Under the condition of global warming, the vegetation growth state in the Qilian Mountains has significantly changed. Studying the relationship between vegetation and climate in the Qilian Mountains is crucial for ecological construction in northwest China. Based on GIMMS NDVI data and ERA5 temperature and precipitation data from 1982 to 2022, this paper analyzes the variation trend of NDVI in the Qilian Mountains and its correlation with climate factors. The results show that high NDVI values are mainly concentrated in the eastern section (0.6-0.8), and NDVI values gradually decreased from east to west. Over the past 40 years, NDVI in the Qilian Mountains has showed an overall increasing trend, primarily due to its significant increases during the growing season. However, NDVI decreased during the non-growing season in some parts of the middle and eastern Qilian Mountains. NDVI changes in the Qilian Mountains were generally and positively correlated with temperature and precipitation. However, during the growing season, NDVI and precipitation were negatively correlated in the eastern part of the Qilian Mountains, while NDVI and temperature were negatively correlated in the western part during the non-growing season. There is a significant coupling mode between NDVI in summer and temperature, precipitation during the corresponding period in the Qilian Mountains. Overall, the increase in temperature and precipitation has been beneficial to the rise in NDVI. However, the increase in NDVI during the growing season in the eastern Qilian Mountains is primarily attributed to the rise in temperature.
Soil temperature and moisture are the important parameters in land surface process, and they are also important physical parameters in boundary conditions of atmospheric numerical model. This paper tried to obtain spatial-temporal evolution of soil moisture of the model through the machine learning method according to the memory characteristics of soil moisture. Considering the influence of soil temperature on soil moisture, the soil temperature and moisture of ERA5 reanalysis at depths of 0-7, 7-28, 28-100, 100-289 cm are used as predictors to predict changes of soil moisture on a monthly and seasonal scale based on convolutional neural networks (CNN). The results show that the method proposed in this paper is reliable and can effectively predict soil moisture 6 months in advance. The mean bias of predicted soil moisture in the shallow layer (0-28 cm) and deep layer (28-289 cm) is less than 0.05 and 0.02 m3·m-3, respectively. In the humid area, the mean bias is basically within 0.03 m3·m-3, showing a good effect.The prediction method and results presented in this paper can be used for both soil drought prediction and the initial and boundary conditions for numerical models.
Based on the SRTM (shuttle radar topography mission)data, the ground clutter and other clutters around Tianshui radar station were filtered, then the Z-I function with localized parameters was established on the basis of six precipitation processes with three types in Southeast Gansu after filtering the ground clutter and other clutters of radar data, and at last the reflectivity factor of Xifeng new generation weather radar in Qingyang was compared with the one in Tianshui within the coincidence range. The results show that SRTM data can well simulate the distribution of ground clutter; radar reflectivity is ahead of precipitation; the Z-I function with localized parameters in Tianshui, which had a smaller A and bigger b, is significantly different to common ones; Tianshui new generation weather radar may have a systematic problem of low echo intensity.
Based on the hourly precipitation data at 81 national meteorological observation stations of Gansu Province from 1981 to 2018 and NCEP reanalysis data, the climate and circulation characteristics of extreme rainstorms were emphatically analyzed in different falling areas of Gansu Province. The results are as follows: (1) The extreme rainstorm weathers occurred mainly in Longnan, Tianshui, Pingliang and Qingyang of eastern Gansu, and the heavy rainfall centers concentrated in Kangxian and Huixian of Longnan. The extreme rainstorms were classified into four types including eastern Gansu, southern Gansu, southeastern Gansu and dispersion patterns, according to the falling areas of rainstorms. (2) The extreme rainstorms were easily to occur in July and August in Gansu, especially in mid-August. The extreme rainstorms in southern Gansu were earlier than in eastern Gansu. The precipitation of extreme rainstorms at night was more than in the daytime as a whole, the night rain characteristic was remarkable in Gansu, especially in southern Gansu and southeastern Gansu. In additional, the convective characteristic was significant in Gansu. (3) There were 2.5, 5 and 10 years period of extreme rainstorms in Gansu during 1981-2018, and the 2.5-year periodic oscillation was obvious. (4) The extreme rainstorms in Gansu were correlated with the subtropical high, and the falling area of rainstorm was significantly related to the location of subtropical high. Moreover, the extreme rainstorms in eastern Gansu were also related to the easterly airflow at the bottom of northern high ridge, the extreme rainstorms with dispersion pattern were related to the tropical low pressure in South China Sea, while the extreme rainstorms in southern and southeastern Gansu depended on the intensity and location of short-wave trough in Tibet Plateau.
In this paper，the performance of WRF model is validated by simulating wind velocity in January and April over northwestern China，and the errors between simulated and observed values were investigated ． Results show that WRF performance was better in April，when wind speed was big and steady，surface heating strong and temperature increasing faster，but in January wind speed was lower but gust frequently occurred． The relative error in 48 hours at different levels was under 10%，the correlation coefficient ( above0． 8) between observed and simulated values was significant at 90% in April． In January，the mean value of relative error in 48 hours at different levels was in the range of 20%，and the absolute value of the correlation coefficient was more than 0． 30 and significant at the 99% confidence level． During the two simulation durations，the simulated value was obviously smaller than observed value when wind speed was high． Due to wind dominated by higher level momentum transfer downwards，terrain and thermodynamic function of land surface，the PBL parameters scheme in model is crucial in simulation of wind，especially in northwest China，where vegetable is sparse and terrain complex．
