Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.
Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of: i) the snow melt onset; ii) the snow gliding and wet-snow avalanches; iii) the release of snow contaminants and iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to be obtained. On the other hand, active microwave sensors such as the Synthetic Aperture Radar (SAR) mounted on board of satellites, are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 days over several places in the world. In this paper we analyze the correlation between the multi-temporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering two hydrological years. We found that the multi-temporal SAR measurements allow the identification of the three melting phases that characterize the melting process i.e., moistening, ripening and runoff. In detail, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water, and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease, but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially-distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.
Abstract. Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.
High-resolution (HR) snow cover maps derived by remotely sensed images are an asset for data assimilation in hydrological models. However, the current satellite missions do not provide daily HR multispectral observations suitable for an accurate snow monitoring in alpine environments. On the contrary, low-resolution (LR) sensors acquire daily information of snow cover fraction (SCF) but at an inappropriate spatial scale. This paper proposes a novel approach that combines multi-source and multi-scale acquisitions to infer the daily HR snow cover area (SCA) for mountainous basins. The approach builds on the assumption that inter-annual snow patterns are both affected by the local geomorphometry and meteorology. We derive these patterns through a hierarchical multi-step approach based on historical statistical analyses on a long and sparse HR time-series. At each step, we obtain HR snow cover maps with higher number of reconstructed pixels but decreasing level of confidence. Historical data are used to estimate the probability that a HR pixel is covered by snow according to two possible multi-scale strategies: i) HR gap-filling, or ii) LR downscaling. These analyses lead to the identification of the patterns that regularly appear given certain conditions. When no systematic patterns are observed, we reinforce the inference of the pixel class by a generalized additive model (GAM) that exploits not only the historical data but also explicit geomorphometric, global snow and multi-temporal properties. The proposed approach has been validated on a catchment in the Sierra Nevada, USA, for three hydrological years (2017)(2018)(2019) showing an average overall accuracy of 92%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.