- Email: [email protected]
Analysis of rainfall climate on the Njemps Flats, Baringo District, Kenya
Journal of Arid Environments (2002) 50: 445–458 doi:10.1006/jare.2001.0917, available online at http://www.idealibrary.com on
Analysis of rainfall climate on the Njemps Flats, Baringo District, Kenya
E.C. Kipkorir Faculty of Agricultural and Applied Biological Sciences, Institute for Land and Water Management, K.U. Leuven University, Vital Decosterstraat 102, 3000 Leuven, Belgium (Received 7 February 2001, accepted 24 August 2001) Rainfall and reference crop evapotranspiration are analysed for Njemps Flats, a semi-arid area in Baringo District, Kenya. Examining the monthly rainfall totals, there is no significant difference in rainfall amount between two stations 17 km apart but daily rainfall totals tend to become independent. Rain-feed agriculture is very risky. Rainfall inclusion in irrigation scheduling causes difficulties. Annual and monthly rainfall was homogenous between 1965 and 2000. The primary reason for the worst droughts in the area is the failure of April–May rainfall. Examining annual rainfall, the lightest rainfall events have become more frequent. The heaviest rainfall events are infrequent but they make up a significant percentage of the total rainfall. Annually, the number of rain-days has decreased with time but rainfall shows no similar decrease, inferring that the rainfall amount per rain-day is increasing. Rainfall amount for the months of May and November has decreased with time while rainfall amounts per rain-day for the months of April, July and August are increasing. Land and water management schemes in the area must be designed to cope with drought periods as part of supply characteristic of the area. # 2002 Elsevier Science Ltd. Keywords: dry spell duration; evapotranspiration; frequency analysis; rainfall; storm; semi-arid
Introduction In the next few decades the world’s population is expected to grow from 6 billion today to at least 8 billion by the year 2025, with about 90% of the increase being added to the developing world. Therefore, it is clear that achieving food security in developing countries will continue to pose major challenges to decision-makers in the next few decades. Kenya consists of about 80% arid and semi-arid land supporting about 20% of population and over 50% of livestock (Gwynne, 1981; Bernard, 1985). Kenya, which is one of the developing countries, has currently a population of 30 million with a growth rate of about 3%. This, combined with the limited land availability in the agriculturally productive lands, has resulted in the extension of E-mail: [email protected] 0140-1963/02/030445 + 14 $35.00/0
# 2002 Elsevier Science Ltd.
446
E.C. KIPKORIR
farming into agriculturally less productive land, mainly the drier areas, resulting in rapid land degradation. Land degradation resulting from population and livestock increase, and associated overgrazing, is a long-established problem in most of the dry land areas of Kenya (Bryan & Southerland, 1989). With the current high population growth rate, the present pressure on marginal lands seems to be greater than ever. The Njemps Flats, located to the west of lake Baringo in Baringo District in the Rift Valley Province of Kenya, is a typical example of such marginal lands. In this area, the number of development projects and rehabilitation programs for soil and water conservation has increased in the recent past. Some of the major aims of these projects are management of scarce water resources for human consumption and agriculture, rainwater harvesting, and soil moisture conservation. Rainfall is the most important environmental factor limiting the development of semi-arid regions of the tropics. Soil moisture management in semi-arid areas of the tropics is faced with the following constraints: limited and unreliable rainfall, high variability in rainfall pattern and high evapotranspiration rate. Therefore, it is necessary to understand the spatial and temporal variability of the amount of rainfall received and its relationship with evapotranspiration rate in order to develop effective management strategies. A frequency analysis of rainfall and evapotranspiration rates in the semi-arid Njemps Flats is becoming increasingly important since there have been dramatic stock and human population increases. The critical importance of rainfall to the area was noted in the drought years of 1984 and 2000 when very high herd reductions occurred. However, scarcity of meteorological stations, poor-quality data, and the problem of obtaining recent data complicate frequency analysis of rainfall and evapotranspiration in semi-arid areas. The present study analyses the rainfall and evapotranspiration on the Njemps Flats in a semi-arid area. Of particular interest in this study is (i) the frequency of drought, dry, normal and wet years, (ii) when wet spells are likely to occur, (iii) frequency analysis of dry spells during the main growing season and (iv) distribution of daily rainfall totals by frequency and amount. The rainfall parameters analysed were rainfall variability, homogeneity of rainfall, monthly rainfall distribution, rainfall periodicity, frequency of drought and wet years and annual rain-days. The evapotranspiration parameter analysed was monthly evapotranspiration distribution. The study therefore provides essential data for several local research programs, rehabilitation projects and irrigation scheduling in the irrigation projects located in the area. The study area There were only two meteorological stations, Perkerra Research station (next to Marigat) and Snake Farm (on the shores of Lake Baringo and 17 km from Marigat), located within the Njemps Flats in Baringo District in the Rift Valley Province of Kenya (Fig. 1). The two meteorological stations are 17 km apart. The general elevation of the Flats varies between 920 and 1230 m a.s.l. The study area is centred about latitude 00130N and longitude 36100E. Monthly average temperatures range from 241C in August to 261C in March, with monthly mean maximum temperatures ranging from 311C to 341C (Kamar, 1992). The soil texture found in the area ranges from silt loam to clay with silt loam dominating the surface soils. Although potentially fertile, the silt loam crusts readily and encourages surface runoff. Materials and methods Daily rainfall data were obtained from Snake Farm for the period 1966–1998, and from Perkerra Research Station for the period 1965–2000. The data length was 33
ANALYSIS OF RAINFALL CLIMATE
447
Figure 1. Location map of the study area.
and 36 years, respectively, which are both greater than the 30 years of climatic data needed to do accurate climatic analyses for the tropics (Stewart, 1988). The two weather stations were compared in order to determine if the rainfall records at the stations are related. This was done by establishing the relationship between daily cumulative rainfall at the two stations and by comparing summary statistics for the rainfall in the two stations. Monthly reference evapotranspiration for the period 1983–1993 was calculated using the data for Perkerra Research Station. These were the only available data for the period of study (1965–2000) for the two stations. The calculation method used was the FAO Penman–Monteith equation (Allen et. al., 1998), given in Eq (1). 0 408ðRn GÞ þ g½900=ðT þ 273Þu2 ðes aa Þ ðEqn1Þ ETo ¼ þ gð1 þ 0 34u2 Þ where ETo is the reference evapotranspiration (mm day1), Rn is the net radiation at the crop surface (MJ m2 day1), G is the soil heat flux density (MJ m2 day1), T is the air temperature at 2 m height (1C), u2 is the wind speed at 2 m height (m s1), es is the saturation vapour pressure (kPa), ea is the actual vapour pressure (kPa), (esea) is the saturation vapour pressure deficit (kPa), D is the slope vapour pressure curve (kPa 1C1) and g is the psychrometric constant (kPa 1C1).
448
E.C. KIPKORIR
The monthly reference crop evapotranspiration, frequency analysis was done using the RAINBOW software program (Raes et al., 1996). The program is designed to test the homogeneity of hydrologic records and to execute a frequency analysis of rainfall and evaporation data. The program is especially suitable for predicting the probability of occurrence of either low or high rainfall amounts. In RAINBOW either the normal or Gumbel (extreme value type 1) distribution can be selected. Using the mean annual precipitation and mean annual reference evapotranspiration, the Agro-climatic zonation was calculated using the United Nations Educational, Scientific and Cultural Organization (UNESCO) aridity index (AI ) given in (Rodier, 1985). P ðEqn2Þ AI ¼ ETo where P is the mean annual rainfall (mm) and ETo is the mean annual reference crop evapotranspiration (mm). Variability and trends in rainfall pattern were highlighted by a 3-year moving average. Annual and monthly rainfall data were first tested for normality using the RAINBOW program. This was done by comparing the rainfall data to the theoretical normal probability distribution curve and assessing how well the data fitted the normal distribution curve. If the data did not fit the normal distribution curve, the data were first transformed using logarithm or square root functions and then regressed to the theoretical normal distribution curve (Raes et al., 1996). The regression coefficient of determination (R2) was taken as a measure of goodness of fit. Homogeneity of the rainfall for the years 1965–2000 was studied by testing the cumulative normalized deviation from the mean at 90%, 95% and 99% confidence levels (Buishand, 1982). Monthly and annual dependable rainfall (20%, 50%, 80% and 90% exceedance) was calculated using normal distribution, transforming the data where necessary, using RAINBOW. The 20% dependable rainfall is expected on average to be exceeded in 1 out of 5 years, 50% in 1 out of 2 years, 80% in 4 out of 5 years and 90% in 9 out of 10 years. The frequency of droughts, dry, normal and wet years was estimated as follows: the 20% and 80% dependable rainfall were calculated using RAINBOW. Each year with annual rainfall above the 20% dependable rainfall was taken to be a wet year, each year with annual rainfall less than the 80% dependable rainfall was taken to be a dry year. Each year with annual rainfall in between 20% and 80% dependable rainfall was taken to be a normal year (Raes et al., 1996). A drought year was defined as a year with annual rainfall less than 90% dependable rainfall on the assumption that droughts in East Africa region occur with a frequency greater than 1 in 10 years. The number of droughts, dry, normal and wet years were then counted and expressed as a percentage of the total number of years on record for the station. This gave estimates for the frequency (probability of occurrence) of drought, dry, normal and wet years for the 1965–2000 period. The accuracy of these estimates will increase as the length of the climatic record increases. The probability of a wet day was estimated using Eqn (3). The number of days (ni) that were wet for each date in the year were counted and expressed as a fraction of the total number of years (Ns) on record for the station. This gave an estimate for the probability of wet days for each day of the year. A 30-year period is long enough to establish a longterm climatology for a region or station (Aldabadh et al., 1982). Since the climatic record was 33 and 36 years (1965–2000), the sample estimate is reasonably closer to the population’s probability of wet days. A day was considered to be wet when there was more than 1 mm of rainfall and dry when rainfall was 1 mm or less. The probability of wet day vs. time was plotted to show the time when it is likely to be dry or wet. PðwetÞ ¼
ni Ns
ðEqn3Þ
ANALYSIS OF RAINFALL CLIMATE
449
The distribution of dry spell of ND days (where ND varies from 1 to an observable maximum for the station) was estimated by a frequency analysis of historic dry spell data (Binh et al., 1994). This was achieved by counting the number of times (frequency) a dry spell of a specified duration occurred during the main growing season in each year of the recorded period of the station (1965–2000) followed by computation of respective weighted dry spell duration. Using this information, the distribution of dry spell duration during the main growing season by frequency was obtained, using the RAINBOW software. A major application of dry spell analysis is to predict extended drought durations during the growing season, which forms a basis for planning the crop production strategies (Sharma, 1996). The distribution of daily rainfall totals by amount and frequency was obtained using a frequency analysis of historic daily rainfall. This was achieved by counting the number of times (frequency) a daily rainfall of specified amount occurred during the recorded period for the station (1965–2000). Daily rainfall in the Njemps Flats almost always falls in association with one weather event. Thus, it is reasonable to assume that the daily rainfall totals can be considered as being equivalent to storm totals (Rowntree, 1988). Using this information, the distribution of monthly storm totals by frequency and amount was obtained by frequency analysis of historic daily rainfall in each month, using the RAINBOW software.
Results and discussion Annual rainfall data were compared for a period of 33 years (1966–1998) for the two stations (Fig. 2). A statistically significant correlation was established at an a ¼ 0?05 for annual rainfall between the two stations (i.e. r ¼ 0?81), but this correlation reduces to r ¼ +0?38 for daily rainfall over the same period. Monthly comparisons of rainfall between the two stations over the 33-year period indicated that rainfall at both stations for all months was also related at statistically significant level (a ¼ 0?05) (i.e. r values
Deviation from mean annual rainfall (mm)
500 400 300 200 100 0 − 100 − 200 − 300 − 400 − 500 1965
1970
1975
1980
1985
1990
1995
2000
Year Figure 2. Time series of deviations from the mean annual rainfall for Perkerra Research Station (1965–2000) and Snake Farm (1966–1998): Perkerra Research Station ( ); Snake ). Farm (
450
E.C. KIPKORIR
varied from +0?50 in September to +0?92 in February). Cumulative daily rainfall at Perkerra Research for the 33-year period was approximately 3% lower than at Snake Farm. The coefficient of determination (r2) is estimated as 0.99 (Fig. 3). The mean annual rainfall and the 95% confidence band was 654758 and 674763 mm for Perkerra Research and Snake Farm stations, respectively. For the annual precipitation totals, therefore, there is no significant difference between the two stations. However, once the temporal scale is reduced to the daily scale rainfall totals at the two stations become more independent. Since differences between the two stations were not significant, the complete rainfall record for Perkerra Research (1965–2000) was assumed to be representative of the semi-arid Njemps Flats (700 km2). The relationship between monthly dependable (20%, 50% and 80% levels) rainfall and reference crop evapotranspiration is shown in Fig. 4. The reference crop evapotranspiration is calculated using the FAO Penman–Monteith equation (Allen et al., 1998). From Fig. 4, it can be seen that the expected rainfall in dry and normal years is less than the reference crop evapotranspiration throughout the year. In a wet year only the months of April and July expect rainfall that is higher than the reference crop evapotranspiration. From the results shown in Fig. 4, the moisture deficit is estimated to be about 670, 1250 and 1700 mm year1 for wet, normal and dry years respectively. Therefore, rain-feed agriculture is very risky in this area. Furthermore, using the same figure it is found that the monthly 20% and 80% dependable reference crop evapotranspiration is in the range 712 mm, the monthly mean value; therefore, it can be concluded that reference crop evapotranspiration is conservative. The AI value is calculated for the two stations using Eqn (2). The P value is taken as 630 mm for Perkerra Research Station and 670 mm for Snake Farm while the ETo value is taken as 1800 mm. The mean AI for the two stations is 0?36, and AI values that range between 0?2 and 0?5 are classified as semi-arid. Therefore the Njemps Flats can be classified as a semi-arid area. The results of homogeneity tests for annual and monthly rainfalls for the two stations show that annual and monthly rainfall was homogenous between 1965 and 2000. The annual rainfall in the two stations was nearly normally distributed. The coefficient of determination (r2) values for normal distribution were 0?98 and 0?99 for the two stations. The distribution of drought, dry, normal and wet years for the
Cumulative daily rainfall perkerra research station (mm)
24,000
20,000
16,000
12,000
8000
4000
0
0
4000
8000
12,000
16,000
20,000
24,000
Cumulative daily rainfall snake farm (mm) Figure 3. Relationship between daily cumulative rainfall (.) at Perkerra Research Station and Snake Farm stations, 1966–1998. ( – is 1:1 line).
Dependable rainfall and evapotranspiration, _ ETo (mm month 1)
ANALYSIS OF RAINFALL CLIMATE
451
200 180 160 140 120 100 80 60 40 20 0 Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month Figure 4. Monthly variation in dependable reference crop evapotranspiration (ETo) and monthly dependable rainfall (20%, 50% and 80% levels) at Perkerra research station: 80% Dep ETo ( ); 20% Dep Rainfall ( ); 50% Dep Rainfall ( ); 80% Dep Rainfall ( ); 20% Dep ETo ( ); 50% Dep ETo ( ).
1965–2000 period is shown in Fig. 5. From the figure it can be seen that in two out of 3 years, the rainfall received is within the normal range, in one out of 9 years there is drought, in one out of 6 years the year is wet and in one out of 18 years it is dry. The monthly rainfall data were required to be transformed using logarithm or square root functions, and the transformed data were found to be nearly normally
Figure 5. Distributions of drought, dry, normal and wet years in Njamps Flats (1965–2000).
452
E.C. KIPKORIR
Table 1. Monthly variation in dependable rainfall at Perkerra research station
Month
Monthly dependable rainfall (mm)
January February March April May June July August September October November December
Transformation
20%
50%
80%
35 49 89 153 100 98 146 127 44 66 71 38
5 15 34 81 67 56 84 70 23 38 36 16
0 0 3 31 33 26 39 30 8 17 18 2
Logarithm Square root Square root Square root Non Square root Square root Square root Square root Square root Logarithm Square root
distributed, with coefficient of determination (r2) ranging from 0?94 to 0?99. The results are shown in Table 1. Inter-annual variability in Njemps Flats annual rainfall was highlighted by calculating a 3-year moving annual rainfall average for the two stations. Typical inter-annual variability in rainfall is highlighted in Fig. 6. From Fig. 6, it appears that the Njemps Flats rainfall is periodic whereby wet periods are followed by dry periods and again by wet periods and so on. This suggests a relationship with El Nino and further research is needed to demonstrate such a relationship. In order to examine wet day probabilities, a 10-day moving average was applied to the data from Perkerra Research station; the results are shown in Fig. 7. From Fig. 7 it can be seen that the probability of a day being wet throughout the year is less
1000 900
Annual rainfall (mm)
800 700 600 500 400 300 200 100 0 1965
1970
1975
1985
1980
1990
1995
2000
Year Figure 6. Three-year moving annual rainfall averages at Perkerra Research Station (1965– 2000) and Snake Farm stations (1966–1998): Perkerra Research Station (3 year moving average ( ); Snake Farm (3 year moving average ( ).
ANALYSIS OF RAINFALL CLIMATE
453
0.40
Probabilty of a wet day
0.35 0.30 0.25 0.20 0.15 0.10 0.05 0
1
29
57
85
113
141
169
197
225
253
281
309
337
365
Day of the year Figure 7. Probability of a wet day (10-day moving average) during the year (1 January–31 December) for Perkerra Research Station.
than 0?4. At a probability level of 0?20 of a day being wet, three periods can be identified. The first period starting on 30 March and ending on 30 May is about 61 days long. The second period starting on 12 June and ending on 8 September is about 88 days long. The third period starting on 15 October and ending on 29 November is about 45 days long. The first two periods form the main growing season. For these periods the results show that if rainfall is included in irrigation scheduling it causes difficulties because the probability of a day being wet is low. In order to examine the frequency of dry spell duration during the main growing season (30 March–8 September), the weighted dry spell duration was calculated using the data from Perkerra Research Station, the results are shown in Fig. 8. Using this analysis, it can be shown that during the main growing season, dry spell duration was moderately weak correlated with time (r ¼ +0?35). During the main growing season, it can also be shown that the number of rain-days was inversely correlated with time (r ¼ 0?33), and rainfall was significantly correlated with number of rain-days (r ¼ +0?70). Though the number of rain-days has decreased with time during the main growing season at Perkerra Research Station, rainfall shows no similar decrease (r ¼ +0?04). Therefore, it can be inferred from these data that dry spell duration during the main growing season is increasing. This may result in significant decline in crop yield (Oladipo & Kyari, 1993). The weighted dry spell duration data required to be transformed using logarithm function and the transformed data were found to be normally distributed. The dry spell duration events corresponding to 20%, 50% and 80% probability of exeedance during the main growing season were estimated as 6?1, 4?5 and 3?4 days. From Figs. 8 and 2 it can be seen that the 1984 and 2000 droughts were the worst. In both these events the primary reason for the drought was the failure of April–May rainfall. The annual distribution of storms is summarized in Fig. 9 which shows both the frequency distribution of storms producing various rainfall amounts (Fig. 9(a)) and estimate of percentage of annual rainfall falling in storms within each class (Fig. 9(b)). The lightest rainfall events are more frequent, with 82?4% of the storms producing
454
E.C. KIPKORIR
12
Weighted dry spell duration (days)
11 10 9 8 7 6 5 4 3 2 1 0 1965
1970
1975
1980
1985
1990
1995
2000
Year Figure 8. Time series of weighted dry spell duration during the main growing season (30 March–8 September) for Perkerra Research Station (1965–2000).
less than 10 mm but accounting for only 43?1% of the total rainfall. Equivalent figures for 1 mm are 27?2% and 1?9%. From the graphs (Fig. 9), it can be seen that 12?2% of the storms and 45?9% of the total rainfall amounts equal or exceed the 15 mm required for rainwater harvesting. Although heavier rainfall events are relatively infrequent, they make up a significant percentage of the total rainfall. Only 3?8% of the storms produce 30 mm of rainfall or more, yet they account for 24?7% of the annual rainfall total. Equivalent figures for 50 mm rainfall events are 1?2% and 10?7% of the total rainfall. The monthly storm data required to be transformed using logarithm function and the transformed data were found to be normally distributed. The results are shown in Table 2. The results show that rainstorm activity is quite variable within the period of record, and nine different months have provided the greatest daily rainfall amounts. Although the heaviest daily total in the 36-year period was received in March (121?9 mm), July has tended to have the heaviest daily totals, followed by April, June and August in decreasing order (Table 2). Catastrophic events, such as breakdown of conservation works, are therefore most likely to occur in these months. If 15 mm is taken to be the minimum amount of daily rainfall required to guarantee effective rainwater harvesting (Roberts, 1985), Table 2 demonstrates that the probability of such a storm occurring in any 1 year is highest in July (P ¼ 0?372), followed by April (P ¼ 0?359), June (P ¼ 0?324) and August (P ¼ 0?321). Thus, the probability of receiving a storm favourable to rainwater harvesting is highest within the main growing season. The relationship between annual rainfall and annual number of rain-days is shown in Fig. 10. Using this analysis, it can be shown that annual rainfall was significantly correlated with the annual number of rain-days (r ¼ +0?74), and the annual number of rain-days was inversely correlated with time (r ¼ 0?37). Though the number of rain-days has decreased with time at Perkerra Research Station, rainfall shows no similar decrease (r ¼ +0?03). Therefore, it can be inferred from these data that the rainfall amount per rain-day is increasing while the frequency of rain-days is decreasing. Sutherland et al. (1991) found similar results in their earlier
ANALYSIS OF RAINFALL CLIMATE
455
100 90
Cumulative % frequency
80 70 60 50 40 30 20 10 0 0
10
20
30
40
50
60
70
80
90
100
110
120
130
90
100
110
120
130
Daily rainfall (mm)
(a) 100 90
Cumulative % depth
80 70 60 50 40 30 20 10 0 0 (b)
10
20
30
40
50
60
70
80
Daily rainfall (mm)
Figure 9. The distribution of daily rainfall at Perkerra Research Station by (a) frequency, and (b) amount.
study, in the same region, for the time period 1958–1986. The analysis, not shown here, for the months April, May, July, August and November, demonstrates that rainfall was significantly correlated with number of rain-days (r ¼ +0?81, +0?70, +0?73, +0?75, +0?76, respectively). Further, the number of rain-days for the months April, May, July, August and November was inversely correlated with time for most of the months (r ¼ 0?16, 0?19, 0?12, 0?31, +0?09, respectively). The correlation coefficients between rainfall and time for the months April, May, July, August and November were r ¼ +0?01, 0?18, +0?08, +0?02, 0?12, respectively. Therefore, it can be inferred from these data that rainfall amounts for the months of May and November have decreased with time. Finally, rainfall amounts per rain-day for the months of April, July and August are increasing while the frequency of rain-days is decreasing.
456
Table 2. The distribution of monthly storm totals by frequency and amount on the Njemps Flats, 1965–2000
Feb
Mar
93?4 68?9 62?0 58?2 53?9 49?1 43?7 37?2 29?1 27?2 25?1 22?9 20?5 17?8 14?8 11?3 7?0 4?3 2?4 1?6 1?1
88?5 65?4 58?9 55?2 51?2 46?7 41?5 35?3 27?7 25?9 23?9 21?8 19?5 17?0 14?1 10?7 6?7 4?1 2?3 1?5 1?0
121?9 105?6 78?4 70?8 66?5 61?7 56?3 50?2 42?9 33?7 31?5 29?2 26?7 23?9 20?8 17?4 13?3 8?3 5?2 2?9 1?9 1?3
Apr
May
Jun
Jul
87?0 78?9 74?3 69?2 63?5 56?9 49?0 39?0 36?6 34?1 31?2 28?2 24?7 20?8 16?1 10?3 6?6 3?8 2?6 1?8
75?1 58?0 53?0 50?2 47?0 43?4 39?3 34?3 27?8 26?2 24?5 22?7 20?6 18?3 15?6 12?4 8?2 5?5 3?3 2?3 1?7
120?9 89?2 80?3 75?3 69?8 63?6 56?5 48?1 37?7 35?2 32?5 29?7 26?5 23?1 19?1 14?6 9?0 5?6 3?1 2?0 1?4
118?7 89?6 81?3 76?6 71?4 65?5 58?7 50?6 40?3 37?8 35?2 32?3 29?1 25?6 21?5 16?7 10?7 6?9 3?9 2?7 1?9
Aug
74?5 67?8 64?0 59?7 54?9 49?3 42?7 34?2 32?1 29?9 27?5 24?9 21?9 18?5 14?5 9?4 6?1 3?5 2?4 1?7
Sep
Oct
Nov
37?2 34?2 32?5 30?6 28?4 25?9 22?8 18?8 17?8 16?7 15?5 14?2 12?7 11?0 8?8 6?0 4?1 2?6 1?9 1?4
50?1 39?4 36?3 34?5 32?5 30?2 27?5 24?2 19?9 18?9 17?8 16?5 15?1 13?5 11?7 9?4 6?4 4?4 2?7 2?0 1?5
62?9 48?6 44?4 42?1 39?4 36?4 33?0 28?8 23?4 22?1 20?6 19?1 17?4 15?4 13?1 10?4 6?9 4?6 2?8 2?0 1?4
Dec
49?0 44?6 42?0 39?2 36?1 32?4 28?1 22?5 21?2 19?7 18?1 16?4 14?4 12?2 9?5 6?2 4?0 2?3 1?6 1?2
Return period
Exceedence probability
143 100 50 40 35 30 25 20 15 10 9 8 7 6 5 4 3 2 1?5 1?2 1?1 1?05
0?007 0?010 0?020 0?025 0?028 0?033 0?040 0?050 0?067 0?100 0?111 0?125 0?143 0?167 0?200 0?250 0?333 0?500 0?667 0?833 0?909 0?950
E.C. KIPKORIR
Jan
457
120
1200
110
1100
100
1000
90
900
80
800
70
700
60
600
50
500
40
400
30
300
20
200
10
100
0 1965
1970
1975
1980
1985
1990
1995
Annual rainfall (mm)
Annual rain days
ANALYSIS OF RAINFALL CLIMATE
0 2000
Year Figure 10. Relationship between annual rainfall and annual number of rain-days, Perkerra Research Station (1965–2000): annual raindays ( ); annual rainfall ( ).
Conclusions Available daily, monthly and annual rainfall data were analysed for Njemps Flats (700 km2). Two rainfall stations were considered, the results showed that at temporal scale of a year or a month, there is no significant difference in the annual and monthly rainfall amounts between the two stations. However, at temporal scale of a day, rainfall amounts at the two stations become more independent. The relationship between monthly dependable rainfall and reference crop evapotranspiration shows that rain-feed agriculture is very risky in the Njemps Flats. This is illustrated by Fig. 4, where at a monthly rainfall dependable level of 50% and 80%, the expected rainfall is less than reference crop evapotranspiration for all the months in the year. Therefore, it can be concluded that in Njemps Flats, rainfall has high variability while reference crop evapotranspiration is conservative. Annual and monthly rainfall for Njemps Flats was found to be homogenous between 1965 and 2000. In two out of 3 years, the rainfall received is within the normal range, in one out of 9 years there is drought, in one out of 6 years the year is wet and in one out of 18 years it is dry. The 1984 drought was the worst followed by that of 2000. In both these events the primary reason for the drought was the failure of April–May rainfall. Annual rainfall is normally distributed while the monthly rainfall is lognormally or square-root normally distributed. The Njemps Flats annual rainfall is periodic whereby wet periods are followed by dry periods and again by wet periods and so on. In the Njemps Flats, the probability of a day being wet throughout the year is less than 0?4. At a probability level of 0?20 of a day being wet, three periods can be identified. The first starts on 30th of March and ends on 30th May, the second starts on 12th June and ends on 8th of September and the third starts on 15th October and ends on 29th November. The first two periods form the main growing season. In these periods, rainfall inclusion in irrigation scheduling will cause difficulties because the probability of a day being wet is very low. Examining the annual rainfall, the lightest rainfall events are more frequent. Although heavier rainfall events are infrequent, they make up a significant percentage
458
E.C. KIPKORIR
of the total rainfall. Rainstorm activity is highly varied, most of the months have a high chance of providing the peak rainfall. Catastrophic events, such as breakdown of conservation works, are most likely to occur in April, June, July and August. The probability of receiving a storm favourable to rainwater harvesting is highest within the growing season (April, June, July and August). Annual rainfall was significantly correlated with the annual number of rain-days, and the annual number of rain-days was inversely correlated with time. Though the number of rain-days has decreased with time, rainfall shows no similar decrease. Therefore, it can be inferred that the rainfall amount per rain-day is increasing while the frequency of rain-days is decreasing. In the growing season months of April, May, July, August and November, rainfall is significantly correlated with number of raindays. The number of rain-days for the months April, May, July and August was inversely correlated with time. Therefore, it can be inferred that the rainfall amount for the months of May and November has decreased with time, and rainfall amounts per rain-day for the months of April, July and August are increasing while the frequency of rain-days is decreasing. References Aldabadh, A.S., Rashid, N. & Ramamothy, M.V. (1982). Dry day analysis for planning supplemental irrigation schemes. Transactions of American Society of Agricultural Engineering, 25: 150–153, 159. Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, Vol. 56, Rome, Italy, p. 301. Bernard, F. (1985). Planning and environmental risk in Kenyan dry lands. Geographical Review, 75: 58–70. Binh, N.D., Murty, V.V.N. & Hoan, D.X. (1994). Evaluation of possibility for rain-fed agriculture using a soil moisture simulation model. Agricultural Water Management, 26: 187–199. Buishand, T.A. (1982). Some methods for testing the homogeneity of rainfall. Journal of Hydrology, 58: 11–27. Bryan, R.B. & Southerland, R.A. (1989). Erosion and soil conservation in semi-arid tropical region: Rift Valley Province, Kenya. Sixth International Soil Conservation Conference, Ethiopia, 6–18 November, 1989. Gwynne, M.D. (1981). Issues in the development of Kenya’s semiarid areas. In: Campbell, D. & Migot-Adholla, S.E. (Eds), The Development of Kenya’s Semiarid Lands, pp. 11–24. Nairobi: Institute for Development Studies, University of Nairobi, Occasional Paper No. 36. Kamar, M.J. (1992). The effect of mulches on hydrological processes, soil moisture and crop yield, in highly crusting tropical semi-arid soils, Baringo, Kenya. Ph.D. thesis, University of Toronto, Canada. p. 196. Oladipo, E.O. & Kyari, J.D. (1993). Fluctuations in the onset, termination and length of the growing season in Northern Nigeria. Theoretical and Applied Climatology, 47: 241–250. Raes, D., Mallants, D. & Song, Z. (1996). RAINBOWFa software package for analysing hydrological data. In: Blain, W.R. (Ed.), Hydrological Engineering Software VI, pp. 525–534. Southampton, Boston: Computational Mechanics Publications. Roberts, M. (1985). Progress Report November 1983 to March 1985. Fuel and Fodder Project, Baringo District, Nakuru, Kenya. Rodier, J. A. (1985). Aspects of arid zone hydrology. In: Rodda, J.C. (Ed.), Facets of Hydrology II, pp. 205–247. Rowntree, K.M. (1988). Storm rainfall on the Njemps Flats, Baringo District, Kenya. Journal of Climatology, 8: 297–309. Sutherland, R.A., Bryan, R.B. & Wijendes, D.O. (1991). Analysis of the monthly and annual rainfall climate in a semi-arid environment, Kenya. Journal of Arid Environments, 20: 257–275. Sharma, T.C. (1996). Simulation of the Kenyan longest dry and wet spells and the largest rainsums using a Markov model. Journal of Hydrology, 178: 55–67. Stewart, J.I. (1988). Response Farming in Rainfed Agriculture. Davis, U.S.A: WHARF Foundation Press.
Analysis of rainfall climate on the Njemps Flats, Baringo District, Kenya
E.C. Kipkorir Faculty of Agricultural and Applied Biological Sciences, Institute for Land and Water Management, K.U. Leuven University, Vital Decosterstraat 102, 3000 Leuven, Belgium (Received 7 February 2001, accepted 24 August 2001) Rainfall and reference crop evapotranspiration are analysed for Njemps Flats, a semi-arid area in Baringo District, Kenya. Examining the monthly rainfall totals, there is no significant difference in rainfall amount between two stations 17 km apart but daily rainfall totals tend to become independent. Rain-feed agriculture is very risky. Rainfall inclusion in irrigation scheduling causes difficulties. Annual and monthly rainfall was homogenous between 1965 and 2000. The primary reason for the worst droughts in the area is the failure of April–May rainfall. Examining annual rainfall, the lightest rainfall events have become more frequent. The heaviest rainfall events are infrequent but they make up a significant percentage of the total rainfall. Annually, the number of rain-days has decreased with time but rainfall shows no similar decrease, inferring that the rainfall amount per rain-day is increasing. Rainfall amount for the months of May and November has decreased with time while rainfall amounts per rain-day for the months of April, July and August are increasing. Land and water management schemes in the area must be designed to cope with drought periods as part of supply characteristic of the area. # 2002 Elsevier Science Ltd. Keywords: dry spell duration; evapotranspiration; frequency analysis; rainfall; storm; semi-arid
Introduction In the next few decades the world’s population is expected to grow from 6 billion today to at least 8 billion by the year 2025, with about 90% of the increase being added to the developing world. Therefore, it is clear that achieving food security in developing countries will continue to pose major challenges to decision-makers in the next few decades. Kenya consists of about 80% arid and semi-arid land supporting about 20% of population and over 50% of livestock (Gwynne, 1981; Bernard, 1985). Kenya, which is one of the developing countries, has currently a population of 30 million with a growth rate of about 3%. This, combined with the limited land availability in the agriculturally productive lands, has resulted in the extension of E-mail: [email protected] 0140-1963/02/030445 + 14 $35.00/0
# 2002 Elsevier Science Ltd.
446
E.C. KIPKORIR
farming into agriculturally less productive land, mainly the drier areas, resulting in rapid land degradation. Land degradation resulting from population and livestock increase, and associated overgrazing, is a long-established problem in most of the dry land areas of Kenya (Bryan & Southerland, 1989). With the current high population growth rate, the present pressure on marginal lands seems to be greater than ever. The Njemps Flats, located to the west of lake Baringo in Baringo District in the Rift Valley Province of Kenya, is a typical example of such marginal lands. In this area, the number of development projects and rehabilitation programs for soil and water conservation has increased in the recent past. Some of the major aims of these projects are management of scarce water resources for human consumption and agriculture, rainwater harvesting, and soil moisture conservation. Rainfall is the most important environmental factor limiting the development of semi-arid regions of the tropics. Soil moisture management in semi-arid areas of the tropics is faced with the following constraints: limited and unreliable rainfall, high variability in rainfall pattern and high evapotranspiration rate. Therefore, it is necessary to understand the spatial and temporal variability of the amount of rainfall received and its relationship with evapotranspiration rate in order to develop effective management strategies. A frequency analysis of rainfall and evapotranspiration rates in the semi-arid Njemps Flats is becoming increasingly important since there have been dramatic stock and human population increases. The critical importance of rainfall to the area was noted in the drought years of 1984 and 2000 when very high herd reductions occurred. However, scarcity of meteorological stations, poor-quality data, and the problem of obtaining recent data complicate frequency analysis of rainfall and evapotranspiration in semi-arid areas. The present study analyses the rainfall and evapotranspiration on the Njemps Flats in a semi-arid area. Of particular interest in this study is (i) the frequency of drought, dry, normal and wet years, (ii) when wet spells are likely to occur, (iii) frequency analysis of dry spells during the main growing season and (iv) distribution of daily rainfall totals by frequency and amount. The rainfall parameters analysed were rainfall variability, homogeneity of rainfall, monthly rainfall distribution, rainfall periodicity, frequency of drought and wet years and annual rain-days. The evapotranspiration parameter analysed was monthly evapotranspiration distribution. The study therefore provides essential data for several local research programs, rehabilitation projects and irrigation scheduling in the irrigation projects located in the area. The study area There were only two meteorological stations, Perkerra Research station (next to Marigat) and Snake Farm (on the shores of Lake Baringo and 17 km from Marigat), located within the Njemps Flats in Baringo District in the Rift Valley Province of Kenya (Fig. 1). The two meteorological stations are 17 km apart. The general elevation of the Flats varies between 920 and 1230 m a.s.l. The study area is centred about latitude 00130N and longitude 36100E. Monthly average temperatures range from 241C in August to 261C in March, with monthly mean maximum temperatures ranging from 311C to 341C (Kamar, 1992). The soil texture found in the area ranges from silt loam to clay with silt loam dominating the surface soils. Although potentially fertile, the silt loam crusts readily and encourages surface runoff. Materials and methods Daily rainfall data were obtained from Snake Farm for the period 1966–1998, and from Perkerra Research Station for the period 1965–2000. The data length was 33
ANALYSIS OF RAINFALL CLIMATE
447
Figure 1. Location map of the study area.
and 36 years, respectively, which are both greater than the 30 years of climatic data needed to do accurate climatic analyses for the tropics (Stewart, 1988). The two weather stations were compared in order to determine if the rainfall records at the stations are related. This was done by establishing the relationship between daily cumulative rainfall at the two stations and by comparing summary statistics for the rainfall in the two stations. Monthly reference evapotranspiration for the period 1983–1993 was calculated using the data for Perkerra Research Station. These were the only available data for the period of study (1965–2000) for the two stations. The calculation method used was the FAO Penman–Monteith equation (Allen et. al., 1998), given in Eq (1). 0 408ðRn GÞ þ g½900=ðT þ 273Þu2 ðes aa Þ ðEqn1Þ ETo ¼ þ gð1 þ 0 34u2 Þ where ETo is the reference evapotranspiration (mm day1), Rn is the net radiation at the crop surface (MJ m2 day1), G is the soil heat flux density (MJ m2 day1), T is the air temperature at 2 m height (1C), u2 is the wind speed at 2 m height (m s1), es is the saturation vapour pressure (kPa), ea is the actual vapour pressure (kPa), (esea) is the saturation vapour pressure deficit (kPa), D is the slope vapour pressure curve (kPa 1C1) and g is the psychrometric constant (kPa 1C1).
448
E.C. KIPKORIR
The monthly reference crop evapotranspiration, frequency analysis was done using the RAINBOW software program (Raes et al., 1996). The program is designed to test the homogeneity of hydrologic records and to execute a frequency analysis of rainfall and evaporation data. The program is especially suitable for predicting the probability of occurrence of either low or high rainfall amounts. In RAINBOW either the normal or Gumbel (extreme value type 1) distribution can be selected. Using the mean annual precipitation and mean annual reference evapotranspiration, the Agro-climatic zonation was calculated using the United Nations Educational, Scientific and Cultural Organization (UNESCO) aridity index (AI ) given in (Rodier, 1985). P ðEqn2Þ AI ¼ ETo where P is the mean annual rainfall (mm) and ETo is the mean annual reference crop evapotranspiration (mm). Variability and trends in rainfall pattern were highlighted by a 3-year moving average. Annual and monthly rainfall data were first tested for normality using the RAINBOW program. This was done by comparing the rainfall data to the theoretical normal probability distribution curve and assessing how well the data fitted the normal distribution curve. If the data did not fit the normal distribution curve, the data were first transformed using logarithm or square root functions and then regressed to the theoretical normal distribution curve (Raes et al., 1996). The regression coefficient of determination (R2) was taken as a measure of goodness of fit. Homogeneity of the rainfall for the years 1965–2000 was studied by testing the cumulative normalized deviation from the mean at 90%, 95% and 99% confidence levels (Buishand, 1982). Monthly and annual dependable rainfall (20%, 50%, 80% and 90% exceedance) was calculated using normal distribution, transforming the data where necessary, using RAINBOW. The 20% dependable rainfall is expected on average to be exceeded in 1 out of 5 years, 50% in 1 out of 2 years, 80% in 4 out of 5 years and 90% in 9 out of 10 years. The frequency of droughts, dry, normal and wet years was estimated as follows: the 20% and 80% dependable rainfall were calculated using RAINBOW. Each year with annual rainfall above the 20% dependable rainfall was taken to be a wet year, each year with annual rainfall less than the 80% dependable rainfall was taken to be a dry year. Each year with annual rainfall in between 20% and 80% dependable rainfall was taken to be a normal year (Raes et al., 1996). A drought year was defined as a year with annual rainfall less than 90% dependable rainfall on the assumption that droughts in East Africa region occur with a frequency greater than 1 in 10 years. The number of droughts, dry, normal and wet years were then counted and expressed as a percentage of the total number of years on record for the station. This gave estimates for the frequency (probability of occurrence) of drought, dry, normal and wet years for the 1965–2000 period. The accuracy of these estimates will increase as the length of the climatic record increases. The probability of a wet day was estimated using Eqn (3). The number of days (ni) that were wet for each date in the year were counted and expressed as a fraction of the total number of years (Ns) on record for the station. This gave an estimate for the probability of wet days for each day of the year. A 30-year period is long enough to establish a longterm climatology for a region or station (Aldabadh et al., 1982). Since the climatic record was 33 and 36 years (1965–2000), the sample estimate is reasonably closer to the population’s probability of wet days. A day was considered to be wet when there was more than 1 mm of rainfall and dry when rainfall was 1 mm or less. The probability of wet day vs. time was plotted to show the time when it is likely to be dry or wet. PðwetÞ ¼
ni Ns
ðEqn3Þ
ANALYSIS OF RAINFALL CLIMATE
449
The distribution of dry spell of ND days (where ND varies from 1 to an observable maximum for the station) was estimated by a frequency analysis of historic dry spell data (Binh et al., 1994). This was achieved by counting the number of times (frequency) a dry spell of a specified duration occurred during the main growing season in each year of the recorded period of the station (1965–2000) followed by computation of respective weighted dry spell duration. Using this information, the distribution of dry spell duration during the main growing season by frequency was obtained, using the RAINBOW software. A major application of dry spell analysis is to predict extended drought durations during the growing season, which forms a basis for planning the crop production strategies (Sharma, 1996). The distribution of daily rainfall totals by amount and frequency was obtained using a frequency analysis of historic daily rainfall. This was achieved by counting the number of times (frequency) a daily rainfall of specified amount occurred during the recorded period for the station (1965–2000). Daily rainfall in the Njemps Flats almost always falls in association with one weather event. Thus, it is reasonable to assume that the daily rainfall totals can be considered as being equivalent to storm totals (Rowntree, 1988). Using this information, the distribution of monthly storm totals by frequency and amount was obtained by frequency analysis of historic daily rainfall in each month, using the RAINBOW software.
Results and discussion Annual rainfall data were compared for a period of 33 years (1966–1998) for the two stations (Fig. 2). A statistically significant correlation was established at an a ¼ 0?05 for annual rainfall between the two stations (i.e. r ¼ 0?81), but this correlation reduces to r ¼ +0?38 for daily rainfall over the same period. Monthly comparisons of rainfall between the two stations over the 33-year period indicated that rainfall at both stations for all months was also related at statistically significant level (a ¼ 0?05) (i.e. r values
Deviation from mean annual rainfall (mm)
500 400 300 200 100 0 − 100 − 200 − 300 − 400 − 500 1965
1970
1975
1980
1985
1990
1995
2000
Year Figure 2. Time series of deviations from the mean annual rainfall for Perkerra Research Station (1965–2000) and Snake Farm (1966–1998): Perkerra Research Station ( ); Snake ). Farm (
450
E.C. KIPKORIR
varied from +0?50 in September to +0?92 in February). Cumulative daily rainfall at Perkerra Research for the 33-year period was approximately 3% lower than at Snake Farm. The coefficient of determination (r2) is estimated as 0.99 (Fig. 3). The mean annual rainfall and the 95% confidence band was 654758 and 674763 mm for Perkerra Research and Snake Farm stations, respectively. For the annual precipitation totals, therefore, there is no significant difference between the two stations. However, once the temporal scale is reduced to the daily scale rainfall totals at the two stations become more independent. Since differences between the two stations were not significant, the complete rainfall record for Perkerra Research (1965–2000) was assumed to be representative of the semi-arid Njemps Flats (700 km2). The relationship between monthly dependable (20%, 50% and 80% levels) rainfall and reference crop evapotranspiration is shown in Fig. 4. The reference crop evapotranspiration is calculated using the FAO Penman–Monteith equation (Allen et al., 1998). From Fig. 4, it can be seen that the expected rainfall in dry and normal years is less than the reference crop evapotranspiration throughout the year. In a wet year only the months of April and July expect rainfall that is higher than the reference crop evapotranspiration. From the results shown in Fig. 4, the moisture deficit is estimated to be about 670, 1250 and 1700 mm year1 for wet, normal and dry years respectively. Therefore, rain-feed agriculture is very risky in this area. Furthermore, using the same figure it is found that the monthly 20% and 80% dependable reference crop evapotranspiration is in the range 712 mm, the monthly mean value; therefore, it can be concluded that reference crop evapotranspiration is conservative. The AI value is calculated for the two stations using Eqn (2). The P value is taken as 630 mm for Perkerra Research Station and 670 mm for Snake Farm while the ETo value is taken as 1800 mm. The mean AI for the two stations is 0?36, and AI values that range between 0?2 and 0?5 are classified as semi-arid. Therefore the Njemps Flats can be classified as a semi-arid area. The results of homogeneity tests for annual and monthly rainfalls for the two stations show that annual and monthly rainfall was homogenous between 1965 and 2000. The annual rainfall in the two stations was nearly normally distributed. The coefficient of determination (r2) values for normal distribution were 0?98 and 0?99 for the two stations. The distribution of drought, dry, normal and wet years for the
Cumulative daily rainfall perkerra research station (mm)
24,000
20,000
16,000
12,000
8000
4000
0
0
4000
8000
12,000
16,000
20,000
24,000
Cumulative daily rainfall snake farm (mm) Figure 3. Relationship between daily cumulative rainfall (.) at Perkerra Research Station and Snake Farm stations, 1966–1998. ( – is 1:1 line).
Dependable rainfall and evapotranspiration, _ ETo (mm month 1)
ANALYSIS OF RAINFALL CLIMATE
451
200 180 160 140 120 100 80 60 40 20 0 Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month Figure 4. Monthly variation in dependable reference crop evapotranspiration (ETo) and monthly dependable rainfall (20%, 50% and 80% levels) at Perkerra research station: 80% Dep ETo ( ); 20% Dep Rainfall ( ); 50% Dep Rainfall ( ); 80% Dep Rainfall ( ); 20% Dep ETo ( ); 50% Dep ETo ( ).
1965–2000 period is shown in Fig. 5. From the figure it can be seen that in two out of 3 years, the rainfall received is within the normal range, in one out of 9 years there is drought, in one out of 6 years the year is wet and in one out of 18 years it is dry. The monthly rainfall data were required to be transformed using logarithm or square root functions, and the transformed data were found to be nearly normally
Figure 5. Distributions of drought, dry, normal and wet years in Njamps Flats (1965–2000).
452
E.C. KIPKORIR
Table 1. Monthly variation in dependable rainfall at Perkerra research station
Month
Monthly dependable rainfall (mm)
January February March April May June July August September October November December
Transformation
20%
50%
80%
35 49 89 153 100 98 146 127 44 66 71 38
5 15 34 81 67 56 84 70 23 38 36 16
0 0 3 31 33 26 39 30 8 17 18 2
Logarithm Square root Square root Square root Non Square root Square root Square root Square root Square root Logarithm Square root
distributed, with coefficient of determination (r2) ranging from 0?94 to 0?99. The results are shown in Table 1. Inter-annual variability in Njemps Flats annual rainfall was highlighted by calculating a 3-year moving annual rainfall average for the two stations. Typical inter-annual variability in rainfall is highlighted in Fig. 6. From Fig. 6, it appears that the Njemps Flats rainfall is periodic whereby wet periods are followed by dry periods and again by wet periods and so on. This suggests a relationship with El Nino and further research is needed to demonstrate such a relationship. In order to examine wet day probabilities, a 10-day moving average was applied to the data from Perkerra Research station; the results are shown in Fig. 7. From Fig. 7 it can be seen that the probability of a day being wet throughout the year is less
1000 900
Annual rainfall (mm)
800 700 600 500 400 300 200 100 0 1965
1970
1975
1985
1980
1990
1995
2000
Year Figure 6. Three-year moving annual rainfall averages at Perkerra Research Station (1965– 2000) and Snake Farm stations (1966–1998): Perkerra Research Station (3 year moving average ( ); Snake Farm (3 year moving average ( ).
ANALYSIS OF RAINFALL CLIMATE
453
0.40
Probabilty of a wet day
0.35 0.30 0.25 0.20 0.15 0.10 0.05 0
1
29
57
85
113
141
169
197
225
253
281
309
337
365
Day of the year Figure 7. Probability of a wet day (10-day moving average) during the year (1 January–31 December) for Perkerra Research Station.
than 0?4. At a probability level of 0?20 of a day being wet, three periods can be identified. The first period starting on 30 March and ending on 30 May is about 61 days long. The second period starting on 12 June and ending on 8 September is about 88 days long. The third period starting on 15 October and ending on 29 November is about 45 days long. The first two periods form the main growing season. For these periods the results show that if rainfall is included in irrigation scheduling it causes difficulties because the probability of a day being wet is low. In order to examine the frequency of dry spell duration during the main growing season (30 March–8 September), the weighted dry spell duration was calculated using the data from Perkerra Research Station, the results are shown in Fig. 8. Using this analysis, it can be shown that during the main growing season, dry spell duration was moderately weak correlated with time (r ¼ +0?35). During the main growing season, it can also be shown that the number of rain-days was inversely correlated with time (r ¼ 0?33), and rainfall was significantly correlated with number of rain-days (r ¼ +0?70). Though the number of rain-days has decreased with time during the main growing season at Perkerra Research Station, rainfall shows no similar decrease (r ¼ +0?04). Therefore, it can be inferred from these data that dry spell duration during the main growing season is increasing. This may result in significant decline in crop yield (Oladipo & Kyari, 1993). The weighted dry spell duration data required to be transformed using logarithm function and the transformed data were found to be normally distributed. The dry spell duration events corresponding to 20%, 50% and 80% probability of exeedance during the main growing season were estimated as 6?1, 4?5 and 3?4 days. From Figs. 8 and 2 it can be seen that the 1984 and 2000 droughts were the worst. In both these events the primary reason for the drought was the failure of April–May rainfall. The annual distribution of storms is summarized in Fig. 9 which shows both the frequency distribution of storms producing various rainfall amounts (Fig. 9(a)) and estimate of percentage of annual rainfall falling in storms within each class (Fig. 9(b)). The lightest rainfall events are more frequent, with 82?4% of the storms producing
454
E.C. KIPKORIR
12
Weighted dry spell duration (days)
11 10 9 8 7 6 5 4 3 2 1 0 1965
1970
1975
1980
1985
1990
1995
2000
Year Figure 8. Time series of weighted dry spell duration during the main growing season (30 March–8 September) for Perkerra Research Station (1965–2000).
less than 10 mm but accounting for only 43?1% of the total rainfall. Equivalent figures for 1 mm are 27?2% and 1?9%. From the graphs (Fig. 9), it can be seen that 12?2% of the storms and 45?9% of the total rainfall amounts equal or exceed the 15 mm required for rainwater harvesting. Although heavier rainfall events are relatively infrequent, they make up a significant percentage of the total rainfall. Only 3?8% of the storms produce 30 mm of rainfall or more, yet they account for 24?7% of the annual rainfall total. Equivalent figures for 50 mm rainfall events are 1?2% and 10?7% of the total rainfall. The monthly storm data required to be transformed using logarithm function and the transformed data were found to be normally distributed. The results are shown in Table 2. The results show that rainstorm activity is quite variable within the period of record, and nine different months have provided the greatest daily rainfall amounts. Although the heaviest daily total in the 36-year period was received in March (121?9 mm), July has tended to have the heaviest daily totals, followed by April, June and August in decreasing order (Table 2). Catastrophic events, such as breakdown of conservation works, are therefore most likely to occur in these months. If 15 mm is taken to be the minimum amount of daily rainfall required to guarantee effective rainwater harvesting (Roberts, 1985), Table 2 demonstrates that the probability of such a storm occurring in any 1 year is highest in July (P ¼ 0?372), followed by April (P ¼ 0?359), June (P ¼ 0?324) and August (P ¼ 0?321). Thus, the probability of receiving a storm favourable to rainwater harvesting is highest within the main growing season. The relationship between annual rainfall and annual number of rain-days is shown in Fig. 10. Using this analysis, it can be shown that annual rainfall was significantly correlated with the annual number of rain-days (r ¼ +0?74), and the annual number of rain-days was inversely correlated with time (r ¼ 0?37). Though the number of rain-days has decreased with time at Perkerra Research Station, rainfall shows no similar decrease (r ¼ +0?03). Therefore, it can be inferred from these data that the rainfall amount per rain-day is increasing while the frequency of rain-days is decreasing. Sutherland et al. (1991) found similar results in their earlier
ANALYSIS OF RAINFALL CLIMATE
455
100 90
Cumulative % frequency
80 70 60 50 40 30 20 10 0 0
10
20
30
40
50
60
70
80
90
100
110
120
130
90
100
110
120
130
Daily rainfall (mm)
(a) 100 90
Cumulative % depth
80 70 60 50 40 30 20 10 0 0 (b)
10
20
30
40
50
60
70
80
Daily rainfall (mm)
Figure 9. The distribution of daily rainfall at Perkerra Research Station by (a) frequency, and (b) amount.
study, in the same region, for the time period 1958–1986. The analysis, not shown here, for the months April, May, July, August and November, demonstrates that rainfall was significantly correlated with number of rain-days (r ¼ +0?81, +0?70, +0?73, +0?75, +0?76, respectively). Further, the number of rain-days for the months April, May, July, August and November was inversely correlated with time for most of the months (r ¼ 0?16, 0?19, 0?12, 0?31, +0?09, respectively). The correlation coefficients between rainfall and time for the months April, May, July, August and November were r ¼ +0?01, 0?18, +0?08, +0?02, 0?12, respectively. Therefore, it can be inferred from these data that rainfall amounts for the months of May and November have decreased with time. Finally, rainfall amounts per rain-day for the months of April, July and August are increasing while the frequency of rain-days is decreasing.
456
Table 2. The distribution of monthly storm totals by frequency and amount on the Njemps Flats, 1965–2000
Feb
Mar
93?4 68?9 62?0 58?2 53?9 49?1 43?7 37?2 29?1 27?2 25?1 22?9 20?5 17?8 14?8 11?3 7?0 4?3 2?4 1?6 1?1
88?5 65?4 58?9 55?2 51?2 46?7 41?5 35?3 27?7 25?9 23?9 21?8 19?5 17?0 14?1 10?7 6?7 4?1 2?3 1?5 1?0
121?9 105?6 78?4 70?8 66?5 61?7 56?3 50?2 42?9 33?7 31?5 29?2 26?7 23?9 20?8 17?4 13?3 8?3 5?2 2?9 1?9 1?3
Apr
May
Jun
Jul
87?0 78?9 74?3 69?2 63?5 56?9 49?0 39?0 36?6 34?1 31?2 28?2 24?7 20?8 16?1 10?3 6?6 3?8 2?6 1?8
75?1 58?0 53?0 50?2 47?0 43?4 39?3 34?3 27?8 26?2 24?5 22?7 20?6 18?3 15?6 12?4 8?2 5?5 3?3 2?3 1?7
120?9 89?2 80?3 75?3 69?8 63?6 56?5 48?1 37?7 35?2 32?5 29?7 26?5 23?1 19?1 14?6 9?0 5?6 3?1 2?0 1?4
118?7 89?6 81?3 76?6 71?4 65?5 58?7 50?6 40?3 37?8 35?2 32?3 29?1 25?6 21?5 16?7 10?7 6?9 3?9 2?7 1?9
Aug
74?5 67?8 64?0 59?7 54?9 49?3 42?7 34?2 32?1 29?9 27?5 24?9 21?9 18?5 14?5 9?4 6?1 3?5 2?4 1?7
Sep
Oct
Nov
37?2 34?2 32?5 30?6 28?4 25?9 22?8 18?8 17?8 16?7 15?5 14?2 12?7 11?0 8?8 6?0 4?1 2?6 1?9 1?4
50?1 39?4 36?3 34?5 32?5 30?2 27?5 24?2 19?9 18?9 17?8 16?5 15?1 13?5 11?7 9?4 6?4 4?4 2?7 2?0 1?5
62?9 48?6 44?4 42?1 39?4 36?4 33?0 28?8 23?4 22?1 20?6 19?1 17?4 15?4 13?1 10?4 6?9 4?6 2?8 2?0 1?4
Dec
49?0 44?6 42?0 39?2 36?1 32?4 28?1 22?5 21?2 19?7 18?1 16?4 14?4 12?2 9?5 6?2 4?0 2?3 1?6 1?2
Return period
Exceedence probability
143 100 50 40 35 30 25 20 15 10 9 8 7 6 5 4 3 2 1?5 1?2 1?1 1?05
0?007 0?010 0?020 0?025 0?028 0?033 0?040 0?050 0?067 0?100 0?111 0?125 0?143 0?167 0?200 0?250 0?333 0?500 0?667 0?833 0?909 0?950
E.C. KIPKORIR
Jan
457
120
1200
110
1100
100
1000
90
900
80
800
70
700
60
600
50
500
40
400
30
300
20
200
10
100
0 1965
1970
1975
1980
1985
1990
1995
Annual rainfall (mm)
Annual rain days
ANALYSIS OF RAINFALL CLIMATE
0 2000
Year Figure 10. Relationship between annual rainfall and annual number of rain-days, Perkerra Research Station (1965–2000): annual raindays ( ); annual rainfall ( ).
Conclusions Available daily, monthly and annual rainfall data were analysed for Njemps Flats (700 km2). Two rainfall stations were considered, the results showed that at temporal scale of a year or a month, there is no significant difference in the annual and monthly rainfall amounts between the two stations. However, at temporal scale of a day, rainfall amounts at the two stations become more independent. The relationship between monthly dependable rainfall and reference crop evapotranspiration shows that rain-feed agriculture is very risky in the Njemps Flats. This is illustrated by Fig. 4, where at a monthly rainfall dependable level of 50% and 80%, the expected rainfall is less than reference crop evapotranspiration for all the months in the year. Therefore, it can be concluded that in Njemps Flats, rainfall has high variability while reference crop evapotranspiration is conservative. Annual and monthly rainfall for Njemps Flats was found to be homogenous between 1965 and 2000. In two out of 3 years, the rainfall received is within the normal range, in one out of 9 years there is drought, in one out of 6 years the year is wet and in one out of 18 years it is dry. The 1984 drought was the worst followed by that of 2000. In both these events the primary reason for the drought was the failure of April–May rainfall. Annual rainfall is normally distributed while the monthly rainfall is lognormally or square-root normally distributed. The Njemps Flats annual rainfall is periodic whereby wet periods are followed by dry periods and again by wet periods and so on. In the Njemps Flats, the probability of a day being wet throughout the year is less than 0?4. At a probability level of 0?20 of a day being wet, three periods can be identified. The first starts on 30th of March and ends on 30th May, the second starts on 12th June and ends on 8th of September and the third starts on 15th October and ends on 29th November. The first two periods form the main growing season. In these periods, rainfall inclusion in irrigation scheduling will cause difficulties because the probability of a day being wet is very low. Examining the annual rainfall, the lightest rainfall events are more frequent. Although heavier rainfall events are infrequent, they make up a significant percentage
458
E.C. KIPKORIR
of the total rainfall. Rainstorm activity is highly varied, most of the months have a high chance of providing the peak rainfall. Catastrophic events, such as breakdown of conservation works, are most likely to occur in April, June, July and August. The probability of receiving a storm favourable to rainwater harvesting is highest within the growing season (April, June, July and August). Annual rainfall was significantly correlated with the annual number of rain-days, and the annual number of rain-days was inversely correlated with time. Though the number of rain-days has decreased with time, rainfall shows no similar decrease. Therefore, it can be inferred that the rainfall amount per rain-day is increasing while the frequency of rain-days is decreasing. In the growing season months of April, May, July, August and November, rainfall is significantly correlated with number of raindays. The number of rain-days for the months April, May, July and August was inversely correlated with time. Therefore, it can be inferred that the rainfall amount for the months of May and November has decreased with time, and rainfall amounts per rain-day for the months of April, July and August are increasing while the frequency of rain-days is decreasing. References Aldabadh, A.S., Rashid, N. & Ramamothy, M.V. (1982). Dry day analysis for planning supplemental irrigation schemes. Transactions of American Society of Agricultural Engineering, 25: 150–153, 159. Allen, R.G., Pereira, L.S., Raes, D. & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper, Vol. 56, Rome, Italy, p. 301. Bernard, F. (1985). Planning and environmental risk in Kenyan dry lands. Geographical Review, 75: 58–70. Binh, N.D., Murty, V.V.N. & Hoan, D.X. (1994). Evaluation of possibility for rain-fed agriculture using a soil moisture simulation model. Agricultural Water Management, 26: 187–199. Buishand, T.A. (1982). Some methods for testing the homogeneity of rainfall. Journal of Hydrology, 58: 11–27. Bryan, R.B. & Southerland, R.A. (1989). Erosion and soil conservation in semi-arid tropical region: Rift Valley Province, Kenya. Sixth International Soil Conservation Conference, Ethiopia, 6–18 November, 1989. Gwynne, M.D. (1981). Issues in the development of Kenya’s semiarid areas. In: Campbell, D. & Migot-Adholla, S.E. (Eds), The Development of Kenya’s Semiarid Lands, pp. 11–24. Nairobi: Institute for Development Studies, University of Nairobi, Occasional Paper No. 36. Kamar, M.J. (1992). The effect of mulches on hydrological processes, soil moisture and crop yield, in highly crusting tropical semi-arid soils, Baringo, Kenya. Ph.D. thesis, University of Toronto, Canada. p. 196. Oladipo, E.O. & Kyari, J.D. (1993). Fluctuations in the onset, termination and length of the growing season in Northern Nigeria. Theoretical and Applied Climatology, 47: 241–250. Raes, D., Mallants, D. & Song, Z. (1996). RAINBOWFa software package for analysing hydrological data. In: Blain, W.R. (Ed.), Hydrological Engineering Software VI, pp. 525–534. Southampton, Boston: Computational Mechanics Publications. Roberts, M. (1985). Progress Report November 1983 to March 1985. Fuel and Fodder Project, Baringo District, Nakuru, Kenya. Rodier, J. A. (1985). Aspects of arid zone hydrology. In: Rodda, J.C. (Ed.), Facets of Hydrology II, pp. 205–247. Rowntree, K.M. (1988). Storm rainfall on the Njemps Flats, Baringo District, Kenya. Journal of Climatology, 8: 297–309. Sutherland, R.A., Bryan, R.B. & Wijendes, D.O. (1991). Analysis of the monthly and annual rainfall climate in a semi-arid environment, Kenya. Journal of Arid Environments, 20: 257–275. Sharma, T.C. (1996). Simulation of the Kenyan longest dry and wet spells and the largest rainsums using a Markov model. Journal of Hydrology, 178: 55–67. Stewart, J.I. (1988). Response Farming in Rainfed Agriculture. Davis, U.S.A: WHARF Foundation Press.