Snow density is an important parameter that characterizes the characteristics of snow accumulation and is also an important indicator for converting snow depth into snow water equivalent. It plays an important role in estimating snow water resources in mountainous areas, managing water resources such as snowmelt floods, predicting natural disasters, and conducting climate research. This data will provide data support for water resource assessment and hydrological process simulation on the Qinghai Tibet Plateau.
Using data from 132 national level meteorological stations on the Qinghai Tibet Plateau from 1960 to 2020, the Chinese regional surface meteorological element driven dataset, and the satellite fusion snow depth dataset as the main data sources, we compared the performance of several machine learning models in snow density simulation under different surface types and selected the optimal model. We integrated ground, satellite, and reanalysis data to generate a monthly snow density dataset for the Qinghai Tibet Plateau.
An accuracy check of the multi-year average snow density data from 132 national meteorological stations on the Qinghai Tibet Plateau revealed an average root mean square error of 0.019 g/cm3and an average relative error of 11.88%, indicating high accuracy of the data.
The data quality is good.
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CSTR:31253.11.sciencedb.j00001.00823
doi.org/10.57760/sciencedb.13892