沙漠研究 19-1, 57- 60 (2009 )Journal of Arid Land Studies“Desert Technology IX ”
Refereed Paper

Long-term-discharge Analysis Using the EPA Method for the Tana River in Kenya
Hiromu OKAZAWA1, Hiromichi TOYODA1, Shinji SUZUKI1, Sawahiko SHIMADA1, Ryuzo NISHIMAKI2
Abstract: The Tana River is Kenya’s largest river, with a length of 1,000 km and a catchment
area of 95,000 km2, and that has one of the famous drainage basins in the country. This study
aims to clarify the trends in the long-term-discharge based on hydrological records of the Tana
River for the past 52 years. The long-term-discharge data of the Tana River show that thedischarge of the river has been increasing every year since 1988. The annual maximum
discharge of the Tana River has been increasing since 1983, and the maximum discharge during
the wet season (October to December) has been increasing. In recent years, it is probable thatthe occurrence cycle of heavy rainfall has shortened, and it is estimated that flooding will
become more frequent.
Keywords: Basin, Discharge, Long-term-trend analysis, Precipitation, River

1. Introduction
The Tana River is Kenya’s largest river, with a length of 1,000 km and a catchment area of 95,000
km2. It flows past the foot of Mt. Kenya and into the Indian Ocean. The Tana delta that locates in thelowest reaches of Tana River consists of 2,000 km2 of alluvial land. To utilize the rich land and water
resources of the delta, large-scale agricultural land development has been conducted since 1980s, under theTana Delta Irrigation Development Project (Agwata, 2005). Agriculture in the delta suffered catastrophic
damage from abnormal weather and floods caused by El Niño in December 1997 and December 2006.
Because climate change has been an obstacle for water use and flood control projects in the basin, it isnecessary to clarify the long-term hydrologic changes of the Tana River and to devise responses. This
study aims to clarify the trends in the long-term hydrological environment (i.e., changes in precipitationand discharge) based on hydrological records of the Tana River for the past 52 years.
2. Survey site and analysis method
The Tana River flows past the foot of Mt. Kenya before
emptying into the Indian Ocean. It has the largest catchment area(95,000 km2) of any river in Kenya. Alluvial land measuring
2,000 km2 lies at the river mouth. Because the Tana River is an
unimproved river with a gentle gradient, it has flooded repeatedly
and its main course has changed many times over the ages. TheTana Riversurvey site is Garissa (0˚29’S, 39˚38’E), a city at the middle
Garissareaches of the river.
We used discharge data of the Tana River observed by theKenya Meteorological Department for 1944 to 1995 (52 years).
The data were monthly average discharge, maximum discharge
and minimum discharge. For analysis of precipitation, we used
the monthly total precipitation observed by the Kenya
Meteorological Department for 1959 to 2006 (47 years).
Fig.1. Investigated point.
To clarify the long-term changes in the Tana River’s
discharge, we used the method of the Economic Planning Agency of Japan (the EPA Method).
Long-term-trend analysis is a method to qualitatively clarify a given trend. In this paper, we assume thatchanges in long-term-discharge are the combined result of four components (T, C, S, and I), and westatistically separate the TCSI components to understand the trend of each component. The details of theTCSI components are discussed below. The EPA method was used in separating the TCSI components.
1

Faculty of Regional Environment Science, Tokyo University of Agriculture,
1-1-1 Sakuragaoka, Setagaya-ku, Tokyo, 156-8502, Japan
2
Japan International Cooperation Agency, 2-1-1 Yoyogi, Shibuya-ku, Tokyo, 151-8558, Japan58

In the EPA Method, a trend in the time-series data of discharge is classified into four categories ofvariation for use in the following equation.
Discharge = T･C･S･I
Where, T (Trend) is long-term variation, which usually shows gradual increase or decrease; C (Cyclicalcomponent) is mid-term cyclical variation (i.e., over 12 months), S (Seasonal variation) is 12-monthvariation, in which the variation for a given certain season tends to be similar from year to year; and I(Irregular variation) is other variation.

Discharge（m3/s）

3000
3. Results and Discussion
MaxMean
3.1 Long-term-change in river dischargeMin
10 years moving average
The changes in the annual minimum,
2000
maximum and average discharges of the Tana
River for the years 1944 to 1995 are shown inMaxFigure 2. The long-term-change in each of the1000
three discharges is shown in the 10-year movingaverage.
Mean0MinThe change in the minimum discharge is1940
1950
1960
1970
1980
1990
2000
small, and the discharge is stable for a longYear (y)period. The average discharge fluctuates in theFig. 2.FigureTime
Seriesdatadischarge(Max,332 Time
seriesdataofof discharge(Max,Mean, MeanMin)
range from 57 m /s to 423 m /s. The changes inMin).
the moving average show that even though there isa gentle peak around 1970, the average discharge has been roughly stable. The range of fluctuation in themaximum discharge is 245 m3/s to 2,111 m3/s. The largest value is 8.6 times the smallest value. The
10-year fluctuations in the moving average of the maximum discharge show that the maximum discharge
peaks in the first half of 1970’s and starts to increase again in 1983.

1200
Max

1000
Discharge（m3 /s）

3.2 Annual changes in discharge
The fluctuation in annual discharge of the Tana
River obtained from the data for the analyzed periods
is shown in Figure 3.
The monthly average
discharge shows two major changes in discharge ineach year in this region: one change during the rainyseasons and another during the dry seasons. We
divided one year into four quarters: the first quarter isfrom January to March, the second quarter is from
April to June, the third quarter is from July to
September, and the fourth quarter is from October toDecember. We defined the first and third quarters as
dry seasons, and the second and fourth quarters as
rainy seasons. The discharge for each quarter was
calculated by obtaining the average discharge from thethree-month discharge data for each quarter.

Mean

800

Min600400
200012345678910 11 12MonthFigure 3 Annualchangesdischarge (Max,(Max,Mean, Min)
Fig. 3. Annualchanges
inindischarges
Mean,Min).

3.3 Discharge changes in each quarterLong-term changes in the discharge of each quarter are shown in Figure 4. Figure 5 shows theten-year moving average for each quarter that was calculated using the data in Figure 4. From Figure 5, it
is known that the rate of change for the third quarter is relatively stable. The rates of change in discharge
for the first, second, and fourth quarters show that there are peaks around 1970. The rates of change indischarge for the second quarter are relatively stable after the peak, but the discharges for the first andfourth quarters start to increase again in 1988. The increase in the discharge for the first quarter isparticularly steep. The discharges for the first quarter showed a marked increase, which we hypothesized
was caused by the wet season in the forth term extending into the first quarter due to recent climate change.

591000
First quarter (Jan. to Mar.)
Second quarter (Apr. to June)

Discharge（m3 /s）

800

Third quarter (Jul. to Sep.)
Fourth quarter (Oct. to Dec.)

600
40020001940

1945

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

Year (y)Fig. 4. Discharge
for eachforquarter.
Figure 4fluctuation
Discharge fluctuationeach quarter
600

First quarter (Jan. to M ar.)
Second quarter (Apr. to June)
Third quarter (Jul. to Sep.)
Fourth quarter (Oct. to Dec.)

Discharge（m3 /s）

500
400
300
200
10001940

1945

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

Year (y)Fig. 5. Ten-yearof discharge
forforeach
Figuremoving5 Ten-yearaverage
moving averageof discharge
eachquarter.
quarter
800
TCSI
TC
Linear approximation

TC = 0.266･y + 133

700
600
TCSI, TC （-）

500
400
300
200
100
0
1944

1949

1954

1959

1964

1969

1974

1979

1984

1989

1994

Year (y)6 Resultsof TCSI separation(EPA method)
Fig. 6. ResultsFigureof TCSI
separation(EPA method).
1800
1600
1400
1200
1000
800
600
400
200
0

Other quarter

1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2005
2006

Precipitation （mm）

4th quarter

Year (y)Figure 7 Annual andprecipitationand precipitation
the fourthquarter (Oct.
Dec.)
Fig. 7. Annual precipitation
precipiration
for theforfourthquarter(Oct.totoDec.).

60

3.4 Long-term-trend analysis for discharge
The measured discharge and the result of Trend and Cycle (TC) are shown in Figure 6. The
approximation curve for TC is shown in the same figure. The approximation curve for TC is the curvethat shows long-term variation in discharge. The approximation curve shows that the values areincreasing year by year.
3.5 Long-term change in annual precipitation
The annual precipitation observed in Garissa for the 47 years from 1959 to 2006 and the total
precipitation for the fourth quarter of each year is shown in Figure 7. The average annual precipitation
during the observed years is 392 mm/y. Yearly precipitation exceeded 500 mm/y ten times during theobserved period: in 1960 (661 mm/y), 1961 (757 mm/y), 1968 (1612 mm/y), 1978 (724 mm/y), 1979 (531mm/y), 1989 (640 mm/y), 1997 (950 mm/y), 1998 (639 mm/y), 2002 (560 mm/y) and 2006 (600 mm/y).
Two successive years saw precipitation exceeding 500 mm/y, and the cycle of occurrence of heavy
precipitation was 7 to 10 years. In recent years, however, the cycle may have become shorter; heavy
precipitation occurred in 2002, only four years after the previous heavy precipitation. It is conjectured
that abnormal weather caused by El Niño, which has surfaced globally, has affected the interval between
heavy rainfalls to some degree.
The average for total precipitation in the fourth quarter (October to December) is 187 mm. The
average precipitation for the 10 years from 1997 to 2006 is 231 mm, which is 44 mm greater than theaverage for total precipitation. The precipitation in the October to December quarter in recent years has
been greater than that for previous years.
The precipitation during the fourth quarter in 1997 and 2006, in which the observed area experienced
flood damage, was 690 mm and 378 mm, respectively. These two are the greatest and third greatest
values in the observed period (The second greatest is 529 mm, for 1961). It is understood that heavy
precipitation, whose probability of occurrence was once low, has become more frequent in recent years.
4. Conclusions
To clarify the hydrological changes in the Tana Delta region, we examined the long-term- changes inthe discharge of the Tana River and precipitation in the catchment area observed in Garissa. Thefollowings are the findings.
(1) The maximum discharge of the Tana River has tended to increase every year since about 1983.
(2) The discharge in the quarter from October to December (one of the two rainy seasons) has been
increasing.
(3) As results of EPA analysis, long-term variation (T) and mid-term cyclical variation (C) are increasing
year by year.
(4) In recent years, the event cycle for large-scale precipitation, which had been 7 to 10 years, has become
shorter.
(5) A comparison of the average precipitation during fourth quarter for the observed period (1959 to 2006)
to that for the most recent 10-year period (1997 to 2006) finds that the precipitation in the October toDecember quarter in recent years has been greater than that for previous years.
The precipitation findings were obtained using the data for the years before 1997 in which flood
damage occurred. The discharge and precipitation data revealed that the precipitation and discharge havetended to increase in recent years. Precipitation in the rainy season of October to December has tended toshow particularly great increases, to which can be attributed the increasing discharge of the Tana River inthis quarter. It is probable that the cycle of heavy rainfall occurrence has shortened in recent years, and it
is estimated that flooding will become more frequent.
Reference
Agwata J.F. (2005): Water Management in the Tana Basin of Kenya: Potential Conflicts and Interventions. Asia Journal of Water,Environment and Pollution, 2(2): 69-74.