Child-centred Disaster Risk and Vulnerability Assessment. Mozambique

Transcription

1 Child-centred Disaster Risk and Vulnerability Assessment Mozambique December 2013 Dr Hanna Schmuck Consultant

2 Table of Contents Acknowledgements... 1 Abbreviations and Acronyms... 2 Foreword Introduction Methodology Disaster Risk Index Vulnerability Index Overlaying high disaster risk with high child vulnerability Limitations of the Assessment Disaster Risk Indicators Floods Cyclones Droughts Child Vulnerability Indicators Multi-dimensional Deprivation Education Nutrition HIV/AIDS Summary of the Results of the Assessment Disaster Risk Child Vulnerability Overlaying Disaster Risk and Child Vulnerability Conclusions... 25

3 Annexes Annex 1: Floods: District Risk in alphabetical order Annex 2: Floods: District Risk Ranking Annex 3: Cyclones: District Risk in alphabetical order Annex 4: Cyclones: District Risk Ranking Annex 5: Droughts: District Risk in alphabetical order Annex 6: Droughts: District Risk Ranking Annex 7: Municipalities Risk Ranking Floods, Cyclones and Droughts Annex 8: Child Vulnerability Index in alphabetical order Annex 9: Child Vulnerability Index by rank Annex 10: Maps on Flood and Cyclone Risk in Cities Annex 11: Risk Maps based on hydrological and meteorological data Maps: Map 1: Flood Risk by Districts Map 2: Cyclone Risk by Districts Map 3: Drought Risk by Districts Map 4: Multi-dimensional Deprivation by Districts Map 5: EP 1 Drop-out rate by Districts Map 6: Growth faltering by Districts Map 7: HIV/AIDS prevalence of pregnant women by Districts Map 8: Multi-Hazard High Risk Map 9: Child Vulnerability by Districts Map 10: Overlay of child vulnerability with high disaster risk... 24

4 Acknowledgements A number of organisations and individuals have provided valuable input to this Child-centred Disaster Risk and Vulnerability Assessment. I am grateful to all of them. In particular, I would like to thank the following: Dr Antonio Queface and Tomásio Januário from the University of Eduardo Mondlane in Maputo have provided technical support to extract data from the database for the Disaster Risk Indices. Olanda Bata and Antonio Mavie from FEWS NET Mozambique have provided information on previous risk mapping and helped in data interpretation. Birgit Weyss, Consultant for UNICEF, has developed the child vulnerability index. Hanoch Barlevi, Disaster Risk Reduction and Emergency Specialist of UNICEF Mozambique, who commissioned this assessment, has provided excellent support and cooperation in all aspects. And it was him who has developed the maps a key output of this exercise. Other staffs of UNICEF Mozambique have also provided important input - too many to mention here. I myself, however, have the final responsibility for the results of the assessment and this report and hope that it serves in improved targeting of the most vulnerable children of this country. Dr Hanna Schmuck, Maputo in December 2013; 1

5 Abbreviations and Acronyms AIDS ART CENOE Acquired immune deficiency syndrome Anti-retroviral therapy National Emergency Operational Centre DHS Demographic and Health Survey, 1996/97, 2003 and 2011 EP1 1 st cycle of primary education (grades 1-5) EP2 2 nd cycle of primary education (grades 6-7) FEWS NET GDP HCT HIV IAF INE INGC Famine Early Warning Systems Network Gross domestic product Humanitarian Country Team (of the UN) Human immunodeficiency virus Inquérito aos Agregados Familiares (Household Survey), 1996/97 and 2002/03 National Statistics Institute National Institute for Disaster Management (Instituto National de Gestão das Calamidades ) IOF Inquérito ao Orçamento Familiar (Household Budget Survey), 2008/09 INSIDA MICS MISAU MMAS NGO PARP PMTCT UN UNICEF WFP National Survey on Prevalence, Behavioural Risks and Information about HIV and AIDS Multiple Indicator Cluster Survey Ministry of Health Ministry of Women and Social Action Non-governmental organization Poverty Reduction Action Plan Prevention of mother-to-child transmission United Nations United Nations Children s Fund World Food Programme 2

6 Foreword Disaster Risk Reduction and Climate Change Adaptation are cross-cutting themes in joint programmes of the Government of Mozambique and UNICEF. A Child-centred Risk and Vulnerability Assessment is a fundamental step to improve UNICEF s targeting of those children which live in particularly disaster prone regions. Apart from that, it aims to ensure the integration of disaster risk reduction in the development process rather than considering it as a separate component. In view of this, this exercise was carried out parallel and complementary to the Situation Analysis (SitAn). The results and recommendations of this assessment can also help in advocating and fund-raising for disaster risk reduction and climate change programming. This exercise is also based on UNICEF s refocus on equity. Thorough analysis among and within countries showed that reaching the most deprived children and families is not only more cost effective but also accelerates the progress towards achieving the Millennium Development Goals. The study Narrowing the Gap shows that child mortality, undernutrition and other deprivations are concentrated in those harder to reach groups and that targeting them would save many more lives and have much greater impact than implementing interventions in better off areas. 1 UNICEF at a global level is therefore increasing its efforts in trying to reach the most vulnerable and underserved children and families. This report first provides a short overview on disasters in Mozambique and child vulnerability. It then explains which methodology was used to establish the disaster risk and child vulnerability indices. The next chapter provides basic information on the occurrence and impact of floods, cyclones and droughts in Mozambique and shows the risk maps for these three disasters. The tables on the district level risk ranking, which form the basis of these maps, are in the Annex. Chapter 4 explains the indicators which were used to establish the child vulnerability index: multi-dimensional deprivations, education, nutrition and HIV/AIDS. Since the issue of children s vulnerability and deprivations is elaborated thoroughly in the above mentioned Situation Analysis carried out by UNICEF, no deeper analysis is provided here. Instead, the result of the ranking the maps are presented, while the background data can be found in the Annex. Chapter 5 summarises the main results of the exercise and the last chapter draws some conclusions in view of the future use of this assessment. 1 Narrowing the Gap to meet the Goals, UNICEF See: 3

7 1 Introduction Mozambique ranks third amongst the African countries most exposed to risks from weatherrelated hazards, such as floods, cyclones and droughts. Worldwide, Mozambique ranks on place 43 out of 173 countries. 2 According to recent studies commissioned by the country s National Institute for Disaster management (INGC), as much as 25% of the population is at risk from these disasters. 3 Statistics show that hazards have increased significantly over the past fifty years. Floods occur mostly in the rainy season from December to February along major river basins, low coastal plains, and areas with drainage problems. The risk is highest in the central and southern region. Cyclones affect the entire coast from October to April, but with highest wind impact along the central area. Droughts occur Number of Events Disaster Trends in Mozambique Flood Storm 0 Drought Decade Source: Extracted from EM-DAT ( primarily in the central and southern region of the country. Epidemics, especially water-borne diseases and malaria, are widely spread and outbreaks peak during and after disasters. Malaria, for example, remains the main cause of mortality in Mozambique - 30% of all deaths 4 registered in hospitals. The situation is becoming worse due to the impact of climate change. Children, which constitute 50.5% of Mozambique s population, are one of the most vulnerable groups. The impact of climate change will challenge their well-being in future even more. 5 Models on the impact of climate change predict a 25% increase in the magnitude of large flood peaks in the Limpopo and Save Rivers and a reduction in the flow of the Zambezi River of 15%. The temperature is projected to increase between 1 o and 2.8 o C by Critical sectors that will be at higher risk include agriculture, infrastructure, power, water and sanitation, health, and nutrition. 6 More than 80% of Mozambique s population depends economically on agriculture and most families cultivate no more than three hectares. Subsistence farming is most prevalent and makes families highly vulnerable to climatic conditions. The loss of harvest or income caused by cyclones, droughts or floods can easily push a household below the poverty line. According to Government figures, the national average of poverty in 2011 was 54.7 % 2 Alliance Development Works, United Nations University Institute for Environment and Human Security (2012): World Risk Report, Germany & USA; 3 INGC (2009): Synthesis report. Study on the impact of climate change on disaster risk in Mozambique. [van Logchem B and Brito R (ed.)]. INGC, Mozambique. 4 and 5 A good overview and summary of children s vulnerability concerning various sectors in Mozambique is: Save the Children (2011): Brief Lives. Child survival and development in Mozambique, Maputo. A comprehensive literature review and latest results of research on the impact of extreme weather events and climate change on children can be found in: Intergovernmental Panel on Climate Change (IPCC) (2012): Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, Cambridge; 6 INGC (2009); see also World Bank, GFDRR, ISDR (2011) and Mozambique Dashboard at 4

8 compared to 54.1 % in So, despite strong economic growth of about 7% the rate of poverty is stagnant, if not rising because with a population growth of 2.4% the actual number of poor Mozambicans may be increasing. Crop failure increases food insecurity and can lead to undernutrition. According to the World Food Programme (WFP), around 25% of the population suffers from acute food insecurity at some point in the year and approximately 34% remains chronically food-insecure and lacks an adequate diet. 8 Chronic undernutrition in children under five remains alarmingly high at 42.6 % and thereby is one of the highest in Africa. 9 Nutritional deficiencies are exacerbating the effects of HIV/AIDS % of Mozambicans between 15 and 49 years old are infected. 10 The epidemic causes not only declines in productivity and the loss of skilled workers, but also increases the vulnerability of children by making them chronically sick or orphans. About 25% of children between 10 and 14 and 31% of children between 15 and 17 years old are orphaned or vulnerable because of AIDS. 11 Lack of or poor quality education continues to be a main factor contributing to vulnerability of children. Adult illiteracy is only slowly declining, but still 59.8% amongst women aged 15 to 49 and 32 % amongst men. Though the coverage of primary schools has improved considerably, the net attendance ratio for primary education is still only 77.1%. 12 Drop-out rates have increased. Though education is compulsory and free through the age of 12 years, matriculation fees are charged and are a burden for many families. Studies reveal that learning achievements are alarming. Standardised tests carried out in 2007 found that 22% of grade 5 pupils were functionally illiterate and 33% were not functionally numerate Methodology A number of UNICEF Country Offices have carried out child-centered multi-hazard risk assessments and used different methodologies and formats to present the results. In the case of Mozambique the assessment was carried out through a document review, extracting and evaluating data from databases and consultation meetings. Based on the outcomes of these activities, the disaster risk and vulnerability indices were established and the maps developed. The disaster risk index was aggregated from the three most prevalent disasters 7 International Monetary Fund (2011): Poverty Reduction Action Plan (PARP) However, a recent World Bank Study challenges the figures from the Government: The poverty rate in 2003 was 56 % and thereby higher than Government figures, whilst it reduced to 52% by 2009 ( World Bank, 2012: Poverty in Mozambique: New Evidence from Recent Household Surveys); 8 WFP (2010): Comprehensive Food Security and Vulnerability Assessment; see also SETSAN (2009): Relatório da Monitoria da Situação de Segurança Alimentar e Nutricional em Moçambique 9 INE, MISAU & MEASURE DHS (2013) Moçambique, Inquérito Demográfico e de Saúde 2011, Instituto Nacional de Estatística & Ministério da Saúde, Maputo, & MEASURE DHS/ICF International, Calverton, Maryland, USA; 10 Instituto Nacional de Saúde, Instituto Nacional de Estatistica e ICF Macro (2010): Inquérito nacional de prevalencia, riscos comportamentais e informacao sobre HIV e SIDA em Mocambique (INSIDA), Mocambique; 11 National Statistics Institute (2009): Final Report of the Multiple Indicator Cluster Survey 2008; 12 Instituto Nacional de Saúde, Instituto Nacional de Estatistica (2013): Inquérito Demogréphico e de Saùde 2011; Maputo; 13 Southern African Consortium for Monitoring Educational Quality (SACMEQ) in 2007; for a deeper analysis see UNICEF (2013): Situation Analysis (draft); 5

9 in Mozambique - floods, cyclones and droughts. The child vulnerability index was established from multi-dimensional deprivation, education, nutrition and HIV/AIDS. A key challenge for the exercise was to get reliable data covering all districts of the country. Meetings with key stakeholders and experts therefore played an important role to validate and confirm the information obtained from documents and statistics. The institutions involved Government offices, national and international organisations and academics. A first round of meetings was held when selecting the indicators and a second round of meetings was held to discuss the preliminary outcomes of the assessment. 2.1 Disaster Risk Index Presently, institutions and organisations dealing with disaster risk management in Mozambique are using maps showing high, medium and low risk for floods, cyclones and droughts which were developed by FEWS NET in cooperation with INGC and other government offices in 2005 (see Annex). These maps are not based on human impact, but scientific data. According to FEWS NET staff in Maputo, the following data was used to establish those maps: Floods: based on hydrological and meteorological information from INAM, INGC, DNA, National Oceanic and Atmospheric Administration (NOAA) satellites and different mathematical models from the year 1975 to 2000; Cyclones: based on information between 1930 and 2000 and a combination of historic records from publications and scientific data collected by National Institute of Meteorology; Droughts: based on data from 1981 to 2000 on the normalized difference vegetation index (NDVI); the NDVI is derived from data collected by National Oceanic and Atmospheric Administration (NOAA) satellites, and processed by the Global Inventory Monitoring and Modelling Studies group (GIMMS) at the National Aeronautical and Space Administration (NASA); UNICEF decided to develop risk indices and maps which a) Provide information for all districts in Mozambique. b) reflect human impact based on historical data as indicator for risk c) include information for the last 30 years up to date, The only database containing this information is the free and open-source Disaster Information System (DesInventar) database. It provides information on the place and year of a disaster and number of affected from the years 1979 to mid The database is hosted by the INGC with technical and financial assistance by UNDP/GRIP and UNISDR. Maputo s University of Eduardo Mondlane (UEM) is providing the local technical assistance. 14 DesInventar is a conceptual and methodological tool for the generation of National Disaster Inventories and the construction of databases of damage, losses and in general the effects of disasters. also known as Global Risk Information Platform (GRIPWEB): 6

10 The above mentioned existing maps and the experts who developed them were nevertheless an important reference to compare, validate and in some case correct data extracted from the database. This refers especially for information on droughts, as data appeared to be exaggerated, which will be explained more in detail below. About 15% of the 128 districts showed dubious results when doing the ranking and for these final decisions whether to classify them as low, medium or high risk were based on opinion of experts. EM-DAT, which is managed by the Centre for Research on the Epidemiology of Disasters (CRED) in Belgium, was used as complementary source of information, especially to compare the information on number of affected on the biggest disasters, such as the floods of 2000 and 2001 and the drought of Before using DesInventar, meetings were held with UEM staff that had collected and entered the data. The information available in the database was collected through extensive field work: A team of the UEM had travelled to most of the districts and collected around files from the period 1979 to June 2012 from district administrations and other local institutions. The information was scrutinised for duplication and viable information and reduced to so-called data cards which were then entered into the database. The disaster risk index was established for districts and municipalities (cities) separately, since the nature and impact of a disaster is quite different in a densely populated, urban are compared to a rural setting. Apart from that, the index was developed separately for floods, cyclones and droughts through the following procedure: 1. Extracting data on floods, cyclones and droughts from the open-source DesInventar database ( an example how a data card looks like: 15 The web-site of Prevention web ( has also disaster statistics based on EM-DAT. The EM-DAT contains information on disasters which fulfil at least one of the following criteria: a) Ten or more people reported killed, b) Hundred or more people reported affected, c) Declaration of a state of emergency or c) Call for international assistance. Information is collected from various sources such as UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies. EM-DAT is internally updated on a daily basis. However, the publicly accessible information is updated every three months, after all the data has been validated and cross-checked with different sources. 7

11 2. Organising the data per district and municipality, cross-checking questionable and doubtful information with original data cards in the database, cross-checking exceptional events with the EMDAT-database and making amendments. In summary, the following figures are the basis of the risk indices and maps: 16 For Districts: Number of events Floods 889 8,751,282 Cyclones 124 1,698,553 Droughts 610 5,399,429 Number of Affected Total 1,623 15,849,264 For Municipalities: Number of events Number of Affected Floods ,435 Cyclones ,077 Droughts ,393 Total 101 1,354, Calculation of impact: total number of affected people over the last 33 years; 4. Calculation of exposure: number of affected per district inhabitants per district (as per 2007 census) x 33 (as from 1979 to 2013) 5. Establishment of ranking from low over medium to high risk based; decision on transition from low to medium and from medium to high risk based on big discrepancies in number of affected and exposure and experts opinion; 6. Establishment of final risk indices for floods, cyclones and droughts with four categories (see Annex): 0 = no information (grey) 1 = low risk (green) 2= medium risk (yellow) 3 = high risk (red) 7. Development of risk maps (see chapter 3); 16 The numbers of reported casualties was not considered for the indices, as in Mozambique they are low compared to these kind of disasters in other countries (see chapter 3). They would also not change the result of the risk index. For floods, the number of reported casualties at district level in the last 33 years resulted in 1.568, for cyclones in 205 and for droughts in 342 after making the mentioned amendments. In the municipalities, 57 casualties were ascribed to floods, 101 to cyclones and 0 to drought. 8

12 2.2 Vulnerability Index A child vulnerability index at district level does not yet exist for Mozambique. In 2010, UNICEF Mozambique has carried out a comprehensive review on the situation of children in Mozambique. 17 In the second half of 2013, parallel to the child-centred disaster risk and vulnerability assessment, UNICEF updated this report through its Situation Analysis of Children in Mozambique by focussing on recent trends and developments, taking into account the latest data on a wide range of child-related indicators. 18 However, both analyses have been carried out at provincial level. District level data and analysis are still very limited. Since this exercise was to be carried out at district level, the choice for indicators was very restricted and guided by the following questions: a) For which indicators is data on district level available and to a reasonable degree reliable? b) Which indicators are appropriate and meaningful to describe children s vulnerability within the social and cultural context of Mozambique? c) Which kind indicators encompass several aspects of children s vulnerability? Four indicators were used: a) Multi-dimensional deprivation, b) education, c) nutrition and d) HIV/AIDS. All indicators were given equal weight, since the result by giving different weights was similar. The source of the data for (a) is a study carried out under the Ministry of Planning and Development. 19 The data of (b) was provided by the Ministry of Education and the data for (c) and (d) were provided by the Ministry of Health. In detail, the child vulnerability index was calculated based on the performance of a particular district in relation to the national average: Districts with indicators worse than 10,1 % than the national average: value 2 = high vulnerability (red); Districts with indicators within the range of 10 % above or below the national average: value 1 = average vulnerability (yellow); Districts with indicators 10,1 % better than the national average: value 0 = low vulnerability (green); The value of the composite index was calculated by summing up the vulnerability values of the four indicators (high = 2, medium = 1, low = 0); Based on the result, a child vulnerability index was established ranging from 0 (lowest) to 8 (highest), as shown in the Annex. To develop the child vulnerability map, the 8 values were divided into three categories: 17 UNICEF (2010): Child poverty and disparities in Mozambique 2010, Maputo 18 These were the 2011 Demographic and Health Survey (DHS), recent administrative data, policy and planning documents, and studies. The report also analyses the trends into a longer perspective by drawing on data from successive rounds of national surveys, including the DHS in 1997, 2003 and 2011, the Multiple Indicator Cluster Survey (MICS) in 2008, and the household budget surveys (IAF and IOF) in 2002/03 and 2008/ Government of Mozambique, Ministry of Planning and Development, National Directorate of Studies and Policy Analysis (2013): Small Area Estimates of District Level Welfare: A Multi-Dimensional Approach, Discussion papers No. 70, July 2013, by Channing Arndt, M. Azhar Hussain, Vincenzo Salvucci, Finn Tarp and Lars P. Østerdal; 9

13 0-2 = low vulnerability 3-4 = medium vulnerability 5-8 = high vulnerability 2.3 Overlaying high disaster risk with high child vulnerability The multi-hazard risk map was overlaid with the child vulnerability map to visualise in which districts children are both highly prone to disasters as well as vulnerable in the sectors of nutrition, education, HIV/AIDS and poverty. 2.4 Limitations of the Assessment The biggest challenge of the assessment was availability of data which covers all districts and the reliability of the data eventually available. Since the data was collected and entered in the respective report by human beings it contains errors. In some cases it was possibly also manipulated for political or economic means. Main limitations of the data used for the child vulnerability index: Data on child vulnerability at district level is hardly available and therefore only four indicators could be used to establish the child vulnerability index. The data on nutrition is facility based. It covers only those children who reached the health centre. Apart from that, it obviously contains measuring inaccuracies, as the data is mostly less than five %. This is probably rather on the conservative side, because Mozambique s average for chronic undernutrition for children under five years old is 42.6 % (DHS 2011) and for acute undernutrition 6%. The data on HIV/AIDS only refers to the sub-set of the population - women who are pregnant and accessed antenatal care. Apart from that, during data collection by health personnel at the facilities there is the likelihood of double counting of women already HIV positive at entry with those tested HIV positive during ante-natal care. This may result in an overestimation of the positivity rate for HIV. However, it can be assumed that mistakes in data entry are consistent throughout the districts. Whilst the data does not provide fully reliable information it still allows to compare between districts, which was the purpose of the assessment. Main limitations of the data used for the disaster risk indices: According to managers of the database from the University of Eduardo Mondlane, a considerable number of reports on disasters can assumed to be lost or destroyed through climatic conditions, pests, civil war etc. Hence, the data used for the indices only shows trends, but does not give an accurate picture. The definition of drought is unclear. The term often seems to have been used as synonym for food insecurity, which may be caused by other factors than climatic conditions (see chapter 3). 10

14 Since July 2012 the DesInventar database was not updated due to lack of funding. Numbers of affected by the massive floods of 2013 were obtained from the INGC and hence considered for the flood risk index. But information on other disasters with less impact which may have happened since mid 2012 in other parts of the country was not obtained and hence not considered. Nevertheless, the result of the ranking overlaps in most cases with the risk maps developed by FEWS NET and INGC and this reveals that the data is reasonably reliable and allows to compare between districts and to identify trends. To reconfirm the relative accurateness of the information obtained from the DesInventar database, opinions of experts were taken to validate the result. 3 Disaster Risk Indicators 3.1. Floods Historical analyses show that many of the approximately 100 rivers of Mozambique are exceeding the danger level every two to three years. 20 However, due to an increase of population density along the rivers and the impact of climate change, they are affecting more and more people. Heavy rains are responsible for the floods both in Mozambique as well as upstream in neighbouring countries. They can also be aggravated by water discharge from dams within Mozambique, such as the Cahora Bassa, or in neighbouring countries, such as the Kariba in Zambia. The most likely time for floods to occur is from November to March in the southern region of the country and from January to April in the central and northern regions. There is also a high probability of flooding following tropical storms or cyclones. They can aggravate already on-going river floods, as they bring extremely strong and heavy rainfall within a short period and their occurrence coincides with the rainy (flood) season from October to April. According to the result of the risk assessment, people living in districts along the Limpopo, Incomáti and Save (in the south) and the Búzi, Pungue, Zambezi and Licungo (in the centre) were most affected by floods in the last three decades (see map on the next page). With regard to urban centres, people in parts of the cities of Beira, Maputo and Xai Xai were most exposed to flooding (see map in the Annex). Floods destroy homesteads, agricultural land, household items, infrastructure and crops and render people temporarily homeless. Daily productive activities in general are severely disrupted. Epidemics, such as cholera and malaria, are strongly linked to floods and lead to fatalities, whilst drowning is rather rare. The most recent floods were in the first quarter of 2013, the worst in the past 13 years. The toll are 478,892 affected, of which 172,479 were displaced. There were 117 casualties, out 20 INGC (2009): Synthesis report. Study on the impact of climate change on disaster risk in Mozambique. [van Logchem B and Brito R (ed.)], page 3; 11

15 of which 45 in Gaza Province alone. However, worth while noting is that 39 of the 117 reported deaths were caused by lightening. 21 Children as one of the most vulnerable groups were affected in several ways: They were separated from their families, suffered psychotraumatic shocks, had less to eat, suffered from diseases such as diarrhoea and all kinds of infections, and in general had little space to play and meet as they stayed in cramped conditions in camps. 20% of students in Gaza could not attend school for a number of weeks. 21 Républica de Moçambique, Ministério da Planificação e Desenvolvimento: Diagnóstico Preliminar e Acções de Reconstrução Pós-Calamidades 2013, Maputo, Março de 2013; 12

16 Map 1: Flood Risk by Districts 13

17 3.2 Cyclones Tropical storms or cyclones occur along the coastal regions of Mozambique and are caused by tropical depressions in the Indian Ocean. 22 They are the strongest and the most destructive meteorological events worldwide due to strong winds and torrential rains. The biggest impact in Mozambique is the destruction of crops and fruit trees (such as cashew and coconut), the destruction of homesteads and infrastructure. They also lead to injuries, but same as for floods only occasionally cause casualties, mainly from falling trees or collapsing buildings. As mentioned in the previous section, cyclones bring intense rainfall which can cause flooding. Tropical storms or cyclones occur along the coast in the central and northern regions of the country between October and April. Of the 56 cyclones that passed through the Mozambique Channel between 1980 and 2007, 15 struck the Mozambican coast. Their frequency and intensity has increased in the past years: Whilst between 1980 and 1993 only two cyclones were classified in categories 3 to 5, seven with the same categories were observed between and The result of the risk mapping shows that people living in the coastal districts in Nampula and Inhambane Province were most affected by cyclones in the past thirty years (see map on the next page). With regard to municipalities, people living in the cities of Nampula and Nacala were most affected (see map in the Annex). 22 A tropical storm is called a cyclone when the wind speed exceeds 118 km/h (see 23 INGC (2009): Synthesis report. Study on the impact of climate change on disaster risk in Mozambique. [van Logchem B and Brito R (ed.)]; 14

18 Map 2: Cyclone Risk by Districts 15

19 3.3 Droughts Different from a flood or cyclone, a drought is a slow onset hazard which can last one or two years or for a much longer period. Since over 95% of the food crops in Mozambique are produced under rain-fed conditions and 80% of the population live from agriculture, droughts can lead to severe problems. 24 They do not only affect agricultural production, but also sanitation and hygiene of people and animal husbandry, as water holes dry up. During and after droughts communities not only experience food shortages and hence undernutrition, but also restricted access to water and outbreak of communicable diseases. In the longerterm, droughts lead to the increase of prices of food and agricultural products. So-called secas (droughts) are considered a chronic problem in some regions of southern and central part Mozambique, which is confirmed by the result of the risk mapping: People in the districts of the Provinces of Gaza and Tete were most affected, followed by people living in some inland districts of Inhambane (see map on next page). However, entries classified under droughts in the DesInventar database show that the issue of drought in Mozambique is complex and has to be looked at critical: Local authorities reported a drought for some district in the Provinces of Gaza and Maputo every year since Also the number of deaths due to drought tends to be exaggerated. A key example 25 are deaths due to lack of rain in Gaza Province in Drought is often given as the reason for low agricultural productivity or a failed harvest. However, the term drought is context-specific and hence can be subjective. Climatic conditions in some parts of Mozambique are definitely not comparable to those in the Sahel or other extremely arid regions of the world. Droughts in Mozambique, according to the NAPA (2007), are inter-linked with desertification. They result from low levels of precipitation or changes in their spatial and temporal distribution (hence natural, climate driven causes) combined with anthropogenic (or human) activities, such as the overuse of agricultural land, over-grazing, bush fires, fire wood gathering, charcoal production and industrial forestry. 26 The study of INGC (2009) 27 confirms that droughts in Mozambique are a result of environmental degradation and inadequate use of land: Shifting cultivation through slash and burn is the traditional farming method. Bush land or trees were only cut to the extent of the need by an extended family for their own food supply through cultivation. The timber was a by-product used for cooking and, in some areas, construction of shelters for people and animals. The increase of population reduced the time the vegetation had to re-grow on fallow land, resulting in reductions in soil fertility and increase of erosion of fertile soil and FAO (2011): Nutrition Country Profile, Republic of Mozambique; 25 This kind of information is often not cross-checked or looked into and therefore according to other databases, such as EM-DAT, drought has caused more casualties than floods or cyclones in the past. 26 Ministry for the Coordination of Environmental Affairs (MICOA) (2007): National Adaptation Programme for Action (NAPA); 27 INGC (2009): Study on the impact of climate change on disaster risk in Mozambique, Synthesis report; 16

20 Map 3: Drought Risk by Districts 17

21 4 Child Vulnerability Indicators 4.1 Multi-dimensional Deprivation District level poverty rates are not available for Mozambique, but a study under the Ministry of Planning and Development has ranked all districts based on a multi-dimensional deprivation index. 28 This ranking has been used for the composite child vulnerability index. The mentioned study applied an approach based on so-called first order dominance (FOD) to small area estimation. Districts were ranked by status according to five welfare indicators using census data from The above mentioned study based the ranking on the following five binary welfare indicators: 1. Safe water: piped water inside or outside the house/yard, spring water, hand pumped well water, or mineral/bottled water; Map 4: Multi-dimensional Deprivation by Districts 2. Sanitation: having access to flush toilet, toilet with septic tank, or an improved latrine; 3. Education: households where at least one household member has some education; 4. Electricity. households with electricity for lighting; 5. Radio: households with a functioning radio; As shown on the map, the highest number of districts are in Nampula (fourteen), followed by Zambézia (eleven), Niassa (nine), Cabo Delgado (eight), Tete (seven), and Sofala (five. 28 Government of Mozambique, Ministry of Planning and Development, National Directorate of Studies and Policy Analysis (2013): Small Area Estimates of District Level Welfare: A Multi-Dimensional Approach, Discussion papers No. 70, July 2013, by Channing Arndt, M. Azhar Hussain, Vincenzo Salvucci, Finn Tarp and Lars P. Østerdal; 18

22 4.2 Education Education is compulsory and free through the age of Map 5: EP 1 Drop-out rate by Districts 12 years. The lower primary school, EP 1, consists of five years of schooling (Grades 1 to 5) and the upper primary, EP 2, comprises two years (Grades 6 and 7). The official age of entry into school is 6 years. One main factor of drop-outs from EP 1 is late enrolment and repetition of students, because when they reach a certain age parents or care-takers tend to send them to work. Data on drop-out rates from EP 1 are available from district level and were taken as one indicator for the vulnerability index. The data was provided by the Ministry of Education and is from the year It can be considered as reliable, as it is a standard procedure for all schools to collect it. The data (see Annex) is the % of children who discontinued attending school in grade 5 from the total number of children enrolled in the beginning of the year. 29 As can be seen from the map, Niassa Province has the highest number of districts with EP 1 drop-out rates (13 districts), followed by Sofala (nine districts), Tete (seven districts) and Cabo Delgado (five districts). 4.3 Nutrition The only data providing information from district level on child nutrition is collected by health facilities during routine visits by parents with children under five (0 to 59 months). The data for 2012 (all months) was provided by the Ministry of Health and gives information on growth faltering, which is defined as the percentage of children who do did not gain weight, or who lost weight, over a period of three visits (which would normally be a time span of at least three months). Map 6: Growth faltering by Districts The map shows that Tete and Nampula have the highest number of districts (eight) with growth faltering, followed by Sofala (six), Zambézia, Niassa and Manica (five). 29 According to concerned government offices, the drop-out rate is the difference between enrolled students in the beginning of the school year and the number of students who reach the end of the school year. The difference is then divided by the number of enrolled students in the beginning of the school year and multiplied by the factor

23 4.4 HIV/AIDS District level data on HIV prevalence rates does not exist yet for Mozambique. In 2009, the first national Map 7: HIV/AIDS prevalence of pregnant women by Districts HIV/AIDS survey was undertaken and provides information on, amongst others, HIV prevalence at provincial level. 30 The only data presently available from district level that can be used as a proxy to give an indication on HIV/AIDS is routine data from health facilities on women attending antenatal care (PMTCT). The information used for the indicator is from the year 2012 for all months and was provided by the Ministry of Health. As can be seen on the map, Zambézia Province has the highest number of districts (12) with high HIV prevalence of pregnant women in antenatal care, followed by Gaza (ten districts), Maputo (nine districts) and Sofala (four districts). 30 Instituto Nacional de Saude, Instituto Nacional de Estatistica e ICF Macro (2010): Inquerito nacional de prevalencia, riscos comportamentais e informacao sobre HIV e SIDA em Mocambique (INSIDA), Mocambique; INS & MEASURE DHS (2010) Mozambique: National Survey on Prevalence, Behavioral Risks and Information about HIV and AIDS (2009 INSIDA), Key findings, Instituto Nacional de Saúde, Maputo, & MEASURE DHS/ICF Macro, Calverton, Maryland, USA 20

24 5 Summary of the Results of the Assessment The maps developed on the basis of the indices were presented in the previous chapter whilst the results of the indices themselves are in the Annex of this document. This chapter summarises the results of the indices and mapping. 5.1 Disaster Risk Floods: The highest number of high flood prone districts is in Gaza Province with seven districts, followed by Sofala with five, Zambézia with four and Tete with three districts. Beira, Maputo and Xai Xai are the most affected cities. Province Gaza Sofala Zambézia Tete Maputo Inhambane Manica Cabo Delgado District Bilene Macia, Chibuto, Chókwè, Guijá, Mabalane, Massingir, Xai-Xai Buzi, Caia, Chemba, Machanga, Marromeu Chinde, Maganja da Costa, Mopeia, Morrumbala Magoe, Mutarara, Zumbu Manhiça Govuro Tambara Macomia Cyclones: Most of the high cyclone risk districts are Nampula Province (seven), followed by Inhambane Province with three districts. With regard to urban areas, people living in Nacala, Ilha de Moçambique and Nampula are most affected. Province Nampula Inhambane Sofala District Angoche, Memba, Mogincual, Monapo, Mossuril, Nacala-a-Velha, Meconta Govuro, Vilankulo, Inhassoro Machanga Droughts: Gaza Province has the highest number of drought prone districts (seven), followed by Tete (five) and Inhambane (four). With regard to municipalities, inhabitants of Tete are the most affected by drought, though it is difficult to understand which impact drought can have for an urban population since they do not live from farming. 31 Province Gaza Tete Inhambane Sofala District Chibuto, Chicualacuala, Chigubo, Guijá, Mabalane, Massangena, Massingir Cahora-Bassa, Changara, Chiúta, Magoe, Mutarara Funhalouro, Govuro, Mabote, Panda Chibabava 31 For this reason, a risk map for droughts for cities was not developed. 21

25 Multi-hazard high risk: Inhabitants of the district Govuro in Inhambane Province are affected by all three disasters. Affected by floods as well as by droughts are people in Chibuto, Guijá, Mabalane, Massingir (all in Gaza Province), Machanga (Sofala), Mutarara and Magoe (both in Tete Province). Map 8: Multi-Hazard High Risk 22

26 5.2 Child Vulnerability According to the results of the composite vulnerability index as shown in the map below, children in Sofala Province are most vulnerable (eight districts), followed by Tete (seven districts), Zambézia (six districts), Cabo Delgado (five districts), Nampula and Niassa (each four districts), Manica (three), Gaza (two districts) and Inhambane (one district). Map 9: Child Vulnerability by Districts 23

27 5.3 Overlaying Disaster Risk and Child Vulnerability The concept of overlaying the map of child vulnerability with the map of multi-hazard risk allows to capture in which districts children have a challenging situation concerning their well-being and at the same time are prone to one, two or even three types of natural hazards. The table and the map below show the result. Province Sofala Tete Zambézia Gaza Inhambane District Caia, Chemba, Chibabava, Machanga Changara, Chiúta, Mutarara, Magoe Maganja da Costa, Mopeia, Morrumbala Chicualacuala, Chigubo Panda Map 10: Overlay of child vulnerability with high disaster risk 24

28 6 Conclusions This assessment is a first step towards improved targeting of children which live under a double burden: High vulnerability in their well-being and disaster risk. To validate and fully understand the situation of children in Mozambique additional district level data is required. To properly reduce vulnerability of children, there is a need for a more in depth analysis in the identified high risk districts. Such an assessment should take in consideration cultural, social-economic, political and environmental aspects that contribute to vulnerability. Also, the DesInventar database should be updated to ensure that the recent impact of disasters is properly reflected. Combined with statistical data, a field assessment on child deprivation and the impact of disasters on children would be useful which takes also the view of the inhabitants of the concerned districts, especially parents or care-takers of children and the children themselves. Apart from that, a mapping of capacities needs to be undertaken: The availability and quality of facilities which support and foster children s well-being (health centres, schools, etc.) as well as disaster risk management structures (local disaster risk management committees, trained community volunteers etc.). Local, indigenous coping capacities would complement the analysis. To improve geographic and thematic targeting, a mapping of past and on-going interventions from organisations and institutions on disaster risk management and child well-being would be necessary. Based on its outcome, UNICEF and other humanitarian organisations can decide whether they need to intervene on a short-, medium or longer term and through which kind of activities to improve the situation of children in Mozambique. Disaster Risk Reduction should not be seen as a separate component, but integrated in all development sectors. 25

29 Annexes Annex 1: Floods: District Risk in alphabetical order Risk Ranking Floods Province District Risk Cabo Delgado Ancuabe 1 Cabo Delgado Balama 1 Cabo Delgado Chiúre 1 Cabo Delgado Macomia 3 Cabo Delgado Mecúfi 1 Cabo Delgado Meluco 2 Cabo Delgado Mocimboa da Praia 1 Cabo Delgado Montepuez 2 Cabo Delgado Mueda 1 Cabo Delgado Muidumbe 2 Cabo Delgado Namuno 1 Cabo Delgado Nangade 1 Cabo Delgado Palma 2 Cabo Delgado Pemba 1 Cabo Delgado Quissango 1 Gaza Bilene Macia 3 Gaza Chibuto 3 Gaza Chicualacuala 2 Gaza Chigubo 1 Gaza Chókwè 3 Gaza Guijá 3 Gaza Mabalane 3 Gaza Mandlakazi 2 Gaza Massangena 2 Gaza Massingir 3 Gaza Xai-Xai 3 Inhambane Funhalouro 1 Inhambane Govuro 3 Inhambane Homoine 1 Inhambane Inharrime 1 Inhambane Inhassoro 2 Inhambane Jangamo 0 Inhambane Mabote 2 Inhambane Massinga 1 Inhambane Morrumbene 1 Inhambane Panda 1 Inhambane Vilankulo 2 Inhambane Zavala 1 Manica Báruè 1 Manica Gondola 1 Manica Guro 1 Manica Machaze 1 Manica Macossa 0 Manica Manica 1 Manica Mossurize 1 Manica Sussundenga 1 Manica Tambara 3 Maputo Boane 2 Maputo Magude 2 Maputo Manhiça 3 Maputo Cidade de Maputo 1 Maputo Marracuene 2 Maputo Matutuíne 2 Maputo Moamba 1 Maputo Namaacha 2 Nampula Angoche 1 Nampula Nacarôa 1 Nampula Lalaua 1 Nampula Malema 1 Nampula Meconta 1 Nampula Monapo 1 Nampula Mogincual 1 Nampula Mossuril 1 26 Province District Risk Nampula Muecate 1 Nampula Murrupula 1 Nampula Nacala-a-Velha 1 Nampula Eráti 1 Nampula Nampula (Rapale) 0 Nampula Ribáuè 0 Niassa Cuamba 1 Niassa Lago 1 Niassa Lichinga 1 Niassa Majune 1 Niassa Mandimba 1 Niassa Marrupa 1 Niassa Maúa 1 Niassa Mavago 0 Niassa Mecanhelas 1 Niassa Mecula 1 Niassa Metarica 1 Niassa Muembe 1 Niassa N'gauma 1 Niassa Nipepe 1 Niassa Sanga 1 Sofala Buzi 3 Sofala Caia 3 Sofala Chemba 3 Sofala Cheringoma 2 Sofala Chibabava 2 Sofala Dondo 2 Sofala Gorongosa 0 Sofala Machanga 3 Sofala Maringue 2 Sofala Marromeu 3 Sofala Muanza 1 Sofala Nhamatanda 2 Tete Angónia 1 Tete Cahora-Bassa 2 Tete Changara 2 Tete Chifunde 1 Tete Chiúta 2 Tete Macanga 0 Tete Magoe 3 Tete Maravia 1 Tete Moatize 2 Tete Mutarara 3 Tete Tsangano 0 Tete Zumbu 3 Zambezia Alto Molocue 1 Zambezia Chinde 3 Zambezia Gilé 1 Zambezia Gurué 1 Zambezia Ile 1 Zambezia Inhassunge 1 Zambezia Lugela 1 Zambezia Maganja da Costa 3 Zambezia Milange 1 Zambezia Mocuba 1 Zambezia Mopeia 3 Zambezia Morrumbala 3 Zambezia Namacurra 2 Zambezia Namarroi 1 Zambezia Nicoadala 2 Zambezia Pebane 1

30 Annex 2: Floods: District Risk Ranking Province District Risk Inhambane Jangamo 0 Manica Macossa 0 Nampula Mecubúri 0 Nampula Nampula (Rapale) 0 Nampula Ribáuè 0 Niassa Mavago 0 Sofala Gorongosa 0 Tete Macanga 0 Tete Tsangano 0 Cabo Delgado Ancuabe 1 Cabo Delgado Balama 1 Cabo Delgado Chiúre 1 Cabo Delgado Mecúfi 1 Cabo Delgado Mocimboa da Praia 1 Cabo Delgado Mueda 1 Cabo Delgado Namuno 1 Cabo Delgado Nangade 1 Cabo Delgado Pemba 1 Cabo Delgado Quissango 1 Gaza Chigubo 1 Inhambane Funhalouro 1 Inhambane Homoine 1 Inhambane Inharrime 1 Inhambane Massinga 1 Inhambane Morrumbene 1 Inhambane Panda 1 Inhambane Zavala 1 Manica Báruè 1 Manica Gondola 1 Manica Guro 1 Manica Machaze 1 Manica Manica 1 Manica Mossurize 1 Manica Sussundenga 1 Maputo Cidade de Maputo 1 Maputo Moamba 1 Nampula Angoche 1 Nampula Nacarôa 1 Nampula Lalaua 1 Nampula Malema 1 Nampula Meconta 1 Nampula Memba 1 Nampula Mogovolas 1 Nampula Moma 1 Nampula Monapo 1 Nampula Mogincual 1 Nampula Mossuril 1 Nampula Muecate 1 Nampula Murrupula 1 Nampula Nacala-a-Velha 1 Nampula Eráti 1 Niassa Cuamba 1 Niassa Lago 1 Niassa Lichinga 1 Niassa Majune 1 Niassa Mandimba 1 Niassa Marrupa 1 Niassa Maúa 1 Niassa Mecanhelas 1 Niassa Mecula 1 Niassa Metarica 1 Niassa Muembe 1 Niassa N'gauma 1 Niassa Nipepe 1 Niassa Sanga 1 27 Province District Risk Tete Angónia 1 Tete Chifunde 1 Tete Maravia 1 Zambezia Alto Molocue 1 Zambezia Gilé 1 Zambezia Gurué 1 Zambezia Ile 1 Zambezia Inhassunge 1 Zambezia Lugela 1 Zambezia Milange 1 Zambezia Mocuba 1 Zambezia Namarroi 1 Zambezia Pebane 1 Cabo Delgado Meluco 2 Cabo Delgado Montepuez 2 Cabo Delgado Muidumbe 2 Cabo Delgado Palma 2 Gaza Chicualacuala 2 Gaza Mandlakazi 2 Gaza Massangena 2 Inhambane Inhassoro 2 Inhambane Mabote 2 Inhambane Vilankulo 2 Maputo Boane 2 Maputo Magude 2 Maputo Marracuene 2 Maputo Matutuíne 2 Maputo Namaacha 2 Sofala Cheringoma 2 Sofala Chibabava 2 Sofala Dondo 2 Sofala Maringue 2 Sofala Nhamatanda 2 Tete Cahora-Bassa 2 Tete Changara 2 Tete Chiúta 2 Tete Moatize 2 Zambezia Namacurra 2 Zambezia Nicoadala 2 Cabo Delgado Macomia 3 Gaza Bilene Macia 3 Gaza Chibuto 3 Gaza Chókwè 3 Gaza Guijá 3 Gaza Mabalane 3 Gaza Massingir 3 Gaza Xai-Xai 3 Inhambane Govuro 3 Manica Tambara 3 Maputo Manhiça 3 Sofala Buzi 3 Sofala Caia 3 Sofala Chemba 3 Sofala Machanga 3 Sofala Marromeu 3 Tete Magoe 3 Tete Mutarara 3 Tete Zumbu 3 Zambezia Chinde 3 Zambezia Maganja da Costa 3 Zambezia Mopeia 3 Zambezia Morrumbala 3

31 Annex 3: Cyclones: District Risk in alphabetical order Province District Risk Cabo Delgado Ancuabe 0 Cabo Delgado Balama 0 Cabo Delgado Chiúre 0 Cabo Delgado Macomia 0 Cabo Delgado Mecúfi 0 Cabo Delgado Meluco 0 Cabo Delgado Mocimboa da Praia 0 Cabo Delgado Montepuez 0 Cabo Delgado Mueda 0 Cabo Delgado Muidumbe 1 Cabo Delgado Namuno 0 Cabo Delgado Nangade 0 Cabo Delgado Palma 0 Cabo Delgado Pemba 0 Cabo Delgado Quissango 0 Gaza Bilene Macia 0 Gaza Chibuto 1 Gaza Chicualacuala 0 Gaza Chigubo 0 Gaza Chókwè 0 Gaza Guijá 0 Gaza Mabalane 0 Gaza Mandlakazi 0 Gaza Massangena 0 Gaza Massingir 0 Gaza Xai-Xai 1 Inhambane Funhalouro 0 Inhambane Govuro 3 Inhambane Homoine 0 Inhambane Inharrime 1 Inhambane Inhassoro 3 Inhambane Jangamo 0 Inhambane Mabote 1 Inhambane Massinga 1 Inhambane Morrumbene 0 Inhambane Panda 0 Inhambane Vilankulo 3 Inhambane Zavala 0 Manica Báruè 0 Manica Gondola 0 Manica Guro 0 Manica Machaze 0 Manica Macossa 0 Manica Manica 0 Manica Mossurize 0 Manica Sussundenga 0 Manica Tambara 0 Maputo Boane 0 Maputo Magude 1 Maputo Manhiça 1 Maputo Cidade de Maputo 1 Maputo Marracuene 1 Maputo Matutuíne 0 Maputo Moamba 0 Maputo Namaacha 1 Nampula Angoche 3 Nampula Nacarôa 2 Nampula Lalaua 0 Nampula Malema 1 Nampula Meconta 3 Nampula Mecubúri 1 Nampula Memba 3 Nampula Mogovolas 2 Nampula Moma 2 Nampula Monapo 3 Nampula Mogincual 3 Nampula Mossuril 3 28 Province District Risk Nampula Muecate 2 Nampula Murrupula 1 Nampula Nacala-a-Velha 3 Nampula Eráti 0 Nampula Nampula (Rapale) 1 Nampula Ribáuè 0 Niassa Cuamba 0 Niassa Lago 0 Niassa Lichinga 0 Niassa Majune 0 Niassa Mandimba 0 Niassa Marrupa 0 Niassa Maúa 0 Niassa Mavago 0 Niassa Mecanhelas 0 Niassa Mecula 0 Niassa Metarica 0 Niassa Muembe 0 Niassa N'gauma 0 Niassa Nipepe 0 Niassa Sanga 0 Sofala Buzi 2 Sofala Caia 0 Sofala Chemba 0 Sofala Cheringoma 0 Sofala Chibabava 1 Sofala Dondo 1 Sofala Gorongosa 0 Sofala Machanga 3 Sofala Maringue 0 Sofala Marromeu 1 Sofala Muanza 0 Sofala Nhamatanda 1 Tete Angónia 0 Tete Cahora-Bassa 0 Tete Changara 0 Tete Chifunde 0 Tete Chiúta 0 Tete Macanga 0 Tete Magoe 0 Tete Maravia 0 Tete Moatize 0 Tete Mutarara 0 Tete Tsangano 0 Tete Zumbu 0 Zambezia Alto Molocue 0 Zambezia Chinde 1 Zambezia Gilé 0 Zambezia Gurué 0 Zambezia Ile 0 Zambezia Inhassunge 1 Zambezia Lugela 0 Zambezia Maganja da Costa 2 Zambezia Milange 0 Zambezia Mocuba 0 Zambezia Mopeia 0 Zambezia Morrumbala 2 Zambezia Namacurra 0 Zambezia Namarroi 0 Zambezia Nicoadala 0 Zambezia Pebane 2

32 Annex 4: Cyclones: District Risk Ranking Province District Risk Zambezia Alto Molocue 0 Cabo Delgado Ancuabe 0 Tete Angónia 0 Cabo Delgado Balama 0 Manica Báruè 0 Gaza Bilene Macia 0 Maputo Boane 0 Tete Cahora-Bassa 0 Sofala Caia 0 Tete Changara 0 Sofala Chemba 0 Sofala Cheringoma 0 Gaza Chicualacuala 0 Tete Chifunde 0 Gaza Chigubo 0 Cabo Delgado Chiúre 0 Tete Chiúta 0 Gaza Chókwè 0 Niassa Cuamba 0 Nampula Eráti 0 Inhambane Funhalouro 0 Zambezia Gilé 0 Manica Gondola 0 Sofala Gorongosa 0 Gaza Guijá 0 Manica Guro 0 Zambezia Gurué 0 Inhambane Homoine 0 Zambezia Ile 0 Inhambane Jangamo 0 Niassa Lago 0 Nampula Lalaua 0 Niassa Lichinga 0 Zambezia Lugela 0 Gaza Mabalane 0 Tete Macanga 0 Manica Machaze 0 Cabo Delgado Macomia 0 Manica Macossa 0 Tete Magoe 0 Niassa Majune 0 Niassa Mandimba 0 Gaza Mandlakazi 0 Manica Manica 0 Tete Maravia 0 Sofala Maringue 0 Niassa Marrupa 0 Gaza Massangena 0 Gaza Massingir 0 Maputo Matutuíne 0 Niassa Maúa 0 Niassa Mavago 0 Niassa Mecanhelas 0 Cabo Delgado Mecúfi 0 Niassa Mecula 0 Cabo Delgado Meluco 0 Niassa Metarica 0 Zambezia Milange 0 Maputo Moamba 0 Tete Moatize 0 Cabo Delgado Mocimboa da Praia 0 Zambezia Mocuba 0 Cabo Delgado Montepuez 0 Zambezia Mopeia 0 Inhambane Morrumbene 0 Province District Risk Cabo Delgado Mueda 0 Niassa Muembe 0 Tete Mutarara 0 Zambezia Namacurra 0 Zambezia Namarroi 0 Cabo Delgado Namuno 0 Cabo Delgado Nangade 0 Niassa N'gauma 0 Zambezia Nicoadala 0 Niassa Nipepe 0 Cabo Delgado Palma 0 Inhambane Panda 0 Cabo Delgado Pemba 0 Cabo Delgado Quissango 0 Nampula Ribáuè 0 Niassa Sanga 0 Manica Sussundenga 0 Manica Tambara 0 Tete Tsangano 0 Inhambane Zavala 0 Tete Zumbu 0 Sofala Chibabava 1 Gaza Chibuto 1 Zambezia Chinde 1 Maputo Cidade de Maputo 1 Sofala Dondo 1 Inhambane Inharrime 1 Zambezia Inhassunge 1 Inhambane Mabote 1 Maputo Magude 1 Nampula Malema 1 Maputo Manhiça 1 Maputo Marracuene 1 Sofala Marromeu 1 Inhambane Massinga 1 Nampula Mecubúri 1 Cabo Delgado Muidumbe 1 Nampula Murrupula 1 Maputo Namaacha 1 Nampula Nampula (Rapale) 1 Sofala Nhamatanda 1 Gaza Xai-Xai 1 Sofala Buzi 2 Zambezia Maganja da Costa 2 Nampula Mogovolas 2 Nampula Moma 2 Zambezia Morrumbala 2 Nampula Muecate 2 Nampula Nacarôa 2 Zambezia Pebane 2 Nampula Angoche 3 Inhambane Govuro 3 Inhambane Inhassoro 3 Sofala Machanga 3 Nampula Meconta 3 Nampula Memba 3 Nampula Mogincual 3 Nampula Monapo 3 Nampula Mossuril 3 Nampula Nacala-a-Velha 3 Inhambane Vilankulo 3 29

33 Annex 5: Droughts: District Risk in alphabetical order Province District Risk Cabo Delgado Ancuabe 0 Cabo Delgado Balama 1 Cabo Delgado Chiúre 1 Cabo Delgado Macomia 1 Cabo Delgado Mecúfi 1 Cabo Delgado Meluco 1 Cabo Delgado Mocimboa da Praia 1 Cabo Delgado Montepuez 1 Cabo Delgado Mueda 1 Cabo Delgado Muidumbe 0 Cabo Delgado Namuno 1 Cabo Delgado Nangade 1 Cabo Delgado Palma 1 Cabo Delgado Pemba 0 Cabo Delgado Quissango 1 Gaza Bilene Macia 2 Gaza Chibuto 3 Gaza Chicualacuala 3 Gaza Chigubo 3 Gaza Chókwè 2 Gaza Guijá 3 Gaza Mabalane 3 Gaza Mandlakazi 2 Gaza Massangena 3 Gaza Massingir 3 Gaza Xai-Xai 2 Inhambane Funhalouro 3 Inhambane Govuro 3 Inhambane Homoine 2 Inhambane Inharrime 2 Inhambane Inhassoro 2 Inhambane Jangamo 1 Inhambane Mabote 3 Inhambane Massinga 2 Inhambane Morrumbene 2 Inhambane Panda 3 Inhambane Vilankulo 2 Inhambane Zavala 1 Manica Báruè 2 Manica Gondola 0 Manica Guro 2 Manica Machaze 2 Manica Macossa 1 Manica Manica 1 Manica Mossurize 2 Manica Sussundenga 2 Manica Tambara 2 Maputo Boane 1 Maputo Magude 2 Maputo Manhiça 2 Maputo Cidade de Maputo 0 Maputo Marracuene 2 Maputo Matutuíne 2 Maputo Moamba 2 Maputo Namaacha 2 Nampula Angoche 1 Nampula Nacarôa 2 Nampula Lalaua 1 Nampula Malema 1 Nampula Meconta 1 Nampula Mecubúri 0 Nampula Memba 2 Nampula Mogovolas 0 Nampula Moma 0 Nampula Monapo 2 Nampula Mogincual 1 30 Province District Risk Nampula Mossuril 2 Nampula Muecate 0 Nampula Murrupula 1 Nampula Nacala-a-Velha 2 Nampula Eráti 1 Nampula Nampula (Rapale) 0 Nampula Ribáuè 0 Niassa Cuamba 1 Niassa Lago 0 Niassa Lichinga 0 Niassa Majune 1 Niassa Mandimba 0 Niassa Marrupa 1 Niassa Maúa 0 Niassa Mavago 1 Niassa Mecanhelas 0 Niassa Mecula 0 Niassa Metarica 0 Niassa Muembe 0 Niassa N'gauma 0 Niassa Nipepe 0 Niassa Sanga 0 Sofala Buzi 1 Sofala Caia 2 Sofala Chemba 2 Sofala Cheringoma 1 Sofala Chibabava 3 Sofala Dondo 2 Sofala Gorongosa 1 Sofala Machanga 2 Sofala Maringue 1 Sofala Marromeu 2 Sofala Muanza 1 Sofala Nhamatanda 0 Tete Angónia 0 Tete Cahora-Bassa 3 Tete Changara 3 Tete Chifunde 1 Tete Chiúta 3 Tete Macanga 0 Tete Magoe 3 Tete Maravia 2 Tete Moatize 2 Tete Mutarara 3 Tete Tsangano 0 Tete Zumbu 2 Zambezia Alto Molocue 1 Zambezia Chinde 1 Zambezia Gilé 1 Zambezia Gurué 0 Zambezia Ile 1 Zambezia Inhassunge 1 Zambezia Lugela 0 Zambezia Maganja da Costa 1 Zambezia Milange 0 Zambezia Mocuba 1 Zambezia Mopeia 2 Zambezia Morrumbala 1 Zambezia Namacurra 0 Zambezia Namarroi 0 Zambezia Nicoadala 2 Zambezia Pebane 1

34 Annex 6: Droughts: District Risk Ranking Province District Risk Cabo Delgado Ancuabe 0 Cabo Delgado Muidumbe 0 Cabo Delgado Pemba 0 Manica Gondola 0 Maputo Cidade de Maputo 0 Nampula Mecubúri 0 Nampula Mogovolas 0 Nampula Moma 0 Nampula Muecate 0 Nampula Nampula (Rapale) 0 Nampula Ribáuè 0 Niassa Lago 0 Niassa Lichinga 0 Niassa Mandimba 0 Niassa Maúa 0 Niassa Mecanhelas 0 Niassa Mecula 0 Niassa Metarica 0 Niassa Muembe 0 Niassa N'gauma 0 Niassa Nipepe 0 Niassa Sanga 0 Sofala Nhamatanda 0 Tete Angónia 0 Tete Macanga 0 Tete Tsangano 0 Zambezia Gurué 0 Zambezia Lugela 0 Zambezia Milange 0 Zambezia Namacurra 0 Zambezia Namarroi 0 Cabo Delgado Balama 1 Cabo Delgado Chiúre 1 Cabo Delgado Macomia 1 Cabo Delgado Mecúfi 1 Cabo Delgado Meluco 1 Cabo Delgado Mocimboa da Praia 1 Cabo Delgado Montepuez 1 Cabo Delgado Mueda 1 Cabo Delgado Namuno 1 Cabo Delgado Nangade 1 Cabo Delgado Palma 1 Cabo Delgado Quissango 1 Inhambane Jangamo 1 Inhambane Zavala 1 Manica Macossa 1 Manica Manica 1 Maputo Boane 1 Nampula Angoche 1 Nampula Lalaua 1 Nampula Malema 1 Nampula Meconta 1 Nampula Mogincual 1 Nampula Murrupula 1 Nampula Eráti 1 Niassa Cuamba 1 Niassa Majune 1 Niassa Marrupa 1 Niassa Mavago 1 Sofala Buzi 1 Sofala Cheringoma 1 Sofala Gorongosa 1 Sofala Maringue 1 Sofala Muanza 1 Tete Chifunde 1 31 Province District Risk Zambezia Alto Molocue 1 Zambezia Chinde 1 Zambezia Gilé 1 Zambezia Ile 1 Zambezia Inhassunge 1 Zambezia Maganja da Costa 1 Zambezia Mocuba 1 Zambezia Morrumbala 1 Zambezia Pebane 1 Gaza Bilene Macia 2 Gaza Chókwè 2 Gaza Mandlakazi 2 Gaza Xai-Xai 2 Inhambane Homoine 2 Inhambane Inharrime 2 Inhambane Inhassoro 2 Inhambane Massinga 2 Inhambane Morrumbene 2 Inhambane Vilankulo 2 Manica Báruè 2 Manica Guro 2 Manica Machaze 2 Manica Mossurize 2 Manica Sussundenga 2 Manica Tambara 2 Maputo Magude 2 Maputo Manhiça 2 Maputo Marracuene 2 Maputo Matutuíne 2 Maputo Moamba 2 Maputo Namaacha 2 Nampula Nacarôa 2 Nampula Memba 2 Nampula Monapo 2 Nampula Mossuril 2 Nampula Nacala-a-Velha 2 Sofala Caia 2 Sofala Chemba 2 Sofala Dondo 2 Sofala Machanga 2 Sofala Marromeu 2 Tete Maravia 2 Tete Moatize 2 Tete Zumbu 2 Zambezia Mopeia 2 Zambezia Nicoadala 2 Gaza Chibuto 3 Gaza Chicualacuala 3 Gaza Chigubo 3 Gaza Guijá 3 Gaza Mabalane 3 Gaza Massangena 3 Gaza Massingir 3 Inhambane Funhalouro 3 Inhambane Govuro 3 Inhambane Mabote 3 Inhambane Panda 3 Sofala Chibabava 3 Tete Cahora-Bassa 3 Tete Changara 3 Tete Chiúta 3 Tete Magoe 3 Tete Mutarara 3

35 Annex 7: Municipalities Risk Ranking Floods, Cyclones and Droughts Floods Province Municipality Risk Nampula Nacala 0 Niassa Lichinga 0 Manica Chimoio 2 Tete Tete 2 Zambezia Quelimane 2 Cabo Delgado Pempa 1 Inhambane Maxixe 1 Inhambane Inhambane 1 Maputo Matola 1 Nampula Ilha de Moçamb. 1 Nampula Nampula 1 Gaza Xai-Xai 3 Maputo Maputo 3 Sofala Beira 3 Cyclones Province Municipality Risk Cabo Delgado Pemba 0 Inhambane Maxixe 0 Manica Chimoio 0 Maputo Matola 0 Niassa Lichinga 0 Tete Tete 0 Gaza Xai-Xai 1 Inhambane Inhambane 1 Maputo Maputo 1 Sofala Beira 2 Zambezia Quelimane 2 Nampula Ilha de Moçamb. 3 Nampula Nacala 3 Nampula Nampula 3 Droughts Province Municipality Risk Cabo Delgado Pemba 0 Inhambane Maxixe 0 Manica Chimoio 0 Maputo Maputo 0 Maputo Matola 0 Niassa Lichinga 0 Zambezia Quelimane 0 Gaza Xai-Xai 1 Sofala Beira 1 Nampula Nampula 1 Nampula Ilha de Moçamb. 2 Inhambane Inhambane 2 Nampula Nacala 2 Tete Tete 3 32

36 Annex 8: Child Vulnerability Index in alphabetical order Province District Deprivation rank 33 EP1 dropout Nutrition (bad growth) HIV/AIDS Vuln. Index Cabo Delgado Ancuabe 96 5,5% 1,0% 9,7% 2 Cabo Delgado Balama 83 9,9% 1,2% 1,8% 2 Cabo Delgado Chiúre 108 5,9% 2,8% 4,9% 4 Cabo Delgado Macomia ,0% 1,2% 8,7% 4 Cabo Delgado Mecúfi 85 9,4% 1,6% 14,4% 3 Cabo Delgado Meluco 128 4,6% 1,0% 3,8% 2 Cabo Delgado Mocimboa da Praia 81 11,0% 7,1% 9,4% 5 Cabo Delgado Montepuez 66 5,6% 1,7% 5,8% 1 Cabo Delgado Mueda 63 12,9% 1,1% 24,0% 5 Cabo Delgado Muidumbe 89 10,1% 1,4% 28,7% 6 Cabo Delgado Namuno 123 5,6% 0,9% 2,8% 2 Cabo Delgado Nangade 78 9,9% 5,9% 58,0% 7 Cabo Delgado Palma 142 7,4% 2,0% 4,7% 3 Cabo Delgado Pemba 45 5,6% 1,3% 5,1% 0 Cabo Delgado Cidade da Pempa 11 1,0% 2,0% 13,2% 2 Cabo Delgado Quissango 126 8,5% 2,6% 4,2% 5 Gaza Bilene Macia 20 5,8% 2,2% 24,6% 4 Gaza Chibuto 23 4,2% 2,0% 25,9% 3 Gaza Chicualacuala 51 10,2% 2,9% 26,9% 6 Gaza Chigubo ,7% 2,9% 24,1% 8 Gaza Chókwè 25 1,6% 1,2% 42,8% 2 Gaza Guijá 41 5,9% 0,8% 24,3% 2 Gaza Mabalane 46 8,1% 1,4% 22,6% 3 Gaza Mandlakazi 49 4,1% 1,8% 24,2% 3 Gaza Massangena 98 8,2% 1,3% 14,5% 4 Gaza Massingir 37 10,5% 1,4% 12,9% 3 Gaza Xai-Xai 27 6,4% 1,5% 33,8% 2 Gaza Cidade da Xai-Xai 3 3,8% 1,3% 25,9% 2 Inhambane Funhalouro 62 9,0% 1,1% 11,7% 1 Inhambane Govuro 33 5,1% 1,3% 15,0% 1 Inhambane Cidade de Maxixe 10 2,9% 1,4% 11,0% 0 Inhambane Homoine 21 2,7% 0,5% 13,2% 1 Inhambane Inharrime 32 6,5% 0,5% 8,4% 0 Inhambane Inhassoro 54 8,9% 0,3% 17,6% 3 Inhambane Jangamo 26 1,3% 0,6% 8,0% 0 Inhambane Cidade de Inhambane 6 2,9% 0,9% 8,4% 0 Inhambane Mabote 84 4,8% 0,8% 16,6% 3 Inhambane Massinga 30 7,8% 0,5% 12,7% 1 Inhambane Morrumbene 38 6,0% 0,6% 9,8% 0 Inhambane Panda 113 9,4% 1,0% 16,1% 5 Inhambane Vilankulo 68 5,5% 1,3% 14,0% 2 Inhambane Zavala 60 3,4% 0,8% 8,0% 0 Manica Báruè 36 12,0% 0,8% 7,5% 2 Manica Gondola 24 5,4% 2,2% 14,1% 3 Manica Cidade de Chimoio 2 9,3% 2,2% 15,8% 5 Manica Guro 72 9,8% 1,1% 9,4% 2 Manica Machaze 58 9,0% 2,2% 26,8% 5 Manica Macossa 80 5,3% 4,2% 7,0% 3 Manica Manica 18 13,0% 2,2% 12,7% 5 Manica Mossurize ,0% 0,6% 11,4% 4 Manica Sussundenga 64 8,4% 1,9% 13,8% 4 Manica Tambara 132 4,6% 2,1% 4,8% 3 Maputo Boane 16 4,8% 0,4% 28,9% 2 Maputo Magude 43 7,8% 3,9% 19,2% 4 Maputo Manhiça 34 7,6% 1,1% 24,4% 2 Maputo Cidade da Matola 7 4,9% 0,7% 23,7% 2 Maputo Cidade de Maputo 6 3,3% 3,9% 25,4% 4 Maputo Marracuene 19 1,9% 1,1% 28,0% 2 Maputo Matutuíne 42 13,1% 0,4% 26,3% 4 Maputo Moamba 31 6,9% 0,3% 21,2% 2 Maputo Namaacha 22 5,7% 1,8% 24,3% 3

37 Province District Deprivation rank 34 EP1 dropout Nutrition (bad growth) HIV/AIDS Vuln. Index Nampula Angoche 76 8,3% 2,9% 10,9% 4 Nampula Nacarôa 139 9,6% 5,0% 5,3% 5 Nampula Lalaua 105 6,9% 2,2% 5,3% 4 Nampula Malema 70 3,1% 1,7% 7,1% 1 Nampula Meconta 77 12,3% 0,9% 6,8% 3 Nampula Mecubúri 133 9,4% 1,4% 13,4% 4 Nampula Memba 143 5,8% 1,2% 6,9% 2 Nampula Ilha de Moçamb ,9% 3,7% 19,0% 7 Nampula Mogovolas ,5% 1,5% 2,8% 4 Nampula Moma 118 7,4% 0,6% 6,7% 2 Nampula Monapo 103 8,4% 1,7% 7,3% 3 Nampula Mogincual ,4% 1,3% 4,2% 4 Nampula Mossuril 135 1,9% 2,8% 9,2% 4 Nampula Muecate 130 9,8% 1,9% 9,3% 4 Nampula Murrupula ,1% 1,0% 3,6% 4 Nampula Cidade de Nacala 35 9,0% 1,1% 10,3% 1 Nampula Nacala-a-Velha ,1% 1,8% 4,3% 5 Nampula Eráti 138 8,0% 4,0% 5,5% 4 Nampula Nampula (Rapale) ,3% 2,5% 4,2% 6 Nampula Cidade de Nampula 13 5,5% 1,8% 12,8% 2 Nampula Ribáuè 57 7,0% 3,4% 8,3% 2 Niassa Cuamba 28 13,5% 2,0% 7,0% 3 Niassa Lago 59 15,7% 2,3% 9,7% 4 Niassa Lichinga 61 17,9% 2,0% 4,9% 3 Niassa Cidade de Lichinga 15 8,0% 3,1% 7,4% 2 Niassa Majune 95 10,5% 2,1% 8,1% 6 Niassa Mandimba 91 14,3% 3,5% 5,8% 6 Niassa Marrupa 122 7,9% 1,8% 4,2% 3 Niassa Maúa 125 9,6% 2,9% 5,1% 5 Niassa Mavago 92 11,4% 2,0% 11,2% 5 Niassa Mecanhelas 93 8,7% 1,9% 2,9% 4 Niassa Mecula 65 23,7% 0,2% 7,0% 3 Niassa Metarica ,5% 1,2% 4,9% 4 Niassa Muembe 88 14,9% 0,6% 7,8% 4 Niassa N'gauma 82 12,5% 1,2% 3,6% 3 Niassa Nipepe ,2% 0,7% 6,3% 4 Niassa Sanga 44 19,2% 1,6% 4,6% 2 Sofala Buzi 52 11,8% 1,7% 12,8% 3 Sofala Caia 56 16,6% 2,4% 13,3% 5 Sofala Chemba 90 13,2% 3,6% 6,2% 6 Sofala Cheringoma 74 10,5% 2,1% 9,2% 5 Sofala Chibabava 131 8,5% 1,7% 15,9% 5 Sofala Cidade da Beira 8 4,6% 1,0% 22,4% 2 Sofala Dondo 17 10,7% 3,5% 22,8% 6 Sofala Gorongosa ,5% 2,2% 6,3% 6 Sofala Machanga ,0% 1,3% 15,3% 6 Sofala Maringue ,9% 3,2% 3,4% 6 Sofala Marromeu 50 7,1% 1,7% 9,5% 1 Sofala Muanza 79 5,2% 1,1% 6,7% 1 Sofala Nhamatanda 40 12,6% 1,9% 12,1% 3 Tete Angónia 53 15,3% 1,2% 5,2% 2 Tete Cahora-Bassa 47 8,4% 3,0% 10,4% 3 Tete Changara 97 8,5% 3,9% 12,9% 6 Tete Cidade de Tete 14 0,0% 3,1% 17,9% 4 Tete Chifunde 86 13,5% 3,0% 5,6% 5 Tete Chiúta ,8% 1,8% 12,2% 5 Tete Macanga 107 5,0% 2,3% 1,4% 4 Tete Magoe ,5% 1,5% 25,0% 6 Tete Maravia ,6% 2,7% 4,5% 6 Tete Moatize 48 8,3% 2,0% 10,2% 2 Tete Mutarara ,3% 3,6% 5,6% 6 Tete Tsangano 69 12,2% 3,5% 14,4% 6 Tete Zumbu 144 5,3% 0,9% 18,0% 4 Zambezia Alto Molocue 101 2,1% 0,5% 21,8% 4 Zambezia Chinde 73 5,7% 1,8% 32,5% 4

38 Province District Deprivation rank EP1 dropout Nutrition (bad growth) HIV/AIDS Vuln. Index Zambezia Gilé 94 5,3% 3,1% 6,7% 4 Zambezia Gurué ,3% 1,1% 28,7% 6 Zambezia Ile 99 7,9% 0,5% 5,6% 2 Zambezia Inhassunge 104 6,5% 4,8% 32,6% 6 Zambezia Lugela 87 6,6% 1,4% 14,5% 2 Zambezia Maganja da Costa 145 8,7% 1,4% 24,1% 5 Zambezia Milange 137 4,9% 0,3% 18,6% 4 Zambezia Mocuba 39 8,3% 1,8% 12,7% 3 Zambezia Mopeia ,5% 2,8% 18,2% 8 Zambezia Morrumbala 109 6,3% 5,7% 22,3% 6 Zambezia Namacurra 55 6,2% 0,8% 33,4% 2 Zambezia Namarroi 75 5,1% 2,0% 3,8% 2 Zambezia Cidade de Quelimane 29 5,1% 1,3% 32,6% 2 Zambezia Nicoadala 112 6,4% 1,5% 21,3% 4 Zambezia Pebane 136 5,0% 2,9% 17,8% 6 35

39 Annex 9: Child Vulnerability Index by rank Province District Vuln. Rank Gaza Chigubo 8 Zambezia Pebane 8 Cabo Delgado Quissango 7 Nampula Nampula (Rapale) 7 Cabo Delgado Palma 6 Gaza Massangena 6 Nampula Murrupula 6 Niassa Metarica 6 Niassa Nipepe 6 Sofala Buzi 6 Sofala Caia 6 Sofala Chemba 6 Sofala Machanga 6 Sofala Maringue 6 Tete Tsangano 6 Tete Chiúta 6 Tete Macanga 6 Tete Magoe 6 Tete Mutarara 6 Zambezia Maganja da Costa 6 Zambezia Mopeia 6 Zambezia Morrumbala 6 Zambezia Nicoadala 6 Cabo Delgado Nangade 5 Cabo Delgado Chiúre 5 Cabo Delgado Namuno 5 Inhambane Panda 5 Manica Sussundenga 5 Manica Mossurize 5 Manica Tambara 5 Nampula Memba 5 Nampula Muecate 5 Niassa Muembe 5 Niassa Marrupa 5 Sofala Cidade da Beira 5 Sofala Muanza 5 Sofala Gorongosa 5 Tete Cidade de Tete 5 Tete Chifunde 5 Zambezia Inhassunge 5 Cabo Delgado Montepuez 4 Cabo Delgado Muidumbe 4 Gaza Bilene Macia 4 Gaza Chicualacuala 4 Manica Manica 4 Manica Machaze 4 Maputo Moamba 4 Maputo Cidade de Maputo 4 Maputo Cidade da Matola 4 Nampula Ilha de Moçamb. 4 Nampula Nacarôa 4 Nampula Lalaua 4 Nampula Mecubúri 4 Nampula Moma 4 Nampula Monapo 4 Nampula Mogincual 4 Nampula Mossuril 4 Nampula Nacala-a-Velha 4 Niassa Lago 4 Niassa Lichinga 4 Niassa Majune 4 Niassa Mavago 4 Niassa Maúa 4 Tete Angónia 4 Tete Maravia 4 36

40 Province District Vuln. Rank Tete Zumbu 4 Zambezia Chinde 4 Zambezia Ile 4 Zambezia Alto Molocue 4 Zambezia Gurué 4 Zambezia Milange 4 Cabo Delgado Mueda 3 Cabo Delgado Macomia 3 Gaza Chibuto 3 Gaza Massingir 3 Gaza Xai-Xai 3 Gaza Cidade da Xai-Xai 3 Inhambane Vilankulo 3 Inhambane Zavala 3 Manica Cidade de Chimoio 3 Manica Guro 3 Manica Macossa 3 Maputo Namaacha 3 Nampula Meconta 3 Nampula Ribáuè 3 Niassa Sanga 3 Niassa Cidade de Lichinga 3 Niassa Mandimba 3 Niassa Mecanhelas 3 Niassa Mecula 3 Sofala Nhamatanda 3 Sofala Cheringoma 3 Tete Changara 3 Zambezia Lugela 3 Cabo Delgado Ancuabe 2 Cabo Delgado Balama 2 Cabo Delgado Mecúfi 2 Cabo Delgado Mocimboa da Praia 2 Cabo Delgado Meluco 2 Gaza Chókwè 2 Gaza Guijá 2 Gaza Mabalane 2 Gaza Mandlakazi 2 Inhambane Mabote 2 Manica Báruè 2 Manica Gondola 2 Maputo Boane 2 Maputo Magude 2 Maputo Manhiça 2 Maputo Marracuene 2 Maputo Matutuíne 2 Nampula Cidade de Nampula 2 Nampula Angoche 2 Nampula Mogovolas 2 Nampula Eráti 2 Niassa Cuamba 2 Niassa N'gauma 2 Sofala Chibabava 2 Tete Cahora-Bassa 2 Tete Moatize 2 Zambezia Mocuba 2 Zambezia Cidade de Quelimane 2 Zambezia Gilé 2 Zambezia Namacurra 2 Zambezia Namarroi 2 Cabo Delgado Cidade da Pempa 1 Inhambane Cidade de Inhambane 1 Inhambane Cidade de Maxixe 1 Inhambane Funhalouro 1 Inhambane Inhassoro 1 Nampula Cidade de Nacala 1 Nampula Malema 1 Sofala Dondo 1 Sofala Marromeu 1 37

41 Province District Vuln. Rank Cabo Delgado Pemba 0 Inhambane Govuro 0 Inhambane Homoine 0 Inhambane Inharrime 0 Inhambane Jangamo 0 Inhambane Massinga 0 Inhambane Morrumbene 0 38

42 Annex 10: Maps on Flood and Cyclone Risk in Cities 39

43 Annex 11: Risk Maps based on hydrological and meteorological data 40