The eastern Xinjiang Basin is desperately short of water. Most rivers in the basin originate in the high eastern Tianshan, which has abundant precipitation and numerous alpine glaciers. Fieldwork conducted on three reference glaciers around Mt. Bogda in 1981 and 2009 suggests that they all strongly melt in summer, a process that has tended to accelerate in recent decades. Based on topographic maps from 1962 and 1972 and 2005/2006 satellite imagery, we investigated 203 glaciers near Mt. Bogda and 75 glaciers near Mt. Harlik. The results show that the surface area of the Mt. Bogda glaciers decreased by 21.6% (0.49% a⁻¹) from 1962 to 2006. This was accompanied by a 181 m decrease in length and a 28% drop in ice volume. In the Mt. Harlik region, areal extent was reduced by 10.5% (0.32% a⁻¹), length by 166 m, and volume by 14% between 1972 and 2005. South-facing glaciers lost more of their area than those that are north facing, yielding an areal loss of 25.3% and 16.9% for southern and northern slopes of Mt. Bogda, respectively, and 12.3% and 6.6% for the comparable slopes of Mt. Harlik. Glaciers smaller than 0.5 km² in area experienced the strongest retreat, whereas glaciers larger than 2 km² in area experienced gentle recession but may be the main contributors in the future to river runoff. Glacial ablation in eastern Xinjiang tends to be strong, and the water resources in this region are deteriorating. Also, a heavy reduction in the capacity of the local karez system, as well as a significant change in river runoff, can be related to glacial retreat. Combined, this will adversely affect the downstream city of Urumqi and the Turfan Basin.

How to measure and quantitatively assess hydrological drought (HD) in the inland river basins of Northwest China is a difficult problem because of the complicated geographical environment and climatic processes. To address this problem, we conducted a comprehensive approach and selected the Aksu River Basin (ARB) as a typical inland river basin to quantitatively assess the hydrological drought based on the observed data and reanalysis data during the period of 1980–2010. We used two mutual complementing indicators, i.e., the standardized runoff index (SRI) and standardized terrestrial water storage index (SWSI), to quantitatively measure the spatio-temporal pattern of HD, where the SRI calculated from the observed runoff data indicate the time trend of HD of the whole basin, while SWSI extracted from the reanalysis data indicate the spatial pattern of HD. We also used the auto-regressive distribution lag model (ARDL) to show the autocorrelation of HD and its dependence on precipitation, potential evapotranspiration (PET), and soil moisture. The main conclusions are as follows: (a) the western and eastern regions of the ARB were prone to drought, whereas the frequency of drought in the middle of the ARB is relatively lower; (b) HD presents significant autocorrelation with two months’ lag, and soil moisture is correlated with SWSI with two months’ lag, whereas PET and precipitation are correlated with SWSI with 1 month’ lag; (c) the thresholds of HD for annual PET, annual precipitation, and annual average soil moisture are greater than 844.05 mm, less than 134.52 mm, and less than 411.07 kg/m², respectively. A drought early warning system that is based on the thresholds was designed.

The global warming effect has been accelerating rapidly and poses a threat to human survival and health. The top priority to solve this problem is to provide reliable renewable energy. To achieve this goal, it is important to provide fast and accurate solar radiation predictions based on limited observation data. In this study, a fast and accurate solar radiation nowcasting method is proposed by combining FY-4A satellite data and the McClear clear sky model under the condition of only radiation observation. The results show that the random forest (RF) performed better than the support vector regression (SVR) model and the reference model (Clim-Pers), with the smallest normalized root mean square error (nRMSE) values (between 13.90% and 33.80%), smallest normalized mean absolute error (nMAE) values (between 7.50% and 24.77%), smallest normalized mean bias error (nMBE) values (between −1.17% and 0.7%) and highest R² values (between 0.76 and 0.95) under different time horizons. In addition, it can be summarized that remote sensing data can significantly improve the radiation forecasting performance and can effectively guarantee the stability of radiation predictions when the time horizon exceeds 60 min. Furthermore, to obtain the optimal operation efficiency, the prediction results were interpreted by introducing the latest SHapley Additive exPlanation (SHAP) method. From the interpretation results, we selected the three key channels of an FY-4A and then made the model lightweight. Compared with the original input model, the new one predicted the results more rapidly. For instance, the lightweight parameter input model needed only 0.3084 s (compared to 0.5591 s for full parameter input) per single data point on average for the 10 min global solar radiation forecast in Yuzhong. Meanwhile, the prediction effect also remained stable and reliable. Overall, the new method showed its advantages in radiation prediction under the condition that only solar radiation observations were available. This is very important for radiation prediction in cities with scarce meteorological observation, and it can provide a reference for the location planning of photovoltaic power stations.

China is the biggest emitter of greenhouse gases (GHGs) in the world, and agricultural GHG emission accounts for nearly a fifth of the total emission in China. To understand the carbon absorption and emission characteristics of agricultural production systems in those arid oasis areas, a typical representative city in northwestern China, Zhangye City, was selected for study.The emission factor method was used to analyze and calculate the characteristics of changing carbon emission dynamics in the whole agricultural production system in Zhangye city region (38,592 km²) from 2010 to 2021.The results revealed that carbon emissions during agricultural planting mainly come from fertilizers, which account for the highest proportion (47.9%) of total carbon emissions in agricultural planting. Animal enteric fermentation emissions from local livestock farming are the main contributor (86%) to GHG emissions. The annual average carbon absorption intensity is 4.4 t C-eq ha⁻¹ for crop and 2.6 t C-eq ha⁻¹ for the agricultural production system. The ratio of total carbon emissions from agricultural production to carbon sequestration of crops is 1:1.7. We find that the total carbon sequestration slightly exceeds its total carbon emissions in the study region, with an annual average of 41% for its sustainable development index. Carbon emissions of the agricultural production system in this oasis area are mainly driven by the livestock industry, mostly CH₄ emissions from cattle raising.Reducing the local carbon emissions from the livestock industry, typically the cattle raising, will play a crucial role in reducing carbon emissions from this local agricultural production system and maintaining its net positive carbon balance.

The Plum Rains process is a complex process, and its spatiotemporal variations and influencing factors on different time scales still need further study. Based on a dataset on the Plum Rains in the Yangtze River Delta, from 33 meteorological stations during the period of 1960 to 2012, we investigated the spatiotemporal variations of Plum Rains and their relation with the East Asian Summer Monsoon (EASM), the El Niño-Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO) using an integrated approach that combines ensemble empirical mode decomposition (EEMD), empirical orthogonal function (EOF), and correlation analysis. The main conclusions were as follows: (1) the plum rainfall (i.e., the rainfall during the period of Plum Rains) showed a trend of increasing first and then decreasing, and it had a three-year and six-year cycle on the inter-annual scale and a 13-year and 33-year cycle on the inter-decadal scale. The effect of the onset and termination of Plum Rains and the daily intensity of plum rainfall on plum rainfall on the inter-annual scale was greater than the inter-decadal scale, (2) the EOF analysis of plum rainfall revealed a dominant basin-wide in-phase pattern (EOF1) and a north-south out-of-phase pattern (EOF2), and (3) ENSO and EASM were the main influencing factors in the three-year and six-year periods, respectively.