Abstract. Selin Co, located within permafrost regions surrounded by glaciers, has exhibited the greatest increase in water storage among all the lakes on the Tibetan Plateau over the last 50 years. Most of the increased lake water volume has been attributed to increased precipitation and the accelerated melting of glacier ice, but these processes are still not sufficient to close the water budget with the expansion of Selin Co. Ground ice meltwater released by thawing permafrost due to continuous climate warming over the past several decades is regarded as another source of lake expansion. This study presents the first attempt to quantify the water contribution of ground ice melting to the expansion of Selin Co by evaluating the ground surface deformation. We monitored the spatial distribution of surface deformation in the Selin Co basin using the small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) technique and compared the results with the findings of field surveys. Then, the ground ice meltwater volume in the watershed was calculated based on the cumulated settlement. Finally, this volume was compared with the lake volume change during the same period, and the contribution ratio was derived. SBAS-InSAR monitoring during 2017–2020 illustrated widespread and large subsidence in the upstream section of the Zhajiazangbu subbasin, where widespread continuous permafrost is present. The terrain subsidence rate was normally between 5 and 20 mm a−1, indicating rapid ground ice loss in the region. The ground ice meltwater was released at a rate of ∼57×106 m3 a−1, and the rate of increase in lake water storage was ∼485×106 m3 a−1 during the same period, with ground ice meltwater contributing ∼12 % of the lake volume increase. This study contributes to explaining the rapid expansion of Selin Co and equilibrating the water balance at the watershed scale. More importantly, the proposed method can be extended to other watersheds underlain by permafrost and help in understanding the hydrological changes in these watersheds.
To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover.
Abstract. Based on a snow depth dataset retrieved from meteorological stations, this experiment explored snow indices, including snow depth (SD), snow covered days (SCDs), and snow phenology variations, across China from 1951 to 2018. The results indicated that the snow cover in China exhibits regional differences. The annual mean SD tended to increase, and the increases in mean and maximum snow depth were 0.04 cm and 0.1 cm per decade, respectively. SCDs tended to increase by approximately 0.5 days per decade. The significant increases were concentrated at latitudes higher than 40° N, especially in Northeast China. However, in the Tibetan Plateau, the SD and SCDs tended to decrease but not significantly. Regarding the snow phenology variations, the snow duration days in China decreased, and 25.2 % of the meteorological stations showed significant decreasing trends. This result was mainly caused by the postponement of the snow onset date and the advancement of the snow end date. Geographical and meteorological factors are closely related to snow cover, especially the change in temperature, which will lead to significant changes in snow depth and phenology.
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