This dataset shows water bodies in Africa including lakes, reservoir, and lagoon. Data is curated from RCMRD Geoportal. The Regional Centre for Mapping of Resources for Development (RCMRD) was established in Nairobi – Kenya in 1975 under the auspices of the United Nations Economic Commission for Africa (UNECA) and the then Organization of African Unity (OAU), today African Union (AU). RCMRD is an inter-governmental organization and currently has 20 Contracting Member States in the Eastern and Southern Africa Regions; Botswana, Burundi, Comoros, Ethiopia, Kenya, Lesotho, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Somali, South Africa, South Sudan, Sudan, Swaziland, Tanzania, Uganda, Zambia and Zimbabwe. To learn more about RCMRD, please visit http://www.rcmrd.org/

South Africa inland water bodies/features (including lakes, canals) with descriptions. Provided by DIVA-GIS

Geo-referenced point database on dams in Africa.

Supplemental Information:

This dataset is described extensively on the website https://www.fao.org/aquastat/en/databases/dams. On this website, the dataset is also published in Excel to facilitate the publication of information on dams without geographical co-ordinates. It is accompanied by an explanatory document that provides specific information about the references used, and brief notes on the more complicated dams. The shapefile consists of the following information: a) GIS generated codes (FID); b) coordinates in decimal degrees (DDLONG, DDLAT); c) 'coordinates' broken down into eight codes (LATDIR with an N or an S for North or South, LATDEG, LATMIN and LATSEC for degrees, minutes and seconds latitude and LONGDIR with an W or E for West or East and LONGDEG, LONGMIN and LONGSEC for degrees minutes and seconds longitude); d) items described in details on the website, such as river basin and administrative unit; e) completion date; f) height; g)surface area; h) main purpose.

This dataset served also as a basis for the Global reservoirs and dams (GRanD) database, which resulted in the article: Lehner, B., Reidy Liermann, C., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J., Rödel, R., Sindorf, N., Wisser, D. 2011. High resolution mapping of the world’s reservoirs and dams for sustainable river flow management. Published in the Journal Frontiers in Ecology and the Environment.

For a wider distribution and to support other projects at FAO this map is also distributed in a DVD as part of a publication entitled: Jenness, J., Dooley, J., Aguilar-Manjarrez, J., Riva, C. African Water Resource Database. GIS-based tools for inland aquatic resource management. 2. Technical manual and workbook. CIFA Technical Paper. No. 33, Part 2. Rome, FAO. 2007. 308 p.

Contact points:

Metadata contact: AQUASTAT FAO-UN Land and Water Division

Online resources:

Download - Database of dams in Africa (Excel file)

Geo-referenced dam databases on AQUASTAT website

Note: This layer does not have clickable pop-ups at this time.Watersheds, also called drainage or catchment basins, are areas of land where precipitation drains into a common body of water such as a lake, river, or ocean. This includes precipitation from clouds like rain or snow, groundwater, and other bodies of water within the basin. Watersheds are powerful components of the natural landscape, and it is important to understand the factors that impact their condition. The size and shape of a drainage basin is determined by many features of its landscape. Often, the first that comes to mind is an area’s topography. The steepness of hills and mountains, along with the distance between a precipitation source and bodies of water, also determine how quickly it reaches its destination. Additionally, different soil types impact water movement, with some types (like sand) much more permeable than others (like clay). If the surface is too impermeable for precipitation to reach the soil in the first place, which is the case in developed areas covered by roofs and pavement, it forms runoff and reaches bodies of water without spending time as groundwater. Extremely large drainage areas are made of a number of tributary basins, which collect precipitation in streams and then deliver water to the major rivers. Watersheds can be made of any number of smaller drainage basins, which is called a river system.The elevated boundary between areas drained by different basins is called a divide, and a continental divide completely separates large river systems to different regions of a continent. In North and South America, the Great Continental Divide runs along the peaks of the Rocky Mountains and Andes, with water to the west running into the Pacific Ocean and to the east into the Atlantic. Another continental divide exists along the Himalayan Mountains in South Asia and continues along the coast of the Arabian Peninsula and eastern Africa, directing precipitation into the Indian Ocean. On the other side of this divide, to the north of the Himalayas, exists a feature called an endorheic basin—in these regions, precipitation never reaches an ocean, but is retained in a smaller body of water like a lake or inland sea.Knowing the extent of watersheds is important for both natural and sociopolitical reasons. Scientists interested in hydrology and ecology often study entire drainage basins because the majority of the precipitation, sediments, nutrients, and pollutants flowing through a watershed originated there, too. Many conservation efforts protect watersheds as holistic units as well, called watershed management, and some countries and states even have governing bodies for basins in their territory. In the field of geopolitics, the study of how international relations are influenced by geographical factors, watersheds can be the cause of conflict or of harmony through mutual governance and accountability.This map layer was created using a model that predicts water flow with elevation data. It separates one watershed into two, by predicting flow then using GIS to add additional information to the model such as catchment boundaries, lake shorelines, and rivers.Each time a divide is created, the model makes a new level—these levels are called hydrologic units. Hydrologic units break the globe up into regions, subregions, basins, subbasins, watersheds and sub watersheds. Each hydrologic unit has a unique code called a hierarchical hydrologic unit code (HUC). Regions, for example, have a two-digit code. An additional two digits are added for each subsequent scale until sub watersheds, which has twelve digits. Not all of the watersheds are clickable at this time. Check back as we add data for areas outside the United States.Watershed conservation is a very important part of keeping water clean and safe. The Nature Conservancy explains that there are a lot of ways to help protect your watersheds, like conserving water, disposing of waste and chemicals safely, or choosing to walk or bike instead of drive. Add the Protected Areas layer to the map to find the areas of your watershed that need special care.

Broad typologies of irrigation systems in project countries were identified by analyzing distribution of area equipped for irrigation in relation to climatic conditions, (proximity to) water resources and coastline, and dominant land cover. The distribution of irrigation systems is derived from the Global Map of Irrigation Areas input files, but caution is needed as not all information is validated or updated. It is foreseen that the country level analysis will better refine this preliminary review. The land cover (FAO, 2014) input can help identifying valley bottom and wetlands where water is managed under no or partial control, most commonly found in humid and sub-humid climates. Proximity to (perennial) rivers and water bodies give an indication on whether the irrigation area is serviced by surface or groundwater, although caution is needed at this scale, as reliable information on irrigation infrastructures is not consistently available. Proximity to coastline and deltas are used to characterize irrigation areas which rely on coastal aquifers.

This dataset is about book subjects and is filtered where the books is A geographical survey of Africa : its rivers, lakes, mountains, productions, states, populations, &c. with a map on an entirely new construction, to which is prefixed a letter to Lord John Russell regarding the slave trade and the improvement of Africa, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

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.

Shape files on Morocco's administrative regions, population, infrastructure and in-country water bodies

Seamless and robust derivative of SWB, Double-Lined River, and Inshore Island Features from SRTM-SWBD data tiles. Data source: NASA Surface Water Body Database. Highest resolution water body and coastal mask of Africa

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Pearson’s correlation coefficients between XW incidence and covariates used for the African Great Lakes Region map, ordered from highest positive to high negative.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

We mapped Low Elevation Coastal Zones at or below 10m in elevation and adjacent to the coastline for West Africa, from Senegal to Nigeria. This analysis was conducted using MERIT DEM data, which was created by removing multiple error types from SRTM3 v2.1 and AW3D-30m v1 to reduce vertical height bias (Yamakzai et al. 2018). Given this increased vertical accuracy, MERIT DEM can map 10-meter LECZs with an 89% accuracy (Gesch 2018).

To determine the 10-meter LECZ, we identified pixels that had a value less than 10 and were adjacent to the coast or a coastal water body. We also masked permanent water bodies from the zone to better visually represent the surrounding land areas most at risk.

Map projection : It is currently Africa Albers Equal Area Conic (WGS84).

Data links

<!·MERIT DEM : https://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_DEM/

<!-https://www.wabicc.org/mdocs-posts/mapping-west-africas-low-elevation-coastal-zones/

<·file:///C:/Users/ProDesk%20400/Downloads/Mapping%20West%20Africa&%23039%3Bs%20Low%20Elevation%20Coastal%20Zones.pdf

Data source :

This data layer was developed using MERIT DEM data, which is created by removing multiple error types from SRTM3 v2.1 and AW3D-30m v1 to reduce vertical height bias. This dataset was produced by Yamakzai et al. 2018.

Citation (s)

Cori G., 2019. Mapping weest Africa’s low elevation costal zones. USAID, WA BiCC, Tetra Tech.

Gesch, D., 2018. Best Practices for Elevation-Based Assessments of Sea-Level Rise and Coastal Flooding Exposure. Frontiers in Earth Science, 6.

Gunduz, Orhan & Tulger Kara, Gülşah. (2015). ‘Influence of DEM Resolution on GIS-Based Inundation Analysis’. 9th World Congress of the European Water Resources Association (EWRA). İstanbul, Turkey.

Kulp, S. and Strauss, B., 2015. ‘The Effect Of DEM Quality On Sea Level Rise Exposure Analysis’. AGU Fall Meeting. 2015.

Leon, J., Heuvelink, G. and Phinn, S., 2014. Incorporating DEM Uncertainty in Coastal Inundation Mapping. PLoS ONE, 9(9), p.e108727.

Yamazaki D., D. Ikeshima, R. Tawatari, T. Yamaguchi, F. O'Loughlin, J.C. Neal, C.C. Sampson, S. Kanae & P.D. Bates. A high accuracy map of global terrain elevations. Geophysical Research Letters, vol.44, pp.5844-5853, 2017 doi: 10.1002/2017GL072874.

Geographic coverageSenegal to Nigeria

Layer creation date : 7/31/20

Contacts : Cori Grainger (cori.grainger@tetratech.com), Vaneska Litz (vaneska.litz@tetratech.com), Stephen Kelleher (Stephen.Kelleher@wabicc.org).

The goal of developing HydroSHEDS was to generate key data layers to support regional and global watershed analyses, hydrological modeling, and freshwater conservation planning at a quality, resolution and extent that had previously been unachievable.