DISASTER RISK PROFILE Flood Drought The Gambia

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

This project is funded by the European Union

© CIMA Research Foundation PROJECT TEAM International Centre on Environmental Monitoring Via Magliotto 2. 17100 Savona. Italy Authors [1] August 2018 Roberto Rudari Sjaak Conijn [3] Silvia De Angeli [1] Africa Disaster Risk Profiles are co-financed by the Hans de Moel [2] EU-funded ACP-EU Natural Disaster Risk Reduction Luca Ferraris [1;4] Program and the ACP-EU Africa Disaster Risk Financing Elisabetta Fiori [1] [1] Program, managed by UNISDR. Tatiana Ghizzoni Isabel Gomes [1] Marco Massabò [1] Lauro Rossi [1] DISCLAIMER Eva Trasforini [1] This document is the product of work performed by CIMA Research Foundation staff. Scientific Team The views expressed in this publication do not Nazan An [6] necessarily reflect the views of the UNISDR or the EU. Chiara Arrighi [1;5] The designations employed and the presentation of the Valerio Basso [1] material do not imply the expression of any opinion Guido Biondi [1] [1] whatsoever on the part of the UNISDR or the EU Alessandro Burastero Lorenzo Campo [1] concerning the legal status of any country, territory, city Fabio Castelli [1;5] or area, or of its authorities, or concerning the Mirko D'Andrea [1] delineation of its frontiers or boundaries. Fabio Delogu [1] Giulia Ercolani[1;5] Simone Gabellani [1] Alessandro Masoero [1] Ben Rutgers [3] RIGHTS AND PERMISSIONS Franco Siccardi [1] The material in this work is subject to copyright. Francesco Silvestro [1] Because UNISDR and CIMA Research Foundation Andrea Tessore [1] [6] encourage dissemination of its knowledge, this work Tufan Turp Marthe Wens [2] may be reproduced, in whole or in part, for non-commercial purposes as long as full attribution to Editing and Graphics this work is given. Silvia De Stefano [1] Citation: CIMA, UNISDR (2018). The Gambia Disaster Andrew Eddy, Canada [1] Risk Profile. Rita Visigalli

Supporting Team Any queries on rights and licenses, including subsidiary Simona Pozzati [1] rights, should be addressed to CIMA Research Luisa Colla [1] Foundation: Monica Corvarola [1] [7] Via Armando Magliotto, 2 - 17100 Savona - Italy; Iain Logan Rich Parker [8] Phone: +39 019230271 - Fax: +39 01923027240 Elisa Poggi [1] E-mail: [email protected] Martino Prestini [1] www.cimafoundation.org With the support of the UNISDR Design and layout: CIMA Research Foundation Regional Oce for Africa

Printing: Jollygraf, Italy CIMA Research Foundation [1] Video Production: Don’t Movie, Italy Vrije Universiteit Amsterdam [2] Wageningen University & Research [3] [4] In collaboration with: Università di Genova Università di Firenze [5] Bogazici University [6] GEG [7] Training in Aid [8] INDEX

Introduction...... P.4

Probabilistic Risk Profile: Methodology...... P.5

Probabilistic Risk Profile: Components...... P.6

Country Climate Outlook...... P.7

Country Socio-Economic Outlook...... P.9

A “Sendai Driven” Presentation...... P.10

Results Floods...... P.11

Results Droughts...... P.15

Probabilistic Risk Assessment for Risk Management...... P.19

Glossary & References...... P.20 THE GAMBIA DISASTER RISK PROFILE IINTRODUCTION

Disasters are on the rise, both in terms of frequency and As part of the programme “Building Disaster Resilience to magnitude. Natural Hazards in Sub-Saharan African Regions, Countries From 2005-2015, more than 700 thousand people worldwide and Communities”, UNISDR engaged CIMA Research have lost their lives due to disasters that have affected over Foundation for the preparation of 16 Country Risk Profiles for 1.5 billion people, with women, children and people in Floods and Droughts for the following countries: Angola, vulnerable situations disproportionately affected. The total Botswana, Cameroon, Equatorial Guinea, Gabon, The Gambia, economic loss was more than US$ 1.3 trillion. Disasters Ghana, Guinea Bissau, Kenya, Kingdom of Eswatini, Ivory inordinately affect lower-income countries. Sub-Saharan Coast, Namibia, Rwanda, São Tomé and Príncipe, Tanzania, Africa, where two-thirds of the world’s Least Developed and Zambia. Countries are located, is prone to recurrent disasters, largely due to natural hazards and climate change. The Country Risk Profiles provide a comprehensive view of hazard, risk and uncertainties for floods and droughts in a The Sendai Framework for Disaster Risk Reduction 2015 – changing climate, with projections for the period 2050-2100. 2030 emphasises the need to manage risk rather than The risk assessment considers a large number of possible disasters, a theme already present in its predecessors, the scenarios, their likelihood, and associated impacts. A Yokohama Strategy and the Hyogo Framework for Disaster significant amount of scientific information on hazard, Risk Reduction. Specifically, the Sendai Framework calls for exposure, and vulnerabilities has been used to simulate strong political leadership, commitment, and involvement of disaster risk. all stakeholders at all levels from local to national and international, with a view to “prevent new and reduce existing disaster risk through the implementation of The EU PROGRAMME “Building Disaster integrated and inclusive economic, structural, legal, social, Resilience to Natural Hazards in Sub-Saharan health, cultural, educational, environmental, technological, African Regions, Countries and Communities” political, and institutional measures that prevent and In 2013, the European Union approved 80 reduce hazard exposure and vulnerability to disaster, million EUR financing for the programme increase preparedness for response and recovery, and thus “Building Disaster Resilience to Natural strengthen resilience”. Hazards in Sub-Saharan African Regions, Countries and Communities”. The programme Understanding disaster risk is the Sendai Framework’s first is being implemented in Africa by four partners: the African Union Commission, the priority for action: “policies and practices for disaster risk United Nations Oce for Disaster Risk management should be based on an understanding of Reduction (UNISDR), the World Bank’s Global disaster risk in all its dimensions of vulnerability, capacity, Facility for Disaster Reduction and Recovery exposure of persons and assets, hazard characteristics and (WB/GFDRR), and the African Development the environment”. The outputs of disaster risk assessment Bank’s ClimDev Special Fund (AfDB/CDSF). The programme provides analytical basis, tools should be the main drivers of the disaster risk management and capacity, and accelerates the effective cycle, including sustainable development strategies, climate implementation of an African comprehensive change adaptation planning, national disaster risk reduction disaster risk reduction and risk management across all sectors, as well as emergency preparedness and framework. response.

THE GAMBIA DISASTER RISK PROFILE | INTRODUCTION

P.4 THE GAMBIA DISASTER RISK PROFILE PROBABILISTIC RISK PROFILE: METHODOLOGY

PROBABILISTIC RISK ASSESSMENT

Understanding disaster risk is essential for sustainable Average Annual Loss (AAL) is the expected loss per year, development. Many different and complementary methods averaged over many years. While there may actually be little and tools are available for analysing risk. These range from or no loss over a short period of time, the AAL also accounts qualitative to semi-quantitative and quantitative methods: for much larger losses that occur less frequently. As such, AAL probabilistic risk analysis, deterministic or scenario analysis, represents the funds that would be required annually in order historical analysis, and expert elicitation. to cumulatively cover the average disaster loss over time.

This disaster risk profile for floods and droughts is based on Probable Maximum Loss (PML) describes the maximum loss probabilistic risk assessment. Awareness of possible perils that could be expected corresponding to a given likelihood, that may threaten human lives primarily derives from expressed in terms of annual probability of exceedance or its experience of past events. In theory, series of historical loss reciprocal, the return period. For example, in the figure below, data long enough to be representative of all possible the likelihood of a US$ 100 million loss is on average once in disastrous events that occurred in a portion of territory would a decade, a loss of US$ 1 billion is considered a very rare event. provide all necessary information for assessing future loss Typically, PML is relevant to define the size of reserves that, for potential. Unfortunately, the availability of national historical instance, insurance companies or a government should have information on catastrophic natural hazard events is limited, available to manage losses. and data on the economic consequences is even less common. The methodology is also used to simulate the impact of climate change [SMHI-RCA4 model, grid spacing 0.44° - A modelling approach is needed to best predict possible about 50 km - driven by ICHEC-EC-EARTH model, RCP 8.5, present and future scenarios, taking into consideration the 2006-2100 and, future projections of population and GDP spatial and temporal uncertainties involved in the analysed growth (SSP2, OECD Env-Growth model from IIASA SSP process. A realistic set of all possible hazardous events Database)]. (scenarios) that may occur in a given region, including very Results are disaggregated by different sectors, using the same rare, catastrophic events, is simulated. For each event, categories of Sendai Framework indicators: direct economic potential impacts are computed in terms of economic losses loss (C1), agricultural sector (C2), productive asset and service or number of people and assets affected, considering publicly sector (C3), housing sector (C4), critical infrastructures and available information on Hazard, Exposure, and Vulnerability. transportation (C5). Finally, statistics of losses are computed and summarised through proper quantitative economic risk metrics, such as:

Annual Average Loss (AAL) and Probable Maximum Loss os s

(PML). L Million$]

In computing the final metrics (PML, AAL), the uncertainties [ that permeate the different steps of the computations are 1000 explicitly quantified and taken into account: uncertainties in the hazard forcing, uncertainties in the exposure values and 700 their vulnerabilities.

100

On average once On average once Very rare in a decade in a lifetime events Return Period [Years]

THE GAMBIA DISASTER RISK PROFILE | PROBABILISTIC RISK PROFILE

P.5 THE GAMBIA DISASTER RISK PROFILE PROBABILISTIC RISK PROFILE: RISK COMPONENTS

A modelling chain composed of climate, hydrological, and hydraulic models using all the available information, in terms of rainfall, temperature, humidity, wind, and solar radiation, HAZARD to best predict possible flood and drought scenarios. A set of mutually exclusive and collectively exhaustive possible hazard scenarios that may occur in a given region or process, phenomenon or country, including the most catastrophic ones, is generated and expressed in terms of human activity that may frequency, extension of the affected area and intensity at different locations. cause loss of life, injury or other health impacts, property damage, social and economic disruption or environmental degradation.

Flood hazard map for 1 in a 100 years probability evaluated under current climate conditions, the scale of blues represents different water depth values.

The direct losses on the different elements at risk are evaluated applying vulnerability VULNERABILITY functions, which link the hazard intensity to the expected loss (economic loss or number conditions determined by of affected people), considering also the associated uncertainty. Vulnerability functions physical, social, economic are differentiated for each typology of exposed element and take into account local and environmental factors factors, such as typical constructive typologies for infrastructures or the crop seasonality or processes which increase for the agricultural production. For flood, vulnerability is a function of the water depth. The the susceptibility of an only exception is represented by agricultural production, for which it is a function of the individual, a community, season in which the flood is occurring. In the case of agricultural drought, the losses are assets or systems to the computed in terms of lack of production for different crops from a nominal expected production. A similar approach is used for hydrological drought, when the loss of impacts of hazards. hydropower production is evaluated.

The losses caused by floods and droughts are evaluated on population, GDP and a series of critical sectors (education, health, transport, housing, and productive and agricultural sectors). The critical sectors are created clustering all 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 EXPOSURE country. The total number of people and the National GDP (in US$) are considered in both current (2016) and future (2050) scenarios. The critical sectors are characterised in terms people, property, systems, of their economic value (in US$), using the most updated information available. or other elements present in hazard zones that are thereby subject to potential losses.

Exposure distribution, the different colors represent different types of assets.

AGRICULTURAL SERVICE PRODUCTIVE HOUSING TRANSPORTATION OTHER CRITICAL SECTOR [C2] SECTOR [C3] ASSETS [C3] SECTOR [C4] SYSTEM [C5] INFRASTR.[C5]

UNISDR terminology on Disaster Risk Reduction: https://www.unisdr.org/we/inform/publications/7817

THE GAMBIA DISASTER RISK PROFILE | PROBABILISTIC RISK PROFILE

P.6 THE GAMBIA DISASTER RISK PROFILE COUNTRY CLIMATE OUTLOOK

OVERVIEW TEMPERATURE AND PRECIPITATION Republic of The Gambia has a sub-tropical climate with two TRENDS IN CURRENT CLIMATE variations of distinct dry and rainy seasons. The dry season [°C] generally starts in October and ends around mid-June each 29,5 year. From November to mid-May there is uninterrupted dry 29,0 weather, with temperatures as low as 16°C. Hot, humid 28,5 weather predominates the rest of the year, with a rainy 28,0 season from June to October; during this period, 27,5 temperatures may rise as high as 43°C [1]. 27,0

26,5 CLIMATE TRENDS [year] 1970 1990 1980 1985 1995 2000 2005 2010 2015 Similarly to other Western Africa countries, temperature 1975 observations indicate that Republic of The Gambia has Mean Annual Temperature

experienced a considerable increase in temperature over Average of Mean Annual Temperature recent years. An analysis of climate data from 1970 to 2015 [2] Trend Mean Annual Temperature shows an average temperature rise of more than 1°C. Trends for precipitation are not as clear as those for air temperatures,

and are variable in time and space. [mm]

Average annual precipitation for Republic of The Gambia is 1150 approximately 807 mm, while the mean number of wet days 1050 950 is around 74 per year. 850 750 650 550

450 [year] 1970 1980 1985 1990 1995 2000 2005 2010 2015 1975

Annual Precipitation

Average of the Annual Precipitation

RIVERS OF THE GAMBIA Republic of The Gambia can be practically identified with the basin of the homonymous river since it consists of little more than the downstream half of the river and its two banks. The Gambia river is 1.120 km long, rising in the Republic of Guinea and flowing westward through Senegal and The Gambia into the Atlantic Ocean. From its source in the highlands of the Fouta Djallon, The Gambia follows a winding course to its mouth, near which, on the island of St. Mary’s Island is located the capital Banjul. The dividing and reuniting of river channels has created several islands along the river’s middle course, of which the two largest are Elephant Island and MacCarthy Island. The annual flooding of the fertile alluvial loams of the middle flats makes them especially suitable for intensive rice cultivation. On the light sandy and well-drained soils of the higher slopes, peanuts grow particularly well. The Gambia is one of the most navigable of African rivers; its chief value, therefore, has been its transportation function. [3]

Photo Credits: GAMBIA River - Landsat / Copernicus, Data SIO

THE GAMBIA DISASTER RISK PROFILE | COUNTRY CLIMATE OUTLOOK Immagini ©2018 Landsat / Copernicus,Data SIO , NOAA, U.S. Na vy, NGA, GEBCO ,Dati car togr 20 km P.7 CLIMATE PROJECTIONS FOR THE GAMBIA LOW There is a wealth of climate projection studies over multiple INTERMEDIATE HIGH time spans and multiple scales. Climate models are tools that EMISSIONS EMISSIONS EMISSIONS the scientific community uses to assess the trends of weather SCENARIO SCENARIO SCENARIO conditions over long periods. In a recent study [4], temperature and precipitation change from global climate models of the Substantial Some Continued Coupled Model Intercomparison Project Phase 5 (CMIP5) were and sustained stabilisation high reported. For different greenhouse emission scenarios (see reductions in in emissions emissions IPPC’s Emissions Scenarios), three future periods (2025-2049, greenhouse 2050-2074 and 2071-2095) were compared against the gas emissions reference period 1980-2004. In all periods, models show a raising increase of temperature RCP 2.6 RCP 4.5 RCP 8.5 from low to high emissions scenarios. This is more evident in IPCC’s Emissions scenarios for Climate Projections long term period projections. In high emission scenarios (RCP 8.5), model projections show an increase between 1.5°C and CHANGE IN TEMPERATURE RCP 2.6 RCP 4.5 RCP 8.5 4°C for the mid term period (2050-2074) and an increase 7 between 2°C and 5.5°C for the long term period (2071-2095). 6 Future change in precipitation seems to be less predictable, in 5 the three periods, where the variability is large for all considered emission scenarios (containing both negative and 4 positive changes). 3

Climate Projections 2 Time Frame (RCP 8.5 - High emission scenario ) 1 Increase in temperature from Mid-term 1.5°C to 4°C 0 Future 2025 - 2049 2050 - 2074 2071 - 2095 [C°] (2050-2074) divergent change Model used in risk profile in precipitation (from -25% to +40%) CHANGE IN PRECIPITATION RCP 2.6 RCP 4.5 RCP 8.5 700 Increase in temperature from 600 Far Future 2°C to 5.5°C 500 (2071-2095) 400 divergent change 300 in precipitation 200 (from -35% to +50%) 100 0 CLIMATE PROJECTIONS USED IN THIS RISK PROFILE -100 The results of the Risk Profile referring to climate change have -200 -300 been obtained using a climate projection model based on a -400 high emission scenario (SMHI-RCA4 model, grid spacing 0.44° 2025 - 2049 2050 - 2074 2071 - 2095

- about 50 km- driven by the ICHEC-EC-EARTH model, RCP 8.5, [mm] Model used in risk profile 2006-2100) [5, 6, 7] In this study, it was decided to use a high-resolution model, The high emission scenario was maintained as representative of accurately calibrated on the African domain, to better capture the worst climate change scenario, enabling the analysis of a full climate variability, important for assessing extremes. Regional range of possible changes. However, in this specific case, the model projections were checked for consistency against the regional model predicts a slightly higher increase in full ensemble of available global models in the area. The temperature (almost 4°C in the long term future), with respect model forecasted changes in temperature and annual to the global ensemble. As regards to annual precipitation at precipitation by the end of the century, in line with the range country level, a slight decrease in precipitation is predicted in of variability of the global models analyzed in the study by the far future, in line with the mean value of the global Alder and Hostetler [5]. ensemble.

THE GAMBIA DISASTER RISK PROFILE | COUNTRY CLIMATE OUTLOOK

P.8 THE GAMBIA DISASTER RISK PROFILE COUNTRY SOCIO-ECONOMIC OUTLOOK

OVERVIEW POPULATION GDP Republic of The Gambia is the smallest country on the African mainland. The estimated population is around 2 million and, with 176 people per square kilometer, it is one of the most densely populated countries in Africa[8]. Most of the people (57%) are concentrated around urban and peri-urban centers

and the the median age is 17.2 years[9]. Republic of The Gambia 2016 Projection 2016 Projection has a small economy that relies primarily on tourism, 0.963 rain-dependent agriculture, and remittances[8]. 2.038 [Million People] [Billion$] Three-quarters of the population depends on the agriculture sector for its livelihood and it provides for about one-third of 3.273 27.680 GDP, making Republic of The Gambia largely reliant on 2050 Projection 2050 Projection sucient rainfall. Small-scale manufacturing activity features the processing of cashews, groundnuts, fish, and hides. The Gambia's reexport trade accounts for almost 80% of goods exports and China has been its largest trade partner for both exports and imports for several years. The country has sparse natural resource deposits. It relies heavily on remittances from workers overseas and tourist receipts. Remittance inflows to The Gambia amount to about one-fifth of the country’s GDP. [10]

PROJECTIONS Recently, climate scientists and economists have built a range of new “pathways” that examine how national and global societies, demographics and economics might lead to different plausible future development scenarios over the next hundred years [11,12]. The scenarios range from relatively optimistic trends for human development, with “substantial investments in education and health, rapid economic growth and well-functioning institutions” [13], to more pessimistic economic THE GAMBIA and social development, with little investment in education or AREA : 11.295 km2 health in poorer countries, coupled with a fast-growing population and increasing inequalities. POPULATION DENSITY : 176 people/km2

SOCIO-ECONOMIC PROJECTIONS MEDIAN AGE : 17.2 years USED IN THE RISK PROFILE The “middle of the road” scenario envisages that historic HDI - HUMAN DEVELOPMENT INDEX : 0.46 development patterns will persist throughout the 21st century. LIFE EXPECTANCY AT BIRTH : 61.2 years The results shown in this report refer to this scenario. According to these conditions, the population of Republic of The Gambia MEAN YEARS OF SCHOOLING : 3.4 years in 2050 will increase by 60% compared to 2016 (World Bank Data), whereas GDP is expected to increase by almost 30-fold. EMPLOYMENT TO POP. RATIO (AGES > 15) : 53.7%

EMPLOYMENT IN AGRICULTURE : 27.1%

EMPLOYMENT IN SERVICES : 57.6%

data from: http://hdr.undp.org/en/countries/profiles/ https://data.worldbank.org/indicator/

THE GAMBIA DISASTER RISK PROFILE | COUNTRY SOCIO-ECONOMIC OUTLOOK

P.9 THE GAMBIA DISASTER RISK PROFILE A “SENDAI DRIVEN” PRESENTATION

The Sendai Framework guides the organisation of the risk In the third page of results for floods, the most relevant profile results. sectors for the country are selected and their AAL distribution is presented in present and future climate conditions in The first page of results reports the annual average number of relation to the exposure distribution considered in the study. people affected by flood. This indicator refers to Sendai The results are broken down on the fourth and final page Framework Target B: “Substantially reduce the number of according to different return periods and presented in terms affected people globally by 2030” and specifically to Sendai of PML curve of the total damage across all sectors. The indicator B1: “Number of directly affected people attributed contribution of the different sectors is highlighted in each to disasters“. range of return periods of the total damage.

In the following pages, other indicators that contribute to In the drought results section, the second page reports the Sendai Framework Targets C and D are reported. spatial distribution of main effects on livestock units, and direct agricultural (crop) losses; the latter map can be A series of indicators that contribute to increasing knowledge associated to indicator C2. In addition, agricultural of the country on Sendai Framework Target C: “Reduce direct production loss is also broken down into crop types, and the disaster economic loss in relation to global gross domestic annual average number of working days lost in agricultural product (GDP) by 2030” are considered. Specifically, the sectors is shown. indicator C1 “Direct economic loss attributed to disasters” is computed as a compound index, obtained as a sum of In the third page, two indicators related to Target C are several indicators computed in our fully probabilistic risk reported: methodology. These single indicators can be reconciled to • C1 Direct economic loss the Sendai Target C indicators. • C2 Direct agricultural loss attributed to disasters (based on the main crops) In the flood results section, the following Target C indicators In the case of The Gambia, only direct agricultural loss are addressed in the second page: contributes to the overall drought economic loss, therefore C1 and C2 indicators are coincident. •C2 Direct agricultural loss attributed to disasters (based on different return periods. the main crops). In the fourth page, the spatial distribution of some drought •C3 Direct economic loss to all other damaged or destroyed indicators is shown. productive assets attributed to disasters. In this risk profile, C3 DIRECT is split into two components: productive assets (industrial ECONOMIC buildings and energy facilities) and service sector C1 LOSS (governmental and service buildings). •C4 Direct economic loss in the housing sector attributed to AGRICULTURAL C2 disasters. SECTOR •C5 Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters. In this PRODUCTIVE C3 ASSETS risk profile C5 is split into two components: transportation systems (roads and railways) and other critical infrastructures SERVICE (health and education facilities). C3 SECTOR

Inside the same page (the second of flood results), Sendai HOUSING Framework Target D: “Substantially reduce disaster damage C4 SECTOR to critical infrastructure and disruption of basic services, among them health and educational facilities, including SENDAI INDICATORS C5 TRANSPORTATION through developing their resilience by 2030” is addressed SYSTEMS providing the count of affected critical infrastructures (e.g. health and education facilities and kilometres of OTHER CRITICAL C5 INFRASTRUCTURES transportation network).

THE GAMBIA DISASTER RISK PROFILE | A “SENDAI DRIVEN” PRESENTATION

P.10 THE GAMBIA DISASTER RISK PROFILE RESULTS | FLOODS

[B1] ANNUAL AVERAGE NUMBER OF AFFECTED PEOPLE

0 - 40

40 - 80

80 - 150

150 - 2000 Present Climate Future Climate 2000 - 11.000

[People affected/Y]

KEY MESSAGES

• Flood is a relevant natural hazard in Republic of The Gambia, affecting on average about 12,700 people every year, almost 0.62% of the total population of the country. ANNUAL AVERAGE NUMBER OF POTENTIALLY AFFECTED PEOPLE [B1]

The potentially affected people are geographically • Present Climate 0.62% concentrated in the Kanifing Municipal Council and West

Coast region. Future Climate 0.25%*

Future Climate & The potentially affected GDP in flooded areas is also high, Socio Economic 0.25%** • Projection cumulating on average almost 1.21% of the total GDP at country level. [People affected/Y] 0 2000 4000 6000 8000 10.000 12.000 14.000

• Future climatic conditions and projections of future growth * % computed with reference to the total 2016 Population / GDP in population and GDP show an expected amount of affected ** % computed with reference to the total 2050 Population / GDP population similar to the present conditions, while the affected GDP is expected to increase by an order of magnitude (10 times) by 2050. ANNUAL AVERAGE POTENTIALLY AFFECTED GDP

• Future climatic conditions alone, due to more likely Present Climate 1.21% decrease in precipitation predicted by the climate model used in this risk profile, would bring an overall risk decrease, if only Future Climate 0.52%* the present population and GDP will remain exposed. Future Climate & Socio Economic 0.52%** However, as shown in the climate session, other climate Projection models show an opposite behaviour which would imply an Loss [Million$/Y] 0 50 100 150 increase in precipitation in future. The future prediction is highly uncertain.

• Taking into consideration the above-mentioned circumstances, where projections highlight the predominance of future development with respect to the climate change effects, risk-informed decision-making for sustainable development is becoming more and more critical.

THE GAMBIA DISASTER RISK PROFILE | FLOODS RESULTS

P.11 THE GAMBIA DISASTER RISK PROFILE RESULTS | FLOODS

[C1] DIRECT ECONOMIC LOSS

0 - 0.1 0.1 - 0.2 0.2 - 0.4 0.4 - 0.8 0.8 - 1.2 AAL AAL AAL Present Climate [Million$/Y] Future Climate

KEY MESSAGES • The direct economic loss in The Gambia distributes • The proportion of the different sectors to the overall loss geographically in line with the exposure concentration, with does not significantly change in future climate conditions. highest values in Upper River and Kanifing Municipal Council regions (see p.3). The pattern is maintained in future climate • Considering the present exposed assets, average annual conditions, except for the Kanifing Municipal Council where a losses tend to decrease in future climate conditions, for all reduction is experienced in relative terms. sectors. However, as already discussed for GDP and population, risk figures may change when considering future • The value of direct economic losses in terms of AAL exposure evolution. amounts to about $US 3.7 million in present climate, or roughly 0.20% of the total exposure value. The larger portion of losses is due to the productive sector, housing and transportation (roads), that together account for more than 80% of the overall loss.

DIRECT 0.20% Present Climate C1 ECONOMIC LOSS 0.07% Future Climate

0.10% C2 AGRICULTURAL SECTOR 0.04%

PRODUCTIVE 0.20% C3 ASSETS 0.06%

SERVICE 0.23% C3 SECTOR 0.07%

HOUSING 0.22% C4 SECTOR 0.07%

SENDAI INDICATORS 0.31% C5 TRANSPORTATION AFFECTED SYSTEMS 0.11% INFRASTRUCTURES [D]

OTHER CRITICAL 0.00% C5 INFRASTRUCTURES 0.00% [D8] 34 Present climate Transportation System [km/Y] Future12 climate AAL [Million$/Y] 0 1 2 3 4

THE GAMBIA DISASTER RISK PROFILE | FLOODS RESULTS

P.12 THE GAMBIA DISASTER RISK PROFILE RESULTS | FLOODS

KEY MESSAGES

• Comparison between present and future climate AALs shows a general decrease in all regions and sectors. • For housing, service and productive sectors, Upper River and Kanifing Municipal Council are the two most affected regions, in present climate conditions, whereas in future climate conditions only Upper River seem to be significantly affected. • Greater losses in transportation sector (roads) are found in the three upstream regions (Upper River, Central River and Lower River) for both present and future climatic conditions.

EXPOSURE DISTRIBUTION AAL - Present Climate

0 - 0.02 0.02 - 0.1 C3 0.1 - 0.2 PROD. C3ASSETS AAL - Future Climate 0.2 - 0.4 0.4 - 0.5 AAL [Million$/Y]

AAL - Present Climate

0 - 0.02 0.02 - 0.05 0.05 - 0.1 C3 0.1 - 0.15 SERVICES AAL - Future Climate 0.15 - 0.2 AAL [Million$/Y]

AAL - Present Climate

0 - 0.02 0.02 - 0.05 C4 0.05 - 0.1 HOUSING AAL - Future Climate 0.1 - 0.2 0.2 - 0.3 AAL [Million$/Y]

AAL - Present Climate

0 - 0.02 0.02 - 0.05 C5 0.05 - 0.1 TRANSP. 0.1 - 0.2 SYSTEMS AAL - Future Climate 0.2 - 0.3 AAL [Million$/Y]

THE GAMBIA DISASTER RISK PROFILE | FLOODS RESULTS

P.13 THE GAMBIA DISASTER RISK PROFILE RESULTS | FLOODS

KEY MESSAGES PROBABLE MAXIMUM LOSS CURVE (PML) C1 - DIRECT ECONOMIC LOSS • In both present and future climate conditions, the PML curves steeply rise until the 10-year loss, meaning 30 that considerable losses may be experienced relatively frequent. In present climate conditions, another 25 change of slope is observed at 100-year loss, and after that tends to be more flat. Total risk will be considerably 20 $] reduced by strategically minimizing extensive risk (i.e. due to more frequent events). 15

Both low and high losses may be experienced less 10 • Loss [Million frequently in future climate conditions. However, given the high level of uncertainty in future climate 5 prediction, also worse scenarios may still be possible (compare climate section at p.8). 0 50 100 150 200 250 300 350 400 450 500 Return Period [Years] • The share of losses across sectors and for the different return periods does not consistently change in Present Climate Future Climate future (except for agricultural sector).

• From rare events (from 25 to 500 return period) losses in agricultural sector not significantly change in future.

PROBABLE MAXIMUM LOSS CURVE (PML) ACROSS ALL SECTORS C1 - DIRECT ECONOMIC LOSS

Present Climate <10 Future Climate

10-25

25-100

100-200

200-500

0 5 10 15 20 25 30 Loss [Million$]

AGRICULTURAL SECTOR [C2] SERVICE SECTOR [C3] PRODUCTIVE ASSETS [C3] HOUSING SECTOR [C4] TRANSPORTATION SYSTEM [C5] OTHER CRITICAL INFRASTRUCTURE [C5]

THE GAMBIA DISASTER RISK PROFILE | FLOODS RESULTS

P.14 THE GAMBIA DISASTER RISK PROFILE RESULTS | DROUGHTS

[B1] ANNUAL AVERAGE NUMBER OF AFFECTED PEOPLE

0 - 10.000

10.000 - 25.000

25.000 - 50.000

50.000 - 100.000

100.000 - 200.000 Present Climate Future Climate [People affected/Y]

Annually average potentially affected population by at least 3 months of drought conditions, as calculated through the standardized precipitation-evapotranspiration index (SPEI) using a 3-month accumulation period.

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.

ANNUAL AVERAGE NUMBER OF KEY MESSAGES POTENTIALLY AFFECTED PEOPLE [B1] • With respect to present conditions (1951-2000 climate), the Present Climate 14% probability of occurrence of severe drought conditions (3 months of precipitation – evapotranspiration deficits) will Future Climate 39%* more than double in the future (2050-2100 climate): from Future Climate & annual average chance of 31% to 78%. Socio Economic 39%** Projection Currently, on average 216 thousand people (14%) are [People affected/Y] 0 200.000 400.000 600.000 800.000 1.000000 • annually potentially affected by severe droughts, i.e. at least * % computed with reference to the total 2016 Population ** % computed with reference to the total 2050 Population three months of drought conditions. In the future, this number is expected to increase to 39% (on average 938 thousand people if population growth is accounted for). Both the ANNUAL AVERAGE POTENTIALLY AFFECTED GDP western and upper river regions will reach over a hundred thousand people annually hit by droughts in the future.

Present Climate 15% • Under current climate conditions, annually on average 15% Future Climate 45%* of the GDP (108 million USD) is potentially affected by

Future Climate & drought. The future climate scenario shows the effect of Socio Economic 45%** climate change: 3 times more GDP value will be affected, in Projection every province of Gambia. Specifically, the GDP affected by [Million$/Y] 0 2000 4000 6000 8000 10.000 droughts is expected to increase, reaching 45% of its total * % computed with reference to the total 2016 GDP value in the future. ** % computed with reference to the total 2050 GDP

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P.15 THE GAMBIA DISASTER RISK PROFILE RESULTS | DROUGHTS

These maps show the annual average KEY MESSAGES amount of livestock units hit by more than 3 months of drought conditions, based on the standardized precipitation - evapotranspiration index In the future 3 times more livestock is expected to be Present Climate • 0 - 10.000 annually affected by droughts: The percentage of annually 10.000 - 25.000 affected livestock will rise from 23% (80 thousand livestock 25.000 - 50.000 50.000 - 75.000 units) to 75%. While now, only livestock in the eastern part 75.000 - 100.000 of Gambia is affected, the amount of livestock affected by Future Climate [Units/Y] droughts will increase over the whole country due to climate change. 80 | 23% ANNUAL AVERAGE NUMBER Present climate Under present climate conditions, agricultural (crop) OF POTENTIALLY AFFECTED • LIVESTOCK UNITS* | 75% losses are very low throughout the whole country. [k-Livestock units] Future260 climate *Livestock is a summation of all livestock animals (referred to as livestock units) using FAO conversion factors However, these losses are estimated to substantially increase in each province of Gambia under future climate conditions. AGRICULTURAL PRODUCTION LOSS 12.000 • Under future climate conditions, large increases in crop production losses are expected for all crops, ranging from 3 (maize) to 5 (sorghum) times more. Physical losses are 10.000 highest for millet and groundnut, while relative losses are largest for maize (41%) under future climate. 8000 • A 5-fold increase in the number of working days lost is projected under future conditions, from 0.7% to 3.6%, relative to the total amount of working days required for 6000 Loss [Ton/Y] crop cultivation.The loss of working days, expressed as a percentage of the average amount of days required for 4000 harvesting, is circa 3 times higher. Future Climate 2000

140 | 0.71% Present Climate Present climate 0 ANNUAL AVERAGE NUMBER OF WORKING DAYS LOST | 3.56% 12% 10% 1.6% 7.9% [k-Days] Future700 climate 2.9% 3.9% 2.0% 13.4% 40.8% 13.7% Groundnut Maize Millet Rice Sorghum

C2 - DIRECT AGRICULTURAL LOSS

0 - 0.5 0.5 - 1 1 - 1.5 1.5 - 2 2 - 3 AAL [Million$/Y]

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C2 is computed considering only the direct loss suffered because of a loss of agricultural DIRECT 2.8% Present Climate ECONOMIC (crop) production with respect to a reference C1 production in the present climate. The crops LOSS 11.3% Future Climate considered in the analysis are the ones that contribute at country level to at least 85% of SENDAI AGRICULTURAL 2.8% the total gross crop production value. It INDICATORS C2 SECTOR 11.3% might therefore happen that crops that have an important role in the local agriculture (commercial or for subsistence) might be AAL [Million$/Y] 0 1 2 3 4 5 6 7 8 neglected in the overall analysis.

KEY MESSAGES

• Under present climate conditions, the direct economic loss from crop production is estimated at circa 3%. Under future climate conditions, this loss percentage increases 4-fold to more than 11% of total gross production value from crops.

• Agricultural income losses (defined as the production below a threshold, which is calculated as the lowest 20% production under present climate conditions) show a gradual increase in expected losses when return periods go up from 10 to 200 years under present climate conditions.

• Agricultural income losses in the future climate increase strongly for all return periods. The relative increases are higher for more frequent losses, i.e. for the return periods of 10, 20 and 50 years.

PROBABLE MAXIMUM LOSS (PML) C2 - AGRICULTURAL LOSS

Present Climate 10 Future Climate

20

50

100

200

0 10 20 30 40 50 60 70 Loss [Million$]

HYDROPOWER PLANTS The Gambia has no hydroelectricity generating potential. part of the reservoir), a downstream Senegalese reach and However, it is working with Guinea, Guinea-Bissau and then a Gambian downstream reach to the sea. The main Senegal, through their regional institution the Gambia River objective of the Sambangalou Hydroelectric development Basin Development Organisation (OMVG) to improve energy project is to generate 128 MW capacity of hydro capacity to supply and security in the member states. The biggest project meet the projected growth of electricity demand in the West in which The Gambia is involved is the construction of Africa region, to be shared between Senegal, Guinea, Guinea Sambangalou dam. The project zone covers Guinea (reservoir Bissau and the Gambia in proportions to be determined [14, 15]. and relocation zone), Senegal (main dam works, including

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SPEI Standardised Precipitation-Evapotranspiration Index

These maps denote the average annual chance of a 0 - 25 meteorological drought occurring (%). Droughts are defined as 3 Present Climate 25 - 40 months of precipitation minus evapotranspiration values 40 - 60 considerably below normal conditions; calculated through the 60 - 80 Standardized Precipitation - Evapotranspiration Index (SPEI; see 80 - 100 ‘Drought’ in Glossary). This is particularly important for areas Probability [%] dependent on rainfall for their water resources. For both present of 3-months Drought and future climate conditions upstream regions are the most Future Climate affected. The probability of a 3-month drought ranges from 80 to 100%, in future conditions.

SSMI - Standardized Soil Moisture Index

These maps denote the average annual chance of a subsurface drought occurring (%). Droughts are defined as 3 months of soil 0 - 30 moisture conditions considerably below normal conditions; Present Climate 30 - 45 calculated through the Standardized Soil Moisture Index (SSMI; 45 - 60 see ‘Drought’ in Glossary). This is particularly important for 60 - 80 agricultural areas and nature. For both present and future 80 - 100 climate conditions upstream regions are the most affected. The Probability [%] probability of a 3-month drought ranges from 60% to 100%, in of 3-months Drought future conditions. This is particularly critical especially considering that also streamflow values will decrease (SSFI box): Future Climate both rainfed and irrigated agriculture will be severely threatened.

SSFI - Standardized Streamflow Index

These maps denote the average annual chance of a hydrological drought occurring(%). Droughts are defined as 3 months of 0 - 20 stream flow levels considerably below normal conditions; 20 - 30 calculated through the Standardized StreamFlow Index (SSFI; see Present Climate 30 - 50 ‘Drought’ in Glossary). This is particularly important for areas 50 - 70 dependent on rivers for their water resources. Comparison 70 - 80 between present and future climate conditions shows an average Probability [%] increase from around 20-30% to 60-80%. While in present of 3-months Drought conditions, the Upper River is most affected region, in future the downstream part of the river has more chance to be affected by Future Climate a 3-month drought. This is more problematic if one considers that most of the population is concentrated in downstream regions.

WCI Water Crowding Index These maps show the percent of population experiencing water 0 - 10 scarcity, based on local (83 km2) average water available per person 10 - 20 3 Present Climate per year. People living with less than 1000 m /person/year, are in 20 - 30 water scarcity. Specifically, areas with high concentrations of people 30 - 50 are dependent on outside water resources (west of Gambia). With 50 - 60 climate change and population growth, more people will be [% Population/Y] exposed to water scarcity in absolute numbers (due to population growth), but it does not change much in relative terms. Both in future and present climate conditions, the three downstream Future Climate regions (Kanifing Municipal Council, West Bank and North Bank) are the most affected, in line with population concentration.

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P.18 THE GAMBIA DISASTER RISK PROFILE PROBABILISTIC RISK ASSESSMENT FOR RISK MANAGEMENT

METRICS FOR RISK MANAGEMENT Risk information may be used to put in place a broad range high ratio of AAL to capital investment and reserves. Social of activities to reduce risk, from improving building codes and development will be challenged if there is a high ratio of AAL designing risk reduction measures, to carrying out to social expenditure. Moreover, limited ability to recover macro-level assessments of the risks to prioritise investments. quickly may significantly increase indirect disaster losses. Risk metrics can help discern the contributions of different Countries that already have compensatory mechanisms such external factors (such as demographic growth, climate as effective insurance in place and that can rapidly change, urbanization expansion, etc.) and provide a net compensate for losses will recover far more quickly than measure of progress of disaster risk reduction policies those that do not. Such mechanisms may include insurance implementation. and reinsurance, catastrophe funds, contingency financing AAL can be interpreted as an opportunity cost, given that arrangements with multilateral finance institutions, and resources set aside to cover disaster losses could be used for market-based solutions such as catastrophe bonds (UNISDR, development. Monitoring AAL in relation to other country 2011 and 2013). economic indicators, such as GDP, capital stock, capital The PML curve is particularly useful in order to articulate a full investment, reserves, and social expenditure, would provide DRR strategy. The PML curve describes the loss that can be indications of country fiscal resilience, broadly defined as experienced for a given return period. Knowing the different comprising internal and external savings to buffer against level of losses expected on a certain frequency can help to disaster shocks. Economies can be severely disrupted if there understand how to organise a strategy combining different is a high ratio of AAL to the value of capital stock. Similarly, risk reduction, mitigation, or avoidance actions. future economic growth can be compromised if there is a

PML CURVE The PML curve can be subdivided into layers. Extensive Risk country level, and a mechanism of risk transfer has to be put Layer: this layer is typically the one associated with risk in place (e.g. insurance and reinsurance measures). The reduction measures (e.g. flood defences, local vulnerability remaining layer of the curve determines the Residual Risk reduction interventions). Immediately after this extensive (catastrophic events), which is the risk that is considered layer is the Mid Risk Layer, which builds up cumulative losses acceptable/tolerable due to the extreme rarity of the events from higher impact events. The losses of this layer are able to determine such loss levels. Due to this rarity, there normally mitigated using financial funds, like a contingency are no concrete actions to reduce risk beyond preparedness fund, that are normally put in place and managed by the actions that tend to ease the conditions determined by the country itself. event (e.g. civil protection actions, humanitarian aid The losses that compose the Intensive Risk Layer (severe, coordination). infrequent hazard events) are dicult to finance on the [$ ] os s L

ed Residual risk layer t c e p

x Intensive risk layer E Risk Transfer

Middle risk layer Contingency Fund

Extensive risk layer Disaster Risk Reduction Measures Return Period [year]

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P.19 THE GAMBIA DISASTER RISK PROFILE GLOSSARY & REFERENCES

AFFECTED PEOPLE and GDP FLOOD* Affected people are the ones that may experience short-term Flood hazard in the risk assessment includes river (fluvial) or long-term consequences to their lives, livelihoods or health flooding and flash flooding. This risk profile document and in the economic, physical, social, cultural and considers mainly fluvial flooding and flash floods in the main environmental assets. In the case of this report “affected urban centres. Fluvial flooding is estimated at a resolution of people from Floods” are the people living in areas 90 m using global meteorological datasets, a global experiencing a flood intensity (i.e. a flood water level) above a hydrological model, a global flood-routing model, and an certain threshold. Analogously, in this report “affected people inundation downscaling routine. Flash flooding is estimated from Droughts” are the people living in areas experiencing a by deriving susceptibility indicators based on topographic and drought intensity (i.e. a SPEI value) below a certain threshold. land use maps. Flood loss curves are developed to define the The GDP affected has been methodologically defined using potential damage to the various assets based on the modelled the same thresholds both for floods and droughts. inundation depth at each specific location.

AVERAGE ANNUAL LOSS (AAL)* LOSS DUE TO DROUGHT (CROPS) Average Annual Loss (also Average Damage per year) is the Economic losses from selected crops result from multiplying estimated impact (in monetary terms or number of people) gross production in physical terms by output prices at farm that a specific hazard is likely to cause, on average, in any gate. Losses in working days have been estimated as function given year. It is calculated based on losses (including zero of crop-specific labour requirements for the cultivation of losses) produced by all hazard occurrences over many years. selected crops. Annual losses have been computed at Admin 1 level as the difference relative to a threshold, when an CLIMATE MODEL* annual value is below this threshold. The threshold equals the A numerical representation of the climate system based on 20% lowest value from the period 1951-2000 and has also the physical, chemical and biological properties of its been applied for the future climate. Losses at national level components, their interactions and feedback processes, and have been estimated as the sum of all Admin 1 losses. accounting for some of its known properties. Climate models are applied as a research tool to study and simulate the PROBABLE MAXIMUM LOSS (PML)* climate, and for operational purposes, including monthly, PML is the value of the largest loss that could result from a seasonal, and interannual climate predictions. disaster in a defined return period such as 1 in 100 years. The term PML is always accompanied by the return period DISASTER RISK* associated with the loss. The potential loss of life, injury, or destroyed, or damaged assets which could occur to a system, society, or a community RESIDUAL RISK* in a specific period of time, determined probabilistically as a The disaster risk that remains in unmanaged form, even when function of hazard, exposure, vulnerability, and capacity. effective disaster risk reduction measures are in place, and for which emergency response and recovery capacities must be DROUGHT maintained. Drought refers to dryer than normal conditions of a specific area (i.e. variability). Not to be confused with aridity, which RESILIENCE* refers to a lack of water resources in absolute terms. In this The ability of a system, community or society exposed to profile, drought hazard is denoted by various indicators, hazards to resist, absorb, accommodate, adapt to, transform, covering a range of the drought types. Both hydrological and and recover from the effects of a hazard in a timely and agricultural droughts are explicitly addressed, both in terms of hazard, exposed population and GDP, and corresponding ecient manner, including through the preservation and losses related to crop production and hydropower generation. restoration of its essential basic structures and functions Drought hazard and risk are modelled based on distributed through risk management. rainfall and a hydrological model solving the water balance (same one as used for the flood risk assessment). Drought RETURN PERIOD* conditions are defined as months with standardised index Average frequency with which a particular event is expected values below a threshold varying between -0.5 and -2, to occur. It is usually expressed in years, such as 1 in X number according to the aridity index of that area, assessed for of years. This does not mean that an event will occur once January, February, March, etc. separately. (Humid areas have every X numbers of years, but is another way of expressing the low thresholds, corresponding with the driest 2% of months as found in the period 1951-2000, while semi-arid and arid areas exceedance probability: a 1 in 200 years event has 0.5% have thresholds linked to respectively the driest 6% and 15% chance to occur or be exceeded every year. of this reference period).

*UNISDR terminology on Disaster Risk Reduction: https://www.unisdr.org/we/inform/publications/7817

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RISK* RISK TRANSFER* The combination of the probability of an event and its The process of formally or informally shifting the financial negative consequences. While in popular usage the emphasis consequences of particular risks from one party to another, is usually placed on the concept of chance or possibility, in whereby a household, community, enterprise, or State technical terms the emphasis is on consequences, calculated authority will obtain resources from the other party after a in terms of “potential losses” for some particular cause, place, disaster occurs, in exchange for ongoing or compensatory and period. It can be noted that people do not necessarily social or financial benefits provided to that other party. share the same perception of the significance and underlying causes of different risks.

*UNISDR terminology on Disaster Risk Reduction: https://www.unisdr.org/we/inform/publications/7817

[1] http://www.accessgambia.com/information/climate-weather.html

[2] Harris, I. P. D. J., Jones, P. D., Osborn, T. J., & Lister, D. H. (2014). Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. International Journal of Climatology, 34(3), 623-642.

[3] https://www.britannica.com/place/Gambia-River

[4] Alder, J. R., & Hostetler, S. W. (2015). Web based visualization of large climate data sets. Environmental Modelling & Software, 68, 175-180.

[5] Abba Omar, S. & Abiodun, B.J., How well do CORDEX models simulate extreme rainfall events over the East Coast of South Africa? Theor Appl Climatol (2017) 128: 453. https://doi.org/10.1007/s00704-015-1714-5

[6] Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Büchner, M., Cerezo-Mota, R., ... & Sushama, L. (2012). Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. Journal of Climate, 25(18), 6057-6078.

[7] Nikulin G, Lennard C, Dosio A, Kjellström E, Chen Y, Hänsler A, Kupiainen M, Laprise R, Mariotti L, Fox Maule C, van Meijgaard E, Panitz H-J, Scinocca J F and Somot S (2018) The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble, Environ. Res. Lett., doi:10.1088/1748-9326/aab2b4

[8] Republic of The Gambia overview, WorldBank, https://www.worldbank.org/en/country/gambia/overview

[9] http://www.worldometers.info/world-population/gambia-population/

[10] CIA FactBook, Gambia https://www.cia.gov/library/publications/the-world-factbook/geos/ga.html

[11] Keywan Riahi et al., The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, January 2017, Pages 153-168

[12] Richard H. Moss et al., The next generation of scenarios for climate change research and assessment, Nature volume 463, pages 747–756 (11 February 2010)

[13] Brian C. O’Neill et al., The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, 2016, doi:10.5194/gmd-9-3461-2016

[14] http://www.au-pida.org/view-project/747/

[15] http://addisababa.mfa.ir/uploads/Sambangalou_Dam_19964.pdf

The results presented in this report have been elaborated to the best of our ability, optimising the publicly data and information available. All geographic information has limitations due to scale, resolution, data and interpretation of the original sources.

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w w w .pr event i o n w e b.ne t/r esi l i ent- afri c a w w w .uni sdr . o r g

RISK PROFILES ARE AVAILABLE AT: africa.cimafoundation.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.