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 Journal Article   Open Access   Published 
 Journal of Environmental Science Revolution (ISSN 2435-726X)  Crossmark

Impact of climate-induced disasters on the sustainability of cities: An urban resilience perspective on floods  2025, 4 (1): 1-17  DOI 10.37357/1068/JESR/4.1.01

Mutu Tantrige Osada Vishvajith Peiris  

Department of Urban Planning and Design, University of Hong Kong, Pok Fu Lam, Hong Kong

Climate change has intensified extreme rainfall and flood events, posing significant threats to ur-ban sustainability. Floods, among the most catastrophic disasters, disrupt livelihoods and irreversi-bly damage economies, making disaster risk reduction critical for achieving safe, inclusive, and sus-tainable cities in line with the Sustainable Development Goals. Urban resilience, reflecting a city’s ability to respond, recover, and maintain core functions during disasters, is challenging to assess due to complex urban system interactions and the non-linear nature of climate emergencies. This study examines resilience through land use changes as indicators of urban sustainability against flood disasters, using Colombo City, Sri Lanka, as a case study. The research evaluates urban flood resilience (UFR) based on ten natural, physical, and social parameters, integrating urban growth simulation, flood modeling, and geospatial assessments at a 30-meter resolution. Land use catego-ries; waterbodies, wetlands, vegetation, and urban built-up areas; were analyzed alongside resili-ence classifications ranging from flood-susceptible to highly responsive. Results reveal that high-resilience areas are concentrated in vegetated high elevations and urban zones with effective drainage systems, while low-resilience areas are heavily populated floodplains and impervious city-center areas with limited greenery. Regression analysis confirms that impervious surfaces exacer-bate flood risk, while vegetation and wetlands provide long-term resistance to extreme rainfall. The findings emphasize the need for green infrastructure-oriented drainage networks and sustainable urbanization to mitigate pluvial floods. Incorporating land use changes and socio-economic factors highlights the importance of disaster preparedness at the grassroots level for effective mitigation strategies. From an urban planning perspective, this approach aids in guiding future land use changes, prioritizing sustainable growth, and informing decision-makers on resource allocation to enhance flood resilience in cities.
 
  1. Liu Z, He C, Zhou Y, Wu J (2014) “How much of the world’s land has been urbanized, really? A hierarchical framework for avoiding confusion” Landsc Ecol (vol. 29, no. 5, pp. 763–771) https://doi.org/10.1007/s10980-014-0034-y
  2. Urban development: Overview (2023) Text/HTML World Bank Group. (https://www.worldbank.org/en/topic/urbandevelopment/overview) Accessed: 16 February 2025
  3. World urbanization prospects: The 2018 revision (2019) United Nations. 123 p. ISBN: 978-92-1-004314-4 (https://www.un-ilibrary.org/content/books/9789210043144) Accessed: 16 February 2025
  4. Wu J (2014) “Urban ecology and sustainability: The state-of-the-science and future directions” Landsc Urban Plan (vol. 125, pp. 209–221) https://doi.org/10.1016/j.landurbplan.2014.01.018
  5. Evans JP (2011) “Resilience, ecology and adaptation in the experimental city” Trans Inst Br Geogr (vol. 36, no. 2, pp. 223–237) https://doi.org/10.1111/j.1475-5661.2010.00420.x
  6. Blunck T (n.d.) “Natural disaster risks - Rising trend in losses | Munich Re” Munich Re. (https://www.munichre.com/en/risks/natural-disasters.html) Accessed: 16 February 2025
  7. Transforming our world: the 2030 Agenda for Sustainable Development (2015) (vol. 15, no. 116, pp. A/RES/70/1)
  8. Garmezy N (1993) “Vulnerability and resilience” Studying lives through time: Personality and development Washington, DC, US, American Psychological Association - pp. 377–398. https://doi.org/10.1037/10127-032
  9. Ribeiro PJG, Pena Jardim Gonçalves LA (2019) “Urban resilience: A conceptual framework” Sustain Cities Soc (vol. 50, pp. 101625) https://doi.org/10.1016/j.scs.2019.101625
  10. Serre D, Heinzlef C (2018) “Assessing and mapping urban resilience to floods with respect to cascading effects through critical infrastructure networks” Int J Disaster Risk Reduct (vol. 30, pp. 235–243) https://doi.org/10.1016/j.ijdrr.2018.02.018
  11. Sharma M, Sharma B, Kumar N, Kumar A (2023) “Establishing conceptual components for urban resilience: taking clues from urbanization through a planner’s lens” Nat Hazards Rev (vol. 24, no. 1, pp. 04022040) https://doi.org/10.1061/NHREFO.NHENG-1523
  12. Datola G, Bottero M, De Angelis E, Romagnoli F (2022) “Operationalising resilience: A methodological framework for assessing urban resilience through System Dynamics Model” Ecol Model (vol. 465, pp. 109851) https://doi.org/10.1016/j.ecolmodel.2021.109851
  13. Du M, Zhang X, Wang Y, Tao L, Li H (2020) “An operationalizing model for measuring urban resilience on land expansion” Habitat Int (vol. 102, pp. 102206) https://doi.org/10.1016/j.habitatint.2020.102206
  14. Zhang M, Yang Y, Li H, van Dijk MP (2020) “Measuring urban resilience to climate change in three Chinese cities” Sustainability (vol. 12, no. 22, pp. 9735) https://doi.org/10.3390/su12229735
  15. Meerow S, Newell JP, Stults M (2016) “Defining urban resilience: A review” Landsc Urban Plan (vol. 147, pp. 38–49) https://doi.org/10.1016/j.landurbplan.2015.11.011
  16. Defossez S, Vinet F, Leone F (2017) “Vulnerability to flooding: Progress and limitations” In: Vinet F - editor. Floods Amsterdam, Netherlands, Elsevier - pp. 241–257. https://doi.org/10.1016/B978-1-78548-268-7.50014-6 (https://www.sciencedirect.com/science/article/pii/B9781785482687500146) Accessed: 16 February 2025
  17. Patankar AM (2017) “Colombo: Exposure, vulnerability, and ability to respond to floods” Policy Res Work Pap Ser (pp. 1–40)
  18. Sendai framework for disaster risk reduction 2015-2030 | (2015) Geneva, Switzerland, United Nations (UN). (https://www.undrr.org/publication/sendai-framework-disaster-risk-reduction-2015-2030) Accessed: 16 February 2025
  19. Peiris MTOV (2024) “Assessment of urban resilience to floods: A spatial planning framework for cities” Sustainability (vol. 16, no. 20, pp. 9117) https://doi.org/10.3390/su16209117
  20. Arkema KK, Griffin R, Maldonado S, Silver J, Suckale J, et al. (2017) “Linking social, ecological, and physical science to advance natural and nature‐based protection for coastal communities” Ann N Y Acad Sci (vol. 1399, no. 1, pp. 5–26) https://doi.org/10.1111/nyas.13322
  21. Waleed M (2023) “Mastering machine learning based land use classification with python: A comprehensive guide!” Medium (https://waleedgeo.medium.com/lulc-py-78cb954673d) Accessed: 16 February 2025
  22. Fl A (2004) “Chapter 9: Hydrologic soil-cover complexes” National Engineering Handbook Bakersfield, USA, United States Department of Agriculture - pp. 1–14.
  23. Cai H, Lam NS-N, Zou L, Qiang Y, Li K (2016) “Assessing community resilience to coastal hazards in the lower Mississippi river basin” Water (vol. 8, no. 2, pp. 46) https://doi.org/10.3390/w8020046
  24. Folke C (2006) “Resilience: The emergence of a perspective for social–ecological systems analyses” Glob Environ Change (vol. 16, no. 3, pp. 253–267) https://doi.org/10.1016/j.gloenvcha.2006.04.002
  25. Walker B, Holling CS, Carpenter SR, Kinzig A (2004) “Resilience, adaptability and transformability in social-ecological systems” Ecol Soc (vol. 9, no. 2) https://doi.org/10.5751/ES-00650-090205 (http://www.scopus.com/inward/record.url?scp=20444454972&partnerID=8YFLogxK) Accessed: 16 February 2025
  26. sweetviz: A pandas-based library to visualize and compare datasets. (n.d.) (https://github.com/fbdesignpro/sweetviz) Accessed: 16 February 2025
  27. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, et al. (2011) “Scikit-learn: Machine learning in Python” J Mach Learn Res (vol. 12, no. 85, pp. 2825–2830)
  28. Wolfram S (1984) “Cellular automata as models of complexity” Nature (vol. 311, no. 5985, pp. 419–424) https://doi.org/10.1038/311419a0
  29. Kelly (Letcher) RA, Jakeman AJ, Barreteau O, Borsuk ME, ElSawah S, et al. (2013) “Selecting among five common modelling approaches for integrated environmental assessment and management” Environ Model Softw (vol. 47, pp. 159–181) https://doi.org/10.1016/j.envsoft.2013.05.005
  30. Olmedo MTC, Paegelow M, Mas J-F: editors (2017) “Geomatic approaches for modeling land change scenarios” Berlin, Germany, Springer. 552 p. ISBN: 978-3-319-60802-0
  31. Al-Ageili M, Mouhoub M, Piwowar J (2017) “Remote Sensing, Gis and cellular automata for urban growth simulation” Comput Inf Sci (vol. 10, no. 4, pp. 38–49) https://doi.org/10.5539/cis.v10n4p38
  32. Yeh AG-O, Li X (2001) “A constrained CA model for the simulation and planning of sustainable urban forms by using GIS” Environ Plan B Plan Des (vol. 28, no. 5, pp. 733–753) https://doi.org/10.1068/b2740
  33. Tripathy P, Kumar A (2019) “Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics” Cities (vol. 90, pp. 52–63) https://doi.org/10.1016/j.cities.2019.01.021
  34. Mallawatantri A (2016) “Medium to long-term multi-stakeholder strategy and action plan for management and conservation of the Kelani River Basin, 2016-2020. Natural resource profile of the Kelani River Basin” IUCN. p. ISBN: 978-955-0205-41-7 (https://portals.iucn.org/library/node/45949) Accessed: 16 February 2025
  35. Manawadu L, Wijeratne VPIS (2021) “Anthropogenic drivers and impacts of urban flooding- A case study in Lower Kelani River Basin, Colombo Sri Lanka” Int J Disaster Risk Reduct (vol. 57, pp. 102076) https://doi.org/10.1016/j.ijdrr.2021.102076
  36. Perera C, Nakamura S (2023) “Conceptualizing the effectiveness of flood risk information with a socio-hydrological model: A case study in Lower Kelani River Basin, Sri Lanka” Front Water (vol. 5, pp. 1–17) https://doi.org/10.3389/frwa.2023.1131997
  37. Fl A (2007) “Chapter 7: Hydrologic soil groups” National Engineering Handbook Bakersfield, USA, United States Department of Agriculture - pp. 1–14.
  38. Ross CW, Prihodko L, Anchang JY, Kumar SS, Ji W, et al. (2018) “Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling” ORNL DAAC https://doi.org/10.3334/ORNLDAAC/1566 (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1566) Accessed: 16 February 2025
  39. Peiris MTOV, Randeniya N, Bopitiyegedara N (2024) “Role of Environmental Factors in Managing Flood Risk Perception of Vulnerable Communities: A Study of Kelani River Basin, Colombo, Sri Lanka” https://doi.org/10.2139/ssrn.4852629 (https://papers.ssrn.com/abstract=4852629) Accessed: 16 February 2025
  40. Mallawatantri A (2016) “Medium to long-term multi-stakeholder strategy and action plan for management and conservation of the Kelani River basin: 2016-2020” Central Environment Authority of Sri Lanka. (https://portals.iucn.org/library/sites/library/files/documents/2016-013.pdf) Accessed: 16 February 2025
  41. Seneviratne R (2022) “Metro Colombo urban development project” Democratic Socialist Republic of Sri Lanka. (http://pub.curwsl.org/dyn/PCR-2023_03-04_19_00_00.pdf) Accessed: 16 February 2025
  42. Enhancing socio-ecological resilience in the urban wetlands of Colombo (n.d.) Enhancing Socio-Ecol Resil Urban Wetl Colombo (https://www.undp.org/srilanka/press-releases/enhancing-socio-ecological-resilience-urban-wetlands-colombo) Accessed: 16 February 2025
  43. Bose S, Mazumdar A (2023) “Urban flood risk assessment and mitigation with InVEST-UFRM model: a case study on Kolkata city, West Bengal state (India)” Arab J Geosci (vol. 16, no. 5, pp. 320) https://doi.org/10.1007/s12517-023-11412-2
  44. Hewawasam V, Matsui K (2022) “Assessing Community Perceptions on Urban Flood Resilience in Sri Lanka” Geosciences (vol. 12, no. 11, pp. 406) https://doi.org/10.3390/geosciences12110406
  45. Renschler C, Frazier A, Arendt L, Cimellaro GP, Reinhorn AM, et al. (2010) “A framework for defining and measuring resilience at the community scale: The PEOPLES resilience framework” New York, USA, State University of New York. (https://www.eng.buffalo.edu/mceer-reports/10/10-0006.pdf) Accessed: 16 February 2025
  46. 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 (vol. 504, pp. 69–79) https://doi.org/10.1016/j.jhydrol.2013.09.034
  47. Community Based Resilience Analysis (CoBRA): Conceptual framework and methodology (2013) New York, USA, UNDP. (https://www.undp.org/sites/g/files/zskgke326/files/publications/CoBRRA_Conceptual_Framework.pdf) Accessed: 16 February 2025
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