Climate change in Northwest China could lead to the change of the hydrological cycle and water resources. This paper assessed the influence of climate change on runoff in the Manas River basin as follows. First, the temporal trends and abrupt change points of runoff, precipitation, and mean, lowest and highest temperature in yearly scale during the period of 1961–2015 were analyzed using the Mann-Kendall (MK) test. Then the correlation between runoff and climatic variables was characterized in a monthly, seasonal and yearly scale using the partial correlation method. Furthermore, three global climate models (GCMs) from Coupled Model Inter-comparison Project Phase 5 (CMIP5) were bias-corrected using Equidistant Cumulative Distribution Functions (EDCDF) method to reveal the future climate change during the period from 2021 to 2060 compared with the baseline period of 1961–2000. The influence of climate change on runoff was studied by simulating the runoff with the GCMs using a modified TOPMODEL considering the future snowmelt during the period from 2021 to 2060. The results showed that the runoff, precipitation, and mean, lowest and highest temperature all presented an increasing trend in yearly scale during the period of 1961–2015, and their abrupt change points were at a similar time; the runoff series was more strongly related to temperature than to precipitation in the spring, autumn and yearly scales, and the opposite was true in winter. All GCMs projected precipitation and temperature, and the runoff simulated with these GCMs were predicted to increase in the period from 2021 to 2060 compared with the baseline period of 1961–2000. These findings provide valuable information for assessing the influence of climate change on water resources in the Manas River basin, and references for water management in such regions.

Climate change and land use/cover change (LUCC) are two major factors that alter hydrological processes. The upper reaches of the Tarim River, situated in the northwest region of China, experience a dry and less rainy climate and are significantly influenced by human activities. This study comprehensively assessed the impacts of individual and combined climate changes and LUCCs on streamflow. Three general circulation models (GCMs) were utilized to predict future climate changes under three shared socioeconomic pathways (SSP119, SSP245, and SSP585). Cellular Automata–Markov (CA–Markov) was employed to predict future LUCC under three scenarios (i.e., ecological protection, historical trend, and farmland development). Streamflow for the period 2021–2050 was simulated using the calibrated MIKE SHE model with multiple scenarios. The results showed that from 2021 to 2050, increments in both average annual precipitation and average annual temperature under the three SSPs were predicted to lead to an increased streamflow. In comparison to the conditions observed in 2000, under three LUCC scenarios for 2030, the grassland area decreased by 1.04% to 1.21%, while the farmland area increased by 1.97% to 2.26%, resulting in reduced streamflow. The related changes analysis indicated that the variation in streamflow during winter is most significant, followed by spring. The study predicted that climate change would increase streamflow, while LUCC would decrease it. Due to the greater impact of LUCC, considering the combined effect of both factors, runoff would decrease. The contribution analysis indicated that climate change contributed between −7.16% and −18.66%, while LUCC contributed between 107.16% and 118.66%.

Analyzing the changes in vegetation under different factors is crucial for ecological protection in arid areas. The spatial-temporal variations of vegetation in the lower reaches of the Tarim River (LRTR) from 2000 to 2020, were analyzed using the Theil-Sen estimator and the Mann-Kendall test. The future trends of NDVI are projected to use the Hurst exponent method. The driving mechanisms of vegetation changes were analyzed using the GeoDetector method and multivariate residual analysis. The NDVI values in the LRTR significantly increased during the study period, indicating good vegetation recovery. The overall vegetation level remains poor and was primarily concentrated around the riverine areas. There is still a risk of vegetation degradation in most areas of the future LRTR. Compared to climate change, vegetation was more affected by human activities. Human activities have helped restore the riparian vegetation and prevented the degradation of vegetation far from the river. Therefore, distance from river channels is the strongest explanatory factor (q = 0.078) for vegetation changes, followed by precipitation, and temperature, while changes in slope have minimal impact on vegetation. Statistics have found that when two factors are combined, their impact on vegetation change is stronger. These findings are beneficial for identifying vegetation evolution patterns in LRTR and providing theoretical support for the government to carry out ecological restoration.