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The use of watershed geomorphic data in flash flood susceptibility zoning: a case study of the Karnaphuli and Sangu river basins of Bangladesh

  • Mohammed Sarfaraz Gani AdnanEmail author
  • Ashraf Dewan
  • Khatun E. Zannat
  • Abu Yousuf Md Abdullah
Original Paper
  • 257 Downloads

Abstract

The occurrence of heavy rainfall in the south-eastern hilly region of Bangladesh makes this area highly susceptible to recurrent flash flooding. As the region is the commercial capital of Bangladesh, these flash floods pose a significant threat to the national economy. Predicting this type of flooding is a complex task which requires a detailed understanding of the river basin characteristics. This study evaluated the susceptibility of the region to flash floods emanating from within the Karnaphuli and Sangu river basins. Twenty-two morphometric parameters were used. The occurrence and impact of flash floods within these basins are mainly associated with the volume of runoff, runoff velocity, and the surface infiltration capacity of the various watersheds. Analysis showed that major parts of the basin were susceptible to flash flooding events of a ‘moderate’-to-‘very high’ level of severity. The degree of susceptibility of ten of the watersheds was rated as ‘high’, and one was ‘very high’. The flash flood susceptibility map drawn from the analysis was used at the sub-district level to identify populated areas at risk. More than 80% of the total area of the 16 sub-districts were determined to have a ‘high’-to-‘very-high’-level flood susceptibility. The analysis noted that around 3.4 million people reside in flash flood-prone areas, therefore indicating the potential for loss of life and property. The study identified significant flash flood potential zones within a region of national importance, and exposure of the population to these events. Detailed analysis and display of flash flood susceptibility data at the sub-district level can enable the relevant organizations to improve watershed management practices and, as a consequence, alleviate future flood risk.

Keywords

Flash flood Watershed hydrology Morphometric analysis Geomorphology GIS Bangladesh 

Notes

Acknowledgements

We thank the anonymous reviewers for their careful reading of our manuscript and insightful comments and suggestions.

References

  1. Abdel-Fattah M, Saber M, Kantoush SA, Khalil MF, Sumi T, Sefelnasr AM (2017) A hydrological and geomorphometric approach to understanding the generation of wadi flash floods. Water (Switzerland).  https://doi.org/10.3390/w9070553 Google Scholar
  2. Abdelkareem M (2017) Targeting flash flood potential areas using remotely sensed data and GIS techniques. Nat Hazards 85:19–37.  https://doi.org/10.1007/s11069-016-2556-x CrossRefGoogle Scholar
  3. Abdullah AYM, Masrur A, Adnan MSG, Baky MAA, Hassan QK, Dewan A (2019) Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sens 11:790CrossRefGoogle Scholar
  4. ACAPS (2015) Flash floods in Cox’s Bazar, Bandarban and Chittagong Districts June–July 2015. Assessment Capacities Project, GenevaGoogle Scholar
  5. Adnan SG, Kreibich H (2016) An evaluation of disaster risk reduction (DRR) approaches for coastal delta cities: a comparative analysis. Nat Hazards 83:1257–1278CrossRefGoogle Scholar
  6. Adnan MSG, Haque A, Hall JW (2019) Have coastal embankments reduced flooding in Bangladesh? Sci Total Environ 682:405–416.  https://doi.org/10.1016/j.scitotenv.2019.05.048 CrossRefGoogle Scholar
  7. Ahmed B, Dewan A (2017) Application of bivariate and multivariate statistical techniques in landslide susceptibility modeling in Chittagong City Corporation, Bangladesh. Remote Sens 9:50.  https://doi.org/10.3390/rs9040304 CrossRefGoogle Scholar
  8. Bajabaa S, Masoud M, Al-Amri N (2014) Flash flood hazard mapping based on quantitative hydrology, geomorphology and GIS techniques (case study of Wadi Al Lith, Saudi Arabia) Arabian. J Geosci 7:2469–2481.  https://doi.org/10.1007/s12517-013-0941-2 Google Scholar
  9. BBS (2015) Bangladesh Disaster-related Statistics (2015) Climate change and natural disaster perspectives Bangladesh Bureau of Statistics (BBS). Ministry of Planning, DhakaGoogle Scholar
  10. Bhatt S, Ahmed SA (2014) Morphometric analysis to determine floods in the Upper Krishna basin using Cartosat DEM. Geocarto Int 29:878–894.  https://doi.org/10.1080/10106049.2013.868042 CrossRefGoogle Scholar
  11. Biswas S, Vacik H, Swanson ME, Haque SS (2012) Evaluating integrated watershed management using multiple criteria analysis: a case study at Chittagong Hill tracts in Bangladesh. Environ Monit Assess 184:2741–2761CrossRefGoogle Scholar
  12. Brakenridge GR (2018) Global Active Archive of Large Flood Events, Dartmouth Flood Observatory, University of Colorado. http://floodobservatory.colorado.edu/Archives/index.html
  13. Brammer H (1990) Floods in Bangladesh: I. Geographical background to the 1987 and 1988 floods. Geogr J 156:12–22.  https://doi.org/10.2307/635431 CrossRefGoogle Scholar
  14. Bronstert A, Niehoff D, Bürger G (2002) Effects of climate and land-use change on storm runoff generation: present knowledge and modelling capabilities. Hydrol Process 16:509–529CrossRefGoogle Scholar
  15. Choudhury NY, Paul A, Paul BK (2004) Impact of costal embankment on the flash flood in Bangladesh: a case study. Appl Geogr 24:241–258.  https://doi.org/10.1016/j.apgeog.2004.04.001 CrossRefGoogle Scholar
  16. Collier C (2007) Flash flood forecasting: what are the limits of predictability? Q J R Meteorol Soc 133:3–23CrossRefGoogle Scholar
  17. Davis J (2002) Statistics and data analysis in geology, 3rd edn. Wiley, LondonGoogle Scholar
  18. Dewan A (2013) Floods in a megacity: geospatial techniques in assessing hazards, risk and vulnerability. Springer, Dordrecht, pp 119–156CrossRefGoogle Scholar
  19. Dewan TH (2015) Societal impacts and vulnerability to floods in Bangladesh and Nepal weather and climate. Extremes 7:36–42.  https://doi.org/10.1016/j.wace.2014.11.001 Google Scholar
  20. Diakakis M (2011) A method for flood hazard mapping based on basin morphometry: application in two catchments in Greece. Nat Hazards 56:803–814.  https://doi.org/10.1007/s11069-010-9592-8 CrossRefGoogle Scholar
  21. Elnazer AA, Salman SA, Asmoay AS (2017) Flash flood hazard affected Ras Gharib city, Red Sea, Egypt: a proposed flash flood channel. Nat Hazards 89:1389–1400.  https://doi.org/10.1007/s11069-017-3030-0 CrossRefGoogle Scholar
  22. Farhan Y, Anaba O, Salim A (2017) Morphometric Analysis and flash floods assessment for drainage basins of the Ras En Naqb Area, South Jordan using GIS Applied Morphometry and Watershed Management Using RS, GIS and Multivariate Statistics (Case Studies), p. 413Google Scholar
  23. Field CB, Barros V, Stocker TF, Dahe Q (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  24. Global Active Archive of Large Flood Events (2018) Dartmouth flood observatory. University of Colorado. http://floodobservatory.colorado.edu/Archives/index.html
  25. Horton RE (1932) Drainage-basin characteristics Eos. Trans Am Geophys Union 13:350–361.  https://doi.org/10.1029/TR013i001p00350 CrossRefGoogle Scholar
  26. Horton RE (1945) Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Bull Geol Soc Am 56:275–370CrossRefGoogle Scholar
  27. Hughes RM, Kaufmann PR, Weber MH (2011) National and regional comparisons between Strahler order and stream size. J N Am Benthol Soc 30:103–121.  https://doi.org/10.1899/09-174.1 CrossRefGoogle Scholar
  28. Kabenge M, Elaru J, Wang H, Li F (2017) Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index. Nat Hazards 89:1369–1387CrossRefGoogle Scholar
  29. Kamal ASMM, Shamsudduha M, Ahmed B, Hassan SMK, Islam MS, Kelman I, Fordham M (2018) Resilience to flash floods in wetland communities of northeastern Bangladesh. Int J Disaster Risk Reduct 31:478–488.  https://doi.org/10.1016/j.ijdrr.2018.06.011 CrossRefGoogle Scholar
  30. Kron W (2005) Flood risk = hazard values vulnerability. Water Int 30:58–68CrossRefGoogle Scholar
  31. Land resource information management system (2014) Bangladesh Agricultural Research Council (BARC). http://www.barc.gov.bd
  32. Lin L et al (2019) Improvement and validation of NASA/MODIS NRT global flood mapping. Remote Sens 11:205CrossRefGoogle Scholar
  33. Melton MA (1957) An analysis of the relations among elements of climate, surface properties, and geomorphology. Columbia University, NewyorkCrossRefGoogle Scholar
  34. Planchon O, Darboux F (2002) A fast, simple and versatile algorithm to fill the depressions of digital elevation models. Catena 46:159–176CrossRefGoogle Scholar
  35. Plate EJ (2002) Flood risk and flood management. J Hydrol 267:2–11CrossRefGoogle Scholar
  36. Rahman MS, Di L (2017) The state of the art of spaceborne remote sensing in flood management. Nat Hazards 85:1223–1248CrossRefGoogle Scholar
  37. Rahman R, Salehin M (2013) Flood risks and reduction approaches in Bangladesh. In: Shaw R, Mallick F, Islam A (eds) Disaster risk reduction approaches in Bangladesh. Springer, Tokyo, pp 65–90CrossRefGoogle Scholar
  38. Rahman MS, Ahmed B, Di L (2017) Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria. J Mt Sci 14:1919–1937CrossRefGoogle Scholar
  39. Rahman M, Di L, Yu E, Lin L, Zhang C, Tang J (2019) Rapid flood progress monitoring in cropland with NASA SMAP. Remote Sens 11:191CrossRefGoogle Scholar
  40. Rai PK, Mohan K, Mishra S, Ahmad A, Mishra VN (2017) A GIS-based approach in drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci 7:217–232.  https://doi.org/10.1007/s13201-014-0238-y CrossRefGoogle Scholar
  41. Sarker AA, Rashid AKMM (2013) Landslide and flashflood in Bangladesh. In: Shaw R, Mallick F, Islam A (eds) Disaster risk reduction approaches in Bangladesh. Springer, Tokyo, pp 165–189.  https://doi.org/10.1007/978-4-431-54252-0_8 CrossRefGoogle Scholar
  42. Schmidt J, Hennrich K, Dikau R (2000) Scales and similarities in runoff processes with respect to geomorphometry. Hydrol Process 14:1963–1979.  https://doi.org/10.1002/1099-1085(20000815/30)14:11/12%3c1963:AID-HYP48%3e3.0.CO;2-M CrossRefGoogle Scholar
  43. Schumm SA (1956) Evolution of drainage systems and slopes in badlands at Perth Amboy, New Jersey. Bull Geol Soc Am 67:597–646.  https://doi.org/10.1130/0016-7606(1956)67%5b597:eodsas%5d2.0.co;2 CrossRefGoogle Scholar
  44. Seibert J, McGlynn BL (2007) A new triangular multiple flow direction algorithm for computing upslope areas from gridded digital elevation models. Water Resour Res.  https://doi.org/10.1029/2006wr005128 Google Scholar
  45. Shen X, Anagnostou EN, Mei Y, Hong Y (2017) A global distributed basin morphometric dataset. Sci Data 4:160124.  https://doi.org/10.1038/sdata.2016.124 CrossRefGoogle Scholar
  46. Smith KG (1950) Standards for grading texture of erosional topography. Am J Sci 248:655–668CrossRefGoogle Scholar
  47. Strahler AN (1952) Hypsometric (area-altitude) analysis of erosional topography. Bull Geol Soc Am 63:1117–1142.  https://doi.org/10.1130/0016-7606(1952)63%5b1117:haaoet%5d2.0.co;2 CrossRefGoogle Scholar
  48. Strahler AN (1957) Quantitative analysis of watershed geomorphology Eos. Trans Am Geophys Union 38:913–920.  https://doi.org/10.1029/TR038i006p00913 CrossRefGoogle Scholar
  49. Stralher A (1964) Quantitative geomorphology of drainage basins and channel net work. Handb Appl Hidrol 4:76Google Scholar
  50. Wieczorek ME (2012) Flow-based method for stream generation in a GIS. United States Geological Survey, August 6Google Scholar
  51. WorldPop (2017) Bangladesh 100 m population, version 2. University of Southampton.  https://doi.org/10.5258/soton/wp00533
  52. Youssef AM, Pradhan B, Hassan AM (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.  https://doi.org/10.1007/s12665-010-0551-1 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Environmental Change Institute (ECI), School of Geography and the EnvironmentUniversity of OxfordOxfordUK
  2. 2.Department of Urban and Regional PlanningChittagong University of Engineering and Technology (CUET)ChittagongBangladesh
  3. 3.School of Earth and Planetary SciencesCurtin UniversityPerthAustralia
  4. 4.School of Public Health and Health Systems, Faculty of Applied Health SciencesUniversity of WaterlooWaterlooCanada

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