Wind shear is one of the crucial parameters in wind resource assessment and also serves as a vital parameter and basis for determining wind turbines’ selection and hub height. Existing studies have only focused on typical underlying surface areas, but a relatively limited comprehensive analysis of wind shear characteristics in different complex environments remains. This study analyzes the daily and monthly variations in wind shear index (α) at the station scale based on the observations from 754 wind measurement towers across land surfaces in China. The distribution and empirical values of wind shear in different wind regions and underlying surface types are also investigated. The research findings indicate that the wind shear index derived from fitting the complete annual average wind speeds at multiple height levels of meteorological towers can accurately characterize the stratification state of the atmospheric boundary layer. The variation pattern of solar radiation influences the daily α value in typical regions. In mountainous and desert areas, the monthly variation tends to be higher in autumn and winter and lower in spring and summer. However, its monthly variation shows relatively smaller fluctuations in plain regions. The comprehensive α value over land regions in China is 0.135. The α values for I, II, III, and IV wind fields are 0.111, 0.163, 0.1, and 0.153, respectively. Its values for mountainous, plains, grassland, and desert regions are 0.12, 0.273, 0.123, and 0.104, respectively. By conducting statistical analysis on α values across different wind regions, guidance is provided for extrapolating surface wind speeds to hub-height wind speeds. This serves as a reference for wind energy resource assessment, wind turbine selection, and hub height determination in the atmospheric boundary layer of China.

Analyzing historical and reanalysis datasets for wind energy climatic characteristics offers crucial insights for wind farms and short-term electricity generation forecasting. However, large-scale wind farms in Chinese deserts, the Gobi, and barren areas often lack sufficient wind measurement data, leading to challenges in assessing long-term power generation revenue and introducing uncertainty. This study focuses on the Tengger Desert as the study area, processes the Coupled Model Intercomparison Project Phase 6 (CMIP6) data, and analyzes and compares wind energy’s future characteristics utilizing a developed deep learning (DL) downscaling algorithm. The findings indicate that (1) the Convolutional Neural Network (CNN) downscaling model, with the Weather Research and Forecasting Model (WRF) numerical simulation results as the targets, exhibits spatial distribution consistency with WRF simulation results in the experimental area. (2) Through testing and validation with three practical wind measurements, the annual average wind speed error is below 4%. (3) In the mid-term future (~2050), the average wind speed in the experimental area remains stable with a multi-year average of approximately 7.00 m·s⁻¹. The overall wind speed distribution range is significant, meeting the requirements for wind farm development.

The intense changes in glaciers in the southeastern Tibetan Plateau (SETP) have essential impacts on regional water resource management. In order to study the seasonal fluctuations of glaciers in this region and their relationship with climate change, we focus on the Yigong Zangbo River Basin in the SETP, extract the annual and seasonal variations of glaciers in the basin during 2018–2023, and analyze their spatio-temporal characteristics through the seasonal-trend decomposition using the LOESS (STL) method. Finally, combining the Extreme Gradient Boosting (XGBoost) model and the Shapley additive explanations (SHAP) model, we assess the comprehensive impact of meteorological factors such as temperature and snowfall on glacier changes. The results indicate that glaciers in the Yigong Zangbo River Basin experienced remarkable mass loss during 2018–2023, with an average annual melting rate of −0.83 ± 0.12 m w.e.∙yr⁻¹. The glacier mass exhibits marked seasonal fluctuations, with increases in January–March (JFM) and April–June (AMJ) and noticeable melting in July–September (JAS) and October–December (OND). The changes over these four periods are 2.12 ± 0.04 m w.e., 0.93 ± 0.15 m w.e., −1.58 ± 0.19 m w.e., and −1.32 ± 0.17 m w.e., respectively. Temperature has been identified as the primary meteorological driver of glacier changes in the study area, surpassing the impact of snowfall. This study uses advanced altimetry data and meteorological data to monitor and analyze glacier changes, which provides valuable data for cryosphere research and also validates a set of replicable research methods, which provides support for future research in related fields.