Monthly multi-year average snow density grid dataset on the Tibetan Plateau

Snow density is an important parameter to characterize the characteristics of snow cover, and it is also an important index for converting snow depth into snow water equivalent, which plays an important role in the estimation of snow water resources in mountainous areas, the management of water resources such as snowmelt floods, natural disaster forecasting, and climate research. Taking the data from 132 day-by-day national meteorological stations on the Tibetan Plateau from 1960 to 2020, the Chinese regional surface meteorological element-driven dataset, and the satellite-fused snow depth dataset as the main data sources, comparing the performance of several machine learning models in the simulation of snow density by different surface types, and selecting the optimal model, we integrated the ground, satellite, and reanalysis data to produce the Tibetan Plateau Monthly snow density dataset is produced for the Tibetan Plateau. An accuracy check with month-by-month multi-year average snow density data from 132 national meteorological observatories on the Tibetan Plateau found that, the average root mean square error is 0.019 g/cm3, and the average relative error is 11.88%, indicating that the data has high accuracy. This dataset will provide data support for water resources assessment and hydrological process simulation on the Tibetan Plateau.