This map features Africa Land Cover at 30m resolution from MDAUS BaseVue 2013, referencing the World Land Cover 30m BaseVue 2013 layer.Land cover data represent a descriptive thematic surface for characteristics of the land's surface such as densities or types of developed areas, agricultural lands, and natural vegetation regimes. Land cover data are the result of a model, so a good way to think of the values in each cell are as the predominating value rather than the only characteristic in that cell.Land use and land cover data are critical and fundamental for environmental monitoring, planning, and assessment.Dataset SummaryBaseVue 2013 is a commercial global, land use / land cover (LULC) product developed by MDA. BaseVue covers the Earth’s entire land area, excluding Antarctica. BaseVue is independently derived from roughly 9,200 Landsat 8 images and is the highest spatial resolution (30m), most current LULC product available. The capture dates for the Landsat 8 imagery range from April 11, 2013 to June 29, 2014. The following 16 classes of land use / land cover are listed by their cell value in this layer: Deciduous Forest: Trees > 3 meters in height, canopy closure >35% (<25% inter-mixture with evergreen species) that seasonally lose their leaves, except Larch.Evergreen Forest: Trees >3 meters in height, canopy closure >35% (<25% inter-mixture with deciduous species), of species that do not lose leaves. (will include coniferous Larch regardless of deciduous nature).Shrub/Scrub: Woody vegetation <3 meters in height, > 10% ground cover. Only collect >30% ground cover.Grassland: Herbaceous grasses, > 10% cover, including pasture lands. Only collect >30% cover.Barren or Minimal Vegetation: Land with minimal vegetation (<10%) including rock, sand, clay, beaches, quarries, strip mines, and gravel pits. Salt flats, playas, and non-tidal mud flats are also included when not inundated with water.Not Used (in other MDA products 6 represents urban areas or built up areas, which have been split here in into values 20 and 21).Agriculture, General: Cultivated crop landsAgriculture, Paddy: Crop lands characterized by inundation for a substantial portion of the growing seasonWetland: Areas where the water table is at or near the surface for a substantial portion of the growing season, including herbaceous and woody species (except mangrove species)Mangrove: Coastal (tropical wetlands) dominated by Mangrove speciesWater: All water bodies greater than 0.08 hectares (1 LS pixel) including oceans, lakes, ponds, rivers, and streamsIce / Snow: Land areas covered permanently or nearly permanent with ice or snowClouds: Areas where no land cover interpretation is possible due to obstruction from clouds, cloud shadows, smoke, haze, or satellite malfunctionWoody Wetlands: Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate periodically is saturated with, or covered by water. Only used within the continental U.S.Mixed Forest: Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. Only used within the continental U.S.Not UsedNot UsedNot UsedNot UsedHigh Density Urban: Areas with over 70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation where constructed materials account for >60%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.Medium-Low Density Urban: Areas with 30%-70% of constructed materials that are a minimum of 60 meters wide (asphalt, concrete, buildings, etc.). Includes residential areas with a mixture of constructed materials and vegetation, where constructed materials account for greater than 40%. Commercial, industrial, and transportation i.e., Train stations, airports, etc.MDA updated the underlying data in late 2016 and this service was updated in February 2017. An improved selection of cloud-free images was used to produce the update, resulting in improvement of classification quality to 80% of the tiles for this service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data across the ArcGIS platform. It can also be used as an analytic input in ArcMap and ArcGIS Pro.This layer has query, identify, and export image services available. The layer is restricted to an 16,000 x 16,000 pixel limit, which represents an area of nearly 300 miles on a side. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Pollen. The data include parameters of pollen (population abundance) with a geographic location of South Africa, Southern Africa. The time period coverage is from 20035 to 338 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

This dataset provides values for PRECIPITATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

WATCH Forcing Data 20th Century. A meteorological forcing dataset (based on ERA-40) for land surface and hydrological models (1901-2001). Data generated in 2 tranches with slightly different methodology: 1901-1957 and 1958-2001. Five variables are at 6 hourly resolution and five variables are at 3 hourly resolution:

Tair_WFD_ - 2m Air temperature (K)

Tmin_WFD_ - 2m Minimum air temperature (K)

Tmax_WFD_ - 2m Maximum air temperature (K)

PSurf_WFD_ - 10m Surface pressure (Pa)

Qair_WFD_ - 2m Specific umidity (kg/kg)

Wind_WFD_ - 10m Wind speed (m/s)

LWdown_WFD_ - Downwards long-wave radiation flux (W/m-2)

SWdown_WFD_ - Downwards short-wave radiation flux (W/m-2)

Rainf_WFD_GPCC_ - Rainfall rate GPCC bias corrected and undercatch corrected

Snowf_WFD_GPCC_ - Snowfall rate GPCC bias corrected and undercatch corrected (kg/m-2/s)

Rainf_WFD_CRU_ - Rainfall rate CRU bias corrected and undercatch corrected (kg/m-2/s)

Snowf_WFD_CRU_ - Snowfall rate CRU bias corrected and undercatch corrected (kg/m-2/s).

This data set has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 1 (WP1). WP1 (Past climate variability) aims to provide consolidated data to other WPs in ClimAfrica, and to analyze the interactions between climate variability, water availability and ecosystem productivity of Sub-Saharan Africa. Various data streams that diagnose the variability of the climate, in particular the water cycle, and the productivity of ecosystems in the past decades, have been collected, analyzed and synthesized. The data streams range from ground-based observations and satellite remote sensing to model simulations. More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Supplemental Information:

The Water and Global Change (WATCH) programme was an Integrated Project funded under the European Union's Sixth Framework Programme. It ran from February 2007 to July 2011. For the first time it brought together the hydrological, water resources and climate research communities at an international level. Together they analysed, quantified and predicted the components of the global water cycle and the related water resources - for the present and for the future. They also evaluated the associated uncertainties, and clarified the vulnerability of global water resources within key societal and economic sectors.

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Tanya A. Warnaars

Resource constraints:

Please contact the data originator to get info of data access and use

Online resources:

Researchers provide extensive analysis of the world's water cycle

Scenarios of major production systems in Africa

CLIMAFRICA – Climate change predictions in Sub-Saharan Africa: impacts and adaptations

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power" by Sterl et al. (2022).

It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.

Five separate datasets are included:

Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022).

Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input.

Folder (3) provides the corresponding shapefiles.

Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 2, 5 or 10 depending on country size.

Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening).

Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022).

Countries/territories included in the dataset:

Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Central African Republic
Chad
Congo Republic
Democratic Republic of the Congo
Djibouti
Egypt
Equatorial Guinea
Eritrea
Eswatini
Ethiopia
Gabon
The Gambia
Ghana
Guinea
Guiné-Bissau
Côte d'Ivoire
Kenya
Lesotho
Liberia
Libya
Madagascar
Malawi
Mali
Mauritania
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Senegal
Sierra Leone
Somalia
South Africa
South Sudan
Sudan
Togo
Tunisia
Uganda
Tanzania
Zambia
Zimbabwe

References

Sterl, S., Hussain, B., Miketa, A. et al. An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. Sci Data 9, 664 (2022). https://doi.org/10.1038/s41597-022-01786-5

See also

Sterl, S. (2024). Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10650822

The future Climate Predictions for Africa (CP4A-future) data were produced using the Met Office's Unified Model (4.5km horizontal grid spacing), the IMPALA (Improving Model Processes for African cLimAte) project ran a ten-year timeslice simulation that is representative of end the 21st century (2095-2105) using a 30-year averaged sea surface temperature (SST) anomaly (2085-2115 relative to 1975-2005). Parameters include (but not limited to); near-surface air temperature, outgoing longwave radiation, surface latent heat flux and surface sensible heat flux. The NERC funded IMPALA project is within the Future Climate for Africa (FCFA) programme.

Here a subset of variables of the data produced is provided in NetCDF format for community reuse, variables are available at a range of temporal frequencies.

Land Surface Temperature (LST) is a key indicator of land surface states, and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. A daily, day and night, LST data set for continental Africa, including Madagascar, was derived from Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km resolution) data for the 6-year lifetime of the NOAA-14 satellite (from 1995 to 2000) using a modified version of the Global Inventory Mapping and Monitoring System (GIMMS) (Tucker et al., 1994). The data were projected into Albers Equal Area and aggregated to 8 km spatial resolution. The data were cloud-filtered with CLAVR-1 algorithm (Stowe et al., 1999). The LST values were estimated with a split-window technique (Ulivieri et al., 1994) that takes advantage of differential absorption of the thermal infrared signal in bands 4 and 5. The emissivity of the surface was generated using a land cover classification map (Hansen et al., 2000) combined with the FAO soil map of Africa (FAO-UNESCO, 1977) and additional maps of tree, herbaceous, and bare soil percent cover (DeFries et al., 2000). Collateral products include cloud mask, time-of-scan, latitude and longitude, and land/water mask files.The data are in flat binary files. Each data file contains 1152 columns and 1152 rows, in signed integer format (2 bytes), with 8 km by 8 km spatial resolution. A unique map exists for each day and each night of the 6-year NOAA-14 lifetime. The data are best used to infer broad temporal and spatial trends rather than pixel-by-pixel values.

Cascade was a NERC funded consortium project to study organized convection and scale interactions in the tropical atmosphere using large domain cloud system resolving model simulations. This dataset contains data from the xfjea simulation which ran using the Met Office Unified Model (UM) at 4km horizontal resolution over the domain 20W-20E, 5S-28N which encompasses the west african monsoon. Cascade Africa simulations are used to study African Easterly Waves. This dataset contains 4km Africa model measurements from xfjea run.

South Africa ZA: Average Precipitation in Depth data was reported at 495.000 mm/Year in 2014. This stayed constant from the previous number of 495.000 mm/Year for 2012. South Africa ZA: Average Precipitation in Depth data is updated yearly, averaging 495.000 mm/Year from Dec 1962 (Median) to 2014, with 12 observations. The data reached an all-time high of 495.000 mm/Year in 2014 and a record low of 495.000 mm/Year in 2014. South Africa ZA: Average Precipitation in Depth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Land Use, Protected Areas and National Wealth. Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.; ; Food and Agriculture Organization, electronic files and web site.; ;

South Africa Visitor Arrivals: Africa: Zambia data was reported at 16,364.000 Person in Aug 2018. This records an increase from the previous number of 16,002.000 Person for Jul 2018. South Africa Visitor Arrivals: Africa: Zambia data is updated monthly, averaging 8,552.000 Person from Jan 1979 (Median) to Aug 2018, with 403 observations. The data reached an all-time high of 21,776.000 Person in Dec 2013 and a record low of 357.000 Person in Jul 1982. South Africa Visitor Arrivals: Africa: Zambia data remains active status in CEIC and is reported by Statistics South Africa. The data is categorized under Global Database’s South Africa – Table ZA.Q005: Visitor Arrivals: by Transportation Mode and Country.