Flood Hazard Assessment and Associated Impacts on Public Health

Asian Institute of Technology The Netherlands Thailand

Thesis No.

FLOOD HAZARD ASSESSMENT AND ASSOCIATED IMPACTS ON PUBLIC HEALTH

A Case Study of Msimbazi River in Dar es Salaam City, Tanzania

Charles Gisima Werema

Flood Hazard Assessment and Associated Impacts on Public Health

A Case Study of Msimbazi River in Dar es Salaam City, Tanzania

Charles Gisima Werema

A thesis submitted in partial fulfilment of the requirements for the degree of Master of Engineering at Asian Institute of Technology with specialization in Urban Water Engineering and Management

and

the degree of Master of Science at the UNESCO-IHE

Examination Committee: Dr. Sutat Weesakul (Chairperson) Prof. Mukand Babel Dr. Thammarat Koottatep Prof. Zoran Vojinovic (UNESCO-IHE)

Nationality: Tanzanian

Previous Degree: Bachelor of Science in Environmental Engineering Ardhi University, Tanzania

Scholarship Donor: BMGF/UNESCO-IHE/AIT Fellowship

Asian Institute of Technology School of Engineering and Technology and School of Environment, Resources and Development Thailand May 2014

ACKNOWLEDGEMENT

I would like to express the deepest appreciation to my committee chairs Dr. Sutat and Professor Zoran Vojinovic, who have shown the attitude and substance of brilliance towards making this study a success. They continually and persuasively conveyed a spirit of adventure in regard to this research. It is obvious that without their compassionate and close guidance, supervision and constant help this thesis would not have been possible.

It is also my sincere pleasure to extend my gratitude to my committee members, Professor Mukand Babel and Dr. Thammarat Koottatep, for their comprehensive knowledge of which was demonstrated in guidance through areas of catchment hydrology, pollution and public health related affairs as associated to flood hazard assessment.

In addition, thanks to all my UWEM colleagues, for their constant motivation, encouragement and moral support throughout the whole master program. I recognise with grateful thanks tireless efforts put by the two institutions; UNESCO-IHE and AIT for initiating, coordinating and monitoring this new program. I comprehend the difficulty in soliciting scholarship; however these two institutions mobilized funds from different fellowships for financing my studies in full scholarship.

Finally, I would like to appreciate magnificent support from my friends and family members. They stayed keen during the whole period and they made sure everything goes smoothly in my absence and still they offered both moral and material support to their level best wherever necessary.

ABSTRACT

Natural and anthropogenic hazards pose invaluable consequences to human life and physical assets. Among the recent world’s growing hazards are related to flood and pollution. In order to deal with such consequences effectively we need better understanding on the science behind them. This study is assessing flood hazard and associated waterborne diseases caused by faecal coliform pathogens dispersed during flood. Dar es Salaam city is used as a case study in this study. The selected study area is a flood prone area having poor sanitation facilities. The flood event of year 2011 is referred. Escherichia coli are used as indicator organisms. MIKE11 hydrodynamic and advection- dispersion models were used in simulating both flood hazard and e-coli concentration dispersion respectively. The results show that the downstream and all areas located within 500m from the river bank are more exposed to flood hazard and waterborne diseases. However, flood depth is not linearly proportional to e-coli concentration, thus it is not recommended to represent bacteria concentration in terms of flood depth. The conclusion drawn is that, for any physical flood damage, there is a cost associated to treatment of waterborne diseases which is about 10% of the total hazard cost.

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TABLE OF CONTENTS CHAPTER TITLE PAGE Acknowledgement ii Abstract iii List of tables v List of figures vi List of abbreviations vii 1 INTRODUCTION 1 1.1 General 1 1.2 Background 2 1.3 Problem statement 5 1.4 Recent state of research in the study area 10 1.5 Research objectives 11 1.6 Scope 12 2 LITERATURE REVIEW 13 2.1 Introduction 13 2.2 Flood hazard in Africa 13 2.3 Flood hazard and pollution in Dar es Salaam city 13 2.4 Data requirements for flood modelling 18 2.5 Flood hazard assessment 19 2.6 Estimation of flood damage 21 2.7 Public health assessment 23 2.8 Public health impacts 24 2.9 Cost implications on waterborne diseases 29 2.10 Model selection and calibration 30 3 METHODOLOGY 31 3.1 Introduction 31 3.2 Study area 32 3.3 Model selection and calibration 32 3.4 Data analysis 35 3.5 Methodology for flood hazard assessment 42 3.6 Methodology for public health impacts assessment 44 . 3.7 Combined flood damage and public health impacts 46 3.8 Rainfall return periods and future hazards 47 4 RESULTS AND DISCUSSION 49 4.1 Introduction 49 4.2 Flood hazard assessment 51 4.3 Public health impacts assessment 57 4.4 Comparison: Flood damage cost and diseases’ treatment costs 61 4.5 Flood hazard during previous flood events 62 4.6 Possible hazards in the future 64 4.7 Proposed mitigation measures 65 5 CONCLUSION AND RECOMMENDATIONS 68 5.1 Conclusion 68 5.2 Recommendations 69 REFERENCES 71 APPENDICES 74

LIST OF ABBREVIATIONS

AIT Asian Institute of Technology AD Advection-Dispersion BOD Biochemical Oxygen Demand BMGF Bill and Melinda Gates Foundation COD Chemical Oxygen Demand CDB Central District Business DO Dissolved Oxygen DHI Danish Hydraulic Institute DSM Dar es Salaam city DTM Digital Terrain Model 1D One Dimensional 2D Two Dimensional GIS Geographic Information System GPS Global Positioning System HD Hydrodynamic HQ Hazard quotient IDF Intensity-Duration-Frequency JNIA Julius Nyerere International Airport LiDAR Laser Interferometry Detection and Ranging MNRH Muhimbili National Referral Hospital MRAN Msimbazi River Action Network MRB Msimbazi River Basin MRFP Msimbazi River Flood Plain NH4+ Ammonia solution PPP Purchasing power parity RG Rain Gauge RR Rainfall Run-off SUD Sustainable Urban Drainage SW Solid Wastes TBS Tanzania Bureau of Standards TDI Tolerable daily intake TMA Tanzania Meteorological Agency UDSM University of Dar es Salaam UNDP United Nations Development Program UDS Urban Drainage System UNESCO United Nations’ Education and Science Organisation UTM Universal Transverse Mercator USD United States Dollars UWEM Urban water engineering and Management WHO World Health Organisation WSP Waste Stabilization Pond WWF Wet Weather Flow

LIST OF TABLES

TABLE TITLE PAGE

1.1 Population in the Dar es Salaam city 3 1.2 Major point sources to Msimbazi River 8 1.3 Average waterborne disease during flood for Dar es Salaam city 9 2.1 Flood scenarios in some African cities 13 2.2 Model parameters and data requirements 18 2.3 Direct and indirect flood damage costs 22 2.4 Guidelines for flood damage estimation 22 2.5 Common pathogenic and associated water-borne diseases 25 2.6 Water related infections and modes of transmission 26 3.1 Model calibration parameters 34 3.2 Data collection 35 3.3 River chainage data 40 3.4 Flood damage cost estimates 43 3.5 Guidelines for qualitative public health impacts assessment 44 3.6 Direct and indirect cost estimates for diseases treatment 45 3.7 Estimated river flow rates 47 4.1 People exposed to flood hazard 52 4.2 Number of houses exposed to flood hazard 54 4.3 Comparison of flood damage costs across the study area 55 4.4 Overall flood damage cost 56 4.5 Number of people exposed to waterborne diseases 58 4.6 Comparison of diseases treatment cost 59 4.7 Overall Public health impacts 60 4.8 Total cost: flood damage and diseases treatment costs 61 4.9 Flood hazard assessment based on previous flood events 62 4.10 Possible flood damages and disease treatment cost in the future 64

TABLE OF FIGURES

FIGURE TITLE PAGE

1.1 Location of Dar es Salaam city 2 1.2 Dar es Salaam city-major areas 3 1.3 Msimbazi River run-off hydrograph 4 1.4 Msimbazi River discharge at Kigogo observation point 4 1.5 Msimbazi River catchment 5 1.6 Pollution in Msimbazi River 7 1.7 Flood at Jangwani 8 1.8 Cholera cases in the study area 10 2.1 Transmission pathway of faecal-oral diseases 28 2.2 Cholera cost estimate model 29 3.1 Study model 31 3.2 Location of rain gauge stations in the study area 32 3.3 Rainfall Run-off model calibartion 33 3.4 MIKE11 model calibration 34 3.5 Twenty years’average rainfall pattern for Dar es Salaam city 37 3.6 Average monthly rainfall pattern for Dar es Salaam city (2011) 38 3.7 Digital elevation model 39 3.8 Terrain of the study area 39 3.9 Msimbazi river network (part of) 41 3.10 Upstream river cross-section 42 3.11 Typical flood damage curve 44 3.12 Waterborne diseases cost estimates model 45 3.13 Flood hazard and public health assessment framework 46 3.14 Maximum and average river flows 48 3.15 Maximum flood depth 48 4.1 Case study boundary 50 4.2 Flood prone area under Msimbazi basin 50 4.3 Maximum flooded area 51 4.4 Flood hazard map 53 4.5 Comparison of flood damages based on houses exposed 54 4.7 Cummulative flood damage cost 56 4.8 Escherichia coli concentration map 57 4.9 Comparison of people exposed to waterborne diseases 58 4.10 Comparison of diseases treatment cost 59 4.11 Overall diseases’ treatment cost 60 4.12 Comparison of flood and treatment costs 61 4.13 Hazard assessment based on previous flood events 63 4.14 Comparison of total cost in various flood return periods 65

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CHAPTER 1 INTRODUCTION 1.1 General

Flood is a situation in which water temporarily covers land where it normally does not. This water may originate from the sea, lakes, rivers, canals or sewers. It can also be rainwater. Flood can also be defined in a more quantifiable manner as "an overwhelming flow of water onto land that is normally dry and which under certain circumstances can cause unprecedented losses and devastation" (Vojinovic, 2012). In practice, urban flood is attributed to lack of capacity in natural and constructed drainage systems which in turn results with increased overland flow through the built-up environment.

Flood can be described according to speed (flash flood), geography or cause of flooding. Flood resulted from river or flood defence failure is termed as fluvial flood, in contrast to pluvial flood which is due to heavy rainfall such that run-off exceeds the capacity of drainage systems. There are also flash floods and ground water flooding that is due to rapid response of ephemeral streams to rainfall and rise of groundwater table. On the other hand, tidal rise or storm surcharges may result in coastal flooding (Vojinovic 2012).

Flood hazard is attributed to the velocity with which water is moving, depth above the ground level its duration and the rate of water rise. Flood hazards are natural phenomena, but damage and losses from floods are the consequence of human action. Population growth, urbanisation and climate variability are major factors that intensify urban flooding with subsequent pollution hazard. Urbanisation aggravates flooding by obstructing the natural water flow patterns, changing natural land cover by roofs, roads and pavements; all these human interventions result in an increased flow of water into rivers and canals than there would be under natural conditions. As a result, even storms with low intensity and short duration produce high surface run-offs that increase flows and over-stress the drainage systems.

Flooding in urban areas can be due to one or more reasons as mentioned above, and therefore is more specifically termed as 'urban flood' typically for urban areas. Urban flooding is specific in the fact that it is more related to urban planning and the capacity of drainage system in place. Urban areas need proper drainage system of optimum capacity due to the fact that urbanisation changes natural land surface characteristics such that there is little open land with natural infiltration characteristics, hence almost all the precipitation is converted into surface run-off. Therefore, even rainfall of moderate intensity can cause flooding if drainage system (natural or man-made) does not have that capacity necessary to drain away the resulting run-off. In some cases such drains, despite being insufficient to convey the wet weather flows (WWF), are also obstructed by human activities like buildings, crude solid wastes dumping and sand mining along the river banks. Moreover, with combine drainage system, sewage may spill over through manholes, mixes with surface water sources and ultimately deposited in the open environment. In locations with poor sanitation system, such sewage finds its way into canals and rivers; despite of increasing run-offs this intensifies pollution both in the drainage system and in the flood plain. This poses a threat to public health due to the fact that flood carries various contaminants including pathogens, which are vectors of human infections. This study aims at developing a methodology on how assess public health problems associated with flood in the urban area with poor sanitation facilities. The developed

framework will be applied on the Msimbazi River in Dar es Salaam which is considered to be the most polluted river in the city and characterised with frequent flood cases. 1.2 Background

Dar es Salaam is the largest city in Tanzania, it covers an area of about 1,350 km2 with a population of about 4.5 million people (Census 2012). This population is equivalent to 10% of the country's population. According to (Kassenga and Mbuligwe 2007), the physical growth rate of the city is around 7.2% whereas the population growth rate is about 8% while the average national growth rate according to 2012 national census is 2.8% per year. This makes it the most populated and fastest growing city in east Africa and hence a regionally important economic centre. It is located in the eastern part of the country along the Indian Ocean extending between 0 and 260m above mean sea level, near the coast and in the hills near the outskirts respectively. Other, major urban areas include; Arusha, Mwanza, Dodoma, Mtwara and Mbeya as shown in figure 1.1 below.

Legend

Main land

Water bodies

Dar Es salaam

Figure 1.1: Location of Dar es Salaam city

The city has three municipalities namely Kinondoni, Ilala and Temeke (figure 1-2). Table 1.2 below shows area coverage and population for each municipality.

Table 1.1: Population in the Dar es Salaam city

Municipality Area coverage (km2) Population Kinondoni 531 1,875,844 Ilala 210 1,289,923 Temeke 652 1,446,612

Figure 1.2: Dar es Salaam city-Major Areas

The city experiences an annual rainfall ranging between 1,000 to 1,400mm (Mbuligwe and Kaseva 2005), with two distinct rainy seasons during March-May and October- December. Flood in the city is mainly due to both pluvial and fluvial flood and in a little extent coastal flooding. Figure 1.3 below shows average monthly flow (in m3/s) for Msimbazi River in year 2011 and the corresponding flood as shown in figure 1.4. Flood events normally occur during rainy seasons as shown in figures 1.3 and 1.4 below.

Figure 1.3: Msimbazi River run-off hydrograph

Figure 1.4: Msimbazi River discharge at Kigogo observation point

It can be deduced that the maximum rains occur during October-December rain season as compared to March-May season. Figure 1.4 shows a response of the river flow due to

heavy storms for two months of November and December, 2011. The flood duration of at least twenty days from 10th - 31st December resulted with devastating flood.

The Msimbazi River crosses the major city area as shown in Figure 1.5 below, separating the city centre from major suburban areas. It has three major tributaries, namely the Luhanga, Ubungo and Sinza. The Msimbazi River discharges into the Indian Ocean at an average rate of about 2.5m3/s and 15.5m3/s during the dry and wet seasons respectively. (Mbuligwe and Kaseva 2005). According to Rwenyagira (1988), the flood plain covers about 15% of the total city area and descends from about 260m near its source to between 2 and 35m above mean sea level as it enters the Indian Ocean (Mbuligwe and Kaseva 2005). Parts of the valley accommodate human settlements, handcraft works, garages, playgrounds and gathering places for religious congregations and political meetings and more recently a central communal bus park station and offices along the city major highway.

Figure 1.5: Msimbazi River catchment

In the last two decades the Msimbazi River flood plain (MRFP) has been invaded by various human activities that have obstructed the river channel resulting in heavy pollution and exacerbating flooding. The flood plain also serves as a receiving body for sewage from industries, abattoir, and referral hospital as well as domestic sewage. In general all these activities have outstretched both flood hazards as well as pollution into the flood plain.

1.3 Problem statement

Flood hazard should not be assessed solely based on damages to buildings, infrastructure or immediate fatalities. The recent approach towards assessing flood consequences requires integration of negative impacts associated to contaminants being dispersed by flood waves. This is due to the fact that various substances either suspended or bound to sediments are transported and deposited thus polluting the environment including water sources and ultimately results in human diseases. As pointed out by (Sauer, Schanze et al. 2007), a broader management of flood hazards is supposed to integrate procedures to assess multiple hazards caused by different substances which influence different receptors. Therefore, with the current uncertainties that the world is exposed to (climate change, rapid population growth, water scarcity, urban pollution and increased records of flood events), this gap need to be filled by developing models to assess contaminants fate and dispersion during flood. Most contaminants originate from crud dumping of solid wastes and indiscriminate discharge of sewage on the environment including both surface and underground waters. This study will focus public health problems due to water pollution based on faecal coliform concentrations as transported by advection-dispersion during flood in the Msimbazi river flood plain.

1.3.1 Urban rivers and pollution

Rivers and their flood plains have many social, economic, ecological and environmental attributes that necessitate them being spared from pollution as much as possible. They provide countless in-stream and consumptive uses; provide major link in the ecosystem; support flora and fauna; improve aesthetic and landscape quality; moderate climate and in some cases can be used to generate hydropower. However, pollution can prevent most rivers from providing all these services. River pollution is mainly due to excessive dumping of wastes (liquid or solids) to the extent that the natural purification of the river is limited. Pollution may be due to discharge of agrochemicals from agricultural fields, of industrial effluents and of domestic sewage. In this regard, urban rivers are more prone to pollution due to their close proximity to various pollution sources.

More particularly, urban rivers in developing countries are vulnerable to pollution risk due to less stringency on land use laws, unplanned settlements and less investment in sewerage and drainage systems. This results with indiscriminate dumping of wastes. According to Vojinovic (2011), health hazards presented by faecal contamination of watercourses in developing countries are serious to the extent that they cause mortality rates up to 5 - 10% worldwide. The problem is worsened by the informal sector which is growing rapidly in such countries as a result of poverty, urban migration and unemployment.

Figure 1.6 below shows human encroachment and pollution in Msimbazi River flood plain.

Figure 1.6: Pollution in Msimbazi River (Photo taken on 09 Jan, 2014 DSM)

1.3.2 Dar es Salaam city: Flood and pollution hazards

DSM city lacks proper infrastructural services like sanitation, drainage systems, flood defence structures, solid waste management services. This is due to the fact that about 70% of the city is unplanned (Chaggu 2002); as a result informal settlements constitute a major part of the city. The fact that these settlements are within the flood-prone areas and wetlands increases the flood hazard and flood-induced pollution for most of the city’s residents. However, about 45% of the city experience a high water table (between 1- 2m and hence risks due to flood and contamination of both surface and underground waters during the rainy season is always high (Chaggu 2002).

The Dar es Salaam city has been experiencing floods almost each year and the situation has become worse during the last decade. This might be attributed to overcrowding and climate variability especially due to the fact that it is located along the coast where is also vulnerable to sea surcharges. The most recent worst scenario was during November- December, 2011, where the heaviest rains estimated to be equivalent to 50-years return period. This rain resulted in unprecedented flooding that devastated many areas of the city, causing over 20 casualties and leaving nearly 5,000 people homeless (Mwananchi, 2011). Consequently, large part of the city was polluted by sewage from domestic on-site facilities and failure of urban drainage system (UDS). As a result sewage dispersed over the over the environment and polluted both surface and underground waters. It is obvious that there were high faecal coliform concentrations in mixed with such water sources during the flood. The evidence of this fact is the outbreak of diarrhoeal/water-borne diseases like dysentery and cholera among other water-borne diseases, a situation that persisted over six months following the flood occurrence.

The visual assessment of flood depth is estimated at a maximum of about 0.5 to 3.0m (figure 1.7) in the extreme events. The flood duration was about of four days, this was exacerbated by high tides in Indian Ocean which resulted with back flow effect into flood plain.

Figure 1.7: Flood at Jangwani (Photo taken on 27 March, 2014 at Dar es Salaam)

According to (Kazinja 2001), urbanization influx exacerbate flood and pollution hazards due to, increasing unplanned settlements and a lack of investment in urban infrastructures including drainage systems to ensure proper functioning. However, only 15% of the city residents have access to the centralized sewerage system (Mbuligwe and Kaseva 2005). This means that more that 80% of the city residents including those in the flood plains use on-site sanitation facilities including pit latrines and septic tanks. During the rainy season most of the facilities flood as well resulting with sewage outfalls on the open environment which eventually carried by flood dispersed in the flood plain, advected downstream and ultimately mixed and deposited in water sources and in the soil. The high water table of about two metres has effects on both underground and surface waters. The risk due to pollution from human excreta is eminent since most people deliberately empty their toilets during rainy season as a measure of restoring the pit storage capacity and avoiding digging new pits as there is no space and is always costly. On the other hand, there are several sewer outfalls directed into the river from Muhimbili National referral hospital (MNRH), Vingunguti abattoir and some textile industries as shown in table 1.2 below. These point sources into the Msimbazi River are categorised into three major groups namely, industrial sources, wastewater treatment facilities (mainly WSP) and institutional sources.

Table 1.2: Major point sources into Msimbazi River Industries WSP and solid wastes dumpsites Institutions Tanzania Breweries Ubungo WSP Muhimbili National hospital Urafiki textile Vingunguti WSP Vingunguti abattoir Dar brew Buguruni WSP Tanzania dairies Vingunguti dumpsite Robbialac paints Tabata dumpsite Ubungo farm implements Ubungo power station According to (Mbuligwe and Kaseva 2005), the Msimbazi River is considered as the worst example of urban river pollution in Tanzania, where its catchment encompasses pollution

from on-site sanitation outfalls, industrial effluent sewer outfalls, leachate from solid waste dumps, abattoir effluent outfall and hospital waste. On the other hand, the pollution load is increased downstream as the flood carries more sewage with individual discharges acting as point sources. As stated by (Benedini 2011(Benedini 2011), the concentration of contaminants in water or in the environment at a given time depends among other factors the reaction coefficient of respective material; whilst the contaminant concentration is higher at any point next to the source in contrast to a point located downstream. This implies that readily degradable materials shall take shorter time as compared to resistive materials. For the case of pathogens, those with high decay rate and less growth rate will have less concentrations and hence less public health problems as compared to one having less decay rate and high growth rate. According to S.Mgana (2013), faecal coliform count at the upstream close to the river source ranges between 75 – 100 per 100mL while at the river mouth is about 250,000 – 400,000 per 100mL. Therefore, faecal pollution increases heavily downstream.

Past experience has proved that, flood victims evacuate their homes for a short period, during acute flood event and returns back as the water depth decreases without further notice. According (Aarne 2003), floating and suspended solids pose public health hazards due to the fact that pathogenic organisms adsorb on these solids. Therefore, despite of minimum flood hazard (decrease of flood depth and velocity), after a couple of days, pollution hazards due to contaminants dispersed by flood waves lasts longer and may cause more devastation to mankind than what might have caused by the corresponding flood event. This can be justified by consistent waterborne diseases prevalence in the Dar es Salaam city after a 2011 flood event. As stated by Brdjanovic (2008), the classic waterborne diseases being cholera and typhoid fever which frequently ravages densely populated areas with poor sanitation and water supply infrastructure can be effectively controlled by protecting water sources from pollution. Some of the common waterborne diseases in case study include ascariasis, diarrhoea, hookworms, schistosomiasis, cholera and typhoid. After a rapid increase of water-borne diseases during flood in Dar es Salaam city, the Muhimbili national hospital conducted a study to assess the relationship between rainfall pattern and occurrence of diarrhoeal diseases and extent of water-borne diseases during rainy season. Table 1.3 below shows an average exceedence number and percentage of water-borne disease cases during flooding event as compared to the rest of the year.

Table 1.3: Average exceedence waterborne disease during flood for Dar es Salaam city. Type of disease Exceedence cases Per cent (%) Ascariasis 5630 12 Diarrhoea 8700 19 Hookworms 9050 20 Schistosomiasis 250 1 Cholera 21980 48 Typhoid 620 1 Total 46230

The above diseases are caused by swallowing contaminated food, water, accidental swallowing soil and swimming in the river or ocean. The indicator for human feces contamination is the escherichia coli (e-coli). One of the major waterborne diseases in the study area is cholera. There is a clear link between increases of cholera cases during flood

season in study area as compared to dry season. Figure 1.8 shows cholera cases within the study area in year 2011.

Figure 1. 8: Cholera cases across the case study in 2011.

The observation shows that there is a considerable increase of cholera cases during March- May and October-December which are rainy seasons which corresponding flooding. Therefore, there is a linear relationship between rainfall/flood and waterborne diseases in the study area. This can be demonstrated when comparing figure 1.8 above and figure 3.3 in chapter three.

1.4 Recent state of research in the study area

Some studies have been conducted to assess the sanitation status of DSM city, as related to pollution problems. There are also specific studies that have focused on MRB pollution as well as the status of settlements in flood plains of the DSM city and its implication for flood hazards. According to (Mohammed 2002), which assessed sewerage capacity and associated pollution sources along the coast of DSM city, it was revealed that sewerage system for DSM city was constructed in late 1940s, despite of subsequent rehabilitation in the 1970s it is highly exhausted and does not meet the current sewage conveyance demand for the city. This poses a threat of raw sewage outfalls into open environment.

One of the best is a study by Mbuligwe and Kaseva (2005), which assessed the pollution and self-purification of the Msimbazi river. In this study it was discovered that the river is highly polluted as explained in Section 1.3 above. Moreover, the UDSM through its chemistry department conducted a study by Othman (1999) on heavy metal depositions in the Msimbazi river in which it was concluded that the river contains high concentrations of copper, zinc, cadmium and lead beyond both the TBS and WHO standards. Over 80% of the city population use on-site sanitation facilities and mainly pit latrines for excreta disposal (Chaggu 2002).

The study on eco-health impacts of urban tropical wastewater in Dar es Salaam with a specific case study of the Msimbazi River was conducted by Mchugh ( 2007). In his study it was shown that bacteriological pollution was in the range of 107counts/100ml of faecal coliform which is an indicator organism for human faecal pollution. There is another study conducted by (Kassenga and Mbuligwe 2007), which assessed the impacts of solid waste leachate on the Msimbazi river water quality. In this study it was clear that the leachate from the solid waste dump along the river banks has contributed to the river pollution including the groundwater. It was also verified that sediments along the river banks are also contaminated with both biological pollution indicators and heavy metals.

According to (Casmiri,2008), regarding the vulnerability of DSM city due to impacts of climate change, five flood prone areas in the city were identified and Casmir proposed some adaptation and mitigation measures. Another study on how to rescue the Msimbazi river from heavy metals and biological waste was conducted by (MRAN 2005) which was more of a social study where key stakeholders and their responsibilities towards forming a collaborative network for monitoring the water quality in the river were assessed, aiming at complying with the applicable rules and regulations. (Kazinja 2001) also conducted a study on water management in flood-prone areas in Dar es Salaam city where the author suggested some key measures to be taken into consideration for alleviating water and wastewater problems in Dar es Salaam city.

The recent state of research in the study area shows that most of the studies focused mostly in heavy metals and bacteriological pollution without considering the public health impacts that might result from such contaminants. However, there is no publication showing that there is any study which was done assessing flood damages and its associated public health problems in the Dar es Salaam city.

1.5 Research objectives 1.5.1 Overall objective

The main objective of this study is to develop an urban flood framework to be used to assess flood hazard and associated public health problems. The developed framework will be tested on a case study of Dar es Salaam city.

As stated by Diadovski, Hristova et al. (2002); the integration of deterministic and statistical models for catchment and river systems pollution is necessary in water ecosystems management in providing understanding on contaminants dispersion and deposition when flood-water overtops the river embankments during flood. Moreover, as part of the output of the study, both structural and non-structural flood and pollution mitigation measures will be proposed. Therefore, this study will provide baseline information social, administrative and economical decision making as far as flood hazard and public health problems are concerned.

1.5.2 Specific objectives

The specific objectives of this research will include

i) To generate flood hazard map across the study area ii) To estimate flood damage costs across the study area iii) To develop e-coli concentration map across the study area iv) To ascertain possible waterborne diseases and associated treatment costs v) To recommend possible interventions for both flood hazard and public health impacts 1.5.3 Research questions

The following questions will be addressed by this research; i) What factors contributing to flooding in the study area? ii) How does e-coli concentration behave during flood iii) What is the relationship between public health problems and e-coli pollution in the study area? iv) What is the relationship between e-coli concentration and flood hazard?

1.6 Scope and limitations of the study

The scope of this study can be summarised based on the expected outcome as follows:

 The overall study area of about 20-hectares within Msimbazi River basin, extending 3km upstream from the Indian Ocean. This area covers five wards namely Kigogo, Jangwani, Magomeni, Upanga and Hana Nasif. The study area is shown in figure 4.2 with a total population of over 160,000 people and about 27,000 households  Both flood hazard and public health impacts assessment was done based on MIKE11 hydrodynamic and advection-dispersion model simulations results respectively. Flood depth and associated damage cost were used as indicators for flood hazard whilst waterborne diseases and corresponding costs for treatment were used as indicators for public health assessment. A flood map is shown in figure 4.4 while e-coli pollution map is shown in figure 4.8.

The limitations of this study are as follows:

 The catchment was assumed to have a uniform slope of 4 o/oo and uniform river roughness coefficient of 0.033.  All buildings are normal residential houses, raised at least 0 .5m above the ground, detached houses, with medium density of 50 dwellings per hectare.

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

An incessant literature work necessary for this study was done aiming at acquiring broad and recent knowledge on flood hazards and public health problems. The literature covers theoretical and methodological approach as well as engineering and social principles with regards to flood hazards and its social implications. The major work in this chapter is to realise the gap in social and health impacts caused by flood consequences and hence formulate appropriate methodology and correct indicators for both flood damage and public health problems assessment. Also literature review forms a basis for critical results interpretation and discussion as well as guidelines towards proposing appropriate intervention measures in controlling flood and dealing with social and health problems caused by flood in the study area.

2.2 Flood hazard in Africa

All countries and communities regardless location or economic strengths are vulnerable to flood hazards due to various factors including climate variability. However, less developed countries are the most vulnerable due to the fact that they are more exposed and have less adaptation capacity. General overview of flood hazards in Africa especially pluvial floods, shows that river flow patterns will change more than long-term average flows and that prolonged heavy rains may increase in volume and occurrence (Douglas, Alam et al. 2008). Recent flood trend shows that many African countries especially in urban centres are experiencing flood more frequently since early 1990s. Table 2-1 below summarises recent flood cases in some African cities.

Table 2.1: Flood scenarios in some African cities

City/Country Flooding Number of Cause of flood Post flood Other since people effects consequences affected Accra-Ghana 1980s N/D Climate change, Loss of Contamination sea surcharges, business, of water sources, Urbanization hunger, interruption of poverty transportation, Kampala- 1980s N/D Unregulated Water-borne Loss of shelters Uganda shelters, climate diseases, change, poverty inadequate of drainage system Lagos-Nigeria 1980 and More than Unregulated Water-borne Displacement of 2002 100 shelters, climate diseases, more than change, poverty 10,000 people in inadequate of yr 2002 drainage system

City/Country Flooding Number of Cause of flood Post flood Other since people effects consequences affected Maputo- Sea surcharges, Water borne Property loss, Mozambique climate change, diseases pollution swampy, Nairobi- 2002 More than Direct storm, Water borne Displacement of Kenya 40 river overflow diseases more than 10,000 people Addis Ababa 2002 More than Direct storm, Water borne Displacement of 100 river overflow diseases more than 10,000 people Source: (Douglas, Alam et al. 2008)

2.2.1 Flood adaptation in African cities

Adaptation towards flood hazards is the biggest challenge facing many urban centres in most of African cities. This is due to the fact that the Governments have not invested enough in sustainable drainage systems (SUD) and flood defence infrastructure, whilst individuals cannot afford putting in place flood defence or adaptive infrastructures sustainably. The community organisations embark on routine cleaning of drainage systems to minimize flood impacts regardless the fact that in most cases such drainage channels are not sufficient.

Options for flood hazard adaptation in Nairobi slums for instance, include traditional approaches like bailing water out of the house, placing children on tables and digging trenches around the house premises before and during floods; constructing temporary dykes, securing structures with waterproof materials; shifting from houses located in low land and sometimes using sandbags to prevent the ingress of water Likewise, flood victims do vacate temporarily to stay with friends or neighbours whilst others temporarily moved to lodges and public places like mosques, churches and schools. Similarly, individuals living in slums in Kampala, Uganda do participate in cleaning drainage channels as means of flood adaptation (Douglas, 2006). It can be concluded that the flood impacts in African is worsened by lack of rescue equipments coordination during flood disaster.

2.3 Flood hazard and public health consequences in Dar es Salaam city

As stated by Casmiri (2008), most of flood prone areas in DSM city, are natural flood basins which have been occupied by human settlements. The flood frequency and consequences is being escalated by increasing peak flows, combined with higher spring tides, sewage outfall into the drainage systems, river and natural drainage systems encroachment as well as increasing urbanisation. Also according to (Douglas, 2006), floodwaters can carry all sorts of organic and inorganic wastes into people’s homes. In this regard, despite of physical damages during flood, there is a growing concern about public health problems associated with flood in such areas with inadequate sanitation facilities.

2.3.1 Public health problems

According to (Wölz, Engwall et al. 2008), run-off resulted from floods mobilizes contaminants either in suspension, dissolved or particulate form. The dissolved

contaminants propagate along the drainage systems and across the catchment. As a consequence, during flood events, such sediments increase eco-toxicological effects to aquatic, terrestrial organisms as well as human being during and after flood events. The spatial distribution of contaminants during flood pollution is authentic and need to be routed in order to understand their composition and load. The pollution can be assessed by through a range of parameters such as bacterial or pathogenic count, BOD, COD, NH4 concentration, heavy metals among others depending on the requirement of the study, availability of data and convenience. During flood for instance, faecal bacteria dynamics studied in Morrisville catchment stream in New Zealand indicated that during the flood e- coli count doubled as compared dry season (Nagels, Davies-Colley et al. 2002).

Preliminary water quality analysis from Msimbazi river indicated relatively extremely high and variable levels of faecal and bacterial pollution, with total faecal coliform counts in the order of 105 to 107/100mL (Mchugh 2007). During his study, it was realised that the amount of bacterial contamination brought into the Indian Ocean by the Msimbazi River is high to the extent that it exceeds World Health Organisation (WHO) thresholds for marine recreational and shellfish harvesting waters. Also According to Lymo (2006), faecal contamination above WHO standards poses a threat of water borne diseases outbreak among the communities around such environment due to possibilities of swallowing pathogenic organisms through water and food.

There are various sources of pollution that bring contaminants into the Msimbazi River basin, including Vingunguti solid waste dumpsite located just adjacent to the River are one of the pollution sources. According to (Kassenga and Mbuligwe 2007), the capacity of this dumpsite is exhausted such that it cannot accommodate more waste generated by the city, which is estimated to be more than 1,900 tons/day. Other sources are as mentioned in section 1.3.2 under table 1.1. However, recently some measures to reduce pollution in the river were taken by the city authorities by closing down the Vingunguti dumpsite although the fact is; it will take some time for the leachate to stop draining into the river.

2.3.2 Flood vulnerable areas in Dar es Salaam city

As mentioned earlier, Dar es Salaam is vulnerable to floods as it is the case for any other city, not only because of poor land use planning and insufficient drainage system, but also due to high tides and coastal erosion. Most vulnerable areas being Msasani, Msimbazi, Jangwani, Mikocheni and city centre (Casmiri 2008).

i) Msasani

This area covers an area of about 60-ha with a mixed land use pattern including residential, commercial & institutional settlements. The area has a big role in the drainage system, due to the fact that two main storm water channels exist. According to the 1979 city master plan this area was declared as hazardous land. However, due to the potential of this area American embassy, a referral private hospital, shopping malls and apartments for former senior government officials, it seems socially secure and economically convenient for city residents and hence experiences rapid development , and it is one of the fastest growing settlements in Kinondoni Municipality (Casmiri 2008).

ii) Msimbazi

Despite frequent flooding, this area continues to be populated exposing residents to flood hazards and flood-related health problems. The influx of people has been accelerated by a number of factors such as easy access to unregulated farming and building plots, proximity to the CBD and low-cost housing (Casmiri 2008).

iii) Jangwani

This is a slum area along the city centre highway, it is a low laying land with localised wetlands and it experiences devastating floods each rain season. The Msimbazi River passes through this valley increasing the risk to dwellers that are at the mouth of the river. According to (Casmiri 2008), this area was declared uninhabitable land by the Ministry of Lands and Human Settlement Development due to its susceptibility to flood and health problems.

iv) City centre

According to (Casmiri 2008), this is the area that faces extreme flood in the city. The problem is exacerbated by poor infiltration and an old and insufficient drainage system.

v) Mikocheni

The problem has been exacerbated by diversion of a natural storm-water drainage channel by human settlements (Casmiri 2008). This study will focus on flood hazards in city due to Msimbazi river overflow during heavy storms.

2.3.3 Uncertainties

i) Climate change

Climate change is making weather less predictable, rains more uncertain and heavy storm rainfalls more likely. The unpredictability of rainfall is shown both by observations, such as the large fluctuations in the water levels in large Lakes and Oceans. Thunderstorms appear to have increased in frequency and intensity. Urban areas experience more flood- related problems due to the fact that most land is built up and hence attains higher temperatures than surrounding areas and creates a local air circulation that produces an urban heat island. Dust particles caught up in this circulation act as nuclei on which moisture in clouds condenses, forming rain droplets that eventually may develop into the large rain drops of a major thunderstorm. Climate change also works in an indirect way to aggravate urban flooding. In any case urban migration further adds to the urban activities that increase the flow of rainwater into rivers and thus the intensity of local flooding (Douglas, 2006).

As a consequence, human settlements are affected by climate change through several processes including flood. however, the extent of such effects will depend on economic status of the societies (Wittig, König et al. 2007). The majority of African communities including Dar es Salaam, living in flood prone areas are poor with little economic resilience against flood hazards and hence vulnerable to fatalities. Therefore, flood

impacts and climate change in general for the African context is among other factors depending on localities and quality of settlements.

Adaptation towards flood and water borne diseases in Africa is another major challenge due to the fact that individuals and public disaster-emergency brigades cannot handle such impacts satisfactorily. As mentioned by (Khandlhela and May 2006), in one of the flood scenario in the Limpopo province in South Africa, it was discovered that flood victims were still unsettled five months after the flood event; the reasons being poor coordination of relief measures among the stakeholders. The potential impacts of climate change by itself might not be adverse to urban floods and public health problems. According to (Ngusaru 2000), the risk is increased by other stresses which are mainly human induced like unsustainable resource use, and development that has a negative impact on natural climate adaptation. These stresses include overexploitation of resources (mining, pollution, decreasing fresh water availability, sediment starvation and urbanization).

ii) Urbanization

In the year 2006 it was estimated that around one third of the urban population in developing countries (approximately 1 billion people) lives in slums while in Africa alone 72% of urban residents lives in slums (Vojinovic 2012). As mentioned earlier, Dar es Salaam city has a growth rate above 2.8% per year with population density about 3,100 people per square kilometre. This growth rate makes it challenging towards provision of basic services to the urban dwellers in a sustainable manner. Hence, in Africa, urbanisation is linked to less opportunities, as over 45% of urban population lacks proper sanitation in the last decade (Ramin 2009). As a consequence, such poor sanitation practices in cities contribute to contamination of both water sources and land. Consequently, as a result during flood, pit latrines and polluted rivers act as key reservoirs that perpetuate faecal coliforms into the environment resulting with eruption of waterborne diseases. According to Chaggu (2002), Tanzania’s urbanization rate has been growing in the order of 6 - 11% for the last 30 years resulting in rapid increase in the urban population. This increase has an implication on pollution due to the increased pressure on existing infrastructures, services and elevates growth of unregulated settlements, unemployment, urban poverty and crude dumping of solid waste as well as sewage. Flood hazard are high with repeated cases during each rainy season and the situation is worsened by unregulated settlements that are mainly within flood plains and depressions either without or with poor sanitation facilities.

iii) Existing drainage system

Lack of investment in urban infrastructures including drainage systems has increased the problem of the DSM city flooding and related sanitation problems (Kazinja 2001). As mentioned by (Kassenga and Mbuligwe 2007), the available capacity of the drainage system is obstructed by human activities including crude dumping of solid waste in the drainage canals, overwhelmed by sewage from different sources such that the system floods more easily and frequently. Due to the fact that only 15% of the city residents have access to the centralized sewerage system (Chaggu 2002), during the rainy season most of the on-site sanitation facilities floods as well, resulting in pit latrine outfalls and adds up to the drainage system flow and hence accelerates flood.

iv) Poverty

Urban poor share some common challenges irrespective of location; this may include poor housing, lack of employment, exposure to disasters, environmental and health hazards and poor infrastructures (Vojinovic 2012). Since they are deprived of basic financial and economic privileges, such as loans from financial institutions, most urban poor often end up securing land in vulnerable areas like in floodplains. Others construct their shelters on steep, unstable hillsides, or along the foreshore on former mangrove swamps or tidal flats. Already vulnerable to destructive floods, damaging landslides or storm surges, climate change is making the situation of the urban poor worse. Urban poor are exposed to all types of flood and flood-related pollution. According to (Douglas, 2006), construction of unregulated shelters by poor people in slum areas has reduced infiltration and the resulting runoff has increased 6 times as compared to natural terrain.

v) High Tides

DSM city, extends along the coast of the Indian Ocean, has been hit by high tides and sea surcharges. Most parts of the city are in low-land as low as 2m above mean sea level at the Msimbazi river entrance to the sea. The most devastating flood of 2011 was claimed to be due to a coincidence of high tides and heavy storm (Mwananchi 2011).

2.4 Data requirements for flood modelling

Flood modelling using hydrodynamic flood model employs the conservation of mass and momentum equations to determine the river hydraulic characteristics such as depth, velocity, top width, discharge and cross-section area (Lai, Tu et al. 2012). In river discharge and catchment inundation modelling, there are parameters that influence the discharge and flood depth. These parameters can be divided into three categories, geometrical parameters, model parameters and boundary condition data. For simplification, geometrical parameters can be assumed not changing significantly during flood whilst model parameters and boundary conditions vary significantly and hence have an effect on the water levels (Havinga, Vermeulen et al. 2006).

Table 2.2: Model parameters and data requirements

Geometrical Model parameters Boundary conditions parameters River depth, River GIS maps Previous flood depth width DEM/DTM (x, y, z) Previous flood data River chainage, River Catchment and sub-catchment Rainfall data cross-section area Average run-off/ direct run-off Water quality parameters AD factor River tributaries Channel roughness Water level at the sea

However, amount of data input into the model depends on among other factors the desired use and accuracy of the model results as well as data availability. However, some of common data includes, land use maps, LiDAR, topographical surveys, historical flood data, photographs and videos as well as flood levels (Saul, Djordjevic et al. 2010). Table 2-2 below summarises key model parameters that govern the data collection for flood and pollution modelling exercise

2.5 Flood hazard assessment

Risk mapping, risk analyses, financial appraisals of probable losses as well as risk- oriented flood design have been gaining more and more attention in recent years and require reliable estimations of flood losses. In engineering and technical assessments, risk is commonly defined as the damage that occurs or is exceeded with a certain probability. Risk encompasses two aspects hazard and vulnerability. While flood hazard assessments describe the intensity (extent and inundation depths) and probability of a flood scenario in a given region and timeframe, vulnerability analyses address the consequences of flood. Currently, such analyses are often restricted to the quantification of direct, tangible losses and thus look at the elements that are exposed to the inundation and their susceptibility to the hydrological load (Seifert, 2008).

As stated by Seifert (2008), the central idea in current flood loss estimation is the concept of loss functions, in which the direct monetary loss is related to the type or use of the affected building and the inundation depth at that building. These functions are an internationally accepted standard approach for assessing urban flood losses. Loss functions can provide the absolute loss in monetary values or the loss ratio such as percentage of the asset value that is damaged. In the latter case, the asset values of the exposed elements also have to be estimated in order to get loss information in monetary terms although current loss functions may have a large uncertainty. One reason might be that flood loss is also influenced by other factors such as flow velocity, flood duration, contamination, building characteristics, private precautionary measures or flood warning. These aspects are, however, neglected in most of the flood loss models (Seifert, 2008). However, the reliability of flood loss and risk estimates is fairly unknown, since loss models are rarely validated. This might be due to limited or missing observations and data about (extreme) flood scenarios.

In modern developments flood routing can be done conveniently using hydrodynamic models. This is the science of quantifying both physical and social processes in the physical world through the use of underlying mathematics. The science of modelling assumes that all processes can be represented either individually or in an integrated manner by some symbolic equations or relationships based in science (physical models) or measured data (data driven models). There are different types of models depending on the way it operates in the real world. Physical models operate within defined numerical equations whereas conceptual models represent the simplified physical world in space or lumped quantity linking the input and the output in the model space. On the other hand, data-driven models have no pre-defined mathematical equations hence they use values which are trained to replicate the connection between the output and input in model space (Vojinovic 2011).

2.5.1 Hydrodynamic flood modelling

Flood inundation modelling can be represented either in one dimensional (1D) or two dimensional (2D) mapping. In practice, the 1D flow represents the flow pattern in pipes or channels whilst the 2D modelling is for overland flows originating from excess rainfall runoff that cannot be accommodated by the available capacity of natural and constructed drainage system (Vojinovic 2011). Therefore the best approach to incorporating both stream and overland flow is to integrate 1D river flow into 1D or 2D overland flow to get a unified 1D/1D or 1D/ 2D model (Saul, Djordjevic et al. 2010). In this particular study a 1D flow modelling for the Msimbazi River will be performed and extended to 2D overland flow mapping using MIKE11 hydrodynamic model.

According to (Vojinovic 2012), river flow can be modelled through various approaches ranging from simple conceptual models to complex 1D/2D models. Unlike the pipe flow modelling where it is necessary to define structures like manholes and weirs, in river modelling separate cross-sections are defined at each node where the corresponding depth is calculated. Cross-sections are usually introduced at 100 to 500m intervals for exceptional to steep sections or where the river flow direction changes. As far as flow hydraulics is concerned it is necessary to identify and define all features that may affect flow. Features like varying river cross-sections, change in gradient, change in depth, weirs and control structures (if any), need to be identified and defined clearly for accurate hydraulic modelling. The governing equations include the hydraulic resistance (Manning’s constant, n) and lateral inflows which leads to the basic equation used in MIKE11.

Where z is the water surface elevation Q is the flow rate B is the wetted cross-sectional width A is the wetted cross-sectional area t is the time x is the distance along the channel, Source (Lai, Tu et al. 2012) K is the conveyance of the channel and g is the gravitational acceleration q is the side discharge per unit channel length.

The continuity equation expresses the mass conservation, while the momentum equation is actually the fundamental law of dynamics, written for fluids. The only assumption in these equations is that the fluid is Newtonian which an excellent approximation for water is indeed. 2.5.2 Flood mapping

The MIKE11 simulated with extended cross-section to cover the flood plain can produce a 2D flood map. This model solves the shallow water equations by means of a finite difference equation. The simulation is governed by continuity, mass and momentum conservation equations integrated in two dimensional flow dimensions. The basic equations are described above.

According to (DHI, 2012), MIKE11 may be used to produce two dimensional maps based on the one-dimensional simulations. The maps are made as dfs2-files (rectangular grids) and constructed through interpolation in space of the grid point results. Thus the maps constructed in this way are viewed as a two dimensional interpretation of results from a one-dimensional model. For MIKE 11 simulations producing a map as output, the maps will therefore be calculated only within the extent of cross sections defined in the mapping area. Moreover, in such flood mapping, no calculation of map-values that takes place outside the cross sections extent, and the maps obtained, therefore represents exactly what the model calculates during the actual simulation whereas various simulation results may be mapped including water level, water depth, and velocity as well as advection dispersion component. It is upon the user to specify the desired type of map out as there are different types like, minimum values, maximum or dynamic. Depending on the user selections additional information may be required such as the advection dispersion component, the time span for which the map is to be produced and the storing frequency (DHI, 2012).

A study conducted in Mill creek in south-western Ohio, on flood mapping employed the use of MIKE11 in developing 2D flood maps (Snead, 2005). In this study flood depths and water levels was studied and mapped across the flood plain to help the engineers and the whole community with additional information on flood hazard that can occur from Mill creek. Flood mapping displays different colour codes depending on the water depth in a specific area. Qualitatively colour codes represent the extent of damage caused by flood ranging from low to very high or extreme. On the other hand, flood depth can be categorised from 0 to 2m or above depending on the historical data from previous flood events for the area in question.

2.6 Estimation of flood hazard

A hazard can be defined as the probability of the occurrence of potentially damaging events (Balica, Popescu et al. 2013). 'Potentially damaging' means that there are elements exposed to a hazard which may be harmed. Flood hazards include events with diverse characteristics, like structures located in the floodplain, people and water sources susceptible for pollution. Heavy rainfall, coastal or fluvial waves, or storm surges represent the source of flood hazard. Generally these elements are characterised by the probability of flood event with a certain magnitude and other characteristics. Traditionally, flood hazard quantification takes into account floodwater depth and velocity in time and space. This is due to the fact that flood hazard is a function of depth, velocity and duration as well as rate of water rise. For instance a 20 and 50-year flood events can have water depths of 1.5m and 3m respectively. Therefore, in order to estimate the flood hazard, some flood parameters like flow velocity, depth and duration at any given point need to be taken into account. The flood hazards can be presented in maps and by flood damage curves.

Flood damage can be estimated by considering negative impacts or loss brought about by flood. According to Genovese (2006), there are intangible and tangible losses caused by flood ranging from human life, environment, as well as economic loss. Therefore the best approach towards estimating flood damage cost is by using probable maximum loss which incorporates both tangible and intangible losses.

As mentioned above, the extent of flood damage caused by flood is subject to water depth, flow velocity duration and sometimes rate of water rising; however, water depth plays a central role as far as flood damage assessment is concerned especially when velocity and

other parameters are negligible or constant. The damage can be expressed qualitatively or quantitatively depending on the requirement for a particular study. Table 2.4 below summarises a general rule on flood damage estimation.

Table 2.3: below summarises types of costs brought about by flood damage.

Type of damage cost Associated damage Direct costs Physical damage to assets and inventories. This is valued as replacement costs Indirect cost i Flow effects like output loss and expected earnings ii Business interruption iii Environmental damage iv Cleaning and evacuation costs

Relief costs Cost due to providing support services to flood victims, like health, food, care, water and sanitation services. Replacement costs Costs incurred to restore assets to the standard that existed before flood Reconstruction costs Cost incurred to re-build a structure up to its original state.

Table 2.4: Guidelines for flood damage estimation

Flood depth Damage category Description Less than 0.3m Low May not cause significant damage, still suitable for cars, though can bring some inconveniences for wading especially for kids Less than 0.8m Medium Still just for cars but much incontinence in wading and may bring damage to some extent in some parts of the house, floors and lateral piping. Less than 1.8m High More damage to buildings and assets Up to 2m Very high Huge damage where no developments can be Above 4m Extreme encouraged Source (Vojinovic, 2008)

In estimating the flood damage cost, it is necessary to assign a financial term to flood damage corresponding to each flood depth. The cost is what is called the maximum probable loss in the sense that it comprises both direct and indirect costs brought about by flood water at that particular depth. This approach results with flood damage curves, where flood damage cost is plotted against corresponding flood depth to ascertain total annual damage cost for a particular case (Vojinovic, 2008). The costing for each flood depth is a case based because it depends on the properties within the flooded area. For instance the industrial area will have different costing from residential setup. Similarly, rural settlement will have different costing from urban due to quality of houses, assets in the house housing density.

2.7 Public health assessment

Exposure to public health problems can be defined by the population exposed to it as a response to flood event. This is due to the fact that despite of the contaminants being transported and deposited during flood they also remain in the environment for some time after a flood. Public health assessment should also consider the extent of possible water borne diseases as determined by type of pathogens and their respective concentrations. Water borne diseases caused during flood are results from pollution of both surface and underground water sources. Therefore before assessing such public health problems it necessary to develop an appropriate model do assess a specific contaminant. Physical models are useful in describing pollution processes as related to urban ecosystem, catchment pollution and river system. As stated by Diadovski (2002), hydrological, hydro- chemical and hydro-biological characteristics in the river ecosystem and dynamic of the point and diffuse contaminants in the catchment is necessary in ascertaining the water quality (pollution level) in the river system (Diadovski, Hristova et al. 2002).

There are parameter(s) of interest to be specified in representing both organic and inorganic contaminants in water (Sibil, Berkun et al.). While BOD can provide an insight of microbial presence of which are diseases causing microorganisms (pathogens) other inorganic contaminants like heavy metals may represent carcinogenic behaviour upon human consumption. Since faecal coliform are responsible for most waterborne diseases, this study shall adopt escherichia coli concentrations as an indicator organism for assessing extent of public health problems.

2.7.1 Escherichia coli modelling

Faecal coliform contaminants transportation and dispersion by flood wave in the river or in the flood plain can be studied using advection-dispersion model when simulated simultaneously with hydrodynamic model in either MIKE11 or MIKE21 or both. The advection-dispersion (AD) module is based on the one-dimensional equation of conservation of mass of a dissolved or suspended material. The module requires output from the hydrodynamic module, in time and space, in terms of discharge and water level, cross-sectional area and hydraulic radius. The advection-dispersion equation is solved numerically using an implicit finite difference scheme which, in principle, is unconditionally stable and has negligible numerical dispersion. This equation contains a correction term in order to reduce the third order truncation error. This correction term makes it possible to simulate advection-dispersion of concentration profiles with steep fronts (DHI, 2012). The one-dimensional (vertically and laterally integrated) equation for the conservation of mass of a substance in a solution, i.e. the one-dimensional advection- dispersion equation is as shown below.

Where; C : concentration q : lateral inflow D : dispersion coefficient x : space coordinate A : cross-sectional area t : time coordinate K : linear decay coefficient C2 : source/sink concentration

The equation reflects two transport mechanisms; advective (or convective) transport with the mean flow; dispersive transport due to concentrations gradients. The dispersion coefficient, D, is described as a function of the mean flow velocity, V, as shown below.

D = aVb

Where ‘a’ is the dispersion factor and b the dispersion exponent; typical value ranges for D is 1 - 5m2/s (for small streams), 5 - 20m2/s (for rivers). Both the ‘dispersion factor’ and the ‘dispersion exponent’ can be specified. If the dispersion exponent is zero then the dispersion coefficient D becomes constant (equal to the dispersion factor). By default the dispersion is zero (i.e. there is only advective transport and no dispersion). The minimum dispersion coefficient’ and the ‘Maximum dispersion coefficient’ parameters are used to control the range of the calculated dispersion coefficients’ (DHI, 2012).

The main assumptions underlying the advection-dispersion equation are; i) The considered substance is completely mixed over the cross-section, implying that a source/sink term is considered to mix instantaneously over the cross-section. ii) The substance is conservative or subject to a first order reaction (linear decay) iii) Fick's diffusion law applies, i.e. the dispersive transport is proportional to the concentration gradient (DHI, 2012).

As stated by Vojinovic (2011), water being an excellent solvent, thus the concentration of a particular contaminant in water is being advected with the mean velocity of the flow, diffused due to turbulence and dispersed according to the three-dimensional structure of the flow. Therefore due to such three-dimensional flow, the flood contaminants spill over the river banks and are dispersed further to the built-up environment. The type and amount of contaminants varies in time and space and it depends on the source. However, the contaminant concentration may be assumed constant for all other sources except for industries. This is due to the fact that most manufacturing industries generate relatively constant flow-rates during production but the flow-rates changes drastically during clean- up and shut-down (Metcalf 2004). In the both MIKE11 and MIKE21 hydrodynamic model, various contaminants can be simulated including BOD and faecal coliform concentrations.

2.7.2 Escherichia coli mapping

As mentioned by (Amini, 2005), in his study for mapping soil pollution due to heavy metals in Zayandehroud River in central Iran, the aim of pollution mapping is to estimate an average concentration of a particular contaminant in sampled locations. This gives an insight of an estimated average or probability of exceeding certain threshold level. Various pollution levels can be represented differently; one of the simplest representation being colour mixture technique. The technique uses standard colour coding system (red, green and blue) in the representation procedure. In this procedure, colour of each pixel is calculated as an average of standard colours and then the results are defined in the properly interpreted legend (Amini, 2005).

2.8 Public health impacts

Flood water carries various contaminants that can pollute not only water resources but also general environment including soil. However, the immediate public health impacts can be

noticed when this happens to water resources. Contamination of water resources by flood contaminants can be due to mixing with water supply system due to pipe failure or through weak joints in the system especially when there is a heavy flood wave. On the other hand, underground water recharge can happen when water table is high. The possibility of flood contaminants mixing with open water sources like dams, rivers, or shallow wells is even more obvious.

Bacterial contamination in both water supply and coastal waters pose a hazard on public health as regards to drinking and recreational activities as well jellyfish consumers. As stated by Porter, (2004), land use characteristics have an effect on the faecal coliform densities where in the study done in South Carolina coast in estimating relationship between land use and faecal coliform pollution it was realised that storm water run-off contains very high faecal coliform concentration from urbanised area as compared to less urbanised one. The degree of imperviousness and use of on-site sanitation systems were reasons for high faecal coliform concentrations in surface water and storm run-off (D. Porter, 2004). The more common types of contaminants include pathogenic organisms, oxygen demanding organic substances, plant nutrients which stimulate algal blooms, inorganic and organic toxic substances, heavy metals and oil.

2.8.1 Escherichia coli and water-borne diseases

Many serious human diseases such as cholera, typhoid, bacterial and amoebic dysentery, enteritis, polio and infectious hepatitis are caused by waterborne pathogens. In addition, malaria, yellow fever and filariasis are transmitted by insects that have aquatic larvae. Faecal pollution of water resources by untreated or improperly treated sewage is a major cause for the spread of water-borne diseases. To a lesser extent, disease causing organisms may also be derived from animal rearing operations and food processing factories with inadequate sewage treatment facilities. As stated by Troussellier, (2004), in most developed nations, the spread of waterborne infectious diseases has been largely arrested through the introduction of water and sewage treatment facilities and through improved hygiene. But in many developing countries, such diseases are still a major cause of death, especially among the young. A strong correlation exists between the infant mortality rates of various countries and the percentage of the population with access to clean water and sewage disposal facilities. Table 2.5 below summarises some common pathogenic micro- organisms, their major reservoirs and the disease that they cause to human.

Table 2.5: Common Pathogenic and associated water-borne diseases

Name and type pathogenic Major diseases Major reservoirs and micro-organisms primary sources 1. Bacteria Salmonella typhi Typhoid fever Human faeces Salmonella paratyphi Paratyphoid fever Human faeces Other Salmonella Salmonellosis Human and animal faeces Shigella spp. Bacillary dysentery Human faeces Vibrio cholera Cholera Human faeces and freshwater zooplankton Enteropathogenic E. coli Gastroenteritis Human faeces

Name and type pathogenic Major diseases Major reservoirs and micro-organisms primary sources Yersinia enterocolitica Gastroenteritis Human and animal faeces Campylobacter jejuni Gastroenteritis Human and animal faeces Legionella pneumophila and Acute respiratory illness Thermally enriched water related bacteria (legionellosis) Leptospira spp. Leptospirosis Animal and human urine Various mycobacteria Pulmonary illness Soil and water Opportunistic bacteria Variable Natural waters 2. Viruses Polio viruses Poliomyelities Human faeces Coxsackie viruses A Aseptic meningitis Human faeces Coxsackie viruses B Aseptic meningitis Human faeces Echo viruses Aseptic meningitis Human faeces Other enteroviruses Encephalities Human faeces Rotaviruses Gastroenteritis Human faeces Adenoviruses Upper respiratory and Human faeces gastrointestinal illness Hepatitis A virus Infectious hepatitis Human faeces Hepatitis E virus Infectious hepatitis; Human faeces miscarriage and death Norovirus Gastroenteritis Fomites and water 3.Protozoa Acanthamocba castellani Amoebic Human faeces meningoencephalitis Balantidium coli Balantidosis (dysentery) Human and animal faeces Cryptosporidium Homonis Cryptosporidiosis Water(comma) human and (comma) C. parvum (gastroenteritis) other mammal faeces Entamoeba histolytica Amoebic dysentery Human and animal faeces Giardia lamblia Giardiasis (gastroenteritis) Water and animal faeces Naegleria fowleri Primary amoebic Warm water meningoencephalitis 4. Helminths Ascaris lumbricoides ascariosis Animal and human faeces

Source: Troussellier, (2004)

Table 2.6 below showing types of water related infections and modes of transmission to human body

Table 2.6: Water related infections and modes of transmission

Group Description Diseases Diseases spread through water in Cholera, Typhoid, Bacillary Water-borne which water acts as a passive carrier dysentry, Infectious hepatitis, diseases for the infecting pathogens. These Leptospirosis, Giardiasis, diseases depend also on sanitation Gastroenteriris etc. Diseases spread by vectors and insects Yellow fever, Dengue fever, that live in or close to water. Stagnant Encephalitis, Malaria, Filariasis Waterborne ponds of water provide the breeding (all by mosquitoes), Sleeping diseases place for the disease spreading vectors sickness (Tsetse fly), such as mosquitoes, flies and insects. Onchocerciasis (Simulium fly) etc. Diseases caused by infecting agents Schistosomiasis, Dracunculosis, spread by contact with or ingestion of Bilharziasis, Philariosis, Water-based water. Water supports an essential Oncholersosis, Threadworms and diseases part of the life cycle of infecting other helminths agents such as aquatic snails Diseases caused by the lack of Scabies, Trachoma (eye- adequate quantity of water for proper infection), Leprosy, Conjuctivitis, Water-washed maintenance of personal hygiene. Salmonellosis, Ascariasis, diseases Some are also depended on poor Trichuriasis, Hookworm, sanitation. Amoebic dysentery, Paratyphoid fever etc.

Source: Troussellier, (2004)

The major sources of pathogenic organisms causing most diarrhoeal diseases are human or animal excreta with non-improved sanitation being medium of transmission. This includes dry sanitation involving re-use, hands, water-borne sewage and non-recycling latrines. The pathway to human being includes vector insects like flies, as well as soil, surface water and ground water contamination. The interface though which a person can get infected is mainly through food, drinking water, swimming/recreation and accidental ingestion of soil. Figure 2.1 below summarises e-coli-human pathway.

Figure 2.1: Transmission pathway of faecal-oral diseases Source: {Annette Prüss, 2002}

2.8.2 Public health hazard quotient

A hazard quotient (HQ) is the ratio of the potential exposure to a substance and the level at which no adverse effects are expected. If the hazard quotient is calculated is less than 1, then no adverse health effects are expected as a result of exposure. If the hazard quotient is greater than 1, then adverse health effects are possible. However, the hazard quotient is not a probability for occurrence of adverse health effects, and is unlikely to be proportional to risk. (http://en.wikipedia.org/wiki/Hazard_quotient, retrieved on 19, April, 2014).

HQ= {DoseSoilIngestion+DoseWaterIngestion+DoseFoodIngestion+DoseParticleInhalation+ DoseDermalContact}

TDI Where; HQ is hazard quotient and TDI is the tolerable daily intake.

The hazard quotient is expressed considering possibility of ingesting contaminants through all possible pathways including, soil, water, food, inhalation and dermal contact. In most cases, hazard quotient is used for estimating risks associated with persistent organic contaminants. It can be adopted for other contaminants with slight modifications. However, using hazard quotient in assessing risks associated with faecal coliform is not scientifically justifiable due to the fact that this will involve many assumptions which can render the study void. For instance most effluent standards restrict e-coli to zero in terms of TDI due to the fact that even at very low concentrations still pathogens cannot be tolerated because of adverse health risks associated with them. This means the only

realistic value for TDI in relation to e-coli should be zero, which cannot fit in the hazard quotient equation above.

2.9 Cost implication on waterborne diseases

There are different indicators that can be used to assess the impacts of pollution on public health problems. The number of people exposed to waterborne diseases, immediate or latter mortalities and cost of treatment. As stated by Lenhart, (2010), cholera outbreak worldwide causes enormous loss of life and financial devastation to families and healthcare systems. Also the centre for health security developed a model for estimating total costs due to treatment of water related infections where average cost for a specific country can calculated by adding the direct and indirect costs associated with case incidence. Costs are reported in purchasing power parity (PPP) (Lenhart, 2010). Therefore in order to make a good comparison between flood damage and associated impacts on public health is better to include cost implication on treatment of water related diseases.

The approach to estimate treatment cost assumes that each disease case is severe enough to require medical treatment and, in the absence of treatment, would result in some days of lost productivity and that carry a measurable probability of death. Such cases have different costs implication depending on factors like level of treatment being health centre hospital or ambulatory/clinic. There is also a cost associated to any disease case which did not receive any treatment which resulted with death or survival. Figure 2.2 below shows an example of cholera treatment cost estimates model.

Figure 2.2: Cholera cost estimate model Source: (Lenhart, 2010)

2.10 Model selection and calibration

Despite of the fact that physical based can forecast flow and determine flow in a more detailed manner within the flow domain they also take into account most of the physical processes. They are more appropriate for reduced amounts of boundary condition data (Vojinovic 2012). MIKE 11 software is one of physical model that can be used to simulate flow in rivers and open channels. Such model is expected to produce simulated values as closely as possible to the actual measured/observed variables. In order to achieve this some parameters have to be adjusted iteratively within the reasonable range. For river flow modelling, the parameter to be adjusted during calibration is the run-off coefficient/ river bed flow resistance. This need to be varied iteratively until the model flow closely resembles the measured flow.

As pointed out by Vojinovic (2012), force-fitting the model by adjusting the parameters outside their normal range of values might result in a model that generates errors in the model output even with the correct parameters. For advection-dispersion model the flow should be adjusted against contaminants concentration for calibration. After successful model calibration, the model should be confirmed by running the model with another set of input data to verify whether the results are reasonable enough to trust the model results during simulation.

Assessment of flood hazard based on physical damages to assets and inconveniences to human life without considering impacts to public health and general environment might not bring a comprehensive meaning that it deserves. This is due to the fact there is a considerable amount of contaminants transported and dispersed by flood waves that need to be considered along with physical flood damage assessment. So far, literature review has demonstrated the vast gap physical flood damage and its pollution implications such that most studies and flood management practices focuses more on management of damages to assets while giving lesser attention to pollution impacts or in some cases not considered at all. Conversely, most flood-related studies and flood management projects tend to treat flood and contaminants dispersion in the catchment separately. Therefore the integrated approach for assessment and management of the single or multiple contaminant hazards due to extreme flood is still missing. This is the gap that needs to be investigated. This study is laying a fundamental basis towards the aspect of integrated flood-pollution hazard assessment with some common indicators like people exposed and cost implication to both flood and pollution (public health) hazards.

CHAPTER 3 METHODOLOGY 3.1 Introduction

The development of flood hazard and public health problems assessment methodology consists of a combination of both flood hazard and public health indicators. The methodology is used to assess cost associated to treatment of waterborne diseases as well as cost associated to physical flood damages. Figure 3.1 below summarises general methodology used in this study towards linking flood and associated impacts on public health.

Literature Review Model selection Data collection & analysis

River flow & Catchment Rainfall data Flood data data River flow rates ModeL building and set-up

River flood and Inland flood and pollution modelling Pollution assessment Intervention measures

Flood Mapping Pollution Mapping

Flood damage Pollution/Public assessment health assessment

Total hazard assessment

Indicators -Flood depth -No. of people exposed -Water-borne infections -Flood damage costs -Diseases treatment cost Results and discussion

Conclusion and Recommendation

Figure 3.1: Study model

3.2 Study area

The Msimbazi catchment occupies an area of about 183.5 km2. There are three rain gauge stations which were used to estimate rainfall run-off in the study area. However, only two rain gauges that fit well in the catchment were sufficient in estimating rainfall run-off in the ratio of 77.25% and 22.75% for the upstream and downstream rain gauge stations respectively. Consider figure below indicating the catchment and location of rain gauge stations.

Figure 3.2: Rain gauge stations in the study area

3.3 Model selection and calibration

This study employed the use of MIKE11 hydrodynamic and advection-dispersion models as simulation tools for river flow, flooding and contaminants dispersion in the flood plain. The river section in question covers a total length of about 2.8km and the total flood area of about 20ha. Both the rainfall runoff and MIKE11 hydrodynamic model calibration was done using observed discharge in 2011. For the RR the auto-calibration was employed while for the MIKE11 hydrodynamic model manual calibration was done.

For the MIKE11 manual calibration, river bed resistances were used as a calibration parameter for hydrodynamic model whilst dispersion factor was used for calibration of advection-dispersion model. The results obtained from MIKE11 were extended to cover the larger area in the flood plain in order to assess both inland flood hazard and associated pollution. Simulation was done based on fixed time step of one-day from 1st November to 31st December, 2011. On the other hand, river extension was done by extending the cross- section and interpolating extra points between the actual river cross-sections. The cross- section extension adopted the bathymetry spacing of 90m between one points to another with corresponding elevations. In MIKE11 model, the map was selected as an output in order to include both flood and e-coli concentration map in the results’ files.

Figure 3.3: RR model calibration

3.3.1 Flood modelling

The MIKE11 hydrodynamic model was used in simulating flood. The initial conditions was set at water level between 1.9 – 2.1m-amsl at the downstream and the observed discharge for April 2011 was used calibration while the discharge for November and December was used for model simulation analysis. The Manning’s equation provided the basis for model calibration.

Where: Q = Flow rate, (m3/s) v = Velocity, (m/s) A = Flow area, (m2) n = Manning’s roughness coefficient R = Hydraulic radius, (m) S = Channel slope, (m/m)

During calibration, the default bed resistance of 0.0 was adjusted iteratively till the discharge value downstream agrees with the measured value (figure 3.2). The river section was assumed to be trapezoidal in shape, consider table 3.1 showing other river section parameters which were kept constant in the Manning equation during calibration.

Table 3.1: Model calibration parameters

Parameter Value Top width 82m Bottom width 20m Av. depth 2m

Wetted perimeter 82m Cross-section area 102m2 Hydraulic radius 1.24m Slope 0.004m/m

The Manning’s ‘n’ = 0.033 was adopted for simulation. Since the river portion to be modelled is almost a straight section with only 3km length, then the bed resistance was assumed to be uniform and equal to the river resistance.

Figure 3.4: MIKE11 model calibration

3.3.2 Escherichia coli modelling

Escherichia coli concentration dispersion in the flood plain were done using advection- dispersion model in MIKE11. Upstream discharge was the boundary conditions on the upstream while the e-coli concentration was set as the boundary condition downstream. The model component was specified as e-coli concentration indicated in 1/100ml. The initial concentration (upstream) was set at 40,000-bacteria/100ml while the downstream

value of 100,000-bacteria/100ml was adopted. These figures are based on the measured data from the river in 2011.

The model calibration is based on the dispersion coefficient ‘D’ as stated in section 2.7 in chapter two and decay rate ‘k’. The value of ‘k’ is derived from the exponential bacteria decay equation. However, the value of decay constant was fixed based on the assumption that the e-coli in question are ordinary heterotrophic bacteria under aerobic conditions. Consider the bacteria decay equation below;

-kt C (t) = C (0) e

Where C is the e-coli concentration at any time t; Co initial e-coli concentration; k decay rate and t is the time in days. The bigger the k the faster the decay rate of bacteria and hence less problem we have. Hence with low ‘k’ value more public health problems we have in the environment because more bacteria will survive longer. However, the number of bacteria present at any time depends also on the half-life for that particular specie.

The bacteria decay constant ‘k’ was specified as 0.6 considering these fecal coliform in this case as ordinary heterotrophic bacteria under aerobic condition. During model calibration, the dispersion coefficient default value of 2m2/s was adjusted iteratively till the simulated bacteria concentration agrees with the observed concentration. The value of 12m2/s was adopted for simulation since it gave almost same bacteria concentration as the observed value. Default dispersion exponent equal to 1, was adopted, while the range for dispersion factor was set at minimum of 0 and maximum at 100.

3.4 Data analysis

Data collection exercise involved acquisition of all necessary information for both flood and pollution hazard assessment. Various data type were collected ranging from river geometry and flow characteristics, population of people in the study area, hospital data related to water-borne diseases, maps, land use data (population and housing density), historical flood data (flood depth and damage cost), digital elevation models, tidal levels, faecal coliform concentrations and historical waterborne disease cases. Most of the collected data are based on flood event corresponding to a 1 in 50 year rainfall return period recorded in Dar es Salaam city in December 2011. Table 3.2 below summarises some of the data collected.

Table 3.2: Data collection

Type of data Location Application in the study Duration Population Six villages within the In estimation of number Projected to 2014 study area of people exposed to based on 2012 flood and pollution census hazard Cholera cases Six villages within the In estimation of number For year 2011 study area of people exposed to cholera hazard Chainage Three kilometres from Estimating study area - downstream coverage

Type of data Location Application in the study Duration Cross-section Six points, three In defining river shape - kilometres from and estimating river downstream width and depth Faecal coliform At a point three Model calibration and For year 2011 concentration kilometres upstream estimating E-coli and one point before concentration in the river mouth river and across the flood plain Tidal levels A the Indian Ocean Downstream boundary year 2011 condition Rainfall Three gauging stations Understanding 30 years (1991- relationship between 2011) river flow and corresponding rainfall intensity River flows Three kilometres Model calibration and year 2011 upstream and one estimating river flood point before river and corresponding mouth inland flooding DEM Study area In estimating catchment - terrain, slope and bathymetry Topo maps Study area In generating both flood - and pollution maps Field photos Study area In verifying the extent of 2013/2014 the problem Flood damage cost Study area Damage cost analysis 2011 Incidence of Hospital Estimation of people Average values waterborne diseases exposed to various infections Disease treatment Hospital Cost implication on Average values costs public health problems

3.4.1 Methodology for river flow assessment

Rainfall has a substantial influence upon flood modelling and hence the way of acquiring, processing and analysing rainfall data determines the accuracy of resulted flood modelling. As mentioned by (Vojinovic 2012), rainfall run-off modelling errors are always associated with uncertainties in rainfall estimates. In this regard, the shape, timing and peak of a rainfall event are subject to both spatial and temporal rainfall variability across the catchment. In this study, rainfall data was used to determine the catchment hydrography, realizing the river response to rainfall run-off, relating rainfall patterns with waterborne diseases. Also rainfall data was used to estimate the run-off/river discharges using the RR simulation using MIKE by DHI software. In reality for this particular case, the dry weather flow due to sewage outfalls in the river is very significant and hence there is a considerable discrepancy between the river flow using the RR simulation and the actual observed flow. However, this was taken into consideration during model

calibration. This is due to the fact that river flow carters for both sewage (dry weather flow) inflow into the river and rainfall-runoff. The use of rainfall data in estimating river flow might be less than the actual river flow and hence misleading. The linear relationship between catchment hydrography and rainfall pattern as shown in figure 1.3 and 1.4 under chapter one indicates a positive response of the river flows due to rainfall. Rainfall data for the last 20 years indicate that the 2011 was the maximum rainfall ever as compared to the previous rainfall records. Figure 3.5, below showing 20-years’ time series rainfall pattern between 1991 - 2011 as recorded by three rain gauge stations namely Airport, Dar es Salaam port and Ocean road in Dar es Salaam city.

Figure 3.5: Twenty years’ rainfall pattern for Dar es Salaam city

Rainfall measured by all three stations has slight variations due to the fact that sometimes a certain stations might not capture the rainfall whereas other station does. However, all stations indicated that the 2011 rainfall was the maximum over twenty years. Consider figure 3.6 below showing average monthly rainfall variation as recorded by three gauging stations for year 2011.

Figure 3.6: Average monthly rainfall for Dar es Salaam city (2011)

The observation from all three rain gauge stations depicts two rain seasons, March – May and October – December. These are the months that the city experiences heavy floods also. High rainfall was recorded by all stations, which ranges between 210 – 250 mm/month for the first rainfall season and between 90 – 380 mm/month for the second rainfall season. The Ocean road rain gauge stations seems to have captured well the rainfall raise around October–December for the year in question due to the fact that this was the period of devastating flood in the Dar es salaam city. The flood event in 2011, the major flood occurred between late November and December. Since Msimbazi River is the major recipient of most of the run-off the river flow within that period can explains the behaviour of flood event as shown under figure 1.4 in chapter one.

3.4.2 Methodology for flood and e-coli mapping

Both flood hazard and e-coli concentration were represented by maps. This involves colour coding to display extent of hazard in terms of flood depth and e-coli concentration for flood damage and public health impacts respectively. The gathered geographical information system maps were used to demarcate the study area. The DEM was created through internet link (http://www.cgiar-csi.org/data/srtm-90m-digital-elevation-database- v4-1) and then converted into dsf2 file to be used as input map in MIKE11 during flood

and e-coli modelling with a cell specified cell size of 100x100m. Demarcation of the study area was done and the initial resulted map is as shown in figure 3.7 below.

Figure 3.7: Digital elevation model

The topography of the flood plain is explained by plotting the DEM across the study area as shown in horizontal terrain. The land terrain has its implication on the slope and hence indicators for flood damage assessment.

35 Terrain of the Study Area

30 msl) - 25

Elevation in metres metres in (a Elevation 5

121

181

241

301

361

421

481

541

601

661

721

781

841

901

961

1021

1081

1141

1201

1261

1321

1381

1441

1501

1561

1621

1681

1741 1801 Points Across the Study Area

Figure 3.8: Terrain of the study area

The figure above shows that the elevation across the study area ranges between 30m-amsl away from the sea and about 0-amsl at the river mouth. The trend shows that most of the area lies between 12 and 22m-amsl. The catchment was assumed to have uniform slope of about 4o/oo. With such slope the flood velocity is expected to be very marginal and hence plays a minimum role towards flood damage.

Flood map was displayed based on flood depth while the public health impacts’ map was based on e-coli concentration. Escherichia coli is used as a measure for pollution due to the fact that is a major pathogenic organisms responsible for most of waterborne diseases as mentioned under section 1.3 in chapter one. E-coli concentrations dispersion is observed in the flood plain as a result of AD module simulation in an extended MIKE11 across the flood plain. Both flood hazard and e-coli concentration maps were overlaid with the digitised topographical maps in order to generate clear maps.

3.4.3 Boundary conditions

Boundary conditions define the model simulation domain. For this study river flows at Kigogo (3km upstream) and water levels at the Indian Ocean were used as model boundary conditions. These data were recorded during the 2011 flood event in Msimbazi.

3.4.4 River network and cross-section

The river network was developed by assigning each node a name based on the actual names of such locations in the study area as well as its distance from the upstream end. The river network has seven major nodes spanning for a distance of 2,895m downstream. The nodes were named according their actual names existing in the study area for clear distinction during simulations, results interpretation and discussion. The upstream end is at Kigogo Street whilst the downstream end is Salender Bridge at the river mouth to the Indian Ocean. The major river cross-sections were taken at an average interval of 500m, two points on either side of the river. For flood mapping reasons by MIKE11 more interpolations was done on intervals and points to cover approximately 1500m on either sides of the river. Consider table 3.3 below showing river network. Similarly, horizontal network representation and cross-section at chainage 497.420m in the river network is as shown in figure 3.7 and 3.8 respectively.

Table 3.3: River chainage data

Figure 3.9: Msimbazi river network (part of)

Consider figure 3.8 which shows river cross-section at chainage 497.420m (located at Kigogo area) upstream as displayed in the MIKE11 model.

Figure 3.10: River cross-section at chainage 497.420m

3.4.5 Estimation of number of people exposed

The current population in the study was used in estimating the number of people exposed to both flood and pollution during flood event. The population data were projected based on 2012 National census using a National growth rate of 2.8%. The number of exposed is function of area covered by flood/pollution and population density. The population density for Dar es Salaam is about 3,100 people per square kilometer. It was deduced that only 30% of people exposed to flood are exposed to public health impacts.

3.5 Methodology for flood hazard assessment

Flood hazard is the level of difficulties in life and damage to properties. Flood hazard can be assessed based on several parameters including flood depth, duration, flood wave velocity and rate of rising of water level. One or more parameters can be considered in hazard assessment depending on the characteristics of the study area and floods (Keo, 2012). In this particular study, the area is small flood plain with flat topography. Considering the characteristic of this kind of topography, flood depth was used in flood hazard assessment. The flood wave velocity is very marginal while flood duration is assumed constant throughout the flood plain. Based on the fact that the study area is mainly a residential the assessment is done at the municipal level and the costs include the

damage to the structure of the residences and their content. As far as inundations are concerned, this indicator is the most important one because it allows the assessment of the most significant source of economic loss in the study area.

Computation of inland flooding was estimated by extending river cross-sections beyond actual river widths. This is due to the fact that the inland flooding is solely due to river flooding. This results with flood computed flood area and flood depth across the flood plain. The river flow resulted from rainfall intensity corresponding to 50-year return period. All the calibrated parameters as described in section 3.2 above were kept constant throughout simulation. The simulations were performed for two months which have high rainfall, starting from 1st of November to 31st of December and then the results expressed in terms of daily flood depth. Maximum flood was observed at any flow above 13.6m3/s. The computed water surface-elevation at all computational points was recorded in an output file at intervals of 10 minutes. A digital water-depth map was generated categorizing flood hazard with 0-m being the minimum and over 2.0m being highest depth.

Table 3.4: Flood damage cost estimates

Flood depth (m) Av.damage cost Category Description (USD) 0.0 - 1.0 2,000 Low Floor, opportunity cost + minor damages 1.0 - 1.5 7,000 Moderate All as above + Damage to house equipments, eg electrical installations, beds, chairs, tables’ television sets, cars. 1.5 – 2.0 12,000 High All as above + possible death to kids, disables, patient and the aged Above 2.0 20,000 Very high All as above + possible destruction of the whole house

Flood damage assessment was done based on flood damage costs and probable number of housing units affected. Each flood depth estimated in the flood mapping by MIKE11 was assigned a value based on total loss (direct and indirect losses) as it was found in the fieldwork by interviewing affected individuals. The American dollars is used as a currency for economic loss, where the value ranges between 2000 and 20000 USD for flood depth below 1.0 and above 2.0m respectively. The overall flood damage cost was done by aggregating all affected houses following in a common flood depth category. It was assumed that all houses are at least 50cm above the ground at all detached. On the other hand, numbers of housing units were estimated based on the fact that the study area follows on medium density housing with 50 houses per hectare.

Figure 3.9 below shows a typical flood damage curve for Dar es Salaam city

Figure 3.11: Typical flood damage curve

3.6 Methodology for assessment of public health impacts

Public health impacts assessment was done based on e-coli concentration. Two indicators were necessary for this, one is average number of people exposed to waterborne diseases and the second indicator is the cost implication due to treatment of such diseases. Both the average number of people exposed to various waterborne diseases during flood and respective diseases’ treatment cost were done based on the hospital data from the case study, Also the online waterborne diseases’ cost calculator was used as a guideline. On the other hand, qualitative analysis on the public health impacts was done based on the concentration of e-coli in different location in the study area as described in table 3.5 below.

Table 3.5: Guidelines for qualitative public health impacts

E-coli concentration (mg/100ml) Description 0 – 40,000 Low 40,000 – 60,000 Moderate 60,000 – 80,000 High 80,000 – 100,000 Very high Above 100,000 Extreme

Consider table 3.6 below showing average costs of treatment of waterborne diseases. Total cost his include direct and indirect costs. Indirect cost is associated to lost productivity by a patient or people taking care of the patient. Figure 3.9 below shows a model for diseases costing as it was customized from the on-line diseases costing calculator

Lowers Life expectance by 10-15%

Death Hospitalised 75 – 90% depending on 3 – 5 productive available resources days lost 2-3 days hospital days Treated Survival 1-3 days ambulatory visits

50-75% depending on resources available Ambulatory 3 -4 productive days lost 1 – days Water-borne infection ambulatory visits requiring treatment Lowers Life expectance by 50-80%

Death

Not treated 5 -7 Productive Survival days lost

Figure 3.12: Waterborne diseases cost estimates model

This model assumes that for any victim of water related infection is either get treated or no treatment all, with corresponding survival or death. In each case there is a cost component attached. On the other hand, for any treatment or survival there is a lost opportunity due to loss of productivity for both a patient and a care-taker. The total cost as shown in table below is the average sum of such direct and indirect costs associated with waterborne infections as they might apply to any other infections. However, the cost associated to death is not included.

Table 3.6: Direct and indirect cost for diseases treatment

Type of disease Average cost per case (USD) Ascariasis 150 Diarrhoea 220 Hookworms 150 Schistosomiasis 250 Typhoid 430 Cholera 720

Source: Muhimbili National Hospital, Dar es salaam-Tanzania

3.7 Combined flood damage and public health impacts

Physical flood damage cost is compared by diseases’ treatment cost. Also the sum of both impacts is aggregated so as to estimate the possible total financial loss that can be experienced by the community in the study per each flood event of similar intensity. Figure 3.10 below summarizes the approach used in to assess physical flood damage and public health impacts in such an integrated fashion.

Flood hazard & Public health assessment

Indicators

River flow, HD flood modelling Inland flood inundation AD e-coli modelling & e-coli dispersion

Flood depth and e-coli E-coli concentration Flood mapping concentration mapping

Flood damage -Damage costs Possible water-borne -No. of people exposed assessment -Water-borne cases diseases

Average treatment cost for Av. Treatment cost for each disease diseases

Total damage cost & Total disease treatment cost

Figure 3.13: Flood hazard and public health assessment framework

3.8 Rainfall return periods and future hazards

Rainfall data for the last 20-years were used to simulate the Rainfall-Runoff model in MIKE by DHI. As stated above, the observed discharge for 2011 rainfall/flood event was used for calibration. The obtained discharge showed that the 2011 river flow was the maximum and this is the why it resulted with extreme flood. Moreover, the obtained discharge for 20-years was used in simulating annual river flow in MIKE11 in aiming at getting the maximum annual water depth in the study area. The maximum water level for each year was used as an input parameter in the hotstat software in order to generate return various rainfall return periods which was then used in estimating possible hazard in the future.

Table 3.7: Estimated river flow rates

Year Maximum Average Minimum Standard deviation discharge Discharge discharge 1991 11.00 2.60 0.00 2.04 1992 12.06 4.77 0.00 2.16 1993 11.50 4.77 0.00 2.09 1994 12.00 4.96 0.00 2.13 1995 15.31 2.24 0.00 2.23 1996 13.86 3.26 0.00 2.55 1997 10.18 2.61 0.00 2.19 1998 15.51 3.01 0.00 2.26 1999 9.84 2.35 0.00 1.77 2000 9.16 2.07 0.00 1.86 2001 10.14 2.00 0.00 2.13 2002 14.40 2.47 0.00 2.76 2003 7.11 1.54 0.00 1.36 2004 10.21 1.63 0.00 1.72 2005 10.60 1.96 0.00 1.84 2006 10.18 2.22 0.00 2.15 2007 9.21 2.26 0.00 1.91 2008 9.70 1.80 0.00 1.94 2009 9.63 1.24 0.00 1.54 2010 14.83 1.54 0.00 2.17 2011 15.10 2.82 0.00 2.77

Figure 3.14 below shows maximum and minimum flow in the Msimbazi River for the last 20-years while figure 3.15 indicates the resulting flood depth for the corresponding flood in each year.

Figure 3.14: Maximum and average river flows

Figure 3.15: Estimated maximum flood depths in the study area

CHAPTER 4

RESULTS AND DISCUSION

4.1 Introduction

The major objective of this study is to link physical flood damage and public health problems resulted from contaminants dispersed by flood waves during extreme flood event. All the results are based on the flood from Msimbazi River corresponding extreme flood event of November-December, 2011. The simulated flood depth within the study area, the flooded area was classified into four hazard categories based on three marginal depths, 1.0, 1.5 and 2.0m. The basis of selecting such marginal depths is due to observation and interviews from the field visit where most of average plinth level of most houses/dwellings is about 0.5 to 1.0m above the ground level. Therefore, when flood depths are between 0 – 1.0m there is minimum physical damage as compared to the rest of other marginal depths. However, when flood depth is more than 1.0m there is possibility of death especially for children, aged and sick people. On the other hand, flood depth between 1.5 and 2.0m is more devastating where property damage is extensive and high possibility of death, both wading and vehicle movements are not possible. Hence, flood hazards are expected to vary non-linearly at each marginal depth. Based on the marginal depths, the area flooded under each depth category was calculated and used to estimate number of houses exposed to each corresponding flood depth. Each marginal depth was assigned a corresponding average damage cost in order to get the total damage cost across the study area. This was done based on the information from the field visit on expenses incurred by individuals during the previous flood events in the study area. Furthermore, due to the fact that flooding occurs in a residential area, average number of people exposed to were estimated.

In assessing public health impacts, 30% of the total population exposed to flood hazard was taken to be exposed to various waterborne diseases. As e-coli disperse in the flood plain it finds its way to water resources like shallow wells, accidental inflow into the water distribution network, mixing with water in open ponds or Ocean which are sources of water for human consumption. Figure 4.8 shows e-coli concentration map. People get infected by swallowing, bathing or swimming in contaminated water. Similarly, some infection may be through consumption of contaminated jellyfishes. Public health problems are assessed in terms of possible exposure to waterborne diseases and corresponding treatment cost for each disease. Common waterborne diseases referred in this study for assessing public health problems are cholera, typhoid, schistosomiasis, ascariasis, diarrhoea and hookworms. Each disease was assigned an average cost of treatment and the total cost was aggregated based on the total number of people exposed to all diseases per year or flood event.

The study area comprises of five wards in the flood plain namely, Kigogo, Jangwani, Magomeni, Upanga and Hana Nasif. Study area map is shown in figure 4.2 below. Flood hazard assessment was done for each ward before summing up the total damage for the whole study area. The same approach was also using for public health impacts assessment, thus finally a combined cost due to flood damage and disease treatment cost across the whole study area was estimated and compared.

Figure 4.1: Case study boundary

Figure 4.2: Flood prone area under Msimbazi basin

4.2 Flood hazard assessment

The number of people exposed to flood hazard is a function of total flood area and population density. Table 4.1 and figure 4.1 below shows the maximum flood area across the study area and the corresponding population and houses exposed. The number of people exposed is a function of number of houses and size of household.

4.2.1 People exposed to flood hazard

Table 4.1 below summarizes total people exposed to flood hazard across the flooded area. Number of houses under flood is estimated as a function of flooded area and housing density whilst number of people is a function of size of household per each house.

Figure 4.3 below indicate a plan view of the maximum flood area across the flood plain.

Figure 4.3: Maximum flooded area

Table 4.1: People exposed to flood hazard

Total Maximum flood Exposure to flood Village Population area (m2) Houses Population Kigogo 60,766 1,900 10 60 Jangwani 18,613 31,400 157 942 Magomeni 25,735 31,400 157 942 Upanga west 14,214 41,200 206 1236 Hanna Nasif 39,146 100,200 501 3006 Total 158,474 206,100 1,031 6,186

There is less flooded area upstream because of river cross-section. The river at Kigogo is deeper as compared to other sections downstream due to the fact that there is less encroachment at this area. On the other hand there much floods at Jangwani areas because this area is a valley with shallow river depth and hence is the most vulnerable area for flood. Magomeni area is also highly flooded, not only because of the shallow river but also due to encroachment were most houses are as close as 10m from the river bank and hence the river conveyance is restricted not only by buildings but also by solid wastes dumping in the river. On the other hand, downstream areas, Han Nasif and Upanga have are highly flooded due to the fact that they lies almost at the same level with the sea and hence in case of a slight sea surcharges they are the most vulnerable. Also most of Upanga area is paved and hence it generates much run-off during rainfall that increases flood to the area. Total number of people exposed to flood hazard is directly proportional to flooded area. This is based on the population density. As shown in table 4.1 above, over 6,000 people are exposed to flood of different intensities in any flood event. Despite Kigogo being the most populated area, it has few people exposed to flood due to relatively low flooded area as compared to the rest of the area in the flood plain. 4.2.2 Flood hazard map

The hazard map generated across the flood plain is as shown below. The hazard level based on flood depth is as represented by colour coding. The largest area, almost all the flood plain is covered by flood depth up to 1.5m whereas areas located within 500m from the river bank are experiencing moderate flood with exception of very few locations on the right bank of Kigogo street part of Magomeni and downstream at Hana Nasif which are exposed to very high flood hazard. The upstream and downstream locations seem to have more floods than the middle section of the flood plain. Downstream location at Hana Nasif have higher flood hazard due to accumulated flow downstream and the fact that it is at lower elevation that which is only 2m above the sea level. Generally, areas on the right- hand side of the river are exposed to very high flood level as compared to those on the left side. On the other hand, more area on the left side of the river bank is exposed to flood hazard though not of big magnitude as on the right side of the river. This is due to the fact that the terrain is slopping to the right side of the river but not much encroachment as compared to the left side. The flood hazard map is as shown hereunder.

Figure 4.4: Flood map

4.2.3 Flood damage assessment

Flood damage assessment is done based on two indicators; namely number of houses exposed to flood damages and associated damage cost. As explained in the study methodology, number of houses exposed to flood damage is a function of flooded area and housing density. The damage cost ranges from 2,000 and 20,000 USD for low and very high flood hazard respectively. These figures are based on the interviews from people who were affected by flood in the previous years from the case study. The flood depth less than 2.0m might not have significant damage to buildings and assets but still it has a cost component attached to it due the fact that it destroys floors and brings inconveniences that lead to opportunity cost. Table below shows flood depth with the corresponding possible damage

Based on the flooded area for each location in the study area, numbers of houses exposed to flood damage were estimated. Table 4.2 below shows total number of housing units exposed in each location across the flood plain. Jangwani area has more houses exposed to flood up to 36% of all flooded houses. As it was explained in the previous sections Jangwani is the valley with a lot of residential houses staying in the flood plain area illegally. The location upstream more houses exposed to flood than the rest of the flood plain. This may be attributed by encroachment, sea surcharges and accumulated discharges. Estimation of the distribution of housing units exposed to a particular flood depth is based on simulation results from the flood maps and historical flood-depth data as collected from the study area where individuals within the flood area were interviewed on the level of water they experienced in 2011 flood event.

4.2.4 Comparison of flood hazard in the study area

Comparison of flood hazard based on both houses exposed to a particular water depth and associated damage cost across the study area is done based on the five wards in the study area as shown in table 4.2 below.

i) Houses exposed

Table 4.2: Number of houses exposed to flood hazard

No. of houses exposed Flood depth (m) Kigogo Jangwani Magomeni Upanga Hana Nasif 0 - 1 1 19 19 72 150 1 - 1.5 5 107 107 29 225 1.5 - 2.0 3 9 9 56 75 >2 1 22 22 49 50 Total houses 10 157 157 206 501

Figure 4.5: Comparison of flood damage based on houses exposed

Most of the houses are exposed to flood depth between 1.0 – 1.5m, while almost 50% of the flooded houses being at the downstream end within Hana Nasif ward. Upstream area at Kigogo is less flooded. On the other hand, Magomeni and Jangwani area have almost similar hazard but less flood depth with large flooded area because they are in a valley.

ii) Flood damage costs across the study area

Table 4.3: Comparison of flood damage costs across the study area.

Damage cost (USD) Flood depth (m) Kigogo Jangwani Magomeni Upanga Hana Nasif 0 - 1 1,400 37,680 37,680 144,200 300,600 1 - 1.5 36,400 747,320 747,320 201,880 1,578,150 1.5 - 2.0 40,800 113,040 113,040 667,440 901,800 >2 12,000 439,600 439,600 988,800 1,002,000 Total damage cost 90,600 1,337,640 1,337,640 2,002,320 3,782,550

Figure 4.6: Comparison of flood damage cost

Total flood damage cost is about 3.8 million USD with much damage cost in Hana Nasif area because it has most of the houses exposed. However, Most of the flood damage cost is due to flood depth of 1.0 – 1.5m in each ward. Generally, flood damage cost increases linearly downstream. This is due to the fact that water depth increases as well as flooded area and hence more houses exposed.

iii) Overall flood damage cost

Table 4.4: Overall flood damage cost

Flood depth No. of houses exposed Damage cost (in USD) 256 512,140 0 - 1.0 479 3,355,030 1.0 - 1.5 150 1,798,440 1.5 - 2.0 146 2,913,400 above 2.0 1,031 8,579,010 Total

The most part of the study area falls under the flood depth of 1.0 – 1.5m and hence much of the damage cost is within this category. This is due to the fact that over 40% of the houses are exposed to flood depth of this range. Figure 4.7 below indicates cumulative flood damage cost for an extreme flood event.

iv) Cumulative damage cost

Figure 4.7: Cummulative flood damage cost

The rainfall data that resulted with the river flow corresponding to the flood event of this magnitude is of 100-years return period. Therefore the probability of such an event to occur each year is 1%. Consequently, the possibility of people in the study suffering such flood damage cost each year is 1%.

4.3 Public health impacts assessment

Public health assessment was done based on fecal coliform concentration and possible waterborne diseases caused by e-coli pathogens.

The e-coli concentration levels are as categorized in chapter three. Figure 4.8 below shows e-coli concentration in different locations across the study area.

Figure 4.8: E-coli concentration map

The downstream locations, mostly the right side of the river bank are exposed mostly to high concentrations of e-coli and hence are more vulnerable to public health problems as compared to the rest of the study area. These areas are also exposed to physical flood damage as compared to the upstream. However, most of the areas are in moderate exposure as few locations in the upstream end far from the river bank are at low exposure. Generally, the more you go far from the river the less exposed to e-coli concentration and hence less vulnerable to waterborne diseases associated to e-coli.

4.3.1 People exposed to public health problems

The number of people exposed to public health problems is only 30% of the total population under flood hazard. This is due to the data collected from the filed which shows an exceedence of 30% of water-borne diseases during flood season as shown in table 1.3 in chapter one. However, it should be noted that these estimates are based on

hospital data collected from the field, hence they might be underestimated due to the fact that, many individuals infected with waterborne diseases may not access health care also not all diagnosed cases are reported to public health or health ministry officials.

4.3.2 Comparison of waterborne diseases in the study area

i) Diseases distribution across the study area

Table 4.5: Number of people exposed to waterborne diseases

Number of people exposed Type of disease Kigogo Jangwani Magomeni Upanga Hana Nasif Ascariasis 0 34 34 44 108 Diarrhoea 0 54 54 70 171 Hookworms 1 57 57 74 180 Schistosomiasis 0 3 3 4 9 Cholera 2 136 136 178 433 Typhoid 0 3 3 4 9 Total 3 286 286 374 911

Figure 4.9: Comparison of people exposed to waterborne diseases

About 1,800 people are exposed to public health problems during exterme flood event. This is about 30% of the total popualtion exposed to flood hazard. The number of people exposed to public health problems increares linearly downstrem with cholera being the

most dominannt disease. Han Nasif has more disease cases as comparae to the rest of the areas followed by Upanga and jangwani area while Kigogo is the least affected. Only Hana Nasif have about 50% of diseases cases which is about 900 people.

ii) Comparison of average diseases treatment cost

Table: 4.6: Comparison of diseases treatment cost

Average disease treatment cost Type of disease Kigogo Jangwani Magomeni Upanga Hana Nasif Ascariasis 0 5,094 5,094 6,660 16,236 Diarrhoea 0 11,829 11,829 15,466 37,704 Hookworms 150 8,490 8,490 11,100 27,060 Schistosomiasis 0 708 708 925 2,255 Cholera 1,440 97,805 97,805 127,872 311,731 Typhoid 0 1,217 1,217 1,591 3,879 Total 1,590 125,143 125,143 163,614 398,864

Figure 4.10: Comparison of diseases treatment cost

The total diseases treatment cost is about 800,000 USD across the study area while major cost is spent on treating cholera due to the fact that the more people suffers cholera as compared to other infections with a relative high treatment cost as well as compared to other diseases. However, the burden is more in Hana Nasif area which carries almost 50% of the treatment cost. This is due to the fact it is more exposed to e-coli pollution as compared to other locations.

4.3.3 Overall public health impacts

The overall public health impacts is expressed in terms of total number of people exposed to waterborne diseases and the total cost for treatment of such diseases across the study area. Table 4.7 below shows total people exposed and corresponding treatment cost

Table 4.7: Overall Public health impacts

Type of disease cost per case USD) Total people exposed Total cost (USD) Ascariasis 150 221 33,084 Diarrhoea 220 349 76,828 Hookworms 150 369 55,290 Schistosomiasis 250 18 4,603 Typhoid 430 18 7,916 Cholera 720 884 636,653 Total 1,860 814,374

Figure 4.11: Overall diseases’ treatment cost

Overall diseases treatment cost is about 800,000 USD while only cholera contribute to 78% of the treatment cost followed by diarrhoea about 9% while the rest of the diseases are not very dominant as they share a treatment cost of about 14% collectively.

4.4 Comparison between physical flood damage cost and diseases’ treatment costs

Table 4.8: Total cost associated to both flood damage and diseases treatment costs

Type of disease Total treatment cost Flood depth (m) Total damage cost (USD) (USD) Ascariasis 33,084 0 .0 - 1.0 512,140 Diarrhoea 76,828 1.0 - 1.5 3,355,030 Hookworms 55,290 1.5 -2.0 1,798,440 Schistosomiasis 4,603 above 2.0 2,913,400 Typhoid 7,916 Cholera 636,653 Total 814,374 8,579,010 Grand total 9,215,663 USD

Per cent 9% 91%

Figure 4.12: Comparison of flood and treatment costs

Total financial cost due to extreme flood event, combining physical damage loss and cost due treatment of waterborne disease is about nine million USD. The physical flood damage dominants the loss as it contributes to over 90% of the overall loss. However, the loss due to treatment is about 10% which is significance as far as socio-economic status of the people in the flood prone areas are concerned. Consider figure 4.12 above showing overall cost.

4.5 Flood hazard during previous flood events

The historical flood hazard for the past 20-years in the study area including both flood duration and depth as indicators is shown under table 4.9 and figure 4.13 below. Due to the fact that duration might have more influence in assessing impacts on public health hazards it is assigned a relative weight of 66% while depth is given 34% in the hazard assessment. The hazard categorization is as shown in the table below. The flood depth is the maximum water level for each year and the corresponding duration as obtained during model simulation.

Table 4.9: Flood hazard assessment based on previous flood events

Year Max. flood depth(m) Duration (days) Hazard_duration Hazard_depth Total hazard Description 1991 0.8 3 0.09 0.00 0.09 low hazard 1992 2.14 2 0.00 0.16 0.16 Moderate 1993 2.46 3 0.09 0.18 0.27 Moderate 1994 3.06 3 0.09 0.22 0.32 Moderate 1995 3.14 4 0.13 0.23 0.36 Moderate 1996 2.51 2 0.06 0.18 0.25 Moderate 1997 1.6 10 0.31 0.12 0.43 High 1998 2.64 5 0.16 0.19 0.35 Moderate 1999 2.28 5 0.16 0.17 0.32 Moderate 2000 1.41 4 0.13 0.10 0.23 Moderate 2001 2.58 8 0.25 0.19 0.44 High 2002 3.06 9 0.28 0.22 0.51 High 2003 3.67 11 0.35 0.27 0.61 Very high 2004 3.53 6 0.19 0.26 0.45 High 2005 2.52 2 0.06 0.18 0.25 Moderate 2006 1.68 4 0.13 0.12 0.25 Moderate 2007 2.6 4 0.13 0.19 0.32 Moderate 2008 2.49 3 0.09 0.18 0.28 Moderate 2009 3.69 8 0.25 0.27 0.52 High 2010 3.07 14 0.44 0.22 0.66 Very high 2011 4.65 21 0.66 0.34 1.00 Extreme Key: Hazard range Category 0.0 - 0.2 Low Duration weight = 0.66 0.2 - 0.4 Moderate Depth weight = 0.34 0.4 - 0.6 High 0.6 - 0.8 Very high 0.8 - 1.0 Extreme

Figure 4.13: Hazard assessment based on previous flood events

It can be deduced that, flood hazard is more determined by water depth rather than duration especially for small catchments where there is little variation in flood durations. The 2011 flood was the extreme event in the sense that it has the maximum duration (21- days) and maximum flood depth of above three metres. Other years with significant hazards are 1995, 2003 and 2009. On the other hand, year 1991, 1997 and 2000 had minimum hazards as compared to the rest. Generally, the trend shows that there flood hazard in the study area is increasing. This calls upon attention from the Dar es Salaam city authority towards proper mitigation measures in order to control the situation.

4.6 Possible hazards in the future

In order to understand how vulnerable the study area is exposed to similar or even worse flood events in the future, the assessment for rainfall return period which can result with similar or more damages and diseases as compared to the 2011 event was done. This was done based on the maximum flood depths for the last 20-years to predict the average water depth in each return period. The hotstat software was used in predicting the return period and corresponding flood depth as shown under table 4.10 below. The physical damage cost was assigned to each depth in a similar manner as explained in chapter three while the diseases’ treatment cost is taken as 10% of the physical damage cost as it was deduced in section 4.4 above.

Table 4.10: Possible flood damages and disease treatment cost in the future

Rainfall return Period Flood depth (m) Physical damage cost (USD) Cost due to diseases treatment (USD) Total cost (USD) 2 0.31 2,062,000 206,200 2,268,200 5 0.73 2,062,000 206,200 2,268,200 10 1.40 7,217,000 721,700 7,938,700 20 1.80 12,372,000 1,237,200 13,609,200 25 2.20 20,620,000 2,062,000 22,682,000 50 3.10 20,620,000 2,062,000 22,682,000 100 4.60 20,620,000 2,062,000 22,682,000 200 7.72 20,620,000 2,062,000 22,682,000 500 8.59 N.A 1,000 9.20 N.A 2,000 9.78 N.A 5,000 10.50 N.A 10,000 11.01 N.A

Key Flood depth (m) Cost USD) Total houses = 1,031 0 - 1.0 2,000 1.0 -1.5 7,000 1.5 - 2.0 12,000 Above 2.0 20,000

The assessment is based on the total exposed number of houses in the study area which is about 1,031. The flood depth more than eight meters for the return periods of over 200 years as predicted by the hotstat model seems is unrealistic and hence not considered. However, any flood depth above two meters is assigned a damage cost of about 20,000USD.

Figure 4.14: Comparison of total cost in various flood return periods

The rainfall of higher return period will have more flood depth and hence more damage cost. Also, total cost is dominated by physical flood damage as cpmapared to treatment costs. However, damage cost inceaeses linearly upt to a return period of 50-years and above that the cost remains almost constant regardless the flood depth. Therefore, the probability of the study area getting a financial loss of about 22 million USD each year in 2% bacause the flood event corresponding to rainfall event of 50-years return period caused brings a loss of similar amount.

4.7 Proposed mitigation measures

Some possible control measures to overcome consequences due to both and public health impacts are suggested as follows;

4.7.1 Flood consequences control measures

Flood hazard control measures proposed here are case sensitive because the study area is a built-up environment. Both structural and non-structural measures are possible options as discussed below.

i) Improving channel conveyance

This can help reducing flood risk by increasing a river’s capacity. Clearing vegetation, solid wastes, sands and debris along the river banks reduces the hydraulic resistance and increases the river cross-section. Therefore, this will increase flow velocity downstream and ultimately to the Indian Ocean. On the other hand, increasing the channel depth by dredging can as well increase the river cross-section. However, dredging might not be

feasible throughout the case study as most of the river sections are not stable due to the fact that most areas are characterised by sandy soil. If the river banks are not stable enough sediment will start building up immediately after dredging until the equilibrium is reached and hence reduce cross-section. Similarly, river conveyance can be enhanced through increasing the river’s slope by decreasing its length. This can be achieved through the use of meander cut-offs. However, this approach might increase the erosion upstream and the sedimentation downstream. Improvements through channel conveyance solves localised flood, but does not solve the flooding problem in its broader term as it passes the risk further downstream. However, for this particular case it can work appropriately due to the fact that the recipient body downstream is a Sea. This approach might not be feasible if there was any sort of settlements downstream.

ii) Dyking

This consists of earthen embankments built between the river and the area to be protected. Dykes restrict the flood water’s flow to/from the river side. This will increase the stage in the river. However, care should be taken in the meandering sections where according to literature for such and urban area height of 3ft is recommended. Moreover, due to the fact that most of the areas within the flood plain in which dykes are supposed to be built are already occupied by human settlement, a detailed assessment is necessary as regards to dyke costs versus flood damage costs, environmental issues, social and political considerations. Generally this is the most applicable approach towards flood control as it has proved successful in most areas.

iii) Reservoirs

These are one of the most direct methods of flood control through storing surface runoff; thus, attenuating flood-waves and storing flood water to be redistributed without exceeding downstream flood conditions. Due to the fact that most areas within the flood plain are already occupied by people, it might be difficult or expensive to find space for positioning storage ponds. However, it is possible to build small ponds along the flood plain depending on space availability whereas large ones can be built upstream outside the urban area where space can be relatively easy to secure.

4.7.2 Public health impacts control measures

Both point and non-point sources are contribute to Msimbazi river pollution and ultimately to the whole catchment. Since point sources are discrete and hence can be readily identifiable and, as a result, they are relatively easy to monitor and regulate. In particular for Msimbazi basin this accounts for sewage (wastewater of mainly domestic origin, containing among others, human excreta) from both domestic and industrial wastewaters is discharged from point sources. On the other hand, non-point sources are distributed in a diffused manner. The location and origin of non-point sources are sometimes difficult to establish and they are therefore less amenable to control. Runoff from large urban or agricultural catchments, carrying loads of sediments and pathogens/nutrients, are examples of non-point sources of water pollution. For Msimbazi river, the possible non-point source of pollution might be oil spills of filling stations (which not a concern for this study) and leachate from solid waste dumpsites.

i) Minimisation of contaminant generation

Reduction of the quantity of waste or contaminants generated from both domestic and industrial sources is obviously the most desirable approach to this kind of pollution. Since it conserves resources that would otherwise be wasted, and at the same eliminates the cost of removing contaminant after they are produced, it is the cheapest and most effective alternative. This can be achieved by enforcing having industries having wastewater treatment plants and wherever possible recycling of treated water within their premises for irrigating their compounds or flushing in their toilets. For small scale industries and household they should have proper functioning onsite excreta disposal facilities (septic tanks) if they cannot access the sewerage system.

ii) Extension of sewerage system

The Dar es Salaam city authority should extend their sewerage system coverage as it is only covering the CDB which is about 15% of the city area. This would help removing all sewage to the treatment plant rather than accumulating in the domestic area where it is then released crudely during rainy season. Extension of sewerage system will reduce both flood and pollution hazard in the sense that, dry weather flow will be taken to the treatment plant and disposed to the sea instead of the river and at the same time minimizing pollution of the river as the problem currently is basically due to onsite excreta disposal systems in the case study.

iii) Wastewater Treatment

Most of the wastewater treatment plants in Dar es salaam city are not working efficiently and still they are realising there effluents to Msimbazi river either directly or through its tributaries. The Government should enforce compliance of effluent standards for all parameters. However, there is evidence that despite of these Wastewater treatment plants being inefficient the overall treatment capacity for the city is not sufficient. This might be the reason of why the water and sanitation authority are not willing to extend the sewerage system because the treatment capacity is already exceeded. The city needs more wastewater treatment plants to accommodate the current demand.

iv) Proper solid wastes management

Leachate from solid wastes gets in the river from some solid waste ponds. However, crude dumping of solid along the river despite of polluting the river it also restricts the river conveyance and results with local flood. Therefore proper handling of solid wastes at the house level and by the city authorities from generation, collection, transportation and management of the dumpsite is necessary in order to reduce nuisance that ends up pollution the river and the whole catchment.

CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

Dar es Salaam, the capital city of Tanzania has faced severe flood disasters several times and the situation is getting worse in recent years since 1995. This is attributed by the low elevation across the city due to the fact that the city lies along the Indian Ocean and majority of areas are natural flood plains which were converted to commercial, industrial or residential establishments. The city receives relatively high rainfall (1000 – 1500 mm per year) as compared to most of other parts of the country which get less than 800mm per year. With increased urbanization changes affects natural infiltration and results with high run-offs. Also lack of sewerage system coupled with inadequate drainage system exacerbates both pollution and flood hazards in the city. Especially for Msimbazi basin, most of the settlements are within flood plain; this has proved by the maximum river flow width during river flow simulation. Also the habit of indiscriminate dumping of solid wastes in the river reduces effective river depth and at the same time increasing flow resistance and hence elevate flood vulnerability.

The 2011 flood event is the most devastating scenario due to the fact that its duration was longer as compared to any of the previous events; nevertheless, this year’s (2014) situation is even worse; where at least 20 people died, more than 10 major bridges connecting the city to different suburbs collapsed and more than 10,000 people evacuated their homes. This trend is alarming for possibility of more intensified flood consequences in the future. Therefore, more efforts on designing proper flood protection, warning and evacuation system is necessary. There is no doubt that possibilities for flood situation might become worse in the city and across the region in the future. However, it is not possible to prevent all levels of flood intensity. Thus, the extreme flood events might be neglected due to relatively high investment cost; still mitigation measures for such extreme events are needed. Therefore, flood and associated pollution/public health assessment done in this study provides basic understanding of possible physical, social and financial consequences brought about by such hazards in order for the Government to make informed decisions, such as optimal investment for the best option towards proper mitigation measures. There is no published work to substantiate that this kind of study, where public health problems are examined as part of flood consequences has ever been done before in Dar es Salaam city. Therefore, the theoretical contributions from this study can be summarized as follows:

 Based on the study area the flood depth is the major determinant of flood hazard when velocity is marginal due to minimum slope. However, flood duration is the determining factor for the level of flood hazard for two or more flood events having similar flood depth, and indeed it is more significant when public health issues are included as part of flood consequences.  Flood hazard assessment should not base on physical damages and related cost only, rather it should consider population exposed to flood and this can be done using housing density and average size per household.  Downstream locations as far as 1000m from the sea (Upanga and Hana Nasif) are more exposed to both flood and pollution as compared to intermediate and upstream locations.

 Msimbazi and Magomeni wards have more flooded area but low flood depth as compared to the rest of the study area. This is due to the fact that these two areas are valleys.  Generally flood hazard zoning in the study area can be expressed in marginal depths and distance from the river bank as follows;

200m from river bank are very high hazard zone

200 – 500m from river bank are high hazard zone

500 – 1000m from river bank are moderate hazard zone

Beyond 1000m from river bank are low hazard

 The residents within the study area can use this information for self-preparedness to minimize the impact of both flood and pollution to their life and livelihood  Poor land use planning in the city where people have establishments residences and business in the flood plain increased both flooding and pollution in the study area.  Almost all people in the flood plain are exposed to flood hazard of which about 30% area are exposed to waterborne infections for any extreme flood event.  It is not possible to estimate pollution hazard as a function of flood depth due to the fact that contaminants concentration(s) does not depend on flood depth  30% of all people exposed to flood hazard are exposed to water-borne infections whilst only 10% of total cost is due to disease treatment cost. This means for any physical flood damage, there is about 10% of the cost associated to public health problems caused by flood especially when flood is occurring in urban area.

5.2 Recommendations

i) For Dar es Salaam city authorities and communities in the flood prone areas

 The city authority should think of putting in place proper drainage and sewerage system to avoid both flood and sanitations problems that may result from even rainfall of small intensity  The city authority should implement land use plans to avoid people continuing establishing settlements in the flood prone areas  Despite the study area being in the flood plain, it is still potential for other development if necessary investment can be done. Hence the city should consider SUD system as an option due to the potentiality of this area.  Intervention measures as proposed can be adopted upon thorough analysis on feasibility of the best option.

ii) For further studies

 Further studies is necessary to include other parameters for flood hazard assessment like rate of water level rise and more details in flood duration as well as effects of persistent organic contaminants and heavy metals since the

possibility of such contaminants being in the river is high due to outfalls from industrial effluents  Effects of encroachments and solid wastes dumping in the river valleys should be studied further.  Point sources should be identified and assessed as regards to discharges and sewage characterisations in order to establish proper pollution monitoring plans  Since Msimbazi basin is not the only flood plain in Dar es Salaam city, more studies should be done to cover the whole catchment. This will help in more effective monitoring and control plans for flood risks than dealing with sub- catchments.  Further studies are necessary as regards to pathogens, to identify their types and growth/decay rates in order to know their spatial concentrations at any time and space. This will help understanding for instance how long does such pathogens stay in the environment after being deposited.

REFERENCES

Aarne, V. P. (2003). Wastewater treatment plant design. London, IWA Publishing.

Annette P., et al; (2002). Estimating the Burden of Disease from Water, Sanitation, and Hygiene at a Global Level; World Health Organization, Protection of the Human Environment, Geneva, Switzerland;

Amini, M., et al. (2005). "Continuous soil pollution mapping using fuzzy logic and spatial interpolation." Geoderma

Balica, S. F., et al. (2013). Parametric and physically based modelling techniques for flood risk and vulnerability assessment: A comparison." Environmental Modelling & Software

Benedini, M. (2011). Water Quality Models for Rivers and Streams. State of the Art and Future Perspectives. Eur Water

Brdjanovic, D. (2008). Bilogical Wastewater Treatment - Principles, Modelling and Design. London, IWA Publishing.

Casmiri, D. (2008). Vulnerability of Dar es Salaam city to impacts of climate change. EPMS, Dar es Salaam.

Census, T. N. (2012). National Census for Human and Settlement. Mwananchi. Dar es Salaam, Mwananchi communication Limited.

Chaggu, E. (2002). Excreta Disposal in Dar es Salaam." University of Dar Es Salaam, Dar Es Salaam city

D.E. Porter, et al. (2004). Using geographic information systems and regression analysis to evaluate relationships between land use and fecal coliform bacterial pollution

DHI (2012). A Modelling System for Rivers and Channels-Reference Manual. Denmark, MIKE by DHI

Diadovski, I., et al. (2002). A model for assessment and prognosis of river water quality. Chemical and biochemical engineering quarterly

Douglas, I., et al. (2008). Unjust waters: climate change, flooding and the urban poor in Africa." Environment and Urbanization

Genovese, H. (2006). A Methodological Approach to Land-use based Flood Damage Assessment in Urban Areas: Prague case study. Rome, Italy, European Community.

Havinga, F., et al. (2006). Data-assimilation of hydro-meteorological model input and hydraulic parameters for flood forecasting in the river Rhine.

Kassenga, G. and S. Mbuligwe (2007). Impacts of solid waste disposal site on soil, surface water and groundwater quality in Dar es Salaam City, Tanzania. Journal of Sustainable Development in Africa

Kazinja, A. (2001). Improvement of Water Management in Flood Prone Areas: Case study of Tanzania.

Khandlhela, M. and J. May (2006). Poverty, vulnerability and the impact of flooding in the Limpopo Province, South Africa. Natural Hazards

Lai, Y., et al. (2012). Development of a water quality modelling system for river pollution index and suspended solid loading evaluation. Journal of Hydrology.

Lenhart, S. (2010). Modelling Optimal Intervention Strategies for Cholera. Tennessee, unversity of Tennessee

Lymo, T. (2006). Assessment of Faecal Bacteria Contamination in Sewage and non- Sewage Impacted Mangrove Ecosystems along the Coast of Dar es Salaam.

Mbuligwe, S. and M. Kaseva (2005). Pollution and self-cleansing of an urban river in a developing country: A case study in Dar es Salaam, Tanzania." Environmental management

Mchugh, N. (2007). Eco-health impacts of Urban tropical wastewater in Dar es Salaam- Tanzania: Msimbazi River Case study.

Metcalf, E. (2004). Wastewater Engineering - Treatment and Re-use. New York, McGraw Hill.

Mohammed, M. (2002). Coastal Water Quality and Pollution Assessment. Zanzibar, Unversity of Dar es salaam; Dar Es Salaam Tanzania

MRAN (2005). "Heavy Metals and Biological wastes in Msimbazi river, University of Dar es salaam; Dar Es Salaam-Tanznaia.

Nagels, J., et al. (2002). Faecal contamination over flood events in a pastoral agricultural stream in New Zealand. Water Science & Technology

Ngusaru, A. S. (2000). Tanzania Coastal Management Partnership; University of Zanzibar, Zanzibar

Othman, C. (1999). Peciation of Cadmium, Copper, Lead and Zinc in the Waters of River Msimbazi, Dar es Salaam-Tanzania.

Portier, et al. (1999). Discriminant analysis of ribotype profiles of Escherichia coli for differentiating human and nonhuman sources of fecal pollution. Applied and Environmental Microbiology

Ramin, B. (2009). Slums, climate change and human health in sub-Saharan Africa. Bulletin of the World Health Organization

S.Mgana (2013). Dar es Salaam Rivers rehabilitation and Management Programme. Dar es Salaam, Ministry of Water.

Sauer, A., et al. (2007). Development of a GIS-based risk assessment methodology for flood contaminants. Information technologies in Environmental Engineering, Springer

Saul, A. J., et al. (2010). Integrated Urban Flood Modelling. Flood Risk Science and Management

Seifert, J. (2008). Methods for the evaluation of Direct and Indirect Flood Losses. Toronto, Canada, Institute for Catastrophic Loss Reduction.

Sibil, R., et al. The comparison of different mathematical methods to determine the BOD parameters, a new developed method and impacts of these parameters variations on the design of WWTPs. Applied Mathematical Modelling.

Snead, D. B. (2005). Flood Analysis using MIKE11 Software - Mill Creek Cincinnanti, Ohio. Ohio. UNDP (2011). Dar es Salaam flood, December 2011, Mwananchi News Paper, Dar Es Salaam Tanzania

Vojinovic, Z.,and R. Price (2011). Urban Hydroinformatics-Data, Models and Decision Support for Integrated Urban Water Management. London, IWA Publishing.

Vojinovic, Z., (2012). Flood Risk and Social Justice- From Quantitative to qualitative Flood Risk Assessment and Mitigation. London, IWA Publishing.

Wittig, R., et al. (2007). A study of climate change and anthropogenic impacts in West Africa. Environmental Science and Pollution Research-International, Accra Ghana

Wölz, J., et al. (2008). Changes in toxicity and Ah receptor agonist activity of suspended particulate matter during flood events at the rivers Neckar and Rhine: Environmental Science and Pollution Research, Cologne

APPENDICES

Appendix A-1: Rainfall and flood data

(i) Tidal levels (m) December, 2011

Flood duration (days) 1 2 3 4 5 6 7 8 9 Hours 1 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 2 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 3 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 4 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 5 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 6 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 7 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 8 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 9 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 10 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 11 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 12 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 13 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 14 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 15 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 16 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 17 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 18 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 19 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 20 1.9 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 21 2.1 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 22 2.1 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 23 2.1 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9 24 2.1 2.1 2.2 2.1 1.9 1.9 1.9 1.9 1.9

(ii) Rainfall for last 20-years for Ocean road rain gauge station

Year Rainfall 1991 1,056 1992 1,146 1993 1,155 1994 1,284 1995 1,313 1996 1,267 1997 1,372 1998 1,141

Year Rainfall 1999 1,255 2000 935 2001 881 2002 1,390 2003 585 2004 1,095 2005 901 2006 1,425 2007 842 2008 903 2009 596 2010 964 2011 1,536

(iii) Rainfall for last 20-years for Dar es Salaam port rain gauge station

Year Rainfall 1991 997 1992 982 1993 889 1994 1,218 1995 1,079 1996 977 1997 772 1998 1,008 1999 1,191 2000 819 2001 1,113 2002 1,218 2003 580 2004 881 2005 835 2006 1,312 2007 765 2008 906 2009 461 2010 810 2011 1,383

(iv) Rainfall for last 20-years for Dar es Salaam Airport rain gauge station

Year Rainfall 1991 1,056 1992 1,146 1993 1,155 1994 1,284 1995 1,313 1996 1,267 1997 1,372 1998 1,141 1999 1,255 2000 935 2001 881 2002 1,390 2003 585 2004 1,095 2005 901 2006 1,425 2007 842 2008 903 2009 596 2010 964 2011 1,536 (v) Rainfall time series for twenty years

(v) Average flood depth distribution

Wards Kigogo Jangwani Magomeni Upanga HanaNasif

Flood No. of %ge No. of %g No. of %g No. of %g No. of %g depth peopl peopl e peopl e peopl e people e (m) in e e e e 2011 0 - 1 1 7 2 9 1 12 3 35 3 30 1.0 - 5 52 6 72 6 68 1 14 5 45 1.5 1.5 - 3 34 1 4 1 6 3 27 1 15 2.0 >2.0 1 6 1 15 2 14 3 24 1 10 Total houses 10 10 10 10 10 visited

(vi) Flood mapping

(a) Demarcating area to be mapped

c) Flood depth

(vii) River flow width

(viii) E-coli simulation results

Appendix A-2: River network and flow data

(i) Upstream average discharge for 2011

January 10.78 February 16.23 March 27.37

April 54.92 May 33.18 June 20.13 July 6.89 August 9.87 September 22.40 October 26.34 November 128.59 December 67.70

(ii) Downstream average discharge for 2011

January 13.79 February 20.77 March 35.03 April 70.30 May 42.47 June 25.76 July 8.82 August 12.64 September 28.68 October 33.72 November 164.59 December 86.66

(iii) River network

Node x y Name 1 527805.0 9246422 RIVER_0.00 Upstream 2 528302.4 9246703 RIVER_497.42 3 528846.4 9247615 RIVER_1041.40 4 529172.1 9248540 RIVER_1367.10 5 529625.2 9249132 RIVER_1748.76 6 530006.8 9249374 RIVER_2322.25 7 530218.5 9249107 RIVER_2895.74 Downstream

(iv) Average daily river flow for November and December, 2011

Q WL Q WL Date (m3/s) (m) (m3/s) (m) 1 9.50 1.1 1.53333 1.2 2 5.62 1.1 0.14907 1.2 3 5.62 1.1 1.40556 1.2

4 0.23 1.1 1.40556 1.2 5 5.62 1.1 1.53333 1.2 6 5.62 1.1 1.49074 1.2 7 5.62 1.1 3.51389 1.2 8 5.62 1.1 0.19167 1.2 9 5.62 1.1 0.10648 1.2 10 2.13 1.1 1.40556 1.2 11 4.15 1.1 1.40556 1.2 12 2.00 1.1 0.80926 1.2 13 3.13 1.1 1.40556 1.2 14 0.28 1.1 1.40556 1.2 15 5.62 1.1 1.40556 1.3 16 0.45 1.1 1.40556 1.3 17 1.92 1.1 1.40556 1.3 18 5.62 1.1 1.40556 1.3 19 1.60 1.1 11.862 1.3 20 5.62 1.1 10.712 1.3 21 5.62 1.2 13.6722 1.3 22 5.62 1.2 1.00093 1.3 23 5.62 1.2 0.14907 1.3 24 5.62 1.2 0.87315 1.3 25 5.62 1.2 1.40556 1.2 26 5.62 1.2 1.40556 1.2 27 5.62 1.2 0.0213 1.2 28 0.77 1.2 0.14907 1.2 29 5.62 1.2 0.25556 1.2 30 1.24 1.2 1.40556 1.2 31 1.40556 1.2

(v) River cross-section

Node width Elevations (m) L Middle-1 Middle-2 R 1 48 15 9 11 13 2 59 10 8 8 11 3 62 7 6 6.6 7 4 70 6 6 5.8 6.6 5 76 5.6 4 6 6.6 6 82 6 5 4 6 7 104 5 4 3 4

(vi) Estimating river width and flood depth

Appendix A-3: Social and diseases data

(i) Population data

Popn Av. Growth rate Year difference; n=2 Projected Ward 2012 Population, 2014 Kigogo 57,613.00 2.8 2 60,884.50 Jangwani 17,647.00 2.8 2 18,649.07 Magomeni 24,400.00 2.8 2 25,785.53 Upanga 13,476.00 2.8 2 14,241.22 west Hanna Nasif 37,115.00 2.8 2 39,222.54

(ii) Cholera cases in 2011

Kigogo Jangwani Magomeni Upanga west Hanna Nasif Jan 114 203 192 45 76 Feb 52 34 41 29 24 Mar 67 107 96 48 52 Apr 125 202 218 92 86 May 174 162 236 92 88 Jun 91 106 124 44 37 Jul 23 14 28 9 12 Aug 37 22 19 11 17 Sep 82 77 49 29 36

Oct 281 201 319 104 133 Nov 302 295 390 144 97 Dec 334 304 481 206 214 Total 1682 1727 2193 853 872

(iii) Average increases of water-borne disease cases during rainfall season

Type of disease Reported cases Fatalities %ge each disease Ascariasis 5631 0 12 Diarrhoea 8706 2 19 Hookworms 9048 0 20 Schistosomiasis 249 0 1 Cholera 21981 16 48 Typhoid 618 7 1 Total 46,233 25

APPENDIX A-4: Flood damage public health impacts for individual wards/Nodes

i) Flood damages

a) Kigogo Street

Flood depth(m) No. of Housing units Total damage cost (USD) 0.0 – 1.0 1 1,400 1.0 – 1.5 5 36,400 1.5 – 2.0 3 40,800 Above 2.0 1 12,000 Total damage cost 10 90,600

b) Jangwani

Flood depth(m) No. of Housing units Total damage cost (USD) 0.0 – 1.0 12 24,264 1.0 – 1.5 97 679,392 1.5 – 2.0 5 64,704 Above 2.0 20 404,400 Total damage cost 135 1,172,760

c) Magomeni

Flood depth(m) No. of Housing units Total damage cost (USD) 0.0 – 1.0 16 32,352 1.0 – 1.5 92 641,648 1.5 – 2.0 8 97,056 Above 2.0 19 377,440 Total damage 135 1,148,496

d) Upanga

Flood depth(m) No. of Housing units Total damage cost (USD) 0.0 – 1.0 38 75,740 1.0 – 1.5 15 106,036 1.5 – 2.0 29 350,568 Above 2.0 26 519,360 Total damage 108 1,051,704

e) Hana Nasif

Flood depth(m) No. of Housing units Total damage cost (USD) 0.0 – 1.0 36 72,900 1.0 – 1.5 55 382,725 1.5 – 2.0 18 218,700 Above 2.0 12 243,000 Total damage 108 917,325

ii) Public health problems for individual wards/Nodes

a) Kigogo

Type of disease No. of people exposed Ascariasis 0 Diarrhoea 0 Hookworms 1 Schistosomiasis 0 Cholera 2 Typhoid 0 Total 3

b) Jangwani

Type of disease No. of people exposed Ascariasis 34 Diarrhoea 54 Hookworms 57 Schistosomiasis 3 Cholera 136 Typhoid 3 Total 286

c) Magomeni

Type of disease No. of people exposed Ascariasis 34 Diarrhoea 54 Hookworms 57 Schistosomiasis 3 Cholera 136 Typhoid 3 Total 286

d) Upanga

Table 4.14: Possible water-borne diseases at Upanga area

Type of disease No. of people exposed Ascariasis 44 Diarrhoea 70 Hookworms 74 Schistosomiasis 4 Cholera 178 Typhoid 4 Total 374 e) Hana Nasif

Type of disease No. of people exposed Ascariasis 108 Diarrhoea 171 Hookworms 180 Schistosomiasis 9 Cholera 433 Typhoid 9 Total 911

Appendix A-4: Model calibrations

Rainfall-runoff calibration

Appendix A-5: Estimating rainfall returns period using hydrostat.

Hydrostat output results

PROGRAM HYDSTAT OUTPUT

INPUT NVAR = 1

Hs at Msimbazi

NUMBER OF POINTS= 21 DISTRIBUTIO N TYPE =

0.13 2.19 4.51 4.1 5.19 6.56 2.11 1.67 2.33 1.64 3.63 6.92 3.72 2.68 2.57 3.72 2.65 4.54 4.74 4.12 3.24

Hs at Msimbazi

PARAMETERS OF THE NORMAL DISTRIBUTION (NO. 1)

(UNBIASED MOMENT ESTIMATORS)

LOCATION PARAMETER= 11.80762 (MEAN) SCALE PARAMETER= 2.87764 (STD.DEV.)

ORDER CUMULATIVE DISTRBUTION

I P(X>Xi) Xi

1 99.9 0.3076 2 99.5 2.729 3 99 3.996 4 95 2.729 5 90 3.996 6 80 5.0419 7 60 5.3466 8 50 2.729 9 20 3.996 10 10 5.0419 11 5 5.3466 12 4 2.729 13 2 3.996 14 1 5.0419 15 0.5 5.3466 16 0.2 2.729 17 0.1 3.996 18 0.05 5.0419 19 0.02 5.3466 20 0.01 7.2