Exploring the Optimal Cost-Benefit Solution for a Low Impact Development Layout by Zoning, as Well as Considering the Inundation Duration and Inundation Depth
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data Collection
2.2. Analytical Framework
2.3. Partition Matrix Construction
2.4. Simulations of LID Combination Scenarios
- Initial scenario: 0 GR + 0 PP + 0 VS
- Scenario I (I): 10%GR + 10%PP + 10%VS
- Scenario II (II): 20%GR + 20%PP + 20%VS
- Scenario III (III): 30%GR + 30%PP + 30%VS
- Scenario IV (IV): 40%GR + 40%PP + 40%VS
- Scenario V (V): 50%GR + 50%PP + 50%VS
- Scenario VI (VI): 60%GR + 60%PP + 60%VS
- Scenario VII (VII): 70%GR + 70%PP + 70%VS
- Scenario VIII (VIII): 80%GR + 80%PP + 80%VS
- Scenario IX (IX): 90%GR + 90%PP + 90%VS
- Scenario X (X): 100%GR + 100%PP + 100%VS
2.5. Cost-Benefit Analysis of Optimal LID Solution
3. Results
3.1. Spatial Distributions of Different ISI Values
3.2. Overall Impact of LID
3.3. Performance of Different Zones
3.4. Optimal Combination of Costs and Benefits
4. Discussion
4.1. Optimal Combination of Costs and Benefits
4.2. Necessity for Resilient Subdivisions
4.3. Reasons for Cost–Benefit Analysis of LID Construction
4.4. Limitations and Future Directions
5. Conclusions
- Overall, the degree of inundation reduction increased, but the rate of reduction decreased slowly as the density of LID construction increased. These findings demonstrated that the implementation of LID strategies obtained cost benefits;
- Optimal cost–benefit solutions exist for single LID strategies. When multiple LID strategies were implemented, the effect was not simply the sum of both, but instead synergistic or antagonistic effects were obtained;
- Dividing the study area based on the degree of flooding severity is essential because of the difference in performance in different zones at the urban watershed scale. We found the optimal solution in terms of the cost-benefit ratio in the Vulnerable Zones. However, the main purpose of this study was to find different optimal combinations for diverse study areas and to identify a universal law rather than determining specific values;
- The LID strategy achieved effective results at improving the inundation resilience, but it was unable to completely prevent flooding at all of the inundation points due to various factors, such as the construction scope, distribution pattern, and the type parameters. In future research, the comprehensive application of multiple resilience improvement strategies will be the main approach employed to build a resilient city by maximizing the resilience enhancement effect.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Types | Structure | Parameters | Value | Unit |
---|---|---|---|---|
GR | Surface | Berm height | 3 | mm |
Vegetation volume fraction | 0.1 | |||
Surface roughness | 0.017 | |||
Surface slope | 1 | % | ||
Soil | Thickness | 100 | mm | |
Porosity | 0.5 | |||
Field capacity | 0.2 | |||
Wilting point | 0.024 | |||
Conductivity | 30 | mm/h | ||
Conductivity slope | 5 | |||
Suction head | 60 | mm | ||
Drainage mat | Thickness | 3 | mm | |
Void fraction | 0.5 | |||
Roughness | 0.1 | |||
PP | Surface | Berm height | 2 | mm |
Vegetation volume fraction | 0 | |||
Surface roughness | 0.014 | |||
Surface slope | 1 | % | ||
Pavement | Thickness | 100 | mm | |
Void ratio | 0.25 | |||
Impervious surface fraction | 0 | |||
Permeability | 250 | mm/h | ||
Clogging factor | 0 | |||
Storage | Thickness | 150 | mm | |
Void ratio | 0.4 | |||
Seepage fate | 1.2 | mm/h | ||
Clogging factor | 0 | |||
VS | Surface | Berm height | 80 | mm |
Vegetation volume fraction | 0 | |||
Surface roughness | 0.24 | |||
Surface slope | 1 | % | ||
Soil | Thickness | 20 | mm | |
Porosity | 0.5 | |||
Field capacity | 0.2 | |||
Wilting point | 0.1 | |||
Conductivity | 5 | mm/h | ||
Conductivity slope | 10 | |||
Suction head | 3.5 | mm |
- Residences in urban villages would not be reconstructed with GR;
- The highways would not be reconstructed by PP for considering the pressure resistance of permeated pavement and other factors;
- PP could be used for reconstruction in several land use types with conditions such as the roads and parking lots in communities;
- vs. would be used in the green space in urban villages, commercial residential land, industrial land and water-permeable surfaces in park and green land.
Types | Commercial Residential Land | Urban Village | Industrial Land | Park and Green Land | Roads | Others |
---|---|---|---|---|---|---|
GR | √ | — | √ | — | — | — |
PP | √ | √ | √ | √ | — | — |
VS | √ | √ | √ | √ | — | — |
Types | Structures | Costs | Units |
---|---|---|---|
GR | Protection layer | 1.88 | USD/m2 |
Plant | 6.57 | USD/m2 | |
Soil | 86.30 | USD/m2 | |
PP | excavation | 4.69 | USD/m2 |
Filter fabric | 1.88 | USD/m2 | |
Disposal | 15.95 | USD/m2 | |
Asphalt pavement | 182.91 | USD/m2 | |
VS | Plant | 6.57 | USD/m2 |
Soil | 86.30 | USD/m2 | |
excavation | 4.69 | USD/m2 |
Intensity of GR | ISI (m·min) | Reduction (m·min) | Relative Cost (Million USD) | CEI (m·min/Million USD) |
---|---|---|---|---|
0% | 33,371 | |||
10% | 31,130 | 2241 | 14.84 | 151.01 |
20% | 28,533 | 4838 | 29.68 | 163.01 |
30% | 26,861 | 6510 | 44.52 | 146.23 |
40% | 24,847 | 8524 | 59.36 | 143.60 |
50% | 22,940 | 10,431 | 74.20 | 140.58 |
60% | 21,219 | 12,152 | 89.04 | 136.48 |
70% | 19,672 | 13,699 | 103.88 | 131.87 |
80% | 18,274 | 15,097 | 118.86 | 127.01 |
90% | 17,046 | 16,325 | 133.70 | 122.10 |
100% | 15,926 | 17,446 | 148.54 | 117.45 |
Intensity of VS | ISI (m·min) | Reduction (m·min) | Relative Cost (Million USD) | CEI (m·min/Million USD) |
---|---|---|---|---|
0% | 28,533 | |||
10% | 28,452 | 82 | 2.94 | 27.891 |
20% | 28,386 | 148 | 5.74 | 25.784 |
30% | 28,309 | 224 | 8.68 | 25.806 |
40% | 28,185 | 348 | 11.48 | 30.314 |
50% | 28,090 | 443 | 14.42 | 30.721 |
60% | 28,004 | 529 | 17.22 | 30.720 |
70% | 27,938 | 595 | 20.16 | 29.514 |
80% | 27,863 | 670 | 23.10 | 29.004 |
90% | 27,790 | 743 | 25.90 | 28.687 |
100% | 27,708 | 825 | 28.84 | 28.61 |
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Type | Use | Source |
---|---|---|
Land use data | Simplified to six types: commercial residential land, urban village, industrial land, park and green land, roads and others | Shenzhen Municipal Government |
Digital Elevation Model(DEM) | Extracting slope and aspect, and identifying the catchment area | Geospatial Data Cloud (http://www.gscloud.cn/) |
Soil data | Reference for soil infiltration rate | Natural resource survey results in Shenzhen |
Pipe network data | Characterizing the drainage capacity of urban systems | Shenzhen Municipal Government |
Hourly rainfall data | Input for rainstorm model | Shenzhen Meteorological Data System (https://data.szmb.gov.cn/) |
Building census data | Identifying non-submerged areas | Shenzhen City Planning and Land Resources Committee |
Inundation points in shapefile format | Calibrating and validating the model parameters | Shenzhen SanFang headquarters and Guangming New District Urban Construction Bureau |
Inundation Area | Average Inundation Depth | |||
---|---|---|---|---|
Value (km2) | Reduction Rate | Value (m) | Reduction Rate | |
Initial scenario | 1.596 | 0.09 | ||
Scenario I | 1.386 | 13% | 0.08 | 15% |
Scenario II | 1.231 | 23% | 0.07 | 28% |
Scenario III | 1.126 | 29% | 0.06 | 38% |
Scenario IV | 1.026 | 36% | 0.05 | 45% |
Scenario V | 0.941 | 41% | 0.05 | 50% |
Scenario VI | 0.882 | 45% | 0.04 | 53% |
Scenario VII | 0.842 | 47% | 0.04 | 56% |
Scenario VIII | 0.801 | 50% | 0.04 | 58% |
Scenario IX | 0.767 | 52% | 0.04 | 60% |
Scenario X | 0.741 | 54% | 0.03 | 62% |
Density | Reduction Rate | CEI (m·min/Million USD) | ||||
---|---|---|---|---|---|---|
GR | PP | VS | GR | PP | VS | |
10% | 4% | 5% | 0.2% | 131.43 | 215.00 | 27.14 |
20% | 9% | 9% | 0.4% | 137.14 | 212.14 | 30.71 |
30% | 13% | 14% | 0.5% | 139.29 | 215.00 | 27.86 |
40% | 19% | 18% | 0.6% | 147.14 | 219.29 | 26.43 |
50% | 24% | 23% | 0.9% | 150.71 | 221.21 | 29.29 |
60% | 29% | 28% | 1.0% | 152.86 | 221.76 | 27.14 |
70% | 34% | 32% | 1.3% | 153.57 | 220.00 | 29.29 |
80% | 39% | 37% | 1.7% | 152.86 | 218.57 | 34.29 |
90% | 44% | 41% | 2.0% | 150.71 | 216.43 | 35.00 |
100% | 47% | 45% | 2.1% | 147.86 | 212.86 | 34.29 |
ISI (m·min) | Reduction (m·min) | Reduction Rate | Cost (Million USD) | CEI (m·min/Million USD) | |
---|---|---|---|---|---|
Initial scenario | 46,308 | — | — | — | — |
Scenario III | 32,781 | 13,527 | 29% | 82.32 | 164.29 |
Optimal solution | 28,090 | 18,218 | 39% | 102.48 | 177.86 |
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Wu, J.; Chen, Y.; Yang, R.; Zhao, Y. Exploring the Optimal Cost-Benefit Solution for a Low Impact Development Layout by Zoning, as Well as Considering the Inundation Duration and Inundation Depth. Sustainability 2020, 12, 4990. https://doi.org/10.3390/su12124990
Wu J, Chen Y, Yang R, Zhao Y. Exploring the Optimal Cost-Benefit Solution for a Low Impact Development Layout by Zoning, as Well as Considering the Inundation Duration and Inundation Depth. Sustainability. 2020; 12(12):4990. https://doi.org/10.3390/su12124990
Chicago/Turabian StyleWu, Jiansheng, Ying Chen, Rui Yang, and Yuhao Zhao. 2020. "Exploring the Optimal Cost-Benefit Solution for a Low Impact Development Layout by Zoning, as Well as Considering the Inundation Duration and Inundation Depth" Sustainability 12, no. 12: 4990. https://doi.org/10.3390/su12124990