%0 Dataset
%T Interdecadal Maximum Freezing Depth Dataset of Seasonally Frozen ground in China
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/ea7a6327-ad4b-460e-af5a-e67fffafae97
%W NCDC
%R 10.12072/ncdc.nieer.db6551.2024
%A SHENG Yu
%A WANG Shuo
%K Seasonal frozen soil;interdecadal maximum freezing depth;machine learning method
%X With global climate change, the freezing depth of seasonal permafrost has also undergone significant changes. Engineering construction facilities in seasonal freezing areas often need to consider the impact of seasonal freezing on the main structure, with frost heave being the most typical. Due to the fact that freezing conditions directly determine the scope and degree of frost heave, many engineering measures for preventing and controlling frost heave diseases involve the important freezing characteristic of freezing depth. This dataset mainly relies on the observation data of the maximum freezing depth from 650 meteorological monitoring points distributed in the seasonally frozen soil areas of China from 1971 to 2020; Climate reanalysis data including temperature, snow cover thickness, surface solar radiation, and precipitation (ERA5 Land); Soil dataset (GSDE) and digital elevation model (DEM). The foundation of using machine learning methods to study the annual maximum freezing depth variation in seasonal frozen soil areas in China has emerged. Using machine learning models to predict the maximum freezing depth, and then obtaining interdecadal maximum freezing depth raster data in China's seasonally frozen soil regions. The data is the interdecadal maximum freezing depth raster data of China's seasonally frozen soil region from 1971 to 2020, with a spatiotemporal resolution of 0.1 ° and data format in TIFF files. In the past, the determination of freezing depth mainly relied on limited measured data, which made it difficult to comprehensively reflect the full extent of freezing depth on a large scale. This dataset, based on machine learning methods, displays the maximum freezing depth values of seasonal frozen soil areas in China from 1971 to 2020.