HS6.4 | Remote Sensing of Seasonal Snow
EDI PICO
Remote Sensing of Seasonal Snow
Co-organized by CR6
Convener: Rafael Pimentel | Co-conveners: Ilaria ClemenziECSECS, César Deschamps-BergerECSECS, Claudia Notarnicola
PICO
| Tue, 16 Apr, 10:45–12:30 (CEST)
 
PICO spot 3
Tue, 10:45
Snow constitutes a freshwater resource for over a billion people world-wide. A high percentage of this water resource mainly comes from seasonal snow located in mid-latitude regions. The current warming situation alerts that these snow water storages are in high risk of being dramatically reduced, affecting not only the water supply but also the ecosystems of these areas. Therefore, understanding seasonal snow dynamics, its possible changes and their implications have become crucial for water resources management.

Remote sensing has been used for decades as the primary technique to monitor snow properties and their hydrological implications across scales. The recent technical advances favoured the study of snow properties at finer spatio-temporal resolution, helping to understand better the snow dynamics (e.g., the interaction of snow with small-scale, quick snow changes within a day, rain on snow events, snow-vegetation interaction).

This session focuses on studies linking the use of remote sensing of seasonal snow to hydrological applications with the aim of: (i) better quantifying snow characteristics (i.e., snow grain size, snow depth, albedo, pollution load, snow specific area, liquid water content and snow density), (ii) understanding snow-related processes and dynamics (snowfall, melting, evaporation, wind redistribution and sublimation), (iii) improving snow modelling and, (iv) assessing snow hydrological impacts and snow environmental effects. Works covering techniques and data from different technologies (time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies, or other new applications), different spatial scales (from the plot to the global), and temporal scales (from instantaneous to multiyear), are welcome.

Session assets

PICO: Tue, 16 Apr | PICO spot 3

Chairpersons: Rafael Pimentel, César Deschamps-Berger, Claudia Notarnicola
10:45–10:50
Optical
10:50–10:52
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EGU24-11557
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ECS
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Highlight
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Virtual presentation
Alejandro Corbea-Pérez, Carmen Recondo, and Javier F. Calleja

In this work, we analyze the relationship between albedo and temperature using albedo and Land Surface Temperature (LST) MODIS collection 6 (C6) and in situ data at Livingston Island, Maritime Antarctica. It is known that the relationship between temperature and albedo could have an important impact on global climate models, especially in places where permafrost distribution is complex, as in the South Shetland Islands (SSI) archipelago. Our results show that LST is not well correlated with albedo, which is consistent with the fact that air temperature (Ta) and surface temperature (Ts) do not separately explain the albedo drop, as previous work in the study area has shown. The best agreement was obtained between Aqua and Terra LST and in situ albedo, while the comparison between albedo MODIS and LST yields the worst results, which could be due to the difference in pixel size of MODIS albedo and LST products (500 m and 1000 m, respectively). However, for Ta versus albedo for all data, the decreasing slope of the fit suggests that higher temperatures are associated with lower snow albedo values. This reaffirms the idea that in polar areas, due to their characteristics, the decrease in snow albedo depends not only or mainly on temperature, but also on multiple factors such as the evolution of snow grain size and precipitation rates, among others. 

How to cite: Corbea-Pérez, A., Recondo, C., and Calleja, J. F.: Correlation of Land Surface Temperature and air temperature with albedo in Maritime Antarctica using MODIS and in situ data., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11557, https://doi.org/10.5194/egusphere-egu24-11557, 2024.

10:52–10:54
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EGU24-10881
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Virtual presentation
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Alexander Kokhanovsky, Maximillian Brell, Karl Segl, and Sabine Chabrillat

We present a software suite SNOWTRAN aimed at the solution of forward and inverse problems of snow optics. The numerical procedure is based on the approximate solutions of the radiative transfer equation and the geometrical optics approximation for local optical parameters of snow such as the probability of photon absorption and the average cosine of the single light scattering angle. The model is validated using EnMAP and PRISMA spaceborne imaging spectroscopy data close to the Concordia research station in Antarctica. The SNOWTRAN is applied for the determination of the total ozone, the precipitable water vapor, the snow grain size and the assessment of the snowpack vertical inhomogeneity using EnMAP imagery over the Aviator Glacier and in the vicinity of the Concordia research station in Antarctica. The remote sensing results based on EnMAP measurements revealed a large increase in precipitable water vapor at the Concordia research station in February 2023 linked to warming event, and a 4 times larger grain size at Aviator Glacier compared to the Concordia station.

How to cite: Kokhanovsky, A., Brell, M., Segl, K., and Chabrillat, S.: SNOWTRAN: a software package for the solution of direct and inverse problems of snow optics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10881, https://doi.org/10.5194/egusphere-egu24-10881, 2024.

10:54–10:56
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PICO3.1
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EGU24-16806
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ECS
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On-site presentation
Daniela Hollenbach Borges, Inge Grünberg, Jennika Hammar, Nick Rutter, Thomas Krumpen, and Julia Boike

Snow cover plays a pivotal role in the Arctic's climate, hydrology, and ecology, making the understanding of its deposition and accumulation dynamics crucial. Snow depth and its duration can directly influence soil temperature: the insulating properties of snow increase with greater snow depth, which prevents soil temperatures from declining in winter.  Trail Valley Creek, NWT, Canada, is located at the northern boundary of the tundra-taiga transition zone, approximately 45 km north of Inuvik, and is underlain by continuous permafrost. The region’s rapid warming points to a trend of vegetation changes such as shrub expansion northwards into the tundra.

Topography and vegetation cover are the main drivers of spatial variation of snow depth across different landscapes, while wind significantly influences snow redistribution. This reallocation causes snow to accumulate preferably in terrain features such as valleys and leeward sides of ridges, and taller vegetation, as their height and intricate structure can favour snow trapping. Understanding the relationships among snow distribution, topography features, and vegetation types is vital, though it is often limited by the scarcity of high-resolution data with broad spatial cover.

To investigate the spatial snow distribution in Trail Valley Creek, we analyzed how snow depth varies according to different topography classes and slope aspects, as well as the region’s different vegetation classes and heights. 

For this purpose, we explored records from Aerial Laser Scanning (ALS) collected during both winter and summer of 2023, covering an area of over 170 km2. We generated a high-resolution Digital Elevation Model (DEM) from the winter snow-covered surface (2023-04-02), a Digital Terrain Model (DTM) from the summer snow-free terrain (2023-07-10), and by combining both, created a 1-m resolution snow depth map of the area. Additionally, we used 3129 Magnaprobe ground-based snow depth measurements for validation (2023-03-26 to 2023-03-29). 

For the topography analysis, we classified the slope aspects, and subdivided the terrain into 10 geomorphological classes using the geomorphons approach. This method calculates terrain forms, such as plateaus, slopes, ridges and valleys, and their associated geometry using a machine vision approach. To analyze the role of vegetation cover, we used a 13-class map that categorizes land-cover features and vegetation types, such as graminoids, shrubs and trees, and vegetation height rasters, derived from the ALS summer data.

Snow is the main driver of the hydrological system in Trail Valley Creek, and the outcomes of this study will provide insights in the important interplay between vegetation, snow depth and terrain characteristics in a permafrost landscape.

How to cite: Hollenbach Borges, D., Grünberg, I., Hammar, J., Rutter, N., Krumpen, T., and Boike, J.: Snow accumulation patterns from 2023 Airborne Laser Scanning data in Trail Valley Creek, Western Canadian Arctic, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16806, https://doi.org/10.5194/egusphere-egu24-16806, 2024.

10:56–10:58
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PICO3.2
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EGU24-1915
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ECS
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On-site presentation
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Catherine Breen, Carrie Vuyovich, John Odden, Dorothy Hall, and Laura Prugh

Snow covers a maximum of 47 million km2 of Earth ’s northern hemisphere each winter and is an important component of the planet ’s energy balance, hydrology cycles, and ecosystems. Monitoring regional and global snow cover has increased in urgency in recent years due to warming temperatures and declines in snow cover extent. Optical satellite instruments provide large-scale observations of snow cover, but cloud cover and dense forest canopy can reduce accuracy in mapping snow cover. Remote camera networks deployed for wildlife monitoring operate below cloud cover and in forests, representing a virtually untapped source of snow cover observations to supplement satellite observations. Using images from 1181 wildlife cameras deployed by the Norwegian Institute for Nature Research (NINA), we compared snow cover extracted from camera images to Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products during winter months of 2018–2020. Ordinal snow classifications (scale = 0–4) from cameras were closely related to normalized difference snow index (NDSI) values from the MODIS Terra Snow Cover Daily L3 Global 500 m (MOD10A1) Collection 6 product (R2 = 0.70). Tree canopy cover, the normalized difference vegetation index (NDVI), and image color mode influenced agreement between camera images and MOD10A1 NDSI values. For MOD10A1F, MOD10A1’s corresponding cloud-gap filled product, agreement with cloud-gap filled values decreased from 78.5% to 56.4% in the first three days of cloudy periods and stabilized thereafter. Using our camera data as validation, we derived a threshold to create daily binary maps of snow cover from the MOD10A1 product. The threshold corresponding to snow presence was an NDSI value of 40.50, which closely matched a previously defined global binary threshold of 40 using the MOD10A2 8-day product. These analyses demonstrate the utility of camera trap networks for validation of snow cover products from satellite remote sensing, as well as their potential to identify sources of inaccuracy.

How to cite: Breen, C., Vuyovich, C., Odden, J., Hall, D., and Prugh, L.: Evaluating MODIS snow products using an extensive wildlife camera network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1915, https://doi.org/10.5194/egusphere-egu24-1915, 2024.

10:58–11:00
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PICO3.3
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EGU24-13030
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ECS
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On-site presentation
Mostafa Bousbaa, Abdelghani Boudhar, Christoph Kinnard, Haytam Elyoussfi, Nadir Elbouanani, Abdelaziz Htitiou, Bouchra Bargam, Karima Nifa, and Abdelghani Chehbouni

Remote sensing technologies provide continuous and detailed observations of various land surface parameters, including snow cover, vegetation, land surface temperature, soil moisture, and evapotranspiration, offering invaluable information at various scales and contexts. One of the major uses is the precise mapping and monitoring of seasonal snow cover dynamics, which are essential for water management and global water balance modeling. Since an intelligent ecosystem based on accurate snow cover estimation requires a collection of high-resolution satellite images, both temporally and spatially, to capture snow dynamics, particularly in semi-arid areas where snowfall is extremely variable. These requirements can be difficult to achieve based on a single sensor, mainly due to the trade-offs between the temporal, spectral, and spatial resolutions of the available satellites. In addition, atmospheric conditions and cloud contamination can increase the number of missing satellite observations. However, there is a promising solution to these limitations. Exploiting the complementary capabilities of the new-generation multispectral sensors aboard Landsat-8 (L8) and Sentinel-2 (S2), with spatial resolutions ranging from 10 to 30 meters, offers an unprecedented opportunity to significantly advance the accuracy of snow cover mapping. Hence, this study aims to investigate the effectiveness of the combined use of optical sensors through deep learning-based spatiotemporal image fusion to capture snow dynamics and produce detailed and dense Normalized Snow Difference Index (NDSI) time series in a semi-arid context. Three distinct deep learning models, namely Very Deep Super Resolution (VDSR), Super Resolution Unet (SR-Unet), and Residual Convolutional Neural Network (RCNN), were evaluated and compared to fuse L8 and S2 data. The findings indicate that all three approaches can provide accurate estimates for a coarse-resolution image at a given fusion date, although there are notable disparities in prediction quality between the different approaches. Specifically, R-squared values were measured at 0.94, 0.92, and 0.96 for RCNN, SR-Unet, and VDSR, respectively, with corresponding root mean square error (RMSE) values of 0.09, 0.11, and 0.08. Our results suggest that the VDSR model is particularly effective in producing high-resolution merged snow time series and can compensate for the absence of ground snow cover data.

How to cite: Bousbaa, M., Boudhar, A., Kinnard, C., Elyoussfi, H., Elbouanani, N., Htitiou, A., Bargam, B., Nifa, K., and Chehbouni, A.: Towards a Deep Learning-based Spatio-temporal Fusion Approach for Accurately Improving Snow Cover Mapping: A Case Study in the Moroccan Atlas Mountains with Performance Evaluation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13030, https://doi.org/10.5194/egusphere-egu24-13030, 2024.

11:00–11:02
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PICO3.4
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EGU24-11542
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ECS
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On-site presentation
Martí Navarro Planes, Cristina Cea López, Xavier Pons, Lluís Pesquer Mayos, and Lluís Gómez Gener

An accurate quantification of the spatiotemporal dynamics of seasonal snow is essential for understanding and predicting the impacts of climate change on mountain regions and their feedback on global climate. This is especially critical in southernmost Mediterranean mountains such as the Pyrenees, where the extent of seasonally snow-covered zones (or persistent snow areas) are expected to decrease more abruptly than other mountain regions of the world. Here we use 40 years of Landsat satellite images (from 1984 to 2023) to study the trend in snow surface area and snow persistence (the fraction of time that the snow remains on the ground) across different spatial scales (from catchment to region) within the Central Pyrenees, Spain. In addition, snow surface data has been correlated with altitude and incident solar radiation to understand the role of topography on driving snow persistence distribution patterns.

How to cite: Navarro Planes, M., Cea López, C., Pons, X., Pesquer Mayos, L., and Gómez Gener, L.: Trends in snow persistence at the Central Pyrenees derived from 40 years of Landsat satellite images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11542, https://doi.org/10.5194/egusphere-egu24-11542, 2024.

11:02–11:04
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PICO3.5
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EGU24-11620
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ECS
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On-site presentation
youssra El jabiri, Abdelghani Boudhar, Abdelaziz Htitiou, Eric Sproles, Mostafa Bousbaa, Hafsa Bouamri, and Abdelghani Chehbouni

The lack of knowledge about the temporal variability, or snow cover phenology and its spatial variation poses enormous challenges to water resource managers who mostly rely on a few weather stations with limited spatial coverage which prevents them from having a complete understanding of snow changes as a whole. Meanwhile, the free availability, wide-coverage, frequent updating, and long-term time horizon make data from programs such as Landsat and Sentinel-2 a valuable data source for reliable snow data information at an unprecedented spatial scale.

In this context, this research aims to derive the snow phenology parameters (first day of snowfall, last day of snow melt; and snow duration) over Morocco’s Atlas Mountains by combining over 10,000 images from Landsat-8 and Sentinel-2 satellites for four hydrological years (2016-2021) to create a harmonized product with a time interval of about 3 days using Google Earth Engine platform. The time series produced allowed us to create detailed maps of snow cover and extract a homogeneous normalized difference snow index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence.

  The results showed that derived seasonality snow metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be observed. The experimental results demonstrate that the proposed workflow can accurately derive snow seasonality timing with almost a day and a half delay than the in-situ observed dates and with an overall accuracy equal to 0.96.

  We expect these results to benefit various applications such as hydrological modeling, natural hazards, and regional climate change studies.

How to cite: El jabiri, Y., Boudhar, A., Htitiou, A., Sproles, E., Bousbaa, M., Bouamri, H., and Chehbouni, A.: Towards operational mapping and estimation of snow cover phenology parameters in the Atlas Mountains, Morocco, using multi-sensor satellite data and Google Earth Engine. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11620, https://doi.org/10.5194/egusphere-egu24-11620, 2024.

11:04–11:06
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PICO3.6
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EGU24-20345
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On-site presentation
Leveraging 40 years of hydroclimate measurements to estimate trends in rain-snow transition elevation and runoff efficiency at Reynolds Creek Experimental Watershed, Idaho, USA
(withdrawn)
Andrew Hedrick, Patrick Kormos, Sarah Godsey, and Ernesto Trujillo
Microwave
11:06–11:08
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PICO3.7
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EGU24-1603
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On-site presentation
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Jean-Pierre Dedieu, Marion Momber, Olivier Champagne, Anna Wendleder, Benoit Montpetit, Eric Bernard, Jean-Michel Friedt, Olga Zolina, and Hans-Werner Jacobi

In the Arctic, extreme weather conditions such as rain-on-snow events (ROS) make the monitoring of the snowpack with remote sensing techniques increasingly relevant and necessary. In recent years, remote sensing methods based on active radar images (SAR) are well described for mapping the spatial extent of ROS events in the terrestrial Arctic (Vickers, 2022; Bartsch, 2023). However, few methods are proposed to validate the relationship between ROS and elevation for such events over glaciers likely due to the lack of in-situ measurement networks in these high latitude areas. Svalbard provides several meteorological and snow monitoring sites, which is a great value for detecting the occurrence of these ROS events and validation of the remote sensing methods.

The purpose of this study is to investigate the spatial and temporal effects of recent ROS events over the Brøgger peninsula (210 km2) in Svalbard (N 78°55’ / E 11° 55’), using remote sensing methods, local meteorological measurements and reanalyses. For each ROS event of the 2017-2023 time period, remote sensing SAR maps of wet snow (Nagler and Rott, 2000) are produced from images obtained with the TerraSAR-X (DLR) and RCM (CSA) high resolution sensors (5-m), respectively at X- and C-band frequency.

The validation of the affected areas is based (i) on ERA5 reanalysis data used to estimate the altitude of the 0°C isoline and (ii) on a network of temperature sensors installed on the Austre Lovén glacier. SAR maps, ERA5 isoline, and in-situ data are in good agreement, resulting in altitude differences between 10 and 25 m for the transition of wet and dry snow, depending on the event.

Although optical images availability is limited due to polar night and cloud cover during precipitation, it was further possible to use optical Planet images at high temporal and spatial resolution (3-m) to determine the ROS impact after the events on the properties of the snow cover. The decreasing signal of the red-edge and near-infrared bands indicate higher snow densities and a stronger wetness of the snowpack, which closely aligns with in-situ observations through snow stratigraphy.

How to cite: Dedieu, J.-P., Momber, M., Champagne, O., Wendleder, A., Montpetit, B., Bernard, E., Friedt, J.-M., Zolina, O., and Jacobi, H.-W.: Spatial extent of rain-on-snow (ROS) events in the Arctic (Svalbard) : combining wet snow maps from TerraSAR-X and Radarsat Constellation Mission with ERA5 reanalysis, glaciological measurements, and optical Planet images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1603, https://doi.org/10.5194/egusphere-egu24-1603, 2024.

11:10–11:12
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PICO3.9
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EGU24-14040
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On-site presentation
Narae Kang, Jungsoo Yoon, and Seokhwan Hwang

In this study, we attempted to classify snowfall patterns using multiple dual-polarization radars and quantitatively review the amount of snowfall observed from radar using a ground observation network (snow depth). In order to more quantitatively compare the difference between radar reflectivity and precipitation (snow) intensity compared to ground observed snow depth, comparison was made on an hourly basis, taking into account the Korea Meteorological Administration's snow observation data provision period (1 hour). Radar observation data were compared with precipitation intensity based on cumulative reflectivity, differential reflectivity, and specific differential phase difference. Compared to radar reflectivity, there were various delays ranging from 2 to 7 hours from the time the precipitation intensity accumulated with the snow depth. In addition, the difference between the time of increase in snow cover is judged to be an error generated by the wind, and it is necessary to expand the range of radar pixels as well as the blinding factor to take into account the influence of wind.

 

Acknowledgments

This research was supported by a grant(2022-MOIS61-003(RS-2022-ND634022)) of Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Kang, N., Yoon, J., and Hwang, S.: Snow-depth Spatial Distribution Analysis Technology linked to Ground Observation Network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14040, https://doi.org/10.5194/egusphere-egu24-14040, 2024.

11:12–11:14
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PICO3.10
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EGU24-11677
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ECS
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On-site presentation
Devon Dunmire, Hans Lievens, Isis Brangers, Lucas Boeykens, and Gabriëlle De Lannoy

Despite the critical importance of understanding trends in snow depth and mass for making informed decisions about water resources and adaptation to climate change, these properties are challenging to quantify, especially in remote, mountainous areas with complex topography.  The increasing availability of frequent, high resolution synthetic aperture radar (SAR) observations from active microwave satellites has provided the opportunity to provide high-resolution estimates of mountain snow depth at large spatial and frequent temporal scales. As a result, novel approaches have been developed for SAR-based snow depth retrievals utilizing C-band microwave imagery. These SAR-based methods are not without their own set of limitations and are challenged by shallow snowpacks, high vegetation cover, and wet snow conditions. Here, we seek to overcome these existing challenges by developing a machine learning approach to estimate snow depth over the European Alps using Sentinel-1 imagery, an optical satellite-based snow cover product, and static information such as elevation, slope, aspect, topographical position index and forest cover fraction. We demonstrate that our machine learning approach can more accurately estimate snow depth than existing methods at independent in-situ test sites throughout the Alps and has especially improved performance in deep snow and wet snow conditions. Using feature importance scores, we also investigate when and where the Sentinel-1 data provides the most benefit for snow depth estimation. Our approach optimizes the use of Sentinel-1 imagery by learning when these observations are effective for retrieving snow depth, while relying on other topographical information when Sentinel-1 observations are not suitable.

How to cite: Dunmire, D., Lievens, H., Brangers, I., Boeykens, L., and De Lannoy, G.: A novel machine learning approach for estimating snow depth in the European Alps from Sentinel-1 imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11677, https://doi.org/10.5194/egusphere-egu24-11677, 2024.

11:14–11:16
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PICO3.11
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EGU24-11034
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ECS
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On-site presentation
Pedro Torralbo Muñoz, Rafael Pimentel Leiva, María José Polo Gómez, and Claudia Notarnicola

Monitoring snowmelt dynamics in mountainous catchments  is essential for comprehending downstream water release, streamflow response and consequently, for a better management of water resources at the catchment scale.  In a consolidated snowpack when external inputs become positive, the snow turns into wet snow and the melting phase begins. This change modifies the dielectric constant of the snowpack which can be detected remotely using information in the microwave region of the electromagnetic spectrum. Sentinel-1 (S-1) synthetic-aperture radar (SAR) has emerged as a widely utilized technique for this purpose due to its frequent acquisitions and all-weather capability. 

This study seeks to, first, explore the capabilities of C-band S-1 SAR imagery, which has been demonstrated in previous studies in other regions such as the Alps, in capturing multi-seasonal snowmelt dynamics and, second to linked these wet-dynamics to changes in streamflow response over Mediterranean mountain areas . The study was carried out at two scales: plot and catchment: At the plot scale, the Refugio Poqueira experimental site, which is located at 2500 m a.s.l. was chosen. At the catchment scale, the headwaters of the Poqueria River, which is a  snow-driven catchment  located in the southern face of Sierra Nevada, was selected. Four hydrological years with high hydroclimatic variability, from 2016-2017 to 2019-2020, were used in the study to capture the heterogeneity of the area. 

The general change detection approach for identifying wet snow was adapted for these regions, utilizing the average S-1 SAR image from the preceding summer as  reference imagery and employing a threshold of −3.00 dB for discriminating wet snow. This adaptation was validated using Landsat images as a reference dataset, yielding a general accuracy of 0.79. The local scale analysis demonstrates that S-1 SAR imagery was  able to capture four types of melting cycles including the well-known main melting event during the spring season. The other three melting cycles are linked to the Mediterranean mountains climate and can occur throughout the hydrological year. When applied at the catchment scale, distributed melting-runoff onset maps were developed to enhance understanding of the spatiotemporal evolution of melting dynamics. Finally, a linear correlation between melting dynamics and streamflow was established for prolonged melting cycles, with a determination coefficient (R2) ranging from 0.62 to 0.83 and an average delay of approximately 21 days between melting onset and streamflow peak.

Acknowledgments: This work has been funded by the project PID2021-12323SNB-I00, HYPOMED—“Incorporating hydrological uncertainty and risk analysis to the operation of hydropower facilities in Mediterranean mountain watersheds”.

How to cite: Torralbo Muñoz, P., Pimentel Leiva, R., Polo Gómez, M. J., and Notarnicola, C.: Using Multitemporal Sentinel-1 imagery for wet snow dynamics characterization in Mediterranean mountain catchments: a case study in Sierra Nevada, Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11034, https://doi.org/10.5194/egusphere-egu24-11034, 2024.

11:16–11:18
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PICO3.12
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EGU24-15736
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On-site presentation
Hyunho Jeon, Seulchan Lee, and Minha Choi

Over the past decade, heavy snow has caused the third-largest amount of disaster damage in South Korea, following typhoons and heavy rain. To prevent damage from heavy snow effectively, it is necessary to forecast weather conditions. The Korea Meteorological Administration uses the Local Data Assimilation and Prediction System (LDAPS) to forecast hydrometeorological factors. However, the performance of LDAPS snow depth data is inferior to that of other models and requires correction. In this study, a cumulative distribution function (CDF) matching was used to correct LDAPS snow depth data. The CDF matching was carried out by utilizing ERA5-Land snow depth data to generate snow depth forecasting data for 12, 24, and 36-hour intervals. The forecasting data for snow depth is expected to generate snow disaster risk prediction data that can help reduce disaster losses on the Korean Peninsula.

How to cite: Jeon, H., Lee, S., and Choi, M.: A new snow depth forecast data using cumulative distribution function matching in South Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15736, https://doi.org/10.5194/egusphere-egu24-15736, 2024.

11:18–12:30