Journal of Environmental and Agricultural Sciences (JEAS). Sagero et al., 2018. Volume 14: 1-15
Open Access – Research Article
Investigation of Rainfall Variability over Kenya (1950-2012)
Philip Obaigwa Sagero 1,2,* Chris A. Shisanya1 , George L. Makokha1
1Department of Geography, Kenyatta University, P.O Box 43844-00100, Nairobi, Kenya
2 Kenya Meteorological Departments, P.O. Box 30259-00100, Nairobi, Kenya
Abstract: This study investigates the rainfall variability from 33 stations, for a period of 63 years (1950 – 2012) over Kenya. Coefficient of Variability and non-parametric MannKendall test at 5% level of significance was used to assess the variability and trend, respectively. The Sen’s slope estimator was then used to find the magnitude of the changeover the time period. It was observed that October to December season had a high inter-annual variability, with most stations having greater than 50% of the Coefficient of Variation compared to March to May season, which had only 4 stations with over 50% Coefficient of Variation. Insignificant decreasing trend was noted during the March – May rains, while insignificant increasing trend in October – December rains. On a spatial scale, a negative trend was noted in the northern part of the country and the opposite condition was noted in western regions of the country, around Lake Victoria area. The results indicate a likelihood of negative impact on sectors of the economy that relay on rainfall, because of negative trend in March – May long rains, which accounts for most of rains received annually in Kenya. Although the October – December rains have an increasing trend, they are unreliable due to high inter-annual variability.
Keywords: Rainfall; climate change; Trend analysis; Mann-Kendall; Coefficient of Variation.
*Corresponding author: Philip Obaigwa Sagero, E-mail: firstname.lastname@example.org, email@example.com
Cite this article as: Sagero, P.O., C.A. Shisanya and G.L. Makokha. 2018. Investigation of rainfall variability over Kenya (1950-2012). Journal of Environmental and Agricultural Sciences. 14: 01-15.
Rainfall is highly variable over East Africa at spatial as well as temporal scale (Indeje et al., 2001). On temporal scale, the variability is attributed to a number of factors among them: Indian Ocean Dipole, El Nino Southern Oscillation, anticyclones, jet streams, Madden-Julian Oscillating (Owiti et al., 2008; Black et al., 2003; Ogwang et al., 2015; Berhane and Zaitchik, 2014; Hogan et al., 2015). However, spatial variability is attributed to the orography and abundance of water bodies (Ongoma et al., 2015; Nyakwada, 2009). Due to this high variability and anomalies in rainfall, trend analysis has been of great interest to researchers in the region (Amin et al., 2018; Darand et al., 2017; Funk et al., 2014; IPCC 2014; Li et al., 2018; Sun et al., 2018; Yang et al., 2014).
Climate change induced irregularities in amount and frequency of extreme precipitation events are likely to increase across the globe (IPCC, 2007; Forsythe et al., 2017; He et al., 2017; Knapp et al., 2017; Siam and Eltahir, 2017; Zhang, 2018). Recent extreme events in Kenya including the droughts of 2011 and 2014, although these were spatially localized, however, attributed to climate change (Ongoma et al., 2015; Marthews et al., 2015). Projected increasing global warming is likely to significantly increase seasonal variability in precipitation over East Africa. By 2050 increasing precipitation trend is predicted during October to December (OND), while decreasing trend for March to May (MAM) (Hulme et al., 2001). These spatiotemporal variations will have serious implications leading to future water vulnerabilities for water security at regional as well as global scales (Vorosmarty et al., 2000; Milly et al., 2005; Eakin and Luers. 2006; Ben-Mohamed, 2010; Ahmed et al., 2014; Forsythe et al., 2017; Gao et al., 2017; Xia et al., 2017; Zhang et al., 2017).
In Kenya climate change is costing huge socioeconomic losses (Awuor et al., 2008; KabuboMariara and Karanja, 2007; Mati, 2000; Muthoni et al., 2017; Ochieng et al., 2016). According to Niang et al. (2014), the African continent has warmed by 0.5 °C and the annual temperatures are projected to rise by 3 to 4 °C by the end of 21st century. This is likely to negatively impact on rainfall and consequently affect the development of many African countries including Kenya. Approximately 60% of Kenya’s economy is dependent on rain fed agriculture (Ongoma et al., 2013; Funk et al., 2008; Ngetich et al., 2014). Therefore, understanding of spatiotemporal anomalies in rainfall is mandatory in order to device effective adaptation measures to the changing climate and ensure food and water security (Haile, 2005; Funk et al., 2008; Lobell et al., 2008; Battisti and Naylor. 2009; Challinor et al., 2014; Rehmani et al., 2014; Conway et al., 2015).
Analyzing the rainfall variability fundamental in climate change detection and attribution studies (Meehl et al., 2000). In recent times, several studies on rainfall trends and variability have been carried out at different spatial scales. Studies across East Africa region reveal that there has been high rainfall variability within the region (Hulmeet al., 2001; Shisanya et al., 2011; Moyo et al., 2012; Wagesho et al., 2013; Ngaina and Mutai, 2013; Ongoma et al., 2015; Nicholson, 2016).
Fig. 1. Map of study area showing the position of Kenya and the spatial distribution of rainfall observing stations used in the study.
In Kenya, recent study by Opiyo et al. (2014) focused on rainfall and temperature trends and variability in Turkana County, north western parts of Kenya. As per their findings, MAM rainfall has a decreasing trend while OND had an increasing trend. In a different study, Wakachala et al. (2015) looked into rainfall variability in the Great Rift Valley of Kenya and observed a high variability of rainfall over the semi-arid region of Rift Valley and a decreasing rainfall trend during the MAM season.
This study aims to investigate seasonal and annual rainfall variability over Kenya by using observed station rainfall data, for over 63 years (1950-2012) through non-parametric statistical technique. The findings of this study are intended to provide information on the long term rainfall trend which could indicate the magnitude of climate variability and change at local level. Obtained information will potentially help in formulation of mitigating and adaptation strategies for various spheres of society that are likely to be vulnerable to changing climatic conditions (Preston et al., 2011).
Table 1. List of rainfall datasets
2. Data and Methodology
2.1. Study Area
The area of study is Kenya and is located between longitudes 34°E to 42°E, and latitudes 5°N to 5°S (Fig. 1), with a total area of about 582650 km2 . Kenya has a varied topography; the highest point in the area is Mount Kenya (5199 m above seas level), while the coastal area is near mean sea level. The regional inland lake system includes Lake Victoria (68,400 km2 ), which is a source of moisture and energy (Indeje, 2001). The Indian Ocean lies to the south east boundary of the country. These features significantly influence the local circulation pattern and hence affect the spatial and temporal rainfall variability in Kenya (Asnani and Kinuthia, 1979).
Kenya experiences bimodal rainfall regime with the ‘long rains’ season coming MAM and the ‘short rains’ being reported in OND (Yang et al., 2015; Camberlin and Philippon, 2002; Mutai and Ward, 2000; Ogwang et al., 2015). These rainy seasons coincide with periods of the year when the InterTropical Convergence Zone (ITCZ) is overhead at the equator (Anyah and Semazzi., 2006). The intervening periods are relatively dry. However, there are rainfallenhancing mechanisms in the region, which contribute to substantial rains over the western and coastal parts of East Africa in June-August (JJA). These mechanisms include the migration of warm and moist Congo air mass, and the East Africa low level jet (EALLJ), respectively (Farmer, 1988; Anyamba and Kiangi, 1985; Davies and Vincent, 1985; Ininda, 1995).
Monthly rainfall data was collected from Kenya Meteorological Department (KMD). The stations were selected based on completeness and quality of data set and spatial distribution over the county. It had different length, ranging from 1950 to 2012 (Table 1). While the monthly gridded data was sourced from Climate Research Unit (CRU), spanning from 1901 to 2010, it is gauge-based, gridded at 0.5° by 0.5° resolution. The data is discussed in detail by Harris et al. (2014). Ongoma and Chen, (2017) validated the CRU and GPCC data against the observed data over Kenya, as per their findings, the CRU data replicate the observed data with a high correlation coefficient of 0.97, the same results were also obtained by Ogwang et al. (2015).
Coefficient of variation (CV) was used to assess the variability of seasonal and annual rainfall data for all the 33 stations. Comparative values of CV among different stations were also conducted to determine the spatial differences in variability between different locations of the rainfall station. It was calculated using equation 1;
The magnitude of the trend in the seasonal and annual series was determined using Sen’s estimator (Sen, 1968) and statistical significance of the trend was analyzed using Mann-Kendall (MK) test (Mann, 1945; Kendall, 1975). The Sen’s slope method is nonparametric approach which gives a robust estimation of trend (Yue et al., 2002). It calculates the slope as a change in measurement in respect to change in time. The slope (S) is calculated by equation 2;
For =1,2,3……., N
The S value is obtained by summing the counts of all the data series, xi and xj are the values of the series and i,j the years, being i =j +1
where and are data values at times j and k ( j > k ), respectively. The median of these N values of is Sen’s estimator of slope which is calculated as given in equation 3;
A positive indicates an upward (increasing) trend and a negative (decreasing) trend in the time series.
The MK was used to check the null hypothesis of no trend versus the alternative hypothesis of the existence of increasing or decreasing trend. The statistic (S) is calculated using equation 4;
This statistic represents a number of positive differences minus the number of negative difference for all the data considered. For a large sample (N>10), the test is conducted using a normal distribution with the mean and variance as expressed in equation 5;
The standard normal (Z-statistics) is then computed as shown in equation 6;
If the computed value of Z>Zα/2, the null hypothesis (Ho) is rejected (here α = 0.05) level of significance in two sided test, but if of Z
Fig. 2. Spatial distribution of average annual rainfall over Kenya.
Table 2. MAM and OND seasonal means (mm) for 33 stations and their percentage (%) contribution to the Annual rainfall
3. Results and Discussion
3.1. Rainfall Variability
Spatial rainfall pattern (Fig. 2) and variability (Fig. 3) highlighted prevalence of wetter areas are in the western region bordering Lake Victoria, central highland of Rift Valley and part of coastal region with an annual mean rainfall ranging from 1000 to 2200 mm while, the dry areas confined to the northern part and south eastern region with an average rainfall of below 700 mm (Table 2).
Fig. 3. Monthly rainfall variability for Moyale, Kakamega, Malindi and Eldoret Airport based on 1950 -2010 observed monthly dataset.
Table 3. The Mean, Standard deviation and Coefficient of Variability (CV) for March-April-May (MAM), October-November-December (OND) and Annual rainfall
Fig. 4. Decadal March-May rainfall anomaly relative to base period (1984 – 2014) average rainfall over Kenya.
Table 4. Mann-Kandall (MK) statistics, Sen’s slope and P-value for MAM, OND and Annual for different station (highlighted in red show statistical significant at 5% level)
Most of the stations displayed bimodal rainfall characteristics with exception of some station over western region which displayed a trimodal pattern (Fig. 4). For the bimodal, the rainfall seasons are; March April May, whereby the rains start in March and peak in April and ends in May (Fig. 4a) with exception of coastal stations (Mombasa, Mtwapa, Msabaha and Malindi) (Fig 4c) which had their peaks in May and ends in June. The second rain season begins in October ends in December with a peak in November. Western region has a third rain season; June to August (Fig 4b and d). It is worth nothing that Eldoret Airport station has its main rainfall in June to August season (Fig. 3d). The results are in agreement with the precipitation trends reported by previous studies (Davies and Vincent, 1985; Ininda 1994; Liebmann et al., 2014; Yang et al., 2015; Ongoma and Chen, 2016).
The analysis of the contribution of the two main rainfall season (MAM and OND) to the annual rainfall, shows that MAM contribute between 30 to 50% of the annual rainfall, with the lowest percentage in Nyahururu station which contribute 27% of the total annual rainfall in that station and the highest in Mandera station at 51.8% of the total annual rainfall (Table 2). On the other hand, OND season contribution ranges from as low as 16% in Lamu station to as high as 53.6% in Makindu station. It was also noted that the south eastern lowland (Voi, Makindu, Machakos) much of its annual rainfall is from the short rain season (OND).
Fig. 5. Decadal October-December (OND) rainfall anomaly relative to base period (1984 – 2014) average rainfall over Kenya.
The two main seasons contributed less than 50% of the annual rainfall for Eldoret and Nyahururu station, JJA is the main rainfall season for the two stations. Overly, the two seasons (MAM and OND) are important rainfall season for Kenya and any changes in the trend will likely affect the economy which majorly relies on rain fed agriculture.
OND season displayed a high degree of variability, with 67% of all stations (22) recording CV greater than 50%, while the MAM season had a low variability with only five stations recording CV of more than 50% (Table 4).
On the other hand, the annual rainfall had a low variability with only four stations having CV of greater than 50%. On spatial scale, stations in the arid areas had high CV greater than 50% for both seasons (MAM and OND). The wet areas (Western and Central highlands) had a low CV (Table. 2). CV measures the reliability of rainfall and the low value indicates very reliable rainfall and vise verse. Therefore, short rains with high variability are unreliable as compared to long rains. The high variability in short rains is connected with the large scale factors: El Nino southern Oscillation Index (ENSO) and Indian Ocean Dipole (IOD), which results to more inter-annual variability. The two synoptic systems: ENSO and IOD are known to influence OND rainfall more than as it does to MAM rainfall (Liebmann et al., 2014; Ogallo, 1988; Ogallo, 1989).
3.2. Trend Analysis
Figures 4 and 5 show decadal rainfall anomaly for MAM and OND seasons, respectively, for a base period 1984 – 2014. From the Figure 4, it is evident that the last two decades: 1990s and 2000s, MAM season has experienced the highest reduction in rainfall, relative to the long term mean of 1984 to 2014. The earlier decades of 1911 – 1920, 1961 – 1970s where the wettest (excessive rainfall) decade relative to long term mean. For the OND season, the earlier decades of 1901 to 1950, has been the driest compared to the recent decade of 1991 to 2010, which have shown a significant increase in rainfall during this season (Fig. 5).The reduction in rainfall during MAM season and significant increase in OND season concur with the observation made in recent studies by Liebmann et al. (2014) and Yang et al. (2014).
Fig. 6. Spatial distribution of MAM (A), OND (B) and Annual (C). Mann-Kendall (MK) at 5% level of significance.
From the trend analysis, a declining trend was noted in MAM season and increasing trend in OND season for most of the station analyzed (Table 4, Fig. 6). However, the trends are statistically insignificant at 5% significance level for both seasons. But some stations (Marsabit and Moyale) displayed statistically significance decreasing trend for MAM season, while Kitale and Nakuru stations had a significant increasing trend for OND season (Fig. 3). Nanyuki displayed a statistically significant decreasing trend for OND season.
Insignificant trends were noted in all station for annual rainfall, with exception of Moyale which had significantly decreasing trend of 3.52 mm/year (Table 4). The increase in OND rainfall has been attributed to the warming of the western Indian Ocean (Liebmann et al., 2014). Yang et al. (2014) observed that the decrease in MAM rainfall over East Africa, and attributed this to natural decadal variability rather than anthropogenic influence.
The trends and variability of rainfall data were analyzed for different stations for a period ranging from 1950 – 2012. High variability was noted during the ‘short rain’ season as compared to ‘long rain’ season. On spatial scale, the stations in the northern region of the study area showed higher CV (>50%) than other station in both seasons hence, a high annual rainfall variability. It was also observed that in recent years, March-May rainfall had decreased compared to long term mean of 1984 – 2014, while that of OND increased during the same period. This was confirmed by the trend analysis of the observed station data; it was found out that MAM rainfall had an insignificant decrease at 5% significant level, while that of OND had an insignificant increase in rainfall at the same significance level. The annual rainfall showed an insignificant decreasing and increasing trend for different stations, with only one station Moyale showing a statistically significant decrease in annual rainfall (3.52 mm/ year). The results give a basis for further analysis on the impact of this reduction/increase in the different sectors of the economy that are rainfall dependent.
List of abbreviations: CV: Coefficient of Variation; OND: October-November- December; MAM: March-April-May.
Acknowledgements: This work was supported by Australian Government through the Department of Environment and Energy for funding the PhD study P.O. Sagero, from which this paper is derived. The authors also express their appreciation to Kenya Meteorological Department for providing the monthly station data and Climate Research Unit (CRU), Kenya for the Gridded data used in the study.
Conflict of Interest: The authors have not declared any conflict of interest.
Author Contribution: POS analyzed the data and wrote the paper, and all authors discussed the results and implications and commented on the manuscript at all stages. All authors contributed extensively to the work presented in this paper.
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