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.
Posters on site: Wed, 30 Apr, 14:00–15:45 | Hall A
Three River Basins (TRB) in southwest China: the Jinsha, Lancang and Nu River, which originate from the Qinghai–Tibet Plateau (QTP), are distributed with cryosphere elements such as snow, permafrost, and glacier. The upstream and midstream areas of the TRB show a trend of warming and wetting, but the downstream regions with the temperature rising shows a trend of decreasing precipitation under climate change. This has led to the vanishing cryosphere and changes in the hydrological process of the TRB. How the changes in a single meteorological forcing or cryosphere element influence the amount and seasonality of streamflow remains unclear. This study simulated the different streamflow components and analyzed the amount and seasonality of streamflow in all sub-basins using a distributed hydrological model by controlling the changes of cryosphere elements and meteorological forcing during 1961-2020. The results showed that the contribution of snowmelt to streamflow in the midstream sub-basins was relatively high during April-June with a decreasing trend; the increasing glacier meltwater contributed to the streamflow in the source areas from June to September, especially in the Jinsha and Nu River; groundwater affected by permafrost degradation exhibited an increasing trend in the downstream reaches of the TRB. The spring rise timing was advanced and the recession timing was delayed with the reduction of snowfall fraction and the degradation of permafrost, and this effect gradually weakened from the upstream to the downstream areas of the TRB. Less glacier generally delayed the timing of summer streamflow in all reaches of the study basins. The seasonal variation of streamflow in the TRB decreased with the vanishing cryosphere. In addition, results of two experiments by using the multiyear mean precipitation and air temperature as forcing manifested that precipitation was the dominant factor causing changes in annual runoff and seasonal variation of streamflow, and increase in air temperature played a significant role in reducing the runoff and streamflow seasonality of the TRB. These findings shed light on the difference of changes in the streamflow process between sub-basins under climate change and provide a useful reference for water resource management in future for the TRB in southwest China originating from the QTP.
How to cite: Huang, Y. and Yang, Y.: Impact of Changing Cryosphere on Streamflow Seasonality of Three River Basins in Southwest China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3907, https://doi.org/10.5194/egusphere-egu25-3907, 2025.
Variations in Arctic snow cover, including Finland, can impact the ecosystem, hydrological cycle, biodiversity, and many other physical processes. Getting a consistent picture of long-term changes in relevant snow cover pattern (SCP), including phenology, duration of snow cover and snow-free days, is crucial for understanding the regional dynamics of the water resources. Prevalent SCP assessments excluded critical features such as the first and last days with maximum snow cover, which are essential for a thorough spatiotemporal analysis. To address these gaps, this study utilized a novel convolution-based method coupled with K-means clustering to analyze SCP features using ERA5-Land data spanning from 2000 to 2020 across Finland. This approach was employed to cluster the country into four distinct regions based on SCP, enhancing our understanding of spatiotemporal variability and dynamics. The largest cluster spanned 114,738 km2 with maximum snow cover duration (Dmax) lasted 189 days of 220 snow-covered duration (Dtotal). Conversely, the smallest cluster in southern and coastal areas covered 41,630 km², experiencing Dmax of 85 out of 123 days of Dtotal. Using K-nearest neighbours method and based on the mentioned four clusters, the 20 annual SCP features images of Finland were classified. The effect of air temperature and precipitation in the classification’s results were also investigated. To assess the accuracy of annual classification, and to analyze snow cover dynamics in relation to air temperature and precipitation, three indices were obtained to measure anomalies occurred during snow accumulation period, the period with maximum snow cover, and snowmelt period.
How to cite: Faal Gandomkar, R., Saboori, M., Patro, E. R., Ala-Aho, P., and Torabi Haghighi, A.: Novel Approach to Spatiotemporal Analysis of Snow Cover Pattern using ERA 5 Dataset Over Finland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4154, https://doi.org/10.5194/egusphere-egu25-4154, 2025.
Mountain glaciers play a crucial role in regulating water resources and are highly sensitive to climatic shifts. In this study, we applied and tailored the snow-firn dynamics model by Banfi and De Michele (2021) to analyze the snowpack and firn characteristics of Georgian glaciers. Meteorological data from a station (Shovi) near the Buba Glacier, located in the Racha region of Georgia in the Caucasus, were utilized. The model integrates snow and firn processes through mass balance, densification, and melt dynamics, allowing for detailed simulations of seasonal and interannual variability. By incorporating site-specific variables such as precipitation, temperature, snow cover, and wind speed, we simulated snow accumulation, firn densification, and melt processes. The model's performance was evaluated under local conditions, demonstrating its capability to replicate
seasonal variations in snow retention and density distribution. Using the Python programming language, our analysis highlights the critical role of wind-driven erosion and seasonal temperature thresholds in shaping snow-firn transitions. These findings offer valuable insights into the dynamics of Georgian glaciers, particularly in this highly active region characterized by numerous glaciers, substantial precipitation, and glacier-related
disasters. This work contributes to advancing glacier monitoring and informing regional climate impact assessments.
How to cite: Beridze, S. and De Michele, C.: Assessing Snow and Firn Dynamics in Georgian Glaciers Using Local Data and Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21295, https://doi.org/10.5194/egusphere-egu25-21295, 2025.
Catchment storage affects the runoff response to snowmelt or rainfall events. Although changes in snow cover will affect streamflow responses, there is currently a lack of knowledge of how changes in snow cover will affect groundwater storage. This is important as summer low flows are often sensitive to changes in groundwater storage. This study aims to investigate how groundwater level fluctuations differ during rainfall versus snowmelt events and if the effect of the precipitation phase on groundwater recharge and storage varies across a catchment.
This study uses data from high-frequency measurements from 44 wells from the 20-ha Studibach catchment in the pre-alps in Switzerland (2010-2024 data), 48 wells from the C6 and C2 catchments in the Krycklan catchment in Sweden (2018-2024 data), and five wells from the Rokytka catchment in the Vydra catchment in Czech Republic (2013-2023 data). It analyses the response of the groundwater during periods with snow cover (rain-on-snow events), during snowmelt events, and rainfall events and whether these differences depend on the location of the catchment (as represented by, for example, the topographic wetness index, slope, and land cover). Furthermore, it determines the correlation between the site characteristics and how this differs for these three types of events. These results will be helpful to understand better how changes in snow due to cover climate change will affect groundwater recharge and storage, and thus streamflow.
How to cite: Bremer, K., van Meerveld, I., Bishop, K., Jeníček, M., Vlček, L., and Seibert, J.: The impact of snow cover and precipitation phase on groundwater recharge, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15593, https://doi.org/10.5194/egusphere-egu25-15593, 2025.
High elevation streamflow integrates the hydrological response to snow accumulation and melt. Accordingly, streamflow observations hold valuable but often underutilized information about snow water equivalent (SWE), offering a means to reconstruct historical SWE dynamics. We develop an inverse hydrological modelling framework to derive SWE estimates from streamflow: the framework generates a range of prior SWE scenarios, feeds them into a hydrological model and computes their likelihood based on how well corresponding streamflow simulations match observed streamflow. A critical step hereby is the choice of model performance metrics to be used as likelihood functions. To test our framework, we perform a range of tests in a synthetical setting, where we use known SWE data that we feed into the hydrological model and then apply the inversion framework to retrieve the SWE time series from the streamflow alone. The goal of this synthetic setting is to determine which streamflow metrics select realistic SWE scenarios (measured in terms of errors between the known SWE time series and generated scenarios).
Our results reveal that classical streamflow metrics, such as the Kling-Gupta and Nash-Sutcliffe efficiencies, show no correlation with any SWE timing or magnitude error metrics. Accordingly, minimizing these streamflow metrics does not result in an efficient selection of SWE scenarios. In contrast, minimizing the mismatch of selected streamflow signatures, such as the baseflow index and the mean melt-season discharge, does lead to a selection of SWE scenarios with smaller errors. Overall, however, our results show that correlations between streamflow performance metrics and SWE performance metrics are generally weak and show significant year-to-year variability, indicating that streamflow metrics are often not informative for reconstructing SWE.
Our synthetic modeling experiments are conducted in the Dischma catchment in Switzerland, using the OSHD Swiss SWE reanalysis product as the synthetic SWE observations (Mott et al., 2023). Synthetic streamflow observations are generated by feeding OSHD snow melt and MeteoSwiss rainfall into the hydrological model.
Our findings are relevant for future studies aiming to evaluate or calibrate SWE simulations against streamflow observations, and will help us in the application of the inverse hydrological framework to real-world SWE reconstructions.
Mott, R., Winstral, A., Cluzet, B., Helbig, N., Magnusson, J., Mazzotti, G., Quéno, L., Schirmer, M., Webster, C., and Jonas, T.: Operational snow-hydrological modeling for Switzerland, Front. Earth Sci., 11, 1228158, https://doi.org/10.3389/feart.2023.1228158, 2023.
How to cite: Wiersma, P., Schaefli, B., Peleg, N., Magnusson, J., and Mariéthoz, G.: Inference of snow dynamics from streamflow observations: choose your metrics wisely, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19745, https://doi.org/10.5194/egusphere-egu25-19745, 2025.
Mountain catchments serve as critical water towers, where the interplay between snow dynamics and baseflow plays a fundamental role in regulating water availability across both seasonal and interannual timescales. While current mesoscale studies have challenged traditional conceptualizations of baseflow and revealed diverse landscape roles, the mechanisms linking snow conditions to baseflow generation across elevation gradients remain poorly understood. This study examines these mechanisms using the HBV model applied to 93 catchments across Czechia and Swiss mountain regions (1965-2019). Our preliminary findings revealed elevation-dependent patterns in baseflow generation, with increases in annual and summer baseflow fractions during periods of increased snowfall. Snow water storage (SwS) emerged as a critical buffer in high-elevation catchments, maintaining stable baseflow patterns despite changing climate conditions. We identified distinct temporal lag effects between snowmelt and baseflow generation that vary with elevation, leading to significant differences in seasonal flow dynamics between lower and higher elevation catchments. These insights advance our understanding of mountain snow hydrology and offer valuable implications for water resource management in snow-dominated regions under increasing climate pressure.
How to cite: Acheampong, J. N. and Jenicek, M.: Snowmelt Contribution to Seasonal Baseflow Dynamics: Multi-Catchment Analysis of Hydrological Responses in Mountain Catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6220, https://doi.org/10.5194/egusphere-egu25-6220, 2025.
Snow water resources play a crucial role in Mediterranean mountainous regions, serving as natural reservoirs that sustain water supply during dry seasons. The accurate estimation of Snow Water Equivalent (SWE) is fundamental for water resource management, though direct measurements remain sparse in mountainous terrain. Current SWE estimation approaches face distinct challenges: for instance, large-scale reanalysis products derived from land-surface models (such as ERA5-Land) are limited by sparse data assimilation of mountain observations, the coarse scale of the modeling grid (often running in the 10+ km), a poor representation of orographic precipitation, and globally optimized parameterizations that may not suit complex mountain environments. On the other hand, hydrological models are constrained by uncertainty in input data, precipitation-phase determination and simplified snow thermodynamics. These limitations necessitate systematic evaluation across different terrain types to improve mountain snow monitoring.
This study compares SWE estimates across Northern Italy by evaluating large-scale reanalysis products and rainfall-runoff modeling against the high-resolution IT-SNOW dataset (Avanzi et al., 2023). The IT-SNOW reference dataset provides validated SWE estimates across Italy at 500m spatial resolution with comprehensive data assimilation from satellite and in-situ measurements. The evaluation examines regional and global reanalyses at various spatial scales, alongside the SWE simulations obtained at catchment scale with the GR6J rainfall-runoff model (Coron et al., 2017) coupled with the CemaNeige snow routine (Valéry et al., 2014), locally calibrated against the observed streamflow. By analysing over 100 catchments during 2010-2023, we assess the performance of these estimates across diverse topographical and climatic conditions to identify their strengths and limitations.
Our methodology involves a two-scale evaluation approach: at the gridded scale, we compare IT-SNOW with reanalysis products; at the catchment scale, we evaluate the CemaNeige-GR6J rainfall-runoff model simulations of the SWE volumes. Both analyses span seasonal and interannual timescales to assess the variations of SWE estimates.
The findings of this comparative analysis advance our understanding of SWE estimation methods across the Italian mountainous regions by systematically evaluating the strengths and limitations of different estimation approaches. Future research will focus on integrating SWE estimates from IT-SNOW into the rainfall-runoff model calibration phase, aiming to develop more robust hydrological models capable of better representing snow dynamics.
REFERENCES
Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Pignone, F., Bruno, G., ... & Ferraris, L. (2023). IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021), Earth Syst. Sci. Data, 15, 639–660. doi: https://doi.org/10.5194/essd-15-639-2023.
Coron, L., Thirel, G., Delaigue, O., Perrin, C., & Andréassian, V. (2017). The suite of lumped GR hydrological models in an R package. Environmental modelling & software, 94, 166-171. doi: https://doi.org/10.1016/j.envsoft.2017.05.002.
Valéry, A., Andréassian, V., & Perrin, C. (2014). ‘As simple as possible but not simpler’: What is useful in a temperature-based snow-accounting routine? Part 1–Comparison of six snow accounting routines on 380 catchments. Journal of hydrology, 517, 1166-1175. doi: https://doi.org/10.1016/j.jhydrol.2014.04.059.
How to cite: Sarigil, G., Avanzi, F., Neri, M., and Toth, E.: Assessment of Snow Water Equivalent Estimates from Reanalysis and Rainfall-Runoff Modeling in Northern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9237, https://doi.org/10.5194/egusphere-egu25-9237, 2025.
In High Mountain Asia (HMA), declines in water stored in glaciers and seasonal snowpacks have been widespread in recent decades. Changes are however highly heterogeneous, with glaciers in the Pamirs experiencing near-neutral mass balance while fast rates of mass loss are observed in the Southeastern Tibetan Plateau. Modeling can provide an understanding of mass balance seasonality and mountain hydrology at a spatial and temporal resolution not achievable by observations, and validated simulations can extend over long time scales. Quantifying the water balance at high elevations requires the estimation of snowfall amounts, which is challenging due to uncertainties in reanalysis products and rare precipitation measurements. Differences in accumulation regimes and precipitation decadal variability complicate the assessment of precipitation phase change and its role in glacier and snow mass changes under warming conditions.
In this study, we leverage in-situ hydro-meteorological observations and climate reanalysis to run a mechanistic, highly resolved land-surface model and reconstruct snow and glacier mass changes since 1970 at three benchmark glacierized catchments with contrasting climatic conditions in HMA. The catchments cover areas between 100 and 200 km2, span elevations ranging from 2000 to 6000 m a.s.l., and are located in the Northwestern Pamir (Kyzylsu), Nepalese Himalayas (Trambau-Trakarding) and Southeastern Tibetan Plateau (Parlung No.4). The land-surface model is run at hourly and 100 meters resolution, and its performance is evaluated using in-situ snow depth, surface albedo, remotely sensed snow cover and multi-decadal geodetic glacier mass balance.
At all sites, we find declining trends in snowfall, snow depth and glacier mass balance between 1970 and 2023. A decline in the snowfall to total precipitation ratio was found at all sites (-0.005, -0.005 and -0.03 decade-1 at Kyzylsu, Trambau-Trakarding and Parlung No.4 respectively), but was only pronounced at the Southeastern Tibetan site. The decadal variability in precipitation amount, rather than phase, controls most of the snowfall and glacier mass changes, although the shift in precipitation type from snowfall to rainfall had a substantial contribution to the recent snowfall decline at Parlung No.4 (30% of the snowfall decrease between 1970-1999 and 2000-2023), where we simulate the most rapid glacier mass loss, in agreement with regional assessments of geodetic mass balances. Glacier mass loss has only been marked at Kyzylsu since 2018, following a near-neutral mass balance period characteristic of the Pamir-Karakoram Anomaly. Positive runoff trends were found at Parlung No.4 (+6%/dec) and Trambau-Trakarding (+2%/dec), but not at Kyzylsu (-2.5%/dec) where the recent increase in ice melt only partially compensated for reduced precipitation and for a relative increase in evapotranspiration. Future simulations should assess how snowfall, glacier mass balance and runoff trends will evolve as climate warming strengthens in these catchments.
How to cite: Jouberton, A., E. Shaw, T., Miles, E., Kneib, M., Fugger, S., McCarthy, M., Sato, Y., Fujita, K., and Pellicciotti, F.: Reconstructing the water balance of selected glacierized catchments in High Mountain Asia since 1970, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10423, https://doi.org/10.5194/egusphere-egu25-10423, 2025.
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, overparameterization, and high computational demands make them challenging to use for operational purposes. In contrast, simple lumped models require less input data, standard snow parameters and are well-suited for operational applications.
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 evaluate and compare different approaches, ranging from direct substitution to a mass-conserving statistical downscaling method. The methodology is applied in the Non Valley catchment, Italy, where water is important for agriculture, hydropower, and other uses.
Our initial results from 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. This approach aims to preserve the computational efficiency and feasibility of lumped models while incorporating the improved physical representation of snow processes and spatial variability from a physically-based snow model.
Acknowledgement
The work of J.M.W. has been funded by Fondazione CARITRO Cassa di Risparmio di Trento e Rovereto, grant number 2022.0246.
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 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12258, https://doi.org/10.5194/egusphere-egu25-12258, 2025.
In the context of climate change, the declining contribution of snowmelt to runoff underscores the need for precise quantification of glacier meltwater contributions. Accurate differentiation between snowmelt and ice melt is crucial but challenging, requiring detailed knowledge of glacier surface land cover. High-resolution optical remote sensing data from platforms like Landsat and Sentinel-2 provide a valuable tool for assessing glacier surface cover, leveraging their rich spectral information to distinguish snow from ice effectively.
The inherent limitation of cloud occlusion in optical imagery can be mitigated by integrating high-resolution datasets with daily low-resolution observations through gap-filling techniques. These land cover maps can be used as input for a range of melting models, from simple temperature-index models to more complex physically-based models.
A preliminary study was conducted on the Hintereisferner glacier in Austria, a well-documented site with extensive historical data. The study compared glacier melt estimates derived from gap-filled high-resolution satellite data, orthorectified and classified webcam imagery, and terrestrial laser scanner (TLS) data (Voordendag et al. 2023). Results revealed strong agreement between the satellite-based melt maps and those derived from webcam and TLS measurements, demonstrating the potential of this approach.
Future applications will focus on reference glaciers in the Andes, where glacier melt contributions to downstream water resources are more significant than in Alpine catchments. The methodology aims to enhance our understanding of glacier dynamics and support water resource management in regions heavily reliant on glacier-fed runoff.
This work has been done in the context of the project SNOWCOP. This project has received funding from the European Union’s Horizon Research and Innovation Actions programme under Grant Agreement 10180133.
References:
Voordendag, A., Goger, B., Klug, C., Prinz, R., Rutzinger, M., Sauter, T., & Kaser, G. (2023). Uncertainty assessment of a permanent long-range terrestrial laser scanning system for the quantification of snow dynamics on Hintereisferner (Austria). Frontiers in Earth Science, 11, 1085416.
How to cite: Barella, R., Toum, E., Pitte, P., Masiokas, M., and Marin, C.: Combined Use of Remote Sensing Data and Melting Models for Estimating Glacier Melt Contributions to Runoff, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16624, https://doi.org/10.5194/egusphere-egu25-16624, 2025.
In semi-arid regions of the Mediterranean, snowmelt and precipitation are vital water sources for downstream communities. Here, snow-covered mountain peaks serve as natural water reservoirs, playing a crucial role in regulating river flow and replenishing groundwater. This research leverages remote sensing to compensate for the lack of ground-based hydroclimatic data, focusing on the latest version of the MODIS snow cover product (version 6, V6). The study aims to refine the Normalized Difference Snow Index (NDSI) threshold and develop localized models for fractional snow cover (FSC) estimation tailored to the Moroccan Atlas Mountains. For this purpose, 448 Sentinel-2 scenes across six different regions in the Atlas Mountains were used to adjust the NDSI threshold and develop FSC models. Moreover, 8419 MOD10A1 and 7561 MYD10A1 images covering the period from March 2000 to June 2023 were processed to improve cloud filtering and generate a high-precision daily snow cover product for the region. Significant improvements were achieved in reducing cloud-covered pixels from 25.7% to 0.4%. Two NDSI MODIS threshold selection schemes were tested: the standard global threshold of 0.4 and a locally optimized threshold of 0.2. The local threshold demonstrated superior accuracy, significantly reducing snow cover estimation errors compared with the global threshold (0.4) for both Terra and Aqua MODIS images. The newly developed FSC models demonstrate high accuracy, displaying high correlation coefficients (average of 0.84) and low error measures when comparing MODIS-derived FSCs with high-resolution Sentinel-2 data. The improved daily snow cover product was compared with high-resolution snow maps obtained from Sentinel-2 satellite imagery in different regions of the Moroccan Atlas. On average, the product showed a mean correlation coefficient of 0.96, a mean absolute error of 0.22%, and a mean reasonable negative bias of -0.17%. This research concludes that the improved daily snow cover product offers a robust understanding of the spatio-temporal dynamics of snow extent. These advancements offer considerable potential improvements to modelling snowmelt contribution to the water balance, supporting efficient water resource management in the southern Mediterranean region.
How to cite: Bousbaa, M., Boudhar, A., Kinnard, C., Vivone, G., Elyoussfi, H., A. Sproles, E., Bargam, B., Nifa, K., and Chehbouni, A.: Remote Sensing of Mountain Snow from Space: Developing Accurate Snow Products for Efficient Water Resource Management in Morocco’s Atlas Mountains, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13326, https://doi.org/10.5194/egusphere-egu25-13326, 2025.
The cryosphere is one of the regions most profoundly affected by climate change. Since snowmelt plays a critical role in runoff generation, understanding its evolving contribution to runoff in the context of global warming is essential for informed water resource management and planning. Existing snow modules embedded in hydrological models typically focus on energy exchanges at the snowpack surface, neglecting internal changes in temperature and density. As a result, these models often fail to accurately capture variations in snow depth.
This study addresses these limitations by developing a multiple layer snow model based on a Lagrangian framework, incorporating liquid water and air content within the snowpack. Conservation equations for energy and mass were established for the surface, inner, and bottom layers of the snowpack, and the fourth-order Runge-Kutta method was employed to solve equations. The model effectively simulates temperature and density profiles of snow layers, as well as the timing and location of melting and refreezing events within the snowpack. Additionally, the snow and rain separation algorithm was enhanced by integrating multiple meteorological datasets.
Applied to the Sanjiangyuan region in China with corrected precipitation data, the model yielded improved simulations of snow depth, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.77. Furthermore, the spatial distribution of snow cover aligned more closely with remote sensing observations, highlighting the model's enhanced accuracy and applicability.
How to cite: Jia, Y., Huang, Y., and Zhang, S.: A Lagrangian-Based Multi-Layer Snow Model for Improved Snowpack Simulation in the Sanjiangyuan Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14202, https://doi.org/10.5194/egusphere-egu25-14202, 2025.
Posters virtual: Fri, 2 May, 14:00–15:45 | vPoster spot A
EGU25-19490 | ECS | Posters virtual | VPS11
Modelling Snow-Glacier Melt Runoff Dynamics In Bhilangana BasinFri, 02 May, 14:00–15:45 (CEST) | vPA.18
The vast network of glaciers in the Himalayas serves as a vital source of freshwater for the main river systems. These are essential in determining a region's climate and hydrology. Ecological balance, agricultural output, and hydrological systems all depend on these glaciers. However, the stability of hydrological systems and long-term water availability have become major concerns in recent decades due to the acceleration of glacier melting brought on by climate change. In this study, globally available gridded satellite and reanalysis datasets, including ERA5, IMDAA, IMD, APHRODITE, and others, were evaluated to identify the most accurate dataset for the Bhilangana Basin. A thorough performance evaluation was conducted to assess the suitability of these datasets for the region. Furthermore, a hybrid rainfall dataset was developed using a bias correction approach to improve accuracy and reliability, ensuring a more robust representation of precipitation dynamics. The Spatial Processes in Hydrology (SPHY) model was utilized to examine the dynamics of snow-glacier melt during the years 2020–2023. The performance matrix revealed that the ERA5 dataset performed better than other datasets except the hybrid precipitation dataset. The average variation during 2000-2023 in snow q was found in the range of 15 to 26 percent, rain q from 12 to 58 percent, glacier q from 56 to 18 percent and base q from 8 to 18 percent. The analysis further revealed that 11 parameters were found to be critical in influencing the model's output e.g. Degree day factor for snow(DDFS), Glacier debris degree day factor(DDFG), Tcritical, Glacier melt frac runoff. The SPHY model's applicability for studying snow-glacier melt runoff dynamics and the significance of combining various climate datasets to precisely forecast the water resource scenarios in glaciated basins are further highlighted by this study.
How to cite: Joshi, B., Singh, V., Chandola, V. K., and Singh, A.: Modelling Snow-Glacier Melt Runoff Dynamics In Bhilangana Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19490, https://doi.org/10.5194/egusphere-egu25-19490, 2025.
