Abstract
The frequency of urban waterlogging is increasing significantly under the combined influence of natural factors (precipitation and terrain) and anthropogenic factors (drainage system and urbanization). Previous studies had explored the effect of landscape pattern and topography on waterlogging based on historical waterlogging events records. However, the research on current waterlogging issues based on historical records had limitations since the impact factors of waterlogging are inconsistent due to the changes of surface and meteorological conditions. This paper applied a hydrological and hydrodynamic model named InfoWorks ICM, to simulate the urban waterlogging depth (UWD). Under the consistent surface and meteorological conditions, UWD were selected as the dependent variable to analyze the influence of landscape pattern and topography on waterlogging at multiple scales. Pearson correlation analysis and stepwise regression models were used to discover the relationship between these indices. According to the results, in terms of landscape composition, the percentages of built-up area and urban green space have the most significant influence on waterlogging. In addition, organizing average built-up area patch sizes and integrating green spaces with complex shape and high connectivity can improve the state of urban waterlogging. Besides, the rational allocation of topographic gradient is an effective measure at small scale. The adjusted R2 of regression model were 0.723 at 400 m analysis scale, 0.323 at 600 m analysis scale, and 0.193 at 800 m analysis scale, indicating that attention should be paid to scale effect in similar research. This research can provide a reference for mitigating urban waterlogging disasters.
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References
Abbas A, Salloom G, Ruddock F et al (2019) Modelling data of an urban drainage design using a Geographic Information System (GIS)database. J Hydrol 574:450–466. https://doi.org/10.1016/j.jhydrol.2019.04.009
Babaei S, Ghazavi R, Erfanian M (2018) Urban flood simulation and prioritization of critical urban sub-catchments using SWMM model and PROMETHEE II approach. Phys Chem Earth 105:3–11. https://doi.org/10.1016/j.pce.2018.02.002
Bai Y, Chen Y, Alatalo JM et al (2020) Scale effects on the relationships between land characteristics and ecosystem services- a case study in Taihu Lake Basin. China Sci Total Environ 716:137083. https://doi.org/10.1016/j.scitotenv.2020.137083
Bisht DS, Chatterjee C, Kalakoti S et al (2016) Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study. Nat Hazards 84:749–776. https://doi.org/10.1007/s11069-016-2455-1
Burger G, Sitzenfrei R, Kleidorfer M, Rauch W (2014) Parallel flow routing in SWMM 5. Environ Model Softw 53:27–34. https://doi.org/10.1016/j.envsoft.2013.11.002
Chen Y, Zhou H, Zhang H et al (2015) Urban flood risk warning under rapid urbanization. Environ Res 139:3–10. https://doi.org/10.1016/j.envres.2015.02.028
Cheng T, Xu Z, Hong S, Song S (2017) Flood risk zoning by using 2D hydrodynamic modeling: a case study in Jinan City. Math Probl Eng. https://doi.org/10.1155/2017/5659197
De Reu J, Bourgeois J, Bats M et al (2013) Application of the topographic position index to heterogeneous landscapes. Geomorphology 186:39–49. https://doi.org/10.1016/j.geomorph.2012.12.015
Du S, Van Rompaey A, Shi P, Wang J (2015) A dual effect of urban expansion on flood risk in the Pearl River Delta (China) revealed by land-use scenarios and direct runoff simulation. Nat Hazards 77:111–128. https://doi.org/10.1007/s11069-014-1583-8
Du S, Xiong Z, Wang Y, Guo L (2016) Remote sensing of environment quantifying the multilevel effects of landscape composition and configuration on land surface temperature. Remote Sens Environ 178:84–92. https://doi.org/10.1016/j.rse.2016.02.063
Fahy B, Brenneman E, Chang H, Shandas V (2019) Spatial analysis of urban flooding and extreme heat hazard potential in Portland. Or Int J Disaster Risk Reduct 39:101117. https://doi.org/10.1016/j.ijdrr.2019.101117
Gao F, He B, Xue S, Li Y (2020) Impact of landscape pattern change on runoff processes in catchment area of the Ulungur River Basin. Water Sci Technol Water Supply 20:1046–1058. https://doi.org/10.2166/ws.2020.027
Hou H, Wang R, Murayama Y (2019) Scenario-based modelling for urban sustainability focusing on changes in cropland under rapid urbanization: a case study of Hangzhou from 1990 to 2035. Sci Total Environ 661:422–431. https://doi.org/10.1016/j.scitotenv.2019.01.208
Hu T, Fan J, Hou H et al (2021) Long-term monitoring and evaluation of land development in a reclamation area under rapid urbanization: a case-study in Qiantang New District, China. L Degrad Dev 32:3259–3271. https://doi.org/10.1002/ldr.3980
Huang T, Wang Y, Zhang J (2017) Simulation and evaluation of low impact development of urban residential district based on SWMM and GIS. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/74/1/012009
Huong HTL, Pathirana A (2013) Urbanization and climate change impacts on future urban flooding in Can Tho city. Vietnam Hydrol Earth Syst Sci 17:379–394. https://doi.org/10.5194/hess-17-379-2013
H. Wallingford (2012), InfoWorks ICM help v3.0. UK
Iwahashi J, Pike RJ (2007) Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology 86:409–440. https://doi.org/10.1016/j.geomorph.2006.09.012
Kirtman B, Power SB, Adedoyin AJ, et al (2013) Near-term climate change: projections and predictability. Clim Chang 2013 Phys Sci Basis Work Gr I Contrib to Fifth Assess Rep Intergov Panel Clim Chang 9781107057:953–1028. https://doi.org/10.1017/CBO9781107415324.023
Li Y, Hu T, Zheng G et al (2019) An improved simplified urban storm inundation model based on urban terrain and catchment modification. Water (switzerland) 11:1–16. https://doi.org/10.3390/w11112335
Liu F, Liu X, Xu T et al (2021) Driving factors and risk assessment of rainstorm waterlogging in urban agglomeration areas: a case study of the guangdong-hong kong-macao greater Bay area. China Water (switzerland). https://doi.org/10.3390/w13060770
Liu J, Liu X, Wang Y et al (2020) Landscape composition or configuration: which contributes more to catchment hydrological flows and variations? Landsc Ecol 35:1531–1551. https://doi.org/10.1007/s10980-020-01035-3
Liu S, Lin M, Li C (2019) Analysis of the effects of the river network structure and urbanization on waterlogging in high-density urban areas—a case study of the Pudong New Area in Shanghai. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph16183306
McGarigal K, Marks BJ (1995) FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. Gen Tech Rep US Dep Agric for Serv. https://doi.org/10.2737/PNW-GTR-351
McMinn WR, Yang Q, Scholz M (2010) Classification and assessment of water bodies as adaptive structural measures for flood risk management planning. J Environ Manag 91:1855–1863. https://doi.org/10.1016/j.jenvman.2010.04.009
Nayeb Yazdi M, Ketabchy M, Sample DJ et al (2019) An evaluation of HSPF and SWMM for simulating streamflow regimes in an urban watershed. Environ Model Softw 118:211–225. https://doi.org/10.1016/j.envsoft.2019.05.008
Quan RS (2014) Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation. Nat Hazards 73:1569–1585. https://doi.org/10.1007/s11069-014-1156-x
Ren X, Wang S, Yang P et al (2021) Performance evaluation of different combined drainage forms on flooding and waterlogging removal. Water (switzerland) 13:1–15. https://doi.org/10.3390/w13212968
Sang YF, Yang M (2017) Urban waterlogs control in China: more effective strategies and actions are needed. Nat Hazards 85:1291–1294. https://doi.org/10.1007/s11069-016-2614-4
Shi Y, Zhai G, Zhou S et al (2019) How can cities respond to flood disaster risks under multi-scenario simulation? A case study of Xiamen, China. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph16040618
Sidek LM, Jaafar AS, Majid WHAWA et al (2021) High-resolution hydrological-hydraulic modeling of urban floods using infoworks icm. Sustain. https://doi.org/10.3390/su131810259
Song J, Wang J, Xi G, Lin H (2021) Evaluation of stormwater runoff quantity integral management via sponge city construction: a pilot case study of Jinan. Urban Water J 18:151–162. https://doi.org/10.1080/1573062X.2020.1860237
Song Y, Song X, Shao G (2020) Effects of green space patterns on urban thermal environment at multiple spatial-temp. Sustain. https://doi.org/10.3390/SU12176850
Su B, Huang H, Li Y (2016) Integrated simulation method for waterlogging and traffic congestion under urban rainstorms. Nat Hazards 81:23–40. https://doi.org/10.1007/s11069-015-2064-4
Tang X, Shu Y, Lian Y et al (2018) A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier. Sci Total Environ 630:264–274. https://doi.org/10.1016/j.scitotenv.2018.02.172
Tehrany MS, Jones S, Shabani F (2019) Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. CATENA 175:174–192. https://doi.org/10.1016/j.catena.2018.12.011
Wang H, Zhao Y, Zhou Y, Wang H (2021) Prediction of urban water accumulation points and water accumulation process based on machine learning. Earth Sci Inf 14:2317–2328. https://doi.org/10.1007/s12145-021-00700-8
Wang L, Hou H, Weng J (2020) Ordinary least squares modelling of urban heat island intensity based on landscape composition and configuration: a comparative study among three megacities along the Yangtze River. Sustain Cities Soc 62:102381. https://doi.org/10.1016/j.scs.2020.102381
Wang Z, Lai C, Chen X et al (2015) Flood hazard risk assessment model based on random forest. J Hydrol 527:1130–1141. https://doi.org/10.1016/j.jhydrol.2015.06.008
Wu J, Lu J (2021) Spatial scale effects of landscape metrics on stream water quality and their seasonal changes. Water Res 191:116811. https://doi.org/10.1016/j.watres.2021.116811
Wu J, Sha W, Zhang P, Wang Z (2020) The spatial non-stationary effect of urban landscape pattern on urban waterlogging: a case study of Shenzhen City. Sci Rep 10:1–15. https://doi.org/10.1038/s41598-020-64113-1
Wu J, Zhang P (2017) The effect of urban landscape pattern on urban waterlogging. Dili Xuebao/Acta Geogr Sin 72:444–456. https://doi.org/10.11821/dlxb201703007
Xue F, Huang M, Wang W, Zou L (2016) Numerical simulation of urban waterlogging based on FloodArea model. Adv Meteorol. https://doi.org/10.1155/2016/3940707
Yang Y, Ng ST, Dao J et al (2021) BIM-GIS-DCEs enabled vulnerability assessment of interdependent infrastructures—a case of stormwater drainage-building-road transport Nexus in urban flooding. Autom Constr 125:103626. https://doi.org/10.1016/j.autcon.2021.103626
Yao L, Chen L, Wei W (2017) Exploring the linkage between urban flood risk and spatial patterns in small urbanized catchments of Beijing, China. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph14030239
YE C, XU Z, LEI X, et al (2021) Flood simulation and risk analysis based on infoworks ICM: case study in the Baima River District of Fuzhou City. J Beijing Norm Univ Sci 57:5–8+73. https://doi.org/10.12202/j.0476-0301.2021088
Yin Z, Yin J, Xu S, Wen J (2011) Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging. J Geogr Sci 21:274–284. https://doi.org/10.1007/s11442-011-0844-7
Yu H, Zhao Y, Fu Y, Li L (2018) Spatiotemporal variance assessment of urban rainstorm waterlogging affected by impervious surface expansion: a case study of Guangzhou. China Sustain. https://doi.org/10.3390/su10103761
Zambrano L, Pacheco-Muñoz R, Fernández T (2018) Influence of solid waste and topography on urban floods: the case of Mexico City. Ambio 47:771–780. https://doi.org/10.1007/s13280-018-1023-1
Zhang H, Cheng J, Wu Z et al (2018a) Effects of impervious surface on the spatial distribution of urban waterlogging risk spots at multiple scales in Guangzhou. South China Sustain. https://doi.org/10.3390/su10051589
Zhang H, Wu C, Chen W, Huang G (2017) Assessing the impact of climate change on the waterlogging risk in coastal cities: a case study of Guangzhou, South China. J Hydrometeorol 18:1549–1562. https://doi.org/10.1175/JHM-D-16-0157.1
Zhang H, Yang Z, Cai Y et al (2021a) Impacts of climate change on urban drainage systems by future short-duration design rainstorms. Water (switzerland). https://doi.org/10.3390/w13192718
Zhang Q, Wu Z, Guo G et al (2021b) Explicit the urban waterlogging spatial variation and its driving factors: the stepwise cluster analysis model and hierarchical partitioning analysis approach. Sci Total Environ 763:143041. https://doi.org/10.1016/j.scitotenv.2020.143041
Zhang Q, Wu Z, Tarolli P (2021c) Investigating the role of green infrastructure on urban waterlogging: evidence from metropolitan coastal cities. Remote Sens. https://doi.org/10.3390/rs13122341
Zhang Q, Wu Z, Zhang H et al (2020) Identifying dominant factors of waterlogging events in metropolitan coastal cities: the case study of Guangzhou. China J Environ Manag 271:110951. https://doi.org/10.1016/j.jenvman.2020.110951
Zhang Y, Xia J, Yu J et al (2018b) Simulation and assessment of urbanization impacts on runoff metrics: insights from landuse changes. J Hydrol 560:247–258. https://doi.org/10.1016/j.jhydrol.2018.03.031
Zhao G, Pang B, Xu Z et al (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ 615:1133–1142. https://doi.org/10.1016/j.scitotenv.2017.10.037
Zhao Y, Xia J, Xu Z et al (2021) Impact of urban expansion on rain island effect in jinan city, north china. Remote Sens 13:1–16. https://doi.org/10.3390/rs13152989
Zheng Y, Li Y, Hou H et al (2021) Quantifying the cooling effect and scale of large inner-city lakes based on landscape patterns: a case study of hangzhou and nanjing. Remote Sens. https://doi.org/10.3390/rs13081526
Zheng Z, Gao J, Ma Z et al (2016) Urban flooding in China: main causes and policy recommendations. Hydrol Process 30:1149–1152. https://doi.org/10.1002/hyp.10717
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The authors are very grateful for funding provided by Zhejiang Provincial Natural Science Foundation of China (LY19D010004), National Nature Sciences Foundation of Hangzhou (20191203B19).
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Wang, L., Li, Y., Hou, H. et al. Analyzing spatial variance of urban waterlogging disaster at multiple scales based on a hydrological and hydrodynamic model. Nat Hazards 114, 1915–1938 (2022). https://doi.org/10.1007/s11069-022-05453-1
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DOI: https://doi.org/10.1007/s11069-022-05453-1