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Analysis of rainfall and temperature time series to detect long-term climatic trends and variability over semi-arid Botswana

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Abstract

Arid and semi-arid environments have been identified with locations prone to impacts of climate variability and change. Investigating long-term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible inhomogeneities and years of intervention are analysed using four absolute homogeneity tests. Trends in the climatic variables were determined using Mann–Kendall and Sen’s Slope estimator statistics. Association of El Niño Southern Oscillation (ENSO) with local climate is also investigated through multivariate analysis. Results from the study show that rainfall time series are fully homogeneous with 78.6 and 50% of the stations for maximum and minimum temperature, respectively, showing homogeneity. Trends also indicate a general decrease of 5.8, 7.4 and 18.1% in annual, summer and winter rainfall, respectively. Warming trends are observed in annual and winter temperature at 0.3 and 1.5% for maximum temperature and 1.7 and 6.5% for minimum temperature, respectively. Rainfall reported a positive correlation with Southern Oscillation Index (SOI) and at the same time negative association with Sea Surface Temperatures (SSTs). Strong relationships between SSTs and maximum temperature are observed during the El Niño and La Niña years. These study findings could facilitate planning and management of agricultural and water resources in Botswana.

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References

  • Akinsanola A A and Ogunjobi K O 2015 Recent homogeneity analysis and long-term spatio-temporal rainfall trends in Nigeria; Theor. Appl. Climatol. 128 275–289, https://doi.org/10.1007/s00704-015-1701-x.

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

    Article  Google Scholar 

  • Alexandersson H and Moberg A 1997 Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends; Int. Int. J. Climatol. 17 25–34.

    Article  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.

    Article  Google Scholar 

  • Batisani N and Yarnal B 2010 Rainfall variability and trends in semi-arid Botswana: Implications for climate change adaptation policy; Appl. Geogr. 30 483–489.

    Article  Google Scholar 

  • Buishand T A 1982 Some methods for testing the homogeneity of rainfall records; J. Hydrol. 58 11–27.

    Article  Google Scholar 

  • Byakatonda J, Parida B P, Kenabatho P K and Moalafhi D B 2016 Modeling dryness severity using artificial neural network at the Okavango Delta, Botswana; Glob. Nest. J. 18 463–481.

    Google Scholar 

  • Costa A C and Soares A 2009a Trends in extreme precipitation indices derived from a daily rainfall database for the south of Portugal; Int. J. Climatol. 29 1956–1975.

    Article  Google Scholar 

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

  • Croakin C and Tobias P 2006 NIST/SEMATECH e-Handbook of Statistical Methods, National Institute of Standards and Technology/SEMATECH; US Commerce Department’s Technology Administration.

    Google Scholar 

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

    Article  Google Scholar 

  • Dai A 2011a Drought under global warming: A review; Wiley Interdiscip. Rev. Clim. Change 2 45–65, https://doi.org/10.1002/wcc.81.

  • Dai A 2011b Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008.

  • Edossa D C, Woyessa Y E and Welderufael W A 2014 Analysis of droughts in the central region of South Africa and their association with SST anomalies; Int. J. Atmos. Sci., https://doi.org/10.1155/2014/508953.

  • Giorgi F and Lionello P 2008 Climate change projections for the Mediterranean region; Global Planet. Change 63 90–104.

    Google Scholar 

  • Gocic M and Trajkovic S 2013 Analysis of changes in meteorological variables using Mann–Kendall and Sen’s slope estimator statistical tests in Serbia; Global Planet. Change 100 172–182, https://doi.org/10.1016/j.gloplacha.2012.10.014.

  • Hänsel S, Medeiros D M and Matschullat J et al. 2016 Assessing homogeneity and climate variability of temperature and precipitation series in the capitals of north-eastern Brazil; Front. Earth Sci. 4 1–21, https://doi.org/10.3389/feart.2016.00029.

  • Hansen J, Sato M and Ruedy R et al. 2016 Global Temperature in 2015; Colomb. Univ., pp. 1–6.

  • Huang J, Ji M and Xie Y et al. 2016 Global semi-arid climate change over last 60 years; Clim. Dyn. 46 1131–1150.

    Article  Google Scholar 

  • Jury M R 2002 Economic impacts of climate variability in South Africa and development of resource prediction models; J. Appl. Meteorol. 41 46–55.

    Article  Google Scholar 

  • Jury M R 2013 Climate trends in southern Africa; S. Afr. J. Sci. 109 1–11.

    Google Scholar 

  • Kandji S T, Verchot L and Mackensen J 2006 Climate change climate and variability in southern Africa: Impacts and adaptation in the agricultural sector; World Agroforestry Centre (ICRAF), United Nations Environment Programme (UNEP), Africa.

  • Kenabatho P K, Parida B P and Moalafhi D B 2012 The value of large-scale climate variables in climate change assessment: The case of Botswana’s rainfall; Phys. Chem. Earth 50-52 64–71, https://doi.org/10.1016/j.pce.2012.08.006.

  • Kendall M G 1975 Rank Correlation Methods; 4th edn, London.

  • Khan M I, Liu D and Fu Q et al. 2016 Recent climate trends and drought behavioral assessment based on precipitation and temperature data series in the Songhua River Basin of China; Water Resour. Manag. 30 4839–4859.

    Google Scholar 

  • Lettenmaier D P 1976 Detection of trends in water quality data from records with dependent observations; Water Resour. Res. 12 1037–1046.

    Google Scholar 

  • Mann H B 1945 Nonparametric tests against trend; Econom. J. Econom. Soc. 13(3) 245–259.

    Google Scholar 

  • Matalas N C and Langbein W B 1962 Information content of the mean; J. Geophys. Res. 67 3441–3448.

    Article  Google Scholar 

  • Menzel A, Sparks T H and Estrella N et al. 2006 European phenological response to climate change matches the warming pattern; Glob. Change Biol. 12 1969–1976.

    Article  Google Scholar 

  • Modarres R and da Silva V de P R 2007 Rainfall trends in arid and semi-arid regions of Iran; J. Arid Environ. 70 344–355.

  • Morán-Tejeda E, Bazo J and López-Moreno J I et al. 2016 Climate trends and variability in Ecuador (1966–2011); Int. J. Climatol. https://doi.org/10.1002/joc.4597.

  • Nazemosadat M J, Samani N, Barry D A and Niko M M 2006 ENSO forcing on climate change in Iran: Precipitation analysis; Iran. J. Sci. Technol. Trans. B Eng. 30 1–11.

  • Nicholson S E, Leposo D and Grist J 2001 The relationship between El Niño and drought over Botswana; J. Clim. 14 323–335.

    Article  Google Scholar 

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

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

  • Nyenzi B and Lefale P F 2006 El Niño Southern Oscillation (ENSO) and global warming; Adv. Geosci. 6 95–101.

    Article  Google Scholar 

  • Omoyo N N, Wakhungu J and Oteng’i S 2015 Effects of climate variability on maize yield in the arid and semi arid lands of lower eastern Kenya; Agric. Food Secur. 4 8, https://doi.org/10.1186/s40066-015-0028-2.

  • Pachauri R K and Reisinger A 2007 IPCC fourth assessment report; IPCC, Geneva.

    Google Scholar 

  • Parida B P and Moalafhi D B 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.

  • Partal T and Kahya E 2006 Trend analysis in Turkish precipitation data; Hydrol. Process. 20 2011–2026, https://doi.org/10.1002/hyp.5993.

  • Pettit A N 1979 A non-parametric approach to the change-point detection; Appl. Stat. 28 126–135.

    Article  Google Scholar 

  • Rahman M R and 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.

  • Recha C W, Makokha G L and Traore P S et al. 2012 Determination of seasonal rainfall variability, onset and cessation in semi-arid Tharaka district, Kenya; Theor. Appl. Climatol. 108 479–494.

    Article  Google Scholar 

  • Reza Y M, Javad K D, Mohammad M and Ashish S 2011 Trend detection of the rainfall and air temperature data in the Zayandehrud basin; J. Appl. Sci. 11 2125–2134.

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

  • Sabzevari A A, Zarenistanak M, Tabari H and Moghimi S 2015 Evaluation of precipitation and river discharge variations over southwestern Iran during recent decades; J. Earth Syst. Sci. 124 335–352, https://doi.org/10.1007/s12040-015-0549-x.

  • Sen P K 1968 Estimates of the regression coefficient based on Kendall’s tau; J. Am. Stat. Assoc. 63 1379–1389.

  • Shahid S 2010 Rainfall variability and the trends of wet and dry periods in Bangladesh; Int. J. Climatol. 30 2299–2313, https://doi.org/10.1002/joc.2053.

  • Shifteh Some’e B, Ezani A and Tabari H 2012 Spatio-temporal trends and change point of precipitation in Iran; Atmos. Res. 113 1–12, https://doi.org/10.1016/j.atmosres.2012.04.016.

  • Statistics Botswana 2009 Botswana water statistics; Gaborone, Botswana.

  • Statistics Botswana 2015 Statistics Botswana Annual Agricultural Survey Report 2013, Gaborone, Botswana.

  • Stocker T F, Qin D and Plattner G K 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).

  • Tabari H, Somee B S and Zadeh M R 2011 Testing for long-term trends in climatic variables in Iran; Atmos. Res. 100 132–140, https://doi.org/10.1016/j.atmosres.2011.01.005.

  • Tabari H and Talaee P H 2011 Temporal variability of precipitation over Iran: 1966–2005; J. Hydrol. 396 313–320, https://doi.org/10.1016/j.jhydrol.2010.11.034.

  • Thiel H 1950 A rank-invariant method of linear and polynomial regression analysis, Part 3. In: Proceedings of Koninalijke Nederlandse Akademie van Weinenschatpen A, pp. 1397–1412.

  • Usman M T and Reason C J C 2004 Dry spell frequencies and their variability over southern Africa; Clim. Res. 26 199–211, https://doi.org/10.3354/cr026199.

  • Von Neumann J 1941 Distribution of the ratio of the mean square successive difference to the variance; Ann. Math. Stat. 12 367–395.

    Article  Google Scholar 

  • Wijngaard J B, Klein Tank A M G and Können G P 2003 Homogeneity of \(20^{{\rm th}}\) century European daily temperature and precipitation series; Int. J. Climatol. 23 679–692, https://doi.org/10.1002/joc.906.

  • Yue S and Hashino M 2003 Long Term Trends of Annual and Monthly Precipitation in Japan; JAWRA J. Am. Water. Resour. Assoc. 39 587–596, https://doi.org/10.1111/j.1752-1688.2003.tb03677.x.

  • Yue S, Pilon P, Phinney B and Cavadias G 2002 The influence of autocorrelation on the ability to detect trend in hydrological series; Hydrol. Process 16 1807–1829, https://doi.org/10.1002/hyp.1095.

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Acknowledgements

This study was supported by the Mobility for Engineering Graduates in Africa (METEGA) and Carnegie Cooperation of New York through RUFORUM in the form of research funds. The climatic data used were provided by Department of Meteorological Services (DMS) of Botswana. The authors are grateful for the support from the two entities. Gulu University is highly appreciated for granting study leave to the first author. They also wish to thank the two anonymous reviewers for their valuable comments that enriched this study.

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Correspondence to Jimmy Byakatonda.

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Corresponding editor: Subimal Ghosh.

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Byakatonda, J., Parida, B.P., Kenabatho, P.K. et al. 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 (2018). https://doi.org/10.1007/s12040-018-0926-3

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  • DOI: https://doi.org/10.1007/s12040-018-0926-3

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