Articles | Volume 29, issue 23
https://doi.org/10.5194/hess-29-6935-2025
© 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-29-6935-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Dec 2025
Research article |
| 02 Dec 2025
| 02 Dec 2025
High-resolution snow water equivalent estimation: a data-driven method for localized downscaling of climate data
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, USA
Gregoire Mariethoz
Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Manuela Girotto
Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, USA
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Leire Retegui-Schiettekatte, Manuela Girotto, Maike Schumacher, Mohammad Shamsudduha, Henrik Madsen, and Ehsan Forootan
Hydrol. Earth Syst. Sci., 30, 6623–6659, https://doi.org/10.5194/hess-30-6623-2026, https://doi.org/10.5194/hess-30-6623-2026, 2026
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An improved method is used to integrate satellite-derived terrestrial water storage and surface soil moisture observations into a hydrological model within the Brahmaputra River basin (South Asia). This leads to a more realistic representation of the water stored in the land, allowing us to better understand its changes in space and time. The information the results provide is crucial in this basin due to its increasing water demand and vulnerability to extreme events because of climate change.
Pau Wiersma, Ralf Loritz, Sanika Baste, Katharina Hafner, and Grégoire Mariéthoz
EGUsphere, https://doi.org/10.5194/egusphere-2026-5373, https://doi.org/10.5194/egusphere-2026-5373, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Despite their long record length and information on catchment-wide snowmelt, streamflow observations have not previously been used to constrain snow reanalyses. We show that a bidirectional LSTM can learn the snowmelt-streamflow relationship and subsequently reconstruct snowmelt from streamflow in unseen years and catchments. Combining streamflow and meteorology as inputs yields the best performance, while a streamflow-only model already widely outperforms the ERA5-Land snowmelt benchmark.
Francesco Avanzi, Stefano Terzi, Mariapina Castelli, Francesca Munerol, Margherita Andreaggi, Marta Galvagno, Andrea Galletti, Tessa Maurer, Christian Massari, Grace Carlson, Manuela Girotto, Giacomo Bertoldi, Edoardo Cremonese, Simone Gabellani, Umberto Morra di Cella, Marco Altamura, Lauro Rossi, and Luca Ferraris
Hydrol. Earth Syst. Sci., 30, 5769–5790, https://doi.org/10.5194/hess-30-5769-2026, https://doi.org/10.5194/hess-30-5769-2026, 2026
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Snow droughts are periods with below-average snow accumulation and are becoming more frequent in a warming climate, yet their ecosystem and societal impacts remain poorly known. Using 13 years of data from 38 Italian catchments, we show that snow droughts reduced snow duration, increased winter melt-out events, and cut summer runoff by ~50 %. Photosynthesis increased by up to 10 % due to earlier meltout. These events also caused widespread water-supply reductions, especially in foothills.
Loïc Gerber, Mathieu Gravey, and Grégoire Mariéthoz
EGUsphere, https://doi.org/10.5194/egusphere-2026-4283, https://doi.org/10.5194/egusphere-2026-4283, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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We tested whether environmental maps can be reconstructed better by selecting similar past situations locally and combining past maps in three ways, including simple averaging and two MPS-based stochastic methods. Each method has advantages: averaging was fastest and most accurate overall, while stochastic methods better preserved some spatial variability and high values. Overall, the simpler averaging approach remains the best baseline, but the preferred method depends on the goal.
Harsh Beria, Lionel Benoit, Grégoire Mariethoz, Natalie Claire Ceperley, and Bettina Schaefli
EGUsphere, https://doi.org/10.5194/egusphere-2026-4249, https://doi.org/10.5194/egusphere-2026-4249, 2026
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Mountain streams can rise within minutes of heavy rain, but much of the streamwater may have been stored underground for far longer. We combine hydrological measurements with stable water isotopes to simulate streamflow in a Swiss Alpine catchment. The simulations show that >80 % of summer streamflow came from groundwater, while direct rainfall contributions were small and brief. Fast stream rises therefore do not necessarily mean that rainwater moves quickly through the landscape.
Pau Wiersma, Jan Magnusson, Nadav Peleg, Bettina Schaefli, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 30, 3331–3350, https://doi.org/10.5194/hess-30-3331-2026, https://doi.org/10.5194/hess-30-3331-2026, 2026
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Streamflow observations contain information about snow, but their potential to constrain seasonal snow mass reconstructions remains underexplored. Using inverse hydrological modeling, we show that streamflow is particularly effective at constraining catchment-aggregated melt rates, but that non-uniqueness in the snow–streamflow relationship and uncertainties in the inverse modeling chain can easily limit inversion performance.
Steven A. Margulis, Maya Hildebrand, Xiaolan Xu, Manuela Girotto, Hans-Peter Marshall, Rashmi Shah, Elias Deeb, Simon Yueh, Dara Entekhabi, and Jessica Lundquist
EGUsphere, https://doi.org/10.5194/egusphere-2026-1594, https://doi.org/10.5194/egusphere-2026-1594, 2026
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This paper demonstrates the feasibility of using a Bayesian framework for estimating snow water equivalent (SWE) from InSAR phase data at C-, L-, and P-band wavelengths. The Bayesian approach shows promise as a value-added approach to estimating SWE from phase delay measurements that are not only accurate at the measurement times, but interpolate between measurements and extrapolate beyond measurements and provide distributional (uncertainty) estimates for SWE.
Shima Azimi, Manuela Girotto, Riccardo Rigon, Gaia Roati, Silvia Barbetta, and Christian Massari
EGUsphere, https://doi.org/10.5194/egusphere-2026-793, https://doi.org/10.5194/egusphere-2026-793, 2026
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Even ground-based precipitation observations, often considered the most reliable, can introduce substantial uncertainty into snow modeling due to sparse gauge coverage at high elevations in mountainous catchments.This challenge motivates the present study, in which we propose a data assimilation framework that integrates satellite-based snow depth into a hydrological model to correct snowfall estimates over the Italian Alps, with implications for water management in data-scarce mountain regions.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
The Cryosphere, 19, 4759–4783, https://doi.org/10.5194/tc-19-4759-2025, https://doi.org/10.5194/tc-19-4759-2025, 2025
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Reliable snow water equivalent and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we propose a Long Short-Term Memory neural network for operational use in snow hydrology. Once trained, it reduces computational cost by 70 percent compared to the Ensemble Kalman Filter, with a slight decrease in performances. This deep learning approach provides a scalable, efficient, and cost-effective modeling solution for hydrology.
Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz
Earth Syst. Dynam., 15, 735–762, https://doi.org/10.5194/esd-15-735-2024, https://doi.org/10.5194/esd-15-735-2024, 2024
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We aim to combine multiple global climate models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α pooling", aggregating the cumulative distribution functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments with European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Proc. IAHS, 385, 121–127, https://doi.org/10.5194/piahs-385-121-2024, https://doi.org/10.5194/piahs-385-121-2024, 2024
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This study assesses the impact of climate change on the timing, seasonality and magnitude of mean annual minimum (MAM) flows and annual maximum flows (AMF) in the Volta River basin (VRB). Several climate change projection data are use to simulate river flow under multiple greenhouse gas emission scenarios. Future projections show that AMF could increase with various magnitude but negligible shift in time across the VRB, while MAM could decrease with up to 14 days of delay in occurrence.
Fabio Oriani, Gregoire Mariethoz, and Manuel Chevalier
Earth Syst. Sci. Data, 16, 731–742, https://doi.org/10.5194/essd-16-731-2024, https://doi.org/10.5194/essd-16-731-2024, 2024
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Modern and fossil pollen data contain precious information for reconstructing the climate and environment of the past. However, these data are only achieved for single locations with no continuity in space. We present here a systematic atlas of 194 digital maps containing the spatial estimation of contemporary pollen presence over Europe. This dataset constitutes a free and ready-to-use tool to study climate, biodiversity, and environment in time and space.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Nadav Peleg, Herminia Torelló-Sentelles, Grégoire Mariéthoz, Lionel Benoit, João P. Leitão, and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1233–1240, https://doi.org/10.5194/nhess-23-1233-2023, https://doi.org/10.5194/nhess-23-1233-2023, 2023
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Floods in urban areas are one of the most common natural hazards. Due to climate change enhancing extreme rainfall and cities becoming larger and denser, the impacts of these events are expected to increase. A fast and reliable flood warning system should thus be implemented in flood-prone cities to warn the public of upcoming floods. The purpose of this brief communication is to discuss the potential implementation of low-cost acoustic rainfall sensors in short-term flood warning systems.
Elias C. Massoud, Lauren Andrews, Rolf Reichle, Andrea Molod, Jongmin Park, Sophie Ruehr, and Manuela Girotto
Earth Syst. Dynam., 14, 147–171, https://doi.org/10.5194/esd-14-147-2023, https://doi.org/10.5194/esd-14-147-2023, 2023
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In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
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Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
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Short summary
Our study presents a method to estimate high-resolution snow water equivalent (HR-SWE) using low-resolution climate data (LR-CD). By using a data-driven approach, we analyze historical weather patterns from LR-CD to generate HR-SWE maps. Machine learning and statistical relationships between LR-CD and HR-SWE enable estimation for dates without HR-SWE data. This method enhances water resource management and climate impact assessments, especially in data-scarce regions.
Our study presents a method to estimate high-resolution snow water equivalent (HR-SWE) using...
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