With digital information technology based on limited data, disaster simulation review is an important guideline for analyzing disaster mechanisms, planning post-disaster reconstruction, and improving defense capability. Taking the “7.20” flash flood in the Wangzongdian river basin as a research area, a hydrological-hydrodynamic model was established using limited measured data. The results showed that the extreme rainstorm caused flooding in mountainous areas and the collapse of subgrade water damming, and the high-level flood quickly flowed into Wangzongdian Village in a short distance, which was the main cause of this serious disaster. Considering the collapse due to the congestion of the upstream bridge, the simulated flood flow in Wangzongdian Village reached 782 m³/s, which was basically consistent with the post-disaster survey results, with a relative error of only +8%. The modeling strategy proposed in this paper is applicable in the review of sudden heavy rainstorms and flash floods and can provide technical guidance for future flash flood simulation review analysis in other areas.

Hubei province is located in the center of China with 56% total area characterized with mountainous area. Thus, flash flood caused by extreme rainfall has become one of the significant obstacles that highly affect the social and economic development of the province. In order to scientifically understand the mechanism of flash flood disasters and provide technological support to the local flood prevention and control work, the IWHR designed and developed a new distributed hydrological model named China-FFMS that can simulate the evolution of natural disasters and make an assessment by setting the flood water sources in line with the flow discharge. The FFMS was further applied to simulate the 8.12 flash flood disaster that occurred in the Liulin county of Hubei province on 12 August (“8.12”) and fed by the data collected from the national flash flood disaster investigation and assessment. The calculated peak flow was 666.22 m³/s with an error of +13% compared with postdisaster investigation data (589 m³/s). The results showed that using a multisourced modelling approach, e.g., mixing spatiotemporal variables and sources, to simulate the flash flood process was able to accurately reproduce the flood process and the consistence of the flow discharge, thereby explaining the underlying reason of the disaster formation and evolution. Regarding the case of the Liulin county, the main factor leading to the disaster was the overlapped peak flow where the Dunne flood peak of three different tributaries from the upper reach met together at the same time. Moreover, the peak flow of the Lianhua river at the downstream of Liulin County also arrived at the same time as the upstream peak, which obstructed the flood progress and increased the damage of the disaster. According to the analysis, several suggestions and recommendations are proposed such as the improvement of the forecast and early warning system of the upstream areas, the optimization of the current flood defense plan, and the enhancement of the residents’ awareness of flash flood disasters.

The activities of coal mining often lead to the formation of underlying runoff areas and have great impacts on both the basin hydrological cycle and water resource management. In order to investigate the changes in the hydrological processes of the coal goaf catchment, this paper analyzes and explores the coal mining influences on the hydrological processes in a small watershed in the Yangquan Basin of China. To disentangle the mining process, a distributed hydrological model, which highlighted the integration of sub-hydrological processes, was developed and implemented in the study area. The calibration and validation results indicated that the developed model simulated streamflow well. This was indicated by the Nash–Sutcliffe model efficiency (NS) and the Coefficient of Correlation (r²) for monthly runoff. The model was first calibrated in the period of 1990–2004 and then validated in the period of 2005–2018. Different scenarios were simulated and cross-compared in order to study the mining effects; the rainfall and runoff of each hydrological station are positively correlated in 2009–2018, and the scenario of change in mining area is negatively correlated with runoff in 2009–2018. The contribution of the changing input variables (rainfall and coal mining area) to the runoff of the Yangquan Basin was analyzed qualitatively and quantitatively; the impact contribution rates of mining activities are 85.96% and 39.34% during the mining and recovery periods in Yangquan station, respectively. The hydrological simulations provided a better understanding of runoff changes in the Yangquan Basin. The analysis results indicate that the hydrologic response to the mining process in Yangquan Basin is changing, and it thus draws attention to other mining places over the world. Methods used in this study can be applied in other regions to orientate the policy-making process.

To address the challenges of complex multi-objective trade-offs, tightly coupled physical constraints in reservoir dam safety dispatching, and fulfill the significant cognitive gaps in human–machine interaction, a framework with four deep cognitive layers and a physical computation layer is proposed which integrates large language models (LLMs) with multi-agent collaboration. The framework stratifies cognitive intelligence into interface translation, strategic cognition, tactical reasoning, and operational understanding layers; performs computation in the physical computation layer; and achieves deep coupling among agents in different layers through the Blackboard information sharing mechanism. The physical computation layer consists of the gate-opening discharge, water-level storage capacity, runoff and inflow, downstream risk calculation agents and a Pareto multi-objective optimizer to realize non-dominated sorting of multi-dimensional objectives encompassing dam safety, ecological loss, downstream risk, and operational complexity. Illustrative case analysis indicates that this framework can effectively parse user requirements expressed in natural language, generate dispatching schemes conforming to physical constraints, achieve error control and quantify the downstream risk. This research provides a scalable framework for the implementation of intelligent reservoir dispatching and can enhance the intelligence of digital twins of river basins.