Analysis of the monthly and annual rainfall climate in a semi-arid environment, Kenya

Journal ofArid Environments (1991) 20: 257-275

Analysis of the monthly and annual rainfall climate in a semi-arid environment, Kenya R. A. Sutherland*:j:, R. B. Bryant and D. Oostwoud Wijendest * Department ofGeography, University of Hawaii at Manoa, 445 Porteus Hall, 2424 Maile Way, Honolulu, Hawaii 96822, U.S.A. and t Department of Geography, University of Toronto, Scarborough Campus, 1265 Military Trail, Scarborough, Ontario MIC lA4, Canada (Received 9 December 1989, accepted 30 May 1990) Evidence from several African countries has indicated that there has been a trend towards decreasing rainfall in dryland areas. Analysis of rainfall data from the semi-arid portion of the Baringo District, Kenya, has indicated no such trend since 1958. However, the number of raindays has decreased significantly for the months of March, July and October. Rainfall between meteorological stations separated by 20 km was not significantlydifferent at the monthly and yearly timescales. Published data indicate that there is no relationship for rainfall between these two meteorological stations at the daily time-scale. This is primarily a function of the localised nature of convective rainfall in the area. Various rainfall indices point to 1984 as the most severe drought on record in this region. Significant stock reductions resulted from the failure of the long rains (AprilMay), and greatly reduced rainfall in July-August. Land managers in the area must accept that drought is an aperiodic, recurring characteristic of a dryland rainfall regime. Land management schemes for water harvesting, agriculture and forage production must be designed to cope with drought periods as part of the supply characteristics of the area.

Introduction

Kenya, along with other African countries, is faced with the problem of supplying a useable water supply to rural populations in ecologically marginal dryland areas. In Kenya, dryland areas cover 80% of the country, with 20% of the population and well over 50% of the livestock (Gwynne, 1981; Bernard, 1985). Presently, Kenya is faced with the highest annual population growth rate in the world (=4%). This, combined with limited land availability in the agriculturally productive highlands, has resulted in immigration to marginal areas. Thus, in spite of the ecological limitations, the drylands of Kenya are now experiencing high immigration rates, and some parts have had population growth rates two to 10 times the country's average. Degradation in the Baringo District, particularly the Njemps Flats (Fig. 1), is not a new problem since it has been recognised since the 1930s (Anderson, 1984). However, this problem has taken on new meaning with the significant influx of people into this marginal dryland area. This has resulted in a dramatic increase in the number of development projects for soil and water conservation in the area to the west of Lake Baringo. These :j:Author to whom correspondence should be addressed.

0140-1963/911030257

+ 19 $03'00/0

© 1991 Academic Press

Limited

R. A. SUTHERLAND ET AL.

258

various projects have been summarised by Fox (1988). The major aim of the water conservation components of these projects has been to wisely manage the scarce water resource for human consumption, agriculture and especially forage production. Water harvesting has been an important component of the Baringo Pilot Semi-arid Area Project (BPSAAP, 1984; Critchley, 1984), and the Baringo Fuel and Fodder Project (BFFP, M. Roberts, pers. comm., 1986). Water harvesting and moisture conservation are also important components of the Baringo Land Reclamation Project (BLRP, Bryan & Sutherland, 1989; Wairagu, 1989). Within dryland areas, precipitation is the most important hydrological variable (Rodier, 1985). Therefore, it is necessary to understand the spatial and temporal variability of this 'resource' in order to develop effective management strategies. An analysis of the rainfall regime in the semi-arid Baringo area is becoming increasingly important since there have been dramatic stock and human population increases, combined with the increased expansion of rain-fed agriculture. The critical importance of rainfall to the area was noted in the drought year of 1984 when herd reductions of 60 to 80% occurred (Homewood & Lewis, 1987). However, analysis of rainfall regimes in drylands is complicated by scarcity of raingauges, poor data quality, and the problem of obtaining recent information (Rodier, 1985; Dennett, 1987). Long-term annual rainfall series in African dryland countries are limited, and in dryland Kenya rainfall data prior to 1930 is almost non-existent (Ogallo, 1983). Some authorities would argue that long-term records are not needed, since it is more appropriate to use the last 20 or 30 years of annual rainfall data to plan effective development and rehabilitation strategies (e.g. Dennett et al., 1985). The present study analyses the monthly and annual rainfall regime of the area to the west of Lake Baringo, Kenya. The major objectives were to statistically analyse the temporal variation in monthly and annual rainfall in the area, to identify drought periods using various indices, and to document variations in rainfall between two stations separated by less than 20 km.

The study area There were only two meteorological stations, Perkerra and Snake Farm, in the semi-arid area to the west of Lake Baringo, which is in the Gregory (Kenya) Rift Valley (Fig. 1). This is one of the most severely degraded semi-arid areas in Kenya. Woody vegetation covers 5 to 20% of the area to the west of Lake Baringo. The dominant woody vegetation species are Acaciamellifera, A. tortilis, A. senegal and Boscia coriaceae. The herbaceous cover ranges from 0 to 1%, and the dominant species are Abutilon spp., Berlenia spp. and Aristida spp. (Wahome, 1984). Monthly average temperatures range from 24°C in August to 26°C in March, with monthly mean maximum temperatures ranging from 31 to 34°C. The Lake SUDAN

ETHIOPIA

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5 !

L. Turkana Tugen

Hills

0030'N • Kabornet

Figure 1. Location map of the study area, with the two meteorological stations noted, i.e. Perkerra Research Station and Snake Farm. • , Study area; -, rainfall stations.

259

ANALYSIS OF RAINFALL CLIMATE 28,....------------,170 27

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Figure 2. Monthly variation in radiation receipt (.) and potential evapotranspiration (PET) (0), Perkerra. Baringo area receives high solar radiation inputs for most of the year (Fig. 2), and high rates of potential evapotranspiration (PET), as calculated using the Penman method (Fig. 2). Monthly PET values are generally 2: 120 mm. The rainfall climatology of Kenya, and East Africa in general, is governed by rainbearing systems associated with the passage of the Intertropical Convergence Zone (ITCZ). The rainfall climatology of Kenya has been described in detail by Barring (1988) and is briefly summarised below. The rainfall regime of Kenya is dominated by two mainly dry 'monsoon' seasons, and two rainy seasons associated with the movement of the ITCZ. The most southerly position of the ITCZ occurs in January, resulting in the establishment of the north-east trades. During December to February the trades bring comparatively dry air of mainly continental origin, thus bringing stable conditions and low rainfall. The ITCZ moves northward and is centred over the Kenyan Highlands in April. From March to June the north-east flow is weakened and a low-pressure system over Lake Victoria gives rise to convergent easterly flow bringing moist air from the Indian Ocean (BPSAAP, 1984). This produces the first rains of the year, known in East Africa as the 'long rains'. The most northerly position of the ITCZ occurs in July, over the Sudan. From June to September the south-east trade winds bring maritime air from the Indian Ocean. Despite the maritime origin of the air, this is a dry season for large parts of the country. From September through to November, the ITCZ moves southward, resulting in easterly flow, bringing moisture in October and November. The second rainy season in Kenya is known as the 'short rains'. This pattern describes a very simplified double wet season equatorial model for East Africa and Kenya (Davies et al., 1985). However, in reality the local picture is more complex because of the influence of the north-south trending mountain ranges, and the Rift Valley. Several locations in west-eentral Kenya have one, two or three rainy seasons (Davies et al., 1985). Rainfall data and statistical analysis Monthly and annual rainfall data were obtained from the Snake Farm for the period 196686, and from the Perkerra Research Station for the period 1958-86. The Kenya Meteorological Department operates tilting siphon recording raingauges at their stations. The raingauge has its aperture 58 em above-ground, with an orifice of 12·7 cm (Barring, 1988). A strip-chart recorder is connected to the recording raingauge, and the rainfall trace

260

R. A. SUTHERLAND ET AL.

has a time resolution of 10 min, and a depth increment resolution of 1 mm (Barring, 1988). Supplemental rainfall data were available for 1985 and 1986 from six manual raingauges located throughout the Njemps Flats (M. Roberts, pers. comm., 1987). Traditional (classical) summary statistics included in this paper are the arithmetic mean, standard error of the mean, coefficient of variation, and the 95% confidence band of the mean. Normality was tested using two indices, the skewness coefficient with an a = 0'05, and the Kolmogorov-Smirnov test with an a = 0·15 (Lister, 1982; Zar, 1984). Robust statistics such as the trimean, the 20% trimmed mean, the median, the 95% confidence interval of the median, and the l H-spread were included in statistical summaries when data were not normally distributed, or when the sample size was small (i.e. <20). These were included since the 'classical' summary statistics are poor estimators oflocation or scale when the distribution is not normal, or when outliers are present. This was documented in the Princeton study (Andrews et al., 1972) where six statisticians concluded that, for more than 60 estimators of central tendency for n ::; 40, the arithmetic mean was generally the worst estimator. Comparisons of annual rainfall between Snake Farm and Perkerra for the period 196686 used both parametric and non-parametric statistical tests. The non-parametric MannWhitney U test was used with an a = 0'05 to test for differences between stations. Also, the parametric Fisher's protected least significance difference (PLSD) test was used with an a = 0'05. Rainfall variability indices were calculated for annual data using two different approaches; the first was the simple climate departure index (z-score or standard score), which was calculated from SCDI (Z)

=X -

°0

fA-

(1)

where SCDI (Z) represents the simple climate departure index (standard score), X a given annual value, fA- the arithmetic mean of the distribution, and 0 the standard deviation of the distribution. For a normal probability distribution, approximately all values are within ± three standard deviations of the mean. Similarly, there is a 68% probability that a value will lie within the interval fA- ± 1 0, and 95% probability that the value is between fA- ± 2 a. The second index is the van Rooy (1965) rainfall anomaly index (RAI), which has been modified to account for non-normality, and is calculated as follows for positive anomalies MRAI

= +1

[ (RF - TM RP ) ] (TM H - TM R P )

(2)

and for negative anomalies (3)

where MRAI represents the modified annual rainfall anomaly index, RF the actual rainfall for a given year, TM RP the trimean rainfall for the total length of record, TMH the trimean of the 10 highest values of rainfall on record, and TM L the trimean of the 10 lowest values of rainfall on record. The RAI of van Rooy has been shown to be a very effective index for detecting drought periods, and it compared favourably with the more complex indices of Palmer and Bholme-Mooley (Oladipo, 1985). The original RAI involved arbitrary coefficients of ±3 rather than ± 1, and it used the arithmetic mean. The trimean was substituted since the 10 highest and lowest values were not normally distributed. A monthly normalised deviation index was determined using an approach similar to Rao et al. (1986), and was calculated as follows

ANALYSIS OF RAINFALL CLIMATE

MND[

= (RF M

-

RFL T ) . 100

RFL T

261

(4)

where MND[ represents th~onthly normalised deviation index for the period of April-Mayor July-August, RFM the mean ~il-May or July-August rainfall for each individual year of record (1958-86), and RFL T the long-term mean April-Mayor July-August rainfall. The two periods, April-May and July-August, were chosen since these are the dominant peaks in the bimodal rainfall distribution. Thus, a failure of one or both of these peaks can have significant consequences for forage production and stock production. Time series analysis was used to describe the temporal variation of annual or monthly rainfall. This technique has been widely used in hydrology (Shaw, 1983) and in meteorology (Rodhe & Virji, 1976). An important guide to the properties of a time series is provided by a series of quantities called sample autocorrelation coefficients which measure the correlation between observations at different distances apart (Chatfield, 1984). The autocorrelation function (rL) was calculated as follows (5) with n-L CL

=n ~

L

I

(Xi - X)(Xi-L - x)

(6)

i=l

I

n

S2

=n ~

1

(Xi -

xt

(7)

i=l

where CL is the sample autocovariance, n the number of sample locations, L the number of lags, xi the property value at a given location, x the sample mean, and; the sample variance. Autocorrelation analysis provides a way of determining the average extent of mutual dependence of adjacent observations (Lanyon & Hall, 1981). Therefore, if L = 2, Xl is correlated with X3' X2 with X4' etc.; if L = 5, Xl is correlated with xs, X2 with X6' etc. The value of rt. varies from + 1 (positive serial correlation) to -1 (negative serial correlation). To interpret a set of autocorrelations, an autocorrelogram is established in which tt. is plotted against L. At L = 0, rt. = + 1 because the members of each pair are identical, and a perfect correlation is achieved (Campbell, 1979). The significance of the autocorrelation function was tested at an a = 0·05. Autocorrelation analysis was used on only normally distributed data, or when n 2: 30, since the Central Limit Theorem can be invoked and autocorrelation analysis can be used. To check for linear trends, the Pearson product moment correlation coefficient was used with an a = 0'05. Grouped monthly data were deseasona1ised using seasonal differencing to remove the frequency of one cycle per year (cf. Chatfield, 1984: pp. 248-250). Autocorrelation was not appropriate for 9 of the 12 individual months since the rainfall distribution was not normally distributed. In cases of non-normality, the non-parametric rank version of von Neumann's test was used to detect lag one trends, cycles or autocorrelation (Bartels, 1982; Gilbert, 1987).

Results

Annual rainfall comparison: Perkerra-Snake Farm stations Annual rainfall data were compared for a period of21 years (1966-86) for the two stations (Fig. 3). Both the Mann-Whitney U test and Fisher's PLSD indicated no statistically

R. A. SUTHERLAND ET AL.

262 1200

1000

E E

800

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600

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"0 ::J

c:

c: <{

400

200

a

1955

Figure 3. Annualrainfall time series for Perkerra (--e-) and SnakeFarm (-0--) stations. significant difference in annual rainfall between the two stations. In addition, a significant correlation was established at an a = 0'05 for annual rainfall between stations (i.e. r = +0'80). Cumulative rainfall at Perkerra for the 2l-year period was approximately 7% lower than at Snake Farm (Fig. 4). The mean annual rainfall and the 95% confidence band was 632 ± 77 mm and 671 ± 86 mm for the Perkerra and Snake Farm stations, respectively (Table 1). Since differences between stations were not significant, the complete rainfall record for Perkerra (1958-86) was assumed to be representative of the semi-arid Njemps Flats (700 knr'). To test this assumption, annual rainfall at Perkerra, for the years 1985 and 1986, was compared with data from six supplemental stations, plus data from Snake Farm. The median rainfall value and 95% confidence band for the seven sites in 1985 was 793 ± 127 mm, and in 1986 the values were 583 ± 81 mm. Rainfall data from Perkerra in 1985 (715 mm) and 1986(511 mm) fall within the 95% confidence intervals of the median for the seven stations. Therefore, these limited data indicate that the annual rainfall at Perkerra did not differ significantly from the other stations within the Njemps Flats. Thus, this information when combined with the Perkerra-Snake Farm comparisons for 21

Figure 4. Relationship between cumulative rainfall at Perkerraand SnakeFarm stations, 1966-86.

263

ANALYSIS OF RAINFALL CLIMATE

years indicates that annual rainfall recorded at Perkerra is representative of other areas within the Njemps Flats. The mean rainfall value for the area is approximately 636 ± 66 rom year -1 (Table 1), and when this is related to PET the moisture deficit is about 1100 to 1200 mm year-I. These values correspond closely with the soil moisture deficit of H20 mm for Marigat (Fig. 1) calculated using a water balance approach, and a soil water storage capacity of 100 mm (Obasi & Kiangi, 1977).

Annual rainfall variability indices There are several approaches to defining droughts; the present paper emphasises rainfall as the critical parameter. This is supported by research in other dryland areas of Africa by Berndtsson (1987), Ojo (1987) and Hulme (1989).

Cumulative deviation from themean

A measure of annual variability and long-term trends may be achieved by plotting the cumulative departure from the arithmetic mean for the period of record (Berndtsson, 1987). This procedure is appropriate only when data are normally distributed. Normality tests for annual Perkerra rainfall data indicate that the distribution is normal (Table 1). Data indicate that during the 7-year period 1977-83, 6 of the years had rainfall values above the mean (Fig. 5). This period was followed by the lowest annual rainfall on record (1984,265 rom), which was approximately 370 mm below the mean annual value for the area. This was considered by many local residents as the worst 'drought' in memory, and this was reflected in significant decreases in stock population (Homewood & Lewis, 1987). This perception was also partly due to their adjustment to above-average rainfall for the previous 6 out of7 years. The 1984 drought was also experienced in other areas of Africa, such as the Sahel (Dennett et al., 1985), Sudan (Walsh et al., 1988; Hulme, 1989) and in West Africa (Ojo, 1987).

Simpleclimate departure index

Variations ofthe SeDI (a-score) have been presented by Ojo (1987) and Suckling (1987). The SeDI for Perkerra does not indicate any long-term persistence in rainfall above or below a z-score of zero (Fig. 6). Approximately 76% of all rainfall years fall within ± 1 a of the mean, and 93% within ±2 a of the mean. Hulme (1989) used the z-score statistic for Table 1. Summary statistics for annual rainfall (mm) at Perkerra andSnakeFarm

meteorological stations

Statistic

Perkerra (1966-86)

Snake Farm (1966-86)

Perkerra (1958-86)

Arithmetic mean Standard error of mean 95% confidence band Coefficient of variation (%) Maximum Minimum Skewness* K-St

632 37 76 26'6 1085 265 0·29 0'158

671 41 85 27'7 1025 349 0·08 0'116

636 32 66 27·4 1085 265 0·18 0·134

"The critical value for skewness with n = 21 at a = 0'05 is ±0-77, and for n = 29 at a = 0-05 the critical value is ±0·67 (Lister, 1982). tThe critical value for K-S (Kolmogorov-Smirnov test) with n = 21 at a = 0-05 is 0-163, and for n = 29 the value is 0'140 (Zar, 1984: 583).

R. A. SUTHERLAND ET AL.

264 600

E

.5 :Ec: 'E

400

c:

200

c

Cl>

E E

,g c g

~

-200

Cl> "0

~

:g

:; E

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::J

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1990

Figure 5. Timeseriesofcumulative deviations ofannualrainfallfromthe mean,Perkerra, 1958-86.

central Sudan (1900-86) and found a significant persistence of drought since the late 1960s. A similar conclusion was reached for West Africa by Ojo (1987) who found that since 1967 droughts have been relatively persistent. However, no such persistence was recorded for the annual rainfall data from Perkerra.

Modified rainfall anomaly index

The MRAI ranges from ""'+4 in 1977 to -2 in 1984 (Fig. 7). As with the SeDI (Fig. 6) there is no evidence of drought persistence in the region. Values of the seDI <-1 a and MRAI < -1 correlate with the local perception of drought, and this is expressed in significant herd reductions in the area. Herd reductions of ""'50% were reported in the Baringo area during 1980 (Little, 1981, cited in Homewood & Lewis, 1987), and reductions of between 60 and 80% occurred in 1984. Data on herd reductions for earlier periods are unavailable. However, as a first-order approximation drought can be considered to occur when annual rainfall is less than ""'430 mm, regardless of the rainfall the previous year, since PET is > 1700 mm year -1.

Q;

8 "'I

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3,-----------------, 2

+2 ..

+1 .. ~

::J

5 Q.

0

~

o

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-I

-I..

-2

-2 ..

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E

en

Figure 6. Simple climatic departure index for annual rainfallat Perkerra, 1958-86.

ANALYSIS OF RAINFALL CLIMATE

265

5.------------------, 1977

4

-2

1984 1990

Figure 7. Modified rainfallanomaly index for Perkerra, 1958-86.

Annual rainfall trends The 29-year rainfall record was divided into two distinct periods: (1) five S-year periods and one 4-year period; and (2) two lO-year periods and one 9-year period. These separate period lengths were analysed for differences using Fisher's PLSD and the non-parametric Kruskal-Wallis test. These tests indicated that there were no significant differences in rainfall between S-year periods and no difference between 10-year periods. Thus, there has been no persistent wet or dry period on record at Perkerra. Regression indicated that there was no significant linear trend in annual rainfall with time. The autocorrelogram for annual rainfall at Perkerra (Fig. 8) indicated that there was no significant serial correlation. Rainfall received in I year at Perkerra was not correlated with rainfall received during any other year. In other words, a wet year is not necessarily followed by a wet year. These data have implications for planning purposes in the region, and land managers must expect significant variation from year to year. Annual rainfall was significantly correlated with the annual number of raindays (r = +0'62), and the annual number of raindays was inversely correlated with time (r = - 0'60; Fig. 9). Though the number of raindays has decreased significantly at Perkerra, rainfall I·or----------------, c::

.s o c:: ... c:: o

"

Q.5

~

~

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~ :§ c::

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0·0

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266

R. A. SUTHERLAND ET AL. 1 8 0 . . . . - - - - - - - - - - - - - - - - - . , 1400 160

1200

140 ~

o

1000

E 5

800

.E

120

-0

.<::: 100 ~

-0

::>

c: c

c

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80


600

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400


c

40 200

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1955

Figure 9. Relationship between annualrainfall and annualnumberof raindays, Perkerra, 1958-86. shows no similar decrease. Therefore, it can be inferred from these data that the rainfall amount per rainday is increasing while the frequency of raindays is decreasing. Hutchinson (1985) found that the annual number of raindays in Gambia has decreased since the 1940s, and concomitantly annual rainfall has also decreased. This differs from the situation in Baringo where the amount of rainfall has not decreased with time.

Monthly rainfall comparison: Perkerra-Snake Farm As with annual rainfall data, the overlapping period of station records (1966-86) was used in initial comparisons. Since many of the months were characterised by non-normal distributions, the non-parametric Mann-Whitney U test was used to test for central tendency differences. Monthly comparisons of rainfall between the stations indicated no significant differences between stations over the 21-year period. Thus, January rainfall recorded at Perkerra is not significantly different from Snake Farm rainfall for the period of record. The non-parametric Spearman correlation coefficient (r s) indicated rainfall at both stations for all months was significantly (a = 0·05) related (i.e. r s values varied from +0·53 in July, to +0·90 in March). Therefore, at the temporal scale of 1 month or 1 year, there is no significant difference in rainfall amount between stations separated by 20 km in this area. However, once the temporal scale is reduced to the daily scale rainfall totals at the two stations become independent (Rowntree, 1988). This results from the localised nature of convective semi-arid rainfall, and individual rainstorms are often less than 10 km in diameter in dryland areas (Sharon, 1972). In the Namib Desert Sharon (1981) noted that correlation coefficients between daily rainfall amounts decrease to zero for locations 20 to 40 km apart. Berndtsson (1987) found that rainstorms occurred with a typical cell size of approximately 6 to 7 km 2 in Tunisia, which borders on the Sahelian zone. Rowntree (1988) found that the correlation coefficient for daily rainfall at Perkerra and Snake Farm was not significant, i.e, r = +0·07. The localised nature of rainfall was experienced at Kampi Ya Samaki (Fig. 1) on 6 March 1986. During the March storm event, 50 mm of rainfall was recorded in less than 1 h; however, 8 km to the south-west of Kampi, at the Katiorin catchment (Fig. 1), less than 5 mm was recorded. Cumulative monthly rainfall for the 21-year period was compared between stations and differences ranged from +21% in October (i.e, Perkerra > Snake Farm) to -32% in December (i.e. Snake Farm> Perkerra). These results can be compared with the cumulative annual rainfall totals between stations which differed by 7%. This confirms the

ANALYSIS OF RAINFALL CLIMATE

267

statement made by Dennett (1987), 'in general, as the time-scale increases, spatial variation decreases, e.g. annual totals vary less than monthly totals which vary less than daily rainfalls'. Since there were no significant differences between stations for monthly rainfall, the complete monthly data set for Perkerra (1958-86) was used for the statistical summary (Table 2). Classical statistics such as the arithmetic mean and the coefficient of variation were tabulated simply for comparative purposes, since for non-normal distributions these statistics are inappropriate (i.e. 9 out of 12 months). This factor has generally been ignored in the sciences. However, robust statistical procedures (Hoaglin et al., 1983) have started to make significant advances in fields such as geosciences (e.g. Lister, 1984; Rock, 1988). Robust estimates are designed to be less sensitive than the simple arithmetic mean to extreme values or to departures from normality. They provide averages which are statistically more 'efficient', that is, both more accurate and more precise than the traditional non-robust measures (Rock, 1988). Thus, in Table 2, robust statistics are included, and it can be noted that there are significant differences between the non-robust arithmetic mean and the various robust measures of central tendency, especially for the drier months of November to February. Monthly rainfall distributions were asymmetric and all were positively skewed (skewed to the right) indicating most months were dominated by low rainfall values interspersed with a few high values; thus the median rainfall value is less than the arithmetic mean in all cases. Positive skewness is most noticeable for the dry-season months of November to February when median rainfall is less than 30 mm per month. The degree of skewness for all grouped monthly rainfall data is shown in Fig. 10. In constructing the frequency histogram, the monthly rainfall class limits were chosen to give 20 classes, which is in the range (15 to 20) suggested by Shaw (1983). Results indicate that the maximum frequency of occurrence is in the first interval 0 to 15 mm (Fig. 10). This reflects the large number of months with limited rainfall (25% of allmonths had monthly rainfall less than 15 mm), and in fact the distribution is similar to theJ-distribution described by Shaw (1983; pp. 227228). Approximately 55% of all months had rainfall values less than 45 mm. This information is essential to land managers in the Baringo area in order to choose the proper perennial grass species which is robust enough to survive several months of negligible rainfall. These rainfall data are also useful in defining probabilities of monthly rainfall in the area. The procedures outlined by Dunne & Leopold (1978) were used to develop probability curves for monthly rainfall at Perkerra (Fig. 11). Three probability curves are shown in Fig. 11,90%,50% and 25%. Each curve can be interpreted as follows: for the month of January there is a 90% chance that rainfall will be less than 75 mm, a 50% chance it will be less than 7 mm, and a 25% chance it will be less than 2 mm. Such curves are useful planning tools for development projects in the Baringo area.

Monthly rainfall regime at Perkerra The monthly rainfall distribution at Perkerra is bimodal with primary peaks in April-May (27% of total annual rainfall) and July-August (28% of total annual rainfall; Fig. 12). The October-November period which corresponds with the 'short rains' in East Africa only accounts for 12% of the total rainfall. The Perkerra station is classed as a Type E rainfall station since the July-August peak ;::::: April-May peak ('long rains') > OctoberNovember period ('short rains'; Davies et al., 1985). This relationship was established statistically using the Mann-Whitney U test, i.e. there were no significant differences between the July-August and April-May peaks. However, both peaks were significantly greater than the October-November peak. The July-August peak is the major contributor to the noticeable departure of the Perkerra area from the 'classical' two-rainy season distribution discussed earlier in this paper. Davies et al. (1985) suggested that the JulyAugust peak is caused by the westerlies which are inherently unstable and are often

N 0\ 00

ANALYSIS OF RAINFALL CLIMATE

269

100--.----------------,

80

~

E

40

::>

z

20

30

60

90

120 150 180 210 240 270 300 Rainfall (rrm)

Figure 10. Frequency distribution of monthly rainfall at Perkerra, 1958-86. (n = 348 months.)

associated with convective development, especially over the highlands such as the Tugen Hills. The April-May and July-August peaks are very persistent in the Perkerra rainfall record. Only twice during the 29-year record was the October-November rainfall greater than the other two peaks. This situation occurred in 1961 and in 1984; both were unusual years in East Africa. One of the wettest years on record in East Africa occurred in 1961; this region received heavy and persistent rainfall late in the year due to the southward passage of the ITez. During 1984, East Africa arid other parts of Africa experienced the worst drought on record. The reason for the severe drought in the Baringo area was the failure of rainfall in April-May and in July-August. During this drought year the AprilMay rainfall contribution was only 14% ofthe total rainfall (compared with the long-term value of 27%), the July-August contribution was 19% (long-term value = 28%), and the October-November contribution was 42% (long-term value = 12%). Thus, the failure of the long rains early in the year resulted in negligible forage production for grazing and browsing animals, and the increased October-November contribution was too little, and too late for stock recovery. Failure of the April-May rains might provide a preliminary signal for development agencies in the area to prepare for a potential drought occurrence.

Figure n. Probabilities (90%, 50% and 25%) of monthly rainfall at Perkerra being less than a , 25%. specific value....., 90%; -0-, 50%;

R. A. SUTHERLAND ET AL.

270 90

80 70

EE

60

.E 50 c

.~

40

c

.,"

'0

:::;:

30 20 10

o

J

FMAMJ

J

ASOND

Monlh

Figure 12. Bimodal rainfall regime for the Perkerra meteorological station, 1958-86.

The monthly normalised deviation index (MND I ) for the two critical periods (AprilMay and July-August) during 1984were in-phase (Fig. 13). This was the only time during the 29-year period when MND I values for these periods were in-phase and <-0'60. Significant herd reductions in 1980and 1984occurred when the MNDI value was < -0'60 for the July-August period. In 1980 the MND I value for April-May was positive; however, significant herd reductions occurred. Thus, these limited data indicate that even if the April-May MND I value is positive, significant herd reductions can still occur if the July-August rains fail (i.e. MND I < -0'60).

Monthly rainfalltrends The autocorrelation function (rd could only be calculated for the months of March, May and October, since their distributions were normal. Correlograms (not shown) indicated that rL was not significant for any lag. This means for example, that rainfall in March of 1 year is not significantly correlated (i.e. is independent) with rainfall in March for lags (i.e. years) 2: 1. For the remaining 9 months, the von Neumann test statistic was computed, 2·5 2'0

.,

.)(

1·5

"0

.s c

1·0

.2

.~

0-5

~ ~

co

(}OI---All+-lrl-lt~~r#JH-+-H+-aH+t----I

:::;: -0·5

-1,0

Figure 13. Deviation of April-May (0) and July-August (e) rainfall from the long-term trimean rainfall for Perkerra.

271

ANALYSIS OF RAINFALL CLIMATE

400

l'Or-:-~--------------------,

( b)

90

I·Or;---;--------------------,

(c)

.sc ti c

Q.5

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.!:! '6

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s

:; -0,5


-1·0 0

IO~~.L.1. ~~.. J;M_LL.l~cr_U~.l.-L""7""OI.. L.J-'r;O:-'-'-lo. . L; ;!90 Lag

Figure 14. (a) Variability of monthly rainfall, Perkerra, 1958-86; (b) autocorrelogram for all monthly rainfall data, n/four lags, prior to deseasonalising (note cycle of approximately 12 months); and (c) autocorrelogram of monthly rainfall following deseasonalising (note significant lag 1 autocorrelation).

and results indicated that there was no lag-one serial correlation for any of the months examined; i.e. rainfall for a specific month is independent of the rainfall received in the samemonth 1 year later. Allmonthlyrainfalldata weregrouped (n = 348months), and the variationfrom 1958 to 1986 is shownin Fig. 14(a). There wasno lineartrend in the relationshipbetweenmonthly

272

R. A. SUTHERLAND ET AL.

precipitation and time. Autocorrelation analysis was applied to the data and the correlogram for n/four lags is shown in Fig. 14(b). A periodic (significant at the 5% level), lowfrequency cycle of approximately 12 months was noted in the time series. This cycle was removed by deseasonalising the original data in order to observe the underlying autocorrelation structure. Following deseasonalising, the following lags were still statistically significant, lag = 1, lag = 21, lag = 47, and lag = 83 [Fig. 14(c)]. The significant tt. value for lag 1 'ri. = +0'15) suggests some short-term correlation structure, such that a month which is wetter or drier than 'normal' is likely to be followed by 1 month which is also wetter or drier than their appropriate averages (cf. Chatfield, 1984). The physical meaning of the significant rL values for lags 21, 47, and 83 are unclear. However, they may simply represent a stochastic (random or irregular) component in the monthly rainfall time series. A time series is said to be completely random when rt. = 0 for all lags >0 (Shaw, 1983); this was not the case for Perkerra rainfall since there was some underlying structure which has physical meaning (i.e. lag = 1 and lag = 12). Much has been written on decreased rainfall in semi-arid and arid African countries since the 1960s. For the Baringo area there has been no significant trend in annual rainfall over the 29-year record. Rainfall for individual months was correlated with time using the Spearman correlation coefficient. The only month which showed a statistically significant (a = 0'05) change with time was April. The rs value was +0'38, thus indicating that April rainfall has increased with time; however, the relationship is weak, i.e. ~ = 0'14. Monthly raindays were correlated with time, and the following months showed a statistically significant decrease with time: March tr, = -0'54); July tr, = -0'39); and October (rs = -0'42). Since rainfall in these months did not show a similar significant decrease with time, it can be inferred that the amount of rainfall per rainday has increased for these 3 months.

Discussion and conclusions Available monthly and annual rainfall data were analysed for a relatively small area. The results presented in this paper are probably representative of a semi-arid area of approximately 700 knr', As Ojo (1987) stated, any empirical data and information obtained for any area may be valid within reasonable bounds only in such an area for which the data and information were derived. Thus, the data from Perkerra and Snake Farm should not be extrapolated to all semi-arid areas in Kenya. At the time-scale of a month or a year rainfall did not vary significantly between stations separated by 20 km, However, if the time-scale is reduced to daily measurements there is no significant correlation between stations (Rowntree, 1988). This variability corresponds with the localised nature of convective rainfall, with individual cells of limited extent. Rainfall variability indices were used to identify droughts and to establish some arbitrary values for drought identification. Rainfall was used in drought index calculations since it is the most important hydrological variable and generally one of the only meteorological measurements made in semi-arid areas. In addition, simple indices with rainfall as the only input perform comparatively well with more complicated indices in depicting periods and density of drought (Oladipo, 1985). The data from Perkerra indicate the droughts, particularly the 1984 event, are isolated features and are not part of a trend towards decreasing rainfall. Monthly or annual rainfall values showed no significant decrease with time. However, the number of annual raindays has decreased, resulting from significant rainday decreases in March, July and October. These data indicate that there is a tendency towards increasing amounts of rainfall per rainday. This can have significant impact on design structures in the area for water conservation projects. Rainfall data and drought indices point to 1984 as the most anomalous year on record. There are several reasons why this event had such a dramatic influence on stock populations in the area. Prior to 1984,6 of the 7 previous years had above-average rainfall

ANALYSIS OF RAINFALL CLIMATE

273

values as indicated by the cumulative deviations of annual rainfall from the mean (Fig. 5), from the simple climatic index (Fig. 6), and from the modified rainfall anomaly index (Fig. 7). The primary reason for drought in the area was the failure of the April-May rainfall (i.e. the long rains), and this was exacerbated by the failure of the July-August rains. These rainfall periods, particularly the long rains, are crucial to agriculture and forage productivity in the area. During the 29-year record at Perkerra, 1984 was the only year when April-May and July-August rainfall deviations were in-phase below an MNDI value of -0'60 (Fig. 13). It is essential for land managers in the area to consider the time of rainfall failure in the region, primarily in the periods of April-May and July-August. Nieuwolt (1978) argued for such an approach in East Africa when she stated that the use of single-month drought frequencies disregards the effects of the time of occurrence which is often decisive regarding the consequences of a drought for agricultural or forage production. Thus, drought in the Baringo area should be considered as one of the supply characteristics of rainfall in the region. As Hulme (1987) noted, 'drought cannot be separated from rainfall; and drought must be seen as a part of climate not apart from it' . Land management programmes must be designed in such a way as to cope with the probability of drought being an aperiodic, as well as a recurring phenomenon (Glantz & Katz, 1985). Therefore, utilisation of probability curves for monthly rainfall (i.e. Fig. 11) should provide an improvement over previous design frameworks. This approach is useful in combination with the knowledge that there has been no long-term decrease in monthly or annual rainfall, but in addition, there has been a tendency towards a decreased number of raindays in the area. Research in the Baringo District was made possible by the Kenyan Government. Research staff at the Perkerra ResearchStation and johnathan LeakeyatSnake Farm are gratefullyacknowledged for making rainfall data available to the authors. Supplemental rainfall data from six locations in the Njemps Flats were provided by Murray Roberts, and his contribution is appreciated. The research has been funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), and the International Development Research Council of Canada (IDRC).

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