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Estimating flooding extent at high return period for ungauged braided systems using remote sensing: a case study of Cuvelai Basin, Angola

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Abstract

Floods are the most expensive natural hazard experienced in many places in the world. The current study aimed at estimating the flooding extent at high return periods in the Cuvelai Basin, southern Angola, where no flow, rainfall or accurate topographic data are available. The flooding study thus relies on remote sensing information: archival optical satellite images, data retrieved from the global flood detection system (GFDS) and Tropical Rainfall Measurement Mission data to help characterize flooding events and determine their extents for high return periods, well beyond the available remote sensing record. Landsat and Earth Observing-1 Mission satellite images are used as optical images. The GFDS provides a monitoring of ongoing flood events everyday. Comparison revealed that the GFDS values in the wetland areas are always less than the other satellite flooding extent by about 25 km2. Frequency analysis was undertaken on the annual maxima flooded areas for monitored GFDS locations using Gumbel distribution. The frequency analysis shows that the potential inundation areas for the 100-year flood event increase by 25 % (±5 %) more than the 10-year event. The remote sensing for the 2009 Landsat image is used to get approximately the flooded areas for the 10-year return period for the whole basin. To assess flooding areas for higher return periods such as the 100-year event, the flooded areas are increased based on the frequency analysis ratio results to give the 100-year inundation extents. Interpolation is undertaken for areas where no data are available from the GFDS website. The Cuvelai Basin inundation areas are thus estimated for non-recorded flooding events.

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References

  • Adjei KA, Ren L, Appiah-Adjei EK, Kwabena Kankam-Yeboah K, Anning Agyapong AA (2012) Validation of TRMM data in the black volta basin of Ghana. J Hydrol Eng 17(5):647–654

    Article  Google Scholar 

  • Awadallah AG, Awadallah NA (2013) A novel approach for the joint use of rainfall monthly and daily ground station data with TRMM data to generate IDF estimates in a poorly gauged arid region. Open J Mod Hydrol 3(1):1–7

    Article  Google Scholar 

  • Awadallah AG, ElGamal M, ElMostafa M, ElBadry H (2011) Developing intensity-duration-frequency curves in scarce data region: an approach using regional analysis and satellite data. Engineering 3(3):215–226

    Article  Google Scholar 

  • Brakenridge GR, Nghiem SV, Anderson E, Mic R (2007) Orbital microwave measurement of river discharge and ice status. Water Resour Res 43(4):W04405. doi:10.1029/2006WR005238

    Article  Google Scholar 

  • BTE (2001) Economic costs of natural disasters in Australia. Report 103, Bureau of Transport Economics, Canberra

  • De Groeve T, Riva P (2009) Global real-time detection of major floods using passive microwave remote sensing. In: Proceedings of the 33rd international symposium on remote sensing of environment stress, Italy, May 2009

  • Disaster Relief Emergency Fund (DREF) (2009) Operation, international federation of red cross and red crescent societies Angola: floods. http://www.ifrc.org/docs/appeals/09/MDRAO003.pdf

  • Disaster Relief Emergency Fund (DREF) (2010) Angola floods; Operation MDRAO003. http://reliefweb.int/sites/reliefweb.int/files/resources/AB12EA6D6127AA22C125770D0040DDEE-Full_Report.pdf

  • Disaster Relief Emergency Fund (DREF) (2012) http://www.ifrc.org/docs/appeals/annual11/MAA0001011ar.pdf

  • El Adlouni S, Bobée B, Ouarda TBMJ (2008) On the tails of extreme event distributions in Hydrology. J Hydrol 355:16–33

    Article  Google Scholar 

  • Global Flood Detection System (GFDS) (2014) JRC European Commission. http://old.gdacs.org/flooddetection

  • Haile AT, Rientjes THM, Gieske A, Gebremichael M (2009) Rainfall variability over mountainous and adjacent lake areas: the case of Lake Tana basin at the source of the Blue Nile River. J Appl Meteorol Climatol 48(8):1696–1717

    Article  Google Scholar 

  • Haile AT, Rientjes T, Habib E, Jetten V (2011) Rain event properties and dimensionless rain event hyetographs at the source of the Blue Nile River. J Hydrol Earth Syst Sci 15:1023–1034. doi:10.5194/hess-15-1023-2011

    Article  Google Scholar 

  • Hughes DA (2006) Comparison of satellite rainfall data with observations from gauging station networks. J Hydrol 327(2006):399–410

    Article  Google Scholar 

  • INRS (2008) Guide for the use of the decision support system (DSS) for HYFRAN plus software. Institut National de Recherche Scientifique, Eau, Terre et Environnement, Québec

    Google Scholar 

  • Kugler Z. De Groeve T (2007) The global flood detection system. Office for official publications of the European communities, EUR 23303 EN

  • Li L, Hong Y, Wang J, Adler RF, Policelli FS, Habib S, Irwn D, Korme T, Okello L (2009) Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa. Nat Hazards 50(1):109–123

    Article  Google Scholar 

  • Mendelsohn J, Weber B (2011) The Cuvelai Basin—its water and people in Angola. Development Workshop Angola and RAISON Namibia. http://www.dw.angonet.org/sites/default/files/online_lib_files/CUVELAI%20BASIN%20-%20BACIA%20DO%20CUVELAI.pdf

  • NASA (1999) TRMM precipitation radar algorithm instructional manual (Version 1)

  • National Flood Risk Advisory Group (2008) Flood risk management in Australia. Aust J Emerg Manag 23(4):21–27

  • Nicholson S (2005) On the question of the “recovery” of the rains in the West African Sahel. J Arid Environ 63(3):615–641

    Article  Google Scholar 

  • Smith MJ, Edwards EP, Priestnall G, Bates PD (2006) Exploitation of new data types to create digital surface models for flood inundation modelling. FRMRC Research report UR3, June 2006. pp 78

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Acknowledgments

This study was undertaken as part of the “Flood Management Study”, included in the “Integrated Water Resources Management of Cuvelai Basin” project, awarded to Dar Al-Handasah, Shair and Partners, Consulting Firm. In addition, the authors acknowledge the efforts of F. Nassar, A. El Mofty and M. Sader, GIS specialists, who made enormous inputs to this work and transformed the tedious remote sensing methodology to reality. Also, special thanks to R. Boalch and P. Speight, environmental experts, H. El Badry, Principal, F. El-Khoury, Director, Dar Al-Handasah who reshaped parts of this paper and challenged the authors to overcome the lack of data.

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Correspondence to A. G. Awadallah.

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Awadallah, A.G., Tabet, D. Estimating flooding extent at high return period for ungauged braided systems using remote sensing: a case study of Cuvelai Basin, Angola. Nat Hazards 77, 255–272 (2015). https://doi.org/10.1007/s11069-015-1600-6

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