Abstract
Under a warming climate, an improved understanding of the water stored in snowpacks is becoming increasingly important for hydropower planning, flood risk assessment and water resource management. Due to inaccessibility and a lack of ground measurement networks, accurate quantification of snow water storage in mountainous terrains still remains a major challenge. Remote sensing can provide dynamic observations with extensive spatial coverage, and has proved a useful means to characterize snow water equivalent (SWE) at a large scale. However, current SWE products show very low quality in the mountainous areas due to very coarse spatial resolution, complex terrain, large spatial heterogeneity and deep snow. With more high-quality satellite data becoming available from the development of satellite sensors and platforms, it provides more opportunities for better estimation of snow conditions. Meanwhile, machine learning provides an important technique for handling the big data offered from remote sensing. Using the Överuman Catchment in Northern Sweden as a case study, this paper explores the potentials of machine learning for improving the estimation of mountain snow water storage using satellite observations, topographic factors, land cover information and ground SWE measurements from the spatially distributed snow survey. The results show that significantly improved SWE estimation close to the peak of snow accumulation can be achieved in the catchment using the random forest regression. This study demonstrates the potentials of machine learning for better understanding the snow water storage in mountainous areas.
Highlights
Snow is an important component of the Earth’s system for regulating climatic processes (Sturm et al 2017), through its strong feedbacks related to albedo (Groisman et al 1994, Déry and Brown 2007), and plays an important role in the water cycle
Time-series AMSR2 Tb for characterizing snow depth in Mjölkbäcken Continuous snow depth measurements from the Mjölkbäcken station were used to explore the strength of time-series AMSR2 Tb for estimating snow depth
As seen from figure 4(d), the performance of Tb for characterizing snow depth in the catchment is significantly better in periods with limited snowmelt
Summary
Snow is an important component of the Earth’s system for regulating climatic processes (Sturm et al 2017), through its strong feedbacks related to albedo (Groisman et al 1994, Déry and Brown 2007), and plays an important role in the water cycle. Climate change is rapidly altering snow conditions in the world (Peng et al 2013, Chen et al 2015, Pulliainen et al 2020), and spatially distributed and temporally dynamic observations of snow conditions are highly valuable for water resource management, hydropower planning, flood risk assessment and climate studies (Bormann et al 2018). In areas where snowmelt is the primary source for hydropower, knowledge of snow water storage can be of crucial importance for both economic (e.g. hydropower production) and environmental purposes (e.g. flood risk assessment). In 2015, the spilling water resulted in an economic loss of roughly 1–6 million Euros in Umeälven (Gustafsson 2016), the third largest river in terms of hydropower production in Sweden
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