Abstract
The complex terrain, shallow snowpack, and cloudy conditions of the Tibetan Plateau (TP) can greatly affect the reliability of different remote sensing (RS) data, and available station data are scarce for simulating and validating the snow distribution. Aiming at these problems, we design a synthesis method for simulating the snow distribution in the TP where the snow is patchy and shallow in most regions. Different RS data are assimilated into the SnowModel, using the ensemble Kalman filter method. The station observations are used for the validation of assimilated snow depth. To avoid the scale effect during validation, we design a random sampling comparison method by constructing a subjunctive region near each station. For years 2000 to 2008, the root-mean-square error of the assimilated results are in the range [0.002 m, 0.008 m], and the range of Pearson product-moment correlation coefficients between the in situ observations and the assimilated results are in the range [0.61, 0.87]. The result suggests that the snow depletion curve is the most important parameter for the simulation of the snow distribution in ungauged regions, especially in the TP where the snow is patchy and shallow. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Highlights
The Tibetan Plateau (TP) is the highest and most extensive highland in the world and has been called “Third Pole” and “Asian water tower.”[1]
Some studies have indicated an overestimation of snow depth (SD) from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data;[43] this can be attributed to the limitation of the current AMSR-E algorithm to account for the large grains that typically develop in snowpacks.[10]
We present a synthesis simulation method for the TP region in which available in situ observations are sparse, utilizing different remote sensing (RS) data, a model, and an assimilation method
Summary
The Tibetan Plateau (TP) is the highest and most extensive highland in the world and has been called “Third Pole” and “Asian water tower.”[1]. Snow is an important water resource supply to adjacent rivers and basins, such as the Yellow River, the Yangtze River, the Mekong Basin, the Brahmaputra Basin, and the Ganges Basin. It has an important influence on the water budget of lake basins in the TP besides that of glaciers and precipitation.[2,3] Snow cover in the TP heavily influences regional climate and global climate. Many different methods for monitoring and modeling snow distribution have been used, including remote sensing (RS), distributed snow models, and synthesis methods such as assimilation. Li et al.: Synthesis method for simulating snow distribution utilizing remotely sensed data
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