cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000255
title: Saturated Vapour Pressure (kPa) May

description: The average energy of the molecules in a liquid such as water is governed by temperature. The higher the temperature, the higher the average energy. Some molecules have higher energies than the average, and if they are on the surface of the liquid, can move fast enough to escape the inter-molecular attractive forces holding the liquid together. They then evaporate from the surface of the liquid. This process can continue until the space above the liquid is saturated with water molecules. Equilibrium is then reached with as many molecules leaving the surface of water as are re-entering it. At this point the pressure exerted by that vapour is called the saturated vapour pressure. The symbol used for saturated vapour pressure is ea and the unit of pressure is kPa. Since the molecular kinetic energy is greater at higher temperature, more molecules can escape the surface and the saturated vapour pressure is correspondingly higher. This relationship is illustrated in the shaded box.

Daily values of saturated vapour pressure were computed from the 50 year daily time series of temperature generated for South Africa at each one of 429 700 raster points at one arc minute interval, using techniques developed by Schulze and Maharaj (2004).
DOI: 10.15493/bea.data.250320-8
title: Nutrient loss - scenarios

description: Decision support tool for the calculation of nutrient losses associated with different forestry residue extraction scenarios.
DOI: 10.15493/dea.carbon.10000037
title: Tmin 1986-2015 90th Percentile

description: The current long term annual climate conditions for the 30-year period 1986-2015. The minimum daily temperature values simulated by six different global circulation models (GCMs), i.e. csiro, gfdl20, gfdl21, miroc, mpi, and ukmo were downscaled with the Cubic Conformal Atmospheric Model (CCAM) developed by CSIRO (Australia) and updated and modified by the CSIR (South Africa). The mean minimum daily temperature was calculated per year for each of the six downscaled GCMs for the 30-year period. The 90th percentile values are based on the mean 30-year minimum daily temperature per year from the six downscaled GCMs. Data are in raster format with a 0.5º resolution using a projected latitude / longitude coordinate reference system with `+proj=utm +zone=48 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0`.
DOI: 10.15493/nccis.data.10000006
title: Linear trend in annual nighttime warmest temperature (1931-2015) defined as the annual maximum value of minimum temperature (TN)

description: An updated analysis of trends in South African temperatures is presented in The Third National Communication (TNC) to UNFCCC published in 2018. The analysis builds on the studies of Kruger & Sekele (2012) and Mackellar et al. (2014). 

A total of 27 weather stations was used for for temperature trends analysis spanning the period 1931-2015. Apart from the trends in the annual mean minimum, maximum and average temperatures, and average diurnal temperature range, the analyses also includes the determination of trends in extreme temperatures, of which the indices considered are based on those developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) (Wang and Feng, 2013).  The base period, from which the annual index values of all indices are determined (except the annual maxima and minima) was selected as 1981 – 2010, which can be considered to be the present general norm for similar trend studies. The trends were tested for significance at the 95% confidence level.

The analysis demonstrates that, South Africa has been warming significantly over the period 1931-2015. Over the western parts of the country, including much of the Western and Northern Cape, and also in the east over Gauteng, Limpopo and the east coast of KwaZulu-Natal, the observed rate of warming has been 2 °C/century or even higher – in the order of twice the global rate of temperature increase.  Associated increases in the annual number of hot days have also occurred, but there have been decreases in the annual number of cold nights over most of the country.
DOI: 10.15493/saeon.fynbos.25b7608
title: 25B manual monthly rainfall August 1976 - February 2008

description: Monthly rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, 1.2 meters above ground in the Bosboukloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.beeh.10000346
title: Extremes (1950 - 2000) Of Minimum Temperatures (C) October

description: When days with very low or very high temperatures are experienced, the question is invariably asked as to whether that `extreme` minimum or maximum temperature was a record, either for the country or province, or for a specific location or month of the year. It is for that reason that `extremes` of temperatures, at both ends of the spectrum, were extracted from the daily temperature database generated by Schulze and Maharaj (2004) for the 51 year period (in this case) from 1950 to 2000, and analysed in tabular and mapped form. While these maps/tables are informative and interesting, there are nevertheless some limitations as to their accuracy, for reasons which are discussed in the shaded blocks containing some scientific background to the analyses.Limitations over South Africa to mapping extremes of temperatures, the generation of daily temperature time series at one arc minute spatial resolution at the 429 700 grid points covering South Africa depends on the selection of "best" control stations, extrapolating to altitudes above that of the highest-lying station in a particular region, as well as computational procedures regarding initial and final daily temperature values (Schulze and Maharaj, 2004). With respect to extreme minimum temperatures, no account has been taken of cold air drainage into valleys, which may aggravate minimum extremes.

Extremes of temperatures were derived from over 970 qualifying stations` records for the 51 year (1950 - 2000) time series of quality controlled daily maximum and minimum values generated at each of the 429 700 one arc minute (i.e. 1.7 x 1.7 km) raster points covering South Africa, using the regional/seasonal lapse rate and infilling techniques developed by Schulze and Maharaj (2004). In order to avoid analysing and mapping anomalous temperature extremes resulting from incorrect recordings, the extremes which were derived are therefore taken as the means of the two highest or lowest values found in the temperature time series at each grid point.
DOI: 10.15493/sarva.saws.10000142
title: Root Mean Square Difference between the nine ensemble member change anomalies of the annual total rainfall for the median for 2066 - 2095 relative to 1976-2005, under the RCP 8.5 pathway

description: Root Mean Square Difference for annual total rainfall (mm per year) change from the median projected for 2066-2095, 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 rainfall averages, which are used to generate projections of annual 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/nccis.data.10000024
title: Projected changes in annual mean temperature (°C) under RCP 8.5 for the 2015-2035 time period based on the Australian Community Climate and Earth System Simulator (ACCESS1-0)

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/sarva.csag.10000206
title: Maximum of Minimum 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.saws.10000097
title: Seasonal total rainfall (mm per 3-month season) change from the 90% percentile projected for 2066-2095, relative to present (1976 - 2005), for DJF season under the RCP 4.5 pathway

description: Seasonal (DJF) rainfall (mm per month) change from the 90% percentile projected for 2066-2095, 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 rainfall 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.10000179
title: Seasonal mean near-surface (2m) temperature (°C) change from the 10% percentile projected for 2066 - 2095, relative to present (1976 - 2005), for MAM season under the RCP 8.5 pathway

description: Seasonal (MAM) mean near-surface (2m) temperature (°C) change from the 10% percentile projected for 2066-2095, 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.csag.10000424
title: Minimum of Minimum Temperature 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.fynbos.141220-7
title: Jonkershoek forestry stock map 1973

description: Map of forestry plantation compartments for the Jonkershoek valley, Western Cape, South Africa as of 1973.
DOI: 10.15493/sarva.231120-2
title: 2016 National Senior Certificate (NSC) matric results per municipal district

description: The National Senior Certificate (NSC) matric results is a measure of performance of all the secondary public schools that wrote the 2016 NSC Examination. This indicator was calculated for each municipal district using the average results obtained by the total number of secondary schools in a municipal district. The master list of schools dataset was linked to the National Senior Certificate (NSC) dataset using the Natemis (a unique number assigned to each school) to calculate the total number of secondary schools in a municipal district.
DOI: 10.15493/sarva.csag.10000401
title: Mean 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.251120-12
title: National wetland map 5 confidence map

description: The 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.

For the purpose of the SAIIAE, higher levels of certainty are associated with areas that have been visited in-field by a wetland specialist(s) over multiple seasons and cycles of the hydroperiod of a wetland, and are therefore likely more accurately represented in the dataset. Accuracy reflects the degree to which the spatial boundaries and/or attributes of the wetlands match those in the real world. A higher confidence score would imply an increase in accuracy, assuming that the spatial boundaries of a wetland had been informed by water, soil and vegetation indicators, and recorded with differential GPSs. Low confidence scores would imply the inverse.

The confidence rating map should always be consulted to assess whether the NWM5 is fit for purpose. The confidence of rivers, estuarine and coastal ecosystems is not reflected in this confidence map. In general, areas mapped as Low confidence may have a 50% omission error, though the commission error may have been reduced to < 10% compared to the previous versions of the NWMs. The appropriate scale of use would be 1:50 000. Where the confidence rating is Low to Medium, Medium and Medium to High, the omission error was estimated to be < 30% and the commission error < 10%.
DOI: 10.15493/dea.carbon.10000020
title: LADA Loss of Cover

description: During the Land Degradation Assessment in Drylands (LADA) project, the South African National Assessment of Land Degradation and Conservation was done between 2008 and 2011. 728 Contributing specialists throughout the country contribute their knowledge and experience on land degradation and sustainable land management during a series of 34 Participatory Expert Assessment Workshops. It is important to notice that the results of these national assessments are qualitative in nature and based on the perceptions of contributing specialists based on the assumption that they all know their respective assessment areas well enough to report on land degradation and conservation attributes. One of the land degradation types considered during the LADA National Assessment was the reduction in vegetation cover and the increase in bare and unprotected soil. Since a positive correlation exist between Soil Organic Carbon Stocks (SOCS) and vegetation cover, areas where loss of cover has been identified by contributing specialists as one of the main degradation types, could have a negative impact on the available SOCS.
DOI: 10.15493/saeon.fynbos.10000011
title: Land cover derived terrestrial habitat change map for South Africa (1990-2018)

description: The primary building blocks of the map were the 1990, 2013/4 and 2018 national land cover products developed by GEOTERRAIMAGE (GTI) between 2015 and 2019. These products were reclassified and combined into a simple land cover change map, and then refined using additional input data on historical field crop boundaries and artificial waterbodies. Land cover information for Lesotho and Swaziland (not included in the national land cover data sets) was added to the layer using the European Space Agency's Beta Africa Land Cover product (2016) . The coastal areas of the map were reclassified using a detailed mask of the coastal zone developed by Harris et al. (2019). An earlier version of this map that excluded the 2018 time point (v5.1) was used in the terrestrial ecosystem assessments undertaken as part of the National Biodiversity Assessment 2018 (Skowno et al. 2019).
DOI: 10.15493/sarva.energy.10000019
title: AWB-PYR: All Woody Biomass - Fast Pyrolysis (MW)

description: Biomass feasibility assesment dataset extracted from the BioEnergy Atlas for South Africa.

* Assessments are made of impacts of economy of scale, logistics, and placement on techno-economic feasibility of feedstock and processing combinations, leading to identification of a portfolio of feasible project options. These options are illustrative of areas where bioenergy is likely to be a competitive, sustainable alternative.

* Original dataset: https://dx.doi.org/10.15493/BEA.DATA.10000028

* BioEnergy Atlas Synopsis Report: https://dx.doi.org/10.15493/SAEON.BEA.DOCS.10000001
DOI: 10.15493/saeon.fynbos.2a9208
title: 2A manual weekly rainfall April 1992 - March 2008

description: Weekly rainfall data collected by a Snowdon gauge with orifice at 1.2 meters above ground at the Swartboschkloof weather station in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/saeon.fynbos.3b4491
title: 3B manual monthly rainfall January 1944 - September 1991

description: Monthly rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, with orifice at 1.2 meters above ground in the Tierkloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.saws.10000208
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the median for 2036 - 2065 relative to 1976-2005, for the JJA season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (JJA) rainfall (mm per month) 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 rainfall 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/dea.carbon.10000015
title: 2013-14 SA National Land-Cover – broad parent classes

description: The 2013-14 South African National Land-cover dataset produced by GEOTERRAIMAGE as a commercial data product has been 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.
 
 For the purposes of the Carbon Atlas, the original 72 land cover classes were reclassified to 12 broad parent classes according to the definitions in Appendix A and hierarchical classes in Appendix E of GTI's Data User and Metadata Report. The colour scheme is similar to the colours used in the 72 original classes.
 This new layer serves as a reference for the Carbon layers and was resampled to an equivalent resolution of 1 km. Re-projection from UTM35 to Albers Conic Equal Area projection WGS84 was done.
DOI: 10.15493/sarva.csag.10000066
title: Median monthly precipitation (mm) 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/