4.2. Weights with Subjective and Objective Approaches
We used two different weights, as shown in
Table 3 and
Table 4. As noted above, “Delphi” corresponds to an average of expert opinions obtained from Delphi surveys. The weights for indicators (
i.e., criteria) or for components (sensitivity, adaptive capacity, and climate exposure) based on the expert opinions gathered in the Delphi survey were quite different from those based on the data characteristics given by Shannon’s entropy, as expected. Considering the weights for different vulnerability components (sensitivity, adaptive capacity, and climate exposure), the entropy-based method led to relatively smaller weights for climate exposure according to both the flood and water scarcity vulnerabilities; however, the Delphi survey led to relatively larger weights. Nevertheless, the entropy-based weights for sensitivity were considerably higher than the Delphi-based weights for flood and water scarcity vulnerability. The opposite weights were derived with adaptive capacity, meaning that the entropy-based weights for flood damage and the Delphi-based weights for water scarcity were higher than the others.
Table 3.
Weighting and indicator values for flood vulnerability.
Table 3.
Weighting and indicator values for flood vulnerability.
Criteria | Weighting Value | Indicator Value |
---|
Delphi | Entropy | Min. | Avg. | Max. |
---|
Sensitivity | 0.27 | 0.42 | | | |
Low-lying area of less than 10 m (km2) | 0.10 | 0.067 | 0.0 | 17.5 | 266.2 |
Low-lying household of less than 10 m | 0.10 | 0.061 | 0.0 | 2.5 | 61.9 |
Area ratio with banks (%) | 0.07 | 0.153 | 0.0 | 2.6 | 21.1 |
Population density (persons/km2) | 0.12 | 0.095 | 0.19 | 38.7 | 271.8 |
Total population (persons) | 0.10 | 0.148 | 0.833 | 202.8 | 1040 |
Regional average slope (°) | 0.11 | 0.190 | 0.8 | 11.5 | 23.0 |
Percentage of road area (%) | 0.07 | 0.156 | 0.7 | 5.6 | 26.2 |
Cost of flood management over last three years (106 Korean won) | 0.16 | 0.081 | 0.0 | 342.5 | 21129.5 |
Population affected by flood management over last three years (10 persons) | 0.15 | 0.049 | 0.4 | 200.8 | 103.8 |
Adaptive Capacity | 0.34 | 0.38 | | | |
Financial independence (%) | 0.13 | 0.192 | 7.4 | 28.0 | 90.5 |
Number of civil servants per population (persons/103 people) | 0.07 | 0.208 | 25.0 | 55.0 | 90.8 |
GRDP (106 Korean won) | 0.11 | 0.180 | 8.7 | 87.7 | 236.5 |
Number of civil servants related to water | 0.13 | 0.104 | 0.0 | 0.4 | 7.9 |
Ratio of improved river section (%) | 0.14 | 0.208 | 16.0 | 72.6 | 100.0 |
Capacity of drainage facilities (m3/min) | 0.21 | 0.105 | 0.0 | 48.1 | 459.0 |
Flood controllability of reservoirs (106 m3) | 0.21 | 0.003 | 0.0 | 11.3 | 616.0 |
Climate Exposure | 0.39 | 0.19 | | | |
Daily maximum precipitation (mm) | 0.31 | 0.205 | 58.4 | 80.8 | 162.6 |
Days with over 80 mm of rainfall (days) | 0.23 | 0.189 | 0.0 | 0.7 | 2.5 |
5-day maximum rainfall (mm/5 days) | 0.19 | 0.205 | 92.6 | 141.6 | 273.1 |
Surface runoff (mm/day) | 0.16 | 0.197 | 0.0 | 0.1 | 0.3 |
Summer precipitation (June–September) (mm) | 0.11 | 0.205 | 311.8 | 605.1 | 933.9 |
In terms of sensitivity to flood damage, the expert groups placed more weight on the cost and population associated with past flood damage, but the data-driven weights were relatively low for those indicators. Additionally, a reservoir’s flood controllability (i.e., capacity) in terms of adaptive capacity for flood damage also differed between the Delphi and entropy approaches, meaning that reservoir capacity was critical for flood vulnerability but lacking in its assessment function. The indicators for water scarcity vulnerability, such as household water consumption and reservoir capacity, also presented discrepancies.
Table 4.
Weighting and indicator values for water scarcity vulnerability.
Table 4.
Weighting and indicator values for water scarcity vulnerability.
Criteria | Weighting Value | Indicator Value |
---|
Delphi | Entropy | Min. | Avg. | Max. |
---|
Sensitivity | 0.31 | 0.38 | | | |
Population density (persons/km2) | 0.11 | 0.078 | 0.19 | 38.7 | 271.8 |
Total population (persons) | 0.10 | 0.117 | 8.3 | 202.6 | 1040 |
Water supply (L/person/day) | 0.07 | 0.143 | 299.7 | 359.5 | 444.1 |
Grain production per area (ton/km2) | 0.07 | 0.092 | 0.0 | 29.5 | 300.1 |
Livestock production per area (km2) | 0.06 | 0.101 | 0.6 | 65.4 | 630 |
Groundwater withdrawal (m3/year) | 0.08 | 0.119 | 0.02 | 15.7 | 103.3 |
River water withdrawal (m3/year) | 0.09 | 0.118 | 0.0 | 152.8 | 762.1 |
Household water consumption (103/m3/year) | 0.15 | 0.051 | 0.2 | 11.5 | 143.2 |
Industrial water usage (103 m3/year) | 0.14 | 0.077 | 0.0 | 13.2 | 279.7 |
Agriculture water usage (103 m3/year) | 0.13 | 0.103 | 0.01 | 68.0 | 743.5 |
Adaptive Capacity | 0.38 | 0.33 | | | |
Financial independence (%) | 0.12 | 0.152 | 7.4 | 27.9 | 90.5 |
Civil servants per population (persons/104 people) | 0.05 | 0.159 | 25.0 | 55.0 | 90.8 |
GRDP (106 Korean won) | 0.09 | 0.139 | 8.7 | 87.7 | 236.5 |
Number of civil servants related to water (persons) | 0.09 | 0.085 | 0.0 | 0.4 | 7.9 |
Water supply distribution ratio (%) | 0.15 | 0.165 | 74.5 | 89.6 | 100.0 |
Groundwater capacity (103 m3/year) | 0.14 | 0.139 | 0.32 | 46.8 | 327.0 |
Reservoir for water supply capacity per area (103 m3) | 0.21 | 0.093 | 0.0 | 1.3 | 22.3 |
Recycled water usage per area (103 ton/year) | 0.15 | 0.068 | 0.18 | 14.9 | 210.0 |
Climate Exposure | 0.31 | 0.29 | | | |
Maximum number of continuous non-rainy days (days) | 0.22 | 0.178 | 13.9 | 21.0 | 26.2 |
Winter (Dec, Jan and Feb; DJF) precipitation (mm) | 0.18 | 0.173 | 0.5 | 1.1 | 3.4 |
Spring (Mar, Apr and May; MAM) precipitation (mm) | 0.21 | 0.178 | 1.2 | 1.9 | 3.0 |
Winter (DJF) evapotranspiration (mm) | 0.10 | 0.125 | 0.2 | 1.9 | 13.8 |
Spring (MAM) evapotranspiration (mm) | 0.13 | 0.175 | 0.04 | 2.3 | 5.6 |
Underground outflow (mm) | 0.15 | 0.171 | 0.0 | 0.3 | 0.7 |
4.3. Vulnerability Rankings and Scores
We assessed flood and water scarcity vulnerability based on district size using two different weights, as shown in Equations (8)–(15). First, we normalized the performance values because they are mutually incompatible.
Table 3 and
Table 4 show that most of the measurement units are different; therefore, their magnitudes vary substantially. To make the various criterion scores compatible, they must be transformed into a common measurement unit while ensuring that the scores for each criterion range from 0 to 1. The normalization technique that uses the maximum or minimum values,
i.e., Equation (9), is limited in considering large variations in the percentage or fractional ranges of the performance values. For example, performance values for the cost of flood management range from approximately 0 to 21.1 million Won; the ratios relative to the average range from approximately 0 to 62. Moreover, the performance values for the water supply range from approximately 330 to 444 L/person/day; the ratios relative to the average range from 0.83 to 1.2. Such smoothing effects of normalization can be overcome by other normalization techniques, such as the Z-score method, which, to some extent, uses the average and standard variation of the performance values. However, such a method is limited in that criteria with relatively large ranges of performance values might over-dominate the final scores.
In terms of flood vulnerability (
Table 5 and
Figure 2a and
Figure 3a), G3 is the most vulnerable group, which is followed by G1. However, G4, G5, and G6 are relatively negatively vulnerable. Based on these findings, districts with 200,000–300,000 inhabitants are vulnerable to flood damage in a changing climate. Therefore, preventive measures for flood damage should be planned in these medium-sized districts. In terms of sensitivity rankings, G3 and G1 are highly vulnerable; G2 is the next most vulnerable group. The flood damage for these three regions may be adversely affected by climate-related stimuli. In terms of adaptive capacity, G1, G2, and G3 are the most vulnerable,
i.e., they do not have the ability to evolve to accommodate environmental hazards or policy changes for increases in flood damage. In terms of climate exposure, G5 is the most vulnerable, while G6, G1, and G2 formed the second most vulnerable group; therefore, these groups have a high likelihood of exposure to extreme climate stimuli. Therefore, G1 is overly weak in terms of adaptive capacity, and districts belonging to G1 should plan to enhance their ability to accommodate environmental hazards.
In terms of water scarcity (
Table 6 and
Figure 2b and
Figure 3b), G1 is the most vulnerable region, whereas G5 and G6 are relatively stable districts. Contrary to the results for flood vulnerability, districts with more than 500,000 inhabitants are vulnerable to water scarcity in a changing climate; therefore, preventive measures that ensure more stable water resources should be investigated. In terms of sensitivity and adaptive capacity, G1, G2 and G4 are highly vulnerable, whereas G3, G5, and G6 are relatively less vulnerable. Because G1, G2, and G4 have a high likelihood of extreme drought conditions, a numerical analysis of water scarcity should be conducted.
Table 5.
Flood vulnerability according to the Delphi and entropy methods. Sensitivity, adaptive capacity, and climate exposure are normalized values.
Table 5.
Flood vulnerability according to the Delphi and entropy methods. Sensitivity, adaptive capacity, and climate exposure are normalized values.
Method | Symbol | Sensitivity | Adaptive Capacity | Climate Exposure | C* | Ranking |
---|
TOPSIS with Delphi | G1 | 0.510 | 0.656 | 0.370 | 0.363 | 2 |
G2 | 0.474 | 0.565 | 0.411 | 0.306 | 3 |
G3 | 0.546 | 0.498 | 0.412 | 0.871 | 1 |
G4 | 0.460 | 0.487 | 0.413 | 0.263 | 4 |
G5 | 0.354 | 0.412 | 0.419 | 0.163 | 5 |
G6 | 0.193 | 0.311 | 0.394 | 0.023 | 6 |
TOPSIS with entropy | G1 | 0.515 | 0.715 | 0.465 | 0.398 | 2 |
G2 | 0.490 | 0.555 | 0.498 | 0.331 | 3 |
G3 | 0.513 | 0.494 | 0.489 | 0.886 | 1 |
G4 | 0.427 | 0.521 | 0.488 | 0.266 | 4 |
G5 | 0.297 | 0.471 | 0.477 | 0.138 | 5 |
G6 | 0.165 | 0.403 | 0.474 | 0.004 | 6 |
Figure 3.
Rankings for flood and water scarcity vulnerability for each group based on the Delphi and Entropy methods. (a) Flood vulnerability; (b) Water scarcity vulnerability.
Figure 3.
Rankings for flood and water scarcity vulnerability for each group based on the Delphi and Entropy methods. (a) Flood vulnerability; (b) Water scarcity vulnerability.
Table 6.
Water scarcity vulnerability according to the Delphi and entropy methods. Sensitivity, adaptive capacity, and climate exposure are normalized values.
Table 6.
Water scarcity vulnerability according to the Delphi and entropy methods. Sensitivity, adaptive capacity, and climate exposure are normalized values.
Method | Symbol | Sensitivity | Adaptive Capacity | Climate Exposure | C* | Ranking |
---|
TOPSIS with Delphi | G1 | 0.782 | 0.581 | 0.590 | 1.000 | 1 |
G2 | 0.698 | 0.439 | 0.537 | 0.568 | 2 |
G3 | 0.670 | 0.482 | 0.493 | 0.538 | 3 |
G4 | 0.582 | 0.501 | 0.522 | 0.438 | 4 |
G5 | 0.520 | 0.507 | 0.471 | 0.286 | 5 |
G6 | 0.476 | 0.526 | 0.393 | 0.224 | 6 |
TOPSIS with entropy | G1 | 0.669 | 0.696 | 0.531 | 1.000 | 1 |
G2 | 0.518 | 0.518 | 0.470 | 0.246 | 4 |
G3 | 0.571 | 0.499 | 0.466 | 0.300 | 3 |
G4 | 0.531 | 0.531 | 0.498 | 0.325 | 2 |
G5 | 0.543 | 0.505 | 0.448 | 0.215 | 5 |
G6 | 0.543 | 0.523 | 0.378 | 0.125 | 6 |
Furthermore, the two rankings determined using the Delphi and entropy methods are identical for flood vulnerability; however, these rankings are slightly different for water scarcity vulnerability (
Figure 3). G2 is the second most vulnerable district according to the Delphi weights and the fourth most vulnerable based on the entropy method, while G4 is fourth and second, respectively. Furthermore,
Figure 4 presents the rankings based on the average vulnerability scores from the Delphi and entropy methods. Based on a comparison of the Delphi-based vulnerability and the entropy-based vulnerability, the results suggest that it is crucial to consider multiple possibilities for criteria weights to make robust decisions.
Figure 4.
Distribution of flood and water scarcity vulnerability in districts based on the average vulnerability scores determined using the Delphi and entropy methods. (a) Flood vulnerability; (b) Water scarcity vulnerability.
Figure 4.
Distribution of flood and water scarcity vulnerability in districts based on the average vulnerability scores determined using the Delphi and entropy methods. (a) Flood vulnerability; (b) Water scarcity vulnerability.