DISASTER RISK PROFILE Mozambique Drought Flood Landslide_@ Earthquake Cyclone * .7.Building Disaster Africa Disaster Risk Financing Initiative ::*.*,:..Resilience in '** **.*."".ub-Saharon Africa WORLDBANKGROUP G FDRR" 02019 The World Bank The International Bank for Reconstruction and Development The World Bank Group 1818 H Street, NW Washington, D.C. 20433, USA July 2019 Africa Disaster Risk Profiles are financed by the EU-funded ACP-EU Africa Disaster Risk Financing Program, managed by the Global Facility for Disaster Reduction and Recovery. DISCLAIMER This document is the product of work performed by GFDRR staff, based on information provided by GFDRR's partners. The findings, analysis and conclusions expressed in this document do not necessarily reflect the views of any individual partner organization of GFDRR, including, for example, the World Bank, the Executive Directors of the World Bank, UNDP, the European Union, or the governments they represent. Although GFDRR makes reasonable efforts to ensure all the information presented in this document is correct, its accuracy and integrity cannot be guaranteed. Use of any data or information from this document is at the user's own risk and under no circumstances shall GFDRR or any of its partners be liable for any loss, damage, liability or expense incurred or suffered which is claimed to result from reliance on the data contained in this document. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denomination, and other information shown in any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any terri- tory or the endorsement or acceptance of such boundaries. The European Union is not responsible for any use that may be made of the information contained therein. RIGHTS AND PERMISSIONS The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to the Office of the Publisher, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2422; e-mail: pubrights@worldbank.org. DISASTER RISK PROFILES Overview National Risk Profiles The Africa Disaster Risk Financing (ADRF) Initiative is To create an enabling environment for dialogue on risk one of five Result Areas of the European Union (EU) - financing strategies and to further the understanding of Africa, Caribbean and Pacific (ACP) cooperation program disaster risk, national risk profiles have been developed Building Disaster Resilience in Sub-Saharan Africa, which for eight countries in the region. The risk profiles provide is implemented by several partners, including the African visual information and data on the hazards, exposure, Development Bank (AfDB), African Union Commission and risk for multiple hazards in each country. The profiles (AUC), the United Nations International Strategy for provide an overview of which hazards, sectors and regions Disaster Reduction (UNISDR) and the World Bank (WB)- are most at risk of disasters, and contribute most to the managed Global Facility for Disaster Reduction and Recovery national level of risk. (GFDRR). The Program's overall objective is to strengthen the resilience of Sub-Saharan African regions, countries Specifically, the national risk profiles provide the estimated and communities to the impacts of disasters, including the impact of disasters on population, building stock, transport potential impact of climate change, to reduce poverty and networks, critical facilities, and agriculture at the national promote sustainable development. and sub-national levels. These profiles can guide initial strategic dialogue on financial protection and / or risk The ADRF Initiative, launched in 2015 and implemented reduction investment opportunities to manage disaster by GFDRR and the World Bank, supports the development risk, as well as help identify priorities for more detailed risk of risk financing strategies at regional, national and local assessments if specific interventions are to be made. levels to help African countries make informed decisions to improve post-disaster financial response capacity to mitigate the socio-economic, fiscal and financial impacts of disasters. One of the operational components to achieve this objective is to create an enabling data environment for risk financing. NN This aims to build the understanding and awareness of 40 s o a disaster and climate risks in Sub-Saharan Africa, providing a fundamental input to developing disaster risk financing Cabo Verde strategy, approaches, and tools for financing risks. One of the activities is to develop national-level multiple-peril Ethiopia country risk profiles using globally available and readily Kenya accessible local datasets, in combination with scientifically proven methodologies. These are used to catalyze dialogue Malawi with government counterparts in the region on the primary disaster risks they face to formulate Disaster Risk Mali Management strategies, such as financial protection and Mozambique risk reduction investment programs. Furthermore, the risk profiles provide datasets that are a critical input for Niger developing risk financing and insurance strategies. Un ( Q Uganda aa0 DISASTER RIO ~~~~~6 .'054 '<* N~4'~%S 'b< 4, 4P$ 4P 00 'Z' ( DISASTER RISK PROFILE I INTRODUCTION DISASTER RISK PROFILES Use The cost or number affected is estimated for most hazards at three time periods: a decade (this refers to the 1 in These risk profiles provide a preliminary view of disaster 10 year return period, or 10% chance of a loss being risk at the national level, and distribution of risk across exceeded in any given year); a person's lifetime (1 in 50, or regions of the country and types of assets. They enable 2% in any year), or for an extreme event (1 in 250, or 0.4% the identification and prioritization of risk drivers, to in any year). guide risk management activities and identify the need for further, more detailed risk assessment. Hazard and Vulnerability Data Due to limitations in the content and resolution of the publicly available global and national level exposure and Drought hazard analysis comprises agricultural hazard data used in their development, these profiles do (soil moisture deficit) and hydrological (river flow) not provide sufficient detail for taking final decisions on drought. Drought duration and deficit volume per year disaster management investments and policies, or for are determined by event-based modeling to estimate planning subnational and local scale mitigation projects, population affected by water scarcity. Monetary loss such as construction of flood defenses. Such decisions reflects the loss in yield and long term average price for should be informed by a local, and possibly sector-specific each modelled per crop. disaster risk assessment, which estimates risk at a higher resolution with more locally-specific exposure, hazard, River flood risk (urban/surface flooding is excluded) is and vulnerability input data. estimated at 1km resolution using global meteorological data, global hydrological and flood-routing models. Loss These risk profiles present a substantial part of the estimates are generated by simulating rainfall statistics analysis results. However, it has not been possible to for 10,000 years based on 40 years of previous rainfall present all results in these documents. Full results for all data. Damage functions for four types of buildings, asset types are available from GFDRR Innovation Lab. and for roads/railways, are used to estimate loss as a function of flood depth. Population are considered 'affected' if flooding of any depth occurs in the same Risk 1km area. Agriculture loss is estimated by assuming that catastrophic flooding will result in a loss of the annual Risk calculations require input data describing the hazard, crop yield. assets ('exposure'), and vulnerability of those assets. Earthquake hazard describes the distribution of ground Disaster risk to structural and infrastructure assets is shaking intensity (i.e., peak ground acceleration), based on quantified here by estimating the cost to repair and/ the locations of known seismic faults and location/size of or replace assets damaged or destroyed in a disaster, previous earthquakes. Losses are estimated using fragility i.e. due to ground shaking, flood depth or wind speed, and vulnerability models that translate ground shaking over various time horizons. Assets analyzed are private into the expected level of (a) damage to different types of and government-owned building stock, critical facilities structure, and (b) displacement of roads and rails. Based (education and health), and transport networks (road, rail, on damage to buildings, a casualty model has been used and bridges). to estimate the risk of fatalities as well as the population affected by ground shaking. This study includes losses Risk to population is quantified by assessing the number due to damage from earthquake ground shaking only. of people that are expected to be affected by the hazard. Secondary hazards (liquefaction and fire following an earthquake) are not accounted for. Landslide hazard is For volcanoes, an indicative measure of volcano risk considered under the separate landslide section, where is given by estimating population and value of assets ground shaking is considered as a potential trigger of exposed to the volcanic hazards (no estimation of impact landslides. is made). Landslide susceptibility has been defined across each Losses additional to those incurred due to physical country using an assessment of factors that increase damage are not included in this analysis (e.g., business potential for landslides (including slope, vegetation interruption due to disrupted infrastructure or supply and soil types) combined with landslide trigger events chains). (rainfall and seismic shaking) to create landslide hazard maps. Long-term average annual cost to structures and transport networks has been estimated using vulnerability of different asset types to landslides, based on extensive literature review, empirical data, and expert judgement. 2 DISASTER RISK PROFILE I METHODOLOGY DISASTER RISK PROFILES Average annual population affected, and fatalities, are Transportation data include roads, railways, and bridges, estimated. where present. Road surface type (paved, unpaved) is also included where available. Agriculture exposure is Volcanic eruption scenarios at a small number of key described by crop type and subnational distribution, volcanoes are used to estimate the population, and average annual yield, and crop price for risk calculations. replacement cost of structures and infrastructure exposed to ashfall hazard (i.e. are located in an area that could Replacement costs for building stock and critical facilities receive ash in an eruption) and topographic analysis is are calculated using construction cost per square meter used to determine the assets and population exposed to for each building or facility type, and cost per kilometer flow hazards. Full quantification of risk at all volcanoes for roads based on road type and for railway lines, is not possible due to limited information on potential based on terrain. Estimates of replacement cost were frequency and eruption style at many volcanoes in Sub- developed through interviews with local engineering and Saharan Africa. construction professionals (numbers and sources varied in each country). These were validated and adjusted Cyclone and storm surge hazards are assessed using where necessary using several sources, including site a record of historical cyclone tracks and wind field surveys and international literature on construction. modelling, to determine maximum wind speeds on Replacement costs used are representative of typical land and accompanying water levels along the coast. building infrastructure and replacement costs for the Vulnerability of structures to wind and surge is estimated entire country. Subnational variations in costs and based on previously observed damage sustained at building distributions (due to cost of materials and labor) different wind speeds and literature on flood depth impact will vary and are not accounted for. of different types of structures. Asset Database Open and freely available national, regional, and global data sets are used to develop, for the first time, a database of population and multiple built asset types for risk analysis. This is used to inform this risk assessment, in a region where there is significant variability in the availability and content of inventories describing building stock and infrastructure. Population density is described using WorldPop data. Building stock is described using six development types: rural, residential, high-density residential, informal, urban, and industrial, based on land use data and satellite imagery In each cell of a 0.5 km resolution grid, the number of buildings and total floor area of each development type is given. The number of buildings is further disaggregated into different construction types to account for the impact different levels of structural vulnerability in the risk analysis. Critical facilities include education and health facilities. Where possible, the assets have been analyzed using accurate geolocation given in an available building inventory. However, many assets had no geolocation given and were distributed using building density as a proxy for their location; the proportion of geolocated assets varies by country. Education facilities (classified as primary school, secondary school, or universities) and health facilities (hospital or clinics) have been assigned an estimated construction type based on interviews with structural engineers in each country and used to approximate construction cost per square meter. DISASTER RISK PROFILE I METHODOLOGY 3 RISK SUMMARY I 0ll GDP $15 billin* 0 Population 28 million* *2015 estimates ozambique's population stood Development Index is 0.4322) and 46% 24% of GDP with a predominance of at 28 million in 2015 but is of the population lives below the poverty the mining sector. The service sector growing at a rate of 3% each year. line'. contributes the remaining 46%'. Approximately 67% of the population lives in rural areas'. The urban population Mozambique's agricultural sector The large majority of Mozambique's is concentrated in various cities of which accounts for 30% of GDP and 77% agriculture is rain-fed, with harvests the capital city Maputo is the largest with of overall employment'. The main vulnerable to rainfall variability In 1.2 million inhabitants'. The country agricultural products are sugar, copra, Mozambique, chronic food insecurity is at is one of the least developed countries cashew nuts, tea, and tobacco. The 24 percent'. The food insecurity is in part in the world (Mozambique's Human industrial sector accounts for a further caused by floods and droughts. 2010 Population 2010 GDP People per km, US$ per kml 0 200 0 100,000 17.'- W. vow .. 1,61, ,q; - ," - or figh-rise developments close to the coast in downtown Maputo, Mozambique 4 DISASTER RISK PROFILE I RISK SUMMARY RISK SUMMARY All $ arounts are In USD 11OZ4,11BIOUE QDrought (oFlood (4Landslide @ Earthquake (aCyclone C yclones pose the most significant can be substantially higher in dry years. Future changes in Mozambique's and recurring risk to Mozambique, Flooding poses a threat to lowland, population and economy, coupled with affecting 2 million people per year highland, and urban areas, with 200,000 changes in climate-related hazards, on average in the coastal regions. people affected by floods each year, on are expected to increase the impacts of average, droughts and floods. Droughts and floods also affect many people: 600,000 people are affected by A much smaller number of people are at drought every year, but this number risk from earthquakes and landslides. Modeled Impact on Population* *AlI data is from 2010 Cyclone Flood Affected Population Affected Population 0, 00c 0 0o0o0 0 0 0 0 0 0 0 0 0 0 Modeled Impact* *Al data is from 2010 Population Cyclone Drought Flood Earthquake Landslide 2 million 10% 600,000 1% 200,000 01 10,000 CL 0.1% 100 Hazard Summary Table HAZARD IMPACT Agricultural income loss of $20 million per year, on average, with the greatest contribution to loss from Zambezia and Manica. On average, 185,000 people and around 350 education and healthcare facilities are exposed to flood each year. Landslide is a very localized hazard, but could cause up to $1 million of damage to building stock and put over 100 people at risk per year, on average. Damaging earthquakes are infrequent, but it is estimated that around 70,000 people could experience strong ground shaking at least once every 50 years. Around 2 million people are affected by cyclone-strength wind and associated storm surge per year, on average. DISASTER RISK PROFILE I RISK SUMMARY 5 .0 CYCLONE The Mozambique coastline is prone The analysis, however, did not take into to cyclones making landfall during account the compounding effect of the rainy season between October rainfall and/or high river floods. This and March. It forms the western boundary combination can worsen the situation of the an active cyclone belt in the south- significantly, as has been observed west Indian Ocean Basin. Some cyclones during the 2000 floods when originating from this belt may travel into Cyclone Eline hit Mozambique. the Mozambique Channel and can result in extreme storm surge, waves, wind and rainfall along the coast of Mozambique. The frequency of tropical cyclones occurring across any one segment of the Mozambique coastline is about once per year. Recent events are Cyclone Bonita (1996), Eline (2000), Funso (2012) and Dineo (2017). The actual effect of Cyclone wind and storm each storm on coastal water levels will surge hazard vary greatly depending on the local Cyclone: 10-minute maximum characteristics (e.g. shape and depth) of sustained wind speed (m/s) at the coastline and shelf, the storm track, and l in 100-year the vulnerability of the affected locations. 27 36 This analysis included the effect of wind Surge: 1 in 100yr coastal flood extent and surge damage as a result of a cyclone. Many cyclone tracks have been generated by resampling to define a reliable prediction of return periods of surge levels aputo along the coastline. Modeled Impact Key Facts * Cyclone Leon-Eline in 2000 is one of the Population most damaging cyclones in Mozambique in 1-in-25 year 300,000 people exposed recent history. This cyclone occurred when 1-in-50 year 800,000 people exposed flooding was already widespread throughout 1-in-250 year 2 million people exposed the country. Total impact of this compounded Buildings disaster was estimated at $500 million of 1-in-25 year $400 million damage damage and 700 deaths. 1-in-50 year $500 million damage * Cyclone Funso hit Mozambique in 2012 and 1-in-250 year $700 million damage affected more than 3 million people with Education and Health Facilities tropical storm-force winds. The Zambezia 1-in-25 year 1,000 facilities exposed Province was hit hard during this storm, which 1-in-50 year 2,500 facilities exposed left more than 50,000 people homeless in 1-in-250 year 6,500 facilities exposed Mozambique. Transport Desinventar reports that about 300,000 people Tranpor ya1in Mozambique are affected by cyclones on 1-in-25 year - 1,500 km exposed average every year, based on the period 1994 1-in-50 year m 3,500 km exposed to 2012 1-in-250 year 8,500 km exposed Agriculture 1-in-25 year $300 million crop damage 1-in-50 year $350 million crop damage 1-in-250 year $400 million crop damage AAL = Average Annual Loss; 1-in-10 year return period equates to a 10% annual probability; 1-in-50 to 2% annual probability; and 1-in-250 to 0.4% annual probability. 6 DISASTER RISK PROFILE I CYCLONE SCYCLONE C yclone risk is presented as population exposed to cyclone wind and storm surge. These two (overlapping) effects are intended to be indicative of the cyclone risk. In addition to this, indicative estimates are provided of the likelihood and magnitude of damage to building stock due to wind and surge. Also, the total exposed value of critical facilities and transport systems are listed. The cyclone impact in Mozambique is greatest in the coastal regions where the cyclone retains its intensity. Nampula and Inhambane show relatively high impact M on population from cyclone winds, whereas Sofala, Cyclone Nampula and Zambezia are most affected by storm Exposed Population* surge. The annual expected damage to building stock is about $70 million each from wind and surge. For extreme events, the damage to building stock can o 00 00 0 go far beyond $100 million. The exposure of critical I NO a facilities such as education and healthcare facilities ( * (see maps below) is much less. Roads and railroads *Exposed values refer to assets and are, however, exposed to cyclone winds due to the population located in areas which m experience Tropical Cyclone strength winds (>119 km/h or 33 M/s) Maputo Asset Distribution Average Annual Loss per Province Average Annual Loss per Province Relative to national total Buildings 10 million 10% $ Damage 7.5 1 5 0.1 2.5 0.01 0 0 Education and Health 40 facilities 10% Facilities 30 1 Facilities 20 0.1 exposed* 10 0.01 0 0 Transport 80km10% Km exposed* 60 1 40 0.1 20 U0.01 0 r0 DISASTER RISK PROFILE ICYCLONE 7 FLOOD Mozambique has many rivers draining from the central African highland plateau into the Indian Ocean. Main rivers are the Zambezi in the central part and Limpopo in the southern part of Mozambique which belong to the largest rivers in Africa. Here, the flood potential of the Limpopo and the Zambezi can be seen in the main map. Other smaller river basins in the northeastern region also result in flood hazard in the various catchments. In Mozambique, the greatest flood potential occurs during and following the November to March rainy season with peak flows in March and April. The coincidence with incoming tropical U Flood Prone Areas cyclones sometimes worsens the 1 in 100-year flood extent flood situation, as occurred during the 2000 floods when Cyclone Eline hit Mozambique. The national scale of these profiles means the focus is on river flooding, and surface flooding (including urban flood) is not included in the risk estimates. ,Maputo Modeled Impact Population AAL 200,000 people exposed Key Facts 1-in-25 year 350,000 people exposed 1-in-50 year 450,000 people exposed * Flooding in 2000 killed nearly 700 people with Buildings 1400 km- agricultural land and 20,000 cattle AAL m $500 million damage lost. The property damage was estimated at 1-in-25 year $1 billion damage $500 million (2000). 1-in-50 year $1.5 billion damage * According to the Desinventar database of Education and Health Facilities disaster impacts, there have been over 11 AAL 350 facilities exposed million people affected by flooding and over 1-in-25 year 750 facilities exposed 1400 people killed in Mozambique since 1990. 1-in-50 year 1,500 facilities exposed In that time, over 180,000 Hectares of crops Transport have been damaged and 40,000 cattle lost. AAL m 300 kilometers exposed 1-in-25 year 650 kilometers exposed 1-in-50 year 1,000 kilometers exposed Agriculture AAL $15 million crop damage 1-in-25 year $35 million crop damage 1-in-50 year $45 million crop damage AAL = Average Annual Loss; 1-in-25 year return period equates to a 4% annual probability; 1-in-50 to 2% annual probability; and 1-in-250 to 0.4% annual probability. 8 DISASTER RISK PROFILE I FLOOD FLOOD Cyclone risk is presented as population exposed Cabo to cyclone wind and storm surge. These two De gado (overlapping) effects are intended to be indicative of the cyclone risk. In addition to this, indicative estimates are provided of the likelihood and magnitude of damage to building stock due to wind and surge. Also, the total exposed value of critical facilities and transport systems are listed. Over the long term, tropical cyclones are not frequently expected in Mozambique, though the changing climate could increase the frequency and severity of cyclones. About once in a person's lifetime, an event Flood would be expected to affect about 800,000 people and Exposed Population (average per year) 500,000 buildings with tropical cyclone strength winds (windspeeds >119km/h), resulting in damage on the 0 order of $500 million to the national building stock. 0 0 0 0 The cyclone impact in Mozambique is greatest in the coastal regions where the cyclone retains its intensity. Nampula and Inhambane show relatively high impact on population from cyclone winds, whereas Sofala, M Nampula and Zambezia are most affected by storm SMaputo surge. Asset Distribution Average Annual Loss Average Annual Loss per Province Per Province Relative to national total Buildings o00 million 2% $ Damage 75 1.5 50 1 25 0.5 0 0 Education and Health 80 facilities 2% Facilities 60 1.5 Facilities 40 1 exposed 20 0.5 0 0 Transport e km 2% Km exposed -30 1.5 20 1 10 0.5 0 0 DISASTER RISK PROFILE FLOOD 9 k DROUGHT Droughts are sustained periods of estimating the impact of rainfall deficits below-normal water availability. on crop yield. The loss in yield translates Droughts occur due to natural to a loss of agricultural income based on atmospheric variability (e.g. El Nifio crop price data. conditions), desertification, land degradation. Increasing rainfall variability The bars below indicate the number of and extremes are increasing the drought people located in areas affected by water hazards which is already common scarcity, and the loss of agricultural particularly in the Horn of Africa. income due to the effects of drought on crop yield. This risk profile assesses the impacts on population due to hydrological drought creating conditions of water scarcity, which occurs when water availability per person per year drops below a certain threshold: 1700 cubic meters per person per year. Water availability of less than 500 cubic meters per person per year is considered severe water scarcity. This analysis uses a combination of water scarcity thresholds and a measure of the volume of water flowing in rivers to assess drought risk to population, Combined Drought Hazard Index referred to here as the 'Combined Drought Hazard Index. very low very high Risk is considered greatest where river flows show a deficit in flow volume over a period of greater than three months, and the amount of water available for human use is below the severe water scarcity above. Shorter droughts can also result in water scarce conditions. j Maputo This profile also estimates the effects of agricultural drought on agricultural income from a wide variety of crops. Loss of agricultural income is assessed by Key Facts * Droughts are a recurrent hazard in Mozambique. The Desinventar database of disaster impacts reports that over 11 million Modeled Impact people have been affected by drought since 1984. Population * The worst drought in recent decades began in 1982, and by 1984 100,000 people had died Annual average 600,000 people affected and another 750,000 required food assistance. 1-in-10 year ~1.5 million people affected Inhambane, Gaza and Maputo were worst affected'. Agricultural Income Loss AAL no $20 million 1-in-10 year $65 million 1-in-50 year $200 million 1-in-200 year $350 million AAL = Average Annual Loss; 1-in-10 year return period equates to a 10% annual probability; 1-in-50 to 2% annual probability; and 1-in-200 to 0.4% annual probability. 10 DISASTER RISK PROFILE I DROUGHT ydrological drought risk (water prevalence of rain-fed agriculture rather scarcity) is greatest in Inhambane. than irrigation. In this region, more than 0.5 million people live in areas expected The different in distribution of drought to suffer water scarcity each year. The risk shown in the maps, of affected selected socioeconomic and climate population and agricultural crop loss is scenarios suggest that by 2050, the due to the use of hydrological drought number of people affected by drought indicators (river flow volume) for will significantly increase, both relatively impact on population, and precipitation and absolutely The 2050 population is as the indicator of drought for rainfed expected to increase by about 140% to agriculture, and the different distribution around 65 million. The number of people of these different drought types. affected by drought each year at that time is expected to be over 3 million. The hydrological drought analysis is conducted at large scale units (water provinces), which can be larger than Cabo administrative provinces. The model Delgado assumes that water resources are Niassa redistributed via rivers within a natural 'water province', and while people in Nampu la one part of the water province may Tete experience water scarcity, those water scarce conditions may be alleviated bym-Z greater water availability in other parts of the same water province, reducing the population affected in the province as a ofa whole. a On average, once every 10 years a loss Agricultural Drought of $65 million in agricultural income is Agricultural Income Loss (AAL) expected to occur in Mozambique, with the equivalent of 4.5 million labor days o * lost. Manica, Gaza and Zambezia are the Iham ne P ' Provinces that are estimated to suffer G 10 0 greatest loss of crop yield and agricultural income. Agricultural income loss would be expected to be about the same by 2050, without accounting for changes in crop Maputo prices. The agricultural drought analysis does not account for the effect of water transfer within provinces, due to the Asset Distribution Average annual affected (water scarcity) Average Annual Loss per Province Per Province Relative to national total Population Pop. exposed 100,000 20% to water 10,000 10 scarcity 1,000 1 100 0.1 0 0 DISASTER RISK PROFILE I DROUGHT 11 * EARTHQUAKE Earthquakes pose the threat of would be expected to occur at least once building damage and collapse, in a person's lifetime. Buildings of poor particularly where seismic-resistant and moderate quality construction could design of buildings is not generally sustain damage, but extent of damage applied, as in Mozambique. They can also would be strongly dependent on local cause damage and disruption to transport seismic and construction factors. Very networks and essential services due to strong earthquakes with moderate ground motion displacing roads, rails, damage could be bridges and other essential services. expected at least once Earthquakes can cause sufficient ground in about 500 years. shaking to trigger rockfalls and landslides The east coast and in areas susceptible to such hazards (i.e. areas north of Beira steep terrain). to Lake Nyassa could expect light shaking at Mozambique is located at the southern least once in a person's end of the East African Rift, and seismicity lifetime and moderate associated with this system extends from shaking at least once in Earthquake Hazard the north into central Mozambique. The a 500-year period. Away 1 in 100-year Peak Ground main areas of seismic hazard are on the from the these areas, the Acceleration (g) east coast (where the Eastern branch of hazard is much lower. 0 AN 50 the Rift tracks offshore), and along the Western branch of the Rift beneath and south of Lake Nyasa. The highest hazard occurs northwest of Lichinga in Niassa Province and in Manica Province, southwest of Beira. In these areas, strong ground shaking SMaputo Modeled Impact Key Facts Population * The Mw7.0 Machaze earthquake in February 1-in-10 year 20,000 people affected 2006 was the largest earthquake in southern 1-in-50 year 70,000 people affected Africa for 100 years. The earthquake was 1-in-250 year 200,000 people affected felt widely in the region and caused 5 deaths and 40 injuries. Three hundred houses were Buildings partially damaged. Damage was limited 1-in-10 year m $45 million damage relative to the earthquake magnitude due to 1-in-50 year m $200 million damage the epicenter being located in a rural area and 1-in-250 year $650 million damage having moderate ground shaking intensity. Education * This study includes losses to due to damage 1-in-10 year $600,000 damage from earthquake ground shaking only. 1-in-50 year $2.5 million damage Secondary hazards (liquefaction and fire 1-in-250 year $8 million damage following an earthquake) are not accounted Health for. Landslide hazard is considered under 1-in-10 year $150,000 damage the separate landslide section, where ground 1-in-50 year $1 million damage shaking is considered as a potential trigger of 1-in-250 year $2.5 million damage landslides. Transport 1-in-10 year m $150,000 damage The distribution of earthquake risk is 1-in-50 year - $1.5 million damage 0 determined by modeled earthquake hazard 1-in-250 year $7.5 million damage events, the location where assets intersect with these hazards, and the vulnerability of those assets. AAL = Average Annual Loss; 1-in-10 year return period equates to a 10% annual probability; 1-in-50 to For more detail, see the Methodology section. 2% annual probability; and 1-in-250 to 0.4% annual probability. 12 DISASTER RISK PROFILE I EARTHQUAKE EARTHQUAKE It is possible that, at least once in a person's lifetime, an earthquake could occur that affects 70,000 people with at least light ground shaking (see bars, opposite). In such an earthquake, there is likely to be at least light to moderate d building damage in some areas. The cost of this would be approximately $200 million, with potential for $3 million of damage to education and health facilities. Manica and Sofala provinces contribute most to the annual average affected Earthquake population, followed by Niassa. Highest Average population affected in a given year potential damage to building stock also occurs in these provinces. In contrast, Maputo shows much lower potential oo o impacts from earthquakes due to low I hazard in that area. In the provinces most at risk, by 2050 there could be almost twice as many people affected and 10 times the building damage costs on average each year, than in 2010. *Maputo Asset Distribution Average Annual Loss Average Annual Loss per Province Per Province Relative to national total Buildings 4 million 0.03% Damage 30.02 2 0.01 1 0.001 0 0 Education and Health 100,000 0.03% Facilities 75,000 0.02 $ Damage 50,000 0.01 25,000 0.001 0 0 Transport 40,000 0.03% $ Damage 0 30,000 0.02 20,000 0.01 10,000 0.001 0 0 DISASTER RISK PROFILE EARTHQUAKE 13 (4 LANDSLIDE There is moderate landslide There is insufficient data on historical hazard in many areas of northern landslides to produce estimates of Mozambique and in southern loss for multiple return periods, as areas, in the region of Maputo. High provided for other hazards. Data on hazard is most prevalent in Nampula and the annual expected frequency of Zambezia, and on the western border different landslides is available with Zimbabwe, in Manica Province. and is used to generate the annual risk estimate. In the capital city Maputo, road construction has led to steepening of slopes and increased landslide hazard. Many instances of such localized hazard are not captured well in this national- scale analysis, and require higher resolution analysis to fully describe the hazard there. Surface erosion and the formation of gullies is common during Rainfall-triggered intense rainfall events in Maputo City landslide hazard index Damage due to landslide has been very low very high estimated across the whole country using a novel method that enables estimation of annual average risk using landslide susceptibility factors combined with earthquake and rainfall triggers, and the potential impact of different size landslides on the population, buildings, and transport networks. Maputo Key Facts * Mount Tumbine, in Zambezia Province is prone to landslides during periods of heavy rainfall from January to March, with slope Modeled Impact failure reported in 1949, 1958, 1968, 1993 and 1998. Reduction in surface vegetation and Population increased amounts of loose surface material Average Annual Loss 100 people exposed due to deforestation contribute to slope instability here. Buildings * In 1998, 200 people were killed and 4,000 AAL $1 million damage displaced by multiple debris flows that destroyed homes, roads, bridges and water Education supply. One thousand hectares of crops were AAL $30,000 damage also destroyed. Health AAL $10,000 damage Transport AAL m $100,000 damage 14 DISASTER RISK PROFILE I LANDSLIDE (4 LANDSLIDE n average, each year over 100 Cab people are at risk of being affected De gd by landslides, with the modelling suggesting a long term average of around 10 fatalities per year nationally. The annual average cost of damage Tete to building stock is expected to be $100,000 in any given year. This analysis also suggests transport networks could sustain damage of $100,000 per year on average. Socio-economic changes Sof and climate change induced increases Mandce in rainfall-triggered landslides could, Landslide by 2050, double the number of people Affected Population affected annually. The most at-risk regions of Mozambique are Maputo, Nampula and Zambezia (see In e main map). These regions contribute az the majority of losses to the national landslide risk profile. I aputo Asset Distribution Average Annual Loss Average Annual Loss per Province Per Province Relative to national total Buildings 200,000 U .002% $ Damage 150,000 .0015 100,000 .001 50,000 .0005 0 0 Health 4,000 .002% Facilities 3,000 .0015 $ Damage 2,000 .001 1,000 .0005 0 0 Education 4,000 .002% Facilities 3,000 .0015 $ Damage 2,000 .001 1,000 .0005 0 0 DISASTER RISK PROFILE LANDSLIDE 15 DISASTER RISK PROFILES Glossary Notes Average annual loss ' Central Intelligence Agency, The World Factbook, 2015, https://www.cia.gov/library/publications/ Average annual loss (AAL) is the estimated impact (in mon- ho tb /. etary terms or number of people) that a specific hazard is likely to cause, on average, in any given year. It is calculated 2 United Nations Development Programme, based on losses (including zero losses) produced by all haz- Human Development Report 2015: Work for ard occurrences over many years. Human Development (New York: United Nations Development Programme, 2015), http://hdr.undp. Exposure org/en/data. Exposure refers to the location, characteristics, and value " http://wwwl.wfp.org/countries/mozambique. of assets such as people, buildings, critical facilities, and transport networks located in an area that may be subject to 4 https://reliefweb.int/report/mozambique/ a hazard event. mozambique-droughtfloods-mar- 1982-undro- situation-reports-21-30. Hazard Hazard refers to the damaging forces produced by a peril, such as ground shaking induced by an earthquake or water inundation associated with flooding. Risk Disaster risk is a function of hazard, exposure, and vulner- ability. It is quantified in probabilistic terms (e.g., Average Damage Per Year, and return period losses) using the im- pacts of all events produced by a model. Vulnerability Vulnerability is the susceptibility of assets to the forces of a hazard event. For example, the seismic vulnerability of a building depends on a variety of factors, including its struc- tural material, quality of construction, and height. DISASTER RISK PROFILE I GLOSSARY AND NOTES ACKNOWLEDGMENTS These risk profiles were prepared by a team comprising Alanna Simpson, Emma Phillips, Simone Balog, Stuart Fraser, Brenden Jongman, Mathijs van Ledden, Rick Murnane, and Anne Himmelfarb. The core team wishes to acknowledge those that were involved in the production of these risk profiles. First, we would like to thank the financial support from the European Union (EU) in the framework of the African, Caribbean and Pacific (ACP)-EU Africa Disaster Risk Financing Initiative, managed by GFDRR. In the GFDRR secretariat we would like to particularly thank Francis Ghesquiere, Rossella Della Monica, and Hugo Wesley. We would also like to extend our appreciation to the World Bank Africa Disaster Risk Management Team, including Niels Holm-Nielsen, Ana Campos, Oscar Ishizawa, Michel Matera, Francis Nkoka, Christoph Pusch, Jean-Baptiste Migraine, and Giovanni Prieto Castellanos. Thank you to the Disaster Risk Financing and Insurance Team: Julie Dana, Barry Maher, and Benedikt Signer. Our thanks to all the organizations who produced the risk assessment analysis: Arup; British Geological Survey (BGS); Center for International Earth Science Information Network (CIESIN); CIMA Foundation; Deltares; Evaluaci6n de Riesgos Naturales (ERN); Global Volcano Model (GVM); ImageCat Inc.; Plant Research International (PRI); Risk Engineering + Design (RED); SecondMuse; University of Bristol; University of Colorado; and VU University Amsterdam, Institute for Environmental Studies (VU-IVM). Finally, we are grateful to Axis Maps for creating the data visualizations and these risk profiles. DISASTER RISK PROFILE I ACKNOWLEDGMENTS