[Objectives] Accurate risk assessment is critical for effective flash flood prevention and mitigation. However, existing flash flood risk assessment methods often ignore geographical similarity between different flood-prone areas. This omission can lead to biased negative samples, fragmentation of spatial correlations among similar units, and ultimately reduced accuracy and reliability of results. [Methods] To address this limitation, this study proposes a novel flash flood risk assessment approach that incorporates geographical similarity. First, catchments are adopted as the basic analysis units, and a comprehensive factor system is constructed based on a risk framework comprising meteorological, underlying, and exposure factors. Second, a geographical similarity-constrained sample generation method is then proposed to optimize the selection of negative samples (non-flash-flood events) based on the similarity of underlying factors among catchments. Finally, a weighted directed graph is constructed based on the geographical similarity among catchments, and a graph neural network with embedded geographical similarity is developed to achieve accurate risk assessment. [Results] The proposed method is applied to the Hengduan Mountains, where 884 positive samples and 884 samples are generated. Results are compared with those from existing flash flood sample generation methods and traditional machine learning-based risk assessment approaches. Findings show that: (1) Compared with the existing random generation and environmentally balanced sample generation methods, the proposed method improves overall accuracy by 24.76% and 22.28%, respectively. (2) Compared with three machine learning-based risk assessment methods(Random Forest, XGBoost, and LightGBM), the proposed geographical similarity-embedded graph neural network improves overall accuracy by 2.84%, 3.41%, and 2.17%, respectively. (3) The flash flood risk assessment results from all four models indicate that high-risk flash flood zones in the Hengduan Mountains are primarily distributed in the southeastern region, the Sanjiang River valley in the southwest, and along rivers in the northern area. [Conclusions] By incorporating geographical similarity into both the sample generation and model construction, the proposed method significantly enhances the accuracy of flash flood risk assessment and provides valuable support for scientific decision-making in flash flood prevention and management.

[Objectives] The extraction of watershed hydrological information is crucial for water resource management, flood forecasting, and ecological protection. Traditional hydrological modeling often employs quadrilateral grids for spatial discretization. However, due to issues such as inconsistent adjacency, shape distortion, and inaccurate representation of topological structures, watershed extraction often results in staircase-like and parallel river line features in finer details, especially at curved sections and bifurcation points of rivers. In contrast, hexagonal grids, with their isotropy, improved boundary effects, and uniform spatial distribution, are better at preserving the morphology of curves and bifurcation points. They thereby enable more accurate simulation of hydrological processes and watershed extraction. [Methods] This study adopts the H3 hexagonal grid system, using the Jiuyuangou watershed as the study area. A 30-meter resolution SRTM 1 Digital Elevation Model (DEM) was used to design a hydrological analysis algorithm based on hexagonal grids. The methodology includes hexagonal grid generation, DEM resampling, depression filling, flow direction analysis, and flow accumulation. The quality of flow accumulation and river network extraction was evaluated. Firstly, the study compared the percentage of hexagonal and quadrilateral grids contributing to total grids across flow values ranging from 1 to 15. Results showed that hexagonal grids demonstrated greater concentration in low flow values and maintained more stable cumulative frequency growth with increasing flow values, avoiding over-concentration in high flow value ranges. Additionally, a higher-resolution Jiuyuangou river network (12.5 m) was used as the standard river network. Points were randomly sampled in proportion to the river line segment length at intervals of 100, 200, 300, 400, and 500 points. The average distance to the nearest quadrilateral and hexagonal grids was then calculated. [Results] The results show that the average offsets for quadrilateral grids were 28.16 m, 30.45 m, 30.57 m, 30.84 m, and 30.79 m, respectively. For hexagonal grids, the average offsets were 24.03 m, 25.63 m, 23.49 m, 23.78 m, and 24.99 m, respectively. Hexagonal grids consistently exhibited smaller average offsets than quadrilateral grids, demonstrating higher precision in river network extraction and better reflection of terrain characteristics. [Conclusions] Compared to traditional quadrilateral grids, hexagonal grids exhibit superior spatial consistency and accuracy in flow accumulation and river network extraction. This provides a more efficient and precise solution for hydrological modeling and watershed analysis.

[Objectives] The Digital Elevation Model (DEM) is a critical input in urban stormwater flood simulations, as its accuracy directly impacts the reliability of simulation results. While DEMs are widely applied in urban hydrology to model water flow over terrain, most existing studies primarily focus on surface elevation representation, often neglecting the essential role of surface confluence characteristics in flood accumulation and diffusion processes. This limitation is particularly evident in complex urban terrains, where traditional DEMs, which primarily represent topographic elevation, fail to effectively capture the dynamic processes of surface confluence. As a result, the accuracy of flood simulations, especially in urban areas with intricate terrain features, is significantly constrained. [Methods] To address this issue, this study proposes an enhanced DEM method that integrates the confluence characteristics of urban terrain objects. This approach combines morphological and hydrological analyses to systematically investigate the confluence relationships among urban terrain features, such as roads, buildings, and green spaces. By incorporating these characteristics into the traditional DEM under confluence relationship constraints, the proposed method enables more realistic simulations of urban flood events. The enhanced DEM accounts not only for terrain elevation but also for the terrain's capacity to convey, retain, and redistribute water. This approach provides a more precise representation of surface runoff dynamics, especially in urban environments characterized by complex and variable surface confluence behaviors. [Results] This methodology was applied in a case study of the Gulou District in Nanjing, China. The enhanced DEM was validated through numerical simulations of stormwater runoff and flooding using the Storm Water Management Model (SWMM) combined with an active diffusion algorithm. These models simulate how rainfall interacts with urban surfaces, and the enhanced DEM was evaluated to determine whether incorporating hydrological features of urban terrain improves flood simulation accuracy. The study compared the simulation results using the traditional DEM and the enhanced DEM. The results show that: (1) Enhancing the DEM altered the flow direction of runoff in 10.78% of grid cells, with notable changes observed near urban terrain objects and their boundaries, such as roads and buildings. These changes in flow direction highlight the enhanced DEM's improved ability to model water flow in complex urban environments accurately. Statistical measures, such as the standard deviation and coefficient of variation, also showed significant improvements in the enhanced DEM compared to the original DEM. (2) Simulations based on the enhanced DEM showed a decrease in flooded areas and an increase in average water depth compared to the original DEM. These results suggest that the enhanced DEM more effectively captures the effects of urban terrain objects on runoff paths, runoff distribution, and flood extent, providing a more realistic representation of urban flooding. The simulation results also aligned more closely with real-world observations, further validating the enhanced model. [Conclusions] The proposed enhanced DEM method successfully integrates the confluence characteristics of urban terrain objects, providing a more accurate representation of surface confluence characteristics. This method significantly improves the reliability of stormwater numerical simulation, offering a more effective tool for urban stormwater management. By addressing the limitations of traditional DEMs, which fail to account for complex urban runoff behavior, this approach contributes to the advancement of fine-scale urban flood modeling. The enhanced DEM provides a valuable technical foundation for urban flood prediction, stormwater management, and disaster mitigation, offering new insights for more sustainable and resilient urban planning.

Compared with urban rainstorm hazardous events (such as waterlogging, flood, debris flow, etc.), existing studies pay less attention to the feature composition and objective assessment of risks associated with small-scale and diversified rainstorm cascading events (such as house damage, subway inundation, etc.), making is difficult to meet the goals of refined city management. At the same time, constructing risk assessment models for rainstorm cascading events faces constraints due to incomplete risk features in sample data. To address these issues, this paper proposes a risk assessment model for urban rainstorm cascading events that integrates risk features and spatial features, considering the spatial correlation between them. Firstly, for the risk scenarios of different rainstorm cascading events, the risk features are extracted from data sources such as grassroots officials' inspection, citizen reporting, and social media posts. Secondly, using the spatial localization of the original risk samples as a connection, an improved marginal Fisher method is employed to mine spatial features from multi-source spatial data to supplement the missing risk features. Finally, using a machine learning approach, the relationship between risk features and risk categories is established, leading to the construction of a risk assessment model for multi-category rainstorm cascading events. Experimental results from Wuhan, Hubei Province, China, show that the proposed method effectively addresses the problem of incomplete features in the construction of risk feature models through multi-source spatial feature mining, enabling the construction of diversified rainstorm cascading event risk assessment models. The overall accuracy, F₁-score and AUC increased by 23%, 24%, and 25%, respectively. Additionally, the complexity and diversity of spatial features highlighted the risks of subjective and arbitrary feature fusion, which can negatively affect the performance of machine learning model construction by adding irrelevant features. The proposed method mitigates this issue with an adaptive feature selection approach. Furthermore, grassroots officials’ inspection records contributed the most to the construction of urban rainstorm cascading event risk assessment models, followed by citizen-reported texts, and finally, social media data. Compared to traditional disaster event risk assessment methods, urban rainstorm cascading event risks have smaller risk granularity and involve more complex and diverse risk types and features. Traditional comprehensive evaluation models face challenges of subjectivity in manual evaluation, while traditional disaster loss curve methods encounter high experimental costs and data scarcity. The method proposed in this paper utilizes objective data to generate multidimensional risk features and establishes relationships between diverse risk levels, resulting in a machine learning-based risk prediction model that is more suitable for small-scale risk assessment scenarios.

Disaster early warning plays an important role in disaster reduction management by proactively disseminating disaster information to guide residents in taking timely evacuation actions, thus effectively reducing disaster losses and casualties. The dynamic response process of residents to disaster early warning information and the assessment of the effectiveness of different flood disaster early warning strategies are pressing issues. This paper proposes a simulation method for urban rainstorm flood disaster early warning strategies based on Agent-Based Modeling (ABM). Firstly, three warning strategies are established: rainfall forecast-based, flood inundation-based, and population exposure-based. Secondly, individual risk perception is assessed by considering a variety of socio-demographic characteristics, and a probabilistic model of individual travel decision-making is constructed. Based on this, an agent-based model for urban flood disaster early warning strategies is developed. Finally, taking Futian District in Shenzhen, China as a case study, residents' travel behavior and flood risk are simulated and analyzed with different flood warning strategies under 20-year, 50-year, and 100-year return period rainstorm scenarios. The results show that: (1) The ABM simulation model, considering residents' perception of flood disaster risk and the probability of individual travel decision-making, accurately simulates residents' travel response behavior and changes in flood disaster risk under different warning strategies. It provides a scientific and comprehensive evaluation of the effectiveness of urban flood disaster early warning strategies; (2) Different warning strategies lead to significant differences in population travel response behavior, resulting in varying effectiveness in reducing urban rainstorm flood disaster risk. Faced with a 20-year rainfall scenario, flood inundation-based and population exposure-based early warning strategies help residents in the study area quickly identify high-risk areas, significantly reducing the risk to buildings and roads. Faced with a 20-year return period rainstorm scenario, the study area shows minimal changes in residents' travel behavior under rainfall forecast-based warnings. However, flood inundation-based, and population exposure-based warning strategies help residents rapidly identify high-risk areas, significantly reducing the number of people heading to red and orange warning zones. This results in a noticeable decrease in risks to buildings and roads; (3) Under different rainstorm scenarios, the effectiveness of various flood disaster early warning strategies varies. In the face of smaller rainstorm scenarios, refined flood disaster early warning strategies, such as flood inundation-based, and population exposure-based, demonstrate effectiveness in reducing urban flood disaster risk. However, when dealing with extreme rainstorm scenarios, adopting a unified flood disaster early warning strategy, such as rainfall forecast-based, is more effective than a refined warning strategy. Therefore, urban flood disaster early warning systems should be tailored to local conditions and varying circumstances, establishing a graded, zonal, and scenario-based warning system.

Glacial lakes, as the primary carriers of glacier meltwater, can postpone the loss of local glacier freshwater resources to some degree. However, they also offer a breeding ground for Glacial Lake Outburst Floods (GLOFs) and other mountain natural disasters (e.g., landslides, mudslides, etc.). In the mountain glacier zones, glacial lakes play a crucial role in the chain of glacier-related disaster risk. The sudden release of a massive volume of water occurs when a glacial lake dam breaches, is overtopped, or is influenced by other events such as earthquakes and avalanches of ice or rock, which poses a major danger to the downstream infrastructure, possessions, and lives of residents living in high-altitude mountains. Glacial lake evolution and glacial changes are closely related to each other. As glaciers shrink and recede, glacial lakes develop and expand. Effective prevention and management of glacial lake disaster risk requires knowledge of glacial lake changes, in addition to retrospective and investigative studies on past glacial lake outburst flood events. However, due to the distribution of glacial lakes in high-altitude mountain regions, its susceptibility to global warming, and the difficulty in accessing these areas, remote sensing monitoring has emerged as the most practical technical method and provides opportunities for analyzing global climate change and assessing natural disasters. Recent research has indicated an increase in the frequency and impact of GLOFs incidents, emphasizing the growing significance of studying these disasters. Based on this, in this study, we first identified key research areas in recent years through the metrological analysis of the literature on the remote sensing monitoring of glacial lakes and GLOFs. Second, focusing on three main directions of the research on glacial lakes and GLOFs (109 important research literatures), namely remote sensing monitoring of glacial lakes and GLOFs, response analysis of glacial lake evolution in the context of climate change, and glacial lake risk assessment with case studies of GLOFs, ten essential topics of recent research advances at home and abroad as well as the shortcomings of current studies are systematically summarized and analyzed. Finally, the direction of future research is prospected, including extraction of glacial lake morphology using artificial intelligence and GLOFs events inventory, glacier-glacial lake (especially for proglacial, supraglacial lake) system evolution and its relationship to climate change, glacial lake monitoring, and early warning and disaster prevention. Our review offers references for the management and adaptive planning of glacial lake and mountain glacier related catastrophes.

Optical remote sensing technology can efficiently capture a wide range of surface water information. However, in challenging applications such as flood monitoring, it is often difficult to obtain high-quality optical remote sensing images, due to cloudy, rainfall, or other inclement weather conditions. Synthetic Aperture Radar (SAR) technology can provide images of both day and night and unaffected by cloud coverage, which provides obvious advantages over optical remote sensing. In this paper, we summarize the methods of surface water body extraction using SAR data, analyze the advantages and disadvantages of Sentinel-1 Dual-Polarized Water Index (SDWI), and propose a Dual-Polarized First-Principal-Component Water Index (DFWI). In addition, aiming at the problem that radar shadow is easily confused with waters in SAR images, three kinds of ascending-descending polarimetric radar water body extraction methods are proposed: Ascending-Descending VV-Polarization (AD-VV), Ascending-Descending VH-Polarization (AD-VH), and Ascending-Descending Dual-Polarized First-Principal-Component Water Index (AD-DFWI) methods. Finally, two experimental sites, Yunnan Province, China, and Hatay, Turkey, are selected for water body extraction, which corresponded to normalized monitoring and emergency monitoring of water bodies, respectively. The accuracy of the water body extraction is evaluated and analyzed using four metrics：User Accuracy, Producer Accuracy, False Alarm Rate, and F1-score. The experimental results show that: (1) Compared with the single-polarization method and the SDWI method, the classification accuracy of our proposed method is significantly improved. The AD-DFWI method achieves the highest accuracy in both experimental areas, e.g., the F1-score index reaches 97.83% and 88.33%, respectively; (2) Our ascending-descending polarimetric SAR water extraction methods not only solve the problem of the fusion between shadows and waters but also generate a more prominent bimodal distribution histogram for improved discrimination of water and non-water. In flat areas that are not affected by radar shadows, the DFWI method can also obtain high-precision water body extraction results. This method is not only suitable for Sentinel-1 dual-polarization data but can also be theoretically extended to fully polarized data such as GF-3, ALOS-2, RADARSAT-2, and TerraSAR-X. If multiple satellite constellations can be used to obtain the ascending and descending orbit data at the same time in the future, or the revisit time of remote sensing satellites can be shortened, the accuracy of the ascending-descending polarimetric SAR water body extraction methods will be further improved. The method proposed in this paper can provide a reference for challenging applications such as surface water extraction and flood monitoring in the future.

Coastal megacities are typically situated in low-lying and densely populated areas. The occurrence of storm surge compound flooding has the potential to result in catastrophic social, economic, and ecological impacts for these coastal cities. The rising sea levels and the increased intensity and frequency of tropical cyclones caused by global warming will exacerbate the challenges faced by coastal cities. Therefore, accurately assessing compound flooding events caused by tropical cyclones is critical to protecting coastal areas from inundation. However, research on the impact of climate change on the risk of tropical cyclone induced compound flooding in coastal areas is still limited. In this study, we used the EC-EARTH3P climate model and selected a dataset of climate change tropical cyclone trajectories synthesized by the STORM model. This dataset is generated using historical data from the International Best Track Archive for Climate Stewardship (IBTrACS) to simulate synthetic tropical cyclones under future climate conditions. Subsequently, we used the coupled Delft3D FLOW & WAVE hydrodynamic model to simulate the impact of storm surge compound water levels on coastal areas due to the nonlinear effects of tropical cyclones wind fields and waves. Furthermore, we investigated the contributions of tropical cyclones and sea level rise to coastal storm surge compound flooding under different Shared Socioeconomic Pathways (SSPs) scenarios, taking the Shanghai city, located within an estuary and along the coastline of China, as our case study. The results showed that climate change had a significant impact on storm surge compound flooding. The future compound flooding disasters exhibited spatial variations in shanghai and differences in water level heights, influenced by future cyclone paths and intensities. Among these areas, Chongming district was the most seriously affected area by storm surge compound flooding. In addition, sea level rise under different climate scenarios will lead to more severe flood hazards in the Shanghai area. We found that although sea level rise will further intensify the impact of storm surge compound flooding in Shanghai, tropical cyclones will have a greater influence on future compound flooding in the city. The spatial risk analysis framework for compound flooding hazards under climate change designed in this study can also be applied to research future storm surge compound flooding hazards in other coastal megacities. Our research findings not only provide a foundational basis for policymakers and flood risk managers to identify risk vulnerable areas, but also provide significant implications for coastal adaptation measures and urban emergency response planning.

Being able to penetrate clouds and fog, Synthetic Aperture Radar (SAR) imagery has been widely used in flood mapping and flood detection regardless of time and weather condition. Improving the accuracy of flood maps retrieved from SAR images is of both scientific and practical significance. However, errors in SAR-derived flood maps can come from SAR image measuring principles, image acquisition and pre-processing system, water detection algorithms, and the remarkable temporal dynamics of the flooding process. The aim of this paper is to provide an extensive literature review of flood detection using SAR images (about 108 peer reviewed journal papers), including SAR data sources, flood detection methods, application of auxiliary information, accuracy evaluation, and challenges and opportunities for future research. Based on the articles reporting flood detection methods, it is found that the threshold segmentation methods such as the OTSU and KI algorithms are computationally fast and have been most widely used. The classification methods (e.g., the support vector machine and K-means clustering algorithms) have the flexibility to account for both subjectivity and objectivity, and the change detection method using the difference and ratio algorithms can effectively suppress over-detection and image geometric errors. Additionally, combining SAR images with four major types of auxiliary data to increase flood detection accuracy has become a hot topic in the past decades. Specifically, terrain information such as Digital Elevation Model (DEM), Height Above Nearest Drainage (HAND), and topographic slope can effectively reduce the impacts of shadows and exclude non-flooded areas. SAR image textural and multispectral optical information (e.g., Landsat data and aerial photos) can enhance the recognition ability of water features. Land cover/use data facilitate removing non-water features that are similar to water features, and hydrological data can help excluding permanent water bodies from temporary flood areas. From the perspectives of SAR image types, image preprocessing, detection algorithms, and accuracy assessment, major challenges are further discussed including insufficient understanding of the complexity of SAR backscattering information, limited progress in improving the signal-to-noise ratio during image pre-processing, lack of versatile flood detection algorithms, and low availability of high-quality verification data. While opportunities for future SAR-based flood detection research include combination of auxiliary information in detection algorithms, use of multiple rather than single threshold for water detection, and transition from deterministic toward probabilistic flood mapping.

Timely and accurate flood extraction can improve the ability of emergency management departments to respond to flood disasters and thus reduce the impact of disasters. Synthetic Aperture Radar (SAR) remote sensing is not affected by cloud and rain and acts as an effective tool for flood disaster monitoring. However, rapid and automated sample acquisition for accurately obtaining flood information still poses challenges in the post-disaster emergency stage. In addition, due to the satellite’s limited revisit period, flood extent extraction based on remote sensing images has a certain time gap. Therefore, how to delineate flood extent in real time is an urgent problem to be solved. In this paper, combining SAR images, Volunteered Geographic Information (VGI), and other multi-source data, an automated flood inundation mapping and estimation method is proposed to identify the flood in a nearly real-time way. Firstly, the Albert+CNN text classification model is constructed to extract flood information from social media. Secondly, based on the outlier elimination method, the Sentinel-1 SAR data are classified using VGI data such as social media and OSM instead of manual sampling, and the flood extent is extracted. Thirdly, in combination with social media, water level data, and other multi-source data, the flood inundation without SAR data is simulated based on SNIC segmentation and cost distance methods. The results show that the text classification model based on Albert+CNN model constructed in this paper can accurately classify the flood information of social media, with the classification accuracy up to 83.6%. The automatic sample generation method based on outlier elimination can effectively assist flood classification from SAR data. The accuracy of flood classification using the generated samples and random forest is 92.7%, which is more than 10% higher than that of the OSTU-based methods. Using VGI data and multi-source data such as water situation and DEM to simulate the flood extent can further increase the temporal resolution of flood monitoring. In this study area, two periods of flood simulation results are provided during the flood process, improving the flood disaster information. This study is helpful to improve the efficiency of flood information extraction and provide reference for VGI to support the emergency management of flood disasters. In the future, the picture information in VGI data can be comprehensively used to extract flood depth and hydrological models could also be used to further improve the flood situation awareness and prediction ability.

Rapidly and accurately monitor the flood disaster is very important, which can protect people's life and property safety and realize the sustainable development of society. The polarimetric synthetic aperture radar (PolSAR) image can obtain all-day and all-weather information of flood disaster by transmitting and receiving electromagnetic microwave of different polarizations, which can provide more favorable data support for flood disaster monitoring. However, the traditional flood disaster monitoring methods based on PolSAR images are seriously affected by speckle noise and the class imbalance between changed class and un-changed class leads to low accuracy of disaster monitoring. To address issues, a novel flood disaster monitoring method based on the improved Hotelling-Lawley Trace (HLT) statistic operator and deep learning for small area change using bi-temporal PolSAR images was proposed in this paper. Within this method, the HLT statistic operator was firstly constructed by considering the neighborhood information of PolSAR images, which can reduce the influence of speckle noise and spatial heterogeneity for the generation of the difference image in this paper. Secondly, the Two-stage Center-Constrained Fuzzy C-Means clustering (TCCFCM) algorithm and the Deep Convolutional Generative Adversarial Network (DCGAN) were introduced to build a robust method of sample select and express when lacked the changed samples over disaster areas, which can solve the problem of the class imbalance between changed and un-changed class. Finally, the Deep Convolutional Wavelet Neural Network (DCWNN) was constructed to achieve accurate monitoring of flood disasters. In order to verify the feasibility and robustness of the proposed method, the Radarsat-2 images covered Wuhan city before and after flood disaster were selected in July, 2016. The qualitative and quantitative results show that the proposed method can reduce the missed alarm rate and false alarm rate of flood disaster monitoring, and significantly improve the Overall Accuracy (OA) and Kappa coefficient (Kappa) of disaster monitoring, i.e. the false alarm rate and missed alarm rate of flood disaster monitoring are reduced respectively 1.5% and 2%, meanwhile, the OA and Kappa coefficient of flood disaster monitoring are significantly improved 3% and 0.02 when compared with the traditional methods of flood disaster monitoring, which can provide technical support of emergency relief work for relevant departments.

The prevention and mitigation of flash flood disasters is a weak link in flood prevention and disaster mitigation at present in China. Early warning is one of the most effective way of defense against flash flood disasters. Emergency mapping can provide strong technical support for early warning information release and decision-making deployment in flash flood disasters. For flash flood disasters with high suddenness and drastic changes in spatial and temporal process, traditional emergency mapping methods are difficult to meet the actual needs in timeliness. Using the SCS model in this research which has few parameters and simple structure can quickly analyze and calculate the possibility of flash flood disasters. It is combined with the framework of "Model calculation- Emergency mapping- Decision-making support" to improve the timeliness of flash flood disaster risk emergency mapping. In the main flood season of 2020, the Yangtze River Basin experienced the most severe flood since 1998. It is urgently needed to carry out the emergency mapping research of flash flood disaster risk. The Yangtze River Economic Belt is taken as the research area to carry out the research. The research results include the following three aspects: (1) the improved SCS model is used to calculate the flash flood disaster risk for each discretized subunit based on meteorological data. Through the construction of early-stage basic database and rapid data processing tools, and the design of standard mapping templates, the efficiency of model calculation and emergency mapping is improved and the standardization of emergency mapping can be guaranteed. The mapping time of a single emergency thematic map is improved to about 50 minutes. (2) The combination of real-time model calculation and emergency mapping, and the use of 24h daily rainfall forecast data realizes dynamic and continuous updating of thematic maps. (3) The study is effectively applied to the flash flood disaster prevention of the Yangtze River Economic Belt during the 2020 flood season. A total of 150 thematic maps of flash flood disaster risk are produced to support the decision-making deployment in national emergency management and it achieves good results. This study also provides new ideas and possibilities for emergency services for various sudden natural disasters in the new era.

With global warming, the frequency of extreme weather events and major meteorological disasters is increasing globally. It is important to study the relationship between climate change and the frequency of meteorological disasters for disaster prevention and mitigation in the context of climate change. In this paper, a method is proposed for automatic extraction of spatial and temporal events of meteorological disasters based on natural language processing technology. Because there is a huge amount of spatial and temporal information of meteorological disasters available in literature and web data. Specifically, (1) A coarse-to-fine method was proposed to build a training corpus of meteorological disaster annotations based on professional literature. Firstly, a unified meteorological disaster knowledge system oriented to textual events is constructed to address the problems of ambiguity and incompatibility of different literature materials. Then a coarse annotation method based on chapter structure was constructed, and a Labeled LDA model-based and a fine-grained annotated corpus screening method based on TF-IDF and N-gram models were developed for long texts (modern texts) and short texts (literary texts), respectively, solving the problem of rapid corpus construction; (2) A method for automatic classification of spatiotemporal events of meteorological disasters based on the BERT-CNN model, which integrates contextual semantic features and local semantic features at multiple granularities, was developed for the integrated processing of short and long texts; (3) Using this method, the spatiotemporal events of meteorological disasters were automatically extracted from the textual and web data, and their macro F1 values reached 89.09% and 80.06%, respectively. The spatiotemporal distributions of major events of meteorological disasters were highly correlated with professional statistics; (4) Based on the above results, the spatiotemporal evolution of disasters in various historical periods in China was also reconstructed. We found that the overall volume of disaster data in each period showed a gradual increasing trend, with heavy rainfall disasters, floods, and droughts being the main types of disasters in China. Our method enables both the automatic extraction of long text events from the web and the automatic detection of short text events from literatures, providing a new technique for application of text data to meteorological disaster research and monitoring.

In the summer of 2020, heavy rains made the Poyang Lake region witnessed a big flood event. Remote sensing earth observation technology can help to map and assess the flood hazard quickly and efficiently. Therefore, two 2020 satellite images acquired on April 15 (Landsat-8 OLI) and July 14 (Sentinel-1A SAR) were selected in this study to represent the dates before and after the flood to evaluate the disaster. Using remote sensing thematic information extraction, Random Forest classification and change detection technology, the inundation area and the area of major land cover types within the inundation areas were revealed. Associated with the hydrological, meteorological and topographical data, the specific flooded sites and the factors causing the disaster were identified and analyzed. The results show that the flood-inundation extent in the Poyang Lake region in 2020 is 1961.95 km2, including 760.54 km2 of farmland, 71.59 km2 of forest, 992.02 km2 of grassland, 26.97 km2 of soil, and 110.83 km2 of built-up land. Poyang County was most severely affected in this flood event, with a total inundated area of 514.35 km2. The next two are Xinjian County with 330 km2 and Yugan County with 310 km2. The main hydro-meteorological and topographical factors that caused the flood are considered to be: (1) higher water level than that in the 1998 flood; (2) failure of timely discharge of water due to backflow of the Yangtze River; (3) breach of the embankments.

Due to climate change and urbanization, rainstorms are becoming more and more frequent and highly unevenly distributed in urban areas. However, it is difficult to have sufficient monitoring data for rainfall events because of insufficient installments of monitoring stations. Usually, the spatial resolution of meteorological forecast data is still coarse, which leads to the low accuracy of simulation and prediction results from urban flood models. Therefore, it is necessary to improve the spatial resolution of rainfall data to enhance the accuracy of urban flood simulation. In this paper, three catchment basins in Shenzhen River Basin are selected. Analytical data are extracted from 118 rainfall events lasting no less than 5 minutes from 31 meteorological stations within and around the study area during 2018—2020. The discrete observation data from meteorological stations are interpolated into continuous grids. According to the drainage zoning area, the grid size is initially set as 200 m×200 m.The gridded data is then aggregated into 400 m×400 m, 800 m×800 m, 1600 m×1600 m, 3200 m×3200 m, and 6400 m×6400 m to achieve coarser spatial resolution rainfall datasets. The basin boundary is used during the aggregation to ensure that the average rainfall in the basin remains constant. The gridded rainfall data at different spatial resolutions are used as the input of an urban flood model to simulate the flooding processes of outlet sections in three watersheds. The model results of three watersheds are compared finally. By comparing the changes of flood peak at the outlets of each basin, the influence of rainfall spatial resolution on river flood peak is further analyzed by combing the spatial parameters such as rainfall center and basin center. The results of three watersheds show that for 118 rainfall processes, with the decrease of spatial resolution, the amplitude of flood at the watershed outlets gradually increases. When rainfall is evenly distributed in the whole basin, the variation ranges of flood peaks in the three basins are 5.04%, 8.73%, and 7.11%, respectively, compared to the rainfall data with 200 m×200 m grid size. The influence of spatial resolution is strongly correlated with the relationship between rainfall center and watershed geometric center, which strongly relates to the direction from watershed geometric center to watershed outlet. The research provides a reference for improving the accuracy of urban flood models by involving the variable of rainfall, which enable researchers to better assess the risk of river floods.

The ability of disaster prevention and mitigation in mountainous and rural areas is weak. Mountain flood disasters have strong suddenness, and the response time of disaster avoidance is short, which are likely to cause casualties. Reasonable design of avoidance routes can help reduce losses caused by disasters. Based on GIS, this paper designs disaster avoidance routes in Tianbaizhi Village, Xingtai City, in the Hebei Province. The Digital Elevation Model (DEM) and orthophoto of the research area are obtained through Unmanned Aerial Vehicle (UAV) photogrammetry. Factors that influence disaster avoidance route planning including slope, cost distance, and function of cost path are used to calculate the initial avoidance route in the mountainous areas. The area where the mountain flood ditch is located and the area with a large slope are used as the interrupted area of the disaster avoidance route. The existing mountain roads are fully considered and rationally used to optimize the final disaster avoidance route. Based on the distance and average slope of the disaster avoidance route, the disaster avoidance route obtained by this method is quantitatively compared with the original disaster avoidance route. The result shows that (1) Among the 20 long-distance disaster avoidance routes designed, this method shortens the length of 18 routes, reduces the average slope of 10 routes, and controls the increase of the average slope of the other 8 routes within 2°; (2) Compared with the original route, the maximum length of each route can be shortened by 329.74 m, and the average slope only increases by 0.68°. The disaster avoidance distance is shortened by adding a small amount of average slope, which improves the disaster avoidance efficiency; (3) The length of remaining two routes increases by 15.21 m and 9.57 m, respectively, resulting in an average slope reduction of 8.43° and 1.43°. The average slope of the routes is reduced by adding a small amount of distance, which improves the safety of disaster avoidance routes; (4) The southern part of Tianbaizhi Village is close to the disaster avoidance point, and the avoidance of disasters is relatively difficult. In contrast, the northern part of Tianbaizhi Village has a longer route to the disaster avoidance point. The longest distance is 1210.02 m, and the difficulty of disaster avoidance is greater. The optimized disaster avoidance path has been separated from the barrier area with large slope, and the degree of overlap between disaster avoidance path and the mountain flood ditch is greatly reduced. The method proposed in this study provides a sufficient scientific basis for relevant decision-makers.

In recent years, Heilongjiang (Amur) River, a border river between China and Russia, is frequently flooded, which brings huge population casualties and economic losses to China and Russia in the basin. Strengthening flood monitoring in the basin is an urgent need for both countries. Influenced by terrible cloudy and rainy weather during the flooding season, the function of traditional optical remote sensing images are restricted. Thus, it is difficult to obtain cloud-free images during the flood. In this paper, through making full use of the advantages of all-weather radar data, a method of monitoring flood in large area based on Sentinel-1 synthetic aperture radar data is proposed. The probability density distribution of backscattering coefficient of SAR image is fitted by Gamma distribution and Gaussian distribution. The global threshold is obtained automatically to segment the initial water by iterating a posteriori probability difference. The misclassification type that looks like water in the initial water classification is refined and removed based on auxiliary data. Moreover, the uniformity of the extracted flood is improved by the post-processing of morphological operation. It can be concluded from the results that, firstly, compared with the traditional segmentation algorithm, the method proposed in this paper carries out the piecewise fitting of the probability density function based on the distribution law of the backscattering coefficient of the SAR image. It also divides the global statistics into local relations, which significantly improves the poor performance, caused by the large difference between water and non-water pixels, of the conventional segmentation algorithm. The flood distribution year by year from 2017 to 2020 is obtained. The overall accuracy of the results is between 87.78% and 94.89% while the Kappa coefficient is between 0.76 and 0.89. Secondly, especially for large semi-arid areas, this paper uses the relationships among backscattering characteristics, topography, and other auxiliary information, to ensure that water can be effectively preserved while the non-water objects similar to the backscattering coefficient of water can be removed. Thirdly, the results reflect that areas along the middle and lower reaches of Heilongjiang (Amur) River, such as Khabarovsk and Amursk, have frequent floods. Meanwhile, the flood area shows an increasing trend as a whole. The research shows that the time series monitoring of flood spatial extent based on radar data can provide scientific support for flood development dynamic monitoring in Heilongjiang River basin in China and Russia.