Natural Hazards

, Volume 89, Issue 3, pp 1369–1387 | Cite as

Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index

  • Martin Kabenge
  • Joshua Elaru
  • Hongtao Wang
  • Fengting LiEmail author
Original Paper


The frequency in occurrence and severity of floods has increased globally. However, many regions around the globe, especially in developing countries, lack the necessary field monitoring data to characterize flood hazard risk. This paper puts forward methodology for developing flood hazard maps that define flood hazard risk, using a remote sensing and GIS-based flood hazard index (FHI), for the Nyamwamba watershed in western Uganda. The FHI was compiled using analytical hierarchy process and considered slope, flow accumulation, drainage network density, distance from drainage channel, geology, land use/cover and rainfall intensity as the flood causative factors. These factors were derived from Landsat, SRTM and PERSIANN remote sensing data products, except for geology that requires field data. The resultant composite FHI yielded a flood hazard map pointing out that over 11 and 18% of the study area was very highly and highly susceptible to flooding, respectively, while the remaining area ranged from medium to very low risk. The resulting flood hazard map was further verified using inundation area of a historical flood event in the study area. The proposed methodology was effective in producing a flood hazard map at the watershed local scale, in a data-scarce region, useful in devising flood mitigation measures.


Flood hazard index Remote sensing Data-scarce areas Analytical hierarchy process 


  1. Aich V, Liersch S, Vetter T, Andersson J, Müller E, Hattermann F (2015) Climate or land use?—attribution of changes in river flooding in the Sahel Zone. Water 7:2796CrossRefGoogle Scholar
  2. Anderson JR, Hardy EE, Roach JT, Witmer RE (2001) A land use and land cover classification system for use with remote sensor data, vol 964. Geological survey professional paper. US Government Printing Office First Printing, 1976Google Scholar
  3. Armah FA, Yawson DO, Yengoh GT, Odoi JO, Afrifa EK (2010) Impact of floods on livelihoods and vulnerability of natural resource dependent communities in Northern Ghana. Water 2:120–139CrossRefGoogle Scholar
  4. Arnell NW, Gosling SN (2016) The impacts of climate change on river flood risk at the global scale. Clim Change 134:387–401. doi: 10.1007/s10584-014-1084-5 CrossRefGoogle Scholar
  5. Arnoldus H (1980) An approximation of the rainfall factor in the Universal Soil Loss Equation. An approximation of the rainfall factor in the Universal Soil Loss Equation, pp 127–132Google Scholar
  6. Asadullah A, McIntyre N, Kigobe M (2008) Evaluation of five satellite products for estimation of rainfall over Uganda/Evaluation de cinq produits satellitaires pour l’estimation des précipitations en Ouganda. Hydrol Sci J 53:1137–1150CrossRefGoogle Scholar
  7. Asare-Kyei D, Forkuor G, Venus V (2015) Modeling flood hazard zones at the sub-district level with the rational model integrated with GIS and remote sensing approaches. Water 7:3531–3564CrossRefGoogle Scholar
  8. Ashouri H et al (2015) PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull Am Meteorol Soc 96:69–83CrossRefGoogle Scholar
  9. Basnet B, Vodacek A (2015) Tracking land use/land cover dynamics in cloud prone areas using moderate resolution satellite data: a case study in Central Africa. Remote Sens 7:6683CrossRefGoogle Scholar
  10. Bonacci O, Ljubenkov I, Roje-Bonacci T (2006) Karst flash floods: an example from the Dinaric karst (Croatia). Nat Hazards Earth Syst Sci 6:195–203CrossRefGoogle Scholar
  11. Busby JW, Cook KH, Vizy EK, Smith TG, Bekalo M (2014) Identifying hot spots of security vulnerability associated with climate change in Africa. Clim Change 124:717–731CrossRefGoogle Scholar
  12. Butler D, Kokkalidou A, Makropoulos CK (2006) Supporting the siting of new urban developments for integrated urban water resource management. In: Integrated urban water resources management. Springer, pp 19–34Google Scholar
  13. Chau VN, Holland J, Cassells S, Tuohy M (2013) Using GIS to map impacts upon agriculture from extreme floods in Vietnam. Appl Geogr 41:65–74. doi: 10.1016/j.apgeog.2013.03.014 CrossRefGoogle Scholar
  14. de Sherbinin A (2014) Climate change hotspots mapping: what have we learned? Clim Change 123:23–37CrossRefGoogle Scholar
  15. Dembélé M, Zwart SJ (2016) Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa. Int J Remote Sens 37:3995–4014CrossRefGoogle Scholar
  16. Demek J (1972) Manual of detailed geomorphological mapping. Academia, PragueGoogle Scholar
  17. Deng Z, Zhang X, Li D, Pan G (2015) Simulation of land use/land cover change and its effects on the hydrological characteristics of the upper reaches of the Hanjiang Basin. Environ Earth Sci 73:1119–1132. doi: 10.1007/s12665-014-3465-5 CrossRefGoogle Scholar
  18. Eggermont H, Van Damme K, Russell JM (2009) Rwenzori mountains (mountains of the moon): headwaters of the white Nile. In: The Nile. Springer, pp 243–261Google Scholar
  19. EIMCO (2007) Environmental impact statement for the proposed waste composting plant and landfill for Kasese Town council. Enviro-impact and management consults (EIMCO), Kasese Town council, Kasese, UgandaGoogle Scholar
  20. Elkhrachy I (2015) Flash flood hazard mapping using satellite images and GIS tools: a case study of Najran City, Kingdom of Saudi Arabia (KSA). Egypt J Remote Sens Space Sci 18:261–278Google Scholar
  21. Feng X, Porporato A, Rodriguez-Iturbe I (2013) Changes in rainfall seasonality in the tropics Nature. Clim Change 3:811–815CrossRefGoogle Scholar
  22. Hulme M, Doherty R, Ngara T, New M, Lister D (2001) African climate change: 1900–2100. Clim Res 17:145–168CrossRefGoogle Scholar
  23. IFRC (2013) Uganda: Kasese floods. The international federation of red cross and red crescent (IFRC), Kampala, UgandaGoogle Scholar
  24. Islam M, Sado K (2000) Flood hazard assessment for the construction of flood hazard map and land development priority map using NOAA/AVHRR data and GIS–a case study in Bangladesh. Hydrol Sci J des Sci Hydrol 45:337–357CrossRefGoogle Scholar
  25. Jacobs L et al (2016) Reconstruction of a flash flood event through a multi-hazard approach: focus on the Rwenzori Mountains, Uganda. Nat Hazards 84:851–876. doi: 10.1007/s11069-016-2458-y CrossRefGoogle Scholar
  26. Jenks GF (1967) The data model concept in statistical mapping. Int Yearb Cartogr 7:186–190Google Scholar
  27. Kazakis N, Kougias I, Patsialis T (2015) Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: application in Rhodope–Evros region, Greece. Sci Total Environ 538:555–563CrossRefGoogle Scholar
  28. Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67:251–264CrossRefGoogle Scholar
  29. Kostadinov S, Mitrovic S (1994) Effect of forest cover on the stream flow from small watersheds. J Soil Water Conserv 49:382–386Google Scholar
  30. Kourgialas NN, Karatzas GP (2011) Flood management and a GIS modelling method to assess flood-hazard areas—a case study. Hydrol Sci J J des Sci Hydrol 56:212–225CrossRefGoogle Scholar
  31. Lillesand T, Kiefer RW, Chipman J (2014) Remote sensing and image interpretation. Wiley, New JerseyGoogle Scholar
  32. Lins K, Kleckner R (1996) Land cover mapping: an overview and history of the concepts. Gap analysis: a landscape approach to biodiversity planning, pp 57–65Google Scholar
  33. Masoudian M (2009) The topographical impact on effectiveness of flood protection measures Bündnisse zur, vol 18. Kassel University Press GmbH, KasselGoogle Scholar
  34. Napolitano P, Fabbri A (1996) Single-parameter sensitivity analysis for aquifer vulnerability assessment using DRASTIC and SINTACS. IAHS Publ Ser Proc Rep Int As Hydrol Sci 235:559–566Google Scholar
  35. Nosetto M, Jobbágy E, Brizuela A, Jackson R (2012) The hydrologic consequences of land cover change in central Argentina. Agric Ecosyst Environ 154:2–11CrossRefGoogle Scholar
  36. Nyarko BK (2002) Application of a rational model in GIS for flood risk assessment in Accra, Ghana. J Spat Hydrol 2(1):1–14Google Scholar
  37. Ologunorisa T, Abawua M (2005) Flood risk assessment: a review. J Appl Sci Environ Manag 9:57–63Google Scholar
  38. Ouma YO, Tateishi R (2014) Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: methodological overview and case study assessment. Water 6:1515–1545CrossRefGoogle Scholar
  39. Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48:119–126. doi: 10.1016/0034-4257(94)90134-1 CrossRefGoogle Scholar
  40. Renard KG, Freimund JR (1994) Using monthly precipitation data to estimate the R-factor in the revised USLE. J Hydrol 157:287–306CrossRefGoogle Scholar
  41. Renno DC, Twinamasiko J, Mugisa CP (2012) Kasese district poverty profiling and mapping 2011–2012. Belgian Technical Cooperation, KaseseGoogle Scholar
  42. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15:234–281CrossRefGoogle Scholar
  43. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48:9–26CrossRefGoogle Scholar
  44. Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1:83–98Google Scholar
  45. Sauer VB (2002) USGS, the national flood frequency program, version 3: a computer program for estimating magnitude and frequency of floods for ungaged sites. USGSGoogle Scholar
  46. Schipper L, Pelling M (2006) Disaster risk, climate change and international development: scope for, and challenges to, integration. Disasters 30:19–38CrossRefGoogle Scholar
  47. Schmitt TG, Thomas M, Ettrich N (2004) Analysis and modeling of flooding in urban drainage systems. J Hydrol 299:300–311CrossRefGoogle Scholar
  48. Sorooshian S, Hsu K-L, Gao X, Gupta HV, Imam B, Braithwaite D (2000) Evaluation of PERSIANN system satellite–based estimates of tropical rainfall. Bull Am Meteorol Soc 81:2035–2046CrossRefGoogle Scholar
  49. 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 504:69–79CrossRefGoogle Scholar
  50. 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 512:332–343CrossRefGoogle Scholar
  51. Ticehurst C, Dutta D, Karim F, Petheram C, Guerschman JP (2015) Improving the accuracy of daily MODIS OWL flood inundation mapping using hydrodynamic modelling. Nat Hazards 78:803–820. doi: 10.1007/s11069-015-1743-5 CrossRefGoogle Scholar
  52. Tingsanchali T, Karim F (2010) Flood-hazard assessment and risk-based zoning of a tropical flood plain: case study of the Yom River, Thailand. Hydrol Sci J 55:145–161. doi: 10.1080/02626660903545987 CrossRefGoogle Scholar
  53. Tong X et al (2016) Estimating water volume variations in Lake Victoria over the past 22 years using multi-mission altimetry and remotely sensed images. Remote Sens Environ 187:400–413CrossRefGoogle Scholar
  54. UBOS (2016) The national population and housing census 2014, Main report edn. Uganda bureau of statistics, Kampala, UgandaGoogle Scholar
  55. USACE-HEC (2013) Geospatial hydrologic modeling extension, HEC-GeoHMS v10.1 user’s manual. U.S. army corps of engineers, hydrologic engineering center, Davis, USAGoogle Scholar
  56. USDA (1972) SCS national engineering handbook, section 4: hydrology. USDA soil conservation serviceGoogle Scholar
  57. Wahlstrom M, Guha-Sapir D (2015) The human cost of weather-related disasters 1995–2015. UNISDR, CRED, Geneva, SwitzerlandGoogle Scholar
  58. Winsemius HC et al. (2016) Global drivers of future river flood risk. Nature Clim Change 6:381–385. doi: 10.1038/nclimate2893.
  59. Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033CrossRefGoogle Scholar
  60. Yalcin G, Akyurek Z (2004) Analysing flood vulnerable areas with multicriteria evaluation. In: 20th ISPRS congress, 2004, pp 1–6Google Scholar
  61. Yan B, Fang NF, Zhang PC, Shi ZH (2013) Impacts of land use change on watershed streamflow and sediment yield: an assessment using hydrologic modelling and partial least squares regression. J Hydrol 484:26–37. doi: 10.1016/j.jhydrol.2013.01.008 CrossRefGoogle Scholar
  62. Yang L, Meng X, Zhang X (2011) SRTM DEM and its application advances. Int J Remote Sens 32:3875–3896CrossRefGoogle Scholar
  63. Yohe G, Malone E, Brenkert A, Schlesigner M, Meij H, Lee D (2006) Geographic distributions of vulnerability to climate change. Integr Assess J 6(3):35–44Google Scholar
  64. Yu B, Rosewell C (1996) Technical notes: a robust estimator of the R-factor for the universal soil loss equation. Trans of the ASAE 39:559–561CrossRefGoogle Scholar
  65. Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24:583–594. doi: 10.1080/01431160304987 CrossRefGoogle Scholar
  66. Zhang Y, Guindon B (2009) Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover. Int J Digit Earth 2:89–108CrossRefGoogle Scholar
  67. Zhao H, Chen X (2005) Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In: Proceedings. 2005 IEEE international geoscience and remote sensing symposium, 2005. IGARSS’05, 2005. IEEE, pp 1666–1668Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Martin Kabenge
    • 1
  • Joshua Elaru
    • 1
  • Hongtao Wang
    • 1
  • Fengting Li
    • 1
    Email author
  1. 1.State Key Laboratory of Pollution Control and Resource Reuse Study, College of Environmental Science and EngineeringTongji UniversityShanghaiChina

Personalised recommendations