Climate Dynamics

, Volume 53, Issue 3–4, pp 1247–1260 | Cite as

Temporal statistical analysis and predictive modelling of drought and flood in Rundu–Namibia

  • Miguel Vallejo OrtiEmail author
  • Kaleb G. Negussie


Namibia is a semi-arid country characterized by the alternation of long drought periods and short episodes of intense rain, which often causes great stress to plants, animals and people. Thus, a deep understanding of the spatio-temporal distribution of rainfall is required to minimize their negative impacts, affecting food security. The temporal occurrence of drought and rainy events in the North East of Namibia (Rundu area) is described and studied for a series of monthly rainfall from 1940 until 2015. Inter-arrival times analysis is conducted to model the occurrence of extreme (high and low) rainfall events through a Poisson Point Process (PPP). Adapting the definitions of drought and flood to the water demands of crops in Rundu, it is deduced that the average inter-arrival time for droughts is smaller than for rainy years, presenting 3 and 10 years respectively. Results of PPP are presented on Lorenz Curves for different study cases (more than one, two and three events per time unit). From the PPP results it can be extracted that the probability of suffering a drought in a period of 5 years in Rundu is approximately 70%, while this likelihood is only 40% for floods. Considering the occurrence of three or more events in a time period of 10 years, the probability is almost 50% for drought and less than 10% floods. Point Process (PP) analysis demonstrates that Poisson Distribution can be used to model the occurrence of drought and floods in Rundu area, being especially precise to model the presence of one event in periods between 1 and 10 years.


Crop production Drought ENSO Flood Food security Lorenz curve Namibia Poisson point process Rainfall 



This work was supported by the Namibian University of Science and Technology (NUST). Our sincere gratitude to Namibia Meteorological Service for providing the data used in this study. Special thanks are extended to the M. Carrión and K. Carter for their assistance in the writing and proof reading of the article as well as to Dr Zimmerman and Red Cross Technical Staff for the technical advices regarding conservation agriculture in Rundu. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.


  1. Baddeley A (2007) Spatial point processes and their applications. Stoch Geom 1892:1–75. CrossRefGoogle Scholar
  2. Baldeh M, Samba C, Tuffour K, Boya A (2016) Poisson process and its application to the storm water overflows. Comput Water Energy Environ Eng 5:47–53. CrossRefGoogle Scholar
  3. Bove R (2002) Using the poisson distribution to approximate the binomial distribution. In: Westchester University Courses. pp 7–10Google Scholar
  4. Clapham C, Nicholson J (2009) The concise Oxford dictionary of mathematics, 5th edn. Oxford University Press, OxfordCrossRefGoogle Scholar
  5. Daley DJ, Vere-Jones D (2003) An introduction to the theory of point processes: volume I: elementary theory and methods, 2nd edn. Springer, HeidelbergGoogle Scholar
  6. Das SK, Gupta RK, Varma HK (2007) Flood and drought management through water resources development in India. WMO Bull 56:179–188Google Scholar
  7. Driver P (2014) Rainfall variability over Southern Africa. Dissertation, University of Cape TownGoogle Scholar
  8. Du Pisani AL (2003) Determining drought indices in Namibia. Agricola 13:15–24Google Scholar
  9. Gallager RG (2011) Poisson processes. In: Discrete stochastic processes, 2nd edn. MIT OpenCourseWare, ‎Cambridge, pp 69–103Google Scholar
  10. Golden Gate Weather Services (2018) El Niño and La Niña Years and Intensities. In: Golden Gate Weather Serv. Accessed 5 Oct 2018
  11. Jacobi I (2008) Crop production: joint presidency committee (NAU and the NNFU). John Meinert Printing, WindhoekGoogle Scholar
  12. Johnson T (2010) Introduction to spatial point processes. In: Spatial point processes. Centre for Research in Statistical Methodology (CRiSM), Coventry, pp 1–10Google Scholar
  13. Jury M (2010) Climate and weather factors modulating river flows in southern Angola. Int J Climatol 30:901–908. Google Scholar
  14. Kapolo IN (2014) Drought conditions and management strategies in Namibia. Namibia Meteorological Services. Accessed 8 Jan 2018
  15. Kerdiles H, Rembold F, Pérez-hoyos A (2015) Seasonal monitoring in Namibia in northern and central Namibia. Eur Comm Jt Res Cent 21:3–15Google Scholar
  16. Landman WA, Mason SJ (1999) Sea-surface temperatures and summer rainfall over South Africa and Namibia. Int J Climatol 19:1477–1492 .;2-W CrossRefGoogle Scholar
  17. Last G, Penrose M (2017) Poisson processes. In: Lectures on the poisson process. Cambridge University Press, Cambridge, pp 9–25CrossRefGoogle Scholar
  18. Lu X, Wang L, Pan M et al (2016) A multi-scale analysis of Namibian rainfall over the recent decade—comparing TMPA satellite estimates and ground observations. J Hydrol Reg Stud 8:59–68. CrossRefGoogle Scholar
  19. Lübbecke JF, Burls NJ, Reason CJC, McPhaden J (2014) Variability in the South Atlantic anticyclone and the Atlantic Niño mode. J Clim 27:8135–8150. CrossRefGoogle Scholar
  20. Lupo A, Kininmonth W (2013) Global climate models and their limitations. In: Idso CD in climate change reconsidered II: physical science part one. The Heartland Institute, Chicago, pp 69–75Google Scholar
  21. Marshall J, Plumb RA (2008) Atmosphere, ocean, and climate dynamics: an introductory text. Int Geophys Ser 93:139–159Google Scholar
  22. Masih I, Maskey S, Mussá FEF, Trambauer P (2014) A review of droughts on the African continent: a geospatial and long-term perspective. Hydrol Earth Syst Sci 18:3635–3649. CrossRefGoogle Scholar
  23. Mendelsohn J, Obeid S el, de Klerk N, Vigne P (2006) Farming systems in Namibia. RAISON (research and information services of Namibia). ABC Press, WindhoekGoogle Scholar
  24. Ministry of Environment and Tourism (2013) National climate change strategy and action plan 2013–2020. Republic of Namibia - Ministry of Environment and Tourism, Windhoek, pp 14–36Google Scholar
  25. Moreira EE, Mexia JT, Pereira LS (2012) Are drought occurrence and severity aggravating? A study on SPI drought class transitions using log-linear models and ANOVA-like inference. Hydrol Earth Syst Sci 16:3011–3028. CrossRefGoogle Scholar
  26. Moskowitz C, Seshan V, Riedel E, Begg C (2008) Estimating the empirical Lorenz curve and Gini coefficient in the presence of error with nested data. Stat Med 27:3191–3208. CrossRefGoogle Scholar
  27. Mueller H-G, Wu S, Zhang Z (2014) Functional data analysis for point processes with rare events. Stat Sin 23:1–23. Google Scholar
  28. Munday C, Washington R (2017) Circulation controls on southern African precipitation in coupled models: the role of the Angola Low. J Geophys Res Atmos 122:861–877. CrossRefGoogle Scholar
  29. Namibia Early Warning and Food Information Unit (2018) Crop prospects and food security situation report. WindhoekGoogle Scholar
  30. National Drought Task Force (1997) National drought policy and strategy. Ministry of Agriculture Water and Forestry, NamibiaGoogle Scholar
  31. Pazvakawambwa GT, Ogunmokun AA (2013) A time-series forecasting model for Windhoek Rainfall, Namibia. University of Namibia. Accessed 10 Jan 2018
  32. Raič M, Toman A (2017) Solved problems in random processes. Ljubljana, SloveniaGoogle Scholar
  33. Randall DA, Wood RA, Bony S et al (2007) Climate models and their evaluation. In: Climate change 2007: the physical science basis. contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 591–662Google Scholar
  34. Rasmussen JG (2011) Temporal point processes: the conditional intensity function. Aalborg University. Accessed 18 Nov 2017
  35. Ratna SB, Ratnam JV, Behera SK et al (2014) Performance assessment of three convective parameterization schemes in WRF for downscaling summer rainfall over South Africa. Clim Dyn 42:2931–2953. CrossRefGoogle Scholar
  36. Red Cross (2012) Namibia Red Cross’s integrated food security intervention. International Federation of Red Cross. Accessed 17 Dec 2017
  37. Rojas O, Li Y, Renato C (2014) Understanding the drought impact of El Niño on the global agricultural areas: an assessment using FAO’ s Agricultural Stress Index (ASI). Food and Agriculture Organization of the United Nations, RomeGoogle Scholar
  38. Smith JA, Karr AF (1983) A point process model of summer season rainfall occurrences. Water Resour Res 19:95–103. CrossRefGoogle Scholar
  39. Waagepetersen R, Guan Y (2009) Two-step estimation for inhomogeneous spatial point processes. J R Stat Soc Ser B (Stat Methodol) 71:685–702. CrossRefGoogle Scholar
  40. World Bank Group (2017) Climate change knowledge portal. Accessed 15 Nov 2017

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Namibia University of Science and TechnologyWindhoekNamibia

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