This dataset provides a continent-wide, raster-based spatial representation of Earth's surface heat flow across the African tectonic plate for the year 2013, modeled under a historical baseline scenario. The data quantifies the amount of thermal energy moving from the Earth's interior to its surface, with values explicitly expressed in watts per square meter (W/m²). This resource is critical for identifying geothermal potential, understanding tectonic anomalies, and supporting broader geoscientific and energy research.

This layer is part of the Geothermal Atlas for Africa developed within the LEAP-RE project

This dataset compiles geophysical models detailing the thickness of the Earth's outer layers, ranging from surface sedimentary basins down to the base of the lithosphere. The collection serves a dual purpose: it provides broad, global baseline data for sediment distribution, while also offering high-resolution, integrated regional models specifically focused on the African plate.

Dataset Contents:

I. Global Baselines

• Global Sediment Thickness (Laske & Masters, 2013): The global sediment thickness baseline extracted from the CRUST1.0 model. This provides broad, foundational coverage across both continental and oceanic domains. (Values expressed in km).
• Global Oceanic Sediment Thickness (Straume et al., 2019): A high-resolution, 5-arc-minute grid detailing total sediment thickness specifically for the world's oceans and marginal seas, compiled from extensive new data and previous work. (Values expressed in m).

II. African Regional Models

• Combined Sediment Thickness (Africa): A synthesized, optimized sedimentary thickness map for Africa and its surrounding regions. This layer intelligently merges the Laske & Masters (2013) and Straume et al. (2019) maps, prioritizing the Straume data wherever overlaps occur to maximize resolution. (Values expressed in m).
• Crustal Thickness (Globig et al., 2016): An Africa-specific crustal thickness map derived from the integrated modeling of elevation, geoid data, and rigorous thermal analysis. (Values expressed in km).
• Lithosphere Thickness (Globig et al., 2016): An Africa-specific lithospheric thickness map, complementing the crustal layer, derived using the same integrated elevation, geoid, and thermal modeling approach. (Values expressed in km).

These layers are part of the Geothermal Atlas for Africa developed within the LEAP-RE project

• Direct Use;
• High Temperature Heat Pump;

• base case scenario;
• upside scenario, modelling a better fluid flow;
• up up side, modelling both better fluid flow and higher underground temperatures.

• base case scenario;
• upside scenario;
• up up side scenario.

The hydropower installed capacity indicates the amount of energy a hydropower plant can produce in its turbines. In 2016, hydropower accounted for 54% of the installed capacity in Eastern Africa, 58% in Central Africa and 30% in Western Africa with fourteen countries having a hydropower share above 50% and eight countries above 70%. These highly hydropower-dependent countries are particularly prone to electricity cuts due to the lack of water caused by severe droughts. This map shows the location and installed capacities of hydropower plants above 5MW installed capacity in Africa for year 2016, representing 95% of the total hydropower installed capacity in Africa.

This dataset shows Regional locations and general geologic setting of known deposits of major nonfuel mineral commodities in Africa

• Cooling Absorption System;
• Refrigeration Absorption System;
• Binary ORC Power Plant;
• Flash Power Plant;
• Heat for Direct Use;
• Heat for High Temperature Heat Pump;

• base case scenario;
• upside scenario, modelling a better fluid flow;
• up up side, modelling both better fluid flow and higher underground temperatures.

Higher energy demands in Africa has led to a wide expansion of the number of hydropower sites, mainly between the 1960s and 1980s. The construction of dams causes impoundments of rivers and reservoirs in the regions of dam influence, with higher evaporation and water temperatures due to increased water surfaces. This map shows the he surface area (sqkm) of African reservoirs subject to hydropower production for the year 2016. It includes reservoirs of associated hydropower plants with installed capacities above 5MW. Apart from this, only reservoirs with a detected dam-caused impoundment of water surface are considered.

The EC JRC global map of forest cover provides a spatially explicit representation of forest presence and absence for the year 2020 at 10m spatial resolution.

The year 2020 corresponds to the cut-off date of the Regulation from the European Union "on the making available on the Union market and the export from the Union of certain commodities and products associated with deforestation and forest degradation" (EUDR, Regulation (EU) 2023/1115). In the context of the EUDR, the global forest cover map can be used as a non-mandatory, non-exclusive, and not legally binding source of information. Further information about the map and its use can be found on the EU Observatory on Deforestation and Forest Degradation (EUFO) in the section on Frequently Asked Questions.

Forest means land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10%, or trees able to reach those thresholds in situ, excluding land that is predominantly under agricultural or urban land use. Agricultural use means the use of land for the purpose of agriculture, including for agricultural plantations (i.e. tree stands in agricultural production systems such as fruit tree plantations, oil palm plantations, olive orchards and agroforestry systems) and set- aside agricultural areas, and for rearing livestock. All plantations of relevant commodities other than wood, that is cattle, cocoa, coffee, oil palm, rubber, soya, are excluded from the forest definition.

The global map of forest cover was created by combining available global datasets (wall-to-wall or global in their scope) on tree cover, tree height, land cover and land use into a single harmonized globally-consistent representation of where forests existed in 2020.

The workflow consisted in first mapping the global maximum extent of tree cover circa the year 2020 from the combination of ESA World Cover 2020 and 2021, WRI Tropical Tree Cover 2020, UMD Global land cover and land use 2019, Global Mangrove Watch 2020, and JRC Tropical Moist Forest 2020 datasets. In the second step, a series of overlays and decision rules were applied to reduce this maximum extent of tree cover and align it with the Forest definition using datasets covering cropland and commodity expansion (ESA World Cereal, UMD Global land cover and land use 2019, UMD Global Cropland Expansion, High-resolution global map of smallholder and industrial oil palm plantations, and WRI Spatial Database of Planted Trees), land use change (UMD global forest cover loss, JRC Tropical Moist Forest, IIASA Global Forest Management), built-up (JRC Global Human Settlement), and water (JRC Global Surface Water).

The detailed mapping approach will be described in a separate technical report expected to be released by March 2024. The accuracy of this map has not been yet assessed but will be reported as soon as available.

Please also refer to the list of known issues and to the JRC Data Catalogue entry.

The Africa Topographic Potisiont layer classifies the African landscape (including Madagascar, Comoros Islands, and other coastal Islands near continenal Africa) into uplands and lowlands/depressions at a 100m resolution. Published by the USGS in 2009, this dataset was created for the Africa Terrestrial Ecosystems mapping project. The classification is derived from the Compound Topographic Index (CTI) , which integrates slope (from SRTM elevation data) and flow accumulation (from HydroSHEDS data) to model potential water flow.

African countries have an evident potential for solar energy. Knowing what amount of solar radiation reaches the earth's surface is of particular interest for solar photovoltaic (PV) installations. It can be represented by the average annual potential energy production (or yield): the total amount of electricity (kWh) produced in one year by a 1 kWp PV system at optimal angle, expressed in kWh/kWp.

Increasing water scarcity and water quality issues are serious constraints, especially for Northern Africa. A comprehensive assessment of spatial and temporal precipitation frequency is the initial step for defining public policies relating to water resources management and environmental monitoring. In the agricultural sector, a detailed knowledge of precipitation patterns is necessary to identify the most appropriate crop varieties for the region and to effectively manage climate related uncertainties. Precipitation frequency is also a central source of information for hazard mitigation and management. This layer represents the average variability of precipitation (L-CV) around the annual mean value for the period 1981-2017. The larger the L-CV, the more variable the annual precipitation is from year to year.

Increasing water scarcity and water quality issues are serious constraints, especially for Northern Africa. A comprehensive assessment of spatial and temporal precipitation frequency is the initial step for defining public policies relating to water resources management and environmental monitoring. In the agricultural sector, a detailed knowledge of precipitation patterns is necessary to identify the most appropriate crop varieties for the region and to effectively manage climate related uncertainties. Precipitation frequency is also a central source of information for hazard mitigation and management. This layer shows the average annual precipitation (mm/year) for the period 1981-2017 across the continent.

At any given place on Earth, complex human-environment interactions are at play. They include differing rates and magnitudes of drivers (e.g. overgrazing, climate change, agricultural practices) and differing consequences in land degradation (e.g. soil erosion, changes in productivity, loss of biodiversity). The occurrence of multiple global change issues at a location suggests a potential for land degradation, at least in some form. This layer depicts where global change issues relevant to land degradation coincide at a global scale. It helps identifying local or regional areas of concern where land degradation processes may be underway.

Whether you’re monitoring crops, modelling green energy installations or soil sealing, combatting loss of natural resources or just helping countries meet their Sustainable Development Goals, chances are high that you’ll need an accurate and spatially detailed map on land cover and land use. Earth Observation satellites, like those from EU’s flagship programme Copernicus, are key to providing such maps, at a global scale, with free and open access. Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. Dynamic land cover maps include transitions of land cover classes over time and hence captures land cover changes. This dataset shows the land cover for the baseline year 2019 with a discrete classification in 23 classes aligned with UN-FAO's Land Cover Classification System.

Healthy coral reefs provide a home for millions of aquatic species and numerous ecosystemic services. Yet they are severely threatened. When stressed, corals expel the symbiotic algae living in their tissues and become white (bleached) and vulnerable. The NOAA Coral Reef Watch daily global 5km satellite coral Bleaching Alert Area (7-day maximum) is a composite product that summarizes the current Degree Heating Week (a cumulative measurement of both intensity and duration of heat stress) and Coral Bleaching HotSpot (occurrence and magnitude of instantaneous heat stress) values. At a glance, this layer outlines the current locations, coverage, and potential risk level of coral bleaching heat stress.

Percentage of children aged 6-8 that currently attends, or in the current school year attended, school. This indicator measures the potential educational impact of bringing electricity to schools; therefore, the impact of binging electricity will be higher where the educational attendance is low.

This dataset provides the P50 projections for a Direct Heat geothermal system.

Direct Heat refers to the use of geothermal energy for applications such as space heating, industrial processes, or agriculture. In the utilized model, it is assumed a minimum production temperature of at least 60°C and a reinjection temperature of 40°C.

P50 is a statistical term used in risk analysis and forecasting. P50 represents the median scenario, meaning there is a 50% probability that the actual outcome will be higher than this value and a 50% probability it will be lower. It serves as the baseline "best estimate."

• Coefficient of Performance (COP) [-]: A measure of efficiency, defined as the ratio of useful heating or cooling provided to the work (energy) required to operate the Chiller. Higher values indicate greater efficiency.

• Doublet Net Production [MW]: The actual useful power output (Megawatts) generated by the ""doublet"" (one production well + one injection well), subtracting any energy used by pumps or auxiliary equipment.

• Levelized Cost of Energy (LCOE) [US$ct/kWh]: The average revenue per unit of energy needed to recover the cost of building and operating the plant over its entire life cycle. It essentially represents the break-even price.

• Net Present Value (NPV) [million US$]: The current financial value of all future cash flows the project will generate, minus the initial investment costs. A positive NPV generally indicates a profitable project.

• Optimized Depth of the Aquifer [m]: The calculated ideal drilling depth (meters) to reach the geothermal reservoir that balances drilling costs with temperature/flow benefits.

• Pressure needed to drive pumps [Bar]: The total pressure difference required to drive the thermal loop through the reservoir and wellbore.

• Production Flow Rate [m³/h]: The volume of geothermal fluid extracted from the reservoir per hour (cubic meters per hour).

• Temperature at Reservoir [°C]: The natural temperature (Celsius) of the geothermal fluid within the subsurface rock formation before it is brought to the surface.

• Transmissivity [Dm]: A measure of how easily fluid flows through the porous aquifer. It is the product of hydraulic conductivity and aquifer thickness (often measured in Darcy-meters). High transmissivity means easier extraction.

• Well Distance [m]: The physical distance (meters) between the production well (extraction) and the injection well (return). Proper spacing prevents ""short-circuiting,"" where cool injected water lowers the temperature of the production well too quickly.

This dataset provides the P50 projections for a Direct Heat with Heat Pump geothermal system.

Direct Heat with Heat Pump refers to a scenario where a geothermal system is integrated with an industrial heat pump to upgrade low-grade heat. This allows for production from geothermal temperatures as low as 40°C, which are then heated by the pump to a delivery temperature of 80°C. The reinjection temperature is set to the production temperature minus 20°C, with a absolute minimum of 15°C.

P50 is a statistical term used in risk analysis and forecasting. P50 represents the median scenario, meaning there is a 50% probability that the actual outcome will be higher than this value and a 50% probability it will be lower. It serves as the baseline ""best estimate.""

• Coefficient of Performance (COP) [-]: A measure of efficiency, defined as the ratio of useful heating or cooling provided to the work (energy) required to operate the Chiller. Higher values indicate greater efficiency.

• Doublet Net Production [MW]: The actual useful power output (Megawatts) generated by the ""doublet"" (one production well + one injection well), subtracting any energy used by pumps or auxiliary equipment.

• Levelized Cost of Energy (LCOE) [US$ct/kWh]: The average revenue per unit of energy needed to recover the cost of building and operating the plant over its entire life cycle. It essentially represents the break-even price.

• Net Present Value (NPV) [million US$]: The current financial value of all future cash flows the project will generate, minus the initial investment costs. A positive NPV generally indicates a profitable project.

• Optimized Depth of the Aquifer [m]: The calculated ideal drilling depth (meters) to reach the geothermal reservoir that balances drilling costs with temperature/flow benefits.

• Pressure needed to drive pumps [Bar]: The total pressure difference required to drive the thermal loop through the reservoir and wellbore.

• Production Flow Rate [m³/h]: The volume of geothermal fluid extracted from the reservoir per hour (cubic meters per hour).

• Temperature at Reservoir [°C]: The natural temperature (Celsius) of the geothermal fluid within the subsurface rock formation before it is brought to the surface.

• Transmissivity [Dm]: A measure of how easily fluid flows through the porous aquifer. It is the product of hydraulic conductivity and aquifer thickness (often measured in Darcy-meters). High transmissivity means easier extraction.

• Well Distance [m]: The physical distance (meters) between the production well (extraction) and the injection well (return). Proper spacing prevents ""short-circuiting,"" where cool injected water lowers the temperature of the production well too quickly.

Seagrass is found on all continents except Antarctica, covering roughly 0.1% of the ocean floor. However, its global extent remains inadequately mapped, with estimates varying between 160,387 km² and 670,000 km², posing a significant challenge for conservation efforts. The UNEP-WCMC seagrass dataset indicates that Africa harbors approximately 12% of the world’s seagrass.

Seagrass is found on all continents except Antarctica, covering roughly 0.1% of the ocean floor. However, its global extent remains inadequately mapped, with estimates varying between 160,387 km² and 670,000 km², posing a significant challenge for conservation efforts. The UNEP-WCMC seagrass dataset indicates that Africa harbors approximately 12% of the world’s seagrass.

The Global Gridded Relative Deprivation Index (GRDI) characterizes the relative levels of multidimensional deprivation and poverty, where a value of 100 represents the highest level of deprivation and a value of 0 the lowest. GRDI is built from sociodemographic and satellite data inputs that were spatially harmonized, indexed, and weighted into six main components to produce the final GRDI layer.

Mining has major economic, environmental and societal consequences, yet knowledge and understanding of its global footprint are still limited. These polygons represent the global mining land use detected via remote sensing analysis of high-resolution, publicly available satellite imagery. The dataset comprises 74,548 polygons, covering ~66,000 km2 of features like waste rock dumps, pits, water ponds, tailings dams, heap leach pads and processing/milling infrastructure.

This layer provides estimates of the percentage of children aged 2 to 10 years (PfPR2–10) with detectable Plasmodium falciparum parasites in 2022. The estimates are generated using geostatistical models based on point prevalence surveys, routine surveillance data, and a wide range of geospatial covariates representing mosquito habitat and environmental conditions. The data are available annually from 2000 onward, covering all malaria-endemic countries, at a spatial resolution of 5 × 5 km.

The map is based on Copernicus Global Land Cover data which shows actual land cover (what physically covers land across the globe—forests, grasslands, croplands, lakes, wetlands, built-up area, etc) at 100m x 100m resolution. The land cover data for Africa was overlaid on (high resolution) satellite imagery of Africa, which is then used to determine the land use (by visual interpretation) associated with the various land cover patterns on the Copernicus Global Land Cover map.

Copernicus land cover data offers several advantages: it is of high quality, has a high resolution (100m x 100m), and offers time series satellite imagery. More importantly, the Copernicus Global Land Service is a continuous process and its datasets are updated annually. This means that the Soils4Africa map of agricultural land can be updated every time the land cover data for the new year becomes available (the map is currently based on 2019 data).

The Copernicus dataset already includes the category ‘cropland,’ which by definition is part of agricultural land. The Soils4Africa map broadens the scope of that dataset by infering information on agricultural use and including other kinds of agricultural land use such as grazing pastures and plantations.

When land is mapped as other than ‘cropland'-- like ‘shrubland’ for example-- it is more difficult to interpret and determine whether it is under agricultural use. The Copernicus dataset includes information about ‘fractional cover’-- or the percentage of a particular pixel under a particular kind of land cover (for example, 30% of a 100m x 100m pixel could be forest and 20% could be shrubland). The Soils4Africa map takes into account how fractional cover varies over an area to establish rules for interpreting its land cover data to determine whether it is under agricultural use and for what purpose. These rules were validated by comparing it with ground level information on land use (or land use pattern) for specific areas drawn from the interpretation of satellite imagery from Google Earth.

For example, ground-level observation shows that forest cover upwards of 30% in a given area when matched by shrubland cover of over 30%, is characterized by woody vegetation with a smooth canopy. Therefore, such area is more likely to be under plantations rather than natural forest. Thus, such an area should be counted as agricultural land, even if less than 15% of it is under crops.

African countries have an evident potential for solar energy. Knowing the amount of solar radiation reaching the earth's surface is of particular interest for photovoltaic installations. It can be represented by the Global Horizontal Irradiation: the total amount of energy received from the Sun by a surface horizontal to the ground during a period of time, expressed in Wh/m2. This map shows the yearly average (2005-2015) global horizontal irradiation (kWh/m2).

Organisms and non-living elements of the environment such as climate, soil, and water are connected through the movement of nutrients and energy in ecosystems. Ecosystems represent specific areas where organisms and environmental conditions create a network of interactions and are affected by forces such as disturbance (temporary change in environmental conditions that causes a pronounced change) and succession (process of change in the species structure of an ecological community over time). This layer maps the Ecosystems of Africa, based on Africa's climate regions, topography and lithology (bedrock), with a 100m spatial resolution.

Tropical moist forests have a huge environmental value. They play an important role in biodiversity conservation, terrestrial carbon cycle, hydrological regimes, indigenous population subsistence and human health (1-5). They are increasingly recognized as an essential element of any strategy to mitigate climate change. Deforestation, and degradation compromise the functioning of tropical forests as an ecosystem, lead to biodiversity loss and reduced carbon storage capacity. Deforestation and fragmentation are increasing the risk of virus disease outbreaks. This map shows where deforestation occurred in the last three decades (between 1982 and 2020) and the year when the forest cover has been deforested for the first time (followed or not by a regrowth).

Tropical moist forests have a huge environmental value. They play an important role in biodiversity conservation, terrestrial carbon cycle, hydrological regimes, indigenous population subsistence and human health (1-5). They are increasingly recognized as an essential element of any strategy to mitigate climate change. Deforestation, and degradation compromise the functioning of tropical forests as an ecosystem, lead to biodiversity loss and reduced carbon storage capacity. Deforestation and fragmentation are increasing the risk of virus disease outbreaks. This map shows where degradation occurred in the last three decades (between 1982 and 2020) and the year when the forest has been degraded for the first time (and remained degraded up to 2020).

This dataset provides the P50 projections for a geothermal system utilizing a Chiller unit.

Chiller refers to an absorption chiller or heat pump integrated into the geothermal system. It is used to provide cooling (by utilizing the geothermal heat) or to extract additional heat from the return fluid, thereby increasing the system's overall thermal harvest.

P50 is a statistical term used in risk analysis and forecasting. P50 represents the median scenario, meaning there is a 50% probability that the actual outcome will be higher than this value and a 50% probability it will be lower. It serves as the baseline "best estimate."

• Coefficient of Performance (COP) [-]: A measure of efficiency, defined as the ratio of useful heating or cooling provided to the work (energy) required to operate the Chiller. Higher values indicate greater efficiency.

• Doublet Net Production [MW]: The actual useful power output (Megawatts) generated by the "doublet" (one production well + one injection well), subtracting any energy used by pumps or auxiliary equipment.

• Levelized Cost of Energy (LCOE) [US$ct/kWh]: The average revenue per unit of energy needed to recover the cost of building and operating the plant over its entire life cycle. It essentially represents the break-even price.

• Net Present Value (NPV) [million US$]: The current financial value of all future cash flows the project will generate, minus the initial investment costs. A positive NPV generally indicates a profitable project.

• Optimized Depth of the Aquifer [m]: The calculated ideal drilling depth (meters) to reach the geothermal reservoir that balances drilling costs with temperature/flow benefits.

• Pressure needed to drive pumps [Bar]: The total pressure difference required to drive the thermal loop through the reservoir and wellbore.

• Production Flow Rate [m³/h]: The volume of geothermal fluid extracted from the reservoir per hour (cubic meters per hour).

• Temperature at Reservoir [°C]: The natural temperature (Celsius) of the geothermal fluid within the subsurface rock formation before it is brought to the surface.

• Transmissivity [Dm]: A measure of how easily fluid flows through the porous aquifer. It is the product of hydraulic conductivity and aquifer thickness (often measured in Darcy-meters). High transmissivity means easier extraction.

• Well Distance [m]: The physical distance (meters) between the production well (extraction) and the injection well (return). Proper spacing prevents "short-circuiting," where cool injected water lowers the temperature of the production well too quickly.

This dataset provides the P50 projections for an Organic Rankine Cycle (ORC) geothermal power plant.

Organic Rankine Cycle (ORC) refers to a binary power plant configuration where geothermal fluid (brine) is used to heat a secondary organic working fluid. This process allows for efficient electricity generation from medium-to-low temperature resources (typically 90°C to 150°C). The organic fluid vaporizes, drives a turbine, and is then condensed back into a liquid to repeat the cycle. The geothermal brine is reinjected into the reservoir after passing through the heat exchanger.

P50 is a statistical term used in risk analysis and forecasting. P50 represents the median scenario, meaning there is a 50% probability that the actual outcome will be higher than this value and a 50% probability it will be lower. It serves as the baseline ""best estimate.""

• Coefficient of Performance (COP) [-]: A measure of efficiency, defined as the ratio of useful heating or cooling provided to the work (energy) required to operate the Chiller. Higher values indicate greater efficiency.

• Doublet Net Production [MW]: The actual useful power output (Megawatts) generated by the ""doublet"" (one production well + one injection well), subtracting any energy used by pumps or auxiliary equipment.

• Levelized Cost of Energy (LCOE) [US$ct/kWh]: The average revenue per unit of energy needed to recover the cost of building and operating the plant over its entire life cycle. It essentially represents the break-even price.

• Net Present Value (NPV) [million US$]: The current financial value of all future cash flows the project will generate, minus the initial investment costs. A positive NPV generally indicates a profitable project.

• Optimized Depth of the Aquifer [m]: The calculated ideal drilling depth (meters) to reach the geothermal reservoir that balances drilling costs with temperature/flow benefits.

• Pressure needed to drive pumps [Bar]: The total pressure difference required to drive the thermal loop through the reservoir and wellbore.

• Production Flow Rate [m³/h]: The volume of geothermal fluid extracted from the reservoir per hour (cubic meters per hour).

• Temperature at Reservoir [°C]: The natural temperature (Celsius) of the geothermal fluid within the subsurface rock formation before it is brought to the surface.

• Transmissivity [Dm]: A measure of how easily fluid flows through the porous aquifer. It is the product of hydraulic conductivity and aquifer thickness (often measured in Darcy-meters). High transmissivity means easier extraction.

• Well Distance [m]: The physical distance (meters) between the production well (extraction) and the injection well (return). Proper spacing prevents ""short-circuiting,"" where cool injected water lowers the temperature of the production well too quickly.

This dataset comprises a collection of shapefiles detailing the geographical locations and attributes of various wells across Africa, with a strong focus on geothermal exploration and assessment. Together, these layers offer a valuable resource for geothermal energy planning, subsurface geological analysis, and infrastructure mapping. The collection includes the following spatial data layers:

• Wells: Maps the locations of various existing well types and provides critical geothermal metrics where available, including well names, temperature correction states, thermal gradients, heat flow, and heat production data.
• BGS Well: A compiled spatial layer derived from scientific literature that categorizes well locations and types.
• Planned wells: Highlights the prospective locations of future geothermal wells across the continent.

These layers are part of the Geothermal Atlas for Africa developed within the LEAP-RE project"

The current version of the GDW database (version 1.0) aims to catalogue all types of anthropogenic instream barriers. While initial mapping efforts prioritize major dams that form reservoirs, as well as run-of-river barriers on larger rivers where more information is readily available, the dataset comprises two distinct but interconnected spatial layers:

• Barrier and Dam Locations (Point Layer): Contains barrier and dam locations, geospatially referenced as point coordinates and co-registered to the global river network of HydroSHEDS.
• Reservoir Extents (Polygon Layer): Contains associated reservoir polygons, which includes data representing the maximum storage capacity of the reservoirs measured in million cubic meters.

This dataset shows the location of hydrocarbon provinces with proven petroleum reserves. This data was compiled as part of the USGS's World Energy Project (WEP).

This dataset is part of the LEAP-RE project collection. For more information visit https://www.leap-re.eu/

Many African countries, especially in the Sub-Saharan region highly depend on hydropower which is one of the energy sources that are most affected by droughts. At the same time hydropower has a huge impact on water consumption (mainly through evaporation from reservoir surfaces) in comparison with other fuel types despite having higher densities of plants and installed capacities. Hydropower accounts for 15% of Africa’s energy production. This map shows the energy production (GWh) of hydropower plants with an installed capacity above 5MW, aggregated for each hydropower-generating country in Africa for year 2016.

Development test dataset: random 0-100 values on the 0.35° Nature Africa grid clipped to the DRC. Not real data.