Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model

Wilson Kalisa, Tertsea Igbawua, Fanan Ujoh, Igbalumun S. Aondoakaa, Jean Nepomuscene Namugize and Jiahua Zhang ()
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Wilson Kalisa: Qingdao University
Tertsea Igbawua: Federal University of Agriculture
Fanan Ujoh: London South Bank University
Igbalumun S. Aondoakaa: Federal University of Agriculture
Jean Nepomuscene Namugize: College of Kigali
Jiahua Zhang: Qingdao University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 106, issue 3, No 15, 2047-2076

Abstract: 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.

Keywords: Quantile regression; Precipitation; Drought; Rainy season; Climate change; East Africa; Ecological zones (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-021-04530-1

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