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African megacities adapt to a climate crisis

You can read the story at DW.

Sources, references and studies

This section lists all resources used.

The following sources contributed expertise to this article

Interviewees Affiliation
Charles Ndika Akong World Health Organizations
Anthony Nyong African Development Bank
Christian Benimana African Design Center
David Dodman International Institute for Environment and Development
Mpho Matsipa University of Witwatersrand
Maimunah Mohd Sharif UN Habitat
Frank Kapangala Institute for Environment and Development Sustainability
Shuaib Lwasa Makerere University
Najib Bateganya Kampala City Council Authority
Precious Akanonu Centre for the Study of the Economies of Africa
Doreen Adengo Adengo Architecture
Phoebe Shikuku International Red Cross and Red Crescent Federation
Helmy Abouleish SEKEM
Nathalie Jean-Baptiste CityLab Dar es Salaam
Ebenezer Amankwaa United Nations University
Mats Eriksson Stockholm International Water Institute
Dolapo Ayokunle Fasawe Lagos State Environmental Protection Agency
Martin Manuhwa Federation of African Engineering Organizations
Hani Sewilam American University of Cairo
Gugu Nonjinge Afrobarometer
Archimedes Muzenda The African Urban Institute
Eloise Marais University of Leicester
Chukwumerije Okereke Alex Ekwueme Federal University
Ibrahim Togola Mali Folkecenter
Ronald Lwakatare Dar Rapid Transit Agency
Chris Kost Institute for Transportation and Development Policy

Further Reading

This is a selection of studies, reports and data sources that were useful in the research.


Afrobarometer: Climate Awareness

IPCC: 1.5 Degrees Celsius

UN: Urban Population Projections

Africapolis: Urban Agglomerations

IEA: Africa Energy Outlook

Environmental Research Letters: (Moran et al., 2018)

World Resources Institute Cities: (Du et al., 2019)

UNFCCC: Climate Finance 2018


Lagos State Adaptation Strategy

Canadian Centre for Climate Modelling and Analysis

WHO: Heat and Health

Journal of Building Engineering: (Rincon et al., 2019)


Uganda National Environment Act 2019

Sustainability: (Aryampa et al., 2019)

Kampala City Council Authority: Waste Management PPP

Journal of the Air and Waste Management Association: (Kinobe et al., 2015)

World Bank Group: Green Urban Development

Global Green Growth Institute: Kampala Municipal Solid Waste

UN Habitat: Climate Change Assessment Kampala


Egypt and the Levant: (Finkelstein et al., 2017)

Earth's Future: (Coffel et al., 2019)

Geological Society of America: (Stanley and Clemente, 2017)

Transparency International: Egyptian Military

Journal of Cleaner Production: (Wahba et al., 2018)

Dar es Salaam

Center for Development Research: (Feye et al., 2014)

University of Twente: (Maliwa, 2019)

African Development Bank: Environmental Impact Assessment

Global Labour Institute: Nairobi BRT

International Growth Center: Ghana BRT

Data sources and analysis

This section contains references to all the datasets used and - where applicable - outlines as to how analysis was conducted.

Climate change vulnerability vs population growth

Data for climate change vulnerability was provided by Verisk Maplecroft, a British research and consultancy company, that regularly releases a self-generated "Climate Change Risk Vulnerability Index". For data licensing reasons, DW was allowed to use and visualize the data, however we are not allowed to publish them, which is why the raw data for this is not included in this repository.

The data source for population growth is the United Nations. They regularly publish a "Data Booklet (on) the World's Cities". For this analysis we scraped the data out of the 2018 pdf version into a csv-file. We calculated the growth rate between the two years (value_2030 - value_2018) / value_2018 * 100 and matched it with the climate change vulnerability data via VLOOKUP.

We included only cities with more than 1 million inhabitants and with an assigned climate vulnerability index.

Lagos temperature scenarios


We used the Linux command-line tools ncks and ncdump, which are part of the netcdf-bin package on Debian and netcdf on Homebrew. Install this in the normal way.

We downloaded modelled daily maximum near-surface air temperatures (variable name tasmax) from the Canadian Centre for Climate Modelling and Analysis.

The model data we are using is the CanRCM4-AFR-44 data. This is a regional climate model for the African region at a 50km grid resolution. Because this is a large-scale model, the granularity of the estimates should not be overstated, either on a temporal or spatial dimension.

We use the RCP4.5 run, based on a central emissions pathway.

We will average the "daily" figures over five years to provide broad-brush illustrative temperature examples for the Lagos area.

Scenario 1: "Present day" temperatures

The "present day" temperatures are based on the 2016-2020 file.

  1. Download data as .nc file.

  2. Run ncks to extract the grid square we want:

ncks -v tasmax -d rlon,3.3841 -d rlat,6.4550
  1. Run ncdump to turn the nc file into a text file.
ncdump > tasmax_lagos_2016_2020.txt

As there is no off-the-shelf way to turn the text file into a CSV, simply copy and paste the daily values into a spreadsheet.

We then calculate the mean average over 5 years for each calendar day of the year.

Scenario 2: Future temperatures

We repeat the steps above for 2046-2050 data.

Scenario 3: With humidity factor.

We download the Near-Surface Specific Humidity (huss) and Surface Air Pressure (ps) variables for the same time periods. These will allow us to calculate the Relative Humidity, which can be combined with the temperature to indicate a notional Heat Index ("feels like") temperature.

The daily maximum temperatures and maximum humidity will not necessarily coincide, meaning our estimates could be on the high side. However, as a broad indication of possible humidity effects it is sufficiently rigorous.

The formula for Relative Humidity was sourced from the Earth Science Stack Exchange.

We then apply a formula from the US National Oceanic and Atmospheric Administration to calculate the Heat Index temperature according to the Rothfusz regression.

Output data

The final output data used in the visualisation can be found here.

Waste production in Kampala

The figures for waste production in Kampala in 2011 compared to 2017 originate from a joint study by Western Syndey University and Makerere University.

Traffic and planned bus lines in Dar es Salaam

The traffic situation was derived from the "Dar es Salaam Transport Policy and System Development Master Plan" produced by Tanzanian authorities with help of the Japan International Cooperation Agency (JICA) in 2008. Although from 2008, this document comprises the most recent and comprehensive data on the traffic situation in Dar es Salaam and is guiding offical transportation policy developments up to 2030, according to a 2017 World Bank Report.

For our analysis we chose to derive the traffic situation from Figure 5.2.51 (p.34) highlighting the morning peak hours, given that greater economic damage occurs if people get late to work due to traffic jams than if people get late home. The study differentiates between seven different travel speed categories. For our story, we included the category where the average travel speed in the morning peak hours is between 0 and 10 km/h. Additionally we grouped the category 10-20 and 20-30 km/h into a second category displayed.

The status of planned/existing bus lines were taken from a brochure by the Dar Rapid Transit Agency (DART). The pdf file includes a map outlining where bus lines are supposed to operate and in what phases they are supposed to be built. Information on what phases are already implemented was taken from the official Bus Rapid Transit (BRT) website, that states so far only phase one is listed as operating.


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