This line shapefile represents rivers in South Africa. The bulk of the data, for this layer, was captured from the South African Provincial Map Series (2nd edition 2001), i.e. LIMPOPO 1:700 000, NOTHERN CAPE 1:1 200 000,KWAZULU - NATAL 1:700 000, WESTERN CAPE 1:800 000, EASTERN CAPE 1:800 000, MPUMALANGA 1:600 000, NORTH WEST 1:700 000, FREE STATE 1:700 000,GAUTENG 1:300 000

HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) provides hydrographic information in a consistent and comprehensive format for regional and global-scale applications. HydroSHEDS offers a suite of geo-referenced data sets (vector and raster), including stream networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances, and river topology information. HydroSHEDS is derived from elevation data of the Shuttle Radar Topography Mission (SRTM) at 3 arc-second resolution. Available HydroSHEDS resolutions range from 3 arc-second (approx. 90 meters at the equator) to 5 minute (approx. 10 km at the equator) with seamless near-global extent.

Citation:Title: HydroSHEDS (BAS) - Africa drainage basins (watershed boundaries) at 30s resolutionCredits: World Wildlife Fund (WWF)Publication Date: 2006Publisher: U.S. Geological SurveyOnline Linkages: http://hydrosheds.cr.usgs.govhttp://www.worldwildlife.org/hydroshedsOther Citation Info: Please cite HydroSHEDS as: Lehner, B., Verdin, K., Jarvis, A. (2006): HydroSHEDS Technical Documentation. World Wildlife Fund US, Washington, DC. Available at http://hydrosheds.cr.usgs.gov.

This layer package was loaded using Data Basin.Click here to go to the detail page for this layer package in Data Basin, where you can find out more information, such as full metadata, or use it to create a live web map.

NBA 2018 Rivers assessment layer. This data set is part of the first version of the South African Inventory of Inland Aquatic Ecosystems (SAIIAE) 2018 released in July 2018. A second update of the SAIIAE 2018 was issued with the launch of the NBA 2018, and includes the condition, Ecosystem Threat Status (ETS) and Ecosystem Protection Level (EPL) information for the rivers. The river lines data set is associated with the National Wetland Map 5 (NWM5) issued with the SAIIAE version 1 and 2. Dr Lindie Smith-Adao generated this rivers data set by 2018/06/29.

The data set consists of southern Africa subset of the CPEP Global River Discharge Data Set. The Climate, People, and Environment Program (CPEP) global river discharge data set is a compilation of monthly mean discharge data for over 2600 sites worldwide. The data sources are RivDis 2.0, the United States Geological Survey, and Brazilian National Department of Water and Electrical Energy. The period of record is variable, from 3 years to greater than 100. The purpose of this compilation is to provide detailed hydrographic information to the climate research community in as general a format as possible. Data is provided in units of meters cubed per second (m**3/sec) in ASCII format. Data from stations with less than 3 years of information or with basin area less than 5000 km2 were excluded from this compilation. Therefore the original sources may have more sites available. No further documentation is available on this data set. Users should refer to the data originators for documentation. More information can be found at: ftp://daac.ornl.gov/data/safari2k/hydrology/river_discharge_cpep/comp/cpep_discharge.pdf.

The National Wetland Map version 5 (NWM5) shows the distribution of inland wetland ecosystem types across South Africa and includes estuaries and the extent of some rivers. A confidence map was compiled to identify areas where wetland extent and hydrogeomorphic (HGM) units (which contributed to defining the inland wetland ecosystem types together with the regional setting) attained at a higher level of certainty compared to other areas. Higher levels of certainty are associated [code 5 in field Confidence_nr] with areas that have been visited in-field by a wetland specialist(s) over multiple seasons and cycles of the wetland hydroperiod, and are therefore more accurately represented in the dataset. Codes 4 to 1 indicate lower levels of confidence that the extent and HGM unit are represented well. If the Estuaries are used, please cite Van Niekerk et al., 2019. Technical Report of the Estuarine Ecosystems for the NBA 2018. For queries on the National Wetland Map 5 and associated Confidence Map datasets please contact the Principal Investigator [HvDeventer@csir.co.za] and cc the Freshwater@sanbi.org.za. For contributions and queries regarding future revisions of the National Wetland Map please contact Freshwater@sanbi.org.za. Updates will be incorporated into the National Wetland Map 6 which is under way.

This southern African subset of the Global Hydrographic data set (GGHYDRO) Release 2.2 is organized into 19 files containing terrain type, stream frequency counts, major drainage basins, main features of the cryosphere surface, and ice/water runoff per year for the entire Earth's surface at a spatial resolution of 1 degree longitude by 1 degree latitude. This southern African subset of the Global Hydrographic data is provided in both ASCII GRID and binary image files formats. More information and selected thumbnails images can be found at: ftp://daac.ornl.gov/data/safari2k/hydrology/hydrographic_gghydro/comp/gghydro_readme.pdf . GGHYDRO Data Set Categories (Data File): 1. Exposed land not covered by swamp, intermittent water bodies, glacier ice, sand dunes, saltmarsh or salt flats (LAND); 2. Perennial freshwater lakes (FLAK); 3. Swamp, marsh and other wetlands(SWMP); 4. Saltwater, whether marine or terrestrial (SLTW); 5. Intermittent water bodies (ILAK); 6. Glacier ice, including shelf ice but excluding pack ice (GLAC); 7. Sand dunes (DUNE); 8. Saltmarsh (SMRS); 9. Salt flats (SFLT); 10. Land + Swamp + Sand dunes + Saltmarsh (DSRF); 11. Perennial rivers (FRIV); 12. Intermittent rivers (IRIV); 13. Land mask (MS05); 14. Major drainage basins (BAS1); 15. Smaller drainage basins (BAS2); 16. Main features of the cryosphere (CRYO); 17. Surface runoff of water (kg/m**2/yr) (RNOF); 18. Estimated root-mean-square error of RNOF (%) (RNER); and 19. Runoff of ice ( kg/m**2/yr ) (RICE).

Understanding the importance of climate in determining species distribution and how it might change as a function of spatial grain size is a vital issue for species distribution modeling (SDM), yet it is often not accounted for in models and has not been extensively addressed in under sampled areas in tropical forests. Using extensive field sampled vegetation plots data on species occurrences and current climate conditions we modeled 150 vascular plant species in the Okavango River Basin, to map their current projected suitable climate space at 2, 5, 10, 20, and 50 km2 pixel resolution. Relationships between the variable importance scores and variable identity and their interaction with predictor spatial grain were investigated using Generalized Linear Models and post-hoc analysis. We found variation in the relative influence of temperature and precipitation variables across the spatial grains. The importance of the determinants of species distribution may change between species but such changes are less determined by the predictor's spatial grain. Potential evapotranspiration consistently exhibited the greatest influence in determining species and richness distribution across fine to coarse spatial grains. We found that the spatial grain of predictors had no effect on the model predictive power and that varying predictor spatial grains had only negligible effects on the model performance measured by AUC and Kappa statistics. The spatial grains of the climatic predictors used showed no effect on species richness pattern either. Our results indicate that in areas with relatively low topographic variation, modeling at coarse spatial grain for conservation purposes can be acceptable. Moreover, we show that in tropical areas that have comparatively homogeneous climatic conditions along large spatial extents the variable importance is not influenced by predictor spatial grain. For projections of contemporary species suitable climate space in relatively flat and topographically homogeneous areas which often have a climatically homogenous landscape, more attention must be given to the identity of the selected predictor variables for modeling the species distributions than to their spatial grain size. We suggest that in species distribution modeling for conservation planning, assessment of the input datasets spatial grain should be informed and guided by knowledge of the landscape level topographic conditions, as protocol.