The Yellow River Basin is an important grain-production base in China, playing a crucial role in the country’s agricultural production and overall national economy and social development. However, due to the impact of climate change, China’s food security is facing challenges. Therefore, this article takes the Yellow River Basin as an example to reveal the temporal and spatial evolution patterns of the main crop yields in the basin. Based on a coupled statistical downscaling model (SDSM) and ten General Circulation Models (GCMs) from CMIP5, it estimates the future temporal and spatial evolution characteristics of rainfall and evaporation in the basin. Furthermore, a distributed crop-growth model (AquaCrop) is constructed to reveal the temporal and spatial evolution patterns of agricultural irrigation water requirements from a future perspective, clarifying the impact of multi-source uncertainty on the prediction uncertainty of agricultural irrigation water needs. The results indicate that the ten climate models constructed in this study can be effectively applied to the Yellow River Basin, and their ability to capture light-rain events is superior to that of moderate- and heavy-rain events. The simulation accuracy of the AquaCrop model significantly improves with an increase in precipitation frequency. The agricultural irrigation water demand in the middle and upper reaches of the basin is greater than that in the lower reaches, and the uncertainties from GCMs and RCPs have a significant impact on the uncertainty of agricultural irrigation water demand. The research results provide important references for formulating agricultural development plans for irrigation areas under climate-change conditions and for developing response measures for irrigation areas to cope with climate change.

Accurate runoff forecasting is vital yet challenged by the increasing non-stationarity of hydrological systems, which often exceeds the capacity of traditional single models. Ensemble forecasting, as an effective approach, integrates multiple models’ information to enhance forecasting performance and assess uncertainty. However, existing methods (such as Bayesian Model Averaging and BMA) still have limitations in dealing with complex hydrological scenarios, particularly in the construction and optimization of forecast intervals. This paper proposes a novel hydrological ensemble interval forecasting method based on constrained multi-model weight optimization (Constrained Multi-Model Weight Optimization, CMWO). CMWO utilizes a set of heterogeneous deterministic models to generate members, assigns dynamic optimization weight intervals to enhance flexibility, and employs a multi-objective framework to minimize interval width and errors subject to a ≥95% coverage constraint. Taking the Huangjinxia Reservoir in the upper reaches of the Hanjiang River as a case study, the CMWO method was systematically applied and evaluated for decadal-scale runoff forecasting and comprehensively compared with widely used BMA methods and individual models. The results show that CMWO significantly outperforms in improving point forecast accuracy (measured by RMSE, KGE, etc.) and interval forecast quality (evaluated by PICP, PIAW, CRPS, etc.), especially in generating narrower, more informative prediction intervals while ensuring high reliability. The CMWO method proposed in this study provides a competitive new tool for the effective management of forecasting uncertainty in complex hydrological systems.

Inadequate water supply during droughts, leading to socio-economic drought, has become a global issue. In this context, the drought resistance and disaster mitigation capabilities of reservoirs play a crucial role during drought events. Taking the downstream Yellow River Basin (DYRB) as the study area, this research analyzes the evolution and characteristics of socio-economic drought in the region from 1956 to 2016 at different time scales (3 months, 6 months, 9 months, and 12 months). The copula function is used to calculate the joint recurrence period of socio-economic drought in the downstream area. In addition, this study constructs a reservoir optimization operation model to explore the drought resistance capabilities of water supply strategies in response to downstream socio-economic droughts. The results show that the indices across the four time scales indicate that the DYRB faced the most severe socio-economic drought from the 1990s to the early 21st century, with long durations and widespread impacts. Compared with conventional scheduling methods, water supply restriction strategies can cope with more severe socio-economic droughts. However, the maximum drought resistance capacity corresponding to its recurrence period still cannot cope with the socio-economic droughts of the early 21st century. Therefore, the implementation of basin-wide unified water planning is of great importance to improve drought resistance capacity.

With the intensification of global warming, droughts and heatwaves occur frequently and widely, which have a serious impact on the healthy growth of vegetation. The challenge is to accurately characterize vegetation vulnerability under compound heat and drought stress using correlation-based methods. This article uses the Haihe River Basin, an ecologically sensitive area known for experiencing droughts nine out of ten years, as an example. Firstly, using daily precipitation and maximum temperature data from 38 meteorological stations in the basin from 1965 to 2019, methods such as univariate linear regression and the Mann–Kendall mutation test were employed to identify the temporal variation patterns of meteorological elements in the basin. Secondly, the Pearson correlation coefficient and other methods were applied to determine the most likely months for compound dry and hot events, and the joint distribution pattern and recurrence period of concurrent high temperature and intense drought events were explored. Finally, a vegetation vulnerability assessment model based on Vine Copula in compound dry and hot climates was constructed to quantify the relationship of the response of watershed vegetation to different extreme events (high temperature, drought, and compound dry and hot climates). The results indicated that the basin’s precipitation keeps decreasing, evaporation rises, and the supply–demand conflict grows more severe. The correlation between the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) is strongest at the 3-month scale from June to August. Meanwhile, in most areas of the basin, the Standardized Normalized Difference Vegetation Index (sNDVI) is positively correlated with the SPI and negatively correlated with the STI. Compared to a single drought or high-temperature event, compound dry and hot climates further exacerbate the vegetation vulnerability of the Haihe River Basin. In compound dry and hot climates, the probability of vegetation loss in June, July, and August is as high as 0.45, 0.32, and 0.38, respectively. Moreover, vegetation vulnerability in the southern and northwestern mountainous areas of the basin is higher, and the ecological risk is severe. The research results contribute to an understanding of the vegetation’s response to extreme climate events, aiming to address terrestrial ecosystem risk management in response to climate change.