent climate projections agree that the Arctic is warming, differences remain in how much and in other climate variables such as precipitation. This presents a challenge for stakeholders who need to develop mitigation and adaptation strategies. We tackle this problem by using the storyline approach to generate four plausible and actionable realisations of end-of-century climate change for the Arctic, spanning its most likely range of variability.
Nicolaj Hansen, Andrew Orr, Xun Zou, Fredrik Boberg, Thomas J. Bracegirdle, Ella Gilbert, Peter L. Langen, Matthew A. Lazzara, Ruth Mottram, Tony Phillips, Ruth Price, Sebastian B. Simonsen, and Stuart Webster
The Cryosphere, 18, 2897–2916, https://doi.org/10.5194/tc-18-2897-2024, https://doi.org/10.5194/tc-18-2897-2024, 2024
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We investigated a melt event over the Ross Ice Shelf. We use regional climate models and a firn model to simulate the melt and compare the results with satellite data. We find that the firn model aligned well with observed melt days in certain parts of the ice shelf. The firn model had challenges accurately simulating the melt extent in the western sector. We identified potential reasons for these discrepancies, pointing to limitations in the models related to representing the cloud properties.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
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How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Jeremy Carter, Amber Leeson, Andrew Orr, Christoph Kittel, and J. Melchior van Wessem
The Cryosphere, 16, 3815–3841, https://doi.org/10.5194/tc-16-3815-2022, https://doi.org/10.5194/tc-16-3815-2022, 2022
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Climate models provide valuable information for studying processes such as the collapse of ice shelves over Antarctica which impact estimates of sea level rise. This paper examines variability across climate simulations over Antarctica for fields including snowfall, temperature and melt. Significant systematic differences between outputs are found, occurring at both large and fine spatial scales across Antarctica. Results are important for future impact assessments and model development.
Nicolaj Hansen, Sebastian B. Simonsen, Fredrik Boberg, Christoph Kittel, Andrew Orr, Niels Souverijns, J. Melchior van Wessem, and Ruth Mottram
The Cryosphere, 16, 711–718, https://doi.org/10.5194/tc-16-711-2022, https://doi.org/10.5194/tc-16-711-2022, 2022
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We investigate the impact of different ice masks when modelling surface mass balance over Antarctica. We used ice masks and data from five of the most used regional climate models and a common mask. We see large disagreement between the ice masks, which has a large impact on the surface mass balance, especially around the Antarctic Peninsula and some of the largest glaciers. We suggest a solution for creating a new, up-to-date, high-resolution ice mask that can be used in Antarctic modelling.
Anna Vaughan, Will Tebbutt, J. Scott Hosking, and Richard E. Turner
Geosci. Model Dev., 15, 251–268, https://doi.org/10.5194/gmd-15-251-2022, https://doi.org/10.5194/gmd-15-251-2022, 2022
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We develop a new method for climate downscaling, i.e. transforming low-resolution climate model output to high-resolution projections, using a deep-learning model known as a convolutional conditional neural process. This model is shown to outperform an ensemble of baseline methods for downscaling daily maximum temperature and precipitation and provides a powerful new downscaling framework for climate impact studies.
Ruth Mottram, Nicolaj Hansen, Christoph Kittel, J. Melchior van Wessem, Cécile Agosta, Charles Amory, Fredrik Boberg, Willem Jan van de Berg, Xavier Fettweis, Alexandra Gossart, Nicole P. M. van Lipzig, Erik van Meijgaard, Andrew Orr, Tony Phillips, Stuart Webster, Sebastian B. Simonsen, and Niels Souverijns
The Cryosphere, 15, 3751–3784, https://doi.org/10.5194/tc-15-3751-2021, https://doi.org/10.5194/tc-15-3751-2021, 2021
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We compare the calculated surface mass budget (SMB) of Antarctica in five different regional climate models. On average ~ 2000 Gt of snow accumulates annually, but different models vary by ~ 10 %, a difference equivalent to ± 0.5 mm of global sea level rise. All models reproduce observed weather, but there are large differences in regional patterns of snowfall, especially in areas with very few observations, giving greater uncertainty in Antarctic mass budget than previously identified.
Andrew Orr, Hua Lu, Patrick Martineau, Edwin P. Gerber, Gareth J. Marshall, and Thomas J. Bracegirdle
Atmos. Chem. Phys., 21, 7451–7472, https://doi.org/10.5194/acp-21-7451-2021, https://doi.org/10.5194/acp-21-7451-2021, 2021
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Reanalysis datasets combine observations and weather forecast simulations to create our best estimate of the state of the atmosphere and are important for climate monitoring. Differences in the technical details of these products mean that they may give different results. This study therefore examined how changes associated with the so-called Antarctic ozone hole are represented, which is one of the most important climate changes in recent decades, and showed that they were broadly consistent.
Andrew Orr, J. Scott Hosking, Aymeric Delon, Lars Hoffmann, Reinhold Spang, Tracy Moffat-Griffin, James Keeble, Nathan Luke Abraham, and Peter Braesicke
Atmos. Chem. Phys., 20, 12483–12497, https://doi.org/10.5194/acp-20-12483-2020, https://doi.org/10.5194/acp-20-12483-2020, 2020
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Polar stratospheric clouds (PSCs) are clouds found in the Antarctic winter stratosphere and are implicated in the formation of the ozone hole. These clouds can sometimes be formed or enhanced by mountain waves, formed as air passes over hills or mountains. However, this important mechanism is missing in coarse-resolution climate models, limiting our ability to simulate ozone. This study examines an attempt to include the effects of mountain waves and their impact on PSCs and ozone.
Matt Amos, Paul J. Young, J. Scott Hosking, Jean-François Lamarque, N. Luke Abraham, Hideharu Akiyoshi, Alexander T. Archibald, Slimane Bekki, Makoto Deushi, Patrick Jöckel, Douglas Kinnison, Ole Kirner, Markus Kunze, Marion Marchand, David A. Plummer, David Saint-Martin, Kengo Sudo, Simone Tilmes, and Yousuke Yamashita
Atmos. Chem. Phys., 20, 9961–9977, https://doi.org/10.5194/acp-20-9961-2020, https://doi.org/10.5194/acp-20-9961-2020, 2020
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We present an updated projection of Antarctic ozone hole recovery using an ensemble of chemistry–climate models. To do so, we employ a method, more advanced and skilful than the current multi-model mean standard, which is applicable to other ensemble analyses. It calculates the performance and similarity of the models, which we then use to weight the model. Calculating model similarity allows us to account for models which are constructed from similar components.
Rebecca Dell, Neil Arnold, Ian Willis, Alison Banwell, Andrew Williamson, Hamish Pritchard, and Andrew Orr
The Cryosphere, 14, 2313–2330, https://doi.org/10.5194/tc-14-2313-2020, https://doi.org/10.5194/tc-14-2313-2020, 2020
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A semi-automated method is developed from pre-existing work to track surface water bodies across Antarctic ice shelves over time, using data from Sentinel-2 and Landsat 8. This method is applied to the Nivlisen Ice Shelf for the 2016–2017 melt season. The results reveal two large linear meltwater systems, which hold 63 % of the peak total surface meltwater volume on 26 January 2017. These meltwater systems migrate towards the ice shelf front as the melt season progresses.
Reinhold Spang, Lars Hoffmann, Rolf Müller, Jens-Uwe Grooß, Ines Tritscher, Michael Höpfner, Michael Pitts, Andrew Orr, and Martin Riese
Atmos. Chem. Phys., 18, 5089–5113, https://doi.org/10.5194/acp-18-5089-2018, https://doi.org/10.5194/acp-18-5089-2018, 2018
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This paper represents an unprecedented pole-covering day- and nighttime climatology of the polar stratospheric clouds (PSCs) based on satellite measurements, their spatial distribution, and composition of different particle types. The climatology has a high potential for the validation and improvement of PSC schemes in chemical transport and chemistry–climate models, which is important for a better prediction of future polar ozone loss in a changing climate.
Lars Hoffmann, Reinhold Spang, Andrew Orr, M. Joan Alexander, Laura A. Holt, and Olaf Stein
Atmos. Chem. Phys., 17, 2901–2920, https://doi.org/10.5194/acp-17-2901-2017, https://doi.org/10.5194/acp-17-2901-2017, 2017
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We introduce a 10-year record (2003–2012) of AIRS/Aqua observations of gravity waves in the polar lower stratosphere. The data set was optimized to study the impact of gravity waves on the formation of polar stratospheric clouds (PSCs). We discuss the temporal and spatial patterns of gravity wave activity, validate explicitly resolved small-scale temperature fluctuations in the ECMWF data, and present a survey of gravity-wave-induced PSC formation events using joint AIRS and MIPAS observations.
Reinhold Spang, Lars Hoffmann, Michael Höpfner, Sabine Griessbach, Rolf Müller, Michael C. Pitts, Andrew M. W. Orr, and Martin Riese
Atmos. Meas. Tech., 9, 3619–3639, https://doi.org/10.5194/amt-9-3619-2016, https://doi.org/10.5194/amt-9-3619-2016, 2016
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We present a new classification approach for different polar stratospheric cloud types. The so-called Bayesian classifier estimates the most likely probability that one of the three PSC types (ice, NAT, or STS) dominates the characteristics of a measured infrared spectrum. The entire measurement period of the satellite instrument MIPAS from July 2002 to April 2013 is processed using the new classifier.
A. Orr, J. S. Hosking, L. Hoffmann, J. Keeble, S. M. Dean, H. K. Roscoe, N. L. Abraham, S. Vosper, and P. Braesicke
Atmos. Chem. Phys., 15, 1071–1086, https://doi.org/10.5194/acp-15-1071-2015, https://doi.org/10.5194/acp-15-1071-2015, 2015
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya
Implementation of global soil databases in the Noah-MP model and the effects on simulated mean and extreme soil hydrothermal changes
Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts
Towards a robust hydrologic data assimilation system for hurricane-induced river flow forecasting
Enhanced evaluation of hourly and daily extreme precipitation in Norway from convection-permitting models at regional and local scales
Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River
High-resolution land surface modelling over Africa: the role of uncertain soil properties in combination with forcing temporal resolution
Investigating the global and regional response of drought to idealized deforestation using multiple global climate models
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?
Leveraging a radar-based disdrometer network to develop a probabilistic precipitation phase model in eastern Canada
Assessment of seasonal soil moisture forecasts over the Central Mediterranean
Saudi Rainfall (SaRa): Hourly 0.1° Gridded Rainfall (1979–Present) for Saudi Arabia via Machine Learning Fusion of Satellite and Model Data
Do land models miss key soil hydrological processes controlling soil moisture memory?
Observation-driven model for calculating water-harvesting potential from advective fog in (semi-)arid coastal regions
Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets
Barriers of urban hydro-meteorological simulation: a review
Downscaling the probability of heavy rainfall over the Nordic countries
Modelling convective cell life cycles with a copula-based approach
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
Enhanced hydrological modelling with the WRF-Hydro lake/reservoir module at Convection-Permitting scale: a case study of the Tana River basin in East Africa
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
Storyline Analytical Framework for Understanding Future Severe Low-Water Episodes and Their Consequences
The role of land-atmosphere coupling in subseasonal surface air temperature prediction
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 VIC models
Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France
Global catalog of soil moisture droughts over the past four decades
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
Potential for historically unprecedented Australian droughts from natural variability and climate change
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
Marc Girona-Mata, Andrew Orr, Martin Widmann, Daniel Bannister, Ghulam Hussain Dars, Scott Hosking, Jesse Norris, David Ocio, Tony Phillips, Jakob Steiner, and Richard E. Turner
Hydrol. Earth Syst. Sci., 29, 3073–3100, https://doi.org/10.5194/hess-29-3073-2025, https://doi.org/10.5194/hess-29-3073-2025, 2025
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We introduce a novel method for improving daily precipitation maps in mountain regions and pilot it across three basins in the Hindu Kush Himalaya (HKH). The approach leverages climate model and weather station data, along with statistical or machine learning techniques. Our results show that this approach outperforms traditional methods, especially in remote ungauged areas, suggesting that it could be used to improve precipitation maps across much of the HKH, as well as other mountain regions.
Kazeem Abiodun Ishola, Gerald Mills, Ankur Prabhat Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci., 29, 2551–2582, https://doi.org/10.5194/hess-29-2551-2025, https://doi.org/10.5194/hess-29-2551-2025, 2025
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Global soil information introduces uncertainty into models that simulate soil hydrothermal changes. Using the Noah with Multiparameterization (Noah-MP) model with two different global soil datasets, we find under-represented soil properties in wet loam, causing a dry bias in soil moisture. This bias is more pronounced and drought categories are more severe in the SoilGrids dataset. We conclude that models should incorporate detailed, region-specific soil information to minimize model uncertainties.
Simon Moulds, Louise Slater, Louise Arnal, and Andrew W. Wood
Hydrol. Earth Syst. Sci., 29, 2393–2406, https://doi.org/10.5194/hess-29-2393-2025, https://doi.org/10.5194/hess-29-2393-2025, 2025
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Seasonal streamflow forecasts are an important component of flood risk management. Here, we train and test a machine learning model to predict the monthly maximum daily streamflow up to 4 months ahead. We train the model on precipitation and temperature forecasts to produce probabilistic hindcasts for 579 stations across the UK for the period 2004–2016. We show skilful results up to 4 months ahead in many locations, although, in general, the skill declines with increasing lead time.
Peyman Abbaszadeh, Fatemeh Gholizadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 29, 2407–2427, https://doi.org/10.5194/hess-29-2407-2025, https://doi.org/10.5194/hess-29-2407-2025, 2025
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The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three southeastern US watersheds during hurricanes, HEAVEN assimilates real-time United States Geological Survey (USGS) streamflow data, boosting forecast accuracy.
Kun Xie, Lu Li, Hua Chen, Stephanie Mayer, Andreas Dobler, Chong-Yu Xu, and Ozan Mert Göktürk
Hydrol. Earth Syst. Sci., 29, 2133–2152, https://doi.org/10.5194/hess-29-2133-2025, https://doi.org/10.5194/hess-29-2133-2025, 2025
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We compared hourly and daily extreme precipitation across Norway from HARMONIE Climate models at convection-permitting 3 km (HCLIM3) and 12 km (HCLIM12) resolutions. HCLIM3 more accurately captures the extremes in most regions and seasons (except in summer). Its advantages are more pronounced for hourly extremes than for daily extremes. The results highlight the value of convection-permitting models in improving extreme-precipitation predictions and in helping the local society brace for extreme weather.
Ningpeng Dong, Haoran Hao, Mingxiang Yang, Jianhui Wei, Shiqin Xu, and Harald Kunstmann
Hydrol. Earth Syst. Sci., 29, 2023–2042, https://doi.org/10.5194/hess-29-2023-2025, https://doi.org/10.5194/hess-29-2023-2025, 2025
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Hydrometeorological forecasting is crucial for managing water resources and mitigating extreme weather events, yet current long-term forecast products are often embedded with uncertainties. We develop a deep-learning-based modelling framework to improve 30 d rainfall and streamflow forecasts by combining advanced neural networks and physical models. With the flow forecast error reduced by up to 33 %, the framework has the potential to enhance water management and disaster prevention.
Bamidele Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 29, 1659–1683, https://doi.org/10.5194/hess-29-1659-2025, https://doi.org/10.5194/hess-29-1659-2025, 2025
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We studied how soil and weather data affect land model simulations over Africa. By combining soil data processed in different ways with weather data of varying time intervals, we found that weather inputs had a greater impact on water processes than soil data type. However, the way soil data were processed became crucial when paired with high-frequency weather inputs, showing that detailed weather data can improve local and regional predictions of how water moves and interacts with the land.
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025, https://doi.org/10.5194/hess-29-1637-2025, 2025
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Deforestation has a significant impact on climate, yet its effects on drought remain less understood. This study investigates how deforestation affects drought across various climate zones and timescales. Findings indicate that deforestation leads to drier conditions in tropical regions and wetter conditions in arid areas, with minimal effects in temperate zones. Long-term drought is more affected than short-term drought, offering valuable insights into vegetation–climate interactions.
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 29, 1295–1317, https://doi.org/10.5194/hess-29-1295-2025, https://doi.org/10.5194/hess-29-1295-2025, 2025
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The study provides a detailed characterisation of flash drought in the UK for 1969–2021. The spatio-temporal distribution and trends of flash droughts are highly variable, with important regional and seasonal contrasts. In the UK, flash drought development responds primarily to precipitation variability, while the atmospheric evaporative demand plays a secondary role. We also found that the North Atlantic Oscillation is the main circulation pattern controlling flash drought development.
Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley J. Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025, https://doi.org/10.5194/hess-29-1201-2025, 2025
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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.
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025, https://doi.org/10.5194/hess-29-1135-2025, 2025
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Precipitation data from an automated observational network in eastern Canada showed a temperature interval where rain and snow could coexist. Random forest models were developed to classify the precipitation phase using meteorological data to evaluate operational applications. The models demonstrated significantly improved phase classification and reduced error compared to benchmark operational models. However, accurate prediction of mixed-phase precipitation remains challenging.
Lorenzo Silvestri, Miriam Saraceni, Bruno Brunone, Silvia Meniconi, Giulia Passadore, and Paolina Bongioannini Cerlini
Hydrol. Earth Syst. Sci., 29, 925–946, https://doi.org/10.5194/hess-29-925-2025, https://doi.org/10.5194/hess-29-925-2025, 2025
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This work demonstrates that seasonal forecasts of soil moisture are a valuable resource for groundwater management in the areas of the Central Mediterranean where longer memory timescales are found. 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.
Xuetong Wang, Raied S. Alharbi, Oscar M. Baez-Villanueva, Amy Green, Matthew F. McCabe, Yoshihide Wada, Albert I. J. M. Van Dijk, Muhammad A. Abid, and Hylke Beck
EGUsphere, https://doi.org/10.5194/egusphere-2025-254, https://doi.org/10.5194/egusphere-2025-254, 2025
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Our paper introduces Saudi Rainfall (SaRa), a high-resolution, near real-time rainfall product for the Arabian Peninsula. Using machine learning, SaRa combines multiple satellite and (re)analysis datasets with static predictors, outperforming existing products in the region. With the fast development and continuing growth in water demand over this region, SaRa could help to address water challenges and support resource management.
Mohammad A. Farmani, Ali Behrangi, Aniket Gupta, Ahmad Tavakoly, Matthew Geheran, and Guo-Yue Niu
Hydrol. Earth Syst. Sci., 29, 547–566, https://doi.org/10.5194/hess-29-547-2025, https://doi.org/10.5194/hess-29-547-2025, 2025
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Soil moisture memory (SMM) shows how long soil stays moist after rain, impacting climate and ecosystems. Current models often overestimate SMM, causing inaccuracies in evaporation predictions. We enhanced a land model, Noah-MP, to include better water flow and ponding processes, and we tested it against satellite and field data. This improved model reduced overestimations and enhanced short-term predictions, helping create more accurate climate and weather forecasts.
Felipe Lobos-Roco, Jordi Vilà-Guerau de Arellano, and Camilo del Río
Hydrol. Earth Syst. Sci., 29, 109–125, https://doi.org/10.5194/hess-29-109-2025, https://doi.org/10.5194/hess-29-109-2025, 2025
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Water resources are fundamental for the social, economic, and natural development of (semi-)arid regions. Precipitation decreases due to climate change obligate us to find new water resources. Fog harvesting (FH) emerges as a complementary resource in regions where it is abundant but untapped. This research proposes a model to estimate FH potential in coastal (semi-)arid regions. This model could have broader applicability worldwide in regions where FH could be a viable water source.
Kyle R. Mankin, Sushant Mehan, Timothy R. Green, and David M. Barnard
Hydrol. Earth Syst. Sci., 29, 85–108, https://doi.org/10.5194/hess-29-85-2025, https://doi.org/10.5194/hess-29-85-2025, 2025
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We assess 63 gridded ground (G), satellite (S), and reanalysis (R) climate datasets. Higher-density station data and less-hilly terrain improved climate data. In mountainous and humid regions, dataset types performed similarly; however, R outperformed G when underlying data had low station density. G outperformed S or R datasets, although better streamflow modeling did not always follow. Hydrologic analyses need datasets that better represent climate variable dependencies and complex topography.
Xuan Chen, Job Augustijn van der Werf, Arjan Droste, Miriam Coenders-Gerrits, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-3988, https://doi.org/10.5194/egusphere-2024-3988, 2025
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Our research highlights the need to integrate urban land surface and hydrological models to better predict and manage compound climate disasters in cities. We find that inadequate representation of water surfaces, hydraulic systems, and detailed building representations are key areas for improvement in future models. Coupled models show promise but face challenges at regional and neighbourhood scales. Interdisciplinary communication is crucial to enhance urban hydrometeorological simulations.
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
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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.
Chien-Yu Tseng, Li-Pen Wang, and Christian Onof
Hydrol. Earth Syst. Sci., 29, 1–25, https://doi.org/10.5194/hess-29-1-2025, https://doi.org/10.5194/hess-29-1-2025, 2025
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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.
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
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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
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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.
Ling Zhang, Lu Li, Zhongshi Zhang, Joël Arnault, Stefan Sobolowski, Anthony Musili Mwanthi, Pratik Kad, Mohammed Abdullahi Hassan, Tanja Portele, and Harald Kunstmann
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-278, https://doi.org/10.5194/hess-2024-278, 2024
Revised manuscript accepted for HESS
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To address challenges related to unreliable hydrological simulations, we present an enhanced hydrological simulation with a refined climate model and a more comprehensive hydrological model. The model with the two parts outperforms that without, especially in migrating bias in peak flow and dry-season flow. Our findings highlight the enhanced hydrological simulation capability with the refined climate and lake module contributing 24 % and 76 % improvement, respectively.
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
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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
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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
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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.
Gabriel Rondeau-Genesse, Louis-Philippe Caron, Kristelle Audet, Laurent Da Silva, Daniel Tarte, Rachel Parent, Élise Comeau, and Dominic Matte
EGUsphere, https://doi.org/10.5194/egusphere-2024-2595, https://doi.org/10.5194/egusphere-2024-2595, 2024
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The 2021 drought in Quebec showcased the province’s potential vulnerability. This study uses a storyline approach to explore impacts of future extreme droughts under +2 °C and +3 °C warming scenarios. By combining regional climate and hydrological simulations, it highlights the potential for severe water deficits. Collaboration with water management experts helped project the impacts of those future extreme droughts and their consequences for ecosystems and human activities.
Yuna Lim, Andrea M. Molod, Randal D. Koster, and Joseph A. Santanello
EGUsphere, https://doi.org/10.5194/egusphere-2024-2312, https://doi.org/10.5194/egusphere-2024-2312, 2024
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To better utilize a given set of predictions, identifying “forecasts of opportunity” has great value. It can help anticipate when prediction skill will be higher. This study reveals that when strong L-A coupling is detected 3–4 weeks into a forecast, the prediction skill for surface air temperature at this lead increases across the Midwest and northern Great Plains. Regions experiencing strong L-A coupling exhibit warm and dry anomalies, leading to improved predictions of abnormally warm events.
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
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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
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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
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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
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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
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
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
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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.
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
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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.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, and Miroslav Trnka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1434, https://doi.org/10.5194/egusphere-2024-1434, 2024
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We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, which was clustered into 775/630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
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
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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
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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
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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
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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.
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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Andermann, C., Bonnet, S., and Gloaguen, R.: Evaluation of precipitation data sets along the Himalayan front, Geochem. Geophy. Geosy., 12, Q07023, https://doi.org/10.1029/2011GC003513, 2011. a, b, c
Anders, A. M., Roe, G. H., Hallet, B., Montgomery, D. R., Finnegan, N. J., and Putkonen, J.: Spatial patterns of precipitation and topography in the Himalaya, Special Paper of the Geological Society of America, 398, 39–53, 2006. a
Andersson, T. R., Bruinsma, W. P., Markou, S., Requeima, J., Coca-Castro, A., Vaughan, A., Ellis, A.-L., Lazzara, M. A., Jones, D., Hosking, S., and Turner, R. E.: Environmental sensor placement with convolutional Gaussian neural processes, Environmental Data Science, 2, e32, https://doi.org/10.1017/eds.2023.22, 2023. a, b
Bannister, D., Orr, A., Jain, S. K., Holman, I. P., Momblanch, A., Phillips, T., Adeloye, A. J., Snapir, B., Waine, T. W., Hosking, J. S., and Allen-Sader, C.: Bias correction of high-resolution regional climate model precipitation output gives the best estimates of precipitation in Himalayan catchments, J. Geophys. Res.-Atmos., 124, 14220–14239, https://doi.org/10.1029/2019JD030804, 2019. a, b, c, d, e, f, g, h
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020. a
Bhardwaj, A., Ziegler, A. D., Wasson, R. J., and Chow, W. T.: Accuracy of rainfall estimates at high altitude in the Garhwal Himalaya (India): A comparison of secondary precipitation products and station rainfall measurements, Atmos. Res., 188, 30–38, https://doi.org/10.1016/j.atmosres.2017.01.005, 2017. a, b, c
Bookhagen, B. and Burbank, D. W.: Topography, relief, and TRMM-derived rainfall variations along the Himalaya, Geophys. Res. Lett., 33, L08405, https://doi.org/10.1029/2006GL026037, 2006. a
Bookhagen, B. and Burbank, D. W.: Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge, J. Geophys. Res.-Earth, 115, F03019, https://doi.org/10.1029/2009JF001426, 2010. a, b
Bui, T. D., Nguyen, C., and Turner, R. E.: Streaming sparse Gaussian process approximations, Adv. Neur. In., 30, 14220—4239, https://doi.org/10.17863/CAM.21293, 2017. a
Cannon, F., Carvalho, L. M., Jones, C., Hoell, A., Norris, J., Kiladis, G. N., and Tahir, A. A.: The influence of tropical forcing on extreme winter precipitation in the western Himalaya, Clim. Dynam., 48, 1213–1232, https://doi.org/10.1007/s00382-016-3137-0, 2017. a
Chen, Y., Sharma, S., Zhou, X., Yang, K., Li, X., Niu, X., Hu, X., and Khadka, N.: Spatial performance of multiple reanalysis precipitation datasets on the southern slope of central Himalaya, Atmos. Res., 250, 105365, https://doi.org/10.1016/j.atmosres.2020.105365, 2021. a, b
Collier, E. and Immerzeel, W. W.: High-resolution modeling of atmospheric dynamics in the Nepalese Himalaya, J. Geophys. Res.-Atmos., 120, 9882–9896, https://doi.org/10.1002/2015JD023266, 2015. a
Copernicus Climate Change Service: ERA5 monthly averaged data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2024. a
Cutajar, K., Pullin, M., Damianou, A., Lawrence, N., and González, J.: Deep gaussian processes for multi-fidelity modeling, arXiv [preprint], https://doi.org/10.48550/arXiv.1903.07320, 2019. a
Dahri, Z. H., Ludwig, F., Moors, E., Ahmad, B., Khan, A., and Kabat, P.: An appraisal of precipitation distribution in the high-altitude catchments of the Indus basin, Sci. Total Environ., 548, 289–306, https://doi.org/10.1016/j.scitotenv.2016.01.001, 2016. a, b, c, d
Dahri, Z. H., Ludwig, F., Moors, E., Ahmad, S., Ahmad, B., Ahmad, S., Riaz, M., and Kabat, P.: Climate change and hydrological regime of the high-altitude Indus basin under extreme climate scenarios, Sci. Total Environ., 768, 144467, https://doi.org/10.1016/j.scitotenv.2020.144467, 2021a. a
Dahri, Z. H., Ludwig, F., Moors, E., Ahmad, S., Ahmad, B., Shoaib, M., Ali, I., Iqbal, M. S., Pomee, M. S., Mangrio, A. G., Ahmad, M. M., and Kabat, P.: Spatio-temporal evaluation of gridded precipitation products for the high-altitude Indus basin, Int. J. Climatol., 41, 4283–4306, https://doi.org/10.1002/joc.7073, 2021b. a
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation data 2010 (GMTED2010), US Department of the Interior, US Geological Survey [data set], https://doi.org/10.3133/ofr20111073, 2011. a, b, c
Daulton, S., Balandat, M., and Bakshy, E.: Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization, Adv. Neur. In., 33, 9851–9864, https://doi.org/10.48550/arXiv.2006.05078, 2020. a
Daulton, S., Balandat, M., and Bakshy, E.: Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement, Adv. Neur. In., 34, 2187–2200, https://doi.org/10.48550/arXiv.2105.08195, 2021. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Dimri, A.: Bias correction demonstration in two of the Indian Himalayan river basins, J. Water Clim. Change, 12, 1297–1309, https://doi.org/10.2166/wcc.2020.119, 2021. a
Dimri, A., Niyogi, D., Barros, A., Ridley, J., Mohanty, U., Yasunari, T., and Sikka, D.: Western disturbances: a review, Rev. Geophys., 53, 225–246, https://doi.org/10.1002/2014RG000460, 2015. a
Drucker, H., Burges, C. J., Kaufman, L., Smola, A., and Vapnik, V.: Support vector regression machines, in: Advances in Neural Information Processing Systems, 2–5 December 1996, Denver, CO, USA, https://proceedings.neurips.cc/paper_files/paper/1996/file/d38901788c533e8286cb6400b40b386d-Paper.pdf (last access: 11 November 2024), 1996. a
Duan, K., Xu, B., and Wu, G.: Snow accumulation variability at altitude of 7010 m a.s.l. in Muztag Ata Mountain in Pamir Plateau during 1958–2002, J. Hydrol., 531, 912–918, 2015. a
Duvenaud, D., Lloyd, J., Grosse, R., Tenenbaum, J., and Zoubin, G.: Structure discovery in nonparametric regression through compositional kernel search, in: Proceedings of the 30th International Conference on Machine Learning, 17–19 June 2013, Atlanta, Georgia, USA, 1166–1174, https://doi.org/10.48550/arXiv.1302.4922, 2013. a
Gerlitz, L., Conrad, O., and Böhner, J.: Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach, Earth Syst. Dynam., 6, 61–81, https://doi.org/10.5194/esd-6-61-2015, 2015. a
Gordon, J., Bruinsma, W. P., Foong, A. Y., Requeima, J., Dubois, Y., and Turner, R. E.: Convolutional conditional neural processes, arXiv [preprint], https://doi.org/10.48550/arXiv.1910.13556, 2019. a
Gutiérrez, J. M., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R., Rössler, O., Wibig, J., Wilcke, R., Kotlarski, S., San Martín, D., Herrera, S., Bedia, J., Casanueva, A., Manzanas, R., Iturbide, M., Vrac, M., Dubrovsky, M., Ribalaygua, J., Pórtoles, J., Räty, O., Räisänen, J., Hingray, B., Raynaud, D., Casado, M. J., Ramos, P., Zerenner, T., Turco, M., Bosshard, T., Štěpánek, P., Bartholy, J., Pongracz, R., Keller, D. E., Fischer, A. M., Cardoso, R. M.,Soares, P. M. M., Czernecki, B., and Pagé, C.: An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment, Int. J. Climatol., 39, 3750–3785, https://doi.org/10.1002/joc.5462, 2019. a
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset, Scientific Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020. a
Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in regional climate predictions, B. Am. Meteorol. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, Co., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Ho, T. K.: Random decision forests, in: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, IEEE, 14–16 August 1995, Montreal, QC, Canada, 278–282, https://doi.org/10.1109/ICDAR.1995.598994, 1995. a
Hussain, S., Song, X., Ren, G., Hussain, I., Han, D., and Zaman, M.: Evaluation of gridded precipitation data in the Hindu Kush–Karakoram–Himalaya mountainous area, Hydrolog. Sci. J., 62, 2393–2405, https://doi.org/10.1080/02626667.2017.1384548, 2017. a, b
Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M., and Bierkens, M. F. P.: Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff, Hydrol. Earth Syst. Sci., 19, 4673–4687, https://doi.org/10.5194/hess-19-4673-2015, 2015. a, b
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B., Elmore, A. C., Emmer, A., Feng, M., A Fernández, A., Haritashya, U. K., Kargel, J. S., Koppes, M. N., Kraaijenbrink, P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T. H., Pellicciotti, F., Rajaram, H., Rupper, S. B., Sinisalo, A., Shrestha, A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T.-D., and Baillie, J. E. M.: Importance and vulnerability of the world's water towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020. a, b
Japan Aerospace Exploration Agency: GPM Data Utilization Handbook, Tech. rep., Japan Aerospace Exploration Agency, https://www.eorc.jaxa.jp/TRMM/documents/PR_algorithm_product_information/doc_pr_v8/GPM_data_util_handbook_V6_20181004_E.pdf, (last access: 27 August 2021), 2018. a
Ji, X., Li, Y., Luo, X., He, D., Guo, R., Wang, J., Bai, Y., Yue, C., and Liu, C.: Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin, Atmos. Res., 242, 104964, https://doi.org/10.1016/j.atmosres.2020.104964, 2020. a, b, c
Karki, R., ul Hasson, S., Gerlitz, L., Schickhoff, U., Scholten, T., and Böhner, J.: Quantifying the added value of convection-permitting climate simulations in complex terrain: a systematic evaluation of WRF over the Himalayas, Earth Syst. Dynam., 8, 507–528, https://doi.org/10.5194/esd-8-507-2017, 2017. a
King, A. D., Alexander, L. V., and Donat, M. G.: The efficacy of using gridded data to examine extreme rainfall characteristics: a case study for Australia, Int. J. Climatol., 33, 2376–2387, https://doi.org/10.1002/joc.3588, 2013. a
Krishnan, R., Shrestha, A. B., Ren, G., Rajbhandari, R., Saeed, S., Sanjay, J., Syed, M. A., Vellore, R., Xu, Y., You, Q., and Ren, Y.: Unravelling climate change in the Hindu Kush Himalaya: rapid warming in the mountains and increasing extremes, The Hindu Kush Himalaya assessment: Mountains, climate change, sustainability and people, Springer, 57–97, https://doi.org/10.1007/978-3-319-92288-1_3, 2019. a
Kumar, M., Hodnebrog, Ø., Daloz, A. S., Sen, S., Badiger, S., and Krishnaswamy, J.: Measuring precipitation in Eastern Himalaya: Ground validation of eleven satellite, model and gauge interpolated gridded products, J. Hydrol., 599, 126252, https://doi.org/10.1016/j.jhydrol.2021.126252, 2021. a, b
Lalchand, V., Tazi, K., Cheema, T. M., Turner, R. E., and Hosking, S.: Kernel Learning for Explainable Climate Science, in: 16th Bayesian Modelling Applications Workshop at UAI, 5 August 2022, Eidhoven, the Netherlands, https://doi.org/10.48550/arXiv.2209.04947, 2022. a
Leander, R. and Buishand, T. A.: Resampling of regional climate model output for the simulation of extreme river flows, J. Hydrol., 332, 487–496, 2007. a
Lederer, A., Conejo, A. J. O., Maier, K. A., Xiao, W., Umlauft, J., and Hirche, S.: Gaussian process-based real-time learning for safety critical applications, in: International Conference on Machine Learning, PMLR, 18–24 July 2021, online, 6055–6064, https://doi.org/10.48550/arXiv.2006.09446, 2021. a
Le Gratiet, L. and Garnier, J.: Recursive co-kriging model for design of computer experiments with multiple levels of fidelity, Int. J. Uncertain. Quan., 4, 365–386, https://doi.org/10.1615/Int.J.UncertaintyQuantification.2014006914, 2014. a
Li, L., Gochis, D. J., Sobolowski, S., and Mesquita, M. D.: Evaluating the present annual water budget of a Himalayan headwater river basin using a high-resolution atmosphere-hydrology model, J. Geophys. Res.-Atmos., 122, 4786–4807, https://doi.org/10.1002/2016JD026279, 2017. a
Libertino, A., Allamano, P., Laio, F., and Claps, P.: Regional-scale analysis of extreme precipitation from short and fragmented records, Adv. Water Resour., 112, 147–159, https://doi.org/0.1016/j.advwatres.2017.12.015, 2018. a
Liu, H., Ong, Y.-S., Shen, X., and Cai, J.: When Gaussian process meets big data: A review of scalable GPs, IEEE T. Neur. Net. Lear., 31, 4405–4423, https://doi.org/10.1109/TNNLS.2019.2957109, 2020. a
Lund, J., Forster, R. R., Rupper, S. B., Deeb, E. J., Marshall, H., Hashmi, M. Z., and Burgess, E.: Mapping snowmelt progression in the Upper Indus Basin with synthetic aperture radar, Front. Earth Sci., 7, 318, https://doi.org/10.3389/feart.2019.00318, 2020. a
Lyu, Y. and Yong, B.: A novel double machine learning strategy for producing high-precision multi-source merging precipitation estimates over the Tibetan Plateau, Water Resour. Res., 60, e2023WR035643, https://doi.org/10.1029/2023WR035643, 2024. a
MacKay, D. J. C.: Bayesian non-linear modelling for the prediction competition, ASHRAE Tran., 100, 1053–1062, https://doi.org/10.1007/978-94-015-8729-7_18, 1994. a
Maraun, D. and Widmann, M.: Statistical downscaling and bias correction for climate research, Cambridge University Press, https://doi.org/10.1017/9781107588783, 2017. a
Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and Finkelnburg, R.: Precipitation seasonality and variability over the Tibetan Plateau as resolved by the High Asia Reanalysis, J. Climate, 27, 1910–1927, https://doi.org/10.1175/JCLI-D-13-00282.1, 2014. a, b
Mei, Y., Maggioni, V., Houser, P., Xue, Y., and Rouf, T.: A nonparametric statistical technique for spatial downscaling of precipitation over High Mountain Asia, Water Resour. Res., 56, e2020WR027472, https://doi.org/10.1029/2020WR027472, 2020. a, b
Meng, J., Li, L., Hao, Z., Wang, J., and Shao, Q.: Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River, J. Hydrol., 509, 320–332, https://doi.org/10.1016/j.jhydrol.2013.11.049, 2014. a, b
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a
Ning, C., Wang, Y., Nan, Z., Chen, H., and Liu, C.: Study on correction of daily precipitation data of the Qinghai-Tibetan plateau with machine learning models, in: 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 10–15 July 2016, Beijing, China, 517–520, https://doi.org/10.1109/IGARSS.2016.7729128, 2016. a, b
Norris, J., Carvalho, L. M., Jones, C., Cannon, F., Bookhagen, B., Palazzi, E., and Tahir, A. A.: The spatiotemporal variability of precipitation over the Himalaya: evaluation of one-year WRF model simulation, Clim. Dynam., 49, 2179–2204, https://doi.org/10.1007/s00382-016-3414-y, 2017. a, b
Norris, J., Carvalho, L. M., Jones, C., and Cannon, F.: Deciphering the contrasting climatic trends between the central Himalaya and Karakoram with 36 years of WRF simulations, Clim. Dynam., 52, 159–180, https://doi.org/10.1007/s00382-018-4133-3, 2019. a, b
Norris, J., Carvalho, L. M., Jones, C., and Cannon, F.: Warming and drying over the central Himalaya caused by an amplification of local mountain circulation, Npj Climate and Atmospheric Science, 3, 1, https://doi.org/10.1038/s41612-019-0105-5, 2020. a
Orr, A., Listowski, C., Couttet, M., Collier, E., Immerzeel, W., Deb, P., and Bannister, D.: Sensitivity of simulated summer monsoonal precipitation in Langtang Valley, Himalaya, to cloud microphysics schemes in WRF, J. Geophys. Res.-Atmos., 122, 6298–6318, https://doi.org/10.1002/2016JD025801, 2017. a, b, c
Orr, A., Ahmad, B., Alam, U., Appadurai, A., Bharucha, Z. P., Biemans, H., Bolch, T., Chaulagain, N. P., Dhaubanjar, S., Dimri, A., P., Dixon, H., Fowler, H. J., Gioli, G., Halvorson, S. J., Hussain, A., Jeelani, G., Kamal, S., Khalid, I. S., Liu, S., Lutz, A., Mehra, M. K., Miles, E., Momblanch, A., Muccione, V., Mukherji, A., Mustafa, D., Najmuddin, O., Nasimi, M. N., Nüsser, M., Pandey, V. P., Parveen, S., Pellicciotti, F., Pollino, C., Potter, E., Qazizada, M. R., Ray, S., Romshoo, S., Sarkar, S. K., Sawas, A., Sen, S., Shah, A., Shah, M. A. A., Shea, J. M., Sheikh, A. T., Shrestha, A. B., Tayal, S., Tigala, S., Virk, Z. T., Wester, Ph., and Wescoat Jr., J. L.: Knowledge priorities on climate change and water in the Upper Indus Basin: A horizon scanning exercise to identify the top 100 research questions in social and natural sciences, Earths Future, 10, e2021EF002619, https://doi.org/10.1029/2021EF002619, 2022. a, b
Palazzi, E., Von Hardenberg, J., and Provenzale, A.: Precipitation in the Hindu-Kush Karakoram Himalaya: observations and future scenarios, J. Geophys. Res.-Atmos., 118, 85–100, https://doi.org/10.1029/2012JD018697, 2013. a, b, c
Panday, P. K., Thibeault, J., and Frey, K. E.: Changing temperature and precipitation extremes in the Hindu Kush-Himalayan region: An analysis of CMIP3 and CMIP5 simulations and projections, Int. J. Climatol., 35, 3058–3077, https://doi.org/10.1002/joc.4192, 2015. a
Perdikaris, P., Raissi, M., Damianou, A., Lawrence, N. D., and Karniadakis, G. E.: Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling, P. Roy. Soc. A-Math. Phy., 473, 20160751, https://doi.org/10.1098/rspa.2016.0751, 2017. a
Potter, E., Fyffe, C., Orr, A., Quincey, D., Ross, A. N., Rangecroft, S., Medina, K., Burns, H., Llacza, A., Jacome, G., Hellström, R., Castro, J., Hosking, J. S., Cochachin, A., Klein, C., Loarte, E., and Pellicciotti, F.: Projected increases in climate extremes and temperature-induced drought over the Peruvian Andes, 1980–2100, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9576, hhttps://doi.org/10.5194/egusphere-egu22-9576, 2022. a
Rasmussen, C. E. and Williams, C. K.: Gaussian processes for machine learning, vol. 1, MIT Press, https://doi.org/10.7551/mitpress/3206.001.0001, 2006. a, b, c, d
Remesan, R. and Holman, I. P.: Effect of baseline meteorological data selection on hydrological modelling of climate change scenarios, J. Hydrol., 528, 631–642, https://doi.org/10.1016/j.jhydrol.2015.06.026, 2015. a
Salzmann, N., Huggel, C., Rohrer, M., and Stoffel, M.: Data and knowledge gaps in glacier, snow and related runoff research – A climate change adaptation perspective, J. Hydrol., 518, 225–234, https://doi.org/10.1016/j.jhydrol.2014.05.058, 2014. a
Sanjay, J., Krishnan, R., Shrestha, A. B., Rajbhandari, R., and Ren, G.-Y.: Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models, Advances in Climate Change Research, 8, 185–198, https://doi.org/10.1016/j.accre.2017.08.003, 2017. a, b
Schreiner-McGraw, A. P. and Ajami, H.: Combined impacts of uncertainty in precipitation and air temperature on simulated mountain system recharge from an integrated hydrologic model, Hydrol. Earth Syst. Sci., 26, 1145–1164, https://doi.org/10.5194/hess-26-1145-2022, 2022. a
Shukla, A. K., Ojha, C. S. P., Singh, R. P., Pal, L., and Fu, D.: Evaluation of TRMM precipitation dataset over Himalayan catchment: the upper Ganga basin, India, Water, 11, 613, https://doi.org/10.3390/w11030613, 2019. a, b
Sigdel, M. and Ma, Y.: Variability and trends in daily precipitation extremes on the northern and southern slopes of the central Himalaya, Theor. Appl. Climatol., 130, 571–581, https://doi.org/10.1007/s00704-016-1916-5, 2017. a
Singh, D., Sharma, V., and Juyal, V.: Observed linear trend in few surface weather elements over the Northwest Himalayas (NWH) during winter season, J. Earth Syst. Sci., 124, 553–565, https://doi.org/10.1007/s12040-015-0560-2, 2015. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the advanced research WRF version 3, NCAR technical note 475, NCAR, Boulder, CO, USA, 113 pp. https://doi.org/10.5065/D68S4MVH, 2008. a
Sun, H., Yao, T., Su, F., He, Z., Tang, G., Li, N., Zheng, B., Huang, J., Meng, F., Ou, T., and Chen, D.: Corrected ERA5 precipitation by machine learning significantly improved flow simulations for the third pole basins, J. Hydrometeorol., 23, 1663–1679, https://doi.org/10.1175/JHM-D-22-0015.1, 2022. a, b, c
Tazi, K.: Downscaled ERA5 monthly precipitation data using Multi-Fidelity Gaussian Processes between 1980 and 2012 for the Upper Beas and Sutlej Basins, Himalayas, NERC EDS UK Polar Data Centre [data set], https://doi.org/10.5285/b2099787-b57c-44ae-bf42-0d46d9ec87cc, 2023. a, b
Tazi, K.: Code for `Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5', Zenodo [code], https://doi.org/10.5281/zenodo.14106487, 2024. a
Tazi, K., Lin, J. A., Viljoen, R., Gardner, A., John, S., Ge, H., and Turner, R. E.: Beyond Intuition, a Framework for Applying GPs to Real-World Data, in: ICML Workshop on Structured Probabilistic Inference and Generative Modeling, 22 July 2023, Honolulu, HI, USA, https://doi.org/10.48550/arXiv.2307.03093, 2023. a, b
Titsias, M.: Variational learning of inducing variables in sparse Gaussian processes, in: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 16–18 April 2009, PMLR, Clearwater Beach, FL, USA, 567–574, http://proceedings.mlr.press/v5/titsias09a/titsias09a.pdf (last access: 11 November 2024), 2009. a
Tresp, V.: A Bayesian committee machine, Neural Comput., 12, 2719–2741, https://doi.org/10.1162/089976600300014908, 2000. a
VALUE: VALUE ECA&D local observations, VALUE [data set], http://www.value-cost.eu/data (last access: 13 November 2024), 2024. a
Vaughan, A., Tebbutt, W., Hosking, J. S., and Turner, R. E.: Convolutional conditional neural processes for local climate downscaling, Geosci. Model Dev., 15, 251–268, https://doi.org/10.5194/gmd-15-251-2022, 2022. a, b
Wester, P., Mishra, A., Mukherji, A., and Shrestha, A. B.: The Hindu Kush Himalaya assessment: mountains, climate change, sustainability and people, Springer Nature, https://doi.org/10.1007/978-3-319-92288-1, 2019. a
Wilson, A. and Nickisch, H.: Kernel interpolation for scalable structured Gaussian processes (KISS-GP), in: International Conference on Machine Learning, PMLR, 7–9 July 2015, Lille, France, 1775–1784, https://doi.org/10.48550/arXiv.1503.01057, 2015. a
Winiger, M., Gumpert, M., and Yamout, H.: Karakorum–Hindukush–western Himalaya: assessing high-altitude water resources, Hydrol. Process., 19, 2329–2338, https://doi.org/10.1002/hyp.5887, 2005. a
Wulf, H., Bookhagen, B., and Scherler, D.: Differentiating between rain, snow, and glacier contributions to river discharge in the western Himalaya using remote-sensing data and distributed hydrological modeling, Adv. Water Resour., 88, 152–169, https://doi.org/10.1016/j.advwatres.2015.12.004, 2016. a, b, c
Xiang, Y., Zeng, C., Zhang, F., and Wang, L.: Effects of climate change on runoff in a representative Himalayan basin assessed through optimal integration of multi-source precipitation data, Journal of Hydrology: Regional Studies, 53, 101828, https://doi.org/10.1016/j.ejrh.2024.101828, 2024. a, b, c
Yadav, B. C., Thayyen, R. J., Jain, K., and Dimri, A. P.: Himalayan Re-gridded and Observational Experiment (HiROX): Part I–Development, J. Earth Syst. Sci., 133, 22, https://doi.org/10.1007/s12040-023-02217-8, 2024. a
Yan, Y., Wang, H., Li, G., Xia, J., Ge, F., Zeng, Q., Ren, X., and Tan, L.: Projection of future extreme precipitation in China based on the CMIP6 from a machine learning perspective, Remote Sens.-Basel, 14, 4033, https://doi.org/10.3390/rs14164033, 2022. a
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1, 2012. a, b
Yin, Z.-Y., Zhang, X., Liu, X., Colella, M., and Chen, X.: An assessment of the biases of satellite rainfall estimates over the Tibetan Plateau and correction methods based on topographic analysis, J. Hydrometeorol., 9, 301–326, https://doi.org/10.1175/2007JHM903.1, 2008. a, b, c
Zhang, L., Li, X., Zheng, D., Zhang, K., Ma, Q., Zhao, Y., and Ge, Y.: Merging multiple satellite-based precipitation products and gauge observations using a novel double machine learning approach, J. Hydrol., 594, 125969, https://doi.org/10.1016/j.jhydrol.2021.125969, 2021. a
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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.
This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a...
