Agricultural water resources face growing pressure from rising food demand and environmental changes. In large agricultural irrigation areas, water and land use is closely linked to energy consumption, carbon emissions, and food production. Therefore, regulating the water–land–energy–food–carbon nexus under multiple external changes is essential for achieving sustainable agriculture. This study aims to optimize water and land allocation in large agricultural irrigation areas to enhance yields and reduce carbon emissions under different external environments and production conditions. A spatial–temporal synergistic optimization and regulation model for water and land resources in large agricultural irrigation zones is developed. Based on 191 representative irrigation districts in Heilongjiang Province, multiple scenarios are constructed, including water-saving irrigation, climate change and low-carbon irrigation energy transitions. Optimal solutions are identified using the Non-dominated Sorting Genetic Algorithm III. The results indicate that, after optimization in the current scenario, crop production increased by 2.13%, carbon emissions decreased by 1.23%, and irrigation energy productivity rose by 9.33%. Concurrently, water-saving irrigation should be prioritized in western regions. This study provides an efficient water management pathway for major food production regions.

Among natural disasters, flash floods are the most destructive events, causing significant damage to the economy and posing a serious threat to human life and property. Comprehensive risk assessment of these sudden floods is a key strategy to mitigate their impact. Accurate analysis of flash flood hazards can greatly enhance prevention efforts and inform critical decision‐making processes, ultimately improving our ability to protect communities from these fast‐onset disasters. This study analyzed the driving forces of flash flood disaster‐causing factors in Heilongjiang Province. Meanwhile, nine different categories of variables affecting the occurrence of flash floods were selected, and the degree of influence of each driving factor on flash floods was quantitatively analyzed, and the driving force analysis of the driving factors of flash floods in Heilongjiang Province was carried out by using the geographic probe model. This paper employs an uncertainty approach, utilizing a statistical‐based interval weight determination technique for evaluation indices and a two‐dimensional information‐based interval number sorting method. These methodologies are combined to construct a comprehensive flash flood risk assessment model. On this basis, the model was implemented in six regions within China's Heilongjiang province to evaluate and prioritize flash flood risks. The resulting risk ranking for these areas was as follows: Bayan Shuangcheng Boli Suibin Hailun Yian. The findings demonstrate that the interval number‐based evaluation method effectively handles uncertainty, providing a more reliable risk grading system. This approach, by leveraging modern scientific advances and risk quantification techniques, is crucial for improving disaster management and mitigating flash flood impacts.

Mountain floods, one of the most common and destructive natural disasters worldwide, pose significant challenges to disaster prevention due to their sudden onset, high destructive power, and severe localized impacts. This study proposes an innovative flash flood early warning system based on a distributed hydrological model ensemble. The main objective is to improve the prediction and early warning accuracy of flash flood disasters by integrating multi-source data and regional modeling. The system simulates flood flow and risk levels under different rainfall scenarios to provide timely warnings in mountainous areas. A case study of a heavy rainfall event in Ma Jia Natural Village, Jiangxi Province was used to validate the system’s performance. Through regionalized parameter calibration within the ensemble, the system achieved Nash–Sutcliffe Efficiency (NSE) values exceeding 0.88, while the simulated peak discharges deviated from observed values by only 1.5%, 9.5%, and 4.8% under 3 h, 6 h, and 24 h rainfall scenarios, respectively, demonstrating the improved quantitative accuracy of flood prediction enabled by the ensemble-based framework. The system showed high consistency with observed data, accurately predicting flood responses at 3, 6, and 24 h time scales and providing reliable risk warnings. This approach not only enhances warning accuracy across multiple temporal scales but also supports risk-level early warnings at both river-section and village scales, offering significant practical value for the prevention of mountainous flood disasters.