Flood Risk Assessment Based on Fuzzy Synthetic Evaluation Method in the Beijing-Tianjin-Hebei Metropolitan Area, China
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Indicator Selection and Data Analysis
- Digital elevation model data (DEM, m): DEM can reflect the absolute elevation and topographic fluctuations of the study area. In general, flooding is more likely to occur at lower altitudes than at higher elevations. The original resolution of DEM is 90 × 90 m.
- Slope data (SL, degree): The slope reflects the topographic change and runoff velocity. In general, steep slopes of the mountain areas promote runoff generation, whereas low-lying level regions are prone to severe flooding and waterlogging due to poor drainage. SL was extracted from the DEM data using GIS technique.
- Rainfall intensity data (RI, normalized value): By analyzing the daily precipitation data of 126 precipitation stations in the study area from 1981 to 2010, the maximum three-day precipitation was calculated to characterize rainfall intensity [38]. Then, spatial interpolation was performed using Kriging interpolation and the RI was generated.
- Rainstorm frequency (RF, times/year): Rainstorm refers to a heavy rainfall event with precipitation of more than 50 mm within 24 hours in China. The rainfall frequency of each rain gauge station was calculated based on the daily precipitation data from 1981 to 2010.
- Drainage density (DD, km/3 km2): Based on the linear density analysis technology of ArcGIS, the drainage density was analyzed using the river vector data in the study area [16]. The search radius was 3 km and a data layer of 1 × 1 km was generated.
- Normalized difference vegetation index (NDVI): This indicator can effectively reflect the distribution of vegetation coverage on the spatial scale of the study area. The vegetation not only regulates the surface runoff but also prevents soil erosion [39].
- Soil texture (ST): Different soil textures have various infiltration capacities and a specific impact on surface runoff. In this study, we used the soil infiltration capacity according to the code values in the Harmonized World Soil Database (HWSD). The soil texture classification criteria in this database were divided into 13 categories by the US Department of Agriculture (USDA) [1]. As shown in Table 1, a large code value indicates a strong infiltration capacity.
- Gross domestic product (GDP, yuan/km2): GDP indicates the prosperity degree of the national economy of a country or region, which is an essential indicator of flood vulnerability. In this paper, we collected the GDP data from 2015 with a resolution of 1 × 1 km.
- Population density (PD, people/km2): This indicator reflects the spatial distribution of human exposure in the research area in 2015.
- Grain output (GO, kg/hm2): Food is a special commodity and an essential strategic reserve related to people’s livelihoods. This data mainly considers the grain yield of five crops (wheat, corn, rice, soybean, and potato) in the study area.
- Road network density (RND, km/km2): The strong hydrodynamic conditions during the flood will inevitably destroy roads. Furthermore, secondary geological disasters caused by floods, such as landslides and debris flows in mountainous areas, pose severe threats to road and rail network systems.
- Per capita disposable income (PDI, 10,000 yuan/people): PDI can reflect the overall socio-economic level of a region. The higher the PDI, the stronger the disaster resistance and the faster the post-disaster recovery and reconstruction.
- The ratio of children and the elderly (RCE, %): Children (0 to 14 years old) and older people (65 years and older) are more vulnerable to flooding due to age and some physiological reasons.
- Average schooling years (ASY, year): ASY is an important indicator to measure the degree of popularization of higher education in a country or region (over six years old). Generally, ASY is positively related to people’s awareness of disaster prevention and reduction.
3. Methodology
3.1. Overall Framework of the Evaluation Procedure
3.2. Fuzzy Synthetic Evaluation (FSE)
3.2.1. Fuzzy Classification
3.2.2. Membership Function and Comprehensive Evaluation
3.2.3. Comprehensive Evaluation
3.3. Calculation of the Weights for Indices Based on the CW Method
4. Results and Discussions
4.1. Weight Calculation Results
4.2. Distribution Characteristics of Flood Risk in the BTH Area
4.3. Results Validation
4.4. Comparison with other Methods
4.5. Flood Risk Mitigation
4.5.1. Engineering Measures
4.5.2. Non-Engineering Measures
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
Appendix A. Membership Functions of Flood Risk Assessment in the BTH Area
Appendix B. Detailed Calculation Steps of Combined Weight for Risk Assessment
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ST | Sand | Loamy Sand | Sandy Loam | Sandy Clay Loam | Loam | Sand Clay | Silt Loam |
---|---|---|---|---|---|---|---|
IC | 13 | 12 | 11 | 10 | 9 | 8 | 7 |
ST | Silt | Clay loam | Silty clay loam | Clay | Silty clay | Clay (heavy) | |
IC | 6 | 5 | 4 | 3 | 2 | 1 |
Indices | Time | Original Resolution | Data Sources |
---|---|---|---|
DEM | / | 90 × 90 m | Computer Network Information Center of the Chinese Academy of Sciences (http://www.gscloud.cn/) |
SL | / | 90 × 90 m | Same as above |
RI | 1981–2010 | Vector data | National Meteorological Information Center (http://data.cma.cn/) |
RF | 1981–2010 | Vector data | Same as above |
DD | 2017 | Vector data | National Geomatics Center of China (http://www.ngcc.cn/ngcc/) |
RND | 2017 | Vector data | Same as above |
NDVI | 2018 | 1 × 1 km | Resource and Environment Data Cloud Platform of the Chinese Academy of Sciences (http://www.resdc.cn/) |
GDP | 2015 | 1 × 1 km | Same as above |
GO | / | 1 × 1 km | Same as above |
ST | / | 1 × 1 km | Food and Agriculture Organization of the United Nations (http://www.fao.org/home/en/) |
PD | 2015 | 1 × 1 km | Peking University Geographic Data Platform (https://geodata.pku.edu.cn/) |
PDI | 2017 | County unit | Beijing Regional Statistical Yearbook (2018), Tianjin Statistical Yearbook (2018), Hebei Economic Yearbook (2018) |
RCE | 2017 | County unit | Same as above |
ASY | 2000 | County unit | Fifth population census data of the National Bureau of Statistics of the People’s Republic of China (2000) |
Flood Disaster Risk | Indicators | Fuzzy Classification (Discontinuities) | Indicator Attributes | ||||
---|---|---|---|---|---|---|---|
d1 | d2 | d3 | d4 | d5 | |||
Hazard | Digital elevation model data (DEM, m) | 1500.00 | 1000.00 | 500.00 | 50.00 | 5.00 | negative |
Slope data (SL, degree) | 20.00 | 10.00 | 6.00 | 2.00 | 0.20 | negative | |
Rainfall intensity data (RI, Normalized value) | 0.21 | 0.37 | 0.53 | 0.69 | 0.85 | positive | |
Rainstorm frequency (RF, times/year) | 0.50 | 1.50 | 2.50 | 3.50 | 4.50 | positive | |
Drainage density (DD, km/3 km2) | 0.00 | 0.12 | 0.24 | 0.36 | 0.48 | positive | |
Normalized difference vegetation index (NDVI) | 0.90 | 0.81 | 0.72 | 0.63 | 0.54 | negative | |
Soil texture (ST) | 13.00 | 12.00 | 11.00 | 9.00 | 7.00 | negative | |
Vulnerability | Gross domestic product (GDP, yuan/km2) | 50.00 | 200.00 | 1000.00 | 3000.00 | 5000.00 | positive |
Population density (PD, people/km2) | 1.00 | 200.00 | 400.00 | 800.00 | 1600.00 | positive | |
Road network density (RND, km/km2) | 0.00 | 0.55 | 1.10 | 1.65 | 2.20 | positive | |
Grain output (GO, kg/hm2) | 967.94 | 2581.17 | 4463.27 | 6668.02 | 8872.77 | positive | |
Per capita disposable income (PDI, 10000 yuan/people) | 2.64 | 2.29 | 2.01 | 1.76 | 1.55 | negative | |
Ratio of children and the elderly (RCE, %) | 23.00 | 25.00 | 26.00 | 27.00 | 28.00 | negative | |
Average schooling years (ASY, year) | 9.40 | 8.80 | 8.20 | 7.60 | 7.20 | negative |
Indicator | Hazard | Vulnerability | |||||||
---|---|---|---|---|---|---|---|---|---|
Hazard | 1 | 4/3 | |||||||
Vulnerability | 3/4 | 1 | |||||||
λmax = 2; CI = 0; RI = 0; CR = 0 | |||||||||
Indicator | RI | RF | DEM | DD | SL | NDVI | ST | ||
RI | 1 | 1 | 2 | 2 | 3 | 4 | 5 | ||
RF | 1 | 1 | 2 | 2 | 3 | 4 | 5 | ||
DEM | 1/2 | 1/2 | 1 | 2 | 3 | 3 | 4 | ||
DD | 1/2 | 1/2 | 1/2 | 1 | 2 | 3 | 4 | ||
SL | 1/3 | 1/3 | 1/3 | 1/2 | 1 | 2 | 3 | ||
NDVI | 1/4 | 1/4 | 1/3 | 1/3 | 1/2 | 1 | 3 | ||
ST | 1/5 | 1/5 | 1/4 | 1/4 | 1/3 | 1/3 | 1 | ||
λmax = 7.215; CI = 0.036; RI = 1.320; CR = 0.027 | |||||||||
Indicator | GDP | PD | GO | RND | PDI | RCE | ASY | ||
GDP | 1 | 1 | 3 | 4 | 5 | 7 | 8 | ||
PD | 1 | 1 | 3 | 4 | 5 | 7 | 8 | ||
GO | 1/3 | 1/3 | 1 | 3 | 4 | 5 | 7 | ||
RND | 1/4 | 1/4 | 1/3 | 1 | 3 | 5 | 5 | ||
PDI | 1/5 | 1/5 | 1/4 | 1/3 | 1 | 4 | 5 | ||
RCE | 1/7 | 1/7 | 1/5 | 1/5 | 1/4 | 1 | 3 | ||
ASY | 1/8 | 1/8 | 1/7 | 1/5 | 1/5 | 1/3 | 1 | ||
λmax = 7.574; CI = 0.096; RI = 1.320; CR = 0.073 |
Indicator | AHP (Analytic Hierarchy Process) | EW (Entropy Weight) | CW (Combined Weight) |
---|---|---|---|
DEM | 0.0999 | 0.0062 | 0.0058 |
SL | 0.0485 | 0.0024 | 0.0011 |
RI | 0.1450 | 0.0224 | 0.0303 |
RF | 0.1450 | 0.0192 | 0.0260 |
DD | 0.0777 | 0.0224 | 0.0162 |
NDVI | 0.0343 | 0.0496 | 0.0159 |
ST | 0.0206 | 0.0504 | 0.0097 |
GDP | 0.1313 | 0.3133 | 0.3843 |
PD | 0.1313 | 0.3414 | 0.4188 |
RND | 0.0438 | 0.0651 | 0.0266 |
GO | 0.0712 | 0.0954 | 0.0635 |
PDI | 0.0279 | 0.0018 | 0.0005 |
RCE | 0.0142 | 0.0078 | 0.0010 |
ASY | 0.0094 | 0.0028 | 0.0002 |
Risk Levels | Very High | High | Moderate | Low | Very Low |
---|---|---|---|---|---|
Historical floods (%) | 34.13 | 15.87 | 38.10 | 8.73 | 3.17 |
Risk Levels | Very High | High | Moderate | Low | Very Low |
---|---|---|---|---|---|
Dead points (%) | 77.50 | 11.70 | 6.30 | 2.70 | 1.80 |
Historical Floods | Inundated Area (km2) | Percentage of Total Area (%) | Overlapping Ratio with Different Risk Levels (%) | ||||
---|---|---|---|---|---|---|---|
Very High | High | Moderate | Low | Very Low | |||
1939 | 31713 | 17.54 | 34.90 | 15.50 | 15.60 | 6.40 | 27.70 |
1963 | 24342 | 13.46 | 22.30 | 17.20 | 26.30 | 7.30 | 27.00 |
Flood Risk | Scenario 1 | Scenario 2 |
---|---|---|
Index Distribution in TAR and AR5 | Index Distribution in AR5 | |
Hazard | RI, RF | RI, RF |
Exposure | GDP, PD, GO, RND | GDP, PD, RND |
Sensitivity | DEM, SL, DD, NDVI, ST | GO, DEM, SL, DD, NDVI, ST |
Adaptive Capacity | PDI, RCE, ASY | PDI, RCE, ASY |
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Wang, G.; Liu, Y.; Hu, Z.; Lyu, Y.; Zhang, G.; Liu, J.; Liu, Y.; Gu, Y.; Huang, X.; Zheng, H.; et al. Flood Risk Assessment Based on Fuzzy Synthetic Evaluation Method in the Beijing-Tianjin-Hebei Metropolitan Area, China. Sustainability 2020, 12, 1451. https://doi.org/10.3390/su12041451
Wang G, Liu Y, Hu Z, Lyu Y, Zhang G, Liu J, Liu Y, Gu Y, Huang X, Zheng H, et al. Flood Risk Assessment Based on Fuzzy Synthetic Evaluation Method in the Beijing-Tianjin-Hebei Metropolitan Area, China. Sustainability. 2020; 12(4):1451. https://doi.org/10.3390/su12041451
Chicago/Turabian StyleWang, Guangpeng, Yong Liu, Ziying Hu, Yanli Lyu, Guoming Zhang, Jifu Liu, Yun Liu, Yu Gu, Xichen Huang, Hao Zheng, and et al. 2020. "Flood Risk Assessment Based on Fuzzy Synthetic Evaluation Method in the Beijing-Tianjin-Hebei Metropolitan Area, China" Sustainability 12, no. 4: 1451. https://doi.org/10.3390/su12041451
APA StyleWang, G., Liu, Y., Hu, Z., Lyu, Y., Zhang, G., Liu, J., Liu, Y., Gu, Y., Huang, X., Zheng, H., Zhang, Q., Tong, Z., Hong, C., & Liu, L. (2020). Flood Risk Assessment Based on Fuzzy Synthetic Evaluation Method in the Beijing-Tianjin-Hebei Metropolitan Area, China. Sustainability, 12(4), 1451. https://doi.org/10.3390/su12041451