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FS 3.116

High mountain hydrology and cryosphere: observations, modelling, prospects

Details

Full Title
FS 3.116: High mountain hydrology and cryosphere under global change: observations, modelling, prospects
Scheduled
TBA
Location
TBA
Convener
Chris Debeer
Co-Conveners
John Pomeroy, Ignacio López Moreno, James McPhee,
Assigned to Synthesis Workshop
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Thematic Focus
Cryo- & Hydrosphere, Monitoring, Multi-scale Modeling
Keywords
Catchment Hydrology, Alpine, Observation, Prediction

Description

High mountain headwaters (snowy forest or tundra, or glaciated basins) are the sources of rivers that supply freshwater for much of humanity. There is a global need to better understand high mountain atmospheric, hydrological and cryospheric processes, improve their prediction as coupled systems, and diagnose their sensitivities to global change to promote water sustainability. Critical research questions such as are there consistent measurement strategies that can be implemented in high mountains, does the predictability of water and energy cycling vary across the high mountain ranges, what improvements are possible in predictions by resorting to high resolution coupled models, to what degree do existing model routines have global validity and can high mountain headwaters be managed to achieve sustainability under global change are important to the World Climate Research Programme’s International Network for Alpine Research Catchment Hydrology (INARCH), a cross-cut project of the GEWEX Hydroclimatology Panel. INARCH has recently conducted a Common Observing Period Experiment (COPE) in 38 mountain research basins to collect high-quality observations from field and remote sensing campaigns. This session welcomes contributions addressing any of the critical research questions, and particularly welcomes contributions on observations from instrumented mountain catchments, theoretical advances and on evaluation of hydrological and atmospheric models using observations to better understand model performance and to see if models reproduce known aspects and regimes of the coupled atmospheric-cryospheric-hydrological system.

Submitted Abstracts

ID: 3.8058

Remote Sensing-based River Discharge Estimation for a Small River Flowing Over the High Mountain Regions of the Tibetan Plateau

Mulugeta Genanu Kebede
Wang, Lei

Abstract/Description

River discharge, as one of the most essential climate variables, plays a vital role in the water cycle. Small-scale headwater catchments including high-mountain regions of Tibetan Plateau (TP) Rivers are mostly ungauged. Satellite technology shows its potential to fill this gap with high correlation of satellite-derived effective river width and corresponding in-situ gauged discharge. This study is innovative in estimating daily river discharge using modified Manning equation (Model 1), Bjerklie et al. (2003) equation (Model 2), and Rating curve approach (Model 3) by combining river surface hydraulic variables directly derived from remote sensing datasets with other variables indirectly derived from empirical equations, which greatly contributes to the improvement of river flow measurement information especially over small rivers of TP. We extracted the effective width from Landsat image and flow depth via hydraulic geometry approach. All the input parameters directly or indirectly derived from remote sensing were combined and substituted into the fundamental flow equations/models to estimate discharges of Lhasa River. The validation of all three models’ results against the in-situ discharge measurements shows a strong correlation (the Nash–Sutcliffe efficiency coefficient (NSE) and the coefficient of determination (R2) values ≥ 0.993), indicating the potentiality of the models in accurately estimating daily river discharges. Trends of an overestimation of discharge by Model 1 and underestimation by Model 2 are observed. The discharge estimation by using Model 3 outperforms Model 1 and Model 2 due to the uncertainties associated with estimation of input parameters in the other two models. Generally, our discharge estimation methodology performs well and shows a superior result as compared with previously developed multivariate empirical equations and its application for other places globally can be the focus of upcoming studies.

ID: 3.8239

Hyper-resolution decametric modelling of alpine catchments : development of a data processing framework to represent small scale-snow hydrological processes, over complex topography

Alix Reverdy
Cohard, Jean-Martial; Voisin, Didier; Gupta, Aniket; Vermaut, Sarah; Liger, Lucie; Arnaud, Laurent; Barral, Hélène; Coulaud, Catherine; Le Lay, Matthieu

Abstract/Description

Mountain socio-ecosystems are under increasing pressure from anthropogenic forcings (warming, precipitation change and nutrient inputs). Understanding and projecting the consequences of these changes for local biodiversity and downstream water resources, requires to be able to model transfer of energy and water by vertical and lateral fluxes. The determination of these water paths is particularly challenging in mountain terrains, where small scale snow, topographic and geomorphological processes drive hydrology. Conceptual and semi-distributed hydrological models fail to represent the complexity of these water paths and land surface model often neglect lateral fluxes, making both approaches limited in studying trajectories of mountain socio-ecosystems.
To overcome these limitations, we applied the data-intensive and calibration-light critical zone model ParFlow-CLM3.5, to a highly instrumented alpine catchment (6.2 km², 1950-3100 m.a.s.l) near the Lautaret Pass, in the French Alps. Specific efforts were directed toward the representation and definition of small-scale snow hydrological processes, modifying significantly the timing, amount, and location of water fluxes above and below the surface.
Limitations of the initial snow scheme were overcome by refining the snow/rain transition dependencies on meteorological factors, by improving the snow albedo aging routine, by accounting for Saharan dust events and by selecting relevant spatial distribution methods for meteorological forcings. The snow/rain transition was evaluated with a disdrometer. Meteorological forcings are distributed based on topography (slope effect on radiation and windspeed, shading, reillumination by longwave radiation), altitude (precipitation, temperature and humidity gradients), and remote sensing measurements (snow redistribution).
In this presentation we will focus on these snow scheme improvements, and the ability of the model to represent the dynamic of the snow cover during the season at decametric resolution. This will be evaluated spatially with drone, Sentinel-2, and Pleiades images (snow height, snow cover), locally with albedo, snow height and Snow Water Equivalent, and hydrologically with streamflow observations. Further on, this work aims to show that distributed and physics-based hydrological modelling is feasible over complex alpine terrain, with reduced field data needs, and to provide a reproducible framework.

ID: 3.8250

Spatio-temporal assessment of areal fragmentation and volume of snow cover in the central Himalaya

Surajit Banerjee
Sati, Vishwambhar Prasad

Abstract/Description

Central Himalaya, the third-largest ice mass globally, is the source of major rivers like the Ganges, upon which almost 655 million people from India, Nepal, and Bangladesh rely for livelihood. However, climate change is the largest threat to its snow cover. Therefore, this study utilizes remotely sensed data to examine spatio-temporal variations in snow cover area, volume, and areal fragmentation over thirty years. Different landscape metrics (class area, number of patches, patch density, largest patch index, mean patch size, edge density, and perimeter area ratio) are used and a novel index is developed to study fragmentation. NDSI is used to map the snow cover and area volume scaling based on empirical observations to estimate the volume. Despite fluctuations, a trend of decline emerges in thick and thin snow cover (from 10768 km² to 3258.6 km² in thick snow, and from 3798 km² to 6863.56 km² in thin snow during maxima, whereas, from 1678.44 km² to 539.66 km² in thick snow and from 2414.12 km² to 1300.56 km² in thin snow during minima). Thick snow cover fluctuated with a period of decline up to 2006, followed by a slight recovery and subsequent reduction in 2021. Conversely, thin snow cover shows a gradual increase up to 2006, followed by a rapid decline in 2021, highlighting the region’s high susceptibility to warming. Furthermore, it was found that B4, C2, C3, and C4 were the grids with very high fragmentation of thick snow. However, C1, D2, and E4 were marked as highly fragmented for thin snow cover. The research underscores the urgent need for adaptive and mitigation measures to impact climate change in the fragile cryosphere of the Central Himalayas.

ID: 3.8423

Impact of climate change on snow cover in the Pyrenees, Alps, and Andes Mountains, derived from 40 years of Landsat data

Andreas Dietz
Roessler, Sebastian; Baumhoer, Celia; Cereceda-Balic, Francisco; Saavedra, Freddy; Gascoin, Simon; Barrou Dumont, Zacharie

Abstract/Description

Climate change has a substantial impact on snow cover in mountain regions, often leading to shorter snow cover duration, later snow cover onset, earlier snow melt, higher snow line elevations, less water stored in the snowpack, and subsequent effects on flora, fauna, tourism, hydropower generation, and agriculture. It is essential to understand these developments and dynamics to be able to anticipate these effects and potentially mitigate their impact on society and biodiversity. The Landsat satellites provide an ideal collection of high-resolution remote sensing datasets collected since the early 1980s, which allow for a detailed analysis of the long-term trends of snow cover in mountain regions worldwide. Recording in intervals of up to 16 days and being affected by cloud cover makes an extensive processing and aggregating necessary in order to retrieve continuous time series ready for trend analyses and predictions of future developments. The first part of the presentation will therefore focus on the methodology which was developed and applied to the Landsat archives to extract the long-term trends and predictions.
The second part of the presentation will include detailed results for the European Alps, the Pyrenees, and the Andes Mountains around Santiago de Chile. After analyzing 40 years of snow cover data derived from all available Landsat satellites, the catchments within almost all investigated mountain ranges depict negative trends, with snow line elevations receding up to 20 m per year. Predictions of potential future snow line developments have been calculated based on several methods and will be presented as well.

ID: 3.8529

Case study: Monitoring snout fluctuation and accelerating retreat of Passu Glacier in Hunza River Basin, Pakistan

Syed Hammad Ali
Muneeb, Fakhra

Abstract/Description

The global variability in glacier systems is changing, mainly due to fluctuation of glaciers from one region to another as a result of climate change. This research investigates changes in Passu Glacier between 2011 and 2024 in the context of the glacier’s response to climate change in Karakoram region, particularly the glacier’s snout. The study utilizes multi-temporal satellite images and field based surveys to analyse fluctuation of glacier snout over a period of 14 years. Field based surveys, which played an important role in understanding the behaviour of this glacier. These surveys began in 2011, determined the glacier’s snout periphery, which has aided in monitoring the glacier in subsequent years. In 2012, the glacier exhibited marked extreme instability when it retreated approximately 100m within a year. Follow up field survey in 2013, demonstrating that the glacier had indeed changed to a more retreating state, though at a slower pace increasing the chances of remaining in a more stable state. Surveys conducted between 2015 and 2016 confirmed that the glacier’s retreat was continuing, suggesting that it was part of a long term process rather than a single fluctuation. The most alarming change happened during the year 2024 survey when it was revealed that the glacier’s terminus had retreated by approximately 650 meters since the last survey conducted in 2015. This sharply increases the rate of retreat, underscoring the glacier’s greater sensitivity to climate change. Active meltwater also streaming through the snout identified along with substantial mass loss in the glacier, suggesting that there were significant ablation processes occurring. These surveys added information that helped better comprehend the more general pattern of glacier behaviour for whole Karakoram region. Data from high resolution satellites collected to aid the accurate boundary delineation exhibited further retreating glaciations. The findings emphasize the importance of integrating satellite-based analyses with in-situ observation to monitor glacier behaviour within the context of regional climate dynamics, underscoring the need for continuous monitoring to predict and mitigate potential impacts on local communities caused by glacier related hazards which relies heavily on glacial runoff for irrigation, drinking water, and even hydropower in this region.

ID: 3.8782

Advancing snow representation and regional water budget estimation in complex terrains within the Community Earth System Model

Samar Minallah
Swenson, Sean

Abstract/Description

Accurately quantifying energy and water balance in complex terrains require careful consideration of the interplay of steep topography, spatial heterogeneity in land characteristics, and microclimatic variability. Earth system models typically operate at resolutions of hundreds of kilometers and lack the details to capture hillslope processes and topography-driven feedback crucial for assessing regional-scale hydrology, ecology, and hydroclimates.
This work aims to improve the representation of snow processes in mountainous regions using the Hillslope Hydrology configuration of the Community Terrestrial Systems Model (CTSM; the land component of the Community Earth System Model CESM) that accounts for terrain characteristics (aspect, relief, and slope). Topography profoundly influences snowpack dynamics by modifying the absorption and reflectance of incoming solar radiation, gradients of temperature and precipitation with elevation, and redistribution of snowfall due to wind. By explicitly modeling these processes, the CTSM Hillslope Hydrology configuration can significantly improve estimates of snowpack evolution, seasonal and sub-seasonal streamflow, and the terrestrial energy and water budgets in topographically complex domains.

ID: 3.9087

Inferring precipitation information from multi-year aerial snow depth observations to improve input quality to a snow energy balance model

Joachim Meyer
Hedrick, Andrew; Trujilo, Ernesto

Abstract/Description

The snow dominated headwaters in the mountains of the Western United States (US) are an essential seasonal water resource. Predicting the spring release timing and magnitude requires accurate spatial information of snow distribution and extent during the accumulation and ablation seasons. One way of trying to capture the accumulation phase of seasonal snow is through the use of spatially distributed snow models that simulate the snowpack from meteorological observations. One of the dominant inputs that dictates a model’s ability to accurately simulate snow cover is precipitation, which to date is either recorded through sparse in-situ measurements stations or modelled with numerical weather prediction models (NWP). The NWP models are initialized with spatial or point observations that often have limited coverage in mountain areas, leading to underestimation or missing areas of snowfall. Approaches to improve the quality of precipitation input data for snow models include the use of spatial snow depth distribution patterns to inform precipitation/snowfall estimation, correcting the homogeneous precipitation outputs of standard interpolation methods with measured accumulation patterns. The number of spatial snow depth observations have increased over the recent years, with lidar-based platforms dominating the underlying technology. In this work, we evaluate the use of snow distribution maps to inform the precipitation input of a spatially distributed snow energy balance model to improve simulated snow depth accuracy. The lidar-based depth maps were recorded over multiple winter seasons (2020 – 2024) in Mores Creek, Idaho, US, and capture a range of accumulation seasons from below to above average years. Normalization across flights during the accumulation period showed a promising potential to derive precipitation information that can be used across water years to reduce the need for repeated observations and enhance the scalability of this approach.

ID: 3.9109

QOMS：A Comprehensive Observation Station for Climate Change Research on the Top of Earth

Yaoming Ma

Abstract/Description

Mount Everest (Qomolangma), the highest mountain on Earth, is an unrivaled natural research platform for understanding multispheric interactions over heterogeneous landscapes. The land–atmosphere interactions in this iconic mountain region have paramount importance for weather and climate predictions at both regional and global scales; however, observing and modeling these interactions is inherently challenging due to the extreme environment. The scarcity of multiscale observations hinders progress in this field. Thus, establishing a comprehensive network to systematically observe the land–atmosphere interactions across multiscales in this unrivaled region, is the basis for gaining a better understanding of weather, climate, and climate change. As one of the 69 national observation and research stations in China, the Qomolangma Special Atmospheric Processes and Environmental Changes (QOMS) observation network of land–atmosphere interactions has been established over the northern slope of Mount Everest since 2005. This network consists of six sites with different underlying surfaces, which significantly improves the observational capabilities for the climate system. These observations have promoted the understanding of land–atmosphere interactions and hydrological processes and their impacts on multiscale weather patterns, atmospheric circulations, and climate and have provided data support for informing and guiding model development and remote sensing monitoring. Facing an unprecedented opportunity with enormous development possibilities, we emphasize the considerable potential of these observations for understanding and predicting weather and climate in the Himalayas and beyond. Additionally, we expect to extend the future focus to model–data fusion and to societally relevant applications, such as natural disaster prevention and climate change mitigation and adaptation.

ID: 3.9110

QOMS：A Comprehensive Observation Station for Climate Change Research on the Top of Earth

Yaoming Ma

Abstract/Description

ID: 3.9247

Towards Bedmap Himalayas: helicopter survey of glacier thickness

Hamish Daniel Pritchard
Goldberg, Daniel; Recinos Rivas, Beatriz

Abstract/Description

We report on uniquely extensive new glacier thickness profiles covering 200 km of Himalayan glaciers in the Solu Khumbu basin around Everest. Glacier thickness maps are needed to determine the size of the world’s remaining mountain ice reserve and to model glacier dynamic response to melting, and projections of future mass loss are highly sensitive to the initial thickness distribution. Modelling of the global ice distribution involves largely unknown parameters that must be tuned with ice thickness measurements, but these are rare and skewed to the European Alps. In the Himalayan headwaters of the Brahmaputra, Indus and Ganges basins, home to 800 million people and 41,000 glaciers, the thickness of only 6 glaciers is reported, for example, with profiles covering only ~10 km. This data scarcity reflects the difficulties of surveying remote, high and sometimes debris-covered glaciers, which we overcame by using a purpose-built low-frequency helicopter radar. We describe this new survey, the challenges involved in extracting ice thickness from radar data, and the opportunities for systematic calibration of glacier thickness models throughout the Himalayas.

ID: 3.9383

Snowmelt Contribution to Seasonal Baseflow Dynamics: Multi-Catchment Analysis of Hydrological Responses in Mountain Catchments

Johnmark Nyame Acheampong
Jeníček, Michal

Abstract/Description

Mountains are significant water towers, with generally steep gradients, seasonal snow-driven hydrology, and elevation-dependent climate zones that impact different hydrological responses. Understanding runoff mechanisms in mountain catchments is critical, especially given the context of climate change. Snowmelt runoff dominates mountain catchments when compared to liquid precipitation, highlighting its vulnerability to changes in snow accumulation and early snowmelt. The interactions between snow and baseflow dynamics are critical in managing water availability over seasons and interannual periods. However, there is a gap in relating snow conditions to baseflow across elevation gradients, as current mesoscale research have questioned traditional baseflow concepts. This work aims to address this gap by utilizing the HBV model applied to 93 catchments across Czechia and Swiss mountain regions (1980-2020). The model was modified with a non-linear function, reducing outflow to two boxes for improved fast flow and baseflow representation, better capturing storage-discharge dynamics in snow-dominated catchments. 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.

ID: 3.9481

Rain-on-snow (ROS) in the Southern Alps of New Zealand: Observations, characteristics and forecasting

Rasool Porhemmat
Conway, Jono

Abstract/Description

Rain-on-snow (ROS) events are critical hydrometeorological phenomena influencing snowpack dynamics, river discharge, and flood risks in alpine regions. In the maritime Southern Alps of New Zealand, ROS events significantly modulate seasonal snowmelt, triggering avalanches and extreme runoff, particularly during winter and spring. Maritime snow-dominated regions are especially vulnerable in a warming climate, where even small temperature increases can drive substantial changes in snow accumulation and melt processes. This study examines observed ROS events using high-elevation meteorological and snowpack data, focusing on their seasonal distribution, atmospheric drivers, and impacts on catchment hydrology. We utilized the Snow and Ice Network (SIN) dataset, alongside data from the Pisa Range, Brewster Glacier, and Mt Belle (Milford Road), to identify and classify ROS events based on their occurrence during snow accumulation and snowmelt periods. While spring ROS events are more frequent, our findings confirm that winter ROS events also exhibit significant hydrological impacts, highlighting the need to account for year-round ROS dynamics in hydrological modelling and flood risk assessments. Additionally, we investigated the relationship between atmospheric rivers (ARs) and ROS events over seasonal snowpacks in the Southern Alps. Case study analyses revealed that AR-related ROS events are associated with an increase in vertical integral of heat flux, leading to anomalously warm mid- and lower-tropospheric temperatures and a freezing level rise to ~725-650 hPa. Air temperature increases of up to 10°C were recorded near the Main Divide, contributing to rapid snowmelt rates (up to 200 mm day⁻¹). These events were further intensified by turbulent latent heat flux and rain-heat flux, exacerbating snowmelt-driven floods. Streamflow data from alpine rivers confirm that high melt rates combined with AR-driven precipitation can lead to major flooding events in upper terrains of the Southern Alps during both winter and spring. The findings of this study contribute to the development of a national-scale snowmelt forecasting system, improving flood prediction accuracy and hazard assessments across New Zealand. This work underscores the importance of integrating ROS processes into hydrological models, particularly in the context of a warming climate and increasing extreme weather events.

ID: 3.9718

SPASS – new gridded climatological snow datasets for Switzerland: Method and Potential

Christoph Marty
Michel, Adrien; Jonas, Tobias; Steijn, Cynthia; Mülchi, Regula; Kotlarski, Sven

Abstract/Description

Gridded information on past and present snow cover is essential for climate services in snow-dominated regions like the Alps. As part of the SPASS project (SPAtial Snow climatology for Switzerland), we developed the first long-term gridded datasets of daily snow water equivalent (SWE) and snow depth, covering Switzerland at 1 km resolution since 1962. We describe our method for generating these datasets and their evaluation for climatological analyses. Two dataset families were produced: 1) Climatological dataset (since 1962): Bias-adjusted snow model results using a quantile-mapping method. 2) Assimilation-based dataset (since 1999): A higher-quality snow model that incorporates snow depth observations. Comparing both datasets shows good overall performance, particularly in bias and correlation. Errors remain acceptable, except for ephemeral snow and short time aggregations like weeks. Validation against in-situ station data for yearly, monthly, and weekly values at different elevations indicates only slightly better performance for the higher-quality dataset, confirming the robustness of the quantile-mapping method. A trend analysis of yearly mean snow depth from both station-based and gridded data revealed strong agreement in trend direction and significance across elevations. However, at low elevations, gridded datasets tend to overestimate the strength of decreasing trends. Overall, our results confirm that the new snow datasets perform well but may show the largest uncertainties at low elevations, single grid points, or short time periods. Despite some limitations, these high-resolution snow datasets offer valuable insights into long-term trends and climate variability, supporting applications such as anomaly maps and elevation-based trend analysis.

ID: 3.10548

Evaluation and Correction of Precipitation Types measure by PARSIVEL2 Disdrometer in a Tropical Glacier Environment

Maria Pérez
Valdivia, Jairo

Abstract/Description

In high mountain regions with complex terrain. The precipitation types are poorly studied due to sparse observations and lack of robust instruments. This study focuses on evaluating precipitation types measured by a PARSIVEL2 disdrometer installed at 4,709 meters on the Huaytapallana tropical glacier in the Peruvian Andes. The instrument records the shadow of the precipitation particles passing through the optical laser to determine the diameter and calculate their falling velocity. Based on this information, it internally calculates the rainfall intensity (mm/h) and classifies the types of precipitation for each recorded minute. With one year of data collected, rain, drizzle, drizzle with rain, snow, hail, soft hail, and drizzle rain with snow were identified as precipitation types. According to this, the drizzle with rain type is highest at 30.6%, followed by snow at 26.2%. To analyze the precipitation amount (mm), a total of 114 precipitation events were identified, which were compared with the record from the Pluvio2 weighing rain gauge. In the presence of soft hail, snow, and hail for more than an hour, the total precipitation value doubled that the precipitation value registered by Pluvio2. This difference is due to the type classification and the high diameter of the particles detected by the disdrometer. To correct this, the types were reclassified according to empirical diameter-fall velocity relationships of particles. This method proved effective for solid events, as the root mean square error was reduced from 5.6 to 2.6 mm between the disdrometer and rain gauge values, in contrast to liquid events it increased from 0.51 to 1.3 mm. The initial precipitation values compared to the corrected values represent a 92% correlation, evidencing the similarity of the method to the instrument’s internal algorithm. However, unlike PARSIVEL2, the method considers types such as wet snow and graupel, which need to be validated with observational measurements in future research.

ID: 3.10552

Producing emergent snowcover behaviours using re-analysis forcing data

Christopher Marsh
Vionnet, Vincent; Mudryk, Lawrence; Menounos, Brian; Pomeroy, John

Abstract/Description

Mountain snowpacks are a source of freshwater for billions of people globally. However, these snowpacks are under profound threat due to climate change as patterns of snowfall and ablation change. There is a significant and timely need to diagnose how these snowpacks are currently changing and how they will change under future climates to better provide estimates of freshwater availability. Mountain snowpacks are influenced by a set of cascading emergent behaviours, where periods of snowfall, wind redistribution events and avalanches shape the snowpacks before spring and summer ablation via spatially distributed energy fluxes. The complex spatial pattern of snowpack ablation impacts freshwater inputs to local ecology, stream flows, and groundwater recharge. Contextualizing the current and predicted snowpack changes against historical trends motivates using global and continental reanalysis products to provide historical forcing data for snowpack simulations. However, these reanalyses are spatially coarse (~10 to 25 km), and it has not been well established if key forcing variables, such as wind speed and direction, are sufficient to drive snowdrift permitting scale snow models. In this work, the multiscale Canadian Hydrological Model (CHM) is driven with ERA5-land (global) and the Canadian Surface reanalysis (CaSR; North America) reanalysis at a snowdrift permitting resolution to evaluate their capacity to be used to produce historical estimates of high-resolution mountain snowpacks. The INARCH COPE Kananaskis region of the Canadian Rockies is used to evaluate downscaled windfields and simulated snowpack against in situ observations and lidar snow depth.

ID: 3.10717

Data-Driven Model Transferability for Streamflow Simulation in Southern Tibetan Plateau Catchments

Insaf Aryal
Maharjan, Saurav; Jade G. Genoguin, Marvin

Abstract/Description

Long-term streamflow data is crucial for managing water resources, providing insights into water availability and variability over time. It supports irrigation, drinking water supply, and industrial needs while helping predict and mitigate extreme events like floods and droughts. Additionally, streamflow data is essential for assessing climate change impacts on hydrological systems, aiding policymakers in developing adaptive strategies. However, continuous streamflow measurement in mountainous regions like the southern slope of the Tibetan Plateau is highly challenging due to rugged terrain, extreme weather, and remote locations. Steep slopes and high altitudes make installing and maintaining monitoring equipment costly and difficult. Seasonal variations, such as monsoonal floods and snowmelt, cause rapid discharge changes, complicating measurements. Limited accessibility and resource constraints further hinder the establishment of reliable hydrological monitoring networks. To address these challenges, innovative approaches like satellite-based observations and data-driven models are needed to complement ground-based measurements. Developing physically based models requires extensive spatial data, computational resources, and a deep understanding of hydrological processes, making them complex and resource-intensive. In contrast, data-driven models like Transformers offer a scalable solution for simulating streamflow in data-scarce regions. This study leverages ERA5 precipitation data and available observed streamflow records to develop a Transformer-based model for streamflow simulation from 1940 to 2023. The model is initially trained in data-rich catchments to ensure accuracy before being transferred to sub-catchments with limited data. This transferability approach allows the model to utilize shared hydrological characteristics and forcing data, making reliable predictions in poorly monitored regions. By enhancing the applicability of streamflow models across diverse basins, this method ensures hydrological predictions are accessible for effective water resource management in data-scarce areas.

ID: 3.10896

Incorporating snow persistency data into a hydrological modelling framework

Michele Bozzoli
Bertoldi, Giacomo; Premier, Valentina; Marin, Carlo; Formetta, Giuseppe; Bavay, Mathias; Cordano, Emanuele

Abstract/Description

Alpine regions are particularly vulnerable to climate change, with snowmelt dynamics playing a key role in their hydrological processes. Snow water equivalent (SWE) is a crucial indicator of snowmelt; however, its measurements are scarce and limited to specific locations, making it challenging to obtain accurate spatial estimates. Remote sensing products offer a valuable solution by providing spatialized observations. Recently, it has been developed a multi-source data approach to generate high-resolution (20 m) daily snow cover area (SCA) maps using optical remote sensing data from MODIS, Landsat, and Sentinel-2, SAR data from Sentinel-1, and in situ observations. This study explores the integration of this method with the semi-distributed hydrological model GEOframe to reconstruct high-resolution (20 m) SWE in the Dischma alpine catchment (Kanton Graubünden, Switzerland, ~40 km²) and in the Venosta catchment (Südtirol, Italy, ~1500 km²). The simulated discharges are assessed by comparing them with observed discharges at the outlet of the two basins, while the modelled SWE is evaluated against high-resolution SWE maps.

The GEOframe model accurately replicates the observed discharges for both catchments, achieving a KGE of 0.904 and 0.810, for the Dischma and Venosta catchment, respectively. 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 using the multi-source remote sensing approach previously mentioned. The SWE estimates derived from the proposed approach show good correlations with observations, particularly in the Dischma catchment. The proposed method indicates that combining high-resolution remote sensing data with hydrological models effectively captures SWE spatial patterns, and at the same time the catchment-averaged SWE is bound to the water mass balance estimated by the hydrological model.

ID: 3.11114

Synergizing Hydrological Data Assimilation with Deep Learning across scales

Giulia Blandini
Avanzi, Francesco; Campo, Lorenzo; Gabellani, Simone; Aalstad, Kristoffer; Mazzolini, Marco; Girotto, Manuela; Yamaguchi, Satoru; Hirashima, Hiroyuki; Ferraris, Luca

Abstract/Description

In mountain regions, today’s snow is tomorrow’s water. Indeed, the snowpack functions as a natural freshwater reservoir and is a primary source of streamflow, particularly during spring and summer,regulating the hydrological cycle. Accurate estimates of snow water equivalent and snow depth are essential for characterizing seasonal water storage, predicting water availability, and managing cascading socio-hydrologic impacts—particularly in an era of increasing climate variability and drought events.
However, operational snow hydrology models are subject to significant uncertainties, including structural deficiencies, meteorological inputs, and parameter variability. Moreover, ground-based snow measurements are often affected by instrumental noise and lack of representativeness, while remote sensing products suffer from coarse spatial resolution and retrieval uncertainties. To address these limitations, data assimilation methods, such as the Ensemble Kalman Filter, optimally combine models and observations to enhance snow state estimates. Despite their effectiveness, such ensemble-based data assimilation approaches can be computationally expensive, limiting operational feasibility. To mitigate this issue, we propose a deep learning-based framework that leverages Long Short-Term Memory networks. Trained on Ensemble Kalman Filter outputs from seven diverse study sites, our model achieves comparable accuracy while significantly reducing computational costs by 70% compared to a parallelized Ensemble Kalman Filter. On average, the network introduces only a minor root mean square error increase for snow water equivalent (+6 mm) and snow depth (+6 cm), with 12 out of 14 site-specific cases outperforming open-loop estimates. Building on these results, we extend the framework to a 2D semi-distributed implementation. To improve the spatial interpolation of the analysis correction, we incorporate recent developments in spatial snow data assimilation leveraging an abstract distance metric defined by topographical features and melt-out climatology to enhance spatial information propagation. This approach is validated across three hydrological basins in California, Norway, and Italy, leveraging high-quality observations from INARCH sites. By integrating deep learning into data assimilation, this research enhances hydrological forecasting in high mountain environments, enabling scalable and efficient snowpack modeling with uncertainty quantification for decision making.

ID: 3.11138

A Reference Precipitation Dataset for Hydrological Modeling in Central Asia

Jingheng Huang
Pohl, Eric

Abstract/Description

Accurate hydrological modeling is essential for water resource management in Central Asia, where meltwater from snow and glaciers support approximately 136 million people. Precipitation uncertainties significantly impact the accuracy of hydrological models, yet the scarcity of in-situ meteorological observations in the high mountain regions of Central Asia has led to widespread reliance on gridded precipitation products. These gridded products vary in quality and often require correction before use in hydrological modeling. Inverse hydrological modeling, where observed outputs (like river discharge) are used to infer unknown inputs (like precipitation), has been proven effective in estimating long-term average precipitation volumes, but accurately capturing interannual precipitation variability remains an unresolved challenge. Here, we develop an inverse modeling framework that integrates total discharge, baseflow, snow cover fraction, and glacier mass balance to simultaneously constrain both precipitation quantities and interannual variability across Central Asia’s river basins. A key innovation of this approach is the use of baseflow to constrain annual precipitation variability. By leveraging the strong relationship between winter snowpack accumulation and subsequent baseflow, this method enables a more accurate representation of interannual precipitation dynamics in the region. This work yields a benchmark precipitation dataset for hydrological modeling applications while also providing a systematic assessment of runoff regimes throughout this critical region.

ID: 3.11277

Modeling snow cover variability in Berchtesgaden National Park, Germany using the distributed snow model openAMUNDSEN

Brage Storebakken
Rottler, Erwin; Warscher, Michael; Strasser, Ulrich

Abstract/Description

Site-specific studies of snow cover in different landscapes are crucial for understanding snow cover variability across various climatic zones. Here, we present a modeling approach using the fully distributed, physically based snow model openAMUNDSEN to simulate snow cover in Berchtesgaden National Park, Germany. This region, located in the eastern European Alps, features highly complex topography with elevation differences of up to 2000 meters over just 3.5 km. We forced the model with meteorological data from 20 automatic weather stations across the 210 km² study area, performing simulations at a spatial resolution of 50 × 50 m. For model evaluation, we used fractional snow cover estimates from Sentinel-2 satellite imagery, point snow depth measurements in open areas, and snow metrics derived from ground temperatures measured under forest canopies. Ground temperature measurements were obtained through a distributed network of 150 microclimate loggers (TOMST TMS-4). Our results indicate that the model reproduces the overall variability of snow cover across the park’s diverse landscapes, although some discrepancies arise at specific sites. These findings advance our understanding of spatial snow cover variations in a complex alpine environment.

ID: 3.11393

Evaluation of a New Process-Based Snow Accumulation Model in Needleleaf Forests

Alex Cebulski
Pomeroy, John

Abstract/Description

Forested mountains characterize the snowy headwaters of many major river basins, where snow accumulation can be reduced by up to 50% through canopy snow interception and subsequent ablation. Accurate modelling of the seasonal subcanopy snowpack requires a comprehensive understanding of these processes. However, existing theories on snow interception and canopy snow ablation have uncertain application in differing environments and spatial scales. Recent observations in the Canadian Rockies have revealed novel insights into snow interception and canopy snow ablation processes. Subcanopy throughfall and canopy structure measurements from aerial LiDAR, combined with high-frequency lysimeter measurements of canopy snow load and unloading challenged the theory behind existing parameterizations and informed the development of improved process representations. This presentation describes a new snow interception parameterisation that calculates throughfall based on forest structure and models canopy snow ablation through a combined approach: an energy balance method for snowmelt and empirically-derived functions for snow unloading. The new parameterisation better captures observed stand-scale processes, including enhanced interception efficiency during wind-driven snowfall events and subsequent increases in canopy snow unloading due to wind, snowmelt, and sublimation. The improved canopy snowmelt energy balance also accounts for sub-zero temperature melting driven by longwave emission from warm canopy elements and limitations on nighttime sublimation imposed by radiative cooling. To assess the effectiveness of these new parameterisations, both the new and traditional routines were implemented in the Cold Regions Hydrological Modelling platform and evaluated against observations of subcanopy snow water equivalent and canopy snow load at sites not used in model development, including the continental climate Marmot Creek, Alberta; subarctic climate Wolf Creek, Yukon Territory; and coastal climate Russell Creek, British Columbia (all in Canada). Preliminary results demonstrate substantially improved subcanopy snow predictability, with R² values increasing from 0.4 using existing methods to 0.7 with the revised routine. This enhanced process-based understanding of snow interception and canopy snow ablation shows promise for broader application in water resource assessment of forested, snow-dominated basins.

ID: 3.11576

Hidden Water Pathways: Investigating Groundwater Recharge in an Ice-Rich Proglacial Environment

Michel Baraër
Charonnat, Bastien; Valence, Eole; Tjoelker, Adam; Masse-Dufresne, Janie; McKenzie, Jeff

Abstract/Description

As glaciers retreat in the Canadian subarctic, many leave behind massive ice deposits within the ground, amplifying the hydrological influence of proglacial areas. This study examines groundwater recharge dynamics in the ice-rich proglacial zone of Shár Shaw Tagà (Grizzly Creek), a glacierized catchment in the St. Elias Mountains, Yukon. Using hydrometeorological monitoring, wavelet coherence analysis, and mass balance modeling, we assess the contribution of different cryospheric components to aquifer recharge. Field observations from summer 2022 reveal that springs at the study area’s outlet exhibit spatially heterogeneous flow patterns—some maintaining perennial discharge, while others respond primarily to rainfall events. Qualitative analysis and wavelet coherence results, based on pressure transducer measurements of electrical conductivity, water temperature, and water level, suggest limited or no direct connection between the springs, glaciers, and proglacial zone. A water balance model estimating seasonal and annual contributions from ice melt and precipitation indicates a significant water budget deficit at the system’s outlet. These findings highlight the crucial role of proglacial areas in subsurface water transfer, suggesting that deglaciating catchments may experience a disconnection between glacial drainage and downstream surface and shallow groundwater flows. Understanding these hidden pathways is essential for predicting hydrological changes in rapidly evolving proglacial environments.

ID: 3.11597

The Role of Snow in Soil Freeze and Thaw dynamics Across an Elevational Gradient in the Snake Range, Nevada, USA

Kabir Rasouli
McEvoy, Daniel; Albano, Christine; Ammatelli, Joseph; Heggli, Anne

Abstract/Description

This study evaluates the role of snow accumulation and melt in soil temperature and moisture across varying elevations in the Snake Range in eastern Nevada in the USA. In-situ observations were used from the Nevada Climate-Ecohydrology Assessment Network along with gridded datasets with different spatial resolutions, including Western Land Data Assimilation System (WLDAS, 1 km by 1 km), Weather Research and Forecasting model for Contiguous USA (CONUS404, 4 km by 4 km), and Land Information System (LIS, 25 km by 25 km). The analysis focuses on soil temperature, soil moisture dynamics, freeze-thaw transitions, and snowpack characteristics to assess model performance under diverse climatic and topographic conditions. Key findings reveal that WLDAS consistently demonstrates the highest accuracy in modeling soil temperature and moisture across most elevations, particularly it performs best at mid-elevations (1580–2200 m). It achieves the lowest mean absolute error (MAE), and root mean square error (RMSE) values, accurately capturing freeze-thaw dynamics and snowpack timing. CONUS404 shows improved performance during winter months at high elevations but struggles with seasonal transitions and wet periods with volumetric soil moisture content greater than 0.3 compared to WLDAS. Coarser-resolution models like LIS and CONUS404 exhibit larger biases in snowpack timing and depth compared to WLDAS, which more accurately captures snow accumulation start and snow-free dates across elevations. Observed wet soil conditions occur earlier at lower elevations (2200 m) during April-May due to delayed snowmelt. Cold soil temperature biases are more pronounced in dry years potentially due to an underestimation of snow depth by models, resulting in reduced snow insulation. Higher-resolution models like WLDAS performs best in simulating localized processes such as freeze-thaw transitions and late-season wet soils, particularly at middle to high elevations. The results emphasize the need for improved modeling of shallow snowpacks, particularly during dry years, to enhance predictions of soil temperature and moisture dynamics critical for hydrological and ecological applications. Changes in snowpack dynamics due to climate warming—such as reduced snow cover duration and earlier melt—are expected to exacerbate these challenges by altering the timing and extent of soil freeze-thaw cycles and reducing the snow depth.

ID: 3.11621

Monitoring long-term trends in precipitation phase over a highly instrumented semiarid experimental watershed in the Great Basin, USA

Andrew Hedrick
Meyer, Joachim; Trujillo, Ernesto; Williams, C. Jason; Kormos, Patrick

Abstract/Description

The Reynolds Creek Experimental Watershed (RCEW; 240 km^2 area) in southwestern Idaho, USA was designated in 1961 as an outdoor laboratory to monitor hydro climatology and grazing practices in semiarid agricultural rangelands. Growing season streamflow in the RCEW is strongly influenced by water stored in large snow drifts that persist until the late summer. However, a large proportion of the catchment exists within the elevational rain to snow transition zone, signaling that small shifts in precipitation phase from snow to rain may alter the streamflow regimes that downstream farmers and ranchers have depended on for more than a century. This work will quantify both seasonal and elevational changes in precipitation phase over the last 40 years. Beginning in the early 1980s, automated meteorological stations were steadily installed across the catchment elevation gradient (1099 – 2240 m asl), enabling detailed monitoring of when and where precipitation fell as snow. This spatial analysis is culled from an hourly, 10-meter resolution gridded dataset of air temperature, relative humidity, wind speed and direction, incoming solar radiation, and precipitation derived from station measurements for the time period 1 October 1983, to 30 September 2023. Calculated hourly wet-bulb temperature was used to parse falling precipitation as either snow, rain, or mixed snow and rain across the watershed using a thresholding approach. Here, we present the workflow used to derive precipitation phase, the associated trends in the spatial dataset, the relationships between those trends and measured streamflow, and finally the ways in which these trends may relate to other semiarid catchments around the globe.

ID: 3.11681

The International Network for Alpine Research Catchment Hydrology: Goals and Recent Achievements

John Pomeroy
Lopez Moreno, Ignacio; DeBeer, Chris

Abstract/Description

The International Network for Alpine Research Catchment Hydrology (INARCH) is a 10-year cross-cut project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel (GHP) of the World Climate Research Programme that strives to i) measure and understand high mountain atmospheric, hydrological, cryospheric, biological and human-water interaction processes, ii) improve their prediction as coupled systems, iii) diagnose their sensitivities to climate change and propose how they may be managed to promote water sustainability under global change. At its core is a global network of highly-instrumented mountain observatories and experimental research sites, 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. There are now 38 research basins and sites in 18 countries and six continents, with more continuing to join the network. INARCH has proposed a research and monitoring agenda for the the International Year for Glaciers Preservation – 2025 and the UN Decade of Action for the Cryospheric Sciences 2025-2034 and is poised to deliver advances in monitoring, science and application to both initiatives. INARCH has been conducting a Common Observing Period Experiment (COPE) across dozens of research basins in mountain regions around the globe as a focal network activity. This is a globally unique effort that is producing a world-class set of observations and data, model applications and diagnostic comparisons, new process understanding and insights, and better prediction of the changing mountain water cycle in the headwaters of many of the world’s major river basins. This presentation will introduce INARCH and the objectives for this session which are to bring forward observations from instrumented mountain catchments, theoretical advances, and evaluation of hydrological and atmospheric models using observations to better understand model performance and to see if models reproduce known aspects and regimes of coupled atmospheric-cryospheric-hydrological system around the world.

ID: 3.11686

Future Glacier and Snow Hydrology of Headwater Basins in the Canadian Rockies Hydrological Observatory using Dynamically Downscaled Climate Model Forcing

John Pomeroy
Fang, Xing

Abstract/Description

The Canadian Rockies are the headwaters of rivers that flow to three oceans and supply freshwater to a vast portion of North America. These mountains are partly glaciated and most runoff is generated from the spring melt of the seasonal snowpack. By late century, under RCP 8.5 scenarios, atmospheric models predict that temperatures will have risen on average 5 oC and precipitation will increase about 15%. However, Regional Climate Models (RCM) have not had sufficient resolution to accurately simulate the sharp gradients of high mountain precipitation and climate and this has introduced uncertainty into hydrological predictions using these forcings. Here, mountain climate is dynamically downscaled using the Weather Research and Forecasting (WRF) model to 4 km. WRF was run over North America for two 20-year periods as part of Global Water Futures, 1995-2015 and 2080-2100, downscaling boundary conditions from coarser-scale RCMs. The WRF forcing was evaluated against observations from four well-instrumented headwater research basins that comprise the Canadian Rockies Hydrological Observatory. Marmot Creek is mostly montane and subalpine forest; Fortress Mountain is subalpine and alpine; Helen Creek is alpine, lake and rock and Peyto Glacier is glaciated. Whilst current climate WRF dynamics are synthetic and so differed from observations – the basic nature of mountain meteorological patterns and extremes was captured sufficiently well for evaluating climate change. Future forcing with WRF was used to run the basin-specific models created using the Cold Regions Hydrological Modelling platform (CRHM) that described snow redistribution, sublimation, energy balance melt, snow and glacier albedo, evapotranspiration, runoff and subsurface hydrology with a high degree of realism. The CRHM models were set up for current and future levels of glaciation, and depressional storage. The results show reduced snow season, similar peak accumulation, earlier peak flows, higher spring flows and reduced summer flows in the non-glaciated basins, with drastically reduced flows and much earlier but reduced peaks in the currently glaciated basin. The importance of forest and glacier cover in modulating the translation of climate change to hydrological change, including compensating hydrological processes that dampen change, is emphasized.

ID: 3.11774

Diurnal energy cycles and net water vapor fluxes in mountain watersheds

Ethan Gutmann
Lundquist, Jessica; Kirshbaum, Daniel; Hogan, Danny; Schwat, Eli; Adler, Bianca

Abstract/Description

Mountain meteorology and hydrology are difficult to model due to fine spatial scales and strong land-atmosphere feedbacks. Mountain topography organizes both atmospheric motions and hydrologic processes affecting both mountain hydrology and weather predictions. Here we use data from the SAIL, SPLASH, and SOS field campaigns to study mountain valley winds and water vapor fluxes in the East River Basin. We will combine these observations with satellite data and high-resolution snow and atmospheric modeling to quantify feedbacks between surface fluxes, winds, and cloud cover. Surface stations along a transect in the East River basin are used to measure valley-scale flows over different catchment areas and their interaction with local and basin scale snow cover. Initial results show that daytime upvalley circulation patterns vary with snow cover presence, and that there is a strong non-linear feedback between valley wind strength and surface sublimation fluxes due to the importance of blowing snow. Winter surface fluxes are linked to wind speed, snow shear strength, and available energy, controlling mid-winter sublimation. This has important implications for the parameterization of mountains in atmospheric and macroscale hydrology models. This research will lead to enhanced mountain hydroclimate models while highlighting biases in existing high-resolution atmospheric simulations, especially regarding cloud and snow cover feedbacks in high alpine terrain.

ID: 3.11785

Integrating process-based snow and glacier mass balance modelling at high spatio-temporal resolution

Giulia Mazzotti
Huss, Matthias; Quéno, Louis; Magnusson, Jan; Kneib, Marin; Farinotti, Daniel; Jonas, Tobias

Abstract/Description

The temporary storage of water in the form of snow and ice and its delayed release as melt is a key feature of Alpine hydrological regimes. Rising temperatures are driving drastic changes in the Alpine cryosphere, including shorter snow seasons and accelerating glacier retreat. While these have profound implications on runoff amounts and seasonality in snow- and glacier-fed catchments, quantifying the contribution of snow and glacier melt to streamflow remains challenging. In particular, the trade-off between model complexity and input data availability often leads to the use of rather simple representations of cryospheric processes in hydrological models, which mostly disregard key drivers of snow distribution dynamics.
Here, we explore the application of FSM2trans, a recent fully distributed snow model based on mass and energy balance and including redistribution processes by wind and avalanches, to partially glacierized Alpine catchments. Simulations are, for the first time, evaluated against glaciological datasets, including spatially distributed in-situ measurements of winter accumulation across the glacier surface and point mass balance timeseries reconstructions at selected ablation stakes. This comparison allows detecting accumulation biases and areas with excessive snow transport in the simulations, and serves as the basis for finetuning FSM2trans for applications in high-alpine glacierized terrain. Comparison of FSM2trans simulations with existing, interpolation-based model estimates of glacier accumulation patterns corroborates the added value of process-based snow modelling for characterizing spatiotemporal accumulation dynamics. Enhanced representation of snow accumulation and depletion over glaciers is expected to enable increased spatial and temporal resolution of glacier mass balance estimates, especially where no in-situ measurements are available. As ultimate goal, this effort aims to provide improved surface water inputs from cryospheric components to hydrological models, thereby allowing for a better characterization of snow and glacier contributions to runoff in mountain catchments.

ID: 3.11900

Climate Forcing of Himalayan Snowmelt: Correlating Aerosol Optical Depth with Snow Cover Changes in Mago Basin, Arunachal Pradesh, India

Kainat Aziz
Kartha, Suresh A

Abstract/Description

Snow cover dynamics in high-altitude Himalayan regions are critical for regional hydrology, climate regulation, and ecosystem balance. However, recent trends indicate accelerated snow ablation, driven by rising temperatures and increased atmospheric aerosol loading. This study examines the interplay between snow cover ablation and aerosol optical depth (AOD) in the Mago region of Arunachal Pradesh, an ecologically sensitive area experiencing significant cryospheric transformations.
To quantify interannual fluctuations in snow accumulation and ablation, we utilized MODIS-derived snow cover data, which enabled the assessment of seasonal and long-term variability. Concurrently, aerosol loadings were analyzed using MERRA-2 reanalysis data, allowing for a detailed evaluation of AOD and its specific components, including dust, black carbon, and other anthropogenic pollutants. Our findings indicate persistently high AOD values in the region, correlating with enhanced rates of snow ablation. Notably, for the years when ablation rates were significantly higher, AOD values were also observed to be elevated, suggesting a strong link between aerosol deposition and snowmelt acceleration. Among the aerosols, black carbon and fine particulate matter exhibit a pronounced influence on snowmelt processes. These light-absorbing particles, when deposited on the snowpack, reduce surface albedo, thereby increasing the absorption of solar radiation. This positive radiative forcing effect contributes to localized surface warming, leading to a decrease in snow persistence and an increase in meltwater runoff.
The results of this study underscore the critical role of atmospheric pollutants in modulating the energy balance of snow-covered surfaces. Given the reliance of downstream communities on glacial and snow-fed hydrological systems, understanding these interactions is essential for improving predictive models of snowmelt-driven discharge, assessing water resource sustainability, and developing mitigation strategies for cryospheric degradation. Furthermore, this research highlights the necessity of integrating aerosol-climate interactions into regional climate adaptation frameworks to mitigate the cascading effects of enhanced snowmelt on water security and disaster risk management in the Eastern Himalayas.

ID: 3.11915

Evaluation and Comparison of Mass Balance Methods on the Rutor Glacier (Aosta Valley, Italy)

Federico Grosso
Pogliotti, Paolo; Morra di Cella, Umberto; Isabellon, Michel

Abstract/Description

The Rutor Glacier, one of the largest in the Aosta Valley, Italy, has been extensively monitored over the last few decades. This study presents a comparative analysis of glaciological and geodetic mass balance methods for the period 2008–2024, aimed at evaluating the transition from a purely glaciological to a purely geodetic approach and assessing the consistency of the historical mass balance series by examining differences in cumulative mass balance. Glaciological mass balance measurements from 2005 to 2019 were conducted using direct measurements of snow depth and density at the end of winter and ice melt using stakes and residual snow at the end of summer. From 2020 to 2024, a hybrid approach was adopted, combining a predominantly glaciological method for the accumulation area—ensuring precise estimates of snow density—with a geodetic method for the ablation area to obtain accurate and spatialized estimates of ice volume changes by using Difference of DEM (DoD). Thanks to the availability of DEMs and a historical mass balance dataset, starting from 2005, this study aims to compare the cumulative mass balance data derived from both approaches. The 2008 DEM, produced using LiDAR technology with a ground resolution of 2 meters, and the 2024 DEM, obtained from aerial surveys with a 0.5-meter resolution, were coregistered to ensure accurate alignment. The calculation of the DEM difference allows for a more precise estimation of volume changes over the 16-year period. The study aims to assess the consistency of the historical mass balance data series by analyzing differences in cumulative balance between the two approaches: the glaciological approach, which divides the glacier surface into areas with homogeneous behavior, and the geodetic approach, applied to a single area covering the entire glacier. This integrated approach emphasizes the strengths and limitations of each method and highlights the importance of combining complementary techniques for accurate glacier monitoring. The findings contribute to ongoing efforts to improve mass balance assessment methods and enhance the understanding of glacier dynamics in the face of climate change.

ID: 3.12066

Investigating transitional rock glaciers through ERT and IP measurements

Julia Agziou
Casotti, Clément; Bock, Josue; Cusiocanqui, Diego; Revil, Andre; Schoeneich, Philippe

Abstract/Description

Rock glaciers are well-known as visible landforms of creeping mountain permafrost. Studies over the last decades revealed a global and recent acceleration that correlates with the rise in air temperatures. However, its impact on degraded permafrost conditions has been studied in a lesser extent than those of active rock glaciers. Even fewer studies have explored the possibility of deactivated rock glaciers ‘reaching an active state’ (RGIK, 2023). However, 20% of transitional rock glaciers in the French Alps were found to exhibit speeds higher than expected.
In this poster, we aim to bring insights on the internal characteristics of three rock glaciers through a joint inversion of Electrical Resistivity Tomography (ERT) and Induced Polarization (IP) data in a comparative approach. Four longitudinal pseudo-sections will be presented for Lanserlia, Chanrouge (Vanoise massif) and Vieux Marinet (Ubaye massif) rock glaciers considering their differences in activity and dynamical characteristics.
First, the range of resistivity values varies significantly from one site to another. The interpretation of thick resistive layers could indicate the presence of a frozen core at all three sites, although the location, thickness, and homogeneity of these horizons differ. Chanrouge rock glacier has the thickest frozen core and this thickness decreases with elevation. Lanserlia exhibits distinct resistive horizons between the lower and higher areas, either between the two profiles where a thinner and smaller frozen layer is visible on one of them. Last, the Vieux Marinet rock glacier exhibits a thick and large discontinuous permafrost body. Neither high nor continuous resistive values are observed on the upper and active unit, where the presence of multiple conductive horizons are moreover present
This study contributes to a better understanding of the role of internal structure, particularly the presence of ice and water, in the motion of these landforms. The joint use of ERT and IP allows for better identification of different transitional trajectories ERT signatures. The IP data particularly provides additional insights into subsurface properties by helping to differentiate ice, water, and fine-grained sediments, thereby refining the hydrogeological interpretation.

ID: 3.12072

Glacier futures for the INARCH COPE catchments simulated using the Open Global Glacier Model

Lindsey Nicholson
Schmitt, Patrick; Prinz, Rainer

Abstract/Description

INARCH conducted a Common Observing Period Experiment (COPE) over 2022–2024 as a focal network activity to collect a well-documented and described data set of mountain meteorology and hydrology from INARCH basins over the two-year period as the basis for mountain hydrology model intercomparisons. Of the 35 basins monitored 15 contain some glacier cover, and for these basins we use the Open Global Glacier Model (OGGM) to project the glacier evolution for the W5E5v2.0 climate dataset spanning climate scenarios SSPs 119 to 585. This provides a benchmark dataset of how the glacier and meltwater runoff trajectories in these catchments are seen from the perspective of a global/regional glacier model, which will facilitate comparison to how glaciers and their future hydrological contributions are represented in mountain hydrology models. We present this dataset and discuss the regional similarities and differences across the COPE catchments.

ID: 3.12176

High-resolution hydrometeorological and snow data for the Dischma catchment in Switzerland

Jan Magnusson
Bühler, Yves; Quéno, Louis; Cluzet, Bertrand; Mazzotti, Giulia; Webster, Clare; Mott, Rebecca; Jonas, Tobias

Abstract/Description

We present an hourly hydrometeorological and snow dataset with 100m spatial resolution from the alpine Dischma watershed and its surroundings in eastern Switzerland, including station measurements of variables such as snow depth and catchment runoff. This dataset is particularly suited for different modelling experiments using distributed and process-based models, including physics-based snow and hydrological models. Additionally, the data are highly useful for testing various snow data assimilation schemes and for developing models representing snow–forest interactions. The dataset covers 7 water years from 1 October 2016 to 30 September 2023. The complete domain spans an area of 333 km2 with altitudes ranging from 1250 to 3228 m. The Dischma Basin, with its outlet at 1671m elevation, occupies 42.9 km2. Included in the dataset are highresolution (100 m) hourly meteorological data (air temperature, relative humidity, wind speed and direction, precipitation, and long- and shortwave radiation) from a numerical weather predication model and rain radar, land cover characteristics (primarily forest properties), and a digital elevation model. Notably, the dataset includes snow depth acquisitions obtained from airborne lidar and photogrammetry surveys, constituting the most extensive spatial snow depth dataset derived using such techniques in the European Alps. Along with these gridded datasets, we provide daily quality-controlled snow depth recordings from seven sites, biweekly snow water equivalent measurements from two locations, and hourly runoff and stream temperature observations for the Dischma watershed. The data compiled in this study will be useful to further develop our ability to forecast snow and hydrological conditions in high-alpine headwater catchments that are particularly sensitive to ongoing climate change.

ID: 3.12266

Reconstruction of Consecutive GLOFs from Small Proglacial Lake with Possible Downstream Impacts in Central Himalaya

Lin Peng
Zhang, Qiong; Zhang, Guoqing

Abstract/Description

Glacial lake outburst floods (GLOFs) is a major glacial hazard, occurring when a vast amount of water is suddenly released from a naturally dammed proglacial lake due to overtopping or dam breaching. Central Himalaya is a hotspot for GLOFs due to rapid alpine glacier retreat and fluctuating climate patterns, casting threats to vulnerable downstream societies. Therefore, research on GLOFs is crucial for understanding the hazard-triggering mechanism and improving our ability to predict and respond to these events, ultimately protecting lives and minimizing socioeconomic damage. This study focuses on two consecutive GLOFs (2015 and 2016) from the same proglacial lake in the Yindapu region, Central Himalaya. Remote sensing data from Landsat and Sentinel-2, along with the 2-dimensional Hydrologic Engineering Center’s River Analysis System (HEC-RAS) are used to identify possible mechanisms of the two closely occurring GLOFs, and to assess their impacts on downstream glacial lake and valley areas. The fifth generation ECMWF climate reanalysis (ERA5) is utilized for detecting regional climate signals preceding these outburst floods, providing insights into climate change as an indicator. The expected results will enhance our understanding of the cascading effects of repetitive GLOFs in the Central Himalaya, contributing to improving GLOFs early warning systems. These insights would be helpful for risk mitigation strategy development to protect communities in the remote and underprivileged glaciated mountain regions.

ID: 3.12428

Evaluating a variable-resolution physically based snow model through intensive monitoring at high-mountain experimental catchments: lessons learned and future directions.

James Mcphee
Courard, Maria; Blanch, Diego

Abstract/Description

Lateral redistribution can play a significant role in determining patterns of snow spatial variability, which in turn influences temporal signatures of snowmelt-driven runoff. However, processes influencing spatial variation operate at different scales, and their representation in a unified modeling framework is challenging. On the other hand, high-mountain experimental catchments provide a rich source of information on many aspects of the snowpack mass and energy fluxes and have led to conceptual improvements in many modeling contexts. However, reconciling observations from experimental catchments and large-scale hydrological models remains elusive. In this work we evaluate the performance of the Canadian Hydrological Model (CHM), a variable-resolution, physically based snowpack model, against different sources of information obtained at the Estero Las Bayas experimental catchment and at the Rio Yeso headwater basin, in the Andes Cordillera of central Chile. We explore the impact of different sources of uncertainty and modeling decisions on the model’s ability to reproduce the observed snowpack variability and suggest directions for the application of small-scale insights toward large-domain implementation. CHM snow cover (SCA) simulations were evaluated against remotely sensed SCA across two seasons in a local scale domain (~150 km2). CHM was found to be skillful at predicting peak-season SCA extent, although a consistent overestimation was observed, most notably during melt periods where simulated SCA depleted more slowly than satellite retrievals . This effect was more pronounced during snow drought conditions, where multiple melt cycles were observed during winter. This application also helped in identifying challenges regarding wind field generation modeling approaches, showing important wind speed distribution biases at the point measurement sites available.

ID: 3.12741

Assimilating hydrological variables with particle filters to improve distributed snow modeling and spatial precipitation patterns

Cristóbal Sardá
Courard, María; Cortés, Gonzalo; McPhee, James

Abstract/Description

Understanding snow accumulation and distribution in high mountain regions, as well as snow water equivalent (SWE), is crucial for accurate hydrological forecasting in snow-influenced catchments. A major challenge in these systems is the scarcity and complexity of snow-related observations; furthermore, hydrological models are required to represent snow processes at larger scales. However, both observations and models are subject to different sources of uncertainty, affecting prediction accuracy. Data assimilation techniques help improve predictions in such uncertain scenarios. Among these, particle filters (PF) are particularly suitable for nonlinear systems. This study aims to reduce uncertainty in key snowpack variable predictions in the Andes Mountains by implementing PF and assimilating hydrological variables. Additionally, it explores the variability in spatial precipitation patterns by updating and correcting state variables through discretization into elevation bands. The study domain is El Yeso Basin in Chile, and the hydrological model used is the Canadian Hydrological Model (CHM), a physically based distributed model that represents terrain using irregular triangular elements. Specifically, the work focuses on state variables such as SWE, input variables derived from numerical weather prediction, and the use of observations from remote sensing products such as SCA/fSCA. This approach constrains uncertainty, enabling updated predictions with reduced variability.

ID: 3.12876

Utilization of a new high resolution land cover map to improve simulations of the water balance components in a forested high mountain catchment (National Park Berchtesgaden, Germany)

Erwin Rottler
Storebakken, Brage; Warscher, Michael; Strasser, Ulrich

Abstract/Description

Climatic changes are expected to alter the relative importance of the water balance components in high mountain catchments. Thereby, snowfall amount, the timing of snowmelt and the amount of evapotranspiration are expected to be affected. One way to quantify the water balance components in high mountain catchments is through snow-hydrological modeling. In this study, we build a workflow to create a new high resolution (i.e. 10 m) land cover map based on satellite data from the Copernicus Land Cover Monitoring Service (CLMS). This map is specifically tailored for the use with snow-hydrological models that include snow-canopy processes, where usually the leaf area index is the most important parameter. To quantify the benefit of this new high resolution land cover map, we conduct snow-hydrological simulations using the physically-based, open source model openAMUNDSEN in different configurations. We increase the model complexity step-wise starting with simulations using a simple temperature index approach and ending with energy balance based snow cover simulations including lateral snow redistribution and snow-canopy processes using the new high resolution land cover map. The study area is the National Park Berchtesgaden (Germany), which is characterized by a complex mountain topography including coniferous and deciduous forests in the lower elevations. A dense climate station network as well as a long history in snow, climate and forest research makes it the ideal research site for our analysis. Preliminary results suggest that the selection of model configuration has considerable impact on the quantification of water balance components and that high resolution land cover maps improve the modeled small scale variability characterizing forested high mountain catchments.

ID: 3.13103

Representation of snow dynamics in a forested area of the Southern Andes using the FSM and CHM models

Elizabeth Ramirez
McPhee, James; Krogh, Sebastián; Bernal, Anelim; Moraga, Miguel

Abstract/Description

The interaction between forests and snow occurs in different regions of the world and plays a crucial role in the hydrological cycle, as forests modify the mass and energy balance of the snow cover and, consequently, key processes such as snowmelt and its contribution to river flow. This topic has been widely studied in the Northern Hemisphere (NH), but in the Southern Hemisphere (SH), there are very few studies on the subject. In this region, the extent of this interaction is more limited than in the NH, and the dominant forest species are different. Additionally, these vegetation-snow interaction zones in the SH are often associated with key biodiversity hotspots. This study aims to represent the snowpack using two models: the Factorial Snow Model (FSM) version 2.1.0 at a point scale and the Canadian Hydrological Model (CHM) at a spatial scale in a vegetation-snow interaction zone located at latitude -36.9° S in the Andes Mountains. The region has a cold Mediterranean climate with winter precipitation, an average annual precipitation of 2400 mm, and a deciduous forest with species of the Nothofagus genus. The FSM model is used to verify whether the default parameterizations in this new version of the model can accurately represent the in situ measured snow depth at the study site, while the CHM model is used to spatially represent the snowpack. To implement CHM, we used the ALOS PALSAR DEM, the Land Cover Map of Chile, and the CLSoilMaps soil type product to generate the grid. For meteorological data, we used CR2MET and the ECMWF-HRES product. We conducted experiments with the CHM model, both with and without wind-driven snow transport. We validate the model results with satellite images of snow cover and point measurements of snow depth. Preliminary results show significant spatial variability in snow cover, highlighting major challenges in accurately representing snow cover in forested areas.

ID: 3.13115

Improving understanding and prediction of the mountain water cycle – summary and outcomes from the INARCH Common Observation Period Experiment, 2022–2024

Chris Debeer
Pomeroy, John; López-Moreno, Ignacio; McPhee, James; O'Hearn, Stephen

Abstract/Description

The International Network for Alpine Research Catchment Hydrology (INARCH) 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 are being used for a series of hydrological process diagnostic modelling evaluations and analyses, emphasizing atmospheric, snow, glacier, and water processes in high mountain terrain. This is to better understand why models produce various behaviours and to assess if models benchmark various known aspects and regimes of the coupled atmospheric-cryospheric-hydrological system, not only for open sites, but for sparse forest, non-needleleaf vegetation, glaciated, and alpine windblown sites. In the end, COPE will produce a valuable and unique set of observations, model simulations and intercomparisons, new process understanding and insights, and better prediction of the changing mountain water cycle. This presentation will review major activities undertaken as part of the COPE and the outcomes and advancements towards addressing key INARCH science questions.

ID: 3.13137

Integrating cryosphere processes into groundwater modeling for alpine headwaters under climate change

Odile De La Ruë Du Can
Roques, Clément; Renard, Philippe; Halloran, Landon

Abstract/Description

Mountain regions, as Alpine headwaters, are undergoing rapid transformations due to global climate change, with significant impacts on the cryosphere. Changes in snow cover, glacier dynamics, and permafrost thawing are driving shifts in hydrological regimes, directly affecting water availability for downstream ecosystems and human systems. A critical knowledge gap exists in understanding the connectivity between the cryosphere and groundwater, which is essential for accurate modeling of water cycles in these sensitive regions. This study presents a process-based approach to model cryospheric dynamics and couple them with a groundwater model, focusing on Alpine headwaters. Using remote sensing, meteorological, and field data, we assess the relative importance of cryospheric processes on groundwater recharge. The primary goal is to enhance understanding of the physical processes involved and their evolution under climate change. We hypothesize that groundwater recharge in Alpine headwaters will be modified as cryospheric features change. This insight will help assess the ability of mountain groundwater systems to buffer reductions in cryospheric water storage and the long-term modifications of their contribution to streamflow. A secondary objective addresses the challenges of data collection in remote mountain regions, where limited data availability and difficult access are persistent obstacles. Using well-studied sites, we establish and test methodologies to document the role of cryosphere features in groundwater dynamics. The approach developed will aim to be adaptable to the data constraints typical of mountainous regions, providing a robust framework for future research in mountain hydrology under global change.

ID: 3.13256

Three decades of snow water equivalent dynamics in the Po River Basin, Italy: Trends and Implications

John Mohd Wani
Roati, Gaia; Dall’Amico, Matteo; Di Paolo, Federico; Tasin, Stefano; Gleason, Kelly E.; Brian, Marco; Rigon, Riccardo

Abstract/Description

Seasonal snowpack is a key component of the mountain cryosphere, acting as a vital natural reservoir that regulates runoff downstream in snow-fed basins. In mid- and low-elevation mountain regions such as the European Alps, snow processes, such as accumulation and melt are highly sensitive to climate change, having direct implications for hydrological forecasting and water availability.
In this study, we analyse a 30-year (1991–2020) long dataset of snow water equivalent (SWE) in the Po River district, Italy, encompassing portions of the Alps and Apennines. The dataset is available at a 500×500 m spatial resolution and at a daily temporal scale. The data was generated using the “J-Snow” modelling framework, which integrates the physically based GEOtop model with in-situ snow height observations and remotely sensed snow cover products (e.g., MODIS).
The Po River basin, the largest in Italy, is among the most sensitive hydrological basins in Europe and has experienced frequent droughts, including a severe snow drought event in 2022. Therefore, these kinds of long-term spatial datasets help to monitor and analyse the spatial and temporal changes in the SWE and provide vital insights for addressing the snow drought alerts in the study region.
We computed key snow phenology metrics and their trends, such as snow persistence, first snow date (FSD), snow disappearance date (SDD), peak SWE, peak SWE timing, and regional snow line elevation. Our initial results show that the long-term basin-wide SWE volume equals 3.34 Gm³ and a mean snow-cover area of 15,471 km². Additionally, elevation-wise analysis of snow phenology metrics show that the most pronounced changes occur below 2000 m a.s.l. Changes in snow-water storage start, snowmelt timing, and its variability can directly affect the water availability in snow-fed basins, with significant implications for both ecosystems and human populations.

Acknowledgement
The work of J.M.W. has been funded by Fondazione CARITRO Cassa di Risparmio di Trento e Rovereto, grant number 2022.0246.

ID: 3.13317

Energy Balance Modeling of Albedo Evolution and Mass Balance on Gulkana Glacier, AK

Claire Wilson
Rounce, David

Abstract/Description

Glacier mass loss in Alaska is accelerating in part due to albedo feedbacks which have not yet been methodically incorporated into glacier models. For example, wildfires deposit black carbon which darkens snow and accelerates melt rates. We present a new glacier energy balance model with a 1D snow layer scheme that allows accumulation and percolation of black carbon and dust and calculates albedo using a fully coupled aerosol radiative transfer model, SNICAR. The model is first applied to Gulkana Glacier, Alaska where a robust in situ dataset exists for forcing, calibration and validation. To enable regional scale-up, the model’s performance is compared under two forcing scenarios: first, using in situ meteorological measurements from an on-ice automatic weather station, and second, using statistically downscaled climate reanalysis data. The model is then assessed against seasonal and annual point mass balance, end-of-winter snow depth and density, and daily surface height change over the 2024 melt season. By performing a grid search on two parameters, we assess tradeoffs between error metrics and validate the model on the 2024 melt season observations. The model framework allows for analysis of albedo feedbacks and their impact on glacier mass loss in Alaska.

ID: 3.13323

A global reference framework of water isotopic signatures in glacierized catchment

Melanie Vital
Sapper, Sarah; Dàvila Roller, Luzmilla; Fernandoy, Francisco; Jeonghoon, Lee; Masse-Dufresne, Janie; Bakhriddin, Nishonov; Persoiu, Aurel; Gorritty, Marcelo; Saidaliyeva, Zarina; Shahgedanova, Maria; Pu, Tao; Temovski, Marjan; Vreca, Polona; Wade, Andrew; Vystavna, Yuliya

Abstract/Description

The livelihoods of millions globally rely on meltwater from glacierized catchments, which serve as essential sources of drinking water, agriculture, and hydropower. However, climate warming is significantly altering the water storage functions of these catchments, creating major challenges for water resource management in mountain regions. In recognition of the United Nations’ designation of 2025 as the International Year of Glacier Protection and the commitment to Sustainable Development Goal 6 (Clean Water and Sanitation), it is crucial to understand and address these changes while developing adaptive management strategies. Despite their importance, the relative contributions of glacier melt, snowmelt, precipitation, groundwater, and other endmember sources to streamflow remain poorly quantified in many glacierized regions. This knowledge gap hampers efforts to predict and manage water resources under changing climatic conditions. Isotope-based techniques provide a powerful means to distinguish and quantify these contributions, offering valuable insights into the current and future availability of water in glacierized catchments. As part of the International Atomic Energy Agency’s (IAEA) coordinated research project, Understanding Hydrological Processes in Glacierized Catchments under Changing Climate using Isotope-Based Methodologies, we developed a comprehensive database of isotopic signatures for key streamflow endmembers. These endmembers, which vary depending on the specific catchment, include for example glacier melt, snowmelt, precipitation, groundwater and outflow from rock-glaciers and ice-cored moraines. Our database includes stable isotopes of oxygen (δ¹⁸O) and hydrogen (δ²H) of endmembers, compiled from over 80 published studies worldwide and supplemented with our own collected data from study areas spanning South America, North America, Europe and Asia. This framework provides expected variations in isotopic signature of endmembers in different regions and supports the assessments of how contributions shift with seasonal and inter-annual climate variations. These insights are critical for evaluating changes in total discharge volumes and informing sustainable water management strategies to mitigate the impacts of climate change on mountain hydrology.

ID: 3.13533

Seasonality and Albedo Dependence of Cloud Radiative Forcing in the Upper Colorado River Basin

William Rudisill
Feldman, Dan; Cox, Christopher; Riihimaki, Laura; Sedlar, Joseph

Abstract/Description

Mountains create and enhance their own clouds, which both scatter and absorb shortwave radiation from the sun and absorb and re-emit longwave radiation from the ground and atmosphere. However, the impacts of clouds on the surface radiation balance in high elevation, snow-covered mountain terrain are poorly quantified. Capturing these effects are among the primary challenges faced by physically based snow energy balance modeling. In this study, we use ground observations of clouds and surface radiation collected by the Surface Atmosphere Integrated Field Lab (SAIL) campaign and partner organizations in the upper elevations (2880 m.a.s.l) of the Upper Colorado River Basin (UCRB) over a 21-month period from September 2021 to June 2023 to estimate Cloud Radiative Forcing (CRF) in the shortwave, longwave, and the net effect for a single, intensively monitored site. Longwave warming dominates over snowpacks in the winter when snow albedos are high (0.8-0.9) and the background atmospheric precipitable water vapor is low (<0.5 cm), yielding a maximum monthly average net CRF of +34.7 W·m−2 in winter, meaning that clouds increase the surface net radiation relative to clear skies during this time period. Perhaps paradoxically, clouds generally increase net radiation over bright snowpacks even at solar noon. However, for a brief two-to-three week period over melting, low-albedo snowpacks (0.5-0.6) impacted by dust impurities, CRF switches sign, and clouds reduce the net radiation available for melt production relative to clear skies. In the summer over non-snow covered ground, CRF reaches a minimum monthly average -47.6 W·m−2 with hourly minima of -600 W·m−2. Sensitivity tests elucidate the role of the surface albedo on the net CRF. The results suggest that net CRF will both increase in magnitude and lead to a more persistent cooling effect on the net radiation budget as snow cover declines across mountains.

ID: 3.13769

Projecting Glacier Mass Loss and recession in the Kashmir Himalaya under 21st Century Climate Change Scenarios

Shakil Romshoo
Abdullah, Tariq; Bashir, Jasia

Abstract/Description

This study evaluates the impacts of 21st century climate change on glacier mass and area in the Jhelum basin, a major tributary of the Indus basin, using advanced climate modelling and glacier dynamics assessments. Employing bias-corrected projections from thirteen CMIP6 climate models under the most plausible Shared Socioeconomic Pathways (SSPs) climate change scenarios, the research integrates temperature-index mass balance modelling and volume-area scaling to predict changes in glacier extent and mass by the end of 21st century. Key findings reveal a projected temperature increase ranging of 1.9°C to 3.8°C, with precipitation rising by 2.5% to 14% by the end of the century. This climate change is predicted to result in a significant mass loss, with annual mass balances declining to -6.8 ± 1.9 m w.e. a⁻¹ under SSP585, leading to a significant 55.3 ± 16.1% reduction in glacier area by the 2080s. Under SSP245, glacier coverage is expected to decrease by 34.7 ± 12.1%, underscoring the sensitivity of the region’s cryosphere to climate change. Notably, glacier mass loss is expected to be five times greater by 2100 under the SSP585 scenario compared to the baseline period (1980–2017). Calibration of simulated mass balance estimates was conducted against direct observations for three key glaciers in the study area; Kolahoi (2014–2019), Hoksar (2013–2018) and Nehnar (1975–1984). The study emphasizes the severe implications of significant mass loss and glacier recession under climate change scenarios by the end of the 21st century, particularly for water availability in sectors reliant on meltwater during critical seasons. These findings highlight serious implications for water, food, and energy security, both regionally and downstream. The results emphasize the urgent need to integrate these insights into regional water resource management and climate change adaptation strategies for glacier-fed water systems in the Himalayan region.

ID: 3.21208

Cryosphere-groundwater interaction processes in mountain regions: How to measure and model to support climate change adaptation?

Odile De La Ruë Du Can
Roques, Clement; Renard, Philippe

Abstract/Description

Mountain regions are undergoing rapid transformations due to global climate
change, including significant alterations to the water cycle. These modifications
include more frequent and intense extreme events such as floods and droughts,
which disrupt water availability and quality, and threaten food production and
access to drinking water. The scarcity of observational data in these remote
regions leaves a major gap in our understanding of mountain areas and limits
our capacity to anticipate and adapt to future risks.
This study investigates the role of the cryosphere – including snow, glaciers,
rock glaciers and permafrost – in the mountain hydrological cycle, focusing on its
interactions with groundwater systems. Using a synthetic, physics-based model,
we explore the dominant physical processes driving these interactions and assess
their effects on key hydrological indicators such as water temperature, electrical
conductivity, and geochemical tracers.
Model outputs are compared with field observations to develop methods tai-
lored to data-scarce mountain regions that integrate cryosphere–groundwater
interactions into hydrogeological modeling. This approach aims to quantify
evolving cryosphere-driven processes and to develop scalable monitoring strate-
gies. The results contribute to a broader understanding of hydrological change
from catchment to regional scales, ultimately supporting more informed water
resource management in high-mountain environments.

FS 3.115: Drought in mountain regions

FS 3.117: The future of low-latitude alpine ecosystems in a changing world: what to expect from nature-based solutions?

FS 3.116

High mountain hydrology and cryosphere: observations, modelling, prospects

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