The Hydrological Study of Poorly Gauged Lake Jipe Catchment in Tanzania Using Simple
Tools
P.M. Ndomba1*, F. W. Mtalo1, A. Killingtveit2, & J. Nobert1
1
University of Dar es Salaam (UDSM), Tanzania.
2
Norwegian University of Science and Technology (NTNU), Trondheim, Norway
*Corresponding author email: pmndomba2002@yahoo.co.uk
ABSTRACT
The hydrology of most catchments remains unstudied due to lack of adequate data and
customized tools or models. In this study characterization of groundwater and surface water
interaction in one of the poorly gauged catchments in North Eastern part of Tanzania, Lake
Jipe catchment, was achieved using simple qualitative and quantitative tools and readily
available data. The approach entailed statistical analysis of hydrologic cycle components and
long term water balance analysis. Hydrological variables such as water levels in Lake Jipe
and daily rainfall amounts for gauging stations located within the sub-catchments; the major
inflow contributing Lumi River water levels; flow discharges at the outlet, 1DC2A gauging
station; and Lake water levels of the nearby Lake Chala were correlated. A strong correlation
is confirmed if the computed correlation coefficient, r, is higher than the corresponding
statistical Table value, r_table, at 5% probability level of significance, p, and degree of
freedom, N-2. In order to understand the effect of delay in runoff delivery to the lake the
analysis was conducted for unlagged and 15 days lagged flow discharges. Besides, the water
balance analysis on annual time scale and in long term perspectives was done to complement
regression analysis. The study found that there is a strong positive correlation, i.e. r = 0.35,
between water levels of Lake Jipe and water levels of Lumi River and rainfall is weakly
correlated to base flow. Besides, the water balance analysis indicated that rainfall does not
account for all the hydrological output fluxes in the catchment. Water levels of the Lakes
Chala and Jipe are strongly negatively correlated. Agreement of the results of various
methods probably suggests that there exists an external source of inflow to the system as has
been reported by researchers working in neighbouring catchments. It may be concluded that
catchment rainfall alone does not account for groundwater flow into the Lake and the main
source of inflow to Lake Jipe is the Lumi River catchment. In order to improve the results
data on Lumi River Flow discharges and recent water levels of Lakes Chala and Jipe is
recommended.
Keywords: Correlation, Hydrology, Lake Jipe Catchment, Regression analysis, Water
balance analysis.
1. INTRODUCTION
Lake Jipe and its wetland situated in a semi arid region have supported many livelihoods in
terms of their water source as well as economic support. However, human activities within
the catchment have impacted negatively to the lake’s environment and water resources that
the population can hardly enjoy its benefits as they did in the 1960’s (Muchiri, 2005). Being a
transboundary water system shared between Kenya and Tanzania, its management is rather
complicated. The fact that the water body and its wetland is the only main water source, then
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there is need for better management of the resource as well as the catchment area (Muchiri,
2005).
Management of water resources in the catchment is not well coordinated considering that the
Pangani River Basin Water Office has not yet established itself in the proposed lower levels
and with the district water office that is managing the water on the Tanzanian concentrating
more on supply coverage. A Ministry of Water officer equally manages the resources on the
Kenyan part of catchment but with more or less duplication of activities by various offices
dealing with water resources. In general there is no visible collaboration efforts in the
management of the water resources between the two countries. However the current Republic
of Kenya Water Act 2002 and the Republic of Tanzania, Ministry of Water, National Water
policy 2002 try to address most of the issues. Lack of data and monitoring system put the
whole water affair at stake as no conclusive decisions can be made without the information.
However, the hydrology of most catchments including Lake Jipe catchment remains
unstudied due to lack of adequate data and customized tools or models.
The general objective of this study was therefore to develop a conceptual understanding of the
roles that surface water inflows and groundwater recharge play in maintaining water levels in
ungauged Lake Jipe catchment
2. MATERIALS AND METHODS
2.1 Description of the study area
Lake Jipe is an International water body on the Kenya and Tanzania border and to the Southeast of Mt Kilimanjaro (Figs. 1 & 2). The lake approximate altitude is 700 metres above sea
level and at about 3o 35’ South and 37o,45’ East. The lake is approximately 12 km long with
the width varying between 2 and 3 km and a depth of about 3 meters but with some depth
points at 8 metres (personal communication with Kenya Wildlife Services officer) .The Lake
is a shallow fresh water lake to the south of Taveta Township on Kenyan side. The length of
the lake is about 12 km and an average width of 2.5 km. The surface area of the lake is known
to vary considerably and sometimes, it is reported to have almost dried up (Ndomba and
Gurandsrud, 2004). The lake storage capacity varies between 20 and 60 Mcm during the dry
and wet years.
2
Chala
Tanzania
Ky_Jipe
Tz_Jipe
Fig.1 Lake Jipe catchment area and spatial distribution of regular hydrometeorological monitoring stations. (Note: Tz_Jipe and Ky_Jipe represent Tanzanian and
Kenyan Side Jipe water levels gauging stations).
The lake and its wetland ecosystem are considered important due to its unique fish species
and other varied Fauna and flora species. The lake also acts as a reservoir for Ruvu River and
consequently Pangani River after draining the southeastern slopes of Kilimanjaro and the
north Pare Mountains of Tanzania (Fig. 2). The lake and the wetland are mainly served by a
series of spring associated with the Lumi River. Njoro Kubwa, Kitobo and Njukiini springs
are the main springs that guarantee sufficient flows to the lake even during the dry months.
The water of the lake is a vital resource to the riparian communities and their livestock,
situated in a largely semi arid to arid environment. It provides crucial watering and feeding
resources of the wild game of Tsavo West National Park (e.g. elephants, hippopotamus,
buffaloes, zebra, giraffes, and Oryx).
The climate of the lake Jipe catchment ranges from arid to semi arid with the annual rainfall
of about 600 mm but with the higher grounds, (North Pare and Kilimanjaro Mountains)
receiving about 2000 mm. It has two rain seasons, the long rains between March to May and
the short rains from November to December. Temperatures are high with an average
minimum of 19.5 o C and Maximum of 29.5o C. Evaporation is also high most times of the
year with a total evaporation of about 2000 mm per year.
3
Fig. 2 Lake Jipe catchment drainage and administrative townships locations
Several creeks drain the Southeastern slopes of Mt Kilimanjaro and feed the Lumi River and
consequently the Lake Jipe catchment. The drains to Lumi River move southward through
gentle and relatively flat area, with the only major interruption been the lake Chala (a
volcanic crater lake). South of the Taveta town, there is an appreciable southeast slope to the
lake Jipe with a general plain of about 710 metres above mean sea level (m.a.s.m.l) which
again drains into the lake. Before the Lumi River enters the lake, it transverse through a large
swamp.
On the western side of the lake, the rivers and water of the North Pare Mountains also drain to
the lake through several other creeks. The slope on these mountains is steep and drains high
volumes run off of high velocity during the rainy seasons. The main streams in the catchment
include Butu, Mwanjo, Ngeleni, Muvaraini, Kisokoro and Lumi (Marue and Motale).
However only the Lumi River that has some flow to the lake throughout the year.
The lake Chala is located to the north of lake Jipe watershed and also shared between
Tanzania and Kenya. The crator lake is about 4 Km2 and about 90 metres deep and at 800
m.a.m.s.l with no surface in-let or out let (Ndomba and Gurandsrud, 2004). Both the sub
surface in-flow and outflow are said to be diffuse and has a steep gradient from the water
surface to about 65 metres below then gradual inclination to the flat bottom, as reported in
Muchiri (2005). Steep slope cliffs naturally protect the lake, and hence limited external
interference from human beings as well as animals. The lake is estimated to hold a volume of
between 300 to 350 Million cubic metres (Mcm) of clean and fresh water and the water levels
hardly changes appreciably.
The southeastern slopes of Kilimanjaro are made up of several volcanic types of rocks
associated with the long and complex volcanic history of the mountain. The area northwest of
Lake Chala consists of mainly the agglomerates and ashes while northeast consists of
basement rocks. In the south towards Lake Jipe, the area is covered by calcareous tuffaceous
grits overlying the basalt and ash sequence.
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2.2 Methodology
Characterization of groundwater/surface water interaction was done using qualitative and
quantitative tools. The latter approach entailed activities such as correlations between input
hydrological variables (i.e. daily rainfall for stations located within sub-catchments and water
levels at the Lumi gauging station) and output hydrological variables; i.e., Lake Jipe water
levels and outflows, flow of Ruvu at Kifaru bridge (Figs. 1 & 2 ).
The levels in the two lakes have been monitored for some time using digital data logger and
manual gauge. However, it should be noted that only portion of the data set was used in this
study because sometimes during the sampling programme the flow from main contributing
tributary (Lumi River) to Lake Jipe was diverted. The authors believe that such a modification
of flows could affect the statistical inferences. Therefore, this analysis excludes portion of
paired data set under modified state. The water levels data was sourced from the Pangani
River Basin Research project (Ndomba and Gurandsrud, 2004).
A strong correlation is confirmed if the computed correlation coefficient, r, is higher than the
corresponding statistical Table value, r_table, at 5% probability level of significance, p, and
degree of freedom, N-2 (StatSoft, 2006). In order to understand the effect of delay in runoff
delivery to the lake the analysis was conducted for unlagged and 15 days lagged flow
discharges.
Another method of characterizing the role of groundwater/surface runoff to water contents in
Lake Jipe was based on catchment water balance analysis.
2.2.1 Correlation analysis
Initially, correlation analysis was conducted between input hydrological variables (rainfall on
Lake Jipe catchment and water level at Lumi gauging station) and output hydrological
variables (flow discharges at station 1DC2A (Lake Jipe outlet) and Lake Jipe water levels at
Ky_Jipe). The analysis was conducted to identify location of water sources and to understand
the main lake water content contributing processes (surface runoff, SURF, and baseflow,
BASE). SURF time series was obtained by filtering the total flow using a baseflow filter
developed by Arnold and Allen (1999). The analysis was carried out in a period between
1/1/1981 and 31/12/1981.
It should be noted also that only six rainfall stations (09337006, 09337031, 09337045,
09337075, 09337132, 09337110) and 2 flow gauging stations (1DC2A and Lumi gauging
station) could be sourced for this analysis. Nevertheless, the rainfall stations were considered
to represent the main runoff contributing sub catchments (Lumi, Pare Mt. and Intervening
catchments and Taveta) (Fig. 1 and Table 1). In order to understand the effect of delay in
runoff delivery to the lake and the outlet of the catchment (1DC2A), the flow discharges were
lagged by 15 days (Table 1). This approach was expected to capture delay in groundwater
flow delivery to the Lake.
2.2.2 Water Balance Analysis
A typical lake water balance could not be done because the inflow data to the lake is not
available, but some estimates have been made. An annual water balance analysis was
conducted between 1976 and 1991 using Eq.1. The components incorporated into the water
balance model are areal precipitation, P (mm), actual evaporation, E (mm), total outflow
runoff, Q (mm), and the error term. The pan evaporation data was used to estimate the actual
evaporation, E. It should be noted that the Evaporation, E, estimation is based on average crop
coefficients of 0.8 (kc*ks). The outflow runoff, Q, was estimated from stream flow runoff
data at 1D2A gauging station, the outlet of Jipe catchment. The hydro-meteorological data
5
was sourced from Ministry of Water and Water Resources Engineering Department Database,
University of Dar-es-Salaam.
P – ( E + Q ) = Error term ……………………………………………………… (1)
This analysis assumes that natural systems such as catchment, restores/re-stabilizes itself as a
function of time. Therefore, the water balance analysis both on annual basis and in a longer
term perspectives, would register zero error term in Eq. 1 above. Otherwise, negative error
term indicates that rainfall as input in the Eq. 1 above does not account for the entire output,
i.e. E plus Q. In the latter case external source of water other than rainfall could be associated
to sustaining lake water levels.
3. RESULTS AND DISCUSSIONS
As noted earlier that a number of approaches have been used to characterize the
ground/surface water interaction and role it plays in water balance of Lake Jipe, therefore, this
section presents result of each method separately.
3.1 Correlation of hydrological variables
One would note from Table 1 that there is a strong positive correlation between water levels
at Lumi gauging station and Lake Jipe at 5% probability level of significance, for
hydrological variables which are not lagged. Independent correlation analysis between
outflow at 1DC2A and Lake Jipe water levels indicates that they are strongly correlated with
correlation coefficient, r of 0.971. Probably, from this result one would suggest that lake
storage is small compared to inflows (i.e. it does not have a significant flood regulation
function). The authors would like to agree with such an assertion because Lumi River joins
Lake Jipe near its outlet. That means regulation effect of the lake Jipe is minimal. Besides, the
analysis indicates that Lake Jipe water levels are highly correlated with BASE than SURF.
Rainfall from Lumi catchment is poorly correlated with BASE, Rainfall from Lumi catchment
is strongly correlated with Lumi water levels.
Table 1
Correlation between input and Output hydrological variables
1DC2A flow
JIPE WL
T/Flo
w
BAS
E
SUR
F
JIPE WL
T/Flo
w
BAS
E
SUR
F
Lagged by
days
1DC2A flow
15
Unlagged
Output
Hydrological
variables
r_tab Input Hydrological variables
le at 933700 9337031 9337045
p=5
6
%
Lumi
Interveni Interveni
ng
ng
0.057
0.065
0.237
0.075
0.089
0.234
0.050
0.053
933707
5
933713
2
933711
0
LUM
I WL
Pare
Mt.
0.221
0.246
Lumi
Taveta
0.015
0.033
0.064
0.083
0.354
0.395
0.021
0.088
0.197
0.117
-0.030
0.065
0.297
0.204
0.106
0.249
0.484
0.169
0.139
0.495
-0.091
-0.035
0.005
0.057
0.147
0.213
-0.107
-0.021
-0.130
-0.082
-0.027
0.022
0.087
0.180
-0.057
0.033
0.145
-0.077
-0.085
0.016
0.110
0.024
0.070
0.267
0.109
-0.041
0.020
0.244
6
On the other hand rainfall from Lumi catchment is strongly correlated with SURF. All rainfall
stations, except 9337132 of Lumi subcatchment are strongly correlated with Lake Jipe water
levels. Lagging output hydrological variables such as flow discharges at the outlet and Lake
Jipe water levels by 15 days decreases the correlation between BASE and rainfalls (Table 1).
In particular Lumi catchment rainfalls become poorly correlated with Lake Jipe WL and
BASE. There is strong positive correlation between Lumi water levels and all output
hydrological variables, i.e., Lake Jipe water levels, total flow, BASE and SURF for both unlagged and lagged experiments.
Secondly, independent correlation analysis between Lakes Chala and Jipe water levels was
also conducted (Table 2). The data used was concurrent daily water levels measurements
from the Lakes between April and December, 2005.
Table 2. Correlation between Water levels of Lakes Chala and Jipe for year 2005
Water Levels at Lake
Jipe
Season
Unlagged
Lagged by 15 days
Lagged by 30 days
Apr. 26 - May 29
r_table
p=5%
0.33
at Water Levels
at Lake Chala
-0.81
Nov. 1-Dec. 20
0.28
-0.59
May 11 – May 29
0.47
-0.83
Nov.16 - Dec. 20
0.32
-0.79
Dec.1 - Dec. 20
0.45
-0.66
From Table 2 above, one would note that water levels of the two Lakes are strongly
negatively correlated. Another notable observation is that lagging of water levels at Lake Jipe
does not improve or change the statistical inferences in the table. Probably, this result
suggests that Lake Chala and Lake Jipe are not hydraulically connected, instead they have
inverse relationships. It should be noted however that, concurrent data set for other
environmental variables such as Lumi Water levels as presented in Table 1 was not available
for such an analysis. The results from both Tables 1 and 2 suggest that rainfall alone does not
account for BASE or groundwater flow into Lake Jipe. Therefore, other sources of water
other than Lake Chala and rainfall could explain baseflow or groundwater contribution to the
Lake.
Water balance
The results of water balance analysis are presented in Table 3 below. The error term in the
table, -149.2 mm, was computed as the difference between long term annual aerial
precipitation, 1406 mm, and sum of evaporation and runoff (i.e. 1496 + 59 = 1555 mm). The
percent error in Q (253 %) was computed as the percentage ratio of absolute error term (149.2 mm) to long term stream flow runoff, Q (59 mm).
Table 3.
Annual Water balance analysis between 1976 and 1991
Water balance components
P (mm)
E (mm) Q
(mm)
1406
1496
59
Water balance Error terms
Error (mm) %Error in %
Q
Error in
P
-149.2
253
11
7
Similarly, the percentage error in P (11 %) was computed as the percentage ratio of absolute
error term (-149.2 mm) to long term annual aerial precipitation, (1406 mm). The negative
error term (i.e. -149.2 mm) as computed in Table 3 above supports the contention that rainfall
alone does not account for the output (E plus Q) on annual basis or in the longer term. This
result compares favorably with the correlation analysis findings. Probably, this suggests that
there exists an external source of inflow to the system as has been reported by Birhanu (2005)
working in neighboring Kikuletwa catchment (1DD1). Birhanu (2005) showed that springs
yield of about 11 m3/s in 1DD1 catchment could not be explained by rainfall amount alone.
Besides, as reported in Muchiri (2005) Chemical and isotopic analysis of the Lake Chala
waters and that of the springs below it showed a marked difference. This coupled with the low
estimated outflow, points against the possibility of it been the main feeder of the springs
draining into Lake Jipe down stream in the catchment (Afrisco, 1994). These results suggest
that springs and base flow could ot be accounted rainfall and surface waters in the catcahment
CONCLUSSIONS AND RECOMMENDATIONS
Hydrologic study in Lake Jipe has found that there is a strong positive correlation (0.971)
between water levels of Lake Jipe and water levels of Lumi River at Lumi gauging
station.The results of correlation analysis showed that rainfall is weakly correlated to base
flow into lake Jipe suggesting that catchment rainfall alone does not account for groundwater
flow into the Lake. The analysis suggests that the main source of inflow to Lake Jipe is the
Lumi sub-catchment.
The Authors have four major recommendations to improve the hydrologic study results of
Lake Jipe catchment. A bathymetric survey or spot measurements of bed elevation of Lake
Jipe are recommended for future water balance or routing studies. Besides, water-level
monitoring in Lake Chala and Jipe should be continued and extended. The Authors also
recommend further monitoring and modeling of groundwater and surface water interactions in
Lake Jipe to study the role of groundwater recharge to the lake.
REFERENCES
Afrisco Ltd, 1994. Lake Chala water resources developments project evaluation report.
Arnold, J.G., and Allen, P.M., (1999). Automated methods for estimating baseflow and
ground water recharge from streamflow records. Journal of the American Water Resources
Association 35(2): 411-424.
Birhanu, B.Z., (2005). Application of a GIS based SWAT model in simulating the available
water resources in a Pangani basin subcatchment. A Dissertation submitted for degree of
Master’s of Science in Water Resources Engineering at University of Dar es salaam.
Mashauri, (2002): “Assessment of Water Quality, upstream of Nyumba ya Mungu
Reservoir”. A workshop proceeding
Muchiri, S. N. (2005). Land And Water Use Impacts, And The Resources Management. Lake
Jipe Catchment Case Study. A dissertation submitted in partial fulfillment of the requirements
for the degree of Masters of Integrated Water Resources Management of the University of
Dar es Salaam.
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Musyoki, M. M. and Mwandotto, B. A. J. (1999). Presentation of results/reports on the
assessment of management needs for the watershed wetlands and waters of Lake Jipe. Paper
presented at the Lake Jipe Cross-border Workshop, 13th - 15th October, 1999.
Ndomba, P. M., and Gurandsrud, Åsta E., (2004). Installations of Water Level Loggers and
Gauges in Lake Chala and Lake Jipe, Pangani River Basin. Field Report, for Pangani Basin
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Republic of Kenya, 2002. Kenya gazette supplement No 107 (The Water act 2002).
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http://www.statsoft.com/textbook/stathome.html. Accessed on October 2006
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