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An integrated index system for urban rainstorm risk evaluation has been developed. Meanwhile, an information diffusion method (IDM) and variable fuzzy sets (VFSs) were employed to evaluate the dangerousness, sensitivity, and vulnerability risk of urban rainstorm disasters, respectively. Then, the comprehensive risk zoning map was drawn. Finally, Jiangsu Province has been taken as a case study area. Due to heavy rainfall in short-term and low rainstorm resistance ability, Wuxi, Changzhou, Nanjing, and Suzhou have higher dangerousness while Wuxi, Changzhou, and Nanjing have higher sensitivity. And because of potential losses in urban rainstorm disaster, Wuxi and Suzhou have higher vulnerability than other cities. The comprehensive risk zoning map showed that most cities of Jiangsu Province are at the moderate risk level, and the northwestern cities have lower risk level than the southern cities. The results are consistent with the actual situation of Jiangsu Province, and the study can provide some decision-making references for the urban rainstorm management.

With the global climate change and the rapid developments of the urbanization, many cities are suffered extreme rainstorm events frequently [

In the last decades, several disaster risk assessment systems for urban rainstorm have been developed. Lyu [

Meanwhile, GIS (the Geographic Information System) and remote-sensing imagery [

This paper takes Jiangsu Province as the study area. Jiangsu Province (Figure ^{5} km^{2}, accounting for 1.1% area of China. The geomorphology of Jiangsu is mainly plains and the elevation of most area is lower than 50 m [

Geographic location of Jiangsu Province, China.

Jiangsu Province is one of the most urbanized regions in China. In recent years, urban rainstorm disasters have occurred more frequently and caused huge losses in Jiangsu Province. Thus, urban rainstorm has become one of the most important factors that restrict the development of Jiangsu Province.

This paper uses Jiangsu Statistical Yearbook data and China City Statistical Yearbook data to collect the statistical information of all cities in Jiangsu Province including economic, social, demographic, urban construction, environmental, and other related city statistics. The meteorological data and the rainfall statistics are collected from the meteorological stations of cities in Jiangsu Province. Other historical precipitation statistics are provided by the Jiangsu climate center. Among these data, the continuous rainfall days and heavy rain days are collected by monthly from 2010 to 2016 from the Jiangsu climate center.

The index weight reflects the relative importance of each index in the risk assessment index system. In this paper, the AHP (Analytic Hierarchy Process) and entropy weight are combined to calculate the weights of indices. The AHP is a subjective method to determine the weight of indices based on experts’ experience [

Suppose the weights calculated by AHP are the subjective weights of indices. The subjective weights of indices can be represented as follows:

Then, the expert’s own weights are calculated by the entropy weight method, and it can be calculated as follows:

Finally, the weight fusion vector of subjective weights and expert's own weights can be expressed as

IDM is a kind of a fuzzy mathematical processing method which can be used to optimize the fuzzy information of samples by means of an appropriate diffusion model [

The principle of the information diffusion method can be expressed as follows: suppose

The principle of the information diffusion method.

The calculation steps of the information diffusion are as follows:

Firstly, the risk levels of urban rainstorm disasters are divided into five levels, i.e., lowest risk, lower risk, moderate risk, higher risk, and highest risk. The information carried by

Secondly, in order to make each set of sample values identical, the diffusion function

The probability of samples located in

Finally, the exceeding probability

According to the classification standards of exceeding probability, the critical value of each evaluation index corresponding to the risk level of urban rainstorm is obtained, so the level classification standards of urban rainstorm risk assessment indices can be obtained.

Variable fuzzy set theory is mainly used in the dynamic analysis of fuzzy phenomena [

Fuzzy variable evaluation method calculates the evaluation level of urban rainstorm disasters scientifically by changing the model and its parameter combination, and it can improve the reliability of risk assessment results. The fuzzy variable evaluation method mainly includes the following steps:

Generating index eigenvalue matrix

Suppose there is a sample set _{,} where

Establishing index standard eigenvalue matrix

Suppose there are

Calculating the relative membership matrix of index level

The interval matrix and the bound matrix of variable set of indices can be determined by referring to the standard value matrix of indices and the actual situation of the area. Then, according to the different eigenvalues of samples

Determining the weight of each index and the relative membership degree

According to equation (

Finally, according to the principle of the largest degree of membership, we can obtain the risk levels of urban rainstorm disasters.

In this paper, an integrated risk assessment index system of urban rainstorm disasters was established (see Table

Risk assessment index system of urban rainstorm disaster.

Target layer | Primary indices | Secondary indices |
---|---|---|

Urban rainstorm disaster risk | Dangerousness | Continuous rainfall days (_{11}: days) (monthly) |

Heavy rain days (_{12}: days) (monthly) | ||

Maximum rainfall in 24 h (_{13}: mm) | ||

Monthly total rainfall (_{14}: mm) | ||

Precipitation anomaly percentage (_{15}: %) | ||

Sensitivity | Urban average elevation (_{21}: m) | |

Urban green coverage rate (_{22}: %) | ||

Urban drainage network density (_{23}: km/km^{2}) | ||

Urban water area percentage (_{24}: %) | ||

Impermeable construction land (_{25}: km^{2}) | ||

Vulnerability | Density of affected population (_{31}: People/km^{2}) | |

GDP of unit area (_{32}: 100 million yuan/km^{2}) | ||

Disaster relief investment level (_{33}: %) | ||

Public emergency response capability (_{34}) |

The dangerousness indices reflect the abnormal conditions and factors of external natural environment. The risk of urban rainstorm disasters can be attributed to short-term rainfall far exceeding normal situations or long-term rainfall in cities, which will lead to the arranged discharge of rainwater beyond the capacity of urban drainage network. Generally, the larger the dangerousness is, the higher the risk of urban rainstorm disasters is. This paper chooses continuous rainfall days (_{11}: days), heavy rain days (_{12}: days), maximum rainfall in 24 h (_{13}: mm), monthly total rainfall (_{14}: mm), and precipitation anomaly percentage (_{15}: %) as the evaluation indices of dangerousness.

The sensitivity indices represent that a particular region is potential to the destruction and influence of disasters due to various natural and social factors [_{21}: m), urban green coverage rate (_{22}: %), urban drainage network density (_{23}: km/km^{2}), urban water area percentage (_{24}: %), and impermeable construction land (_{25}: km^{2}) were selected as the sensitivity indices.

The vulnerability indices describe the potential losses of the area exposed to the risk [_{31}: people/km^{2}), GDP of unit area (_{32}: 100 million yuan/km^{2}), disaster relief investment level (_{33}: %), and public emergency response capability (_{34}). The public emergency response capability (_{34}) can be quantified by expert scoring. Some experts are asked to score the index, and the average score is calculated as the index value.

Based on the risk assessment index system and IDM-VFS model, firstly the AHP was combined with the entropy method to determine the weights of the risk indices of urban rainstorm disasters; secondly, the IDM was adopted to determine the classification standards of the risk indices; thirdly, the disaster risk values in dangerousness, sensitivity, and vulnerability can be calculated by the VFS model, respectively. Finally, the comprehensive disaster risk levels were obtained and the risk zoning map was drawn.

Index weights are determined by combined AHP and the entropy weight method. The weights of risk assessment indices are shown in Table

The index weights of urban rainstorm disasters.

Primary indices | Secondary indices | Weight |
---|---|---|

Dangerousness | Continuous rainfall days (days) | 0.0630 |

Heavy rain days (days) | 0.0735 | |

Maximum rainfall in 24 h (mm) | 0.0945 | |

Monthly total rainfall (mm) | 0.0665 | |

Precipitation anomaly percentage (%) | 0.0425 | |

Sensitivity | Urban average elevation (m) | 0.0772 |

Urban green coverage rate (%) | 0.0577 | |

Urban drainage network density (km/km^{2}) | 0.0927 | |

Urban water area percentage (%) | 0.0735 | |

Impermeable construction land (km^{2}) | 0.1279 | |

Vulnerability | Density of affected population (people/km^{2}) | 0.0424 |

GDP of unit area (100 million yuan/km^{2}) | 0.0916 | |

Disaster relief investment level (%) | 0.0452 | |

Public emergency response capability | 0.0408 |

The level classification standards of each risk assessment index of urban rainstorm disasters are determined by IDM. Firstly, the index values can be taken as samples of information diffusion, then the exceeding probability of each index also can be calculated. Finally, the level classification standards of each risk assessment secondary index are obtained (see Table

Level classification standards of risk assessment secondary indices.

Secondary indices | First level (lowest) | Second level (lower) | Third level (moderate) | Forth level (higher) | Fifth level (highest) |
---|---|---|---|---|---|

Continuous rainfall days (days) | <1 | 1∼2 | 2∼4 | 4∼6 | >6 |

Heavy rain days (days) | <1 | 1∼3 | 3∼5 | 5∼7 | >7 |

Maximum rainfall in 24h (mm) | <25 | 25∼50 | 50∼100 | 100∼200 | >200 |

Monthly total rainfall (mm) | <50 | 50∼124 | 124∼236 | 236∼378 | >378 |

Precipitation anomaly percentage (%) | <4 | 4∼15 | 15∼40 | 40∼100 | >100 |

Urban average elevation (m) | >35 | 35∼20 | 20∼10 | 10∼5 | <5 |

Urban green coverage rate (%) | >50 | 50∼40 | 40∼30 | 30∼20 | <20 |

Urban drainage network density (km/km^{2}) | >32 | 32∼24 | 24∼16 | 16∼10 | <10 |

Urban water area percentage (%) | >30 | 30∼20 | 20∼15 | 15∼10 | <10 |

Impermeable construction land (km^{2}) | <90 | 90∼148 | 148∼245 | 245∼440 | >440 |

Density of affected population (people/km^{2}) | <1265 | 1265∼2355 | 2355∼3375 | 3375∼4430 | >4430 |

GDP of unit area (100 million yuan/km^{2}) | <0.8 | 0.8∼1.2 | 1.2∼3 | 3∼5 | >5 |

Disaster relief investment level (%) | >17 | 15∼17 | 13∼15 | 9∼13 | <9 |

Public emergency response capability | >90 | 90∼80 | 80∼70 | 70∼60 | <60 |

According to Table

Risk values from June to August in 2016 in Nanjing.

Month | Dangerousness | Sensitivity | Vulnerability |
---|---|---|---|

June | 4.11 | 3.82 | 2.87 |

July | 4.21 | 3.82 | 2.85 |

August | 2.46 | 3.82 | 2.88 |

Average | 3.59 | 3.82 | 2.87 |

From Table

Risk values of urban rainstorm from 2010 to 2016 in Nanjing.

Year | Dangerousness | Sensitivity | Vulnerability |
---|---|---|---|

2010 | 3.44 | 3.64 | 2.78 |

2011 | 3.75 | 3.66 | 2.78 |

2012 | 3.46 | 3.68 | 2.81 |

2013 | 3.32 | 3.68 | 2.76 |

2014 | 3.21 | 3.73 | 2.84 |

2015 | 3.61 | 3.79 | 2.86 |

2016 | 3.59 | 3.82 | 2.87 |

Average | 3.48 | 3.72 | 2.82 |

From Table

Variation tendency of risk values in terms of three subsystems from 2010 to 2016: (a) dangerousness; (b) sensitivity; (c) ulnerability.

By calculating the average risk values in different cities in terms of dangerousness, sensitivity, and vulnerability, the risk levels of urban rainstorm disasters are shown in Table

Comprehensive risk level in Jiangsu Province.

City | Dangerousness | Sensitivity | Vulnerability | Risk level |
---|---|---|---|---|

Nanjing | 3.48 | 3.72 | 2.82 | 4 |

Wuxi | 3.67 | 3.46 | 3.37 | 4 |

Xuzhou | 2.47 | 2.39 | 2.53 | 2 |

Changzhou | 3.62 | 3.58 | 3.03 | 4 |

Suzhou | 3.53 | 3.07 | 3.26 | 3 |

Nantong | 3.28 | 2.71 | 3.08 | 3 |

Lianyungang | 2.81 | 2.98 | 2.33 | 3 |

Yancheng | 2.83 | 2.74 | 2.46 | 3 |

Yangzhou | 3.02 | 2.45 | 2.87 | 3 |

Zhenjiang | 3.17 | 2.34 | 3.06 | 3 |

Taizhou | 2.86 | 2.57 | 2.94 | 3 |

Huai’an | 2.57 | 2.69 | 2.66 | 3 |

Suqian | 2.63 | 2.08 | 2.47 | 2 |

The dangerousness of Wuxi, Changzhou, Nanjing, and Suzhou is higher, while that of Xuzhou, Huai’an, and Suqian is lower from 2010 to 2016. The major influence factors of dangerousness are sustained rainfall and strong rainfall intensity in short duration. And the precipitation decreased from south to north gradually.

The sensitivity of Wuxi, Changzhou, and Nanjing is higher, while that of Xuzhou and Suqian is lower. The sensitivity of urban rainstorm disasters mainly depends on the natural and social environment of the cities and the disaster resistance level. The conditions of the different cities in Jiangsu Province are uneven. Because different cities have different natural and social environments, Zhenjiang, Xuzhou, and Suqian have higher altitudes and less impervious construction area which makes them have lower sensitivity. Wuxi, Changzhou, and Nanjing are more advanced, so they have more impervious construction area which decreased the ability of disaster resistance, resulting in higher sensitivity [

Lianyungang, Yancheng, and Suqian are located in the lowest vulnerability area, while Wuxi and Suzhou are the highest cities. The vulnerability of urban rainstorm is the reflection of the vulnerable degree of social economy and human society capability to disasters. Lianyungang, Yancheng, and Suqian have lower GDP of unit area and affected population density so they belong to the lower disaster vulnerability cities. The GDP of unit area in Wuxi and Suzhou is more than 500 million

The comparisons of different cities in terms of dangerousness, sensitivity, and vulnerability, respectively, in Jiangsu Province are shown in Figure

Comparisons of different cities in terms of dangerousness, sensitivity, and vulnerability, respectively, in Jiangsu Province.

Based on the assessment results, the comprehensive risk zoning map in Jiangsu Province can be drawn (Figure

Distribution of urban rainstorm risk in Jiangsu Province.

Urban rainstorm risk assessment involves many factors; thus, this paper established an integrated index system in terms of dangerousness of hazard-formative factors, sensitivity of hazard-inducing environments, and vulnerability of hazard-affected body. Then, the IDM and VFS models were coupled to assess the comprehensive risk of the urban rainstorm. In the coupled model, the IDM was adopted to determine the classification standards of the VFS. The assessment results of Jiangsu Province showed that most cities are at the moderate risk level, and the northwestern cities have lower risk than southern cities. In the dangerousness subsystem, due to the heavy rainfall in short-term, Wuxi, Changzhou, Nanjing, and Suzhou have higher risk than Xuzhou, Huai’an, and Suqian from 2010 to 2016. In the sensitivity subsystem, because of low urban rainstorm resistance capability, Wuxi, Changzhou, and Nanjing have higher risk than other cities. In the vulnerability subsystem, Wuxi and Suzhou have higher risk while Liangyungang, Yanchang, and Suqian have lower risk. The assessment results can help the local government to improve the rainstorm resistance capability and reduce the losses caused by rainstorm disasters.

In this paper, Jiangsu Province is a typical city suffering from frequent urban rainstorm disasters in recent years. According to the characteristics of regional urban rainstorm disasters, the risk assessment index system of urban rainstorm disasters is constructed. Based on the IDM and VFS model, the risk assessment model is established to assess the risk of rain and flood disasters in 13 cities of Jiangsu Province from 2010 to 2016. Then, according to the assessment results, the risk map of urban rainstorm disaster is drawn by ArcGIS and the assessment results are analyzed. Finally, the corresponding control measures are put forward which can provide decision-making reference for Jiangsu Province and other cities.

The continuous rainfall days, heavy rain days, maximum rainfall in 24 h, monthly total rainfall, precipitation anomaly percentage, the urban average elevation, urban green coverage rate, urban drainage network density, urban water area percentage, impermeable construction land, the density of affected population, GDP of unit area, disaster relief investment level, and public emergency response capability data used to support the findings of this study are available from the corresponding author upon request.

The authors declare that there are no conflicts of interest regarding the publication of the paper.

This research was supported by the National Key Research and Development Program of China (grant no. 2019YFC0409000), the National Natural Science Foundation of China (grant no. 41877526), the Fundamental Research Funds for the Central Universities (grant no. B200204018), the Water Conservancy Science and Technology Project of Jiangsu Province (grant no. 2017060), and the Humanities and Social Sciences Fund of Ministry of Education of China (grant no. 18YJA630009).