cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/bea.data.10000067
title: Production of Sugar Cane Sugar on Subsistence and Underutilised Farmland

description: Data was derived from the following sources: * Extent of underutilised and subsistence farmland, data obtained from Department of Agriculture, Forestry, and Fisheries. * On such land, Sugar Cane potential was calculated from data published by Schulze, Hull, and Maharaj (2007) on cane-growing potential. * Sugar and Residue production was calculated based on sugar yields, and aggregated to meso-zones for planning and feasibility analysis. * Sugar and Residue (Bagasse) ratios were derived from literature
DOI: 10.15493/sarva.dws.10000027
title: Rivers of South Africa 1:500 000

description: The coverage is a South African surface drainage network for GIS, originally based on a 1994 1:500 000 rivers coverage from the Chief Directorate of National Geo-spatial Information. This version is made available `as is` for the convenience of those who need a medium-scale river coverage for display or modelling: the developers would welcome any comments relating to errors or areas for improvement.
DOI: 10.15493/sarva.csag.10000115
title: Number of days for Maximum Temperature over 90th percentile for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.beeh.10000108
title: Pinus elliottii Growth Areas and Yield Estimation

description: Pinus elliottii a native of the southern USA also goes by the name of slash pine. It is an evergreen conifer with very long needles, an extensive root system and a moderate taproot (Hill, 2001). P. elliottii is the hardiest of the three main species of pine grown in South Africa (the other two being P. patula and P. taeda) and produces a relatively hard, durable wood which, because of its high resin content, however, limits its marketability (Smith, 1998). Normally planted in pits, it is weed sensitive (Smith, 1998).

Climatically optimum growth areas for Pinus elliottii have been defined in the ICFR`s Forestry Toolbox (Kunz, 2004), by mean annual precipitation (MAP) and mean annual temperature (MAT), as illustrated in This figure also shows the climatic conditions under which suboptimum growth would take place because conditions may at times be too cold and damage from snow occurs, or because occasional drought may occur. Optimum and sub-optimum growth areas of P. elliottii were mapped by applying these criteria to the one arc minute (1` x 1` of a degree latitude/longitude) databases of MAP (Lynch, 2004) and MAT (Schulze and Maharaj, 2004) for South Africa. Using Smith`s (1994) rule based approach and considering only his climatic criteria and not soil or management factors, Pinus elliottii yield estimates were done.
DOI: 10.15493/dea.carbon.10000088
title: South African Land Cover 2018 Class F Pivot Agriculture Resampled to a 1km Grid

description: Raster-based land cover dataset representing the full South African landscape for the full year 2018. Derived from 20m Sentinel 2 imagery acquired between 01 January 2018 and 31 December 2018. Land cover information classes based on new Gazetted land cover standards and legend content used in 2013-14 national land cover data. All land cover and land-use classes generated using automated modelling procedures for full operational repeatability and change detection. Class 38–39 of the 2018 South African National land cover dataset was clustered to derive South African land cover 2018 Class F Pivot Agriculture for the current product and resampled to a 1000 x 1000m grid resolution Albers Equal Area map projection +proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs
DOI: 10.15493/sarva.csag.10000307
title: Number of days for Minimum Temperature over 90th percentile for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.saws.10000045
title: Seasonal mean near-surface (2m) temperature (°C) change from the median projected for 2036-2065, relative to present (1976 - 2005), for SON season under the RCP 4.5 pathway

description: Seasonal (SON) mean near-surface (2m) temperature (°C) change from the median projected for 2036-2065, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature averages, which are used to generate projections of seasonal change. The projections are generated using the medium to low (RCP4.5) pathway which associates CO2 concentrations of approximately 560ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.saws.10000159
title: Seasonal mean near-surface (2m) temperature (°C) change from the median projected for 2036-2065, relative to present (1976 - 2005), for JJA season under the RCP 8.5 pathway

description: Seasonal (JJA) mean near-surface (2m) temperature (°C) change from the median projected for 2036-2065, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature averages, which are used to generate projections of seasonal change. The projections are generated using the high (RCP8.5) pathway which associates CO2 concentrations of approximately 950ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.csag.10000152
title: Minimum of Maximum Temperature for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.beeh.10000309
title: Monthly Means Of Daily Average Temperature (C) October

description: Under conditions of natural vegetation, mean temperatures are used to distinguish between three broad thermal divisions of plants, viz. mega-thermal plants, which require mean monthly temperatures above 20 degree Celsius for at least 4 months of the year; micro-thermal plants, which grow where 8 months or longer have means below 10 degree Celsius; and the meso-thermal plants of the mid-latitudes, which cover most of South Africa, and whose physiology is adapted to the strong seasonal rhythms which are experienced over most of the southern African subcontinent. Month-by-month variations of mean temperature influence the geographic range and the optimum growth areas of certain vegetation and crop species, especially those with life-cycles extending one year or less. 

Assuming a daily average temperature to be represented by the mean of that days minimum and maximum temperatures, these averages were derived from the 50 year (1950 – 1999) time series of quality controlled daily minimum and maximum temperatures which were generated from over 970 qualifying temperature stations using infilling and record extension techniques developed by Schulze and Maharaj (2004). From these point location time series, 50 years of daily mean temperature values were derived at each of 429 700 one arc minute (1.7 km x 1.7 km) grid points covering South Africa, using regionally and seasonally determined lapse rates and other physically appropriate spatial interpolation approaches (Schulze and Maharaj, 2004). From the daily values, monthly means and standard deviations of daily values for each month of the year could be computed for purposes of mapping and production of tables of statistics for each of the nine provinces of the RSA as well as for Swaziland and Lesotho.
DOI: 10.15493/bea.data.10000069
title: Production of Sorghum on Subsistence and Underutilised Farmland

description: Data was derived from the following sources: * Extent of underutilised and subsistence farmland, data obtained from Department of Agriculture, Forestry, and Fisheries. * On such land, sorghum potential was calculated from data published by Schulze and Maharaj (2007) on sorghum-growing potential. * Grain and Residue production was calculated based on grain yields, and aggregated to meso-zones for planning and feasibility analysis. * Grain and Residue ratios were derived from literature
DOI: 10.15493/sarva.beeh.10000060
title: Monthly Rainfall and its Inter-Annual Variability (March)

description: Monthly rainfall totals are necessary to many water resources as well as agricultural problems and decisions for which MAPs, or even wet/dry season precipitation totals be they high or low, are of relatively little consequence, because an intra-year distribution of rainfall is required (Schulze, 1997). Monthly rainfall values then serve as an important tool in describing such an intra-year distribution. It should, however, be borne in mind that the use of the calendar month is but a time step of convenience for describing temporal patterns of rainfall, in that it breaks up annual precipitation into components of time long enough to smooth out many of the irregularities of daily rainfalls (Schulze, 1997). Nevertheless, large differences in rainfall can exist from one month to the next. Some of these differences result from major rainfall generating mechanisms changing from one month to the next.

How the maps of median monthly rainfall were derived since time series of monthly rainfall are more variable than those of annual rainfall, the raster surfaces of median monthly rainfall at 1 arc minute spatial resolution (i.e. at 1` x 1` latitude/longitude spacing; 1.7 x 1.7 km; with 429 700 raster points making up South Africa) were calculated by expressing the median rainfall value of a given month at each qualifying rainfall station as a ratio of the MAP surface which was generated by Lynch (2004) using Geographically Weighted Regression. These ratios were then interpolated by Inverse Distance Weighting (IDW) onto the rectangular raster of 1 arc minute. This interpolated raster was then multiplied by the raster of MAP values to give 1` x 1` values of that month's median rainfall. The procedure was then repeated for each of the 12 months of the year (Lynch, 2004).
DOI: 10.15493/sarva.csag.10000416
title: Number of days for Maximum Temperature over 90th percentile for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/dea.carbon.10000082
title: South African Land Cover 2014 Class P Built-up Resampled to a 1km Grid

description: The 2013-14 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 8 multispectral imagery, acquired between April 2013 and March 2014. In excess of 600 Landsat images were used to generate the land cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land cover dataset, which covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. The dataset contains 72 x land cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 8 standard map projection format as provided by the USGS. Class 42–72 of the 2013-14 South African National land cover dataset was clustered to derive South African land cover 2014 Class P Built-up for the current product and resampled to a 1000 x 1000m grid resolution Albers Equal Area map projection +proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs
DOI: 10.15493/saeon.fynbos.42022022
title: Surrey Hills Farm rainfall data

description: Annual rainfall data collected in the Eastern Cape near Port Elizabeth. The data was collected using a Snowden standard rain gauge. This record forms part of a collection of historical rainfall data submitted to SAEON. The data were generated by farmers on a voluntary basis and have been published as submitted.
DOI: 10.15493/sarva.csag.10000446
title: Consecutive Dry Days for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/saeon.fynbos.8a3668
title: 8A manual daily rainfall April 1936 - April 1968

description: Daily rainfall data collected by a Snowdon gauge with orifice at 1.2 meters above ground in the Langrivier catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.csag.10000240
title: Maximum of Minimum Temperature for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/nccis.data.10000038
title: Projected changes in annual mean temperature (°C) under RCP 8.5 for the 2080-2099 time period based on the National Centre for Meteorological Research Coupled Global Climate Model, version 5 (CNRM-CM5)

description: The analyses of future climate change over South Africa as described in the Third National Communication, are from the projections of the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Assessment Report (AR) 5 of the Intergovernmental Panel on Climate Change (IPCC). These projections are used to inform on the uncertainty range of the large-scale climate change futures over the southern African region. At the Council for Scientific Industrial Research (CSIR), a dynamic regional climate model CCAM (conformal-cubic atmospheric model) of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) was used to downscale CMIP5 CGCM projections to 50 km resolution over Africa. These downscalings were for both Representative Concentration Pathway (RCP) 8.5 and Representative Concentration Pathway (RCP) 4.5 of AR5 of the IPCC. RCP 4.5 describes a future with relatively ambitious emission reductions whereas RCP 8.5 describes a future with no reductions in emissions. Emissions in RCP 4.5 peak around 2040, then decline and in RCP 8.5 emissions continue to rise throughout the 21st century. The change in temperature is expressed as an anomaly, the difference between the average climate over a period of the last several decades (1971-2000), and the projected climate (short to medium term 2021 to 2050). The simulations were performed on supercomputers of the CSIRO and on the Centre for High Performance Computing (CHPC) of the Meraka Institute of the CSIR in South Africa.
DOI: 10.15493/dea.carbon.10000008
title: Total Biomass Organic Carbon of Transformed areas (gC/sq.m)

description: In areas which have been transformed to cultivation or forestry plantations municipal level agricultural senses data were used to assign the biomass values to areas covered by the specific fractional land cover or crop type.
DOI: 10.15493/bea.data.10000066
title: Waste Water Treatment Sites

description: Data was digitised from addresses provided in the Department of Water and Sanitation listing of waste water treatment works.
DOI: 10.15493/sarva.csag.10000318
title: Number of days for Maximum Temperature under 10th percentile for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/saeon.egagasini.191121-1
title: SkiMonkey mounted MicroCAT CTD data from around Prince Edward Island on SA Agulhas II Voyage 036, April 2019

description: The 2019 Marion Relief Voyage offered the 7th opportunity to conduct multi-disciplinary, shipbased marine research focusing on links and interactions between air and sea, ocean physics, chemistry, biogeochemistry and biology, including microbial, planktic and benthic communities on board South Africa’s polar research and supply vessel MV SA Agulhas II en route to, and around, the sub-Antarctic Prince Edward archipelago. The overall aim was to continue contributing to an oceanographic observation and monitoring system directed by South African research institutions in support of South Africa’s needs – and where possible, in support of global requirements of inter alia the Global Oceans Observing System (GOOS), the Southern Ocean Observing System (SOOS), the Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR), and the Agreement on the Conservation of Albatrosses and Petrels (ACAP). With the declaration on 9 April 2013 of the Prince Edward Islands (PEIs) as South Africa’s first offshore Marine Protected Area (MPA), the outcomes of this cruise will further contribute toward an integrated view and a better understanding of the functioning of the combined island/marine PEI ecosystem.
 This accession contains raw and processed SkiMonkey mounted SBE-37 MicroCAT CTD data collected from part of the Marion Island Relief Voyage on the SA Agulhas II Voyage 036, which was at sea between 11 April and 16 May 2019. At each station the SkiMonkey III towed camera system, with the MicroCAT CTD attached, was deployed off the stern of the ship, from the plankton towing winch at a speed of 1 m/s until contact with the seafloor was made. Once on the seafloor, the system was towed behind the vessel at a speed of 1 knot for approximately 20 minutes. Following this, the system was retrieved and the data were downloaded and backed-up appropriately. Note that latitude and longitude information were only recorded at two points per station, when the system touched down on the seafloor and when it was retrieved from the seafloor (i.e. Start_Lat_DD, Start_Lon_DD, End_Lat_DD, End_Lon_DD). The download is divided into station level folders, within which the user can find ReadME documents describing the variables recorded in the data, a _StationLevelData sheet describing the instrument deployment, a _RawData file with data extracted as is from the instrument, and a _CleanData file with processed data as described in the metadata lineage statement.
description: During deployments, data were stored on the system's internal memory and subsequently downloaded upon retrieval (ASCII engineering units). Quality control processes and procedures were then conducted on the raw data (filenames including _RawData). This included removing unused columns, deleting data collected while the system was out-of-water, and then saving the data in a useable comma delimited format (filenames including _CleanData).