A Climate Trend Analysis of Addis Ababa (Ethiopia’s Sprawling Capital)

Surabhi Singh
11 min readSep 18, 2017

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Ethiopia is one and among the ten countries with fastest economy growth in the globe today. Climate Change poses a huge challenge to Ethiopians as experienced in 1974, 1984, 2002 and 2015/2016 droughts and various floodings. Ethiopia hopes to capitalize on its current economic growth by becoming more resilient to the impacts of climate change, while developing its economy in a carbon neutral way. To do so it has prepared the Climate Resilient Green Economy Strategy (CRGE) which aims at transforming country’s development model by leapfrogging to modern energy efficient technologies.

Addis Ababa is the capital city of Ethiopia. Located in the foothills of the Entoto Mountains and standing 7,726 feet (2,355 meters) above sea level, it is the third highest capital city in the world.

Local and regional ecosystems provide important functions that benefit urban residents including providing a habitat for biodiversity, primary productivity, storm water absorption and retention, air pollution removal and heat mitigation . The absence of functional ecosystem services incurs health cost to citizens and government.

Addis Ababa is sprawling in height and width, with growth in urban extent outpacing population growth. The result of this growth is an estimated 46% vacant or underutilized land. At the same time, the city center has extremely high density (up to 30,000 people per km2), concentrating around 30% of the population on 8% of the land, generally with poor living conditions. With an increasing built up area, the city is experiencing a decrease in the critical functions of its ecosystem services. Massive and rapid urbanization increasingly demands more water, energy, food, land and housing, causing rapid land cover change and alterations in biochemical cycles and hydro systems, loss of biodiversity and soil degradation .

A critical look at the situations in 1999 and 2014 shows that the built-up area increased from 134 to 200 km. This implies that the city is consuming the ecosystems at a speed of 4.5 km2 annually.

More importantly, the downward trend in Addis Ababa’s ecosystems significantly weakens the resilience to stress and shocks of the whole urban system (such as urban food insecurity and vulnerability to droughts or floods) and worsens the likely impacts of climate change related hazards on the city.

Risks associated directly with climate change in Addis Ababa mainly consist of floods, which induce landslides and water scarcity. Addis Ababa is exposed to riverine and flash floods as well as river overflows caused by extreme rainfall events and upper catchment area activities, such as land-use management or scarce watershed planning. Vulnerability to flooding is intimately linked with residential development encroaching on riverbanks, non-permanent construction materials (i.e. mud and wood), and poor drainage systems along roadways (World Bank, 2015)24. With limited availability of drainage services and continued mismanagement of storm water drainage facilities, flash floods occur following heavy rains, resulting in property damage, particularly in those Woredas located along rivers and streams.

Apart from human and material losses, flooding can also trigger outbreaks of diseases such as malaria, dengue fever and water-borne diseases such as cholera and dysentery.

Ecosystem Study:

Source : Addis Ababa

Ecosystems are not only an environmental matter; they also represent economic value. The presence or absence of functional ecosystems and their environmental services have impact on the strength of the economy and on the well being of people (e.g. air purification, noise reduction, urban cooling and absorbing storm/flood water runoff). In an analysis (based on the land cover), the demand and supply of ecosystem services (ES) for the city of Addis Ababa was assessed and mapped to determine whether these are strong or weak. The ES supply and demand assessment was based on methodology developed by McPhearson et al. which assigns an ES supply score from 0 to 10 to each land-cover type. The ES analyzed included: 1) carbon sequestration; 2) carbon storage, 3) air pollution removal (PM10); 4) air temperature regulation; and 5) runoff mitigation.

It emerges from the analysis that not all ecosystems are providing ES supply to the same extent. While carbon sequestration, air pollution removal and local climate mitigation are mainly supplied in the North, carbon storage and runoff mitigation are supplied in a more homogeneous way within the city boundaries.

The map shows vulnerability weighted according to the population density per sub-city. It shows that Addis Ketema has the highest demand for ES. This is mainly the outcome of high vulnerability and high population density, followed by Arada and Lideta. Nifassilk Lafto, Kolfe and Gulele show somewhat average scores. On the other hand, Yeka, Kirkos and Akaki Kality represent the better off scenarios, while Bole has very low demand score because of its residents’ low vulnerability. Comparison of demand and supply shows need for urgent interventions in Addis Ketema, Arada, Lideta and Kirkos.

Emissions :

Global Context : To achieve the 2 Degree Celsius global goal, the world needs to cut GHGs emissions from its current level of 54 GtCo2e to 42 GtCo2e by 2030.

Ethiopia aims to achieve carbon neutral middle income status by 2025 and reduce national GHGs emissions from current levels (150 MtCo2e) to 145 MtCo2e by 2030. If no action is taken ,GHGs emissions from cities in Ethiopia will increase by six fold from 20 MtCo2e in 2011 to 125 MtCo2e by 2030. About 42%of the increase or 105 MtCo2e is expected from urban sectors (industry, transportation, building ,waste). Although urban sectors currently contribute less than 15% (20 MtCo2e) of Ethiopia’s total emissions, their potential growth is tremendous.

Note: This GHG inventory covers Co2 , Methane CH4 and nitrous oxide emissions.

Addis Ababa’s 2012 BASIC+ emissions total is 4.89 MtCo2e or roughly 1.6 tCo2e per capita. Emissions from transportation, residential buildings and waste make up the largest share.They contribute about 28%, 47% and 13% respectively of Addis Ababa’s total emissions.

Stationary energy, waste and in-boundary on-road transportation have highest GHG mitigation potentials. Kerosene and biomass fuels are the main sources of residential GHG emissions. Out of the 1.71 MtCo2e emissions from stationary energy sources, almost 75% is from the combustion of kerosene and 12% from wood and charcoal. Zero emission grid supplied electricity and low electricity prices are among the main reasons for low GHG emissions in Addis Ababa.

Currently only about 17% of the population in Ethiopia lives in cities, with nearly 5% in Addis Ababa. The CRGE estimates that the urban population will grow at 4.4% annually, and will surpass 30 million people by 2030. More and more cities will be expanding at an unprecedented rate increasing the GHGs emissions at alarming rates.

Historical Climate Dynamics :

Time series of the land area averaged seasonal temperature changes between 1963 and 2012, for the four seasons: DJF, MAM, JJA and SON. Source: Daron (2014), using data from the CRU TS3.22 dataset

There is broad evidence at the regional level supporting increasing trends in temperature from climate baselines. Mean temperatures across the region have increased by 1 to 3°C over the past 50 years . Several studies have reported a warming trend has occurred in the region during the last five to six decades at a rate broadly consistent with wider African and global trends. There is also evidence of increasing changes in extreme temperature events such as extreme maximum temperature, warm days, warm nights and the duration of warm spells.

Rainfall trends over the past 50 years are less evident than for temperature, and there are large variations in the direction and magnitude of changes across the region. Over past half century there has been substantial multi-decadal variability in rainfall.

The change in rainfall between 1963 and 2012 at each grid cell, according to a linear trend, for the four seasons: DJF, MAM, JJA and SON. Source: Daron (2014), using data from the CRU TS3.22 dataset

There are major data and knowledge gaps in terms of creating a complete understanding of regional trends to the rainfall record. Studies in Ethiopia have reported the absence of systematic evidence for consistent changes in the amount, frequency or intensity of extreme events in the country. However, most of the studies confirmed the presence of high inter-annual and intra-seasonal rainfall variability, which is accompanied by drought and flood risks.

Future Climate Changes :

Temperature Changes :

Time series of temperature change relative to 1986–2005 averaged over land grid points in East Africa in December to February and June to August. Thin lines denote one model simulation and thick lines are the multi-model mean. On the right the 5th, 25th, 50th, 75th and 95th percentiles of the distribution of 20-year mean changes are given for 2081–2100 for the four RCP scenarios. Source: IPCC (2013).

IPCC projections for East Africa include warming of 0.2°C (low scenario) to more than 0.5°C (high scenario), 5–20% increase in precipitation from December-February (wet months) and 5–10% decrease in precipitation from June-August (dry months) (IPCC, 2014).

Climate projections generated by UNDP (cited in DFID, 2009) for Ethiopia highlight the likelihood of mean temperature increases of 1°C in 2020s and up to 3.9°C to 2080s. Using a multi-model dataset the National Meteorological Agency of Ethiopia indicates that the mean annual temperature is likely to rise significantly when compared with the 1961–90 level by a maximum of 1.1oC by 2030, 2.1oC by 2050 and 3.4oC by 2080 (Weldegebriel and Prowse, 2013).

Rainfall Changes :

There is a tendency of models to project drying across the region in the October to March period but at present there is insufficient evidence to support either a shift to drier or wetter conditions in the future in most locations . The impacts of future climate change on different sectors are complicated by the spread of model projections and the complex nature of natural and societal systems in eastern Africa.

Time series of precipitation change relative to 1986–2005 averaged over land grid points in East Africa in October to March and April to September. Source: IPCC (2013).

Mean annual precipitation in East Africa is projected to increase by 7 per cent by the decade of 2080 to 2090, though projections range from a decline of 3 per cent to an increase of 25 per cent . Seasonal variations in rainfall patterns are also expected, with some models projecting a mean increase in East Africa of 13 per cent from December to February and 4 per cent from June to August by the period from 2080 to 2090. The number of extreme wet seasons in East Africa may increase by 5 to 20 per cent . However, several studies have stressed the disagreement between global climate models in representing rainfall amounts over east African highlands, and topographic influences on models are not well understood. That said regional climate modelling work has substantially improved precipitation simulation compared to their driving general circulation models (GCMs).

Utility of Climate Model Information :

The use of climate model data from general circulation models (GCMs) and regional climate models (RCMs) for both seasonal near-term forecasting and more medium-term decision making and planning has increased in recent decades across the region. Cross-referencing knowledge of climate change with plans such as the Poverty Reduction Strategy Papers, ), for example, can provide a useful starting point to identify national level risks from climate change.

However, based on work in Ethiopia, Conway and Schipper (2011) found that even where rainfall model convergence is apparent, current trends and physical interpretations on the ground often counter IPCC multimodel projections. GCM uncertainties remain a barrier to prioritization of climate change adaptation by decision makers, and improvements are needed in how uncertainties in projections are articulated and approaches should be guided more by management objectives. There remains a shortage of accurate regional climate model (RCMs) data and lack of capacity to interpret inherent uncertainties within climate model outputs. Deficiencies include the lack of good quality and timely dissemination of data to the local level; the ineffective packaging, explanation and translation of climate information; and the lack of analysis of climate data to produce forecasts and scenarios, especially at the local level

Risks, Impacts and Vulnerability :

This section looks at the social-ecological risks associated with environmental dynamics, of which climate change is only one of several interlinked drivers of change. Effects of climate change will be compounded by widespread poverty, human diseases and high population growth rates that are expected to intensify demand for food, water and livestock forage within the region.

Eight inter-related risks that affect people’s livelihoods and well being in semi-arid areas of East Africa are: rainfall variability, drought, flood hazards, resource degradation, resource conflict, food insecurity, human health, and plant and animal diseases; recognizing that each is a product of multiple factors and causes.

Key Vulnerable Groups :

  1. Women
  2. Children
  3. Pastoralists
  4. Small-hold Farmers
  5. Conflict Afflicted Communities

Development trends are changing vulnerability in semi-arid areas, and there are climate dimensions to this: as livelihood strategies and access to resources and assets respond to broader development changes taking place such as agricultural development, land use policy and urbanization, these can have an impact on climate-related risk and knock-on effects on people’s ability to cope.

Population growth, especially in rural areas, is affecting access to land and resources. In the face of population pressure, specialization of agricultural systems is seen by some to be inevitable to ensure regional food security. This intersects with climate change impacts as more marginal extensive systems are becoming increasingly risky to the point where livelihoods may have to change substantially, as climate change effects exacerbate problems for vulnerable and poor people living in these marginal areas.

Changes in land distribution also affect vulnerability via a number of channels, and lack of access to land and other natural resources may be a key constraint to improved livelihood opportunities for the poorest and most vulnerable.

In the case of Ethiopia, although the government has set policies for equitable distribution in land access and use, there is substantial evidence that this may not hold in practice and land is fragmented into tiny parcels. More than 85% of farming households operate on less than 2 hectares and, about 40% operate on less than 0.5 hectares (USAID, 2012). Fragmentation in this context arguably discourages sustainable land management practices like rotation, agroforestry, inter-cropping and soil erosion control, as well as acting as a barrier to modernized agricultural activity as economies of scale cannot be achieved (USAID, 2012). Restrictions on land redistribution means access is severely constrained for smallholder farmers. For pastoralists in these areas this has led to shortage of rangeland and inter-community conflicts due to increasing competition for water and pasture resources.

National agricultural policies have a profound impact on the lives and livelihoods of those living in rural areas. In some cases, while policies may work for people living in particular agro-climatic zones, in other regions policies may act to exacerbate vulnerability. This has eroded biological and institutional diversity of informal seed systems causing increased vulnerability and threat to food security. Policy focus on certain forms of crop agriculture, for example, has been highlighted as a key contributor to the marginalization of, and development failures in, drylands of Africa.

Addis Ababa’s low environmental sustainability can, and will, compromise the city’s economic viability, worsen climate change and decrease dramatically the resilience of the city to hazards if a ‘business as usual’ scenario is pursued.

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