It is well understood that snow is a complex, porous material, whose microstructural changes directly affect its physical properties. Therefore, – to gauge the snow's role within the climate system – it is of interest to accurately measure and characterize the spatio-temporal variability of snow surfaces and snowpacks.

On a local scale, for example inside a snowpit during a field campaign, snow measurements are often taken in a manual, point-like fashion resulting in single, one-dimensional profiles with a sampling resolution of a few centimeters. At this resolution thin layers are difficult to observe and spatial inhomogeneities of the snowpack are missed. State-of-the-art X-ray microtomography (μ‑CT) scans of snow provide excellent spatial resolution,¹ however, the added experimental constraints prevent sampling extended spatio-temporal domains.

To address some of these limitations, we propose to use near-infrared (NIR) photography² with 940 nm illumination to determine the snow's specific surface area (SSA) and density. Our device – called SnowImager – achieves millimeter resolution and covers a spatial extent of a few square meters, such as the surface area of a snowpit wall. While the SSA is determined directly from the measured NIR image using the well-established asymptotic radiation transfer theory,^3–6 the density dependence is introduced by physically truncating the illuminating and back-scattered light. It results non-trivially from the lateral component of the sub-surface scattering process and enables us to recover density profiles that compare well to reference data from density cutter and μ‑CT measurements. As a demonstration, we present the spatial variability of an Antarctic snowpack at an unprecedented level of detail, revealing an extremely high spatial variability of the snow microstructure.

Using near-infrared photography enables accurate and fast determination of snow material properties, whenever millimeter spatial resolution and a spatial extent of several square meters are required. It is thus ideally suited to simultaneously capture thin layers within the snowpack and spatial inhomogeneities over a centimeter to meter scale, which is relevant as ground truth measurement for climate research, remote sensing and avalanche forecasting among others.

1. Kerbrat, M. et al., Atmos. Chem. Phys. 8, 1261–1275 (2008).

2. Matzl, M. & Schneebeli, M., J. Glaciol. 52, 558–564 (2006).

3. Bohren, C. F. & Barkstrom, B. R., J. Geophys. Res. 79, 4527–4535 (1974).

4. Warren, S. G., Rev. Geophys. 20, 67–89 (1982).

5. Kokhanovsky, A. A. & Zege, E. P., Appl. Opt. 43, 1589–1602 (2004).

6. Libois, Q. et al., The Cryosphere 7, 1803–1818 (2013).

How to cite: Mewes, L., Walter, B., Buchli, J., Büchel, V., Suter, M., Schneebeli, M., and Löwe, H.: Determining snow material properties from near-infrared photography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17505, https://doi.org/10.5194/egusphere-egu24-17505, 2024.

Quantifying the mechanical properties of snow is crucial for various applications, including the assessment of slope stability, vehicle mobility on snow-covered terrain, and the understanding of snowpack evolution. To build our understanding of snowpack evolution, we utilize a novel non-contacting laser ultrasound system (LUS). This system collects ultrasonic wavefield data from tens to hundreds of kilohertz in a controlled cold lab environment, allowing us to interpret acoustic measurements and measure mechanical properties on a microscale and upscale this to the field scale.

 We investigated the relationship between P-wave velocity changes and snow properties such as density, snow crystal type, and metamorphism through sintering. We controlled the density of the snow samples by adjusting the volume while maintaining the same mass. We controlled the microstructure by manipulating the supersaturation and temperature (controlling air and water temperatures within an artificial snow maker) within a cold lab to make artificial snow of a specific crystal type (i.e., Dendritic, plate, column, and needle snow crystals). Homogeneous snow samples, each composed of their own single crystal type, were created and compacted to a density of 250 kilograms per cubic meter.

Over a period of 72 hours, we measured acoustic wave propagation through  these artificial snow samples to periodically observe changes in waves peed during metamorphism. This allowed us to monitor changes in mechanical properties as sintering occurred, for different snow crystal types. We also measured snow microstructure and micromechanical properties with destructive techniques, using the SnowMicroPen and MicroCT. Finally, we examined the relationship between velocity changes and snow crystal types, specifically in terms of sintering time. Our findings suggest that the crystal type, as influenced by time under isothermal temperature conditions, affects the observed bulk mechanical properties and their rate of change.  Observations of ultrasonic wavefields show that snow strengthened by a factor of 1 to 2 within 72 hours, depending on the snow crystal type. 

How to cite: McCaslin, J., Mikesell, T., Marshall, H.-P., and Courville, Z.: Characterization of snow mechanical properties using laser ultrasound: Role of snow crystal type, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-728, https://doi.org/10.5194/egusphere-egu24-728, 2024.

Monitoring the amount of snow, its spatiotemporal distribution as well as the onset and amount of snow-melt induced runoff generation are key challenges in alpine hydrology. Cryo-hydro-gravimetry is a non-invasive method of observing temporal gravity variations after the reduction of all other geophysical signals as the integral of all cryospheric and hydrological mass variations including snow accumulation and ablation. It has an accuracy of up to 9 decimals on a wide spectrum from high temporal resolution of up to 1 min to several years within footprints up to approx. 50 km². At the Zugspitze Geodynamic Observatory Germany (ZUGOG) with its worldwide unique installation of a superconducting gravimeter at a high-alpine summit (2.962 m a.s.l.), this method is applied for the first time on top of a well-instrumented, snow-dominated catchment. We use this instrumental setup in synthesis with in situ measured data, detailed physically-based snowpack modelling with Alpine3D as well as satellite-based snow depth maps derived by stereo photogrammetry. We will give an introduction into the novel sensor setup and will show first results, including the sensitivity of the integrative gravimetric signal regarding the spatially distributed snowpack and the cryo-hydro-gravimetric signal changes since 2019. The amount of the simulated snow water equivalent within the footprint of the gravimeter correlates well with the gravimetric signal (Pearson correlation coefficient r = 0.98). Based on the applied snowpack modelling approach including the snow depth maps for precipitation scaling, topography information as well as Newton’s Law of Gravitation, the gravimetric signal contribution and footprint can be described spatiotemporally over winter periods.

How to cite: Koch, F., Gascoin, S., Achmüller, K., Schattan, P., Wetzel, K.-F., Rehm, T., Schulz, K., and Voigt, C.: Tracking high-alpine snow mass evolution using signals of a superconducting gravimeter combined with snowpack modelling and stereo satellite imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5825, https://doi.org/10.5194/egusphere-egu24-5825, 2024.

Seasonal alpine snow is subject to fluctuating meteorological conditions with diurnal temperature cycles around the freezing point and a mix of snow and rain. Throughout the season, fresh snow accumulations repeatedly contribute to the snowpack whereas older layers beneath contain snow at various stages of the metamorphosis often including melting and refreezing periods. The increasing complexity of the snowpack throughout the snow season affects the interaction of radar signals with the snowpack and underlying ground.

Numerous radar/SAR missions, operating at different frequencies, aim to retrieve snow parameters such as snow mass, snow water equivalent, and snow cover extent. These include missions like CRISTAL, TSMM, ROSE-L, and NISAR, each utilizing specific frequency bands to study the temporal variations in snow properties. Understanding the vertical structure of seasonal snow and its interaction with radar signals at various microwave frequencies from L- to Ka-band is therefore essential.

In our study, we investigated tower-mounted rail-based tomographic SAR measurements obtained within the ESA SnowLab project in Davos Laret, Switzerland. The SAR tomography technique provides non-destructive measurements of the vertical structure of the snowpack by means of vertical profiles of radar backscatter, co-polar phase differences, and interferometric phase differences. The measurements were taken with the ESA SnowScat and the ESA Wide-Band Scatterometer, covering a wide range of frequency bands. Additional data on snow characteristics and meteorology complemented the radar measurements. We present time series of SAR tomographic profiles over entire snow seasons at different frequency bands (1-6 GHz, 12-18 GHz, and 28-40 GHz) with reference snow characterizations obtained from snow pits and SnowMicroPen measurements. Detailed analyses include depth-resolved co-polar phase differences, anisotropy, and differential interferometric phase, revealing insights into changes in snow properties over time.

The high-resolution SAR tomographic profiles offer valuable information on microwave interactions with seasonal alpine snow. Analysis of vertical radar backscatter profiles indicates relative changes in location and intensity within the snowpack, correlating with factors like melting and refreezing cycles, snow accumulation, and liquid water content.

We find that distinctive features of seasonal snow, such as melt-freeze crusts, varying penetration depths, and anisotropy can be tracked over time using a SAR tomography approach. To exploit this information for snow mass and structure retrieval, further research tailored to specific spaceborne SAR mission objectives is required. The ESA SnowLab time series of SAR tomographic profiles is a rich dataset covering a broad spectrum of frequencies and providing an opportunity to advance the understanding of scattering mechanisms in alpine snow for various spaceborne SAR missions. The comprehensive coverage includes frequency bands relevant to existing and future mission concepts.

How to cite: Frey, O., Wiesmann, A., Werner, C., Caduff, R., Löwe, H., and Jaggi, M.: High-resolution snow parameter/structure retrieval from tower-based radar time series of seasonal snow obtained with the ESA SnowScat and the ESA Wideband Scatterometer in SAR tomographic profiling mode, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17561, https://doi.org/10.5194/egusphere-egu24-17561, 2024.

No current remote sensing technique can accurately measure snow water equivalent (SWE) from space for mountain hydrologic applications. Optical sensors are robust for measuring the fractional snow-covered area (fSCA) at various spatial and temporal resolutions. Yet, these optical methods are limited by cloud cover and do not provide information on SWE. Synthetic aperture radar (SAR) can penetrate clouds, has a fine spatial resolution, and various algorithms allow us to quantify both SWE magnitude and changes. However, SAR cannot discriminate between snow-free and snow-covered areas when the snow is dry. To address this SWE monitoring challenge, we evaluate a multisensor approach that leverages the strengths of both optical and radar sensors. Our study aims to better understand the variability between common snow cover data products and how that uncertainty propagates into InSAR-based SWE retrieval techniques. We analyzed four UAVSAR InSAR pairs from one flight line over the Sierra Nevada, CA, during the SnowEx 2020 campaign and compared six satellite-based snow cover products. First, we computed InSAR-based SWE change estimates using in situ snowpack data. We then compared the summed SWE change values with a moving window analysis to quantify product variability. Lastly, we tested the volumetric SWE results for statistical differences. Results show that moderate-resolution (375–500 m) NDSI-based products provide broadly similar volumetric SWE change results to those using more complex spectral unmixing and machine learning retrieval methods. This suggests that the readily available moderate-resolution snow cover products from MODIS are adequate for an optical-radar SWE monitoring approach. Future work should focus on understanding how sub-canopy snow in forested regions affects snow cover product accuracy and variability. Furthermore, near-real-time, high-resolution cloud- and gap-filled optically-derived snow cover data will be important for supporting water resources decision-making.

How to cite: Tarricone, J., Palomaki, R., Rittger, K., Marshall, H.-P., Nolin, A., and Vuyovich, C.: Comparing multisensor optical-radar approaches for snow water equivalent retrievals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4137, https://doi.org/10.5194/egusphere-egu24-4137, 2024.

In the dry Chilean North, the impact of the mountain snowpack on freshwater availability in the adjacent lowlands areas is crucial. The correlation between the snow water equivalent record and regionally averaged river discharges suggests that ~85% of the streamflow variance could be explained by the snowpack record alone. As seasonal snow cover depends on few winter events, there is a large year-to-year variability in the snow water equivalent (SWE). Typically, there are some dry years with very low annual precipitation which are compensated by wet years. However, since around 2010, the almost continuous extraordinarily dry conditions (so-called Central Chile “mega drought”) and increased water consumption in the region have led to significant stress on the water system. Hence, for an efficient water allocation and water management, it is crucial to know the actual SWE stored in the mountain snowpack. Until now, decisions have been based on scarce point measurements of the SWE or snow area estimates from MODIS. A drawback of these estimates is the large uncertainty that hampers efficient water allocation with important implications for water security of different sectors such as hydropower, agriculture and domestic use. 

To address this problem, we have developed a new operational SWE Estimation Tool for water resources decision making in the Coquimbo region (SWEET-Coquimbo), able to estimate current SWE in near real-time with a latency of ~10 days. SWE is estimated using a data assimilation framework that combines bias-corrected meteorological forcing ensembles from reanalysis data (ERA5, 5-day latency), hydrological modeling (Snowmodel) and satellite observations (Landsat) of the snow-covered area. SWEET-Coquimbo is placed in an open-access web platform, visualizing the current state of the SWE of five main catchments. The data can be downloaded and used for research and diagnostic purposes. 

The newly generated data show SWE for the period 2000-2023. We can now better understand the response of the regional snow cover to the Central Chile megadrought on snow cover and general trends in SWE over the last two decades. SWEET-Coquimbo has allowed, for the first time, a catchment-based estimation of the water available from the snowpack, which can now be used to improve seasonal runoff forecasts. Furthermore, our method has a great potential to be validated and applied to other mountainous regions with sparse in-situ data, as it does not rely directly on in-situ data.

How to cite: Schauwecker, S., Ayala, Á., Cortés, G., Yáñez, E., MacDonell, S., Goubanova, K., and Orrego, C.: SWEET (Snow Water Equivalent Estimation Tool): A new tool to generate updated SWE estimates for poorly monitored regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13564, https://doi.org/10.5194/egusphere-egu24-13564, 2024.

Mountain precipitation is a key feature of the hydrological cycle since it feeds snowpack, glaciers, river runoff and supports ecosystems and human life both locally and downstream. However, available precipitation datasets are affected by large uncertainties in mountain regions, especially during the cold season when most of the precipitation falls as snow: on one hand, commonly used precipitation gauges can have systematic losses up to 80-100% in case of snow precipitation, mainly owing to wind undercatch; on the other hand, reanalysis datasets generally provide much larger precipitation amounts when compared to observations and observation-based datasets. So, an accurate quantification of the snowfall component is crucially needed to reduce the uncertainty on mountain total precipitation in the cold season.    

In this work we present an extensive analysis of snowfall precipitation over the Greater Alpine Region (GAR) considering snowfall data from different data sources, including long-term in-situ observations, reanalysis and gridded datasets. We analyze: i) the most comprehensive observational dataset of monthly fresh snow depth (commonly employed as a measure of snowfall precipitation), consisting of more than 2000 in-situ station time series, covering 6 alpine countries (Switzerland, Austria, Germany, Slovenia, Italy and France); ii) the snowfall dataset provided by the ECMWF ERA5 global reanalysis at 0.25° spatial resolution, and iii) the HISTALP gridded snowfall dataset at 0.08° spatial resolution, which is based on temperature and precipitation observations. We compare the three datasets over the last decades to investigate i) climatological features of seasonal and monthly snowfall over the GAR; ii) snowfall variability and trends in relation to elevation; iii) snowfall trends in relation to temperature and total precipitation, based on the best available observational datasets; iv) uncertainties in the snowfall climatology and trends, by comparing the different data sources. This study provides a first comprehensive evaluation of the quality of ERA5 and HISTALP snowfall datasets against ground observations. Moreover, by quantifying the snowfall component, it contributes to better characterize mountain precipitation in the cold season.  

How to cite: Terzago, S., Gatti, L. M., Arnone, E., and Matiu, M. C.: Snowfall variability, trends and their altitudinal dependence in the European Alps from ERA5, HISTALP and in-situ observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17624, https://doi.org/10.5194/egusphere-egu24-17624, 2024.

Surface snow cover is an important and highly interactive component of global and regional climate systems and has already clearly responded to past warming trends in many regions of the world. Moreover, it is a key ingredient for tourism industry, water supply, irrigation, and hydro-power generation in many mountainous and high-latitude regions. Accurate information about the past, present and future evolution of snow cover is therefore of high importance.

In this context, we here present and evaluate a newly developed gridded SWE climatology for Switzerland, available at daily resolution since 1961 and at a 1 km grid spacing. The climatology is based on a variant of the snow cover model of the Operational Snow Hydrological Service (OSHD) of Switzerland, driven by gridded atmospheric input and bias-adjusted towards in-situ snow depth measurements. In accordance with previous works, the analysis shows that the Swiss snow cover has changed strongly over the last decades. The comparison of two climatological long-term periods, 1962-1990 and 1991-2020, in terms of mean September-May SWE and the number of snow days (SWE > 10 mm) within the snow season, reveals a decrease in both indicators over the majority of the country. Low elevations < 1000 m show relative decreases larger than 50% of the mean SWE and larger than 30% regarding the mean number of snow days (about -22 days). The largest absolute difference of mean SWE is found at medium elevations between 1500 and 2000 m with a decrease of about 45 mm (about -26%).

The validation of the new snow climatology indicates a high general agreement with in-situ observations and independent remote sensing products. Larger uncertainties and limitations are found at the highest elevations (> 3000 m). They originate from different sources, such as temporal inconsistencies in the gridded input data of the underlying OSHD snow model or the lack of stations at high elevations that are needed for the bias adjustment of the model. Nevertheless, the new snow climatology is able to provide adequate information on past snow cover for Switzerland as a whole and will, among others, serve as a reference for the development of future snow cover scenarios.

How to cite: Kotlarski, S., Danioth, S., Gubler, S., Muelchi, R., Michel, A., Jonas, T., Steger, C. R., and Marty, C.: Swiss snow cover in a changing climate: Evaluation of a long-term, high-resolution SWE climatology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11117, https://doi.org/10.5194/egusphere-egu24-11117, 2024.

The current and future evolution of snow cover in the Vosges massif (N-E of France) was simulated at a resolution of 4 km with the regional climate model MAR (version 3.14) forced by the ERA5 reanalysis. Thanks to the adjustment of only few parameters, MAR (initially developed for the polar regions) was optimized and validated with respect to daily observations of temperature, precipitation and height of the snowpack. Over the 62 winters (DJF) 1960-2021, MAR suggests a statistically significant decrease of about a factor of two in the average snow depth due to the significant increase in temperatures (~+2°C/62 years). Although precipitation has slightly increased (+10-20%/62 years) due to a non-significant strengthening of the westerly circulation, it falls more and more in the form of rain, especially below 1000 m. Above 1000 m, it does not snow less than before but there is more melting reducing the snowpack between two snow events. By extrapolating current trends, an anomaly of +2.5°C (resp. +3.8°C) compared to the winters of 1960-90 would be sufficient to no longer have snowpack on average below 750m (resp. 1000m). This trend is fully confirmed by MAR forced by 5 global models (EC-EARTH3, IPSL-CM6A-LR, MIROC6, MPI-ESM1-HR, NorESM2) from the CMIP6 database using both SSP245 and SSP585 scenarios over 1980-2100. In 2050, the average winter snow cover at 1000m will be reduced by half and will become almost non-existent in 2100 following SSP585. While with SSP245, MAR suggests skiing conditions still possible until 2100 above 1000m.

How to cite: Fettweis, X., Ambroise, B., David, P.-M., Ghilain, N., and Paul, P.: Present and future evolution of the winter snow cover in the French Vosges massif with the help of the regional climate MAR model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17058, https://doi.org/10.5194/egusphere-egu24-17058, 2024.

While airborne Light Detection and Ranging (LiDAR) surveys are routinely used to measure snow volume in many types of mountain watersheds, those that are heavily forested and lie within maritime environments have been largely ignored to date. Here, we summarise our findings from a four-year study (2020-2023) of eight watersheds within the coastal rainforests of southwest British Columbia, which collectively represent an area of >330 km². Aerial LiDAR surveys were conducted 3 to 5 times per year between March and June in order to measure snow depth across each watershed. Spatiotemporally-distributed snow density was estimated using a random forest model incorporating weather station data, LiDAR-derived metrics and in-situ snow density observations. At peak snow volume, we find typical mean catchment-wide snow water equivalent values of ~600-1200 mm, verified by a widespread field campaign consisting of > 25,000 in-situ measurements of snow depth and density. We show that, typically, ~60-90 % of the snow water volume is stored at mid-elevations of between 800 and 1500 m, where air temperatures are close to melting point and forest cover is prevalent, leaving the snowpack vulnerable to early seasonal melt onset and impacts due to forest management. We find that while peak measured snow volume typically represents ~20-40 % of surface runoff, providing an important buffer towards droughts within the region, snowmelt volumes can be insufficient to safeguard downstream water supply during extreme seasonal drought events. Overall, the results of this work provide valuable insights into the vulnerability of the snowpack in coastal maritime regions and the potential knock-on effects of a changing snowpack on regional water security.

How to cite: Bisset, R., Floyd, B., Menounos, B., Bishop, A., Marchenko, S., Marshall, P., and Geospatial, H.: Quantifying snow water storage from aerial LiDAR surveys in eight Pacific coastal watersheds, British Columbia, Canada, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22543, https://doi.org/10.5194/egusphere-egu24-22543, 2024.

Satellite datasets are especially useful to monitor the cryosphere in vast and remote environments, such as the Arctic, where seasonal snowpack controls permafrost distribution, surface runoff, plant growth and animal survival rate. The recent availability of free, high-precision and high-resolution elevation datasets show promises to map snow depth on a large scale, a key bulk variable of the snowpack. Here, we mapped the snow depth distribution across Iceland (65°N) using elevation data from ICESat-2, a photon-counting laser altimetry satellite, and the ArcticDEM, a large set of digital elevation models from satellite stereoimages. The snow depth was retrieved through comparison of acquisitions with snow-on conditions (ICESat-2, ArcticDEM) and snow-free (summer ArcticDEM). Despite the heterogeneous spatial coverage of the two datasets, negative impacts of clouds, polar night and a shallow snowpack often close to the limit of detection, we successfully retrieved snow depth from 2018 to 2023, at monthly resolution. By leveraging large publicly available datasets, this approach is promising to further monitor the snowpack in other regions of the Arctic.

How to cite: Deschamps-Berger, C., Belart, J., Gunnarsson, A., Revuelto, J., Aðalgeirsdóttir, G., and Lopez-Moreno, J. I.: Mapping snow depth in the Arctic with public satellite elevation datasets, a case study in Iceland with ICESat-2 and the ArcticDEM, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11228, https://doi.org/10.5194/egusphere-egu24-11228, 2024.

Snow is an important hydrological component in Central Asia. The snowmelt contributes to about 50 % of total water formation in the region, depending on geographic conditions. Many hydro-meteorological phenomena such as floods or drought conditions can be triggered by snowmelt amounts in Central Asia. The amount of snow accumulation in the mountains of Tian-Shan and Pamir also defines the availability of water for summer months to be used for agricultural production or re-filling of reservoirs for energy production in the winter period. Thus, it is of high importance to better understand the seasonal variation of snow and if the over the global average climate warming in the region is affecting the processes related to snow accumulation and melt.

In this study, we analyze 22 years of daily Moderate Resolution Imaging Radiometer (MODIS) snow cover data that was processed using the MODSNOW-Tool, including cloud elimination. Additionally, observed snow depth data from meteorological stations were used to estimate trends related to snow cover change. We used several parameters such as snow cover duration, snow depth, snow cover extent, and snowline elevation to analyze changes.  We conducted this analysis in 18 river basins across the Central Asian domain with each river basin having different geographic conditions and the results show varying tendencies. In many river basins, a clear decrease of snow cover was found to be significant, whereas in some river basins also increase in the snow cover extent in particular months could be identified. We attributed the changes related to snow cover to available historical temperature and precipitation records from meteorological stations to better understand the driving forces. The results of this study indicate seasonal snow cover variations but also potential water shortages in particular months as well as water abundance in months where water demand is not high in Central Asia.

How to cite: Gafurov, A., Mamaraimov, A., Friedrich, B., and Gafurov, A.: Seasonal snow cover variations in Central Asia based on remote sensing data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19588, https://doi.org/10.5194/egusphere-egu24-19588, 2024.

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. 
Therefore, it is a fundamental task to analyze the consistency and assess the accuracy of the snow cover products retrieved from the two sensors. 
In this study, snow cover products from MODIS/Terra, MODIS/Aqua, VIIRS/SNPP and VIIRS/JPSS-1, were evaluated in terms of Normalized Difference Snow Index (NDSI) consistency and accuracy assessment using higher resolution images of Landsat and Sentinel-2 snow cover products. Paired comparisons were performed among the four products in five major snow distribution regions over the world: Northeast China (NE), Northwest China (NW), the Qinghai–Tibet Plateau (QT), Northern America (NA), and European Union (EU). The two categories of snow products are utilized: The L3 Daily Tiled products, referenced by their Earth Science Data Type (ESDT) names of VJ110A1, VNP10A1, MOD10A1, MYD10A1, and L3 Daily Cloud-Gap-Filled (CGF) products, VJ110A1F, VNP10A1F, MOD10A1F, MYD10A1F. The important conclusions demonstrated as follows.
(1) During the snow season, the four types of 10A1 snow products demonstrated good consistency, with higher R values and smaller BIAS under clear sky. VIIRS exhibited a higher snow cover percentage than MODIS. By combining the four 10A1snow products, it is effective and feasible to produce cloud-free snow products.
(2) The consistency of the four 10A1F snow products was lower than that of the 10A1 products under clear skies. SNPP showed good consistency with JPSS-1, and the same to TERRA with AQUA.
(3) In the 10A1F products based on the previous day's clear-sky cloud-filling algorithm, VJ1 and VNP products exhibited larger fluctuations compared to MOD and MYD products. Among the 10A1F products, the smaller fluctuations and higher snow cover percentage of MODIS, along with a cloud persistence duration higher than VIIRS, led to an overestimation in MODIS's 10A1F snow products.
(4) The snow-cloud confusion is existing both in products with the same sensor and with different sensors for the 10A1products, and the latter is much larger than the former, the percentage of which is approximately 10% in the five regions.
(5) High-resolution snow product validation indicates that VIIRS has higher accuracy in both snow products than MODIS. 
(6) The newest JPSS-1 snow cover products display good agreement with that of SNPP. The pixels with the flag of ‘no decision’ in VNP10A1, MOD10A1, MYD10A1 are labelled as land, waterbody, and mostly clouds in VJ110A1 product, respectively.               
Above all, in spite of existing sensor differences affecting consistency of snow cover products, the paired comparisons indicated that under clear skies, the four snow products exhibit good consistency, with higher consistency observed in snow products from the same sensor. The evaluations by higher resolution snow products assured the high accuracy. It is effective and feasible to produce cloud-free snow products considering the overestimation of 10A1F products.

How to cite: Liu, A. W. and Che, T.: Consistency and Accuracy Assessment of Snow Cover Products from Terra, Aqua, SNPP and JPSS-1 Satellites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9925, https://doi.org/10.5194/egusphere-egu24-9925, 2024.

Snow is a crucial factor determining plant species distributions in alpine and arctic environments. Therefore, metrics like the duration of snow cover are important predictors to model plant distributions. Many studies employed snow cover metrics derived from optical satellite image time series. Such satellite-derived observations are easily accessible and highly consistent, making them a viable choice for current and past conditions. However, an inherent limitation is their applicability for future projections of snow cover, which is only possible by establishing statistical relationships to ancillary data sets. Snow cover simulated by a physically-based snow model could circumvent these constraints, but it was rarely employed for predicting alpine plant species distributions. Increasing availability of input data, computational power and data sets for validation nowadays allow for modeling at reasonably high resolutions.

To this end, we report first results of several modeling experiments, to quantify the differences of using snow cover metrics derived from webcam time series and modeled snow data for a study site of approximately 5 km² in the Stubaier Alps (Tyrol, Austria). Melt-out date is one of most commonly used snow metrics in species distribution models. Hence, we derive the melt-out dates from two seasons (2022 and 2023) of webcam-based and modeled snow cover. Subsequently, we modeled the distribution of 79 plant species with the melt-out dates as predictors along with several proxies for topographic heterogeneity at spatial resolutions of 1 m and 20 m in order to account for the small-scale variability of snow cover in alpine landscapes. The study demonstrates how the usage of modeled and observed snow data affects modeling of high-alpine vegetation distribution. These insights are important for appropriately designing species distribution modeling studies based on modeled rather than observed snow data.

Acknowledgements: This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 883669).

How to cite: Kollert, A., Chytrý, K., Mayr, A., Hülber, K., and Rutzinger, M.: Investigating the usage of physically modeled snow cover vs. webcam-based snow cover for driving plant species distribution models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6297, https://doi.org/10.5194/egusphere-egu24-6297, 2024.

Plant phenology is highly sensitive to climate change, and the Arctic region is experiencing rapid changes in vegetation and snowpack. However, the specific climatic drivers of these changes are poorly understood. This study aimed to investigate the effects of snowpack phenology and environmental variables on the onset of vegetation phenology in the Alaskan Arctic. The results showed that Snow cover end date (SCED) had a stronger correlation with the Start of the growing season (SOS) compared to other factors, with consistent spatial and temporal patterns. Forested vegetation exhibited strong positive feedback between SCED and SOS, while grassland, shrub, and tundra communities showed insignificant positive feedback. Temperature and Fractional photosynthetically active radiation (FPAR) also significantly affected SOS. Snow density and snow depth played a larger role in SOS variation during the short pre-season period. These findings highlight the need for further investigation into the role of snowpack in specific vegetation types, particularly after observing widespread greening. Future studies should consider factors such as changes in snowmelt timing and photoperiod and traditional climatic factors like temperature and precipitation.

How to cite: Mu, Y. and che, T.: Unraveling the Influence of Snow Phenology on Vegetation across Alaskan Plant Communities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9574, https://doi.org/10.5194/egusphere-egu24-9574, 2024.

The Peruvian Andes contains the largest mass of glaciers in the tropics and these glaciers have shown considerable decay over the past 4 decades and into the present day. The historic and future runoff is tied to the cryospheric changes in the region and this could have important consequences for water resources, given the importance of snow and ice melt for dry season runoff. To disentangle the role of the cryosphere in the water cycle in the tropical Andes we run the fully distributed, hourly glacier-hydrological model TOPKAPI-ETH, both in the past (from 1987) and into the future over the upper Rio Santa catchment in the Cordillera Blanca. Meteorological forcing is provided by bias-corrected WRF simulations, which are also used for statistical downscaling of CMIP5 model projections to provide the future climatology. Calibration of model parameters is conducted using a step-wise approach using a wealth of ground-based data and model outputs are evaluated against gauged runoff data and remote sensing estimates of snow cover, glacier cover and glacier mass balance. 

We find that under present conditions (2008-2018) snowmelt is an important contributor to runoff, comprising 16% to 47% of inputs (the range in weekly average as a proportion of all snow melt (on and off-glacier), ice melt and rain contributions) into the catchment with its proportional contribution largest at the beginning of the dry season (early June). Off-glacier snowmelt is important in the wet season, but snow is confined to on-glacier areas by the mid-dry season. Snow cover <5000 m is ephemeral, lasting hours to days, with correspondingly thin average snow depths and rapid fluctuations in the wet season snowline. Meanwhile, ice melt is an important contributor to runoff in the dry season (up to 54% of the inputs in early August) in all glacierised catchments, even those with a small glacier cover, but the wet season contribution is small. We also explore the long term evolution of glaciers and snow cover in the catchment and its implications for catchment runoff. Through the long term modelling we investigate the timing of peak water in the catchment and the key drivers of runoff change in the past. Our future projections will allow us to examine the impact of future climate changes on the glaciers and snow dynamics. A key vulnerability is the impact of temperature increases on the ephemeral snowpack and the consequences for glacier mass balance. We will also investigate the potential implications for catchment runoff and dry season water availability.

How to cite: Fyffe, C., Potter, E., Miles, E., Shaw, T., McCarthy, M., Orr, A., Loarte, E., Medina, K., Fatichi, S., and Pellicciotti, F.: On the importance of the cryosphere in a tropical Andean basin: the past, present and future of the glaciers and runoff in the Rio Santa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18472, https://doi.org/10.5194/egusphere-egu24-18472, 2024.

In semiarid Andean regions, rock glaciers are more prevalent than debris-free glaciers and their relatively extensive areal coverage suggest the existence of significant frozen water reserves. Although there are some doubts whether permafrost landforms constitute a readily available water resource due to the effective thermal insulation provided by the active layer, there is some suggestion that permafrost can act as a primary factor controlling water flow and delivery to the catchment. The hydrogeomorphological connections and water system processes linking different hydrological units impacts the fate of the generated water, making it paramount to understand how water is transmitted from the headwater hydrological system to the wider catchment to better predict future impact of climate change in this important environment. However, unravelling their role is reasonably complicated since in semiarid regions glacial complexes (i.e. combination of glaciers and rock glaciers) are common and contain not only complicated structures but also complex hydrological connections.

In this study, the scientific understanding of the hydrological role of ice-debris glacial landforms is analysed to better understand how the transfer of water by glacier complexes relates to their internal structure. The research analyses the lower section of the Tapado glacier complex, in the Chilean semiarid Andes (30°S), which comprised the lower section of the debris-covered Tapado Glacier, that is in morphologic continuity to a rock glacier and a moraine at lower elevations. Geophysical measurements and elevation changes using uncrewed aerial vehicles (UAVs) were employed to inspect the internal structure of the selected ice-debris units in order to evaluate how it controls hydrological routing and storage, and in the delivery of cryospheric waters to the wider catchment.

Overall, internal structural arrangement and composition affect water routing and storage on the explored ice-debris landforms. Impermeable zones, characterised by massive glacial ice, ground ice or interstitial ice, not only represent a water storage capacity but are also a barrier to water flow. Therefore, at their interface with air-filled debris they also play a role in downstream water transmission, since sectors such as the debris layer (debris-covered glacier), active layer (rock glacier), intra-permafrost sectors (rock glacier), and main interstitial ice-free body of the moraine play important roles in the downglacier flow transfer. In addition, the potential subpermafrost hydrological connection between the rock glacier and the moraine area was recognised to occur as baseflow. Importantly, a potentially relevant hydrological role of the rock glacier is described based on its observed heterogenous internal structure associated with enhanced vertical infiltration compared to the debris-covered glacier. Lastly, in general, the moraine acts as a transmissive medium between generated glacial and snow meltwater and the proglacial area and river, buffering incoming flows due to the existence of interstitial ice within moraine structure, which also potentially enables deep groundwater circulation.

How to cite: Navarro, G., MacDonell, S., Valois, R., de Pasquale, G., and Robson, B.: Impact of Internal Structure on Water Routing in a Semiarid Andean Glacier, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12946, https://doi.org/10.5194/egusphere-egu24-12946, 2024.