Balancing the relationship between rural development and the protection of water resources is a challenging undertaking. This study develops a coupling coordination degree (CCD) model to examine the non-linear interaction between rural development and water environment in the 11 prefectures of Gansu, northwestern China. There are three key findings. First, economic development is the key driver of rural development, whereas social development has relatively little impact. For the water environment subsystem, improved water efficiency has been the key contributor, whereas environmental carrying capacity is secondary. Second, the CCD increased steadily in the studied period, which suggests that the relationship between rural development and water environment has gradually changed from antagonistic to mutually beneficial. However, this change is not occurring rapidly and in fact shows signs of slowing. Third, the complex spatial differences of the CCD are related to the level of economic and social development, the process of urban–rural integration, and regional natural conditions. The findings of this study have great significance for further quantitative analysis of the interaction and mutual feedback mechanism between the rural economy and the water environment in China and support evidence-based policymaking.

Drought is one of the major climate disasters leading to forest degradation in dryland mountains. Hence, revealing the response of forest resilience to drought is crucial to predict forest succession in dryland mountains under future global warming. Here, we chose the Qilian Mountains as the study area and calculated the recovery time and drought intensity along elevation from 1982 to 2020 using the Leaf Area Index (LAI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Then, the forest resilience to drought was calculated using the area of an exponentially fitted curve between drought intensity and corresponding recovery time. Finally, the dominant climate factors underlying altitude differentiation of forest resilience were analyzed using a random forest (RF) regression model, and correlations were determined based on a generalized additive model (GAM). The results indicate that forests in the elevation range of 2600–3900 m exhibited faster recovery rates and greater resilience compared to those in 1700–2600 m. The attributional analysis shows that altitudinal differentiation of forest resilience to drought was mainly constrained by precipitation with a non-monotonic correlation, and resilience was strongest when monthly precipitation reaches 30 mm. In terms of the occurrence of historical drought events, increased potential evapotranspiration improved resilience in the elevation range of 2600–3900 m and enhanced cloud cover initially enlarged the resilience and then decreased it in the elevation range of 3000–3400 m and 3400–3900 m, with resilience being strongest when cloud cover reached 24% and 33%, respectively. Under future climate change, global warming will further exacerbate the drought impact in arid regions, increasing the risk of primary forest collapse. The results of this study provide a scientific basis for predicting the potential changes in vegetation resilience and developing policies for ecological protection in dryland mountains, and we will take addressing the difficult study of the quantitative effects of tree species on resilience altitude differentiation based on ecosystem scales as our future direction.