The Effect of Sponge City Construction for Reducing Directly Connected Impervious Areas on Hydrological Responses at the Urban Catchment Scale
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Preparation
2.2.1. Infiltration Characteristics
2.2.2. Rainfall Characteristics
2.3. Hydrologic and Hydrodynamic Models
2.3.1. Models Description
2.3.2. Model Calibration and Validation
2.4. Identification of Directly Connected Impervious Area
2.5. Design Scenarios
3. Results
3.1. The Distribution of Directly Connected Impervious Area
3.2. Simulated Runoff under Five Design Scenarios
3.3. Sensitivity of Hydrological Performance to DCIA Reduction
4. Discussion
4.1. The Effect of Directly Connected Impervious Area Reduction
4.2. The Effect of the Rainfall Event Return Period
5. Conclusions
- By comparing the simulated results of S1–S5, the results confirmed the effectiveness of sponge city construction in reducing urban stormwater runoff and showed that even though all these design scenarios were implemented with the same area of LID controls, Scenario 5 was the most effective in stormwater management. This indicates that the efficiency of LID controls can be improved by changing the spatial distribution of the LID controls to reduce the DCIA.
- The hydrological performance reduction was positively correlated and linearly dependent on DCIA reduction. The peak flow was the most sensitive parameter to reduction in DCIA, followed by runoff volume and peak time
- By comparing the simulated results for the rainfalls of three return periods, the regression lines for 2-year and 5-year storms were steeper than that for the 10-year storm. This indicates that as the rainfall intensity increased, the hydrological performance was more sensitive to rainfall intensity than reduction in DCIA.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Tang, Z.; Engel, B.A.; Pijanowski, B.C.; Lim, K.J. Forecasting land use change and its environmental impact at a watershed scale. J. Environ. Manag. 2005, 76, 35–45. [Google Scholar] [CrossRef] [PubMed]
- Shuster, W.D.; Bonta, J.; Thurston, H.; Warnemuende, E.; Smith, D.R. Impacts of impervious surface on watershed hydrology: A review. Urban Water J. 2005, 2, 263–275. [Google Scholar] [CrossRef]
- Yang, Y.; Chui, T.F.M. Integrated hydro-environmental impact assessment and alternative selection of low impact development practices in small urban catchments. J. Environ. Manag. 2018, 223, 324–337. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Ding, L.; Ren, M.; Li, C.; Wang, H. Sponge city construction in China: A survey of the challenges and opportunities. Water 2017, 9, 594. [Google Scholar] [CrossRef] [Green Version]
- Yu, S.; Jia, H. China’s ambitious Sponge City initiative: A monumental effort for green/gray infrastructure integration. ASCE EWRI Curr. 2015, 17, 8–9. Available online: http://www.asce.org/ewri (accessed on 22 June 2017).
- Mei, C.; Liu, J.; Wang, H.; Yang, Z.; Ding, X.; Shao, W. Integrated assessments of green infrastructure for flood mitigation to support robust decision-making for sponge city construction in an urbanized watershed. Sci. Total Environ. 2018, 639, 1394–1407. [Google Scholar] [CrossRef]
- Wang, H.; Mei, C.; Liu, J.H.; Shao, W.W. A new strategy for integrated urban water management in China: Sponge city. Sci. China Technol. Sci. 2018, 61, 317–329. [Google Scholar] [CrossRef]
- Qin, H.P.; Li, Z.X.; Fu, G. The effects of low impact development on urban flooding under different rainfall characteristics. J. Environ. Manag. 2013, 129, 577–585. [Google Scholar] [CrossRef] [Green Version]
- Liu, W.; Chen, W.; Peng, C. Assessing the effectiveness of green infrastructures on urban flooding reduction: A community scale study. Ecol. Model. 2014, 291, 6–14. [Google Scholar] [CrossRef]
- Kong, F.; Ban, Y.; Yin, H.; James, P.; Dronova, I. Modeling stormwater management at the city district level in response to changes in land use and low impact development. Environ. Model. Softw. 2017, 95, 132–142. [Google Scholar] [CrossRef]
- Li, Q.; Wang, F.; Yu, Y.; Huang, Z.; Li, M.; Guan, Y. Comprehensive performance evaluation of LID practices for the sponge city construction: A case study in Guangxi, China. J. Environ. Manag. 2019, 231, 10–20. [Google Scholar] [CrossRef] [PubMed]
- Pyke, C.; Warren, M.P.; Johnson, T.; LaGro, J.; Scharfenberg, J.; Groth, P.; Freed, R.; Schroeer, W.; Main, E. Assessment of low impact development for managing stormwater with changing precipitation due to climate change. Landsc. Urban Plan. 2011, 103, 166–173. [Google Scholar] [CrossRef]
- Liu, J.; Kuang, W.; Zhang, Z.; Xu, X.; Qin, Y.; Ning, J.; Zhou, W.; Zhang, S.; Li, R.; Yan, C.; et al. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J. Geogr. Sci. 2014, 24, 195–210. [Google Scholar] [CrossRef]
- Wang, M.; Zhang, D.; Adhityan, A.; Ng, W.J.; Dong, J.; Tan, S.K. Assessing cost-effectiveness of bioretention on stormwater in response to climate change and urbanization for future scenarios. J. Hydrol. 2016, 543, 423–432. [Google Scholar] [CrossRef]
- Bosley, E.K., II. Hydrologic evaluation of low impact development using a continuous, spatially-distributed model. Master’s Thesis, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, July 2008. [Google Scholar]
- Rossman, L.A. Modeling Low Impact Development Alternatives with SWMM. J. Water Manag. Model. 2010, 6062, 167–182. [Google Scholar] [CrossRef] [Green Version]
- Juan, A.; Hughes, C.; Fang, Z.; Bedient, P. Hydrologic Performance of Watershed-Scale Low-Impact Development in a High-Intensity Rainfall Region. J. Irrig. Drain. Eng. 2017, 143, 04016083. [Google Scholar] [CrossRef]
- Panda, R.K.; Pramanik, N.; Bala, B. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Comput. Geosci. 2010, 36, 735–745. [Google Scholar] [CrossRef]
- Thompson, J.R.; Sørenson, H.R.; Gavin, H.; Refsgaard, A. Application of the coupled MIKE SHE/MIKE 11 modelling system to a lowland wet grassland in southeast England. J. Hydrol. 2004, 293, 151–179. [Google Scholar] [CrossRef]
- Noymanee, J.; Theeramunkong, T. Flood Forecasting with Machine Learning Technique on Hydrological Modeling. Procedia Comput. Sci. 2019, 156, 377–386. [Google Scholar] [CrossRef]
- Zăinescu, F.; Vespremeanu-Stroe, A.; Anthony, E.; Tătui, F.; Preoteasa, L.; Mateescu, R. Flood deposition and storm removal of sediments in front of a deltaic wave-influenced river mouth. Mar. Geol. 2019, 417, 106015. [Google Scholar] [CrossRef]
- Mejía, A.I.; Moglen, G.E. Impact of the spatial distribution of imperviousness on the hydrologic response of an urbanizing basin. Hydrol. Process. 2010, 24, 3359–3373. [Google Scholar] [CrossRef]
- Sohn, W.; Kim, J.H.; Li, M.H. Low-impact development for impervious surface connectivity mitigation: assessment of directly connected impervious areas (DCIAs). J. Environ. Plan. Manag. 2017, 60, 1871–1889. [Google Scholar] [CrossRef]
- Jacobson, C.R. Identification and quantification of the hydrological impacts of imperviousness in urban catchments: A review. J. Environ. Manag. 2011, 92, 1438–1448. [Google Scholar] [CrossRef] [PubMed]
- Guo, J.C.Y.; Blackler, G.E.; Earles, T.A.; MacKenzie, K. Incentive Index Developed to Evaluate Storm-Water Low-Impact Designs. J. Environ. Eng. 2010, 136, 1341–1346. [Google Scholar] [CrossRef]
- Lee, J.G.; Heaney, J.P. Estimation of Urban Imperviousness and its Impacts on Storm Water Systems. J. Water Resour. Plan. Manag. 2003, 129, 419–426. [Google Scholar] [CrossRef]
- Roy, A.H.; Shuster, W.D. Assessing impervious surface connectivity and applications for watershed management. J. Am. Water Resour. Assoc. 2009, 45, 198–209. [Google Scholar] [CrossRef]
- Ebrahimian, A.; Wilson, B.N.; Gulliver, J.S. Improved methods to estimate the effective impervious area in urban catchments using rainfall-runoff data. J. Hydrol. 2016, 536, 109–118. [Google Scholar] [CrossRef]
- William, M.; Alley, J.E.V. Effective Impervious Area in Urban. J. Hydraul. Eng. 1983, 109, 313–319. [Google Scholar]
- Brabec, E.; Schulte, S.; Richards, P.L. Impervious Surfaces and Water Quality: A Review of Current Literature and Its Implications for Watershed Planning. J. Plan. Lit. 2002, 16, 499–514. [Google Scholar] [CrossRef]
- Rossman, L.A. Storm water management model user’s manual, version 5.0.; Environmental Protection Agency: Washington, DC, USA, 2010; p. 295. [Google Scholar]
- Cipolla, S.S.; Maglionico, M.; Stojkov, I. A long-term hydrological modelling of an extensive green roof by means of SWMM. Ecol. Eng. 2016, 95, 876–887. [Google Scholar] [CrossRef]
- Knighton, J.; Lennon, E.; Bastidas, L.; White, E.D. Stormwater Detention System Parameter Sensitivity and Uncertainty Analysis Using SWMM. J. Hydrol. Eng. 2016, 21, 05016014. [Google Scholar] [CrossRef]
- Sakshi, S.; Singh, A. Modeling LID using SWMM5 and MIDS Credit Calculator: Credit Valley Conservation’s Elm Drive Case Study. J. Water Manag. Model. 2016, 1–7. [Google Scholar] [CrossRef] [Green Version]
- Warsta, L.; Niemi, T.J.; Taka, M.; Krebs, G.; Haahti, K.; Koivusalo, H.; Kokkonen, T. Development and application of an automated subcatchment generator for SWMM using open data. Urban Water J. 2017, 14, 954–963. [Google Scholar] [CrossRef]
- Ahiablame, L.; Shakya, R. Modeling flood reduction effects of low impact development at a watershed scale. J. Environ. Manag. 2016, 171, 81–91. [Google Scholar] [CrossRef]
- Moriasi, D.N.; Arnold, J.G.; Van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 2007, 50, 885–900. [Google Scholar] [CrossRef]
- Bracmort, K.S.; Arabi, M.; Frankenberger, J.R.; Engel, B.A.; Arnold, J.G. Modeling long-term water quality impact of structural BMPS. Trans. ASABE 2006, 49, 367–374. [Google Scholar] [CrossRef] [Green Version]
- Martin-Mikle, C.J.; de Beurs, K.M.; Julian, J.P.; Mayer, P.M. Identifying priority sites for low impact development (LID) in a mixed-use watershed. Landsc. Urban Plan. 2015, 140, 29–41. [Google Scholar] [CrossRef] [Green Version]
- Kleidorfer, M.; Mikovits, C.; Jasper-tönnies, A.; Huttenlau, M.; Einfalt, T.; Rauch, W. Impact of a Changing Environment on Drainage System Performance. Procedia Eng. 2014, 70, 943–950. [Google Scholar] [CrossRef] [Green Version]
- Palla, A.; Gnecco, I. Hydrologic modeling of Low Impact Development systems at the urban catchment scale. J. Hydrol. 2015, 528, 361–368. [Google Scholar] [CrossRef]
- Gnecco, I.; Palla, A.; Lanza, L.G.; La Barbera, P. The Role of Green Roofs as a Source/sink of Pollutants in Storm Water Outflows. Water Resour. Manag. 2013, 27, 4715–4730. [Google Scholar] [CrossRef]
Land Use | Area | |
---|---|---|
(ha) | (%) | |
Water | 68.14 | 3.15 |
Road | 698.34 | 32.32 |
Roofs | 301.21 | 13.94 |
Parking | 20.09 | 0.93 |
Farmland | 295.87 | 13.69 |
Greenspace | 777.12 | 35.96 |
Sites | |||
---|---|---|---|
Site1 | 9.61 | 0.196 | 0.045 |
Site2 | 4.92 | 1.604 | 0.047 |
Site3 | 3.25 | 0.518 | 0.468 |
Site4 | 7.03 | 0.530 | 0.232 |
Site5 | 10.51 | 0.732 | 0.412 |
Site6 | 15.56 | 1.476 | 0.114 |
Site7 | 9.98 | 1.230 | 1.070 |
Site8 | 19.69 | 1.539 | 0.229 |
Site9 | 7.23 | 1.176 | 1.367 |
Site10 | 7.04 | 0.633 | 0.486 |
Site11 | 4.15 | 1.596 | 0.130 |
LID Control | Parameters | |||||
---|---|---|---|---|---|---|
Permeable Pavement (PP) | Surface | Berm height (mm) | Vegetation (%) | Manning’s n | Surface slope (%) | |
5 | 0 | 0.013 | 1 | |||
Pavement | Thickness (mm) | Void (%) | Imperviousness (%) | Conductivity (mm/h) | Clogging factor | |
150 | 15 | 30 | 72 | 100 | ||
Storage | Thickness (mm) | Void (%) | Conductivity (mm/h) | Clogging factor | ||
180 | 75 | 76 | 100 | |||
Vegetated Swale (VS) | Surface | Berm height (mm) | Vegetation (%) | Manning’s n | Surface slope (%) | Side slope (%) |
300 | 90 | 0.15 | 1 | 33.3 | ||
Rain Garden (RG) | Surface | Berm height (mm) | Vegetation (%) | Manning’s n | Surface slope( %) | |
250 | 90 | 0.1 | 1 | |||
Soil | Thickness (mm) | Porosity | Conductivity Slope | Conductivity (mm/h) | Suction head (mm) | |
100 | 0.5 | 10 | 72 | 38 |
Scenario | LID Controls Applied | DCIA Reduction | Scenario Description |
---|---|---|---|
S1 | PP + VS + RG | 0% | All the LID controls were implemented in impervious areas that are not directly connected to each other. The storm runoff flows to streams via the non-directly connected impervious areas (NDCIA). So, the DCIA was not reduced. |
S2 | PP + VS + RG | 25% | A small part of the LID controls were implemented in the DCIA in order to reduce the DCIA by 25%. The other LID controls were implemented in the NDCIA. |
S3 | PP + VS + RG | 50% | Half of the LID controls were implemented in the DCIA in order to reduce the DCIA by 50%. The other LID controls were implemented in the NDCIA. |
S4 | PP + VS + RG | 75% | Most of the LID controls were implemented in the DCIA in order to reduce the DCIA by 75%. The other LID controls were implemented in the NDCIA. |
S5 | PP + VS + RG | 100% | All the LID controls were implemented in the DCIA, and this scenario disconnected all directly connected impervious areas. So, there was no DCIA in this scenario. |
Design Scenarios | Hydrological Performance | ||||||||
---|---|---|---|---|---|---|---|---|---|
Rainfall Event T = 2 years | Rainfall Event T = 5 years | Rainfall Event T = 10 years | |||||||
Peak Flow | Runoff Volume | Peak Flow Time | Peak Flow | Runoff Volume | Peak Flow Time | PEAK FLOW | Runoff Volume | Peak Flow Time | |
(m3/s) | (m3) | (h:min) | (m3/s) | (m3) | (h:min) | (m3/s) | (m3) | (h:min) | |
S1 | 0.97 | 25,648 | 3:52 | 1.75 | 43,264 | 3:50 | 2.05 | 52,258 | 3:51 |
S2 | 0.61 | 19,020 | 3:57 | 1.21 | 33,376 | 3:53 | 1.58 | 43,120 | 3:53 |
S3 | 0.42 | 14,688 | 3:57 | 0.72 | 21,768 | 3:56 | 1.12 | 35,808 | 3:55 |
S4 | 0.35 | 11,612 | 3:59 | 0.57 | 15,662 | 3:57 | 1.05 | 29,950 | 3:56 |
S5 | 0.24 | 7600 | 4:02 | 0.42 | 14,688 | 4:01 | 0.91 | 22,790 | 4:00 |
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Liang, C.; Zhang, X.; Xia, J.; Xu, J.; She, D. The Effect of Sponge City Construction for Reducing Directly Connected Impervious Areas on Hydrological Responses at the Urban Catchment Scale. Water 2020, 12, 1163. https://doi.org/10.3390/w12041163
Liang C, Zhang X, Xia J, Xu J, She D. The Effect of Sponge City Construction for Reducing Directly Connected Impervious Areas on Hydrological Responses at the Urban Catchment Scale. Water. 2020; 12(4):1163. https://doi.org/10.3390/w12041163
Chicago/Turabian StyleLiang, Changmei, Xiang Zhang, Jun Xia, Jing Xu, and Dunxian She. 2020. "The Effect of Sponge City Construction for Reducing Directly Connected Impervious Areas on Hydrological Responses at the Urban Catchment Scale" Water 12, no. 4: 1163. https://doi.org/10.3390/w12041163
APA StyleLiang, C., Zhang, X., Xia, J., Xu, J., & She, D. (2020). The Effect of Sponge City Construction for Reducing Directly Connected Impervious Areas on Hydrological Responses at the Urban Catchment Scale. Water, 12(4), 1163. https://doi.org/10.3390/w12041163