Coastal Winds of Tanzania

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Frequency and long-term distribution ofcoastal winds of Tanzania

A.M. Dubi

Institute of Marine Sciences, University of Dar es Salaam,P. O. Box 668, Zanzibar, Tanzania

ABSTRACTWind data records of four major coastal locations (Tanga, Dar es Salaam,Zanzibar and Mtwara) of Tanzania covering the period between 1972 and1996 were analysed to determine maximum winds frequency, long-termdistribution and general trends. Results show that Tanga, Dar es Salaamand Mtwara experienced peak speeds during July/August and Zanzibarexperienced peak speeds in January. At Mtwara, maximum wind speedshave been linearly increasing since 1972, whereas at the other threelocations, the speeds show a decreasing trend. The mode for Mtwara is30 knots and that for Tanga, Zanzibar and Dar es Salaam is 20 knots.Mtwara has the highest location parameter B = 26, followed by Zanzibarand Tanga with B = 20. Dar es Salaam has the lowest of them all. Interms of wind activity, Mtwara and Zanzibar experienced offshore winds,while Dar es Salaam was more influenced by coastal winds. Of all thecoastal locations, Mtwara experienced the strongest winds, which certainlyresulted in increased wave activity, stronger currents and wind set-up.

INTRODUCTIONThe United Republic of Tanzania is located on the eastern coast of Africa, between 100' 11 48' S and 29 30' E. It is composed of Mainland Tanzania and the Islands ofZanzibar (Unguja and Pemba). The Indian Ocean borders its entire eastern frontier(Figure 1). Tanzania has an estimated total land area of 945,200km2 with mainlandTanzania covering an area of 942,800km2. The Zanzibar islands cover 2400km2.

The coastline of Tanzania extends for about 800km on the mainland, 430kmaround Unguja Island and 450km around Pemba Island. The coastline of MainlandTanzania extends from the border with Kenya in the north to Ruvuma River in the south(Figure 1). The coastal zone of Tanzania includes five administrative regionsTanga,Coast, Dar es Salaam, Lindi and Mtwara, as well as the three large islands Unguja andPemba (which make up Zanzibar) and Mafia and numerous islets. About two thirds ofthe coastline has fringing reefs, often close to the shoreline, broken by river outletssuch as the Rufiji Delta, Pangani, Ruvuma, Wami and Ruvu. The continental shelf is

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about 5.8km wide, except at the Zanzibar and Mafia channels where the continental shelfreaches a width of about 62km. The nations total estimated shelf area is 17,500km2.

The coastal waters of Tanzania are influenced by two alternating seasons; namelythe southerly and northerly monsoons (Newell, 1957). The southerly monsoons,

Figure 1. The coastline of Tanzania and locations considered in this paper

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beginning in April and ending in October/November, are usually strong and predominantlysoutherly. The northerly monsoons begin in November and end in March. These arelighter winds and are predominantly northerly. Circulation of the coastal waters of EastAfrica is strongly influenced by these winds. The East African Coastal Current (EACC) isan important hydrographic feature of the East African coastal waters. Newell (1957)described the current as one that moves northward throughout the year, but changesspeeds during the two monsoon seasons. During the southerly monsoons, it moveswith a speed of about 4 knots after being accelerated by the trade winds. During thenortherly monsoons, the current is retarded by the northerly winds along this coast.From the equator northwards, it is reversed to flow in the southerly direction. Thereversed current meets the much-decelerated EACC at about 1S, where both aredeflected out to the sea forming the Equatorial Counter Current (Figures 2a and 2b).

As wind has multiple effects on ocean circulation, waves and sea level fluctuations,it is important to study wind patterns in order to evaluate the frequency of maximumwind speeds, the distribution and return speeds of extreme winds. In order to evaluateextreme wind speeds, annual maximum series are fitted to some distribution functions.The commonly employed functions are the Fisher-Tippett (FT) type I & II, the Weibulland log-normal distributions. Other formulae are also cited in the literature. Petruaskasand Aagaard (1970) presented a procedure to select the best fitting distribution functionamong a set of the Weibull and the FT-I distributions advocating the Weibull distribution.According to Goda (1988), the use of the log-normal distribution should not be considereduntil positive evidence for its support is produced. Dubi (1999) used Godas method toevaluate extreme wind speeds of coastal Tanzania.

Lwambuka (1992) compiled 18-year wind data of several locations in Tanzaniaincluding Tanga, Zanzibar, Dar es Salaam and Mtwara. Using the method of moments,he fitted only the FT-I distribution function and found 50-year extreme gust speeds(1.9 x extreme wind speed) to be 30m/s for Tanga, 29m/s for Zanzibar, 27m/s for Dares Salaam and 38m/s for Mtwara.

This paper is a complement for the paper by Dubi (1999) on the evaluation ofextreme wind speeds in relation to the design of coastal structures in Tanzania. Themain objectives of the present paper are to evaluate the frequency of the maximumwind speeds and their cumulative distributions, and to review statistical and otherparameters related to the winds. This information is required in the evaluation of waveclimate and currents in the coastal waters of Tanzania.

DISTRIBUTION FUNCTIONS FOR EXTREME VALUE ANALYSISAs there is no strong theoretical or empirical evidence for selecting a particular probabilitydistribution function, the approach commonly used is to try several candidate distributionswith each data set and select the one that fits best. Following Goda (1988), Dubi(1999) fitted two distribution functions to the annual maximum wind speeds to findextreme wind speeds and 50- and 100-year return wind speeds. The candidate

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Figure 2a. North East Trade Winds (adapted from Linden & Lundin, 1996)

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Figure 2b. South East Trade Winds (adapted from Linden & Lundin, 1996)

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distributions are:

The Fisher-Tippett Type I (FT-I) Distribution given by:

F V V B A( ) exp( exp( ( ) / )= - - - (1)

and

The Weibull Distribution given by:

F(V)=1exp[{(VB)/A}k] (2)

where F(V) is the probability of non-exceedance of the statistical wind speedvariable V, B V is the location parameter, A is the scale parameter and k > 0 is the shapeparameter.

The input data are then arranged in descending order of magnitude from thelargest to the smallest of the wind speeds. Then a probability or plotting position isassigned to each wind speed using Gringortens (1963) formula for FT-I and the modifiedformula (by Goda, 1988) of Petruaskas and Aagaard (1970) as follows:

F VmN

F Vm

k

Nk

mT

m

T

( )..

( ).

.

..

= - - +

= -- -

+ +

10 44012

10 20

0 27

0 200 23

for FT - I

for Weibull(3)

where F (Vm) is the probability of the m-th value of wind speed not being exceeded,m being the rank of the wind speed. NT is the total number of events during the lengthof a record, which in our case, has been taken to be equal to the number of input windspeeds.

The scale and location parameters A and B in equations (1) and (2) are relatedlinearly as:

Vm = AYm + B, m = 1, 2, ......., N (4)

>

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where Vm is the m-th largest value of the wind speed and Ym is the reduced variatefor the m-th value of the wind speed. This reduced variate is related to F (Vm) as:

Y n F V

Y F V

m m

m mk

= - l -ln ( ) , for FT - I

= - ln - ( ) for Weibull

[ ]

{ [ ]}11

(5)

The parameters A and B are evaluated using the Least Squares method appliedbetween the ordered pair of Vm and its reduced variate Ym for the two candidatedistribution functions.

Wind speeds for various return periods are calculated by the following equations(Goda, 1988):

Vr=Ayr+B (6)

where

Vr = wind speed with return period Tr

( )[ ]

yT

y T

rr

r r

= - - -

=

ln ln for FT - I

ln for Weibull1k

1 1l

l(7)

l = NT/K = average number of events per year = 1 in this analysis.Tr = return period in years and K = length of record in years

Since the period of record for this data is relatively short and the level of uncertaintyin extreme estimates with long return periods is high, confidence intervals are calculatedto give a quantitative indicator of the level of uncertainty in the estimated extremewind speeds.

Confidence intervals are calculated following the approach of Goda (1988) forestimating standard deviation of return values when the true distribution is known.Table 1 gives factors by which the standard error is multiplied to get bounds withvarious levels of confidence.

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In order to select the best fit distribution function, the correlation between variablesin the linear equation (4) and the sum of the squares of residuals are used. The sum ofthe squares of residuals is given by:

( )[ ]2

1

2 =

+-=N

mmm BAYVR (8)

The distribution function that gives the highest correlation and the smallest sumof the squares of residuals has been selected.

DATA SETWind data records for Tanga, Dar es Salaam, Mtwara and Zanzibar for the period between1972 and 1996 were obtained for analysis from the respective airport authorities. These3-hourly records of wind speeds and direction at airport stations are recorded by a rotatingcup anemometer mounted at the top of a 10-meter high tower. The maximum value inevery month for each location was taken to constitute the data set. Figure 3 shows themaximum wind speeds recorded for each month of the 25-year period. Figure 4 showsmaximum wind speeds in each year of the 25-year period.

RESULTS

General trend of maximum wind speedsFigure 3 reveals peak speeds of the maximum speeds during January and July. Tanga andZanzibar experienced peak speeds in January, while Mtwara experienced peak speedsJuly. Dar es Salaam experienced peak speeds during both January and July.

Figure 4 shows that maximum wind speeds at Mtwara have been linearly increasingsince 1972, whereas at the other three locations, the speeds show a decreasing trend.Zanzibar winds also exhibit a peak in the period 19791982. Dar es Salaam winds showtwo peaks: one peak is seen in the period 19751979 and another during 198184, afterwhich the speeds decrease. Tanga winds exhibit two peaks: one in the period 19741977and another during 19911993.

Table 1. Confidence interval bounds for extreme wind speeds

Confidence level Confidence interval Probability of exceeding(%) bounds around Vr upper bound (%)

80 1.28s 10.085 1.44s 7.590 1.65s 5.095 1.96s 2.599 2.58s 0.5

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Figure 3. Monthly maximum wind speeds recorded in the period 197296

Frequency of occurrence and cumulative distribution of maximum windspeedsFigures 58 show the frequency of occurrence and cumulative distribution of themaximum wind speeds. Some of the statistical parameters are shown in Table 2.

Figure 4. Annual maximum wind speed recorded in the period 197296

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Figure 5. Frequency of occurrence and cumulative distribution of Tanga winds

Figure 6. Frequency of occurrence and cumulative distribution of Zanzibar winds

Based on the correlation coefficient and the sum of the squares of residuals, Dubi(1999) selected distribution functions that fitted best. They were Weibull (k =1.0) forTanga winds, FT-I for Zanzibar winds, Weibull (k = 2) for Dar es Salaam and Mtwarawinds. Model parameters corresponding to the distribution functions are shown in Tables36. The selected distribution function is shown in bold and italic format.

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Figure 7. Frequency of occurrence and cumulative distribution of Dar es Salaam winds

Table 2. Statistical parameters of the maximum wind speeds

Location

Parameter Tanga Zanzibar Dar es Salaam Mtwara

Mean (kts.) 20.2 21.5 20.3 27.3Mode (kts.) 20.0 20.0 20.0 30.0Minimum (kts.) 18.0 16.0 15.0 20.0Maximum(kts.) 27.0 30.0 26.0 35.0

Figure 8. Frequency of occurrence and cumulative distribution of Mtwara winds

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Table 3. Tanga winds (Mean of sample data = 20.20 knots, std = 2.708knots)

Weibull Distribution

Parameters FT-I k = 0.75 k = 1.00 k = 1.40 k = 2.00

A 2.056 1.730 2.736 4.026 5.529B 19.047 18.150 17.472 16.535 15.302Correlation 0.9330 0.9435 0.9559 0.9449 0.9182Sum of squaresof residuals (R2) 0.2739 0.5444 0.2016 0.2447 0.3159

Return period (yrs) Return wind speed (knots)

50 27.07 28.82 28.17 27.20 26.24100 28.51 31.41 30.07 28.52 27.17

Table 4. Zanzibar winds (Mean of sample data = 21.458 knots, std = 3.007knots)





50 29.43 30.85 30.29 29.42 28.52100 31.09 33.87 32.39 30.92 29.61

Table 5. Dar es Salaam winds (Mean of sample data = 20.00 knots, std =2.828 knots)





50 27.51 28.52 28.19 27.50 26.73100 29.08 31.09 30.13 28.91 27.76

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CONCLUSIONCoastal processes and hydrographic conditions of near-shore and offshore waters arestrongly influenced by winds. From an examination of the monthly and annual maximumwind speeds, the response of beaches and coastal water circulation can be deduced.Beginning 1986 until 1996 Mtwara experienced increasing maximum winds reaching apeak of 35 knots. This trend certainly resulted in increased shoreline retreat due to increasedwave activity, stronger currents and wind set-up. Maximum values of Tanga winds showa peak in the periods 19721976 and 19901993. Zanzibar winds had a peak in 19791981. These are the periods in which increased coastal processes are expected.

Zanzibar and Mtwara have the same location parameter, B, which is approximately20, followed by Tanga with a parameter 17.5. Dar es Salaam has the lowest of all. Interms of wind activity, Zanzibar and Mtwara experience offshore winds, while Dar esSalaam is more influenced by coastal winds. Tanga is influenced mostly by offshore winds.Of all the coastal stations, Mtwara experienced the strongest winds, while Dar es Salaamexperienced the weakest winds.

REFERENCESBowden, K. F. 1983. Physical Oceanography of Coastal Waters. Ellis Horwood Ltd.Dubi, A. M. 1999. Evaluation of extreme wind speeds in relation to the design of coastal

structures in Tanzania. Uhandisi Journal 23(1): 5763.Goda, Y. 1988. On the methodology of selecting design wave heights, pp. 899913: In:

Proceedings, 21st International Conference on Coastal Engineering, American Societyof Civil Engineers, Costa del Sol-Malaga, Spain.

Gringorten, I. I. 1963. A plotting rule for extreme probability paper. Journal of GeophysicalResearch 68 (3): 813814.

Linden, O. & Lundin, C.G. (eds) 1996. Proceedings of the National Workshop on IntegratedCoastal Zone Management in Tanzania.

Lwambuka, L. 1992. Evaluation of wind data records to predict extreme wind speeds inTanzania. Uhandisi Journal 16 (1).

Table 6. Mtwara winds (Mean of sample data = 27.292 knots, std = 3.712knots)





50 36.90 37.93 37.57 36.79 35.91100 38.91 41.13 40.01 38.58 37.24

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Newell, B.S. 1957. A preliminary survey of the hydrography of the British East African Coastalwaters. Fishery Publications No. 9. Her Majestys Stationery Office, London, 21pp.

Petruaskas, C. & Aagaard, P. M. 1970. Extrapolation of historical storm data for estimatingdesign wave heights. Prepr. 2nd Annual Offshore Technology Conference, OTC 1190.

Coastal Winds of Tanzania

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