As a vital ecological barrier and water source for the Beijing-Tianjin-Hebei (BTH) region, the Luan River Basin (LRB) plays a crucial role in maintaining regional ecological balance. However, comprehensive research on Ecological Vulnerability (EV) evaluation in the LRB remains scarce, making EV evaluation and forecasting particularly significant. This study evaluated EV dynamics (2002–2022) via the SRP model with 17 indicators, with driving factors analyzed via Geodetector and 2032 scenarios forecasted by CA-Markov. Key findings revealed: (1) EVI increased from 0.397 to 0.428 (peak at 0.445 in 2017), with Microscopic vulnerability dominating (46.47% average area). (2) Spatially, EV exhibited a “low-medium–high” gradient (lower in northwestern high-altitude areas and higher in southeastern plains), confirmed by significant clustering (Global Moran’s I = 0.889–0.938, p value < 0.001). (3) Geodetector identified elevation (q = 0.855), biological abundance (q = 0.812), annual temperature (q = 0.800), and cultivated land proportion (q = 0.783) as primary driving factors. (4) CA-Markov forecasts for 2032 indicate declines in Potential vulnerability (− 5.83%), Microscopic vulnerability (− 2.01%), and Severe vulnerability (− 2.30%), but increases in Mild vulnerability (+ 6.57%) and Moderate vulnerability (+ 3.57%). These findings provide a scientific basis for evidence-based ecological policies in the LRB, contributing to the promotion of regional sustainable development and the balance between ecological conservation and economic growth.

Against the backdrop of climate change and compounded by human activities, increasing water scarcity has triggered a series of drought disasters, which have already severely impacted both ecological environments and socioeconomic production. The SWAT model, recognized for its strong portability and superior spatial heterogeneity, has gained widespread acceptance in fields such as hydrology and environmental science, and is extensively applied in hydrological simulation studies across large-scale river basins. Hydrological models of the study area can be constructed in the SWAT model to simulate changes in hydrological variables by conducting spatial discretization, parameter specification, and boundary condition definition. Standardized drought index can effectively reflect the spatiotemporal variations in drought disasters, holding significant importance for clarifying and predicting drought characteristics. This study took the Luanhe River Basin as the research area, constructed a watershed hydrological model based on SWAT, and projected changes in the basin’s hydrological processes for the period 2030–2060. Based on the model’s projected data, we calculated drought indices and extracted drought events for the basin. The results indicate the following: (1) During the simulation period, only 30% of the years in the Luanhe River basin had annual runoff above the long-term average, with a range of 228.18 mm. The range of mean annual runoff across sub-basins was 173.32 mm. Drought and uneven water resource allocation over both spatial and temporal scales coexisted, and this issue is expected to intensify under future climate warming and drying. (2) The mid-reaches of the Luanhe River are more prone to drought compared to the upper reaches for its higher water demand. However, due to a stronger capacity for ecological restoration, droughts there are mostly of low intensity in the mid-reaches. In contrast, the upper reaches experience more periods classified as severe or extreme drought, and the drought events encountered are generally more intense than those in the mid-reaches. (3) The method proposed in this study can screen extreme drought events based on outliers in the characteristic values of drought events. Taking the simulation from this study as an illustration, anomalies in drought event characteristic values suggest a potential basin-scale, prolonged extreme drought event in the Luanhe River Basin from June 2038 to July 2042. Proactive drought prevention policies should be formulated for this period. The findings of this study provide guiding significance and practical value for drought assessment, risk management, and policy application in the Luanhe River Basin. This study methodologically combines hydrological model predictions with drought event responses, providing a novel method for predicting basin-scale drought conditions and issuing early warnings for extreme drought events.