Megadroughts are multi-year precipitation deficits that cause severe hydrological, ecological, agricultural or socioeconomic droughts, and they are increasing world-wide in duration, severity and extension. The Chilean Megadrought is among the most severe, persistent and extensive droughts on record in South America (from 2010 to present), and offers an ideal study case to understand the importance of glaciers during periods of water stress. Here, we simulate the response of glaciers in the Central Andes of Chile and Argentina to both the ongoing Chilean Megadrought and to megadroughts projected to occur by the end of the century under climate change scenarios.

We use the TOPKAPI-ETH glacio-hydrological model to simulate the evolution, mass balance and runoff of the 100 largest glaciers in the Southern Andes between 30°S and 40°S, representing a total of 78 km³ of ice volume (63% of the total glacier volume in the region). TOPKAPI-ETH is a spatially distributed physically-based model with parameterisations of mass redistribution due to ice flow, avalanching and ice melt under debris, as well as snow albedo decay and distributed ice albedo, which are key elements to represent the impact of snowfall reduction on surface melt. We run the model at high horizontal (100 m) and temporal (3-hour) resolutions forced by gridded meteorological data. Parameters are calibrated and evaluated for each selected glacier using geodetic mass balance and surface albedo for the period 2000-2019. The model is then used to simulate the period 2000-2099 using outputs from four Global Climate Models (GCM) under a moderate (RCP2.6) and a high (RCP8.5) future greenhouse gas (GHG) emission scenario. End-of-century megadroughts are defined as the driest 10-year period during 2075-2100 for each GCM and RCP. We use the decade 2000-2009 as a reference period, since it has been identified as a period of near-neutral glacier mass balance in the study area. The Chilean megadrought caused a precipitation deficit of −36±11% across glaciers, but total glacier runoff (sum of snowmelt, ice melt and rainfall) during 2010-2019 remained nearly unchanged (decrease of −2%) compared to the 2000-2009 reference period. These small changes were due to a 5% loss in total glacier volume that resulted in a 120% increase in total ice melt. In contrast to the relatively small changes in glacier runoff during 2010-2019, glacier runoff is projected to decrease significantly during end-of-century megadroughts compared to the reference period (2000-2009): by −10±4% under RCP2.6 and by −21±11% under RCP8.5 on an annual basis, and by −35±6% and −50±6% during summer. 

Our results demonstrate that ongoing glacier retreat reduces glaciers’ fundamental capacity to buffer precipitation deficits during extreme droughts, increasing water scarcity for ecosystems and livelihoods in the mountain regions of South America. Crucially, the future megadroughts will occur under substantially warmer conditions than the current megadrought, likely increasing water demand of downstream areas. 

How to cite: Ayala, Á., Muñoz-Castro, E., Farinotti, D., Farías-Barahona, D., Mendoza, P., MacDonell, S., McPhee, J., Vargas, X., and Pellicciotti, F.: Glaciers and their declining role in buffering current and future megadroughts in the Southern Andes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18065, https://doi.org/10.5194/egusphere-egu25-18065, 2025.

The Alps are impacted by dramatic changes in the context of global warming with large implications for hydrology. The Rhône bassin, draining a large part of the french and Swiss Alps, has already been the subject of hydrological modelling using J2000-Rhone. In this study, we present the integration of a glacier algorithm in the hydrological model J2000-Rhône, the validation of snowmelt, icemelt and streamflow, and the future projections of these processes. The results show that snowmelt, icemelt and streamflow are satisfactorly simulated by J2000-glaciers in the Rhone basin. By the end of the 21st century, the major changes will be a large increase of streamflow in winter but a decrease in summer associated to earlier snowmelt, a decrease of precipitation and glacier shrinkage. On the Arve and upper Rhône catchments, the remaining glaciers will still be crucial to sustain the streamflow in dry summers.

How to cite: Champagne, O., Lemoine, A., Gouttevin, I., Cauvy-Fraunié, S., Condom, T., Delaygue, G., and Branger, F.: Glacier melt contribution to future streamflow in the Rhône bassin (France), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11032, https://doi.org/10.5194/egusphere-egu25-11032, 2025.

Glacier meltwater is critical in sustaining streamflow during low-flow periods in mountainous regions. Nevertheless, glacier melt is often poorly or statically assessed in hydrological simulations, leading to partial considerations for effectively managing water, especially during droughts.

This study evaluates the contribution of glacier melt to summer flows and its capacity to mitigate hydrological droughts in the upper Adige River basin, located in the Italian Alps (6900 km²). To achieve this, a new dynamic glacier module was implemented into the ICHYMOD-TOPMELT hydrological model, enabling annual updates of glacier area and improved quantification of meltwater contributions under progressive glacier retreat (from 122 km² in 1997 to 84 km² in 2017).

The hydrological model exhibited robust performance metrics, with Kling-Gupta Efficiency (KGE) values of 0.82 for the overall study period and 0.65 for summer low-flow months. The dynamic glacier module accurately captured observed glacier area, mass balance, and seasonal melt trends. Validation against ASTER-derived glacier mass balance data for 2000–2019 revealed an error of 11%, underscoring the model’s ability to effectively represent long-term glacier dynamics.

Results indicate that glacier melt contributed an average of 5.86% to summer streamflow during the period 1997–2019, with significant spatial variability. In drier, more glacierized subbasins, melt contributions reached up to 30–40%, highlighting its importance in maintaining streamflow where precipitation is scarce. We analyzed also the interplay between snow water equivalent (SWE), temperature, and glacier melt during droughts, with SWE acting as a buffer to delay summer glacier melt under cooler conditions.

Severe drought years (like 2003, 2005, and 2022) demonstrated considerable variability in glacier melt contributions. In 2003, high temperatures and limited SWE led to glacier melt accounting for 14.4% of summer flows. By contrast, colder temperatures in 2005 reduced contributions to 6.3%. In 2022, while high temperatures drove glacier melt, reduced glacier areas led to lower absolute contributions (8.2%) compared to earlier droughts. If we had the same glacier area in 2022 like in 1997, glacier contribution could have been up to 14.6 %.

Findings highlight the declining capacity of glacier melt to buffer against hydrological droughts due to ongoing glacier retreat. With shrinking glaciers, future summer flows in the Adige River basin are expected to become increasingly dependent on precipitation and snowmelt, thereby heightening the vulnerability of water resources to climate variability.

Additionally, simulations showed that models using a static glacier area tend to overestimate glacier melt contributions, emphasizing the necessity of integrating a dynamic glacier modeling framework in hydrological models. These frameworks are crucial for accurately projecting future water availability and informing adaptive water resource management strategies in glacier-fed catchments.

How to cite: Bertoldi, G., Shrestha, S., Terzi, S., Zoccatelli, D., Zaramella, M., Borga, M., Callegari, M., Galletti, A., and Dinale, R.: Evaluating Glacier Melt's Role in Mitigating Hydrological Droughts in Mountainous Regions: Insights from the Adige River Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18668, https://doi.org/10.5194/egusphere-egu25-18668, 2025.

Ongoing glacier retreat is causing the loss of a critical water resource in mountain regions, with wide-ranging downstream impacts. These include shifts in streamflow seasonality, change in water availability, and changes to low-flow conditions, either exacerbating or alleviating them. To date, most hydrological impact studies have relied on model simulations for specific regions or catchments, often driven by future climate change scenarios. However, evidence on the hydrological impact of glacier retreat based on direct observational data is scarce due to the limited accessibility of in-situ data. To address this, we have assembled a comprehensive dataset of streamflow observations from approximately 600 glacierized catchments (10–1000 km²) around the world. By integrating this dataset with geodetic estimates of glacier mass change for each individual glacier globally, we quantify the contribution of net glacier mass loss to streamflow across diverse mountain regions. Our study identifies where decadal glacier mass losses (2000–2010 and 2010–2019) align with observed streamflow trends in both magnitude and direction, and where other hydrological processes are more dominant. Streamflow trends and variations are analyzed both at an annual and seasonal scale with a specific focus on hydrograph characteristics such as high flows, low flows, and the melt season. Our results highlight the spatial heterogeneity of glacier retreat impacts across mountain regions and their downstream implications.

How to cite: Van Tiel, M., Steiner, J., Huss, M., Immerzeel, W., Aguayo, R., Andermann, C., Mager, S., Nepal, S., Pohl, E., Rets, E., V Schuler, T., Stahl, K., van Tricht, L., Yao, T., and Farinotti, D.: Linking observed glacier mass losses and streamflow trends globally, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17124, https://doi.org/10.5194/egusphere-egu25-17124, 2025.

In our study, we focused on the Panj River basin, located in the Eastern Pamirs, a region characterized by extreme climatic conditions, low air temperatures, minimal precipitation, extensive permafrost, seasonal snow cover and graciarization. The Panj River, one of Tajikistan's primary rivers, is a left tributary of the Amu Darya, formed by the confluence of the Vakhandarya and Pamir rivers. Its estuary lies in southeastern Tajikistan.

In Panj river, the seasonal snow reserves significantly influence the timing of snow and glacier melt and determines water availability in the region. In years with minimal snow accumulation, snow cover melts out by early July, with glacier melt beginning shortly thereafter. In contrast, during average snowy years, snow cover melts in the latter half of July, followed by glacial melt approximately 10 days later. During dry winters and low-water years, glacial runoff partially compensates for reduced river flow during the flood season.

To study the runoff formation and temporal contribution to Gunt River, we employed both observational methods (e.g., topographic maps and catalogs) as well as digital techniques using remote sensing data from Landsat and MODIS satellite programs. The MODSNOW-Tool program, which analyzes MODIS snow cover area data and can be used for hydrological forecasting purposes, was used in determining snow cover melt and the onset of glacier melt dates. This enabled precise calculations of snow and glacial runoff in the Gunt River Basin. Additionally, MODIS snow cover data was utilized to forecast water availability during the flood season, providing critical early warnings to water management organizations and emergency services across Central Asia and beyond.

With this presentation we would like to demonstrate achieved results and potentially disseminate scientific outcome to be used by other research communities or decision makers. 

How to cite: Niyazov, J., Gafurov, A., and Gafurov, A.: Glaciation, snow cover and runoff formation in the Gunt River basin analyzed using the remote sensing data. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15080, https://doi.org/10.5194/egusphere-egu25-15080, 2025.

Climate change significantly impacts snow dynamics, thereby affecting water resources, especially in countries like Türkiye, where snow is crucial for water supply. This study focuses on one of the largest Mediterrenain river basins, the Seyhan Basin (21,890 km²), contributing significantly to Türkiye's overall water resources. The basin's extensive agricultural activities and significant hydropower potential necessitate sustainable water management for its long-term sustainability. The basin's complex mountainous topography and its location at the intersection of Mediterranean and continental climate zones, combined with limited data availability, present significant challenges for hydrological modeling. These factors make the Seyhan Basin an ideal region for analyzing changes in snow potential and related water resources.

This study aims to refine spatial snow characterization in this complex Mediterranean basin through comprehensive snow modeling using multi-source data and assimilation. The spatial and temporal accuracy and reliability of Snow Multidata Mapping and Modeling (S3M) model outputs for snow-water equivalent, snow depth, and snow-covered area are assessed. The main S3M model inputs, derived from ERA5-Land, include hourly temperature, relative humidity, shortwave radiation, and precipitation data from 2012 to 2022. Model inputs of temperature and precipitation are validated against observations from 12 meteorological stations within and around the basin. The S3M data assimilation framework improves model estimates by incorporating satellite snow cover area data (Eumetsat H SAF products). Additionally, snow-covered area estimates are compared to MODIS, IMS, and ERA5-Land datasets, while snow-water equivalent measurements from five in situ stations, ERA5-Land and H SAF datasets provide independent validation for SWE outputs. The performance of daily aggregated model results is evaluated using different metrics as RMSE, KGE, and NSE, besides spatial performance analysis as false alarm rate and hit scores for the whole period. The results indicate that NSE performance is 0.90-0.95 for SCA, RMSE is 5-30 mm for SWE and the false alarm rate is calculated as 0.15-0.35 for SCA.

How to cite: Sakallı, M., Şorman, A., Avanzi, F., Gabellani, S., and Şensoy, A.: A Comprehensive Snow Modeling Using Multi-Source Data and Assimilation for a Refined Characterization of a Complex Mediterranean Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-819, https://doi.org/10.5194/egusphere-egu25-819, 2025.

Accurate estimation of spatiotemporal snowpack is crucial for understanding the hydrological processes associated with snowmelt in mountainous regions. Incorporating in-situ and remote sensing observations into physics-based snowpack models through data assimilation techniques can mitigate model uncertainties and improve estimates of snow water equivalent (SWE). However, implementing data assimilation techniques over a large spatial domain remains challenging, due to the sparse and uneven availability of observations across varying spatial and temporal scales. Remote sensing data are also constrained by gaps caused by revisit intervals, cloud cover, and complex topography in mountains. Therefore, this study proposes an adaptive snow data assimilation framework with satellite remote sensing data utilizing the ensemble Kalman filter (EnKF). The adaptive EnKF assimilates daily sparse high-resolution, remote-sensed snow cover data into the snow model of the distributed wflow_sbm hydrological model, applied to the Rhône River basin—a region in France heavily dependent on snow and glacier meltwater for runoff across multiple scales. Using this adaptive EnKF, we simulate spatiotemporal SWE and river runoff in the Rhône River basin from 2016 to 2019. Results demonstrate that snow data assimilation significantly improves streamflow predictions in both spatial and temporal dimensions. Compared to the simulations without assimilation, our model indicates a spatial decrease in snowmelt runoff during winter (October to March) and a spatial increase during the melt season (April to June). These results demonstrate that adaptive data assimilation not only effectively integrates high-resolution satellite data with hydrological models but also enhances the representation of snowmelt processes, leading to more accurate forecasts of river runoff. This approach paves the way for developing snow reanalysis and forecasting tools, seamlessly integrating sparse information from high-resolution satellite observations into physics-based models, offering valuable insights for water resource management in basins governed by snowmelt-driven hydrological processes.

How to cite: Cheng, M., Vossepoel, F. C., Lhermitte, S., and Hut, R.: Adaptive assimilation of spatiotemporal sparse satellite-derived snow cover data into hydrological modelling in the Rhône River basin, France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6433, https://doi.org/10.5194/egusphere-egu25-6433, 2025.

While the importance of dynamic precipitation phase partitioning to get accurate estimates of rain versus snow amounts has been established, hydrology models rely on simplistic static temperature-based partitioning. We evaluate changes in model bias for a suite of snow and streamflow metrics between static and dynamic partitioning. We used the VIC-CropSyst coupled crop hydrology model and performed a comprehensive evaluation using 164 snow telemetry observations across the Pacific Northwest (1997-2015).  We found that transition to the dynamic method resulted in a better match between modeled and observed (a) peak snow water equivalent (SWE) magnitude and timing (~50% mean bias reduction), (b) daily SWE in winter months (reduction of relative bias from -30% to -4%), and (c) snow-start dates (mean reduction in bias from 7 days to 0 days) for a majority of the observational snow telemetry stations considered (depending on the metric, 75% to 88% of stations showed improvements). We also find improvements in estimates of basin-level streamflow and peak SWE over streamflow. However, there was a degradation in bias for snow-off dates, likely because errors in modeled snowmelt dynamics—which cannot be resolved by changing the precipitation partitioning—become important at the end of the cold season.  These results emphasize that the hydrological modeling community should transition to incorporating dynamic precipitation partitioning so we can better understand model behavior, improve model accuracies, better support management decision support for water resources, and prioritize improvements in melt dynamics to improve timing simulations.

How to cite: Singh, B., Liu, M., Abatzoglou, J., Adam, J., and Rajagopalan, K.: Dynamic precipitation phase partitioning reduces model bias for some snow and streamflow metrics across the Northwest US , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7604, https://doi.org/10.5194/egusphere-egu25-7604, 2025.

Accurate Snow Water Equivalent (SWE) data is essential for hydrological modelling, flood forecasting, estimating terrestrial water storage, and understanding climate change impacts on water systems. The high horizontal and vertical heterogeneity of snowfall, snow accumulation and snowmelt restrict the usage of ground-based SWE observations for region-scale estimations. Climate reanalysis products like ERA5-Land provide SWE estimates globally but are often unable to capture local snow processes due to their limited spatial resolution, especially in mountain areas with heterogeneous topography.

To address these limitations, this study presents a Random Forest Regression (RFR)-based approach to downscale ERA5-Land SWE data to a finer spatial resolution using open-source global datasets and in situ SWE measurements. The RFR model was trained on a dataset of SWE observations at 383 snow weather stations between 1999 and 2019. Predictor datasets included climate reanalysis of ERA5-Land SWE and DEM-derived topographical covariates. The SWE downscaling methodology was trained and validated for the Upper Danube River Basin and its applicability in hydrological models is investigated in two case studies in Alpine Europe: CWatM model simulations for the Upper Danube and PCR-GLOBWB simulations for the Rhine-Meuse Basin.

ERA5-Land significantly overestimated SWE with a PBIAS of 444% at snow weather station locations in the Upper Danube River Basin. Applying the downscaling approach significantly reduced this bias to -11%. Downscaled SWE strongly correlated with the observations with an R² of 0.81 and an RMSE of 17.87 mm for the Upper Danube. The downscaled SWE showed improved temporal dynamics of snow accumulation and melt, and enhanced spatial distribution. These initial results highlight the potential of the RFR downscaling approach for improving snowmelt runoff calibration in the two case studies. In the Rhine-Meuse study, we validate the applicability of the RFR model to regions outside the training domain. The open-source and easily accessible nature of the predictor datasets ensures accessibility and adaptability across diverse landscapes.

How to cite: Schults, T., Simons, G., van Etten, J., Lutz, A., Catania, C., Burek, P., Steyaert, J., and Wanders, N.: Spatial downscaling of snow water equivalent estimates for hydrological applications in Alpine Europe using machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8286, https://doi.org/10.5194/egusphere-egu25-8286, 2025.

Numerous studies in the western United States have shown that atmospheric rivers (AR) have been responsible for flood-prone rain-on-snow (ROS) conditions such as intense rainfall, rapid warming of the air, and important snowmelt. Intense AR events were also shown to affect snowpack depth on a seasonal scale. The documentation of such impacts is less extensive in other areas, such as the east coast of North America. Meanwhile, climate projections indicate that the intensity and frequency of extreme events associated with atmospheric rivers will increase for the region, leading to an elevated hydrological impact caused by AR. This study focuses on the Côte-Nord region in Quebec (Canada), which experiences important yearly snow accumulation (>300 mm) and where snowpack monitoring is crucial due to the high hydroelectricity production in the area. The impacts of AR are analyzed by combining hydrometeorological observations with automated snow water equivalent measurements at 42 sites for the 2012–2021 period, as well as atmospheric river intensity scales derived from reanalysis. The ROS events were separated between those accompanied by AR and those that were not, resulting in 149 AR and 58 non-AR events. The intensity of the events was represented by the generated water available for runoff (WAR), which combines net rainfall and snowmelt. A seasonal analysis revealed that early winter was characterized by a high frequency of AR-associated events (36), exhibiting the greatest yearly frequency of high-scale AR events. The median WAR for AR events during this period was 36 mm, with rainfall predominating. In instances of extreme precipitation, WAR was significantly amplified by snowmelt, resulting from the rapid warming of shallow snowpacks. In late winter, there was a more balanced distribution of non-AR (54) and AR (74) events, which were characterized by generally lower intensity scales. This resulted in lower median WAR of, respectively, 30 mm and 19 mm for AR and non-AR events. However, the contribution of snowmelt during these events closely resembled that of rainfall, due to the generally warmer temperatures and the presence of lower-scale AR. The seasonal behaviour of the ROS events suggests a precipitation phase sensibility for WAR generation and variability in energy balance components. The sensitivity to precipitation phase is expected to vary between early and late winter, due to their distinct WAR compositions. Similarly, the event energy balance is bound to differ between early and late winter due to the contrasting conditions provided by low- and high-scale AR. This study underscores the distinctions between early and late winter ROS events and the necessity of accounting for the effects of atmospheric rivers on snowpack dynamics. Additionally, the findings outline considerations for snowpack modelling to more accurately represent extreme weather events projected to increase in frequency in the future.

How to cite: Bédard-Therrien, A., Anctil, F., and Nadeau, D.: Investigating the seasonal influence of atmospheric rivers on runoff generation during rain-on-snow, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12459, https://doi.org/10.5194/egusphere-egu25-12459, 2025.

Climate change significantly impacts global water resources, particularly in snow-fed mountainous basins where reservoir operations and hydropower generation are crucial. This study investigates future snowmelt runoff, water resource management, and hydropower production under climate change scenarios for two headwater basins in Türkiye: Peterek on the Çoruh River in the Eastern Black Sea region and Göksu on the Seyhan River in the Mediterranean region. These basins were selected for their similar topographies but distinct climatic conditions, representing regions predicted to experience varying climate change impacts.

An ensemble of five Global Climate Models (GCMs) from the CORDEX-Europe database, adjusted for local bias corrections, was employed under Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 scenarios, along with Global Warming Levels (GWL) from the Paris Climate Agreement. The HBV-Light model simulated future reservoir inflows (2020–2099), revealing significant reductions in snow accumulation and inflows due to rising temperatures and altered precipitation patterns. Reservoir operations and hydropower generation projections were conducted using the Water Resources Assessment and Planning (WEAP) model, predicting a 4–9% reduction in hydropower generation for the Çoruh Basin and a 10–35% reduction for the Seyhan Basin over the final decades (2076–2099).

To adapt to these changes, four alternative management strategies were evaluated to optimize reservoir operations under climate challenges. This study emphasizes the importance of comprehensive scientific assessments for policymakers in understudied, snow-dominated transboundary river basins, particularly those with significant energy production potential. The findings contribute to improved water and energy management by providing critical insights into climate-driven changes in reservoir storage, flow patterns, and hydropower generation.

How to cite: Sensoy, A., Doğan, Y. O., Uysal, G., and Şorman, A. A.: Future of Snowmelt Hydrology and Hydropower: A Case Study of Türkiye’s Mountainous Basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16691, https://doi.org/10.5194/egusphere-egu25-16691, 2025.

Mid-elevation alpine regions are currently undergoing profound changes, with snow cover regimes shifting from seasonal to ephemeral. At the same time, forests around the world are also undergoing large changes due both natural and human-induced disturbances. Quantifying the impact of these environmental changes on seasonal snow requires physics based models that incorporate the relevant processes, as well as sufficiently detailed datasets of forest structure.

In the last decade, a new generation of snow models have been developed that explicitly represent interactions between forests, snow and meteorology. These models build on airborne lidar data  incorporating the effect of individual tree crowns on radiation transfer and snow interception processes, replacing the use of the leaf-area index and the “big-leaf” approach. However, these new models rely on airborne lidar data with limited spatial extents defined by arbitrary boundaries such as state, municipal and/or isolated hydrological catchments. Large spatial scale and global forest snow modelling is therefore still reliant on the “big-leaf” approach, which is known to have limited performance especially in heterogeneous forest environments. 

This study presents a modelling chain to predict seasonal snow accumulation and ablation in forests based on satellite forest products and global climate forcings (ERA5) applied across the European Alps as a first large-scale use case. The motivation to develop this modelling chain is to facilitate modelling forest snow processes across large spatial scales, especially in previously unstudied remote forested regions around the globe.

The model chain begins with a 10m canopy height model (CHM) derived from Sentinel-2 imagery. The CHM is used with Copernicus Land Monitoring Service forest products as input to the Canopy Radiation Model (CanRad) to calculate the forest structure and radiation transfer input variables for the Flexible Snow Model (FSM2). Forest variables are calculated at 25m sub-grid scale and averaged to run FSM2 at 250 m resolution over the European Alps. The ERA5 meteorological input for FSM2 are downscaled and aggregated at the hillslope scale using the climate downscaling toolkit TopoPyScale (TPS). Throughout the modelling chain, the model outputs are validated using airborne lidar data of both forest structure and snow cover in both the French and Swiss Alps. 

This model chain overcomes large-scale forest snow modelling challenges with 1) an explicit description of snow-canopy interactions, 2) a method compensating for the lack of a global canopy dataset, and 3) reduced computational cost of running large scale simulations. The main advantage of this approach is the ease of use and availability to run over much smaller domains as well as its relevance for global applications in fields such as permafrost, snow and hydrological research.

How to cite: Webster, C., Filhol, S., Mazzotti, G., Rüetschi, M., Queno, L., Fiddes, J., Jonas, T., and Ginzler, C.: High-resolution and large scale modelling of seasonal snow in forests over the European Alps, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18909, https://doi.org/10.5194/egusphere-egu25-18909, 2025.

Switzerland is covered 30% by forests, a considerable part of which receives snow in winter. Accurate information about where, when, and how much snow is stored across Swiss forests remains scarce due to the extensive area, complex terrain, and intricate forest snow processes leading to substantial spatiotemporal variability across scales. The Swiss Operational Snow-Hydrological Service (OSHD) runs a physics-based model system that includes detailed forest snow routines, providing daily nationwide snow distribution and snow melt grids at 250m resolution. While these routines have been validated on multiple occasions and at various research sites within and outside forests, the forest simulations have not yet been evaluated over large areas. As a first step, we therefore validated the model results against remotely sensed snow cover information from 3m Planet Labs RGB imagery. The evaluation revealed a very good match throughout the winter season, across regions and years, and within aspect classes, expressed by an overall mean absolute error in snow cover fraction of only 0.14. These results motivated us to analyze a multi-year dataset from the OSHD model system with regards to the relevance of forest snow for the hydrology of Switzerland. In the period investigated, hydrological years 2017-2024, Swiss forests stored, on average, a fifth of the total snow water equivalent during peak SWE. Yet, if hypothetically all forests in Switzerland were removed or lost, this would increase SWE storage by approximately 5% only. However, this does not render the impact of forests on snow water resources irrelevant. At smaller spatial scales and between years, the differences can be considerable for both the amount and timing of snowmelt runoff. In the Swiss Alps, on average, snow remains longer on the ground in the open, reaching its maximum storage later in winter and having significantly higher ablation rates than in adjacent forests. However, aspect matters as snow in south-facing slopes often deviates from the above with prolonged snow cover durations in the forest due to later melt-out. In summary, this study provides a detailed view of the effects of forests on snow water resources and quantifies how these differ with region, topography, season, and between years.

How to cite: Haagmans, V., Mazzotti, G., Molnar, P., and Jonas, T.: A multi-year analysis of forest snow and its contribution to the water cycle of Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19532, https://doi.org/10.5194/egusphere-egu25-19532, 2025.

Streamflow generation in cryosphere-dominated environments results from the complex interplay of precipitation accumulation and release processes across spatial and temporal scales. While the general streamflow dynamics of such environments are very well understood and relatively easy to simulate, the actual underlying storage-release processes are more difficult to reliably represent in models than what is currently thought.  Using stable isotopes of water to trace hydrological flow paths, estimate water age or attribute streamflow sources has become standard in hydrological process research. The isotopic ratios of oxygen or hydrogen in rainfall and snowfall commonly show substantial differences in alpine environments. Accordingly, it is tempting to think that they represent an ideal tracer to quantify the hydrologic partitioning at various time scales (from the event scale to the seasonal scale) and across a range of processes (ice melt, snow melt, rain-on-snow, infiltration, groundwater recharge, vegetation water uptake, baseflow generation). In this contribution, we discuss the potential of isotope analyses for cryosphere-dominated catchments regarding process research and modeling, what essential insights we can derive from stable isotopes of water for downstream water resources management under a changing climate, and we provide recommendations for future sampling campaigns.  

How to cite: Schaefli, B., Ceperley, N., Fan, X., and Müller, T.: The potential of stable isotopes of water for process analysis and modeling in cryosphere-dominated environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12766, https://doi.org/10.5194/egusphere-egu25-12766, 2025.

This study examines the formation, evolution, and hydrological role of ice layers in snowpacks during dynamic winter conditions, with a focus on liquid water infiltration, moisture redistribution, and structural transformations. The research was conducted at the Sainte-Marthe Experimental Watershed (BVE), located 70 km west of Montreal, Quebec, Canada. From February 8 to April 3, 2023, the study captured 50 freeze-thaw cycles and 7 substantial rain-on-snow (ROS) events, which significantly influenced snowpack properties and hydrological behavior.

 A downward-looking Ground Penetrating Radar (GPR) system was used to provide high-resolution data on snowpack stratigraphy and changes in dielectric properties. Complementary observations, including ultrasonic snow depth sensors, Time-Domain Reflectometry (TDR) probes, and weekly snow pit measurements, supported the GPR interpretations. These data were further contextualized with energy balance analyses to link external meteorological drivers—such as radiative fluxes and precipitation inputs—to internal snowpack processes.

 The results highlight the critical role of ice layers as dynamic hydrological barriers. During a significant ROS event, March 17, the GPR captured a rapid increase in two-way travel time (TWT) and amplitude changes as liquid water accumulated above an impermeable ice lens. Over time, the lens degraded, becoming permeable and enabling deep water infiltration. This permeability shift was corroborated by amplitude data, which revealed contrasting moisture responses above and below the lens. Four other events monitored before and after March 17 served in capturing the evolving influence of ice layers in influencing surface meltwater retention and subsurface flow pathways.

By emphasizing the hydrological dynamics of ice layers, this study advances understanding of snowpack behavior under changing winter conditions. The integration of GPR, field measurements, and energy balance analyses provide a powerful framework for examining the interplay between meteorological inputs and internal snowpack transformations, particularly during critical events involving ice layers.

How to cite: Baraer, M., Goujon, M., Michaud, L., Poulin, A., and Valence, E.: Investigating Ice Layer Dynamics and Hydrological Processes in Snowpacks Using Ground Penetrating Radar and Energy Balance Analyses , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6998, https://doi.org/10.5194/egusphere-egu25-6998, 2025.

The hydrology of the glacier-fed systems plays a critical role in maintaining sustainability, water availability, and livelihood in the downstream region. The Khangeri glacier of the north-eastern Himalaya belongs to the Mago basin, which is a small catchment in the major Brahmaputra river system. Understanding the isotopic characteristics of these cold regions offers a unique lens to decode the dynamics of snow and glacier behaviour to the regional water resources. This study investigates the stable isotopic signature of snow, ice, glacier, and meltwater within the Khangeri glacier system, employing the stable isotopes of oxygen (δ¹⁸O) and hydrogen (δ²H) as tracers. The isotopic analysis was performed using the Liquid Triple Isotopic Water Analyser (L-TIWA) following the conventional analytical procedure for laser-based, off-axis integrated cavity output spectroscopy (ICOS). The isotopic analysis reveals distinct seasonal variations, with heavier isotope enrichment during the premonsoon period and depletion during the postmonsoon period. All the snow samples show regression lines with similar slopes and intercepts greater than the Global Meteoric Water Line (GMWL), but the glacier samples show a regression line with a lesser slope and intercept than the GMWL. This study also identifies the critical processes involved in the fractionation of isotopes during snow/glacier melting and isotope mixing, which shapes the isotopic signature of meltwater coming downstream. This isotopic study offers the significance of this tracer technique in understanding hydrological processes and predicting climate change on cryospheric hydrology.

Keywords: Stable isotope, Snow, Khangeri glacier, North-eastern Himalaya, Mago basin

How to cite: Nanda, M., Narayan, U., Nair, A. M., and Kartha, S. A.: Isotopic insights into cold region hydrology: Decoding isotopic signatures of snow and glacier in Khangeri glacier, North-eastern Himalaya. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15091, https://doi.org/10.5194/egusphere-egu25-15091, 2025.