WFS1.1.01.0.01.1.0text/xmlServiceIdentificationServiceProviderOperationsMetadataFeatureTypeListFilter_Capabilitiestext/xml; subtype=gml/3.1.1resultshitstext/xml; subtype=gml/3.1.1GML2KMLSHAPE-ZIPapplication/gml+xml; version=3.2application/jsonapplication/vnd.google-earth.kml xmlapplication/vnd.google-earth.kml+xmlcsvgml3gml32jsontext/csvtext/javascripttext/xml; subtype=gml/2.1.2text/xml; subtype=gml/3.22ALLSOMEresultshitstext/xml; subtype=gml/3.1.1GML2KMLSHAPE-ZIPapplication/gml+xml; version=3.2application/jsonapplication/vnd.google-earth.kml xmlapplication/vnd.google-earth.kml+xmlcsvgml3gml32jsontext/csvtext/javascripttext/xml; subtype=gml/2.1.2text/xml; subtype=gml/3.2text/xml; subtype=gml/3.1.1GenerateNewUseExistingReplaceDuplicateALLSOMEQueryInsertUpdateDeleteLockbims:nba2018_artificial_wetlands_aeaThe artificial waterbodies layer was part of the South African Inventory of Inland Aquatic Ecosystems (SAIIAE) released with the launch of the NBA 2018. As part of the development of the national wetland map a dataset of artificial water bodies was built using the DRDLR:NGI hydrological features exported from NWM5.2. All non-natural feature types were included in this feature class and were checked for consistency and then integrated by Dr Andrew Skowno (SANBI) with the national 1:50 000 dams map. Dr Heidi van Deventer subsequently added and cleaned the dataset. Reference: Van Deventer, H.; Smith-Adao, L.; Mbona, N.; Petersen, C.; Skowno, A.; Collins, N.B.; Grenfell, M.; Job, N.; Lötter, M.; Ollis, D.; Scherman, P.; Sieben, E.; Snaddon, K. 2018. South African Inventory of Inland Aquatic Ecosystems. South African National Biodiversity Institute, Pretoria. Report Number: CSIR report number CSIR/NRE/ECOS/IR/2018/0001/A; SANBI report number http://hdl.handle.net/20.500.12143/5847.featuresnba2018_artificial_wetlands_aeaurn:x-ogc:def:crs:EPSG:4326-34.77466303934897 16.500308111638844-22.141823856188193 32.81910426354215bims:mv_conservation_areasData from the South African Conservation Areas Database (SACAD) available at https://egis.environment.gov.za. According to the National Environmental Management: Protected Areas Act (Act 57 of 2003), The Minister must maintain a register called the Register of Protected Areas. A Register of Protected Areas was therefore developed for reporting and mapping purposes of PROTECTED AREAS and CONSERVATION AREAS in South Africa. This Register used a database called the Protected and Conservation Areas database (PACA) and comprises of all data required for the Register of Protected Areas (legally declared) as well as data on Conservation Areas (areas responsibly managed for biodiversity conservation but not legally declared as Protected Areas).featuresmv_conservation_areasPolygonurn:x-ogc:def:crs:EPSG:432616.45227814 -46.982257338.0038739 -22.13038443bims:eco_region_1Ecoregions (Level I, version 2005) for South Africa derived from terrain and vegetation, with some consideration of altitude, rainfall, runoff variability, air temperature, geology, soil. Data Source: Department of Water and Sanitation: 2011featureseco_region_1urn:x-ogc:def:crs:EPSG:414816.46921730041504 -34.8342132568359432.89185333251953 -22.124500274658203bims:eco_region_2Ecoregions (Level II, version 2005) for South Africa derived from terrain and vegetation, with some consideration of altitude, rainfall, runoff variability, air temperature, geology, soil. Level II ecoregions are a refinement of the Level I ecoregions, using the same methods. Data Source: Department of Water and Sanitation: 2011featureseco_region_2urn:x-ogc:def:crs:EPSG:385716.469217300415046 -34.8342132568359432.891853332519524 -22.124500274658203bims:freshwater_ecoregions_of_the_worldThe Freshwater Ecoregions Of the World (FEOW) provide a global biogeographic regionalization of the Earth's freshwater biodiversity. FEOW were published in 2008 by the The Nature Conservancy. TNC is the originator of this version of the FEOW. Ecoregion names, codes, and boundaries are identical to those in the published version by TNC and WWF, Inc. Citation for the Ecoregions: Abell et al. 2008. Freshwater Ecoregions of the World: A New Map of Biogeographic Units for Freshwater Biodiversity Conservation. BioScience. Vol. 58 No. 5: 403-414. Link: https://www.feow.org/featuresfreshwater_ecoregions_of_the_worldurn:x-ogc:def:crs:EPSG:4326-34.822227563999945 16.125000300000067-17.735000003999914 36.110275200000046bims:KGClim_2080s_RCP6_FUTURE_SAKoppen-Geiger Climate Zones for the future period 2070 - 2090, using data from KGClim. KGClim is a new, improved 1-km global dataset of historical and future Köppen-Geiger climate classification maps and bioclimatic variables. The dataset provides high-resolution descriptions of long-term (30-yr) climate conditions for historical and future periods. The historical maps are derived from multiple downscaled observational datasets, and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. The data can be downloaded from http://glass.umd.edu/KGClim/ Cui, D., Liang, S., Wang, D., and Liu, Z.: A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables, Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, 2021.featuresKGClim_2080s_RCP6_FUTURE_SAurn:x-ogc:def:crs:EPSG:4326-34.83409999899973 16.451890000000276-22.1250300579999 32.900000000000034bims:geoclassGeomorphological zones for all rivers were generated using Digital Elevation Model (DEM): 90m x 90m resolution Shuttle Radar Topography Mission (SRTM). This is a preliminary classification where slope has been used to classify the rivers using slope classes based on Rowntree and Wadeson (1999). Moolman J, Kleynhans CJ and Thirion C (2002) Channel Slopes in the Olifants, Crocodile and Sabie River Catchments. Department of Water Affairs and Forestry, Institute For Water Quality Studies, Internal Report No.N/0000/00REH/0102. 41pp Rowntree KM and Wadeson RA (1999) A hierarchical geomorphological model for the classification of selected South African Rivers. Water Research Commission Report No. 497/1/99, Water Research Commission, Pretoria. 334pp.featuresgeoclassurn:x-ogc:def:crs:EPSG:4326-34.769989013671875 15.98332977294922-19.791433334350586 33.999252319335945bims:ground_strategic_water_source_2017The Strategic Water Source Areas for groundwater (SWSA-gw) reflect areas that have high groundwater recharge and where the groundwater forms a nationally important resource. The areas are delineated for the purposes of research, and the outcomes are useful to national level planners and decision-makers as an indication of the location of strategic groundwater sources and resources. Sub-national WSAs for groundwater were also identified.The framework for the management of the SWSAs promotes various best-practice groundwater management measures, such as the delineation of wellfield capture zones, and the incorporation of these in spatial planning processes. It is recommended that DWS prioritizes the SWSA-gw, along with other criteria (areas of sole supply, presence of stressed resources), for various management measures allowed for in the NWA, but not yet implemented.featuresground_strategic_water_source_2017urn:x-ogc:def:crs:EPSG:4326-34.81825754675748 16.838025749199293-22.677155357513254 32.88765827861305bims:KGClim_1979_2008_HIST_SAKoppen-Geiger Climate Zones for the historical period 1979-2008, using data from KGClim. KGClim is a new, improved 1-km global dataset of historical and future Köppen-Geiger climate classification maps and bioclimatic variables. The dataset provides high-resolution descriptions of long-term (30-yr) climate conditions for historical and future periods. The historical maps are derived from multiple downscaled observational datasets, and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. The data can be downloaded from http://glass.umd.edu/KGClim/ Cui, D., Liang, S., Wang, D., and Liu, Z.: A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables, Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, 2021.featuresKGClim_1979_2008_HIST_SAurn:x-ogc:def:crs:EPSG:4326-34.83409999899972 16.451890000000276-22.125030057999886 32.90000000000009bims:HydroRIVERS_Reprojected_rendered_shpHydroRIVERS depicts a vectorized line network of all global rivers with a catchment area of at least 10 km² or an average river flow of at least 0.1 m³/sec, or both. HydroRIVERS has been derived from the gridded HydroSHEDS core layers at 15 arc-second resolution. Data are available for download at https://www.hydrosheds.org/products/hydrorivers#downloads. Citation: Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186.featuresHydroRIVERS_Reprojected_rendered_shpurn:x-ogc:def:crs:EPSG:3857-17.99374999999995 -34.7937499999999754.41666666666555 37.33124999999891bims:lada_land_useIn the context of the Land Degradation Assessment in Drylands (LADA) project, the land use system approach for land degradation assessment has as guiding principle that land use is the major driving force of land degradation. Mapping of land use systems has, therefore, becomes a major activity within the LADA project at the global and national level where land use units are considered the basic units in which land degradation and land improvements are mapped. The following spatial layers were found to be reliable enough and at an acceptable level of detail to be used in the production of the LUS map for South Africa: • National Land Cover 2000 (NLC Consortium); • Protected Areas in South Africa 2001 (Biodiversity GIS, South African Biodiversity Institute- SANBI); • VEGMAP 2006 (Mucina & Rutherford, SANBI); • Local Municipalities 2005 (Demarcation Board of South Africa). The LADA Land Use Map for South Africa has 18 land use/cover classes as a result of combining the 4 spatial layers discussed above. In combination with administrative boundaries, a total number of 2447 unique combinations (which were equal to the number of LADA-WOCAT Mapping Questionnaires (QM) to be completed) were derived by integrating the Local Municipality (LM) boundaries with the Land Use Systems data. Data Source: Department of Agriculture, Forestry and Fisheries (DAFF): 2009featureslada_land_useurn:x-ogc:def:crs:EPSG:385716.456732836033222 -34.8305252225219232.85970999979669 -23.98123999904244bims:map_zonesMean annual precipitation (in mm/year). From Schulze R.E. (ed.) (2007). South African atlas of climatology and agrohydrology. WRC Report No. 1489/1/06. Water Research Commission, Pretoriafeaturesmap_zonesurn:x-ogc:def:crs:EPSG:4326-34.85093385565186 16.414927990591522-22.078834182711123 32.89240070190429bims:rivers_nfepaFEPA: River FEPAs and their associated quinary catchments achieve biodiversity targets for river ecosystems and threatened fish species, and were identified in rivers that are currently in a good condition (A or B ecological category). The FEPA status applies to the river reach itself, and indicates that the river should remain in a good condition in order to contribute to national biodiversity goals and support sustainable use of water resources. FishCorrid: Quinary catchments containing rivers that form migration corridors for threatened fish species, as part of the fish sanctuary network. FishFSA: Fish Support Areas are quinary catchments identified as necessary as fish sanctuaries, fish translocation or fish relocation zones that are not in good condition (i.e. A or B ecological category). Phase2FEPA: These were identified in moderately modified rivers (C ecological category), only in cases where it was not possible to meet biodiversity targets for river ecosystems in rivers that were still in good condition (A or B ecological category). The condition of these Phase 2 FEPAs should not be degraded further, and should be rehabilitated to a better condition. Upstream FEPAs: Upstream management catchment required to prevent the downstream degradation of FEPAs and FishFSAs. Source reference: Nel, J.L., Murray, K.M., Maherry, A.M., Petersen, C.P., Roux, D.J., Driver, A., Hill, L., Van Deventer, H., Funke, N., Swartz, E.R., Smith-Adao, L.B., Mbona, N., Downsborough, L. and Nienaber, S. (2011). Technical Report for the National Freshwater Ecosystem Priority Areas project. WRC Report No. K5/1801.featuresrivers_nfepaurn:x-ogc:def:crs:EPSG:4326-34.792832222204964 16.468762878355896-22.123996991513273 32.89081654672433bims:cba_national_mviewA CBA Map is a spatial plan for ecological sustainability. It identifies a set of biodiversity priority areas, called Critical Biodiversity Areas (CBAs) and Ecological Support Areas (ESAs), which, together with protected areas, are important for the persistence of a viable representative sample of all ecosystem types and species as well as the long-term ecological functioning of the landscape as a whole. CBA Maps are typically developed at the provincial, district or metropolitan scale, and are based on a systematic biodiversity plan. Map categories include PA (Protected Areas), CBA (Critical Biodiversity) and ESA (Ecological Support Areas). Source Sanbi 2019featurescba_national_mviewurn:x-ogc:def:crs:EPSG:432616.455625648000023 -34.8324268349999832.85328674400006 -22.12502999999998bims:ecological_support_areas_mviewESAs are required to support and sustain the ecological functioning of Critical Biodiversity Areas (CBAs). Source: SANBI 2016featuresecological_support_areas_mviewurn:x-ogc:def:crs:EPSG:432667183.65170000048 6144999.5312000021686310.5417999988 7527335.1266bims:nwm6_beta_v3_20230714The National Wetland Map 6 Beta is an unpublished version (not public) update of the National Wetland Map. National Wetland Map 6 Beta includes inland wetlands, artificial wetlands (dams), and estuaries. This dataset includes an update of wetland extent in selected catchments, and an update of wetland attributes across the dataset. The National Wetland Map 6 Beta contains the condition, ecosystem threat status, and protection level of the NBA 2018. These will be re-assessed, updated, and released to the public after the completion of the NBA 2024.featuresnwm6_beta_v3_20230714urn:x-ogc:def:crs:EPSG:432616.468761444091797 -34.8214645385742232.88218688964844 -22.125028610229492bims:pestapp_quar_2014featurespestapp_quar_2014urn:x-ogc:def:crs:EPSG:432616.45694923401983 -34.8334541321813532.89228057866289 -22.123983383166678bims:primary_catchment_areaPrimary drainage regions are the broad hydrological catchment areas in South Africa. Data Source: Department of Water and Sanitation: 2011featuresprimary_catchment_areaurn:x-ogc:def:crs:EPSG:385715.644238499999998 -34.8334541000000135.09458159999999 -19.751834900000013bims:protected_areasData from the South African Protected Areas Database (SAPAD) available at https://egis.environment.gov.za. According to the National Environmental Management: Protected Areas Act (Act 57 of 2003), The Minister must maintain a register called the Register of Protected Areas. A Register of Protected Areas was therefore developed for reporting and mapping purposes of PROTECTED AREAS and CONSERVATION AREAS in South Africa. This Register used a database called the Protected and Conservation Areas database (PACA) and comprises of all data required for the Register of Protected Areas (legally declared) as well as data on Conservation Areas (areas responsibly managed for biodiversity conservation but not legally declared as Protected Areas).featuresprotected_areasurn:x-ogc:def:crs:EPSG:4326-50.23333333219722 16.77666780899719-22.135582437033865 43.03970189693305bims:vegmap2018_aea_v22_5_07012019_betapublicIn the classification hierarchy for the terrestrial realm, there exists a classification level that falls between Biome and ecosystem type, known as the Bioregion. Bioregions consider dominant landscape scale attributes and biological data, while also applying the principle of interconnectedness sometimes driven by management boundaries. The concept of a Bioregion acknowledges that a region should not consist of widely dispersed areas but rather share a common boundary, promoting a sense of cohesion. Additionally, each Bioregion should exhibit a coherent climatic profile. The classification of Bioregions relies heavily on the identification of distinct vegetation types. These vegetation types are characterized by unique assemblages of plant species. This definition of the Bioregion was established by Rutherford et. al in 2006. However, it is important to note that the boundaries of Bioregions have undergone revisions with the updated versions of the map. These revisions reflect a better understanding of the ecological dynamics and interconnectedness within different regions, resulting in more accurate and refined classifications. Reference: Rutherford, M.C., Mucina, L. & Powrie, L.W. 2006. Biomes and bioregions of Southern Africa. In: Mucina, L. & Rutherford, M.C. (eds), The vegetation of South Africa, Lesotho and Swaziland: 30-51. SANBI, Pretoria.featuresvegmap2018_aea_v22_5_07012019_betapublicurn:x-ogc:def:crs:EPSG:4326-34.83301470908603 16.4426902101852-22.125831584260148 32.89129939455158bims:surface_strategic_water_source_2017Surface water SWSAs are found in areas with high rainfall and produce most of the runoff. The SWSA-sw study identified 22 areas that were significant at the national level and a further nine that are significant at a sub-national level. They are important because they contribute considerably to the overall water supply of the country. These multi-purpose landscapes are key ecological infrastructure assets for South Africa, supporting growth and development needs. The effective protection of surface water SWSAs areas is vital for national security because a lack of water security will compromise national security and human well-being.featuressurface_strategic_water_source_2017urn:x-ogc:def:crs:EPSG:4326-34.429616472214484 18.312879163108114-22.602072297926966 32.636560333875885bims:combination_saprovince_sadc_boundaryMap of the nine provinces in South Africa, together with neighbouring countries within the SADC region.featurescombination_saprovince_sadc_boundaryurn:x-ogc:def:crs:EPSG:432611.717621287581297 -34.8341740.843516472590245 -10.472588808985627bims:gis_osm_roads_free_1featuresgis_osm_roads_free_1urn:x-ogc:def:crs:EPSG:4326-31.2665393 32.5127293-6.1748001 42.1733303bims:hydr_lines_expNational Geospatial Information I hydrological lines (Rivers and Streams) from 1:50000 topographical maps data, displaying perennial and non-pernnial rivers and streams. Source: National Geospatial Information (NGI) 2016featureshydr_lines_expurn:x-ogc:def:crs:EPSG:385716.448917543114046 -34.8182784416855732.85773878352141 -22.125733493436464bims:rivers_500kRivers of South Africa, originally based on a 1994 1:500 000 rivers coverage from the Chief Directorate of National Geospatial Information. This version is made available 'as is' for the convenience of those who need a medium-scale river coverage for display or modeling. Data available at www.dws.gov.za/iwqs/gis_data/river/rivs500k.aspxfeaturesrivers_500kurn:x-ogc:def:crs:EPSG:385715.983329714507738 -34.76999022634769433.99925271872241 -19.791432141685114gml:Envelopegml:Pointgml:LineStringgml:PolygonLessThanGreaterThanLessThanEqualToGreaterThanEqualToEqualToNotEqualToLikeBetweenNullCheckabsabs_2abs_3abs_4acosAddCoveragesAffineAggregateAndAreaarea2AreaGridarrayasinatanatan2attributeCountBandMergebandsBandSelectBarnesSurfacebetweenboundaryboundaryDimensionboundedByBoundsbufferBufferFeatureCollectionbufferWithSegmentsCategorizeceilcenterLinecentroidclassifyClassifyByRangeClipCollectGeometriesCollection_AverageCollection_BoundsCollection_CountCollection_MaxCollection_MedianCollection_MinCollection_NearestCollection_SumCollection_UniqueConcatenatecontainsContourcontrastconvertconvexHullConvolveCoveragecosCountCoverageClassStatsCropCoveragecrossesdarkendateDifferencedateFormatdateParsedensifydesaturatedifferencedimensiondisjointdisjoint3Ddistancedistance3Ddivdouble2boolendAngleendPointenvenvelopeEqualAreaEqualIntervalequalsExactequalsExactToleranceequalToexpexteriorRingFeatureFeatureClassStatsfloorfootprintsgeometrygeometryTypegeomFromWKTgeomLengthGeorectifyCoverageGetCoveragesValueGetFullCoveragegetGeometryNgetXgetYgetzgrayscalegreaterEqualThangreaterThanGridGroupCandidateSelectionHeatmaphslidIEEEremainderif_then_elseImportinin10in2in3in4in5in6in7in8in9inArrayInclusionFeatureCollectionint2bboolint2ddoubleinteriorPointinteriorRingNInterpolateintersectionIntersectionFeatureCollectionintersectsintersects3DisCachedisClosedisCoverageisEmptyisInstanceOfisLikeisNullisometricisRingisSimpleisValidisWithinDistanceisWithinDistance3DJenksJifflejsonArrayContainsjsonPointerlabelPointlanguagelapplylengthlessEqualThanlessThanlightenlinlistlistMultiplylitemliteratelogLRSGeocodeLRSMeasureLRSSegmentmapGetmaxmax_2max_3max_4midAnglemidPointminmin_2min_3min_4mincircleminimumdiameterminrectanglemixmoduloMultiplyCoveragesNearestNormalizeCoveragenorthFixnotnotEqualTonownumberFormatnumberFormat2numGeometriesnumInteriorRingnumPointsoctagonalenvelopeoffsetOroverlapsPagedUniqueparameterparseBooleanparseDoubleparseIntparseLongparseTimepgNearestpiPointBufferspointNpointOnLinePointStackerPolygonExtractionpolygonizePolyLabellerpowpropertyPropertyExistsQuantileQueryrandomRangeLookupRasterAsPointCollectionRasterZonalStatisticsRasterZonalStatistics2RecodeRectangularCliprelaterelatePatternreprojectReprojectGeometryrescaleToPixelsrintroundround_2roundDoublesaturateScaleCoveragesetCRSshadesimplifysinsizeSnapSpatioTemporalZonalStatisticsspinsplitPolygonsqrtStandardDeviationstartAnglestartPointStoreCoveragestrAbbreviatestrCapitalizestrConcatstrDefaultIfBlankstrEndsWithstrEqualsIgnoreCasestrIndexOfstringTemplatestrLastIndexOfstrLengthstrMatchesstrPositionstrReplacestrStartsWithstrStripAccentsstrSubstringstrSubstringStartstrToLowerCasestrToUpperCasestrTrimstrTrim2strURLEncodeStyleCoveragesymDifferencetantinttoDegreestoRadianstouchestoWKTTransformTransparencyFilltypeOfunionUnionFeatureCollectionUniqueUniqueIntervalVectorToRasterVectorZonalStatisticsverticeswithin