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Non-point source (NPS) pollution has caused serious threats to water quality on a global scale. However, the investigation using a single measure with multi-scenarios for the long-term simulation in karst agricultural watershed is still lacking. In this study, the Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) model was developed to verify the applicability in a karst agricultural watershed. Then, the model was used to determine the best management measures (BMPs) and the reduction rate characteristics under long-term effects (about 10 years) in the watershed. The AnnAGNPS model perform well in simulating in NPS pollution with R2 (0.95 for runoff, 0.93 for TN, and 0.93 for TP, respectively) and NSE (0.95 for runoff, 0.53 for TN, and 0.57 for TP, respectively). The output of total nitrogen (TN) and total phosphorus (TP) primarily occurred in the rainy season (up to 80%). The loss of mass of TN and TP were mainly observed in orchards and woodlands in the upstream of each sub-basin. The results from AnnAGNPS model demonstrated that different BMPs had significant impacts on the reduction of NPS pollution. Furthermore, a same BMPs measure showed it was closely related to land use in the watershed. In the Qifeng River watershed, stubble tillage (ST) showed to be useful with relatively good reduction rates (16.64% for sediment, 17.85% for TN, and 17.80% for TP, respectively). The simulation results indicated that AnnAGNPS was a valuable tool after validation for the planning and management of the watershed in karst areas.

Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (Tm) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

Understanding monitor accurately and predicting drought conditions are necessary to prevent economic losses and natural disasters. Now, there is still a lack of comprehensive assessment of drought prediction in Xinjiang. In order to better predict the future drought trend, firstly we analyzed the trends of SPEI and the magnitude and frequency of drought in selected surface meteorological stations from 1960 to 2017. Secondly, we compared drought trends and spatial distribution characteristics of SPEI and PDSI. Based on historical data from 1960 to 2017, the paper predicted the trends of SPEI and PDSI in the next five years. The results show that the winter SPEI has risen significantly, and there was a SPEI value mutation in 1994. The Spring, Summer and Autumn SPEI show an insignificant downward trend. It is relatively stable on other time scales , and there is no significant sudden change. It is inferred thatthe increasing trend of wetting that may occur under different time scales at NRX in 2021, winter and annual scales will continue to be humidified, and spring, summer, and autumn will continue to be arid. Droughts in NRX occur at least once every three years, which is a high frequency event. The prediction results of the artificial neutral network (ANN) method based on multilayer feed forward network show that probability of humidification at NRX is more likely in the future, and the prediction results of wavelet and Hurst index are basically consistent with that of ANN method.

Based on the extended STIRPAT model and panel data from 2005 to 2015 in 20 industrial sectors, this study investigates the influential factors of carbon intensity, including employee, industry added value, fixed-assets investment, coal consumption, and resource tax. Meanwhile, by expanding the spatial weight matrix and using the Spatial Durbin Model, we reveal the spatiotemporal characteristics of carbon intensity. The results indicate that Manufacturing of Oil Processing and Coking Processing (S7), Manufacturing of Non-metal Products (S10), Smelting and Rolling Process of Metal (S11), and Electricity, Gas, Water, Sewage Treatment, Waste and Remediation (S17) contribute most to carbon intensity in China. The carbon intensity of 20 industrial sectors presents a spatial agglomeration characteristic. Meanwhile, industry added value inhibits the carbon intensity, however, employee, coal consumption, and resource tax promote carbon intensity. Finally, coal consumption appears to have spillover effects, and the employee has an insignificant impact on the carbon intensity of industrial sectors.

Spatiotemporal characteristics of the trade-off among ecosystem services and its mechanism have been exten sively studied. However, studies on the difference in spatiotemporal variation of ecosystem services trade-offs in various climate zones under climate change are limited. This study aims to explore the spatiotemporal charac teristics and driving factors of trade-offs between crop production and soil conservation in different climate zones by employing a case study in various climate zones of the Shaanxi Province. We adopted statistical and GIS-based spatial analyses to explore the spatiotemporal characteristics of the trade-off and used gradient boosting decision tree to quantify the relative contribution of driving factors. Results showed that trade-off between crop pro duction and soil conservation in a middle temperate zone (0.09) is significantly lower than in a warm temperate zone (0.18) and a subtropical zone (0.21). The trade-off clearly exhibits an upward trend from 1995 to 2018 in all climate zones. However, the increasing trend gradually weakened from the middle and warm temperate zones to the subtropical zone. Moreover, changes in the trade-off are spatially heterogeneous in all climate zones. Anthropogenic activities have larger relative contribution (62.27%–71.67%) to the trade-off than environmental factors in all climate zones. Climate zone type has an insignificant effect on the relative contributions of anthropogenic activities, whereas the relative contributions of most environmental factors are significantly different in various climate zones. Fertilization (relative contribution, 26.58%–37.66%), GDP per land area (relative contribution, 8.50%–20.49%), and population density (relative contribution, 9.77%–16.11%) are the most important anthropogenic driving factors in all climate zones. The environmental factors with greatest relative contribution in the middle temperate, warm temperate, and subtropical zones are accumulated tem perature (13.40%), precipitation (26.64%), and accumulated temperature (9.63%), respectively. Thus, differ ences of spatiotemporal characteristics and driving factors of ecosystem services trade-off must be considered in the formulation of targeted polices for agroecosystem management in different climate zones.

Under the backdrop of achieving carbon neutrality and accelerating urbanization, China’s forests face unprecedented pressures. This study explored the spatiotemporal characteristics of forest loss in the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR). The dynamic mechanism of forest loss caused by fire, logging, construction, and pollution was also analyzed using spatial database development, polygon superposition analysis, grid system construction, and coordinate system calculation. The results show that the forest loss in the UAMRYR experienced three stages: continuous acceleration (1990–2010), peak (2010–2015), and slight decline (2015–2020). Rapid urban expansion is the primary cause of forest loss, and the three metropolitan areas had the fastest urban expansion and the most severe forest loss. Due to the success of afforestation efforts, the forest loss caused by fire, logging, and pollution was restored by 80%, while most of the forest losses caused by construction are permanent. Given the current forest loss trends, large expanses of forests in the UAMRYR are at risk of being destroyed and causing serious damage to the region’s ecological environment. Forest losses can be significantly reduced by guiding the rational expansion of cities, supporting afforestation for urban construction projects, strengthening forest fire risk investigation, and implementing ecological reconstruction of polluted areas.

Optical remote sensing data are used widely in many fields (such as agriculture, resource management and the environment), especially for the vast territory of China, however, the application of these data is usually limited by clouds. Although it is valuable to analyze the probability of acquiring cloud-free imagery (PACI), PACI using different sensors at the pixel level across China has not been reported. In this study, the PACI of China was calculated with daily Advanced Very High Resolution Radiometer (AVHRR) cloud mask data from 1990 to 2019. The results showed that (1) PACI varies dramatically in different regions and months in China. The value was larger in autumn and winter, and the largest figure reached 49.55% in October in Inner Mongolia (NM). In contrast, relatively small values occurred in summer, and the minimum value (5.26%) occurred in June in South China (SC). (2) As the climate changes, the PACI has increased significantly throughout the country and most regions, especially in North China (NC), with a growth rate of 1.9% per decade. The results can be used as a reference for selecting appropriate optical sensors and observation times in areas of interest.