Multivariate statistical methods including cluster analysis (CA), discriminant analysis (DA) and component analysis/factor analysis (PCA/FA), were applied to explore the surface water quality datasets including 14 parameters at 28 sites of the Eastern Poyang Lake Basin, Jiangxi Province of China, from January 2012 to April 2015, characterize spatiotemporal variation in pollution and identify potential pollution sources. The 28 sampling stations were divided into two periods (wet season and dry season) and two regions (low pollution and high pollution), respectively, using hierarchical CA method. Four parameters (temperature, pH, ammonia-nitrogen (NH₄-N), and total nitrogen (TN)) were identified using DA to distinguish temporal groups with close to 97.86% correct assignations. Again using DA, five parameters (pH, chemical oxygen demand (COD), TN, Fluoride (F), and Sulphide (S)) led to 93.75% correct assignations for distinguishing spatial groups. Five potential pollution sources including nutrients pollution, oxygen consuming organic pollution, fluorine chemical pollution, heavy metals pollution and natural pollution, were identified using PCA/FA techniques for both the low pollution region and the high pollution region. Heavy metals (Cuprum (Cu), chromium (Cr) and Zinc (Zn)), fluoride and sulfide are of particular concern in the study region because of many open-pit copper mines such as Dexing Copper Mine. Results obtained from this study offer a reasonable classification scheme for low-cost monitoring networks. The results also inform understanding of spatio-temporal variation in water quality as these topics relate to water resources management.

Evaluation of Chinese precipitation extremes is conducted based on large ensemble projections of the present climate and 4-K-warmer climates derived from a high-resolution atmospheric general circulation model. The model reproduced the overall trend and magnitude of total precipitation and extreme precipitation events for China reasonably well, revealing that this dataset can represent localized precipitation extremes. Precipitation extremes are more frequent and more severe in future projections under 4-K-warmer climates than in the representative concentration pathway 8.5 (RCP8.5) scenario of phase 5 of the Coupled Model Intercomparison Project (CMIP5). Our results show that using a large-ensemble simulation can improve the ability to estimate with high precision both the precipitation mean and the precipitation extremes compared with small numbers of simulations, and the averaged maximum yearly precipitation will be likely to increase by approximately 18% under a +4-K future in southern China compared with the past. Finally, uncertainty evaluation in future precipitation projections indicates that the component caused by the difference in six ΔSST patterns is more important in southern China compared with the component due to the atmospheric internal variability. All these results could provide valuable insights in simulating and predicting precipitation extremes in China.

Based on the Gini-coefficients, this study has presented an analysis of the impacts of anthropogenic forcing on the temporal inequality (i.e., increase in unevenness or disparity) of precipitation amounts (PRCPTOT), intensity (SDII), and extremes (R95p and RX5day) at national and regional scales (eight regions) in China. A positive anthropogenic influence on the temporal inequality is found for precipitation extremes over China, especially in southern regions during the period 1961–2005. Projections of future precipitation indices except R95p have a stepped upward trend in temporal precipitation variability with increasing anthropogenic forcing in most regions of China under SSP126, SSP370, and SSP585 scenarios. Except for Southern China (SC) and SWC2, R95p has a significant decrease in the future, and the largest decrease is up to 29.5% in Northwest China under SSP370. Results obtained from this study offer insights into temporal variability of precipitation extremes and help policy makers for managing water-related disasters.

Comprehensive understanding of the long-term trends and seasonality of water quality is important for controlling water pollution. This study focuses on spatio-temporal distributions, long-term trends, and seasonality of water quality in the Yangtze River basin using a combination of the seasonal Mann-Kendall test and time-series decomposition. The used weekly water quality data were from 17 environmental stations for the period January 2004 to December 2015. Results show gradual improvement in water quality during this period in the Yangtze River basin and greater improvement in the Uppermost Yangtze River basin. The larger cities, with high GDP and population density, experienced relatively higher pollution levels due to discharge of industrial and household wastewater. There are higher pollution levels in Xiang and Gan River basins, as indicated by higher NH₄-N and COD_Mn concentrations measured at the stations within these basins. Significant trends in water quality were identified for the 2004–2015 period. Operations of the three Gorges Reservoir (TGR) enhanced pH fluctuations and possibly attenuated COD_Mn, and NH₄-N transportation. Finally, seasonal cycles of varying strength were detected for time-series of pollutants in river discharge. Seasonal patterns in pH indicate that maxima appear in winter, and minima in summer, with the opposite true for COD_Mn. Accurate understanding of long-term trends and seasonality are necessary goals of water quality monitoring system efforts and the analysis methods described here provide essential information for effectively controlling water pollution.

Water resources are increasingly under stress in Central Asia because downstream countries are highly dependent on upstream countries. Water is essential for irrigation and is becoming scarcer due to climate change and human activities. Based on 20 hydrological stations, this study firstly analyzed the annual and seasonal spatial–temporal changes of the river discharges, precipitation, and temperature in the Syr Darya River Basin and then the possible relationships between these factors were detected. Finally, the potential reasons for the river discharge variations have been discussed. The results show that the river discharges in the upper stream of the basin had significantly risen from 1930 to 2006, mainly due to the increase in temperature (approximately 0.3 °C per decade), which accelerated the melting of glaciers, while it decreased in the middle and lower regions due to the rising irrigation. In the middle of the basin, the expansion of the construction land (128.83 km²/year) and agricultural land (66.68 km²/year) from 1992 to 2015 has significantly augmented the water consumption. The operations of reservoirs and irrigation canals significantly intercepted the river discharge from the upper streams, causing a sharp decline in the river discharges in the middle and lower reaches of the Syr Darya River in 1973. The outcomes obtained from this study allowed us to understand the changes in the river discharges and provided essential information for effective water resource management in the Syr Darya River Basin.