Optimization of impervious surface space layout for prevention of urban rainstorm waterlogging: A case study of Guangzhou, China

International Journal of Environmental Research and Public Health

Volumn 16, Issue 19, 2019, Pages

  Yu, Huafei ^a,b   Zhao, Yaolong ^a   Fu, Yingchun ^a  

^a SOUTH CHINA NORMAL UNIVERSITY   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Ant colony optimization; Guangzhou; Impervious surfaces; Optimization of spatial layout; Soil conservation service curve number model; Urban rainstorm waterlogging

Indexed keywords

RAIN; STORM WATER;

ALGORITHM; MODELING; OPTIMIZATION; SOIL CONSERVATION; URBAN DEVELOPMENT; URBAN PLANNING; URBAN RENEWAL; WATERLOGGING;

ARTICLE; CASE STUDY; CHINA; CITY PLANNING; PREVENTION; PROCESS OPTIMIZATION; STORMWATER RUNOFF; URBAN AREA; WATERLOGGING; CITY; ENVIRONMENTAL MONITORING; PROCEDURES; SOIL;

CHINA; GUANGDONG; GUANGZHOU;

CHINA; CITIES; CITY PLANNING; ENVIRONMENTAL MONITORING; RAIN; SOIL;

EID: 85072696458     PISSN: 16617827     EISSN: 16604601     Source Type: Journal    
DOI: 10.3390/ijerph16193613     Document Type: Article

References (94)

1. Campbell, A.; Pradhanang, S.M.; Kouhi Anbaran, S.; Sargent, J.; Palmer, Z.; Audette, M. Assessing the impact of urbanization on flood risk and severity for the pawtuxet watershed, Rhode Island. Lake Reserv. Manag. 2018, 34, 74-87.
2. Löwe, R.; Urich, C.; Domingo, N.S.; Mark, O.; Deletic, A.; Arnbjerg-Nielsen, K. Assessment of urban pluvial flood risk and efficiency of adaptation options through simulations-A new generation of urban planning tools. J. Hydrol. 2017, 550, 355-367.
3. Akter, A.; Mohit, S.A.; Chowdhury, M.A.H. Predicting urban storm water-logging for Chittagong city in bangladesh. Int. J. Sustain. Built Environ. 2017, 6, 238-249.
4. Rojas, O.; Mardones, M.; Rojas, C.; Martínez, C.; Flores, L. Urban growth and flood disasters in the coastal river basin of south-central Chile (1943-2011). Sustainability 2017, 9, 195.
5. Subrina, S.; Chowdhury, F.K. Urban dynamics: An undervalued issue for water logging disaster risk management in case of Dhaka city, Bangladesh. Procedia Eng. 2018, 212, 801-808.
6. Hu, S.L.; Han, C.F.; Meng, L.P. A scenario planning approach for propositioning rescue centers for urban waterlog disasters. Comput. Ind. Eng. 2015, 87, 425-435.
7. Yin, J.; Ye, M.; Yin, Z.; Xu, S. A review of advances in urban flood risk analysis over China. Stoch. Environ. Res. Risk Assess. 2015, 29, 1063-1070.
8. Quan, R.; Zhang, L.; Min, L.; Min, L.; Wang, J.; Niu, H. Risk assessment of rainstorm waterlogging on subway in central urban area of Shanghai, China based on scenario simulation. Nat. Hazards 2014, 73, 1569-1585.
9. Zhang, X.; Hu, M.; Chen, G.; Xu, Y. Urban rainwater utilization and its role in mitigating urban waterlogging problems-A case study in Nanjing, China. Water Resour. Manag. 2012, 26, 3757-3766.
10. Hu, B.-B.; Zhou, J.; Wang, J.; Xu, S.-Y.; Meng, W.-Q. Risk assessment on rainstorm waterlogging of tianjin binhai new area based on scenario simulation. Sci. Geogr. Sin. 2012, 32, 846-852.
11. Chen, Z.; Yin, L.; Chen, X.; Wei, S.; Zhu, Z. Research on the characteristics of urban rainstorm pattern in the humid area of southern china: A case study of Guangzhou city. Int. J. Climatol. 2016, 35, 4370-4386.
12. Chen, P.; Zhang, J.; Zhang, L.; Sun, Y. Evaluation of resident evacuations in urban rainstorm waterlogging disasters based on scenario simulation: Daoli district (Harbin, China) as an example. Int. J. Environ. Res. Public Health 2014, 11, 9964-9980.
13. Chen, P.; Zhang, J.; Jiang, X.; Liu, X.; Bao, Y.; Sun, Y. Scenario simulation-based assessment of trip difficulty for urban residents under rainstorm waterlogging. Int. J. Environ. Res. Public Health 2012, 9, 2057.
14. Wang, X.; Xie, H. A review on applications of remote sensing and geographic information systems (GIS) in water resources and flood risk management. Water 2018, 10, 608.
15. Tang, X.; Shu, Y.; Lian, Y.; Zhao, Y.; Fu, Y. A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier. Sci. Total Environ. 2018, 630, 264.
16. Brunetti, G.; Šimnek, J.; Piro, P. A comprehensive numerical analysis of the hydraulic behavior of a permeable pavement. J. Hydrol. 2016, 540, 1146-1161.
17. Hafsi, R.; Ouerdachi, L.; Kriker, A.E.; Boutaghane, H. Assessment of urbanization/impervious effects on water quality in the urban river annaba (eastern Algeria) using physicochemical parameters. Water Sci. Technol. 2016, 74, 2051-2059.
18. Muhammad, S.; Reeho, K. Retrofitting the low impact development practices into developed urban areas including barriers and potential solution. Open Geosci. 2017, 9, 240-254.
19. Lee, E.S.; Lee, D.K.; Kim, S.H.; Lee, K.C. Design strategies to reduce surface water flooding in a historical district. J. Flood Risk Manag. 2018, 11, S838-S854.
20. Xu, J.; Zhao, Y.; Zhong, K.; Ruan, H.; Liu, X. Coupling modified linear spectral mixture analysis and soil conservation service curve number (scs-cn) models to simulate surface runoff: Application to the main urban area of Guangzhou, China. Water 2016, 8, 550.
21. Angel, S.; Parent, J.; Civco, D.L.; Blei, A.; Potere, D. The dimensions of global urban expansion: Estimates and projections for all countries, 2000-2050. Prog. Plan. 2011, 75, 53-107.
22. Fassman, E.A.; Blackbourn, S. Urban runoff mitigation by a permeable pavement system over impermeable soils. J. Hydrol. Eng. 2010, 15, 475-485.
23. Collins, K.A.; Hunt, W.F.; Hathaway, J.M. Hydrologic comparison of four types of permeable pavement and standard asphalt in eastern north carolina. J. Hydrol. Eng. 2008, 13, 1146-1157.
24. Drake, J.A.P.; Bradford, A.; Marsalek, J. Review of environmental performance of permeable pavement systems: State of the knowledge. Water Qual. Res. J. Can. 2013, 48, 2013.
25. Jarden, K.M.; Jefferson, A.J.; Grieser, J.M. Assessing the effects of catchment-scale urban green infrastructure retrofits on hydrograph characteristics. Hydrol. Processes 2016, 30, 1536-1550.
26. Pappas, E.A.; Smith, D.R.; Huang, C.; Shuster, W.D.; Bonta, J.V. Impervious surface impacts to runoff and sediment discharge under laboratory rainfall simulation. Catena 2008, 72, 146-152.
27. Kaspersen, P.S.; Ravn, N.H.; Arnbjerg-Nielsen, K.; Madsen, H.; Drews, M. Comparison of the impacts of urban development and climate change on exposing european cities to pluvial flooding. Hydrol. Earth Syst. Sci. 2017, 21, 4131-4147.
28. Su, W.; Ye, G.; Yao, S.; Yang, G. Urban land pattern impacts on floods in a new district of China. Sustainability 2014, 6, 6488-6508.
29. Yu, H.; Zhao, Y.; Fu, Y.; Li, L. Spatiotemporal variance assessment of urban rainstorm waterlogging affected by impervious surface expansion: A case study of Guangzhou, China. Sustainability 2018, 10, 3761.
30. Poff, L.R.; Bledsoe, B.P.; Cuhaciyan, C.O. Hydrologic variation with land use across the contiguous united states: Geomorphic and ecological consequences for stream ecosystems. Geomorphology 2006, 79, 264-285.
31. Zheng, P.Q.; Baetz, B.W. Gis-based analysis of development options from a hydrology perspective. J. Urban Plan. Dev. 1999, 125, 164-180.
32. Mejía, A.I.; Moglen, G.E. Spatial patterns of urban development from optimization of flood peaks and imperviousness-based measures. J. Hydrol. Eng. 2009, 14, 416-424.
33. Sang, Y.-F.; Yang, M. Urban waterlogs control in china: More effective strategies and actions are needed. Nat. Hazards 2017, 2017, 1291-1294.
34. Deverel, S.J.; Ingrum, T.; Leighton, D. Present-day oxidative subsidence of organic soils and mitigation in the sacramento-San Joaquin Delta, California, USA. Hydrogeol. J. 2016, 24, 1-18.
35. Pietrucha-Urbanik, K. Failure analysis and assessment on the exemplary water supply network. Eng. Fail. Anal. 2015, 57, 137-142.
36. Pietrucha-Urbanik, K. Assessing the Costs of Losses Incurred as a Result of Failure, International Conference on Dependability and Complex Systems, 2016; Springer: Cham; pp. 355-362.
37. Tabesh, M.; Soltani, J.; Farmani, R.; Savic, D. Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling. J. Hydroinf. 2009, 11, 1-17.
38. Kuang, W.; Chi, W.; Lu, D.; Dou, Y. A comparative analysis of megacity expansions in china and the US: Patterns, rates and driving forces. Landsc. Urban Plan. 2014, 132, 121-135.
39. Zhao, P. Sustainable urban expansion and transportation in a growing megacity: Consequences of urban sprawl for mobility on the urban fringe of Beijing. Habitat Int. 2010, 34, 236-243.
40. Zheng, H.W.; Shen, G.Q.; Wang, H.; Hong, J. Simulating land use change in urban renewal areas: A case study in Hong Kong. Habitat Int. 2015, 46, 23-34.
41. Zheng, H.W.; Shen, G.Q.; Wang, H. A review of recent studies on sustainable urban renewal. Habitat Int. 2014, 41, 272-279.
42. Barosio, M.; Eynard, E.; Marietta, C.; Marra, G.; Melis, G.; Tabasso, M. From urban renewal to urban regeneration: Classification criteria for urban interventions. Turin 1995-2015: Evolution of planning tools and approaches. J. Urban Regener. Renew. 2016, 9, 367-380.
43. Roberts, P.W.; Sykes, H. Urban regeneration. A handbook. Environ. Plan. B Plan. Des. 2000, 28, 319-319.
44. Wang, D.; Gong, Z.G.; Yang, Z.Z. Design of industrial clusters and optimization of land use in an airport economic zone. Land Use Policy 2018, 77, 288-297.
45. Liang, X.; Liu, X.P.; Li, X.; Chen, Y.M.; Tian, H.; Yao, Y. Delineating multi-scenario urban growth boundaries with a ca-based flus model and morphological method. Landsc. Urban Plan. 2018, 177, 47-63.
46. Herzig, A.; Nguyen, T.T.; Ausseil, A.G.E.; Maharjan, G.R.; Dymond, J.R.; Arnhold, S.; Koellner, T.; Rutledge, D.; Tenhunen, J. Assessing resource-use efficiency of land use. Environ. Modell. Softw. 2018, 107, 34-49.
47. Yuan, K.Y.; Lin, Y.C.; Chiueh, P.T.; Lo, S.L. Spatial optimization of the food, energy, and water nexus: A life cycle assessment-based approach. Energy Policy 2018, 119, 502-514.
48. Li, S.; Ye, X.; Lee, J.; Gong, J.; Qin, C. Spatiotemporal analysis of housing prices in China: A big data perspective. Appl. Spat. Anal. Policy 2016, 10, 1-13.
49. Li, X.; Parrott, L. An improved genetic algorithm for spatial optimization of multi-objective and multi-site land use allocation. Comput. Environ. Urban Syst. 2016, 59, 184-194.
50. Safarzadeh, S.; Koosha, H. Solving an extended multi-row facility layout problem with fuzzy clearances using Ga. Appl. Soft. Comput. 2017, 61, 819-831.
51. Xu, L.Y.; Li, Z.X.; Song, H.M.; Yin, H. Land-use planning for urban sprawl based on the clue-s model: A case study of Guangzhou, China. Entropy 2013, 15, 3490-3506.
52. Jabir, E.; Panicker, V.V.; Sridharan, R. Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem. Transp. Res. Part D Transp. Environ. 2017, 57, 422-457.
53. Colorni, A.; Dorigo, M.; Maniezzo, V. Distributed Optimization by Ant Colonies. In Proceedings of the First European Conference on Artificial Life, 1991; Paris, France, 1992; pp. 134-142.
54. QuanLi, X.; Kun, Y.; GuiLin, W.; YuLian, Y. Agent-based modeling and simulations of land-use and land-cover change according to ant colony optimization: A case study of the Erhai Lake basin, China. Nat. Hazards 2015, 75, 95-118.
55. Ma, S.; Li, X.; Cai, Y. Delimiting the urban growth boundaries with a modified ant colony optimization model. Comput. Environ. Urban Syst. 2017, 62, 146-155.
56. Sidiropoulos, E.; Fotakis, D. Spatial water resource allocation using a multi-objective ant colony optimization. Eur. Water 2016, 55, 41-51.
57. Yao, L.; Wei, W.; Yu, Y.; Xiao, J.; Chen, L. Rainfall-runoff risk characteristics of urban function zones in beijing using the scs-cn model. J. Geogr. Sci. 2018, 28, 656-668.
58. Zajíek, A.; Kvítek, T.; Kaplická, M.; Doležal, F.; Kulhavý, Z.; Bystický, V.; Žlábek, P. Drainage water temperature as a basis for verifying drainage runoff composition on slopes. Hydrol. Process. 2011, 25, 3204-3215.
59. Rockwood, D.M. Application of streamflow synthesis and reservoir regulation-“SSARR”-Program to the lower mekong river. Am. J. Obstet. Gynecol. 1969, 169, 817-824.
60. Crawford, N.H.; Linsley, R.K. Digital simulation in hydrology: The stanford watershed model iv. Evapotranspiration 1966, 39, 158-160.
61. Sugawara, M. Tank Model with Snow Component. Study Report of National Research Center for Disaster Prevention: National Research Center for Disater Prevention Science and Technology Agency, Japan, 1984; pp. 293.
62. Lettenmaier, D.P.; Gan, T.Y. Hydrologic sensitivities of the sacramento-San Joaquin River basin, California, to global warming. Water Resour. Res. 1990, 26, 69-86.
63. Huber, W.C.; Singh, V.P. Epa storm water management model-Swmm. Computer Models of Watershed Hydrology; Water Resources, Colorado, USA, 1995; pp. 789-808.
64. Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Williams, J.R. Soil and Water Assessment Tool Theoretical Documentation Version 2009. Texas Water Resources Institute Technical Report, Texas, America, 2011.
65. Ponce, V.M.; Hawkins, R.H. Runoff curve number: Has it reached maturity? J. Hydrol. Eng. 1996, 1, 11-19.
66. Li, C.; Liu, M.; Hu, Y.; Shi, T.; Qu, X.; Walter, M.T. Effects of urbanization on direct runoff characteristics in urban functional zones. Sci. Total Environ. 2018, 643, 301.
67. Shokri, A. Evaluation of scs-cn method using a fully distributed physically based coupled surface-subsurface flow model. In Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria, 23-28 April 2017; p. 1225.
68. Ansari, T.A.; Katpatal, Y.B.; Vasudeo, A.D. Spatial evaluation of impacts of increase in impervious surface area on scs-cn and runoff in nagpur urban watersheds, India. Arab. J. Geosci. 2016, 9, 702.
69. Kayet, N.; Pathak, K.; Chakrabarty, A.; Sahoo, S. Evaluation of soil loss estimation using the rusle model and scs-cn method in hillslope mining areas. Int. Soil Water Conserv. Res. 2018, 6, 31-42.
70. Liu, X. Waterlogging Points in Guangzhou 30 Years Spread 16 Times. Available online: http://gd.qq.com/a/20160105/008699.htm (accessed on 5 January 2016).
71. Wu, X.; Yu, D.; Chen, Z.; Wilby, R.L. An evaluation of the impacts of land surface modification, storm sewer development, and rainfall variation on waterlogging risk in Shanghai. Nat. Hazards 2012, 63, 305-323.
72. Gu, L.; Gu, N. Urban waterlogging and stormwater management Appl. Mech. Mater. 2014, 587-589, 554-557.
73. Lei, Z.; Weng, Q.; Shao, Z. An evaluation of monthly impervious surface dynamics by fusing landsat and modis time series in the Pearl River Delta, China, from 2000 to 2015. Remote Sens. Environ. 2017, 201, 99-114.
74. Li, B.; Zhao, Y.; Fu, Y. Spatio-temporal characteristics of urban stormwaterlogging in Guangzhou and the impact of urban growth. Geo Inf. Sci. 2015, 17, 445-450.
75. Clarke, R. A review of some mathematical models used in hydrology, with observations on their calibration and use. J. Hydrol. 1973, 19, 1-20.
76. Cronshey, R. Urban Hydrology for Small Watersheds; US Dept. of Agriculture, Soil Conservation Service, Engineering Division, America, 1986; pp. 1268-1273.
77. Schoener, G. Urban runoff in the us southwest: Importance of impervious surfaces for small-storm hydrology. J. Hydrol. Eng. 2018, 23, 12.
78. Lal, M.; Mishra, S.K.; Pandey, A. Physical verification of the effect of land features and antecedent moisture on runoff curve number. Catena 2015, 133, 318-327.
79. Washington. Scs National Engineering Handbook “Section 4: Hygrology, Chapter 4; Soial Conservation Service, USDA: Washington, DC, USA, 1985.
80. Fan, F.; Deng, Y.; Hu, X.; Weng, Q. Estimating composite curve number using an improved scs-cn method with remotely sensed variables in Guangzhou, China. Remote Sens. 2013, 5, 1425-1438.
81. Natural Resources Conservation Service. National Engineering Handbook: Part 630; USDA Soil Conservation Service: Washington, DC, USA, 2004.
82. Williams, J.; Jones, C.; Dyke, P. The Epic Model and Its Application. In Proceedings of the International Symposium on Minimum Data Sets for Agrotechnology Transfer, Patancheru, India, 21-26 March 1983; pp. 111-121.
83. Dorigo, M.; Birattari, M.; Stutzle, T. Ant colony optimization. Comput. Intell. Mag. IEEE, April, 2007, 1, 28-39.
84. Weng, Q.; Lu, D. A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States. Int. J. Appl. Earth Obs. Geoinf. 2008, 10, 68-83.
85. Weng, Q.; Hu, X.; Liu, H. Estimating impervious surfaces using linear spectral mixture analysis with multitemporal aster images. Int. J. Remote Sens. 2009, 30, 4807-4830.
86. Lu, J.; Mei, Z.; Zhao, S.; Xiao, Y. Land use optimization allocation based on chaos ant colony algorithm. J. Geo Inf. Sci. 2017, 3, 59-63.
87. Hulshoff, R.M. Landscape indices describing a dutch landscape. Landsc. Ecol. 1995, 10, 101-111.
88. Turner, M.G. Landscape changes in nine rural counties in Georgia. Photogramm. Eng. Remote Sens. 1990, 56, 379-386.
89. Zhang, X.; Guo, X.; Hu, M. Hydrological effect of typical low impact development approaches in a residential district. Nat. Hazards 2016, 80, 389-400.
90. Ahiablame, L.; Shakya, R. Modeling flood reduction effects of low impact development at a watershed scale. J. Environ. Manag. 2016, 171, 81.
91. Mentens, J.; Raes, D.; Hermy, M. Green roofs as a tool for solving the rainwater runoff problem in the urbanized 21st century? Landsc. Urban Plan. 2006, 77, 217-226.
92. Mao, X.; Jia, H.; Yu, S.L. Assessing the ecological benefits of aggregate lid-bmps through modelling. Ecol. Modell. 2017, 353, 139-149.
93. Viola, F.; Hellies, M.; Deidda, R. Retention performance of green roofs in representative climates worldwide. J. Hydrol. 2017, 553, 763-772.
94. Hu, M.; Sayama, T.; Zhang, X.; Tanaka, K.; Takara, K.; Yang, H. Evaluation of low impact development approach for mitigating flood inundation at a watershed scale in China. J. Environ. Manag. 2017, 193, 430-438.