Exploring the Spatial Impact of Green Infrastructure on Urban Drainage Resilience
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview
2.2. GI Types and Modelling Approaches
2.3. Resilience Assessment
2.4. Location Sensitivity Analysis
- The system with no design or operational changes is used as the reference for system performance. GRA is used to evaluate the baseline (or no-GI case) resilience for the different indicators, and it is referred to as .
- A single GI infrastructure with a consistent type is applied in each subcatchment at a time and simulated for resilience assessment. The GI size is determined by the maximum extent possible determined by the land use of the subcatchment.
- Assessment of resilience using GRA considering the different metrics. The impact of different GI types and their location in the system is reflected by a change in the response curve shape.
- The net change in the system’s resilience due to the placement of a GI in a subcatchment is calculated using Equation (1).
2.5. Exploratory Spatial Data Analysis
2.5.1. Visualisation
2.5.2. Global Spatial Autocorrelation
2.5.3. Local Spatial Autocorrelation
- High-high (HH): a high value of net change in resilience in a subcatchment, neighbouring subcatchments have high values of net change in resilience.
- Low-high (LH): a low value of net change in resilience in a subcatchment, neighbouring subcatchments have high values of net change in resilience.
- Low-low (LL): a low value of net change in resilience in a subcatchment, neighbouring subcatchments have low values of net change in resilience.
- High-low (HL): a high value of net change in resilience, neighbouring subcatchments have low values of net change in resilience.
2.5.4. Spatial Weights Matrix
- Queen contiguity: which reflects adjacency relationships as a binary indicator variable denoting whether a subcatchment shares an edge or a vertex with another polygon.
- Rook contiguity: which considers the subcatchment neighbours only when they are sharing an adjacent edge.
- K-nearest neighbours matrices (k = 4, 5, 6): the distances between a given subcatchment and the rest of the set are ranked, and the neighbours are defined as the k closest ones in the ranking [40].
2.6. Case Study
3. Results
3.1. Visualisation
3.2. Global Spatial Autocorrelation
3.3. Local Spatial Autocorrelation
4. Discussion
4.1. GI location and Resilience Enhancement Spatial Relationships
4.2. ESDA and Implications in Urban Planning
4.3. Limitations and Future Research
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Layer | Parameter | Green Roof | Permeable Pavement | Bio-Retention Cell |
---|---|---|---|---|
Surface | Berm height (mm) | 50 | 0 | 150 |
Vegetation fraction (%) | 0.5 | 0 | 0.1 | |
Roughness (Manning’s n) | 0.4 | 0.012 | 0.1 | |
Surface slope (%) | 0 | 1 | 1 | |
Pavement | Thickness (mm) | - | 150 | - |
Void ratio (volume fraction) | - | 0.15 | - | |
Fraction Imperviousness (%) | - | 0 | - | |
Permeability (mm/h) | - | 500 | - | |
Clogging factor | - | 0 | - | |
Soil | Thickness (mm) | 150 | - | 600 |
Porosity (volume fraction) | 0.45 | - | 0.5 | |
Field capacity (volume fraction) | 0.2 | - | 0.2 | |
Wilting point (volume fraction) | 0.1 | - | 0.1 | |
Conductivity (mm/h) | 650 | - | 250 | |
Conductivity slope (-) | 5 | - | 12.5 | |
Suction head (mm) | 49.5 | - | 50 | |
Storage | Height (mm) | - | 300 | 150 |
Void ratio (-) | - | 0.4 | 0.75 | |
Seepage factor (mm/h) | - | 7.0 | 7 | |
Drain | Coefficient (mm/h) | - | 0.5 | 0.5 |
Exponent (-) | - | 0.5 | 0.5 | |
Offset height (mm) | - | 100 | 150 | |
Delay (days) | - | 0 | 0 | |
Drain Mat | Thickness (mm) | 75 | - | - |
Void ration (volume fraction) | 0.75 | - | - | |
Roughness (Manning’s n) | 0.1 | - | - |
Level of Service Loss | Metric | Description | Calculation | Notations |
---|---|---|---|---|
Sewer flooding | Failure Magnitude | The total flood volume in the system | Summation of flood volume at the flooded nodes | SF_M |
Failure Duration | Time taken between the occurrence of flooding to the recovery of normal performance | Average of the duration of the flooding at the flooded nodes | SF_D | |
CSO (combined sewer overflows) | Failure Magnitude | The total CSO volume in the system | Summation of CSO volume at the outfall nodes | CSO_M |
Failure Duration | Time taken between the occurrence of CSO to the recovery of normal performance | Average of the duration of the CSO at the outfall nodes | CSO_D |
SF_M | SF_D | CSO_M | CSO_D | ||||||
---|---|---|---|---|---|---|---|---|---|
GI Type | Spatial Weights | Moran’s I | p-Value | Moran’s I | p-Value | Moran’s I | p-Value | Moran’s I | p-Value |
Permeable Pavement | Queen Contiguity | 0.015 | 0.354 | 0.110 | 0.020 | −0.005 | 0.138 | −0.011 | 0.430 |
Rook Contiguity | 0.012 | 0.346 | 0.111 | 0.011 | −0.047 | 0.169 | −0.006 | 0.487 | |
KNN–4 | 0.070 | 0.052 | 0.156 | 0.001 | 0.059 | 0.058 | 0.001 | 0.448 | |
KNN–5 | 0.088 | 0.024 | 0.152 | 0.001 | 0.048 | 0.074 | 0.003 | 0.397 | |
KNN–6 | 0.076 | 0.020 | 0.135 | 0.003 | 0.034 | 0.118 | 0.012 | 0.305 | |
Bioretention Cell | Queen Contiguity | 0.191 | 0.001 | 0.052 | 0.111 | 0.168 | 0.001 | 0.042 | 0.150 |
Rook Contiguity | 0.181 | 0.004 | 0.052 | 0.114 | 0.172 | 0.001 | 0.043 | 0.145 | |
KNN–4 | 0.200 | 0.002 | 0.087 | 0.022 | 0.199 | 0.001 | 0.039 | 0.146 | |
KNN–5 | 0.210 | 0.001 | 0.040 | 0.126 | 0.175 | 0.001 | 0.044 | 0.096 | |
KNN–6 | 0.215 | 0.001 | 0.041 | 0.086 | 0.160 | 0.001 | 0.043 | 0.087 | |
Green Roof | Queen Contiguity | 0.106 | 0.027 | 0.167 | 0.003 | 0.053 | 0.092 | −0.060 | 0.122 |
Rook Contiguity | 0.101 | 0.022 | 0.164 | 0.001 | 0.053 | 0.111 | −0.070 | 0.092 | |
KNN–4 | 0.170 | 0.002 | 0.193 | 0.001 | 0.107 | 0.006 | −0.057 | 0.101 | |
KNN–5 | 0.165 | 0.001 | 0.159 | 0.001 | 0.088 | 0.017 | −0.045 | 0.138 | |
KNN–6 | 0.167 | 0.001 | 0.135 | 0.001 | 0.059 | 0.033 | −0.053 | 0.067 | |
All GI | Queen Contiguity | 0.015 | 0.289 | 0.110 | 0.012 | −0.019 | 0.370 | 0.195 | 0.001 |
Rook Contiguity | 0.012 | 0.325 | 0.111 | 0.018 | −0.018 | 0.404 | 0.198 | 0.001 | |
KNN–4 | 0.070 | 0.049 | 0.156 | 0.002 | 0.010 | 0.019 | 0.191 | 0.001 | |
KNN–5 | 0.087 | 0.016 | 0.151 | 0.002 | 0.059 | 0.058 | 0.195 | 0.001 | |
KNN–6 | 0.076 | 0.019 | 0.134 | 0.001 | 0.059 | 0.033 | 0.154 | 0.001 |
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Rodriguez, M.; Fu, G.; Butler, D.; Yuan, Z.; Sharma, K. Exploring the Spatial Impact of Green Infrastructure on Urban Drainage Resilience. Water 2021, 13, 1789. https://doi.org/10.3390/w13131789
Rodriguez M, Fu G, Butler D, Yuan Z, Sharma K. Exploring the Spatial Impact of Green Infrastructure on Urban Drainage Resilience. Water. 2021; 13(13):1789. https://doi.org/10.3390/w13131789
Chicago/Turabian StyleRodriguez, Mayra, Guangtao Fu, David Butler, Zhiguo Yuan, and Keshab Sharma. 2021. "Exploring the Spatial Impact of Green Infrastructure on Urban Drainage Resilience" Water 13, no. 13: 1789. https://doi.org/10.3390/w13131789