Home⇛DAVAO RESEARCH JOURNAL⇛vol. 16 no. 2 (2025)

Development and Prioritization of Flood Vulnerability Indicators for Nabunturan, Davao de Oro

Frincess Jade C Cajano | Alex S. Olpenda

Discipline: environmental sciences

 

Abstract:

Flooding remains one of the most prevalent and destructive natural hazards, threatening infrastructure, livelihoods, and communities worldwide. This study conducted a systematic assessment of flood vulnerability in Nabunturan, Davao de Oro, a municipality prone to recurrent flooding. Utilizing the UNESCO-IHE flood vulnerability indicators, the research examined the Social, Economic, Environmental, and Physical components of vulnerability. These indicators were categorized into three subdimensions— exposure, susceptibility, and resilience—and were evaluated using the Fuzzy Delphi Method (FDM) to achieve expert consensus, followed by the Analytical Hierarchy Process (AHP) to prioritize key factors influencing flood vulnerability. The study identified and prioritized 27 flood vulnerability indicators, with normalized weights (ranging from 0 to 1) derived from the Fuzzy Delphi Method (FDM) and Analytical Hierarchy Process (AHP), reflecting the relative importance of each factor. Higher-weighted indicators serve as the basis for prioritization of risk reduction actions and resilience-building efforts. The study revealed the indicators with the highest weights per component, arranged in exposure, susceptibility, and resilience, respectively. Social Component: Population in Flood-prone Areas (0.3114); Past Experience (0.4314); Shelters/Hospitals (0.2685). Economic Component: Land Use (0.5230); Quality of Infrastructure (0.6149); Amount of Investment (1.0). Environmental Component: Degraded Area (1.0); Rainfall (0.5091); Green Area (1.0). Physical Component: Topography (0.2958); Frequency of Occurrence (1.0); Dikes/ Levees (1.0). The weighted indicators can support the computation of a Flood Vulnerability Index (FVI), informing Barangay Disaster Risk Reduction and Management Plans (BDRRMPs), enhancing the Climate and Disaster Risk Assessment (CDRA), and guiding targeted risk reduction and adaptation programs.



References:

  1. 1. Abarquez, I., & Murshed, Z. (2004). Community-based disaster risk management: Field practitioners’ handbook. Asian Disaster Preparedness Center (ADPC).
  2. Abdullah, J. B., & Yusof, S. I. (2018). A fuzzy Delphi method—Developing high-performance leadership standard for Malaysian school leaders. Journal of Education and Social Sciences, 9(2), 5–6.
  3. Alves, P. B. R., Cordão, M. J. d. S., Djordjević, S., & Javadi, A. A. (2021). Place-based citizen science for assessing risk perception and coping capacity of households affected by multiple hazards. Sustainability, 13(1), 302. https://doi.org/10.3390/su13010302
  4. Asian Development Bank. (2020). Nature-based solutions for building resilience in towns and cities: Case studies from the Greater Mekong Subregion. Asian Development Bank.
  5. Asian Development Bank. (2022). Disaster-resilient infrastructure: Unlocking opportunities for Asia and the Pacific. Asian Development Bank. https://doi.org/10.22617/TCS220310-2
  6. Balica, S. F. (2012). Applying the flood vulnerability index as a knowledge base for flood risk assessment. CRC Press / Balkema.
  7. Balica, S. F., Popescu, I., Beevers, L., & Wright, N. G. (2013). Parametric and physically based modeling techniques for flood risk and vulnerability assessment: A comparison. Environmental Modelling & Software, 41, 84–92.
  8. Balica, S. F., & Wright, N. G. (2010). Reducing the complexity of the flood vulnerability index. Environmental Hazards, 9(4), 321–339.
  9. Balica, S. F., Wright, N. G., & van der Meulen, F. (2012). A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards, 64(1), 73–105. https://doi.org/10.1007/s11069-012-0234-1
  10. Bankoff, G., & Hilhorst, D. (2009). The politics of risk in the Philippines: Comparing state and NGO perceptions of disaster management. Disasters, 33(4), 686–704. https://doi.org/10.1111/j.1467-7717.2009.01104.x
  11. Birkmann, J. (Ed.). (2006). Measuring vulnerability to natural hazards: Towards disaster resilient societies. United Nations University Press.
  12. Birkmann, J., Cardona, O. D., Carreño, M. L., Barbat, A. H., Pelling, M., Schneiderbauer, S., & Welle, T. (2013). Framing vulnerability, risk and societal responses: The MOVE framework. Natural Hazards, 67(2), 193–211. https://doi.org/10.1007/s11069-013-0558-5
  13. Cabrera, J. S., & Lee, H. S. (2018). Impacts of climate change on flood-prone areas in Davao Oriental, Philippines. Water, 10(7), 893. https://doi.org/10.3390/w10070893
  14. Cardona, O. D., van Aalst, M. K., Birkmann, J., Fordham, M., McGregor, G., Perez, R., Pulwarty, R. S., Schipper, E. L. F., & Sinh, B. T. (2012). Determinants of risk: Exposure and vulnerability. In C. B. Field, V. Barros, T. F. Stocker, & Q. Dahe (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation (pp. 65–108). Cambridge University Press. https://doi.org/10.1017/CBO9781139177245.005
  15. Climate Change Commission. (2010). National framework strategy on climate change 2010–2022. Office of the President of the Philippines. https://climate.gov.ph/files/NFSCC.pdf
  16. Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242–261. https://doi.org/10.1111/1540-6237.8402002
  17. Cutter, S. L., & Finch, C. (2008). Temporal and spatial changes in social vulnerability to natural hazards. Proceedings of the National Academy of Sciences, 105(7), 2301–2306. https://doi.org/10.1073/pnas.0710375105
  18. Eckstein, D., Künzel, V., & Schäfer, L. (2021). Global Climate Risk Index 2021: Who suffers most from extreme weather events? Weather-related loss events in 2019 and 2000–2019. Germanwatch e.V. https://www.german-watch.org/en/19777
  19. Efraimidou, E., & Spiliotis, M. (2024). A GIS-based flood risk assessment using the Decision-Making Trial and Evaluation Laboratory approach at a regional scale. Environmental Processes, 11(1), 9. https://doi.org/10.1007/s40710-024-00683-w
  20. El Mazi, M., Hmamouchi, M., Er-Riyahi, S., Bouchantouf, S., & Houari, A. (2022). Deforestation effects on soil properties and erosion: A case study in the central Rif, Morocco. Eurasian Journal of Soil Science, 11(4), 275–283. https://doi.org/10.18393/ejss.1098600
  21. Fatemi, F., Ardalan, A., Aguirre, B., Mansouri, N., & Mohammadfam, I. (2017). Social vulnerability indicators in disasters: Findings from a systematic review. International Journal of Disaster Risk Reduction, 22, 219–227. https://doi.org/10.1016/j.ijdrr.2016.09.006
  22. Gaillard, J. C., & Mercer, J. (2013). From knowledge to action: Bridging gaps in disaster risk reduction. Progress in Human Geography, 37(1), 93–114. https://doi.org/10.1177/0309132512446717
  23. Habibi, A., Jahantigh, F. F., & Sarafrazi, A. (2015). Fuzzy Delphi technique for forecasting and screening items. Asian Journal of Research in Business Economics and Management, 5(2), 130–143. https://doi.org/10.5958/2249-7307.2015.00036.5
  24. Hao-Chang, T., An-Sheng, L., Huang-Ning, L., Chen, C. N., & Yu-Chun, L. (2020). An application of the Fuzzy Delphi Method and Fuzzy AHP on the discussion of training indicators for the Regional Competition, Taiwan National Skills Competition, in the trade of joinery. Sustainability, 12(10), 4290. https://doi.org/10.3390/su12104290
  25. Hsu, C.-C., & Sandford, B. A. (2007). The Delphi technique: Making sense of consensus. Practical Assessment, Research and Evaluation, 12(10), 1–8. https://doi.org/10.7275/pdz9-th90
  26. Intergovernmental Panel on Climate Change. (2021). Climate change 2021: The physical science basis. Cambridge University Press. https://doi.org/10.1017/9781009157896
  27. Ismail, N. K., Mohamed, S., & Hamzah, M. I. (2019). The application of the Fuzzy Delphi technique to the required aspect of parental involvement in the effort to inculcate positive attitude among preschool children. Creative Education, 10, 2907–2921. https://doi.org/10.4236/ce.2019.1012216
  28. Jamshed, A., Birkmann, J., Feldmeyer, D., & Rana, I. A. (2020). A conceptual framework to understand the dynamics of rural–urban linkages for rural flood vulnerability. Sustainability, 12(7), 2894. https://doi.org/10.3390/su12072894
  29. Khajehei, S., Ahmadalipour, A., Shao, W., & Moradkhani, H. (2020). A place-based assessment of flash flood hazard and vulnerability in the contiguous United States. Scientific Reports, 10, Article 448. https://doi.org/10.1038/s41598-019-57349-z
  30. Khosravi, K., Nohani, E., Maroufinia, E., & Pourghasemi, H. R. (2016). A GIS-based flood susceptibility assessment and its mapping in Iran: A comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Natural Hazards, 83(2), 947–987. https://doi.org/10.1007/s11069-016-2357-2
  31. Lee, G., Jun, K.-S., & Chung, E.-S. (2013). Integrated multi-criteria flood vulnerability approach using fuzzy TOPSIS and Delphi technique. Natural Hazards and Earth System Sciences, 13(5), 1293–1312. https://doi.org/10.5194/nhess-13-1293-2013
  32. Liu, W., Chen, W., & Peng, C. (2014). Assessing the effectiveness of green infrastructures on urban flooding reduction: A community scale study. Ecological Modelling, 291, 6–14. https://doi.org/10.1016/j.ecolmodel.2014.07.012
  33. Local Government Academy. (2016). Enhanced LGU guidebook on the formulation of local climate change action plan. Department of the Interior and Local Government. https://lga.gov.ph/uploads/publication/attachments/1590498028.pdf
  34. Manakandan, S. K., Ismail, R., Jamil, M. R. M., & Ragunath, P. (2017). Pesticide applicators questionnaire content validation: A fuzzy Delphi method. Medical Journal of Malaysia, 72(4), 228–235.
  35. Mavhura, E. (2017). Building resilience to food insecurity in rural communities: Evidence from traditional institutions in Zimbabwe. Jàmbá: Journal of Disaster Risk Studies, 9(1), a453. https://doi.org/10.4102/jamba.v9i1.453
  36. Mohamed Yusoff, A., Hashim, A., Muhamad, N., & Wan Hamat, W. (2021). Application of Fuzzy Delphi Technique towards designing and developing the elements for the e-PBM PI-Poli module. Asian Journal of University Education, 17(1), 292–304. https://doi.org/10.24191/ajue.v17i1.12625
  37. Mourato, S., Fernandez, P., Pereira, L. G., & Moreira, M. (2023). Assessing vulnerability in flood-prone areas using Analytic Hierarchy Process—Group decision making and geographic information system: A case study in Portugal. Applied Sciences, 13(8), 4915. https://doi.org/10.3390/app13084915
  38. Mrekva, L. (2024). Addressing the different aspects of urban land use and urban flood risk together, and identifying ways to manage them successfully. Védelem Tudomány, 9(4), 115–129. https://doi.org/10.61790/vt.2024.17660
  39. Municipal Disaster Risk Reduction and Management Office – Nabunturan. (2022). Climate and disaster risk assessment (CDRA) 2022. Municipality of Nabunturan.
  40. Municipality of Nabunturan. (2024). Municipality profile 2024. Municipality of Nabunturan.
  41. Nakasu, T., & Amrapala, C. (2023). Evidence-based disaster risk assessment in Southeast Asian countries. Natural Hazards Research, 3(2), 295–304. https://doi.org/10.1016/j.nhres.2023.04.001
  42. Nasiri, H., & Shahmohammadi-Kalalagh, S. (2013). Flood vulnerability index as a knowledge base for flood risk assessment in urban area. Journal of Novel Applied Sciences, 2(8), 266–269.
  43. Nguyen, M.-H., Le, D. P., & Vo, T. T. (2021). Vulnerability to natural disaster and welfare effect: A case study of flood risk in Vietnam’s North Central Region. Journal of Asian and African Studies, 56(4), 581–595. https://doi.org/10.1177/0021909621993499
  44. Orencio, P. M., & Fujii, M. (2013). A localized disaster-resilience index to assess coastal communities based on an analytic hierarchy process (AHP). International Journal of Disaster Risk Reduction, 3, 62–75. https://doi.org/10.1016/j.ijdrr.2012.11.006
  45. Pelone, B. C., & Sanchez, P. A. J. (2024). Development of science and significance-based indicators for social-ecological system flood resilience. NRCP Research Journal, 23(2), 25–49.
  46. Pelone, B. C., Bantayan, N. A., Fermil, F. A., & Cajano, F. J. (2024). Analyzing vulnerability dynamics of the flood-prone landscapes through the context of social-ecological system framework. TWIST, 19(2), 218–228. https://doi.org/10.5281/zenodo.10049652
  47. Philippine Statistics Authority. (2021). 2020 Census of Population and Housing (2020 CPH) population counts declared official by the President. https://psa.gov.ph/content/2020-census-population-and-housing-2020-cph-population-counts-declared-official-president
  48. Razzaghi Asl, S., Rahman, M. A., Tate, E., Lehman, W., & Wing, O. (2025). Social vulnerability correlates of flood risk to crops and buildings. Natural Hazards, 111(2), 1234–1256. https://doi.org/10.1007/s11069-025-07137-y
  49. Rehman, S., Sahana, M., Hong, H., Sajjad, H., & Ahmed, B. (2019). A systematic review on approaches and methods used for flood vulnerability assessment: Framework for future research. Natural Hazards, 96(2), 975–1000. https://doi.org/10.1007/s11069-018-03567-z
  50. Robbani, A., Siswanto, A., & Teddy, L. (2025). Revitalizing flood-prone areas through socio-environmental strategies: A qualitative approach to urban flood management. Calamity: A Journal of Disaster Technology and Engineering, 2(2), 45–60.
  51. Rufat, S., Tate, E., Burton, C. G., & Maroof, A. S. (2015). Social vulnerability to floods: Review of case studies and implications for measurement. International Journal of Disaster Risk Reduction, 14, 470–486. https://doi.org/10.1016/j.ijdrr.2015.09.013
  52. Saaty, T. L. (1980). The analytic hierarchy process (AHP). The Journal of the Operational Research Society, 41(11), 1073–1076.
  53. Saaty, T. L. (2005). Theory and applications of the analytic network process: Decision making with benefits, opportunities, costs, and risks. RWS Publications.
  54. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
  55. Shelton, K., & Creghan, K. A. (2015). Demystifying the Delphi method. In V. C. X. Wang (Ed.), Handbook of research on scholarly publishing and research methods (pp. 215–230). IGI Global. https://doi.org/10.4018/978-1-4666-7409-7.ch019
  56. Sohn, W., Kim, J.-H., Li, M.-H., Brown, R. D., & Jaber, F. H. (2020). How does increasing impervious surfaces affect urban flooding in response to climate variability? Ecological Indicators, 118, 106774. https://doi.org/10.1016/j.ecolind.2020.106774
  57. Tayyab, M., Zhang, J., Hussain, M., Ullah, S., Liu, X., Khan, S. N., Baig, M. A., Hassan, W., & Al-Shaibah, B. (2021). GIS-based urban flood resilience assessment using urban flood resilience model: A case study of Peshawar City, Khyber Pakhtunkhwa, Pakistan. Remote Sensing, 13(10), 1864. https://doi.org/10.3390/rs13101864
  58. United Nations Human Settlements Programme (UN-Habitat). (2019). Urban planning and design at UN-Habitat. https://unhabitat.org/sites/default/files/2019/10/urban_planning_design_branch_portfolio.pdf
  59. United Nations Office for Disaster Risk Reduction. (2020). The human cost of disasters: An overview of the last 20 years (2000–2019).
  60. United Nations Office for Disaster Risk Reduction. (2022). Disaster risk reduction and climate change adaptation: Pathways for sustainable development.
  61. Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
  62. Wisner, B., Blaikie, P., Cannon, T., & Davis, I. (2004). At risk: Natural hazards, people’s vulnerability and disasters (2nd ed.). Routledge. https://doi.org/10.4324/9780203714775
  63. World Bank Group. (2021). World Bank Group Climate Change Action Plan 2021–2025: Supporting green, resilient, and inclusive development. https://hdl.handle.net/10986/35799
  64. Zhou, L., Kori, D. S., Sibanda, M., & Nhundu, K. (2022). An analysis of the differences in vulnerability to climate change: A review of rural and urban areas in South Africa. Climate, 10(8), 118. https://doi.org/10.3390/cli10080118

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