# Climatic gradients along the windward slopes of Mount Kenya and their implication for crop risks. Part 1: climate variability

Pierre Camberlin, Joseph Boyard-Micheau, Nathalie Philippon, Christian Baron, Christian Leclerc, Caroline Mwongera

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## ► To cite this version:

Pierre Camberlin, Joseph Boyard-Micheau, Nathalie Philippon, Christian Baron, Christian Leclerc, et al.. Climatic gradients along the windward slopes of Mount Kenya and their implication for crop risks. Part 1: climate variability. International Journal of Climatology, 2014, 34 (7), pp.2136-2152. 10.1002/joc.3427 . hal-00693335

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# Climatic gradients along the windward slopes of Mount Kenya
## and their implication for crop risks

## Part 1 : climate variability

Pierre Camberlin (1),

Joseph Boyard-Micheau (1),

Nathalie Philippon (1),

Christian Baron (2),

Christian Leclerc (3),

Caroline Mwongera (4)

(1) Centre de Recherches de Climatologie (CRC), UMR 5210 CNRS/ Université de Bourgogne, 6 Bd Gabriel, 21000 Dijon, France

(2) CIRAD, UMR TETIS, Maison de la Télédétection, 500 rue J.F. Breton, Montpellier, F-34093, France

(3) CIRAD, UMR AGAP, Avenue Agropolis, F-34398 Montpellier Cedex 5, France

(4) Montpellier SupAgro, 2 Place Pierre Viala, 34060 Montpellier Cedex 1, France

Submitted to *International Journal of Climatology*

4 July 2011

Revised version : 6 December 2011
Abstract

In tropical mountains, the way topographical gradients translate in terms of intra-seasonal and interannual climate variability (especially rainfall and evapotranspiration) is relatively unknown, yet it is an important issue for agriculture and food security. The eastern slopes of Mount Kenya, in East Africa, with their wide range of agroecological conditions, are appropriate to study these aspects. Daily (monthly) rainfall data from 11 (24) stations at different elevations and exposures are collected for the period 1961-2006. For the elevation belts suitable for farming and agro-pastoral activities, mostly below 2000 m, it is found that not all rainfall characteristics co-vary with altitude.

Four distinct behaviours are revealed. (i) Parameters which show a relatively regular increase / decrease with elevation. This is the case for the duration of the two rainy seasons found across the region, and the mean onset and cessation dates. (ii) Parameters which show a relatively uniform pattern throughout the region, with little differences both horizontally and vertically. This applies to the interannual variability of the October-December seasonal rains, which are quite homogeneously modulated by Indian Ocean sea-surface temperatures. (iii) Parameters markedly influenced by exposure. This applies to daily rainfall intensities, which peak along the southeast-facing slopes, whatever the altitude. The frequency of rainy days shows both a vertical gradient and some influence of exposure. (iv) Parameters which show very weak spatial coherence, or pronounced vertical discontinuities. This is obvious for the cessation date of the March-May rains, whose poorly organised interannual variability contrasts with that of the onset.

Variables involved in potential evapotranspiration (ET₀) computation display either a strong (maximum temperature) or a weak (global radiation) vertical coherence, reflecting the existence / absence of an elevation control on the mean distribution of these variables. However, the interannual variations of global radiation have the strongest influence on those of ET₀.
Keywords :

Kenya, precipitation, evapotranspiration, mountain climates, elevation gradients, rainy season, onset of the rains, interannual variability.
# 1. Introduction

In the dry tropics, mountain environments provide agroclimatic conditions which are generally less stringent than the drier, higher water-demanding environments of the surrounding plains. The general pattern of increasing rainfall with altitude, in the lower and mid slopes, up to a rainfall maximum often found between 1500-2500 m, is a well-known feature of tropical climates (Nieuwolt and McGregor, 1998; Rohr and Killingtveit, 2003; Barry, 2008). However, much less is known about how this translates in terms of rainfall distribution within the rainy season, including aspects such as the onset and end dates of the season, the frequency and daily intensity of the rains. The problem includes both the mean patterns, and the year-to-year variability. In particular, it is not well understood whether elevation (among other topographical features) enhances or reduces the spatial coherence of the rainfall events (i.e., their co-occurrence or independence), and the interannual variability of the rains.

It is generally believed that exceptionally wet years jointly occur at low and high elevations, as evidenced from glacier variations of mass balances reproducing variability in atmospheric moisture or precipitation in adjacent regions (e.g., Hoffmann et al., 2003; Mölg et al., 2009b). However, there are also indications that rainfall variations are not uniform along tropical mountain slopes (Chacon and Fernandez, 1985; Buytaert et al., 2006; Bewket and Conway, 2007).

In Equatorial East Africa, Hastenrath (1984) used high elevation precipitation data from Mount Kenya and compared them with a regional rainfall index. He found little relationship between precipitation amounts on Mount Kenya and the surrounding highlands. Exceptions are for the most extreme seasons, and occasional wet events during the otherwise relatively dry months of January-February and July-August, which seem to be more controlled by the large-scale general
circulation. Mölg et al. (2009a) studied the temporal precipitation variability versus altitude on Mt Kilimanjaro. They found, through a comparison between automatic weather station data on the summit of Kilimanjaro and TRMM estimated rainfall from the surrounding lowlands, that there is a clear consistency between the two sites in terms of seasonal precipitation variability. However, the relative range of variability is greater in the high-elevation zone than in the low-elevation zones. This greater temporal variability is found to mostly result from the frequency of precipitation events, rather than the amount per event. A similar pattern is suggested to occur at high altitude locations over Mount Kenya, as evidenced from data published by Hastenrath (2005). Mölg et al. (2009a) provided evidence that an upslope shift of the zone with maximum precipitation rate occurs as a result of an increase in atmospheric moisture content. They further asserted that the belt of maximum precipitation on tropical mountains most probably migrates vertically under climate change.

Most of these observations refer to high elevation sites, and apart from general aspects of the mean rainfall distribution, little is known about the temporal variability with respect to altitude in the mid- to low-elevation zones. Yet these zones concentrate most of the human population, and any rainfall variation or change may have serious impacts on farming activities. Variations in altitudinal precipitation distribution affect the farmers' vulnerability by enabling them or not to grow crops at lower elevations in certain years, and by fuelling conflicts between lowland pastoralists / farmers and highland farmers in dry years (Liniger et al., 1998). A better understanding of how temporal rainfall distribution varies with elevation is also required in order to downscale both seasonal rainfall forecasts and future climate change projections, then to more accurately evaluate climatic impacts in mountain environments. As the onset is not precisely predictable from year to year, farmers usually favor a "dry sowing". Seeds germinate after the first precipitation event. But there is a risk of seed loss that farmers have to face to when the first
precipitation event is followed by many days without rain. Translating rainfall frequency and daily
intensity in terms of farmers' strategies, food security or farming systems resilience is a crucial
issue that could be considered with a multidisciplinary approach, first requiring a good
understanding of the space-time variations of the rains.

Mount Kenya, with its relatively simple, almost regular topography, constitutes an enlightening
case to study the effect of elevation on climate. Strong vertical gradients, in both temperatures and
precipitation, result in a typical agro-ecological zonation which is illustrative of the main crop belts
found across Kenya (Gichuki et al., 1998a; Jaetzhold et al., 2007). Climatic conditions prevailing
at high elevations over Mount Kenya have been described by Thompson (1966), Davies et al.
(1977) and Hastenrath (1984). Franz et al. (2010) studied the ecohydrology of the Ewaso N'giro
basin, encompassing the north-western slopes of Mount Kenya, and documented trends in the
frequency and intensity of rain storms. Mutiga et al. (2010), for the same river basin, provided
estimates of evaporation rate based on satellite data, and showed an elevation-dependant spatial
variability, as well as a significant interannual variability, though the assessment of the latter was
limited to three years.

The south-eastern (windward) slopes of Mount Kenya were not included in Franz et al. (2010).
They are wetter than the north-western (leeward) slopes, but their lower parts remain quite dry
(600 to 900 mm mean annual rainfall), and the interannual variability of the rains strongly impacts
agricultural activities (Smucker and Wisner, 2008). Droughts may result into total crop failure, as
happened in much of Mbeere and Tharaka districts in 2000 and 2005. The present study focuses on
the inhabited, sometimes densely populated zones, mostly below the belt of mean maximum
rainfall. They range from the semi-arid agro-ecological zone "IL5" ("Inner Lowland"), dedicated
to livestock rearing and some drought-resistant cereal cultivation, to the humid "LH1" zone
("Lower Highland" or "tea-dairy zone"), including the intensively cultivated "Upper Midland" zone or "coffee-tea zone" (Jaetzhold et al., 2007). As discussed above, the aim is to document the vertical (altitudinal) and horizontal variability of precipitation, including its intra-seasonal distribution, along the windward slopes of the mountain. To that end, two parallel altitudinal transects (from about 600 m to 1600 m) and two horizontal altitudinal belts (600-900 m and 1400-1600 m) are defined. The objective is also to assess how the vertical and horizontal rainfall gradients deviate from normal in wet and dry years, and how such deviations are forced by external causes of variability. The role played by Pacific Ocean sea-surface temperature (SST) variability associated with El-Niño-Southern-Oscillation (ENSO) events, and by variations in SST gradients across the equatorial Indian Ocean, are considered. These SST patterns are known to have a significant impact on rainfall in East Africa (Ogallo et al, 1988; Beltrando and Camberlin, 1993; Indeje et al., 2000; Black et al., 2003; Behera et al., 2005; Ummenhofer et al., 2009). The two rainy seasons characteristic of the Mount Kenya region ("Long Rains" around March-May and "Short Rains" around October-December) are studied separately. Onduru and Du Preez (2007) noted that annual rainfall is not adequate to explain the risks posed by rainfall to crop production in the region. Basic aspects of potential evapotranspiration at two different elevations are also considered. The rainfall patterns described in the present study will then be related to crop growth in a forthcoming paper (part 2).

Section 2 describes the data and defines the rainfall variables. In section 3, the patterns of climate variability are presented. Focus is first on the distribution of mean rainfall characteristics with respect to elevation (3.1), then on the interannual variability considering different rainfall variables independently (3.2) and in combination (3.3). The relationship between precipitation and large-scale circulation is next considered (section 3.4). An analysis of reference evapotranspiration is finally provided in section 3.5.
## 2. Data

### 2.1 Rainfall data

The rainfall network consists of 24 stations (figure 1), whose data were obtained from the Kenya Meteorological Department (KMD). Other initially selected stations were discarded due to too many missing values or data unreliability. The stations have monthly rainfall records encompassing the period 1961-2000, with some series up to 2006. Daily records were only available at the 11 stations shown as large dots on figure 1. The stations are in the range between 600 m a.s.l ("Inner Lowland" agro-ecological zone IL5, Jaetzhold et al., 2007) and 1600 m a.s.l ("Upper Midland" agro-ecological zone UM1), with a few higher altitude stations close to or within the forest belt ("Upper Highland" zone around 2000-2500 m). The network is not dense but samples reasonably well the different altitudinal belts, except for the upper part of the Montane Forest belt and the Ericaceous and Alpine belts (Hastenrath, 1984; Ojany, 2008), which are not in the scope of this study since they are well outside the cultivated areas.

Two sectors and associated vertical transects have been defined in order to capture the effects of elevation and exposure on rainfall distribution (fig.1). The northern one is oriented due east. It is described by a vertical transect from Marimanti (681 m) to Marimba (2120 m). The southern sector has a south-east exposure. The corresponding vertical transect runs from Kiritiri (1143 m) to Kamweti Forest Station (2438 m). For the daily analyses, due to the paucity of available data, additional stations not along the transects but in the same sectors have been used. In order to synthesize the effects of elevation, the stations have also been grouped into two main elevation belts. The upper one (group A) comprises stations located between 1400 and 1600 m, the lower
one (group B) between 600 and 900 m. Intermediate stations between 900 and 1400 m are also used but their small number (3) prevents the constitution of a separate group.

Data have been checked for consistency and outliers. The geographical coordinates of all stations have been scrutinized, and the elevation compared to that of the nearest Digital Elevation Model grid point (reference: STRM at a 1 km resolution). Corrections have been made where necessary. Daily rainfall data may incur several types of inconsistencies. Very low (typically <=1mm) daily rainfall is not always properly recorded, particularly at stations which do not belong to the synoptic grid. For this reason, only daily rainfall higher than 1 mm have been considered. Raingauges not monitored by professional observers may also suffer from the fact that rainfall amounts for several consecutive days are occasionally reported as a single (hence larger) daily record. Such cases are not easy to detect. However, a close analysis of day-by-day rainfall records at the various locations failed to identify any such systematic bias at the 11 stations selected. Further discussion on this issue will be found below, since there is a potential effect on daily rainfall intensity.

For the monthly records, a limited number of missing data was estimated by linear regression using the nearest stations. Although this procedure adds some spatial coherence, it was found to have only a minor effect on the results presented at this time-scale, judging from a separate analysis based on complete time-series on a shorter period. No attempt was made to fill the missing data at daily time-scale.

From the daily records, statistics on the distribution of the rains during both rainy seasons have been computed for each year and each station. The onset dates have been determined following criteria similar to those used by Sivakumar (1988), Dodd and Jolliffe (2001), Marteau et al. (2009), and in their study on Tanzania by Mhita and Nassib (1987). The onset date of the rainy season is
defined as the first day of the first 2-day period recording at least 20 mm, not followed by a 10-day dry sequence, receiving less than 5 mm, in the next 20 days. These thresholds are based on preliminary analyses of successful and failed sorghum sowing using the SARRA-H crop model (part 2 of this paper), and previous on-farm studies for West Africa (Sivakumar, 1988; Marteau et al., 2009). To enable comparisons between the onset and the cessation of the rains, a symmetrical definition has been adopted for the cessation date of the rainy season. The onset and cessation dates are determined by considering the September-January and February-June sub-periods, for the Short Rains and the Long Rains respectively. The number of rainy days over 1 mm (NRD) is computed between the onset and the cessation dates of the rainy season. In order to separate the components of the rainy season into independent components, the frequency of rainy days (FRD) during the rainy season is also computed as the number of rainy days divided by the duration of the rainy season. Finally, the daily intensity (INT) of the rains is computed as the total rainfall divided by the number of rainy days. The four variables (onset, cessation, FRD and INT) are, by construction, independent of each other, and inform on different characteristics of the rainy seasons, of particular relevance for agricultural or hydrological applications. Other statistics describing the arrangement of wet and dry spells within the rainy season are computed. They are the mean length of wet spells, the mean length of dry spells, the intensity of wet spells (in mm/day), and the seasonally accumulated number of days in long (over 7 days) dry spells. Dry spells of 7 days or more are often seen as detrimental to crop growth, and are therefore often retained as a threshold in the definition of the rainy season onset (Stern et al., 1981; Sivakumar, 1988).

## 2.2 Other local meteorological data
Reference evapotranspiration has been computed using the Penman-Monteith formula (Allen et al., 1998). Input data are global incoming radiation, minimum and maximum temperature, mean relative humidity and mean wind speed. These parameters are only available at a few stations. To sample the vertical distribution, daily data from Embu (1433m) for the period 1977-2006 have been extracted from the KMD data base, and original KMD records from Marimanti (681 m) have been manually keyed in for the years 1973-1995. Wind and humidity for Marimanti are only available as long-term monthly means.

## 2.3 Large-scale climate indices

For the analysis of the forcing mechanisms which account for the interannual variability of the rains, both the Dipole Mode Index (DMI) and the Niño-3 Index have been used. The DMI depicts zonal SST gradients across the equatorial Indian Ocean, which show maximum interannual variability in northern autumn (Saji et al., 1999 ; Black et al., 2003). The index, based on Kaplan SST data (Kaplan et al., 1998), is the difference between the western equatorial Indian Ocean (50-70°E and 10°S-10°N) and the south eastern equatorial Indian Ocean (90-110°E and 10°S-0°N) SST. It has been extracted from the JAMSTEC website (http://www.jamstec.go.jp) for the years 1961-2006. The Niño-3 index documents the SST component of El-Niño-Southern-Oscillation (ENSO). It is the average SST in the region between 150-90°W and 5°S-5°N, and is extracted from the NOAA Climate Prediction Center website (http://www.cpc.ncep.noaa.gov/data/indices/).

# 3. Results

## 3.1 Elevation and space-time distribution of mean rainfall characteristics
The seasonal distribution of the rains on the windward slopes of Mount Kenya is relatively simple, with two distinct rainy seasons of about three months (October-December "Short Rains" and March-May "Long Rains"). These rainy seasons are associated with the passage of the Intertropical Convergence Zone (ITCZ) over Equatorial East Africa. Rainfall amounts steadily increase uphill, with little change in the overall seasonal distribution (fig.2).

There are however three deviations to this general pattern. First, the increase with altitude halts around 2000-2500 m (Thompson, 1966). Above this level, precipitation decreases, as for many other tropical mountains. In the absence of available stations along the northern transect, it is only shown along the southern one (fig.2), where Kamweti Forest Station at 2438 m displays less rainfall than Kerugoya Forest Station, 400 m below. Second, the rate of rainfall increase with altitude gets enhanced during the northern summer main dry season, especially at its beginning (May) and end (October). This is evident along the southern transect, where May is even the wettest month at high altitude stations (fig.2). This pattern results from cold and dry air advection in the lower troposphere, capped by an inversion layer typical of the northern summer season (Anyamba and Kiangi, 1985), which makes convection only possible as a result of orographic ascent along the steepest, mid- to high-elevation slopes. The net result is a slightly shorter northern summer dry season in the mid- and upper slopes than in the lower ones. Finally, at high elevations, moderate rainfall is found during the northern summer dry season along the southern transect (fig.2), which further reduces the dryness of this season.

Despite the relative simplicity of the topographical setting, these features already point to the role played by minor exposure variations in the rainfall patterns. Further assessment of these mean patterns is carried out using seasonal statistics from daily data.
On the whole, the duration of both rainy seasons shows an increase with altitude (fig.3 a-c). The rate is about 3.6 days per 100 m for the Long Rains, and 4.2 for the Short Rains. This results from relatively linear altitudinal gradients in both the onset and the cessation dates (table 1). Consequently, all variables depending on the duration of the rainy seasons (e.g., seasonal totals, NRD) show a significant linear relationship with elevation (table 1).

Although it is in principle independent from the duration of the rainy season, the distribution of wet and dry spells is also partly elevation-dependant. The mean length of wet spells increases with altitude during both rainy seasons (table 1). However, the mean length of dry spells does not show a corresponding decrease. This means that the more numerous wet days at higher elevations tend to be grouped into longer wet spells, separated from each other by dry spells of similar duration as in the lowlands. The intensity of these wet spells is, during the Long Rains, slightly reduced at high elevations.

When the effect of the rainy season duration is eliminated, the relationship with elevation is at first sight less linear, as exemplified by the scatter of stations obtained for the frequency of rainy days (FRD, fig.3 b-d). A closer scrutiny however reveals that the increase of FRD with altitude is still relatively linear, but with different rates depending on the sector (northern or southern transect) and season. During the Long Rains, in the northern sector, FRD increases slowly with elevation, while in the southern sector it increases faster. During the Short Rains, the pattern is virtually inverted, with the southern sector being drier than the northern one. These differences point to the fact that both altitude and exposure impact FRD. The two sectors actually have slightly different exposures. The northern sector faces east, while the southern one has a south to south-east exposure. It is clear that the effect of exposure to the dominant winds is much stronger at high than at low elevations, in accordance with the steeper slopes. The contrast between the two rainy
seasons is related to the fact that in the Long Rains the synoptic-scale low-level flow (humid and unstable) is more southerly (Oettli and Camberlin, 2005). Hence, elevation on south-facing slopes has a strongly positive effect on rainfall generation, while on east-facing slopes it is less effective. During the Short Rains, the southerly component vanishes, and elevation on south-facing slopes does not have a significant effect any more.

Daily rainfall intensity is even more strongly constrained by exposure. This variable does not show any increase with altitude. In both rainy seasons, the highest daily rainfall amounts are found at the east-facing stations, with values in the range of 17 to 23 mm.day⁻¹ at both lower and upper elevations (fig.4). This result is not an artefact which could have resulted from a grouping of several consecutive rainy days in one, as it sometimes happens at raingauges where the recording standards are not fully met. This situation would be conducive to an abnormally low frequency of rainy days (FRD) for such raingauges, comparatively to (synoptic) meteorological stations located at the same altitude. Such a pattern is not found in fig.3. For instance, Chuka raingauge (with intense rains) has a similar FRD as Meru meteorological station (with lighter rainfall), at the same elevation. The same applies to Mitunguu raingauge and Mwea agro-meteorological station. We thus assume that the increase of rainfall intensities along the east- to south-east facing slopes (whatever the elevation) is real and results from a dynamical effect. Orographic uplift is a key factor, but local wind convergence likely plays also a role, since the spatial pattern remains the same between the two seasons, contrary to FRD. For the Upper Ewaso Ng'iro river basin, located over the north-western slopes of Mount Kenya, Franz et al. (2010) published maps of average rain per storm. Consistent with the above findings, it is shown that the average rain per storm tends to increase towards the south-east, and although it is generally lower on the leeward (north-western) side of Mount Kenya, it does not bear any systematic relationship with the mean seasonal rainfall. For the same river basin, Gichuki et al. (1998b) noted that the distribution of daily rainfall within
the seasons was more regular in the highlands than in the lowlands, where a few heavy storms account for a large percentage of the annual rainfall. This is again in line with the observation made along the south-eastern slopes of Mount Kenya that while the number of rainy days decreases downwards, the intensity of the rains is not necessarily lower.

In summary, three main points emerge from the analysis of mean rainfall patterns in the Eastern Mt Kenya area : (i) elevation has its main effect on the mean duration of the rainy season (with virtually no influence of exposure) and the length of wet spells, (ii) elevation combined with exposure impacts the frequency of rainy days, while (iii) the daily rainfall intensity is strongly controlled by exposure.

**3.2. Spatial patterns of interannual variability**

There are substantial interannual variations of seasonal rainfall over East Africa, particularly for the Short Rains (Hastenrath et al., 1993; Nicholson, 1996; Black et al., 2003; Camberlin, 2010). Over the Mount Kenya area, the interannual variability is also large, and absolute variability tends to increase with the mean rainfall amounts (i.e., it is greater at higher elevations). However, some stations, especially those located in the margins of the wettest areas (e.g., around Meru in the north) show a stronger than expected interannual variability.

The spatial patterns and spatial coherence of interannual rainfall variability are further explored by considering the above-defined rainfall variables. These are important issues, since they determine whether drought / flood years, or other climatic events potentially harmful to agriculture, co-occur at different elevations and within each agro-ecological belt. Decoupling between locations, especially at different elevations, may help the local communities to be more resilient to climatic
crises, by recurring to food trade in exchange of labour for instance. Traditional communities from
the region even used to reduce risks of crop failures by having plots scattered across different
altitudinal zones (Bernard, 1972). Mwimbi people today settled at mid-slope (850-950 m) have
retained some plots at higher elevations, from which they actually come from.

The spatial coherence was first assessed at monthly time-scale, using the full network of 24
stations. All possible couples of stations were considered, and inter-station correlations between
the interannual time-series computed over the period 1961-2000. The correlations were then
plotted against the elevation difference between the corresponding stations (fig.5). Mean
correlations peak at 0.87 in January and decline to 0.32 in July. The scatter is small from October
(beginning of the Short Rains) to April (middle of the Long Rains). Throughout this period there is
no influence of elevation. Drought years as well as wet years tend to co-occur at all elevations.
This independence to elevation starts to alter in April where the inter-station correlations decrease
between stations located in different altitudinal belts. In May the effect of elevation is obvious:
while the correlations between stations located at similar elevations are still high (0.7 to 0.8 on
average), they strongly decrease between stations located in different altitudinal belts. The black
oblique line on fig.5 for May materializes this statistically significant dependency on elevation.
This result suggests that failures of the second part of the Long Rains often occur independently on
the lower and upper slopes. From June, during the main dry season, the inter-station correlations
further decrease though a slight dependency on elevation is still found. The very different
behaviour of the northern winter (January-February) and northern summer (June-September) dry
seasons points to the fact that January-February in some years can be exceptionally wet over
Kenya (with almost no interruption between the two rainy seasons, as in 1979, 1993 and 1998)
while June-September is invariably dry, except for highly localised showers.
Fig.6 completes the picture by showing correlations with respect to both horizontal and vertical distances between stations. The low coherence of the northern summer, in both horizontal and vertical directions, contrasts with the high coherence (again in both directions) of the October to March period. In April and May, the horizontal coherence is still evident, but the vertical coherence markedly declines.

These results strongly suggest that the Long Rains do not have a uniform behaviour. This was also found at larger scale (Kenya / North Tanzania) by Camberlin et al. (2009). The issue is further examined by using statistics of the distribution of the rains during the two rainy seasons. Stations having daily records have been grouped into an upper belt (A, 1400-1600 m : Embu, Chuka, Nkubu, Meru) and a lower belt (B, 600-900 m : Ishiara, Chiokarige, Tunyai, Marimanti). There are fewer stations at intermediate elevations, but their behaviour tends to be closer to that of group B. The median significance level (Joiner, 1969) of the inter-station correlations is shown in table 2. The correlations are computed based on slightly different numbers of years, depending on data availability.

Integrative variables, such as the seasonal total and the NRD display the strongest spatial coherence, in both rainy seasons, and both horizontally and vertically. This result was also found for much of East Africa (Camberlin et al., 2009), although the study did not differentiate horizontal and vertical variations. The seasonal total and the NRD, which combine information on the duration of the rainy season and its internal properties, raise the signal-to-noise ratio. The horizontal coherence in the onset of the rains is also high, especially for the Long Rains. The vertical coherence (between the upper and the lower slopes) is also significant, although slightly weaker. A strong coherence is found at the end of the Short Rains, in contrast with the end of the Long Rains which is spatially more variable. This is in line with the above results based on
monthly data, although for the end of the Long Rains the daily data also show a relative disconnection within each altitudinal belt. This can ascribed to the fact that at high elevations the rainy season is prolonged beyond May. Spatial coherence for May rainfall totals (related to the FRD, for instance) may be obtained even in the absence of synchronicity in the cessation date, which depends on the late occurrence of small-scale convective showers, hence its low spatial coherence.

Within each rainy season, the frequency of rainy days (FRD) shows relatively similar interannual variations inside agro-ecological zone A only, and the top-down correlations are not significant. The spatial coherence of the seasonal mean daily rainfall intensity is even lower.

An important feature is that the onset of the Long Rains, besides its strong spatial coherence, shows a large interannual variability. The reverse is true for the cessation of the Long Rains. Whereas the amplitude of the Long Rains cessation dates is around one month, it is almost two months for the onset (for instance, regional mean onset occurred on 14 February in 1969 and 5 April in 1987). The contrasted spatial consistency and interannual variability of the Long Rains onset and cessation dates are further illustrated in fig.7. The onset, plotted as the median of the local dates, displays large temporal variations (standard-deviation of 19 days, as compared to 12 days for the end dates). However, the onset often occurs almost simultaneously at most locations, as shown by the large dots which denote years in which the inter-station spread does not exceed 3 days. The spread between stations is much higher for the cessation dates. In some years however, during the Long Rains onset period, a wet spell co-occurs at all locations, but is followed by a dry spell longer at low elevations than at high elevations. In such a case, the true onset is delayed in the lowlands, which accounts for the inter-station spread found on a few years (occasional larger bars on fig.7, e.g. 1963, 1972...), and for the overall later mean onset at low elevations (section 3.1).
### 3.3. Classification of years

As shown above, each rainfall descriptor is characterized individually by a distinct interannual variability and spatial coherence, and the rainy season structure results from a combination of many descriptors. It is useful to understand how these descriptors interact to shape rainy seasons and how these interactions are organized along the altitudinal gradient. The analysis is based on 8 descriptors, of which 6 were previously presented (seasonal total, onset and end dates of the rainy season, duration, frequency of rainy days and intensity). In order to further describe the temporal distribution, the frequency of wet spells (F wet sp) and the number of dry days recorded in long dry spells (L dry sp) have been added.

The main objective of the classification is to identify seasons characterized by similar profiles of seasonal rainfall descriptors and to distribute them into several clusters. The classification is based on a data set composed of the 8 above descriptors computed from daily rainfall at 3 stations located along the Mount Kenya gradient, for the 1961-2001 period. The Long Rains and the Short Rains are considered as separate seasons but in the same analysis. The prior spatial coherence analysis (section 3.2) shows that the stations of Embu, Mitunguu and Ishiara, corresponding to the upper, middle and lower slopes, respectively, can be considered as fairly representative of their respective altitudinal belts. The input database comprises the 82 seasons (2 x 41 years) at each of the 3 stations as rows, and the 8 rainfall descriptors as columns. To limit the descriptors collinearity, an initial filtering is performed on the standardized data set (by station) by retaining only the first four principal components, which explain 95% of the total variance. Cluster analysis is performed to seek similarities between the profiles of the 246 (82 x 3) station-seasons, as described by the filtered rainfall variables. Seasons on which the onset is undefined are put aside in
a separate cluster (“cluster 0”) because (i) no seasonal statistics can be computed, and (ii) these
years are highly singular, denoting a total failure of the rains. A *k*-means algorithm (Diday and
Simon, 1976; Ullmann and Moron, 2008) is applied which distributes the station-years into 4
clusters. The number of clusters is determined using the classifiability index developed by
Michelangeli et al. (1995). Note that other solutions (from 4 to 10 clusters) may be equally
acceptable, but in order to underline the most recurrent combinations, only the 4-cluster solution is
presented here, although it gives a simplified view of the seasons’ variety.

Figure 8 shows the profile of each cluster of station-seasons. Black dots highlight, for each cluster, the descriptors deviating significantly (95% confidence level) from the mean. The key descriptor is the duration of the rainy season : it shows significant deviations from the mean for all 4 clusters. CL1 and CL4 depict abnormally long rainy seasons, while CL2 and CL3 represent abnormally short rainy seasons. Among the years with a long rainy season, CL4 (fig.8, bottom right) depicts those which also recorded a frequent daily rainfall occurrence, both factors contributing to a high seasonal rainfall. This typically reflects “good” rainy seasons. CL4 makes up 28 % of the station-seasons, with an equal distribution between the Long Rains and the Short Rains.

CL1 also reflects long rainy seasons, resulting from an exceptionally early onset (fig.8, upper left).

However, the CL1 seasonal rainfall amount is not particularly high because of a poor distribution of the rain events, with many short wet spells separated by very long dry spells. CL1 occurrence is low (11% of all station-seasons), though slightly more frequent in the Short Rains than in the Long Rains.

In contrast to the uniformly “good” years (CL4), CL2 depicts shortened rainy seasons, accompanied by lower than normal rain days frequency, and low mean daily rainfall intensities
(fig.8, upper right). The net result is an outright “bad” season. CL2 is a frequent occurrence, accounting for one third (34%) of all the station-seasons analyzed. It tends to be slightly more common in the Short Rains than in the Long Rains.

Finally, CL3 combines an equally short rainy season (as in CL2), generally due to an early end of the rains, with an abnormally high daily rainfall intensity (fig.8, lower left). The latter helps to ensure that the total rainfall amount is generally above normal. These short but intense seasons are not very frequent (21% of all station-seasons, in equal proportion for the Long Rains and the Short Rains).

The identification of these basic profiles of seasons is useful in that it sheds light on the most recurrent combinations of rainfall variables accounting for “good” and “bad” seasons. An important fact is that the clusters are not spatially constrained (not shown). No particular type is associated with a particular altitudinal level, except for cluster 0 (total failure of the rains), a rare event which occurs slightly more frequently at low than at high elevations. The joint occurrence of a given profile at all 3 stations is not very common: only 25% of the seasons display such a synchronization. This is due to the fact that, besides the influence of highly-coherent variables (in terms of interannual variability) such as the duration of the rains, the classification includes “local information” having a potentially equally strong effect on the quality of the season, such as intensity. However, 85% of the seasons show relatively similar rainy season profiles (i.e., belonging to the same cluster) in at least two elevation belts, implying that most of the time, some spatial coherence exists which gives the rainy season a definite character at regional scale.

An important issue in terms of food security is the possible occurrence of two consecutive bad seasons (i.e., Short Rains in year 0 and Long Rains in year 1, or Long Rains and Short Rains in the
same year). The consecutive occurrence of two CL2 or/and CL1 seasons does regularly occur (on average, 3 times every 10 seasons, i.e. 5 years). However, whereas bad seasons are regularly reported simultaneously at all elevations, it is very unusual that such generalized drought events persist over several consecutive seasons. During the season following a generalized drought, while dryness may persist say at low altitude, better conditions generally prevail at high altitude (e.g., during the 1984 drought). This may be seen as a factor of resilience to drought. It was certainly recognized as such by local farmers who, as noted above often, had (and sometimes still have) plots scattered across different altitudinal zones.

**3.4. Relationships with large-scale climate forcings**

The strong spatial coherence (both horizontal and vertical) of several rainfall variables, as found above, is hypothesized to result from large-scale forcing mechanisms. Rainfall in Equatorial East Africa, during boreal autumn (Short Rains) is strongly affected by SST anomalies. The vertical distribution of the rains has been examined for years showing anomalous ENSO conditions in the Pacific Ocean, and anomalous zonal SST gradients in the Indian Ocean. The Long Rains season was not considered due to its relatively weak association with large-scale climate forcings.

Figure 9 shows the results obtained for OND seasonal rainfall along the Marimanti (681 m)-Marimba (2120 m) transect, in years of positive and negative anomalies of the SST gradient along the equatorial Indian Ocean. DMI is used as an index of this gradient. Composites were preferred to correlation analysis because of the evidence of non-linear relationships with Indian Ocean SST gradients (Black et al., 2003). Relatively similar results were obtained for a composite of years based on the sign of the Niño-3 index, representative of ENSO (not shown), although the response is much weaker than for DMI. Other topographical transects yielded comparable results. It is found
that the impact of Indian Ocean SST on rainfall is evident at all elevations, although a few years
have a different behaviour. The increase in rainfall with altitude is evident in both the negative and
positive composites. However, the gradient is more linear in negative DMI years (i.e., dry years).
In positive DMI years, the increase in rainfall with altitude is very strong at lower elevations, then
more moderate. There is therefore a change in the vertical rate in the lower slopes only.

The changes associated with anomalous SST are substantial. For instance, the average rainfall recorded in negative DMI years at Marimba (2120 m) corresponds to the long-term mean OND rainfall at 1300m (i.e., close to Mitunguu). This means a 800 m altitudinal shift of rainfall belts. By contrast, the average rainfall recorded in positive DMI years at Tunyai (900m) corresponds to the long-term mean OND rainfall at about 1600m, i.e. above Nkubu. Based on the rainfall gradient between Tunyai and Nkubu, a 0.5°C increase in the SST gradient between the western and the eastern Indian Ocean (DMI) translates into a 700 m elevation shift in the rainfall belt along the eastern slopes of Mount Kenya during OND. This is indicative of the extreme sensitivity of East African rainfall, and associated agro-ecological elevation belts, to Indian and Pacific ocean sea-surface temperature variations.

The analysis of intra-seasonal characteristics of the rains reveals that variations in DMI predominantly affect the number of rainy days (NRD), rather than the mean daily rainfall intensity (fig.10). A shorter rainy season strongly contributes to the reduced NRD in negative DMI years in the Indian Ocean. Both elevation zones have their rainy season shortened. This mainly results from an early end of the rains. Positive DMI years do not affect as strongly the duration of the rains. Their effect on rainfall is more consistent at high (belt A) than at low elevations (belt B). It consists in a very wet season, as a result of both more frequent rainy days (FRD) and a higher
intensity of the rains. A strong increase of the number of intense wet spells is also demonstrated, in zone A only (not shown).

In summary, it is found that positive DMI years mainly affect the within season behaviour of the rains (more frequent rainy days, and more intense events in the upper belt). Negative DMI years mainly affect the duration of the rainy season at both high and low elevations. The linearity of the relationship between rainfall and DMI seems to be slightly greater at high elevations than at low elevations. On the whole however, the general response to DMI does not strongly differ between the two elevation belts.

**3.5. Potential evapotranspiration**

Although rainfall variations are the key player for crop growth, estimate of the full water balance requires the computation of potential evapotranspiration. Its spatial variability, due to the contrasted topography of the Mount Kenya area, is high. Its temporal variability, apart from the mean seasonal variations, has seldom been described for East Africa in general, and Mount Kenya in particular. The space-time variations of the individual components of reference evapotranspiration (ET₀) as from the Penman-Monteith equation are therefore discussed below, for the two stations available, Embu (1433m) and Marimanti (681m).

Vertical temperature gradients are strong (fig.11a-b). The lapse rate for maximum temperature (1.09 °C / 100m elevation on average between Marimanti and Embu) is larger than that of minimum temperature (0.8 °C/100m). It is related to the heavy cloudiness which develops at daytime at high elevations, and contributes to limit the temperature rise. Seasonal variations are significant, despite the location on the equator. The highest maximum temperatures are found
shortly before the onset of the rains, in early March and early October at both locations. Maximum temperatures decrease during the wet seasons. A further drop of maximum temperature occurs during the July-August dry season at high elevations, as a result of the low-level temperature inversion and persistent stratocumulus cloud cover which develop over the Eastern Kenya Highlands at this time of the year (Okoola, 1990 & 2000). The cooling is substantial (temperatures 5 to 6 degrees lower than the warmest periods of the year), and may affect plant growth given that it occurs at altitudes where temperature is already a limiting factor. The seasonal variations of minimum temperature are similar to those of maximum temperature, although the peaks are shifted towards the middle of both rainy seasons, and the annual range is smaller. At Embu, minimum temperature remains between 13 and 16°C.

Other variables do not show any clear variation with elevation. Global radiation is quite similar at Embu and Marimanti, although the annual cycle is more pronounced at Embu (fig.11c). Global radiation peaks at 23 MJ/m2/day in February (small dry season) and drops to half this value in July, again as a result of the persistent dry season stratocumulus cloud cover. Similar but damped variations are found at Marimanti. This could be explained by less cloudiness (during the rainy seasons and the northern summer dry season), but a thicker atmospheric layer (+750m). Mean wind speeds (fig.11d) at both locations are low and do not have any clear spatial pattern. Relative humidity shows a weak vertical gradient (fig.11e). It tends to increase upwards, in conjunction with the lower temperature and higher rainfall. However the difference is large only from May to September, i.e. during the main dry season. Orographic uplift, coupled with the trade-wind inversion, makes Embu lie close to the cloud base and explains the persistence of high (65-75%) relative humidity during this period. By contrast, and as the dry season proceeds, humidity gets lower at Marimanti (50% in September).
As a result, mean ET₀ shows significant changes with elevation (fig.11f), except in January-February. The ET₀ decrease with height is mostly a result of the temperature gradient. Solar radiation variations enhance (June-September) or annihilate (January-February) the vertical gradient. The seasonal variations of ET₀ remain modest, except at Embu where the low solar radiation and temperature in the June-August dry season contribute to reduce ET₀ below 3 mm.day⁻¹. During the rainy seasons, ET₀ ranges from 3 to 5 mm.day⁻¹.

Temporal variability is further examined by showing how much each input parameter contributes in the interannual variability of ET₀. The aim is first to assess how large are year to year ET₀ variations, and second to identify which climatic parameters are most likely to have a significant effect on the crops water balance. To that end, all input parameters but one are assigned to their long-term seasonal means. The singled-out parameter uses the observed time-series. ET₀ is then computed. The contribution of each parameter in terms of ET₀ standard-deviations is provided in table 3. The effect of the interannual variations of all parameters combined is also shown. For both rainy seasons, the interannual variability of ET₀ is small: standard-deviations for all parameters combined are at 0.23-0.24 mm.day⁻¹ at Embu (0.13-0.14 mm.day⁻¹ at Marimanti, but using fixed wind speeds), which is roughly 7% of the mean seasonal ET₀ (3% at Marimanti). The higher interannual variability found at Embu is mostly due to larger variations of global radiation at this station. The contribution of temperature is small at both locations. It is negligible for minimum temperature and still very small for maximum temperature, although slightly higher in the lowlands.

The inter-station consistency was also tested by computing correlation coefficients between the interannual variations (1977-1995) at the two stations, separately for each parameter (table 4). There is a strong correlation for maximum temperature, and for both rainy seasons. Unusually
warm years tend to jointly occur over most of East Africa (Plisnier et al., 2000). They are often associated with abnormally dry conditions. Interannual variations of minimum temperature are less consistent ; a weak correlation only exists in March-May. No correlation between the lower and upper zones is found for global radiation. Although global radiation is the most influential variable on ET₀, this absence of a regional signal is likely to make it a more difficult variable to predict. However, there is a strong positive interaction between maximum temperature and radiation, at least at high elevation. Therefore, the overall ET₀ cross-correlation between the two stations is highly significant (table 4), which suggests that anomalies in water demand are spatially coherent.

On the whole, the altitudinal control of mean monthly ET₀ is evident, mostly as a result of the temperature gradient, with additional modulation explained by solar radiation. Interannual variations of ET₀ are moderate. They are mainly explained by those of global radiation. However, the variables contributing to ET₀ interannual variations display either a strong (maximum temperature) or a weak (global radiation) vertical coherence. This is in line with the fact that the mean seasonal cycles of these variables are strongly (maximum temperature) or weakly (global radiation) controlled by elevation.

**4. Conclusions and discussion**

The detailed analysis of the altitudinal and horizontal variations of rainfall on the eastern slopes of Mount Kenya shows a variety of patterns, depending on which characteristics of rainfall are considered. The simple model of increasingly wet conditions upwards, up to the rainfall maximum found above 2000 m, is not unsupported, but other descriptors of the rains (onset and cessation dates of the rainy seasons, frequency and intensity of the rains, wet and dry spells occurrence, spatial coherence indicators...) show much more diversified relationships with elevation.
Parameters controlling potential evapotranspiration also show very diverse relationships with elevation.

To summarize, four different patterns can be identified :

(i) A relatively linear variation with elevation. This is best exemplified by the mean onset and cessation dates of the March-May (Long Rains) and October-December (Short Rains) rainy seasons. As a result, the mean duration of the two rainy seasons is also strongly constrained by elevation, increasing by 3.6 days (4.2 days) for every additional 100m during the Long Rains (Short Rains). Mean maximum and minimum temperature also show a very clear decrease with elevation, at the strong rate of over 1°C per 100 m for maximum temperature.

(ii) A relatively uniform pattern, with small differences both horizontally (within each altitudinal belt) and vertically (between different altitudinal belts). This is the case for the interannual variability of the Short Rains, which are quite homogeneously modulated by sea-surface temperature zonal gradients in the Indian Ocean and by ENSO. At the larger scale of East Africa, both climatic signals are found to affect quite uniformly seasonal rainfall amounts during this season (Indeje et al., 2000). The data available for mean wind speed and mean global radiation, although limited, do not show strong contrasts along the eastern slopes of Mt Kenya either, except during the June-September dry season.

(iii) A distinctive influence of exposure. Mean daily rainfall intensity, during both rainy seasons, best shows the role of exposure. The highest values are found at stations
having an east to south-east exposure, whatever their altitude. This may reflect the part
played by forced orographic ascent. Among other variables, the frequency of rainy days
shows a relatively linear increase with elevation, but clearly constrained by exposure.
Rates differ between east-facing and south-facing locations.

(iv) *Marked distinctions between different altitudinal belts.* This feature appears for the interannual variability of global radiation, as well as several rainy season characteristics. The duration of the Long Rains illustrates this pattern. While correlations within each elevation belt are strong, they are insignificant between the upper and lower elevation belts. This is a result of the independence of rainfall variability at different elevations towards the end of the season, which implies that a late termination of the rains in the lowlands may not necessarily coincide with a late termination at higher elevations, in contrast to the more uniform behaviour of the onset.

These results demonstrate that for many climatic variables involved in the estimation of the water
balance, in a contrasted, yet relatively simple topographical environment such as that of the eastern
slopes of Mount Kenya, an explicit account of the spatial patterns of these variables should be
provided. This goes beyond the standard application of mean vertical lapse rates. The existence of
distinct patterns of variability at different elevation belts, for instance at the end of the Long Rains
and the beginning of the Short Rains, requires that the impacts of these variables on crop yields
should be tested separately for the low and high elevation zones. A potential desynchronization of
bad and good years at different elevations may be expected.

However, some other variables do show a relative independence to local topography. As judged by
their relationship with Indian Ocean sea-surface temperature, there is only a small modulation by
relief of the impact of large-scale dynamics on several rainfall variables. The outcome is that the
results obtained from predictability studies (and possibly climate projections studies), for these
variables, are likely to remain valid (to some degree) at the various elevation belts. This is at least
true for the Short Rains, the season during which the remote control on East Africa climate is the
strongest.

Finally, some variables, like daily rainfall intensity, vary at local scale and display little spatial coherence, as well as very weak teleconnections with large-scale climate forcings. These variables introduce further spatial diversity in the character of each season at the station scale, as demonstrated by a classification of the seasons. However, even for such variables, some form of spatial organization in the mean patterns does exist (controlled by exposure in the case of daily rainfall intensity) which needs to be taken into account in impact studies.

The complex space-time variations of the climatic parameters along the eastern slopes of Mount Kenya, where agriculture is mostly rainfed, have strong potential effects on farming activities. A better understanding of the interactions of rainfall variations from year to year and farmers' strategy, choice of varieties and date of sowings to mitigate the climatic risks is an important issue for present-day agricultural production and future food security. Increasingly research suggests that the varietal and genetic diversity of crops insure the resilience of the traditional farming systems to rainfall variations (Altieri 1999; Smithson & Lenné, 1996). On the eastern slopes of Mount Kenya, seeds are usually self-produced by farmers by selecting plants from their own harvest. However, this strategy is still impacted by climate variability as major drought events (especially those occurring on two consecutive seasons) may result into significant seed losses. More generally, the effects of year-to-year rainfall variations is supposed to strongly affect subsistence crop yields, and hence food security. This aspect will be discussed in part 2 of this
study, based on trials experimentation conducted at two contrasted sites with local and cross-bred
varieties to parameterize a crop model. A particular focus will be devoted to the role played by the
independent or combined variability of each rainfall characteristics (duration, onset date, intensity
of the rainy season...) on crop growth, an issue rarely considered in previous studies.

Acknowledgements

This study is a contribution to the PICREVAT project, funded by the French National Research
Agency (ANR 08-VULN-01-008). Calculations were performed using HPC resources from DSI-
CCUB (Université de Bourgogne).
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List of figures

Fig.1 : Elevation map and station location. The dashed lines show the northern and southern elevation transects. Large dots show stations having daily data.

Fig.2 Mean monthly rainfall amounts along the northern and the southern elevation transects

Fig.3 : Mean duration of the rainy season (top panels) and mean frequency of rainy days (FRD, bottom panels), with respect to elevation. Left panels : March-May "Long Rains"; right panels October-December "Short Rains". The dashed lines show the best fit with elevation (top panels: polynomial regression ; bottom panels: linear regression). The thick blue lines correspond to the southern sector, the thin green lines correspond to the northern sector.

Fig.4 : Mean rainfall intensity per rain-day (mm) for the Long Rains (a) and the Short Rains (b)

Fig.5 : Interannual correlations between monthly rainfall for all possible couples of stations (each small dot corresponds to one couple), plotted with respect to the altitudinal difference between the two stations. Vertical dashed line : 95% confidence level. The central black dots indicate the mean correlation. Regression lines are shown when a statistically significant (95% c.l.) dependency exists between the inter-station correlation and the elevation difference between the stations.

Fig.6 : Interannual correlations between monthly rainfall at all possible couples of stations, plotted with respect to the altitudinal difference between the two stations and their horizontal distance. Small dots in the January panel indicate the spatial sampling of the individual stations to which the reference station (large dot) is correlated.

Fig.7 : Median onset dates (solid line) and cessation dates (dashed line) of the Long Rains, based on data for all available stations. Double vertical lines indicate years with no rainy season at all (i.e., no start detected in at least 25% of the stations – 1973, 1984 and 2000), and gaps indicate seasons with an insufficient number of stations available. Black vertical bars for onset and cessation give the spread between all stations, defined as the median of the inter-station differences. Large dots indicate years for which the inter-station spread does not exceed 3 days. Horizontal lines show the mean dates.
Fig.8 : Classification of seasons with respect to their seasonal rainfall profiles. Box-plots show intra-cluster variability for each descriptor. The percentage of seasons pertaining to each cluster is indicated above (note that cluster 0, corresponding to total rain failure, accounts for 5% of all seasons). Black dots specify when the anomaly from the mean (zero line) is significant (Student t-test, 95%). All variables are shown as standardized anomalies.

Fig.9 : OND Rainfall distribution along the northern transect of Mount Kenya, on average and for the 8 most positive (+ signs) and 8 most negative (stars) anomalies of the Indian Ocean DMI, over the period 1961-2000. The dashed lines show the mean rainfall composite for the two groups of DMI anomalies.

Fig.10 : Composite anomalies (normalised) of all intra-seasonal variables for the 9 most positive and 9 most negative DMI years (OND index, 1961-2006). Group A (upper panels) : upper stations; group B (bottom panels) : lower stations. Stars indicate 95% significance according to a one-sided t-test.

Fig.11 : Mean annual cycles of the climatic parameters involved in the computation of reference evapotranspiration (ET₀), at Embu (1433 m) and Marimanti (681 m).
Table 1 : Relationship between elevation and the long-term means of each rainfall variable at the 11 stations. The inter-station average is provided in the first column of data for each season. Significant values are in bold. NS : not significant.

Table 2 : Median confidence levels for the inter-station correlations between the interannual variations of various seasonal characteristics of the rains (all couples of stations within agro-ecological belt A, within agro-ecological belt B, or between belts A and B). Stars indicate significant correlations at the 99% level (***), 95% level (**) and 90% level (*). Blank : non significant. Period of study : 1961-2006 or shorter depending on stations (minimum number of years : 10). The number of couples of stations is shown between brackets in the second column.

Table 3 : ET₀ standard-deviations (mm.day⁻¹) associated with observed interannual variations of a single parameter (long-term means used for the other parameters), at Embu and Marimanti. Values above 0.1 mm.day⁻¹ are highlighted. The right column indicates the long-term mean ET₀ values.

Table 4 : Correlations between interannual variations of seasonal means of temperature and global radiation at Embu and Marimanti (1977-1995). Stars indicate significant correlations at the 99% level (***), 95% level (**) and 90% level (*). NS : non significant.
<figure><img src="image_3.png" /><figcaption>Fig.1: Elevation map and station location. The dashed lines show the northern and southern elevation transects. Large dots show stations having daily data.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig.2 Mean monthly rainfall amounts along the northern and the southern elevation transects</figcaption></figure>
<figure><img src="image_5.png" /></figure>

Fig.3: Mean duration of the rainy season (top panels) and mean frequency of rainy days (FRD, bottom panels), with respect to elevation. Left panels: March-May "Long Rains"; right panels: October-December "Short Rains". The dashed lines show the best fit with elevation (top panels: polynomial regression; bottom panels: linear regression). The thick blue lines correspond to the southern sector, the thin green lines correspond to the northern sector.

<figure><img src="image_6.png" /></figure>

Fig.4: Mean rainfall intensity per rain-day (mm) for the Long Rains (a) and the Short Rains (b)
<figure><img src="image_7.png" /></figure>

Fig.5: Interannual correlations between monthly rainfall for all possible couples of stations (each small dot corresponds to one couple), plotted with respect to the altitudinal difference between the two stations. Vertical dashed line: 95% confidence level. The central black dots indicate the mean correlation. Regression lines are shown when a statistically significant (95% c.l.) dependency exists between the inter-station correlation and the elevation difference between the stations.
<figure><img src="image_8.png" /><figcaption>Fig.6: Interannual correlations between monthly rainfall at all possible couples of stations, plotted with respect to the altitudinal difference between the two stations and their horizontal distance. Small dots in the January panel indicate the spatial sampling of the individual stations to which the reference station (large dot) is correlated.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig.7: Median onset dates (solid line) and cessation dates (dashed line) of the Long Rains, based on data for all available stations. Double vertical lines indicate years with no rainy season at all (i.e., no start detected in at least 25% of the stations – 1973, 1984 and 2000), and gaps indicate seasons with an insufficient number of stations available. Black vertical bars for onset and cessation give the spread between all stations, defined as the median of the inter-station differences. Large dots indicate years for which the inter-station spread does not exceed 3 days. Horizontal lines show the mean dates.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Fig.8: Classification of seasons with respect to their seasonal rainfall profiles. Box-plots show intra-cluster variability for each descriptor. The percentage of seasons pertaining to each cluster is indicated above (note that cluster 0, corresponding to total rain failure, accounts for 5% of all seasons). Black dots specify when the anomaly from the mean (zero line) is significant (Student t-test, 95%). All variables are shown as standardized anomalies.</figcaption></figure>
<figure><img src="image_11.png" /></figure>

Fig.9: OND Rainfall distribution along the northern transect of Mount Kenya, on average and for the 8 most positive (+ signs) and 8 most negative (stars) anomalies of the Indian Ocean DMI, over the period 1961-2000. The dashed lines show the mean rainfall composite for the two groups of DMI anomalies.
<figure><img src="image_12.png" /></figure>

Fig.10 : Composite anomalies (normalised) of all intra-seasonal variables for the 9 most positive and 9 most negative DMI years (OND index, 1961-2006). Group A (upper panels) : upper stations; group B (bottom panels) : lower stations. Stars indicate 95% significance according to a one-sided t-test.
<figure><img src="image_13.png" /><figcaption>Fig.11 : Mean annual cycles of the climatic parameters involved in the computation of reference evapotranspiration (ET0), at Embu (1433 m) and Marimanti (681 m).</figcaption></figure>
<table><caption>Table 1: Relationship between elevation and the long-term means of each rainfall variable at the 11 stations. The all-station average is provided in the first column of data for each season. The altitudinal gradient is the linear rate of increase for each additional 100 m of elevation. Significant values are in bold. NS: not significant.</caption><thead><tr><th rowspan="3">Rainfall variable</th><th colspan="3">'Long Rains'</th><th colspan="3">'Short Rains'</th></tr><tr><th rowspan="2">11-station average</th><th colspan="2">Altitudinal gradient</th><th rowspan="2">11-station average</th><th colspan="2">Altitudinal gradient</th></tr><tr><th>Rate / 100m elev.</th><th>Statistical significance</th><th>Rate / 100m elev.</th><th>Statistical significance</th></tr></thead><tbody><tr><td>Seasonal total</td><td>510 mm</td><td><strong>+26.7 mm</strong></td><td><strong>95%</strong></td><td>600 mm</td><td><strong>+32.9 mm</strong></td><td><strong>95%</strong></td></tr><tr><td>Number of rainy days</td><td>27 days</td><td><strong>+2.1 days</strong></td><td><strong>99%</strong></td><td>33 days</td><td><strong>+2.1 days</strong></td><td><strong>99.9%</strong></td></tr><tr><td>Frequency of rainy days</td><td>0.42 day<sup>-1</sup></td><td><strong>+0.009 day<sup>-1</sup></strong></td><td><strong>99%</strong></td><td>0.44 day<sup>-1</sup></td><td>+0.002 day<sup>-1</sup></td><td>NS</td></tr><tr><td>Intensity per rain-day</td><td>19.5 mm</td><td>-0.6 mm</td><td>NS</td><td>18.0 mm</td><td>-0.2 mm</td><td>NS</td></tr><tr><td>Duration</td><td>64 days</td><td><strong>+3.6 days</strong></td><td><strong>99%</strong></td><td>74 days</td><td><strong>+4.2 days</strong></td><td><strong>99.9%</strong></td></tr><tr><td>Onset date</td><td>15 Mar</td><td><strong>-1.1 days</strong></td><td><strong>98%</strong></td><td>11 Oct</td><td><strong>-2.8 days</strong></td><td><strong>99.9%</strong></td></tr><tr><td>Cessation date</td><td>17 May</td><td><strong>+2.5 days</strong></td><td><strong>98%</strong></td><td>24 Dec</td><td><strong>+1.4 days</strong></td><td><strong>99%</strong></td></tr><tr><td>Length of dry spells</td><td>3 days</td><td>-0.002 days</td><td>NS</td><td>3.4 days</td><td>+0.006 days</td><td>NS</td></tr><tr><td>Length of wet spells</td><td>2.3 days</td><td><strong>+0.07 days</strong></td><td><strong>99%</strong></td><td>2.6 days</td><td><strong>+0.07 days</strong></td><td><strong>99%</strong></td></tr><tr><td>Intensity of wet spells</td><td>16.2 mm.day<sup>-1</sup></td><td><strong>-0.25 mm.day<sup>-1</sup></strong></td><td><strong>90%</strong></td><td>14.7 days</td><td>-0.24 mm.day<sup>-1</sup></td><td>NS</td></tr></tbody></table>

<table><caption>Table 2: Median confidence levels for the inter-station correlations between the interannual variations of various seasonal characteristics of the rains (all couples of stations within agro-ecological belt A, within agro-ecological B, or between belts A and B). Stars indicate significant correlations at the 99% level (***), 95% level (***) and 90% level (*). Blank: non significant. Period of study: 1961-2006 or shorter depending on stations (minimum number of years: 10). The number of couples of stations is shown between brackets in the second column.</caption><thead><tr><th></th><th></th><th>Seasonal Total</th><th>NRD</th><th>FRD</th><th>Intensity</th><th>Duration</th><th>Onset</th><th>End</th></tr></thead><tbody><tr><td rowspan="3">LONG RAINS</td><td>A: Upper-slopes (5)</td><td>***</td><td>***</td><td>*</td><td></td><td>**</td><td>***</td><td></td></tr><tr><td>B: Lower-slopes (5)</td><td>*</td><td>***</td><td></td><td></td><td>**</td><td>***</td><td></td></tr><tr><td>A vs B (13)</td><td>**</td><td>***</td><td></td><td></td><td></td><td>*</td><td></td></tr><tr><td rowspan="3">SHORT RAINS</td><td>A: Upper-slopes (5)</td><td>***</td><td>***</td><td>**</td><td></td><td>***</td><td>*</td><td>***</td></tr><tr><td>B: Lower-slopes (5)</td><td>***</td><td>***</td><td></td><td></td><td>**</td><td>*</td><td>**</td></tr><tr><td>A vs B (11)</td><td>***</td><td>***</td><td></td><td></td><td>**</td><td>*</td><td>**</td></tr></tbody></table>
<table><caption>Table 3: ET₀ standard-deviations (mm.day⁻¹) associated with observed interannual variations of a single parameter (long-term means used for the other parameters), at Embu and Marimanti. Values above 0.1 mm.day⁻¹ are highlighted. The right column indicates the long-term mean ET₀ values.</caption><thead><tr><th>Season</th><th>Station</th><th>Tn</th><th>Tx</th><th>Rg</th><th>Wind speed</th><th>All combined</th><th>Mean ET₀</th></tr></thead><tbody><tr><td rowspan="2">March-May</td><td>EMBU (1433 m, 1977-2006)</td><td>0.01</td><td>0.06</td><td><strong>0.21</strong></td><td>0.07</td><td><strong>0.24</strong></td><td>3.69</td></tr><tr><td>MARIMANTI (681 m, 1973-1995)</td><td>0.02</td><td><strong>0.10</strong></td><td><strong>0.10</strong></td><td>N/A</td><td><strong>(0.14)</strong></td><td>4.90</td></tr><tr><td rowspan="2">October-December</td><td>EMBU</td><td>0.01</td><td>0.05</td><td><strong>0.16</strong></td><td>0.06</td><td><strong>0.23</strong></td><td>3.61</td></tr><tr><td>MARIMANTI</td><td>0.02</td><td>0.07</td><td>0.08</td><td>N/A</td><td><strong>(0.13)</strong></td><td>4.78</td></tr></tbody></table>

<table><caption>Table 4: Correlations between interannual variations of seasonal means of temperature and global radiation at Embu and Marimanti (1977-1995). Stars indicate significant correlations at the 99% level (***), 95% level (**), and 90% level (*). NS: non significant.</caption><thead><tr><th></th><th>Tn</th><th>Tx</th><th>Rg</th><th>ET₀</th></tr></thead><tbody><tr><td>March-May</td><td>*</td><td>***</td><td>NS</td><td>***</td></tr><tr><td>October-December</td><td>NS</td><td>**</td><td>NS</td><td>***</td></tr></tbody></table>