Runoff generation in catchments that are impacted by snow or ice profoundly differs from rainfed catchments. Yet, our knowledge of snow/ice accumulation and melt and their contribution to runoff remains highly uncertain, because of both limited availability and inherently high spatial variability of hydrological and weather data.
Contributions addressing the following topics (but not limited to) are welcome:
- Experimental research on snowmelt & ice-melt runoff processes and potential implementation in hydrological models;
- Development of novel strategies for snowmelt runoff modelling in various (or changing) climatic and land-cover conditions;
- Evaluation of remote-sensing or in-situ snow products and application for snowmelt runoff calibration, data assimilation, streamflow forecasting or snow and ice physical properties quantification;
- Observational and modelling studies that shed new light on hydrological processes in glacier-covered catchments, e.g. impacts of glacier retreat on water resources and water storage dynamics or the application of techniques for tracing water flow paths;
- Studies addressing the impact of climate change and/or extreme events (e.g., droughts) on the water cycle of snow and ice affected catchments.
- Studies on cryosphere-influenced mountain hydrology and water balance of snow/ice-dominated mountain regions;
- Use of modelling to propose snowpack, snowmelt, icepack, ice melt or runoff time series reconstruction or reanalysis over long periods to fill data gaps
Snow energy balance, particularly radiation balance, is monitored only at a limited number of well-instrumented, research-focused snow study sites in the western United States. This lack of observations limits our ability to force or validate process-based snow models in mountain terrain, an important hurdle to operational adoption. To address this gap, we have prototyped a low-cost, low-power, transportable snow monitoring system capable of transmitting near-real-time snow energy balance relevant observations. Each instrumentation suite measures incoming and reflected broadband shortwave radiation, incoming and emitted longwave radiation, air temperature/relative humidity, and snow depth. Including sensors, power, data logging, and communications infrastructure, each site costs less than USD $10,000, enabling deployment at a scale not feasible with conventional research stations. The systems have been deployed at 11 sites across snow-dominated headwater catchments in the Intermountain West, more than doubling the current number of snow radiation balance observation sites. Two sites are co-located with research sites for validation, and observations are used to drive the 1d SNOBAL model to assess the sensitivity of simulated snow water equivalent to lower-cost instrumentation. This approach complements existing snow monitoring networks, including the ~900-site SNOTEL (Snowpack Telemetry) network, which provides long-term observations for snow mass balance monitoring and index-based streamflow forecasting. SNOTEL sites are intentionally located in sheltered, mid-elevation forest openings and do not capture spatial variability, nor do they measure radiation balance. Low-cost, distributed energy balance observations provide a pathway to complement and extend the observational capabilities of current networks.
How to cite: Skiles, S. M., Roe, W., and Clark, S.: Advancing Snow Observation Systems to Improve Hydrologic Prediction in Mountain Headwaters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22177, https://doi.org/10.5194/egusphere-egu26-22177, 2026.
In cold and wet regions, the forest canopy strongly influences the energy and mass balance of the snowpack by intercepting a large fraction of solid precipitation. Although commonly represented in land-surface models, snow interception remains poorly documented in the field because of the difficulties associated with directly measuring intercepted snow mass. Existing indirect measurement approaches include the use of accelerometers to quantify wind-induced tree sway and relate changes in sway frequency to variations in intercepted snow mass. However, this experimental method has so far been applied at only one site in the western United States, under climatic conditions that differ from those of the boreal forests of eastern Canada.
The objective of this study is to apply the tree sway method in eastern Canada to estimate intercepted snow mass and to improve the parametrization of canopy snow interception in the SVS2–Crocus land surface model.
A total of nine coniferous trees were equipped with accelerometers across three sites to monitor wind-induced tree sway and obtain estimates of intercepted snow mass. Results from the winter 2024–25 show that the sway method captures rapid loading and unloading events, with sway frequency responding to interception and release within a few hours, in agreement with hourly timelapse imagery acquired at each site. The resulting intercepted snow time series is then used to evaluate the canopy interception parametrization in the SVS2–Crocus model, which was forced using in situ meteorological measurements.
Sway-based observations and simulated intercepted snow mass show good agreement in the timing of interception and unloading, with rapid increases during snowfall and subsequent exponential decay. However, the model tends to overestimate slow, continuous unloading and often fails to accurately reproduce rapid unloading events associated with strong winds, warm temperatures, or rain-on-snow events. These results pave the way for improving the parametrization of canopy snow unloading in SVS2–Crocus and, in turn, for more accurately estimating snow cover in forested environments.
How to cite: Barrette, E., Vionnet, V., Bouchard, B., and Nadeau, D. F.: Tree sway monitoring for improved representation of canopy snow interception in cold, wet climates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15976, https://doi.org/10.5194/egusphere-egu26-15976, 2026.
Predicting the volume and timing of snowmelt is essential for applications such as hydropower production planning and flood forecasting. The timing of snowmelt is strongly influenced by the spatial distribution of snow. A more heterogeneously distributed snowpack leads to a longer melt season and lower peak flow than a homogeneously distributed snowpack. Despite the importance of spatial snow distribution for runoff characteristics, large-scale and high-resolution measurements of snow distribution are rare and it is challenging to effectively use such measurements in models when the snow conditions differ substantially across the study region.
We acquired high-resolution airborne lidar snow height maps in three winters for three large hydropower regions in Southern Norway, covering over 1000 km2. We use these to improve snow height simulations and demonstrate how the scans can be assimilated into a physics-based snow model. To this end, we use a snowfall scaling method that aims to implicitly describe preferential deposition and redistribution processes during snow accumulation by altering the snowfall inputs to the snow model. In each grid cell, a scaling factor is chosen such that the modelled snow height matches the observed snow height. Existing methods are often not finding the optimal scaling factor, especially in case snowmelt has started in parts of the scanned regions. We present a new approach that considers estimated snow losses due to melt and sublimation that occurred before the acquisition of the lidar scan. With this improvement, scans taken slightly after melt onset in part of the region can still be used to reliably find the optimal snowfall scaling factors, even if part of the snow is already lost due to melt and sublimation.
We show how similar these snowfall scaling factors are between years, due to repeatable patterns in snow height, and whether this similarity provides opportunities to transfer snowfall scaling factors to different years. Furthermore, we show that higher model resolutions are best suited to represent the observed spatial snow distribution in the model using the proposed snowfall scaling method. The insights of this work can be used to effectively use large area, high-resolution snow height measurements in snow models.
This work is partially funded by Statkraft Energi AS and the Norwegian Research Council (SnowInflow, NFR 346308).
How to cite: Oprel, J., Magnusson, J., Jonas, T., Brunner, M., Holand, K., Stordal, A., and Lappegard, G.: Improving the spatial distribution of snow height in physics-based snow models using large-area airborne lidar-scans, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9801, https://doi.org/10.5194/egusphere-egu26-9801, 2026.
While the assessment of climate model uncertainty is well established, the uncertainty originating from the selection of a surface snow model usually only receives little attention. However, a better understanding of snow model uncertainty currently becomes more and more important, as novel climate model data at the kilometer-scale, innovative downscaling techniques, and increasing computational capacities are among the elements that pave the way for a new phase of high resolution and physically based climate change impact studies assessing cryospheric changes in complex mountain areas. To investigate the uncertainty induced by the selection of the snow model configuration, we simulate the seasonal snow cover in the mountain area of the Berchtesgaden National Park (Germany) under historical conditions (10/2013 - 09/2023) and for a 10-year period characterized by a 1°C warming. Therefore we use a large number of openAMUNDSEN snow model configurations (n = 108) with T-Index, enhanced T-Index as well as energy balance based snowmelt methods, varying land cover maps and spatial resolutions. Forcing data for the 10-year warming period is constructed using the stochastic bootstrap resampler (climate generator) available within the openAMUNDSEN modelling framework. Prior to the estimation of snow model uncertainty, we evaluate the snow model results using satellite-based snow data. Our results suggest that differences in key snow metrics such as snow cover duration and snow disappearance day can be in the same range as the impact of a 1°C warming. The results also support the identification of critical snow model settings that need to be considered, in particular, when using energy balance instead of degree-day snow models to investigate climate change impacts on snow hydrological processes in complex mountain terrain.
How to cite: Rottler, E., Storebakken, B., Warscher, M., Hanzer, F., Bertazza, E., and Strasser, U.: Assessment of snow model uncertainty using a large number of openAMUNDSEN snow model configurations: A study from the Berchtesgaden National Park (Germany), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10888, https://doi.org/10.5194/egusphere-egu26-10888, 2026.
Snow plays a key role in land-surface processes by modulating the surface energy balance, soil thermal insulation and water availability. However, the influence of snow on water and energy fluxes in land surface models remains insufficiently understood. To improve simulations of the coupled water–energy cycle, we developed a snow data assimilation (snow-DA) within the Encore Community Land Model coupled to the Parallel Data Assimilation Framework (eCLM-PDAF; https://github.com/HPSCTerrSys/eCLM), enabling assimilation of both snow depth and snow water equivalent (SWE). In the Snow-DA experiment, we assimilate daily snow depth with a one-dimensional Ensemble Kalman Filter (EnKF), updating the liquid and ice SWE components across all snow layers; snow depth is then adjusted through its correlation with SWE. We evaluated the performance of the snow-DA framework by comparing snowpack variables as well as heat fluxes such as latent heat flux (LE), sensible heat flux (SH), ground heat flux (GH), and soil temperature, between data assimilation (DA) and open-loop (OL) simulations at eleven selected Integrated Carbon Observation System (ICOS) sites across Europe. The sites span different observation periods within 2017–2024. Each OL and DA experiment used 100 ensemble members, generated through perturbation of meteorological variables and key snow related parameters. A multiplicative inflation factor of 0.95 and observation error of 0.2m are applied across all sites. Results across ICOS sites showed that DA substantially improved snow variable estimates compared to OL simulations. On average, the root mean square error (RMSE) of SD decreased by 27.3%, and the correlation coefficient (R) increased by 0.06. DA also improved the timing of snow cover duration, yielding a more realistic seasonal snow cover evolution when compared with satellite-based observations. Although overall changes in land-surface heat fluxes were modest, the improved snowpack reduced RMSE during the melt season for LE by 9.5%, evaporative fraction (EF) by 1.6%, and soil temperature by 20.8%. Although the energy balance was evaluated, and LE and EF improved, snow DA degraded the performance of SH and GH at most sites, indicating possible coupling bias between modeled variables for energy partitioning, or representativeness errors between tower-based and modeled fluxes. Overall, this study enhances the representation of snow processes in a land surface model and encourages further research into the modeling of associated water and energy balance mechanisms. Future work will investigate regional responses of water flux components, including runoff, evapotranspiration, and soil water content, to further examine snow data assimilation impacts on water availability.
How to cite: Guan, B., Strebel, L., Keller, J., Hendricks Franssen, H.-J., De Lannoy, G., and S. Naz, B.: Impact of Snow Data Assimilation on Land-Surface Energy Fluxes at Sites Across Europe Using eCLM-PDAF, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18389, https://doi.org/10.5194/egusphere-egu26-18389, 2026.
Quantifying snow water equivalent (SWE) and melt dynamics across the Pan-Siberian domain is critical for understanding the hydro-climatological conditions of the entire Northern Hemisphere. However, due to the scarcity of data, the strong interaction between vegetation and climate, and representativeness biases of sparsely distributed measurement stations, there is a high degree of uncertainty in current estimations. While traditional monitoring networks provide essential points of reference, their limited spatial coverage and site-selection biases—often favoring open clearings—hinder the accurate assessment of regional snow storage across diverse and complex landscapes.
In this study, we develop and apply a physics-based snow process model designed for data-sparse cold regions, combined with a corresponding regionalization strategy to bridge the gap between sparse point-scale observations and regional snow dynamics. The model was first validated at the Sodankylä site in Finland, demonstrating high performance for both Snow Depth (NSE > 0.78) and SWE (NSE > 0.83), indicating a physically consistent representation of snow density, compaction, and melt processes. The model was then applied across Pan-Siberia by grouping 85 stations into hydro-climatic regimes based on wind, precipitation characteristics, and forest cover. Model parameters were calibrated simultaneously across stations within each regime to derive robust zonal parameter sets, thereby ensuring physical consistency and overcoming parameter equifinality.
The resulting regionalized model achieves robust performance across the majority of the domain (median KGE > 0.75), substantially outperforming global default parameterizations. The results reveal a key physical insight in forest-dominated Taiga regions, where the optimized wind correction factor converges toward zero, confirming the strong canopy sheltering effect and indicating that standard WMO wind corrections systematically overestimate snowfall under forest cover. In contrast, the Cold Continental regime (Yakutia) exhibits a high rain–snow temperature threshold (~+3.7°C), reflecting sublimation-driven cooling under extremely dry atmospheric conditions.
This approach enables the reconstruction of spatially consistent, multi-year snow dynamics across Pan-Siberia, providing a scalable strategy for hydrological modeling in ungauged, cryosphere-dominated regions and offering new insights into the spatiotemporal evolution of Eurasian snow resources.
How to cite: Luo, J. and Menzel, L.: A Physics-Based Regionalized Snow Modeling Framework for the Data-Sparse Pan-Siberian Domain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9883, https://doi.org/10.5194/egusphere-egu26-9883, 2026.
Snow phenology characterizes the cyclical changes in snow and has become an important indicator of climate change in recent decades. Changes in snow phenology can significantly impact climate and hydrological conditions. Previous studies commonly employed fixed threshold methods to extract snow phenology. However, these methods do not account for the variability in snow distribution across the Northern Hemisphere, leading to potential biases of snow phenology. In this study, we observe that snow phenology extracted from different snow data and methods shows significant differences, but consistently underestimates snow duration at low and middle latitudes. Our analysis further indicates that the changes in snow depth exhibits a significant shift around 10% of peak value across the Northern Hemisphere, marking the transition between the snow and non-snow seasons. We further apply the 10% snow depth threshold and investigate the differences between original and newly extracted snow phenology. At low and middle latitudes, the snow cover duration (SCD) extends, the snow cover onset day (SCOD) advances, and the snow cover end day (SCED) delays, especially on the Tibetan Plateau, where the SCD differences can reach 28 days. The change at higher latitudes is reversed. The dynamic snow phenology accounts for the spatial heterogeneity of Northern Hemisphere snow cover, and excludes the influence of inter-annual variability of snow cover on snow phenology extraction, providing a novel perspective for identifying and understanding snow cover variations in the Northern Hemisphere.
How to cite: Wang, L., Miao, X., and Guo, W.: Dynamic identification of snow phenology in the Northern Hemisphere, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16791, https://doi.org/10.5194/egusphere-egu26-16791, 2026.
Snow provides numerous ecosystem and economic services, such as hydropower generation, regulation of stream temperature, and winter tourism. Despite projected increases in winter precipitation, warming temperatures are expected to reduce snowfall and shift precipitation toward rainfall, fundamentally changing snowpack accumulation dynamics, and the associated hazards such as rain-on-snow flooding. This highlights the need for accurate snow projections at locally relevant spatial scales.
Here, we present novel high-resolution (1x1 km²) daily projections of snow water equivalent (SWE) and snow depth for Switzerland, based on the recently released Climate CH2025 scenarios. SWE is simulated for an ensemble of 12 bias-adjusted regional climate models from the EURO-CORDEX initiative using a distributed temperature-index snow model, which is statistically nudged toward a reference SWE dataset (SPASS) – derived from the same model but debiased using data assimilation with observations from 1998-2024.
We project widespread SWE declines across Switzerland, with the largest percentage reductions at low elevations (<1000 m a.s.l.), and a transition from seasonal to ephemeral snowpacks at intermediate elevations. We further assess the added value of high-resolution snow simulations by comparing them with physically-based, but coarser (~12 km) raw EURO-CORDEX SWE projections. While both show consistent large-scale patterns, our higher resolution simulations reveal clearer elevation-dependent signals, especially in topographically complex mountainous landscapes, enabling robust estimation of locally relevant snow indicators. These results offer actionable insights for managing future snow-dependent resources in a rapidly warming climate.
How to cite: Beria, H., Kotlarski, S., Michel, A., Jonas, T., and Marty, C.: Elevation-dependent snow cover changes across Switzerland in the 21st century, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8202, https://doi.org/10.5194/egusphere-egu26-8202, 2026.
SNOWCOP is a Horizon Europe project aimed at developing and evaluating a new high-resolution reanalysis dataset of snow water equivalent (SWE) and glacier ice melt rates for the extra-tropical Andes. The project integrates Copernicus and complementary remote sensing products within a physically based modeling framework to generate daily SWE and ice melt rate maps at 50 m spatial resolution, covering the period from 2002 to the present. These products address a critical observational gap in the region, where ground-based snow and meteorological measurements remain sparse. To support the development and validation of the SNOWCOP workflow, the initial phase of the project focuses on two pilot basins: the Río Maipo (Chile) and the Upper Río Mendoza (Argentina). These basins were selected due to their long term and high-quality instrumental SWE records, making good candidates for method’s evaluation.
We present the first results of a retrospective SWE reconstruction that integrates high-resolution daily snow cover maps with snowmelt modeling. The snow cover products are generated by applying a gap-filling and downscaling algorithm to coarse-resolution snow cover fraction data fused with high-resolution multi-source optical observations (Premier et al., 2021). Several snowmelt modeling approaches are evaluated, including a simple temperature-index (TI) model, an enhanced temperature-index (ETI) model (Pellicciotti et al., 2005), and fully physics-based formulations. Model coefficients are derived through calibration against in-situ observations. Meteorological forcings are obtained from ERA5 reanalysis data and dynamically downscaled using MicroMet (Liston & Elder, 2006). The reconstructed SWE is evaluated against ground-based measurements and compared with an existing SWE reanalysis dataset (Cortés & Margulis, 2017). as well as modeling results produced by our team (CHM model - Marsh et al., 2020).
References
Cortés, G., & Margulis, S. (2017). Impacts of El Niño and La Niña on interannual snow accumulation in the Andes: Results from a high‐resolution 31 year reanalysis. Geophysical Research Letters, 44(13), 6859-6867.
Liston, G. E., & Elder, K. (2006). A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). Journal of Hydrometeorology, 7(2), 217-234.
Marsh, C. B., Pomeroy, J. W., and Wheater, H. S.: The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview, Geosci. Model Dev., 13, 225–247.
Pellicciotti, F., Brock, B., Strasser, U., Burlando, P., Funk, M., & Corripio, J. (2005). An enhanced temperature-index glacier melt model including the shortwave radiation balance: development and testing for Haut Glacier d’Arolla, Switzerland. Journal of glaciology, 51(175), 573-587.
Premier, V., Marin, C., Steger, S., Notarnicola, C., & Bruzzone, L. (2021). A novel approach based on a hierarchical multiresolution analysis of optical time series to reconstruct the daily high-resolution snow cover area. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 9223-9240.
How to cite: Premier, V., Blanch, D., Palma, P. V., Orell, M. I., Toum, E., Masiokas, M., Pitte, P., Cara, L., McPhee, J., and Marin, C.: SNOWCOP: Advancing High-Resolution Retrospective SWE Reconstruction in the Andes of Chile and Argentina with Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17888, https://doi.org/10.5194/egusphere-egu26-17888, 2026.
Much of the freshwater sustaining human societies is generated in the mountains: the mountain cryosphere supports almost a third of the global population for irrigation, drinking water, industry and the environment. At the same time, this crucial resource is undergoing unprecedented changes, with glaciers shrinking, snow decreasing globally and permafrost thawing across continents. This bigger picture masks a very large variability of responses across climates and continents, shaped by processes specific to different mountain ranges. Glaciers and seasonal snow are often assumed to respond to a changing climate in a linear manner, especially at large and global scales, given the complexity of interactions among them and the eco-hydrology of the catchments they sustain. Growing evidence suggests more complex dynamics and threshold effects that will affect the water resources they generate.
In this talk, I will focus on processes and non-linearities in the mountain cryosphere that shape the mountain water cycles across climates, and show how that water cycle is changing across regions as a result. I will focus on a number of specific processes: i) the role of ephemeral and marginal snowpacks on streamflow generation, and their vulnerability to temperature and precipitation shifts, especially in sub-tropical regions; ii) changes in precipitation phase, and their distinct effects on the water cycle depending on precipitation seasonality; iii) the transition from sublimation to melt in a warmer world and how that can change the assumed linear trajectory of water from glaciers and snow in arid areas; iv) the role of evaporative fluxes in the mountain water cycle and how warming promotes increased evapotranspiration that recycle increasing portions of high-altitude precipitation and surface water into the atmosphere. Another key disruption in the functioning of mountain systems is the increasing frequency and intensity of droughts, and I will show some very recent results about how glaciers buffer droughts, and how this capacity might be hampered when droughts become more severe and of longer duration. We use for most of these investigations a fully mechanistic, physically-based modelling framework that represent both the cryosphere, the biosphere and hydrosphere of mountain regions, and I will also briefly touch on the modelling strengths and limitations.
Our results show that the response of the cryosphere to ongoing changes in the climate is very heterogenous. Ephemeral snow in sub-tropical, semi-arid climates has been mostly neglected in modelling assessments, invisible to satellite images, but represents the main contributor to water runoff, and yet this will change in the future with increasing temperatures, which will remove a major source of water. Overall, shifting snowline altitudes and shrinking accumulation areas will move the areas of water generation to higher elevations, altering storage and routing patterns and seasonality, and accelerating the water cycle. Droughts are changing the functioning of mountain systems, with evapotranspiration amplifying water deficits in many mountain regions, while snow droughts enhance this so-called drought paradox. I will conclude with a perspective of future research on mountain processes and water resources.
How to cite: Pellicciotti, F., McCarthy, M., Jouberton, A., Ayala, A., Fyffe, C., Rodriguez, M., Buri, P., Shaw, T., Fontrodona Bach, A., and Tumarkin, Z.: The changing water cycle of the mountains, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16647, https://doi.org/10.5194/egusphere-egu26-16647, 2026.
Changes in snowpack climatology are taking place because of changes in climate. Information is needed on how future changes in climate may affect snowpack regimes.
This information is usually generated by running time stepping models for which time series of forcing data must be supplied. In this study we explore whether it is possible to make reliable estimates of snowpack regime without explicit knowledge of the temporal sequence of forcing data. This could support development of a hydrological theory of seasonal snowpacks. We will test whether it could be enough to know some statistics of the forcing data, rather than the complete time series. In this presentation, we begin by trying to identify where it is necessary to maintain the correlation between temperature and precipitation amount in a temperature index model. Our interest is in correlations at the timescale of a precipitation event; seasonal-scale correlations will be captured separately.
We investigate these questions at 4736 locations in the northern hemisphere, using the NH-SWE database combined with precipitation (P) and temperature (T) data from GHCN. We run the temperature index model once with the original forcing data, and then again with the temperature data displaced by a few days in time from the precipitation data, to reduce their cross-correlation. We calculate statistics of the modelled snowpack for the two model runs (for each station and each year: the start date, peak date and end date for the snowpack, and the peak SWE – snow water equivalent). If the cross-correlation is not important, then the statistics of modelled snowpack should not change much between the two model runs. Since our interest is in snow accumulation and melt, we expect that the most important P-T correlations are at times of year when both rainfall and snowfall are likely to occur.
Initial results show that for the 60% of sites with a positive correlation between P and T-anomaly, neglecting the correlation generally leads to an overestimation of peak SWE (by an average 12%). The overestimation presumably occurs because when the correlation is removed, days below freezing are more likely to be paired with the higher precipitation amounts which tend to occur on days above freezing, and thus the amount of snowfall is increased by neglecting the correlation. For the remaining sites with a negative correlation between P and T-anomaly, neglecting the negative correlation generally leads to a slight underestimation of peak SWE (by an average -4%).
We will also carry out several other similar model experiments with degraded forcing to identify key features of the climate data. The intended endpoint of the work is an improved theory of snowpack hydrology, i.e., a stochastic version of the deterministic theory in Woods 2009 (https://doi.org/10.1016/j.advwatres.2009.06.011)
How to cite: Woods, R., Fontrodona-Bach, A., Larsen, J., and Schaefli, B.: How Much Climate Information Does a (Temperature Index) Snow Model Need?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19982, https://doi.org/10.5194/egusphere-egu26-19982, 2026.
Snow plays a critical role in water resources, the planetary energy balance, glacier nourishment, ecosystem and the winter tourism economy. In recent decades, rising temperatures have led to a decline in snowfall and shifting of patterns. These changes have resulted in reduced snowpack and earlier snowmelt, thereby triggering snow drought conditions. Since the first formal definition of snow drought in 2017, the topic has gained increasing scientific attention, with the first systematic studies published in 2019, followed by significant advancements in subsequent years. However, flash snow droughts (FsD) have not yet been studied and remain unexamined. FsD are short-duration events characterized by rapid onset and intensification, occurring over timescales ranging from weeks to month. These events may arise due to low precipitation accompanied by warm winter. Consequently, establishing a clear definition and identifying FsD hotspots are critical, particularly in regions experiencing imbalanced seasonal snow patterns and low snowpack. In this work, we examine FsD at a 500 m spatial resolution in the North-West Basin part of Afghanistan of the Hindu Kush Himalaya (HKH) using a Snow Water Equivalent Index (SWEI) derived from the High Mountain Asia Snow Reanalysis (HMASR) dataset. The analysis is limited to the 1999–2016 water years due to the unavailability of HMASR data for more recent periods. It is also noted that coarser-resolution datasets may be inadequate for capturing FsD events because of spatial heterogeneity in snow cover dynamics. Our results indicate the recurrence of flash snow droughts (FsDs) with varying durations, notably during February-March 2001, November-December 2010, and January 2011 and 2014. These FsDs fall within the moderate to severe drought categories, based on a threshold of −1. This study highlights the importance of FsD at the local scale for policymaking, mitigation planning, and integrated monitoring frameworks, and it identifies key research gaps to support resilient FsD management.
How to cite: Singh, H., Mehraj, M., and Varade, D.: Flash Snow Drought: Escalating Risks to Mountain Water Resources at local scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19296, https://doi.org/10.5194/egusphere-egu26-19296, 2026.
Glacier contribution to streamflow has mostly been investigated at the scale of relatively small catchments, and more rarely at the scale of the major rivers. In this study, we compare monthly glacier mass changes to monthly estimates of streamflow for the period 1990-2023, both annually and seasonally for 55 major river basins larger than 5700 km2 (0.01 to 20.0 % glaciated). Monthly mass changes for every individual glacier are obtained by temporally downscaling geodetic elevation change observations with the variability from in situ glaciological measurements and global-scale model results. Streamflow is based on GLOFAS and G-RUN global datasets. GLOFAS is a land surface model forced by ERA5 reanalysis that feeds a channel routing model. G-RUN is a machine learning algorithm that predicts monthly runoff based on the Global Soil Wetness Project Phase 3 dataset. Basin scale precipitation and evapotranspiration are estimated from ERA5 reanalysis data.
Annual glacier mass change and thus water release ranges from near zero to 550 mm at the basin-scale and roughly correlates with the percentage of the glacierized area in the basin. The ratio of annual glacier mass change divided by the mean annual discharge, hereafter called the annual glacier contribution, is below 6 % for all the basins larger than 500’000 km2, with the exception of the Indus river with an annual glacier contribution of 25 % (40 mm). The Indus river basin is both highly glacierized (more than 3 %) and arid, explaining such a high ratio.
The glacier seasonal contributions, defined as the water volume derived from glacier mass change during summer months (JJAS in the northern hemisphere and DJFM in the southern hemisphere), divided by the mean discharge in the same months, are always higher than the annual ones. In particular for the basins with low flow during the melt season (e.g. Rapel, Skagit, Rhone, Columbia, Biobio, Po, Rhine), the seasonal contribution is more than three times the annual one. On average 62 % of the glacier mass change originate as a balance contribution, meaning that it corresponds to the seasonal snow accumulation. In contrast, 38 % of the glacier contribution originates from ice melt, i.e. the unsustainable release of the solid water stored in glaciers.
Besides uncertainties in glacier mass change and streamflow data, evaporation (of the glacier meltwater) and groundwater contributions are not treated explicitly, which might lead to overestimations of the glacier contributions. It should thus be seen as a first-order estimate that highlights the major contrasts between basins at the global scale.
How to cite: Brun, F., van Tiel, M., Huss, M., and Mazzotti, G.: Global glacier contribution to streamflow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11091, https://doi.org/10.5194/egusphere-egu26-11091, 2026.
The proportion of annual precipitation that falls as snow, called snow fraction (Sf), in the western United States is declining. The impact of this transition on streamflow timing and magnitude is unclear. Some studies report declining runoff efficiency (streamflow/precipitation, RE) with declining Sf , while others report no change and even increases. The causes of variability in the Sf-RErelationship involve complex interactions between climate and landscape properties. To understand and perhaps mitigate the impact of declining Sf on water resources, it is essential to be able to represent the physical processes and properties controlling that variability in predictive models. While significant insights in SfRErelationships across catchments have been revealed in recent years, few have investigated the variability within a catchment over time. Here, we report Sf-RE relationships from two long-term, highly instrumented catchments in the rain-to-snow transition zone in southwest Idaho, USA. The Dry Creek Experimental Watershed (DCEW) and the Reynolds Creek Experimental Watershed (RCEW) have been monitoring hydrometeorological variables for approximately 25 and 60 years, respectively. Analyzing these long-term records allows us to identify potential physical mechanisms controlling the Sf-RE relationship that short-term, spatially-focused studies cannot. Preliminary results suggest that variability in the alignment of energy and water availability across the rain-dominated to snow-dominated elevation gradient controls how snow fraction impacts streamflow response.
How to cite: McNamara, J.: Impact of decling snow fraction on runoff efficiency in the rain-snow transition zone., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21980, https://doi.org/10.5194/egusphere-egu26-21980, 2026.
Melting glaciers provide crucial seasonal water to communities in high mountain regions. To project how mountain water resources will be impacted by glacial recession requires quantification of current contributions of glacier meltwater to streamflow. This is particularly challenging in monsoon-affected regions, where high glacier melt rates are synchronous with very high precipitation rates. Glacio-hydrological modelling provides a way of estimating meltwater contributions, but confidence in applied conceptual and numerical models is enhanced by observations. Here, stable isotope ratios of oxygen and hydrogen are employed in order to trace relative contributions of multiple sources to the flow of the Dudh Koshi river in northeastern Nepal. Integration of 45 new observations from river, glacial melt, and snow samples with 784 previous observations creates a comprehensive multi-season dataset; these data constrain a mixing model to resolve contributions to river flow that vary seasonally and along the river transect. Preliminary results from the new post-monsoon samples indicate the highest meltwater fractional contribution yet seen in this region.
How to cite: Graves, B., Matthews, T., and Taylor, R.: High-Altitude Himalayan Meltwater Contributions Revealed by Isotopic Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7317, https://doi.org/10.5194/egusphere-egu26-7317, 2026.
Climate change intensifies water scarcity by increasing the frequency of streamflow droughts. Glaciers play a key role in moderating these events by regulating runoff, but their ongoing retreat threatens this natural resilience. Despite case-specific advances, the regional role of Andean glaciers in shaping streamflow droughts across complex climates and landscapes remains highly uncertain. To address this gap, we use a hybrid glacio-hydrological model that combines process-based glacier mass-balance and ice-flow dynamics with a data-driven runoff representation, allowing us to capture both long-term glacier evolution and short-term hydrological responses. This model is applied to a newly developed dataset of 257 glacierized catchments spanning the Andes (“AndeanGC”; 5–56ºS), which consolidates harmonized hydrological observations, remotely sensed glacier characteristics, and gridded meteorological forcing. The hypothetical future glacier extents correspond to projections under three warming storylines that represent plausible global outcomes: a Paris-aligned pathway limiting warming to 1.5 °C, a current-policy trajectory leading to approximately 2.8 °C of warming, and a high-emission pathway reaching about 4.0 °C. We find that glaciers historically provided substantial buffering of streamflow droughts, but this effect diminishes as glaciers shrink. If past droughts had occurred under the smaller glacier extents projected for the late 21st century under current climate policies, their severity and spatial extent would have increased substantially. Consequently, regional water stress would have intensified markedly. These hypothetical scenarios reveal the previously unquantified regional influence of glaciers on past droughts and illustrate the broader consequences of their decline for water resources. They also highlight the critical need to communicate these changes effectively to support climate-resilient planning and policy.
How to cite: Aguayo, R., Zekollari, H., Bolibar, J., van Tiel, M., Ayala, Á., Van Tricht, L., and Ultee, L.: Reimagining how Andean glaciers buffered past streamflow droughts , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3886, https://doi.org/10.5194/egusphere-egu26-3886, 2026.
Mountain glaciers in data-scarce regions are particularly sensitive to climate variability, yet their snow and
firn processes remain poorly constrained due to limited long-term observations/analyzes, especially in the
Caucasus region. Caucasus glaciers are highly sensitive to temperature and precipitation variability due to
their mid-latitude location, steep relief, and strong seasonal contrasts.
In this study, we present a preliminary analysis of snow accumulation and melt dynamics for selected
areas in Georgia (Racha Region, Buba glacier), based on in situ meteorological observations and historical
data. The analysis focuses on preparing meteorological input data for the application of a conceptual
snow–firn model, following the framework proposed by Banfi and De Michele (2021). Meteorological and
snow data from selected Georgian glacierized catchments are analyzed to characterize snow
accumulation and melt dynamics and to prepare input datasets for snow–firn modelling. Particular
attention is given to precipitation phase partitioning, seasonal snow persistence, and data harmonization,
as available observations are often heterogeneous and affected by temporal gaps.
Key variables include snow depth, snow bulk density, snow water equivalent (SWE), and meltwater runoff.
While such variables are monitored at several well-instrumented Alpine sites, allowing for extensive multi-
year model evaluation, comparable long-term and structured datasets remain scarce in the Caucasus
region. However, given the similarities between the Caucasus region and the Italian Alps in terms of
geomorphological and hydrological characteristics, the modelling framework is well suited for application
in the Caucasus context.
How to cite: Beridze, S. and De Michele, C.: Investigating Snow and Firn Processes in a Georgian Glacier through Local Data and Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23257, https://doi.org/10.5194/egusphere-egu26-23257, 2026.
In this contribution, we share observations of a braided river plain adjacent to the little ice age moraine of a land-terminating outlet glacier in West Greenland at around 71°N. During three visits in spring (2023 - 2025), we document a plain of refrozen water. We report on the extent, genesis and decay of the aufeis plain and hypothesize on drivers building it. Time-lapse and high-resolution satellite imagery allow us to assign the build-up of the aufeis during core winter until spring and the decay throughout the melting season. We find that long after the disappearance of the snow cover at the adjacent glacier and ice-free environment, the aufeis still is in place. Using multispectral satellite imagery (Sentinel-2) we derive a time series of aufeis extent ranging from virtually no coverage to almost 0.5 km² for the period 2016-2025, using a random forest classification. DEM differences derived from photogrammetric acquisitions using UAVs enable us to estimate ice volumes between 49x10³ (April 2025) and 110x10³ m³ (April 2024), respectively. To understand atmospheric conditions for meltwater generation, we use automated weather station data near the aufeis plain. As another reason for ice formation, we discuss potential water sources related to groundwater aquifers in porous ground moraine material. Finally, bias-corrected CARRA model output was applied to reconstruct meteorological conditions relevant for aufeis formation. Based on a lagged correlation approach, we find statistically significant (p = 0.05) correlations between cumulative positive air temperature departures and aufeis extent summing up approx. 7 years before the respective icing occurrence. While simplified, we discuss a possible long-term relation between icing extent and meltwater generation.
How to cite: Abermann, J., Truegler, A., Zandler, H., Bergstedt, H., Schalamon, F., Scher, S., and Schöner, W.: Aufeis (Proglacial Icing) in the forefield of a land-terminating outlet glacier in West Greenland – multi-annual and seasonal variability and drivers , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20199, https://doi.org/10.5194/egusphere-egu26-20199, 2026.
Snowmelt is a critical seasonal water source in mountain catchments, yet the dynamics of catchment water storage and release, as well as the redistribution of snowmelt signals into low-flow periods, remain poorly constrained, particularly across the snow–rain transition. In this study, we focus on one diagnostic question: how long does the snow signal persist before it emerging in baseflow, and how does that lag change with elevation and snow regime? We analyse 88 near-natural mountain catchments in Czechia and Switzerland using HBV-Light simulations of snow water equivalent (SWE) and baseflow and apply wavelet coherence to quantify phase-derived SWE–baseflow lags as a signal-based indicator of storage modulation. Across both regions, SWE and baseflow exhibit stable annual coupling, with SWE consistently leading baseflow. Mean lags are systematically longer in higher, colder, and snow-richer catchments, consistent with stronger storage buffering and delayed meltwater release. At the regional scale, the characteristic lag is ~69 days in Czech catchments and ~100 days in Swiss catchments, and the lag increases with elevation in both countries. These storage-linked delays align with stronger snow support to summer baseflow at higher elevations, while mid-elevation catchments near the snow–rain transition show shorter lags and weaker persistence of snow influence. This lag-based indicator provides a compact, transferable way to diagnose where snowmelt most strongly sustains baseflow through storage buffering, and where this mechanism is most likely to weaken as snow seasons shorten under warming.
How to cite: Acheampong, J. N. and Jenicek, M.: Snowmelt–Baseflow Lags as Indicators of Elevation-Dependent Storage Buffering in Snow-Dominated Mountain Catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4540, https://doi.org/10.5194/egusphere-egu26-4540, 2026.
Université de Sherbrooke (UdeS) produced a snow water equivalent (SWE) database for southern Quebec, Canada, using a spatialised particle filter, the snow module of the distributed hydrological model Hydrotel, and two types of snow observations (manual snow surveys, and automated sonic sensors). This gridded database has a spatial resolution of 10 km and covers the southern part of the province of Quebec, below 53°N. This database is used operationally as part of the government’s official flood forecasting system but has never been compared to other similar gridded datasets. This work therefore aims to compare the UdeS-produced SWE grids with reference data from the Canadian database CanSWE, and GMON stations, which measure SWE using gamma ray attenuation. This study also compares the UdeS grids with four other gridded databases that are widely used in hydrology: ERA-5 Land, SNODAS, MERRA and Crocus-ERA-5. Three indices are used to evaluate the ability of these databases to estimate the duration of the snow cover period: the start and end dates of snow cover and the start date of snowmelt. The annual maximum of SWE, the correlation coefficient, bias, and root mean square error (RMSE) are other indices used to evaluate the ability of these databases to estimate SWE values. The main results show that, despite some differences, particularly in the north-eastern part of the study area, all databases accurately estimated the duration of the snow cover period. Except for the MERRA database, which appears to underestimate the SWE in our study area, the results show that all databases perform well. ERA-5 Land appears to perform better, although it overestimates the reference data. UdeS and Crocus perform similarly but underestimate the reference data.
How to cite: Bathelemy, R., Boucher, M.-A., Redondo Tilano, S. A., and Hamelin, J.: Evaluation of the performance of a database for snow water equivalent in southern Quebec, obtained using a spatialised particle filter, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6027, https://doi.org/10.5194/egusphere-egu26-6027, 2026.
Modelling high-alpine hydrology poses significant challenges due to terrain heterogeneity and complex topography. In snow-dominated karst catchments, accurate representation of spatiotemporal snow distribution is essential for simulating aquifer recharge and spring discharge dynamics. However, differences in data availability and landscape complexity can be a limit. Here, we assess the ability of Alpine3D applying snow pattern redistribution to capture the spatiotemporal snow cover variability in two adjacent alpine karst catchments with different spatial snow distribution characteristics.
We present an 11-year (2015-2025) validation of Alpine3D simulations in two adjacent high-alpine karst catchments in the Zugspitze region in Germany (European Alps): the Partnach Spring catchment (15.4 km², 1430-2962 m a.s.l.) and the Hammersbach catchment (17.8 km², 768-2951 m a.s.l.). While both catchments share similar karstified alpine geomorphology, Partnach Spring is characterized by higher elevations on average, limited vegetation, and more persistent snow cover, whereas Hammersbach exhibits stronger elevation gradients, greater forest cover, and higher radiation exposure, leading to more heterogeneous snow accumulation and melt dynamics.
Precipitation and snow were redistributed in order to correct snow water equivalent quantitatively and spatially. Therefore, we used a snow depth map derived by Pléiades stereo satellite images taken on the 9th of April 2021, near peak snow accumulation. Data gaps, e.g. due to shaded areas and very steep terrain were filled using Random Forest trained on terrain attributes, topographic indices, and energy balance parameters. Alpine3D was run at 16 m × 16 m resolution with spatially interpolated meteorological station data on an hourly base and was validated against Sentinel-2 snow cover area (SCA) maps during the melt season (May-August). Snow classification employed dual thresholds (red band reflectance and NDSI) with manual cloud masking and DEM-based shadow removal. Modelled performance was evaluated using pixel-based confusion matrices across multiple dates per year, whereof we will present preliminary results for both catchments.
This multi-catchment approach with different characteristics, but similar meteorological conditions aim to demonstrate the transferability of this snow redistribution method across different alpine environments. The results are valuable insights for improving hydrological predictions in ungauged basins with limited spatially distributed snow observations.
How to cite: Facchinetti, R., Bögl, E., Schattan, P., Knieß, J., Wetzel, K.-F., Schulz, K., and Koch, F.: Sentinel-2 Based Validation of Snow Covered Area of Alpine3D Simulations Applying Snow Depth Pattern Redistribution across Two High-Alpine Karst Catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13523, https://doi.org/10.5194/egusphere-egu26-13523, 2026.
In the European Alps, seasonal snow plays a crucial role in hydrology, functioning as a reservoir by storing precipitation during winter and releasing it during the summer. Snow is highly sensitive to climate change, particularly in low- and mid-elevation mountain regions like the European Alps. In snow-fed basins, any changes in snowmelt contribution to river discharge can significantly impact agriculture, domestic water supply, and hydro power generation.
Hydrological modeling employs a variety of models, ranging from simple lumped models to physically-based, spatially distributed models, to simulate river discharge. These models either have a simple temperature-based or a physically based snow module to simulate the snow dynamics. Distributed, physically based models can provide accurate insights into snow dynamics. However, their high input data requirement, over-parameterization, and high computational demands make them challenging to calibrate for discharge estimation for operational purposes. In contrast, simple lumped models require less input data, standard snow parameters, quick calibration, and are well-suited for operational applications, but, of course, lack spatial details.
In this study, we present an approach to improve both runoff forecasting and spatial snow pattern estimation by integrating the snow water equivalent (SWE) simulations from a physically based GEOtop model into the lumped GEOframe system. We utilize a mass-conserving Topographic Response Unit (TRU) aggregation logic to preserve the spatial variability of melt fluxes across elevation and aspect gradients. The methodology is applied in the Non Valley catchment, Italy, where water is important for agriculture, hydropower, and other uses.
Our results for the period 01-01-2017 to 15-09-2022 at hourly time step show that the GEOframe is able to simulate the discharge very well with a Kling-Gupta Efficiency (KGE) value of 0.87 and 0.72 during the calibration and validation, respectively. Substituting the internal snow module with GEOtop-derived fluxes yielded a KGE of 0.71 without further calibration. This demonstrates that the physically-based snow input successfully maintains the model’s predictive power while providing a more realistic and spatially distributed representation of snow dynamics. This coupling approach preserves the operational efficiency of lumped models while incorporating the improved physical representation and spatial variability essential for modeling mountain hydrology under a changing climate.
Acknowledgement
JMW and RR would like to thank and acknowledge the funding support from Project “SPACE IT UP! ASI Contract n.2024-5-E.0 CUP Master n. I53D24000060005” SAP fund n: 000040104905.
How to cite: Wani, J. M., Bertoldi, G., Bozzoli, M., Andreis, D., and Rigon, R.: Integrating snow-water equivalent simulated by a physically based model into a lumped model in an Alpine catchment in Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17257, https://doi.org/10.5194/egusphere-egu26-17257, 2026.
Glaciers in the Peruvian Andes play a crucial role in sustaining regional water resources for downstream populations and ecosystems, have been experiencing rapid mass loss and retreat in recent decades. The region is characterised by a tropical semi-arid climate with minimal seasonal temperature variability, alternating dry and wet seasons, and high-elevation areas frequently experience air temperatures close to 0 °C. These conditions lead to dynamic glacier energy-balance processes, in which intermittent and ephemeral snow strongly controls melt. Furthermore, these glacierized areas provide water resources to fragile downstream ecosystems, as well as subsistence and commercial agricultural systems, which themselves alter the water balance. It is challenging to reproduce the energy and water balance of such a complex environment using simplified, empirically parameterised models, and integrated, process-based modelling approaches might offer a viable way forward under a changing climate. We use a physically-based land surface modelling framework to disentangle the spatio-temporal variability of the energy and water balance of a large catchment in the Peruvian Andes.
Within this study we focus on the Rio Santa basin (4950 km2), located in the Cordillera Blanca, which contains ~330 km2 of glacier area at elevations of 4300-6300 m a.s.l. We employ the process-based land-surface model Tethys-Chloris to simulate energy and water fluxes over a 9-year period (2010-2018) for the whole catchment. We use downscaled meteorological forcing derived from a WRF climate model simulation forced by ERA5 reanalysis. Meteorological forcings are bias-corrected using observations from multiple automatic weather stations across the catchment. The model is evaluated using in-situ glacier observations, including mass balance, surface albedo, and snow-pit measurements, as well as remote-sensing products covering the catchment.
We present a comprehensive, process-based simulation of the catchment-scale water balance of the Rio Santa basin. We quantify the altitudinal distribution and the spatial, seasonal, and interannual variability of the blue-green-white water balance and its individual components across the entire catchment. We further estimate the energy- and mass-balance components of all glaciers in the Cordillera Blanca (445 glaciers) to identify hotspots of glacier changes and their controls. This allows us to determine the importance of sublimation for controlling glacier mass balance and the role of ephemeral snow in shaping melt rates. A key step forward is the catchment-wide quantification of catchment losses, where we identify the combined role of sublimation and evapotranspiration in the water balance. These results provide a novel process-based understanding of the energy and water balance of the Rio Santa basin to establish a mechanistic baseline simulations to understand future changes in the system.
How to cite: Sato, Y., Fyffe, C., Shaw, T., Varghese, V., Jouberton, A., Rodriguez, M., and Pellicciotti, F.: Process-based modelling of the energy and water balance of the Rio Santa Basin, Peruvian Andes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14610, https://doi.org/10.5194/egusphere-egu26-14610, 2026.
Seasonal snowmelt strongly influences hydrological and ecological processes by controlling the timing of peak soil moisture and subsequent vegetation growth; yet, this relationship is less studied in the Indian Himalayan region, especially in Himachal Pradesh’s snow region. This study investigates the linkage between snow disappearance timing and peak soil moisture using station data across various elevation ranges from 1571 m to 3325 m and the FLDAS dataset (daily, 0.01° resolution), and then their effect on vegetation growth using the MODIS NDVI product (8-day, 250-m resolution). We quantified the spatial and temporal variability in snow recession by fitting an exponential decay model. The recession rate varies between 0.03 and 0.27 across various elevation ranges and temporal periods. The recession rate also exhibits a strong elevation dependency, being low at higher elevations (e.g., Lari, 3325 m m.s.l., k = 0.044 m/day) and high at lower elevations (e.g., Dodra Kawar, 2522 m m.s.l., k = 0.169 m/day). Based on analysis from 2001 to 2020 across nine stations, results show that snow onset occurs in mid-December, followed by snow recession in late February and complete snow disappearance by late March across Himachal Pradesh. We observed an average lag of 3-4 days between the timing of peak soil moisture and snow disappearance, and a correlation of 0.94 (p < 0.05) was observed across various stations. Early melt does contribute to greening, as evidenced by the weaker but still positive correlation (0.54, p < 0.05) between the timing of snow disappearance and the rise in NDVI. The results show that the timing of snowmelt primarily influences soil moisture dynamics and controls vegetation activity in Himalayan catchments.
How to cite: Nanda, A., Bharti, A., and Varade, D.: Understanding the Role of Snowmelt Processes on Soil Moisture Storage and Vegetation Dynamics acrossTopographic Gradients of Himalayan Catchments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16655, https://doi.org/10.5194/egusphere-egu26-16655, 2026.
In the glacier-melt-dominated regions of Kyrgyzstan, accurate cryosphere monitoring is essential for Central Asian water resource forecasting. However, the lack of consistent in situ observations necessitates the integration of diverse remote sensing datasets and physically based models which often vary in their underlying assumptions and resolutions. This study presents a transparent, reproducible framework for the comparative evaluation of heterogeneous snow products in complex terrain, applied to SnowMapper (a NWP-driven physical model) and GlacierMapper (a MODIS-based NDSI product) for the period 2000–2024.
The framework employs spatial harmonization via nearest-neighbor resampling to a common independent grid and temporal alignment across differing calendar conventions. To address variable incompatibility, Snow Water Equivalent (SWE) outputs from SnowMapper are transformed into binary snow/no-snow classifications using literature-derived thresholds. Sensitivity analyses reveal that product agreement is significantly influenced by these methodological transformations. Evaluation using complementary spatiotemporal diagnostics such as fractional snow cover area, balanced accuracy, Cohen’s kappa and snow depletion curves (SDCs) identifies periods of systematic divergence across decadal and seasonal timescales. Results demonstrate that apparent product discrepancies arise not only from physical inconsistencies but also from methodological treatment. This standardized intercomparison approach is transferable across sensors and regions enhancing the reliability of snow-product assessments in data-scarce mountain environments.
How to cite: Akhter, J., Marti, B., Molnar, P., Caduff-Fiddes, J., and Ragettli, S.: A Methodological Framework for Harmonized Comparison of Model-Based and Satellite-Derived Snow Cover Products in Data-Sparse Mountain Regions of Kyrgyzstan. , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13824, https://doi.org/10.5194/egusphere-egu26-13824, 2026.
European Alps are essential sources of water sustaining downstream ecosystems and human activities. In this regard, they are referred to ‘the water towers of Europe’. However, these regions are also among the most sensitive to climate change. Indeed, the high rate of temperature increase, altered precipitation regimes, and “snow droughts”, i.e., a lack of snow accumulation in winter, are significantly impacting the hydrological processes in snow-dominated areas.
In this context, Snow Water Equivalent (SWE) and Actual EvapoTranspiration (AET) are two key variables for understanding the mountain water cycle. Investigating SWE and AET changes is essential to detect whether the hydrological cycle is accelerating in response to climate warming.
Gridded datasets, such as those derived from reanalysis products or satellite-based observations, have significantly enhanced the spatial representation of climatic and environmental variables in topographically complex regions, where the availability of ground-based observational data is often sparse or unevenly distributed due to logistical and environmental constraints.
This study examines the dynamics of SWE and AET over recent years across catchments of varying spatial scales in the Western Italian Alps, based on SWE data from the IT-SNOW dataset and AET data from MODIS. The analysis also includes a comparison between gridded data and in-situ measurements. The main aims are:
- evaluate the ability of gridded datasets to capture key hydrological processes at multiple spatial scales;
- identify shifts in snow regimes and evapotranspiration patterns potentially driven by climate warming;
- assess the reliability of gridded data for local-scale hydrological applications through comparison with ground-based observations.
This publication is part of the project NODES which has received funding from the MUR – M4C2 1.5 of PNRR funded by the European Union - NextGenerationEU (Grant agreement no. ECS00000036). This work was supported by the PRIN 2022 202295PFKP SUNSET Project and by Funding 2023-2025 Fondazione CRT.
How to cite: Gentile, A., Gisolo, D., Hamza, T., Olivero, A., Salis, M., Aqsa, A., Bechis, S., Ferrari, S., Canone, D., and Ferraris, S.: Recent Trends in Snow Water Equivalent and Evapotranspiration in the Western Italian Alps: Emerging Signals of Climate Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6555, https://doi.org/10.5194/egusphere-egu26-6555, 2026.
Drought has recently emerged as a common hazard in Alpine regions, where snow
dynamics strongly influence river flow regimes and play a crucial role in reservoir filling,
irrigation, tourism, and ecosystem sustainability. Reduced snow contributions and
warmer winters, which enhance rainfall at the expense of snowfall, can shift the
hydrological behaviour of Alpine catchments toward regimes typical of lower elevations.
In this study, we assess the main drivers of drought in the Adige River basin, a large
Italian Alpine basin characterized by sub-catchments spanning a wide range of
elevations over the period (1980-2018). We further investigate the seasonality and
characteristics of drought events across elevation bands to identify the drought types
with the greatest impacts in terms of total severity and duration. Our results show that
cold-season snow drought is the dominant drought type over the study period, followed
by snowmelt drought and rainfall deficit drought. Mid- and high-elevation sub-
catchments are particularly affected by cold season and snowmelt drought, whereas
low-elevation areas are mainly impacted by rainfall deficit drought. These findings
highlight the need for adaptation strategies that explicitly account for seasonal drought
processes and elevation-dependent river responses to sustain mountain water systems
under increasing drought conditions, especially given the implications for hydropower
production, irrigation, and tourism.
How to cite: Bouabdelli, S., Morlot, M., Massari, C., and Formetta, G.: From Snow to Rain: Elevation-Dependent Drought Responses and Drivers in a Large Italian Alpine Catchment., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11618, https://doi.org/10.5194/egusphere-egu26-11618, 2026.
Glaciers sustain the global water cycle and preserve natural ecosystems by acting as reservoirs that release consistent freshwater during dry periods. This meltwater supports biodiversity, nutrient balances, river streamflow modulation, and human activities such as irrigation and water supply. Currently, accelerating glacier melt has become an alarming phenomenon, with global glacier mass loss of around 5% since 2000. This contributes to sea-level rise and threatens water supplies for over 2 billion people. In this context, this study aims to assess the impacts of climate change on pristine glaciated catchments in the Apline region, where accelerating ice loss threatens spring and summer river flows that are vital for ecosystems and societies. Using the ROBIN dataset, we calculated seasonal (i.e., spring and summer) indicators of hydrologic alterations (magnitude, timing, extremes) comparing pre-climate change (1931–1950) and post-climate change (1977–2012) baselines for 20 pristine catchments with areas ranging between 22 Km2 -981 Km2.
Results in spring and summer reveal high variability in key flow indicators (including mean monthly and seasonal flows, rise and fall rates, In addition to seasonal minimum and maximum flows) especially across the six largest catchments in the study area (> 345 Km2). Comparing the post-climate change period (1977–2012) to the pre-climate change baseline, results indicated increased interquartile ranges and greater uncertainty of mean values associated with seasonal flow rates. Additionally, they showed irregular occurrences of extremes regarding their timing, frequency, and duration of high and low pulses, as well as flow reversals, for all catchments. These findings indicate increased dispersion, extremes, and instability associated with a meltwater buffer zone.
These results highlight that climate change has a strong impact on pristine Alpine glaciated catchments with limited human intervention, showing how increased glacier melting rates trigger hydrological fluctuations with pivotal implications on water resources management. This situation requires effective adaptation measures concerning increased ice melting rates.
How to cite: Ziani, C., Ribbe, L., Heinle, M., van Dongen-Köster, R., Zentis, C., Vu, T., and Bharati, L.: Assessing climate change impacts on pristine glaciated catchments in the Alpine region using indicators of hydrological alteration, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14775, https://doi.org/10.5194/egusphere-egu26-14775, 2026.
Glacier runoff in High Mountain Asia (HMA) is approaching peak water in many regions, with asynchronous timing across basins due to differences in glacier size, elevation, and climate forcing. Using the Open Global Glacier Model (OGGM v1.6.1), we simulate glacier mass balance (1940–2019), glacier dynamics and runoff (1940–2100) across 17 major basins, driven by bias-corrected GSWP–W5E5 historical forcing and an ensemble of 13 CMIP6 GCMs and four SSP scenarios. Small, low-elevation glaciers have already surpassed their peak runoff and are rapidly vanishing, whereas large, high-elevation glaciers continue to buffer downstream flows into the late 21st century particularly in glacier-rich basins such as the Indus and Tarim. HMA-wide glacier mass is projected to decline by 57–82% between 2001-2100, accompanied by an overall 10 ± 6.5% reduction in glacier runoff. Crucially, basin-scale hydrological shifts are not dictated by average glacier behavior, but by the composition and distribution of glacier classes. Clustering analysis reveals three distinct peak-runoff regimes, early, transitional, and delayed primarily controlled by glacier size, elevation, and regional climate. These staggered peak-runoff patterns highlight pronounced spatial heterogeneity in HMA’s hydrological response and underscore the urgency of basin-specific adaptation strategies in one of Earth’s most densely populated and climate-sensitive mountain regions.
How to cite: Afzal, M. M., Fuming, X., and Liu, S.: Most Asian Glaciers Will Deplete After Mid-Century: Linking Mass Loss to Peak Water Runoff, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16472, https://doi.org/10.5194/egusphere-egu26-16472, 2026.
Reliable estimates of snow water equivalent (SWE) are necessary to understand hydrological variability and snow-related extremes in mountain environments of Central Italy and the Apennines, where snowpacks are generally thin, variable, and still insufficiently observed by conventional monitoring networks. As part of broader efforts to improve how snow processes are represented in complex terrain, this work describes the recent developments using the Multiple Snow Data Assimilation System (MuSA). The primary goal of this work is to generate spatially coherent SWE estimates over Central Italy.
The modelling approach employs a physically based snow model within MuSA, driven by MORE meteorological reanalysis (MOloch-downscaled ERA5 REanalysis), which provides high-resolution atmospheric forcing at ~1.8 km over Italy spanning more than three decades from 1990 onward, enabling consistent multidecadal SWE reconstruction. This extended forcing, available at an hourly scale and with a finer spatial resolution, captures the complex orographic precipitation and temperature gradients that are critical for accurate snowpack simulation in the Apennines. Snow depth observations from Sentinel-1 (S-1) are assimilated as the primary observational input, leveraging their spatial extent and ability to detect snowpack characteristics in areas with limited ground measurements. Given that S-1 snow depth is only available from around 2015 onward, the main objective of this research is to use an observation-constrained MuSA configuration to extrapolate the SWE estimate back to 1990, producing multidecadal records.
The methodological design, data preparation, and assimilation strategy are described to ensure temporal consistency between the observation-rich and pre-observation period. Specific focus is given to basin-scale implementation, uncertainty estimation, and potential scalability to regional-scale applications. Presently, model validation and analysis of SWE are ongoing. This research establishes a concrete framework for long-term SWE estimation in Central Italy. It provides future studies with the opportunity to assess snow variability and extremes at the regional scale in a changing climate.
How to cite: Tariq, M., Alonso-González, E., Girotto, M., Avanzi, F., Rossi, M., Stocchi, P., Tuccella, P., and Massari, C.: Multidecadal snow water equivalent reconstruction in Central Italy using the Multiple Snow Data Assimilation System, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21723, https://doi.org/10.5194/egusphere-egu26-21723, 2026.
The Peruvian Andes have faced substantial glacier loss in recent decades, and as the glaciers have receded, the exposed ground has been gradually occupied by succession vegetation. Previous work assessing the impact of glacier loss on downstream hydrology has tended to assess the cryospheric change in isolation, which may not account for the impact of vegetation changes on the water balance, especially in terms of altering catchment losses. An increasing body of work has demonstrated the importance of snow and glaciers for water resources in this region, especially in the dry season, although continued warming and glacier loss is predicted to decrease these meltwater contributions. Plant growth in deglaciated regions has the potential to compound runoff decreases through increasing evapotranspiration, but few studies have attempted to quantify this. This work aims to provide the first integrated assessment of the combined impact of glacier evolution and post-glacial vegetation succession on water availability in the Peruvian Andes.
Here we quantify these changes by modelling the hydrological and ecological functioning of the Shallap catchment (13.6 km2) in the Rio Santa basin of the Peruvian Andes. We apply the mechanistic land surface model Tethys-Chloris which applies a full energy balance approach to resolving the fluxes over clean and debris-covered ice, snow and vegetation surfaces. The model is applied for a present period (2014-2025), forced by measured meteorological data, and using data from vegetation transects to parameterise the succession vegetation cover. The model is validated against ablation stakes, remotely sensed glacier mass balance, ground temperature, soil moisture and river discharge. We then simulate scenarios of climate, vegetation and glacier change to assess the separate and combined impact of glacier change and plant succession on the energy and water balance into the future. We are able to determine the impact of succession vegetation on evapotranspiration rates and water yield compared to bare soil and glacier cover, and determine the overall potential impact of glacier and vegetation change on downstream runoff. This work will provide a basis for understanding the significance of plant succession for the overall water balance in deglaciating catchments, impacting strategies for larger scale catchment and water management modelling throughout the Andes.
How to cite: Fyffe, C. L., Medina, K., Cruz, R., Loarte, E., Castro, J., Shaw, T. E., Fatichi, S., Granados, H., and Pellicciotti, F.: Understanding the combined impact of vegetation and glacier change on Andean hydrology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17903, https://doi.org/10.5194/egusphere-egu26-17903, 2026.
Water resources availability in the Central Andes of Chile largely depends on glacier melt, which is undergoing accelerated changes as a consequence of global warming. Studying the hydrological response of glacierized systems requires understanding their interaction with catchment-scale processes, which in turn demands detailed observations that are rarely available in glacierized basins. Physically based hydrological modeling offers the opportunity of representing processes at large spatial extents through the informed selection and transference of observable parameters. Here, the Cold Regions Hydrological Model (CRHM) was implemented to evaluate the feasibility of transferring parameters from the intensively monitored Glaciar Echaurren Research Basin (app. 4 km2) to two larger glacierized basins: the Yeso River basin at Termas del Plomo (RYTP, app. 60 km2) and the Mapocho River basin at los Almendros (RMLA, app. 640 km2).
Simulations were performed using both locally calibrated parameters and parameters transferred from the experimental basin, and model performance was evaluated in terms of streamflow, fractional snow-covered area (fSCA), and snow water equivalent (SWE). Full transfer of the calibrated parameters from the small intensive study catchment to the larger RMLA basin resulted in reductions of up to 71% in streamflow KGE and 97% in SWE NSE compared to the basin’s own calibration. In the intermediate RYTP basin, the transfer of snow-related parameters adequately reproduced the seasonal pattern of SWE, although with a −59.2% bias and a 70.6% decrease in streamflow KGE relative to the calibrated version.
Individual parameter transfer revealed that snow-related parameters, such as snow roughness length and active layer thickness, explain a large fraction of the loss in SWE performance. On the other hand, the degradation in streamflow performance was dominated by parameters associated with surface storage processes. Overall, the results indicate that parameter transferability is only partially viable: while some parameters can be generalized across basins with similar characteristics, highly sensitive and locally dependent parameters require site-specific calibration to preserve model representativeness.
How to cite: McPhee, J., Villagra, N., Mendoza, P., and Courard, M.: Glacio-hydrological modeling informed by observations from a research basin in the Central Andes of Chile, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19867, https://doi.org/10.5194/egusphere-egu26-19867, 2026.
The Alps, often referred to as Europe's water tower, are undergoing profound changes as a result of climate change. Declining snow cover, accelerated glacier retreat, and increasingly severe periods of low flow raise urgent questions for water resource management, biodiversity, and energy security. Hydropower, one of the pillars of the European renewable energy strategy, is particularly exposed to these changes. To understand and anticipate these impacts, it is necessary to have detailed modeling of Alpine hydrological systems, combined with reliable climate projections.
To meet these challenges, the spatially distributed hydrological model MORDOR-TS (Garavaglia et al., 2017; Rouhier et al., 2017) now includes an explicit glacier component to simulate glacier dynamics in warming scenarios (Rouzies et al., 2024). Applied to the Isère basin in the French Alps, this model supports strategic decisions relating to hydroelectric exploitation by simulating hydrological responses at the basin scale under different future climate scenarios (Le Lay et al., 2022). However, glaciers remain relatively poorly instrumented today, and their ice volumes are often poorly known, making such modeling inevitably imprecise. In this context, local observations and modeling produced by glaciologists are valuable for better quantifying the relative contribution of glaciers to river flows and improving the robustness of hydrological projections.
This study focuses on the modeling of a representative alpine glacier in the Vanoise massif, which feeds the Doron des Allues River. On this glacier, combining historical glaciological monitoring with radar-based surveys of current geometry and ice volume has enabled a detailed modelling of the glacier geometry, its evolution and meltwater discharge throughout the 21st century. These data are used to refine glacier representation in the MORDOR-TS hydrological model —surface area, volume, and meltwater flows—improving the reliability of basin-scale hydrological simulations, distinguishing between precipitation-driven and glacier-driven contributions. Results confirm strong consistency between the local glaciological model and the regional hydrological model and highlight pathways for further parameterization improvements.
The glacier is projected to almost completely disappear by 2100, with cascading impacts on discharge regimes. Beyond reduced mean flows, significant shifts in seasonal patterns and diminished summer flows are expected—posing challenges for hydropower production, ecosystem resilience, and water allocation. These results highlight the importance of coupling regional hydrological models with high-resolution glaciological data to improve the robustness of climate impact assessments in mountain regions.
These findings underscore the urgency of adaptation strategies for mountain water resources in a warming climate. They also illustrate the value of coupling large-scale hydrological models with high-resolution glaciological data to support energy planning and climate resilience across Europe’s alpine regions.
How to cite: Le Lay, M., Gilbert, A., Pinte, K., Jouet, C., Laarman, O., and Six, D.: Past and Future Evolution of the Gébroulaz Glacier. Modelling the impact on the Hydrology of the Doron des Allues in the Vanoise Massif, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18889, https://doi.org/10.5194/egusphere-egu26-18889, 2026.
The catchment storage-discharge characteristics (CSDC) are usually the highly sensitive parameters in hydrology model and fundamentally decide the baseflow simulation performance. With dramatic climate change, several recent studies had found significant trend of the power-law parameter of CSDC (b0) in cold region. However, studies about the temporal variability of b0 and its driving mechanism in cold region are less consistence because of differences in study area and time scale. In this study, the b0 was firstly calculated from daily recession event in 315 cold catchments, after which the time-varying rule of b0 and its driving mechanism was investigated at events, warm period and decades scales. The results show that the set of calculated b0 have a median of 2.1 around all study catchments and are great different between flow recession events in a specific catchment with the median of its variance in all study catchments is equal to 2.3. Moreover, the b0 increased within warm period in most (78%) cold catchments and had also an increase on the decades scale in 63% cold catchments, stating a significant time-varying characteristic. Correlation analysis presents that permafrost extent degradation, increases in both precipitation (P) and terrestrial water storage (TWS) play the positive roles in b0, while increasing PET play the negative role on the contrary. On the events scale, potential evaporation (PET) is the main control of the b0, followed by the permafrost extent, while P and TWS take a slightly positive effect. During the warm period, permafrost thawing overtakes PET as the main control of the b0, followed by the PET, and the effect of P and TWS can be not negligible. On the decade scale, climate change (i.e, climate warming and wetting) caused permafrost degradation and increases in both P and TWS, which has further increased the b0. These results are of great significance for improving the understanding of the catchment storage-discharge process, and highlight that traditional hydrology modelling with constant CSDC could result in systematic bias in baseflow simulation and prediction in cold region.
How to cite: Xu, Z., Zhou, Y., and Cheng, L.: Temporal variability of catchment storage-discharge characteristics and their driving mechanisms in cold region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2074, https://doi.org/10.5194/egusphere-egu26-2074, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot A
EGU26-2612 | ECS | Posters virtual | VPS9
Evaluating a Long Short-Term Memory (LSTM) approach for Snow Water Equivalent (SWE) downscaling and hydrological modeling in mountainous terrainWed, 06 May, 15:18–15:21 (CEST) vPoster spot A
In the context of the global hydrological cycle, runoff generated from snowmelt plays a key role in water availability, particularly in cold and mountainous regions. In many parts of Canada and the western United States, mountain snowpacks act as natural reservoirs by storing precipitation during cold seasons and releasing it during spring and summer. Accurate estimation of snow water equivalent (SWE) is therefore essential for hydropower reservoir operation, snow-related hazard assessment, and hydrological modeling. However, the coarse spatial resolution of widely available SWE products in northern latitudes, combined with complex mountain topography, introduces substantial uncertainty in their direct application to hydrological models. High-resolution SWE mapping remains a major challenge in these environments. In this study, we propose a multifactor SWE downscaling framework based on a Long Short-Term Memory (LSTM) deep learning approach, applied to the Nechako River watershed in British Columbia, Canada. The framework uses ERA5-Land SWE at 10 km resolution as the target variable, with predictor variables including precipitation, minimum and maximum temperature, solar radiation, and 2-m dewpoint temperature, together with static physiographic information such as elevation and land cover. Daily data from 1981 to 2024 are considered. The model is trained and evaluated at the 10 km resolution before being applied to generate SWE at 5 km resolution, corresponding to the spatial resolution of the CEQUEAU hydrological model. The downscaled SWE fields are designed to retain the large-scale snow patterns provided by ERA5-Land, while adding more spatial detail based on local elevation and land cover. Current work focuses on incorporating these downscaled SWE estimates into the CEQUEAU hydrological model and comparing the resulting runoff simulations with those obtained using CEQUEAU’s internal SWE representation. Rather than aiming to demonstrate clear improvements at this stage, the goal is to better understand how different SWE inputs influence the simulated hydrological response. Preliminary results suggest that LSTM-based downscaling offers a flexible and promising way to generate intermediate-resolution SWE fields in mountainous regions. This approach shows potential as a practical link between coarse-resolution reanalysis products and distributed hydrological models used for water resources and hydropower studies.
How to cite: moghadam, S. H., Arsenault, R., St-Hilaire, A., and Talbot, F.: Evaluating a Long Short-Term Memory (LSTM) approach for Snow Water Equivalent (SWE) downscaling and hydrological modeling in mountainous terrain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2612, https://doi.org/10.5194/egusphere-egu26-2612, 2026.
