With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.

Decadal climate change has evidently posed serious threats and challenges to water resource management across arid regions in China. Thus, vulnerability assessment of water resources has been considered vital to adapt to or cope with the adverse effects of climate change. This paper investigated spatial patterns of decadal climate change and water resources variability in the arid regions of Northwest China based on a GIS analysis. The Budyko hypothesis was used to investigate the climate sensitivity of basin streamflow. A framework was proposed to assess the vulnerability of water resources as a function of exposure, sensitivity, and adaptability. Results indicated that a significant increase in precipitation and water resources (both surface water resources and groundwater recharge) occurred primarily in Xinjiang Province after the 1980s. Water utilization ratio in arid regions was very high, and groundwater abstraction substantially increased as it exceeded the exploitable volume in the Hexi Corridor, the Tuha Basin, and the north of the Tianshan Mountain. Exposure of water resources to drought in arid regions decreased from the 1960s to the 2000s. The sensitivity of water resource response to precipitation changes varied between regions. Water resources were more sensitive to precipitation variability in the river basins in the Hexi Corridor and inner Mongolia. In most arid regions in China, water resources were evaluated with low adaptability and high or severe vulnerability in the 2000s. In the future, the precipitation is projected to increase significantly under the RCP2.6, RCP4.5, and RCP8.5 scenarios. Particularly, water vulnerability will be significantly reduced as the precipitation increases in most river basins under the RCP8.5 scenario. Water resources in the rivers in the Qaidam Basin, the Qinhai Lake Basin, the inner Mongolia, and the north of Kunlun Mountain will have low or medium vulnerability in the 2030s and 2050s. However, in the Tuha Basin and Hexi Corridor, vulnerability will remain severe in the 2050s despite the significant increase in precipitation and implementation of water-saving measures. Hence, measures that will facilitate sustainable water resource management must be identified and implemented.