To reveal the characteristics of apple phenology changes and its response to climate warming on the Tibetan plateau, with a view to providing basic scientific and technological support for local apple cultivation, management, breeding and coping with climate change. Both phenological data and meteorological items measured in the period of apple growth in Tsedang agrometeorological station from 2001 to 2020 were analyzed, which include phenological dates and the daily mean value of several meteorological factors such as mean temperature(T_m), maximum temperature(T_max), minimum temperature(T_min), diurnal temperature range(DTR), precipitation(Pr), relative humidity(RH), sunshine duration(S), and accumulated temperature above 0℃(∑T₀) et al. Statistical methods, including linear regression, Pearson correlation coefficient and stepwise regression, were used to reveal the trends and identify the leading factors caused the changes in apple phenology in south-central Tibet in the past 20 years. The results indicated that: (1) all of the apple phenological dates were postponed in a range from 2.83 to 7.64d·y⁻¹ in south-central Tibet from 2001 to 2020 except for the fruit maturing date (FMD), which exhibited an advanced rate of 1.28d·y⁻¹. The length of fruit growing period (LFG) and flowering duration (FD) were shortened by 8.92d·y⁻¹ and 5.98d·y⁻¹, respectively, while the length of tree growing season(LTG) was slightly extended at an increasing rate of 0.65d·y⁻¹. These results were different from the main apple producing areas in northern China where the autumn phenophase was delayed and the spring phenophase was advanced, and may be attributed to the reduction of ∑T₀ during the growth seasons. (2) For all of apple phenological stages, the T_max has increased with T_min decreasing, leading to a significant larger value of DTR. For most of the phenological stages, RH and S have reduced significantly, while Pr increased before FMD and decreased afterwards. (3) In spring, temperature was identified as the dominant factor with negative correlation with the phenophase in south-central Tibet. In contrast, Pr was the leading factor causing changes in autumn phenophases, which was positively correlated with the FMD and negatively correlated with both the end of leaf coloring date (LCD) and the end of leaf fall date (LFD). (4) For most of the phenology, ∑T₀ has been revealed as the leading factor influencing the length of phenological period. However, Tm has played the dominant roles influencing the length of the whole period and the period from the FMD to LCD, and S can be viewed as the leading factor influencing the length of the period from the LCD to LFD. It has been identified that the first flowering date was advanced by 2.32 days when T_max increased by 1°C in early March, and the LFD was advanced by 8.55 days when Pr changed with 10mm in late October in this study region.

The Mongolian plateau was divided into "vegetation area" and "non-vegetation area" using the threshold value of the multi-year monthly mean NDVI value above 0.1. Based on an examination of the time lagged response of vegetation to precipitation in the "vegetation area" and the association between various data values of land surface temperature and precipitation in the "non-vegetation area", a geographically weighted regression (GWR) model of TRMM 3B43 data with the elevation, slope, slope direction data, and normalized vegetation index (NDVI)/land surface temperature (LST) data was constructed to obtain monthly precipitation downscaling simulations at 1km spatial resolution from May to October 2006−2015 in the different areas. Utilizing information from 141 local meteorological stations, the accuracy of downscaled simulation data was validated. The results showed that, (1) there is a time lag in the response of vegetation to precipitation in the "vegetated area" of the Mongolian plateau, which is about one month. The most significant correlation between daytime and nighttime land surface temperature difference (LST_D_N) values and precipitation is found in the "non-vegetated area" in most months. (2) The descending-scale simulations agree with the meteorological station data, with a monthly-scale correlation coefficient of 0.83 and correlation coefficients ranging from 0.42 to 0.98 for each station. (3) On the growing season and monthly mean scales, the downscaled simulation data are highly accurate, with September and October data being more accurate than TRMM 3B43 data. The overall accuracy of the downscaled simulation data is higher, and together with the filling of areas not covered by the original data above 50°N and the increase in spatial resolution. It can provide essential data support for research on water cycle changes, agricultural and livestock production, and drought monitoring in the region.

To study the temporal and spatial change of vegetation ecological quality index in Shiyang river basin from 2000 to 2020, the MODIS NDVI remote sensing data, DEM data and land use classification map were used based on remote sensing and GIS technology by using transition matrix and linear regression model. And the distribution characteristics of vegetation ecological quality index under different altitude, slope, aspect and vegetation type were analyzed. Understanding the changes of vegetation ecological quality of Shiyang river basin could provide theoretical foundation and scientific bases for ecological protection and construction. The results showed that: (1) the average of the index from 2000 to 2020 was 9.8 and there existed a great gap between the south and the north in this area. There was a positive correlation between vegetation ecological quality index and altitude, slope. (2)The transition matrix of vegetation ecological quality index showed that the vegetation ecological quality was improved from 2000. The proportion of the area of the vegetation ecological quality improved and degraded on the total area were 30.0% and 2.2% from 2000 to 2010, and it were 13.7% and 10.9% from 2010 to 2020. (3)The vegetation ecological quality index was increased 1.2 every decade and the area of the index improved (slope>0.0) accounted for 90.3% of the total area from 2000 to 2020. (4)The area of the vegetation improved fluctuated with the increase of altitude. The area of the vegetation improved increased at first and then decreased with the increase of slope. (5)The proportion of the area in the forest, grassland and bush improved were all above 60.0%. (6)The correlation coefficient between vegetation ecological quality index and temperature and precipitation were 0.352 and 0.281 respectively and temperature was the key influence factor in Shiyang river basin.

In order to demonstrate the feasibility and application prospect of the silage-grain maize double season flexible planting pattern with flexible sowing date of summer maize in North China Plain under the background of the change of water and heat resources and grain demand structure, an experiment was conducted at Yucheng Comprehensive Experimental Station of the Chinese Academy of Sciences from 2020 to 2021, and its annual grain equivalent yield and utilization efficiency of heat and water resources were analyzed. The results showed that: (1) the flexible sowing period of summer maize with high annual heat use efficiency (HUE) of double season maize cropping pattern was from June 1 to June 29, with an average annual HUE of 3.1kg•ha⁻¹·(℃·d)⁻¹. (2) The flexible sowing period of summer maize with high annual water use efficiency (WUE) of double maize cropping pattern was from June 1 to June 15, with an average annual WUE of 2.1kg•m⁻³. (3) Based on the flexible sowing period, which was from June 1 to June 15, the average annual grain equivalent yield was 12886.4kg•ha⁻¹, which was 14.0% lower than the average annual yield of winter wheat-summer maize. Whereas the annual water consumption of double maize cropping pattern decreased by 34.4% and annual water use efficiency increased by 30.9%, compared with the annual average of winter wheat-summer maize. The silage-grain maize double flexible planting pattern can effectively reduce annual crop water consumption, improve annual water use efficiency under the condition of the same yield.

Based on the daily temperature data of 70 meteorological observation stations in Fujian province from 1971 to 2020, the duration days of different range of the daily maximum temperature was defined the evaluation criteria for high-temperature heat damage level of tea plants. The temporal and spatial distribution of heat damage times are counted and analyzed. The risk regions of tea plants high-temperature heat damage in Fujian province were divided. The results showed that in recent 50 years, there were more light and moderate heat damage to tea plants in Fujian province, with a large range of interannual changes, and the occurrence times showed an overall upward trend. The occurrence times and frequencies of heat damage since the 21st century were higher than those in the last century. The frequency of heat damage increased gradually from south to north in tea areas. The areas with high heat damage and high risk are mainly distributed in the low-altitude areas of Wuyi mountains, Shanling mountains and Daiyun mountains to the north of Jiufeng mountains. Farmer should combine the specific geographical and climatic conditions and the results of heat risk regionalization to carry out planting management modes such as planting drought resistant varieties or reducing summer and autumn tea picking, so as to improve the microclimate of tea gardens and reduce the loss of tea income caused by high temperature and heat damage in summer.

The real disaster information is an important reference for preventing and reducing heavy rainfall disaster losses effectively. Taking process rainfall intensity as the index, this paper constructs a heavy rainfall disaster event database matching the multi-source meteorological disasters and disaster-causing processes in Hebei Province during 1984−2020. After manual quality control, 2305 groups of real disaster information and 263 groups of false information are obtained. In this study, correlation analysis was used to determine and select the rainfall eigenfactors that are significantly related to the disaster degree. Based on the One-class support vector machine (OCSVM) and 10 folds cross-validation method, 10 samples were randomly selected to establish the meteorological factor disaster discriminant model and test and optimize, so as to explore the intelligent and easy-to-use intelligent discriminant method of multi-source heavy rainfall disaster credibility. The results showed that: (1) there are eleven rainfall eigenfactors related to disaster degree at 0.01 significant level, which are maximum rainfall, minimum rainfall, average process rainfall, average daily rainfall, average hourly rainfall intensity, hourly maximum rainfall, 3-hour maximum rainfall, 6-hour maximum rainfall, 12-hour maximum rainfall, 24-hour maximum rainfall and rainfall in the first 10 days. (2) Ten models (M1-M10) were established using 11 rainfall eigenfactors. According to the identification accuracy of the real disaster, the optimal model was determined to be M9, with the authenticity rate of 96.4% and the falsification rate of 67.6%, which indicated that the model is one-sided in determining the authenticity of the disaster situation and should be further optimized. (3) Through the autocorrelation test, maximum rainfall, average hourly rainfall intensity, hourly maximum rainfall and rainfall in the first 10 days were taken as input factors. Ten models (M11-M20) were reconstructed, and the optimal model is M20 with 96.2% proof rate and 82.9% false rate. Based on comprehensive analysis, the model established by 4 factors is more reliable than the model established by 11 factors.

Soil surface water content, which exists at the land air interface, is an important variable to simulate the global hydrological, energy and carbon cycles and their interactions, and is crucial for climate and earth system research. In order to evaluate the accuracy and applicability of the soil moisture products of FY-3B, FY-3C and AMSR2 in Xilingol grassland and different land cover types, authors applied time series correlation (R), root mean square error (RMSE) , the average deviation (Bias) and unbiased root mean square error (ubRMSE) were to analyze the reliability on these three grid products. The results showed that: (1) on a daily scale, FY-3B and FY-3C soil moisture products overestimated the land surface water content, while AMSR2 underestimated the soil water content. Each evaluation index showed that the three soil water content products can capture the time change trend of surface soil water content in Xilingol grassland. (2) On a monthly scale, compared with AMSR2, the monthly distribution of FY-3B and FY-3C soil moisture products was more consistent with the measured soil moisture distribution. (3) In the growing season scale, the three satellite soil moisture products reflected the soil moisture status of each grassland subtype. FY-3B and FY-3C soil moisture products had good applicability in areas with high vegetation coverage, AMSR2 had good reliability in areas with low vegetation coverage, and the three products had complementary functions in monitoring soil moisture. FY-3B had the best consistency with the change trend of in-situ soil water content, FY-3B and FY-3C can better reflect the variation of soil moisture in the west and east of the study area, and AMSR2 can well capture the variation of soil moisture in the middle of the study area.