Abstract
Precipitation and temperature are critical climatic variables that drive catastrophic climatic events including droughts and floods. These variables continue to fluctuate, thereby producing even more extreme weather events across different parts of East African region. Using quantile linear regression (QLR) method, this study interrogated wet and dry conditions over a period of 34 years across East African region. The spatio-temporal quantile trends (time coefficient of precipitation) analysis is presented in 5 conditions (quantiles): extreme dry (1st), dry (10th), median (50th), wet (90th) and extreme wet (99th). For annual precipitation, the quantiles indicated a trend value of − 0.294, 0.205, − 0.425, − 0.069 and 0.145, respectively. This shows that the extreme dry (wet) values in annual mean precipitation over the region are decreasing (increasing) over time, while the reverse is the case for the long and short seasons. Differences in the regression coefficients of precipitation variables for the inter-quantile differences show that any increase or decrease in average precipitation changes the shape of the distribution of hydrological parameters, increasing or decreasing spread between the extreme quantiles. The precipitation deciles at different quantiles over 34 years reveal marked variations in the annual mean and the long and short rainy seasons. Finally, the results indicate significant variations in extreme wet and dry conditions across eight ecological zones in East Africa with variable slope along various quantiles. In conclusion, QLR method has shown the ability to provide superior detailed information on extreme wet and dry climatic conditions required for flood mitigation and water resources planning and management.
Similar content being viewed by others
References
Abbas SA, Xuan Y, Song X (2019) Quantile regression based methods for investigating rainfall trends associated with flooding and drought conditions. Water Resour Manag 33:4249–4264. https://doi.org/10.1007/s11269-019-02362-0
AghaKouchak A (2015) A multivariate approach for persistence-based drought prediction: application to the 2010–2011 East Africa drought. J Hydrol 526:127–135. https://doi.org/10.1016/j.jhydrol.2014.09.063
Ayana EK, Ceccato P, Fisher JR, DeFries R (2016) Examining the relationship between environmental factors and conflict in pastoralist areas of East Africa. Sci Total Environ 557:601–611
Ayantabo OO, Li Y, Song S, Yao N (2017) Spatial comparability of drought characteristics and return periods in mainland China over 1961–2013. J Hydrol 550:549–567. https://doi.org/10.1016/j.jhydrol.2017.05.019
Barbosa SM, Scotto MG, Alonso AM (2011) Summarizing changes in air temperature over Central Europe by quantile regression and clustering. Nat Hazards Earth Syst Sci 11:3227–3233. https://doi.org/10.5194/nhess-11-3227-2011
Barron J, Rockström J, Gichuki F, Hatibu N (2003) Dry spell analysis and maize yields for two semi-arid locations in East Africa. Agric For Meteorol 117:23–37. https://doi.org/10.1016/S0168-1923(03)00037-6
Bassett GW Jr, TamKnight MSK (2002) Quantile models and estimators for data analysis. Metrika 55:17–26
Boken VK, Cracknell AP, Heathcote RL (eds) (2004) Monitoring and predicting agricultural drought: a global study. Oxford University Press, Oxford
Borgomeo E, Vadheim B, Woldeyes FB, Alamirew T, Tamru S, Charles KJ, Kebede S, Walker O (2018) The distributional and multi-sectoral impacts of rainfall shocks: evidence from computable general equilibrium modelling for the awash basin. Ecological Economics, Ethiopia. https://doi.org/10.1016/j.ecolecon.2017.11.038
Brefeld U, GÄartner T, Scheffer T, Wrobel S (2006) Efficient co-regularised least squares regression. In: Proceeding of 23rd international conference on machine learning
Bremnes JB (2004) Probabilistic forecasts of precipitation in terms of quantile using NWP model output. Mon Weather Rev 132:338–347. https://doi.org/10.1175/1520-0493(2004)132%3c0338:PFOPIT%3e2.0.CO;2
Buchinsky M (1994) Changes in the U.S. wage structure 1963–1987: application of quantile regression. Econometrica 62:405–458
Cai Y, Reeve D (2013) Extreme value prediction via a quantile function model. Coast Eng 77:91–98. https://doi.org/10.1016/j.coastaleng.2013.02.003
Camberlin P, Okoola RE (2003) The onset and cessation of the ‘long rains’ in eastern Africa and their interannual variability. Theor Appl Climatol 75:43–54. https://doi.org/10.1007/s00704-002-0721-5
Chamaille-Jammes S, Fritz H, Murinadagomo F (2007) Detecting climate changes of concern in highly variable environments: quantile regressions reveal that droughts worsen in Hwange national park. Zimb J Arid Environ 71(3):321–326. https://doi.org/10.1016/j.jaridenv.2007.05.005
Dai A (2011) Drought under global warming: a review. Wiley Interdiscip Rev Clim Change 2(1):45–65
Dai A, Lamb P, Trenberth KE, Hulme M, Jones PD, Xie P (2004) The recent Sahara drought is real. Int J Climatol 24:1323–1331. https://doi.org/10.1002/joc.1083
Depaula G (2020) The distributional effect of climate change on agriculture: evidence from a Ricardian quantile analysis of Brazilian. J Environ Econ Manag. https://doi.org/10.1016/j.jeem.2020.102378
Dinpashoh Y, Mirabbasi R, Jhajharia D, Abianeh HZ, Mostafaeipour A (2014) Effect of short-term and long-term persistence on identification of temporal trends. J Hydrol Eng 9(3):617–625. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000819
Elsner JB, Kossin JP, Jagger TH (2008) The increasing intensity of the strongest tropical cyclones. Nature 455:92–95. https://doi.org/10.1038/nature07234
Emergency response coordination centre 2019. East Africa: floods. DG ECHO daily map 05/11/2019. https://reliefweb.int/sites/reliefweb.int/files/resources/ECDM_20191105_East_Africa_Floods.pdf
Fan L (2014) Quantile trends in temperature extremes in China. Atmos Ocean Sci Lett 7(4):304–308. https://doi.org/10.3878/j.issn.1674-2834.13.0102
Fan L, Chen D (2016) Trends in extreme precipitation indices across China detected using qunatile regression. Atmos Sci Lett 17(7):400–406. https://doi.org/10.1002/asl.671
Fitzenberger B, koenker R, Machado JAF (2002) Economic applications of quantile regression. Spriger, Berlin
Franco-Villoria M, Scott M, Hoey T (2018) Spatiotemporal modeling of hydrological return levels: a quantile regression approach. Environmetrics 30(2):e2522. https://doi.org/10.1002/env.2522
Friederichs P, Hense A (2007) Downscaling of extreme precipitation events using censored quantile regression. Mon Weather Rev 135:2365–2378. https://doi.org/10.1175/MWR3403.1
Funk C, Senay G, Asfaw A, Verdin J, Rowland J, Korecha D, Eilerts G, Michaelsen J, Amer S, Choularton R (2005) Recent drought tendencies in ethiopia and equatorial-subtropical Eastern Africa. FEWS-Net famine early warning system network. Vulnerability to food insecurity: factor identification and characterization report. Number 01/2005. FEWS Net
Funk C, Dettinger MD, Michaelsen JC, Verdin JP, Brown ME, Barlow M, Hoell A (2008) Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proc Natl Acad Sci 105(32):11081–11086
Funk C, Husak G, Michaelsen J, Shukla S, Hoell A, Lyon B, et al. (2013) Attribution of 2012 and 2003–12 rainfall deficits in Eastern Kenya and southern Somalia. In: Peterson T, Hoerling M, Stott P, Herring S (eds). Explaining extreme events of 2012 from a climate perspective. Bulletin of the American meteorological society, 94(9), Si–S74. Accessed 3rd January, 2021 from http://www.jstor.org/stable/26218715
Funk C, Hoell A, Shukla S, Bladé I, Liebmann B, Roberts JB, Robertson FR, Husak G (2014) Predicting East African spring droughts using Pacific and Indian Ocean sea surface temperature indices. Hydrol Earth Syst Sci 18:4965–4978. https://doi.org/10.5194/hess-18-4965-2014
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J (2015) The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci Data 2:150066. https://doi.org/10.1038/sdata.2015.66
Gliessman SR (1992) Agroecology in the tropics: achieving a balance between land use and preservation. Environ Manag 16(6):681–689. https://doi.org/10.1007/BF02645658
Haied N, Foufou A, Chaab S, Azlaoui M, Khadri S, Benzahia K, Benzahia I (2017) Drought assessment and monitoring using meteorological indices in a semi-arid region. Energy Proc 119:518–529. https://doi.org/10.1016/j.egypro.2017.07.064
Haroon AM, Zhang J, Yao F (2016) Drought monitoring and performance evaluation of MODIS-based drought severity index (DSI) over Pakistan. Nat Hazards 84(2):1349–1366. https://doi.org/10.1007/s11069-016-2490-y
Harris I, Jones PD, Osborn TJ, Lister DH (2013) Updated high resolution grids of monthly climatic observations: the CRU TS3.10 dataset. Int J Climatol. https://doi.org/10.1002/joc.3711
He X, Pan M, Wei Z, Wood EF, Sheffield J (2020) A Global drought and flood catalogue from 1950 to 2016. Bull Am Meteorol Soc 101(5):E508–E535. https://doi.org/10.1175/BAMS-D-18-0269.1
Huho JM, Kosonei RC (2014) Understanding extreme climatic events for economic development in Kenya. IOSR J Environ Sci Toxicol Food Technol 8(2):14–24
IPCC (2007) Climate Change 2007: climate change impacts, adaptation and vulnerability. Working group II contribution to the intergovernmental panel on climate change fourth assessment report. Summary for policymakers
IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. In: Special report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge and New York
Jagger TH, Elsner JB (2009) Modeling tropical cyclone intensity with quantile regression. Int J Climatol 29:1351–1361. https://doi.org/10.1002/joc.1804
Kalisa W, Igbawua T, Henchiri M, Ali S, Zhang S, Bai Y, Zhang J (2019) Assessment of climate impact on vegetation dynamics over east africa from 1982 to 2015. Sci Rep 9(1):16865. https://doi.org/10.1038/s41598-019-53150-0
Kalisa W, Zhang J, Igbawua T, Ujoh F, Ebohon OJ, Namugize JN, Yao F (2020) Spatio-temporal analysis of drought and return periods over the East African region using Standardized Precipitation Index from 1920 to 2016. Agric Water Manag 237:106195. https://doi.org/10.1016/j.agwat.2020.106195
Karavitis CA, Alexxandris S, Tsesmelis DE, Athanasopoulos G (2011) Application of the standardized precipitation index (SPI) in Greece. Water 3:787–805. https://doi.org/10.3390/w3030787
Kedem B, Fokianos K (2002) Regression models for time series analysis. Wiley-Interscience, Amsterdam
Kendall MG (1975) Rank auto-correlation methods, 4th edn. Charles Griffin, London
Koenker R (2004) Quantile regression for longitudinal data. J Multivar Anal 91:74–89. https://doi.org/10.1016/j.jmva.2004.05.006
Koenker R (2005) Quantile regression. Cambridge University Press, Cambridge
Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46:33–50. https://doi.org/10.2307/1913643
Koenker R, Schorfheide F (1994) Quantile spline models for global temperature change. Clim Change 28:395–404. https://doi.org/10.1007/BF01104081
Lee K, Baek H, Cho C (2013) Analysis of Changes in Extreme Temperature Using Quantile Regression. Asia Pacific J Atmos Sci 49(3):313–323. https://doi.org/10.1007/s13143-013-0030-1
Li Y, Liu Y, Zhu J (2007) Quantile regression in reproducing kernel Hilbert Spaces. J Am Stat As 102(477):255–268. https://doi.org/10.1198/016214506000000979
Liebmann B, Hoerling MP, Funk C, Bladé I, Dole RM, Allured D, Eischeid JK (2014) Understanding recent Eastern Horn of Africa rainfall variability and change. J Clim 27(23):8630–8645. https://doi.org/10.1175/jcli-d-13-00714.1
Liu Q, Zhang G, Ali S, Wang X, Wang G, Pan Z, Zhang J (2019) SPI-based drought simulation and prediction using ARMA-GARCH model. Appl Math Comput 355:96–107. https://doi.org/10.1016/j.scitotenv.2019.134585
Liu Q, Zhang S, Zhang H, Bai Y, Zhang J (2020) Monitoring drought using composite drought indices based on remote sensing. Sci Total Environ 711:134585. https://doi.org/10.1016/j.amc.2019.02.058
Lott FC, Christidis N, Stott PA (2013) Can the 2011 East African drought be attributed to human-induced climate change? Geophys Res Lett 40:1177–1181. https://doi.org/10.1002/grl.50235
Love R (2009) Economic drivers of conflict and cooperation in the Horn of Africa. Chatham house briefing paper. www.chathamhouse.org/publications/papers/view/109208. December, Accessed 3 Jan 2021
Macharia JM, Ngetich FK, Shisanya CA (2020) Comparison of satellite remote sensing derived precipitation estimates and observed data in Kenya. Agric For Meteorol. https://doi.org/10.1016/j.agrformet.2019.107875
Mann HB (1945) Nonparametric tests against trend. Econometrica 13(3):245–259. https://doi.org/10.2307/1907187
Marasinghe D (2014) Quantile regression for climate data. M.Sc dissertation in mathematical sciences, Clemson University, South Carolina, US. https://tigerprints.clemson.edu/all_theses/1909
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. https://doi.org/10.5194/hess-18-3635-2014
Mazvimavi D (2008) Investigating possible changes of extreme annual rainfall in Zimbabwe. Hydrol Earth Syst Sc Discuss 5:1765–1785. https://doi.org/10.5194/hessd-5-1765-2008
McGuinness S, Bennett J (2007) Overeducation in the graduate labour market: a quantile regression approach. Econ Educ Rev 26:521–531
McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Eighth conference on applied climatology. American Meteorology Society, Anaheim, CA, pp 179–184
Meinshausen N (2006) Quantile regression forests. J Mach Learn Res 7:983–999
Muller JCY (2014) Adapting to climate change and addressing drought–learning from the Red Cross Red Crescent experiences in East Africa. Weather Clim Extremes 3:31–36
Nicholson SE (2017) Climate and climatic variability of rainfall over eastern Africa. Rev Geophys 55:590–635. https://doi.org/10.1002/2016RG000544
Nicholson SE, Funk C, Fink AH (2018) Rainfall over the African continent from the 19th through the 21st century. Glob Planet Change 165:114–127. https://doi.org/10.1016/j.gloplacha.2017.12.014
Ogwang BA, Chen H, Li X, Gao C (2014) The influence of topography on East African october to december climate: sensitivity experiments with RegCM4. Adv Meteorol. https://doi.org/10.1155/2014/143917
Rachmawati RN, Sungkawaa I, Rahayua A (2019) Extreme rainfall prediction using bayesian quantile regression in statistical downscaling modeling. In: Proceedings of 4th international conference on computer science and computational intelligence (ICCSCI), 12–13 September 2019
Rowell DP, Booth BBB, Nicholson SE, Good P (2015) Reconciling past and future rainfall trends over East Africa. J Clim 28(24):9768–9788. https://doi.org/10.1175/jcli-d-15-0140-1
Sharma TPP, Zhang J, Koju UA, Zhang S, Bai Y, Suwal MK (2018) Review of flood disaster studies in Nepal: a remote sensing perspective. Int J Disaster Risk Reduct 8:18–27
Shiau J, Lin J (2016) Clustering Quantile regression-based drought trends in Taiwan. Water Resour Manag 30:1053–1069. https://doi.org/10.1007/s11269-015-1210-9
Tan X, Shao D (2016) Precipitation trends and teleconnections identified using quantile regressions over Xinjiang, China. Int. J. Climatol. https://doi.org/10.1002/joc.4794
Takeuchi I, Le QV, Sears T, Smola AJ (2005) Nonparametric quantile regression. J Mach Learn Res Nonparamteric Quant Estim 7:1001–1032
Tareghian R, Rasmussen P (2013) Statistical downscaling of precipitation using quantile regression. J Hydrol 487:122–135. https://doi.org/10.1016/j.jhydrol.2013.02.029
Tierney JE, Ummenhofer CC, deMenocal PB (2015) Past and future rainfall in the Horn of Africa. Sci Adv 1(9):e1500682. https://doi.org/10.1126/sciadv.1500682
Verschuren D, Laird KR, Cumming BF (2000) Rainfall and drought in equatorial East Africa during the past 1100 years. Nature 403:410–414. https://doi.org/10.1038/35000179
Xuan Y, Abbas SA, Song X, Reeve DE (2017) Quantile regression based method for investigating rainfall trends associated with flooding and drought conditions. Eur Water 59:137–143
Yang H, Huntingford C (2018) Drought likelihood for East Africa. Nat Hazards Earth Syst Sci 18:491–497
Yang W, Seager R, Cane MA, Lyon B (2014) The East African long rains in observations and models. J Clim 27(19):7185–7202. https://doi.org/10.1175/JCLI-D-13-00447.1
Acknowledgements
This work was jointly supported by the CAS Strategic Priority Research Program (No. XDA19030402), the Natural Science Foundation of China (No. 41871253, No. 42071425), and the Taishan Scholar Project of Shandong Province (No. TSXZ201712). We thank the anonymous reviewers and the editor for their valuable comments and suggestions, which have greatly helped us to improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kalisa, W., Igbawua, T., Ujoh, F. et al. Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model. Nat Hazards 106, 2047–2076 (2021). https://doi.org/10.1007/s11069-021-04530-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-021-04530-1