Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression

Stochastic Environmental Research and Risk Assessment

Volumn 31, Issue 7, 2017, Pages 1777-1790

  Wang, Congxiao ^a   Du, Shiqiang ^a   Wen, Jiahong ^a   Zhang, Ming ^a   Gu, Honghuan ^a   Shi, Yong ^b   Xu, Hui ^a  

^a SHANGHAI NORMAL UNIVERSITY   (China)

^b ZHENGZHOU UNIVERSITY   (China)

Author keywords

Explanatory model; Geographically weighted regression (GWR); Inundated streets; Pluvial flood

Indexed keywords

CLIMATE CHANGE; MEAN SQUARE ERROR; REGRESSION ANALYSIS;

CORRECTED AKAIKE INFORMATION CRITERIONS; ENVIRONMENTAL FACTORS; GEOGRAPHICALLY WEIGHTED REGRESSION; ORDINARY LEAST SQUARES REGRESSIONS; PROVIDE GUIDANCES; RAPID URBANIZATIONS; SPATIAL AUTOCORRELATIONS; SPATIALLY EXPLICIT;

FLOODS;

CLIMATE CHANGE; FLOOD CONTROL; NATURAL DISASTER; POLICY MAKING; REGRESSION ANALYSIS; RISK FACTOR; URBAN AREA; URBANIZATION;

CHINA; SHANGHAI;

EID: 84961675194     PISSN: 14363240     EISSN: 14363259     Source Type: Journal    
DOI: 10.1007/s00477-016-1242-6     Document Type: Article

References (53)

1. Battiata J, Collins K, Hirschman D, Hoffmann G (2010) The runoff reduction method. J Contemp Water Res Educ 146(1):11–21. doi:10.1111/j.1936-704X.2010.00388.x
2. Bobrowsky PT (2013) Encyclopedia of natural hazards. Springer, Dordrecht
3. Brunsdon C, Fotheringham S, Charlton M (1998) Geographically weighted regression. J R Stat Soc Ser D 47(3):431–443
4. Cai L (2009) Shantytowns and management ability of national government in modern Shanghai. Hist Rev 02:23–30
5. Cheng XT (2005) Changes of flood control situations and adjustments of flood management strategies in China. Water Int 30(1):108–113
6. Cheng J, Yang K, Zhao J, Yuan W, Wu JP (2007) Variation of river system in center district of Shanghai and its impact factors during the last one hundred years. Scientia Geographica Sinica 27(1):85–91 (in Chinese)
7. Cherqui F, Belmeziti A, Granger D, Sourdril A, Le Gauffre P (2015) Assessing urban potential flooding risk and identifying effective risk-reduction measures. Sci Total Environ 514:418–425. doi:10.1016/j.scitotenv.2015.02.027
8. Cliff A, Ord J (1981) Spatial processes: models and applications. Pion Ltd, London
9. de Smith MJ, Goodchild MF, Longley PA (2015) Geospatial analysis: a comprehensive guide to principles, techniques and software tools. The Winchelsea Press, Winchelsea
10. Du S, Gu H, Wen J, Chen K, Van Rompaey A (2015a) Detecting flood variations in Shanghai over 1949–2009 with Mann–Kendall tests and a newspaper-based database. Water 7(5):1808–1824
11. Du S, Shi P, Van Rompaey A, Wen J (2015b) Quantifying the impact of impervious surface location on flood peak discharge in urban areas. Nat Hazards. doi:10.1007/s11069-014-1463-2
12. Du S, Van Rompaey A, Shi P, Wang JA (2015c) 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. doi:10.1007/s11069-014-1583-8
13. Gaitan S, ten Veldhuis J (2015) Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks. Proc IAHS 370:9–14
14. Gaitan S, ten Veldhuis MC, van de Giesen N (2015) Spatial distribution of flood incidents along urban overland flow-paths. Water Resour Manag 29(9):3387–3399. doi:10.1007/s11269-015-1006-y
15. Gao J, Li S (2011) Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using geographically weighted regression. Appl Geogr 31(1):292–302. doi:10.1016/j.apgeog.2010.06.003
16. Gaume E, Bain V, Bernardara P, Newinger O, Barbuc M, Bateman A, Blaskovicova L, Bloschl G, Borga M, Dumitrescu A, Daliakopoulos I, Garcia J, Irimescu A, Kohnova S, Koutroulis A, Marchi L, Matreata S, Medina V, Preciso E, Sempere-Torres D, Stancalie G, Szolgay J, Tsanis I, Velasco D, Viglione A (2009) A compilation of data on European flash floods. J Hydrol 367(1–2):70–78. doi:10.1016/j.jhydrol.2008.12.028
17. GRIP (2015) Disaster databases. http://174.122.150.229/~gripwebo/gripweb/?q=disaster-database. Accessed 5 Jan 2015
18. Guha-Sapir D, Below R, Hoyois P (2015) EM-DAT: The CRED/OFDA International Disaster Database. www.emdat.be, Université Catholique de Louvain, Brussels, Belgium
19. Harris P, Clarke A, Juggins S, Brunsdon C, Charlton M (2014) Geographically weighted methods and their use in network re-designs for environmental monitoring. Stoch Env Res Risk Assess 28(7):1869–1887. doi:10.1007/s00477-014-0851-1
20. Hood MJ, Clausen JC, Warner GS (2007) Comparison of stormwater lag times for low impact and traditional residential development. J Am Water Resour Assoc 43(4):1036–1046. doi:10.1111/j.1752-1688.2007.00085.x
21. IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) A special report of working groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge
22. Landwehr P, Carley K (2014) Social media in disaster relief. In: Chu WW (ed) Data mining and knowledge discovery for big data. Studies in big data, vol 1. Springer, Berlin, pp 225–257. doi:10.1007/978-3-642-40837-3_7
23. Llasat MC, Llasat-Botija M, Lopez L (2009) A press database on natural risks and its application in the study of floods in Northeastern Spain. Nat Hazards Earth Syst 9(6):2049–2061
24. Merz B, Kreibich H, Lall U (2013) Multi-variate flood damage assessment: a tree-based data-mining approach. Nat Hazards Earth Syst 13(1):53–64. doi:10.5194/nhess-13-53-2013
25. Miller HJ (2004) Tobler’s first law and spatial analysis. Ann Assoc Am Geogr 94(2):284–289. doi:10.1111/j.1467-8306.2004.09402005.x
26. Osborne PE, Foody GM, Suárez-Seoane S (2007) Non-stationarity and local approaches to modelling the distributions of wildlife. Divers Distrib 13(3):313–323. doi:10.1111/j.1472-4642.2007.00344.x
27. Quan RS (2014) Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation. Nat Hazards 73(3):1569–1585. doi:10.1007/s11069-014-1156-x
28. Rashid H (2011) Interpreting flood disasters and flood hazard perceptions from newspaper discourse Tale of two floods in the Red River valley, Manitoba, Canada. Appl Geogr 31(1):35–45. doi:10.1016/j.apgeog.2010.03.010
29. Schubert JE, Sanders BF, Smith MJ, Wright NG (2008) Unstructured mesh generation and landcover-based resistance for hydrodynamic modeling of urban flooding. Adv Water Resour 31(12):1603–1621. doi:10.1016/j.advwatres.2008.07.012
30. Shi P, Wang JA, Yang M, Chen J, Pan Y, Wang P, Fang W, Zhou W, Wang Y, Yuan Y, Xu X, Luo W (2000) Understanding of natural disaster database design and compilation of digital atlas of natural disasters in China. Geogr Inf Sci 6(2):153–158. doi:10.1080/10824000009480544
31. Shi Y, Shi C, Xu SY, Sun AL, Wang J (2010) Exposure assessment of rainstorm waterlogging on old-style residences in Shanghai based on scenario simulation. Nat Hazards 53(2):259–272. doi:10.1007/s11069-009-9428-6
32. Spekkers MH, Kok M, Clemens FHLR, ten Veldhuis JAE (2013) A statistical analysis of insurance damage claims related to rainfall extremes. Hydrol Earth Syst Sci 17(3):913–922. doi:10.5194/hess-17-913-2013
33. Spekkers MH, Kok M, Clemens F, ten Veldhuis JAE (2014) Decision-tree analysis of factors influencing rainfall-related building structure and content damage. Nat Hazards Earth Syst 14(9):2531–2547. doi:10.5194/nhess-14-2531-2014
34. Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79. doi:10.1016/j.jhydrol.2013.09.034
35. Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343. doi:10.1016/j.jhydrol.2014.03.008
36. Tehrany MS, Pradhan B, Jebur MN (2015) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Env Res Risk Assess 29(4):1149–1165. doi:10.1007/s00477-015-1021-9
37. ten Veldhuis JAE, Clemens FHLR, van Gelder PHAJM (2011) Quantitative fault tree analysis for urban water infrastructure flooding. Struct Infrastruct Eng 7(11):809–821. doi:10.1080/15732470902985876
38. Tu J, Xia ZG (2008) Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation. Sci Total Environ 407(1):358–378. doi:10.1016/j.scitotenv.2008.09.031
39. Wang J, Haining R, Cao Z (2010) Sample surveying to estimate the mean of a heterogeneous surface: reducing the error variance through zoning. Int J Geogr Inf Sci 24(4):523–543. doi:10.1080/13658810902873512
40. Wang J, Gao W, Xu S, Yu L (2012) Evaluation of the combined risk of sea level rise, land subsidence, and storm surges on the coastal areas of Shanghai, China. Clim Change 115(3–4):537–558. doi:10.1007/s10584-012-0468-7
41. Warhurst JR, Parks KE, McCulloch L, Hudson MD (2014) Front gardens to car parks: changes in garden permeability and effects on flood regulation. Sci Total Environ 485–486:329–339. doi:10.1016/j.scitotenv.2014.03.035
42. Wong W, Lee J (2005) Statistical analysis of geographic information with ArcView GIS and ArcGIS. Wiley, Hoboken
43. Wu X, Yu D, Chen Z, Wilby R (2012) An evaluation of the impacts of land surface modification, storm sewer development, and rainfall variation on waterlogging risk in Shanghai. Nat Hazards 63(2):305–323. doi:10.1007/s11069-012-0153-1
44. Xie Y (2013) Development of drainage planning in view of frequent urban waterlogging disasters. Chin J City Plan Rev 37(2):45–50
45. Xu Q (1997) History of hydraulic projects in Shanghai. Shanghai Academy of Social Sciences Press, Shanghai (in Chinese)
46. Yang TH, Yang SC, Ho JY, Lin GF, Hwang GD, Lee CS (2015) Flash flood warnings using the ensemble precipitation forecasting technique: a case study on forecasting floods in Taiwan caused by typhoons. J Hydrol 520:367–378. doi:10.1016/j.jhydrol.2014.11.028
47. Yao L, Chen L, Wei W, Sun R (2015) Potential reduction in urban runoff by green spaces in Beijing: a scenario analysis. Urban For Urban Green 14(2):300–308. doi:10.1016/j.ufug.2015.02.014
48. Yin J, Yin Z, Zhong H, Xu S, Hu X, Wang J, Wu J (2011a) Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China. Environ Monit Assess 177(1–4):609–621. doi:10.1007/s10661-010-1660-8
49. Yin Z, Yin J, Xu S, Wen J (2011b) Community-based scenario modelling and disaster risk assessment of urban rainstorm waterlogging. J Geogr Sci 21(2):274–284. doi:10.1007/s11442-011-0844-7
50. Yin J, Yu D, Yin Z, Wang J, Xu S (2013) Modelling the combined impacts of sea-level rise and land subsidence on storm tides induced flooding of the Huangpu River in Shanghai, China. Clim Change 119(3–4):919–932. doi:10.1007/s10584-013-0749-9
51. Yu DY, Shi PJ, Liu YP, Xun B (2013) Detecting land use-water quality relationships from the viewpoint of ecological restoration in an urban area. Ecol Eng 53:205–216. doi:10.1016/j.ecoleng.2012.12.045
52. Yuan Z (1999) Floods and drought in Shanghai. Hohai University Press, Nanjing (in Chinese)
53. Zhang B, G-d Xie, Li N, Wang S (2015) Effect of urban green space changes on the role of rainwater runoff reduction in Beijing, China. Landsc Urban Plan 140:8–16. doi:10.1016/j.landurbplan.2015.03.014