Urban Flood Depth-Economic Loss Curves and Their Amendment Based on Resilience: Evidence from Lizhong Town in Lixia River and Houbai Town in Jurong River of China

Wu, Xianhua; Guo, Jing

doi:10.1007/978-981-16-1319-7_7

Cited by 3 publications

(3 citation statements)

References 47 publications

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“…Data sources in this paper included the storm water management model (SWMM) used to simulate the flooding and inundation data of Zhengzhou urban area for four AEP flood events, respectively. The damage ratio data collected from different cities were used as a sample for the analogous migration (Chen et al., 2019; Ding, 2004; Ke, 2014; Li, 2007; Shi, 2010; Wang et al., 2013; Wu, Lv, et al., 2021; Wu et al., 2016). Urban land use maps were created from actual surveys and remote sensing data acquired from Resource and Environment Science and Data Center (https://www.resdc.cn/).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…Data sources in this paper included the storm water management model (SWMM) used to simulate the flooding and inundation data of Zhengzhou urban area for 1%, 2%, 5%, 10% AEP, respectively (Rossman, 2007; https://www.epa.gov/water-research/storm-water-management-model-swmm). The data on flood damage ratios collected in several cities used for analogous migration were derived from many studies (Chen et al., 2019; Ding, 2004; Ke, 2014; Li, 2007; Shi, 2010; Wang et al., 2013; Wu, Lv, et al., 2021; Wu et al., 2016), and urban land use maps (Figure 1) were created from actual surveys and remote sensing data acquired from Resource and Environment Science and Data Center (https://www.resdc.cn/). Since this paper mainly considered the economic loss caused by the flood to urban systems, the calculated land uses included industrial, commercial, residential and public service land uses.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

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Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties

Wang

et al. 2022

Water Resources Research

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On 15 July 2021, the massive floods that occurred in many German states were seen as a national tragedy by Chancellor Angela Merkel. On 20 July, in Zhengzhou, China, a time-interval rainfall of 201.9 mm between 16:00 and 17:00 broke the global land record. Millions of people were affected and the economic loss amounted to 53.2 billion Chinese yuan. On 1 September, record rainfall caused floods in New York that largely paralyzed the city. Flooding has become a major constraint on urban public security and social and economic development (Alexander et al., 2019;Guo et al., 2021;Lee et al., 2020). Quantifying and evaluating the damage of flooded urban areas not only allows for timely mitigation measures, but also provides a basis for post-disaster recovery and reconstruction (Kreibich et al., 2014;Merz et al., 2010;Schröter et al., 2014). Accurate flood damage assessment results are also essential for the natural disaster insurance industry in post-disaster claims settlement (Diakakis et al., 2020;Wagenaar et al., 2017). Hence, scientific and reasonable flood damage quantification plays an important role in urban flood risk control and rescue decision making.The flood damage assessment process is characterized by multidimensionality, dynamic changes and uncertainties due to multiple factors such as regional rainfall characteristics, geographical conditions and socio-economic levels (Gall, 2017;Schoppa et al., 2020). It is a widely used flood damage assessment method to achieve damage results by combining flood damage ratios with inundation depth, land use and economic factors (Chen et al., 2019;Jongman et al., 2012;Scorzini & Frank, 2017). The damage ratio serves as the link between flood inundation

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Section: Data Availability Statementmentioning

confidence: 99%

Section: Study Area and Data Sourcesmentioning

confidence: 99%

Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties

Wang

et al. 2022

Water Resources Research

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“…However, the differences in the index selection and weight determination methods will affect the results, showing the limitations of this method (Mishra and Singh, 2010;Tanago et al, 2016). The disaster area, population and economic loss are the result of flood and drought disasters and can be used as a good index to measure the risk of flood and drought disasters (Nafarzadegan et al, 2012;Shi et al, 2017;Wu et al, 2016). Among them, the size of the affected crop area and the proportion of production reduction due to disasters can reflect the extent of the impact of disasters on the region.…”

Section: Introductionmentioning

confidence: 99%

Risk analysis and influencing factors of drought and flood disasters in China

Pei

Tian

et al. 2021

Nat Hazards

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The risk analysis of flood and drought disasters and the study of their influencing factors enhance our understanding of the temporal and spatial variation law of disasters and help identify the main factors affecting disasters. This paper uses the provincial administrative region of China as the research area. The statistical distribution of flood and drought disasters was optimized from 10 alternative distributions by the KS test, and the disaster risk was analyzed. Thirty-five indicators were selected from nature, agriculture and the social economy as alternative factors. The main factors affecting flood and drought disasters were selected by Pearson, Spearman and Kendall correlation coefficient test. The results demonstrated that the distribution of floods and drought is right skewed, and the beta distribution is the best statistical distribution for fitting disasters. In terms of time, the risk of flood and drought disasters in all regions showed a downward trend. Economic development and the enhancement of the ability to resist disasters were the main reasons for the change in disasters. Spatially, the areas with high drought risk were mainly distributed in Northeast and North China, and the areas with high flood risk were mainly distributed in the south, especially in Hubei, Hunan, Jiangxi and Anhui. The distribution of floods and drought disasters was consistent with the distribution characteristics of precipitation and water resources in China. Among the natural factors, precipitation was the main factor causing changes in floods and drought disasters. Among the agricultural and socioeconomic factors, the indicators reflecting the disaster resistance ability and regional economic development level were closely related to flood and drought disasters. The research results have reference significance for disaster classification, disaster formation mechanisms and flood and drought resistance.

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A Novel Framework for Urban Flood damage Assessment

Yavari

Neyshabouri

Yazdi

et al. 2022

Water Resour Manage

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The study of non-stationary effects of hydrological time series and land-use changes in urban areas is essential to predict future oods and their probable damage. In the current study, a novel method was proposed for analyzing their simultaneous impact. For this purpose, rainfall frequency and land-use changes analyses were conducted for two different long-term periods, and the results were compared. Then, hydrologic modeling of the catchment was carried out using the HEC-HMS model, and obtained hydrographs were fed to the HEC-RAS2D model for estimating ood inundation areas. Using the economic information of assets and their damage functions, ood damages related to these two periods were evaluated through the HEC-FIA model. The results indicated that in the low return periods (e.g., 2-year ood), the damage in the second period was increased with respect to the rst one but increased for the return periods of 5 to 100 years. Furthermore, surface runoff showed a 4.65% increase due to land-use change and a 12% increase due to rainfall non-stationarity. Moreover, ood damage showed a 136% increase on average, and among the two studied factors, the non-stationarity of rainfalls is considerably more effective on ood intensi cation.

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Urban Flood Depth-Economic Loss Curves and Their Amendment Based on Resilience: Evidence from Lizhong Town in Lixia River and Houbai Town in Jurong River of China

Cited by 3 publications

References 47 publications

Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties

Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties

Risk analysis and influencing factors of drought and flood disasters in China

A Novel Framework for Urban Flood damage Assessment

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