No abstract

To address the problems of traditional hydrological and hydraulic methods of estimating disasters in urban flood areas, such as small scale, poor timeliness, and difficulty of obtaining data, an inversion method of estimating urban flood disaster area based on remote sensing spectroscopy is proposed. In this paper, the spatial distribution of urban flood disasters is first inverted based on large-scale multidimensional remote sensing spectral orthography. Then, spatial coupling inversion of the remote sensing spectrum-urban economy-flood disaster is performed by simulating the urban economic density through single spectral remote sensing at night. Finally, losses at the urban flood area are estimated. The results show that (1) the heavy rain in Henan Province on 20 July is centered in Zhengzhou, and the spatial distribution of urban flood disasters accords with Zipf’s law; (2) the estimated damage to the urban flood area in Henan Province is 132,256 billion yuan, and Zhengzhou has the most serious losses at 43,147 billion yuan, accounting for 32.6% of the entire province’s losses. These results are consistent with the official data (accuracy ≥ 90%, R2 ≥ 0.95). This study can provide a new approach for accurately and efficiently estimating urban flood damage at a large scale.

To address the problems of traditional insurance compensation methods for flood losses, such as difficulty in determining losses, poor timeliness, a complicated compensation process and moral hazard, an urban flood index insurance tiered compensation model integrating remote sensing and rainfall multi-source data was proposed. This paper first extracted the area of water bodies using the Normalized Difference Water Index and estimates the urban flood area loss based on the flood loss model of remote sensing pixels. Second, the tiered compensation mechanism triggered by rainfall was determined, and the urban flood index insurance tiered compensation model was constructed using remote sensing and rainfall multi-source data. Finally, the economic losses and flood insurance compensation in urban flood were estimated. The results show that: (1) the geo-spatial distribution of flood-affected areas by remote sensing inversion is consistent with the actual rainfall characteristics of Henan Province, China; (2) based on the flood losses model of remote sensing pixels, the estimated flood losses for Henan Province are CNY 110.20 billion, which is consistent with the official data (accuracy ≥ 90%); and (3) the proposed model has good accuracy (R2 = 0.98, F = 1379.42, p < 0.05). The flood index insurance compensation in Henan Province is classified as a three-tier payout, with a total compensation of CNY 24,137 million. This paper can provide a new approach to estimate large-scale urban flood losses and the scientific design of flood index insurance products. It can also provide theoretical and technical support to many countries around the world, particularly those with underdeveloped flood insurance systems.