Flash flood susceptibility modelling using geomorphometric approach in the Ushairy Basin, eastern Hindu Kush

  • Shakeel MahmoodEmail author
  • Atta-Ur Rahman


This study focuses on flash flood susceptibility modelling using geomorphometric ranking approach in the Ushairy Basin. In the study area, flash floods are highly unpredictable and the worst hydrometeorological disaster. An advanced spaceborne thermal emission and reflection radiometer global digital elevation model was used as input data in a geographic information system environment to delineate the target basin. A total of 17 sub-basins were delimited using a threshold of \(4 \hbox {km}^{2}\). The attribute information of each sub-basin was analysed to compute the geomorphometric parameters by applying Hortonian and Strahler geomorphological models. The results were analysed and categorised into five classes using statistical techniques, and the rank score was assigned to each class of all parameters depending on their relation with flash flood risk. In this study, 16 parameters were analysed to quantify the geomorphometric number of each sub-basin depicting the degree of flash flood susceptibility. The geomorphometric number of each sub-basin was linked to the geo-database for spatial visualisation. The analysis reveals that extremely high, very high, high and moderate sub-basins susceptible to flash floods were spread over an area of 55%, 8.5%, 23.7%, and 11.5%, respectively. It was found that out of total settlements, 53% are located in the extremely highly and very highly susceptible sub-basins. In the study area, the upper reaches are characterised by snow-covered peaks, steep slopes and high drainage densities (\({>}1.7 \hbox { km/km}^{2})\). The analysis further indicated that the flash flood susceptibility increases with the increase in area, relief and relief ratio of the sub-basins. Model accuracy was assessed using primary data regarding past flood damages and human fatalities. Similarly, socio-demographic conditions of each sub-basin were also compared and linked to the extent of flash flood susceptibility. This study may assist the district government and district disaster management authority of Dir upper to initiate flood risk reduction strategies in highly susceptible zones of the Ushairy Basin.


Susceptibility modelling flash floods damages GIS Hindu Kush 



We would like to thank the anonymous reviewers for their precious suggestions and the District Disaster Management Authority for providing the concerned data. We would also like to thank and acknowledge the residents of the surveyed villages for providing the data.


  1. Aksoy H, Kirca V S O, Burgan H I and Kellecioglu D 2016 Hydrological and hydraulic models for determination of flood-prone and flood inundation areas; Proc. Int. Ass. Hydrol. Sci. 373 137–141.Google Scholar
  2. Borga M, Boscolo P, Zanon F and Sangati M 2007 Hydrometeorological analysis of the August 29, 2003 flash flood in the eastern Italian Alps; J. Hydrometeorol. 8(5) 1049–1067.CrossRefGoogle Scholar
  3. Cantet P, Bacro J and Arnaud P 2011 Using a rainfall stochastic generator to detect trends in extreme rainfall. Stoch. Environ. Res. Risk Assess. 25 429–441.CrossRefGoogle Scholar
  4. Collier C 2007 Flash flood forecasting: What are the limits of predictability?; Quart. J. Roy. Meteor. Soc. 133 3–23.CrossRefGoogle Scholar
  5. Creutin J D and Borga M 2003 Radar hydrology modifies the monitoring of flash-flood hazard; Hydrol. Process. 17(7) 1453–1456.CrossRefGoogle Scholar
  6. Creutin J D, Borga M, Gruntfest E, Lutoff C and Zoccatelli D 2013 A space and time framework for analyzing human anticipation of flash floods; J. Hydrol. 482 14–24.CrossRefGoogle Scholar
  7. Dawood M, Mahmood S, Rahman G and Rahman A 2017 Impact of rainfall fluctuation on river discharge In Hindu Kush region, Pakistan; Abasyn J. Soc. Sci. 10 246–259.Google Scholar
  8. DeGaetano A T 2009 Time-dependent changes in extreme-precipitation return-period amounts in the continental United States; J. Appl. Meteorol. Clim. 48(10) 2086–2099.CrossRefGoogle Scholar
  9. Dottori F, Martina M L V and Figueiredo R 2016 A methodology for flood susceptibility and vulnerability analysis in complex flood scenarios; J. Flood Risk Manag., Scholar
  10. Elmaghraby M, Masoud M and Niyazi B 2014 Assessment of surface runoff in arid, data scarce regions: An approach applied to Wadi Al-Hamd, Al Madinah Al Munawarah, Saudi Arabia; Life Sci. J. 11 271–289.Google Scholar
  11. Elmoustafa A M and Mohamed M M 2013 Flash flood risk assessment using morphological parameters in Sinai Peninsula; J. Mod. Hydrol. 3 122–129.CrossRefGoogle Scholar
  12. El-Shamy I 1992 Recent recharge and flash flooding opportunities in the Eastern Desert, Egypt; Ann. Geol. Sur. Egypt 18 323–334.Google Scholar
  13. Farhan Y and Anaba O 2016 Flash flood risk estimation of WadiYutum (Southern Jordan) watershed using GIS based morphometric analysis and remote sensing techniques; J. Mod. Hydrol. 6(02) 79.CrossRefGoogle Scholar
  14. Gardiner V 1990 Drainage basin morphometry; In: Geomorphological techniques (ed.) Goudie A, Unwin Hyman, London, pp. 71–81.Google Scholar
  15. Gaume E, Bain V, Bernardara P, Newinger O, Barbuc M and Bateman A 2009 A compilation of data on European flash floods; J. Hydrol. 367 70–78.CrossRefGoogle Scholar
  16. Government of Pakistan (GoP) 2000 District census report of Upper Dir. Population census organization, Islamabad.Google Scholar
  17. Horton R E 1945 Erosional development of streams and their drainage basins: Hydrophysical approach to quantitative morphology; Geol. Soc. Am. Bull. 56(3) 275–370.CrossRefGoogle Scholar
  18. Hu H 2016 Rainstorm flash flood risk assessment using genetic programming: A case study of risk zoning in Beijing; Nat. Hazards 83(1) 485–500.CrossRefGoogle Scholar
  19. Ibarra E M 2012 A geographical approach to post-flood analysis: The extreme flood event of 12 October 2007 in Calpe (Spain); Appl. Geogr. 32 490–500.CrossRefGoogle Scholar
  20. IPCC 2014 Climate Change 2014: Synthesis Report; Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) Geneva, Switzerland, 151.Google Scholar
  21. Jonkman S N and Vrijling J K 2008 Loss of life due to floods; J. Flood Risk Manag. 1 43–56.CrossRefGoogle Scholar
  22. Khosravi K, Nohani E, Maroufinia E and 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; Nat. Hazards 83(2) 947–987.CrossRefGoogle Scholar
  23. Kim J, Kuwahara Y and Kumar M 2011 A DEM-based evaluation of potential flood risk to enhance decision support system for safe evacuation; Nat. Hazards 59 1561–1572.CrossRefGoogle Scholar
  24. Korytny L M and Kichigina N V 2006 Geographical analysis of river floods and their causes in southern East Siberia; Hydrol. Sci. J. 51(3) 450–464.CrossRefGoogle Scholar
  25. Krausmann E and Mushtaq F 2008 A qualitative Natech damage scale for the impact of floods on selected industrial facilities; Nat. Hazards 46 179–197.CrossRefGoogle Scholar
  26. Kundzewicz Z W and Jania J A 2007 Extreme hydro-meteorological events and their impacts. From the global down to the regional scale; Geo. Polonica. 80(2) 9–23.Google Scholar
  27. Llasat M C, Llasat-Botija M, Rodriguez A and Lindbergh S 2010 Flash floods in Catalonia: A recurrent situation; Adv. Geosci. 26 105–111.CrossRefGoogle Scholar
  28. Mahmood S, Khan A H and Mayo S M 2016a Exploring underlying causes and assessing damages of 2010 flash flood in the upper zone of Panjkora river; Nat. Hazards 83(2) 1213–1227.CrossRefGoogle Scholar
  29. Mahmood S, Khan A H and Ullah S 2016b Assessment of 2010 flash flood causes and associated damages in Dir Valley, Khyber Pakhtunkhwa Pakistan; Int. J. Disast. Risk RE 16 215–223.Google Scholar
  30. Mahmood S and Rahman A 2019 Flash Flood Susceptibility Modelling using Geo-morphometric and Hydrological Approaches in Panjkora Basin, Eastern Hindu Kush, Pakistan; Environ. Earth Sci. 78(1) 43–58.CrossRefGoogle Scholar
  31. Mazzorana B, Hübl J and Fuchs S 2009 Improving risk assessment by defining consistent and reliable system scenarios; Nat. Hazard Earth Syst. 9(1) 145–159.CrossRefGoogle Scholar
  32. Miller V 1953 A quantitative geomorphic study of drainage basin characteristics in the Clinch Mountain Area, Virginia and Tennessee; Project NR 389–402, Technical Report 3, Department of Geology, ONR, Columbia University, New York.Google Scholar
  33. Muhammad S K 2011 Diversity of vascular plants, ethnobotany and conservation status of Ushairy Valley, District Dir, Upper NWFP Northern Pakistan; Doctoral Dissertation, Quaid-i-Azam University, Islamabad.Google Scholar
  34. Pradhan B 2010 Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing; J. Spatial Hydrol. 9(2).Google Scholar
  35. Rahman A and Khan A N 2011 Analysis of flood causes and associated socio-economic damages in The Hindu Kush region; Nat. Hazards 59(3) 1239.CrossRefGoogle Scholar
  36. Rahman A and Khan A N 2013 Analysis of 2010-flood causes, nature and magnitude in the Khyber Pakhtunkhwa, Pakistan; Nat. Hazards 66(2) 887–904.CrossRefGoogle Scholar
  37. Rahman A and Shaw R (eds) 2015 Floods in The Hindu Kush region: Causes and socio-economic aspects; In: Mountain hazards and disaster risk reduction, Springer, Tokyo, pp. 33–52.Google Scholar
  38. Santangelo N, Santo A, Di Crescenzo G, Foscari G, Liuzza V, Sciarrotta S and Scorpio V 2011 Flood susceptibility assessment in a highly urbanized alluvial fan: The case study of Sala Consilina (southern Italy); Nat. Hazard Earth Syst. 11(10) 2765.CrossRefGoogle Scholar
  39. Schumm S A 1956 Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey; Geol. Soc. Am. Bull. 67(5) 597–646.CrossRefGoogle Scholar
  40. Shehata M and Mizunaga H 2018 Flash flood risk assessment for Kyushu Island, Japan; Environ. Earth Sci. 77(3) 76.CrossRefGoogle Scholar
  41. Singh N and Singh K K 2017 Geomorphological analysis and prioritization of sub-watersheds using Snyder’s synthetic unit hydrograph method; App. Water Sci. 7(1) 275–283.CrossRefGoogle Scholar
  42. Singh P, Thakur J and Singh U C 2013 Morphometric analysis of Morar River Basin, Madhya Pradesh, India, using remote sensing and GIS techniques; Environ. Earth Sci. 68 1967–1977.CrossRefGoogle Scholar
  43. Strahler A 1957 Quantitative analysis of watershed geomorphology; Trans. Am. Geophys. Union 38 913–920.CrossRefGoogle Scholar
  44. Strahler A 1964 Quantitative geomorphology of drainage basins and channel networks; In: Handbook of applied hydrology (ed.) Chow V, McGraw Hill, New York, pp. 439–476.Google Scholar
  45. Tehrany M S, Pradhan B and Jebur M N 2014 Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS; J. Hydrol. 512 332–343.CrossRefGoogle Scholar
  46. Youssef A M, Pradhan B, Gaber A F D, Buchroithner M F 2009 Geomorphological hazard analysis along the Egyptian Red Sea coast between Safaga and Quseir. Nat. Hazards Earth Syst. Sci. 9(3) 751–766.CrossRefGoogle Scholar
  47. Youssef A M, Pradhan B and Hassan A M 2011 Flash flood risk estimation along the St. Katherine road, Southern Sinai, Egypt using GIS based morphometry and satellite imagery; Environ. Earth Sci. 62 611–623.CrossRefGoogle Scholar
  48. Youssef A M, Pradhan B and Sefry S A 2016 Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models; Environ. Earth Sci. 75(1) 12.CrossRefGoogle Scholar
  49. Zhao G, Xue H and Ling F 2010 Assessment of ASTER GDEM performance by comparing with SRTM and ICESat/GLAS data in Central China; In: 2010 18th international conference on geoinformatics, IEEE, pp. 1–5.Google Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of GeographyGovernment College University LahoreLahorePakistan
  2. 2.Department of GeographyUniversity of PeshawarPeshawarPakistan

Personalised recommendations