Angola - Disaster risk profile

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DISASTER RISK PROFILE

Flood

Angola

Building Disaster Resilience to Natural Hazards in Sub-Saharan African Regions, Countries and Communities

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IL

PR

TEC

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Building Disaster Resilience to Natural Hazards in Sub-Saharan African Regions, Countries and Communities ANG LA O

This project is funded by the European Union

Drought

© CIMA Research Foundation International Centre on Environmental Monitoring 2019 - Review This Disaster Risk Profile is a result of the ACP-EU Building Disaster Resilience to Natural Hazards in Sub-Saharan African Regions, Countries and Communities Programme Result 4 - implemented by UNDRR. DISCLAIMER This document is the product of work performed by CIMA Research Foundation staff.

The views expressed in this publication do not necessarily reflect the views of the UNDRR or the EU. The designations employed and the presentation of the material do not imply the expression of any opinion whatsoever on the part of the UNDRR or the EU concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delineation of its frontiers or boundaries.

RIGHTS AND PERMISSIONS The contents of this report are subject to copyright. Because UNDRR and CIMA Research Foundation encourage dissemination of its knowledge, this work may be reproduced, in whole or in part, for non-commercial purposes as long as full attribution is given: CIMA, UNDRR (2019): Angola Disaster Risk Profile Nairobi: UNDRR and CIMA Research Foundation.

Any queries on rights and licenses, including subsidiary rights, should be addressed to CIMA Research Foundation: Via A. Magliotto, 2 - 17100 Savona - Italy • Phone: +39 019230271 - Fax: +39 01923027240 info@cimafoundation.org • www.cimafoundation.org Design and layout: CIMA Research Foundation

In collaboration with:

ANGOLA DISASTER RISK PROFILE

Project Team Authors Roberto Rudari [1] Amjad Abbashar [2] Sjaak Conijn [4] Hans de Moel [3] Auriane Denis-Loupot [2] Luca Ferraris [1;5] Tatiana Ghizzoni [1] Adrien Gignac-Eddy [1] Isabel Gomes [1] Diana Mosquera Calle [2] Katarina Mouakkid Soltesova [2] Marco Massabò [1] Julius Njoroge Kabubi [2] Lauro Rossi [1] Luca Rossi [2] Roberto Schiano Lomoriello [2] Eva Trasforini [1] Marthe Wens [3]

Franco Siccardi [1] Francesco Silvestro [1] Andrea Tessore [1] Tufan Turp [7] Editing Isabel Gomes [1] Adrien Gignac-Eddy [1] Graphics Rita Visigalli [1] Supporting Team Simona Pozzati [1] Luisa Colla [1] Monica Corvarola [1] Anduela Kaja [1] Daniela Perata [1] Tatiana Perrone [1] Elisa Poggi [1] Martino Prestini [1] Maria Ravera [1]

Scientific Team Nazan An [7] Chiara Arrighi [1;6] Valerio Basso [1] Guido Biondi [1] Gulia Bruno [1] Alessandro Burastero [1] Lorenzo Campo [1] Fabio Castelli [1;6] Mirko D’Andrea [1] Fabio Delogu [1] Giulia Ercolani [1;6] Giacomo Fagugli [1] Elisabetta Fiori [1] Simone Gabellani [1] Alessandro Masoero [1] Alessia Matanò [1] Enrico Ponte [1] Ben Rutgers [4]

Country Team José Horácio da Silva [8] Edson Fernando Domingos [8] Marquinha Gabriel Mário [8] Teresa Epako Cadondo [8] Jenoval João Mohongo [8] Paulo Calunga [9] Artur Inácio [9] André Luvenga [10] Bernardo Cândido [11] Ermelinda Calengue [12] Lutumba Agostinho [13] Joseph Rita de Sousa [13] Domingos Pedro Nsoko [14] Narciso Augusto Ambrósio [15]

With the support of the UNDRR Regional Office for Africa CIMA Research Foundation [1] UNDRR [2] Vrije Universiteit Amsterdam [3] Wageningen University & Research [4] Università di Genova [5] Università di Firenze [6] Bogazici University [7] SPCB [8] SPCB (Comando Provincial) [9] INE [10] MEP [11] MINGRI [12] Ministério da Saúde [13] INAMET [14] INRH [15]

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ANGOLA DISASTER RISK PROFILE

Participants in the workshops WORKSHOP 2018

WORKSHOP 2019

Alberto Domingos Mendes Machado André Luvenga Antonio M. L. Gaspar Bernardo Cândido Calunga Paulo Raimundo Carlos Calongo Edson Fernando Emídio Silva Ernesto Kitumba Fernando André Francisco Pascoal Francisco Flávio Germano Jorge Gabriel Francisco Sebastião Inácio Manuel Isel Isabel Epalanga Jenoval Mohongo João João Elias Gabriel José Duarte Varela José Horácio da Silva Júlio Tunecas Lutumba Agostinho Manuel Matanda Lutango Marcelino Neto Palestina Bernardo Patricia Cardoso de Castro Ruth Fortuna Tomás Sebastiao Francisco Mateus Tingao Teresa Epako Candondo Virgílio Miguel Fonseca Wilson Rodrigues da Silva

Agostinho Lutumba Alsau Sambu André Antunes Rufino André Luvenga António Bunga Artur Pinheiro Artur Inácio Baptista Domingos Benvindo Nagague Benvindo Mateus Bernardo Elídio Cândido Carla Serrao Carlos Cardoso Carlos Mendes Dias Carlos Alberto de Oliveira Cláudio Januário Comandante Mateus Conceição Tavares Domingos de Rosa Edson Fernando Ermelinda Calengue Ernesto Chola Ernesto da Fonseca Kitumba Fabião Tulukeni Fátima Santos Fernando Pedro Figueiredo Nassundi Flora Jonas Fausto Ernesto Manuel Gilberto Lourenço Gomes João Neves João Neves José Chilenga José Natal José Horácio da Silva Joseth Rita de Sousa Jovenal João Júlia Valderrama Kumangin Vahza Ladislau de Sousa Laurindo Luís Cambinda Luís Neto Barbosa Madalena Rodrigues Manuel Lutango Marilina Costa Marquinha Mario Maurício Mata Paulo Roque dos Santos Pedro Domingos Nsoko Samba Garcia Sebastian Willemart Sónia da Silva Caxala Teresa Manuel Valentino Kalueyo Victor Pedro Júnior Virgílio Fonseca Virgílio Miguel Fonseca

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ANGOLA DISASTER RISK PROFILE

Acknowledgments This report is the result of extensive research and close collaboration between Angolan governmental institutions, international organizations and scientific research centres.

The scientific consortium was led by CIMA Research Foundation in partnership with the UN Office for Disaster Risk Reduction (ROA), and included the VU University of Amsterdam and the Wageningen University & Research.

During the past two years, as the scientific team collected data and conducted the risk assessment process, the vital contributions and continuous feedback provided by the Angolan institutions once again revealed the importance of collaborative, fruitful relationships for knowledge sharing and horizontal learning.

The consortium would like to express its gratitude and acknowledge the valuable support it received from all of its Angolan partners, namely: the Civil Protection and Fire Service of Angola, the National Institute of Statistics, the Ministry of Agriculture and Forestry, the Ministry of Environment, the Ministry of Education, the Ministry of Health, the Ministry of Energy and Water, the National Institute of Water Resources, the National Institute of Meteorology and Geophysics, the Ministry of Construction and Public Works, the National Road Institute and PRODEL - Public Electricity Company.

The present disaster risk profile is not only the synthesis of insights gained during several months collecting data and conducting risk modelling in Angola, but also the result of having mobilized more than six hundred risk managers from fifteen other African countries during strategic national workshops, consultative meetings and individual interviews. This opportunity, made possible through the implementation of the ACP-EU funded programme “Building Disaster Resilience to Natural Hazards in Sub-Saharan African Regions, Countries and Communities”, allowed us to listen to the real challenges, perceptions and priorities on risk governance and societal needs. As a result, we believe that we have moved towards a common understanding of risk in each of the countries we have had the privilege to work with.

Aligned to the Sendai Framework for Disaster Risk Reduction, and as representatives of the scientific community, the consortium will always encourage countries to increase research on disaster risk causes and scenarios, supporting local authorities in understanding the value of a systematic interface between policy and science for decision-making. Under this lens, three main groups of stakeholders were identified as key beneficiaries of this report: • Policy makers, risk managers and local academia, who wish to develop their risk knowledge and to apply and promote evidence-informed policy making for good public risk governance.

• Civil society leaders, who wish to explore the evolving roles that they may play, through advocacy and awareness, given the foreseen economic, environmental and social changes.

• International Donors and NGOs who wish to identify priority sectors and regions for risk mitigation funding and actions.

As science is first and foremost at the service of humankind, we hope that this report facilitates the translation of knowledge into solutions to reduce disaster losses, increase societal resilience and the capacity to create development models able to provide a better future for all of Earth’s inhabitants.

The Consortium

ANGOLA DISASTER RISK PROFILE | ACKNOWLEDGMENTS 6

ANGOLA DISASTER RISK PROFILE

Foreword Angola has been mostly affected by disasters of hydrometeorological and meteorological origin and in some cases by man-made disasters. About 80% of disasters in Angola are related to water, either due to its excess or its lack.

Floods have been the catastrophic events causing the most casualties and material damage, especially in social infrastructure.

Improving the quality of information on disaster risks in order to facilitate its use in national and cross-border disaster risk management strategies is fundamental for Angola, especially in a context where we expect to see changes in the frequency and magnitude of natural events. It is, therefore, our common responsibility to ensure that such events do not turn into disasters.

Through Angola’s disaster risk profile, an important instrument for the management of flood and drought risk in the country, the National Civil Protection Commission demonstrates its commitment to fulfill the priority of the Sendai Framework for Disaster Risk Reduction: to know the risk of disasters in all its dimensions, namely hazards, exposure, vulnerabilities and the capacities to deal with it.

Under this lens, this Risk Profile will allow us to target investments aimed at disaster risk reduction in the most affected areas, with a cost-benefit perspective and, consequently, to estimate the positive effects for the protection of the population and Angola's sustainable development.

This important instrument also obliges us to assume a more interventionist and proactive stance with regard to prevention, mitigation and preparation of disasters in our country.

Eugénio César Laborinho Ministry of the Interior Coordinator of the National Civil Protection Commission of Angola

ANGOLA DISASTER RISK PROFILE | FOREWORD 7

ANGOLA DISASTER RISK PROFILE | FOREWORD 8

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ANGOLA DISASTER RISK PROFILE

ANGOLA DISASTER RISK PROFILE

Acronyms & Abbreviations AAL

Annual Average Loss

ACP

African, Caribbean, and Pacific group of states

CCA

Climate Change Adaptation

CIMA

International Centre on Environmental Monitoring

DRM

Disaster Risk Management

DRR

Disaster Risk Reduction

EU

European Union

GDP

Gross Domestic Product

IIASA

International Institute for Applied Systems Analysis

IPCC

Intergovernmental Panel on Climate Change

NGO

Non-Governmental Organization

PML

Probable Maximum Loss

PRA

Probabilistic Risk Assessment

RCP

Representative Concentration Pathway

SDGs

Sustainable Development Goals

SSPs

Shared Socio-economic Pathways

STAG

Scientific and Technical Advisory Group (UNDRR)

UN

United Nations

UNDRR

United Nations for Disaster Risk Reduction

USD

United States Dollars

ANGOLA DISASTER RISK PROFILE | ACRONYMS AND ABBREVIATIONS 9

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ANGOLA DISASTER RISK PROFILE

Table of Contents Acknowledgments ............................................................................................................................................................................................................................................... 6 Foreword .................................................................................................................................................................................................................................................................................... 7 Risk information for sustainable development .............................................................................................................................. 12 Sendai - SDG’s Indicators ................................................................................................................................................................................................................. 13 Why a probabilistic risk assessment? .................................................................................................................................................................... 14 Climate Scenarios ............................................................................................................................................................................................................................................. 16 Local data collected table ............................................................................................................................................................................................................. 19 Hazard / Vulnerability / Exposure definitions ..................................................................................................................................... 22 Flood .............................................................................................................................................................................................................................................................................................. 24 Flood Risk Analysis ......................................................................................................................................................................................................................................... 25 Results ........................................................................................................................................................................................................................................................................................ 28 Drought ................................................................................................................................................................................................................................................................................... 34 Drought Risk Analysis .............................................................................................................................................................................................................................. 35 Results ........................................................................................................................................................................................................................................................................................ 38 Risk Profile key messages ............................................................................................................................................................................................................ 48 Policy Recommendations ............................................................................................................................................................................................................. 49 Glossary .................................................................................................................................................................................................................................................................................... 52

ANGOLA DISASTER RISK PROFILE | TABLE OF CONTENTS 11

ANGOLA DISASTER RISK PROFILE

Risk information for sustainable development Over the last few decades, disasters resulting from natural hazards have often derailed hard-earned development plans and progress. Disasters damage infrastructure, lifelines and critical facilities and often result in severe human, financial, cultural and environmental losses. While disasters have direct consequences on development efforts, inversely, “bad development” can itself be a driver of risk. Non-planned urbanization and the building of non risk-resilient infrastructure are some examples of unsustainable development. These approaches to development increase the vulnerability of populations and of existing economic systems, while depleting the natural ecosystems, in a vicious cycle.

Over the last four decades, Sub-Saharan Africa has experienced more than 1000 disasters (World Bank, 2017), affecting approximately 320 million people (Preventionweb 2019).

The majority of disasters in Africa are hydro-meteorological in origin, with droughts affecting the largest number of people and floods occurring frequently along major river systems and in many urban areas. Cyclones, geological events, sea level increase, coastal erosion and storm surges also deeply affect the continent. Sub-Saharan Africa’s disaster risk profiles relate information on natural hazards to each countries’ population and economic exposures and vulnerabilities. These exposures and vulnerabilities are exacerbated by countries’ limited coping capacities and resources for investing in disaster risk reduction and recovery measures. In this context, post-disaster rehabilitation often implies the intervention of international aid or the diversion of national funds originally planned for development interventions, resulting in a tremendous setback for societal development as a whole.

Disasters, however, can be significantly minimised with rigorous scientific risk modelling and through effective institutional and community preparedness. Considering that natural hazard events will likely change in frequency and magnitude due to climate change in the near future, it is necessary to ensure that risk assessments be conducted systematically, so as to provide a quantitative basis for disaster risk reduction and climate adaptation measures. Risk reduction processes must also be based on the effective communication and application of risk information through the strengthening of institutional and human capacities. Risk assessments and the risk information they provide should support risk-informed decision-making towards resilience building across all levels and within all socio-economic development sectors.

Risk reduction and resilience building are embedded in the various global frameworks adopted in 2015 and 2016, such as the Addis Ababa Action Agenda, the Paris Agreement, the Agenda for Humanity, the New Urban Agenda, the Sendai Framework for Disaster Risk Reduction and the 2030 Agenda for Sustainable Development.

These international frameworks are the result of a long-term process developed by different communities, often from different cultural and scientific backgrounds, but their final aim points towards the same sustainable future. As such, the frameworks are closely intertwined and should be coherently implemented. Challenges such as poverty eradication, economic growth, reduction of inequalities and the creation of sustainable cities and settlements are some examples that require a massive, joint effort to design and apply coherent policies and strategies.

ANGOLA DISASTER RISK PROFILE | RISK INFORMATION FOR SUSTAINABLE DEVELOPMENT 12

ANGOLA DISASTER RISK PROFILE

These need to be based on scientific risk information that assesses vulnerability, hazard and exposure to estimate disaster impacts - quantifying population and economic losses across different regions and sectors.

SENDAI / SDG’S INDICATORS

FLOOD

INDICATORS

SENDAI INDICATORS

B1

Number of directly affected people

C1 Direct economic loss attributed to disasters

C2

Direct agricultural loss (Crops)

C3

Direct economic losses to productive asset (Industrial Buildings + Energy Facilities)

C3

Direct economic losses in service sector

C4

Direct economic losses in housing sector

C5

Direct economic losses to transportation systems (Roads + Railways)

C5

Direct economic losses to other critical infrastructures (Health + Education Facilities) Number of destroyed or damaged

D2 health facilities

D1

Damage to critical infrastructure attributed to disasters

D3 Number of destroyed or damaged educational facilities

Number of other destroyed or damaged

D4 critical infrastructure units and facilities (Transportation systems)

GDP of affected areas*

* No official Sendai indicators

Number of potentially affected livestock units* Agricultural & Economic Indicators

Number of working days lost* Food Energy Loss Crop Drought Tolerance

SPEI

Standardised Precipitation-Evapotranspiration Index*

SSMI Standardised Soil Moisture Index* Hazard Index

SSFI

Standardised StreamFlow Index*

SPI

Standardised Precipitation Index*

WCI

Water Crowding Index*

DROUGHT

C

P

SEP

• • • • • • • • • • •

• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •

C Current Climate (1979 - 2018)

C

P

P Projected Climate (2051-2100)

ANGOLA DISASTER RISK PROFILE | RISK INFORMATION FOR SUSTAINABLE DEVELOPMENT 13

SEP

SDG

SDG INDIC.

1

1.5.1

11

11.5.2

13

13.1.1

2

-

1

1.5.2

1

1.5.2

1

1.5.2

1

1.5.2

1

1.5.2

11

-

11

-

11

-

1

1.5.2

2

-

2

-

2

-

2

-

SEP Socio-Economic Projection (2051-2100)

ANGOLA DISASTER RISK PROFILE

Why a probabilistic risk assessment?

The added value of a Probabilistic Risk Assessment (PRA) is often misunderstood, as audiences tend to view it as a highly technical method that is difficult to apply or understand. These difficulties represent a challenge for communicating risk results.

A probabilistic disaster risk profile should be seen as a risk diagnosis instrument, as it provides indications on possible hazardous events and their impact. Both past and probable future events are taken into consideration in a comprehensive risk assessment exercise. In this risk profile two different climate scenarios were considered: • under current climate conditions: with disaster risk assessed using the observed climate conditions in the 1979 - 2018 period; • under projected climate conditions: with disaster risk being assessed under projected climate conditions (projected period 2051 - 2100), considering the IPCC scenario RCP 8.5 which foresees an increase in the global temperature between 1,5°C and 4°C by 2100, and assuming that further risk mitigation measures will not be put in place.

Probabilistic disaster risk profiles consider all possible risk scenarios in a certain geographical area. This means that both low frequency, high loss impact events, as well as high frequency, lower loss impact events are calculated. Included is their probability of occurrence, and all elements of the risk equation (risk = hazard X exposure X vulnerability / capacity), their variability and uncertainty ranges.

R =

H x E

Hazard

Exposure

x

V

Vulnerability

C

Risk

Capacity

Events which have never been historically recorded but might occur in projected climate conditions are also considered in the risk analysis. This feature is particularly useful in the context of climate change which is dramatically increasing uncertainty about future hazard patterns. Thus, societies need to calculate their “worst” possible impacts in order to be prepared. Under this lens, there is no valid alternative to a probabilistic analysis in order to address this uncertainty in a usable, quantitative way.

By assigning a probability of occurrence to each event magnitude, a probabilistic risk profile quantifies the expected direct impacts of disasters through economic metrics and affected population, both at aggregated and at disaggregated levels (ex. affected children, women and people with disabilities, different regions and development sectors). As this risk information is framed within return periods as a conventional probability measure, a PRA approach provides a clear vision of the risk trends: This risk information - expressed in an annual average loss curve (AAL) and a probable maximum loss curve (PML) is calculated both at a national scale, as well as by sector and by region, allowing for a geographic and quantitative comparison of disaster losses, as well as within a country and/or between countries. These analyses and comparison exercises are an important step of the risk awareness processes, key in pushing for risk reduction, risk adaptation and risk management mechanisms to be put in place.

ANGOLA DISASTER RISK PROFILE | WHY A PROBABILISTIC RISK ASSESSMENT? 14

ANGOLA DISASTER RISK PROFILE

PML CURVE INTERPRETATION Large-scale natural hazards don’t necessarily lead to important economic losses.

An event that occurs in a desert for example (i.e. no exposure) would not result in any losses. It is therefore important to understand the estimated losses events are likely to produce. The PML curve shows the likelihood of a certain scenario producing an estimated amount of losses. In this example, under current climate conditions, Country X would experience at least one disaster event, leading to losses equal or greater than 50 million dollars, on average every three to five years, and at least one disaster event, leading to losses greater or equal than 130 million dollars, on average every one hundred years. Under projected climate conditions (IPCC RCP 8.5), losses equal or greater than 50 million dollars can be experienced on average every three to five years, while losses equal or greater than 130 million dollars can be experienced on average once every 25 years. In this case, high frequency disasters - within a return period of 10 years and losses of equal or greater than 70 million dollars will keep a constant pattern, both in current and projected climate conditions. Medium and rare frequency loss events (medium and low likelihood) - with a return period ranging from 25 to 250 years are expected to lead to an increase between 30% and 50% in economic losses in future. The PML curve can be subdivided into three main layers: the Extensive Risk Layer typically associated with risk reduction measures (e.g. flood defences, vulnerability reduction interventions); the Mid Risk Layer that captures higher losses which are commonly mitigated using financial funds at country level, such as the contingency fund; the Intensive Risk Layer (severe and infrequent hazard events) that is normally managed trough risk transfer mechanisms such as insurance and reinsurance measures. The remaining Residual Risk (catastrophic events) is the risk that is considered acceptable/tolerable due to the extreme rarity of the events causing such loss levels. Given their rarity, there are no concrete actions to reduce risk beyond preparedness (e.g. civil protection actions, humanitarian aid coordination).

LIKELIHOOD 200

HIGH

MEDIUM

IN

LOW 0)

ate (2051-210

- 2018) Climate (1979

Intensive Risk Layer Mid Risk Layer

100

Extensive Risk Layer

Contingency Fund Disaster Risk Reduction Measures

50

50

100

150

200

250

SSP4

Challenges for Mitigation

ANGOLA DISASTER RISK PROFILE | WHY A PROBABILISTIC RISK ASSESSMENT? Sustainability Inequality

LOW

15

LOW

Challenges for Adaptation

HIGH

C2

D

C3

D (

C3

D

C4

D

C5

D s

C5

D i

N

D2 h

D1

Damage to critical infrastructure attributed to disasters

SSP3

SSP2

SSP1

C1 Direct economic loss attributed to disasters

Return Period [Years]

Probabilistic disaster risk profiles are used as the first step in cost-benefit analyses of investments and policies for disaster risk reduction. Cost-benefit analyses show decisionmakers the required level of public sector financing and/or insurance mechanisms to support disaster risk management across sectors, an important tool for guiding risk Fossil-fueled Regional management policies.

In the medium and long term, these investments and policies Development Rivalry improve social and economic outcomes, as well as institutional coherence and efficiencies.

The return on these investments (i.e. from the decrease in disaster losses) will free resources, allowing future budgets to address other development challenges, thus creating a virtuous cycle. The integration of disaster risk profiles developed with a probabilistic approach Middle of instils an added value, not only by delivering highly reliable science-based information, the Road but also as a trigger for integrated and resilient development approaches.

SSP5

Numb

Risk Transfer

SENDAI INDICATORS

Current

HIGH

B1

Residual Risk Layer

150

D3 N

e

N

D4 c

(

GDP of

Numb * No official Sendai indicators

Loss [Million$]

Projected Clim

Agricultural & Economic Indicators

Numb

Food E

Crop D

SPEI

Standa

SSMI Stand Hazard Index

SSFI

Standa

SPI

Standa

WCI

Water

ANGOLA DISASTER RISK PROFILE

Climate Scenarios Although the international community is consensually aware of the ongoing global warming due to the emission of greenhouse gases and air pollutants in the atmosphere, its main consequences are expected to be observed in the behaviour of projected climate patterns.

Both historical and projected climate patterns cannot be analysed without considering its intrinsic link with socioeconomic development. Factors like population, economic activity, urbanization, social equality and consumption patterns determine the way energy, land and natural resources are used. These in turn determine the emission of greenhouse gases and air pollutants in the atmosphere. Climate change is expected to impact socio-economic activities by, for example, increasing the frequency and severity of extreme weather events with direct impacts in societal well-functioning and development.

The way this climate-social dependency will translate into projected climate patterns is inherently uncertain, as it depends on human decisions, actions and behaviours yet to be made. But the future is not completely unknown: scenarios can be used to explore what could and what should happen in different decision-making contexts, analysing the consequences on the development of the projected climate conditions. With this in mind, in the late 2000s, the international climate community started developing scenarios to explore how the world might change over the next century, looking at possible trajectories of population, economic growth and greenhouse emissions, both through isolated and integrated approaches.

The Representative Concentration Pathways (RCPs) were developed to describe the different levels of greenhouse gases and other radiative forces that might occur by 2100 - not including any socioeconomic narratives - and are represented in four different pathways: 2.6, 4.5, 6.0, 8.5.

SCENARIO NAME

ASSUMES WE REDUCE CO2 EMISSIONS

PREDICTED TEMPERATURE INCREASE BY 2100

PREDICTED SEA LEVEL RISE BY 2100

RCP 2.6

VERY QUICKLY

(1°C)

0.44 m

RCP 4.5

SOMEWHAT QUICKLY

(1.8°C)

0.53 m

RCP 6.0

MORE SLOWLY

(2.4°C)

0.55 m

RCP 8.5

HARDLY AT ALL

(4.1°C)

0.74 m

Source table: https://www.exploratorium.edu/climate/looking-ahead

Another set of “pathways” were developed in 2016, by modelling the behaviour of socioeconomic factors such as population, economic growth, education, urbanization and the rate of technological development. They are known as the “Shared Socioeconomic Pathways” (SSPs) and are based on five different ways in which the world might evolve: ANGOLA DISASTER RISK PROFILE | CLIMATE SCENARIOS 16

ANGOLA DISASTER RISK PROFILE

LIKELIHOOD 200

HIGH

MEDIUM

INDICATORS

LOW

00)

ate (2051-21

B1

Residual Risk Layer

150

8)

(1979 - 201 rent Climate

Cur

Number of directly affected people

Risk Transfer

Intensive Risk Layer Mid Risk Layer

SENDAI INDICATORS

Loss [Million$]

Projected Clim

Contingency Fund

SSP1 - a world of sustainability focused growth and equality 100 Extensive Risk Layer

C2

Direct agricultural loss (Crops)

C3

Direct economic losses to productive (Industrial Buildings + Energy Facilitie

Disaster Risk SSP2 - a “middle of the road” world where trends broadly follow their historical patterns C3 C1 Reduction Measures

SSP3 - a fragmented world of “resurgent nationalism” 50 SSP4 - a world of ever-increasing inequality

Direct economic loss attributed to disasters

SSP5 - a world of rapid and unconstrained growth in economic output, energy use 50

100

150

200

250

Return Period [Years]

HIGH

SSP5

Middle of the Road

SSP4 Inequality

LOW LOW

C5

Direct economic losses to other critic infrastructures (Health + Education Fac Number of destroyed or damaged

* No official Sendai indicators

Challenges for Mitigation

SSP2 SSP1

C5

of destroyed or damaged Each of the SSPs socioeconomic conditions can D3 Number educational facilities of other destroyed or damag be related to estimates of future energy use D4 Number critical infrastructure units and facilit (Transportation systems) characteristics and greenhouse gas emissions, GDP of affected areas* therefore the connection between SSPs and Number of potentially affected livestock un RCPs is crucial for combined modelling.

Agricultural Number of working days lost* & Economic In our case the SSP2 - middle of the road pathway Indicators has been chosen as one of the most probable Food Energy Loss scenarios for future development. This choice is Crop Drought Tolerance SPEI Standardised Precipitation-Evapotranspiration based on the fact that no specific information SSMIa Standardised Soil Moisture Index* or study was gathered in this direction thus, Hazard SSFI Standardised StreamFlow Index* “central” scenario was thought to be Index the most Standardised Precipitation Index* SPI adequate choice. In the “middle of the road” SSP2, WCI Water Crowding Index* emissions will continue to increase through the end of the century, reaching between 65GtCO2 and 85GtCO2, resulting in a warming of 3.8-4.2°C. As a result, the combination of the RCP8.5 worst case scenario with SSP2 is a consistent choice that highlights the future risk figures in the absence of a strong, global and coordinated commitment towards a climate change mitigation policy.

Regional Rivalry

Sustainability

Direct economic losses in housing se

Direct economic losses to transportat systems (Roads + Railways)

Damage to critical infrastructure attributed to disasters

SSP3

Development

C4

D2 health facilities

D1

Fossil-fueled

Direct economic losses in service sec

Challenges for Adaptation

HIGH

Source image: https://climatescenarios.org/ primer/socioeconomic-development

Both RCP and SSPs were designed to be complementary: while the RCPs set pathways for greenhouse gas concentrations and the amount of warming that could occur by the end of the century, SSPs set the stage on which reductions in emissions will or will not - be achieved. Moreover, SSPs are now being used as important inputs for the latest climate models and will be integrated in the Sixth Assessment of the Intergovernmental Panel on Climate Change (to be published in 2020-2021) and to explore how the climate goals of the Paris Agreement could be met.

SOCIO-ECONOMIC PROJECTIONS FOR ANGOLA This risk profile assumed the SSP2 for its projection analysis: a “middle of the road” world where trends broadly follow their historical patterns. According to these conditions, by 2050, both GDP and population in Angola will triple when compared to 2014 (Local Data and World Bank).

POPULATION

25.8

GDP

2014

[Million People]

122.5 2017

77.4

2050 Projection

[Billion$]

324.7

2050 Projection

7

CHANGE IN TEMPERATURE

RCP 2

6 5 4 3 2

22,5 22 21,5

ANGOLA DISASTER RISK PROFILE | CLIMATE SCENARIOS 17

1 0

[°C]

[°C]

23,0

2025 - 2049

2050 - 2074

Model used in Risk Profi

ANGOLA DISASTER RISK PROFILE

PROJECTING POPULATION GROWTH AND URBANIZATION Population levels are projected to increase in the future, but population distribution is also projected to change dramatically. Urbanization will be a driving force in many African countries in the coming decades. This will affect the riskscape, especially for localized hazard patterns such as the ones on floods. In this profile a model has been used to mimic population growth and urbanization based on a simplified Cellular Automata similar to the one proposed in the literature (i.e. SLEUTH from Clarke and Gaydos, 1998*). Starting from a grid representation of population distribution over the country, the model simulates the evolution of two simultaneous processes triggered by a myriad of seemingly random events: population growth in each location and population migration from one location to another. The evolving population pattern is conditioned by population density, presence of bodies of water, distance that needs to be covered for internal migration and the ability of certain locations/cities to attract populations. The attractiveness of a certain location increases with its vicinity to transport infrastructure and with increasing urban connectivity, so that urbanization is a spontaneous, dynamic evolution of the model. Parameters of the model are calibrated with data and projections of population growth and urbanization from UNWorld Population Prospects**.

Southern suburbs of Luanda, current population density

0 10 30 100 300 1000 3000 10.000 30.000 [People]

Southern suburbs of Luanda, projection for 2050 of population density * Clarke, K.C., and L. Gaydos., 1998. Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore, Int J of Geographic Inf Sci. 12, 699–714. ** https://population.un.org/wpp/

ANGOLA DISASTER RISK PROFILE | CLIMATE SCENARIOS 18

ANGOLA DISASTER RISK PROFILE

Local data collected AREA

DATA TYPE

LEVEL OF DETAILS

SOURCE OF DATA

Commune level or sub municipality level.

National Institute of Angola Statistics (INE)

Sector Specific at national level

National Institute of Angola Statistics (INE)

Population

Population Statistics (age distribution, gender, minorities at national or sub-national scale).

Characterization of dwellings.

Population projection by 2024 for the municipalities and 2050 for the provinces.

GDP

Limites of the new administrative division Sector-specific Gross Domestic Product (GDP) Local Gross Domestic Product (e.g., Regional)

AGRICULTURE CRITICAL INFRASTRUCTURES

EXPOSURE

Livelihood zones

Thirteen spatial zones of the country.

Market prices and agricultural information bulletim(2010 to 2019).

Prices of agricultural products in Angola´s main markets.

Vegetative cycle of the main crops.

Data on family and business agricultural production for the agricultural year 2018-2019 national and provincial level.

Report of the agricultural year 2018-2019.

Location of Critical Infrastructure (e.g., health, education, energy production) possibly with attributes information.

HAZARD

HAZARD LOSS DATA

Temperature variation (minimum, maximum and average)

LOSS

Primary and Secondary Schools Countrywide

President’s Office – egional Administrations and Local Government (PO-RALG) – schools and Health facilities information

Health Facilities including dispesaries, Healthcentres and Hospitals

Ministry of Health

Electroproductive Centers

Ministry of Energy and Water

Hydrometric network Linear critical infrastructure (e.g., railways, roads, etc)

Main river network

National Institute of Water Resources (INRH)

National and Complementary Roads of Angola

Ministry of Construction and Public Works / National Road Institute of Angola (INEA)

Daily and monthly temperature variation in 11 stations in the country,(1971 to 2018)

Annual summary of the daily average flow rates.

Flooding Zones

Public electricity generation company-Prodel

Angola's hydrographic network

Daily and monthly rainfall variation of 11 stations per country (1971 to 2018)

Average rainfall

Ministry of Agriculture and Foresty

National Institute of Meteorology and Geophysics de Angola (INAMET)

31 stations (1951-1974) Data on flood events countrywide (observations from 1976 to 2013).

ANGOLA DISASTER RISK PROFILE | LOCAL DATA COLLECTED 19

Ministry of Energy and Water National Institute of Water Resources-INRH

ANGOLA DISASTER RISK PROFILE

ANGOLA CLIMATE TRENDS Angola is composed of three natural regions: the coastal lowland, characterized by low plains and terraces; an area of hills and mountains, rising inland from the coast into a great escarpment; an area of high plains, called the high plateau (planalto), which extends eastward from the escarpment.1 The dominant climate in Angola varies from humid tropical to dry tropical and is strongly influenced by the country’s geographic position, near to the Atlantic Ocean, its topography, the Benguela cold water current, and the movement of the Intertropical Convergence Zone (ITCZ) where the northern and southern air masses converge. In the Köppen climate classification, the northern part of the country is classified as tropical, while the southern part is temperate with dry winters and stretches of semi-arid and arid desert climates.

Angola map of Köppen climate classification Tropical savanna (Aw)

25.8 25.8 77.4 77.4

122.5 122.5 324.7 324.7

Arid, desert, hot (BWh)

Average of the Annual Precipitation Annual Precipitation Average of the Annual Precipitation ANGOLA DISASTER RISK PROFILE | COUNTRY CLIMATE 20

2015 2015

2015 2015

2010 2010

2010 2010

2000 2000

2005 2005

2005 2005

Annual Precipitation

2000 2000

1995 1995

1995 1995

1990 1990

1990 1990

1985 1985

1985 1985

1975 1975

1980 1980

1980 1980

780

1975 1975

780 860

1970 1970

1970 1970

POPULATION GDP Arid, steppe, hot (BSh) Temperatures fall according to the distance from dry winter, hot summer (Cwa) POPULATION Temperate, 2014 GDP 2017 the equator and altitude, and tend to rise closer to Temperate, dry winter, warm summer (Cwb) the Atlantic Ocean. Thus, at the mouth of the Congo 2014 2017 River, the average annual temperature is about 26°C, 2050 Projection [Million People] 2050 Projection [Billion$] while on the temperate central plateau the average 2050 Projection [Million People] 2050 Projection [Billion$] temperature is 16°C. TEMPERATURE AND PRECIPITATION TRENDS The country has two distinct seasons: a cool dry [°C] (1970 - 2015) Cacimbo season from June to September and a 23,0 [°C] warm, humid, rainy season from October to May. 23,0 22,5 Similarly, to other central-southern African countries, 22,5 22 temperature observations indicate that Angola has 22 experienced considerable temperature increases in 21,5 recent years. An analysis of climate data from 1970 21,5 21,0 [Anos] to 2015 shows an average temperature rise of just under 1°C. This increase is particularly noticeable 21,0 [Anos] Mean Annual Temperature from the 1980s onwards.

Precipitation trends are not as clear as those for air Average of Mean Annual Temperature Mean Annual Temperature temperature and are variable in time and space. Over Trend Mean Annual Temperature Average of Mean Annual Temperature the last 40 years, rainfall has marginally increased Trend Mean Annual Temperature in Angola, with a large difference between very wet [mm] and dry years. The average annual precipitation 1100 for Angola is about 970 mm. The semiarid, narrow [mm] 1020 coastal region has an average annual rainfall of 600 1100 940 mm, with higher rainfall in the north and lower 1020 rainfall in the south.

860 940 [Anos]

[Anos]

ANGOLA DISASTER RISK PROFILE

ANGOLA CLIMATE PROJECTIONS

25.8

122.5

77.4

324.7

7

4.7

jection

2015

2010

2005

2000

5

Trend Mean Annual Temperature

2025 - 2049

RCP 4.5

RCP 8.5

700 600 500 400 300 200 100 0 -100 -200 -300 -400

2071 - 2095

2050 - 2074 2015

Model used in Risk Profile 2010

1985

1980

1975

860

2005

0

2000

940

1995

1

1990

1020

[°C]

2

[°C]

[Anos]

4

1100

1970

1995

Average of Mean Annual Temperature

3

CHANGE IN PRECIPITATION

2025 - 2049

RCP 2.6

2050 - 2074

RCP 4.5

RCP 8.5

2071 - 2095

Model used in Risk Profile

[Anos]

Annual Precipitation

of global models available for the area. Results show that the Regional Model forecasted changes in temperature and annual precipitation which are in line with the range of variability presented by Alder et al.

Within the RCP 8.5 scenario, the Regional Model predicts a moderate temperature rise compared to the Global ensemble. On the contrary, regarding the annual precipitation at the country level, the Regional Model predicts a higher increase in the long-term period than the one predicted on average by the Global ensemble.

The climate indicators used in this risk profile have RCP 2.6 RCP 4.5 RCP 8.5 CHANGE INthe PRECIPITATION Average of Annual Precipitation 700 been obtained using a climate projection model 600 based on the RCP 8.5 - high emission scenario for 500 the period 2006-2100 (SMHI-RCA4 model, grid 400 300 spacing 0.44° about 50 km - driven by the ICHEC200 EC-EARTH model). This Regional high-resolution 100 model that is part of the CORDEX Africa project 4 0 was then accurately calibrated for the African -100 -200 domain, allowing for a better capture of the climate -300 variability and its inherent extremes. Projections -400 deriving from the Regional Model2071 were then 2025 - 2049 - 2095 2050 - 2074 checked for consistency against the full ensemble [mm]

e (2051 - 2100)

0

RCP 2.6

Mean Annual Temperature

6

[mm]

780

CHANGE IN TEMPERATURE

simulations an increase in temperature.

RCP 2.6 RCP 4.5 RCP 8.5 CHANGE INshowed TEMPERATURE 7 The increase was more substantial in the high 6 emission scenario (RCP8.5) and for long-term periods projections. Projections in the high 5 emission scenario showed, moreover, an increase 4 in temperature between 2.2°C and 4.2°C for the 3 mid-term period (2050-2074) and an increase of 2 to 4°C for the long-term period (2071-2095). up 1 Future changes in precipitation were more uncertain, however the models predicted a 0 2025 2049 2071 - 2095 2050 - 2074 moderate increase - around 10% - in precipitation for the medium term. Projections for the longModel used in Risk Profile term period show a divergent change.

[mm]

7

1990

1980

2.5

1975

1970

21,0

1985

2 In a recent study Alder GDP et al. compared the observed temperature and precipitation values 2014 2017 registered in the period 1980-2004 with the estimations of a set of global climate models provided by the Coupled Model2050 Intercomparison Projection [Million People] 2050 Projection [Billion$] Project Phase 5 (CMIP5). Three future periods (2025-2049, 2050-2074 and 2071-2095) were [°C] analysed considering the different Representative 23,0 Concentration Pathways (RCPs) used in the IPCC Fifth Assessment Report for greenhouse emission 22,5 scenarios 3.

22 In all future projections, both long term and 21,5 short term, and for all emission scenarios, model

POPULATION

Model used in Risk Profile

REFERENCES

500

600

Current Climate

AGRICULTURAL SECTOR (C2) 1: http://worldfacts.us/Angola-geography.htm Projected Climate 2: Alder, J. R., & Hostetler, S. W. (2015). Web based visualization of large climate data sets. Environmental Modelling PRODUCTIVE ASSETS (C3) & Software, 68, 175-180.

700 800 3: https://www.ipcc.ch/assessment-report/ar5/ SERVICE SECTOR (C3) 4: https://www.cordex.org/domains/region-5-africa/ HOUSING SECTOR (C4)

ANGOLA DISASTER RISK PROFILE | COUNTRY CLIMATE

TRANSPORTATION SYSTEMS (C5)

21 OTHER CRITICAL INFRASTR. (C5)

ANGOLA DISASTER RISK PROFILE

Hazard process, phenomenon or human activity that may cause loss of life, injury or other health impacts, property damage, social and economic disruption or environmental degradation.

In order to best predict possible flood and drought scenarios, a modelling chain composed of climate, hydrological, and hydraulic models combined with available information on rainfall, temperature, humidity, wind and solar radiation, has been used. A set of mutually exclusive and collectively exhaustive possible hazard scenarios that may occur in a given region or country, including the most catastrophic ones, is generated and expressed in terms of frequency, extension of the affected area and intensity in different locations.

Vulnerability conditions determined by physical, social, economic and environmental factors or processes which increase the susceptibility of an individual, a community, assets or systems to the impacts of hazards.

Direct losses on different elements at risk are evaluated by applying vulnerability functions. This links hazard intensity to the expected loss (economic loss or number of affected people) while counting for associated uncertainty. Vulnerability functions are differentiated by the typology of exposed elements, and also take into account local factors, such as typical constructive typologies for infrastructures or crop seasonality for agricultural production. In the case of floods, vulnerability is a function of water depth. For agricultural production, the vulnerability is a function of the season in which a flood occurs.

In the case of agricultural drought, losses are computed in terms of lack of production for different crops from a nominal expected production. A similar approach is used for hydrological drought, the evaluation of which focuses on loss of hydropower production.

Exposure people, property, systems, or other elements present in hazard zones that are thereby subject to potential losses.

Losses caused by floods and droughts are assessed in relation to population, GDP and a series of critical sectors (education, health, transport, housing, and the productive and agricultural sectors). The sectors are created by clustering all of the different components, which contribute to a specific function (e.g. the health sector is comprised of hospitals, clinics and dispensaries). Publicly available global and national data, properly generated, enables the location of these elements at high resolution, e.g. 90 metres or lower, for the whole country. The total number of people and the national GDP (in USD) are considered in both current (2016) and future (2050) scenarios. The critical sectors are characterised in terms of their economic value (in USD), using the most updated information available.

ANGOLA DISASTER RISK PROFILE | HAZARD - VULNERABILITY - EXPOSURE 22

pixabay.com

23

ANGOLA DISASTER RISK PROFILE

FLOOD Flood Risk Analysis ......................................................................................................................................................................................................................................... 25 Results ........................................................................................................................................................................................................................................................................................ 28

Results

(Fig. & Tab.)

POPULATION (B1)................................................................................................................................................................................................................................................................................................... 28 F01 - F02 : Annual Average Number of potentially affected people.............................................................................. 28 F03 : Annual Average Number of potentially affected people with disability....................................... 28 F04 - F05 - F06 : Potentially affected people - Maps................................................................................................................................................. 29 F07 : Probable Maximum Loss Curve of potentially affected people........................................................................... 29 DIRECT ECONOMIC LOSS (C1)............................................................................................................................................................................................................................................ 30 F08 : Average Annual Loss................................................................................................................................................................................................................................................................ 30 F09 : Average Annual Loss per Sector............................................................................................................................................................................................................... 30 F10 : Damage to critical infrastructures....................................................................................................................................................................................................... 30 F11 - F12 : AAL - Average Annual Loss - Maps............................................................................................................................................................................ 30 F13 : Probable Maximum Loss Curve................................................................................................................................................................................................................. 31 GDP............................................................................................................................................................................................................................................................................................................................................................... 31 F14: Annual Average GDP Affected......................................................................................................................................................................................................................... 31 F15: Annual Average GDP produced in potentially affected areas..................................................................................... 31 DIRECT ECONOMIC LOSS PER SECTOR..................................................................................................................................................................................................... 32 F16 - F17 : AAL - Average Annual Loss in agricultural sector - Maps.................................................................................................32 F18 : Probable Maximum Loss Curve in agricultural sector......................................................................................................................................32 F19 - F20 : AAL - Average Annual Loss in productive assets - Maps....................................................................................................32 F21 : Probable Maximum Loss Curve in productive assets..........................................................................................................................................32 F22 - F23 : AAL - Average Annual Loss in housing sector - Maps.................................................................................................................32 F24 : Probable Maximum Loss Curve in housing sector......................................................................................................................................................32

ANGOLA DISASTER RISK PROFILE | FLOOD 24

ANGOLA DISASTER RISK PROFILE

Flood Risk Analysis Flood risk assessment involves four main steps: flood hazard assessment; identification and characterization of exposed elements; vulnerability assessment and capacity / performance of flood protection/structural mitigation measures in lowering flood damaging conditions. From the combination of these four steps into a flood model we are able to determine risk.

Different procedures and methodologies to determine risk are used worldwide through a variety of models and approaches. Their common aim is to understand the probability that different magnitudes of damaging flood characteristics - considering flood depth, horizontal flood extent, flood velocity and flood duration - will occur over an extended period of time. These estimates can be calculated both in current and projected climate conditions through a consistent analysis of meteorological, geological, hydrological, hydraulic and topographic properties of the watershed, channels, and floodplains, resulting in detailed hazard maps. Hazard maps are then combined with the reproduction of past events patterns and the modelling of projected future events.

Information on the performance capacity of flood protection measures is finally added to the analysis. This workflow allows for the estimation of the “expected” water depth for a certain location and/or individual infrastructures, for a set of reference scenarios.

From this step on, it is possible to explore the full frequency distribution of events and the consequent damage to exposed assets, taking into consideration their different levels of vulnerability.

The probability of a given flood magnitude is expressed in terms of the “return period” (or recurrence interval). Return period is the average time interval, in years, separating two consecutive events equal or exceeding the given flood magnitude. The damage assessment is converted into economic metrics through the computation of the average annual loss - the expected loss per year, averaged over many years - and the probable maximum loss - a relationship describing all the potential losses with a certain probability range.

GLOBAL CIRCULATION MODEL

REGIONAL CIRCULATION MODEL

CIMA’S HYDROLOGICAL MODEL CONTINUUM

=

CIMA’S HYDRAULIC MODEL REFLEX

DETAILED HAZARD MAPS

FLOOD DEFENSES

BIAS CORRECTION FOR OBSERVED TEMPERATURE AND PRECIPITATION

EXPOSURE

FLOOD RISK ASSESSMENT PROCESS

0,70 m

SIMULATION OF 3000 YEARS OF POSSIBLE EVENTS

1,20 m

3,5 m

VULNERABILITY

IMPACT ASSESSMENT Hazard x Exposure x Vulnerability /Capacity AFFECTED PEOPLE

ANGOLA DISASTER RISK PROFILE | FLOOD RISK ANALYSIS 25

ECONOMIC LOSS AAL PML

ANGOLA DISASTER RISK PROFILE

Within this articulated process, access to data is of vital importance to achieve an accurate risk evaluation. Not only is it necessary to feed information to the modelling chain for the identification of possible hazards in specific locations, such as historical series of observed temperature, rainfall and discharges volumes, it is also crucial to feed the damage models with detailed data on population and assets’ levels of exposure and vulnerability. It is only possible to fully understand the economic, social and environmental impacts of past and future possible events with this data. To this end, the present risk profile considers five categories of potentially exposed values. Information about those values were provided by local institutions whenever available. Regional and global datasets were used both as substitutes, when local data was not available, and as data validators, to cross check the consistency of different data sources.

POPULATION Population estimates were obtained through official censuses at the maximum level of detail available - in the case of Angola this information was obtained at the comuna level. Further information on age, gender, schooling and the presence of vulnerable groups were accessed through official datasets at the local or district level.

Global datasets on population were only used in this study to retrieve spatial binary information (population/no-population at any point in space) or information on the relative distribution of population inside a given area. This study considered population according to two different levels of detail:

• the density, meaning the spatial distribution of population across the country; • the statistical composition, according to the population categories used to identify

vulnerable segments (e.g. children, women, people with disabilities).

Projections for future population were produced using the Shared Economic Pathway - SSP2 - “Middle of the Road” where global population growth is expected to be moderate in the first half of the century and levels off in the second half.

BUILT-UP Information on the built-up area refers to two main aspects: to the description of the physical exposure of buildings, in terms of their spatial location inside or outside flood-prone areas; the elements which might influence its vulnerability - such as its occupancy characteristics, the existence of basements, and the typology of its constructive materials; The built-up data prepared for the present risk profile were obtained from the exposure dataset used in the Global Assessment Report 2015 and in the The Atlas of the Human Planet 2017. They have been divided into three sector classes, according to the exposure categories reported in the Sendai Framework Indicators: housing sector distribution, service sector distribution and productive sector distribution (limited to the industrial sector as the energy production is considered separately).

Whenever possible, local data was also included, regarding:

• Number of storeys of residential buildings in urban and rural areas; • Number of storeys of non-residential buildings (e.g. business, government etc.) in

urban areas; • Construction materials in urban and rural areas; • Elevation of the ground floor; • Average construction cost of residential buildings in urban and rural areas per unit surface area; • Average construction cost of non-residential buildings (e.g. business, government etc.) in urban areas per unit surface area.

ANGOLA DISASTER RISK PROFILE | FLOOD RISK ANALYSIS 26

ANGOLA DISASTER RISK PROFILE

GROSS DOMESTIC PRODUCT (GDP) Present national GDP data was obtained from the World Bank national studies (https:// data.worldbank.org/indicator/NY.GDP.MKTP.CD). However, in order to improve the accuracy of the risk results in terms of affected GDP, two increased levels of detail were added from official national data:

• Sub-national gross domestic product (provincial and, if available, municipal level) • Sector-specific gross domestic product (agriculture, industry, services and commerce

(either at national or, if available, at sub-national

Projections of the GDP in 2050 were extracted from the SSP Database from IIASA, using the estimates for the Shared Economic Pathway - SSP2 - ‘Middle of the Road’ scenario where income inequality persists or improves only slowly and challenges to reducing vulnerability to societal and environmental changes remain.

AGRICULTURAL PRODUCTION The data on agricultural production refers mainly to the economic value and the vulnerability of the most relevant crop types in the national agricultural production.

Estimates of crop distribution, useful to understand production and land use patterns, were obtained through the Spatial Production Allocation Model (MapSpam - September 2019 release) and coupled with national production prices.

The use of local data was useful to identify the most relevant crops and to describe its vegetative/growing cycles, used for vulnerability characterization. Thus, the data analysed included:

• List of crops which account for at least 85% of the Total Gross Production Value of all

crops for the country; • The economic values in terms of production cost and/or wholesale price in USD per ton for each crop; • Information on the growing cycle of each crop.

In this risk profile iteration, the selection of crops considered was not only based on their commercial value but also to the role that specific produces play in the average diet of the population. A decrease in the availability of this crops on the national market may result in food insecurity.

CRITICAL INFRASTRUCTURES Critical infrastructures data refer to the description of the physical exposure in terms of spatial location of school, medical and hospital facilities, energy plants, as well as the transport network combined with their economic values. The main added values of this information rely on knowing the exact location of the infrastructure, the typology of its construction materials, and, in the case of roads, also its elevation and the construction costs per km.

As the available information on critical infrastructures - such as education and health structures was not spatially accurate enough for their direct use in the risk assessment, the scientific team opted to aggregate these data at the ward or district level (depending on the available ancillary information) and then redistribute it in space following the localization of built-up areas. This allowed for the maintenance of the information content of the data on each relevant sector, decreasing potential errors linked to considering a wrong localization of the buildings.

ANGOLA DISASTER RISK PROFILE | FLOOD RISK ANALYSIS 27

d e 00)

0

(1979 - 2018) (2051 - 2100) Projected Projected Climate Climate & SEP

Million$/Y

(1979 - 2018) (2051 - 2100)

281

292 912 % 0.23% 0.24% 0.23% 0.24% 0.24%

(1979 - 2018)

(1979 - 2018)

Projected 25.500 Projected Climate 25.500 Climate 0.10% 0.10% 25.600

704

4590

459

POTENTIALLY AFFECTED

710023,0 [°C]

YOUTH 5-14 22,5

22 15-24 TEENAGERS

0.10% People/Y

795021,5

POTENTIALLY AFFECTED

ELDERLY >65

4610

POTENTIALLY AFFECTED

7840

830

840

POTENTIALLY AFFECTED

ADULTS 25-65

0.15% 115.000

ADULTS 25-65 21,0

7950

POTENTIALLY AFFECTED

ELDERLY >65 People/Y

0.15% People/Y

POTENTIALLY AFFECTED

Peop 13.380 Trend Mean Annual Tempera People/Y

POTENTIALLY AFFECTED

0.06%

95.9

0.06%

19

SCHOOLS

Million$/Y

D2 - AFFECTED [UNITS/Y]

HOSPITALS

INFRASTRUCTURES 5 7

POTENTIALLY AFFECTED

MENTAL

POTENTIALLY AFFECTED

Current DISABILITY Climate

YOUTH 5-14

(1979 - 2018)

Projected Climate

PARALYTIC

30.8%

POTENTIALLY AFFECTED

TEENAGERS 15-24

(2051 - 2100)

700 13.5

Million [People/Y] 800 People/Y Projected Climate &

600

500

400

300

200

100

54.4%

42.2

30.8% Million People/Y

Projected

19

2

100

200

0.39

0.71

0.53

0.96Current

0.35

0.63

POTENTIALLY AFFECTED

300 400 0-4 500 CHILDREN

0.60

POTENTIALLY AFFECTED

POTENTIALLY AFFECTED

ELDERLY >65

(1979 - 2018)

People/Y

0

POTENTIALLY AFFECTED

ADULTS 25-65

Million Socio-Economic Current People/Y Projection (2051 - 2100) Climate

7.6 56.2% Million

1

Current Projected Climate Climate HOSPITALS (2051 (1979 - 2018) - 2100)

CHILDREN 0-4

7.6

171

Projected Climate (2051 - [UNITS/Y] 2100) D2 - AFFECTED

TOTAL NUMBER OF PEOPLE WITH DISABILITIES

YOUTH 5-14 0.06

13.5

54.4%

POTENTIALLY AFFECTED

ADULTS 25-65

5

Climate (1979 - 2018)

600

Proje Clim (2051

0.39 800

0.7

0.53

0.9

700

1.07 0.11

ANGOLA DISASTER RISK PROFILE | FLOOD RESULTS POTENTIALLY AFFECTED Projected Million People/Y Million People/Y Climate TEENAGERS 15-24 (2051 - 2100) 28

2000

22

D4 - AFFECTED [KM/Y]

- AFFECTED [UNITS/Y] Annual Average Number of potentially affected People withD3disabilities Fig. F03 SCHOOLS

Current Climate (1979 - 2018)

1995

1980

93.6

1020

1975

95.9

13.3

economic development and therefore the possible 940 changes in values distribution and population 860 concentration, this number could increase more than fourfold, reaching 115.000780 people affected on average every year. This figure, which Projected represents Current Climate Climate 0.15% of the total population, is the result of the (1979 - 2018) (2051 - 2100) Annual PrecipitationAGRICU combined effect of the expected projected hazard D4 - AFFECTED [KM/Y] PRO patterns and the overall increase 171 in population 195 Average of the Annual Precip INFRASTRUCTURES Current Pro associated with the urbanisation foreseen in many Climate CliH D3 -Africa.

AFFECTED [UNITS/Y] countries in (1979 - 2018) (2051 1970

93.6

13.310

FEMALES [B1] - Annual Average Number of potentially affected People [mm] Tab. F02 1100

[B1] - Annual Average Number of potentially affected People Fig. F01

Current Angola Climateis highly impacted by severe weather and (1979 - 2018) extreme climate events. Floods represent one of Projected the most prominent issues in Angola, affecting Climate Current (2051 - 2100)25.000 people every year on average more than 0.06%Climate - 2018) (0.10% of(1979 the total Angolan population). These Million$/Y numbers also 0.06% hold under projected climate Projected Climate conditions (considering one possible projected Million$/Y (2051 - 2100) climate scenario - RCP8.5, worst case scenario) and the hypothesis Million$/Y of unchanged socio-economic patterns. If we consider, however, the socio-

830 84 Mean Annual Temperature

People/Y

Average of Mean Annual Tem

13.310

FEMALES

784

1990

People/Y

70407100

POTENTIALLY AFFECTED

1985

115.000

Socio-Economic Projection (2051 - 2100)

521

5230 0-4 CHILDREN

POTENTIALLY AFFECTED

25.600 0.10%

People/Y Projected Socio-Economic Climate & Projection (2051 - 2100)

52105230

POTENTIALLY AFFECTED

1975

People/Y

Projected Climate &

POTENTIALLY AFFECTED

77.4

2050 Projection

[Million People]

4610 AFFECTED TEENAGERS 15-24POTENTIALLY

(2051 - 2100)

Proje TOT Clim (2051

Projected Climate Climate (1979 - 2018) (2051 - 2100)

1970

(2051 - 2100)

People/Y

2014

Current

Current Climate (1979 - 2018)

CHILDREN 0-4 YOUTH 5-14

25.8

POPULATION

[B1] - ANNUAL AVERAGE NUMBER OF POTENTIALLY AFFECTED PEOPLE

Current Current Climate Climate

0.24%

2000

%

912

1985

POPULATION

(2051 - 2100) 281 292

1980

Million$/Y

(2051 - 2100)

1995

Current Climate

Projected Projected Climate Climate & SEP

1990

ANGOLA DISASTER RISK PROFILE

Current Climate

0.35

0.6T

0.60

1.0

O

ANGOLA DISASTER RISK PROFILE

[B1] - ANNUAL AVERAGE NUMBER OF POTENTIALLY AFFECTED PEOPLE

POPULATION

Projected Climate Conditions (2051 - 2100) Fig. F05

0 - 50 50 - 100 100 - 250 250 - 1000 1000 - 30.000 [People Affected/Y]

Climate & Socio-Economic Projections (2051 - 2100) Fig. F06

Current Climate Conditions (1979 - 2018) - Fig. F04

In terms of geographical distribution, the pattern is rather disperse, with the majority of the population affected concentrated along the coastal areas and southern regions, but also some concentration in the eastern regions. In the projected future, the general pattern is confirmed with a marked increase in the whole country if socio-economic development is considered.

The age and gender structure of the affected people resembles the overall age and gender structure of the country's demography. Angola is a country with a young population and, as such, the majority of the affected people are below 24 years of age. However, it is interesting to focus on the most vulnerable categories, such as children

in their school age and elderly people, which might suffer more serious consequences from the flood event. The majority of affected people are women. This is particularly interesting in terms of vulnerability, especially in rural areas where women attend to the majority of the work in agricultural fields and as such are more vulnerable to unexpected flood episodes. Women also play a prominent role in the rural society and their enhanced exposure to floods should be better analyzed from the socio-economic standpoint.

PML - Fig. F07

[B1] - PROBABLE MAXIMUM LOSS CURVE OF POTENTIALLY AFFECTED PEOPLE

[k-People Affected]

When the PML curves are analysed it is interesting to note that, for more frequent events (below 40-year return period), the curves of present conditions and projected climate conditions with unchanged socio-economic pattern have the same trends, but differ for rarer values.

When considering both climate change and socio-economic development, losses dramatically increase for both frequent and rare events. It is evident that adaptation to this scenario should consider all kind of DRR measures, spanning from structural interventions, to risk transfer, to Early Warning.

Return Period [Years] Current Climate (1979 - 2018)

Projected Climate (2051 - 2100)

Projected Climate & SEP (2051 - 2100)

ANGOLA DISASTER RISK PROFILE | FLOOD RESULTS 29

0.10%

(2051 - 2100)

25.600 ANGOLA DISASTER RISK PROFILE

People/Y

RCP 8.5

Projected Climate &

Socio-Economic Projection (2051 - 2100) Projected Climate (2051 - 2100)

0.10%

Current POTENTIALLY AFFECTED

Climate ADULTS 25-65

People/Y

(1979 - 2018)

115.000

Current Climate

(1979 - 2018)

25.500

People/Y

LOSS Projected

0.10% People/Y

Climate

95 0

People/Y Socio-Economic Projection (2051 - 2100) 600 100 200 300 400 500 Current Climate

115.000 0.15% People/Y

7040

7950

95.9

POTENTIALLY AFFECTED

7840 840 People/Y

13.310

TRANSPORTATION FEMALESSYSTEMS (C5)

Million$/Y

OTHER CRITICAL INFRASTR. (C5)

Current 13.380 Climate

(1979 - 2018)

Projected Climate (2051 - 2100)

171

195

D4 - AFFECTED [KM/Y]

INFRASTRUCTURES

AAL [Million$/Y] 0 40 20 D3 - AFFECTED [UNITS/Y]

affected roads averages 170 annually. A slight D2 - AFFECTED [UNITS/Y] increase in these numbers is expected under 5 HOSPITALS projected climate conditions.

Target C envisages the substantial reduction of direct economic losses by 2030.

Projected The estimated direct Climateeconomic losses from floods (2051 - 2100) 0.06% in Angola is of about 94 million USD per year, 095 which is roughly 0.06% of the total economic Million$/Y value of the assets considered and 0.08% of the 0.06% rojected Climate annual GDP. Million$/Y Housing and agriculture are the (2051 - 2100) most affected sectors, followed by the productive sector, services, transportation and critical facilities. The same values of average direct economic losses will be maintained also under the projected climate Currentconditions.

Climate 60 80 100 120 140 The160 annual number of currently affected health (1979 - 2018) centers and educational centers averages around Projected Climate 20 and 5 respectively, while the kilometres of (2051 - 2100) (1979 - 2018)

93.6

95.9

Current Climate (1979 - 2018) D4 - AFFECTED [KM/Y]

INFRASTRUCTURES

Projected Climate &

SCHOOLS

HOSPITALS

13.5

POTENTIALLY AFFECTED

(1979 - 2018)

42.2 People/Y

5 - 7.5

Projected Climate

Million People/Y

13.5

30.8% Current Climate

Projected Climate &

7.5 - 13 13 - 16

(2051 - 2100)

[Million$/Y]

54.4% Current

1.9

Projected Climate

1.9

7.5%

POTENTIALLY AFFECTED

0.35

POTENTIALLY AFFECTED

0.60

YOUTH 5-14

TEENAGERS 15-24

Projected Climate (2051 - 2100) Loss [Ton/Y]

0.60

1.07

0.06 0.71

0.11

Million People/Y

Million People/Y

0.96 0.63

Current Climate

1.07

Projected Climate

(1979 - 2018) (2051 - 2100) Climate Conditions (2051 - 2100) Fig. F12

k - days / Y

0.06

0.11 1730 12.120

Current Climate

Current ANGOLA DISASTER RISK PROFILE | FLOOD RESULTS Climate 30

(1979 - 2018)

0.63

Million People/Y Million People/Y foresee a general slight increase except for the provinces of Cunene and Lunda Norte. Uncertainty % 0.31% 2.17% in climate predictions and the ongoing evolution of the country’s socio-economic situation could significantly influence these estimates.

7.9

People/Y

0.53

POTENTIALLY AFFECTED AAL - Projected

3.4

Current Climate

POTENTIALLY AFFECTED

CHILDREN 0-4

ELDERLY >65

(2051 - 2100) The impact of 7.5% floods in Angola has a sparse 56.2% Million Million spatial distribution with the majority of economic People/Y People/Y losses expected to Projected occur in Uige, Cuanza Norte, 13% Climate & Benguela and Socio-Economic Cunene.

Results for projected Million People/Y Projection (2051 - 2100) climate conditions (RCP8.5 - worst case scenario)

10% Million

ELDERLY >65

ADULTS 25-65

Million Projected Climate Socio-Economic Climate People/Y Current Climate Projection AAL (2051 -- 2100) (1979 - 2018) Conditions (1979 - 2018) Fig. F11 100 200 300 400 500 600 700 800

42.2

0.35

0.39

POTENTIALLY AFFECTED

2.5 - 5

(1979 - 2018)

0.96

Projected Climate (2051 - 2100)

POTENTIALLY AFFECTED

k - days / Y

AGRIC Projected Climate PR (2051 - 2100)

0.53 7

5

Current ADULTS 25-65 Climate

7

0.71

POTENTIALLY AFFECTED

1 - 2.5

7.6

YOUTH 5-14

22

0.39 22

19

CHILDREN 0-4

Damage to critical infrastructures TEENAGERS 15-24- Tab. F10

54.4%

56.2% Million

195

POTENTIALLY AFFECTED D2 - AFFECTED [UNITS/Y]

Million Socio-Economic People/Y Projection (2051 - 2100)

Current Climate

171

Projected Climate (2051 - 2100) Current Climate (1979 - 2018)

POTENTIALLY AFFECTED D3 - AFFECTED [UNITS/Y]

7.6

Million People/Y

60

19

SCHOOLS

Current Climate Sendai

30.8%

13.380

Current Climate Projected Climate

People/Y

HOUSING SECTOR (C4)

(2051 - 2100)

0.06%

13.310 4590

830

POTENTIALLY AFFECTED

ELDERLY SERVICE >65 SECTOR (C3)

(1979 - 2018)

Million$/Y

People/Y

sector - Fig. F09

4610

TO

PRODUCTIVE ASSETS (C3)

800

Projected Climate

People/Y

POTENTIALLY AFFECTED

ADULTS 25-65

0.06%

RCP 8.5

7100

POTENTIALLY AFFECTED

93.6

840

POTENTIALLY AFFECTED

AGRICULTURAL SECTOR (C2)

700

7840

830 5210

TEENAGERS 15-24

0.10%

7950

POTENTIALLY AFFECTED

FEMALES Average Annual Loss per POTENTIALLY AFFECTED

25.600

Projected Climate &

Projected Climate (2051 - 2100)

5230 ELDERLY >65

[C1] - AVERAGE ANNUAL LOSS YOUTH 5-14

(2051 -- 2100) [C1] Average Annual Loss Fig. F08

4590

POTENTIALLY AFFECTED

CHILDREN 0-4

0.15% [C1] DIRECT ECONOMIC

4610

POTENTIALLY AFFECTED

TEENAGERS 15-24

(1979 - 2018) (2051 - 2100)

Projected Climate

Million Unit/Y 7.2 (1979 - 2018) (2051 - 2100)

1730 %

Projected Climate

10.3

12.120 47.6% Loss 68.5% [Ton/Y]

Loss [Ton/Y

1.200.000

1.000.000

800.000

600.000

400.000

200.000

0

0

0

Y

0

ted ate 2100)

5

ed e 100)

1970

[C1] DIRECT ECONOMIC

LOSS

[C1] - PROBABLE MAXIMUM LOSS CURVE PML - Fig. F13

The PML curves provide important information on the frequency of floods and associated economic losses. Despite the fact that the current average annual losses are 94 million USD per year, floods with losses of at least 100 million USD are expected to occur frequently, with a return period of about 25 years (i.e. on average Climate (1979 - 2018) experienced every 25 years). Under Current projected climate conditions, the same loss becomes more TOTAL frequent: onNUMBER averageOF once in 10 years. Rare flood PEOPLE WITH a significant increase in events would generate DISABILITIES losses: +50% for a return period of about 100 years and +100% for a return period of about 250 years.

Projected Climate (2051 - 21

MENTAL DISABILITY

0

0

780

Loss [Million$]

ed e 100)

860

ANGOLA DISASTER RISK PROFILE

Return Period [Years] Current Climate (1979 - 2018)

Projected Climate (2051 - 2100)

PARALYTIC

[People/Y] 800 700 600 ANNUAL AVERAGE GDP PRODUCED IN POTENTIALLY AFFECTED AREAS

500

400

An indication of the risk incidence on the economy can be drawn from the portion of GDP produced in areas affected by floods.

It is a proxy of direct and indirect potential losses due to floods. Areas subject to flooding over a certain magnitude might suffer indirect losses: assets might be partially or completely damaged and economic activities stopped. In the current climate, the annual GDP produced in areas affected by floods represents on average 0.23% of the total (281 million$/Y).

In the projected future, the proportion relative to the total GDP remains almost the same, around 0.24%, even when considering socio-economic projections (912 million$/Y).

300

200

100

Current Climate

Million$/Y

Projected Climate &

Socio-Economic Projection (2051 - 2100)

People/Y

-1.5

912

5230

5210

POTENTIALLY AFFECTED

7100

7040

4610 TEENAGERS 20 40 60 15-24 80 100 120 140 160 POTENTIALLY AFFECTED

60

40

20

0.10% Annual Average GDP produced People/Y

0

Projected Climate (2051 - 2100)

POTENTIALLY AFFECTED

in potentially affected areas - Fig. F15 POTENTIALLY AFFECTED

ADULTS 25-65

POTENTIALLY AFFECTED

ELDERLY >65

4590

7950

7840

830

840

ANGOLA DISASTER RISK PROFILE | FLOOD RESULTS

People/Y

31 POTENTIALLY AFFECTED

-2

292

Current

YOUTH 5-14

115.000 0.15%

(2051 - 2100)

Climate Projected Climate (1979 - 2018) (2051 - 2100)

(2051 - 2100)

25.600

500

Annual Average GDP Affected - Tab. F14

Projected Climate

160 140 120 100 80 [Million$/Y] People/Y

400

0.23% 0.24% 0.24%

%

Current PRODUCTIVE ASSETS Climate

-1

281

CHILDREN 0-4

25.500 HOUSING SECTOR

300

Projected Projected Climate Climate & SEP

(1979 - 2018) (2051 - 2100)

AGRICULTURAL SECTOR (1979 - 2018) SERVICE SECTOR

GDP 200

100

GDP generated by the productive sector is the most affected, followed by the service sector.

GDP generated by agricultural and housing sectors in potentially affected areas is less. In the projected future, affected GDP of the productive sector and service sector increase slightly.

Current Climate (1979 - 2018)

0.10%

0

FEMALES

13.310

People/Y

13.380

ANGOLA DISASTER RISK PROFILE

DIRECT ECONOMIC

LOSS PER SECTOR AGRICULTURAL SECTOR [C2] AAL Projected Climate Conditions (2051 - 2100) - Fig. F17

PROBABLE MAXIMUM LOSS CURVE Fig. F18 Projected Climate

Loss [Million$]

AAL Current Climate Conditions (1979-2018) - Fig. F16

0 - 0.2 0.2 - 0.5 0.5 - 1 1-2 2 - 3.5

Current Climate

[Million$/Y]

Return Period [Years]

PRODUCTIVE ASSETS [C3] AAL Projected Climate Conditions (2051 - 2100) - Fig. F20

PROBABLE MAXIMUM LOSS CURVE Fig. F21 Projected Climate

Loss [Million$]

AAL Current Climate Conditions (1979-2018) - Fig. F19

0 - 0.3 0.3 - 0.6 0.6 - 0.9 0.9 - 1.2 1.2 - 1.5

Current Climate

[Million$/Y]

Return Period [Years]

HOUSING SECTOR [C4] AAL Projected Climate Conditions (2051 - 2100) - Fig. F23

PROBABLE MAXIMUM LOSS CURVE Fig. F24 Projected Climate

Loss [Million$]

AAL Current Climate Conditions (1979-2018) - Fig. F22

0-1 1-2 2-3 3-5 5 - 7.5

Current Climate

[Million$/Y]

Return Period [Years]

ANGOLA DISASTER RISK PROFILE | FLOOD RESULTS 32

ANGOLA DISASTER RISK PROFILE

The most affected sectors, in terms of direct economic losses, are housing and agriculture, followed by the productive sector. Under current climate conditions, the spatial distribution of the annual average losses differs per sector, except for the provinces of Cunene and Malanje, where high losses are experienced for all three sectors.

Under projected climate conditions, direct economic losses would keep the same pattern or slightly increase for all sectors except in Cunene and Moxico for the productive sector, and Moxico, Cabinda and Lunda Norte for the agricultural sector. Losses in the productive sector seem to increase in the Huíla, Luanda, Cuanza Norte and Zaire. For housing the increase is expected in the north-western regions and Benguela, Huambo, Bengo, Cuanza Norte and Zaire.

commons.wikimedia.org

The PML curves for the three sectors confirm that Angola is exposed to very frequent floods and the frequency of high impact floods can increase significantly under projected climate conditions. The impacts of floods with a low likelihood of occurrence (return period of 200 years) are expected to almost double under projected climate conditions for the housing and productive sectors.

ANGOLA DISASTER RISK PROFILE | FLOOD RESULTS 33

ANGOLA DISASTER RISK PROFILE

DROUGHT Drought Risk Analysis .............................................................................................................................................................................................................................. 35 Results ........................................................................................................................................................................................................................................................................................ 38

Results

(Fig. & Tab.)

POPULATION (B1)................................................................................................................................................................................................................................................................................................... 38 D01 : Annual Average Number of People living in drought affected areas................................................. 38 D02 - D03 - D04 : Potentially affected people - Maps........................................................................................................................................... 38 D05 - D06 : Annual Average Number of People directly affected by drought.................................... 39 D07 - D08 - D09 : Directly affected people - Maps........................................................................................................................................................ 39 DIRECT ECONOMIC LOSS (C1)............................................................................................................................................................................................................................................ 40 D10 : Average Annual Loss............................................................................................................................................. 40 D11 : Average Annual Loss per Sector............................................................................................................................................................................................................. 40 D12 : Probable Maximum Loss Curve in agricultural sector................................................................................................................. 40 D13 : Probable Maximum Loss Curve in hydropower sector............................................................................................................... 40 LIVESTOCK................................................................................................................................................................................................................................................................................................................................. 41 D14 - D15 : Potentially affected livestock units - Maps...................................................................................................................................... 41 D16 : Annual average number of potentially affected livestock units........................................................................ 41 AGRICULTURE.................................................................................................................................................................................................................................................................................................................. 41 D17 - D18 : AAL - Average Annual Loss - Maps........................................................................................................................................................................ 41 D19 : C2 - Direct agricultural loss................................................................................................................................................................................................................................. 41 D20 : Annual average number of working days lost................................................................................................................................................... 42 D21 : Agricultural production loss.............................................................................................................................................................................................................................. 42 D22 - D23 : Food energy supply - Maps...................................................................................................................................................................................................... 43 D24 : Food supply consequences................................................................................................................................................................................................................................. 43 D25 - D26 : Drought tolerant crops - Maize...................................................................................................................................................................................... 44 D27 - D28 : Drought tolerant crops - Bean......................................................................................................................................................................................... 44 HAZARD........................................................................................................................................................................................................................................................................................................................................... 46 D29 - D30 : Combined drought hazard - Maps...................................................................................................................................................................... 46 D31 - D32 : WCI Water Crowding Index - Maps...................................................................................................................................................................... 46 D33 - D34 : SPEI - Standardized Precipitation-Evapotranspiration Index - Maps...................................... 47 D35 - D36 : SSMI - Standardized Soil Moisture Index - Maps............................................................................................................ 47 D37 - D38 : SSFI - Standardized Streamflow Index - Maps..................................................................................................................... 47 D39 - D40 : SPI - Standardized Precipitation Index - Maps.................................................................................................................... 47

ANGOLA DISASTER RISK PROFILE | DROUGHT 34

ANGOLA DISASTER RISK PROFILE

Drought Risk Analysis Drought risk in this report is assessed in four different ways: the analysis of drought hazard and potentially affected population and livestock; the estimation of drought vulnerability of the human population; the calculation of current and projected losses for hydropower production; the estimation of current and projected damage to crop production.

From the combination of these four assessments, one can get a comprehensive understanding of the drought risk. Droughts can arise from a range of hydrometeorological processes that reduce water availability. With varying time gaps between the reduction in availability and a potential impact on the system, these processes can create conditions that are “significantly drier than normal”, and limit moisture availability to a potentially damaging extent (WMO 2016). A drought hazard, interacting with the vulnerable conditions of the exposed people and assets, becomes a disaster when it causes a serious disruption of the functioning of society, leading to losses. (UNISDR 2015).

The social, economic and environmental impacts of droughts stem from their severity, duration and spatial extent; and from the situation of people, production capacities and other tangible human assets exposed to the drought hazard; it is a combination of the drought hazard, exposure and vulnerability to droughts (UNISDR 2015). In order to align the risk profiles with the Sendai Targets, the approach focuses on the: • • • • •

number of affected people B1; sections “Drought hazard, exposure and vulnerability”; agricultural loss (C2; section “Agricultural losses”); productive assets (C3; section “Hydropower losses”); and the direct losses (C1) as sum of C2 and C3.

DROUGHT HAZARD, EXPOSURE AND VULNERABILITY Due to the multi-faceted character of droughts, numerous drought indices exist. One group is the standardized indices, representing anomalies from a normal situation by analysing at least +30 years (preferably 50) in a standardized way. The following five standardized drought indices are used in this risk profile: the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Standardized Evapotranspiration Index (SEI), the Standardized Soil Moisture Index (SSMI) and the Standardized Stream Flow Index (SSFI). Together, they cover all parts of the hydrological cycle.

Larger, longer and/or more intense droughts are more severe and will result in a larger impact. To include these intensity, duration and spatial extent aspects of drought, the total water deficit (i.e. how much less water than average) is calculated as the cumulative sum of the monthly water deficits. This is done using the different indices and also different deficit intensity thresholds. Then, using an artificial intelligence algorithm applying decision trees, the deficits under different thresholds for different indices are matched with reported drought disaster impacts. This is done for each agro-ecological zone in the country, assuming the vulnerability to droughts is similar under similar agro-ecological conditions. As such, local-tailored indices and thresholds can be used to assess the drought hazard under current and projected climate conditions.

ANGOLA DISASTER RISK PROFILE | DROUGHT RISK ANALYSIS 35

ANGOLA DISASTER RISK PROFILE

This drought hazard map can be combined with population, GDP and livestock maps so as to calculate how many people or animals are exposed to different drought events.

From this, the annual average amount of these potentially affected people and animals can be estimated.

No estimations about mortality and the impact of droughts on livelihoods could be made; nor numbers of how many people would be in need of emergency aid because of the drought impact. While the regional vulnerability is included (droughts are defined based on their effects for each agro-ecological zone), human vulnerability (or livestock vulnerability or GDP sectoral vulnerability) are not accounted for. Quantifying vulnerability to droughts as the relation between severity and the expected impacts requires detailed records on droughts losses and damages which are seldom available.

However, one extra proxy for vulnerability was included in the risk profiles: the Water Crowding Index, which quantifies the amount of available water locally available per person.

CIMA’S HYDROLOGICAL MODEL CONTINUUM

REGIONAL CIRCULATION MODEL

GLOBAL CIRCULATION MODEL

FAO CROP YIELD FUNCTION

CROP YIELD MODEL

BIAS CORRECTION FOR OBSERVED TEMPERATURE AND PRECIPITATION

LIST OF MAIN CROPS

CROP VULNERABILITY STANDARDIZED DROUGHT INDICES

EMDAT

DISASTER LOSS LOCAL DATA

ARTIFICIAL INTELLIGENCE ALGORITHM

HARVEST AREA MAPS

EXPOSURE

CROP CROP LOSS PRODUCTION AAL/PML

FOOD SECURITY DROUGHT HAZARD MAPS AGRO-ECOLOGICAL ZONES

IMPACT ASSESSMENT Hazard x Exposure x Vulnerability /Capacity

AFFECTED

PEOPLE

DROUGHT RISK ASSESSMENT PROCESS

LIVESTOCK

GDP

HYDROPOWER LOSS AAL/PML

ANGOLA DISASTER RISK PROFILE | DROUGHT RISK ANALYSIS 36

ANGOLA DISASTER RISK PROFILE

HYDROPOWER LOSSES Assessing the drought risk for hydropower is possible when given sufficient information on hydropower dams and their reservoirs. Using the GRAND database, all dams with 1st or 2nd use “electricity” were selected and their characteristics (location, height, surface area, capacity) retrieved. Using the coordinates of the dam, corresponding discharge, evaporation, and precipitation were retrieved from the hydro-meteorological data.

For each time step, the influx (discharge and precipitation), as well as the outflux (outflow and evaporation) were estimated. Outflow is a function of the storage capacity (minimal, maximal and current) and long term mean average inflow. Using a reservoir capacity equation, the height of the water level in the reservoir was subsequently determined, which was an important parameter for determining the energy that can be generated.

Using a fixed energy price (0.14 USD/kWh) this was translated to a monetary value.

In order to identify losses, a baseline energy production was established. For this, the average annual production over the baseline period (1979-2018) was used. As such, a year with below average production (and thus revenue) was considered a loss (equal to the difference with the baseline average). This way, annual production and loss series were created, allowing to calculate average annual losses and marginal losses for return periods of 5, 10, 25, 50 and 100 years. This was done for current climate conditions, as well as projected climate conditions (keeping the baseline production the same).

AGRICULTURAL LOSSES When there is insufficient moisture in the soil to meet the needs of a particular crop at a given time and location, drought-induced crop losses can occur. To estimate the risk of droughts on the arable sector and the risk to food security, the major crops for each country were selected based on (1) their contribution to the Gross Production Value of all crops in the country and (2) their importance as food for the population. Data have been acquired from FAOSTAT, MAPSPAM, EARTHSTAT, and supplemented with data derived from Angola.

Generally, there is a fairly linear relation between the ratio of actual over potential evapotranspiration and crop yield, as represented by the FAO “water production function”. This relation was tailored for all crops by including a local crop drought sensitivity factor - a factor that is also determined by whether the crop is produced under rain fed or irrigated conditions. Further, reference yield values were then defined by matching the calculated crop yields with data from FAOSTAT. Then, the spatiallyexplicit crop yields' variability as response to changing hydrological conditions could be assessed on a yearly basis, and the annual production could be estimated by multiplying the yields with the harvested areas of the selected crops.

Production losses were calculated as the difference between the production of a year and the 20% lowest value from the current climate. A zero loss was assigned to any year with a production equal or above this 20% threshold. We determined the average annual loss by dividing the summation of these annual losses by the total number of years, including the non-drought years. These calculations can be done in kilograms but also converted to USD by multiplying the production loss with the market price of the evaluated crops .

Additionally, effects of droughts on (1) amount of lost working days (reflecting job opportunities in arable farming), (2) potential loss of food energy supply (in Kcal, calculated as the part of the production which is available for consumption as food for people in Angola, subtracting the production for export and other uses) and the amount of people who could potentially have been fed by this lost production (assuming Minimum Dietary Energy Requirement of 1730 kcal/cap,day and 10% household waste), and (3) production losses from using drought-adapted varieties (reflecting options for adaptation), have been estimated as well.

ANGOLA DISASTER RISK PROFILE | DROUGHT RISK ANALYSIS 37

D3 - AFFECTED [UNITS/Y]

ANGOLA DISASTER RISK PROFILE

SCHOOLS

D2 - AFFECTED [UNITS/Y]

HOSPITALS

POPULATION ANNUAL AVERAGE NUMBER OF PEOPLE LIVING IN DROUGHT AFFECTED AREAS

Curren Climat (1979 - 2

Current Climate Conditions (1979 - 2018) - Fig. D02 POTENTIALLY AFFECTED

0.39

CHILDREN 0-4

Current Climate

(1979 - 2018)

7.6

Projected Climate

Million People/Y

13.5

30.8% Projected Climate &

POTENTIALLY AFFECTED

0.53

POTENTIALLY AFFECTED

0.35

POTENTIALLY AFFECTED

0.60

POTENTIALLY AFFECTED

0.06

YOUTH 5-14

TEENAGERS 15-24

(2051 - 2100)

54.4%

ADULTS 25-65

Million Socio-Economic People/Y Projection (2051 - 2100)

42.2

ELDERLY >65

56.2% Million

Million Peo

0 - 1000

People/Y

1000 - 10.000 Annual Average Number of People living in drought affected areas Fig. D01

10.000 - 50.000 50.000 - 200.000 200.000 - 1.710.000 [People/Y]

With respect to observed conditions (19792018 climate), the probability of occurrence Current Climate of soil moisture and meteorological droughts (1979 - 2018) will increase under projected conditions (2051Projected 2100 climate). While with the average annual Climate precipitation, its variability also increases.

This, (2051 - 2100) together withMillion an increased temperature, thus People/Yevapotranspiration, results higher potential in longer, moreProjected intense and more frequent Climate & meteorological, hydrological and agricultural Million Socio-Economic droughts. Projection (2051 - 2100) People/Y Under current climate conditions, central-southern regions have the largest number of potentially affected people, while the eastern regions have Million to droughts. Throughout less population exposed People/Y the country, on average 7.6 million people (around 30% of the total population of 2014) are annually exposed to droughts. Under projected climate conditions, this number is expected to increase to over 50%. Overall, the amount of people exposed to droughts is expected to increase in Angola, with some significant increases in the east (Moxico area).

Current Climate

(1979 - 2018)

1.9

7.5%

Projected k - days / Y Climate 1730

3.4

%

13%

Conditions (2051 - 2100) Fig. D03

7.9

10%

0.31%

12

2.

Pro Cl

7.2

1

(1979 - 2018) (205

MillionClimate Unit/Y &

Socio-Economic Projections (2051 - 2100) Fig. D04

%

47.6% 68 Current Climate

ANGOLA DISASTER RISK PROFILE | DROUGHT RESULTS

Million$/Y

100

Proj Cli

(2051

7

38

(1979 - 2018)

134

(205

Current Climate

(1979 - 2018)

Current Climate

Pro Cl

Projected Climate

%

1.66% 12.

13.5 95.9

Current POTENTIALLY AFFECTED

0.06% 54.4%

Climate ADULTS(1979 25-65 - 2018)

MillionANGOLA DISASTER RISK PROFILE Million$/Y D4 - AFFECTED [KM/Y] Socio-Economic People/Y POTENTIALLY AFFECTED 171 INFRASTRUCTURES Projection (2051 - 2100)

42.2

ELDERLY >65

D3 - AFFECTED [UNITS/Y]

56.2% Million

SCHOOLS

CHILDREN 0-4

1.9

POTENTIALLY AFFECTED

(1979 - 2018)

Current Climate

7.6 7.5%

30.8%

Million People/Y Million

People/Y

Projected &

Climate Projected Climate &

(2051 - 2100) Projected Climate

3.4 13.5 (2051 - 2100)

13% 54.4% Million

%

Current Climate

(1979 - 2018)

1.9

Projected Climate

Million People/Y

3.4

1.9%

Million$/Y

-2

-2.5

1730

12.120

-3

0.96 0.63

0.31%

1.07

Los

-3.5

1

2.17%

-4

1

-4.5

0.11

Current Projected Climate Climate Million People/Y

1000 - 10.000

13%

%

10.000 - 50.000

Million People/Y

50.000 - 200.000

(2051 - 2100)

1730

k - days / Y

0 - 1000

(2051 - 2100)

Projected Climate

7.2

Million Unit/Y

Current Climate

Million People/Y

Projected Climate

(1979 - 2018) (2051 - 2100) [B1] - Annual Average Number of People directly affected by drought - Tab. D06

(1979 - 2018)

134 10%

-1.5

(2051 - 2100)

Million People/Y

[B1] - Annual Average Number of People directly affected by drought - Fig. D05

7.9

Million Pe

(1979 - 2018)

0.06

ELDERLY >65

People/Y

(1979 - 2018)

HOU

Projected Climate (2051 - 2100)

Current 0.71 Climate

0.39

POTENTIALLY AFFECTED

Million People/Y

Projected

Current Climate (1979 - 2018)

YOUTH 5-14

42.2 7.9 56.2% 10% Million

Climate & Current Socio-Economic Climate Projection (2051 - 2100)

7

0.53 k - days / Y POTENTIALLY AFFECTED 0.35 TEENAGERS 15-24 % POTENTIALLY AFFECTED 0.60 ADULTS 25-65

Projected Climate

Socio-Economic Million Socio-Economic People/Y People/Y Projection (2051 - 2100) Projection (2051 - 2100)

7.5%

5

SER 0.1

-1

POTENTIALLY AFFECTED

Current Climate

PRODUC

Million People/Y

POPULATION

[B1] - ANNUAL AVERAGE NUMBER OF PEOPLE DIRECTLY AFFECTED BY DROUGHT

(1979 - 2018)

22

HOSPITALS

1.0

AGRICULTU

1950.06

19

D2 - AFFECTED [UNITS/Y]

People/Y

0.60

Projected Climate (2051 - 2100)

11.8% Million$/Y

(2051 - Conditions 2100)

(2051 - 2100)

Projected Fig. Current D08 12.120 Climate Climate Loss [Ton/Y]

(1979 - 2018) (2051 - 2100) 1.200.000

Current Climate

% (1979 - 2018)

[People Affected/Y]

841

47.6% 68.5%

Projected Projected Climate Climate

0.31% 2.17% Million$/Y 100

200.000 - 1.710.000

10.3

Million Unit/Y

7.2

%

47.6%

Projected Climate

Current Climate

800.000

1.66% 12.28%

600.000 Climate & Socio-Economic 400.000 10.3 Projections (2051 - 2100) 200.000 Fig. D09 68.5%

(2051 - 2100)

0

Current Climate Conditions (1979 - 2018) - Fig. D07

(1979 - 2018)

745

1.000.000

Projected Climate

Based on these assumptions, in this risk profile, the combination of people vulnerability and coping Million$/Y 100 745 capacity to drought was estimated as a function of rural/urban population concentrations within % 1.66% 12.28% each province. Rural communities with higher levels of isolation were then assumed to suffer wider drought consequences. Computations show that among 7.6 million people (on average, per year) living in areas affected by drought in the current climate, an average of 1.9 million people per year are estimated to be directly affected. This number increases to 3.4 million under projected climate conditions and to 7.9 million if both projected climate conditions and socioeconomic evolution are considered. Cunene is clearly the most affected province.

Individuals’ vulnerabilities and coping capacities to drought conditions depend on their physical, Current social, economic, and environmental factors or Climate (1979 - 2018) processes. People living in urban environments Projected Climate are usually less vulnerable to drought than those (2051 - 2100) 1.9% living in rural communities. Rural communities Million$/Y tend to have a limited short-term coping capacity 11.8%adaptive capacity. and a limited long-term Million$/Y These communities strongly rely on national and provincial disaster management authorities’ efforts to mitigate such adverse effects and, in extreme cases, are forced to migrate elsewhere to satisfy their subsistence needs. In this sense, transport infrastructure plays a key role in providing access to water during an emergency, as remote unconnected communities are more difficult to reach by external relief resources.

134

841

ANGOLA DISASTER RISK PROFILE | DROUGHT RESULTS 39

(2051 - 2100)

2.5% 15.0%

Projected Climate &

1.6% 10.5%

People/Y Million$/Y

CASSAVA MAIZE

Socio-Economic People/Y Projection (2051 - 2100) [Million$/Y] 160 140 120 100 80

7.9

7

60

40

20

0

20

40

People/Y

Million Unit/Y

[C1] DIRECT ECONOMIC LOSS

0.71

[C1] - Direct Economic Losses Fig. D10

-2.5

0.96

-3 Current Climate (1979 - 2018) -3.5

0.63

7.2

10.3

Current Climate

1.9%

Projected Climate

AGRICULTURAL SECTOR (C2)

841

PRODUCTIVE ASSETS (C3)

(2051 - 2100)

Million$/Y -4.5

800.000

(2051 - 2100)

100

745

11.8%

AAL [Million$/Y] 0

Million$/Y 2050-2075 2076-2100

% Current Climate

600.000 400.000 200.000 0

Projected Climate

(1979 - 2018)

Million$/Y

-4

0.11

Projected Climate

Direct Economic Losses per sector - Fig. D11

134

1.07

Current Climate

47.6% 68.5%

%

-1.5 -2

80 100 120 140 160

(1979 - 2018) (2051 - 2100)

-1 Projected Climate (2051 - 2100)

60

ANGOLA DISASTER RISK PROFILE

10% Million

1.66% 12.28%

Projected Climate

100 200 300 400 500 600 700 800

Million People/Y

The Average Annual Loss (direct economic from crops), as a total for the whole country, is much higher under the projected climate then it is in the current climate (from 100 to 744 million USD per year).

This represents a more than sevenfold absolute increase. The relative increase, represented by the values expressed as a percentage of the average Gross Production Value of the selected crops, rises from about ANGOLA 1.7% to almost 12.3% (see table D19). This indicates that a substantial part of the annual crop production might be lost due to intensified droughts under the projected climate. Compared to current climate PML C2 AGRICULTURAL LOSS conditions, losses in hydropower generation (C3) resulting from drought will increase three times, from over 30 to almost 100 million USD per year (for Mabubas, Cambambe, Capanda, Gove and Matala dams).

Loss [Ton/Y] C2 - LOSSES

[Million $] LossLoss [Million$]

800.000

1400

600.000

0

20

40

60

80

2.1% 21.7%

200

3.2% 13.8%

1.6% 10.5%

0

2.7% 19.3%

600

2.5% 15.0%

200.000

Current Climate

400.0001000

Projected Climate

Current Climate

100 120 140 160 180 200

Under current climate conditions, direct economic crop 75.000 losses increase gradually in expected maximum loss when return periods rise from 10 to 200 years. It is worth60.000 noting that these results’ uncertainty becomes higher as we move 45.000 into the very rare losses domain. Under the projected climate, these losses increase substantially in absolute units, 30.000 compared to the situation in the current climate, and relative increases range from more than two times with a 200 year 15.000 return period to circa five times with a ten year return period.

ANGOLA More frequent losses are thus projected to become 0 more important in the future.

PML C3 HYDROPOWER LOSS

CASSAVA MAIZE SWEET VEGETABLES Return Period [Years] RetunPOTATO Period [Years] POTATO Current Climate Projected Climate Projected Climate

250

1

200

Current Climate 150

100

50

0

0

20

ANGOLA

40

60

80

Return Period [Years] Retun Period [Years] Current Climate Projected Climate

PML SENSITIVITY BEAN 150

100

2.5% 4.4%

1800

3.4% 5.7%

1.000.000

90.000 AGRICULTURAL

3.1% 27.6%

2200

3.3% 30.1%

%

[C2] - PML - Fig. D12

0.5% 3.7%

Projected Climate

1.200.000

160.00

120.00

80.00

40.00

Loss [Ton/Y]

Loss [Ton/Y]

[Million $] Loss Loss [Million$]

0

22

BANANA BEAN GROUNDNUT MILLET SORGHUM PEARL

C3 - LOSSES

[C3] - PML - Fig. D13

HYDROPOWER

C3 is computed by exclusively considering losses in hydropower production. These are defined as production levels below average reservoir conditions. Under current climate conditions, frequent losses of once in ten years (on average) are expected to exceed 100 million USD. Hydropower losses are projected to increase in Angola’s projected climate. Losses of just over 160 million, which in current climate conditions are expected once in every 100 years, would be experienced once every five years on average under projected climate conditions. This is a net result of the increase of annual losses in all hydropower stations.

Strikingly, this increase in annual losses is mainly due to very frequent (once every one or two years) events, meaning that many years will have below (current) average production in the projected climate conditions.

2

ANGOLA DISASTER RISK PROFILE | DROUGHT RESULTS 40

450

375

120

300

0.53

POTENTIALLY AFFECTED

YOUTH 5-14

Current 0.35Climate

POTENTIALLY AFFECTED

TEENAGERS 15-24

(1979 - 2018)

0.60171 ADULTS 25-65 INFRASTRUCTURES POTENTIALLY AFFECTED [KM/Y] D4 - AFFECTED

0.06 19

POTENTIALLY AFFECTED [UNITS/Y] D3 - AFFECTED

0.96

ANGOLA DISASTER RISK PROFILE Projected 0.63 Climate

Current Climate (1979 -AGRICULTURAL 2018) SECTOR

-3.5

(2051 - 2100)

AGRICULTURAL SECTOR

-4

1.07 195

PRODUCTIVE ASSETS

PRODUCTIVE ASSETS SERVICE SECTOR

LIVESTOCK

-4.5

0.11

22 ANNUAL AVERAGE NUMBER OF POTENTIALLY AFFECTED Million People/Y D2 - AFFECTED [UNITS/Y] Million People/Y 5 7 LIVESTOCK UNITS HOSPITALS

ELDERLY >65 SCHOOLS

-3

AAL [Million$

HOUSING SECTOR

2050-2075 2076-2100 [Million$/Y] 160 140 120 100 80

60

40

20

0

-1

[Units/Y]

Climate

0.60 Projected Climate

(1979 - 2018) (2051 - 2100)

POTENTIALLY AFFECTED

ELDERLY >65

Million Unit/Y

0.06 10.3

7.2

Million People/Y

%

47.6% 68.5%

Potentially Affected Livestock - Tab. D16 Current Projected Climate Climate (1979 - 2018)

Million$/Y k -% days / Y %

Current

100 Climate

(1979 - 2018)

Projected Climate

745

(2051 - 2100)

2.17%

-3.5 Projected Climate Conditions (2051 - 2100) - Fig. D15 AGRICULTURAL SECTOR

Under current climate conditions, -4 affected livestock (i.e. animals 800.000 PRODUCTIVE ASSETS 1.07 living in areas hit by droughts) amounts to 7.2 million units -4.5 (48%600.000 of the total value). Livestock units are calculated as the AAL [Million sum of all animals in a certain place, weighed by the water and 0.11 2050-2075 2076-2100 400.000 food needs of the animals following FAO conversion factors.

Under projected climate conditions, the number of affected Million People/Y 200.000 livestock is projected to increase to more than 68% of the total livestock population. Major losses, which are concentrated in 0 the southern regions under current climate conditions, would extend to central and eastern regions in the future.

CASSAVA MAIZE POTATO

SWEET VEGETABLES POTATO

AGRICULTURE

(2051 - 2100)

1.66% 1730 12.28% 12.120 0.31%

0.63 1.000.000

3.3%

POTENTIALLY AFFECTED

ADULTS 25-65 Current

-3

1.200.000

0.5% 3.7%

TEENAGERS 15-24

Loss [Ton/Y] 0.96

Projected Climate

2.17% 0.35

Current Climate Conditions (1979 - 2018) - Fig. D14

-2.5

Current Climate

%POTENTIALLY AFFECTED 0.31%

12.120 0.53

2.1% 21.7%

YOUTH 5-14

0.71 300.000 - 1.600.000

3.2% 13.8%

k - days / Y AFFECTED 1730 POTENTIALLY

-2

100.000 - 300.000

(2051 - 2100) 0.39

2.7% 19.3%

CHILDREN 0-4

-1.5

1.6% 10.5%

(1979 - 2018)

0 - 1000 Projected 1000 - 15.000 Climate (205115.000 - 2100)- 100.000

2.5% 15.0%

Current Climate (1979 - 2018) Current Projected Climate Climate POTENTIALLY AFFECTED

BANANA BEAN

C2 - DIRECT AGRICULTURAL LOSS Loss [Ton/Y] 1.200.000

0 - 2.5

2.5 - 6 1.000.000 6 - 13

Million$/Y %

(1979 - 2018)

(2051 - 2100)

100

745

1.66% 12.28%

C2 - Direct Agricultural Loss - Tab. D19

ANGOLA DISASTER RISK PROFILE | DROUGHT RESULTS 41

3.3%

Projected Climate

Under current climate conditions, direct economic crop losses are concentrated in eight provinces (Benguela, Huila, CASSAVA MAIZE POTATO SWEET VEGETABLES Bié and Cuanza Sul, Cuanza Norte, Malanje, POTATO Cabinda, Uíge), which contribute to almost ¾ of the total losses. Under the future climate projections and considering the same crop distribution, increased droughts would cause substantially higher direct economic crop losses in nearly all provinces (except Luanda, Huambo, Cunene). Particularly, the northeastern regions would experience high losses (up to 45 - 100 million dollars/Y).

0.5% 3.7%

Current Climate

AAL - Projected Climate Conditions (2051 - 2100) - Fig. D18

200.000 0

Projected Climate

47.6% 68.5%

400.000

Current Climate

AAL - Current Climate Conditions (1979 - 2018) - Fig. D17

%

[Million$/Y]

2.1% 21.7%

10.3

3.2% 13.8%

Million Unit/Y

2.7% 19.3%

7.2

(1979 - 2018) (2051 - 2100)

13 - 45

45 - 100 600.000

2.5% 15.0%

Projected Climate

1.6% 10.5%

Current Climate

800.000

BANANA BEA

PRODUCTIVE ASSETS

[Million$/Y] 160 140 120 100 80 Million People/Y

-3.5

0.31% -4

2050-2075 2076-2100 Climate Climate

(1979 - 2018) (2051 - 2100)

Million People/Y

Million Unit/Y

rojected Climate

Current Climate

(1979 - 2018)

Loss [Ton/Y]

10.3

400.000

68.5%

200.000

51 - 2100)

745

2.28%

0

CASSAVA MAIZE POTATO

(2051 - 2100)

SWEET VEGETABLES POTATO Loss [Ton/Y]

745

90.000 75.000

1.66% 12.28%

%

CASSAVA MAIZE POTATO

160

120

60.000 45.000

Current Climate

600.000

200.000

Projected Climate

3.2% 13.8%

800.000

2.5% 15.0%

1.000.000

051 - 2100)

ojected Climate

100

1.200.000 Million$/Y

1.6% 10.5%

Projected Climate

10.3

AGRICULTURAL PRODUCTION 0 LOSS

051 - 2100)

2.17%

7.2

47.6% 68.5%

%

2.120

2.17%

-4.5 Annual Average Number of working days lost Current Projected Tab. D20

2.7% 19.3%

eople/Y

12.120

30.000

80

40

15.000 0

BANANA BEAN GROUNDNUT MILLET SORGHUM PEARL

SWEET VEGETABLES POTATO

Agricultural Production Loss - Fig. D21

Crop production losses, induced by drought conditions, have been calculated for ten different crops in Angola. Under current climate conditions, these losses are dominated by cassava (physical units), and if expressed as a percentage of the average crop production, crop losses remain close to or lower than 3.5%. Under the projected climate, large increases in production loss have been calculated for all crops, due to the intensification of droughts compared to the current climate. Relative losses range from 3.7 to 30%, with an increase in factors from 1.7 (millet) to up to 10 (vegetables).

ANGOLA DISASTER RISK PROFILE | DROUGHT RESULTS 42

Loss

0.11

1730

Projected Climate

06

(2051 - 2100)

Current Climate

1.07

(1979 - 2018) -2.5

-3

%

Projected Climate

2.1% 21.7%

60

Current Climate

The estimated average number of lost working days is linked to the crop production losses, because lower crop production is linked to reduced labour requirements, especially during harvest time. This loss has been estimated at roughly 1.7 million working days per year due to droughts under current climate conditions.

[Ton/Y] resulting from increased droughts under TheLoss increase the projected conditions is sevenfold: up to 12 1.200.000 climate AGRICULTURAL SECTOR (C2) million days per year. Thus, many more people may have Current Climate less PRODUCTIVE farmwork ASSETS employment opportunities under this 1.000.000 (C3) Projected Climate projected climate scenario. Compared to the average amount of AAL working days required for land preparation, 800.000 [Million$/Y] 0 100 200 300 400 500 600 700 800 sowing and harvesting, the relative values for lost working days is600.000 0.3% under current climate climate conditions and increases to around 2% under projected climate conditions.

400.000

3.2% 13.8%

0.63

80 100 120 140 160

2.5% 4.4%

35

-2

k - days / Y

60

2.7% 19.3%

0.96

40

3.4% 5.7%

53

20

2.5% 15.0%

0.71

0

1.6% 10.5%

39

20

ANNUAL AVERAGE NUMBER OF WORKING DAYS LOST

-1.5

Projected Climate (2051 - 2100)

2050-2075 2076-2100 40

Million People/Y

AGRICULTURE -1

ent ate 2018)

60

3.1% 27.6%

7

0.11

HOUSING SECTOR ELDERLY >65

PRO

-4.5

3.3% 30.1%

5

0.06

SERVICE SECTOR POTENTIALLY AFFECTED

0.5% 3.7%

22

ANGOLA DISASTER 0.60 1.07 RISK PROFILE

POTENTIALLY AFFECTED

Projected Climate (2051 - 2100) -4

AGRICULTURAL SECTOR ADULTS 25-65

195

19

Current Climate (1979 - 2018)

Projected Climate

171

Projected Climate (2051 - 2100)

2.1% 21.7%

Current Climate 979 - 2018)

%

0.23% 0.24% 0.24%

ANGOLA DISASTER RISK PROFILE

Current Climate (1979 - 201 Current Climate (1979 - 2018) POTENTIALLY AFFECTED

CHILDREN 0-4

POTENTIALLY AFFECTED

YOUTH 5-14

POTENTIALLY AFFECTED

TEENAGERS 15-24

5230 7100

7040

4610

20 - 200

[People/Y] 800

700

600

500

400

300

200 - 500

POTENTIALLY AFFECTED

830

840 2000 - 3500

1000 - 2000

[Million kcal/Day per Year]

People/Y

POTENTIALLY AFFECTED

PARALYTIC

4590

7840 500 - 1000

FEMALES

MENTAL DISABILITY

FOOD ENERGY SUPPLY

7950

ELDERLY >65

AGRICULTURE

5210

POTENTIALLY AFFECTED

ADULTS 25-65

TOTAL NUMBER OF PEOPLE WITH DISABILITIES

Projected Climate (2051 - 2100)

People/Y

13.310

13.380

Current Climate Conditions (1979 - 2018) - Fig. D22

Projected Climate Conditions (2051 - 2100) - Fig. D23

Arable crops contribute to Angola’s food energy supply. Droughts potentially diminish food security situation via lower crop production. This analysis has estimated which part of the crop production is, on average, available for direct consumption as vegetal food in Angola, therefore excluding crops used for export or other purposes (including feed). Contribution to food energy supply is expressed in kcal per day for the whole country. The crops included in this analysis are the same as those considered for Agricultural Production Loss. The highest values of energy supply under current climate conditions are found in the central part from the north to the south, Huambo. Due to increased drought conditions Current underClimate Current except Projected (1979 - 2018) Climate Climate the projected climate conditions, food energy supply should decreases in most provinces (except Luanda (1979 - 2018) (2051 - 2100) AGRICULTURAL SECTOR An evaluation of and Cunene), but would be most severe in the central and eastern parts of the country.

D4 - AFFECTED [KM/Y] PRODUCTIVE ASSETS in both of these the economic loss and energy supply maps shows that the distribution of loss increases 171 195 INFRASTRUCTURES SERVICE SECTOR are very similar.

FOOD SUPPLY CONSEQUENCES D2 - AFFECTED [UNITS/Y] HOSPITALS

19

22

5

7

When droughts diminish crop production, they also diminish the average food energy supply. To illustrate the impact of a lower crop production, the loss of production has been expressed in an equivalent number of people that could have been fed with Current Projected Climate this lost production (compared to the situation under current Climate (1979 - 2018) (2051 - 2100) climate conditions). To convert food energy supply into number POTENTIALLY AFFECTED fed, we applied the Minimum Dietary of people potentially 0.71 0-4 (MDER0.39 Energy CHILDREN Requirement for Angola is 1750 kcal/cap,day*) and an assumed a household waste of 10%. The Figure below POTENTIALLY AFFECTED 0.53 0.96the projected illustrates the 5-14 results for two periods under YOUTH climate to emphasize the projected trends (box plot: 25% - 75%; AFFECTED whiskersPOTENTIALLY to indicate extreme 0.35 values). Our results 0.63show that less TEENAGERS 15-24 people can potentially be fed with a diet containing the MDER, as compared to the situation under current climate conditions, POTENTIALLY AFFECTED nd 0.60 1.07 and that this effect ADULTS 25-65is stronger in the 2 half of the projected climate (from an average 2.25 million people to 3.25 million AFFECTED people).POTENTIALLY It illustrates a possible negative effect on food security 0.06 0.11 ELDERLY >65 for the people in Angola due to increased drought conditions in the selected projected climate.

The values Million should be regarded Million People/Y People/Y as minimum values, because in reality many more people could be affected in their food security situation (more but less severe), when conditions for crop growth worsen.

HOUSING SECTOR

[Million$/Y] 160 140 120 100 80

k - days / Y

1730

12.120

Loss [Ton/Y]

40

20

0

-1.5 -2 -2.5 -3 -3.5

AGRICULTURAL SEC

-4

PRODUCTIVE AS

-4.5

AAL [Mi

2050-2075 2076-2100 Fig. D24 http://www.fao.org/fileadmin/templates/ ess/documents/food_security_statistics/ MinimumDietaryEnergyRequirement_en.xls

ANGOLA DISASTER RISK PROFILE | DROUGHT RESULTS Projected Climate 43 (1979 - 2018) (2051 - 2100)

Current Climate

60

-1

[Equivalent Million People]

D3 - AFFECTED [UNITS/Y]

SCHOOLS

ANGOLA DISASTER RISK PROFILE

00 800

ANGOLA

2

PML C2 AGRICULTURAL LOSS

AGRICULTURE

2200

1800

DROUGHT TOLERANT CROPS Loss [Million $]

ANGOLA

MAIZE

ss [Ton/Y]

.000

160.000

28.000450

120.000

21.000

Loss [Million Ton]

.000

.000

.000

1 Current Projected Climate Climate (1979-2018) (2051-2100)

375

Current Climate

Projected Climate (2051-2100) 40

Current Climate (1979 -Current 2018)- Standard

100 120 140 160 180 200

60

80

100

120

140

160

4

Standard

Return Period [Years]

MAIZE Short Cycle

Drought tolerant

Current - Drought tolerant

Current - Short Cycle

120

Loss [Million Ton]

90

60

30

Current Projected Climate Climate (1979-2018) (2051-2100)

0

Loss [k-tons]

Drought tolerant crops Fig. D27

0

20

40

60

80

100

120

140

160

180

200

Return Period Return Period[Years] [Years]

Current Climate Current Clim - Standard Standard (1979 - 2018) Current Clim - Short Cycle Projected Clim - Drought tolerant

Projected Climate (2051 - 2100)

Standard

BEAN Current Clim - Drought tolerant Drought tolerant Short Cycle Projected Clim - Standard Projected Clim - Short Cycle

Drought tolerant

3

Projected Climate

200

150

7000

ht tolerant

180

PML SENSITIVITY DroughtBEAN tolerant crops - Fig. D28

14.000

Loss [Ton/Y]

80

Retun Period [Years] Current Climate Projected Climate

0 (1979-2018) 0 20

21.000

VITY BEAN

60

Projected Climate Projected - Standard StandardProjected - Drought toleranttolerant Projected - Short Cycle Drought Short Cycle (2051 - 2100) ANGOLA

28.000

0

40

Return Period [Years]

Drought tolerant crops Fig. D25

BEAN

Projected Climate 8) (2051-2100)

20

Loss [k-tons]

olerant d ycle

0

150

200

BEAN

200

225

0 75

Loss [Ton/Y]

Loss [Ton/Y]

180

600

300

7000

40.000

.000 0

Drought tolerant crops - Fig. D26

1000

14.000

80.000

.000

1400

PML SENSITIVITY MAIZE

Short Cycle

We estimated the effect of drought-adapted varieties on crop production for two selected crops: maize (staple crop) and beans (protein crop). For both crops we introduced a short-cycle variety to avoid planting too early in the season, and a variety that has better characteristics to tolerate periods of droughts (e.g. via more extensive root system or physiological response to drought conditions).

Short cycle

ANGOLA DISASTER RISK PROFILE | DROUGHT RESULTS 44

ANGOLA

ANGOLA

ANGOLA DISASTER RISK PROFILE

In most cases, the adaptation to drought, gives these varieties an advantage in drier years, but results in lower yields if conditions are good (i.e. in wet years). However, the analysis to determine the overall effect with all years in a climate (dry and wet) was not possible within this project. Obviously, this overall effect is strongly influenced by the frequency of occurrence of dry/wet years. In order to reduce the occurrence of lower yields in wet years, an Early Warning System can be used in combination with these varieties.

Drought monitoring and seasonal outlook can be used to advise using a drought-adapted variety when the probability of droughts in the growing season is high.

A crop production threshold, that is, a level of production under which a loss can be expected, was determined for the standard variety and under current climate conditions. This threshold was used to compute losses under projected climate conditions and for the two other crop varieties. The results show that for maize, losses should decrease substantially in both climates. The two types of adaptation are not very different with respect to this calculated loss. For beans, losses diminish for the current climate as well as under projected climate conditions, compared to the standard variety, due to intensified drought conditions. Our results show a slightly larger beneficial impact for the drought-tolerant varieties compared to the short-cycle varieties. The only exception is in the case of maize in current climate conditions, where the short-cycle variety seems more beneficial.

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Strong effects are visible in the Probable Maximum Loss estimates for maize: all losses in the projected climate conditions show much higher values, compared to their counterparts in the current climate.

However, the losses of the two drought-adapted varieties under projected climate conditions remain lower than the standard variety in the current climate. This suggests that the adaptation would be able to compensate for the increased drought situation of this projected climate. The situation is less pronounced for beans, where only the short-cycle variety under projected climate is similar to the standard variety in current climate. The graphs illustrate for both crops the potential advantage of using adapted varieties in the dry years of the two climates, compared to the standard variety.

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HAZARD COMBINED DROUGHT HAZARD

0 - 10 10 - 30 30 - 50 50 - 70 70 - 100 Probability[%]

Current Climate Conditions (1979 - 2018) - Fig. D29

Projected Climate Conditions (2051 - 2100) - Fig. D30

These maps show the annual average chance of experiencing a drought (%). By analysing the deficits in effective rainfall (precipitation minus potential evapotranspiration), in subsurface water (soil moisture), and in the rivers (streamflow), and investigating which deficits caused an impact in the past decades, the vulnerability to water deficits was estimated per agro-ecological zone of Angola. Then, it was evaluated how often such meteorological or hydrological deficit occurs under current climate conditions and how often it is expected to occur under projected climate conditions. This results in a drought probability map, showing the annual chance that a drought - of a severity which resulted in a harmful impact in the past - will hit. The results show the highest probability to experience drought is found in the southern and the central-western regions, under current climate conditions. Under projected climate conditions, the probability of droughts would strongly increase towards the east, Moxico and Lunda Sul being the two provinces where major increases are expected. The projected increase in drought risk can be assigned to higher temperatures and larger rainfall variability in a projected climate, and to the close link between drought disasters and evaporation deficits in Angola.

WCI WATER CROWDING INDEX 0 - 20 20 - 40 40 - 60 60 - 80 80 - 100 [% Population/Y]

Current Climate Conditions (1979 - 2018) - Fig. D31

Projected Climate Conditions (2051 - 2100) - Fig. D32

These maps show the percentage of the population per region experiencing water scarcity, based on the water available (precipitation minus actual evapotranspiration) per person per year (<1000 m3/ person/year). Water scarcity indicates that a population depends on water resources from outside their immediate region (~25 km2). Currently, the highest percentage of population in water scarcity can be found in and around the capital (Luanda, Cazenga Cacuaco) and in the south-east (Benguela, Catumbela, Namibe), where almost the entire population is not self-sufficient in water resources coming from their immediate region. Overall, average water availability will decrease under projected climate conditions. With a growing population, large increases in water scarcity are projected in, among others, Dala, Luacano and Tchikala Tcholohanga.

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SPEI - Standardized Precipitation-Evapotranspiration Index These maps denote the average Current Climate Conditions annual chance of a meteorological (1979 - 2018) - Fig. D33 drought occurring (%). Droughts are defined as 3 consecutive months of precipitation minus evapotranspiration values considerably below normal conditions; calculated through the Standardized Precipitation – Potential Evapotranspiration Index (SPEI). Due to temperature increases, this indicator projects a major increase in drought frequency throughout the country.

This is particularly important for areas dependent on rainfall for their water resources.

SSMI - Standardized Soil Moisture Index These maps denote the average annual chance of a subsurface drought occurring (%). Droughts are defined as 3 consecutive months of soil moisture conditions considerably below normal conditions; calculated through the Standardized Soil Moisture Index (SSMI).

It can be noted that the probability of droughts is the highest along the coast and in the south of the country. In the projected climate, there is a general increase in drought probability. Eastern regions in particular might see a sharp increase. This is important for agricultural areas and natural ecosystems.

Projected Climate Conditions (2051 - 2100) - Fig. D34

0 - 20 20 - 40 40 - 60 60 - 80 80 - 100 Probability [%] of 3-months Drought

Current Climate Conditions (1979 - 2018) - Fig. D35

Projected Climate Conditions (2051 - 2100) - Fig. D36

0 - 20 20 - 40 40 - 60 60 - 80 80 - 100 Probability [%] of 3-months Drought

SSFI - Standardized Streamflow Index These maps denote the average annual chance of a hydrological drought occurring (%). Droughts are defined as 3 consecutive months of stream flow levels considerably below normal conditions; calculated through the Standardized StreamFlow Index (SSFI). This indicator is particularly important for areas dependent on rivers for their water resources. The probability of droughts is expected to generally increase, particularly in the eastern regions.

Current Climate Conditions (1979 - 2018) - Fig. D37

SPI - Standardized Precipitation Index

Current Climate Conditions (1979 - 2018) - Fig. D39

These maps denote the average annual chance of a meteorological drought occurring (%). Droughts are defined as three consecutive months of precipitation considerably below normal conditions; calculated through the Standardized Precipitation Index (SPI). In Angola, the likelihood of drought is higher in the south, southeast, and in coastal areas. These areas will also see the largest increase in droughts, according to future climate projections.

This is particularly important for areas dependent on rainfall for their water resources.

HAZARD INDEX

Projected Climate Conditions (2051 - 2100) - Fig. D38

0 - 20 20 - 40 40 - 60 60 - 80 80 - 100 Probability [%] of 3-months Drought

Projected Climate Conditions (2051 - 2100) - Fig. D40

0 - 20 20 - 40 40 - 60 60 - 80 80 - 100 Probability [%] of 3-months Drought

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Risk Profile key messages CLIMATE The climate projections (2050-2100) considered in this risk profile (RCP 8.5) foresee a huge increase in temperature in Angola: especially in the final part of the century (2071-2095), when it would reach 4 degrees at the country scale. No significant changes in rainfall totals are foreseen at the country level.

FLOODS The average value of 25,000 people affected per year in the current climate conditions does not vary if only the climate variations are considered. Taking into account both climate projections and the population increase, however, the average affected people per year would increase more than 4 times (above 100,000 people). Cunene is one of the most affected provinces, in both present and projected climate conditions.

Average Annual Loss due to floods is estimated on average to be just under 100 million USD* in the present and projected climate. Most affected sectors are housing and agriculture.

The increased frequency of extreme events under projected climate conditions: a loss of 125 million USD occurs on average every 200 years in current climate conditions, while in future the same loss would occur on average every 20 years.

DROUGHTS On average almost 1.9 million people are estimated to be directly affected by drought per year. Cunene Province is a hotspot. The situation would worsen significantly under projected climate conditions, where 7.9 million people are estimated to be directly affected if population growth is also accounted for.

Drought risk would increase across the country, including eastern and northern regions.

More than 40% of livestock is currently exposed to risk and would be close to 70% under projected climate conditions. Drought risk for livestock is also expanding eastward and northward.

Average annual economic losses in agriculture due to drought is estimated to be about 100 million USD* under current climate conditions. These and would increase 7 times under projected climate conditions, if no adaptation measures are implemented.

Agriculture production losses would cause a decrease in available food energy supply equivalent to around 3 million potentially fed people.

Hydroelectric losses (deficit compared to current average production) would increase threefold under projected conditions: respectively from above 30 to almost 100 million USD.*

* This risk profile considers prices and exchange rate USD/AOA (United States Dollar / Angolan Kwanza) of 2015.

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Policy Recommendations From 16 to 18 December 2019, at the Hotel Trópico in Luanda, a National Workshop took place to present the updated flood and drought risk profiles, organized by the National Civil Protection Commission (CNPC), the United Nations Regional Office for Disaster Risk Reduction (UNDRR), and CIMA Research Foundation - International Centre for Environmental Monitoring (Italy).

The opening session was led by His Excellency the Chief Commissioner Salvador José Rodrigues, the Interior Secretary of State for Technical Assurance, representing the Minister of the Interior, who was flanked by the Ambassador of the European Union, Mr. Tomas Ulicny, by Mr. Abubakar Sutan Rcai, representing the UN Resident Coordinator in Angola, by the representative of the CIMA Research Foundation, Mr. Lauro Rossi and the representative of the United Nations Regional Office for Disaster Risk Reduction in Africa, Mr. Roberto Schiano Lomoriello.

THE WORKSHOP HAD THE FOLLOWING OBJECTIVES: • To present and evaluate the results of the updated flood and drought risk profile, the data sources used, and the new risk metrics applied to affected people and in different sectors.

• To improve the interpretation of the basic key risk metrics, including Average Annual Loss (AAL), Probable Maximum Loss (PML) and Loss Exceedance Curve (LEC) for floods and droughts; • To promote the nationwide adoption of the risk profile, including developing policy recommendations based on the results of the risk profile and on the local knowledge and experience.

• To increase the national capacity to incorporate disaster risk reduction into investment planning and public economic development systems, in accordance with the Sendai Framework and the 2030 Agenda.

• To understand the budget level for national disaster risk reduction investments and to discuss methodologies to identify public investments and spending in those areas, as well as to understand the direct and indirect economic benefits of investment in Disaster Risk Reduction.

Following working discussions over three days of activities, the workshop’s participants proposed the following National Disaster Risk Reduction Policies: INSTITUTIONAL AND LEGAL FRAMEWORK 1. Having realised the importance of a scientific approach for effective risk management, the participants proposed incorporating the academic sector’s studies and research of climate change phenomena into the design processes for national disaster risk reduction projects and programmes.

2. Considering future projections regarding flood and drought risk, participants proposed that the Angolan Executive consider the risk profile results in budget forecasts and strategic investments, so that the expected impacts can be anticipated and minimized.

3. Similarly, participants proposed the drawing up of a strategic document on the National Policy on Civil Protection and Integral Risk Management, which would result in creating new regulations and procedures.

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4. Based on the worsening of the expected impacts in all sectors of development, as described in the risk profile, participants recommended strengthening the multisectoral monitoring and coordination National Platform for risk reduction, based on Decree 229/10.

5. Considering the difficulty of sharing data between institutions and the need to respond collectively and promptly to risk, participants proposed developing a National Platform for Disaster Risk Reduction, including the public sector, the private sector, the academic community, traditional authorities, students, UN agencies, NGOs and representatives of civil society. The main objective of this action should be to increase the network of partnerships, cooperation and exchange of ideas both nationally and internationally, in order to improve knowledge, technical skills and technological development for risk management.

6. Considering the need to effectively predict and manage risk at various levels (national, provincial and municipal), participants suggested that Provincial and Municipal Civil Protection Commissions should be considered as budgetary units, with the autonomy to manage their resources.

7. To foster continuous dialogue between political interlocutors and the technicalscientific community, a fundamental step for effective risk management, participants proposed creating a thematic technical group in the National Assembly dedicated to Disaster Risk Reduction.

8. Given the need to increase risk awareness and resilience in communities, participants suggested implementing local participatory strategies through the Local Disaster Risk Management Committees.

9. Considering the impacts of climate change predicted for Angola, especially in relation to drought, participants suggested implementing mitigation actions in line with international Framework Agreements, like the Sendai Framework for Disaster Risk Reduction, the Paris Agreement and the 2030 Agenda for Sustainable Development.

STRUCTURAL RISK REDUCTION MEASURES 10. Considering that, according to the risk profile, the risk of drought will increase significantly in the future and the risk of flooding will not decrease, participants proposed setting up mechanisms for diverting, retaining and exploiting rain and river water, based on cost-benefit analyses.

11. Taking into account the provinces and municipalities identified in the risk profile that may suffer the greatest impacts, participants suggested creating safe resettlement zones for people living in risk zones (properly catalogued).

12. Taking into account the provinces identified as being most prone to drought and considering the impact on the population's food security, participants suggested building silos to store cereals, as well as implementing projects to promote local production through rural commerce.

13. Considering the projected future increase in drought risk nationwide, participants proposed research be undertaken on the diversification of agricultural crops/seeds, in order to provide food alternatives for the population. Usage of drought tolerant varieties should be further explored, as they can significantly reduce production losses (reduction of 60% in current climate conditions and 70% in projected climate conditions, for maize).

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14. Considering the expected impacts arising from flood risks, participants proposed building and maintaining macro drainage and flood mitigation infrastructures, giving priority to the provinces and municipalities highlighted by the risk profile.

NON-STRUCTURAL RISK REDUCTION MEASURES 15. Based on the results presented in the 2019 risk profile, participants proposed updating the National Plans and the Provincial Plans for Preparation, Response, Contingency and Recovery (2015-2017).

16. Similarly, participants suggested accelerating the process of implementing the 2018 – 2022 Drought Recovery Framework.

17. Considering the need to access information that allows a continuous analysis of risk and its associated factors, participants proposed creating a common platform for hazard, exposure and vulnerability data storage, with shared access between various entities and sectors.

18. To improve responsiveness to natural events which, according to the risk profile, will be reflected in increased economic losses in various sectors and in the increased number of people affected, participants suggested investing in an early warning system based on detailed risk maps for each province, integrating local knowledge.

19. Considering the overall results of the risk profile, participants suggested that urban plans take into account extreme weather events and be designed based on sustainability criteria and disaster resilience.

RISK AWARENESS AND KNOWLEDGE 20. After analysing the risk profile, participants proposed carrying out information, education, awareness and mobilisation actions, as well as training to communities and professionals on the results contained therein and on topics related to disaster prevention and risk reduction. Likewise, they suggested including subjects related to Disaster Prevention in Schools and Communities on the curriculum for primary and secondary education.

21. In order to make the results of the risk profile known and shared by Angolan society, participants suggested that the different ministerial bodies duly share and communicate results to the various development sectors (agricultural sector, production sector, housing sector, transport sector, services sector, critical infrastructure sector, among others).

22. Based on the results of the risk profile, participants proposed developing and maintaining a Risk Atlas at the national, provincial and municipal levels, which would include a vulnerability assessment to various threats.

Luanda, 18 December 2019

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Glossary AFFECTED PEOPLE and GDP Affected people are the ones that may experience short-term or long-term consequences to their lives, livelihoods or health and in the economic, physical, social, cultural and environmental assets. In the case of this report “affected people from Floods” are the people living in areas experiencing a flood intensity (i.e. a flood water level) above a certain threshold. Analogously, in this report “affected people from Droughts” are the people living in areas experiencing a drought intensity (i.e. a SPEI value) below a certain threshold. The GDP affected has been methodologically defined using the same thresholds both for floods and droughts.

AVERAGE ANNUAL LOSS (AAL) Is the expected loss per year, averaged over many years. While there may actually be little or no loss over a short period of time, AAL also accounts for much larger losses that occur less frequently. As such, AAL represents the funds which are required annually in order to cumulatively cover the average disaster loss over time.

CLIMATE MODEL* A numerical representation of the climate system based on the physical, chemical and biological properties of its components, their interactions and feedback processes, and accounting for some of its known properties. Climate models are applied as a research tool to study and simulate the climate, and for operational purposes, including monthly, seasonal, and interannual climate predictions.

DISASTER RISK* The potential loss of life, injury, or destroyed, or damaged assets which could occur to a system, society, or a community in a specific period of time, determined probabilistically as a function of hazard, exposure, vulnerability, and capacity.

DROUGHT Droughts, defined as unusual and temporary deficits in water supply, are a persistent hazard, potentially impacting human and environment systems. Droughts, which can occur everywhere, should not be confused with aridity, a permanent climate condition. In this profile, drought hazard is represented by a combination of various standardized indices, covering a range of drought types (meteorological, subsurface and surface (hydrological) droughts). In this disaster risk profile, droughts are analysed in terms of hazard, exposed population, GDP and livestock. Drought induced losses are explicitly estimated for crop production and hydropower generation, by linking hazard, exposure and vulnerability (H x E x V) STANDARDIZED DROUGHT INDICES Standardized drought indices represent the ‘abnormality’ of certain water deficits, assessed by analysing the meteorological, sub-surface or surface water balances. Using these indices, drought can be defined as at least three consecutive months with standardised index values below a certain drought threshold, indicating conditions that are significantly dryer than normal for a certain region, given the reference period 1979-2018. On the drought indices maps, the drought probability is calculated using a varying drought threshold [coincidencing with the 5%-25% lowest water availabilities ever recorded]: the dryer the area, the less extreme the water deficit needs to be in order to be considered ‘a drought’.

*UNDRR terminology on Disaster Risk Reduction: https://www.unisdr.org/we/inform/publications/7817 ANGOLA DISASTER RISK PROFILE | GLOSSARY 52

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FLOOD* Flood hazard in the risk assessment includes river (fluvial) flooding and flash flooding. This risk profile document considers mainly fluvial flooding and flash floods in the main urban centres. Fluvial flooding is estimated at a resolution of 90 m using global meteorological datasets, a global hydrological model, a global flood-routing model, and an inundation downscaling routine. Flash flooding is estimated by deriving susceptibility indicators based on topographic and land use maps. Flood loss curves are developed to define the potential damage to the various assets based on the modelled inundation depth at each specific location.

LOSS DUE TO DROUGHT (CROPS) Economic losses from selected crops result from multiplying gross production in physical terms by output prices at farm gate. Losses in working days have been estimated as function of crop-specific labour requirements for the cultivation of selected crops. Annual losses have been computed at Admin 1 level as the difference relative to a threshold, when an annual value is below this threshold. The threshold equals the 20% lowest value from the period 1951-2000 and has also been applied for the projected climate. Losses at national level have been estimated as the sum of all Admin 1 losses.

PROBABLE MAXIMUM LOSS (PML) describes the loss which could be expected corresponding to a given likelihood. It is expressed in terms of annual probability of exceedance or its reciprocal, the return period. Typically, PML is relevant to define the size of reserves which, insurance companies or a government should have available to manage losses.

RESIDUAL RISK* The disaster risk that remains in unmanaged form, even when effective disaster risk reduction measures are in place, and for which emergency response and recovery capacities must be maintained.

RESILIENCE* The ability of a system, community or society exposed to hazards to resist, absorb, accommodate, adapt to, transform, and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions through risk management.

RETURN PERIOD* Average frequency with which a particular event is expected to occur. It is usually expressed in years, such as 1 in X number of years. This does not mean that an event will occur once every X numbers of years, but is another way of expressing the exceedance probability: a 1 in 200 years event has 0.5% chance to occur or be exceeded every year.

RISK* The combination of the probability of an event and its negative consequences. While in popular usage the emphasis is usually placed on the concept of chance or possibility, in technical terms the emphasis is on consequences, calculated in terms of “potential losses” for some particular cause, place, and period. It can be noted that people do not necessarily share the same perception of the significance and underlying causes of different risks.

RISK TRANSFER* The process of formally or informally shifting the financial consequences of particular risks from one party to another, whereby a household, community, enterprise, or State authority will obtain resources from the other party after a disaster occurs, in exchange for ongoing or compensatory social or financial benefits provided to that other party.

*UNDRR terminology on Disaster Risk Reduction: https://www.unisdr.org/we/inform/publications/7817 ANGOLA DISASTER RISK PROFILE | GLOSSARY 53

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Notes

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www.preventionweb.net / resilient-africa www.undrr.org RISK PROFILES ARE AVAILABLE AT: riskprofilesundrr.org

This publication has been produced with the assistance of the European Union.

The contents of this publication are the sole responsibility of CIMA Research Foundation and can in no way be taken to reflect the views of the European Union.