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.
Drought is a water-related, most complex natural disaster which affects a wide range of environmental factors and activities related to agriculture, vegetation, human, wild life and local economies. More specific drought definitions are used around the world according to lack of rain over various time periods, or measured impacts such as reservoir levels or crop losses [
Africa is prone to a variety of hazards especially the occurrence of Hydro-Meteorological hazards (drought and floods). This has increased of recent with devastating impacts and has become more frequent in the 21st century in sub-Saharan Africa (SSA) where droughts account for over 80% of the affected population [
Kenya being the study area has had drought episodes over the past five decades. The recently documented droughts occurred during 2008/2009 and 2010/2011, hitting the arid and semi-arid regions of the country hard [
The main purpose of this study was to characterize drought in Kenya from 1987 to 2016 using climatic data and satellite images. The study concentrated on the last three decades due to availability of remote sensing data and the interest in the recent developments of drought events in the study area. In this study Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI) indices were used. NDVI Anomaly was applied to characterize agricultural drought, (SPEI) to characterize meteorological drought whereas maize production data was used to show the effects of climate change on socio economic activities in Kenya from the years 1987 to 2016.
Several researches have been conducted with similar environmental topics using satellite and climate data and showed successful and satisfactory results. [
Most of the studies carried out 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 recognized. SPI is actually a precipitation-based drought index. This study has tried to fill the gap by using SPEI which includes precipitation, a temperature component and evapotranspiration in its computation. This has allowed the index to account for the effect of temperature on drought through a basic water balance calculation. This in effect has made the research more detailed and a better result.
The Republic of Kenya, lies between 5˚7'N and 4˚39'S longitude and is part of the Greater Horn of Africa along the Indian Ocean [
The country has climatic and ecological extremes with altitude varying from sea level to over 5000 m in the highlands. The mean annual rainfall ranges from less than 250 mm in semi-arid and arid areas to greater than 2000 mm in high potential areas. Soils vary from the coral types on the coast to alluvial, swampy, and black cotton soils along river valleys and plains. The Kenyan highlands have fertile volcanic soils whereas in the semi-arid regions are shallow and infertile. Farming is the primary livelihood of more than 75% of the population, conducted either on subsistence plots in marginal farming areas or on large plantations in the more arable areas [
and poorly distributed rainfall, these areas are used for pastoral farming [
The first step was to identify the variables needed for Spatio-temporal drought characterization. The variables included climate data (Temperature and Precipitation data), Remote sensing satellite data (Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectro radiometer images) and Socio economic data of which in this case was maize yield data. These variables were obtained from secondary sources and covered the period from 1986 to 2016. The Remote sensing satellite datasets were from The United States National Aeronautical and Space Administration and were on monthly basis over the study period. The monthly precipitation data was downloaded from (CHIRPS) Climate Hazards Group Infrared Precipitation station, Temperature data on monthly basis was downloaded from Climate Research unit (CRU) and crop production data (maize) in statistical form was acquired from the ministry of Agriculture, livestock and fisheries (Kenya).
The methodology approached in this study is shown in
The climate data used in this study was historical data series of monthly precipitation (P), and monthly temperature which included both maximum and minimum temperature. Precipitation data was from CHIRPS (Climate Hazards
S/No. | TYPE OF DATA | SOURCE | DURATION |
---|---|---|---|
1 | NOAA AVHRR(NDVI) | USGS | 1987-2000 |
3 | MODIS 13A2 (NDVI) | USGS | 2001-2016 |
4 | Topographic map of Kenya | Survey of Kenya | |
5 | Temperature | Climate Research Unit (CRU) | 1987-2016 |
6 | Rainfall | CHIRPS precipitation data | 1987-2016 |
7 | Crop yield data (Maize production data) | Ministry of Agriculture, Livestock and Fisheries | 1987-2016 |
Group Infrared Precipitation) for the period 1987 to 2016. Temperature data was downloaded from CRU (Climate Research Unit), both the data was in raster format, the area of interest Kenya was clipped and then converted to text files format so as to get the average monthly rainfall, the minimum, maximum and average temperature. Trend line was used to understand both the temperature and rainfall trends.The rainfall and temperature data was used to calculate SPEI (Standardized Precipitation Evapotranspiration Index). The procedure to calculate the Index was similar to that used for the Standardized Precipitation Index (SPI), but it included the role of temperature as per [
Calculation of the Standardized Precipitation-Evapotranspiration Index (SPEI) was done using a time series of the climatic water balance (precipitation minus potential evapotranspiration) so as to get the SPEI values. In this study the equation used was Thornthwaite which computes the monthly potential evapotranspiration (PE) according to [
The Normalized Difference Vegetation Index (NDVI) was derived from NOAA AVHRR (National Oceanic and Atmospheric Agency Advanced Very High Resolution Radiometer) from 1987 to 2000 and the successor the MODIS level-3 product, MOD13A2 respectively from 2001 to 2016. The data was downloaded from Earth Explorer USGS site. Only the NDVI band was extracted. The first
SPEI value | Drought severity class |
---|---|
2.0+ | Extremely wet |
1.5 to 1.99 | Very wet |
1.0 to1.49 | Moderately wet |
0.99 to − 0.99 | Normal |
−1.0 to −1.49 | moderately dry |
− 1.5 to −1.99 | Very dry |
−2.0+ | Extremely dry |
step was to change the NDVI product from Sinusoidal projection, which is not supported in ArcGIS into usable spatial information. All the images were projected into Geographic system with WGS84 datum. NOAA AVHRR data was on monthly basis and image was clipped to area of interest. MODIS NDVI, covered four images which were mosaicked together, the area of interest Kenya was then clipped and then classified using ERDAS IMAGINE and Arc Map 10.2.software. To get the NDVI values the images were converted to text files so as to get the average monthly values which were later used for further analysis.
NDVI can be used as an index to assess vegetation condition through analysis of NDVI anomaly [
The socio economic data in this study was maize production in tonnes. It was collected from the ministry of Agriculture, Livestock and Fisheries headquarters. The data was organized at different regional levels. The production was computed to see the yield trend over the last 30 years (1987 to 2016). To quantify the impact of drought on production of maize crops in Kenya, the totals were found and were correlated with annual SPEI and NDVI and NDVI Anomaly to assess the impacts of climate change on crops.
In this study the Standardized Precipitation and Evapotranspiration Index (SPEI) were used to assess the degree of drought in terms of severity, duration and magnitude using observed climate data. In addition, Satellite image based drought indices was used to detect agricultural drought condition and to show its spatial extent. Correlation analysis was done to understand the response of climate and satellite based drought monitoring indices result on crop yield.
Harmonization of ResultsHarmonization as per [
Both the NDVI, NDVI Anomaly (Agricultural) drought values, Meteorological (Climatic) data and socio economic data (crop production) which included maize were correlated to find the relationship between Agricultural, meteorological and socio economic data in relation to drought magnitude
The results in
The Temperature and Rainfall monthly values were used to compute the SPEI values for the whole country from years 1987 to 2016 on monthly basis. The values were then used to plot SPEI trend graphs in which drought years and months were identified as shown in Figures 4-6 respectively. In this study one
month and three months scales time series were used in the analysis. The one and three month SPEI values reflected short- and medium-term moisture conditions and provided a seasonal estimation of precipitation deficiency. A drought is noted whenever the SPEI value reached a value of -1 and continued until the SPEI value became positive again. The drought was categorized as per [
NDVI Anomaly values were used to characterize agricultural droughts using the
drought categories in
The results in
NDVI anomaly (%) | Drought severity class |
---|---|
Above 0 | No drought |
0 to −10 | Slight drought |
−11 to −25 | Moderate drought |
26 to −50 | Severe drought |
Below −50 | Very Severe drought |
identified by correlating NDVI Anomaly with SPEI one and three months lag respectively. The arrows show the drought years and months, the vertical dotted lines show the duration and the characterization of the event, while the horizontal dotted line show the severity (below it being commencement of drought). The 1987 drought was characterized as short Severe drought year for it covered five months with the severely affected month being October.
1998 to 2001 was characterized as prolonged moderate drought years, for all the consecutive four years had dry spells. In 1998 three months were affected, the month of April being severely affected and characterized as a very dry month. The year 2000 had four dry months the severely affected month being November and the year 2001 had two months that were affected, the severely affected months being November and characterized as very dry.
2005 and 2006 were characterized as a mild drought duration. 2005 had five drought months with the severely affected months being December characterized as a very dry month. 2006 had two dry months, the severely affected month being June and was characterized as a very dry month.
2008 to 2011 were characterized as prolonged severe drought duration. 2008 had four dry months, the severely affected month was October having been characterized as a very dry month. 2009 had 7 dry months, the severely affected month being June (Extremely dry month). 2010 had four dry months, the severely affected month being June (very dry month). 2011 had two dry months, June was the severely affected (extremely dry month).
2015 was a short mild drought year with five dry months. Severely affected month was August. Results show, either short rains or long rains failed or both in the dry years over the study period.
The results are in agreement with some of the results of [
The results in
NDVI and SPEI one month lag relate well as shown in
lag variable can be explained by NDVI. The relationship between SPEI three months lag and NDVI as shown
The results in
NDVI has a strong relationship with Maize yield.
The drought years identified in this study were 1987, 1998, 2000, 2001, 2005, 2006, 2008, 2009, 2010, 2011 and 2015 with months being characterized as being extremely dry, very dry and moderately dry for meteorological drought using SPEI while for Agricultural drought they were characterized as slight, moderate and severe drought using NDVI Anomaly. The correlation analysis of SPEI and NDVI showed that SPEI drought detection has one month lag compared to NDVI. NDVI lag those of SPEI. SPEI correlates well with NDVI Anomaly.
1987 was a short and severe drought year with a total magnitude of −7.2, the months affected were five with the severely affected month being October with −1.9 severity.
1998 to 2001 was characterized as prolonged moderate drought event. It covered three months in 1998 with a magnitude of −3.8; the month of April was characterized as a very dry month with a severity of −1.6. The year 2000 had four months with a magnitude of −5.7, the severely affected month was November with −1.8 and the year 2001 had two months that were affected with a magnitude of −2.8, the severely affected month being November with the severity of −1.6.
2005 and 2006 were characterized as mild drought event. 2005 had five drought months with overall magnitude of −7.3, the severely affected month was December with severity of −1.8 and characterized as a very dry month. 2006 had 2 month of drought with a magnitude −4.2, the severely affected month was June having severity of −1.7 and was characterized as a very dry month.
2008 to 2011 was characterized as a prolonged severe drought year event. 2008 had four dry months with a magnitude of −6.2, the severely affected month was October with severity of −1.8 (very dry month). 2009 had 7 dry months with magnitude of −11.5, the severely affected month was June with severity of −2.0 (Extremely dry month). 2010 had four dry months with a magnitude of −5.6; the severely affected month was June with a severity of −1.8. 2011 had two dry months with a magnitude of −4.4, January was the severely affected month with a magnitude of −3.0 and characterized as an extremely dry month.
2015 was a short mild dry year event with five dry months with an overall magnitude of −6.3. Severely affected month was August with a severity of −1.7. From the results it can be concluded that either short rains or long rains failed or both.
Drought trends in Kenya do not have a fixed pattern and tend to fluctuate from time to time, this is shown from the rainfall, temperature and SPEI graphs. The rainfall, temperature and NDVI maps of the dry years and months show that there is non-uniformity in dryness where some areas along the coast, western, Nyanza and Rift valley tend to be wet whereas the ASAL (Arid and Semi-Arid Lands) that forms about 80% of the total Kenya’s land cover are always dry. This has led to effects of non-uniformity in drought detection.
Agricultural drought similarly does not have a fixed trend. This is seen through NDVI and maize production graphs. However maize production fails in different regions due to non-uniform drought occurrence, this is because regions fall under different hydrological basins which experience different climatic conditions at different times.
The long term historical records of satellite imagery and climatic data have become essential tools in calculating drought severity levels and to determine drought risk prone areas. Similarly this study has achieved a great milestone in the Agriculture sector as mitigation measures can be put in place long before the occurrence of drought. This would reduce loss of livestock and human life as a result of loss of water and food.
Due to availability of satellite imageries agriculture has benefited due to constant drought assessment levels. Government agencies and County based Departments can create drought mitigation plans based on drought monitoring data models.
Drought modelling using meteorological index (SPEI) was not done in this study. This is because meteorological drought index lack spatial extent as such requires many points which are interpolated to model drought. In this study data used was for the whole country which was only a one point data (Kenya). For future research, I recommend for further studies that would be done using the same technology according to hydrological basins.
Future studies should also use same methods to predict future drought in Kenya.
I express my heartfelt thanks to my supervisors, Dr. Arthur W. Sichangi and Dr. Godfrey O. Makokha for their guidance during the entire project phase, my family for the financial support and all the organizations who provided data for this research.
Mutsotso, R.B., Sichangi, A.W. and Makokha, G.O. (2018) Spatio-Temporal Drought Characterization in Kenya from 1987 to 2016. Advances in Remote Sensing, 7, 125-143. https://doi.org/10.4236/ars.2018.72009