The correlation between the consecutive dry days and other indicators was low and negatively correlated with some indicators, while the consecutive wet days were only correlated with a few indicators. In addition, other extreme precipitation indicators were significantly correlated. Results of the Hurst index analysis showed that the trend of extreme precipitation in Ansai District was sustainable.Based on the ground-based observations from seven atmospheric background stations during 2009 to 2018 in monsoon Asia (including BKT station in Indonesia, LLN and WLG stations in China, RYO and YON stations in Japan, TAP station in Republic of Korea, and UUM station in Mongolia), we analyzed the temporal and spatial variation of atmospheric CH4 concentration and its driving factors using harmonic model and maximal information-based nonparametric exploration. The results showed that the CH4 concentration in monsoon Asia varied from 1853.04 to 1935.61 nmol·mol-1, higher than that in Mauna Loa (MLO) station (1838.33 nmol·mol-1) in Hawaii, USA. The CH4 concentration decreased from north to south, with the highest value in TAP station (1935.61 nmol·mol-1) in Republic of Korea and RYO station (1907.19 nmol·mol-1) in Japan. The average seasonal amplitude at YON station in Japan was the largest (108.20 nmol·mol-1); while that at WLG station in China was the smallest (29.48 nmol·mol-1). The seasonal amplitude of TAP station in Republic of Korea changed faster at the rate of 4.49 nmol·mol-1·a-1. Except for WLG and TAP stations, CH4 concentrations were low in summer and high in winter. From the long-term perspective, the CH4 concentration at LLN (7.68 nmol·mol-1·a-1) and WLG (7.56 nmol·mol-1·a-1) stations in China exhibited the most obvious growth trend. Compared with wind speed, temperature and precipitation had greater impact on CH4 concentration, which were negatively associated with CH4 concentration. Local CH4 emission at some stations had a significant positive effect on CH4 concentration.In recent years, soil salinization in the Yellow River Delta under the effects of hydrology, climate and human activities have become increasingly prominent. Based on the 20 Landsat series images of Hekou, Kenli, Dongying districts and Lijin County of Dongying City selected from 1985 to 2018, numerical regression correction method was used to perform image spectral consistency conversion. The partial least squares regression method was used to construct quantitative inversion models of soil salt content. https://www.selleckchem.com/products/bgb-290.html The soil salt content of the study area were retrieved by the best salt prediction model. The temporal and spatial characteristics of soil salt changes in the Yellow River Delta were analyzed. The results showed that the soil salt inversion model constructed with 10 sensitive spectral indices performed higher prediction accuracy, with coefficient of determination R2=0.769 and RMSE=1.125 for calibration, R2=0.752 and RMSE=1.203 for validation, and relative prediction deviation (RPD)=2.08. Using the measured so38). Soil salinity did not correlate with regional precipitation, and was most affected by the Yellow River streamflow in the previous season (R=-0.543).In this study, we collected soil samples from four different land use types (forest land, shrub land, grassland and abandoned land) in Huajiang valley of Guizhou Province, a typical karst rocky desertification area in Southwest China. Correlation analysis and redundancy analysis were used to examine the distribution of available nitrogen (N) and available phosphorus (P) in diffe-rent soil layers from 0 to 30 cm and the relationships between soil environmental factors (soil physical indexes, organic carbon components, electrochemical properties, metal oxides and enzyme activities) and the contents of available N and available P. The results showed that the concentrations of soil total N, total P, available N, available P decreased significantly with the increases of soil depth. The concentrations of soil available N and available P in forest land and shrub land were significantly higher than those in grassland and abandoned land, which were significantly positively correlated with soil organic carbon compositint and the reduced adsorption and fixation of N and P by iron and aluminum oxides.Agriculture is the second-largest source of carbon emission, which is not only a burden for the government to achieve the goal of carbon emission reduction but also is a huge threat to food security and the sustainable development of agriculture. Therefore, how to quantify the impacts of policy cognition of farmers on their low-carbon agricultural technology adoption is of great importance in China. Based on the survey data from 704 farmers in Jianghan Plain, China, we used the entropy method and Heckman sample selection model to quantify the effects of farmers' policy cognitive degree on their low-carbon agricultural technology adoption behavior and adoption intensity. The results showed that the level of farmers' cognition of low carbon policy should be improved. The average level of farmers' policy cognition was only 1.89. The adoption rate of single low-risk, high-efficiency low-carbon agricultural production technology by farmers was relatively high, but that of multiple low-carbon agricultural technologies was low, with an average adoption intensity of only 1.62. Policy cognition could effectively promote farmers' low-carbon agricultural technology adoption behavior and adoption intensity. Farmers' policy cognition had a significant positive impacts on their low carbon agricultural technology adoption behavior and adoption intensity. Local government should take more effective supporting measures, including strengthening the propaganda, enhancing the training and improving the subsidy standard of low-carbon agricultural technology, to improve farmers' low-carbon agricultural technology adoption intensity. Such strategy would help achieve the target of carbon emission reduction and sustainable development of agriculture.It is important to understand the response of vegetation to climate change in Tibetan Pla-teau (TP), an ecological barrier for China and Asia. The spatiotemporal variation of the normalized difference vegetation index (NDVI) of vegetation growing season were analyzed based on the gro-wing season NDVI retrieved from MOD09A1. The relationship between NDVI and climate factors was analyzed by combining the data of meteorological stations in TP from 2001 to 2018. The results showed that NDVI in the growing season showed a slow upward trend during the study period. There was substantial interannual variation of NDVI in different climate regions. The fluctuation magnitude of NDVI value was plateau humid climate region>semi-humid climate region>semi-arid climate region>arid climate region. The proportion of area with increasing and decreasing NDVI in humid climate region, semi-humid climate region, arid climate region, semi-arid climate region on TP were 1.4% and 1.9%, 4.9% and 1.5%, 16.4% and 0.8%, 7.0% and 2.0%, respectively.