Analysis of heavy rainfall events over city: A necessity to lessen flood risks

Program: “Sustainability/survivability science for resilient society adaptable to extreme weather conditions” course at Kyoto University, Japan

Author : Wilbert Timiza Muruke Organization : Meteorological Agency (TMA) Research Work done at Kyoto University-DPRI 2012 under WMO-Kyoto University Fellowship.

Supervised by : Professor Kaoru Takara

Abstract Floods are a threat to many cities especially in the developing countries and cities found near the coastal areas where the population pressure, unplanned settlements and poor infrastructures are most evident. Climate change also poses a higher flood risks in these areas due to increased frequency and intensity of extreme rainfall events. In this study analysis of extreme rainfall over Dar es Salaam city in Tanzania was done. The analysis employed empirical as well as hydrological frequency modeling to annual maximum daily rainfall events for 5 of the meteorological stations found within the city. A flood risk map to facilitate in flood forecasting and warning was also made using GIS techniques. The results suggest an increase in intensity of extreme rainfall events and the GEV families of hydrologic frequency analysis models were found to fit the data set better. The later results lead to better estimation of return periods. These findings are useful information to the city planners (e.g. hydrological designers), decision makers as well as the general public in the fight against flood risks in the city of Dar es Salaam.

Key words : Probability density function; extreme rainfall; Generalized Extreme Value Distribution; Dar es Salaam.

1

Background

Heavy precipitation events rank highest among natural hazards with the most disastrous impacts on infrastructure, ecosystems, and losses of lives primarily due to floods, landslides and mudflows (Kysel´y, 2009). Global warming has influenced precipitation amount, intensity, frequency and type all around the globe. Though warming may cause surface drying followed by drought, also according to Clausius–Clapeyron relation, water holding capacity of the atmosphere increases by about 7% for every 1°C rise in temperature (IPCC. 2007). Evidence of an increase in precipitation intensity for some places in the world especially the last half of the 20 th century can be observed in such studies as done by Frich et al (2002) and (Goswani et al ., 2006) among many others. Thus the increase in frequency of heavy precipitation observed currently and in future is consistent with warming and increasing atmospheric moisture due to global warming (IPCC, 2007). Excessive precipitation also may be caused by increase of aerosols in the atmosphere, which act as the cloud condensation nuclei, and also depend on the atmospheric circulation patterns driven by coupled atmospheric-ocean mechanisms such as El Nino Southern Oscillation (ENSO) Kijazi and Reason (2009). Percentage contribution of very wet days (above the 95th percentile) to the annual precipitation total globally was found to have a trend of 0.21% per decade in the period 1951-2003 as compared to a trend of 0.41% per decade in the period 1979-2003 (IPCC, 2007).

Precipitation is probably the longest observed and mostly widely recorded hydrological phenomenon (Strangeways and Smith, 1985) and is a fundamental element of hydrological cycle. Because of global warming, trends of extreme precipitation have changed significantly. Growing atmospheric concentrations of greenhouse gases are associated with changes in the average climate (Houghton et al., 2001). Recently, the attention of the climatic research has shifted to likely future behavior of the occurrence and intensity of excessive precipitation events under climate change (Casas et al., 2007).

Recent study done in East Africa show that short rain season (October-December) is projected to increase by more than 10% also the long rain season (March to May) is projected to increase by more than 15% (Shongwe et al 2010). This study confirms other similar findings for the Eastern African region experiencing bimodal rainfall pattern including URT (2003) and IPCC (2007). Dar es Salam area is under the bimodal rainfall regime which make it a likely candidate of these findings.

Frequency analysis technique on extreme rainfall provides a better way of mitigating/adapting and hence reducing the risks associated with these events. The results are especially useful in many hydrological designs and flood management. Such analyses are important as they will assist in searching for reasonable and simple distribution that fits the observations (Stedinger et al, 1992). Various studies have been conducted to study behavior of extreme rainfall events which include that of Takara and Stedinger (1994), Takara and Tosa (1999). Some studies have been conducted in eastern and southern African region where Tanzania is located. Among them a study by Kachroo and Mkhandi (2000) and Mkhandi et al. (1996). A study over Lake Victoria/Nile basin flow used Log-Pearson and Gumbel distribution to study the nature of flood discharge over the basin (Opere et al 2006).

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Climate change impacts in various parts of the world are now evident. United Republic of Tanzania being one of the least developed countries is significantly impacted due to the low adaptive capacity. In recent years, frequency of extreme weather events such as drought due to deficiency in rainfall and floods as a result of occasional extreme rainfall events has increased, thus threatening human life and their property. Specific example of drought event is the widespread 2005/2006 drought while a recent flood event experienced over Dar es Salaam city on 20 th to 22 nd December 2011, caused significant impacts to the country economy. About 43 people were reported dead and left many people homeless while causing severe destruction of infrastructure; these include houses, roads and bridges. Cumulated total rainfall for three days amounted to 260.2mm at Julius Nyerere International Airport (JNIA) Station while 156.4mm was recorded on 21th Dec. 2011 alone. This amount of rainfall was a record breaking of 58 years since the establishment of the station in 1953.

Tanzania Meteorological Agency (TMA) has always been on the forefront to issue timely warnings in the events of impending natural hazards such as droughts and floods to the community especially to the most vulnerable. Also bearing in mind that most extreme rainfall events are confined in local or regional scale. For the case of floods, the most vulnerable people are those residing in low lying areas which are acerbated by poor planning of infrastructures including housing and drainages. It is with this aim that identification of flood prone areas and flood risks in the fast growing Dar es Salaam city as a sustainable and adaptive measure to extreme weather event will help decision makers to take more deliberate actions in city planning, environmental management also for the residents to make proper choices of their residencies for our county’s socio-economic well-being.

Main Objective Flood risks reduction for enhancement of socio-economic development in Tanzania.

Specific Objectives • Analysis of past extreme rainfall events and probability of recurrence over Dar es Salaam by: (a) Empirical method and (b) Hydrologic frequency analysis. • Mapping of flood prone areas using GIS

3

Study Area

Climate profile Tanzania has a tropical equatorial type of climate. However its climate has a great diversity due to the country’s diversity in topography and waterbodies. The country is characterized by two rainfall regimes: namely unimodal and bimodal rainfall regime. The seasonal rains over the unimodal regime occur between October and May (Msimu) over the Southern, South-western, Central and Western areas of the country. The bimodal rainfall regime has two rain seasons, the long rain season (Masika) experienced between March and May (MAM) and the short rain season (Vuli) occurring between October and December (OND) over the Northern coast, North-eastern Highlands, Lake Victoria basin and the Islands of (Unguja and Pemba) (Figure 2).

The short rains (OND) are highly variable in space and time as compared to relatively less variable long rains (MAM) over the bimodal and the October to May (Musimu) rains over the bimodal. Annual rainfall varies from 200 mm to 1000 mm over most parts of the country. Higher rainfall amounts are recorded over the highlands to the Northeastern and Southwestern parts. Central Tanzania is a semi - arid region with some parts receiving annual rainfall amount of less than 400 mm. The annual mean temperature range from 25°C to 32°C. In the highlands, average temperatures for the hot (February) and cold (July) months are about 20C and 10C respectively. The rest of the country has temperatures hardly falling below 20C, with highest temperatures along the coastal belt and the western parts of the country. The high temperature season is between October and March while the coldest season occurs between May and August.

Dar es Salaam area (the focus of this study) is located between lat. 6.6S and 7S and long. 39E and 39.5E. Dar es Salaam is one of the fast growing cities in the world and Africa with a population of approximately 3.1 million and an area of about 1000 km 2 , a smaller area when compared to the total country’s area of 947,300 km 2 (Tanzania NBS.2010) About 8% of its land lies below 10m above mean sea level (Kebede et al. 2011). Dar es Salaam City is found in the bimodal rainfall regime. Figure 3 shows the average monthly distribution of rainfall for Dar es Salaam Airport station.

Msimbazi River Profile

Floods management in Dar es Salaam city is incomplete without considering the effect of the Msimbazi river among other factors. The Msimbazi river crosses the city of Dar es salaam, originates from the nearby Kisarawe Mountains in Pwani Region and flows in north-east direction with the length of about 46km (figure 1). On its path it is fed by smaller seasonal tributaries and drains its water into the Indian Ocean. Various socio- economic activities are carried out along the river bank such as small scale vegetable cultivation, fishing on the upper parts, sand excavation for building construction, playgrounds and gathering places. The flood plains are wide as 1100m in some places and in total covers about 41sqkm which is about 15% of total Dar es Salaam city area (Rwenyangira, 1988). At present discharge data for the Msimbazi river are not available but relying on the measurements done by Rwenyangira, the river discharge ranges between 0.215m3/s and 0.448m3/s.

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Dar es Salaam City: Administration

Mbweni

Bunju µ Kunduchi

Goba

Mbezi Kinondoni Kawe Mikocheni Sinza Kinondoni Kimara Hananasif Makuburi Mabibo Kibamba llala Miburani Kurasini Kiwalani Vijibweni Mji Mwema Sandali Mtoni Makangarawe Kibada Yombo Vituka Mbagala Mbagala Kuu Msimbazi Somangira River Pugu Charambe Toangoma Basin Chamazi Kisarawe II Temeke Kimbiji

Wazo Hill (111m) Ubungo Maji (61m) Dar Chemical (Lab) (9m) Dar es Salaam Airport (JNIA)(53m) Pemba Mnazi Dar. Port (6m)

05 10 20 Kilometers

Figure 1: Dar es Salaam city in small administrative constituents (wards) and stations used in the study

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Dar es Salaam City: Administration 050 100 200 Bukoba Musoma Kilometers Mbweni L.Victoria Loliondo -2 Mwanza Bunju µ Kunduchi Moshi Arusha ³ Goba Shinyanga MbuluB Kinondoni I KIA Same Mbezi -4 M Kawe O Mikocheni Msasani Ubungo Sinza Kinondoni Kigoma Singida D Kimara Hananasif Tabora A Tanga Makuburi Mabibo Kivukoni Kibamba Tabata llala ) MlinganoL Pemba Segerea Kigamboni

S Kinyerezi Miburani Kurasini ( Hombolo Kiwalani Vijibweni Mji Mwema e Kipawa Sandali d -6 Zanzibar Ukonga Mtoni u Mpanda Makangarawe Kibada t L i t .T Dodoma Yombo Vituka Mbagala Mbagala Kuu a Morogoro Kitunda Somangira

L a Dar es Salaam n Ilala g U Pugu IlalaIlala Charambe Toangoma a Iringa n N Chamazi y I Kisarawe II ik M Msongola a Temeke -8 O Chanika D Mahenge Kimbiji Mbeya AL Kilwa 30 Msimbazi 20 Lindi L River -10 Pemba Mnazi 10 . N Mtwara Basin y Songea 0

a

-10 s Tunduru a 05 10 20 Kilometers -20

-30 -20 -10 0 10 20 30 40 50 30 32 34 36 38 40 Figure 2: LocationLongitude of Dar (E) es Salaam city and Msimbazi river basin in Tanzania

Figure 3: Dar es Salaam rainfall climatology

Dar es salaam city like the other bimodal regions of Northern parts of Tanzania experience two rainfall regimes namely March to May (MAM) and October to December (OND) rainfall seasons (figure 3). MAM season is longer with more rainfall amount and less variable when compared to the shorter and highly variable OND season.

Most of the extreme rainfall events leading to floods in Dar es Salaam city have been either triggered in large extent by El Nino conditions, Easterly waves or presence of Tropical Cyclone over the Western Indian Ocean which enhances moist winds originating from Congo forest to blow through Tanzania and causes heavy rainfall in most parts (Table 1) and sometimes floods events which causes loss of lives, destruction of infrastructures and disruption of social economic activities (Appendix figure A1).

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Table 1: Historical flood events information for Dar es Salaam from 1983 to 2011 (source TMA)

No. Year Months Monthly Total Rainfall (mm) Meteorological Remarks Causes Observed Long Percentage Term of long mean term mean (%) 1 1983 May 405.6 197.8 205 The rain was enhanced by El Nino. 2 1989 December 175.6 117.8 149 Tropical Cyclone Alberta was to large extent responsible for the heavy rains 3 1995 May 374.2 197.8 189 There was continuous rainfall at least for two days 4 1997 October 250.8 69.3 361 The rain was November 152.0 125.9 121 associated with December 231.0 117.8 196 strong El Nino episode. 5 1998 January 107.3 76.3 141 The rain was February 123.7 54.9 225 associated with March 155.2 138.1 112 strong El Nino April 319.9 254.2 126 episode. 6 2002 April 569.4 254.2 224 The rain was enhanced by El Nino 7 2006 November 240.9 125.9 191 The rain was December 230.4 117.8 196 enhanced by El Nino 8 2010 April 362.2 254.2 142 9 2011 Dec ember 377.2 117.8 320 The rain was About 43 people enhanced by killed and easterly wave destruction of properties and infrastructures. 10 2012 April 263.5 254.2 104 Convective activities during March to May rainfall season.

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Methods

Data Dar es Salaam Rainfall records for 5 stations namely Julius Nyerere Internationa Airport (JNIA) or Dar es Salaam Airport with data spanning from 1953 to 2012, Dar es Salaam Chemical Labaratory (1927-2008), Ubungo Maji (1967-2009), Dar Port (1982-2011) and Wazo Hill (1961-2011). The first three stations are located within the Msimbazi river basin while the other two stations are outside the basin but within boundaries of Dar es Salaam city. All rainfall data set were obtained from Tanzania Meteorological Agency (TMA).

Digital Elevation Model (DEM), Rivers network, District and Ward boundaries for Dar es Salaam were obtained from USGS HydroSHEDS website.

Global rainfall watch maps were provided by Japan Aerospace Exploration Agency (JAXA) .

Frequency analysis techniques.

Distribution function, non-exceedance probability and frequency of extreme events

Cumulative Distribution Function (CDF) is used to describe the probability distribution f(x) of a random variable X. CDF is denoted by the function FX(x) which is the same as the probability of the random variable being equal or less than x:

FX(x) = P[X<= x] (1)

Where f(x) = dF X(x)/dx (2)

FX(x) is also termed as non-exceedance probability.

For a given value or threshold xp with a non-exceedance probability p, there is a corresponding return period or recurrence interval T gives as:

T = 1/n(1-p) (3) Where n represent the annual average number of occurrence of the random variable X. For the case of annual time series n = 1.

Since p = F(x p) taking its inverse we get: -1 xp = F (p) (4) Where xp is the quantile or T-year event

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Probability Density Functions (pdf) for extreme values

There are quite a number of probability density functions (pdfs) for modeling the extreme events of climatic data. The most used pdfs are the extreme value family (GEV, Gumbel, Fretchet, Weibul), Normal family (Normal and Log-Normal), Person and Log-Person and Gamma distributions. Examples of their use can be seen in Takara and Stedinger (1994), Takara and Tosa (1999) and Takara (2009). Table 2 below describes the pdfs used in this study together with their properties. In this study Maximum Likelihood (ML) fitting method was used for parameter estimations .

Table 2: Probability density functions used in this study Name Probability density function Parameters Normal σσσ,μμμ 1 − − 2 () = Generalized √2 σσσ μμμ,μ,,, 1 , k extreme value exp (−(1 + ) ) (1 + ) ≠ 0 (GEV) () = 1 exp− − (−) = 0 Gumbel (EV1) GEV GEV Fretchet (EV2) = 0 GEV Weibull (EV3) < 0 > 0 Lognormal γ σσσ,μμμ,μ,,,γγγγ 1 ln − − − 2 γ () = − √2 ααα,βββ,γγγ Log Pearson III Г ( )

Where Г () = || () exp (−( − ) /)

Г ℎ (α>0) ∝ (∝) = ααα βββ γγγ Gamma , , (Г) (Г) = () exp (−( − ) / ) ℎ

Goodness of fit Sometime it becomes difficult to determine which probability distribution fits the original data sets well because several distributions can give a virtual fit which seem to be similar and perfect to distinguish which give a better fit, goodness-of fit tests are performed. Among these (used in this study) are Standard least square criterion (SLSC), Correlation of coefficient (COR), Aikaike information criterion (AIC) and Maximum log-likelihood (MLL) also used by Takara and Stedinger (1994).

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Standard least square criterion (SLSC) Suppose that S is the reduced or standardized variate for X: S=g(X). For example for for normal (Gaussian) distribution with the mean μμμand standard deviation σσσ.,

S = g(X)=X-μμμ/μ///σσσσ (5)(5)(5)

Let p be a non-exceedance probability. For q*, a specific value of q, define s*: s*=g(F -1(q*))

Let be the order statistics (sorted in an ascending order) for the original data sets {x ,….,x }, and q be the non-exceedance probability {, … … . , } 1 N ( ≤ i ≤ ⋯ ) assigned to yi (i=1,…,N) . Here N is the total number of observations. Using the transformation function g, we obtain: si = g(y) (6)(6)(6) and -1 ri =g(F (qi) (7) Then it follows that:

(8) ∗ ∗ = Where s* q is a specific value of a reduced variate s corresponding to non-exceedance 222 probability q*, and δδδ minminmin is obtained by minimizing:

(9) = ∑( − ) To minimize equation (9) Weibull plotting position formula was used and is given as:

(10) = Wher i is the rank and N is the sample size.

For non-exceedance probability in eqn. 8, q = 0.99 is used because most of hydrological samples have less than 100 observations. Fitting the monthly and annual precipitation from Lake Biwa in Japan using normal, lognormal, exponential, Gumbel and log-Gumbel distributions, Takara (2009) making reference of Takasao et al (1986) concluded that SLSC ≈ 0.02 corresponds to a good fit and for SLSC > 0.03, other distribution other distributions other than the ones they used should be tried. The smaller the SLSC value the better the fit.

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Correlation Coefficient (COR) The correlation coefficient between the ordered statistics yi and ri computed as:

(11) ∑( )() = ∑( ) ∑( ) Where and are the means of y and r, respectively. The larger the value of COR the better the fit.

Maximum log-likelihood (MLL) One of the strength of the Maximum log-likelihood (MLL) method is its preference in giving unbiased and efficiency parameter estimates (Takara 2009). Let be a probability density function corresponding to a cumulative distribution function(; ) , for the variate X, where ɵ is a parameter vector consisting of k parameters. Also having a (; ) series of identical independent observations {x 1,….,x N}, the ML method select the estimates that maximizes the likelihood function:

(12) (, … , ; ) = ∏ ( ; ) For simplicity in computation logarithm is taken on equation (12) and the resulting log- likelihood is used:

(13) = ∑ (; )

Where is the maximum likelihood estimator of . Larger value of MLL indicates better fits.

AIC (Aikaike information criterion) AIC is given by:

AIC = -2(log (maximum likelihood)) + 2k = MLL +2k (14)

Where k is number of parameters; distributions having more parameters tend to have better fits and a model that minimizes the AIC is the best (Takara, 2009).

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Results and Discussion

Extreme rainfall events (Empirical analysis)

Extraction of annual maximum daily rainfall events was done as indicated in table 3. Not all of the events led to floods because there are many factors involved for floods to occur including the amount of rainfall and duration of the events. These extreme rainfall events are somehow in agreement with the historical flood events recorded in table 1. Thus most of them were either triggered in large extent by El Nino conditions, Easterly waves or presence of Tropical Cyclone over the Western Indian Ocean.

Table 3: Annual maximum daily rainfall events at Dar es Salaam Airport (JNIA)

Annul Date of Meteorological Annul Date of Meteorological Max Occurrence Cause Max Occurrence Cause Daily Daily Rainfall Rainfall 167.4 16 -Nov 1953 El Nino 68.1 28 -May 1983 El Nino 89.7 23 -May 1954 El Nino 67.3 21 -Apr 1984 95.5 2-May 1955 64.0 18 -Feb 1985 55.6 21 -Jan 1956 72.0 16 -Oct 1986 94.0 4-May 1957 El Nino 55.7 21 -May 1987 El Nino 81.3 10 -Dec 1958 57.8 9-Jan 1988 86.4 10 -Dec 1959 87.8 31 -Dec 1989 El Nino 77.7 12 -Apr 1960 87.0 19 -Feb 1990 88.1 4-Feb 1961 148.9 25 -Nov 1991 El Nino 62.0 10 -Apr 1962 78.4 7-Apr 1992 El Nino 126.5 10 -Nov 1963 68.8 21 -Nov 1993 66.8 27 -Apr 1964 63.8 7-Dec 1994 El Nino 65.4 16 -Apr 1965 126.5 27 -May 1995 El Nino 57.4 11 -Apr 1966 El Nino 98.2 5-Feb 1996 55.8 21 -Dec 1967 84.8 18 -Oct 1997 El Nino 136.9 6-Apr 1968 69.9 2-May 1998 El Nino 69.1 25 -Apr 1969 110.3 15 -Dec 1999 52.7 4-May 1970 El Nino 61.6 31 -Mar 2000 35.9 7-May 1971 65.3 26 -Mar 2001 80.5 16 -Apr 1972 75.9 1-Apr 2002 70.2 28 -Apr 1973 El Nino 72.0 9-Nov 2003 El Nino 59.1 14 -Jan 1974 66.1 4-Apr 2004 108.4 13 -Nov 1975 135.2 31 -Jan 2005 55.4 15 -Mar 1976 108.9 16 -Dec 2006 El Nino 68.2 26 -Nov 1977 72.8 11 -Apr 2007 El Nino 72.4 6-Apr 1978 76.5 26 -Mar 2008 70.1 4-May 1979 55.8 27 -Apr 2009 94.1 20 -Nov 1980 93.4 27 -Apr 2010 El Nino 61.1 3-May 1981 156.4 21 -Dec 2011 Easterly Wave 81.0 8-May 1982 133.8 11 -Apr 2012

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1970 -1980 1981 -1990 1991 -2000 2000 -2012

Figure 4: Dar es Salaam Annual Maximum daily rainfall from 1970 to 2012.

Figure 4 shows a time series for the annual maximum daily rainfall from 1970 to 2012 at JNIA. The series depict an increasing trend in the amount of rainfall observed during the extreme daily rainfall events more specifically starting from 90s when compared to the 70s and 80s. For other stations there seem to be a decreasing rainfall amounts in recent years, however the pattern is not so well defined (figure 7-10).

Box Plot of Annual Max Daily Rainfall

160

140

120

100 Rainfall(mm) 80 60 40

Figure 5a: Box plot for MaxDl the annual maximum daily rainfall events at Dar es Salaam Airport

With exception of few extreme cases, the annual maximum daily rainfall ranges between 36mm to 135mm. The 25th, 50th and 75th percentiles are at 64.3mm, 72.6mm and 93.85mm respectively (figure 5a).Further analysis for all the stations can be seen in appendix Table A1.

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Figure 5b: Number of extreme rainfall events for the March to May (MAM) and October to December (OND) seasons for Dar es Salaam Airport (JNIA).

A comparison of the number and intensity of extreme rainfall events between the MAM and OND rainfall season as in figure 5b for JNIA and other stations in figures 8-10 was made. Events falling in February were removed from the list while those falling in January were assumed to be an extension of OND season. The result indicates that there were 33 extreme rainfall events in MAM and 22 in OND over JNIA. However the occurrences of OND events seem to be more intense than in MAM (figure 5b). This result support the fact that though OND rainfall season is short (fewer events), highly variable and with less total rainfall when compared to longer, less variable and with more amount of rainfall in total, its (OND) rainfall is erratic and therefore having the most destructive (flood risk) potentials.

Percent Probability Plot of Annual Max Daily Rainfall 100 90 80

70

60

50

Percent 40 30 20 10 0 20 40 60 80 100 120 140 160 180 Rainfall (mm) Figure 6: Percent probability of annual maximum daily rainfall for Dar es Salaam Airport.

For flood management and planning in Dar es Salaam city, it is helpful to have prior understanding of the flood risks at least derived from empirical analysis of extreme rainfall events. From figure 6 it can be observed that, the probability or chance of having

14 rainfall amount exceeding 100mm in one day at JNIA station is about 20% ( 1 out of 5 years), while chances of having more than 75mm in a day is about 50% (1 in 2 years).

Heavy rainfall events (1927-2008) by Day Number:Dar-Chemical 200

150

100 Rainfall (mm) Rainfall

50

0 50 100 150 200 250 300 350 Day of the year

Emperical C.D.F for Dar-Chemical (1927-2008) 100

80 60

Percent 40

20 0 0 50 100 150 200 250 Rainfall (mm)

Figure 7: Heavy Precipitation events plot: Dar. Chemical Station (1927-2008)

From figure 7 the probability of having rainfall amount exceeding 100mm in one day at Dar. Chemical is about 33% (1 out of 3 years), while chances of having more than 75mm in a day is about 65% (1 in 1.5 years).

Heavy rainfall events (1961-2011) by Day Number:Wazo Hill

140 120 100 80 60

(mm) Rainfall 40 20

0 0 50 100 150 200 250 300 350 Day of the year

Emperical C.D.F for Wazo Hill (1961-2011) 100 90 80 70 60 50 Percent 40 30 20 10 0 0 20 40 60 80 100 120 140 Rainfall (mm)

Figure 8: Heavy Precipitation events plots:15 Wazo Hill (1961-2011)

Heavy rainfall events (1967-2009) by Day Number:Ubungo Maji 120 100 80

60 Rainfall (mm) Rainfall 40

20

0 0 50 100 150 200 250 300 350 Day of the year

Emperical C.D.F for Dar-Chemical (1967-2009) 100 90

80 70 60 50 Percent 40 30 20 10

0 0 20 40 60 80 100 120 140 Rainfall(mm)

Figure 9 : Heavy Precipitation events plots: Ubungo Maji (1967-2009)

Heavy rainfall events (1982-2011) by Day Number:Dar Port 160 140

120 100 80

(mm) Rainfall 60

40 20 0 0 50 100 150 200 250 300 350 Day of the year

Emperical C.D.F for Dar Port (1982-2011) 100

80 60

Percent 40

20

0 40 60 80 100 120 140 160 180 Rainfall (mm)

Figure 10 : Heavy Precipitation events plots: DAR Port Station (1982-2011)

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Hydrological frequency analysis .

All eight models were tested for each station and the best models fitting the empirical data were chosen as shown in figures 11&13 which include the Cumulative Distribution Function (C.D.F), Quatile-Quatile (Q-Q) and Probability Difference plots. Visually these plots provide a picture of how best the models fit the datasets. For the Dar es Salaam Airport dataset, GEV, EV1 (Gumbel), EV2 (Frechet) and log-Pearson distributions gave the best fit while Ubungo Maji datset was best fitted by GEV, Gamma and Gumble distributions. For the Dar Chemical Lab., GEV, Log-Pearson and Log-Normal were best. Wazo Hill and Dar Port data sets do not seem to have defined histograms as seen in other stations due to missing data and shorter lengths. Nevertheless an attempt to fit the data sets was done where GEV, Gamma, Log-Normal and Weibul was tried on Wazo Hill while Lognormal and Weibull for Dar Port (Figure 13).

Probability Density Function

Cumulative Distribution Function 0.44 1 0.4 0.9 0.36 0.8 0.32 0.7 0.28 0.6 0.24 0.5 F(x) f(x) 0.2 0.4 0.3 0.16 0.2 0.12 0.1 0.08 0 0.04 50 100 150 200 x 0 40 60 80 100 120 140 160 Frechet (10.629; 200.46; -130.56) x

Histogram Frechet (3P)

Figure 11a : Histogram of extreme rainfall events fitted with Probabilty Distribution

Function (P.D.F)-left and Cumulative Distribution Function (C.D.F)-right for Dar es

Salaam Airport: (EV2 (Frechet 3 Parameters); alpha=10.629;beta=200.46;gamma=-

130.56).

Probability Density Function Cumulative Distribution Function 0.32 1 0.9 0.28 0.8 0.24 0.7 0.2 0.6 0.5 F(x) f(x) 0.16 0.4 0.12 0.3 0.08 0.2 0.1 0.04 0 0 50 100 150 40 60 80 100 120 140 160 x x Histogram Gumbel Max Gumbel Max (16.2; 70) Figure 11b : Histogram of extreme rainfall events fitted with Probability Distribution Function (P.D.F)-left and Cumulative Distribution Function (C.D.F)-right for Dar es Salaam Airport: (EV1-Gumbel Max. Parameters; alpha=16.2;beta=70).

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Table 4: Comparison of the goodness of fit for the annual maximum daily precipitation at Dar es Salaam International Airport for 60 years,1953-2012. P.D.F (q) SLSC CORCORCOR MLLMLLMLL AICAICAIC SLSCSLSCSLSC CORCORCOR MLLMLLMLL AICAICAIC GEV (3) 0.0191 0.9810 -275.49 556.97 * 1 2 4 6 EV2 (Frechet) (3) 0.0264 0.9810 -274.33 554.67 2 2 1 1 Log-Pearson III (3) 0.0285 0.9619 -275.06 556.13 3 4 3 5 LogNormal (3) 0.0353 0.9334 -274.98 555.97 4 5 2 4 Gamma (3) 0.0384 0.9787 -275.95 557.89 5 3 6 3 EV3 (Weibull) (3) 0.0406 0.9810 -277.96 561.93 6 2 7 7 Gumbel (EVI) (2) 0.0422 0.9810 -275.65 555.31 7 1 5 2 Normal (2) 0.0577 0.9334 -284.39 572.78 8 5 8 8

*Ranks

Table 4 shows the result of fitting various probability distributions to the annual maximum daily precipitation at Dar es Salaam Airport station using maximum likelihood method (ML) and four goodness of fit criterions namely SLSC, COR, MLL and AIC. Figures in the brackets indicate number of parameters. All most all the criterions gave good rankings to EV2 (Frechet) (3) followed by GEV (3). Similarly result for Dar Chemical laboratory (Table 5) a station about 10 km away from Dar Airport station had higher ranking with GEV(3), EV1(2) and Log-Normal. However in some criterion such as CORR the coefficients were found to have very small differences rendering any family member of distribution to be a possible candidate (e.g. Log-Normal and Normal or GEVs). This raise a need of further analysis in selecting the perfect distribution e.g the use of resampling method such as jackknife as proposed by Takara (2009). Nevertheless the result suggests that Dar es Salaam extreme rainfall events will give good results when fitted with GEV family distributions and preferably Frechet (EV2 (3)).

Table 5: Comparison of the goodness of fit for the annual maximum daily precipitation at Dar Chemical lab station for 82 years,1927-2008. P.D.F (q) SLSC CORR MLL AIC SLSC COR MLL AIC GEV (3) 0.01526 0.9895 -399.970 805.940 1 1 2 2 LogNormal (3) 0.02875 0.9525 -400.080 806.160 2 4 3 3 Gamma (3) 0.03052 0.985 6 -400.753 807.507 3 2 4 4 Gumbel(EVI) (2) 0.03205 0.9895 -399.966 803.931 4 1 1 1 EV3(Weibull) (3) 0.04340 0.9895 -403.134 812.268 5 1 5 5 Normal (2) 0.04898 0.9525 -409.286 822.571 6 5 6 6 EV2(Frechet) (3) 0.07334 0.9895 -1099.190 2204.379 7 1 8 8 Log -PearsonIII (3) 0.09655 0.9530 -413.795 833.591 8 3 7 7

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Table 5: Return period (T years) estimates for Dar es Salaam International Airport , 1953-2012

P.D.F (q) T=10 T=20 T=30 T=50 T=100 T=200 T=300 T=500 GEV 117.40 137.68 150.63 168.27 194.91 225.10 244.62 271.37 EV2 (Frechet) 117.16 134.52 145.05 158.80 178.45 199.35 212.21 229.11 Log-Pearson III 118.46 136.27 147.03 161.05 181.03 202.29 215.38 232.62 LogNormal 117.53 133.23 142.37 153.93 169.80 185.96 195.58 207.89 Gamma 117.91 131.87 139.65 149.15 161.61 173.70 180.62 189.22 EV3 (Weibull) 120.18 133.28 140.29 148.59 159.10 168.90 174.36 180.99 Gumbel (EVI) 119.21 134.88 143.90 155.16 170.36 185.50 194.35 205.48 Normal 118.57 128.71 133.99 140.13 147.74 154.70 158.54 163.14

Log-normal, Gamma, EV1 and EV2 distributions seem to have good estimates of return periods which mirror the mean of all the distributions (Table 6 and figure 12). However similar reasons as in table 4, further analysis for the variability of quantile estimates for distributions is necessary. For example an extreme rainfall event of 21 December 2011 with a one day rainfall amount of 156.4mm and a 58 years record falls within the range of quantile estimations by the P.D.Fs at 50 years return period, also between 50 to 100 years return periods for the distributions approaching the mean (Table 5).

Figure 12: Pictorial view of return periods by distribution at Dar es Salaam International Airport .

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Probability Density Function:Dar Int. Airport (JNIA) Cumulative Distribution Function:JNIA 0.32 1 0.28 0.9 0.24 0.8 0.7 0.2 0.6

0.16f(x) 0.5 F(x) 0.12 0.4 0.3 0.08 0.2 0.04 0.1 0 0 50 100 150 50 100 150 X x Histogram Frechet (3P) Sample Frechet (3P) Gen. Extreme Value Log-Pearson 3 Gen. Extreme Value Log-Pearson 3

Q-Q Plot:JNIA Probability Difference:JNIA 0.2 160 0.16 140 0.12 0.08 120 0.04 100 0 -0.04 80 -0.08 Quantile (Model) Quantile

Probability Difference -0.12Probability 60 -0.16 40 -0.2 40 60 80 100 120 140 160 50 100 150 x x

Frechet (3P) Gen. Extreme Value Frechet (3P) Gen. Extreme Value Log-Pearson 3 Log-Pearson 3

Figure13a : Dar es Salaam International Airport (JNIA) distribution fit (1953-2012)

Probability Density Function:Ubungo Maji Cumulative Distribution Function:UbungoMaji 1 0.25 0.9 0.8 0.2 0.7 0.6 0.15 0.5 f(x) F(x) 0.4 0.1 0.3 0.2 0.05 0.1 0 0 20 40 60 80 100 120 20 40 60 80 100 120 X x

Histogram Gamma Sample Gamma Gen. Extreme Value Gumbel Max Gen. Extreme Value Gumbel Max

Q-Q Plot:Ubungo Maji Probability Difference:Ubungo Maji 120 0.24 110 0.2 100 0.16 90 0.12 0.08 80 0.04 70 0 60 -0.04 50 -0.08 Quantile (Model) Quantile 40 Probability Difference -0.12Probability 30 -0.16 20 -0.2 10 -0.24 20 40 60 80 100 120 20 40 60 80 100 120 x x

Gamma Gen. Extreme Value Gamma Gen. Extreme Value Gumbel Max Gumbel Max

Figure13b : Ubungo Maji distribution fit: 43years (1967-2009)

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Probability Density Function:Dar Chemical Cumulative Distribution Function:Dar Chemical 0.3 1 0.25 0.9 0.8 0.2 0.7 0.6

0.15f(x) 0.5 F(x) 0.1 0.4 0.3 0.05 0.2 0.1 0 50 100 150 200 0 X 50 100 150 200 x Histogram Gen. Extreme Value Log-Pearson 3 Lognormal (3P) Sample Gen. Extreme Value Log-Pearson 3 Lognormal (3P)

Q-Q Plot:Dar Chemical Probability Difference:Dar Chemical 0.12 200 0.1 180 0.08 160 0.06 0.04 140 0.02 120 0 100 -0.02 -0.04 Quantile (Model) Quantile 80 -0.06 Probability Difference Probability 60 -0.08 40 -0.1 -0.12 50 100 150 200 x 50 100 150 200 x Gen. Extreme Value Log-Pearson 3 Lognormal (3P) Gen. Extreme Value Log-Pearson 3 Lognormal (3P)

Figure 13c : Dar Chemical distribution fit: 82years (1927-2008)

Probability Density Function:Wazo Hill Probability Density Function:Dar Port 0.2 0.18 0.25 0.16 0.14 0.2 0.12

f(x) 0.1 0.15 f(x) 0.08 0.06 0.1 0.04 0.02 0.05 0 40 60 80 100 120 140 0 X 60 80 100 120 140 160 X Histogram Gamma Gen. Extreme Value Lognormal (3P) Histogram Lognormal (3P) Weibull (3P) Weibull Figure 1 3d : Wazo Hill distribution Figure 13e : Dar Port distribution fit:51years (1961-2011) fit:30years (1982-2011)

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Flood risk mapping and early warning

Mbweni

Bunju Moderate Risk Kunduchi

Goba µ Mbezi Kawe Mikocheni Msasani MOST VULNERABLE Ubungo Sinza Kinondoni Kimara Hananasif Makuburi Mabibo Kivukoni Kibamba Segerea Tabata llala Kigamboni Kinyerezi Kurasini Kiwalani Vijibweni Mji Mwema Kipawa Sandali Ukonga Mtoni Kibada Mbagala Kuu Pugu Mbagala Kitunda Makangarawe Somangira Charambe Toangoma

Chamazi Kisarawe II Msongola

Chanika Kimbiji

Low Risk

Elevation (m) High : 268 Pemba Mnazi

Low : 0

02.5 5 10 15 20 Kilometers

Figure 14a: Dar-es-Salaam: Areas vulnerable to flood hazard

Spatial extent of flooding resulting from different return periods is one of the sources of flood management resources to the authorities. Such studies over Dar es Salaam city can be seen in a case study by START, TMA, University of Dar es Salaam and Ardhi University (2011). Figure 14a shows the flood vulnerability for Dar es Salaam city where the light red color indicates the 30m level contour. Also the red circle encloses an area with the highest flood risk in the city. The area is characterized either by high population density, poor and unmanaged drainage infrastructures, unplanned settlement or the passage of the lower section of Msimbazi river. All these factors contribute to higher flood risks during extreme rainfall events. The vulnerability map (figure 14a) is in agreement with the UNEP/ISDR (2011) on global flood risk analysis shown in figure 14b.

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Figure 14b: Flood frequency in Dar es Salaam according to UNEP/ISDR (2011); Insert is the population distribution density.

Figure 15: Global Satellite Mapping of Precipitation (GSMaP) hourly precipitation. (Source: JAXA )

Near real time hourly Global rainfall maps offered by Japan Aerospace Exploration Agency (JAXA) are very useful in flood forecasting and warning which will evade high loss of lives and property. GSMap rainfall is produced 4 hours after observation and updated every hour. Figure 15 shows a GSMap for 21 December 2011 one of three days when coastal area of Tanzania notably Dar es Salaam city experienced heavy rainfall and floods. The storm which resulted to the floods is marked with a red circle on figure 15.

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Conclusion and recommendations

Analysis of annual maximum daily rainfall was conducted for five meteorological stations’ data sets in Dar es Salaam city. The results showed slight increase on rainfall intensity for some stations in particular at Dar es Salaam Airport. GEV distribution functions (EV1, EV2 and EV3) were found to be more appropriate to fit most of the stations’ data set, an indication that GEV will give better estimates of return periods.

Return periods obtained will be useful in flood control decisions such as in the design of the city’s hydrological structures. It is admitted that this work did not expose the entire needed flood risk alleviation ingredients; rather it highlights some of the basic ones which may prove to be useful to begin with before embarking into a more elaborate methods which involve a variety of factors. For more informative flood risk alleviation plan, such factors as detailed topography, land use, hydrological, flooding, river and socio-economic information as well as field surveys are necessary. In this study few of these were used to come up with the flood vulnerability map in figure 14a. In future, further thorough analysis of the flood risks analysis and forecasting in the study area will be required. The use Distributed Hydrological Models even under climate change conditions such as the Cell Distributed Rainfall-Runoff Model developed by DPRI in Kyoto University and The Integrated Flood Analysis System (IFAS) developed by Japanese institutions led by International Centre for Water Hazard and Risk Management (ICHARM) will be very helpful.

Having a prior knowledge of the vulnerabilities present in specific areas, this result will enable meteorological services to develop and issue meteorological products for flood early warning for these areas. This will save the country from the loss of life and properties.

Recommendations

During flood events there is destruction of infrastructures, loss of properties and deaths. However in the past events there is a gap in the documentation of number of casualties and property losses which are necessary for future monitoring and mitigation of similar events. Therefore there is a great need in research of the meteorological causes of extreme rainfall events as well as careful documentation of the losses.

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Future work ò Analysis of extreme rainfall time series using other methods such as Man-Kendal test. ò Use of regional downscaled climate model products for impact analysis including hydrology, agriculture and ecosystem fields. ò Further research on flood risks warning and management using hydrological models such as Cell-Distributed Runoff Model (CDRM) and Integrated Flood Analysis System (IFAS). ò Improvement of this result and replicate to other river basins in Tanzania which are larger than the ones found in Dar es Salaam city. ò Work on the study to find PMP over the larger river basins which are key to many socio-economic activities such as agriculture and Hydroelectric power production in Tanzania.

Acknowledgements • I wish to express my sincere gratitude to the organizers of the fellowship; World Meteorological Organization (WMO), Kyoto University and Tanzania Meteorological Agency (TMA) for making it possible. • I would also like to thank WMO staff at Fellowships division for their kind assistance and cooperation before and during my fellowship. • My appreciation goes to Professor Kaoru Takara for accepting me in his famous laboratory, his keen supervision and guidance through my work and stay. Many other Professors were involved in my programme just to mention a few of them: Professor Masahito Ishihara took good care of me and organized most of the field trips; Professor Yoden Shingeo for the Atmospheric Physics lectures; Professor Taiichi Hayashi for the Southern Japan study visit (Shirahama and Shionomizaki); Professors Hiroyuki Kameda, Yukiko Takeuchi, Kenichiro Kobayashi, Bin He and Hiroaki Negishi in the DRH seminars; • I would like to thank all Takara sensei laboratory members (students) for their kind assistance when I needed any kind of help. • I am so grateful to all GCOE-ARS programme officers not to forget Sono Inoue san, Yonekawa Yoko san and Fujiki san for their care and kind assistance. • It is difficult to mention all of you by names, but I say you all that, you have been an inspirational to me from day one to the end and will always treasure this precious time studying at Kyoto University. May God bless you.

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Appendix :

Table A1: Descriptive Statistics of Maximum Daily rainfall events.

(i) Dar es Salaam Airport (JNIA)

Stat. Max.daily No. of observations 60 Minimum 35.9 mm Maximum 167.4 mm Range 131.5 mm Mean 82.512 mm Std. deviation 27.952 mm Proportion > 50 mm 0.983 Proportion > 75 mm 0.467 Proportion > 100 mm 0.183 Percentiles 20% 50% 80% Rainfall (mm) 61.68 72.2 97.66

(ii) Dar es Salaam Chemical Lab. Stat. Max.daily No. of observations 82 Minimum 26.6 mm Maximum 215.9 mm Range 189.3 mm Mean 92.347 mm Std. deviation 35.818 mm Proportion > 50 mm 0.963 Proportion > 75 mm 0.646 Proportion > 100 mm 0.329 Percentiles 20% 50% 80% Rainfall (mm) 62.52 84.25 124.6

(iii) Dar es Salaam Wazo Hill. Stat. Max.daily No. of observations 51 Minimum 0mm Maximum 140mm Range 140mm Mean 72.677mm Std. deviation 35.331mm Proportion > 50mm 0.765 Proportion > 75mm 0.451 Proportion > 100mm 0.255 Percentiles 20% 50% 80% Rainfall (mm) 46.11 72.2 106.6

(iv) Dar es Salaam Ubungo Maji Stat. Max.daily No. of observations 43 Minimum 0mm Maximum 121.2mm Range 121.2mm Mean 70.63mm Std. deviation 27.404mm Proportion > 50mm 0.860 Proportion > 75mm 0.349 28

Proportion > 100mm 0.140 Percentiles 20% 50% 80% Rainfall (mm) 53.2 66.6 94.4

(v) Dar es Salaam Dar Port Stat. Max.daily No. of observations 30 Minimum 53.2 mm Maximum 173.2 mm Range 120 mm Mean 93.497 mm Std. deviation 32.441 mm Proportion > 50 mm 1.000 Proportion > 75 mm 0.667 Proportion > 100 mm 0.333 Percentile 20% 50% 80% Rainfall (mm) 64.8 82.2 118.4

Flooding of a major road along Msimbazi River in Dar es Salaam in Dec. 2011

Destruction of properties and infrastructures

Figure A1 : Impacts of floods in Dar es Salaam city in December 2011

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