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

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.

Alongside scientific knowledge of hazards that may contaminate water sources, those living and working in rural sub-Saharan Africa may have detailed knowledge of potential contamination hazards and where they are located. Participatory mapping has been used as a component of the OneHealthWater project which aims to draw on that knowledge, to better understand geographic patterns of hazards that could contaminate water sources. The technique in this study involves working with small groups or individuals in 10 villages in Siaya County, who are then asked to map the domestic water sources and possible microbiological contamination hazards onto satellite imagery. The outputs may contribute to a better understanding of the potential hazards that may be found around rural water sources in sub-Saharan Africa and ultimately help to improve management of water safety.

Diarrhoeal disease and lack of access to safe water remain significant public health issues in developing countries. There is also growing concern about the potential for disease, including diarrhoeal infections, to be transmitted from livestock to humans. This project addresses the potential drinking-water contamination risks to human health in rural sub-Saharan Africa, where people and livestock often live in close proximity. Preliminary fieldwork will be carried out in rural Kenya, building on an ongoing study that is simultaneously recording human and livestock disease in ten villages. The fieldwork will test different techniques to identify contamination hazards from livestock, alongside water quality testing and recording of diarrhoea in children. These techniques will include the use of GPS collars to track cattle movements, maps of hazardous areas created by the communities themselves, and also checklists for recording signs of livestock hazards at water sources and around water stored in the home. We will look at how feasible it is to record hazards using these techniques. We will also statistically assess whether we find greater water contamination and greater diarrhoea in children where there are more recorded hazards. Since measurement of water contamination used in such areas is based on bacteria found in both livestock and humans, the project will also work on affordable ways of testing for micro-organisms that are specifically found in livestock faeces versus those found in human faeces. If successful, such techniques could be used to investigate the importance of different sources of faecal contamination of drinking-water. This in turn could help manage the safety of rural water sources like wells and rainwater and better protect drinking-water stored in the home from contamination through livestock. Because this complex problem requires a wide range of expertise, during the project we will strength our academic team to include more disciplines, particularly specialists in child health and social sciences. The tools for identifying hazards from livestock will be made widely available at the end of the project and UK expertise in the microbiological laboratory techniques will be shared with Kenyan collaborators. The experience gained will be used to build up contacts and develop a plan and team for a larger-scale study of livestock hazards, water contamination, and diarrhoeal disease risk in several countries.

This data set is a 30m land use / cover classification product in the Sahel region of Africa every five years from 1990 to 2020. The product is based on a collaborative framework of land cover classification integrating machine learning and multiple data fusion, and integrates supervised land cover classification with existing thematic land cover maps by using Google Earth engine (GEE) cloud computing platform. The classification system adopts FROM_ GLC classification system includes 8 categories: cultivated land, forest, grassland, shrub, wetland, water body, impervious surface and bare land. The data set has been verified by a large number of seasonal samples in the Sahel region. The overall accuracy of the data set is about 75%, and the accuracy of change area detection is more than 70%. It is also very similar to FAO and the existing land cover map. The data set can provide data support for the sustainable use of land resources and environmental protection in the Sahel region 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