Climate change and increasing urbanization have contributed greatly to urban flooding, making it a global problem. The resilient city approach provides new ideas for urban flood prevention research, and currently, enhancing urban flood resilience is an effective means for alleviating urban flooding pressure. This study proposes a method to quantify the resilience value of urban flooding based on the `4R' theory of resilience, by coupling the urban rainfall and flooding model to simulate urban flooding, and the simulation results are used for calculating index weights and assessing the spatial distribution of urban flood resilience in the study area. The results indicate that (1) the high level of flood resilience in the study area is positively correlated with the points prone to waterlogging; the more an area is prone to waterlogging, the lower the flood resilience value. (2) The flood resilience index in most areas shows a significant local spatial clustering effect, the number of areas with nonsignificant local spatial clustering accounting for 46% of the total. The urban flood resilience assessment system constructed in this study provides a reference for assessing the urban flood resilience of other cities, thus facilitating the decision-making process of urban planning and disaster mitigation.

  • Enhancing urban flood resilience is an effective means for alleviating urban flooding pressure.

  • A method to quantify the resilience value of urban flooding based on the ‘4R’ theory of resilience.

  • By coupling the urban rainfall and flooding model to simulate urban flooding and using the simulation results for calculating index weights.

  • Entropy weight method.

Currently, climate change is a great challenge for the planet, which includes not only changing rainfall and flooding patterns but also increasing urbanization, combined with population and economic growth in flood-prone areas, posing an increased risk of urban flooding in many parts of the globe (Sharma et al. 2018; Hemmati et al. 2020). The causes of urban flooding as a global problem include the frequent occurrence of extreme rainstorm events; the yearly decrease in urban subsurface permeability; the yearly increase in surface catchment volume; and the increasing difficulty of drainage pipe infrastructure capacity to meet urban flood mitigation needs, resulting in annual flood losses in many cities worldwide (Sharma 2022). Reportedly, 1.81 billion people worldwide (23% of the world's population) are directly affected by a 100-year flood. Of these, 1.24 billion are inhabitants of South and East Asia, with China (395 million) and India (390 million) accounting for more than one-third of the world's affected population, and low- and middle-income countries accounting for 89% of the world's affected population (Rentschler et al. 2022). To cope with the increasing threat of flooding, improving urban flood resilience is an effective strategy, and it is, therefore, crucial to fully understand and assess the resilience of cities to flooding.

The word ‘resilience’ is derived from the Latin word ‘resilio,’ which means to restore a bent or stretched object to its original shape. The concept of ‘resilience,’ originally used to refer to the ecosystem in the 1960s, was subsequently applied to the ecological and socio-ecological fields and other fields (Pohl 2020; Zhao et al. 2020). In the 21st century, resilient cities are being actively promoted by several national and international organizations have been actively promoting resilient cities, such as the ‘Top 100 Resilient Cities’ project funded by the Rockefeller Foundation in the United States. For example, the ‘Managing Risk and Enhancing Resilience’ adaptation plan proposed by London, UK, in response to persistent flooding, the ‘Urban Resilience Index’ developed by Arup, which is widely used worldwide, and different professional resilience evaluation frameworks including energy resilience framework and water resilience framework.

The application of resilience in disaster management was gradually introduced into urban flooding research, and the concept of urban flood resilience was developed. Urban flood resilience refers to the ability of an urban system to withstand the effects of a disaster in the face of infrastructure damage and economic loss during a flood and to reorganize its resources and quickly return to its original state after a flood (Brown et al. 2012). Urban flood resilience is different from urban flood risk studied by previous authors, although many scholars have conducted studies on urban flood risk and achieved outstanding results. Ma et al. (2021) calculated the weights of risk evaluation indices based on the entropy weighting method and sequential relationship combination assignment method and used the weighted clustering method to establish an urban flood risk evaluation model (Ma et al. 2021). However, flood riskiness is more focused on the possibility of disaster occurrence and represents an outcome. Urban flood resilience, on the other hand, focuses on the ability of urban systems to cope with and recover from floods, representing a process. The ‘4R’ theory of resilience includes four characteristics: robustness, rapidity, resourcefulness, and redundancy (Rözer et al. 2022). Robustness is the ability of a city to resist floods and mitigate economic, social, human, and physical losses caused by floods; rapidity is the ability of a city to recover quickly after a disaster, and the efficiency of a city to recover to a certain level of functionality after a disaster; resourcefulness is the ability to maximize the effectiveness of resources and rationalize the allocation of disaster relief resources in the case of limited reserves of basic disaster relief resources; and redundancy indicates that the key functional facilities in the city should have certain spare modules, which can be replenished in time in the face of a sudden flooding disaster that causes damage to the functions of some facilities so that the whole system can still perform a certain level of functions.

Flood hazard studies based on a resilience framework are feasible, and many related studies have been conducted to validate them. For example, Restemeyer et al. developed a heuristic framework for the qualitative assessment of flood-resilient cities, where resilience is defined as robustness, adaptability, and transformability relative to various assessment measures (Restemeyer et al. 2015). Fox-Lent et al. (2015) and others assessed the post-Hurricane Sandy exposure of coastal communities on the Rockaway Peninsula in New York response capacity for the time course of the destructive event by using a combination of qualitative and quantitative methods to construct a resilience matrix framework (Resilience Matrix Framework) for four phases, namely infrastructure, communication, cognitive, social, and disaster preparedness. This framework assesses the resilience capacity by assigning scores to each system's units in functional areas and time courses for ready monitoring and rapid comparison of resilience gaps with other systems (Fox-Lent et al. 2015). In addition, the Drivers–Pressures–State–Impact–Response (DPSIR) framework developed by Michael Hammond and other scholars has also been proven to be an effective method for assessing urban flood resilience (Hammond et al. 2018). Therefore, a flood hazard study based on the resilience framework is feasible.

In the flood resilience assessment, the concept of assessing indicator systems has been successfully constructed to measure the resilience of flood hazards. Multi-criteria-based indicators and functional-based indicators are two common resilience assessment methods that have been proposed in some studies. Indicators based on multi-criteria indices are the mainstay of resilience assessment, which usually involves constructing a multi-criteria system and then calculating resilience by assigning weights to the indicators (Bertilsson et al. 2019; Chen & Leandro 2019). Miguez & Veról (2016) proposed a composite indicator called the flood resilience index (FRI), which is designed to support the decision-making process to select design alternatives to improve flood response when design criteria are exceeded (Miguez & Veról 2016); Kotzee et al. used principal component analysis to integrate social, ecological, infrastructure, and economic indicators related to flooding to derive a composite index (Kotzee & Reyers 2016). Moura Rezende et al. (2019) also proposed an urban FRI to quantify urban flood resilience (Moura Rezende et al. 2019). Lee et al. constructed a loss function between the flood accumulation volume and disaster through a multidimensional flood hazard analysis to establish a resilience index for urban drainage systems (Lee & Kim 2017). Alexander and others considered improving infrastructure resilience as an important indicator of urban flood resilience, such as improving the resilience of infrastructure networks such as roads, water, electricity, and gas (Alexander 2013; Yang et al. 2021). However, infrastructure resilience is only a part of what affects urban flood resilience, and other factors such as human factors should also be considered.

Many studies have evaluated flood resilience by using different methods such as geography, and the use of remote sensing (RS) and geographic information systems (GIS), combined with other evaluation methods, can be valuable in the study of urban flood resilience (Gao et al. 2022). Presently, in flood resilience research, GIS, RS, and other technologies are valuable tools for acquiring and processing relevant data, constructing models to measure resilience changes, and performing resilience measurement and evaluation (Cariolet et al. 2019). For example, the urban disaster resilience resistance analysis is conducted through high-resolution RS imagery and grid visualization (Chen et al. 2021a). Zhang et al. proposed a method to assess urban flood resilience based on the flood recovery rate of different subdivisions of cities by using all-weather synthetic aperture radar images (i.e., Sentinel-1A images), and then determined the principal component analysis factors affecting flood resilience and their relative importance (Zhang et al. 2022). In addition, AHP methods (Moghadas et al. 2019; Narimani et al. 2021), Bayesian networks (Wu et al. 2020), and flood resilience prediction and evaluation models (Liu et al. 2019) have been widely used in flood resilience research. Chen Changkun and other scholars constructed a comprehensive KL-TOPSIS assessment calculation model for urban resilience based on the TOPSIS method with an improved Kullback–Leibler formula to analyze the urban resilience of Wuhan city under rain and flood disaster scenarios (Chen et al. 2018).

The urban flood resilience indicator system forms the basis of flood resilience research. Studies have been conducted to assess the index system. For example, Xu et al. (2015) established an urban flood resilience evaluation system based on the gray box model from three dimensions, namely resilience, recovery capacity, and adaptive capacity, and evaluated the current situation of flood resilience in 238 prefecture-level and above cities in China based on the input–output method (Xu et al. 2015). Liu et al. (2018a) evaluated post-flood resilience by using the TOPSIS-PSR method (Liu et al. 2018a). Huang Jing et al. (2020) constructed an urban flood resilience evaluation system and simulation model from the perspective of system dynamics (Jing et al. 2020). Based on the ‘pressure–state–response’ (PSR) framework, Liu et al. (2018b) analyzed the pressure, state, and response processes of the urban flooding system; constructed an urban flood resilience evaluation system from three aspects, namely irritation, sensitivity, and adaptability; and evaluated the urban flood resilience of Suzhou, Wuxi, and Changzhou cities in China during three periods, that is, 2009, 2012, and 2015 (Liu et al. 2018b). By analyzing the four phases of the urban recovery process, namely Rescue, Refuge, Rebuild, and Revival, Mu Huiquan et al. established the ReCOVER system for urban resilience evaluation and systematically analyzed the resilience of cities with 62 indicators in the following five dimensions: community and population, government and management, housing and facilities, economy and development, and environment and culture (Miu et al. 2021). An evaluation method of indicators combining social factors and government governance has also been proposed (Zhang 2021). The prioritization of urban flood resilience indicators has also been studied by constructing a network model for urban flood resilience assessment to determine the interdependencies among indicators (Xu et al. 2021).

Although many urban flood resilience studies have been conducted, most of them focus mainly on flood resilience indices and evaluation index models, and there are still limitations and challenges associated with rainfall and flood models and spatial distribution of urban flood resilience. Urban flood simulation is often used to study urban flood risk and is less frequently used in urban flood resilience research. For urban flooding simulation, InfoWorks ICM is an excellent integrated watershed drainage system model that can couple a one-dimensional (1D) pipe network and two-dimensional (2D) ground for detailed simulation, which is more advantageous than models such as the SWMM model and MIKE model. The combination of the InfoWorks ICM model for numerical simulation provides greater computational accuracy and efficiency, and the model is also more realistic. Qualitative and quantitative assessments of urban flood resilience can gradually become a direction of flooding research (Jülich 2017; Lin et al. 2018; Zhou et al. 2020; Chen et al. 2021b). Improving flood resilience measures is one of the effective measures to cope with urban flooding (Lwin et al. 2020). Therefore, this study analyzes the current situation of urban resilience under heavy rainfall scenarios, constructs an urban flood resilience evaluation model based on the ‘4R’ theory of resilience, simulates urban flooding by coupling InfoWorks ICM scenario simulation, and uses the simulation results in the calculation of index weights. Finally, the study proposes a method for quantifying urban flooding resilience values. The spatial distribution of urban flood resilience in the study area and the autocorrelation analysis are analyzed with the built-up area of Xishan District, Kunming as an example, which is of great interest for disaster prevention and control in typical flood-prone areas, and it is also useful in improving urban flood resilience enhancement strategies. Enhancing urban flood resilience to reduce the losses caused by floods can be of great practical significance for the development of cities and the improvement of people's quality of life.

Study area

Geographical location

The study area selected is the built-up area of Xishan District, Kunming City, Yunnan Province, is selected as the study area. Kunming is located in the middle of the Yunnan-Guizhou Plateau, the capital of Yunnan Province, the central city of the Central Yunnan City Cluster, and one of the important central cities in western China. Xishan District is located in the western part of Kunming's municipal area, adjacent to Dianchi Lake, and is one of the 14 districts (municipalities) of Kunming, named after the famous national scenic spot, Xishan Mountain, which stretches between 102°21′-102°45′E and 24°41′-25°36′N. The selected built-up area of Xishan District in this study area includes eight offices, namely Dianchi National Tourism Resort, Qianwei Street Office, Yongchang Street Office, Fuhai Street Office, Jinbi Street Office, Palmshuying Street Office, Xiyuan Street Office, and Majie Street Office. The built-up area of the main city of Xishan District is 50.8 square kilometers, with rainwater and sewage pipes built on 840.6 kilometers, a total of 970 public drainage units (185 in old neighborhoods), and 11 overflow outlets in the rainy season. The study area location is shown in Figure 1.
Figure 1

Location of the study area.

Figure 1

Location of the study area.

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Topography and geomorphology

The study area is located at the junction of the Kang Dian Ancient Land and the Kunming Depression, with distinct undulations and inter-basin ridges. The overall topography is high in the north–west and low in the south–east, and the overall topography slopes slightly toward Dianchi, with the mountain range running north–south. The geomorphological type is more complex, with lake basin karst plateau topography landforms, erosion mesa landforms, karst mesa landforms, erosion, dissolution mesa landforms, and alluvial basin landforms. The topographic relief of the study area is shown in Figure 2.
Figure 2

Topographic elevation map of the study area.

Figure 2

Topographic elevation map of the study area.

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Climate disasters

Due to the geographical location and climate, the spatial and temporal distributions of rainfall in Kunming is uneven, and serious urban flooding disasters occur in Xishan District every rainy season, causing serious socio-economic and environmental disasters. For example, the cumulative average precipitation of the whole Xishan District in 2020 was 916.61 mm, including a single day of heavy rainfall on August 17, with a cumulative rainfall of more than 50 mm in 6 h at several sites, the largest rainfall in the street of Majie, Xishan District, and 24-h rainfall of 152.9 mm. Overnight, the spring city turned into a ‘country of water’, leading to many road interruptions.

To combat the flooding problem, in recent years, Xishan District has been further upgrading flood prevention planning, implementing old neighborhood renovation projects, creating parks, adding new urban green spaces, and building new rainwater and sewage pipes to manage the urban flooding problem at the source. However, flooding is not a simple problem, and new problems are encountered every year, resulting in flooding incidents from time to time, causing serious economic losses. Therefore, it also reveals the lack of urban flood resilience and weak basic drainage facilities, especially the operational capacity and maintenance capacity of urban underground drainage pipelines. Therefore, the study of flood resilience in Xishan District has certain research value and can provide case references for other small urban areas.

Data

The basic geographic data include RS images, DEM, slope data, rainfall data, site type data, and drainage network data. The RS images are acquired from Google Earth with a resolution of 5 m, and DEM data are derived from the Geospatial Data Cloud (www.gscloud.cn). Other data include population density data, GDP, and other socio-economic data from the statistical yearbook, and some of the data are from Yunnan Statistical Yearbook, Yunnan Disaster Reduction Yearbook, Kunming City Yearbook, and National Population Census Data. Among them, the population data in 2020 represent the data of the seventh National Population Census, the drainage network data are obtained from Kunming Survey and Mapping Research Institute, and the flood and emergency response data are obtained from Kunming Flood and Drought Control Command Office.

The basic geographic data were processed, and the slope of the study area was calculated using the slope calculation tool of ArcGIS. The RS images were extracted by supervised classification interpretation of the subsurface, which was mainly extracted into eight categories, namely green land, forest land, cultivated land, buildings, roads, bare soil, hardened surface, and water bodies. The classification of the subsurface is shown in Figure 3.
Figure 3

Land use map of the study area.

Figure 3

Land use map of the study area.

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Infoworks ICM numerical simulation

There are many flood simulation models, such as InfoWorks ICM, Storm Water Management Model (SWMM), and MIKE model. However, the SWMM is a one-dimensional hydrodynamic model that does not include river and two-dimensional surface flood models; the MIKE model achieves simulation effects by externally coupling several software; however, it lacks convenience and computational stability, and its functions are too simple and lack practicality. The InfoWorks ICM model can perform 1D and 2D simulations and has good computational accuracy in the numerical simulation of flooding; it can also perform more realistic simulation of the interaction between the underground drainage network system and the surface impounded water bodies. Therefore, InfoWorks ICM was used to simulate urban waterlogging scenarios, and a series of refinement measures were taken to improve the accuracy of the model, such as generalizing the underground drainage network and refining the division of sub-catchment areas to simulate the actual catchment process of the network and using high-precision topographic data to simulate the waterlogging conditions of buildings, roads, low-lying areas, and other urban characteristics. The InfoWorks ICM model uses a distributed model to simulate the urban rainfall–runoff process. This model is widely used in the assessment of the current status of drainage systems, urban flooding hazard assessment, urban rainfall–runoff control, and storage design assessment.

The InfoWorks ICM model uses the fully solved St Venant equation to calculate the flow velocity and water depth results of the pipe. To solve the St Venant equation, it uses the joint continuous and momentum equations to simulate asymptotic non-constant flow, and it can simulate various complex hydraulic conditions. Its computational equations are as follows (Huang et al. 2017):
formula
formula
formula
formula
formula

where A is the area of the water crossing section, Q is the discharge, t is the time, x is the length in the runoff direction, v is the velocity in the X direction, Z is the water level, g is the gravitational acceleration, τ is the average shear stress around the wet section, γ is the density of water, and R is the hydraulic radius of the wet section; H is water depth; u is the velocity in the Y direction; q1D is the areal discharge; S0,x and S0,y are the slopes in the X and Y directions, respectively; Sf, x and Sf, y are the resistance slopes in the X and Y directions, respectively; and U1D and V1D are the velocity components of Q1D in the X and Y directions, respectively.

InfoWorks ICM scenario simulation requires basic geographic data, DEM data, RS images, drainage network, rainfall, and other data. Among them, the drainage network data need to be generalized and combined, and ArcGIS is used for sub-catchment delineation and attribute calculation. The sub-catchment delineation adopts the Tyson polygon method and combines with the D8 algorithm to automatically delineate the catchment area based on the discrete rainfall inlet nodes and their neighboring nodes. The catchment area is delineated in Figure 4.
Figure 4

Map of catchment area division in the study area.

Figure 4

Map of catchment area division in the study area.

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Different sub-bedding surface types of the production and sink flow coefficient parameters are shown in Table 1. Compared with the traditional whole area using the same fixed runoff coefficient, this study considers different location catchment area flow production characteristics to ensure that the model simulation results are more realistic and reliable. The impervious surface flow production process is relatively stable; hence, the runoff coefficient method is used to predict the rainwater runoff volume. According to the Technical Guide for Sponge City Construction and Outdoor Drainage Code (2016 version) (GB50014-2006), the road runoff coefficient is 0.9, the roof runoff coefficient is 0.9, and the unutilized land is 0.45. The process of permeable surface flow production is relatively complex, and in the actual rainfall process, as the rainfall proceeds, the soil infiltration capacity decreases and the flow production coefficient increases; thus, the fixed runoff coefficient method cannot reasonably simulate the flow production process of permeable surface, and the Horton flow production model is used to simulate and express the flow production process of the permeable surface (Xu et al. 2022).

Table 1

Runoff and concentration coefficients

Underlying surface typeRunoff generation modelFixed runoff coefficientInitial infiltration rateSteady seepage rateDecay rateManning coefficient
Road Fixed 0.9 – – – 0.015 
Building Fixed 0.9 – – – 0.015 
Green space (including forested cultivated land) Horton – 227 110 2.0 0.200 
Bare surface Horton – 77 46 2.5 0.200 
Hardened surface Fixed 0.8 – – – 0.040 
River Fixed 1.0 – – – 0.002 
Underlying surface typeRunoff generation modelFixed runoff coefficientInitial infiltration rateSteady seepage rateDecay rateManning coefficient
Road Fixed 0.9 – – – 0.015 
Building Fixed 0.9 – – – 0.015 
Green space (including forested cultivated land) Horton – 227 110 2.0 0.200 
Bare surface Horton – 77 46 2.5 0.200 
Hardened surface Fixed 0.8 – – – 0.040 
River Fixed 1.0 – – – 0.002 

Entropy value method

The entropy value method is used to calculate the information entropy of each dimension based on the degree of variation of each dimensional attribute value of the target object and then assign an objective value by entropy. Originally, entropy is a concept in thermodynamics used to describe the degree of chaos of a system. Later, in information theory, entropy theory was used to evaluate the orderliness of indicators, and the entropy value method is a weighting method to determine the weight of each indicator system with the judgment matrix composed of the evaluated indicator values. Compared with other methods such as hierarchical analysis, factor analysis, and fuzzy affiliation, the entropy method measures the importance of dimension by the size of the entropy of information value with higher precision and more scientific interpretation of variable dimensions. As an objective assignment method, the entropy method can reduce the errors of human decision-making and avoid the problem of overlapping information among multiple indicators, thereby providing a reasonable basis for the comprehensive evaluation of multiple indicators (Wu et al. 2021). Its calculation steps are as follows:

  • (1)
    First, the original data matrix of the evaluation system is established according to the m partition cross-sections and n indicators covered by the measurement object.
    formula
  • (2)

    Data standardization

To eliminate the effect of the differences in the number and magnitude of different indicators on the evaluation results, the data of the selected indicators need to be standardized. Among the 17 selected indicators, some are called positive indicators as they play a positive role in urban flood resilience and some are called negative indicators as they play a negative role in urban resilience. In this study, we used the extreme value standardization method to standardize the data, which is also a common data standardization method of the entropy method, that is, each indicator data is transformed into the [0, 1] set. The processing formula for indicators is as follows:
formula
formula
where Xij is the value of the jth indicator in the ith partitioned cross-section, max(xj), min(xj) are the maximum and minimum values of the jth indicator, respectively, and Xij+ is the normalized value of the jth indicator in the ith time cross-section.
  • (3)

    Calculation of weights

Based on the results of data normalization, the weight Yij of the jth indicator in the ith time cross-section is calculated as follows:
formula
Then, the information entropy Ej and information redundancy dj of the jth indicator in the index system are calculated as follows:
formula
formula
Finally, the indicator weight wj for the jth indicator is calculated as follows:
formula

Urban FRI

The resilience level of urban systems in different periods can be assessed using the urban resilience indices, namely robustness, wisdom, redundancy, and rapidity. Therefore, an urban FRI can be established to assess the resilience level of urban systems in the face of flooding. The urban FRI is used to evaluate urban flood resilience, and different definitions and calculation methods of FRI have been given by some authors previously (Hettiarachchi et al. 2022; Qi et al. 2022).

The comprehensive evaluation method was used to calculate the urban FRI for the study area. The comprehensive evaluation method is one of the most widely used methods to calculate the evaluation index, which mainly involves mathematically multiplying the values of each indicator after normalization with the combined weights of that indicator and then accumulating them. The formula is as follows:
formula
where wj denotes the weight of each indicator, Xij denotes the normalized value of the indicator, j is the number of indicators, and FRI is the urban flood resilience index; the larger the FRI, the greater is the resilience, and vice versa.

Spatial correlation analysis of resilience

To further analyze the flood resilience-driven role of the study area and the potential for the coordinated development of regional resilience, we used the Moran's I method of spatial autocorrelation analysis to examine the relationship of flood resilience among units in the built-up area of Xishan and determine the spatial correlation and degree of correlation of urban flood resilience. Local spatial autocorrelation, which describes the degree of similarity between a spatial unit and its domain, can indicate the degree to which each local unit obeys the global general trend and is often expressed by Local Moran's I. The calculation formula is as follows:
formula
formula
where wij is the value of one element of the spatial weight matrix w, n is the total number of cities in the study area, and xi and xj are the flood resilience indices of neighboring cities. The local spatial association patterns can be divided into four types: high–high association, low–low association, high–low association, and low–high association. Among them, high–high and low–low correlation patterns are both positive spatial correlations; a high–high correlation means that the location is a high value and other surrounding areas are also high values, representing a high-value aggregation area, whereas a low–low correlation means that the location is a low value and surrounding areas are also low values, representing a low-value cluster area. High–low and low–high correlations are both negative spatial correlations. High–low correlation means that it is a high value but surrounded by low values, and the spatial unit with higher attribute values than the average value is surrounded by areas with the attribute values lower than the average value; low–high correlation means that it is a low value but surrounded by high values. The low–high association means that the spatial unit is low but surrounded by high values, and the spatial unit with attribute values below the mean value is surrounded by the domain with attribute values above the mean value.

Selection of evaluation indicators

To assess the resilience of urban flooding, it is crucial to determine whether the indicators for urban flood resilience assessment have been objectively and scientifically selected. The data of initial screening indicators include rainfall, slope, flood emergency plan, population density, length of drainage network, road network density, number of pumping stations of storage ponds, imperviousness rate, drainage network capacity, flood emergency response, comprehensive disaster mitigation demonstration community ratio, broadcasting coverage, disaster warning forecast, GDP, per capita disposable income, per capita medical point, social security and employment expenditure ratio, depth of road waterlogging, road waterlogging time length, gross regional product (million yuan), and post-disaster reconstruction management funds.

The principles of indicator selection are as follows: (1) Scientificity: It is the basic requirement for any evaluation index system. In urban flooding disaster, due to its large scope of influence and more scattered affected area, any wrong decision amounts to a huge loss. Thus, an evaluation index system must be of high scientificity. (2) Feasibility: The purpose of establishing the resilience assessment system of urban flooding disaster is to enhance the resilience of cities and municipalities in the face of urban flooding disaster. Feasibility is required to achieve the purpose of assessing the expected results, and the selected factors have strong operability. (3) Purposefulness and relevance: An evaluation index system is established to enhance the resilience of urban flooding disaster, and the typical indices that best reflect the situation of the influencing factors of the content to be evaluated are selected. (4) Independence: Each indicator in the index system should be clear in connotation and relatively independent, and any overlap between indicators should be avoided to the maximum possible extent to ensure the accuracy of the evaluation system.

According to the aforementioned principles, 15 urban flood resilience evaluation indices are finally selected, which are as follows: robustness indices comprise mainly road water depth, road network density, population density, and flood-prone points; rapidity indices comprise mainly rainfall intensity, imperviousness, and terrain slope; strategy indices comprise mainly emergency management plan, flood emergency response, comprehensive disaster mitigation demonstration community, and disaster warning forecast; and redundancy indices comprise mainly the number of storage pond pumping stations, GDP, per capita disposable income, and post-disaster reconstruction capital investment. The urban flood resilience evaluation indicators are presented in Table 2.

Table 2

Urban flood resilience evaluation indicators

Primary indicatorsSecondary indicatorsIndicator interpretationIndicator impact (positive/negative)
Urban flood resilience evaluation index Robustness Road flooding depth Maximum water depth of the road surface Negative 
Road network density Reflects urbanization to some extent Negative 
Population density Population density causes congestion and drainage network pressure Negative 
Waterlogged spots The area where the water on the road surface is greater than 30 cm Negative 
 Rapidity Rainfall intensity The intensity of urban rainfall Negative 
Imperviousness The rate of the hardened road surface Negative 
Slope of terrain The slope of the terrain affects the speed and area of water accumulation Negative 
Strategy Emergency Management Plan Emergency management, command and rescue plans in the event of flooding Positive 
Flood Emergency Response Emergency and rescue response is an important part of reducing disaster losses Positive 
Comprehensive Disaster Reduction Demonstration Community Cultivate and educate public awareness of disaster prevention and mitigation Positive 
Disaster warning forecast Refers to effective prediction and warning of heavy rainfall and flooding Positive 
Redundancy Number of pumping stations for storage ponds Storage pond pumping stations can accelerate drainage capacity Positive 
GDP GDP represents the level of regional economic development Positive 
Per capita disposable income The financial capacity of individual residents to recover after a disaster Positive 
Post-disaster reconstruction management funds Funds and materials prepared by relevant government departments for flood control and disaster recovery Positive 
Primary indicatorsSecondary indicatorsIndicator interpretationIndicator impact (positive/negative)
Urban flood resilience evaluation index Robustness Road flooding depth Maximum water depth of the road surface Negative 
Road network density Reflects urbanization to some extent Negative 
Population density Population density causes congestion and drainage network pressure Negative 
Waterlogged spots The area where the water on the road surface is greater than 30 cm Negative 
 Rapidity Rainfall intensity The intensity of urban rainfall Negative 
Imperviousness The rate of the hardened road surface Negative 
Slope of terrain The slope of the terrain affects the speed and area of water accumulation Negative 
Strategy Emergency Management Plan Emergency management, command and rescue plans in the event of flooding Positive 
Flood Emergency Response Emergency and rescue response is an important part of reducing disaster losses Positive 
Comprehensive Disaster Reduction Demonstration Community Cultivate and educate public awareness of disaster prevention and mitigation Positive 
Disaster warning forecast Refers to effective prediction and warning of heavy rainfall and flooding Positive 
Redundancy Number of pumping stations for storage ponds Storage pond pumping stations can accelerate drainage capacity Positive 
GDP GDP represents the level of regional economic development Positive 
Per capita disposable income The financial capacity of individual residents to recover after a disaster Positive 
Post-disaster reconstruction management funds Funds and materials prepared by relevant government departments for flood control and disaster recovery Positive 

Changes in climate and geographic factors in the natural system, such as rainfall and topographic changes, and ‘pressure stimulation’ in the social system, such as population growth and unreasonable land use, can affect the state of the urban flooding system; ‘state change’, such as reduced vegetation cover and higher population density, can make the urban flooding resilient. ‘The system receives the signal of state change and makes ‘response measures’, such as soil infiltration of rainwater, improving flood control engineering, and strengthening flood monitoring, etc., which slow down the system pressure, coordinate and improve the system state, and increase resilience to the urban flooding.

Pressure caused by the factors such as rainfall and population density, topography, and imperviousness of the subgrade accounts mainly for the generation of urban flooding. The greening coverage of the built-up area represents the greening rate of the area, which is conducive to the response and disposal of flooding disasters; the higher the greening rate, the higher is the resistance to flooding disasters; the density of storage ponds and pumping stations reflects, to a certain extent, the ability of post-disaster recovery of the area, and a high water disposal rate is more conducive to post-disaster recovery and reconstruction after the occurrence of flooding disasters and serious water accumulation in the city.

GDP represents the level of regional economic development, reflecting the level of regional economic resilience; disposable income per capita reflects the household economic strength, which is conducive to people's self-help and mutual help in the face of disasters, and to a certain extent, representing the ability of regional disaster prevention and mitigation. The population density has gradually increased, the green space rate of the lower pad surface is decreasing year by year, and the green space area of the built-up area is decreasing continually. All these natural and man-made pressures lead to a reduction in the resistance and release capacity of urban systems to flooding.

Model flow

The urban flood resilience assessment model is shown in Figure 5. This study considered the raster data and vector data of the study area; hence, all data were unified into raster data through raster to vector operation to facilitate subsequent calculations. The data were standardized using ArcGIS 10.6 software, and the raster calculator tool of GIS map algebra and the entropy value method were used to calculate the weights of each indicator. Finally, the comprehensive flood resilience value was calculated by superposition.
Figure 5

Flow chart of the flood resilience assessment model.

Figure 5

Flow chart of the flood resilience assessment model.

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Model simulation validation

Based on the InfoWorks ICM scenario simulation, we determined the simulated water accumulation area, water accumulation depth, and water accumulation time in the study area under different scenarios. The validation of the urban flooding results is mainly judged by comparing the ponding points and ponding depths and by assessing whether the simulated maximum ponding depths are consistent with the reality. Presently, limited waterlogging data are available, and the waterlogging point situation and waterlogging depth can only be judged through the network, media, and photos taken by the public. Through the network information, Kunming city news reports of the method for determining the intersection of Guangfu Road and Qianwei West Road where flooding occurred, the depth of ponding about 40 cm, the simulation results of the area also occurred, the average depth of water is about 0.4 m. Comparing the model simulation results with the official waterlogging data and information released by Kunming city, the obtained waterlogging distribution in the study area and the simulation results are basically consistent with the actual situation, which verifies the feasibility of the flooding model simulation, as shown in Figure 6. The results of the integrated flooding model simulation are used as input data for resilience evaluation, which are highly consistent with the actual situation.
Figure 6

Simulation results of water accumulation point greater than 30 cm.

Figure 6

Simulation results of water accumulation point greater than 30 cm.

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Urban flooding resilience value

The vector data were transformed into raster data by using the ArcGIS conversion function to achieve the unification of the basic units of the indicators. All data were standardized, after which the raster calculator was used to calculate the weight of each indicator by using the entropy value method wj, and the value of each indicator after normalization was calculated by mathematically multiplying it with the comprehensive weight of that indicator to obtain the urban FRI for cities coping with flooding at the raster level. The FRI values for each street office were then determined to assess the flood resilience of the area. Table 3 shows the urban flood resilience weights for the extreme rainfall scenario in 2020 to compare and analyze the resilience of each part of the city's indicators under different scenarios.

Table 3

Calculation results of weights

Primary indicatorsWeightsSecondary indicatorsWeights
Robustness 0.477 Road flooding depth 0.047 
Road network density 0.116 
Population density 0.067 
Waterlogged spots 0.245 
Rapidity 0.213 Rainfall intensity 0.126 
Imperviousness 0.062 
Slope of terrain 0.025 
Strategy 0.065 Emergency Management Plan 0.012 
Flood Emergency Response 0.021 
Comprehensive Disaster Reduction Demonstration Community 0.012 
Disaster warning forecast 0.020 
Redundancy 0.245 Number of pumping stations for storage ponds 0.065 
GDP 0.080 
Per capita disposable income 0.080 
Post-disaster reconstruction management funds 0.020 
Primary indicatorsWeightsSecondary indicatorsWeights
Robustness 0.477 Road flooding depth 0.047 
Road network density 0.116 
Population density 0.067 
Waterlogged spots 0.245 
Rapidity 0.213 Rainfall intensity 0.126 
Imperviousness 0.062 
Slope of terrain 0.025 
Strategy 0.065 Emergency Management Plan 0.012 
Flood Emergency Response 0.021 
Comprehensive Disaster Reduction Demonstration Community 0.012 
Disaster warning forecast 0.020 
Redundancy 0.245 Number of pumping stations for storage ponds 0.065 
GDP 0.080 
Per capita disposable income 0.080 
Post-disaster reconstruction management funds 0.020 

As shown in the table of weight calculation results, among the urban flood resilience indicators, the water accumulation point has the largest weight, followed by the rainfall intensity and road network density in the study unit, and the difference in the weight of other indicators does not have a great impact. This may be because the greater the weight of water accumulation points reflecting flood resilience, the greater is its impact on flooding, which is an important indicator to improve resilience. Another important indicator is the drainage infrastructure such as storage ponds and pumping stations, which reflects the performance of the city in resisting flooding.

According to the comprehensive evaluation method to calculate the urban FRI of each street office in the study area, the comprehensive resilience of each subdistrict was determined, as shown in Table 4. Yongchang street office and Jinbi street office exhibited the highest comprehensive resilience value, and the higher the index value, the lower the resistance and the worse is the ability to resist disasters. The area with the highest degree of resilience is Dianchi National Tourism Resort because it has permeable substrates such as parks, rivers, and lakes, and owing to the excellent drainage capacity of the pipe network, the loss caused by flood disaster is small.

Table 4

Integrated flood resilience by street offices

Street officesUrban FRI
Dianchi National Tourism Resort 6.836072 
Qianwei Street Office 10.535379 
Yongchang Street Office 20.819113 
Fuhai Street Office 11.755251 
Jinbi Street Office 20.480848 
Palmshuying Street Office 15.870557 
Xiyuan Street Office 11.854879 
Majie Street Office 7.50502 
Street officesUrban FRI
Dianchi National Tourism Resort 6.836072 
Qianwei Street Office 10.535379 
Yongchang Street Office 20.819113 
Fuhai Street Office 11.755251 
Jinbi Street Office 20.480848 
Palmshuying Street Office 15.870557 
Xiyuan Street Office 11.854879 
Majie Street Office 7.50502 

Spatial distribution of flood resilience

The quantitative presentation of flood resilience can more intuitively reflect the differences of each partition. The spatial distribution of flood resilience is expressed using the GIS spatial zoning method. Based on the established evaluation system and the collected data, the urban flood resilience values of each zoning district were calculated. The mapping of the spatial distribution of flood resilience in the study area was performed using ArcGIS 10.2, as shown in Figure 7.
Figure 7

Spatial distribution of flood resilience in the study area of Xishan District.

Figure 7

Spatial distribution of flood resilience in the study area of Xishan District.

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The FRI values are divided into five classes according to the natural breakpoint method, that is, the class intervals of 0.01–2, 2–9, 9–20, 20–40, and >40, representing the toughness classes of very low toughness, low toughness, medium toughness, high toughness, and very high toughness, respectively. The lower the toughness value, the worse is the ability of the area to resist flooding, and the more likely is the flooding to occur.

The spatial distribution shows that the areas with higher resilience in Xishan District have a discrete distribution. The resilience of urban flooding in Xishan District is lowest in the area near the center, whereas that of other areas such as Dianchi resort area in the south and Majie Street Office in the west is high. This is because the west of the study area is Xishan Mountain, which is covered by a large area of vegetation, namely Xishan Park, causing a weak impact of flooding. The Dianchi National Resort Center is in the southern part of the study area, near Dianchi, which has more rivers and lakes, high permeability, and surface confluence that can be discharged in a timely manner, which is less likely to cause waterlogging and therefore has relatively high resilience. The central area has relatively low flood resilience due to its developed economy, dense population, and traffic congestion. Some areas of Qianwei street office lie in the low resilience area, which is basically consistent with the official notification data. Some street offices also have more ponding points, which can be partly attributed to the relatively low resilience to flooding due to the low-lying terrain in the area, possibly accounting for more flooding ponding points, as shown in the DEM. The medium resilience area is concentrated in the central part of the study area, which belongs to the area of Kunming city developed earlier, with the urban area being relatively old and the drainage network being difficult to renovate.

Figure 8 shows the Moran's I scatter plot of urban FRI in the built-up area of Xishan. The Moran's index shows the local spatial correlation characteristics, with the Moran's I value being 0.563, which passes the test of significance level α = 0.05, indicating that the regional distribution has some aggregation.
Figure 8

Scatter plot of Moran's I of the urban FRI for the built-up area of Xishan.

Figure 8

Scatter plot of Moran's I of the urban FRI for the built-up area of Xishan.

Close modal
We used GeoDa and ArcGIS software to draw the LISA clustering map of urban FRI in the built-up area of Xishan, as shown in Figure 9. The flood resilience indices of most of the eight offices in the built-up area of Xishan show obvious local spatial clustering effects; the number of areas with nonsignificant local spatial clustering accounts for 46% of the total and the number of areas with significant clustering accounts for nearly 54% of the total, and the overall pattern of local spatial clustering shows the aggregation of core points. Among the local spatial agglomeration types, the ‘low–low’ type areas predominate and are spatially distributed at the edges. The ‘high–high’ and ‘low–high’ areas account for 15 and 3% of the total, respectively, and are spatially distributed in a point pattern.
Figure 9

LISA clustering map of the urban FRI for the built-up area of Xishan.

Figure 9

LISA clustering map of the urban FRI for the built-up area of Xishan.

Close modal

Overall, the spatial correlation of the urban FRI in the built-up area of Xishan is positive and significantly clustered. The comprehensive results show that the urban flood resilience of several street offices in the central city needs to be improved. The changes of urban flood resilience values reflect the resistance and resilience of the city to floods in different areas of development. Because of high population density and public facilities, central areas face greater flood risk than suburban areas in the event of flooding.

Discussion

In the context of climate change, extreme rainfall events are becoming increasingly common, which inevitably poses a great challenge to urban resilience, eventually resulting in changes in the rapidity, strategizing, and redundancy of cities facing flooding change (Brown et al. 2012; Yang et al. 2021). The spatial distribution of flood resilience and LISA clustering diagram show that a high level of urban flood resilience is positively correlated with urban flooding accumulation points, which implies that the flood resilience of areas with extremely easy accumulation points is low, thus indicating that flood resilience depends on the distribution of accumulation points, and the more prone an area is to water accumulation, the lower is its flood resistance and recovery, which is in line with reality.

In this study, some innovations were made in the research method, that is, different data were used for different indicators, and instead of using the fuzzy data from the statistical yearbook exclusively for the calculation, the results obtained by using the actual data combined with the hydraulic model simulations were used as input parameters into the weight calculation.

The study of urban flood resilience evaluation is an important initiative to evaluate the ability of cities to cope with flooding (Restemeyer et al. 2015). Due to the complex structure of the urban flooding system and the presence of many influencing factors, thoroughly analyzing the overall urban flooding system resilience is challenging, which is a limitation of this study. In addition, as some of the road offices have incomplete data, the values for small areas are obtained by rasterizing the data for large areas, which may have some impact on the accuracy of the study result. Fewer studies have been conducted to express and analyze flood resilience at smaller scales (Wu et al. 2022). The study provides an objective assessment of the current situation of urban flood resistance in the study region, from the perspective of spatial distribution characteristics and the change process.

In this study, we constructed the urban flood resilience evaluation indices and flood resilience indices, with eight street offices built in Xishan District, Kunming City, as the study area, and used the index data of the study area in 2020 to perform the simulation. We obtained the data of water accumulation points and depth of water accumulation in the study area by constructing the InfoWorks ICM flood model. Further, by using the simulated data and index data combined with the entropy value method, we determined the comprehensive FRI. The distribution of urban flood resilience strength and the importance of indicators are revealed in GIS. The results showed that the flood resilience of the built-up area of Xishan is positively correlated with the waterlogging points, that is, the more waterlogged an area is, the lower is its flood resilience value. Additionally, the results indicated that the FRI of most areas shows obvious local spatial clustering effect, with the number of areas having nonsignificant local spatial clustering accounting for 46% of the total and the number of areas having significant clustering accounting for 54% of the total.

The study of urban flood resilience evaluation is an important initiative to evaluate the ability of cities to cope with flooding. Due to the complex structure of the urban flooding system and the presence of many influencing factors, thoroughly analyzing the overall urban flooding system resilience is challenging, which is a limitation of this study.

The urban flood resilience assessment system constructed in this study provides a reference to carry out urban flood resilience assessment for other regions, and the assessment results provide a reference for flood control planning and resilient city construction in regions such as dams. The study also found that the strength of flood resilience is strongly associated with the water accumulation point. Water accumulation point is commonly caused by topography, drainage capacity of the drainage pipe network, emergency solutions, and other problems. Solving these problems or improving these capabilities are the key to enhance urban flood resilience.

We would like to thank the Kunming Surveying and Mapping Institute and Kunming Drainage Facilities Management Co., Ltd for their support.

This study was supported by the Postgraduate Research and Innovation Foundation of Yunnan University (2021Y041), Yangtze River Conservancy Commission Yangtze River Academy of Sciences 2022 Open Research Fund (CKWV20221029/KY), 2021 Ministry of Natural Resources Digital Cartography, and Land Information Key Laboratory Open Research Fund (ZRZYBWD202108).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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