# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
###### Abstract
So far, storm tracks can be synthesized rapidly from purely statistical intensity algorithms. However, the effects of natural or anthropogenic climate change could not be encompassed through the above empirical models. Today, some novel intensity models considering environment variables that can be obtained from reanalysis or global climate models have the potential to estimate the future wind hazards under future climate projections. For example, by taking the effects of ocean coupling and environmental wind shear into account, [PERSON] [19] presented a fast, physically motivated intensity algorithm and verified the model's validity. Reanalysis data were adopted by [PERSON] and [PERSON] [18] to develop an improved full track model for TCs based on a statistical dynamics method, described as a beta-and-advection model, to estimate wind hazards on the coast of southeast China. [PERSON] and [PERSON] [20] developed a hidden Markov model (MeHiM), which is dependent on the surrounding large-scale environment such as vertical wind shear, relative humidity, and ocean feedback from reanalysis to simulate the whole process of hurricane intensity evolution. [PERSON] et al. [21] verified the applicability of MeHiM in the Northwest Pacific Ocean and presented a general framework of typhoon full-track simulation. For engineering applications, the wind field model can be classified as the gradient wind field model [1; 2; 14] and the planetary boundary layer (PBL) model [15; 16; 22]. [PERSON] et al. [22] proposed an analytical model with an upper inviscid layer of cyclostrophic balance and a lower friction layer to calculate the wind field in a moving typhoon boundary layer. [PERSON] and [PERSON] [23] upgraded a PBL model by increasing the spatial resolution to simulate a wider variety of radial pressure and wind profile forms. These PBL models have been widely applied in the assessment of hurricane/typhoon wind hazards for the coastal region of the United States and China [5; 6; 7; 8; 9; 16; 17]. In terms of vulnerability analysis of disaster-bearing bodies, [PERSON] et al. [24] analyzed the vulnerability of disaster-bearing bodies in coastal areas of China by considering the characteristics of disaster-causing factors based on natural disaster system theory; [PERSON] et al. [25] assessed the risk of disaster-bearing bodies caused by typhoons in coastal areas of China by establishing an indicator system based on hierarchical analysis; [PERSON] et al. [26], combined with socioeconomic factors, pointed out that the degree of disaster loss was mainly related to population and economic currency; and [PERSON] et al. [27] established a typhoon vulnerability equation for rural housing in Zhejiang Province using insurance data and provided relevant parameters. [PERSON] et al. [28] established a typhoon disaster prediction model based on comprehensive disaster correlation and typhoon disaster-causing factors; and [PERSON] et al. [29] established a neural network based on the principal component analysis method and used the direct economic loss index as an assessment index for five typhoons in Zhejiang Province. Up until now, there have been few studies on the typhoon loss assessment of disaster-bearing bodies based on township-level high-resolution due to the scarcity of public information on township-level disaster loss data. The existing studies [24; 25; 26; 27; 28; 29] have mainly focused on macro-provincial resolution, which is mainly attributed to the limitation of domestic disaster statistics. Although typhoon disaster statistics in China have made great progress in the past decades, there are still many deficiencies in disaster statistics in different provinces, different generations, and different departments, and the lack of uniform data standards makes it difficult to predict quantitative and accurate disaster losses.