A dataset of monthly multi-year average snow density grids on the Tibetan Plateau

<p indent="0mm"> Snow density is an important parameter for characterizing snow cover and functions as a key index for converting snow depth into snow water equivalent. It also plays a vital 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. Sourcing data from <sc>132 day</sc>-by-day national meteorological stations on the Tibetan Plateau from 1960 to 2020, China’s regional surface meteorological element-driven dataset, and the satellite-fused snow depth dataset as the main data sources, we compared the performance of several machine learning models in the simulation of snow density by different surface types, and selected the optimal model. Integrating the ground, satellite, and reanalysis data, we finally produced the dataset of monthly multi-year average snow density grids on the Tibetan Plateau. An accuracy evaluation using month-by-month multi-year average snow density data from 132 national meteorological stations on the Tibetan Plateau showed that an average root mean square error of <sc> 0.019 g/cm³ </sc> and an average relative error of 11.88%, indicating the high accuracy of the dataset. This dataset is expected to provide data support for water resource assessment and hydrological process simulation on the Tibetan Plateau.