Snow is an important part of the hydrological cycle in high-latitude and mountainous regions, influencing global climate, ecosystems, water resource management, and human societies. Accurate, high-resolution snow cover data are increasingly needed for model inputs, predictions, and societal risk management. Snow distribution is influenced by weather and topography, often exhibiting consistent patterns across locations, such as areas prone to faster melting or wind-blown accumulation. Thus, there is major local variation, making modelling and predictions challenging.

This study tests a novel measurement of snow water equivalent in the boreal landscape through the combination of UAV lidar technology, machine learning and ground measurements. We focus on three different study sites in Finnish Lapland, Pallas, Sodankylä and Oulanka, each representing different vegetational and topographical conditions typical of the boreal and sub-arctic landscapes. The field data were collected in four campaigns during the winter of 2023–24 from UAV-based lidar, manual snow course measurements, and snow depth sensor network. Based on measurements, we defined clusters for variable snow accumulation sections in study sites using a k-means machine learning algorithm, and daily snow height estimates were created for each cluster from reference snow depth measurements. The created clusters and their daily snow heights were then used as input for the Δsnow model (Winkler et al., 2021) to estimate catchment-scale daily snow water equivalent (SWE) and its distribution.

Three different clusters were defined in all sites by the lidar-based snow depth maps, typically corresponding to open areas, transition zones and forested areas. Each established cluster represents three different snow development patterns during the winter, from early winter to melt. The clustering approach allowed the upscaling of snow course measurements with reasonable accuracy, producing daily SWE and snow depth estimates that aligned with observed measurements.

The results show a promising contribution of UAV lidar mapping to catchment-scale snow monitoring, providing improved spatial and temporal accuracy for daily snow depth and SWE mapping in different areas. The work is important for estimating snow cover and melting for flood prediction, hydropower operation and water availability estimation.

How to cite: Ylönen, M., Marttila, H., Kuzmin, A., Korpelainen, P., Kumpula, T., and Ala-Aho, P.: Lidar-based estimation of snow depth and SWE in north boreal and sub-arctic sites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5483, https://doi.org/10.5194/egusphere-egu25-5483, 2025.

In the European Alps, snow sometimes takes a blood-like color in late spring due to the presence of snow algal blooms. These blooms decrease snow albedo, accelerating snowmelt and potentially feeding back on snow and glacier decline caused by climate change. In the Alps, so far, only sparse information exists regarding the frequency and location of these blooms. We developed a methodology to identify red snow algal blooms in the European Alps on Sentinel-2 image that enabled to separate red blooms from similarly colored snow due to Saharan dust depositions that occurs frequently in the Alps. The methodology was evaluated using 4600 webcam images. We applied the methodology to 5 years of Sentinel-2 images to generate an atlas of snow algal blooms in the Alps.

The atlas was combined to detailed simulations of the snow and meteorological conditions to identify the drivers of the blooms in the Alps as well as to quantify the maximum contributions of red algal blooms to snow melt. Based on this analysis and on projections on the future snow and meteorological conditions under different emission scenarios, we finally conclude that the occurrences of red snow algal blooms in the European Alps by the end of the century will either stay stable or slightly decrease.

How to cite: Dumont, M., Roussel, L., Gascoin, S., Monteiro, D., Bavay, M., Nabat, P., Ezzedine, J., Fructus, M., Lafaysse, M., Morin, S., and Maréchal, E.: Satellite remote sensing of red algal blooms in the snow of the European Alps , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3516, https://doi.org/10.5194/egusphere-egu25-3516, 2025.

Snow cover extent and related variables are key elements to understand many processes in mountain regions. To constantly monitor and assess the changes in these areas, consistent and accurate data sets are of utmost importance. In this perspective, MODIS sensors offer an unprecedented possibility in terms of time availability from 2000 to present and ground resolution (500 m) (Bormann et al., 2018).

This work presents a unique time series of snow cover extent and snow phenology (snow cover duration-SCD, first snow day-FSD, and last snow day-LSD) for the period 2000-2024 with a ground resolution of 500 m (Notarnicola, 2024). The main input data is the MODIS product, MOD10A1.061, from which the Normalized Difference Snow Index (NDSI) layer was considered and converted to SCF by exploiting the Salomonson and Appel formulation (2004). The snow phenology parameters (SCD, FSD, LSD) were derived from MOD10A1.061 daily maps. The SCD values were obtained from daily snow cover maps by exploiting an auto-regressive approach to reduce the gaps due to cloudiness (Dietz et al., 2012). In this time series, FSD and LSD represent the first and the last date in the hydrological year with snow presence. The whole dataset is available here: https://zenodo.org/records/11181638

Preliminary analysis of the whole datasets indicate that reduction in snow cover duration can reach up to 55 days while the snow cover extent declines up to 13%. These results were obtained on regions showing changes with significance level at 5% in the Mann-Kendall statistics. Interestingly there are some areas in eastern Russia which show a snow cover extent increase up to 15% while snow cover duration indicates an increase as well but not significant in the adopted statistics.  When considering FSD and LSD variables, both mainly indicates a shortening of the snow season with an average of 15 days for both delayed start of the season and anticipated end of the season. These preliminary results on the trends in the period 2000-2024 provide confirmation of behaviour found in the shorter period 2000-2018 (Notarnicola, 2020), highlighting a general decline for main snow variables but as well with a high variability among the different investigated regions.

References

Bormann, K. J., Brown, R. D., Derksen, C., Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Change 8, 924–928, 2018.

Dietz, A.J., Wohner C., Kuenzer, C. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sens. 4, 2432-2454, 2012.

Notarnicola, C., Hotspots of snow cover changes in global mountain regions over 2000-2018. Rem. Sen. Environ. 243, 111781, 2020. https://doi.org/10.1016/j.rse.2020.111781.

Notarnicola, C. Snow cover phenology dataset over global mountain regions from 2000 to 2023,Data in Brief, Volume 56, 2024, 110860, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.110860.

Salomonson, V.V., Appel, I. Estimating the fractional snow covering using the normalized difference snow index. Remote Sens Environ 89, 351-360, 2004.

How to cite: Notarnicola, C.: Assessing snow cover changes in global mountain regions by exploiting MODIS time series from 2000 to 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14994, https://doi.org/10.5194/egusphere-egu25-14994, 2025.

Satellite remote sensing of snow water equivalent (SWE) at high spatiotemporal resolutions remains an unsolved challenge in snow hydrology. While accurate and high resolution measurements of snow surface properties (e.g., snow cover, grain size, albedo) can be derived from multispectral and hyperspectral data, these sensors cannot provide direct SWE information. Synthetic aperture radar (SAR) has the potential to measure SWE directly because the radar signal at sufficiently low frequencies can penetrate a dry snowpack. Depending on the SAR frequency used, both backscatter-based and interferometric (InSAR) approaches have been demonstrated. Here we present recent results from several studies that investigate remote sensing of SWE using airborne L-band (1.26 GHz) and spaceborne C-band (5.405 GHz) InSAR data. Because the InSAR technique is sensitive to changes in atmospheric and soil conditions as well as snow, one way to determine where to apply the technique is to incorporate satellite-based optical snow cover maps alongside the InSAR data. We show that careful selection of optical snow data is necessary because differences in the spatial and temporal resolutions between the optical and InSAR products propagate uncertainties into SWE calculations, which can change the final SWE estimates by more than 100%. Additionally, optical sensors can accurately detect snow cover in forested areas with canopy densities up to 60%, but vegetation effects may cause temporal decorrelation in InSAR data over these environments and prevent the retrieval of SWE information. Using data from two field sites in Colorado, USA, we show that InSAR coherence generally remains sufficiently high over temporal baselines of 12 days or more, allowing unbiased SWE estimates to be obtained across landscapes with canopy densities up to 40%. These results show the potential for SWE monitoring with the L-band InSAR sensor on the NISAR satellite, especially when combined with other SAR (e.g. Sentinel-1) and optical (e.g. Landsat 8/9) satellites.

How to cite: Palomaki, R., Hoppinen, Z., Tarricone, J., Bonnell, R., Lenard, S., and Rittger, K.: Recent developments in remote sensing of SWE using InSAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7808, https://doi.org/10.5194/egusphere-egu25-7808, 2025.

Understanding the spatial and temporal variations in the liquid water content (LWC) of alpine snowpacks is crucial for assessing short-term water availability, which influences hazards such as wet snow avalanches and river floods. Accurate monitoring and forecasting of snow wetness play a vital role in applications ranging from avalanche risk assessment to hydropower management and flood prediction, particularly when integrated with hydrological models.

Remote sensing provides valuable observations of snowpack properties, with Sentinel-1 satellites offering C-band synthetic aperture radar (SAR) data at high spatial and temporal resolutions, enabling the detection of wet snow. Meanwhile, snow models like HyS (De Michele et al. 2013) can simulate the liquid water content of the snowpack.

This study focuses on evaluating the discrepancies between satellite-derived wet-snow products and modeled LWC estimates. Specifically, we compare (1) Sentinel-1-based wet-snow retrievals and (2) HyS model simulations. The analysis is conducted for the Mallero basin, a mid-sized alpine watershed where snowmelt and glacier ablation significantly impact seasonal river discharge, particularly in spring and summer.

The results indicate a strong overall agreement between Sentinel-1 data and HyS model outputs. Short periods of divergence between the two datasets are further analyzed to investigate potential physical processes that may not be fully captured by the model.

How to cite: Norouzi, S., Cazzaniga, G., Arslan, A. N., and De Michele, C.: Assessing Liquid Water Content in a Seasonal Snowpack: A Comparative Analysis of Satellite Observations and the HyS Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21944, https://doi.org/10.5194/egusphere-egu25-21944, 2025.

Snowfall, a crucial indicator of climate change, is essential for freshwater supply and glacier health. Accurately classifying precipitation types, especially in the rain-snow transition zone, is vital for understanding climate impacts. While previous studies have used snowfall fractions for classification, they often overlook the nuances of regional variations and tipping points. High Mountain Asia (HMA), with its complex topography and rapid warming, is an ideal region to study snowfall thresholds. This research aims to: (1) identify key snowfall fraction thresholds to categorize HMA into distinct precipitation dominance categories, (2) project the future evolution of these dominant precipitation types using CMIP6 model data, including estimates of transition times for various precipitation types, and (3) assess uncertainties in snowfall fraction predictions by comparing temperature- and temperature-relative humidity-based precipitation phase identification methods. This research can provide a valuable scientific resource for identifying climate-sensitive areas and regions at high risk of snowfall loss within HMA.

In this study, a continuous piecewise linear regression model was employed to classify HMA into four distinct precipitation regimes: insensitive snowfall-dominated areas, sensitive snowfall-dominated areas, sensitive rainfall-dominated areas, and insensitive rainfall-dominated areas. Our results show that future warming will increase the sensitivity of winter and spring snowfall to climate change, whereas summer and autumn snowfall will become less sensitive. All four precipitation regimes exhibit an upward shift to higher elevations, with varying rates of elevation gain across regions and seasons. Temperature is the primary driver of snowfall loss, whereas relative humidity mitigates it. This study identifies high-risk areas vulnerable to snowfall loss, guiding the development of effective mitigation strategies.

How to cite: Li, Y., Chen, Y., Sun, F., Zhang, X., and Hou, Y.: Warming Triggers Snowfall Fraction Loss Thresholds in High-Mountain Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1624, https://doi.org/10.5194/egusphere-egu25-1624, 2025.