HS2.1.9 | From snow and glacier hydrology to catchment runoff
From snow and glacier hydrology to catchment runoff
Co-organized by CR2
Convener: Francesco Avanzi | Co-conveners: Giulia MazzottiECSECS, Guillaume Thirel, Abror Gafurov, Doris Duethmann
| Mon, 15 Apr, 14:00–15:45 (CEST)
Room 2.17
Posters on site
| Attendance Mon, 15 Apr, 16:15–18:00 (CEST) | Display Mon, 15 Apr, 14:00–18:00
Hall A
Orals |
Mon, 14:00
Mon, 16:15
Water stored in the snow pack and in glaciers represents an important component of the hydrological budget in many regions of the world, as well as a sustainment to life during dry seasons. Predicted impacts of climate change in catchments covered by snow or glaciers (including a shift from snow to rain, earlier snowmelt, and a decrease in peak snow accumulation) will reflect both on water resources availability and water uses at multiple scales, with potential implications for energy and food production.

The generation of runoff in catchments that are impacted by snow or ice, profoundly differs from rainfed catchments. And yet, our knowledge of snow/ice accumulation and melt patterns and their impact on runoff is highly uncertain, because of both limited availability and inherently large spatial variability of hydrological and weather data in such areas. This translates into limited process understanding, especially in a warming climate.

This session aims at bringing together those scientists that define themselves to some extent as cold region hydrologists, as large as this field can be. Contributions addressing the following topics are welcome:
- Experimental research on snow-melt & ice-melt runoff processes and potential implementation in hydrological models;
- Development of novel strategies for snowmelt runoff modelling in various (or changing) climatic and land-cover conditions;
- Evaluation of remote-sensing or in-situ snow products and application for snowmelt runoff calibration, data assimilation, streamflow forecasting or snow and ice physical properties quantification;
- Observational and modelling studies that shed new light on hydrological processes in glacier-covered catchments, e.g. impacts of glacier retreat on water resources and water storage dynamics or the application of techniques for tracing water flow paths;
- Studies on cryosphere-influenced mountain hydrology, such as landforms at high elevations and their relationship with streamflow, water balance of snow/ice-dominated mountain regions;
- Studies addressing the impact of climate change on the water cycle of snow and ice affected catchments.

Orals: Mon, 15 Apr | Room 2.17

Chairpersons: Francesco Avanzi, Doris Duethmann, Giulia Mazzotti
Snow hydrology
On-site presentation
S. McKenzie Skiles, Patrick Naple, Otto Lang, and Joachim Meyer

Seasonal mountain snowmelt is an important contributor to surface water resources and groundwater recharge in the midlatitudes, making forecasting of snowmelt timing and duration critical for accurate hydrologic prediction. Net solar radiation, controlled primarily by snow albedo, is the main driver of snowmelt in most snow covered environments. Lowering of snow albedo from episodic dust deposition has been shown to be an important control on snowmelt patterns in the Rocky Mountains of the Western United States. Here, we compare and contrast trends in dust impacted albedo over the previous two decades with a focus on two regions: 1) the Colorado Rockies, headwaters of the Colorado River, which recieves dust from the southern Colorado Plateau and 2) the Wasatch Mountains (UT), headwaters of the Great Salt Lake, which recieves dust from the Great Basin. Results show that while snow darkening occurs every year, the magnitude of impact is spatially and temporally variable, and there are no emerging relationships that indicate when 'high-impact' dust years will occur. To account for spatial and interannual variability in dust impacted net solar radiation in hydrologic prediction we developed a spatially distributed process-based snowmelt model that incorporates near-real time snow albedo from remote sensing and incoming solar radiation from numerical weather prediction. The model improves simulated timing of snowmelt initiation and duration in all years, even those with lower dust impacts, demonstrating the importance of accurate snow albedo in snowmelt modeling. 

How to cite: Skiles, S. M., Naple, P., Lang, O., and Meyer, J.: Accounting for interannual variability in dust accelerated snowmelt in process-based hydrologic prediction, Rocky Mountains, USA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13919, https://doi.org/10.5194/egusphere-egu24-13919, 2024.

On-site presentation
Connor Shiggins, Douglas Mair, James Lea, and Isabel Nias

The fate of percolating surface meltwater encountering ‘impermeable’ ice layers is uncertain in the accumulation zone of the Greenland Ice Sheet (GrIS). Often, ice layers are considered to retard meltwater and cause lateral runoff. However, modelled and field-based observations in the percolation zone of the GrIS have suggested ice layers are not necessarily impermeable and meltwater can breakthrough, percolating to deeper depths of snow/firn and consequently inferring a greater refreezing capacity within the accumulation zone. The physical and thermal conditions which control the permeability of ice layers remain unclear and effective parameterisation of these processes is lacking for snow/firn modelling of melt, refreezing and runoff. Here we present repeat cold-laboratory experiments which seek to understand how meltwater interacts with thin ice layers (5 to 20 mm) for two differing thermal regime contexts whereby the surrounding snow/firn thermal regime is either (i) below or (ii) at the melting point.

We find that under extreme melt regimes, ice layers continually retard wetted fronts of percolating meltwater when the thermal regime of the snowpack is below the melting point. This barrier results in the snowpack at depth remaining at least ~1oC cooler than snow above the ice layer which is saturated with meltwater. We also find that the ice layer forces ~35% of the percolating meltwater to runoff, cooling the overlying snow and increasing the refreezing capacity of the snow closer to the snowpack surface. The remaining ~65% of meltwater ponds and later refreezes on the ice layer, thickening the impenetrable surface.

When the thermal regime of the surrounding snow/firn is at the melting point, we find that meltwater is able to pond without refreezing, resulting in the ice layer failing and allowing deeper percolation into the snowpack. These findings suggest that the thermal regime of a snowpack is crucial for the structural integrity of an ice layer and thus the permeability of a snowpack.

Consequently, these findings have implications for parameterising meltwater runoff and ice layer integrity in snow and firn models which incorporate ‘impermeable’ barriers in their domains. Future work will continue to explore similar experiments with thicker ice layers (~60 mm) to determine whether ice layer breakthrough is primarily a function of snow/firn thermal regime and/or ice layer thickness. 

How to cite: Shiggins, C., Mair, D., Lea, J., and Nias, I.: Cold-laboratory experiments to observe meltwater and ice layer interactions in snowpacks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7278, https://doi.org/10.5194/egusphere-egu24-7278, 2024.

On-site presentation
Achille Jouberton, Stefan Fugger, Thomas Shaw, Evan Miles, Marin Kneib, Abdulhamid Kayumov, Ardamehr Halimov, Hofiz Navruzshoev, Husraf Kabutov, Firdavs Vosidov, and Francesca Pellicciotti

Mountain glaciers are shrinking at accelerating rates due to enhanced ablation and reduced accumulation. In High Mountain Asia (HMA), recent glacier and snow changes have been highly heterogeneous, due to differences in accumulation regimes and sensitivity of glacier mass balances to temperature increases. The Pamir-Karakoram region is well known for hosting some of the only glaciers featuring neutral or even positive mass balance since the 2000, yet the causes for this anomaly are not fully understood, neither how long it will persist in the future nor its hydrological implications. In the semi-arid basins of Central Asia, snow- and glacier melt sustains most of the annual streamflow, with glacier melt being especially important towards the end of the dry summers. However, very few direct observations exist at high elevation, hindering the quantification of glacier mass inputs which is essential to estimate the long-term sensitivity of glaciers to warming. 

In this study, we combine in-situ hydro-meteorological observations with remote sensing observations to constrain a land-surface model and understand snow accumulation dynamics at a glacierized catchment in the Pamir mountains of Tajikistan. In-situ snow height and mass changes have been collected since 2021 from automatic weather stations, time-lapse cameras and pressure loggers in seasonally frozen lakes, providing a uniquely rich dataset for this region. We use MODIS, Landsat-8 and Sentinel-2 satellite images to derive snow cover dynamics at high spatial and temporal resolutions, and very high-resolution (2m) optical stereo imagery (Pleiades) to derive spatially resolved snow depths. These in-situ and remote-sensing observations are then used to inform a land-surface model that we force with statistically downscaled and bias-corrected reanalysis data (ERA5-Land) at 100m spatial and hourly temporal resolution, from 2015 to 2023.

We use our model to dissect the glacier mass balance seasonal dynamics, to quantify how much mass is gained through snowfall and avalanches, and how much mass is lost through melting and sublimation. We find that glaciers in our catchment receive a large part (58 %) of their annual mass input (1081 mm w.e.) from March to July, suggesting that spring and early summer precipitation events are key to control accumulation and therefore dictate glacier mass balances. Importantly, 11% of the annual snowfall is returned to the atmosphere via sublimation. At the catchment scale, snowmelt contributes to 67% of the annual runoff (625 mm), followed by glacier melt (24%) and rain (9%). When most of the seasonal snowpack has melted out (usually in August), glacier melt becomes the dominant contribution (with 55% in September). In most of the study period years, the glacier mass balance is close to neutral, but it turned negative in the last three years, where warmer conditions have led to more rapid seasonal snowpack melt-out and higher glacier ELAs, deteriorating the health of these previously spared glaciers and casting doubts on their ability to provide fresh water during the dry summers in the longer term.

How to cite: Jouberton, A., Fugger, S., Shaw, T., Miles, E., Kneib, M., Kayumov, A., Halimov, A., Navruzshoev, H., Kabutov, H., Vosidov, F., and Pellicciotti, F.: Snow accumulation dynamics and its contribution to the hydrology of a glacierized catchment in the Northern Pamirs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18443, https://doi.org/10.5194/egusphere-egu24-18443, 2024.

On-site presentation
Vazken Andréassian, Amalya Misakyan, and Artur Gevorgyan

The hydrological analysis of high elevation catchments is particularly difficult for two reasons:

. first, precipitation measurements are scarce at higher elevations,

. second, even when there are precipitation measurements, the collected amounts are strongly biased due to the well-known effect of wind on snowflakes.

Several formulations have been proposed to correct this wind-dependent underestimation of solid precipitation amounts. They all depend on at least one parameter, which must be calibrated for the specific location. At a few locations in the world, a double-fenced shielded raingage can be used to provide a reference precipitation amount, and the parameter of the correction can be determined experimentally. But at most locations, we have no real way to parameterize the adjustment relationship.

We use here a newly released dataset comprising 30 years of data for 11 stations located at high elevation in Armenia, where the precipitation gage network is strongly impacted by snow undercatch. Using ground snow surveys jointly with a degree-day based snow accumulation and melt model, we show that we can propose an adapted parameterization of the correction formula.

How to cite: Andréassian, V., Misakyan, A., and Gevorgyan, A.: Correction of raingages' snow undercatch at meteorological stations using data from snow surveys: an Armenian case study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19969, https://doi.org/10.5194/egusphere-egu24-19969, 2024.

Glacier hydrology
On-site presentation
Bastien Charonnat, Michel Baraer, Janie Masse-Dufresne, Eole Valence, Jeffrey McKenzie, Chloé Monty, Kaiyuan Wang, and Elise Devoie

The deglaciation of high mountain ranges is leading to the expansion of proglacial areas, which encompasses diverse permafrost and ground ice landforms. These features exert an increased influence on the hydrology and hydrogeology of alpine catchments as glaciers retreat. Despite the heightened attention received by rock glaciers for the last decades, their role within the broader hydrological and hydrogeological valley system remains understudied. Previous studies have highlighted rock glaciers’ ability to act as hydrological storage and to buffer water release from alpine catchments. However, there is a lack of studies about their ability to modify the groundwater flow paths in a proglacial valley system and to redistribute glacial meltwater. This study addresses this knowledge gap by investigating how the rock glacier redistributes glacial meltwater in a study catchment.

Shar Shäw Tágà (Grizzly Creek) is a subarctic glaciated catchment located in the St. Elias Mountains, Yukon (Canada) that experiences significant glacial retreat. A non-relict rock glacier at the outlet of a glacial sub-catchment obstructs the valley thalweg, with only a few springs exhibiting minimal discharge from its front. These minor springs contrast remarkably with the substantial discharge observed at higher elevations above the rock glacier.

Water level, water temperature and electrical conductivity variables were monitored in identified springs throughout the summer 2022. Results were compared to meteorological data with wavelet coherence analysis to determine the springs’ origins and drivers. Additionally, multiple sampling campaigns were conducted in the summers of 2022 and 2023 to analyze major ions concentrations and water stable isotopes signatures in the catchment’s streams.

The results reveal that the rock glacier serves as a critical obstacle and deflector to subsurface meltwater, either forcing upstream meltwater to penetrate deeper into the subsurface, or redirecting lateral subsurface flow to resurge at its front, forcing part of the alluvial floodplain shallow aquifer to reach the surface.

While rock glaciers are often considered potential water reservoirs, this study illuminates their dual role as critical deflectors for shallow subsurface flow in proglacial valley systems. They can impede glacial meltwater flow, originating alternative pathways toward deep aquifers or lowlands’ surface waters. Such findings nuance the ability of rock glaciers to store and release glacial meltwater, as they can deflect shallow subsurface flow. Additionally, it shows that rock glaciers can force infiltration and resurgence of water at specific locations, affecting the broader mountain hydrogeological system. Furthermore, it enforces their critical role in the future of water resources supplied by high mountain ranges in a deglaciation context.

How to cite: Charonnat, B., Baraer, M., Masse-Dufresne, J., Valence, E., McKenzie, J., Monty, C., Wang, K., and Devoie, E.: The role of proglacial rock glaciers in redistributing glacial meltwater, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-804, https://doi.org/10.5194/egusphere-egu24-804, 2024.

On-site presentation
Caroline Ehrendorfer, Franziska Koch, Sophie Lücking, Thomas Pulka, Hubert Holzmann, Philipp Maier, Fabian Lehner, Herbert Formayer, and Mathew Herrnegger

The timing and quantity of snow and ice melt in high-alpine regions is of great importance, especially for time-sensitive processes such as hydropower production. In most conceptual hydrological models, the simulations of these components are frequently only based on simple temperature index methods, and the question arises whether these are sufficient to derive useful information on changing runoff seasonality and quantities for hydropower producers.

This study examines the quantitative and seasonal changes in glacier melt contribution to total runoff under climate change in several Austrian high-alpine catchments with hydropower production (Stubaital, Stubachtal, Kölnbrein/Maltatal, Schlegeis/Zillertal). As the estimation of precipitation model inputs for areas with complex terrain is characterised by a high degree of uncertainty, an undercatch-correction adapted for high-alpine areas was applied, integrating information from local weather stations, topography and iterative feedback from the modelled water balance. The conceptual, semi-distributed rainfall-runoff model COSERO was set up for the case study regions.  To cover long-term changes, the model was run for Stubai- and Stubachtal for the reference period (1990-2020) and future scenarios (2021-2100) with daily timesteps. In addition to the daily timesteps, COSERO was also coupled with the physically-based snowpack model Alpine3D for simulations in the Kölnbrein and Schlegeis catchments for recent decades to implement the simulation of relevant components of the water balance including snow and ice processes at an hourly timestep based on more complex energy-balance modelling. Besides air temperature and precipitation, the coupling requires additional hourly meteorological input such as radiation, relative humidity and wind information.

The combination of COSERO with Alpine3D improves results at the hourly timestep, but the conceptual model delivers satisfying results on its own as well. Moreover, the results are in line with literature and show the expected decrease of ice volume and ice melt in coming years. By 2050, the ice melt contribution to total runoff is significantly reduced in all case study areas and seasonality shifts due to less ice melt and earlier snowmelt in the form of more winter and spring runoff and less flow in summer are prevalent. In addition, we show that the modelling of the water balance components in the past can be greatly improved by using the undercatch-corrected precipitation data.


Acknowledgements: We thank the VERBUND Energy4Business GmbH, the Austrian Climate Research Programme (ACRP), the Austrian Research Promotion Agency (FFG), and the ÖBB for funding, fruitful discussions and providing us with data.

How to cite: Ehrendorfer, C., Koch, F., Lücking, S., Pulka, T., Holzmann, H., Maier, P., Lehner, F., Formayer, H., and Herrnegger, M.: Contribution of glacier melt to runoff under climate change using a conceptual hydrological model in selected high alpine regions in Austria, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5329, https://doi.org/10.5194/egusphere-egu24-5329, 2024.

From the cryosphere to general hydrology
On-site presentation
Florentin Hofmeister, Xinyang Fan, Madlene Pfeiffer, Inga Labuhn, Ben Marzeion, Bettina Schaefli, and Gabriele Chiogna

The streamflow generation related to snow and glacier melt is particularly sensitive to temperature fluctuations and, hence, highly affected by global warming. However, the non-linear and complex interaction between streamflow contributions originating from snow melt versus glacier melt and being transferred to stream via the subsurface complicates the investigation of climate-induced changes in high-elevation catchments. We used the physically-based hydrological model WaSiM to simulate the climate-induced changes in the streamflow generation in the Kaunertal (Austria), a highly glaciated Alpine headwater catchment. The simulations extend from the last little Ice Age (i.e., 1850) to 2015. Large-scale climate processes of a general circulation model (GCM) were dynamically downscaled with the Weather Research & Forecasting Model (WRF) to the central Alpine region at a 2 km spatial resolution from 1850 to 2015. The WaSiM model parameters were transferred from a WaSiM configuration driven by station data and partly optimized by a manual calibration on observed streamflow. For model evaluation, a multi-objective approach was chosen considering streamflow, SWE, snow cover duration, and glacier mass balances. The hydrological model results showed a good representation of the individual components and seasonal streamflow generation. However, difficulties exist in the spatial representation of the heterogeneous and small-scale differences in the snowpack. In addition, there are limitations in the simulation of glacier evolution using WaSiM over long periods (> 30 years) in highly glaciated catchments, as WaSiM does not contain an ice flow routine that can simulate the glacier dynamics during advance or retreat. Despite the cascade of uncertainties in this complex model chain (i.e., GCM, WRF, WaSiM), the results of the long-term simulation show interesting dynamics and enable an analysis of streamflow generation for periods where no observational data is available. For instance, glacier melt indicates a high dependence on the development of summer temperatures (i.e., JJA). The rising temperatures led to an earlier onset of snow and glacier melt, which shifted the streamflow regime and increased the daily streamflow magnitude, especially from 1995 onwards. The next step will be the comparison of the hydrological model results with those from other headwater catchments in the eastern Central Alps with a different degree of glaciation. The novelty lies in comparing 165 years of simulated streamflow and the contributions from snow and glacier melt. This comparison will validate the transferability and generalizability of the complex model chain and the simulation results.

How to cite: Hofmeister, F., Fan, X., Pfeiffer, M., Labuhn, I., Marzeion, B., Schaefli, B., and Chiogna, G.: Process-based modeling of the streamflow generation in a highly glaciated Alpine headwater catchment since the last little Ice Age (i.e., 1850)., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2191, https://doi.org/10.5194/egusphere-egu24-2191, 2024.

On-site presentation
Caroline Aubry-Wake and Walter Immerzeel

While mountain groundwater in glacierized regions has gained increasing attention, comprehensive insights of glacier melt contributions to groundwater and their resurfacing patterns remain limited. Our study employs a cryosphere-surface hydrology model in combination with numerical groundwater simulations to estimate the water table variations across the high-altitude Langshisha basin in the Langtang Himalaya (4094-6049 m). We evaluate surface water-groundwater interactions amidst current and projected climatic conditions. Utilizing in-situ weather forcings and evaluated with field measurements, our findings indicate that glacier melt contributes up to 70% of groundwater recharge in the Langshisha basin during the 2012-2020 period. This substantial contribution is attributed to the basin's considerable glacier cover (40%) and its high elevation, where cold temperatures in areas above 5300 m limit melt and are underlain by permafrost, restricting recharge. Groundwater simulations based on these recharge rates reveal a high sensitivity to hydraulic conductivity parameters but are constrained by field measurements of creek exfiltration indicating a water table near the surface along the main streams. The combination of groundwater simulations and field measurements suggests that groundwater exfiltration along the proglacial stream is a predominant surface-water-groundwater exchange mechanism within the basin. Considering the important role of glacier melt in groundwater recharge, our study applies future climate scenarios to gauge the impact of warming trends and glacier retreat on surface water-groundwater dynamics within the basin.

How to cite: Aubry-Wake, C. and Immerzeel, W.: Current and future glacier melt contribution to groundwater dynamics in a high-altitude, Himalaya basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12337, https://doi.org/10.5194/egusphere-egu24-12337, 2024.

On-site presentation
Nico Sneeuw, Shuang Yi, Peyman Saemian, and Mohammad J. Tourian

Spaceborne gravimetry is the only satellite method that can observe terrestrial water storage at a continental scale. The time variable gravity field, observed by GRACE and GRACE-FO, is a measure of mass transport primarily in the global water cycle. In this contribution we analyse the runoff-storage relationship in the GRACE time frame for the pan-Arctic drainage basins. Over these boreal catchments, the conventional hysteresis-type formulation requires algorithmic adaptations in order to accommodate snowload and base-flow during winter periods. We show that the parameters involved in the pan-Arctic runoff-storage relationships are transferable, albeit with a few exceptions, between the various catchments. This remarkable fact allows us access to determining runoff from ungauged drainage areas across the pan-Arctic. As a result we can quantify the total freshwater flux from pan-Arctic basins into the Arctic Ocean.

How to cite: Sneeuw, N., Yi, S., Saemian, P., and Tourian, M. J.: Estimating runoff from pan-Arctic basins through an improved runoff-storage relationship using satellite gravimetry in the GRACE period 2002-2019, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2098, https://doi.org/10.5194/egusphere-egu24-2098, 2024.

Posters on site: Mon, 15 Apr, 16:15–18:00 | Hall A

Display time: Mon, 15 Apr 14:00–Mon, 15 Apr 18:00
Chairpersons: Francesco Avanzi, Giulia Mazzotti, Abror Gafurov
Peter Romanov

A new version of the Global Multisensor Automated Snow and Ice Mapping System (GMASI) has been implemented into operations at NESDIS is summer 2023. The new system is an upgrade of the previous version of the GMASI which was operated since 2006. The system provides information on the snow and ice distribution for NOAA numerical weather prediction and climate models as well as for a number of other atmosphere and land remote sensing products. The retrieval algorithm uses satellite observations in the visible/infrared and in the microwave spectral bands and delivers daily spatially continuous (gap-free) maps of the snow and ice cover.  

Compared to previous version, the new system incorporates data from a larger set of microwave sensors and features an enhanced retrieval algorithm. The spatial resolution of the output maps has been improved from es improved from 4km to 2km.  In the presentation we provide details of the data processing algorithm and of the output product focusing on the improvements and upgrades. We demonstrate that the output of the new GMASI system closely matches the accuracy of snow maps produced within NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) and agrees well to in situ station snow depth report. Improvements to the retrieval algorithm mostly affected reproduction of small-scale features in the snow and ice cover distribution, particularly in alpine areas.  In the same time, large-scale climatologically-important cryosphere features as the continental and hemisphere snow and ice extent estimated with the new snow and ice maps remain consistent with the previous version of the product. 

How to cite: Romanov, P.: Enhancing NESDIS Global Automated Snow and Ice Cover Mapping System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14158, https://doi.org/10.5194/egusphere-egu24-14158, 2024.

Ying Yi, Shiyin Liu, Yu Zhu, Kunpeng Wu, and Fuming Xie

    Many reservoirs have been constructed in the Yangtze River basin, however, spring floods in its source region pose increasingly severe challenges to reservoirs operation and water resources management due to increased climatic variability under global warming. Understanding spring flood variability and their major influential factors under changing climates is crucial to improving water management, agricultural irrigation, reservoir operation, and flood prevention. In this study, we have examined the spring flood characteristics and their influential factors in the source region of the Yangtze River based on station data and multisource remote sensing products during 2001–2020. Late Mays have seen most of the highest spring flood discharge, while some springs have experienced multiple peaks. Extreme spring floods were identified in the years 2012, 2013, 2019, and 2020, with the highest peak discharge (1365.83 m3/s) and longest flood duration (47 days) in 2019. Spring snowmelt played a key role in 2019 spring flood and others were also driven by snowmelt in the UJSB. We defined Snow Water Volume (SWV) as an indicator of the precondition for high spring flood. In 2019, large winter SWV along with spring snowfall melted into meltwater under the rising temperature, resulting in extreme spring flood event in late April. Whereas, in 2012 and 2020, snowmelt and rainfall combined to contribute to the extreme spring flood events in late Mays. In 2013, although snowmelt made a contribution to the first spring flood peak, the flood event at the end of May was primarily contributed by rainfall. Based on spatiotemporal variations in spring SWV and the isotherm of critical temperature for snow melting, the key regions dominating spring floods were identified as the regions with large amount of SWV. Weather pattern analysis showed that the enhanced Westerly jets in winters brought about large snowfall and extended snow cover in the region which can be released as floods triggered by rapid increase in air temperature in the coming spring.

How to cite: Yi, Y., Liu, S., Zhu, Y., Wu, K., and Xie, F.: Spring floods and their major influential factors in the source region of the Yangtze River during 2001–2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2310, https://doi.org/10.5194/egusphere-egu24-2310, 2024.

Michelle Yu, Christopher Paciorek, Alan Rhoades, Mark Risser, and Fernando Perez

Snow Water Equivalent (SWE) is a critical parameter for understanding water availability in regions with seasonal snow cover. Ensuring an accurate representation of SWE across regular spatial and temporal intervals is essential and plays a pivotal role in hydrological and climatological studies. This work critically examines the ablation modeling strategy employed by the University of Arizona daily 4km SWE dataset (UA SWE), a widely adopted SWE gridded product in the United States, highlighting limitations inherent in methodologies that rely solely on temperature data.

Recognizing the utility of a more nuanced perspective to capture the complexities of snowmelt dynamics, we propose a novel method that incorporates a diverse set of meteorological and terrain characteristics as input variables in the predictive modeling of snow ablation. Our approach is further extended to directly model SWE, eliminating the need for intermediate ablation estimation and providing a more intuitive solution for empirical SWE prediction.

Our versatile methodology can be easily applied to produce high-resolution gridded SWE data. By addressing deficiencies in a leading empirical approach, our technique aims to enhance the accuracy of SWE representation at both the point and grid levels.

How to cite: Yu, M., Paciorek, C., Rhoades, A., Risser, M., and Perez, F.: Enhancing Snow Ablation Modeling in the Generation of Gridded Snow Water Equivalent Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5531, https://doi.org/10.5194/egusphere-egu24-5531, 2024.

Snowmelt Contribution to Seasonal Baseflow Dynamics in Mountain Catchments
Johnmark Nyame Acheampong and Michal Jenicek
Vicente Melo Velasco, Evan Miles, Michael McCarthy, Thomas E. Shaw, Catirona Fyffe, and Francesca Pellicciotti

Debris, ranging from thin surface dust to medial moraines and thick, continuous layers in ablation zones, partially covers glaciers all around the world. By modifying energy transfer from the atmosphere to the ice, the supraglacial debris layer fundamentally controls sub-debris melt rates. Debris physical properties such as surface roughness (z0) and thermal conductivity (k) have only been derived from local measurements at a few sites, and modelling studies of debris-covered glaciers have often relied on literature values. The correct representation of these properties in energy-balance models is crucial for understanding the climate-glacier dynamics and how debris-covered glaciers will behave in the future. There are several established methods to derive these properties from field measurements, yet relatively few studies undertake to measure properties for their sites, or to evaluate the resulting property values.

We undertook an observational campaign to investigate supraglacial debris properties at Pirámide Glacier, in the central Chilean Andes. First, we used established approaches, as well as some variations on those approaches, to derive z0 from wind-temperature tower data and k from thermistor strings in the debris at three glacier locations. Second, we determined locally-optimal k and z0 values to reproduce observed ice melt: we optimised k by simulating energy conduction through the debris with the surface temperature as an input, then optimised z0 by running a complete energy-balance model using the observed surface meteorology. We then conducted point-scale energy-balance modelling using the z0 and k values obtained i) with the derivations from field measurements; ii) through optimisation, or; iii) from the typical values found in literature. This allowed us to evaluate how the different methods perform by comparing the modelled and measured ice melt. 

Our results show that deriving local debris properties from measurements is challenging and that measured values can differ significantly from common literature values. The values derived from measured data can vary significantly depending on the method employed. It is important to note that these values can also differ significantly from the values required by an energy-balance model to accurately represent sub-debris ice melt. Furthermore, energy-balance models typically assume a representation of heat transfer within the supraglacial debris layer based solely on conduction and require a bulk thermal conductivity value. This highlights the necessity of efforts to reevaluate measurements in the field and reconsider our definition of debris properties in melt modelling.

How to cite: Melo Velasco, V., Miles, E., McCarthy, M., Shaw, T. E., Fyffe, C., and Pellicciotti, F.: Inferring Debris Properties on Debris-Covered Glaciers: Implications for Glacier Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9702, https://doi.org/10.5194/egusphere-egu24-9702, 2024.

Alexandra von der Esch, Matthias Huss, Marit Van Tiel, Justine Berg, Tarang Patadiya, Pascal Horton, Saurabh Vijay, and Daniel Farinotti

High Mountain Asia is characterized by a substantial glacier coverage with glaciers of varying sizes. These glaciers are crucial in the area's hydrological cycle since they feed large rivers such as the Indus, Ganges, and Brahmaputra rivers. However, ongoing climate change is having a significant negative impact on glacier mass and projections show strong further declines of glacier mass in the future. This is raising concerns about future water security. How big the impact of the evolution of small glaciers (< 2 km2) is towards changing water availability remains to be investigated.

Most studies focus on the regional evolution of glaciers as a whole, which means that small-scale glaciers are often overlooked due to larger glaciers dominating the signal in area and volume changes, despite the fact that small glaciers make up about 30% of the glacierized area in High Mountain Asia. To address this issue, we applied the Global Glacier Evolution Model (GloGEM) to simulate all ca. 100’000 glaciers of High Mountain Asia (Regions 13-15 of the Randolph Glacier Inventory v6.0) under various climate scenarios in the period of 1980-2100. We compared the spatio-temporal variability of the timing of peak water, as well as glacier volume change, between small and large glaciers for a set of approximately 30 catchments in the headwater of Indus, Ganges and Brahmaputra rivers.

We find that there is a larger difference between future scenarios for the timing of peak water for smaller glacier, with it ranging from 2030-2060 and then runoff declining rapidly. Meanwhile, peak water for larger glaciers is likely to occur between 2070-2080 according to an intermediate emission scenario, with glacier runoff decreasing gradually thereafter. As for the ice volume change, smaller glaciers are expected to reach volumes close to zero near the year 2080, while larger glaciers are expected to reach this point only after 2100. The quicker response of small glaciers compared to large glaciers emphasize the need for a particular focus on small glaciers to better understand their responses to climate change and make accurate projections about local and regional scale near future water availability.

How to cite: von der Esch, A., Huss, M., Van Tiel, M., Berg, J., Patadiya, T., Horton, P., Vijay, S., and Farinotti, D.: The importance of small glaciers for accurate projection of future runoff in High Mountain Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15373, https://doi.org/10.5194/egusphere-egu24-15373, 2024.

Jose David Hidalgo Hidalgo, Antonio Juan Collados Lara, David Pulido Velazquez, Eulogio Pardo Iguzquiza, Juan de Dios Gomez Gomez, and Francisco Jose Rueda Valdivia

Snow cover area, which can be obtained from satellite, is a valuable information to simulate streamflow in snow-dominated mountain basins where snowmelt is a major runoff factor. However, usually satellite do not provide long completed snow cover area spatiotemporal series, which are required to calibrate and validate hydrological models. It is due to difference limitations, as presence of clouds, sensor failure, low revisit time or spatial resolution, or recent launch.

Cellular automata models, which use precipitation and temperature as driving variables and some transition rules between cells through calibrated parameters, are capable of capturing the dynamics of snow cover area. Therefore, they can be used to complete and extend the information provided by satellite.

In this work, we simulate long series of daily streamflow in the Canales basin (Sierra Nevada, south Spain) by combining a cellular automata model and the Snowmelt Runoff Model. The Snowmelt Runoff Model is a degree-day model that requires data of temperature, precipitation, and snow cover area and has been widely used in simulation of streamflow in snow-dominated mountainous basins around the world.

The water resources in the Canales basin are regulated by a reservoir, which contributes to supply the Granada city water demand. The main resources stored in reservoir come from Sierra Nevada Mountains during the melting season. Therefore, the dynamics of snow is essential to simulate streamflow in the Canales basin.

It has been also used to simulate future local climate scenarios generated for specific level of warming in peninsular Spain.

Aknowledments: This research has been partially supported by the projects: STAGES-IPCC (TED2021-130744B-C21), SIGLO-PRO project (PID2021-128021OB-I00), SIERRA-CC project (PID2022-137623OA-I00) from the Spanish Ministry of Science, Innovation and Universities, and SER-PM (2908/22) from the National Park Research Program.

How to cite: Hidalgo Hidalgo, J. D., Collados Lara, A. J., Pulido Velazquez, D., Pardo Iguzquiza, E., Gomez Gomez, J. D. D., and Rueda Valdivia, F. J.: Combined use of evolutionary algorithms and hydrological models to simulate snow cover and flow in alpine basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15646, https://doi.org/10.5194/egusphere-egu24-15646, 2024.

Eric Pohl, Mukhammed uulu Esenaman, Ardamehr Halimov, Dominik Amschwand, Tomas Saks, and Jingheng Huang

Central Asia’s mountain rivers are to a large degree fed by snow and ice melt and are a crucial contributor of fresh water downstream for millions of people. The attribution of how these meltwater sources will change their contribution to stream flow in a warmer future are, however, very uncertain. A major reason for this is an extremely sparse hydrometeorological monitoring network. This affects the calibration and validation of large-scale cryo-hydrological models that could be used for the task, or the validation and bias correction of reanalysis and remote sensing data needed to run such models. In combination with uncertainties about the glacier mass balances in the region, hydrological models are facing a pronounced equifinality problem. In order to improve this, and to understand better the glacier response to the current meteorological forcing in different climate zones of Central Asia, we instrumented 8 pro-glacial streams in Kyrgyzstan and Tajikistan with automated runoff gauges running at hourly resolution to also capture diurnal variability. These measurements complement the already (re-)established glaciological monitoring network at most of these sites and allow tackling the equifinality problem by constraining many variables. Here, we present first results from this database that shall serve to improve hydrological model calibration and parameterization, and understand relationships between meteorological forcing, annual glacier mass balance and meltwater generation. We also discuss instrumenting strategies and problems, and uncertainties related to gauge calibration.

How to cite: Pohl, E., uulu Esenaman, M., Halimov, A., Amschwand, D., Saks, T., and Huang, J.: A Central Asian pro-glacial discharge database for improving hydrological models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16055, https://doi.org/10.5194/egusphere-egu24-16055, 2024.

Quantifying Glacier Melt Contribution to Streamflow in Switzerland with CAMELS-CH using Deep Learning
Corinna Frank and Marvin Höge
Jingheng Huang, Eric Pohl, and Juan Carlos Richard-Cerda

Central Asia is a climate change hot spot, facing an unprecedented juxtaposition of regional climate- and water-related issues. Meltwater from the Pamir Mountains plays a crucial role in Central Asia's hydrological cycle, and its response to climate change has been widely investigated using glacial-hydrological models. However, the hydrological simulation in Pamirs is highly uncertain, primarily driven by data scarcity and the complex interplay between climatic factors and glacier dynamics. Ongoing efforts concentrate on including more calibration data and constraining the uncertainty about the exact internal process representation of hydrological models. However, the quality of the groundwater simulation is often neglected. Groundwater reservoirs, buffering meltwaters and providing river flow when little to no surface runoff occurs, are extremely important in the Pamir region. Although physically based groundwater models provide a more detailed picture of the possible evolution of the system, empirical groundwater models are often used in hydrological modeling due to their minimal input data requirements and low computational cost compared to physically based models. However, the traditional empirical groundwater model with single linear storage is not suitable for the Pamir region. The region is characterized by a variety of sedimentary deposits in different landscape morphologies, resulting in varying delays in water recharge, release, and storage capacities. We improved the baseflow representation by coupling two linear groundwater reservoirs (one fast and one slow) into a widely-used hydrological model in the region. A representative catchment in the central Pamir, the Gunt River basin, is used as a case study to demonstrate the importance of groundwater in constraining the hydrological calibration process. Groundwater is the only contribution to winter river discharge in the Gunt basin and can thus be used as an indicator of groundwater parameter constraint. Here we show that the hydrological model can achieve good performance (in terms of daily discharge, seasonal snow cover fraction, and annual glacier mass balance) even when calibrated with only total daily discharge and winter baseflow. Especially the baseflow calibration helps constraining snowmelt onset in spring and improving adjustments of precipitation and temperature, which are the most uncertain sources in hydrological modeling in the region. Despite improvements, degree day factors still show a large variability. The resulting model equifinality problem still leads to predictive uncertainty, indicating that more glacier observations are needed for a sound process understanding. Based on the simulated results, the hydrological cycle in Gunt was analyzed and compared with previous studies.

How to cite: Huang, J., Pohl, E., and Richard-Cerda, J. C.: On the Importance of groundwater constraining in hydrological modeling of the Pamir region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18059, https://doi.org/10.5194/egusphere-egu24-18059, 2024.

Ilaria Clemenzi, David Gustafsson, Viktor Fagerström, Daniel Wennerberg, Björn Norell, Jie Zhang, Rickard Pettersson, and Veijo Pohjola

In cold regions, snow is a crucial component of the cryosphere, experiencing changes such as decreasing snowpack and snow cover. These changes impact the seasonal amount of snow and cause a shift in the timing of spring floods, particularly in mountainous areas. The complex and diverse snow processes and interactions in mountainous environments challenge making accurate predictions on snow and runoff. Moreover, snow is not uniformly distributed in space and time, which emphasizes the importance of monitoring mountain snowpack to enhance the understanding of hydrological processes and improve forecasting in the face of changing conditions. In the past few years, ground penetrating radar and drone acquisitions have emerged as a state-of-the-art methodology for obtaining snow data at high spatial resolution with a significant area coverage compared to traditional point observations. This study used data from ground penetrating radar and drone acquisitions to develop and evaluate a new snowfall distribution function based on wind speed, direction and topography to model wind redistribution in the semi-distributed hydrological model HYPE. We assessed the effect of the new snowfall distribution function compared to the one based on wind direction and topography on the snow distribution close to the accumulation peak in the Överuman catchment, Northern Sweden. We further assessed the impact of the two snowfall distribution functions on the catchment runoff predictions. Results show that the snowfall distribution function based on wind speed and direction better simulated the snow spatial distribution in the catchment than the snowfall function based on wind direction. Ground penetrating radar and drone acquisitions provided complementary model development and evaluation information.

How to cite: Clemenzi, I., Gustafsson, D., Fagerström, V., Wennerberg, D., Norell, B., Zhang, J., Pettersson, R., and Pohjola, V.: Assessing the use of GPR and drone snow data for model development and runoff predictions in Northern Sweden, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18338, https://doi.org/10.5194/egusphere-egu24-18338, 2024.

Domenico De Santis, Christian Massari, Silvia Barbetta, Farhad Bahmanpouri, Viviana Maggioni, Sagar Gupta, Ashutosh Sharma, Ankit Agarwal, and Sumit Sen

The Himalayan region is severely exposed to the flood risk due to the heavy rainfall during the summer monsoon. The dynamics of the hydrological response during extreme events is relatively less understood, because of several complex and interactive processes. In this scenario, the use of rainfall-runoff models capable of adequately taking these processes into account could be crucial for reliable flood forecasting. However, in areas with such complex topography, accurately characterizing meteorological forcing and streamflow dynamics remains a challenging task due to the lack of ground measurements. Furthermore, in highly glacierized Himalayan basins, the significant contribution to streamflow by snow and ice melting has been shown to be progressively increasing due to its sensitivity to climate change, in parallel with the loss in glacier mass.

In this study, a conceptual and parsimonious hydrological model was implemented in semi-distributed mode and calibrated against streamflow and glacier loss volume data simultaneously. The MISDc-2L model was modified to simulate not only the snow accumulation and melt, but also the glacier melting in the ice-covered fraction of sub-basin area, assumed to occur once the seasonal snowpack is locally depleted. The Alaknanda River (one of the two headstreams of the Ganges) was chosen as a case study because it experiences several disastrous flood events in recent years. The basin upstream the Rudraprayag gauge was considered (≈8600 km2), for the period 2000-2020. The Randolph Glacier Inventory v7.0 was employed to locate glacierized areas, while glacier storage change data were extracted from available literature studies. Elevation data from NASADEM and hourly variables from ERA5-Land reanalysis dataset were used. A joint objective function was considered for calibration, including the Kling-Gupta efficiency, a high-flows hydrological signature and the error in glacier stored water loss. The model, constrained with glacier storage change data, was found to be able to provide good hydrological performances, both in calibration and validation, also with specific reference to annual flood peaks.

Despite the simplicity and the flood-oriented approach, the proposed modelling procedure simulated the dominant hydrological processes in a physically plausible way, in a basin with high-altitude glacierized areas in a context of climate change. The goal of adequately characterizing the contribution of glacier melt to total streamflow was pursued by aiming for consistency with additional data sources.


This work was funded by:

-          the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

-          the FLOSET Project 'Probabilistic floods and sediment transport forecasting in the Himalayas during the extreme events’, funded in the context of the 'ITALY-INDIA JOINT SCIENCE AND TECHNOLOGY COOPERATION CALL FOR JOINT PROJECT PROPOSALS FOR THE YEARS 2021 2023'.

How to cite: De Santis, D., Massari, C., Barbetta, S., Bahmanpouri, F., Maggioni, V., Gupta, S., Sharma, A., Agarwal, A., and Sen, S.: Flood modelling of a partly glacierized catchment in the Himalayas in a context of climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19899, https://doi.org/10.5194/egusphere-egu24-19899, 2024.