Alpine snow plays a vital role in regional hydrological cycles and climate systems. Topographic factors exert a significant controlling effect on snow cover distribution in mountainous areas. Understanding the complex relationship between snowpack distribution and topography will be helpful for more effectively estimate high-spatial resolution snow depth distribution. In this study, the snow cover variation with topographic factors (elevation, aspect, and slope) at different periods are analyzed based on remote sensing snow coverage fraction data from August 1, 2000, to July 31, 2020. A new method based on the snow cover area increase rate, is utilized to divide a snow season into three periods (accumulation, stable and melt periods). The variations in snow cover fraction (SCF) and snow cover days (SCD) with topographic factors, as well as their inter-annual changes, in the three different periods are analyzed in the three typical snow regions of China (Xinjiang, Tibetan plateau, Northeast China). The results indicate that the the influence of topography on snow cover distribution display different characteristics in the three typical snow regions and in the three snow periods.
How to cite: Che, T., Dai, L., and Zhu, J.: The Impact of Topography on Snow cover in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1603, https://doi.org/10.5194/egusphere-egu25-1603, 2025.
The headwaters catchments of the Sierra Nevada mountain range in Southern Spain are a clear example of Mediterranean mountain catchments where climate variability enhances the spatiotemporal complexity of snow dynamics. The changeable patterns of snowfall combined with the usually mild and sunny winters result in shallow snowpacks that favor various accumulation and melting cycles and, consequently, the appearance of a characteristic snow patchy distribution. Remote sensing techniques has proven to be the most effective solution to monitor this characteristic snow distribution. Among the different satellite constellations, Landsat still provides the most extended time series with an adequate spatial resolution for capturing the long-term snow spatial variability over these areas. Applying a spectral mixture analysis to the long-term Landsat dataset over the area has allowed us to not only improve the spatial representation of snow that binary classification gave but also to define and idetenfigy the presence of pixels that are not fully covered by snow: mixed pixels.
This work proposes using these mixed pixels as an indicator of snow cover occurrence and persistence and linking its frequency and evolution with snow dynamics, from snowfall to snow ablation patterns. Twenty years of Landsat imagery has been analyzed over an area composed of the five main headwaters in the Sierra Nevada mountain range. A spectral mixture analysis, considering the three main land cover over the region: snow, shallow vegetation, and rocks, was performed to define the land cover partitioning in each pixel in the area. The distributed snow-mixed pixels' spatiotemporal persistence and evolution over the region were statistically analyzed.
The analysis of the occurrence of these pixels shows that their presence can reach up to 40% of the mountain range during some specific years, such as wet and cold years. The clustering of mixed pixels has also allowed us to identify common areas where patchy conditions prevail. A clear differential pattern has been observed between catchments in the southern face, which is highly influenced by the presence of the sea, and in the southern face, which has a more continental climate. Finally, analyzing the temporal evolution of these pixels has allowed for the spatial assessment of areas where snowfalls can be significant and/or frequent. Still, persistence is not enhanced by the local conditions. In general, this work highlights that accounting for subgrid variability is key in this area for understanding snow spatiotemporal patterns, determining the more vulnerable regions facing potential changes in the snow regime due to global warming and climate variability, and further assessing water resources planning through the improvement of hydrological models predictions.
Acknowledgment: This research was funded by the Spanish Ministry of Science and Innovation through the research project PID2021-12323SNB-I00, HYPOMED—“Incorporating hydrological uncertainty and risk analysis to the operation of hydropower facilities in Mediterranean mountain watersheds.”
How to cite: Pimentel, R., Aparicio, J., Andreu, A., and Polo, M. J.: Assessing snow mixed pixels dynamics to better understand snow spatiotemporal variability in Mediterranean mountain catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2954, https://doi.org/10.5194/egusphere-egu25-2954, 2025.
Accurate spatially distributed simulations of snow cover in mountainous regions is highly dependent on the possibility to well constrain the accumulation of solid precipitation. A number of observations and model data can provide direct or indirect assessment of their amount with varying spatial resolutions, spatial coverage and uncertainties. However, the complementarity between the different sources of informations is poorly documented and methodologies to appropriately combine all data are missing.
In this work, we present a new modelling framework taking benefit from (1) radar observations of precipitation, (2), local precipitation gauges, (3) precipitation climatology of a Numerical Weather Prediction model and (4) satellite remote sensing of snow depth. We show over a 900 km² simulation domain in Central French Alps that all data sources help to better constrain precipitation and to obtain more realistic snow depth spatial patterns. Radar observations provide the best temporal chronology of precipitation but the NWP model help to capture better altitudinal and horizontal climatological gradients and to fix spatial artefacts in radar measurements due to ground clutter. The assimilation of satellite snow depth maps is found as highly beneficial to capture spatial patterns of accumulated solid precipitation not well captured by radars and NWP. The added value of snow depth maps is maintained several months after the assimilation date, but they can not solve the errors specific to individual precipitation events. As a result, the most realistic spatial patterns of simulated snow depths are obtained when all sources of data are combined, with appropriate ensemble algorithms and uncertainty quantification.
Finally, we outline short term perspectives to integrate real-time snow observations from optical satellites in the previously described framework. This is an important step in the development of the EDELWEISS high-resolution (250 m) snow modelling system, which is expected to cover all French mountains by 2026.
How to cite: Lafaysse, M., Vernay, M., Augros, C., Haddjeri, A., Imperatore, N., Deschamp-Berger, C., Gascoin, S., and Dumont, M.: Complementarity between snow remote sensing, gauges, radar observations and Numerical Weather Prediction models to better constrain solid precipitation accumulation in spatially distributed snow cover modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3206, https://doi.org/10.5194/egusphere-egu25-3206, 2025.
Optical remote sensing offers the best combination of resolution, coverage and revisit times for monitoring mountain snow cover, but it is limited by cloud cover and topographic shading, and does not directly provide measures of snow mass essential for hydrological applications. Assimilation of snow cover products in snow models allows gap filling. In addition, the use of physically-based models allows rejection of misclassified changes in snow cover that are not energetically possible and hindcasting of snow mass consistent with energy required for observed snow cover depletion. This presentation will demonstrate assimilation of new European Space Agency snow_cci and AlpSnow snow cover products, which represent contributions to the International Network for Alpine Research Catchment Hydrology, using ensembles of perturbed simulations. Trade-offs between resolution, ensemble size, model complexity, accuracy and computational expense will be considered for well-defined seasonal snow cover in the Alps and a more challenging case study of ephemeral snow cover and frequent cloud cover in the Cairngorm Mountains of Scotland.
How to cite: Essery, R., Nemec, J., Howe, L., Schwaizer, G., and Nagler, T.: Gap filling satellite snow cover for mountain catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6832, https://doi.org/10.5194/egusphere-egu25-6832, 2025.
The snowpack stores a substantial part of the seasonal freshwater in cold environments, impacting catchment runoff generation and timing. Alterations of the seasonal snowpack may affect the availability of water resources, with implications for energy production, relying on meltwater from mountain catchments. Spatial and temporal variability of snow processes at multiple scales challenges snowpack monitoring, snow volume estimations and runoff predictions. Drone acquisition techniques have emerged as a new methodology for snowpack monitoring to obtain dense and high spatial-resolution snow data. This study uses drone observations to estimate snow depth close to the accumulation peak in the Överuman catchment, Northern Sweden. We compared the snow depth average and distribution in the catchment areas where drone acquisitions occurred and in the whole catchment. We explored the use of topographic and wind shelter factors and different machine learning methods to obtain snow depth maps of the entire catchment. We further evaluated the impact of aggregating snow depth data at various spatial resolutions on snow spatial distribution and runoff. Results show high correlations of snow depth, especially with wind shelter factors, which are among the selected predictors in cross-validation, together with topographic roughness at a fine spatial scale. Drone observations provided valuable insights into the snow depth variability to improve process understanding and model development.
How to cite: Clemenzi, I., Gustafsson, D., Fagerström, V., Wennerberg, D., Norell, B., Zhang, J., Pettersson, R., and Pohjola, V.: Estimating snow distribution using drones and machine learning in Swedish mountain catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19756, https://doi.org/10.5194/egusphere-egu25-19756, 2025.
Estimating the amount of snow, its evolution and spatiotemporal distribution in complex high-alpine terrain is currently considered as one of the most important challenges in alpine hydrology and water resources management. This is predominantly caused by the lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in vast regions with no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometers scale radius. An introduction into the novel sensor setup will be given including the sensitivity of the integrative gravimetric signal regarding the spatially distributed snowpack and the cryo-hydro-gravimetric signal changes. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations (Pléiades). The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure.
How to cite: Koch, F., Garscoin, S., Achmüller, K., Schattan, P., Wetzel, K.-F., Deschamps-Berge, C., Lehning, M., Rehm, T., Schulz, K., and Voigt, C.: Signals of a superconducting gravimeter at the high-alpine Mt. Zugspitze show that a satellite-derived snow depth image improves the simulation of the snow water equivalent evolution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11449, https://doi.org/10.5194/egusphere-egu25-11449, 2025.
The assessment of snow water equivalent (SWE) is crucial for hydrological studies in glaciated catchments to quantify accumulation and ablation of a seasonal snow cover for both, the glaciated and non-glaciated terrain. Presently, the majority of the SWE assessment on glaciated terrain relies on manual measurements once or a few times per year, given the limited techniques for continuous SWE monitoring and the challenging conditions in a high mountain environment. Cosmic Ray Neutron Sensors (CRNS) offer to overcome these limitations providing sub-daily SWE estimates derived from neutron counts.
This study employs a CRNS installed on an Alpine glacier (Hintereisferner, Austria) over three years, complemented with an additional CRNS for one winter roughly 300 m lower in elevation along the glacier’s central flow line. Comparing CRNS outputs with frequent manual SWE measurements, the results demonstrate close agreement in SWE and snow density. The CRNS were found to be remarkably resilient in harsh conditions, providing nearly continuous hourly data over the measurement period. Additionally, the study evaluates at the CRNS locations the performance of two snow models, which might be considered as end members of model complexity – SNOWPACK and ΔSNOW in its latest version. While SNOWPACK, with its physically-based approach, yields the best results, ΔSNOW stands out for its simplicity, requiring only daily snow depth observations as input and performs almost as well as SNOWPACK in terms of mean absolute SWE error.
Comparing the SWE measurements with winter precipitation from weighing gauges distributed in the catchment gives interesting details of precipitation gradients with elevation. Precipitation gradients interpolated from non-glaciated to glaciated terrain are considerably higher than on non-glaciated terrain only. On the latter, empirical bulk correction factors are frequently applied, which might fail on glaciers due to their different topographic setting. This highlights the need of separate treatment of snow on glaciers in hydrological models for correct SWE representation across the catchment.
How to cite: Prinz, R., Schroeder, M., Binder, M., Schellander, H., Winkler, M., and Nicholson, L.: Snow water equivalent on an Alpine glacier from continuous cosmic ray neutron sensing and numerical modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12602, https://doi.org/10.5194/egusphere-egu25-12602, 2025.
The cryosphere plays a critical role in Central Asia, particularly in terms of water availability for agriculture and energy production via hydropower stations. Glaciers serve as essential sources of both seasonal and long-term water supply, while snow storage in mountainous regions significantly influences seasonal water availability. Consequently, the accurate estimation of water resources stored in glaciers and seasonal snow is vital for the effective management of transboundary water resources. However, limitations in data availability pose significant challenges to comprehensive water resource assessments.
To address these challenges, we aim to demonstrate methodologies for conducting studies under data-limited conditions. This includes leveraging available observations and conducting field campaigns in high-altitude regions to enhance understanding of cryospheric changes. Additionally, we employ remote sensing data to extend observational coverage and address gaps in remote and inaccessible areas. This approach provides a more comprehensive understanding of the cryosphere's role in hydrological forecasting.
Furthermore, we highlight an ongoing project that actively involves local communities in observational data collection, thereby improving both the quality of records and operational understanding of the cryosphere. By expanding in-situ measurement networks, we aim to enhance the accuracy of water resource assessments.
Our ultimate goal is to improve water resource availability assessments in Central Asia and to support policy dialogue on water resource management by integrating scientific knowledge into hydrological forecasting. The methodologies and case studies presented here may also be applicable to other regions with similar geographic and climatic conditions, where water resources are critical for human well-being and data availability remains limited.
How to cite: Gafurov, A., Kalashnikova, O., Sasivarevic, A., Niyazov, D., Selyuzhenok, V., Gafurov, A., and Mamaraimov, A.: Cryosphere monitoring and modeling in Central Asia: integrating in-situ observations, remote sensing, and community-driven approaches, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13117, https://doi.org/10.5194/egusphere-egu25-13117, 2025.
This paper addresses the critical need for high-altitude weather, climate, and environment monitoring in the Himalayas, where the impacts of climate change, such as water and food insecurity, biodiversity loss, and increased extreme events, are increasingly felt. Despite the region’s vulnerability, existing climate monitoring infrastructure remains inadequate, with past efforts to install Automated Weather Stations (AWS) often failing due to sustainability challenges related to maintenance and upkeep. The proposed solution leverages the strategic location of monasteries in remote, high-altitude regions, which serve as centers of teacher-student traditions, many of which are occupied year-round, and can provide secure sites for AWS installation. By training monks, nuns, and lamas to maintain these stations, this approach aims to fill critical data gaps and strengthen adaptation strategies for local communities with the monastic practice of spreading wisdom by fostering community awareness about climate change. Furthermore, this approach addresses potential logistical and bureaucratic barriers, such as permissions within national parks, and using private monastery properties with established accessibility. The project seeks support for equipment installation, volunteer training, and collaborative research to create a robust, sustainable monitoring network while contributing to the global understanding of high-altitude climate dynamics.
How to cite: Pradhananga, D., Smith, J., Crowe, M., Bharati, L., Aryal, K., Kayastha, D., and Manandhar, S.: Filling critical data gaps in High-Altitude Environments of the Himalayan Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8959, https://doi.org/10.5194/egusphere-egu25-8959, 2025.
In the face of climate change transforming snow cover and permafrost in the Arctic, it is important to enhance our understanding of how snowmelt interacts with the environment. Here, we use stable isotopes of 17O, 18O and 2H coupled with hydro-chemical tracers and process-based modelling, to trace snowmelt from the evolution of the snowpack to river runoff and groundwater recharge in a coastal Arctic environment. The study is based on the data obtained from an unglaciated Fuglebekken catchment of 1.27 km2 situated in the southwest Spitsbergen. This area represents sea terraces and coastal mountain catchments that are becoming increasingly common with deglaciation. We reveal the dynamics of the snowmelt partitioning between surface runoff and underground recharge throughout the summer season. Change in isotopic content within the snow profile during snowpack evolution indicates significant fractionation processes. The study underlines the importance of accurately addressing uncertainties when using the isotopic hydrograph separation method and discusses possibilities for tackling these uncertainties.
How to cite: Rets, E., Nawrót, A., Luks, B., and Wachniew, P.: Understanding Snowmelt Interaction with the Environment in a Changing Climate: Insights from a Small Coastal Mountainous Catchment in Svalbard, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19386, https://doi.org/10.5194/egusphere-egu25-19386, 2025.
Despite the importance of snow contributions to the water resources collected by alpine catchments, their precise measurement and monitoring remain challenging due to their complexity and inaccessibility. Stable Isotopes of water (δ18O, δ2H, and δ17O) allow separation of streamflow into water that entered the catchment as snow versus rain. Meanwhile, progress in process-based simulations of snow (physics-based FSM2oshd model) fused with satellite snow cover data has enhanced the accuracy of gridded data products such as snow water equivalent (SWE) and runoff from snow melt (ROS) in mm at a resolution of 250m (Mott, 2023).
Between June 9, 2016, and September 24, 2018, 2548 water samples from the Avançon de Nant (Western Swiss Alps, 13.4 km2, 1200 to 3051 m a.s.l.; see Michelon et al., 2023) were analyzed for δ18O, δ2H, and δ17O and compared with 157 snow and 95 rain samples taken in the catchment during the same period. A simple mixing model of snow and rain was used to estimate the porporation of daily discharge originating from snow (Psnow). Over the same period, the discharge (Q) was measured and and multiplied with Psnow to estimate discharge from snow, Qsnow.
There is a clear seasonality of the correspondence between the ROS and Qsnow: during the low flow period, Qsnow exceeds ROS. In contrast, during the peak flow periods, e.g., during the spring “freshet” period, ROS exceeds Qsnow. Their correlation is statistically significant during the spring freshet (April–June), because direct snow runoff, hving undergone minimal storage, dominates the streamflow. When snow-free periods are excluded, the Qsnow, as determined by isotopes, is more correlated with the ROS than is the total Q. This difference is obscured when including ROS-free periods. The discrepancy we see can be explained by the fact that ROS does not account for storage and release beyond the grid scale, namely the catchment scale, and thus may eventually be the basis for a travel time calculation.
This example in a single catchment allows inter-scale comparisons, moving beyond validation to developing larger-scale monitoring tools. Collecting and analyzing stable isotope samples is labor-intensive and not universally possible. Thus, finding tools that enable the information they deliver to be gleaned from other sources is very useful. Ongoing work compares how these comparisons vary according to other satellite-derived products, such as SWE, at a higher resolution, which may be more ubiquitous.
References
Michelon, A., Ceperley, N., Beria, H., Larsen, J., Vennemann, T., and Schaefli, B.: Hydrodynamics of a high Alpine catchment characterized by four natural tracers, Hydrol. Earth Syst. Sci., 27, 1403–1430, https://doi.org/10.5194/hess-27-1403-2023, 2023.
Mott, R.: Seasonal snow data for Switzerland OSHD - FSM2sohd (1.0), https://doi.org/10.16904/ENVIDAT.404, 2023.
How to cite: Ceperley, N., Schaefli, B., Zakeri, F., and Mariethoz, G.: Comparing Satellite-Derived and Isotope-Based Estimates of Snow Contribution to Runoff in an Alpine Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16352, https://doi.org/10.5194/egusphere-egu25-16352, 2025.
Understanding the interactions between cryosphere and groundwater is pivotal but challenging. This is primarily due to high spatial heterogeneity of subsurface properties and rare spatial in-situ measurements in such environments. Here we discuss the opportunities and challenges of modelling shallow groundwater in a high elevation glaciated alpine catchment: the Martell Valley in the central European Alps (northern Italy). We have performed extensive field measurements of hydroclimatic variables and sampling campaigns for stable water isotope analysis (δ2H, δ18O) since 2022, including river discharge, groundwater level, spring discharge, rainfall, snow, and glacier outlets. To infer additional insights on the system dynamics, we adopted the physics-based hydrological model WaSiM with an integrated groundwater module for hydrological process simulations. We find that (i) shallow groundwater increases nearly as quickly as streamflow to snowmelt and heavy rainfall, as shown by their hydrographs and annual isotope signatures. Because this quick groundwater response is rarely anticipated by the model, this highlights the need for improved subsurface parameterization in hydrological models. (ii) Surprisingly, subsurface lateral flow plays a minor role in river discharge generation at the study site, providing new insights into the hydrological processes in this environment. (iii) Lastly our results underline the challenges of integrating point-scale groundwater observations into a distributed hydrological model, with important implications for future piezometer installation in the field. Through our findings with this coupled modelling-field data study, we synthesize current challenges in modelling high alpine hydro(geo)logical processes.
How to cite: Fan, X., Hofmeister, F., Tarantik, M., Ceperley, N., Schaefli, B., and Chiogna, G.: Modelling shallow groundwater in high elevation alpine catchments: opportunities and challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4147, https://doi.org/10.5194/egusphere-egu25-4147, 2025.
Given the sensitivity of snow to climate change and its critical role in the hydrological cycle of alpine regions, it is essential to accurately project future snow processes in mountainous areas. This study, taking the Manas River Basin (MRB) in Xinjiang China as the test bed, aims to quantify the uncertainties in hydrometeorological variables from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) simulations and further reduce these biases using a Cycle-Consistent Generative Adversarial Network (CycleGAN). The bias-corrected CMIP6 data are then used to drive the SWAT model calibrated with both runoff and snow water equivalent (SWE) through a dual-objective approach for future projections. The results indicate that: (a) model uncertainty is the primary source of uncertainty in the original CMIP6 outputs. CycleGAN demonstrates substantial effectiveness in reducing model uncertainties; (b) most subbasins of the MRB will experience absolute SWE reduction in the future and the changes of SWE vary significantly across elevation bands; (c) The runoff in MRB has an increasing trend in future. As the ratio of rain to snow increases and snowmelt occurs earlier, low flows during the dry period will increase significantly, which will result in higher risk of spring floods. The findings will provide important guidance for projecting future snow dynamics and water resources management in the snow dominated watersheds.
How to cite: Liu, Z., Su, T., Zhu, F., and Duan, Q.: Integrating uncertainty decomposition and CycleGAN bias correction in enhancing future hydrologic projections in a snow-dominated alpine watershed, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6142, https://doi.org/10.5194/egusphere-egu25-6142, 2025.
In glacierized high mountain catchments, streamflow generation is strongly influenced by snow and glacier melt, processes especially sensitive to rising temperatures and ongoing climate change. These vulnerabilities make mountain headwater catchments a research priority; However, limited observational data and complex glacier-snow interactions often challenge conventional hydrological modeling in high-mountainous areas. Although several models have been developed to simulate streamflow dynamics in glacierized settings, many either lack comprehensive glacier representations or oversimplify them, failing to incorporate critical processes such as glacier evolution over time (e.g., glacier retreat).
To address these limitations, we employed the recently developed SWAT-GL model, which integrates a mass balance module and a glacier evolution parameterization to more accurately track changes in glacier volume and extent. Using a degree-day approach and ∆h-parameterization for glacier adjustment, SWAT-GL provides a robust framework for simulating spatiotemporal streamflow dynamics in glacierized catchments.
We applied SWAT-GL to the Valsorey catchment in the canton of Valais, Western Swiss Alps, calibrating the model with in-situ meteorological and streamflow data collected over the past decade. Our analyses revealed pronounced interannual variability in flow intermittency between climatically contrasting years, underscoring the distinct influences of glacier-fed and non-glacier-fed sub-catchments on overall runoff patterns. In particular, we identified notable differences in no-flow occurrences and seasonal streamflow recessions. Glacier-fed streams exhibited prolonged baseflow during warmer periods, while non-glacier-fed streams experienced an earlier onset and more frequent episodes of low or no-flow conditions.
Ongoing work applies future climate change scenarios to explore how continued glacier retreat will reshape these flow regimes and flow intermittency patterns. These findings will provide valuable insights into the resilience and adaptability of alpine hydrological systems.
How to cite: Llanos-Paez, O., Deluigi, N., Hou, J., and Battin, T.: Modeling spatial and temporal streamflow dynamics in a high-mountain catchment using the SWAT-GL model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6577, https://doi.org/10.5194/egusphere-egu25-6577, 2025.
This study investigated the responses of glaciers to changes in air temperature and precipitation in two Central Asian glacierized basins, namely the Ala-Archa basin in Kyrgyzstan, where 16% of the area is covered by glaciers, and the Tailan River basin in China, with 33% glacier coverage. A ∆h-parameterization approach was coupled with the Cold Regions Hydrological Model (CRHM) to simulate glacier dynamics. CRHM uses physically based algorithms to simulate the full range of mountain hydrocryospheric processes such as energy balance snow and ice melt, slope/aspect influence on irradiance, energy balance precipitation phase, blowing snow transport and sublimation, avalanches, firnification and firn to ice conversion, subsurface storage and runoff processes, surface water detention, actual evapotranspiration and hydrograph routing. The Randolph Glacier Inventory (RGI) versions from 1.0 to 7.0 were employed to validate the modeled glacier changes. Bias-corrected ERA5 reanalysis data were used to reconstruct the meteorological and energy conditions on glaciers over the historical period from 1950 to the present. Thanks to the robust physical foundation of CRHM, which requires minimal effort in parameter identification, the contrasting glacier responses in the two basins can be predominantly attributed to differences in local climate, surrounding terrain, and energy processes. The preliminary results suggest a strong dependence of the glacier area response to climate change on terrain characteristics such as slope, aspect, and self-shadowing. Meanwhile, the response of glacier thickness is more sensitive to elevation and the distance from the central flow line. The total glacier area in the Tailan River basin is much less sensitive to warming compared to that in the Ala-Archa basin due to its greater mean glacier thickness. In contrast, the streamflow response in the Tailan River basin is more sensitive to climate warming because of its larger glacier coverage. These modeling findings offer valuable insights into how local glaciation, snow, firn and ice exposure, terrain and climate condition the streamflow response to climate change in Central Asian glacierized basins.
How to cite: He, Z., W. Pomeroy, J., and Tian, F.: Contrasting glacier responses to climate change in Central Asian Basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14123, https://doi.org/10.5194/egusphere-egu25-14123, 2025.
Anthropogenic climate change is strongly impacting mountain regions. The warming rate observed for the Alpine Region is well above the global average. Moreover, crucial water reservoirs such as glacier ice and seasonal snowpack are extremely susceptible to changes due to their inherent dependence both on the persistence of below 0 °C temperatures and the amount of solid precipitation. In recent years, the European Alps have experienced multiple seasons of intense deficit of snow precipitation compared to historical records. Given that the hydrological assets of the Western Italian Alps have a crucial role in the economic activities of Northern Italy, it is necessary to enhance the monitoring and the studies performed on the region. One of the most pressing criticalities that arise dealing with the study of the Alps is the growing need of direct measurements of meteorological and snow-related variables at high-elevation sites. To address this gap, a network of automated stations (ASs) has been established on the Monte Rosa massif in Western Alps, on the border between the Italian regions of Piedmont and Aosta Valley. The network’s responsibility falls under the Laboratory of Alpine Climatology, LabClima, of the University of Turin. Firstly, we present the data collected by the AS installed in the LTER site - Mosso Institute (45°52’30’’ N; 7°52’18’’ E; 2900 m a.s.l.), which is also equipped with sensors for Snow Water Equivalent (SWE) measurement based respectively on Cosmic Rays Sensors (CRS) and Global Navigation Satellite System (GNSS) technologies. The daily SWE datasets are compared with field data collected during measurement campaigns to investigate their potential to increase the temporal density and availability of data for remote and harsh mountain environments. We also present the data acquired by another AS (45°53'46.53"N; 7°50'56.96"E; 3500 m a.s.l.) established in September 2024 near the Garstelet Glacier. The sensors installed retrieve continuous data regarding the main meteorological variables (e.g., air temperature, atmospheric pressure, wind speed and direction, relative humidity, solar radiation, precipitation, and snow height) as well as the temperature of the snowpack layers (measured at 50 cm intervals from the ground level up to 2 m height) and the type of precipitation thanks to the presence of a disdrometer. The variety of data collected by this AS is unprecedented at such elevation in the Italian Alps and could help to address the present gaps of information for end users and future scientific research.
This abstract is part of the project NODES which has received funding from the MUR – M4C2 1.5 of PNRR funded by the European Union - NextGenerationEU (Grant agreement no. ECS00000036). The authors also acknowledge the support of NBFC to University of Turin, Department of Agricultural, Forest and Food Sciences, funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU.
How to cite: Gallarate, M., Colombo, N., Viani, C., Gazzola, E., Henkel, P., Lamm, M., Maiorano, M., Freppaz, M., Giardino, M., and Acquaotta, F.: Exploring the advantages of automated stations to retrieve continuous weather and snow-related data in high-elevation sites (Monte Rosa massif - Western Italian Alps), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9399, https://doi.org/10.5194/egusphere-egu25-9399, 2025.
Mediterranean mountainous basins play a vital role in supplying water and supporting ecosystem services. However, these environments are increasingly threatened by climate change. Recent studies reveal that mountainous catchments in the Mediterranean region, which experienced substantial snow accumulation from 1970 to 1990, are now facing reduced snow levels and faster snowmelt since 2000. These changes can significantly affect the seasonality and volume of runoff and groundwater recharge as well as changes in vegetation phenology. Given the expected drier and warmer Mediterranean region the implications for these cathcments remain poorly understood.
This study explores the eco-hydrological implications of reduced snow accumulation using ground observations from a newly established catchment: Ussita (18 km²), a tributary of the Nera River located in the Apennines within the Monti Sibillini National Park, Central Italy. We analyzed two contrasting hydrological years—2022-2023, which featured substantial winter snow accumulation (up to 300 cm at high elevations) and a hot summer, and 2023-2024, which has thus far recorded a severe snow drought with less than 30 cm at the same locations.
The experimental setup includes an array of instruments: pressure transducers for river and groundwater levels, electrical conductivity meters, soil moisture probes, throughfall collectors, tree talkers, and a weather station. Additionally, stable water isotope data from precipitation, groundwater, soil, and surface water were used to trace water sources across hydrological compartments.
Preliminary results, using these collected data complemented with remote sensing observations of evaporation and gross primary productivity, highlight shifts in runoff seasonality and a faster runoff decline as well as anticipation of the growing season with an anticipation of the decline of soil moisture levels thus underscoring the significant impacts of snow droughts on eco-hydrological dynamics of this cathcment. Ongoing analysis aims to deepen our understanding of these eco-hydrological changes and their broader implications for the region.
How to cite: Massari, C., Dionigi, M., Donnini, M., Di Matteo, L., Fronzi, D., Avanzi, F., Battipaglia, G., Preziosi, E., Cappelletti, D., Spolaor, A., Segura, C., and Penna, D.: Eco-hydrological insights from a snow drought in a Mediterranean mountainous catchment in Central Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17507, https://doi.org/10.5194/egusphere-egu25-17507, 2025.
The International Network for Alpine Research Catchment Hydrology (INARCH, https://inarch.usask.ca) is a cross-cutting project of the GEWEX Hydroclimatology Panel (GHP) to better understand alpine cold regions hydrological processes, improve their prediction, diagnose their sensitivities to global change, and find consistent measurement strategies. At its core is a global network of 38 highly-instrumented mountain observatories and experimental research sites in 18 countries and six continents, which are testbeds for detailed process studies on mountain hydrology and meteorology, developing and evaluating numerical simulation models, validating remotely sensed data, and observing, understanding, and predicting environmental change. INARCH has completed a Common Observing Period Experiment (COPE) over the period 2022–2024, collecting high-quality measurements along with supplementary observations and remote sensing campaigns, to produce a common, coherent, and well-documented and described data set of mountain meteorology and hydrology. These data will be used to address key INARCH science questions and for a series of hydrological process diagnostic modelling evaluations and analyses. The aim is to better understand why models produce various behaviours and to see if models benchmark various known aspects and regimes of the coupled atmospheric-cryospheric-hydrological system. Model diagnostic evaluations will emphasize atmospheric, snow, glacier, and water processes in high mountain terrain and include sparse forest, non-needleleaf vegetation, glaciated, and alpine windblown sites. This has not been done globally in alpine regions and could be potentially very powerful. The presentation will discuss progress in the COPE, an overview of the data management and initial results, and next steps in the analyses.
How to cite: DeBeer, C., Pomeroy, J., López Moreno, I., McPhee, J., and O'Hearn, S.: Improving understanding and prediction of the mountain water cycle – overview and initial results from the INARCH Common Observation Period Experiment, 2022–2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12462, https://doi.org/10.5194/egusphere-egu25-12462, 2025.
Advances in alpine snow and ice hydrology have occurred due to the relentless efforts of field researchers to study snow processes in remote research sites, improvements in automated instrumentation, advances in remote sensing, and improvements in numerical modelling. Crucial has been the joint consideration of the mass and energy conservation equations and phase change in various calculation procedures. For instance, energy budget snowmelt and icemelt methods have replaced calibrated, anti-physical and highly uncertain temperature index melt models. Slope, aspect, remote shading, katabatic flow and wind flow over complex terrain are considered in energy and mass balance calculations. Albedo decay considers changes in grain size and increasingly addresses deposition of impurities such as soot. Snow redistribution by wind and by gravity have been recognized as important processes controlling snow accumulation. Blowing snow redistribution has advanced from flat-earth physics to 3-D complex terrain representations of saltation and suspension transport and sublimation due to turbulent transfer with blowing snow particles. Snow redistribution by forest canopies considers the role of canopy structure in interception and of the competing processes of unloading, sublimation and melt in ablating canopy snow. Snow-soil interactions consider the role of freezing soils on heat flow to snow and infiltration of snowmelt. Snow depth can be measured by LiDAR from planes and drones and snow-covered area and albedo estimated by satellite.
However, several challenges remain unsolved or very uncertain. Advection of latent and sensible heat from bare ground or open water to snow or ice is not fully understood in complex terrain. Ice ablation from glaciers terminating in proglacial lakes is uncertain. Alpine blowing snow calculations do not fully consider the role of terrain roughness and sparse vegetation on transport fluxes and atmospheric exchanges. Wind flow calculations in steep alpine terrain are still problematic and incapable of reliable estimation of wind speed and direction. Intercepted snow calculations lack an understanding of wind erosion and redistribution from forest canopies. Snow avalanche calculations used in hydrology are highly empirical and tuned to regional observations, so lack the flexibility and global robustness of physically based methods. Snow water equivalent observations still depend on gravimetric methods and lack reliable high resolution remote sensing approaches. Snowfall measurements are too sparse and in wind swept terrain are still highly uncertain due to wind-induced undercatch and other gauge errors. Albedo impacts from atmospheric deposition on snow and ice and biological magnifiers such as snow and ice algae are understood but not incorporated in calculations. The role of edge effects such as treelines, glacier edges, canopy gaps and ridges on upscaled hydrological responses are incompletely understood. And the full understanding of what fine-scale processes are emergent or are compensated for in larger scale energy and water budget calculations is still being developed.
This talk considers the advances in and the prospects for improving snow and ice process understanding, parameterisation and prediction in alpine catchments and calls for new research to solve the remaining uncertainties.
How to cite: Pomeroy, J.: Challenges in Alpine Snow and Ice Hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13257, https://doi.org/10.5194/egusphere-egu25-13257, 2025.
The mountainous aquifer system plays an important role in the entire hydrological cycle, facilitating the redistribution of seasonal water resources and providing water supply to downstream watersheds. Due to severely environments and complex geological conditions, it is still a challenge to establish a conceptual model to comprehensively describe the system. Accordingly, this study aims to focus on constructing an alpine hydrogeological model and evaluating the water budget in mountain areas. The selected study site is located at the upstream tributaries of the Beinan River in Taituung County, Taiwan. Through establishment of hydrological monitoring facilities of surface and subsurface and conduction a series of field experiments, such as electrical resistivity tomography (ERT), fiber-optic distributed temperature sensor (FO-DTS) measurements, hillslope infiltration tests, and cross-borehole tracer tests, the aquifer properties, flow paths, and recharge mechanisms can be evaluated. The preliminary results indicate that the shallow aquifer contributed a significantly amount of water to the stream, piratically during the dry seasons. Infiltrated water was primarily passes through the regolith, while flow within the bedrock is predominantly controlled by fractures. Groundwater was mainly stored in the regolith but the water within the fractured rocks may serve as the buffer for the downstream water supply. The conceptual model and water flow paths for high mountain hydrogeology developed in this study can provide a theoretical basis for understanding the hydrological characteristics, hydrological processes, and storage of groundwater resources in mountainous areas. This preliminary model can serve as a reference for subsequent analyses of the impact and feedback of climate change and land use changes on the hydrogeological environment of high mountain areas.
How to cite: Chiu, Y.-C. and Lee, Y.-H.: Development of an Alpine Hydrogeology Model and Water Budget Evaluation– a Case Study of the Upper Beinan River, Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2411, https://doi.org/10.5194/egusphere-egu25-2411, 2025.
Alpine glaciated catchments exhibit complex hydrological streamflow dynamics influenced by temperature effects on snow and ice melt as well as precipitation, resulting in seasonally varying diel streamflow cycles. These cycles shift and become more intense during the summer melt season due to reduced buffering by the declining snow cover and the associated progressive development of more efficient subglacial drainage systems. This variation is of importance, especially for sediment transport, which is commonly a non-linear function of instantaneous discharge above a critical threshold. However, these diel streamflow cycles remain challenging to simulate due to a lack of high-quality meteorological data for remote areas and a general lack of observed streamflow data in highly glaciated catchments for model calibration. Consequently, many classically used hydro-glaciological models, such as those that use a degree-day approach for melt simulation, cannot capture sub-daily streamflow dynamics well, unless they are combined with temporal downscaling to sub-daily timescales. This work aims to develop an innovative downscaling approach that captures the specific features of streamflow patterns in Alpine glacierized catchments.
The work benefits from an exceptionally high-resolution dataset that comprises 15-minute discharge records for 45 years from 7 small, highly-glacierized catchments in the South-Western Swiss Alps (relative glacial cover ranging from 5 to 70%). It adopts a maximum entropy (POME) approach more commonly used to downscale non-glacial discharge records available at the monthly scale. We couple this approach with a semi-distributed hydrological model that predicts mean daily discharge using modeled hydrological characteristics (e.g., snow depth, ice melt rates) to drive the downscaling.
Results show that a simple sigmoid equation can be used to fit the daily flow duration curves of glacierized catchments. Furthermore, the progressive evolution of the sigmoid parameters over the last 45 years shows the influence of rapid climate warming on the dynamics of sub-daily flows. The downscaling method based on daily simulated discharge and informed by simulated hydrological and glacial characteristics offers a promising and transferable solution for reconstructing sub-daily discharge in data-scarce regions, as well as for improving hydrological modeling at high temporal resolutions.
How to cite: Argentin, A.-L., Gianini, M., Schaefli, B., Horton, P., Chavez-Demoulin, V., Pitscheider, F., Repnik, L., Bizzi, S., Lane, S. N., and Comiti, F.: Sub-daily downscaling of discharge in glacierized Alpine catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7009, https://doi.org/10.5194/egusphere-egu25-7009, 2025.
Alpine regions are highly sensitive to the impacts of climate change, with snowmelt dynamics playing a crucial role in their hydrological processes. A representative variable of the snowmelt is the snow water equivalent (SWE). However, SWE measurements are rare and limited to point scales, making it difficult to obtain accurate spatialized estimates. For this reason, remote sensing products offer a unique opportunity to provide spatialized observations. Recently, using optical remote sensing data from MODIS, Landsat and Sentinel-2, SAR data from Sentinel-1 and in situ observations, Premier et al. (2021) developed a multi-source data method to reconstruct daily snow cover area (SCA) maps at high spatial resolution (20 m). In this work, we investigate the effectiveness of combining this approach with a semi-distributed hydrological model (GEOframe) (Formetta et al., 2014) for reconstruct SWE at high spatial resolution (20 m) in the alpine catchment of Dischma, Kanton Graubünden, Switzerland (~40 km²). Modelled results are compared against both observed discharge and high-resolution SWE maps reconstructed using snow depth data retrieved by airplane photogrammetry of Bührle et al. (2022) and then converted into SWE maps using the approach of Jonas et al. (2009).
The GEOframe model can reproduce with high accuracy the observed discharge (KGE=0.904, NSE=0.823). However, being a semi-distributed model, modelled SWE spatial patterns are too coarse and less accurate. We find that the most effective SWE downscaling approach is based on the combination of topographic parameters and the snow persistency estimated by the novel approach of Premier et al. (2021). Comparing SWE estimates based on the novel proposed approach against the observations, we find a mean bias error of + 27.27 mm and a correlation of 0.624. Results suggest that our new method can reproduce SWE spatial patterns quite well, but at the same time the catchment-averaged SWE is bound to the water mass balance estimated by the hydrological model.
The presented approach could be seen by a two-fold perspective. Either a downscaling procedure to improve the capability of a semi-distributed hydrological model to estimate high-resolution SWE pattern in mountain regions, or a method to estimate SWE from multi-source satellite observations using the constraint on catchment-scale water budget coming from a hydrological model.
How to cite: Bozzoli, M., Bertoldi, G., Premier, V., Marin, C., Tonelli, C., Formetta, G., and Bavay, M.: Assimilation of snow persistency information in a hydrological framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8373, https://doi.org/10.5194/egusphere-egu25-8373, 2025.
Precipitation (snow and rainfall) is an essential climate variable for hydrological modelling, flood forecasting, avalanche preparedness and assessing permafrost stability and ecological change. In data sparse regions, such as the Canadian Sub-Arctic, long-term sub-daily precipitation measurements are rare, yet imperative to understanding environmental feedback and the impact of extreme events. The Wolf Creek Research Basin (WCRB) in the southern Yukon, Canada, provides a unique long-term hydrological and climate record across forested, shrub and alpine ecozones. This study presents hourly precipitation recorded in WCRB since 1993 using a variety of instruments. The diversity in measurement techniques and range of monitoring elevations allows for thorough consideration of precipitation phase and lapse rate.
We outline the challenges of maintaining and compiling in-situ, remote monitoring data spanning decades of change. This study facilitates discussion around best practices for cold-region precipitation data products by using transparent data filtering, correction and in-filling. We consider the efficacy and uncertainty of measurement techniques and bias correction methods for wind-induced losses at a site equipped with multiple concurrent instruments, shields and gauges. Our results explore spatiotemporal trends in the preliminary dataset and compare to available data in the southern Yukon. This work provides critical insights into the improvement and longevity of cold region, remote precipitation monitoring and the importance of long-term data sets in a changing climate.
How to cite: Tutton, R., Carey, S., Pomeroy, J., and Janowicz, R.: A Thirty-Year Precipitation Record at Wolf Creek Research Basin, Yukon and the Importance of Bias-Corrected, Sub-Daily Measurements in a Changing Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14633, https://doi.org/10.5194/egusphere-egu25-14633, 2025.
Meteorology is crucial to understand the rapid response of mountain glaciers to climate warming, but is often challenging to observe and simulate due to site inaccessibility, instrument maintenance and the complex interactions between glaciers and their surroundings. Recent, high-resolution, globally-available remote sensing observations create an opportunity to exploit the observed changes in glacier volumes and surface properties to infer bias-corrected high-mountain meteorology from climate reanalysis. In this study, we develop a unified method for model inversion based on Monte Carlo simulation and Bayesian inference, and then evaluate it on four benchmark glaciers with extensive in-situ measurements of surface meteorology (Argentière Glacier and Aletsch Glacier in the European Alps, Abramov Glacier and Parlung No.4 Glacier in High Mountain Asia).
Our approach is a multiparameter optimization that uses a physical-based land-surface model (Tethys-Chloris) driven by an ensemble of statistically-downscaled ERA5-Land reanalysis datasets, with remote-sensing-derived glacier surface mass balance and glacier albedo as targets. With this method, we obtain the bias of air temperature, precipitation and incoming shortwave radiation to correct the reanalysis data during the period 2015-2019 at the four sites. The results show that the derived multiyear meteorology is spatially variable over the glaciers and agrees with independent in-situ observations at each site. The good performance of this method in different climatic conditions paves the way to derive multiyear glacier meteorology on the world’s mountain glaciers and constrain globally a key control on their response to climate change.
How to cite: Ren, S., S. Miles, E., McCarthy, M., Jouberton, A., E. Shaw, T., Buri, P., Kneib, M., Gantayat, P., and Pellicciotti, F.: Inferring glacier meteorology with physical modeling and remote sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17515, https://doi.org/10.5194/egusphere-egu25-17515, 2025.
