Spatio-Temporal Drought Characterization in Kenya from 1987 to 2016

Abstract

Kenya is a drought, famine and hunger prone country, with considerable impact on agriculture, human health and livestock due to its eco-climatic conditions. It contains only a few regions of high and regular rainfall where arid and semi-arid lands cover 80% of the territory, therefore periodical droughts are part of the climate system. Some drought studies undertaken in Kenya used Standardized Precipitation Index (SPI) which could not fully account for drought severity status as the role of temperature increase on drought conditions was not taken into account. This study has tried to fill the gap by using Standardized Precipitation Evapotranspiration Index (SPEI), which includes precipitation, a temperature component and evapotranspiration in its computations. SPEI and the Normalized Difference Vegetation Index Anomaly (NDVI) were applied to characterize drought in Kenya from 1987 to 2016, investigate the drought severity and duration in the same period, assess drought trends together with mapping of spatial distribution of drought in identified months, assessment of Agricultural, meteorological and socio-economic activities. Correlation analysis was done to understand the response of climate and satellite based drought monitoring indices results and the crop yield data. The results and analysis obtained from the study showed that the years 1987, 1998, 2000, 2001, 2005, 2006, 2008, 2009, 2010, 2011 and 2015 were considered as drought years based on their SPEI and NDVI anomaly results. They were classified as extremely dry, very dry and moderately dry for meteorological drought and slight, moderate, severe and very severe for Agricultural drought. SPEI results can be rated as being superior as the element of temperature variation is taken into consideration.

Characterization of Historical Trends of Droughts and Pluvial Scenarios over Kenya, East Africa

“…The dry/wet years of all the grid cells based on the frequency, duration, severity and intensity concurred with similar years as noted over the whole area average. This agrees with previous studies conducted over various parts of the study domain or based on different indices[43, 89, 90]. The driest month for both for SPEI-3 was experienced in pixel 26 and 63 in high altitude region and low-lying arid land, respectively.…”

“…An in-depth analysis at each grid cells was conducted based on frequency, length, severity and magnitude of occurrences of some significant incidences. [42][43][44]91]. The cause of this variation from low lying region to humid vegetative areas could be attributed to surface and atmospheric interactions and dynamics.…”

“…Kenya has been witnessing increase in severe and frequent famine events in the recent decades, exacerbated by the recent decline in March-May (MAM) seasonal rainfall [37][38][39][40]. Numerous studies have been conducted to ascertain drought variabilities, trends and the respective impacts on agriculture, economy, water resources and environment over the study region [41][42][43][44]. These researchers have employed various drought indices recommended by the World Meteorology Organization [11].…”

“…For instance, [43] analyzed correlation between the two indices. On the other hand, [42] used Standardized Precipitation Index (SPI), to characterize seasonal and annual droughts in Laikipia west sub-county from 1984 to 2014, focusing on drought events occurring during the two rain seasons in Kenya.…”

“…However, it is a challenge to find a universally applicable criterion for the whole country due to the complexity of landforms and meteorological conditions. So far, very few studies [42,43] have employed a drought monitoring algorithm that is grounded on current rainfall and temperature anomaly, which can be used as a predictor of the severity and the duration of possible occurrence of extreme climatic events.…”