Rapid flood mapping is crucial in hazard evaluation and forecasting, especially in the early stage of hazards. Synthetic aperture radar (SAR) images are able to penetrate clouds and heavy rainfall, which is of special importance for flood mapping. However, change detection is a key part and the threshold selection is very complex in flood mapping with SAR. In this paper, a novel approach is proposed to rapidly map flood regions and estimate the flood degree, avoiding the critical step of thresholding. It converts the change detection of thresholds to land cover backscatter classifications. Sentinel-1 SAR images are used to get the land cover backscatter classifications with the help of Sentinel-2 optical images using a supervised classifier. A pixel-based change detection is used for change detection. Backscatter characteristics and variation rules of different ground objects are essential prior knowledge for flood analysis. SAR image classifications of pre-flood and flooding periods both take the same input to make sense of the change detection between them. This method avoids the inaccuracy caused by a single threshold. A case study in Shouguang is tested by this new method, which is compared with the flood map extracted by Otsu thresholding and normalized difference water index (NDWI) methods. The results show that our approach can identify the flood beneath vegetation well. Moreover, all required data and data processing are simple, so it can be popularized in rapid flooding mapping in early disaster relief.

Assessment of urban water logging risk depth is mainly based on extreme value of rainstorm and its occurrence frequency as disaster causing factor. Regional waterlogging disaster risk assessment can be determined through regional geographic spatial information coupling calculation; the fundamental reason lies in the lack of an effective method for numerical simulation of waterlogging risk depth. Based on the hydrodynamic principle, FloodArea model realizes the numerical simulation of regional waterlogging depth by hydrologic calculating of runoff generation and runoff concentration of waterlogging. Taking risk assessment in Nanchang city as an example, spatial distribution of urban waterlogging depth was simulated by using FloodArea model in return period of 5 years, 10 years, 50 years, and 100 years. Research results show that FloodArea model can simulate urban waterlogging forming process and spatial distribution qualitatively.

Flood is one of the most frequent natural disasters in the Yangtze River Basin. Flood risk evaluation is of great social significance, especially for large hydrological systems. Rainfall is both temporal and spatial, influencing surface hydrological activities. The water body range is the final outcome of a flood and can be observed from synthetic aperture radar (SAR) images under any weather condition. A flood severity evaluation model is proposed to quantitatively evaluate the flood based on water body range from area disparity and flood duration. Large hydrological objects usually span a wide range and have significant differences. This results in different initial water areas in each region. This approach addresses the issue through normalization processing. In this paper, Sentinel-1 data are used to extract the temporal water body using the adaptive bimodal method, and the water level data were also incorporated to improve the observation frequency for water area. The flood severity evaluation approach can be used to assess flood risk between any region of large hydrological systems or any flood event, regardless of their regional spatial differences and rainfall duration differences. The results show that: (1) In general, the average water body area in 2020 was 20.40% larger than it was in 2019, and the daily water body areas in 2020 were all greater than the average of 2019 with 71.36% of the days in 2020 having an area greater than the maximum in 2019. The flood severity in 2020 was 1.75 times as much as that of 2019; (2) Reach performance indexes in 2020 were in order of Yueyang (2.21) > Jiujiang (2.04) > Hankou (1.44) > Chizhou (1.32), which were inconsistent with the spatial site; (3) Flood event impact indexes in 2020 were in order as No.2 (1.64) > No.3 (1.61) > No.1 (1.44) > No.4 (1.17) > No.5 (1.15); (4) The flood was more likely the result of cumulative rainfall for 30 days.