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Investigating relationship between drought severity in Botswana and ENSO

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Abstract

Influences of El Niño southern oscillation (ENSO) on weather systems have increased the frequency and amplitude of extreme events over the last century. This even continues to exacerbate the already warming earth, with 2014–2016 which coincided with the strongest El Niño years observed as the warmest period in recent past. This study presents an approach of characterizing droughts at various timescales and establishes teleconnections between ENSO and drought severity in Botswana. The study uses Standardized Precipitation Evaporation Index (SPEI) at timescales of 1, 3, 6, 12 and 24 month to characterize droughts and Pearson’s correlations to study the teleconnections between SPEIs and ENSO. Results from the study reveal that extreme droughts are a rare occurrence in Botswana though it is more prone to moderate droughts at 12 month SPEI with a probability of 19% in the north. The highest severe drought probability was 7% recorded in the east. Linear trends indicate increasing dryness of around 0.8% per decade. These results have demonstrated that warm sea surface temperatures combined with negative Southern Oscillation Index correspond to persistent negative SPEI values and thus are likely to result in dry conditions. Significant correlations were observed in the mid austral summer in December and January. Due to this relationship, the drought early warning systems could use ENSO as one of the instruments for predicting drought over the study area and hence in its management.

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References

  • Abramowitz M, Stegun IA (1964) Handbook of mathematical functions: with formulas, graphs, and mathematical tables. Courier Corporation, North Chelmsford

    Google Scholar 

  • Alexandersson H (1986) A homogeneity test applied to precipitation data. J Climatol 6:661–675

    Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) FAO Irrigation and drainage paper no. 56. Rome Food Agric Organ UN 56:97–156

    Google Scholar 

  • Alley WM (1984) The Palmer drought severity index: limitations and assumptions. J Clim Appl Meteorol 23:1100–1109

    Google Scholar 

  • Batisani N (2011) The spatio-temporal-severity dynamics of drought in Botswana. J Environ Prot (Irvine, Calif) 2:803

    Google Scholar 

  • Batisani N (2012) Climate variability, yield instability and global recession: the multi-stressor to food security in Botswana. Clim Dev 4:129–140

    Google Scholar 

  • Beguería S, Vicente-Serrano SM, Reig F, Latorre B (2014) Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int J Climatol 34:3001–3023. https://doi.org/10.1002/joc.3887

    Article  Google Scholar 

  • Bepura C (1999) Drought management: a SADC perspective. In: Proceedings of the international conference on integrated drought management: lessons for sub Saharan Africa, 20–22 Sept

  • Bromwich DH, Rogers AN, Kallberg P et al (2000) ECMWF analyses and reanalyses depiction of ENSO signal in Antarctic precipitation. J Clim 13:1406–1420. https://doi.org/10.1175/1520-0442(2000)013%3c1406:EAARDO%3e2.0.CO;2

    Article  Google Scholar 

  • Byakatonda J, Parida BP, Kenabatho PK, Moalafhi DB (2016) Modeling dryness severity using artificial neural network at the Okavango Delta, Botswana. Glob Nest J 18:463–481

    Google Scholar 

  • Byakatonda J, Parida BP, Kenabatho PK, Moalafhi DB (2018a) Analysis of rainfall and temperature time series to detect long-term climatic trends and variability over semi-arid Botswana. J Earth Syst Sci 127:25. https://doi.org/10.1007/s12040-018-0926-3

    Article  Google Scholar 

  • Byakatonda J, Parida BP, Moalafhi DB, Kenabatho PK (2018b) Analysis of long term drought severity characteristics and trends across semiarid Botswana using two drought indices. Atmos Res. https://doi.org/10.1016/j.atmosres.2018.07.002

    Article  Google Scholar 

  • Byakatonda J, Parida BP, Kenabatho PK, Moalafhi DB (2019) Prediction of onset and cessation of austral summer rainfall and dry spell frequency analysis in semiarid Botswana. Theor Appl Climatol 135:101–117

    Google Scholar 

  • Cai J, Liu Y, Lei T, Pereira LS (2007) Estimating reference evapotranspiration with the FAO Penman–Monteith equation using daily weather forecast messages. Agric For Meteorol 145:22–35

    Google Scholar 

  • Conforti P, Ahmed S, Markova G et al. (2018) Impact of disasters and crises on agriculture and food security- 2017. FAO, Rome, Italy

    Google Scholar 

  • Costa AC, Soares A (2009) Homogenization of climate data: review and new perspectives using geostatistics. Math Geosci 41:291–305. https://doi.org/10.1007/s11004-008-9203-3

    Article  Google Scholar 

  • Dai A (2013) Increasing drought under global warming in observations and models. Nat Clim Change 3:52–58

    Google Scholar 

  • Edossa DC, Woyessa YE, Welderufael WA (2014) Analysis of droughts in the central region of South Africa and their association with SST anomalies. Int J Atmos Sci 2014:508953

    Google Scholar 

  • Engelbrecht F, Adegoke J, Bopape M-J et al (2015) Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ Res Lett 10:85004

    Google Scholar 

  • FAO (2001) Drought impact mitigation and prevention in the Limpopo River Basin: a situation analysis. FAO, Rome

    Google Scholar 

  • Fauchereau N, Trzaska S, Rouault M, Richard Y (2003) Rainfall variability and changes in southern Africa during the 20th century in the global warming context. Nat Hazards 29:139–154

    Google Scholar 

  • Feidas H, Makrogiannis T, Bora-Senta E (2004) Trend analysis of air temperature time series in Greece and their relationship with circulation using surface and satellite data: 1955–2001. Theor Appl Climatol 79:185–208

    Google Scholar 

  • Franks SW (2005) Regional hydrological impacts of climatic change: impact assessment and decision making. International Association of Hydrological Sciences, Foz do Iguaço, Brazil

    Google Scholar 

  • Guha-Sapir D, Below R, Hoyois P (2016) EM-DAT: the CRED/OFDA international disaster database. In: UN Environ. Doc. Repos.

  • Hansen J, Sato M, Ruedy R et al (2016) Global temperature in 2015. GISS, NASA, New York, pp 1–6

    Google Scholar 

  • Hayes M, Svoboda M, Wall N, Widhalm M (2011) The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bull Am Meteorol Soc 92:485–488

    Google Scholar 

  • Heim RR Jr (2002) A review of twentieth-century drought indices used in the United States. Bull Am Meteorol Soc 83:1149–1165

    Google Scholar 

  • Hosking JRM, Wallis JR (2005) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, Cambridge

    Google Scholar 

  • Iglesias A, Garrote L, Cancelliere A et al (2009) Coping with drought risk in agriculture and water supply systems: drought management and policy development in the Mediterranean. Springer, Berlin

    Google Scholar 

  • Lloyd-Hughes B (2012) A spatio-temporal structure-based approach to drought characterisation. Int J Climatol 32:406–418

    Google Scholar 

  • Lorenzo-Lacruz J, Vicente-Serrano SM, López-Moreno JI et al (2010) The impact of droughts and water management on various hydrological systems in the headwaters of the Tagus River (central Spain). J Hydrol 386:13–26

    Google Scholar 

  • 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

    Google Scholar 

  • Mbululo Y, Nyihirani F (2012) Climate characteristics over southern highlands Tanzania. Atmos Clim Sci 2:454–463. https://doi.org/10.4236/acs.2012.24039

    Article  Google Scholar 

  • McEvoy DJ, Huntington JL, Abatzoglou JT, Edwards LM (2012) An evaluation of multiscalar drought indices in Nevada and Eastern California. Earth Interact 16:1–18

    Google Scholar 

  • McKee TB, Doesken NJ, Kleist J, et al (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology, pp 179–183

  • Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391:202–216

    Google Scholar 

  • Morchain D, Urquhart P, Zaremba J (2017) Background paper on Botswana’s draft drought management strategy. Gaborone, Botswana

    Google Scholar 

  • Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27:2103–2111

    Google Scholar 

  • Nalbantis I, Tsakiris G (2009) Assessment of hydrological drought revisited. Water Resour Manag 23:881–897

    Google Scholar 

  • Narasimhan B, Srinivasan R (2005) Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agric For Meteorol 133:69–88

    Google Scholar 

  • Nicholson SE, Kim J (1997) The Relationship of the El Niño Southern Oscillation To African Rainfall. Int J Climatol 17:117–135. https://doi.org/10.1002/(SICI)1097-0088(199702)17:2%3c117:AID-JOC84%3e3.0.CO;2-O

    Article  Google Scholar 

  • Nicholson SE, Leposo D, Grist J (2001) The relationship between El Niño and drought over Botswana. J Clim 14:323–335

    Google Scholar 

  • NOAA-NCDC (2016) Southern Oscillation Index (SOI). www.ncdc.noaa.gov/teleconnections/enso/indicators/soi/data.csv. Accessed 16 Oct 2016

  • NOAA-NCEP (2016) Average sea surface temperetaure (SST) anormalies in region 3.4 of the Equatorial Pacific. http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_change.shtml. Accessed 16 Oct 2016

  • Nyenzi B, Lefale PF (2006) El Nino southern oscillation (ENSO) and global warming. Adv Geosci 6:95–101

    Google Scholar 

  • Pachauri RK, Reisinger A (2007) IPCC fourth assessment report. IPCC, Geneva

    Google Scholar 

  • Parida BP, Moalafhi DB (2008) Regional rainfall frequency analysis for Botswana using L-Moments and radial basis function network. Phys Chem Earth 33:614–620. https://doi.org/10.1016/j.pce.2008.06.011

    Article  Google Scholar 

  • Pettit AN (1979) Anon-parametric approach to the change-point detection. Appl Stat 28:126–135

    Google Scholar 

  • Potop V, Türkott L, Kožnarová V, Možný M (2010) Drought episodes in the Czech Republic and their potential effects in agriculture. Theor Appl Climatol 99:373–388

    Google Scholar 

  • Rahman MR, Lateh H (2017) Climate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model. Theor Appl Climatol 128:27–41. https://doi.org/10.1007/s00704-015-1688-3

    Article  Google Scholar 

  • Rojas O, Li Y, Cumani R (2014) An assessment using FAO’s Agricultural Stress Index ( ASI ) understanding the drought impact of El Niño on the global agricultural areas. FAO, Rome, Italy

    Google Scholar 

  • Sheffield J, Wood EF, Roderick ML (2012) Little change in global drought over the past 60 years. Nature 491:435–438

    Google Scholar 

  • Sivakumar MVK, Motha R, Wilhite D, Wood D (2011) Agricultural drought indices. In: Proceedings of an expert meeting: 2–4 June, 2010, Murcia, Spain. WMO

  • Stagge JH, Tallaksen LM, Xu CY, Van Lanen HAJ (2014) Standardized precipitation-evapotranspiration index (SPEI): sensitivity to potential evapotranspiration model and parameters. In: Proceedings of the FRIEND-Water, pp 367–373

  • Stagge JH, Tallaksen LM, Gudmundsson L et al (2015) Candidate distributions for climatological drought indices (SPI and SPEI). Int J Climatol 35:4027–4040

    Google Scholar 

  • Stocker TF, Qin D, Plattner GK, et al (2013) Climate change 2013: the physical science basis. Intergovernmental panel on climate change, working group I contribution to the IPCC fifth assessment report (AR5)

  • Trambauer P, Maskey S, Werner M et al (2014) Identification and simulation of space–time variability of past hydrological drought events in the Limpopo River basin, southern Africa. Hydrol Earth Syst Sci 18:2925–2942

    Google Scholar 

  • Trenberth KE, Jones PD, Ambenje P et al (2007) Observations: surface and atmospheric climate change, chapter 3. IPCC, London, UK

    Google Scholar 

  • Troup AJ (1965) The “southern oscillation”. Q J R Meteorol Soc 91:490–506

    Google Scholar 

  • Usman MT, Reason CJC (2004) Dry spell frequencies and their variability over southern Africa. Clim Res 26:199–211. https://doi.org/10.3354/cr026199

    Article  Google Scholar 

  • Van Loon AF (2013) On the propagation of drought: how climate and catchment characteristics influence hydrological drought development and recovery. Wageningen University, Wageningen

    Google Scholar 

  • Vicente-Serrano SM, Begueria S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23:1696–1718

    Google Scholar 

  • Vicente-Serrano SM, López-Moreno JI, Gimeno L et al (2011) A multiscalar global evaluation of the impact of ENSO on droughts. J Geophys Res. https://doi.org/10.1029/2011JD016039

    Article  Google Scholar 

  • Vicente-Serrano SM, Chura O, López-Moreno JI et al (2015) Spatio-temporal variability of droughts in Bolivia: 1955–2012. Int J Climatol. https://doi.org/10.1002/joc.4190

    Article  Google Scholar 

  • Wang W, Zhu Y, Xu R, Liu J (2015) Drought severity change in China during 1961–2012 indicated by SPI and SPEI. Nat Hazards 75:2437–2451

    Google Scholar 

  • White DH, Walcott JJ (2009) The role of seasonal indices in monitoring and assessing agricultural and other droughts: a review. Crop Pasture Sci 60:599–616

    Google Scholar 

  • Wijngaard JB, Klein Tank AMG, Können GP (2003) Homogeneity of 20th century European daily temperature and precipitation series. Int J Climatol 23:679–692

    Google Scholar 

  • Wilhite DA, Svoboda MD, Hayes MJ (2007) Understanding the complex impacts of drought: a key to enhancing drought mitigation and preparedness. Water Resour Manag 21:763–774

    Google Scholar 

  • WMO (2009) Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation. WMO, Geneva

    Google Scholar 

  • Yaghoobi AHZ (2012) Handling uncertainty in hydrologic analysis and drought risk assessment using Dempster–Shafer theory. University of British Columbia

  • Yu M, Li Q, Hayes MJ et al (2014) Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index: 1951–2010? Int J Climatol 34:545–558

    Google Scholar 

  • Yuan J, Hartmann DL (2008) Spatial and temporal dependence of clouds and their radiative impacts on the large-scale vertical velocity profile. J Geophys Res. https://doi.org/10.1029/2007JD009722

    Article  Google Scholar 

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Acknowledgements

The authors wish to thank the Mobility for Engineering Graduates in Africa (METEGA) for funding this study. We also pass our gratitude to the editor and anonymous reviewers for their insights in an effort to improve the quality of the manuscript. The Department of Meteorological services (DMS) of Botswana provided the climatological data used in this study for this we are grateful.

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Byakatonda, J., Parida, B.P., Moalafhi, D.B. et al. Investigating relationship between drought severity in Botswana and ENSO. Nat Hazards 100, 255–278 (2020). https://doi.org/10.1007/s11069-019-03810-1

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