Flash floods cause extensive loss of property and human life. Early warning systems present a more efficient approach to flood prevention and mitigation than engineering measures. This article reviews research on flash flood early warnings in China, including long-term prediction methods based on statistical regularity and flood mechanisms, and real-time warning indicators relying on multi-source data and automated systems. Current research shortcomings are discussed, and suggestions for future improvements are proposed. This research can provide public officials with knowledge of flash flood early warnings, influencing policy and protecting people from flash flood disasters.

Due to the frequent and sudden occurrence of urban waterlogging, targeted and rapid risk monitoring is extremely important for urban management. To improve the efficiency and accuracy of urban waterlogging monitoring, a real-time determination method of urban waterlogging based on computer vision technology was proposed in this study. First, city images were collected and then identified using the ResNet algorithm to determine whether a waterlogging risk existed in the images. Subsequently, the recognition accuracy was improved by image augmentation and the introduction of an attention mechanism (SE-ResNet). The experimental results showed that the waterlogging recognition rate reached 99.50%. In addition, according to the actual water accumulation process, real-time images of the waterlogging area were obtained, and a threshold method using the inverse weight of the time interval (T-IWT) was proposed to determine the times of the waterlogging occurrences from the continuous images. The results showed that the time error of the waterlogging identification was within 30 s. This study provides an effective method for identifying urban waterlogging risks in real-time.

Accurate and reliable estimation of actual evapotranspiration (AET) is essential for various hydrological studies, including drought prediction, water resource management, and the analysis of atmospheric–terrestrial carbon exchanges. Gridded AET products offer potential for application in ungauged areas, but their uncertainties may be significant, making it difficult to identify the best products for specific regions. While in situ data directly estimate gridded ET products, their applicability is limited in ungauged areas that require FLUXNET data. This paper employs an Extended Triple Collocation (ETC) method to estimate the uncertainty of Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network (FLDAS), and Maximum Entropy Production (MEP) AET product without requiring prior information. Subsequently, a merged ET product is generated by combining ET estimates from three original products. Furthermore, the study quantifies the uncertainty of each individual product across different vegetation covers and then compares three original products and the Merged ET with data from 645 in situ sites. The results indicate that GLEAM covers the largest area, accounting for 39.1% based on the correlation coefficient criterion and 39.9% based on the error variation criterion. Meanwhile, FLDAS and MEP exhibit similar performance characteristics. The merged ET derived from the ETC method demonstrates the ability to mitigate uncertainty in ET estimates in North American (NA) and European (EU) regions, as well as tundra, forest, grassland, and shrubland areas. This merged ET could be effectively utilized to reduce uncertainty in AET estimates from multiple products for ungauged areas.

In open-channel water transfer projects, hydrodynamic modeling has been one of the tools commonly used by the scheduling staff to perceive the changes in the water condition of the channel. The reliability of the flow monitoring data has not been guaranteed due to restrictions in the flow monitoring method. Flow monitoring errors directly reduce the accuracy of hydrodynamic modeling calculations, making it impossible to accurately grasp the real-time changes in the internal water conditions of the channel. In this paper, by utilizing the characteristics of the hydrodynamic model itself, which can be solved discretely with multiple boundary conditions, the method of correcting the boundary anomaly data is proposed by changing the combination of hydrodynamic boundaries. This solves the problem of the inaccuracy of hydrodynamic model calculations due to errors in the monitoring data and improves the accuracy of the hydrodynamic model calculations. In the actual operation process, it is found that the method proposed in this paper solves the problem of hydrodynamic model misalignment caused by flow observation errors by correcting the flow during the flow adjustment period, which greatly improves the calculation accuracy of the hydrodynamic model. Secondly, for a stable flow period, the method proposed in this paper can quickly capture and correct the local monitoring of abnormal data and improve the overall hydrodynamic simulation accuracy.