Engineering, 2013, 5, 299-302 Published Online March 2013 (
Estimation of Extreme Flows in Nkana Ri v er to Verify the
Adequacy of Naming’ongo Bridge Waterway
Zacharia Katambara*, Joseph J. Msambichaka, Joseph Mkisi
Department of Civil Engineering, Mbeya Institute of Science and Technology, Mbeya, Tanzania
Email: *, *
Received December 26, 2012; revised January 26, 2013; accepted February 5, 2013
The response by the government of Tanzania to food security and poverty alleviation in the Naming’ongo area in Mbozi
District has been to develop Naming’ongo irrigation scheme as well as construct a bridge across River Nkana to con-
nect the farms and other parts of the district to facilitate a reliable transportation of the produce to the market. The Aus-
tralian Water Balance Model was calibrated by using 10 years data from a nearby sub-catchment of Mbarali. The Nam-
ing’ongo Sub-catchment was delineated form a 30 m digital elevation model. The observed rainfall was obtained from
Mbozi Meteorological station. The study approximated the peak flows in River Nkana for a return period of 50 years to
be slight above 560 m3/s. This was considered to be adequate for the proposed structure. The study recommends that
when undertaking human activities such as deforestation and cultivation an account for soil and environmental conser-
vation should be considered. While it is necessary to establish a monitoring system within the catchment, the designs of
future hydraulic structures should incorporate stream flow measuring facilities.
Keywords: Extreme Flow; Australian Water Balance Model; Stream Flow Simulations; Catchment Delineation;
Parameter Estimation
1. Introduction
The scale of hydrological monitoring in many catch-
ments is inadequate and the trend suggests that this will
even decrease due to various reasons. In Tanzania, just
like many other developing countries, there is insuffi-
cient financial support from the respective government as
well as the development partners that would be critical in
enabling the establishment of new or repair of all the
faulty monitoring stations, lack of committed gauge
readers and the accompanying supervision of the data
collection and verification processes, major change in
economic perspective that witnessed increase of unem-
ployment rate [1]. On the other hand, the demand for
food to feed the growing population is increasing and
80% of the food producers are subsistence farmers lo-
cated in rural areas. The majority of these farmers prac-
tice rain fed agriculture making them vulnerable to insuf-
ficient rainfall and longer dry spells that result to food
insecurity. It is against this background that the Tanza-
nian government has undertaken initiatives aimed at im-
proving food security by the establishment of several
irrigation infrastructures and their associated services
across the country as a response. One such initiative is
the Naming’ongo Bridge and Irrigation Scheme located
in Mbeya Region, Tanzania (Figure 1). The initiative
saw a new bridge considered following the failure of the
previous structure within a year of its construction. The
failure was attributed to the effect of floods. It is in the
same vein that this study recognizes the importance of
estimating the design peak discharges associated with the
different return periods. The study, therefore attempts to
estimate discharge with a 50-year return period along the
Nkana River at the project site (Figure 1).
Figure 1. Location of the Naming’ongo project in Mbeya,
*Corresponding author.
opyright © 2013 SciRes. ENG
Several studies have been done to estimate the fre-
quency of discharges as well as rainfall. Kagoda and
Ndiritu (2008) studied the frequency of rainfall using the
Bayesian Inference approach. For longer return periods,
the rainfall magnitudes of Bayesian estimates were found
to be greater than those obtained using the regional storm
index methodology [2]. In another study, pattern recog-
nition fuzzy model employing cause-and effect as well as
statistical analysis was used to estimate the peak dis-
charge at Yichang Station along the Yangtze River [3]
and the results indicated that the approach is effective.
With respect to ungauged catchments, parameters that
satisfactorily represent the Ruaha Catchment response
were obtained by collating the physical catchment prop-
erties (topography, geology, climate and land) and cali-
brated parameters from gauged catchments using the
Geographical Information System [4]. However, the un-
certainty associated with the model parameter estimates
depends on the importance of the collated physical
catchment properties and may be corrected by the use of
weighted regression approach [5].
In this study, the Australian Water Balance Model was
calibrated and the identified model parameters values
were further used to identify physical catchment attrib-
utes that could be used to undertake discharge frequency
analysis in ungauged catchments.
2. Description of the Study Area
The delineated Naming’ongo sub-catchment as shown in
Figure 1 is located in a semi-arid region in the southern
highlands of Tanzania in Mbozi District and covers an
area of 895 km2. Most of the rainfall is received on the
eastern part of the catchment and decreases towards the
western side. The sub-catchment is drained by River
Nkana which is a tributary of River Momba and flows
into Lake Rukwa. The outlet of the catchment is where
the headworks for the irrigation scheme are located. The
irrigation water diverted through the headworks flows
through the lead canal to the rice farming area. In addi-
tion to the silt water that flows in River Nkana that have
been noted of further increasing the soil nutrients suitable
farming, the farms are characterised by alluvial soil that
have been noticed of being suitable for farming. The rice
farming activity is normally carried out during the month
of December through April when the flows in the river
are high.
3. Data and Method
3.1. Australian Water Balance Model
The Australian water balance model (AWBM) was de-
veloped to relate the daily rainfall and daily evapotran-
spiration to runoff [6,7]. The model employs 5 concep-
tual stores, 3 conceptual surface stores to simulate partial
area that generate excess rainfall and the other 2 concep-
tual stores are the surface store and base flow store..
Each surface store’s water balance is calculated inde-
pendently (Equation (1)) and the excess contributes to
the surface runoff store and base flows. At every time
step rainfall is added to each store and evapotranspiration
is subtracted; this ensures that all the conceptual stores’
water is accounted for in each time step.
,1 ,ktkt t t
 (1)
where S is the storage of store k (1 to 3) at time step t, R
is the rainfall and E is the evapotranspiration. When the
amount of rainfall received is higher than the storage
capacity Sk,t + 1, the excess rainfall is generated. When
runoff occurs part of it contributes to base flow. Among
suitable features of the AWBM is its ability to simulate
delayed surface runoff for medium and large catchments,
a fact that makes it a suitable model for the delineated
Naming’ongo sub-catchment and other similar catch-
ment. The commonly used optimization strategy [8-10],
the shuffled complex evolution [11] was employed in
this study to undertake the calibration of the AWBM
model parameters. Following the catchment processes
being complex and nonlinear, the root mean square error
was used as an objective function since it is nonlinear
[12]. The Nash-Sutcliffe and the correlation coefficient
were used as the model performance measures. The val-
ues range from zero to one and a perfect match between
simulated and observed is one [13].
3.2. Data Used
In addition to the 30 m DEM, the other data used in-
cludes 10 years observed data of rainfall, potential eva-
poration, and stream flow data. The data was obtained
from Mbozi Meteorological Station and Rufiji Basin Of-
4. Methodology
The methodology involved:
a) Delineation of the catchment and identification of
the river network: A 30 m digital elevation model (DEM)
using the ARCSWAT an extension of ArcGIS was used
in determining the sub-catchment boundary, area as well
as the river network based on the DEM.
b) Identification of su itable parameters of AWBM: The
AWBM was calibrated using the data for Mbarali catch-
ment. The catchment characteristics of Mbarali have
been observed of having some similarities. Therefore, the
obtained parameters were considered adequate for the
Naming’ongo Project.
c) Simulation of the flows: The model was set for
Naming’ongo Sub-Catchment and the flows in River
Copyright © 2013 SciRes. ENG
Nkana were simulated.
d) Identification of the annual peak flows: The annual
peak flows were identified and thereafter the series were
ranked and the probabilities attached. The values were
extrapolated on a semi-log scale.
5. Results and Discussion
5.1. Calibration of the Model
The upper and lower limits of parameter search range
used were in line with the AWBM model developers’
recommendations and a total of 8 parameters were cali-
brated. The values obtained are within the range as
shown in Table 1. Although there is no suitable explana-
tion with regard to the 3rd surface store, the constrained
portion of the catchment area was found to have a capac-
ity value for the third surface store of zero. The model
performance based Nash-Sutcliff obtained during cali-
bration was 0.48 and the corresponding correlation coef-
ficient was 0.538 for the Mbarali Sub-catchment. The
lower values obtained can be attributed to the location of
the gauging stations on the lower side of the catchment.
The end of the catchment normally receives less rainfall
than the higher altitudes and therefore cannot be consid-
ered as one of the driver stations for the catchment [9].
Figure 2 shows the observed and calibrated flows. The
simulated flow time series plot suggests that satisfacto-
rily trend has been obtained; however, the lower flows
values have been satisfactorily simulated. This can be
attributed to the lower observed rainfall values that are
not representative of the catchment areal estimates and
higher evaporation rate. This calls for the need for estab-
lishing gauging station on the higher altitudes of the
catchment that normally receives higher rainfall and less
evaporation values.
5.2. Simulation Results for River Nkana
The simulation results suggest that the annual peak val-
ues range from 146 m3/s to 395 m3/s (Figure 3). For 50
Table 1. Calibrated parameters.
Parameter Value Lower limit Upper limit
1 A1 0.9998 0 1.0
2 A2 0.0002 0 1.0
3 A3 = 1 A1 A2 0
4 BFI 0.078650 1.0
5 C1 1.444 0 50.0
6 C2 127.70310 200.0
7 C3 194.73910 500.0
8 Kbase 0.0137 0 1.0
9 KSurf 0.6382 0 1.0
Jan-00 May-01Sep-02 Feb-04Jun-05
Time (days)
Flow (m
Figure 2. Observed and simulated flows for the Mbarali
sub-catchment during calibration.
y = -105.72Ln(x) + 651.8
= 0.9554
Chance of nonexceedance (%)
Flood peak (m
Figure 3. Probability of nonexceedance for the annual peak
values along River Nkana.
years return period the probability of nonexceedance is
2% and corresponds to 578 m3/s (Figure 3) and is close
to what was obtained from Mbeya Zone Irrigation Unit.
5.3. Likely Sources of Uncertainties
In estimating peak discharge in an ungauged catchment,
several sources of uncertainties exist and in a situation
where the uncertainties are not accounted for, the ob-
tained values are considered to be approximate values
[14]. The uncertainties can be classified into three cate-
gories, the knowledge uncertainty, natural variability and
decision uncertainty [15]. In the Naming’ongo Sub-
catchment, several factors that are likely to affect the
model results exist. These include the nonexistence of
streamflow gauging stations, the spatial and temporal
variability of the rainfall, the spatial and temporal vari-
ability of the hydrological process and human impact. In
addition, the various assumptions made during the iden-
tification of the two different catchments to be consid-
ered of having similar characteristics, may require an
explicit analysis which not the subject of this study. Fu-
ture activities need to account for land and environmental
conservation when undertaking some human activities
such as deforestation and the cultivation practices. In
addition to this, the current and future development of
Copyright © 2013 SciRes. ENG
Copyright © 2013 SciRes. ENG
many hydraulic structures within the catchment and be-
yond should incorporate stream flow measuring facilities.
6. Conclusion and Recommendation
Following the response by the government of Tanzania
in addressing food security and poverty alleviation in
Naming’ongo area in Mbozi District, is by developing
Naming’ongo irrigation scheme as well as construction
of bridges across River Nkana that connects the farms
and other parts of the district to facilitate a reliable
transportation of the farm produce. The Australian Water
Balance Model that was calibrated by using 10 years
hydrological and meteorological data from a nearby
sub-catchment of Mbarali, and the calibrated model was
then applied to simulate the flows in River Nkana. The
study approximated the average peak flows in River
Nkana for a return period of 50 years to be just above
560 m3/s. This discharge level was considered to be ade-
quate for the proposed structure. The study recommends
that the human activities including deforestation and cul-
tivation should incorporate soil and environmental con-
servation. While it is necessary to establish a monitoring
system within the catchment, the designs of future intake
structures should incorporate stream flow measuring fa-
7. Acknowledgements
The authors acknowledge the support received from the
Mbeya University of Science and Technology. Com-
ments received are also acknowledged.
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