Urban Flood Management through Urban Land Use Optimization Using LID Techniques, City of Addis Ababa, Ethiopia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. LID
2.3. SWMM Model and Simulation Process
2.4. Climate Change
2.5. Design of Rainstorm
2.6. Model Parameter Calibration and Validation
2.7. Design of LID Scenarios
- No LID technique: This does not consider any LID effect and it is therefore considered as a base line scenario.
- LID technique based on infiltration (LID-Infiltration): This scenario consists of LID types that temporarily store runoff and infiltrate into the ground. In this study only the effects of Bio-Retention cells and infiltration trenches were taken among alternative infiltration-based LID techniques. Based on land use types and their area coverage (Table 4) and population density, both LID techniques were proposed on relatively sparsely populated areas and condominium houses as shown in Figure 4.
- LID technique based on water storage (LID-Storage): Rain barrels were proposed to be located in densely populated residential areas. Storage units were set up for runoff control in sub-catchment areas which are prone to flooding by collecting runoff from roof tops. It was proposed that 60% of households use rain barrels for this purpose. There are about 5500 housing units excluding condominium houses and about 3300 rain barrels to be used.
- LID technique based on the combination of infiltration and water storage (LID-Combination): This scenario is a combination of scenario 2 and scenario 3 (Table 5).
2.8. Rainfall Patterns and Durations
3. Results
3.1. Effects of Various LID Scenarios on Rainfall-Runoff Relation of 10 min Duration
3.2. Effects of Various LID Scenarios on Runoff of 30 min Storm Duration
3.3. Effects of Various LID Scenarios on Rainfall-Runoff Relation of 1 h Duration
3.4. Effect of LID Scenarios under Climate Change
4. Discussion
4.1. Impact of Rainfall Patterns on Peak Runoff
4.2. Impact of LID Measures on Runoff Management
5. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Extreme PCP Indices. | RCP 4.5 | RCP 8.5 | Reference | Extent of Study | ||||
---|---|---|---|---|---|---|---|---|
2020s | 2050s | 2080s | 2020s | 2050s | 2080s | |||
Rx1day | 20–25 | 25–30 | 30–35 | 15–20 | 15–20 | 15–20 | [6] | Ethiopia |
---- | 7.22 | ---- | ---- | ---- | ---- | [27] | Addis Ababa | |
---- | ---- | ---- | ---- | ---- | 15–30 | [26] | Worldwide | |
Rx5day | 35–40 | 35–40 | 35–40 | 10–20 | 15–20 | 10–20 | [6] | Ethiopia |
21 | ---- | ---- | ----- | ----- | 17 | [27] | Addis Ababa | |
---- | ---- | ---- | ---- | ---- | 10–30 | [26] | Worldwide | |
SDII | −4–20 | −4–22 | −5–23 | −12–5.5 | −7–8 | −5–18 | [6] | Ethiopia |
---- | ---- | ---- | ---- | ---- | 5–25 | [26] | Worldwide | |
Total pcp | 40–65 | 40–65 | 40–65 | 15 | 10 | −10 | [6] | Ethiopia |
---- | ---- | 29.3 | ---- | ---- | 21 | [27] | Addis Ababa | |
95th p | 20 | 25 | 20 | 25 | 5 | 10 | [6] | Ethiopia |
Pav | ---- | ---- | ---- | ---- | ---- | 5–15 | [26] | Worldwide |
---- | ---- | 5–10 | ---- | ---- | 15–30 | IPCC, 2014 | Global |
No | Model Parameter | Parameter Definition | Value Range | Initial Value | Final Value |
---|---|---|---|---|---|
1 | N-Imperv | Manning coefficients in impervious areas | 0.006–0.05 | 0.01 | 0.014 |
2 | N-Perv | Manning coefficients in pervious areas | 0.08–0.5 | 0.1 | 0.1 |
3 | S-Imperv | Depression storage in impervious areas/mm | 0.011–0.24 | 0.02 | 0.05 |
4 | S-Perv | Depression storage in pervious areas/mm | 0.2–5 | 0.5 | 2 |
5 | Max-Rate | Maximum infiltration rate (mm/h) | 25–75 | 25 | 40 |
6 | Min-Rate | Minimum infiltration rate (mm/h) | 0–10 | 2 | 5 |
7 | Decay | Infiltration decay constant (1/h) | 2–7 | 3 | 5 |
8 | %Zero-Imperv | Percentage of impervious area with no depression storage (%) | 0–100 | 10 | 25 |
No | Land Use (Catchment Type) | Comprehensive Curve Number Coefficient (CCNC) | Equivalent Runoff Coefficient |
---|---|---|---|
1 | Densely built commercial areas | 78–94 | 0.7–0.9 |
2 | Densely built residential areas | 76–92 | 0.6–0.8 |
3 | Sparsely built residential areas | 72–88 | 0.5–0.7 |
4 | Sparsely populated areas | 70–86 | 0.4–0.6 |
No | Land Use Type | % Impervious | Area (ha) | Areal Coverage (%) |
---|---|---|---|---|
1 | Built-up (houses) | 75 | 149.16 | 67.8 |
2 | Parking lots | 50 | 16.06 | 7.3 |
3 | Green areas | 40 | 27.06 | 12.3 |
4 | Roads and pavements | 70 | 24.64 | 11.2 |
5 | Stream | - | 3.08 | 1.4 |
Total | - | 220 | 100 |
No | LID Type | Area (ha) | Coverage (%) | % of Total Area |
---|---|---|---|---|
1 | Bio-Retention cell | 26.2 | 39.7 | 11.9 |
2 | Infiltration trench | 18.4 | 27.9 | 8.4 |
3 | Rain Barrel | 21.3 | 32.4 | 9.7 |
Total | 65.9 | 100 | 30.00 |
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Jemberie, M.A.; Melesse, A.M. Urban Flood Management through Urban Land Use Optimization Using LID Techniques, City of Addis Ababa, Ethiopia. Water 2021, 13, 1721. https://doi.org/10.3390/w13131721
Jemberie MA, Melesse AM. Urban Flood Management through Urban Land Use Optimization Using LID Techniques, City of Addis Ababa, Ethiopia. Water. 2021; 13(13):1721. https://doi.org/10.3390/w13131721
Chicago/Turabian StyleJemberie, Mengistu A., and Assefa M. Melesse. 2021. "Urban Flood Management through Urban Land Use Optimization Using LID Techniques, City of Addis Ababa, Ethiopia" Water 13, no. 13: 1721. https://doi.org/10.3390/w13131721