Floods Early Warning System

Factors Associated with Uptake of Floods Early Warning Information in Butaleja District, Eastern Uganda

Akumu Jennifer

2015/HD07/2405U

Supervisors

Dr. Roy William Mayega

Mr. Abdullah Ali Halage

A Dissertation Submitted to Makerere University School of Public Health in Partial Fulfillment of the Requirements for Master of Public Health Disaster Management, Makerere University – Kampala

November, 2018

ACKNOWLEDGEMENTS My very sincere gratitude to my supervisors; Dr. Roy William Mayega and Mr. Abdullah Ali Halage, for their devotion, patience, skillful guidance, mentorship and time spent to read through my work despite their busy schedules. In a special way, I convey my sincere appreciation to Prof. Christopher Garimoi Orach whom I would consult from time to time when I needed guidance. Thank you so much for the effort you have given this work. To all my lecturers who supported me through this program, very big appreciation to you. With the knowledge and skills which you have imparted in me, I know I will add value to the society I serve.

Special thanks to my family, my husband and my children. I appreciate all the support you rendered me in terms of finance, time and moral support. To Mummy and all my brothers, I appreciate you all for the encouragement. To all my classmates and friends who supported me in one way or the other especially Lucy, Alice, Juliet, Fiston, Charles, Brenda, Susan, Caro, Abii, David, Thomas and Chris am surely humbled.

I would like to appreciate my data collection team in Butaleja. Without you, I would not have managed.

I cannot forget the constant reminder by my Late Daddy, Mr. Okee Maurice who would tell me from time to time to do my master degree. Your desire for quality education will forever live in us.

Above all, I thank God for the gift of life, protection, strength and wisdom which He has graciously given to me.

TABLE OF CONTENTS DECLARATION ...... i ACKNOWLEDGEMENTS ...... ii TABLE OF CONTENTS ...... iii LIST OF TABLES ...... vi ACRONYMS AND ABBREVIATIONS ...... vii OPERATIONAL DEFINITIONS ...... viii ABSTRACT………………………………………………………………………………………………..x

CHAPTER ONE ...... 1 1.0 Introduction and Background ...... 1 1.1 Introduction ...... 1 1.2 Background ...... 2 CHAPTER TWO ...... 5 2.0 Literature Review ...... 5 2.1 Introduction ...... 5 2.2 Floods Early Warning ...... 6 2.3 Floods Early Warning Information ...... 7 2.4 Factors affecting uptake of Floods Early Warning Information (FEWI)...... 8 2.4.1 The uptake of floods early warning information ...... 8 2.4.2 The individual factors associated with uptake of floods early warning information ...... 9 2.4.3 System factors ...... 10 2.4.4 The environmental factors associated with floods early warning information ...... 11 2.4.5 The practices and perceptions associated with floods early warning information ...... 11 CHAPTER THREE ...... 13 3.0 Statement of the problem, Justification, Research questions and Conceptual Framework ...... 13 3.1 Statement of the problem ...... 13 3.2 Justification ...... 14 3.3 Research questions ...... 14 3.4 Conceptual Framework ...... 15 Figure 1: Conceptual Framework of factors associated with uptake of floods early warning information in Butaleja district, eastern Uganda ...... 15 Narrative of the Conceptual Framework ...... 15

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CHAPTER FOUR ...... 17 4.0 Study objectives ...... 17 4.1 General objective ...... 17 4.2 Specific objectives ...... 17 CHAPTER FIVE ...... 18 5.0 Methodology ...... 18 5.1 Study area ...... 18 5.2 Study population ...... 18 5.3 Study design ...... 18 5.4 Sample size ...... 19 5.5 Sampling procedure ...... 20 5.6 Selection criteria ...... 21 5.6.1 Inclusion criteria...... 21 5.6.2 Exclusion criteria ...... 21 5.7 Study variables ...... 22 5.7.1 Dependent variables ...... 22 5.7.2 Independent variables ...... 22 5.8 Data collection procedures ...... 22 5.9 Data management and analysis ...... 23 5.9.1 Data management ...... 23 5.9.2 Data analysis ...... 23 5.9.3 Quality control ...... 24 5.9.4 Field editing of data...... 24 5.9.5 Missing data ...... 24 5.10 Ethical considerations ...... 25 5.11 Study limitations ...... 25 5.12 Dissemination ...... 26 CHAPTER SIX ...... 27 6.0 Results...... 27 6.1 Socio-demographic characteristics of respondents ...... 27 6.2 Knowledge and experiences about floods early warning information ...... 29 6.3 Attitudes toward floods early warning information ...... 32 6.4 System‘s factors affecting uptake of floods early warning information ...... 33

6.5 Environmental factors affecting uptake of floods early warning information...... 34 6.6 Participants‘ practices and perceptions regarding floods early warning information...... 34 6.7 Factors associated with uptake of floods early warning information ...... 37 6.7.1 Socio-demographic factors associated with uptake of floods early warning information; . 37 6.7.2 Knowledge and experiences associated with floods early warning information ...... 38 6.7.3 Attitudes associated with uptake of floods early warning information ...... 41 6.7.4 System‘s factors affecting uptake of floods early warning information...... 42 6.7.5 Environmental factors associated with uptake of floods early warning information ...... 42 6.7.6 Practices and perceptions associated with uptake of floods early warning information; ... 43 6.8 Factors associated with uptake of floods early warning information ...... 46 CHAPTER SEVEN ...... 48 7.0 Discussion ...... 48 7.1 Uptake of floods early warning information ...... 48 7.2 Socio-demographic factors associated with uptake of floods early warning information ...... 49 7.3 Knowledge and experiences on floods early warning information ...... 50 7.4 Attitudes toward floods early warning information ...... 52 7.5 System‘s factors affecting uptake of floods early warning information ...... 53 7.6 Environmental factors associated with uptake of floods early warning information ...... 53 7.7 Practices and perceptions associated with uptake of floods early warning information ...... 54 CHAPTER EIGHT ...... 56 8.0 CONCLUSIONS AND RECOMMENDATIONS ...... 56 8.1 Conclusions ...... 56 8.2 Recommendations ...... 57 REFERENCE ...... 58 APPENDICES ...... 63 Appendix I CONSENT FORM ...... 63 APPENDIX IIa: STRUCTURED QUESTIONNAIRE ...... 66 APPENDIX IIb: STRUCTURED QUESTIONNAIRE ...... 83

LIST OF TABLES Table 1: Sub County Population…..………………………………………….………………..………....20

Table 2: Village population………...………………..…………………………………………………....20

Table 3: Key informant categories……………………...………………………………………………...21

Table 4: Socio demographic characteristics of respondents………………….…………………………..28

Table 5: Knowledge and experiences about floods early warning information………………………….29

Table 6: Attitudes towards floods early warning information……………………………..……………..32

Table 7: Participants‘ practices and perceptions regarding floods early warning information..……...... 35

Table 8: Bi-variable analysis of socio-demographic factors associated with uptake of floods early warning information………………………………..……………………………………………………...38

Table 9: Bi-variable analysis of knowledge and experiences associated with uptake of floods early warning information……………..……………………………………………………..……….………....39

Table 10: Bi-variable analysis of attitudes associated with uptake of floods early warning information..41

Table 11: Bi-variable analysis of environmental factors associated with uptake of floods early warning information………………………………………………………………………………………………...43

Table 12: Bi-variable analysis of practices and perceptions associated with uptake of floods early warning information ……………………………………………………………………………………...45

Table 13: Multi-variable analysis of factors associated with uptake of floods early warning information.47

ACRONYMS AND ABBREVIATIONS AAL Average Annual Loss

CA Constitutional Assembly

EWS Early Warning System

FEWS Floods Early Warning System

FGD Focused Group Discussion

HFA Hyogo Framework for Action

ITU International Telecommunications Union

KI Key Informant

L.C.1 Local Council 1

NAPA National Adaptation Programme of Action

OPM Office of the Prime Minister

PI Principal Investigator

UACE Uganda Advanced Certificate of Education

UCC Uganda Communications Commission

UNDP United Nations Development Program

UNFCCC United Nations Framework Convention on Climate Change

UNOCHA United Nations Office for the Coordination of Humanitarian Affairs

OCHA Office for the Coordination of Humanitarian Affairs

VHT Village Health Team

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OPERATIONAL DEFINITIONS Capacity refers to the resources available to cope with a threat.

Climate refers to the average weather condition of a place or region over time.

Climate change refers to alteration in the weather condition majorly due to global warming.

Early Warning System refers to a holistic mechanism to ensure information is passed prior to a disaster in order to aid preparedness and response.

Early Warning Information refers to the facts on upcoming event or disaster in order to ensure preparedness and response plans are in place.

Floods Early Warning Information refers to official announcement of on upcoming floods.

Emergency refers to a sudden serious event that causes harm to the population and the environment and requires immediate action.

Disaster is a serious disruption in the normal functioning of a society or community that cause widespread loss of life, socio-economic, physical and environmental damages that exceeds the capacity of the affected community to cope using its own resources. Disaster Risk = Hazard x Vulnerability Capacity Flood refers to accumulation of excess amount of water in a place that is meant to be relatively dry.

Hazard refers to an event that has a potential to cause damage.

Modern flood early warning in formation refers to information received from meteorological centers and the early warning sensor installed on river Manafwa.

Resilience refers to capacity of an individual or community to anticipate, absorb and recover from hazards and its stresses without compromising its long term plans.

Traditional flood early warning in formation refers to indigenous knowledge that indicates whether or not it will flood.

Uptake refers to the commitment to hear, understand, believe, personalize and respond to warning information.

Vulnerability refers to the susceptibility to the damaging effects of a disaster.

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ABSTRACT

Background: Globally, millions of people are exposed to floods each year. Efforts to reduce the impact of floods through Floods Early Warning Information (FEWI) are required. Butaleja district which has a vast portion of the total land area covered with wetlands, experiences a lot of floods. The district is generally flat and in a low lying area, this leads to recurrent floods during rainy seasons. The general objective of this study was to assess the factors associated with uptake of floods early warning information in Butaleja district.

Methods: This was a cross-sectional study. It was conducted in four sub counties in Butaleja district. The study population was head of households. A total of 537 respondents, on average 135 per Sub County were randomly and proportionately selected and interviewed. Eight (8) Key Informants were interviewed. Quantitative data was captured using EPI info and analyzed using STATA version 13. Univariate, bi-variate and multivatiate analysis were done. Qualitative data were analyzed using content analysis.

Results: The mean age for the respondents was 41.1 years with a standard deviation of 14.7. The uptake of floods early warning information was at 56% (301/537). About 68.5% (366/534) of the respondents use modern floods early warning information. Majority of the respondents 527 (98.1%) had received floods early warning information regarding their community. Factors associated with uptake of floods early warning information were frequency of floods ranging from 6 months to 1 year (Adjusted PR 1.19, 95% CI 1.03-1.39), those who believe that floods are temporary (Adjusted PR 0.39, 95% CI 0.17-0.89) and those who evacuate their home in times of floods (Adjusted PR 1.45, 95% CI 1.21-1.75). From the qualitative data, low public awareness, inadequate dissemination and misconception influenced uptake of floods early warning information.

Conclusion: Although most (9.81 in 10) respondents receive floods early warning information, just over half reported high uptake. Low public awareness, inadequate dissemination and misconceptions should be addressed to improve uptake of floods early warning information.

CHAPTER ONE

1.0 Introduction and Background

1.1 Introduction

Globally, over 6,000 disasters were recorded accounting for approximately 900,000 deaths, US$ 738 billion material losses and 2,500 million people were affected (Basha, Ravela et al. 2008). Most of these disasters are hydro- meteorological in nature. In 2008, this was identified as a major setback in the economic development of any country (Basha, Ravela et al. 2008). On average 53.2 million people are exposed to floods each year, and in 2009 alone, floods affected about 39.4 million people in Central China, making it the worst disaster (Peduzzi, Chatenoux et al. 2010).

Early warning refers to the provision of information on circumstances such as disasters, where the information given can result in the reduction of the possible risks associated. It is a component in disaster risk reduction which when well conducted and utilized, can prevent loss of life and reduce economic and material impacts of possible disasters (Van Aalst, Cannon et al. 2008). Effective early warning system involves the communities at risk, public education and sensitization on the disaster risks, dissemination of warning information in order to ensure preparedness (Garcia and Fearnley 2012). Early warning system (EWS) consists of four main components which include the risk knowledge, monitoring and evaluation services, dissemination and communication as well as response. For a successful early warning system, each of these components must work effectively and efficiently (Plate 2007).

Early warning information for the natural hazards needs to focus not only on the sound scientific and technical basis but also emphasize strong focus on people who are exposed to risks where by all the relevant factors in that risk are taken into consideration. A good early warning system therefore requires a holistic approach, a question still remains whether this approach is realistic and effective (Basher 2006). Early Warning System enables preparedness, prevention and mitigation plans to be put in place prior to disasters such as floods, by providing fast and accurate information for distinction of normal and abnormal variations (Storey, van der Gaag et al. 2011).

Floods result in massive loss of life and property. Therefore, early warning to communities about upcoming floods offers effective solution since it gives sufficient time to evacuate and protect their property from possible damages (Magomelo, Chikwiriro et al. 2014). Floods are associated with risk and risk cannot be completely eliminated in our lives. However, efforts can be made to reduce the impacts through effective early warning information. Short term predictions have been seen to work better than the long term predictions, and high income countries have implemented better early warnings than the low income countries like Uganda (Zschau and Küppers 2013).

Butaleja district which has about 32% of its total area covered with wetlands experiences a lot of floods (CAO-Butaleja 2009). In March 2010, over 400 people were killed in Bududa district as a result of heavy rains that flooded the area, causing landslides (Atuyambe, Ediau et al. 2011). Despite the periodic early warning information passed by the Meteorological Authority, prevention and mitigation of the impacts of floods has remained unsuccessful in Butaleja district. The district is seasonally experiencing floods with all it‘s related consequences such as disease outbreaks, break down of infrastructure (including schools, hospitals and roads), damage to property, and farmland among others (Armah, Yawson et al. 2010). Understanding the factors associated with the uptake of floods early warning information in Uganda is required, so as to prevent loss of life and reduce economic and material impacts of floods.

1.2 Background

The Global climate change has resulted in widespread drought and floods. Despite all these, John H. Sorensen recognized that there is no 100% reliable warning system in the world. In his report he stated that even United States has no comprehensive national warning strategy and that public warnings are done by different government and private sectors. Much as there is improvement in the dissemination of forecasts and warnings of some hazards such as floods, hurricanes and volcanic eruptions, the warning systems still remain less reliable (Sorensen 2000). The catastrophic flooding in the United States which caused damage to public structures, infrastructure and utilities resulted in an estimated damage of $90 billion in 2005. Nevertheless, uptake of early warning information could have helped in reducing the damages (Jonkman, Bočkarjova et al. 2008).

In Africa, the urban poor face severe flooding due to the unplanned settlement, coupled with improper waste disposal especially in the slum areas. While the local people adapt to floods, it is important for the national and international governments and organizations to put in place policies that will strengthen early warning system (Thomson and Connor 2001). In Ethiopia, 75% of the landmass is estimated to have malaria and this has been attributed to floods. However, generalization of the study findings is difficult since it was restricted to the highlands of Ethiopia (Midekisa, Senay et al. 2012). A study conducted around the communities living around Lake Victoria indicates their vulnerability to floods and floods related diseases like cholera. Therefore interventions which will reduce their vulnerability such as effective early warning information are recommended (Olago, Marshall et al. 2007).

The Hyogo Framework for Action (HFA) emphasizes the need to identify, assess and monitor disaster risks and enhancement of early warning systems in order to build resilience of nations and communities (Twigg 2009). Besides, Uganda submitted its National Adaptation Programme of Action (NAPA) to the United Nations Framework Convention on Climate Change (UNFCCC) in 2007. This indicated the country‘s commitment to address climate change and its impacts, where strengthening meteorological services including early warning system is key (Nyasimi, Radeny et al. 2016). During the Constituent Assembly (CA), there was a call for an end to unbearable and persistent loss of life, suffering and disruption of economic activities by disaster (OPM 2010, Mujuzi 2013). The emphasis here was on ensuring effective early warning systems are in place.

Butaleja district has been facing recurrent floods. This is attributed to the heavy water flow that comes from the Elgon region through river Manafwa. Since Butaleja district is generally flat and in a low lying area, this leads to recurrent floods during rainy seasons. Besides, the main economic activity in Butaleja is rice growing in the low lying areas. Coupled with other human activities like deforestation, Butaleja district face frequent floods leading to widespread destruction of crops, infrastructure and other socio-economic benefits.

A study conducted in conjunction with Uganda Red Cross Society (URCS) in 2013, emphasized the need to put up a sensor system on river Manafwa. The study established that since flooding in Butaleja district always arise when it rains upstream of river Manafwa in the Elgon region, putting up a sensor to detect increase in amount of water on river Manafwa would alert people in

Butaleja of possible flooding (Cecinati 2013). Analysis of precipitation level upstream which is necessary to result in flooding downstream was conducted and a good site for the establishment of the river gauge was established, to provide floods early warning downstream in Butaleja (Kaatz 2014).

The Uganda Communications Commission (UCC) together with the International Telecommunications Union (ITU) installed an early warning system which uses two sensor points; one at Butaleja District Headquarter, with its siren at Namulo Primary school and its sensor at Namulo Bridge; and the other at Himutu Sub county Headquarters, with its siren at Bugombe Primary school and its sensor at Masulula Bridge. The system which has been handed over to Butaleja District Local government is aimed at alerting the community of upcoming floods in order to aid preparedness and reduce damage to lives and property. But the uptake of it‘s messages is still unknown since floods continue to affect the people of Butaleja district (UCC 2015).

CHAPTER TWO

2.0 Literature Review

2.1 Introduction

Flood is one of the most disastrous hazards which is majorly attributed to climate change (Pulwarty and Sivakumar 2014). A study conducted in Europe explored how people respond to floods warning information, and then how the response impacts on the effectiveness of the flood warning information. It is not therefore simply the passing of the information, but the way the information is taken and utilized that determines the effectiveness (Parker, Priest et al. 2009). Furthermore, a study conducted in eastern and western Uganda indicates that the resulting effects of floods include environmental degradation, food insecurity, poverty, damage to infrastructure, morbidity and mortality. Uptake of floods early warning system would be one basic approach in resilience building (Mayega, Tumuhamye et al. 2015).

There are two main types of floods, which are flash floods and slow rising floods. Flash floods have been one of the most significant hazards in Europe. The magnitude of flash floods is greater in the Mediterranean countries than the Continental countries (Gaume, Bain et al. 2009). Flash floods are so difficult to monitor since they develop so fast that makes it difficult to measure scale. However, it is always characterized by massive loss of lives, due to its speed of occurrence and the underlying uncertainty surrounding its occurrence (Borga, Gaume et al. 2008). The study provides limited insight into the hydrological control of flash-floods response.

On the other hand, slow on set floods is more destructive to the economic base and leads to disease outbreak, but it does not result in massive loss of lives due to its nature that allows people to relocate to safer zones or find other coping mechanisms (Laczko and Aghazarm 2009). The study did not explore the driving forces for slow on-set environmental changes.

Human being is becoming more vulnerable to natural disasters mainly due to rapid population growth and increased globalization which has made man more susceptible to disasters like floods. In the United States of America, flash floods is the leading cause of weather - related deaths where approximately 200 deaths are recorded every year (Huppert and Sparks 2006). According to Cannon, floods are the most common hazard affecting the world and the effects are

5 greater among the poor people and in the developing countries like Uganda. This could be attributed to the individual characteristics which may subject one to ill health such as, low economic status and poor social response which may as well impact on the uptake of floods early warning information (Cannon 1993).

2.2 Floods Early Warning

There are various forms of early warning systems which include the traditional and the modern early warning systems. The traditional early warning systems use the indigenous knowledge to tell whether there will be floods or not (Basha, Ravela et al. 2008). While the modern early warning systems use modern technology to anticipate floods (Krzhizhanovskaya, Shirshov et al. 2011).

There is high level of humanitarian needs which impacts greatly on humanitarian health. Utilization of early warning, preparedness and response capacity is relevant in ensuring improved health is attained and maintained during and after humanitarian crisis, though the study emphasized more on the response strategy than on early warning strategy (Olu, Usman et al. 2015). In Mozambique, it was reported that since 1980 there were seven major droughts and seven major floods which all happened as a result of extreme weather events. Much as the government of Mozambique has a policy to facilitate preparedness for floods, their activities are geared more towards response leaving out other components of floods early warning (Hellmuth, Moorhead et al. 2007).

There must be adequate time period for the floods warnings to be effective, so as to give time to plan for mitigation measures ahead of the flood. The mitigation measures may include evacuations, evaluation of flood threat, notification of the emergency personnel and the public which will ensure people‘s health and property are protected. The study did not analyze the effectiveness of these mitigation measures (Carsell, Pingel et al. 2004).

In western Uganda, a study was conducted to assess the inter-relationship between extreme weather conditions resulting in floods, with malaria. Ascertaining the actual malaria burden attributed to floods was instrumental in assessing the effects of floods (Boyce, Reyes et al. 2016). Furthermore, an evidence-based and policy-relevant approach in understanding current and future climatic hazards such as floods and droughts was conducted. The study highlights the

6 importance of adjusting centralized control with bottom-up approaches to enhance reduction of floods related impacts (Huntjens, Pahl‐Wostl et al. 2011).

Considering the widespread socio-economic and physical losses resulting from flooding in Kampala, recommendations have been made to make the city more resilient and put in place policies to support resilient building. Emphasis here is to be innovative in planning, institutionalize readiness and community resilience so as to have reduced floods impacts, but there is call for more support from the City Authority and the government of Uganda (Lwasa 2010). Besides, fatalities related to floods as well as their associated economic and environmental losses have drastically increased over the past half-century, leading to the growing concern to identify the causes of such increased flood damages. Intensive and unplanned human settlement in the flood-prone areas was identified as a major cause of floods. Putting in place policies that discourage human settlement and the introduction of early warning information will help reduce floods and its related impacts not only in the city but in the floods prone areas like Butaleja as well (Di Baldassarre, Montanari et al. 2010).

2.3 Floods Early Warning Information

A study conducted in Europe explored how people respond to flood warning information, and then how the response impacts on the effectiveness of the flood warning information. It is not therefore simply the passing of the information, but the way the information will be taken and utilized that determines the effectiveness (Parker, Priest et al. 2009). Early warning helps in building resilience by ensuring the people get timely and adequate information necessary to protect them from the likely hazards which always have other related consequences including disease outbreaks.

The underlying issue is that people and the institutions have knowledge about the threat, can monitor trends, they are able to communicate and have the capacity to respond. This can be supported by having good scientific base and conducive political ground (Collins 2009). Solutions that would address reduction in floods impacts would greatly save not only lives but also the economy.

2.4 Factors affecting uptake of Floods Early Warning Information (FEWI).

2.4.1 The uptake of floods early warning information Globally, early warning information is a strategy developed to reduce disaster risks. In Australia, there has been a wide range of destruction to lives and property. As the country draws a strategy to go beyond relief and recovery, more focus is on early warning which is seen as a remedy to disaster risks. The uptake of the warning information is not 100%, leading to disaster related losses (Gunasekera, Plummer et al. 2005). The poor uptake of early warning information has been associated with the devastating impacts of the Indian Ocean tsunami of December 26th 2004 which is one of the worst global disasters (Samarajiva 2005). This disaster killed over 280,931 people and had an estimated damage of USD 4.45 billion. High income countries have implemented better early warnings than the low income countries like Uganda (Zschau and Küppers 2013).

In Sri Lanka, the level of uptake of floods early warning information is very low. This has been attributed to the fact that it is difficult to establish and sustain a reliable source of warning information. This makes the communities reluctant to respond in a timely manner. The use of mobile phone for early warning alerts or information was therefore recommended since most people own and use mobile phone on a regular basis (Gow and Waidyanatha 2011).

The Kenya Meteorological Services (KMS) has put in place early warning system which gives early warning information to the community. This has reinforced floods early warning information. Despite this, the response to the early warning information is still doubted, since there are several damages and deaths attributed to floods in Kenya. However there is need to strengthen the existing structure so as to improve monitoring, processing and dissemination of information to the communities (Shilenje and Ogwang 2015). There must be adequate time period for the floods warnings to be effective, so as to give time to plan for mitigation measures ahead of the flood. The mitigation measures may include evacuations, evaluation of flood threat, notification of the emergency personnel and the public which will ensure people‘s health and property are protected (Carsell, Pingel et al. 2004). The study did not analyze the effectiveness of these mitigation measures.

In Uganda, it has been noted that the success of early warning depends on a multi-sectoral and interdisciplinary response and evaluation. But the links between the community based approach with the national and global early warning systems are relatively weak, which could be strengthened through effective knowledge management and actions (Pulwarty and Sivakumar 2014). There was a lot attributed to false and inadequate early warning leading to untold losses yet having an integrated system which captures the risk knowledge, monitoring and evaluation services, dissemination and communication as well as response capability would support making the early warning information more practical. Majority of the people affected either did not receive the early warning information or they received it too late to effect positive response (Samarajiva 2005). Understanding the level of uptake of floods early warning information in Butaleja district will build the knowledge base.

2.4.2 The individual factors associated with uptake of floods early warning information Analysis of floods‘ impacts shows how systems, people‘s health, infrastructure and the economy can be affected. However, any flood vulnerability analysis requires information on elements at risk indicators, exposure indicators and susceptibility indicators. Much as these are known, knowledge, attitude and perception of the individuals may affect their response, as well as individual characteristics influenced by their age, gender, race, education, social relations, poverty, structure, institutional development and proportion of the population with special needs (Messner and Meyer 2006). Besides, it has been argued that individuals who have low risk perception are less likely to respond to warnings and take up preparedness measures compared to those with high risk perception. Therefore, it is not the power of early warning information, but rather the power of the risks expected that determines how individuals respond to early warnings. Furthermore, it has been noted that risk perception is influenced by risk governance and risk communication in ensuring preparedness actions (Wachinger, Renn et al. 2013). The factors responsible for determining risk perception include risk, information, personal and context factors.

People‘s reactions to warnings of impending disaster depend on a number of factors which include their understanding about the risk and ideas about what they can do before they take protective action. To reach the action level, they go through phases such as hearing a warning, forming a personal understanding of what was meant by the warning, believing in the

9 information conveyed, personalizing the risk and deciding on response (Mileti 1995). According to Mileti, there are factors that influence public response which include warning sources, warning message consistencies, message accuracy, warning clarity, certainty of the message, sufficient information, guidance, warning frequency, risk location information and channel of communication. The study however did not assess the level of uptake in relation to the factors.

A study conducted in Eastern Uganda found that 96% of the respondents confirmed that they are aware of the changes in climate change which directly relates to floods and drought, and that different communication channels are used to disseminate warning information ranging from straight talk (direct talk), radios, cell phones, posters and documentaries. Of all those, only straight talk was recommended since the other communication channels mentioned had challenges ranging from costs, coverage, accessibility and literacy level (Kansiime 2012).

Uganda is constantly experiencing short term and long term danger from floods. The Meteorological Authority has been passing floods early warning information, but these have not fully protected the people as it was intended. A study identified some of the challenges facing early warning systems such as defining roles and responsibilities, targeted and sustainable financing, institutionalization and integration, warning interpretation at community level, political obstacle, dissemination of information, accuracy and appropriateness, weaknesses in national or local capacity, narrow focus on preparedness and distinction between early warning systems for slow and rapid onset emergencies. However, these challenges are limited, yet have not been clearly explained (Brown, Cornforth et al. 2014). This study intends to explore the factors associated with the uptake of floods early warning information in Butaleja district.

2.4.3 System factors System‘s factor plays a big role in the uptake of floods early warning system. A study conducted by Begum et al. 2014, emphasized the importance of establishing an effective human resource to facilitate early warning information, as one way to ensure uptake of floods early warning information. This does not only include the technical human resource but also community based human resources at community level (Begum, Sarkar et al. 2014). The study however, did not explore other factors that can affect uptake of floods early warning information other than the human resources. In China, over 90% of the communities take precautions prior to floods to ensure the most minimal damages to their lives and property besides receiving government

10 support like releasing early warning information, post-disaster services, technical assistance, financial assistance and physical support helps to improve adaptation measures (Liang, Jiang et al. 2017).

2.4.4 The environmental factors associated with floods early warning information A study conducted in Italy, found out that environmental factors determines how early warning information are perceived. For instance when floods early warning information are passed during sunny day, people relax and don‘t imagine the feasibility of floods occurring. People respond to floods threats when they see the weather change and become cloudy. Besides, the physical location of those at risk also impacts on response to warning information. (Mileti 1995). Individuals in the flood plain will always monitor the floods themselves and respond to the warning signs and information. Therefore the topography of a place impacts on the way communities respond to floods early warning (Parker, Priest et al. 2009)

In Uganda, uncoordinated city planning that resulted in construction of road infrastructure and buildings along drainage channels lead to seasonal flooding around the city especially in Bwaise. Despite the warning information got, the communities always do not respond adequately and effectively, due to lack of alternative or due to lack of trust in the warning information (Douglas, Alam et al. 2008).

2.4.5 The practices and perceptions associated with floods early warning information The social setting of a community impacts on their response to early warning information. A family which is united during disaster responds better to warning information. Besides, the activities they are engaged in at a time they receive the warning information, impacts on how they act or respond to the information (Mileti 1995). However, no critical analysis was done to explore the linkages between the different activities and uptake of floods early warning information.

Moderate flood is important in Butaleja district in that it supports rice growing which is the main economic activity for the district. However, too much floods turn out to be destructive to human lives as well as socio-economic activities (Kijima 2012). In an attempt to manage climate change which resulted in reduced rains, the government introduced upland rice. But this was less

11 preferred by the farmers, since it is even less preferred by the buyers who always prefer the lowland rice (Oonyu 2011). This as well affects uptake of floods early warning information.

CHAPTER THREE

3.0 Statement of the problem, Justification, Research questions and Conceptual Framework

3.1 Statement of the problem Despite the existence of floods early warning system in Butaleja district, the uptake of its information is thought to be poor. There is limited information on uptake of floods early warning information in the district. Butaleja district is highly prone to severe flooding every year, which pose a lot of socio-economic burden to the district (Aboda 2012). According to UNOCHA Uganda, about 2,159 school pupils from Mazimasa and Himutu sub counties, Butaleja district were displaced by floods in 2010 and many lives were lost (OCHA 2010).

Poor uptake of floods early warning information results in damages and losses associated with floods which have both direct impacts such as loss of lives, damage to property and environmental damage, while the indirect impacts include disease outbreak. The most affected people are the women, children, elderly as well as the farmers who always risk losing their crops to floods.

There are several challenges relating to floods early warning information including; defining roles and responsibilities, institutionalization and integration, political obstacle, warning interpretation at community level, dissemination of information, targeted and sustainable financing, narrow focus on preparedness, accuracy and appropriateness, weaknesses in national or local capacity and distinction between early warning information for slow and rapid onset emergencies.

In 2014, the Uganda Communications Commission (UCC) together with the International Telecommunications Union (ITU) installed an early warning system on river Manafwa. The system which has been handed over to Butaleja District Local government is meant to alert the community of upcoming floods in order to aid preparedness and reduce damage to lives and property (UCC 2015). The impact of the early warning information is however not being felt since the installation of the early warning system in Butaleja district.

Considering the above situation, it is important to know the level of uptake and factors associated with the uptake of floods early warning information so as to avert likely impacts of floods. Current knowledge base on uptake of floods early warning system in Uganda is limited and cannot adequately support good planning. Providing evidence based solution, supported with good data will be necessary. This study will therefore assess the factors associated with uptake of floods early warning information so as to reduce floods related impacts in Butaleja District, Eastern Uganda.

3.2 Justification Despite the global, national and community efforts to conduct early warning system, a lot still remains in question as to whether this has been successful or not. Although there are policies in place to facilitate preparedness, their activities are geared towards response leaving out the other components of floods early warning (Hellmuth, Moorhead et al. 2007).

Floods are associated with environmental degradation, food insecurity, poverty, damage to infrastructure, morbidity and mortality. Uptake of floods early warning information would be one basic approach in resilience building (Mayega, Tumuhamye et al. 2015).

Butaleja district is constantly affected by severe floods. Since flood is a natural hazard, it is impossible to prevent its occurrences. However, its impacts can be reduced through effective uptake of floods early warning information (Osuret, Atuyambe et al. 2016). The study will assess the factors associated with the uptake of floods early warning information so as to provide evidence based solution to reduce the impacts of floods in Butaleja district, thereby improving the socio- economic wellbeing of the community.

3.3 Research questions 1. What is the level of uptake of floods early warning information by the community in Butaleja district, eastern Uganda?

2. What are the individual factors associated with uptake of floods early warning information in Butaleja district, eastern Uganda?

3. What are the environmental factors associated with uptake of floods early warning information in Butaleja district, eastern Uganda?

3.4 Conceptual Framework

Figure 1: Conceptual Framework of factors associated with uptake of floods early warning information in Butaleja district, eastern Uganda

Political factors System’s factor  Policies, by-laws and  Communication ordinances in place  Human resource capacity and their enactment  Political influence in early warning system

Individual factors

Socio-demographic factors  IndivSex  Age Outcomes

 Education level  High uptake  Marital status Uptake of Floods  Low uptake

 Employment status Early Warning  Number of people Information  Tribe  House of resident

Knowledge and experiences  Knowledge of vulnerability  Knowledge of the system (floods warning information in place)  Knowledge of capacity Attitudes Environmental factors  How the community  Topography of the place perceive their risks  Seasons i.e dry season and  Trust in the system wet season Practices and perceptions  Landscape  Household preparedness  Land use  Evacuation plan  Where they defecate  Access to the information

Narrative of the Conceptual Framework

Several factors are associated with the uptake of floods early warning information. Individual factors include socio-demographic factors, knowledge and experiences regarding floods early warning information, attitudes towards floods early warning information, practices and perceptions regarding floods early warning information; Environmental factors include the topography of the area, landscape, land use, the seasonal variation and where they defecate; System‘s factors such as human resources and communication aiding floods early warning information; and political factors include policies, ordinances and bye-laws regarding floods early warning information and their enactment.

CHAPTER FOUR

4.0 Study objectives

4.1 General objective To assess uptake of floods early warning information and its associated factors among communities in Butaleja district in order to reduce the impacts of floods in Butaleja district, eastern Uganda.

4.2 Specific objectives 1. To determine the level of uptake of floods early warning information by the communities in Butaleja district, eastern Uganda.

2. To establish the individual factors associated with uptake of floods early warning information in Butaleja district, eastern Uganda.

3. To establish the environmental factors associated with uptake of floods early warning information in Butaleja district, eastern Uganda.

CHAPTER FIVE

5.0 Methodology

5.1 Study area

This study was conducted in four selected sub counties in Butaleja district. Butaleja district is located in the Eastern part of Uganda. It occupies a total area of 644 square kilometers with about 110 square kilometers covered by open water and swamps. The area is relatively flat making is vulnerable to floods. It has a sub-humid climate with rainy season starting from May to October. The district consists of 10 sub counties, 2 town councils, 62 parishes and 379 villages. It has a population of about 198,500 people and their main economic activity is agriculture.

5.2 Study population

The study population comprised heads of households. Where the head of household was not there, any other adult (18 years and above) was interviewed.

5.3 Study design

This was a cross sectional study. Both qualitative and quantitative techniques were used.

5.4 Sample size

The sample size was determined using the formula of Kish Leslie (1965).

N = Z2pq d2 Where: n = Sample size of the study respondents

Z = A standard Z value of a normal distribution of the observations corresponding to

95 percent confidence level (i.e 1.96) p = Prevalence of low uptake of floods early warning system is 63.3 (Okayo, Odera et al. 2015) q = 1-p d = absolute precision required (i.e. 0.05)

Substituting the formula, the number of respondents was: n = 1.962 (0.633 (1-0.633) 0.052 n = 358

The study considered a design effect of 1.5, adopted from (Rayhan 2008) to cater for multi stage cluster sampling, bringing the new sample size to 537.

For the qualitative data, the KI interviews were conducted. Respondents were purposively selected from Meteorological Authority, Office of the Prime Minister (OPM), and Uganda Red Cross Society (URCS) at national level. At the district level, the Acting District Community Development Officer was interviewed, while at sub county levels, one local leader was purposefully chosen and interviewed from each of the four selected sub counties.

5.5 Sampling procedure

Multi-stage sampling was done to identify the households that participated in the study. From the district level, simple random sampling was done to select 4 sub counties that participated in the study out of the 10 sub counties and 2 town councils in Butaleja district. From the sub counties, a list of all the villages in the four sub counties was generated and using simple random sampling 16 villages were selected. A list of all households in the 16 villages was generated and using simple random sampling, 537 households were selected to participate in the study.

Table 1: Sub County Population

Characteristic Categories Frequency (n) Percentages (%) Mazimasa 193 35.94 Himutu 142 26.44 Sub County Naweyo 102 19 Kachonga 100 18.62

Table 2: Village Population

Sub County Villages Frequencies (n) Percentages (%) Doho A 37 6.89 Lubembe 35 6.52 Manafa 33 6.15 Mazimasa Namehere 33 6.15 Wegga 31 5.77 Nahasalagala 24 4.47 Namulo 36 6.7 Doho 36 6.7 Himutu Bubago 35 6.52 Mahindu 35 6.52 Highland A 34 6.33 Naweyo Lwoba 34 6.33 Namatala 34 6.33 Napologoma 34 6.33 Kachonga Doho Hibira 33 6.15 Umango 33 6.15

For the qualitative data, eight (8) Key Informant (KI) interviews were conducted, till point of saturation. Respondents were purposively selected based of their experience and responsibilities in floods early warning information from Meteorological Authority, Office of the Prime Minister (OPM), and Uganda Red Cross Society (URCS) at national level. At the district level, the Acting District Community Development Officer was purposefully selected and interviewed, while at sub county levels, one local leader was purposefully chosen and interviewed from each of the four selected sub counties. Table 3: Key informant categories

Category Numbers (n) Technical staff from Office of the Prime Minister (OPM) 1 Technical staff from Uganda National Meteorological Authority (UNMA) 1 Technical staff from Uganda Red Cross Society (URCS) 1 Technical staff from Butaleja District Local Government 1 Local Leaders 4 Total 8

5.6 Selection criteria

5.6.1 Inclusion criteria  At the community level, all the head of households or any member of their household above 18 years, who had lived in the same community for over one year and were willing to participate in the study  Community leaders / elders, who had lived in the same community for over one year and were willing to participate in the study  Government staffs from the selected ministries and local government who had worked in the same offices for more than six months.  A technical staff in Uganda Red Cross society who had worked in the same office for over six months.

5.6.2 Exclusion criteria  Those who were either sick or mentally unable to respond to the questions.

 Eligible respondents who declined to provide informed written consent, or decline to participate in the study were excluded.

5.7 Study variables

5.7.1 Dependent variables The dependent variable for this study was uptake of floods early warning information. This was measured by assessing the practices on the existing early warning information, such as those who save money, those who keep food in the house, those who keep food in the granary or food store and those who evacuate their homes in times of floods. .

5.7.2 Independent variables The independent variable included:

 Individual factors which included socio-demographic factors such as sex, age, education level, marital status and income level, number of people in the family, tribe, household items, type of roof, type of wall, type of floor; knowledge such as knowledge of the system, hazards, vulnerability and capacity; attitudes such as how the community perceive their feasibility, temporary and trust; and practices such as access to the system, evacuation plan, and preparedness plan in place.  Access to the existing floods early warning information: this was measured by the number of people who stated that they receive floods early warning information irrespective of whether they used it or not.  Communication channels such as medium of communication (newspapers, radio, television, brochures, leaflets and letters) and sources of information such as the local and national level  Environmental factors such as the topography of the area, land use, landscape, seasons and where they defecate were considered to assess uptake of floods early warning information  Beliefs and social support were assessed.

5.8 Data collection procedures

Quantitative data was collected from a representative sample of participants. During data collection, information regarding uptake of floods early warning information, socio-demographic factors, individual factors (including knowledge, attitudes and practices), communication

22 channels and environmental factors were collected using Structured Questionnaire and respondents were selected using simple random sampling. The questionnaires were both in English and Lunyole for clarity and ease of interpretation. The questionnaires were tested in Napologoma ‗A‘ in Kachonga Sub County, to assess their suitability.

Qualitative data was collected using a Key Informant Interview guide. For Key Informant interviews, the respondents were selected purposively and interviewed using key informants interview guide, till point of saturation.

5.9 Data management and analysis

5.9.1 Data management The data were entered using Epinfo software, cleaned and exported to STATA version 13 software for analysis at univariable, bivariable and multivariable levels. Data were cleaned by the Principle Investigator (PI) and research assistants on a daily basis while in the field to ensure that data was as complete as possible. Two data entry clerks who were competent in computer applications were recruited to ensure timely and quality data entry.

5.9.2 Data analysis Quantitative data were analyzed using STATA version13 in line with specific objectives. In order to determine the level of uptake; individual factors; social cultural factors and environmental factors associated with uptake of floods early warning information; frequency tables and proportions were used. Bivariate analysis was conducted to identify the predictor variables of uptake of floods early warning information such as knowledge and practices. A cross tabulation was done to determine associations between independent and dependent variables using odds ratios with a 95% confidence interval for categorical variables.

Logistics regression was used to assess the effects of the various factors on uptake of floods early warning information and control for potential confounders. Factors that were significant in the bivariate analysis were included in the multivariate analysis.

Qualitative data were coded and analyzed using content analysis. All field notes and recorded interviews were transcribed while listening to the audios. The transcribed data was read by the field team so as to gain a full overview of the data. Content for the study was summarized based

23 on the themes related to the objectives of the study. Verbatim quates from the KIs were presented in the results. The purpose of the qualitative information was to know if the technical staff and community leaders are aware of the factors affecting uptake of floods early warning information, and how they can improve uptake of floods early warning information.

5.9.3 Quality control A total of six (6) research assistants were selected and trained to collect the data. This included training on the questionnaire and the consent form so that they all get collective understanding of the questions. The research assistants recruited were at least with a UACE certificate with experience in data collection and with good command of both English and Lunyole. The tools were translated in Lunyole then back to English to ensure the tools do not change meanings since majority of respondents were Banyole. Pretesting of the tools was done in one of the communities in Butaleja district to ensure quality and validity. Adjustments were done according to findings from the pretest.

Research assistants moved to the communities and respective offices, made appointments with the selected respondents and then interviewed them at an appropriate time. Supervision of data collection was done on a daily basis by the researcher so as to check compliance and address any issue that would affect the data quality.

5.9.4 Field editing of data The questionnaires were reviewed and edited for completeness and consistency on a daily basis. In case of a missing data, a recall to the respondent was made by the responsible research assistant to fill the missing data. The supervisor checked questionnaires on a daily basis to verify completeness, correctness, omission and possible errors. Besides, data were reviewed and edited by the Principal Investigator on a daily basis.

5.9.5 Missing data To avoid losing data, all questionnaires and guides used were given serial numbers to avoid confusion and help tracking. Each research assistant was responsible for the data collected by him or her. He / she then handed over the data to the Principal Investigator (PI).

5.10 Ethical considerations

Ethical approval to conduct the study was obtained from Makerere University School of Public Health Higher Degree research and Ethics committee (MakSPH HDREC). Through (MakSPH HDREC) clearance was sought from the Uganda National Council for Science and Technology (UNCST). Further permission was obtained from the Chief Administrative Officer of Butaleja district and from the respective community Local Council 1 (LC1). Data collection tool was translated into Lunyole which is the local language in Butaleja district. Written informed consent was sought from each participant before starting the interview The consent form had contacts of the IRB chairperson/ 0393 291 397 and the respondents were assured of confidentiality throughout the process. The PI together with the research assistants ensured the respondents know the potential risks and benefits of the study before they were engaged.

5.11 Study limitations

These included;

 Information bias since some questions would require the respondent to talk about his/ her personal life. This was minimized by critically explaining the purpose of the study and ethical issues guiding the study.  Recall bias since the respondent may be required to think backward and forward. The use of event calendars by the research team helped to track backward information therefore reducing recall bias.

A total of 537 participants were selected for the study. However, there were variations in the denominators under results since some participants could not answer all the questions as designed. This brought differences in the denominators for some variables. However, this was not too big to affect the outcome. Above all, the research team created good rapport with the community that enabled them to get the required information without much challenge.

5.12 Dissemination

The study will be submitted to the School of Post graduate studies in partial requirement for the award of master of Public Health Disaster Management. Then later to Makerere University School of Public Health resource centre, Ministry of health, Ministry of water, Meteorological Authority, Office of the Prime Minister (OPM) and Butaleja district will all receive copies of this report. The community in the selected sub counties will be given the results in a dissemination workshop to be organized by the Principal Investigator. The findings of this study will be published in a peer reviewed journal.

CHAPTER SIX

6.0 Results

The results are presented in six sections; section 6.1 addresses socio-demographic characteristics of respondents, section 6.2 focuses on knowledge and experiences about floods early warning information, section 6.3 deals with attitudes towards floods early warning information, section 6.4 explains the system‘s factors, section 6.5 focuses on the environmental factors and section 6.6 focuses on practices and perceptions about floods early warning information.

6.1 Socio-demographic characteristics of respondents

A total of 537 households participated in this study. The mean age was 41.1 years with a standard deviation of 14.7. Majority 56.6% (302/534) of the respondents were male. More than half of the households 52.7% (282/535) had 7 people or more. Most respondents 83.4% (446/535) attained primary education and below. While 96.7% (519/537) of the household head had informal employment. The predominant tribe of the household head was Banyole 86.8% (461/531) (Table 4).

Table 4: Socio-demographic characteristics of respondents

Variables Frequencies (n) Percentages (%) Sex of respondent (n=534)

Male 302 56.5 Female 232 43.5 Age (n=537)

Below 30years 141 26.3 30-39years 125 23.3 40-49years 121 22.5 Above 49years 150 27.9 Sex of household head (n=536)

Male 460 85.8 Female 76 14.2 Marital status

Not married 88 16.4 Married 448 83.6 Number of People in the household (n=535) 6 and below 253 47.3 7 and above 282 52.7 Level of Education of household head

Primary education and below 446 83.4 Secondary education and above 89 16.6 Employment status (n=537)

Formal employment 18 3.4 Informal employment 519 96.6 Tribe of household head (n=531)

Banyole 461 86.8 Others 70 13.2

6.2 Knowledge and experiences about floods early warning information

Results show that 98.5% of the households had ever been affected by floods. Out of these, about 53.9% responded that the frequency of floods in the community is six months and below. Majority 98.1% (527/537) had received floods early warning information regarding their community. Mostly the modern type of floods early warning information 68.5% (366/534) was said to be used in the community. Majority 48.9% (262/536) lived between 6-10 kilometers from Butaleja district headquarter. The household risk of floods was high 77.7% according to most of the respondents. About 55.9% had their income affected and about 52.3% had their property damaged within the medium (4-6) level. Majority 88.5% (469/530) of the respondents reported that their household member contracted a disease during the last floods they experienced in their community (Table 5).

Table 5: Knowledge and experiences about floods early warning information

Characteristic Frequencies (n) Percentages (%)

Household ever been affected by floods (n=537) No 8 1.5 Yes 529 98.5 Frequency of floods in the community (n=534) At least 6 months and below 288 53.9 Every 6 months to 1 year 96 18.0 Every 1 year and above 150 28.1 Received any floods early warning information regarding your community (n=537) No 10 1.9 Yes 527 98.1 Types of Floods Early Warning Information used in this community (n=534) Modern 366 68.6 Traditional 36 6.7 Both 125 23.4 I don‘t know 7 1.3 Floods early warning information frequently preferred (n=537) Modern 452 84.2 Traditional 55 10.2 Both 23 4.3 None 7 1.3 How respondents identify a vulnerable household (n=537) Lives on a lower ground 468 87.2 Proximity to a swamp 387 72.1 Poor drainage around the home 365 68.0 Poor (weak) housing structure 318 59.2 Respondents rating of vulnerability levels Children (n=537)

Not vulnerable 0 0.0 Fairly vulnerable 11 2.0 Vulnerable 207 38.6 Extremely Vulnerable 319 59.4 Women Not vulnerable 0 0.0 Fairly vulnerable 79 14.7 Vulnerable 330 61.5 Extremely Vulnerable 128 23.8 Men Not vulnerable 34 6.3 Fairly vulnerable 277 51.6 Vulnerable 196 36.5 Extremely Vulnerable 30 5.6 Disabled (n=536) Not vulnerable 0 0.0 Fairly vulnerable 27 5.0 Vulnerable 297 55.4 Extremely Vulnerable 212 39.6 Perceived vulnerability of their household to floods (n=534) No 4 0.8 Yes 530 99.2 Distance from Butaleja district headquarter, where early warning sensor was established (n=536) 1-5 Kilometers 146 27.2 6-10 Kilometers 262 48.9 11-20 Kilometers 116 21.6 Over 20 Kilometers 12 2.3 Distance from Himutu Sub county head quarter, where floods early warning sensor was established (n=536) 1-5 Kilometers 128 23.9 6-10 Kilometers 291 54.3 11-20 Kilometers 114 21.2 Over 20 Kilometers 3 0.6 How to know that it is going to flood (n=537) Warning information from meteorological authority 376 70.0 Warning information from district level 303 56.4 Traditional signs (Indigenous knowledge) 114 21.2 How do you gauge household risks to floods (n=537) Low (0-3) 26 4.8 Medium (4-6) 94 17.5 High (7-10) 417 77.7 Extent to which income was affected (n=535) Low (0-3) 36 6.7 Medium (4-6) 299 55.9 High (7-10) 200 37.4 Extent of damage to property (n=535) Low (0-3) 24 4.5 Medium (4-6) 281 52.5 Household member contracted a disease during last floods (n=530) No 61 11.5 Yes 469 88.5

Most of the key informants mentioned that the source and timeliness of floods early warning information determines a lot how the community uptake the floods early warning information. Communities believe more on information which are generated locally within their own communities. Besides, the information passed, should come timely enough to allow time for action.

“You are more likely to see information that comes out of a reliable partner, it is easy to be believed……Information that are generated from the community is easily understood and utilized ... So the framework at which we collect this information will determine its uptake.” A national level KI “And when it comes to dissemination, we really have a challenge. Actually the whole thing is about dissemination. Because early warning information goes with early action, if it doesn’t come in time then that means it won’t save people, the lives and of course the property.” A national level KI

According to one of the key informants, not only lack of knowledge but also inability of the people to appreciate the importance of the information passed, affects uptake of floods early warning information. Besides, not many studies have been done in regards to indigenous knowledge on floods. Furthermore, the information shared always does not indicate losses resulting from floods in a tangible way and does not make people acknowledge the economic losses associated with flood.

“Another one is low public awareness, even when the information is there people are not willing to use it, because they are not aware of the importance of this information. Not only to the local people but also the decision makers we still have a problem in consuming this information”. A national level KI.

“As regards floods of course they could be having some indigenous knowledge about it...... but I must admit that we have not done much as regards to indigenous knowledge apart from the scientific part of flood modeling”. A national level KI.

“Can we explain what they lose in undergoing this kind of situations every year so that they see it in a tangible way? So we now need to get down to the scientific and economic analysis of the losses presented by these floods”. A national level KI

6.3 Attitudes toward floods early warning information

The respondents 99.1% (521/526) overwhelmingly said risks resulting from floods can be avoided. Majority 89.5% (480/536) said floods early warning information received was good. The reliability 90.5% (486/537) , accessibility 91.2% (490/537) and timeliness 82.1% (440/536) of the floods early warning information were rated by the majority of the respondents as ―good‖ (Table 6.).

Table 6: Attitudes toward floods early warning information

Characteristic Frequencies (n) Percentages (%)

Risks resulting from floods can be avoided (n=526) No 5 0.9 Yes 521 99.1 Rate Floods Early Warning Information that you receive in this community (n=536) Poor 56 10.5 Good 480 89.5 Perception about reliability of floods early warning information (n=537) Poor 51 9.5 Good 486 90.5 Perception about accessibility of floods early warning information Poor 47 8.8 Good 490 91.2 Perception about timeliness of floods early warning information (n=536) Poor 96 17.9 Good 440 82.1

According to the key informants, attitude towards floods greatly impacts on uptake of floods early warning information. This is because, they have had this recurrent problem and see them as part of their lives and they have nothing to do about it. However, the floods of 2010 that claimed lives and so many properties, made people to have better uptake of floods early warning information.

“People have lived with the environment for quite a long time, they know it, they have even developed some coping mechanisms and in so doing they try to see how best they can avoid such kind of hazard if at all it comes. ….So what am saying is, the perception of the risk because they know even if it comes, they will still remain”. A national level KI.

“Generally between 2010 and to date, you will realize that uptake of early warning information generally in the country has increased following the death of hundreds of people in Bududa as a result of the landslides, that also affected several other communities downstream that is Tororo and Butaleja, that is why we have not had any major disaster to that magnitude since then”. A national level KI

6.4 System’s factors affecting uptake of floods early warning information

The effectiveness of the early warning system‘s operators (workers) was said to be good 81.6% (417/511) by most of the respondents. And the effectiveness of the floods early warning system installed in river Manafwa was rated as good 81.4% (415/510) by most of the respondents.

However, according to the key informants, system factors affect uptake of floods early warning information as indicated below. The KIs at national level still acknowledge that the process of collecting, managing and disseminating floods early warning information is still poor. Besides the reliability of the machine is not guaranteed according to a community based key informant.

“Dissemination is still poor, we are trying our level best using the available channels, we use radios of course, we use newspapers, we use local government….…… however, these information still do not reach the intended users on time, and this is a big challenge”. A national level KI.

“I think for early warning information to be properly utilized … the processes of collecting, managing and feedback needs a closer community engagement.…..I would say that a lot more community engagement of research institutions like yours, should engage more with the community based practices”. A national level KI

“The machine has been working and serving the purpose. But in some cases even if a child just pours water, it makes noise, so it warns people even when there is no flood, but the traditional signs always communicate facts because when it comes it floods”. Community level KI.

6.5 Environmental factors affecting uptake of floods early warning information.

The different environmental factors were rated differently by the respondents. Most respondents 76.7% (412/537) said seasons determine uptake of floods early warning information. About 42.1% (226/537) said topography determine uptake of floods early warning information. Whereas 40.4% (217/537) said land use determine uptake of floods early warning information. Only 13.2% (71/537) of the respondents said landscape determine uptake of floods early warning information

According to a KI at the district, people respond more to floods early warning information during the rainy seasons because they would always expect floods.

“During rainy seasons you can get a lot of floods. When it rains heavily water becomes too much and it occupies most of the homes, the furniture and even the people in the community suffer a lot , their foods are taken, their houses are taken even their clothes, even most of the things they use they remain with nothing when it floods”. A district level KI.

6.6 Participants’ practices and perceptions regarding floods early warning information

On household practices most respondents 74.1% (398/537) reported that they keep food in the house. Red Cross was mentioned by majority respondents 60.2% (323/537) followed by local government 34.8% (187/537) as organizations that gave them support in times of disease outbreak. More than half 54.4% (287/528) of the respondents evacuate their homes in times of floods. Most of the respondents 62.0% (332/536) defecate in latrines/toilets in times of floods. Most respondents 67.4% (362/537) reported that radios are the most appropriate way of passing floods early warning information (Table 7).

Table 7: Participants’ practices and perceptions regarding floods early warning information

Variables Frequencies (n) Percentages (%) Preparedness measures undertaken (n=537) Keeping food in the house 398 74.1 Keeping food in the granary or food store 204 38.0 Moving to alternative site (Evacuation centre) 202 37.6 Saving money 94 17.5 Don‘t do anything about it 10 1.9 Households received external support during last flood (n=537) Red Cross 323 60.1 Local Government 187 34.8 Central Government 157 29.2 None Supported 69 12.8 UNICEF 65 12.1 World Vision 50 9.3 Evacuate home in times of floods (n=528) No 241 45.6 Yes 287 54.4 Where they defecate in times of floods (n=536) Own latrine/Toilet 332 62.0 Bush 204 38.0 Most appropriate way of passing early warning information (n=537) Early warning system at Namulo 93 17.3 Radio 362 67.4 Others 82 15.3 Perception about level of receptiveness of floods early warning information with regards to accessibility (n=537) Very low 25 4.7 Low 129 24.0 Good 339 63.1 Very good 44 8.2 Perception about level of understanding of floods early warning information (n=536) Very low 11 2.1 Low 131 24.4 Good 349 65.1 Very good 45 8.4 Perception about level of utilization of floods early warning information (n=537) Very low 28 5.2 Low 118 22.0 Good 344 64.1 Very good 47 8.7 Belief about floods (n=535) Floods happen temporarily 304 56.8 Floods are Normal 183 34.2 Flood is a curse 48 9.0

According to the key informants, practices and perceptions are associated with the uptake of floods early warning information. Factors such as failure of the hazard to reach the threshold after a warning and lack of evacuation centres affect uptake of floods early warning information.

“However, at community level you note that there are gaps, either in the delay of the warning information or at times when the warning is issued the hazard doesn’t reach a threshold of causing a disaster and this one has an effect of causing the communities to relax”. A national level KI

“As a country we still don’t have evacuation centres……Previously we have seen in case of evacuation people relocate themselves to the nearby churches, schools, health centres and you know this one affects the operation of such institutions….…So capacity issue is a very big thing”. A national level KI.

“During the floods, they were stopped for a while from going to school, then after we got help from the government and NGOs, like Red Cross, World Vision who renovated those schools then they started fresh learning”. A community level KI

6.7 Factors associated with uptake of floods early warning information

Since the level of uptake of floods early warning information was at 56.0% (301/537), prevalence risk ratios were used to identify factors associated with the uptake of floods early warning information.

6.7.1 Socio-demographic factors associated with uptake of floods early warning information;

The prevalence of uptake of floods early warning information was 15% higher among female respondents compared to the male (Unadjusted PR 1.15, 95% CI 0.95-1.39). However the association was not statistically significant. Besides, uptake of floods early warning information was 9% more likely to occur among the age group 30-39 years compared to other age groups (unadjusted PR 1.09, 95% CI 0.90-1.31). However the association was not statistically significant.

Furthermore, the uptake of floods early warning information was 12% less likely to occur among those who are married than those who are not married (Unadjusted PR 0.88, 95% CI 0.73-1.1). However the association was not statistically significant. Households with seven(7) people and above were 20% more likely to have high uptake of floods early warning information compared to those with six(6) people and below (Unadjusted PR 1.20, 95%CI 1.03-1.40) and the association was statistically significant. Whereas uptake of floods early warning information among households whose heads are Banyole was 39% less likely to occur compared to the other tribes (Unadjusted PR 0.61, 95% CI 0.50-0.74) and the association was statistically significant.

See details of the unadjusted analysis of the socio demographic factors associated with uptake of floods early warning information in table 8 below.

Table 8: Bi variable analysis of socio demographic factors associated with uptake of floods early warning information

Variables High Uptake Low Uptake Crude PR (95% CI) p-value

Sex of respondent Male 163(54.0) 139(46.0) 1.0 Female 136(58.6) 96 (41.4) 1.15(0.95-1.39) 0.150 Age <30 years 84(59.6) 57(40.4) 1.0 30-39 years 81(64.8) 44(35.2) 1.09(0.90-1.31) 0.380 40-49 years 63(52.1) 58(47.9) 0.87(0.70-1.09) 0.227 Above 49 years 73(48.7) 77(51.3) 0.82(0.66-1.01) 0.063 Sex of household head Male 252(54.9) 207(45.1) 1.0 Female 48(63.2) 28(36.8) 1.15(0.95-1.39) 0.150 Marital status Not married 55(62.5) 33(37.5) 1.0 Married 246(54.9) 202(45.1) 0.88(0.73-1.1) 0.164 Number of People in the household Six (6) and below 129(51.0) 124(49.0) 1.0 Seven (7) and above 172(61.0) 110(39.0) 1.20(1.03-1.40) 0.022* Level of Education of household head Primary education and below 253(56.7) 193(43.3) 1.0 Secondary education and above 47(52.8) 42(47.2) 0.93(0.75-1.15) 0.510 Employment status Formal employment 10(55.6) 8(44.4) 1.0 Informal employment 290(56.1) 227(43.9) 1.01(0.66-1.54) 0.964 Tribe of household head Banyole 289(62.7) 172(37.3) 0.61(0.50-0.74) <0.001* Others 10(14.29) 60(85.7) 1.0 *Statistically significant P-value <0.05

6.7.2 Knowledge and experiences associated with floods early warning information The prevalence of uptake among households that have ever been affected by floods is 2.26 times that among those who have not been affected by floods (Unadjusted PR 2.26, 95% CI 0.68-7.53) but the association was not statistically significant. Whereas the prevalence of high uptake was 1.5 times high among those who said frequency of floods in their community is between 6 months to 1 year compared to those who said the frequency of floods in their community is from six (6) months and below (Unadjusted PR 1.5, 95% CI 1.26-1.79). The association was statistically significant.

The uptake of floods early warning information was 48% less likely to occur among those who prefer indigenous knowledge (traditional method) compared to others (Unadjusted PR 0.52, 95% CI 0.35-0.77) and the association was statistically significant. Furthermore, uptake of floods early warning information was 38% less likely to occur among those who are six (6) to ten(10)

38 kilometers away from Butaleja district head quarter than those who are five (5) kilometers and below (Unadjusted PR 0.62, 95% CI 0.54-0.71) and the association was statistically significant. Last but not least, the prevalence of uptake was 3.76 times high among households who reported impacts of floods on property as medium compared to those who reported impacts of floods on property as low (Unadjusted PR 3.76, 95% CI 1.53-9.24) and the association was statistically significant.

See details of the unadjusted analysis of knowledge and experiences associated with uptake of floods early warning information on table 9.

Table 9: Bi variable analysis of knowledge and experiences associated with uptake of floods early warning information

Variables High Low Crude PR (95% CI) p-value Uptake Uptake Household ever been affected by floods No 2(25.0) 6(75.0) 1.0 Yes 299(56.5) 230(43.5) 2.26(0.68-7.53) 0.184 Frequency of floods in the community At least 6 months and below 136(47.2) 152(52.8) 1.0 Every 6 months to 1 year 68(70.8) 28(29.2) 1.5(1.26-1.79) <0.001* Every 1 year and above 96(64.0) 54(36.0) 1.36(1.14-1.61) 0.001* Received any floods early warning information regarding your community No 2(20.0) 8(80.0) 1.0 Yes 299(56.7) 228(43.3) 2.84(0.82-9.83) 0.100 Types of Floods Early Warning Information used in this community Modern 220(60.1) 146(39.9) 1.0 Traditional 14(38.9) 22(61.1) 1.04(0.94-1.16) 0.472 Both 66(52.8) 59(47.2) 0.88(0.79-0.98) 0.022* I don‘t know 0(0.0) 7(100.0) 0.84(0.47-1.48) 0.540 How respondents identify a vulnerable household Lives on a lower ground No 29(42.0) 40(58.0) 1.0 Yes 272(58.1) 196(41.9) 1.38(1.04-1.84) 0.027* Proximity to a swamp No 84(52.8) 75(47.2) Yes 217(57.4) 161(42.6) 1.08(0.92-1.29) 0.340 Poor drainage around the home No 105(61.1) 67(38.9) Yes 196(53.7) 167(46.3) 0.88(0.75-1.02) 0.100* Poor (weak) housing structure No 118(48.2) 127(51.8) 1.0 Yes 183(62.7) 109(37.3) 1.30(1.11-1.52) 0.001* Respondents rating of vulnerability levels Women Fairly Vulnerable 61(77.2) 18(22.8) 1.0 Vulnerable 152(46.1) 178(53.94) 0.60(0.50-0.70) <0.001*

Extremely vulnerable 88(68.8) 40(31.2) 0.89(0.75-1.05) 0.174 Men Not Vulnerable 15(44.1) 19(55.9) Fairly Vulnerable 125(45.1) 152(54.9) 1.02(0.69-1.53) 0.912 Vulnerable 143(73.0) 53(27.0) 1.65(1.12-2.11) 0.011* Extremely vulnerable 18(60.0) 12(40.0) 1.36(0.84-2.19) 0.208 Disabled Not vulnerable Fairly Vulnerable 5(18.5) 22(81.5) Vulnerable 137(46.1) 160(53.9) 2.49(1.12-5.55) 0.026* Extremely vulnerable 158(74.5) 54(25.5) 4.02(1.82-8.92) 0.001* Perceived vulnerability of their household to floods No 1(25.0) 3(75.0) 1.0 Yes 297(56.0) 233(44.0) 2.24(0.41-12.28) 0.352 Distance from Butaleja district headquarter 1-5 Kilometers 122(83.6) 24(16.4) 1.0 6-10 Kilometers 136(51.9) 126(48.1) 0.62(0.54-0.71) <0.001* 11-20 Kilometers 39(33.6) 77(66.4) 0.40(0.31-0.52) <0.001* Over 20 Kilometers 3(25.0) 9(75.0) 0.30(0.11-0.80) 0.016* Distance from Himutu Sub county head quarter where early warning sensor was established 1-5 Kilometers 35(27.3) 93(72.7) 1.0 6-10 Kilometers 200(68.7) 91(31.3) 2.51(1.87-3.37) <0.001* 11-20 Kilometers 62(54.4) 52(45.6) 1.99(1.43-2.76) <0.001* Over 20 Kilometers 3(100.0) 0(0.0) 3.66(2.76-4.85) <0.001* How to know that it is going to flood Warning information from meteorological authority No 91(56.5) 70(43.5) 1.0 Yes 210(55.8) 166(44.2) 0.99(0.84-1.16) 0.886 Warning information from district level No 107(45.7) 127(54.3) 1.0 Yes 194(64.0) 109(36.0) 1.40(1.19-1.65) <0.001* How they gauge household risks to floods Low (0-3) 2(7.7) 24(92.3) Medium (4-6) 58(61.7) 36(38.3) 8.02(2.10-30.70) 0.002* Extent to which income was affected Low (0-3) 7(19.4) 29(80.6) 1.0 Medium (4-6)- moderate 183(61.2) 116(38.8) 3.15(1.61-6.16) 0.001* High (7-10) 111(55.5) 89(44.5) 2.85(1.45-5.62) 0.002* Extent of damage to property Low (0-3) 4(16.7) 20(83.3) 1.0 Medium (4-6) 176(62.6) 105(37.4) 3.76(1.53-9.24) 0.004* High (7-10) 121(52.6) 109(47.4) 3.16(1.28-7.79) 0.013* *Statistically significant P-value <0.05

6.7.3 Attitudes associated with uptake of floods early warning information The uptake of floods early warning information was 42% more likely to occur among those who said risk resulting floods can be avoided than among those who said risk resulting from floods cannot be avoided (Unadjusted PR 1.42, 95% CI 0.48-4.17). However, the association was not statistically significant. Besides, the prevalence of uptake of floods early warning information was 5.72 times more among those who rate floods early warning information received in their community as good, compared to those who rate it as poor (Unadjusted PR 5.72, 95% CI 2.67- 12.23) and the association was statistically significant.

Furthermore, the prevalence of uptake of floods early warning information was 3.84 times higher among those who said their feelings about floods early warning information with regards to reliability was good compared to those who said it was poor (Unadjusted PR 3.84, 95% CI 2.02- 7.30). The association was statistically significant.

See details of the unadjusted analysis of the attitudes associated with uptake of floods early warning information in table 10 below

Table 10: Bi variable analysis of attitudes associated with uptake of floods early warning information

Variables High Uptake Low Uptake Crude PR (95% CI) p-value

Risks resulting from floods can be avoided No 2(40.0) 3(60.0) 1.0 Yes 296(56.65) 225(43.2) 1.42(0.48-4.17) 0.523 Rate Floods Early Warning Information that you receive in this community Poor 6(10.7) 50(89.3) 1.0 Good 294(61.3) 186(38.7) 5.72(2.67-12.23) <0.001* Perception about reliability of floods early warning information Poor 8(15.7) 43(84.3) 1.0 Good 293(60.3) 193(39.7) 3.84(2.02-7.30) 0.001* Perception about accessibility of floods early warning information Poor 6(12.8) 41(87.2) 1.0 Good 295(60.2) 195(39.8) 4.72(2.22-10.0) <0.001* Perception about timeliness of floods early warning information Poor 6(6.3) 90(93.8 1.0 Good 294(66.8) 146(33.2) 10.69(4.91-23.28) <0.001* *Statistically significant P-value <0.05

6.7.4 System’s factors affecting uptake of floods early warning information The prevalence of uptake was 9.33 times high among those who said that the effectiveness of the early warning message‘ interpreters was good compared to those who said the effectiveness was poor (Unadjusted PR 9.33, 95% CI 4.56-19.11). The association was statistically significant. Besides, the prevalence of uptake was 6.59 times higher among those who said that the effectiveness of the early warning machine installed on river Manafwa was good compared to those who said the effectiveness was poor (Unadjusted PR 6.59, 95% CI 3.65-11.90) and the association was statistically significant.

6.7.5 Environmental factors associated with uptake of floods early warning information The uptake of floods early warning information was 98% more likely to occur among those who said seasons determine uptake than those who said seasons do not determine uptake (Unadjusted PR 1.98, 95% CI 1.52-2.58). The association was statistically significant. Besides, uptake of floods early warning information among those whose roofs were made of iron sheet were 11% less likely to occur than those whose roofs were grass thatched (Unadjusted PR 0.89, 95% CI 0.77-1.03). However, the association was not statistically significant.

See details of the unadjusted analysis of the environmental factors that determine uptake of floods early warning information in table 11 below

Table 11: Bi variable analysis of environmental factors associated with uptake of floods early warning information

Variables High Uptake Low Uptake Crude PR (95% CI) p-value

Environmental factors that determine uptake Topography No 238(76.5) 73(23.5) 1.0 Yes 63(27.9) 163(72.1) 0.36(0.29-0.45) 0.000* Seasons No 40(32.0) 85(68.0) 1.0 Yes 261(63.3) 151(36.7) 1.98(1.52-2.58) 0.000* Landscape No 276(59.2) 190(40.8) 1.0 Yes 25(35.2) 46(64.8) 0.59(0.43-0.82) 0.000* Land use No 161(50.3) 159(49.7) 1.0 Yes 140(64.5) 77(35.5) 1.28(1.11-1.49) 0.000* Type of roof Grass thatched 105(60.7) 68(39.3) 1.0 Iron sheet 196(53.8) 168(46.2) 0.89(0.77-1.03) 0.126 Type of wall Mud and wattle 197(59.7) 133(40.3) 1.0 Bricks and blocks 104(50.2) 103(49.8) 0.84(0.72-0.99) 0.037* *Statistically significant P-value <0.05

6.7.6 Practices and perceptions associated with uptake of floods early warning information; The prevalence of uptake of floods early warning information among households who said they received external support from Red Cross was 56% more likely to occur than among those who said they did not receive external support from Red Cross (Unadjusted PR 1.56, 95% CI 1.31- 1.84). The association was statistically significant. Besides, the prevalence of uptake of floods early warning information among those who evacuate their homes in times of floods were 3.57 times more compared to those who do not evacuate their homes in times of floods (Unadjusted PR 3.57, 95% CI 2.82-4.52). The association was statistically significant. On the other side, the prevalence of uptake of floods early warning information was 23% less likely to occur among those who defecate in the bush in times of floods compared to those who defecate in the latrines or toilets in times of floods (Unadjusted PR 0.77, 95% CI 0.65-0.92). The association was statistically significant. Last but not least, the prevalence of uptake of floods early warning information is 39% less likely to occur among those who consider radio as the most appropriate way of passing floods early warning information compared to those who consider early warning

43 system at Namulo (Unadjusted PR 0.61, 95% CI 0.55-0.68) however, the association was statistically significant.

See details of the unadjusted analysis of the practices and perceptions associated with uptake of floods early warning information in table 12 below.

Table 12: Bi variable analysis of practices and perceptions associated with uptake of floods early warning information

Variables High Uptake Low Uptake Crude PR (95% CI) p-value

Preparedness measures undertaken Saving money No 263(59.4) 180(40.6) 1.0 Yes 38(40.4) 56(59.6) 0.68(0.53-0.88) 0.003* Keeping food in the house No 103(74.1) 36(25.9) 1.0 Yes 198(49.7) 200(50.3) 0.67(0.58-0.77) <0.001* Keeping food in the granary or food store No 223(67.0) 110(33.0) 1.0 Yes 78(38.2) 126(61.8) 0.57(0.47-0.69) <0.001* Moving to alternative site (Evacuation centre) No 131(39.1) 204(60.9) 1.0 Yes 170(84.2) 32(15.8) 2.15(1.86-2.49) <0.001* Don’t do anything about it No 296(56.2) 231(43.8) Yes 5(50.0) 5(50.0) 0.89(0.48-1.66) 0.715 Household member contracted the disease No 10(16.4) 51(83.6) 1.0 Yes 284(60.60 185(39.4) 3.69(2.08-6.54) <0.001* Households received external support during last flood Red Cross No 274(54.3) 231(45.7) Yes 27(84.4) 5(15.6) 1.56(1.31-1.84) <0.001* Local government No 228(65.1) 122(34.9) Yes 73(39.0) 114(61.0) 0.60(0.49-0.73) <0.001* Central Government No 206(54.2) 174(45.8) Yes 95(60.5) 62(39.5) 1.12(0.95-1.31) 0.169 UNICEF No 250(53.0) 222(47.0) Yes 51(78.5) 14(21.5) 1.48(1.27-1.73) <0.001* World Vision No 258(53.0) 228(47.0) Yes 43(86.0) 7(14.0) 1.62(1.41-1.87) <0.001* Evacuate home in times of floods No 56(23.2) 185(76.8) 1.0 Yes 238(82.9) 49(17.1) 3.57(2.82-4.52) <0.001* Where they defecate in times of floods Latrine/toilets 204(61.5) 128(38.5) 1.0 Bush 97(49.7) 107(50.3) 0.77(0.65-0.92) 0.003* Most appropriate way of passing early warning information Early warning system at Namulo 85(91.4) 8(8.6) 1.0 Radio 202(55.8) 160(44.2) 0.61(0.55-0.68) <0.001* Others 14(17.1) 68(82.9) 0.19(0.12-0.30) <0.001* *Statistically significant P-value <0.05

6.8 Factors associated with uptake of floods early warning information

At multivariable analysis, the independent factors with p<0.05 at bi-variable analysis and those that were thought to have impacts on the study according to literature were run in a multivariable generalized linear model (GLM) with poison family, a log link and robust standard errors to obtain adjusted prevalence ratios.(PR). Logical model building was used to ascertain the final model. Variables that were entered in the final include; Extent of damage to property, type of floods early warning information used in the community, rating the floods early warning information that they receive, perception on accessibility of floods early warning, how respondents would know that it is going to flood and most appropriate way of passing early warning information.

As shown in table 6.9 below, factors associated with uptake of floods early warning information included; those who indicated that frequency of floods is from six (6) months to 1 year (Adjusted PR 1.19, 95% CI 1.03-1.39), those who had perception on reliability of floods early warning information as good (Adjusted PR 3.17, 95% CI 1.27-7.90), those who had perception on timeliness of floods early warning information as good (Adjusted PR 1.15, 95% CI 1.00-1.34), those who believe that floods are temporary (Adjusted PR 0.39, 95% CI 0.17-0.89), those who said topography determine uptake of floods early warning information (Adjusted PR 0.75, 95% CI 0.60-0.93) and those who said they evacuate their home in times of floods (Adjusted PR 1.45, 95% CI 1.21-1.75).

See details of the adjusted analysis of the independent variables associated with uptake of floods early warning information in table 13 below.

Table 13: Multi variable analysis of factors associated with uptake of floods early warning information

Variables High Low Crude PR (95% P-value Adj.PR (95% P-value Uptake Uptake CI) CI) Extent of damage to property Low (0-3) 4(16.7) 20(83.3) 1.0 Medium (4-6) 176(62.6) 105(37.4) 3.76(1.53-9.24) 0.004* 0.71(0.35-1.41) 0.328 High (7-10) 121(52.6) 109(47.4) 3.16(1.28-7.79) 0.013* 0.71(0.35-1.46) 0.355 Frequency of floods in the community At least 6 months and below 136(47.2) 152(52.8) 1.0 Every 6 months to 1 year 68(70.8) 28(29.2) 1.5(1.26-1.79) <0.001* 1.19(1.03-1.39) 0.017* Every 1 year and above 96(64.0) 54(36.0) 1.36(1.14-1.61) 0.001* 1.06(0.87-1.31) 0.549 Types of Floods Early Warning Information used in the community Modern 220(60.1) 146(39.9) 1.0 Traditional 14(38.9) 22(61.1) 1.04(0.94-1.16) 0.472 1.20(0.95-1.52) 0.131 Both 66(52.8) 59(47.2) 0.88(0.79-0.98) 0.022* 1.12(0.96-1.30) 0.151 Perception on reliability of floods early warning information Poor 8(15.7) 43(84.3) 1.0 Good 293(60.3) 193(39.7) 3.84(2.02-7.30) 0.001* 3.17(1.27-7.90) 0.013* Perception on accessibility of floods early warning information Poor 6(12.8) 41(87.2) 1.0 Good 295(60.2) 195(39.8) 4.72(2.22-10.0) <0.001* 1.15(1.00-1.34) 0.061 Perception on timeliness of floods early warning information Poor 6(6.3) 90(93.8 1.0 Good 294(66.8) 146(33.2) 10.69(4.91-23.28) <0.001* 11.11(5.05-24.41) <0.001* Beliefs about floods Floods are temporary No 294(294) 145(33.0) 1.0 Yes 7(7.1) 91(92.9) 0.11(0.05-0.22) <0.001* 0.39(0.17-0.89) 0.026* Environmental factors that determine uptake Topography No 238(76.5) 73(23.5) 1.0 Yes 63(27.9) 163(72.1) 0.36(0.29-0.45) <0.001* 0.75(0.60-0.93) 0.009* Evacuate home in times of floods No 56(23.2) 185(76.8) 1.0 Yes 238(82.9) 49(17.1) 3.57(2.82-4.52) <0.001* 1.45(1.21-1.75) <0.001* Most appropriate way of passing early warning information Early warning system 85(91.4) 8(8.6) 1.0 Radio 202(55.8) 160(44.2) 0.61(0.55-0.68) <0.001* 1.00(0.89-1.11) 0.951 Others 14(17.1) 68(82.9) 0.19(0.12-0.30) <0.001* 0.75(0.51-1.11) 0.154 *Statistically significant P-value <0.05

CHAPTER SEVEN

7.0 Discussion

7.1 Uptake of floods early warning information

According to this study, uptake of floods early warning information was at 56.0%. This was consistent with another study which indicated that uptake of floods early warning information saved lots of lives and property (Stephens, Edwards et al. 2012). Therefore high uptake of floods early warning information ensures precautionary measures which in return lead to reduced damage to property, income and human suffering. According to this study, since the early warning machine was installed on river Manafwa in 2014, no death of a human being was reported. This can be improved further by ensuring that challenges affecting uptake such as lack of evacuation centers are addressed.

The study also revealed that the vast majority 98.5% of the households interviewed had ever been affected by floods. In a related study conducted in Vietnam, many of the respondents reported that during floods they registered various challenges such as disease outbreak and loss of property (Bich, Quang et al. 2011). Since majority of the households interviewed had ever been affected by floods, the likelihood of high uptake was expected. Effective measures have to be put in place to ensure floods early warning information is received on a timely and reliable manner.

Women have better uptake of floods early warning information compared to men and women who are vulnerable are said to have better uptake compared to those who are less vulnerable. In times of floods, the women are more exposed to the devastating effects of floods due to their physical, emotional and social characteristics which tend to make them more vulnerable. In a related study, women respond better to early warning information and take up preparedness measures as opposed to men (Bahadur, Peters et al. 2015). The study did not explore challenges men face in ensuring uptake of floods early warning information. Therefore engagements that will ensure full men‘s commitment in floods early warning information are recommended.

Furthermore, the study indicated that 82.1% of the respondents said that the timeliness of the floods warning information was good. When floods early warning information is passed too late

48 or too early, it affects effective uptake. Therefore timely early warning information is recommended. In another related study, timeliness of the warning information was considered very important in ensuring proper planning for mitigation measure ahead of floods (Carsell, Pingel et al. 2004). The study did not analyze the effectiveness of these mitigation measures. Consideration should be made to put in place effective mitigation measures.

The use of both modern early warning information and the indigenous knowledge was noted in this study to yield better uptake compared to when only one method was used. This was however contrary to a study conducted in 2011 which gives more reward to the modern warning system. The study applauded the modern technology for using nonstop monitoring which uses remote sensing technology and brings a more reliable information (Krzhizhanovskaya, Shirshov et al. 2011). There is however, need to explore and document the indigenous floods early warning information in Uganda so as to guide future interventions.

7.2 Socio-demographic factors associated with uptake of floods early warning information

According to this study, the type of walls determine uptake of floods early warning information, in that those whose walls were made of bricks and blocks were less likely to take up floods early warning information, compared to those who had mud and wattle, since those who had bricks and blocks feel their house is durable and less susceptible to floods. In line with this, a study conducted in Kenya revealed uptake was at 33.3% among those who had brick/stone. There is need to sensitize all the community members to appreciate that floods affect everyone irrespective of the type of walls one has, so as to improve uptake.

Whereas the study indicated uptake among those who attained secondary education and above was 7% less likely to occur compared to those who attained primary education and below. In a related study conducted in Kenya high uptake was at 31.8% among those who attained secondary education and 33.3% among those who attained post-secondary education. Both studies indicate low uptake among respondents who attained secondary education and above (Okayo, Odera et al. 2015). This could have been attributed to negligence due to alternative sources of livelihood. There is need to strengthen social mobilization for improved uptake of floods early warning information.

This study revealed that those who are married had a high uptake of 54.9%.This was attributed to their level of stability and responsibility in the community. While another study still revealed that the married had a high uptake of 63.1%. The study however did not explain why the married had high uptake (Okayo, Odera et al. 2015). Further investigation can be done to find out why the unmarried have relatively low uptake of floods early warning information.

The households whose heads were Banyole were 39% less likely to use floods early warning information compared to other tribes. This was because the Banyole are the indigenous people in Butaleja district and have lived with the floods for a long time, they know it and have even developed some coping mechanisms to deal with it. Therefore they are reluctant to take up floods early warning information. However, in a related study conducted there was no relationship between tribe and uptake of floods early warning information (Becker, Johnston et al. 2008). This is because warning messages were mostly considered depending on people‘s past experience with floods.

This study also discovered that households with 7 people or more had better uptake of floods early warning information. This could have been attributed to the fact that households with more family members have better capacity to transport their property to safer zones like schools. This contradicts another study which revealed that large families had uptake of about 41.7%, which is relatively low compared to the findings of this study (Okayo, Odera et al. 2015),

7.3 Knowledge and experiences on floods early warning information

This study reveals that households which have ever been affected by floods had better uptake of floods early warning information compared to those that have never been affected by floods. This was in line with another study which indicated that people who have had past effects of floods were more likely to consider uptake of warning messages (Tall, Mason et al. 2012). Experience sharing can be encouraged so that households who had ever been affected by floods can educate others as one way to enhance uptake.

Those who said frequency of floods is every 6 months to1 year had better uptake of floods early warning information. In a related study, the duration and frequency of floods affects uptake in

50 that prolonged floods make people more subject to uptake of floods early warning information (Jha, Bloch et al. 2012). Emphasis should be put to sensitize people to generally take up floods early warning information irrespective of duration and frequency since damage resulting from floods does not necessarily depend on duration and frequency.

This study noted that respondents who receive flood early warning information from the district were 40% more likely to use floods early warning information. This could be attributed to proximity of the district and the level of involvement of the community. In line with the United Nations survey conducted in 2006, local government was known to have direct responsibilities for citizens and ensuring early warning information reach the community. However, the study did not elaborate on people‘s response to those messages (Basher 2006). Butaleja district can improve uptake by ensuring that all the sub counties are equipped to receive and disseminate floods early warning information.

According to this study, uptake of floods early warning information with regards to timeliness was good. However, the qualitative study rated timeliness of the floods early warning information as low since they said by the time they hear the alarm, some homes will have flooded already especially those close to the river banks. In a related study, the United Nations put it that timeliness of the warning information is highly linked to the technology and it determines the level of uptake (Basher 2006). Besides, in another study conducted in 2012, it was discovered that forecasts in their current state are not sufficiently appropriate to information needs and decision making, since the warning either comes very late or too close to effect any preparedness (Tall, Mason et al. 2012). The study did not consider finding out why those who receive information early enough also fail to take up the warning information. Uptake of floods early warning information can be improved further if the district improves on the timeliness and reliability of floods early warning information.

Those who received floods early warning information from meteorological authority were 1% less likely to consider uptake of floods early warning information. But those who received warning information from the district level (including that installed on river Manafwa) were 40% more likely to consider uptake of floods early warning information. In a related study however, warning information from the meteorological authority was highly respected leading to good uptake of the information (Garambois, Roux et al. 2013). This could have been attributed to real

51 time information that relates to action, which is not the case with meteorological authority in Uganda.

Furthermore those who experience moderate damage to income had high uptake of 61.2% while those who experience high damage to income had high uptake of 55.5%. The study also realized that those who had moderate damage to property had high uptake of 62.6% and those who experienced high damage to property had high uptake of 52.6%. A related study conducted in Kenya in 2015 reported that combined economic loss due to floods was at 93%. Both studies reported that income and property damage affects uptake of floods early warning information (Okayo, Odera et al. 2015). This can be strengthened further by translating the damages caused into monetary value so that people can view the losses in a more tangible way.

7.4 Attitudes toward floods early warning information

In this study, those who said risk resulting from floods can be avoided had better uptake of floods early warning information. This is probably because they know they can utilize the floods warning information to prepare and respond to floods. In a related study, those who perceive that risks resulting from floods can be avoided take it that through floods warning information this risk can be avoided and therefore have better uptake just like this study affirms (Wouter Botzen and Van Den Bergh 2012).

Those who appreciate the floods early warning information received in the community had better uptake. According to the analysis, the prevalence of uptake was 5.72 times more among those who rate floods early warning information received in the community as good. This implies that they are positive towards the information received and therefore understand and utilize it better as they appreciate the importance of floods early warning information in saving lives and property. In a global survey conducted by the United Nations in 2006, the Secretary General stated that ―If an early warning system was in place when the tsunami of 26 December 2004 hit the Indian Ocean region, many lives could have been saved‖. All these portray the importance attached to early warning information (Basher 2006).

7.5 System’s factors affecting uptake of floods early warning information

According to this study respondents who indicated trust in the people who operate the early warning sensor and those who communicate the warning information had better uptake of floods early warning information that they receive. This implies that they are positive on the messages they receive. Contrary to this finding, a study revealed that poor communication, coupled with limited languages used during forecast impacts on the uptake of the information (Tall, Mason et al. 2012). Therefore, in order to improve on uptake of floods early warning information, the right languages, with proper communication should be used.

This study also discovered that people who believed in the floods early warning sensor installed in river Manafwa which alarms whenever there is anticipated flood had better uptake of floods early warning information. A related study conducted in 2009 discovered that people‘s trust on the warning system including the machines used is paramount in uptake of the warning information (Roberts, Cole et al. 2009). The study only considered flooding from the main rivers but did not consider floods due to other factors like poor drainage and lowland. However, there is need to educate the communities on operation, maintenance and utilization of the sensor so that it is not misused since some people reported that misuse of the sensor contributes to low uptake.

7.6 Environmental factors associated with uptake of floods early warning information

This study mentioned that topography is the only environmental factor which had scientifically significant association with uptake of floods early warning information. This could be attributed to the fact that people living in the low lying areas were more prone to floods and therefore respond better to floods early warning information. This was in line with a study conducted in 2009 which reported that, the poor people who were living in the low lying areas due to their inability to afford land elsewhere end up living in high floods risk areas (Di Baldassarre, Montanari et al. 2010). Besides, a draft report by United Nations Environment Programme (UNEP) narrated more on location specific environmental threats which include ecosystem changes and loss of wetlands among others (Grasso and Singh 2011). The study did not however show how topography affects uptake of floods early warning information.

7.7 Practices and perceptions associated with uptake of floods early warning information

According to this study, those who save money as they prepare for floods were less likely to uptake floods early warning information as compared to those who do not save money. This has been attributed to the fact that they are able to get alternative source of livelihood during and after floods. However, a study conducted in Karamoja in 2010 reported that those who have saved money and invested in agriculture are more likely to consider uptake of warning information. This is because they become conscious of the losses and consequences of floods (Mubiru 2010).

This study revealed that those who evacuate their homes when there is flood are more likely to consider uptake of floods early warning information. This is more likely attributed to the difficulty experienced before, during and after evacuation. According to a study conducted in Kenya, 41.1% of the respondents reported that they move to another location when there is floods (Okayo, Odera et al. 2015). Besides, as they move, they are exposed to more risks including accidents and loss of property. These make them to have good uptake of floods early warning information.

In this study, households whose members contracted diseases like malaria and diarrhea among others during the last floods were 3.69 times more likely to consider uptake of floods early warning information. A study conducted in Western Uganda indicated that extreme floods resulted in a 30% increase in malaria. Relating to this study, there are all indications that floods prone areas are more at risk of contracting diseases and therefore more likely to respond positively to warning information (Boyce, Reyes et al. 2016). There is need to have disease emergency preparedness plan for floods prone areas to counter act the likely effects of floods on disease out breaks.

This study revealed that respondents who believed that floods are temporary were less likely to take up floods early warning information. In another study, flash floods were considered based on their intensity. People reacted based on the predicted effects of the floods (Garambois, Roux et al. 2013). The study did not explore other beliefs like flood is seasonal. There is need to

54 address community beliefs and misconceptions in order to improve uptake of floods early warning information.

CHAPTER EIGHT

8.0 CONCLUSIONS AND RECOMMENDATIONS

8.1 Conclusions

Although the vast majority 9.81/10 of the respondents received floods early warning information and most acknowledged that it is reliable, uptake was low.

The factors associated with uptake of floods early warning information include number of people in the household, frequency of floods in the community, types of floods early warning information used and preferred in the community, distance from Butaleja district head quarter, distance from Himutu sub county head quarter, reliability and timeliness of floods early warning information, believes that floods are temporary and those who evacuate their homes in times of floods.

Environmental factors such as topography, seasons, landscape and land use were associated with uptake of floods early warning information. This is because those who say topography does not affect uptake, are more likely to take up floods early warning information. Besides, community members are more likely to take up floods early warning information during the rainy seasons, compared to the dry seasons.

8.2 Recommendations

1. Community beliefs and misconceptions should be addressed since this affect uptake of floods early warning information. There is need for sensitization and health education by the government through Butaleja district local government, to ensure the community appreciate floods early warning information and use it to avoid adverse effects of floods. 2. The government through Butaleja district local government should consider constructing evacuation centres to assist the community during floods since there is no evacuation centre. This will reduce the burden imposed on the institutions such as schools and health centres which act as evacuation centres during floods. 3. Further study should be conducted about the operation and maintenance of the floods early warning sensor installed on river Manafwa. Specific focus should be on community involvement in ensuring sustainability and ownership of the machine. 4. The study found out that there are various partners who support the community in times of floods. Therefore coordination between these partners should be strengthened to reduce duplication of services, ensure integrated approach and cost effective interventions.

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APPENDICES

Appendix I CONSENT FORM

Makerere University College Of Health Sciences School Of Public Health Institutional Review Board (Maksph-IRB) Consent Form Title of the study: Factors associated with uptake of floods early warning information in Butaleja district, eastern Uganda.

Principal investigator: Akumu Jennifer, Master of Public Health Disaster Management student, Makekerere University, School of Public, Department of Community Health and Behavioral Change. Phone number: (+265750063036), email:[email protected]

Supervisors: Dr. Roy Mayega, Tel: +256772412455 Mr. Ali Halage, Tel: +256 772663033

Introduction This consent form explains the research study you are being asked to join. Please review this form carefully or (listen carefully to what I will be reading from this form) and ask any questions about the study before you agree to join. You may also ask questions at any time after joining the study.

Purpose of the study: This study intends to examine the factors associated with the uptake of floods early warning system in Butaleja district. Butaleja district is seasonally affected by floods. And yet there is an early warning system established on River. Manafwa. This study will therefore be able to establish factors associated with the uptake of early warning system so as to establish a better solution to improve the uptake, thereby reducing floods related impacts. Procedures

On agreement to participate, you will be requested to participate in an interview for about 30 minutes whereby you will be answering questions from a questionnaire regarding the topic of the study. Your honesty and openness in answering the questions is very helpful to the study. Risks/Benefits This study will require you to spend time during the interview to answer questions which is not part of the main activities of the day according to your plans. The questions also may ask you about topics related to your private life like economic affairs, affairs which some people feel uncomfortable to discuss with others. Outcomes of the study will help us to know the factors associated with the uptake of floods early warning system in Butaleja.. Compensation: No compensation will be given to you for participating in this research study. Protecting data and confidentiality All the information from this interview will be kept confidential except those directly involved in the research. A code instead of the name will be used on the questionnaire to maintain anonymity and the consent form which bears your name will be kept separate from the questionnaire file. All paper data files will be kept confidential in a locked cabinet for three years, at which time they will be destroyed. The questionnaire will be safeguarded in a sealed envelope and later transferred on pass-word protected computer of the researcher. Electronic data will not include your name or any of the participants name and results will not be reported as for an individual but a group or summary. Right to participation, refuse, withdraw and complain Your participation is voluntary and you can choose to take part in the study or not. You can also stop your participation at any time. Your decision to take part, or not and your responses will not affect your access to quality care at any health and nutrition service provision. Should you have question or problems regarding this study, please contact the researcher or supervisors on the number provided above.

STATEMENT OF CONSENT I ………………………………………….. having been explained to the purpose of the research, my roles, benefits and risks involved in the research. I am aware that my information will be kept confidential and that also that participating in the study does not waive my legal rights to withdraw or access quality health services. I have also been given chance to ask any question before signing and therefore, I voluntarily agree to be in the study. Name and Signature of participant______Name and Signature of the Witness______Name and Signature of interviewer______

APPENDIX IIa: STRUCTURED QUESTIONNAIRE

A STRUCTURED QUESTIONNAIRE TO STUDY THE FACTORS ASSOCIATED WITH THE UPTAKE OF FLOODS EARLY WARNING INFORMATION IN BUTALEJA DISTRICT, EASTERN UGANDA.

Identification information

Sub county code: Household Code:

Parish code: Interviewer‘s Names:

Village code: Date of interview:

Section A: Individual factor Response

Socio-demographic factors

1=Male Sex of respondent 2=Female

Age ……………(Years)

1= No formal Education

What is the highest level of formal 2= Primary Education

education you attained 3= Secondary Education

4= Tertiary Education

1=Male Sex of household head (if different 2=Female from respondent) 3= N/A because is the respondent

Current marital status of the head of 1=Single household 2=Married

3=Separated/Divorced

4= Widowed/ Widower

5= N/A because is the respondent

Relationship of respondent to head of 1= Self (is the respondent) household 2= Spouse

3= Child

4= Grand child

5= Other (Specify)……………………….

Which of the following items does A radio A cupboard your household own (tick whichever is present) A television A clock Electricity Cattle

A phone A goat

A refrigerator Chicken/ other birds

A Table Land

A Chair Food store/ Granary

A sofa set A Motorcycle

A bed A bicycle

Other (specify)……………………………………….

…………………………………………………… ….

Type of roof for the house of resident 1= Grass thatched 3= Tiles (Observation) 2 = Iron sheet 4= Others (Specify) …………………..

Type of walls for the house of 1=Grass thatched 3= Iron sheets residence (Observation) 2= Mud/wattle 4= Bricks/blocks

5= Other (Specify)

…………………………………..

…………………………………………………… …..

Type of floor for the house of 1= Cement/tiles residence (Observation) 2= Mud/ cow dung smeared

3= Loose floor surface

4= Other (Specify) …………………………………

…………………………………………………… …

Uptake of floods early warning information

Knowledge regarding Floods Early Warning Information

Has your household ever been affected 0= No by floods? 1= Yes

2=Don‘t know

What is the frequency of floods in this 1= Not at all 4= 1 to 2 years community 2= Up to 6 months 5= Over 2 years

3= 6 months to 1 year

During which months do you (Specify Months)…………………………. experience most floods? (mention all) ……………………………………………..

0= No Have you received any floods early warning information regarding your 1= Yes community? 2= Don‘t know

If yes, how frequent do you receive 1= Every three months this information? 2= Twice a year

3= Once a year

4= More than a year

5= Don‘t remember

What are the different types of Floods 1= Modern 3= Both Early Warning Information used in this community ( Don’t read out 2= Traditional 4= I don‘t know options)

1= Modern Of the different types of floods early warning information that you 2= Traditional mentioned, which one do you 3= Both frequently depend on or prefer? 4= None

1= Reliable 3= Period of warning

2= Accessible 4 = Others (Specify) Why do you depend mostly on this ……………………… type of Floods early warning …. information? (Tick all that apply) ……………………… ….

How do you tell that a household is 1= Proximity to a river or swamp vulnerable to floods? (Mention all that you know) 2= Lives on a lower ground 3= Poor drainage around the home

4= Poor (weak) housing structure

5= Others (Specify)………………………………………..

………………………………………………………… ……..

In your own opinion, how do you rate Children 1= Not vulnerable

69 the vulnerability level of the people in 2= Fairly vulnerable your community? 3= Vulnerable 4=Extremely Vulnerable

Women 1= Not vulnerable

2= Fairly vulnerable

3= Vulnerable

4 = Extremely Vulnerable

Men 1= Not vulnerable

2= Fairly vulnerable

3= Vulnerable

4 = Extremely Vulnerable

Disabled 1= Not vulnerable

2= Fairly vulnerable

3= Vulnerable

4= Extremely Vulnerable

Is your household vulnerable to 0=No floods? 1=Yes

2=Don‘t know

If yes, what shows that your 1= Proximity to a river or swamp household is vulnerable to floods? 2= Lives on a lower ground

3= Poor drainage around the home

4= Poor (weak) housing structure

5= Physical inability

6= Others (Specify)…………………

……………………………………….

Do you know of any hazard/ 0=No risk/threat other than floods that affected this village within the last one 1=Yes year? 2= Don‘t know

If yes, mention the hazard/ risk/threat 1= Landslides that affected this village within the last one year? 2= Famine

3= Drought

4=Diseases (Specify all)……………

……………………………………………

5= Pests (Specify all) ……………………

…………………………………………….

6= Others (Specify)…………………………………..

…………………………………………………… ….

Practices to aid uptake of Floods Early Warning Information

Does this Sub county have 0=No contingency plan? 1=Yes

2= Don‘t know

If yes, do you have access to the 0=No contingency plan? 1=Yes

2= Don‘t know

When was the contingency plan last 1= <1month updated? 2= 1-6 months

3= >6 months -1 year

4= > 1 year

5= Don‘t know

How does your household get ready 1= Save money for floods (household preparedness)? (Tick all that apply) 2= Keep food in the house 3=Keep food in the granary or food store

4= Move to alternative site (Evacuation centre)

5=Don‘t do anything about it

How does your household respond to 1= Construct house with raised floor floods? (Tick all that apply) 2= Dig drainage channel in the compound

3= Dig drainage channel in the gardens

4=Reduce the number of meals eaten per day

5=Move to alternative site (Evacuation centre)

6=Do nothing

0= No Are there instances when you experience disease outbreak in this 1=Yes village during floods? 2=Don‘t know

If yes, which diseases where 1=Malaria prominent / noticeable? (Tick all that apply) 2=Cholera 3=Typhoid

4= Other diarrheal diseases

5= Respiratory tract infections

6= I don‘t know

7= Other diseases (Specify) …………………………

……………………………………………………

….

Did any of your household members 0=No contract the disease? 1=Yes

If yes, did any of your family 0=No members die of the disease? 1=Yes

Who supported you or your 1= Central Government community during this outbreak? (Tick all that apply) 2= Local Government 3= Red Cross

4=Other Humanitarian organizations/NGOs (Name them)……………………………..

……………………………………………

5= None

In times of floods, do you always 0=No evacuate your home? 1=Yes

If no, why not? 1= Nowhere to go

2= Evacuation sites too congested/ not safe

3= Floods temporal, so feel no need

4= It is expensive

5= Difficult to move family

6= Others (Specify)………………………….

……………………………………………….

Access to Floods Early Warning Information

How far is your home from Butaleja 1= 1-5 kilimeters 3= 11-20 Kilometers district head quarter? (Where floods early warning system is installed) 2= 6-10 Kilometers 4= Over 20 Kilometers

How far is your home from Himutu 1= 1-5 kilimeters 3= 11-20 Kilometers Sub county head quarter? (Where floods early warning system is 2= 6-10 Kilometers 4= Over 20 Kilometers installed)

Did you receive prior information 0= No about the last floods you experienced in this community? 1= Yes If yes, through which media of 1= Television 6= Phones communication did you receive this message (Tick all that apply) 2=Radio 7=Community meetings 3=Newspaper 8=Place of Worship

4=Brochures 9=Gatherings

5=Leaflets 10=Others (Specify)…………

……………………………… …

According to you, what is the most 1= Television 6= Phones appropriate way of passing early warning information? 2=Radio 7=Community meetings 3=Newspaper 8=Place of Worship

4=Brochures 9=Gatherings

5=Leaflets 10=Others (Specify)…………

……………………………… …

How frequently do you receive Floods 0= Extremely rarely (Over one year) Early Warning Information? 1= Very rarely (Once a year)

2= Rarely (Bi-annually)

3= Sometimes (Quarterly)

4= Often (Monthly)

5= Very Often (At least Weekly)

Do you always share floods early 0=No warning information with others? 1=Yes

If yes, with whom do you share floods 1= Family members early warning information 2= Friends

3= Community

4= Other (Specify) …………………………………..

…………………………………………………… ….

Attitudes towards Floods Early Warning Information

How do you rate the magnitude of 1= Very big (Affects > 500 people) floods in this community? 2= big (Affects 50-500 people)

3= Small (Affects 10-49 people)

4= Very small (Affects <10 people)

Do you think risks resulting from 0=No floods can be avoided? 1=Yes

2= Don‘t know

If yes, how can it be avoided? 1= Building far from river or swamp (Mention all) 2= Not cultivating on the flood plain

3= Improving drainage around the home

4= Improving housing structure

5= Living on a hill, not flood plain

6= Others (Specify)……………………….

……………………………………………..

On average, what is the duration of 1= < 24 hours 5=> 6 months-12 months floods in this community? 2= >1day < 14 days 6= > 1 year

3= 2 weeks - 4 weeks 7= Not there

4= > 1 month - 6 months

In your opinion, how do you rate the 1= Very unreliable (0-1) Floods Early Warning Information that you receive in this community? 2= Unreliable (2-3) (Scores of 1-10) 3= Somehow reliable (4-5)

4= Reliable (6-7)

5=Very reliable (8-10)

How do you rate the reliability of the Modern 1= Very unreliable (0-1) different forms of floods early warning systems in this community? 2= Unreliable (2-3)

(Scores of 1-10) 3= Somehow reliable (4-5) 4= Reliable (6-7)

5=Very reliable (8-10)

Traditional 1= Very unreliable (0-1)

2= Unreliable (2-3)

3= Somehow reliable (4-5)

4= Reliable (6-7)

5=Very reliable (8-10)

For the last time that you received Reliability 1= Very Poor(0-1) floods early warning information, kindly tell us how you felt with 2= Poor (2-3) regards to the following 3= Fair (4-5)

4= Good (6-7)

5= Very Good (8-10)

Accessibility 1= Very Poor(0-1)

2= Poor (2-3)

3= Fair (4-5)

4= Good (6-7)

5= Very Good (8-10)

Time lag 1= Very Poor(0-1) (Timeframe from warning 2= Poor (2-3) to event) 3= Fair (4-5)

4= Good (6-7)

5= Very Good (8-10)

How do you rate your level of uptake Accessibility 1= Very low 4= Very good of Floods Early Warning Information that you receive with regards to the 2= Low 5= Excellent following? 3= Good

Understanding 1= Very low 4= Very good

2= Low 5= Excellent

3= Good

Utilization 1= Very low 4= Very good

2= Low 5= Excellent

3= Good

Systems factors

0=No Do you think the people collecting and disseminating Floods early warning 1=Yes information, frequently do their work? 2= Don‘t know

If yes, how do you rate the 1= Weak (1-2) 4= Very good (7-8)

77 effectiveness of their work? (Scale of 2= Fairly weak (3-4) 5=Excellent (9-10) 1-10 in terms of performance and capability) 3=Good (5-6)

Do you think the instruments 0=No (machine) used for collecting and 1=Yes disseminating Floods early warning information is effective? 2=Don‘t know

If yes, how do you rate the 1= Weak (1-2) 4= Very good (7-8) effectiveness of the machines? (Scale 2= Fairly weak (3-4) 5=Excellent (9-10) of 1-10 in terms of performance and capability) 3=Good (5-6)

Socio- cultural practices

How many people leave in this 1=1-3 people 4= 10-12 people household? 2=4-6 people 5= above 12 people

3= 7-9 people

What is the highest level of formal 1= No formal Education education of head of household (if different from respondent) 2= Primary Education

3= Secondary Education

4= Tertiary Education

1= Formal employment What is the employment status of the 2= Informal employment head of household? 3= Does not work at all

Tribe of household head 1= Jopadhola 3= Bagwere

2= Banyoli 4= Bagishu

5= Others (Specify)……………………………......

What are the beliefs about floods in 1=Temporal 3=Curse

78 this area? (Write all that is mentioned) 2=Normal 4= Gift from God

5= Others (Specify)………………………………….

How do you know that it is going to 1=Warning information from meteorological flood? (Write all that is mentioned) authority

2= Warning information from district level

3= Traditional signs (List them as mentioned)……………………………………… ….

…………………………………………………… …

4=Don‘t know

Do you perceive your community as at 0=No risk? 1=Yes

2=Don‘t know

How do you perceive your community 1= Very big (Affects more than 50 households) risk? (Give scale) 2= big (Affects 20-49 households)

3= Moderate (Affects 10-19 households)

4= Small (Affects 5-9 households)

5= Very small (Affects less than 5 households)

0=No Do you perceive your household as at 1=Yes risk? 2=Don‘t know

How do you perceive your household 1= Very big (9-10) 4= Small (3-4)

79 risk? (Give scale) 2= big (7-8) 5= Very small (1-2)

3= Moderate (5-6)

Environmental factors

0=No Do you think environmental factors determine the uptake of Floods early 1=Yes warning information? 2=Don‘t know

If yes, what environmental factors 1= Topography 3= Landscape determine the uptake of Floods early warning information? (mention all, 2= Seasons 4= Land use giving reasons) 5= Others (specify) ………………………………….

…………………………………………………… ….

Where do you defecate in times of 1= Latrine/toilets 3= River/swamp floods? 2= Bush 4= Others (Specify)

Where do you mostly get water in 1= Tap 4= Unprotected spring times of floods? 2= Borehole 5= River/lake

3= Protected spring 6= Others (Specify)

……………………… …

Recommendation

What do you suggest to aid 1= Disseminate reliable information improvement in uptake of Floods Early Warning Information? 2= Send timely information (at least two days (Multiple) before event) 3= Use local radios

4= Use local gatherings (churches, markets etc)

5= Use brochures/ leaflets and fliers

6= Sensitize community

7= Others (Specify)………………………….

……………………………………………

KEY INFORMANT INTERVIEW (KII) GUIDE

(Ministry of water, OPM and Meteorological Authority; District technical staff; Community leaders)

1. How frequent do you experience floods? 2. When did you last experience floods? (Probe)  Magnitude (i.e threats to human beings, socio - economic and physical damage).  Duration 3. Do you have any early warning system for floods in Butaleja? (Probe)  Modern Early Warning information  Traditional early warning information 4. Comment on floods early warning information received by the community in Butaleja district?  Modern Early Warning information  Traditional early warning information

Probe (while considering source, type of information, when it’s given, how frequently, in what form it is given, reliability and accessibility).

5. Let us talk about factors that affect uptake, what do you think are the factors affecting uptake of floods early warning information  In the community  In the district 6. How can we improve uptake of floods early warning information in Butaleja district? (Probe for frequency, communication channels, accessibility etc)

APPENDIX IIIb: STRUCTURED QUESTIONNAIRE

EBIWUUSO EBINGHAMBA HUHUSOMA HU MBERA EGINGHAMBAGANA N‘ENGERI OBUBAHA OBULAWULA HUHWETEHERA AMATABA EYIBUNGILIBWAMO MU BUTALEJA DISTULIKITI, EBUNGHWA LYOBA WA UGANDA

Ebinghamba hu batu

Eggombolola: Amasiina g‘ehidaala

Omuluha: Amasiina g‘Omuwuhirisi

Eshalo: Olunaku lw‘Ohuwuhirisa:

Section A: Ebiretebwa omutu Engaluhamo

mubutu

SEbiletebwa embera gyabatu muhitundu

Musaaja

Agaluhamo Muhasi oba Musajja Muhasi

Emyaha Emyaha ………..

Sinasomaho

Mupulayimale Wasoma paka shahunga? Musiniya

Gasoma paka mu matendehero ganghamugulu

Omuhulu w‘ehidaala Muhasi oba Musajja

Musajja (anaba wanjawulo hwali Muhasi hugaluhamo) Sihitinilangho kubanga njalihugaluhamo

ebiwuso.

Ali yeka / Simufumbo

Omuhulu wehidaala ali atye hatyani Mufumbo mumbera gyebyobufumbo. Gawuhana / Ganoba

Namwandu / Semwandu

Sihitinilangho kubanga njalihugaluhamo ebiwuuso

Oluganda olulingho nghagati w‘alihugaluhamo n‘omuhulu Ndiise samwene (alihugaluhamo) w‘ehidala Wamwange / Muhasiwange

Mwana wange

Mwijuhulu wange

Ehindi (Yanjulusa)

Batu banga abaaba muhidaala hino? Batu 1-3 Batu 10-12

Batu 4-6 Basuka abatu 12

Batu 7-9

Sigasomaho

Omuhulu w‘ehidaala gasoma paka Gahoma mupulayimale mushahunga? (anaba wanjawulo Gahoma musiniya humuhulu wehidaala) Gasoma paka mumatendehero aganghamugulu

Omuhulu w‘ehidala gasoma paka Sigasomaho

84 mushahunga (anaba wanjawulo Gahoma mupulayimale humuhulu w’ehidaala) Gahoma musiniya

Gasoma paka mumatendehero aganghamugulu

Oli n‘omulimo ogwetongoye

Omuhulu wehidaala ahola mulimu hi? Oli n‘omulimo ogutali mutongole

Nghanghuma mulimo ogwahola

Omuhulu w‘ehidaala waggwanga hi? Mudama Mugwere

Munyole Mugisu

Ebindi (Yanjulusa) ………………………………

Ladiyo Essaawa y‘Ohuhisenge

Hubitu bino, biringhena ebiri Tiivi Enghombe muhidaala hino? (taho tiiki hubuli Amasaanyalaze Embusi eshilingho) Esimu Enghoho / Ebiyuni ebindi

Fuligi Eloba

Emeza Ehyagi

Ettebe Epikipiki

Ehitanda Egaali

Ekabadda

Ebindi (Yanjulusa) ……………………………………

……………………………………………………

….

Hina ehibahohesa ohusereha enyumba Enyaasi Mategula (Lengerera) Ebatti Ebindi (Yanjulusa) ………

Hina ehibahohesa ohuzimba ebisenge Enyaasi Ebatti byenyumba eyibanghenyuhamo Madosi/Masisye Matafari (Lengerera) Ebindi (Yanjulusa) …………………………………………..

Hiina ehibahohesa ohuzimba nghasi Seminti / tayilo wennyumba (Lengerera) Madosi / banghaha masisye

Nghanghuma ehibataye nghasi

Ehindi (Yanjulusa) …………………………………

Engeri obubaha owulawula huhwetehera amataba mubwangu eyibunghiribwamo

Ohumanya ebinghambagana hu bubaha obulawula huhwetehera amataba mubwangu.

Ehidaala hyemwe hyali hikosewileho amataba? Bbe

Iye

Simanyire

Hinaba ti ye, ohunghwa hu 0-10 (0 nga njeyanghasi ate nga 10 nga

86 njeyanghamugulu), ehidaala hihyo hyakosewa hibbala hi mubyefuna mumataba agamba gabangho.(Nghagati wa 0 ne 10)

Amataba gatera hubango emirundi nga Sigabangho Omwaha 1-2 ginga muhitundu hino? Paka myesi 6 Ninghabitire emyaha 2

Myesi 6 paka hu mwaha 1

Amataba musinga hugafuna mu myesi Yanjulusa emyesi hi? (Loma emyesi gyosi) …………………………………

Bbe Wafuna hu bubaha obunghamba hu Iye hwetehera amataba muhitundu shisho? Simanyire

Hinaba ti ye, mirundi ginga egyotera Buli myesi edatu ohufuna obubaha buno? Mirundi ebiri mu mwaha

Mulundi mulala mu mwaha

Ohusuka omwaha

Sikewulira

Ndomele hu biha byengeri obubaha Byahisungu Engeri gyombi obunghamba hu hwetehera amataba Byahinasi Simanyire amangu eyibuhohesebwamo muhitundu hino?

Hu ngeri ejenjawulo obubaha Eyehisungu

87 obunghamba hu hwetehera amataba Eyehinasi amangu ejolomile, ngeri hi eyotera Engeri gyombi ohwedilaho oba eyosinga ohwesiga? Nghanghuma

Lwahiina eyo njengeri eyosinga Yesigiha Ebiseera ebyo hulawula ohutera ohwesiga muhufuna obubaha Ebindi (Yanjulusa) obunghamba huhwetehera amataba ………… Yolehelera amangu (tiikinga ebyo byosi ……………………… ebitiniraho) …

Omanya otye ti ehidaala hiri Hinaba hupi n‘elungu n‘obuzibu bungi ohulumbibwa Hunghenyuha muhitundu hyanghasi amataba? Amajji gatengama nghango

Ennyumba naffu

Ebindi (Yanjulusa) ………………………………………..

Abaana Sibali nabuzibu

Muhuwona huhwo, abatu b‘omu Baliho n‘obuzibu hitundu hihyo bali n‘obuzibu bubbala Bali n‘obuzibu ohulumbibwa amataba? Bali n‘obuzibu bubbala

Sibali nabuzibu

Abahasi Baliho n‘obuzibu

Bali n‘obuzibu

Bali n‘obuzibu bubbala

Abasajja Sibali nabuzibu

Baliho n‘obuzibu

Bali n‘obuzibu

Bali n‘obuzibu bubbala

Abali Sibali nabuzibu n‘obulemu Baliho n‘obuzibu

Bali n‘obuzibu

Bali n‘obuzibu bubbala

Is your household vulnerable to Bbe floods? Iye Ehidaala hihyo hiri n‘obuzibu Simanyire bwohulumbibwa amataba?

Hinaba hitufu, hiina ehilaga ti ehidaala Ohuba hupi n‘omwala oba elungu hihyo hiri n‘obuzibu ohulumbibwa amataba? Ohuba muhitundu hyanghasi

Amajji ohutengama nghango

Ennyumba nafu

Ohutaba n‘abusobozi

Ebindi (Yanjulusa) ………………………………………….

Omanayireho ahabenje/ehibwatuhira Bbe ohutusaho amataba ehyakosa ehyalo Iye hino mu mwaha mulala egongo? Simanyire

Hinaba hituffu, loma ahabenje/ Eloba Ohulumbuluha ehibwatuhira ehyakosa ehyalo hino Enjala mu mwaha mulala egongo? Enjala

Endwasi (Yanjulusa gyosi) …………………………….

Ebiwuuka (Yanjulusa

byosi)……………………………

Ebindi (Yanjulusa)……………………………………… …..

Ekola ohuyedda hu ngeri obubaha obunghamba huhwetehera amataba mangu eyibunghambibwamo

Amago gago geteheratehera gatye Ohubiiha ebbesa amataba (obwetegefu bwe‘hidaala)? Ohubiiha emeere munnyumba (tikkinga byosi ebitinaraho) Ohubiiha emeere muhyagi / mbihiro y‘emeere

Ohutina anghandi (Ehifo anghohudulumira)

Nghanghuma ehihuholangho

Ehidaala hihyo shiholangohi Ohuzimba Ennyumba eyibatindiliye nihifunire amataba? (Tikkinga byosi nghamuggulu

90 ebitiniraho) Tangho emifulejje mu lunya

Tangho emifulejje mu ndimiro

Hendesa hu mirundi ejimulyamo buli lunaku

Ohutina anghandi (Ebifo anghohudulumira)

Nghanghuma ehihuhola

Bbe Nghalingho ebiseera olumufuna endwasi mubiseera by‘amataba mu Iye hyalo hino? Simanyire

Hinaba hitufu, ndwasi hi ejisinga / Omusujja gw‘esuna ejiwoneherera? Kolera

Typhoid

Endwasi egindi egy‘ohwidukana

Endwasi gy‘ohwisa

Lukusense

Edwasi gy‘olususu

Simanyire

Endwaye egindi (Yanjulusa …………………………….

Nghalingho abehidaala hihyo abafuna Bbe endwasi eyo? Iye

Hinaba hitufu, ngalingho abe‘hidaala Bbe hihyo abafuye endwasi eyo? Iye

Njani agahuyeda oba ohuyedda Gavumenti yanghagati ehitundu shisho mubiseera Gavumenti y‘Ehitundu bye‘ndwasi? (Tikkinga byosi ebitiniraho) Red Cross

Ebitongole by‘ehinakyewa (Bilome) …………………..

Nghanghuma

Mubiseera byamataba, mutera Bbe ohunghwaho mumago genywe? Iye

Hinaba ti bbe, lwahiina bbe? Nghanghuma nga hutina

Ebifo anghohudulumira biri n‘abatu bangi / sibili bitehetehe wulanghi

Amataba gahiseera, mbona ti sihyetagisa

Hya beeyi

Hizibu ohugesya abomumaggo bossi

Ebindi (Yanjulusa) …………………………………..

Ohufuna obubaha obunghamba huhwetera amataba

Ohunghwa hu 0-10(0 nga njesingayo ohuba ngasi, 10 nga njesingayo ohuba ngamugulu) ogerageranya otye ekola

92 ya alarm eyibata hu buligi ye Namulo.

Nghalingho bulengi hi ngagati we Kilometer 1-5 Kilometer 11-20 wuwo ne Butaleja hu distulikiti? Kilometer 6-10 Ohusuuka Kilometer 20 (Eyibataye ebyoma ebilawula amangu hu mataba)

Nghalingho bulengi hi ngagati we Kilometer 1-5 Kilometer 11-20 wuwo ne hu Ggombolola e Himutu? Kilometer 6-10 Ohusuuka kilometer 20 (Eyibataye ebyoma ebilawula mangu hu mataba)

Wafunaho obubaha obwali Bbe nibunghamba humataba Iye agamwasembayo ohufuna muhitundu hino? Hinaba ti ye, obubaha buno Tiivi Amasiimu mwabufuna mu kola hi Ladiyo Mukunghana gy‘ehitundu eyabyamawulire? (Tikkinga byosi ebitiniraho) Amanghulire Mubiffo by‘ohusabiramo g‘epapula

Obutabo Mukunghana

Epapula Obubaha obwamangu e Namulo

Ebindi (Yanjulusa) ……………………….

Ohusinzira hwiwe, ngerihi esinga Tiivi Amasiimu ohuba endangi ohubisya obubaha Ladiyo Ekunghana gy‘ehitundu obwamangu obulawula hu mataba? Amanghulire Mubiffo by‘ohusabiramo g‘epapula

Obutabo Mukumbana

Epapula Ebindi (Yanjulusa)………….

Mirundi jinga egyotera ohufuna Sihitera hubangho nayire (ohusuuka omwaha obubaha obwamangu obulawula hu mulala) mataba? Siteera hubangho (Mulundi mulala mu mwaha)

Siteera (Emirundi ebiri mu mwaha)

Anghandi (Buli luvanyuma lwamyesi enne)

Tera (Buli mwesi)

Batera nnyao (Buli wiiki)

Bulijjo ogabana obubaha abwamangu Bbe obulawula hu mataba nabahyo? Iye

Hinaba ti ye, nj‘ani eyogabana ni naye Abomumago obubaha obwamangu obulawula hu Emihago mataba? Abomuhitundu

Red cross

Abakulembezze

Gavumenti

Ebindi (Yanjulusa) ………………………………………

Endowosa eri hububaha obwamangu obulawula hu mataba

Oloboosa ti hisoboha ohwenghala Bbe ebiibi ebinghwa mu mataba? Iye

Simanyire

Hinaba ti ye, bisobola hwenghalibwa Ohuzimba nghale ne myala oba elungu bitye? (Loma ngeri gyosi) Ohutalima amataba anghagabita

Ohutelesa emifuleje nghango

Ohutelesa ennyumba

Ohunghenyuha husozi, sihu ngira y‘amataba

Ebindi (Yanjulusa) ……………………………….

Nogerageranya, amataba ganghira Nghasi we ssaawa 24 Ohusuuka emyesi 6 banga hi muhitundu hino? paka hu myesi 12

Ohusuuka olunaku Ohusuuka omwaha 1 lulala aye nghasi wenaku 14

Nghagati wa wiiki 2 Nghanghuma ne 4

Ohusuuka omwesi 1 paka hu myesi 6

Muhuwona huhwo, oloma otye hu Sibyesigiha nayile (0-1) bubaha obwamangu obulawula hu Sibyesigiha mataba obumufuna muhitundu hino? (nghagati we 1-10) Byesigihasigihahumo(4-5)

Byesigiha(6-7)

Byesigiha nnyo(8-10)

Owona otye ebiha ebyenjawulo Ebyehisungu Sibyesigiha nayile (0-1) ebyengeri gyohulawula amangu hu Sibyesigiha (2-3) mataba muhitundu hino? Byesigihasigihahumo(4-5)

Byesigiha (6-7)

Byesigiha nnyo (8-10)

Ebyaganibwan Sibyesigiha nayile (0-1) gho Sibyesigiha (2-3) (ohumanya hwango Byesigihasigihahumo(4-5) ohwahale) Byesigiha (6-7)

Byesigiha nnyo (8-10)

Humulundi oguwasembayo ohufuna Obwesigwa Bubi nnyo (0-1) obubaha obwamangu obulawula hu Bubi (2-3) mataba, tulomele njoluwanghulira nihugerageranya hu bino Bulanghilanhiho(4-5)

Bulanghi (6-7)

Bulanghi nnyo(8-10)

Obwangu Bubi nnyo (0-1) bwohwolerera Bubi (2-3)

Bulanghilanhiho(4-5)

Bulanghi (6-7)

Bulanghi nnyo(8-10)

Ohuhuma Bubi nnyo (0-1) ebiseera Bubi (2-3) (Ebiseera ebibitangho Bulanghilanhiho(4-5) ohungwa Bulanghi (6-7) ohulawula paka hu Bulanghi nnyo(8-10) mataba)

Ogerageranya otye engeri Obwangu Hwanghasi Hulanghi nnyo eyimufunamo obubaha obwamangu bwoholerera nnyo obulabula hu mataba obumufuna Hwanghasi Husingila elala muhunghambanya nina bino? Hulanghi

Ohutegera Hwanghasi Hulanghi nnyo nnyo

Hwanghasi Husingila elala

Hulanghi

Ohuhohesa Hwanghasi Hulanghi nnyo nnyo

Hwanghasi Husingila elala

Hulanghi

Embera gyebyoma ekosa engeri obubaha obwamangu obulabula hu mataba eyibufunibwamo

Olobosa ti abatu abahumbanya era Bbe batusa obubaha obulabula Iye huhwetehera amataba batera ohuhola omulimo gwabwe? Simanyire

Hinaba ti ye, ogera otye engeri Bunafu (1-2) Ndanghi nnyo(7-8) eyibaholamo omulimo gwabwe? (Hu Bunafu humo (3-4) Husingila elala(9-10) hipimo hya 1-10 nogerera hu kola n‘obusobozi) Ndanghi

Olobosa ti ebyoma ebyatehebwangho Bbe Uganda Communication Commissions Iye (UCC) ebihohesebwa ohuhumbanya n‘ohutusa obubaha obulabula Simanyire huhweteheratehera amataba bihola njoluhisaniiye?

Bunafu (1-2) Ndanghi nnyo(7-8) Hinaba ti ye, ogera otye ekola y‘eshoma ehyo? (Hu hipimo hya 1-10 Bunafu humo (3-4) Husingila elala(9-10) nogerera hu kola n‘obusobozi) Ndanghi

Ebyobuwangha bya batu ebiholebwa

Biina abatu ebibehilihisamo hu Amataba gahiseera Amataba hinghwabo mataba muhitundu hino? (Nghandiiha Amataba ga bulijjo Amataba hirabo byosi ebilomebwa) ohunghwa eri Hiwumbe

Ebindi (Yanjulusa) ………………………………

Omanya otye ti amataba gajja? Obubaha obulawula ohunghwa muhitongole (Nghandiiha byosi ebilomebwa) ehiteberesa embera y‘ebiseera.

Obubaha obulawula ohunghwa hu distulikiti

Obubonero Abatu obubawoneraho (Ohusinzira huhumanya hwa batu) (Binghandihe njolubabiloma) ………………………………….

Simanyire

Bbe Owona ti ehidaala hihyo hiri hubuzibu Iye bwa mataba? Simanyire

Hinaba ti ye, Hu hipimo hya 0-10 (0 nga njesembayo ngasi era nga 10 njesingayo nghamugulu), Hububbala hi obwowona ti ehiddala hihyo hiri hubuzibu bwa mataba?

Ebiretebwa embera ye’hitundu

Olowoosa ti embera y‘ehitundu eri Bbe nengeri eyekosamo engeri obubaha Iye ogulawula huhwetehera amataba mangu eyibunghilibwamo? Simanyire

Hinaba ti ye, mberahi egy‘ehitundu Embera y‘ehitundu Embera gye loba egyikosa engeri obubaha obwamangu Ebiro Engeri eloba bwohetehera amataba eyilihohesebwa eyibunghilibwamo?(Loma gyosi, nonga esonga) Ebindi (Yanjulusa) ………………………

Nghena eyimunya mubiseera bya Omugwanya Emyala / elungu mataba? Esuggu Ebindi (Yanjulusa)

Nghena eyimusinga ohufuna amaajji Taapu (Omuga ogutali mubiseera bya mataba? muserehe

Nayikonte Omwala / Ennyanja

Omuga omuserehe Ebindi (Yanjulusa)………….

Nghena eyimusinga ohufuna amaajji Taapu (Omuga ogutali nghanaba ninghanghuma mataba? muserehe

Nayikonte Omwala / Ennyanja

Omuga omuserehe Ebindi (Yanjulusa)

Ehilinohuholebwa

Hiina ehyolowosa ti njehinghanga Ohutusa obubaha obwesigiha ohutimbula mungeri amanghulire Ohunghindiha obubaha muhisera (wakili enaku agamba huhwetehera amataba ebiri amataba nihahiri) eyiganghiribwamo? (Bingheraho) Huhohesa Ladiyo gyomuhitundu

Muhoheze ekunghana gy‘omuhitudu (Amakanisa, obutale, nebindi)

Ohuhohesa obutabo/obupapula n‘obupapula bwohugaba

Ohusomesa ehitundu

Ebindi (Yanjulusa)…………………………

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KEY INFORMANT INTERVIEW (KII) GUIDE

(Ministry of water, OPM and Meteorological Authority; District technical staff; Community leaders)

1. Mutera ngali ohufuna amataba? 2. Mwasembayo ngali ohufuna amataba? (Wuusa) a. Gaali gabusito hi? (ohugesa; ebulabe eri abatu, ohukosa embera gya batu mu byefuna, ohukosa ebiwoneherera). b. Ebanga /Ehiseera amataba ehiganghira 3. Loma humanghulire agaloma huhwetehera amataba agafuniwa abatu mu Butaleja distulikiti. a. Ekola eyomulembe huhwetehera amataba b. Ekola abatu babulijjo eyibahohesa ohwetehera amataba. c. Wuusa (Ayenga gessa amanghulire eyigangwa, eshiha hya manghulire, amanghulire oluganghewa, ehiseera ehiganghebwa, engeri eyiganghebwa, amanghurire gesigibwa, amanghulire gafunibwa). 4. Hahulome hubikosa engeri amanaghulire eyigafunibwamo, biina ebyolowosa ti njebikosa engeri amanghulire aganghamba huhwetehera amataba eyigangiribwamo a. Mu batu muhitundu b. Mu distulikiti 5. Husobola hutye ohwongera mungeri amanghulire ag‘ohwetehera amataba eyiganghiribwamo in distulikiti ye Butaleja? a. (Wuusa hu biseera, engeri amangulire eyigafunibwamo, obwangu bwohufuna amanghulire, nebindi) 6. Edistulikiti ye Butaleja eri ni pulani eyohwenghanga amataba? a. Wuusa nghali oluhyatehebwatehebwa, pulani yoleheha? b. Pulani enaba nenghumangho, wuusa lwahiina bayinghuma

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