Analysis of heavy rainfall events over Dar es Salaam 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 : Tanzania 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.
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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
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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 Zanzibar (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 Msasani Ubungo Sinza Kinondoni Kimara Hananasif Makuburi Mabibo Kivukoni Kibamba Tabata llala Segerea Kigamboni Kinyerezi Miburani Kurasini Kiwalani Vijibweni Mji Mwema Kipawa Sandali Ukonga Mtoni Makangarawe Kibada Yombo Vituka Mbagala Mbagala Kuu Msimbazi Kitunda Somangira River Pugu Ilala Charambe Toangoma Basin Chamazi Kisarawe II Msongola Temeke Chanika 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: