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Hydrological connectivity directly affects aquatic ecological processes, water environment and wetland ecological security, which is essential to the stability of arid ecosystems. However, the mechanism between hydrological connectivity and water-related environment has not been revealed completely. To address these issues, we use a landscape connectivity approach to assess the connectivity of water patches for analyzing the hydrological connectivity of the Bosten Lake Basin (BLB), as well as its response to human activities and climate change, based on the Joint Research Centre (JRC) global surface water dataset. It shows that the integral index of connectivity (IIC) of the BLB is low (ranging from 0 to 0.2) from 1990 to 2019, with an increasing interannual trend. The connectivity is higher in wet periods and in oases compared with dry periods and high-altitude mountain regions. Correlation and regression analyses indicate that hydrological connectivity has a strong correlation (r > 0.5, p ≤ 0.05) with water area and water level. The interannual and seasonal trends of eight hydrochemical indices in the Bosten Lake have been investigated to systematically elaborate the complex relationships between hydrological connectivity and water quality in the BLB. Results indicated that better hydrological connectivity can improve water quality, and the minimum of pollutants were observed in high hydrological connectivity period, covering approximately 75% of the high-water quality period. These findings could provide scientific support for the water management in the BLB. [ABSTRACT FROM AUTHOR]

Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of p = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What's more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter. [ABSTRACT FROM AUTHOR]