Abstract
Urban areas are increasingly getting vulnerable to floods due to high-intensity precipitation and increasing concretization. To identify the flood vulnerability status of urban micro-watersheds for an improved mitigation strategy, we propose a Flood Vulnerability Index (FVI) with readily available urban infrastructure and hydrological data. The criteria variables for FVI calculation include urban infrastructure data (building and road density), run-off retention capacity, the fraction of vegetation cover, and open spaces. The Soil Conservation Service Curve Number (SCS-CN) method has been applied to estimate run-off retention using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model. The weighted linear combination of the criteria variables was used to derive the FVI of each micro-watershed. The Analytical Hierarchical Process (AHP) has been utilized for the weight assignment. This method is applied to the Hyderabad City area, India. The city is densely populated and often devastated by urban floods. Results indicate that out of 85 micro-watersheds classified in the region, 24 are highly vulnerable with FVI > 3, requiring immediate flood mitigation action. A near-future flood mitigation strategy is required for 36 micro-watersheds with FVI in the range of 2–3. The remaining 25 micro-watersheds are relatively less vulnerable with FVI < 2. The proposed FVI accounts for the watershed's hydrological behavior, which is highly relevant in flood vulnerability estimation. The developed method is extremely simple to adapt to any city for flood vulnerability estimation and policy planning based on easily available open data.
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Abbreviations
- AHP:
-
Analytical hierarchical process
- DEM:
-
Digital elevation model
- FVI:
-
Flood Vulnerability Index
- HSG:
-
Hydrologic Soil Group
- InVEST:
-
Integrated valuation of ecosystem services and trade-offs
- IPCC:
-
Intergovernmental panel on climate change
- LULC:
-
Land use land cover
- MCDM:
-
Multi-criteria decision making
- QGIS:
-
Quantum Geographic Information System
- RS-GIS:
-
Remote Sensing and Geographic Information System
- SCS-CN:
-
Soil conservation service-curve number
- UGS:
-
Urban green spaces
- WLC:
-
Weighted linear combination
References
Agilan V, Umamahesh NV (2017) Modelling nonlinear trend for developing non-stationary rainfall intensity-duration-frequency curve: Modelling Nonlinear Trend For Developing Non-Stationary Idf Curve. Int J Climatol 37:1265–1281. https://doi.org/10.1002/joc.4774
Albuquerque MB, de Araújo AA, Martinez CENM, Mauad FF, Okawa CMP (2019) Sustainable urban drainage: a brief review of the compensatory techniques of structural and non-structural measures. Rev Eletrônica Em Gest Educ E Tecnol Ambient 23:35. https://doi.org/10.5902/2236117039837
Ali SA, Khatun R, Ahmad A, Ahmad SN (2019) Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Model Earth Syst Environ 5:1083–1102. https://doi.org/10.1007/s40808-019-00593-z
Baroni G, Facchi A, Gandolfi C, Ortuani B, Horeschi D, van Dam JC (2010) Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity. Hydrol Earth Syst Sci 14:251–270. https://doi.org/10.5194/hess-14-251-2010
Bedi P, Mahavir TNG (2020) Smart urban green spaces for smart Chandigarh. In: Vinod Kumar TM (ed) Smart environment for smart cities. Springer, Singapore, pp 111–148
Bera D, Kumar P, Siddiqui A, Majumdar A (2021) Assessing impact of urbanisation on surface runoff using vegetation-impervious surface-soil (V-I-S) fraction and NRCS curve number (CN) model. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-01079-z
Bonetti J, Woodroffe C (2016) Spatial analysis for coastal vulnerability assessment. In: Bartlett D, Celliers L (eds) Geoinformatics for marine and coastal management. CRC Press, Boca Raton, pp 367–395
Borga M, Anagnostou EN, Blöschl G, Creutin J-D (2011) Flash flood forecasting, warning and risk management: the HYDRATE project. Environ Sci Policy 14:834–844. https://doi.org/10.1016/j.envsci.2011.05.017
Borga M, Comiti F, Ruin I, Marra F (2019) Forensic analysis of flash flood response. Wires Water 6:e1338. https://doi.org/10.1002/wat2.1338
Boyaj A, Dasari HP, Hoteit I, Ashok K (2020) Increasing heavy rainfall events in South India due to changing land use land cover. Q J R Meteorol Soc. https://doi.org/10.1002/qj.3826
Braun B, Aßheuer T (2011) Floods in megacity environments: vulnerability and coping strategies of slum dwellers in Dhaka/Bangladesh. Nat Hazards 58:771–787. https://doi.org/10.1007/s11069-011-9752-5
Burden D (2006) Urban Street Trees: 22 benefits specific applications. Glatting Jackson Walkable Communities Inc. https://www.walkable.org/download/22_benefits.pdf
Chan NW, Tan ML, Ghani AA, Zakaria NA (2019) Sustainable urban drainage as a viable measure of coping with heat and floods due to climate change. IOP Conf Ser Earth Environ Sci 257:012013. https://doi.org/10.1088/1755-1315/257/1/012013
Charoenkit S, Piyathamrongchai K (2019) A review of urban green spaces multifunctionality assessment: a way forward for a standardized assessment and comparability. Ecol Indic 107:105592. https://doi.org/10.1016/j.ecolind.2019.105592
Chow V, Maidment D, Mays L (1988) Applied hydrology McGraw-Hill International editions. New York, USA
Das D (2015) Hyderabad: visioning, restructuring and making of a high-tech city. Cities 43:48–58. https://doi.org/10.1016/j.cities.2014.11.008
de Almeida GAM, Bates P, Ozdemir H (2018) Modelling urban floods at submetre resolution: challenges or opportunities for flood risk management? J Flood Risk Manag 11:S855–S865. https://doi.org/10.1111/jfr3.12276
Dhara VR, Schramm PJ, Luber G (2013) Climate change and infectious diseases in India: implications for health care providers. Indian J Med Res 138:847–852
Du J, Fan Z, Pu J (2020) Comparative study on flash flood hazard assessment for Nam Ou River Basin, Lao PDR. Nat Hazards 102:1393–1417. https://doi.org/10.1007/s11069-020-03972-3
Ghaffari Gilandeh A, Sobhani B, Ostadi E (2020) Combining Arc-GIS and OWA model in flooding potential analysis (case study: Meshkinshahr city). Nat Hazards 102:1435–1449. https://doi.org/10.1007/s11069-020-03975-0
Ghosh A, Kar SK (2018) Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India. Nat Hazards 94:349–368. https://doi.org/10.1007/s11069-018-3392-y
Hossain M, Noiri E, Moji K (2011) Climate change and Kala-Azar. Springer, Dordrecht
IPCC (2007) Summary for Policymakers. In: Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK
Jiang Y, Zevenbergen C, Ma Y (2018) Urban pluvial flooding and stormwater management: a contemporary review of China’s challenges and “sponge cities” strategy. Environ Sci Policy 80:132–143. https://doi.org/10.1016/j.envsci.2017.11.016
Kadaverugu R, Sharma A, Matli C, Biniwale R (2019) High resolution urban air quality modeling by coupling CFD and mesoscale models: a review. Asia-Pac J Atmos Sci. https://doi.org/10.1007/s13143-019-00110-3
Kadaverugu A, Gorthi KV, Chintala NR (2021a) Impacts of urban floods on road connectivity—a review and systematic bibliometric analysis. Curr World Environ 16:575–593. https://doi.org/10.12944/CWE.16.2.22
Kadaverugu A, Nageshwar Rao C, Viswanadh GK (2021b) Quantification of flood mitigation services by urban green spaces using InVEST model: a case study of Hyderabad city, India. Model Earth Syst Environ 7:589–602. https://doi.org/10.1007/s40808-020-00937-0
Kadaverugu R, Gurav C, Rai A, Sharma A, Matli C, Biniwale R (2021c) Quantification of heat mitigation by urban green spaces using InVEST model—a scenario analysis of Nagpur City. India Arab J Geosci 14:82. https://doi.org/10.1007/s12517-020-06380-w
Kishtawal CM, Niyogi D, Tewari M, Pielke RA, Shepherd JM (2010) Urbanization signature in the observed heavy rainfall climatology over India. Int J Climatol 30:1908–1916. https://doi.org/10.1002/joc.2044
Leal M, Ramos C, Pereira S (2018) Different types of flooding lead to different human and material damages: the case of the Lisbon Metropolitan Area. Nat Hazards 91:735–758. https://doi.org/10.1007/s11069-017-3153-3
Li L, Eetvelde VV, Cheng X, Uyttenhove P (2020) Assessing stormwater runoff reduction capacity of existing green infrastructure in the city of Ghent. Int J Sustain Dev World Ecol. https://doi.org/10.1080/13504509.2020.1739166
Lima CO, Bonetti J (2020) Bibliometric analysis of the scientific production on coastal communities’ social vulnerability to climate change and to the impact of extreme events. Nat Hazards 102:1589–1610. https://doi.org/10.1007/s11069-020-03974-1
Majra JP, Gur A (2009) Climate change and health: why should India be concerned? Indian J Occup Environ Med 13:11–16. https://doi.org/10.4103/0019-5278.50717
Marvi MT (2020) A review of flood damage analysis for a building structure and contents. Nat Hazards 102:967–995. https://doi.org/10.1007/s11069-020-03941-w
Myers BR, Pezzaniti D (2019) Chapter 6—flood and peak flow management using WSUD systems. In: Sharma AK, Gardner T, Begbie D (eds) Approaches to water sensitive urban design. Woodhead Publishing, Sawston, UK, pp 119–138
Natarajan S, Radhakrishnan N (2019) Simulation of extreme event-based rainfall–runoff process of an urban catchment area using HEC-HMS. Model Earth Syst Environ 5:1867–1881. https://doi.org/10.1007/s40808-019-00644-5
NatCatSERVICE (2020) NatCatSERVICE analysis tool. https://natcatservice.munichre.com/. Accessed 14 May 2020
Nilsen V, Lier JA, Bjerkholt JT, Lindholm OG (2011) Analysing urban floods and combined sewer overflows in a changing climate. J Water Clim Change 2:260–271. https://doi.org/10.2166/wcc.2011.042
Paul S, Ghosh S, Mathew M, Devanand A, Karmakar S, Niyogi D (2018) Increased spatial variability and intensification of extreme monsoon rainfall due to urbanization. Sci Rep 8:3918. https://doi.org/10.1038/s41598-018-22322-9
Phalkey R, Dash SR, Mukhopadhyay A, Runge-Ranzinger S, Marx M (2012) Prepared to react? Assessing the functional capacity of the primary health care system in rural Orissa, India to respond to the devastating flood of September 2008. Glob Health Action 5:1–10. https://doi.org/10.3402/gha.v5i0.10964
Prasad RN, Pani P (2017) Geo-hydrological analysis and sub watershed prioritization for flash flood risk using weighted sum model and Snyder’s synthetic unit hydrograph. Model Earth Syst Environ 3:1491–1502. https://doi.org/10.1007/s40808-017-0354-4
Radwan F, Alazba AA, Mossad A (2019) Flood risk assessment and mapping using AHP in arid and semiarid regions. Acta Geophys 67:215–229. https://doi.org/10.1007/s11600-018-0233-z
Rahman MA, Islam S (2019) Climate change adaptation in urban areas: a critical assessment of the structural and non-structural flood protection measures in Dhaka. In: Huq S, Chow J, Fenton A et al (eds) Confronting climate change in Bangladesh: policy strategies for adaptation and resilience. Springer International Publishing, Cham, pp 161–173
Revi A, Satterthwaite D, Aragón-Durand F, Corfee-Morlot J, Kiunsi RBR, Pelling M et al (2014) Towards transformative adaptation in cities: the IPCC’s fifth assessment. Environ Urban 26:11–28. https://doi.org/10.1177/0956247814523539
Richards DR, Edwards PJ (2017) Using water management infrastructure to address both flood risk and the urban heat island. Int J Water Resour Dev. https://doi.org/10.1080/07900627.2017.1357538
Ross CW, Prihodko L, Anchang J, Kumar S, Ji W, Hanan NP (2018) Global hydrologic soil groups (HYSOGs250m) for curve number-based runoff modeling. 571.82448 MB. https://doi.org/10.3334/ORNLDAAC/1566
Saaty TL (2012) Decision making for leaders: the analytic hierarchy process for decisions in a complex world, Third Revised Edition. RWS publications, Pittsburgh
Saha UD, Bhattacharya S (2021) Application of multi-criteria decision-making approach for ascertaining the avulsion potentiality of the Torsa River course. Model Earth Syst Environ 7:643–667. https://doi.org/10.1007/s40808-020-00967-8
Sharp R, Tallis HT, Ricketts T, Guerry AD, Wood SA, Chaplin-Kramer R et al (2020) InVEST 3.8.0 user’s guide. Nat Cap Proj Stanf Univ Univ Minn Nat Conserv World Wildl Fund. https://storage.googleapis.com/releases.naturalcapitalproject.org/invest-userguide/latest/index.html
Singh A, Sarma AK, Hack J (2020) Cost-effective optimization of nature-based solutions for reducing urban floods considering limited space availability. Environ Process 7:297–319. https://doi.org/10.1007/s40710-019-00420-8
Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlal M (2019) GIS-based MCDM—AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int. https://doi.org/10.1080/10106049.2019.1566405
Stefanidis S, Stathis D (2013) Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat Hazards 68:569–585. https://doi.org/10.1007/s11069-013-0639-5
Suriya S, Mudgal BV, Nelliyat P (2012) Flood damage assessment of an urban area in Chennai, India, part I: methodology. Nat Hazards 62:149–167. https://doi.org/10.1007/s11069-011-9985-3
Turner BL, Kasperson RE, Matson PA, McCarthy JJ, Corell RW, Christensen L et al (2003) A framework for vulnerability analysis in sustainability science. Proc Natl Acad Sci 100:8074–8079
Vemula S, Raju KS, Veena SS, Kumar AS (2019) Urban floods in Hyderabad, India, under present and future rainfall scenarios: a case study. Nat Hazards 95:637–655. https://doi.org/10.1007/s11069-018-3511-9
Wang X, Kinsland G, Poudel D, Fenech A (2019) Urban flood prediction under heavy precipitation. J Hydrol 577:123984. https://doi.org/10.1016/j.jhydrol.2019.123984
Wind TR, Joshi PC, Kleber RJ, Komproe IH (2013) The impact of recurrent disasters on mental health: a study on seasonal floods in Northern India. Prehospital Disaster Med 28:279–285. https://doi.org/10.1017/S1049023X13000290
Xing Y, Liang Q, Wang G, Ming X, Xia X (2019) City-scale hydrodynamic modelling of urban flash floods: the issues of scale and resolution. Nat Hazards 96:473–496. https://doi.org/10.1007/s11069-018-3553-z
Zhou Q, Leng G, Su J, Ren Y (2019) Comparison of urbanization and climate change impacts on urban flood volumes: importance of urban planning and drainage adaptation. Sci Total Environ 658:24–33. https://doi.org/10.1016/j.scitotenv.2018.12.184
Zhou S, Wang Y, Li Z, Chang J, Guo A (2021) Quantifying the uncertainty interaction between the model input and structure on hydrological processes. Water Resour Manag. https://doi.org/10.1007/s11269-021-02883-7
Acknowledgements
The authors sincerely thank the support extended by the Commissioner of Technical Education, Telangana State, and Director of Jawaharlal Nehru Technological University, Hyderabad.
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Conceptualization, methodology: AK; formal analysis and investigation: AK, RK; writing-original draft preparation: AK; supervision: NRC, KVG.
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Kadaverugu, A., Kadaverugu, R., Chintala, N.R. et al. Flood vulnerability assessment of urban micro-watersheds using multi-criteria decision making and InVEST model: a case of Hyderabad City, India. Model. Earth Syst. Environ. 8, 3447–3459 (2022). https://doi.org/10.1007/s40808-021-01310-5
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DOI: https://doi.org/10.1007/s40808-021-01310-5