Across the globe, flood control standards for reservoir engineering appear different due to various deciding factors such as flood features, society, economy, culture, morality, politics, and technology resources, etc. This study introduces an in-depth comparison of flood control standards for reservoir engineering for different countries. After the comparison and analysis, it is concluded that the determination of flood control standards is related to engineering grade, dam type, dam height, and the hazard to downstream after dam-breaking, etc. Each country should adopt practical flood control standards according to the characteristics of local reservoir engineering. The constitutive flood control standards should retain certain flexibility in the basis of constraint force. This review could offer a reference for developing countries in the enactment of flood control standards for reservoir engineering.

Obtaining accurate near-real-time precipitation data and merging multiple precipitation estimates require sufficient in-situ rain gauge networks. The triple collocation (TC) approach is a novel error assessment method that does not require rain gauge data and provides reasonable precipitation estimates by merging data; this study assesses the TC approach for producing reliable near-real-time satellite-based precipitation estimate (SPE) products and the utility of the merged SPEs for hydrological modeling of ungauged areas. Three widely used near-real-time SPEs, including the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) early/late run (E/L) series, and the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Dynamic Infrared Rain Rate (PDIR) products, are used in the Beijiang basin in south China. The results show that the TC-based merged SPEs generally outperform all original SPEs, with higher consistency with the in-situ observations, and show superiority over the simple equal-weighted merged SPEs used for comparison; these findings indicate the superiority of the TC approach for utilizing the error characteristics of input SPEs for multi-SPE merging for ungauged areas. The validation of the hydrological modeling utility based on the Génie Rural à 4 paramètres Journalier (GR4J) model shows that the streamflow modeled by the TC-based merged SPEs has the best performance among all SPEs, especially for modeling low streamflow because the integration with the PDIR outperforms the IMERG products in low streamflow modeling. The TC merging approach performs satisfactorily for producing reliable near-real-time SPEs without gauge data, showing great potential for near-real-time applications, such as modeling rainstorms and monitoring floods and flash droughts in ungauged areas.

With the rapid development of computer network technology, virtual display technology has become a typical representative of modern information technology, which can provide more efficient and intuitive technical means for flood prevention and risk assessment of water conservancy departments, and make more innovations and breakthroughs in the field of 3D simulation. This paper takes the area of Wuyuan County in the upper reaches of Le’an River in Poyang Lake as an example. Based on WebGIS technology, spatial database technology, CTS virtual display technology and combined with TOPMODEL semi-distributed hydrological model and IFMS domestic flood analysis software, it uses computer languages such as C#.NET and Java to carry out secondary development, explore and build a three-dimensional immersive hydrological multi-element virtual reality platform with real-time interactive display function, and realize the dynamics simulation display of three-dimensional data query, water and rain monitoring, flood evolution, submergence analysis, disaster assessment and other scenes under the three-dimensional perspective. It also provides an auxiliary decision-making platform for flood control and disaster reduction in the river basin, and further improves the guarantee level of water safety in the river basin.

Insufficient precipitation observations hinder the bias-correction of Global Climate Model (GCM) precipitation outputs in ungauged and remote areas. As a result, the reliability of future precipitation and water resource projections is restricted for these areas. Open-access quantitative precipitation estimation (QPE) products offer a potential solution to this challenge. This study assesses the effectiveness of three widely used, long-term QPEs, including ERA5, PERSIANN-CDR, and CHIRPS, in bias-correcting precipitation outputs from the CMIP6 GCMs. The evaluation involves the reproduction of precipitation distribution, streamflow simulation utility based on a hydrological model, and the accuracy of extreme indices associated with rainstorm/flood/drought events. This study selects the Beijiang basin located in the subtropical monsoon area of South China as the case study area. The results demonstrate that bias-correction using QPEs improves the performance of GCM precipitation outputs in reproducing precipitation/streamflow distribution and extreme indices, with a few exceptions. PCDR generally exhibits the most effective bias-correction utility, consistently delivering reasonable performance across various cases, making it a suitable alternative to gauge data for bias-correction in ungauged areas. However, GCM outputs corrected by ERA5 tend to overestimate overall precipitation and streamflow (by up to about 25% to 30%), while the correction results of CHIRPS significantly overestimate certain extreme indices (by up to about 50% to 100%). Based on the revealed performance of QPEs in correcting GCM outputs, this study provides references for selecting QPEs in GCM-based water resource projections in remote and ungauged areas.

The southeastern margin of the Qinghai–Tibet Plateau is characterized by complex geological conditions and frequent disasters such as landslide-induced river blockage and subsequent barrier lake breaching. With the construction of cascade reservoirs in the basin, the potential impacts of historical barrier lakes under the current flood control system remain unclear. Reconstructing the breaching process of barrier lakes is of great guiding significance for regional flood control and disaster mitigation, risk management, and the safe operation of cascade reservoirs in the basin. In this study, the Mogangling landslide barrier lake in the Dadu River basin was selected as the research object. We constructed breach flood hydrographs for the 300 m and 600 m breach scenarios and established a one-dimensional and two-dimensional coupled hydrodynamic model of the barrier lake breach flood based on the integrated flood modeling system (IFMS). The model was used to simulate the propagation and inundation processes of the breach flood under natural propagation without reservoirs and under the joint operation of the cascade reservoirs. Spatially distributed socioeconomic data were then combined with the model to assess flood impacts and the potential disaster mitigation effect of the joint operation of the cascade reservoirs. The results show that the joint operation of the cascade reservoirs can reduce the peak discharge of the barrier lake breach flood by 30.7–97.9%, decrease the total inundated area of downstream riverside towns by 70%, and reduce the GDP losses and the affected population by 89% and 87%, respectively. In addition, artificial excavation of a spillway channel can reduce the peak discharge of the natural barrier lake breach by approximately 30%.

The difficulty in accurately determining channel Manning’s roughness has long constrained the accuracy of river flood simulation. At present, Manning’s roughness is mainly assigned using empirical approaches such as lookup tables and visual comparison, which are insufficient to represent the spatial heterogeneity of roughness in natural rivers. Remote sensing provides valuable data support for flood simulation in areas with sparse hydrological gauges. Remote-sensing observations are objective and can be acquired over large areas, but they are temporally discontinuous, whereas hydrodynamic models provide physically meaningful and temporally continuous simulations. These two information sources are therefore naturally complementary, providing a new avenue for Manning’s roughness inversion through remote sensing and hydrodynamic model coupling. To address these challenges, this study proposes a new remote sensing and hydrodynamic model coupling framework centered on roughness inversion. SAR-derived inundation extent is used as a spatial constraint, and an objective function together with particle swarm optimization (PSO) drives an iterative closed loop between the one-dimensional HEC-RAS model and the remote-sensing constraint to autonomously approach the optimal roughness parameters and improve the reliability of flood simulation. To bridge the scale mismatch between one-dimensional hydrodynamic outputs and two-dimensional SAR observations, the HEC-RAS cross-section geometry TIN structure is used to spatialize the simulated water surface, enabling direct comparison between simulated and SAR-observed inundation extents. The Le’an River in Wuyuan, Jiangxi Province, China, was selected as the test reach to evaluate the applicability and reliability of the framework under sparse-gauge conditions. The model was calibrated using the SAR-derived inundation extent, and simulated water levels were subsequently compared with rating-curve-derived reference levels. Under the SAR inundation constraint, the agreement between simulated and observed inundation extents improved, with IoU increasing from 0.763 under the initial parameter set to 0.789. Comparison with rating-curve-derived reference water levels further showed that the optimized Manning’s roughness parameters improved water-level simulation, reducing RMSE from 0.811 m to 0.594 m and increasing NSE from 0.950 to 0.973. These results demonstrate the effectiveness of the proposed method when continuous hydrological observations are unavailable. The framework provides an automated remote-sensing-constrained approach for Manning’s roughness inversion in hydrodynamic models of small and medium-sized rivers with limited in situ hydrological data.