Estimation of Extreme Flows in Nkana River to Verify the Adequacy of Naming’ongo Bridge Waterway

doi:10.4236/eng.2013.53040

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Engineering, 2013, 5, 299-302

http://dx.doi.org/10.4236/eng.2013.53040 Published Online March 2013 (http://www.scirp.org/journal/eng)

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: *[email protected], *[email protected]

Received December 26, 2012; revised January 26, 2013; accepted February 5, 2013

ABSTRACT

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,

Tanzania.

*Corresponding author.

Z. KATAMBARA ET AL.

300

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

SSR

E



 (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-

fice.

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

Z. KATAMBARA ET AL. 301

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

100

120

140

Jan-00 May-01Sep-02 Feb-04Jun-05

Time (days)

Flow (m

/s)

Observed

Simulated

Figure 2. Observed and simulated flows for the Mbarali

sub-catchment during calibration.

y = -105.72Ln(x) + 651.8

= 0.9554

100

200

300

400

500

600

700

110

Chance of nonexceedance (%)

Flood peak (m

/s)

100

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

Z. KATAMBARA ET AL.

302

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-

cilities.

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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