The continuous release of global precipitation products offers a stable data source for flood forecasting in areas without rainfall gauges. However, due to constraints of forecast timeliness, only no/short-lag precipitation products can be utilised for flood forecasting, but these products are prone to significant errors. Therefore, the keys of flood forecasting in areas lacking rainfall gauges are selecting appropriate precipitation products, improving the accuracy of precipitation products, and reducing the errors of precipitation products by combination with hydrology models. This paper first presents the current no/short-lag precipitation products that are continuously updated online and for which the download of long series historical data is supported. Based on this, this paper reviews the utilisation methods of multi-source precipitation products for flood forecasting in areas with insufficient rainfall gauges from three perspectives: methods for precipitation product performance evaluation, multi-source precipitation fusion methods, and methods for coupling precipitation products with hydrological models. Finally, future research priorities are summarized: (i) to construct a quantitative evaluation system that can take into account both the accuracy and complementarity of precipitation products; (ii) to focus on the improvement of the areal precipitation fields interpolated by gauge-based precipitation in multi-source precipitation fusion; (iii) to couple real-time correction of flood forecasts and multi-source precipitation; and (iv) to enhance global sharing and utilization of rain gauge–radar data for improving the accuracy of satellite-based precipitation products.

The Haihe River Basin has experienced frequent floods from extreme rainfall in recent decades, with a catastrophic event striking the Beijing-Tianjin-Hebei region in July 2023. From July 29 to August 1, Liucun Town recorded 473.6 mm of rainfall, while the Wangjiayuan Reservoir station observed 744.8 mm, the highest in Beijing. This study examines the impacts of this flood in Shisanling Town, upstream of the Shisanling Reservoir, using a new-generation distributed hydrological model with calibrated parameters. Based on the “7·29” rainfall event, we simulated flood processes and assessed impacts on population and infrastructure, as well as reservoir discharge scenarios. Results indicate that under the “7·29” rainfall, Shisanling Town’s inundated area could reach 6.2 km², affecting 38 villages (excluding Yuling), with Beixin Village the most severely impacted (1.6 km² submerged). Approximately 2,935 people, 458 houses, 239.5 ha of orchards, and 105.4 ha of farmland would be affected. The flood peak, with an inflow of 1,627 m³/s and river depth of 7.2 m, would reach the reservoir within 48 h. Once the water level exceeds 93 m, a discharge of 80.05 million m³ is required. At the observed release rate of 30 m³/s, drainage would take 31 days. A full discharge downstream would inundate ~218 km² across nine towns, affecting ~650,000 people, 76,000 houses, 100+ road segments, 23,300 ha of farmland, 10 parks, and 16 schools. This study provides critical insights for flood prevention, reservoir management, and emergency evacuation planning in small watersheds under extreme rainfall conditions.

In recent years, there have been frequent extreme weather events that defy traditional understanding. Specifically, mountain flood disasters can cause significant loss of life due to their sudden onset and destructive power. The 7.21 flood event in Xingyang, Zhengzhou, China, recorded a maximum 6 h precipitation of 240.5 mm in the Suo River basin, corresponding to a 500-year return period, and causing fatalities and substantial damage. The central government of China has launched supplementary mountain flood disaster surveys and evaluations involving key towns and villages, following an initial round of surveys in riverside villages, to improve foresight and response capabilities for mountain flood disaster risks under extreme conditions. This paper introduces the contents of the national mountain flood disaster surveys and evaluations of key towns and villages, elaborating on the principles, content, and rules for auditing the national survey and evaluation results. This paper innovatively proposes professional audit criteria, such as early warning indicators, monitoring facility correlations, and hazard zoning, based on a formal audit of the data quality. The implementation of professional audit criteria improved the data accuracy by 85% and reduced false alarms by 40%, enhancing the overall effectiveness of mountain flood disaster prevention. The analysis of the audit results suggests that the audit rules for the survey and evaluation results of key towns are scientific, reasonable, and effective, achieving the expected goals of data quality control. This approach can effectively enhance the practical value of the survey and evaluation outcomes for key towns, laying a solid data foundation for transforming flood disaster prevention from merely “existing” to “optimal”.

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.

X-band rainfall radar systems offer advantages such as high spatial resolution, flexible deployment options, and strong near-surface detection capabilities. However, due to the short electromagnetic wavelength in this band, the radar base data are highly susceptible to multiple coupled factors—including clutter from mountain vegetation, tall buildings, and other terrain features; electromagnetic interference from surrounding radio-frequency equipment; beam obstruction caused by complex topography; and attenuation of rainfall intensity along the precipitation path—resulting in pronounced distortion of raw echoes. This distortion significantly hinders the accuracy of quantitative precipitation estimation in mountainous regions and makes it difficult to meet the operational requirements for precise mountain torrent early warning systems. To address the problem, this study utilizes real-time observational data from field deployments of X-band rainfall radars in mountainous regions to construct a comprehensive, progressive quality control system comprising: refined removal of terrain clutter; electromagnetic interference pre-suppression; dynamic beam obstruction correction; and adaptive rainfall intensity attenuation correction. The terrain clutter suppression algorithm is optimized in this study, and an adaptive beam-blockage compensation algorithm based on the terrain-blockage fraction is further adopted. However, beam-blockage correction exhibits limited improvement in this case, which is likely attributed to the complementary observational coverage provided by higher-elevation radar scans. The echo-missing regions are dynamically corrected according to the terrain-blockage coverage ratio at different elevation angles and azimuths to realize differentiated compensation corresponding to blockage severity so as to effectively restore the true echo intensity obscured by terrain. Furthermore, considering the prominent rain-induced attenuation of radar electromagnetic waves along the propagation path, a dynamic attenuation correction scheme based on path-integrated reflectivity is introduced. The precipitation attenuation coefficient is dynamically calculated point by point from the variation characteristics of real-time echo intensity along the beam path to compensate echo loss, which addresses the limitation that fixed correction parameters cannot adapt to attenuation differences under variable rainfall intensities. Based on the Z-R power-law relationship, the radar echo-derived rainfall is inverted, using hourly measurements from dense ground-based rain gauges as the reference values, and precision is verified using the MB and RMSE—industry-standard meteorological evaluation metrics. Experimental results demonstrate that clutter suppression dominates RMSE reduction (7.37 to 1.91 mm/h). Beam blockage correction shows negligible impacts on RMSE and MB. Attenuation correction delivers marginal MB improvement (−0.52 to −0.51 mm/h) with no measurable RMSE response. After full-chain quality-control optimization, the aggregate RMSE between radar-derived rainfall and gauge observations is 1.91 mm h⁻¹, representing an approximately 74% reduction compared with the raw dataset (from 7.37 to 1.91 mm h⁻¹). Elevated RMSE values of 4.2–5.0 mm h⁻¹ are observed for rainfall intensities between 5 and 10 mm h⁻¹. In addition, the discrepancy between radar and gauge estimates grows as rainfall intensity increases. As the distance between radar and rain gauge increases, radar QPE systematically underestimates light precipitation, while persistent underestimation occurs across all ranges for heavy rainfall. This study enhances the differentiated quality control framework for X-band radar systems in complex mountainous regions, effectively improves the observation quality of radar-derived base data, and provides data support and technical references for dynamic monitoring of flash floods in mountainous river basins.