cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/hcdb-yd02
title: Historical Research Survey Database

description: The historical survey data were transcribed from the Department of Agriculture `Report of the Marine Biologist` (pre-1900) and `Report of the Government Biologist` (1900-1904) publications for the period of 1897-1904. The data from 1920-1949 were transcribed from the Union of South Africa series of `Fisheries and Marine Biological Survey` reports, which in 1935 became the Department of Commerce and Industries` `Fisheries and Marine Biological Survey Division` reports, and from 1937 onwards were published as `The Division of Fisheries Annual Reports`. Although they changed names, these reports essentially represent an (incomplete) series that documents fisheries and oceanography-related surveys and investigations in South Africa (and Namibia and Mozambique) during those periods. The majority of the digitized records stem from trawls, but samples from dredges, plankton nets and other gear are also included. Whereas the initial reports appear to document all biological specimens collected during these surveys, the reports of 1899-1904 focus on trawl catches only, listing roughly 20 common fish species and frequently grouping chondrichthyans into categories such as `sharks`, `sting rays`, `skates` and `dogfish`, with a broad `other fish` group encompassing all taxa not listed separately. During the period 1920-1932, all specimens (collected by various gear) seem to have been recorded, although the identification of some taxa (especially invertebrates) was frequently to a relatively coarse level.
DOI: 10.15493/dea.carbon.10000107
title: Above Ground Litter for 2014 in t/ha

description: Above ground litter consists of downed wood, leaves and dung on the soil surface. It was calculated per biome based on a simple model including litterfall and decay rates as a function of rainfall. This was validated against the fuel load datasets. Biome factors calculated by Powell (2009), Shea et al (1996), Weider & Wright (1995) and van Wilgen et al (1990) were used. NLC2014 were used as the land cover dataset.
DOI: 10.15493/sarva.csag.10000158
title: Rainfall days per month 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/bea.data.250320-2
title: Forestry Residue Availability - South African forestry area information methodology

description: A report describing the methodology used to create the spatial dataset of all large, medium, small-scale and government-owned plantation forests in South Africa.
DOI: 10.15493/sarva.csag.10000071
title: Mean of Maximum 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/nccis.data.10000012
title: Linear trend in annual total precipitation from high daily rainfall (1921-2015) defined as when daily rainfall (RR) > 95th percentile fo the baseline average

description: An updated analysis of trends in South African rainfall 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 60 weather stations were used for the rainfall trend analysis spanning the period 1921-2015. Apart from the trends in the annual rainfall totals, long-term changes in rainfall can manifest in changes in rainfall extremes. An extreme rainfall trend analysis is also performed, based on rainfall extreme indices 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.

Based on the data, there is strong evidence of statistically significant increases in rainfall occurring over the southern interior regions, extending from the western interior of the Eastern Cape and eastern interior of the Western Cape northwards into the central interior region of the Northern Cape, over the period 1921-2015. Extreme daily rainfall events have increased over these same areas, with these increases also being statistically significant and extending northwards into North West, the Free State and Gauteng. Over Limpopo there is strong evidence of statistically significant decreases in annual rainfall totals.
DOI: 10.15493/sarva.saws.10000061
title: Seasonal mean near-surface (2m) temperature (°C) change from the 90% percentile projected for 2066 - 2095, relative to present (1976 - 2005), for JJA season under the RCP 4.5 pathway

description: Seasonal (JJA) mean near-surface (2m) temperature (°C) change from the 90% percentile 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/dea.carbon.10000031
title: Rain 1986-2015 90th Percentile

description: The current long term annual climate conditions for the 30-year period 1986-2015. The total daily rainfall 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 daily rainfall values were summed to yearly totals and the mean annual total rainfall were calculated for each downscaled global circulation model for the 30 year period. The 90th percentile values are based on the mean annual rainfall values for 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/sarva.beeh.10000112
title: Standard Deviations Of Daily Maximum Relative Humidity (%) February

description: Daily minimum and maximum relative humidity is necessary as relative humidity, defined for practical purposes as the ratio of actual to saturated vapour pressure, expressed as a percentage generally varies considerably in the course of a day because, despite actual vapour pressure, ed, remaining relatively constant, saturated vapour pressure, ea, varies curvilinearly with temperature. The minimum RH of a day is thus experienced during the hottest part of the day when ea is high and the ratio of ed : ea is therefore small. Conversely, the maximum RH of a day is experienced around sunrise when temperatures are low, thus ea is low and the ratio of ed : ea therefore approaches unity.

In computations of daily RHmin and RHmax, the uncorrected actual vapour pressure, ed , is assumed not to change significantly in the course of a day. Because uncorrected ed at any specific location has been shown to be predictable over South Africa on a month-by-month basis with a high degree of confidence when using predominantly geographical factors in multiple regression equations for each month the ed at any location is therefore held at a relatively constant value for any given month of the year, varying only slightly on a day-by-day basis within that month because temperature range is a variable in the regression equation. However, at that specific location the saturated vapour pressure, ea , can vary considerably from one day to the next, as well as within a given day between morning and midday, because it is related to temperature. Therefore, RHmin and RHmax can range considerably within a given day and vary markedly from one day to the next.
DOI: 10.15493/sarva.csag.10000154
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.251120-6
title: 2018 Coast ecosystem threat status and protection level

description: First assessment of the integrated coast map including coastal ecosystem types from the terrestrial, marine, and estuarine realms and forms a key part of the National Biodiversity Assessment
DOI: 10.15493/saeon.fynbos.g2h002dt
title: G2H002 Bosboukloof discharge table

description: Conversion table to estimate stream flow in cumec from stage height measured at the V-notch weir in the Bosboukloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.beeh.10000384
title: Uncorrected Vapour Pressure (kPa) November

description: Actual vapour pressure, i.e. the variable expressing atmospheric moisture content through the partial pressure exerted by the water vapour present in the atmosphere, to be a conservative climatic element which could be expressed adequately as a relatively consistent monthly value (cf. shaded box which follows). The monthly uncorrected vapour pressure, ed, at any given location in South Africa was subsequently held constant for that entire month and derivatives of ed, such as daily maximum and minimum relative humidity or daily vapour pressure deficit which depend on ratios of uncorrected actual to saturated vapour pressure, were then computed using daily maximum/minimum temperatures (Schulze and Maharaj, 2004) as inputs to daily saturated vapour pressure calculations, which were in turn used with that location`s monthly uncorrected actual vapour pressure.

The derivation of equations for monthly uncorrected actual vapour pressure, ed, over South Africa included, testing a number of hypotheses on factors affecting ed against observed data across a range of latitudes, distances from the ocean, climatic regimes and other factors; the selection, by iterative procedures, of suitable geographic and climatic variables against which to regress ed; the development of month-by-month multiple regression equations of uncorrected ed against altitude, latitude, longitude and continentality as geographical variables, and temperature range as a climatic variable, and their relative importance in the different seasons of the year. In each case reasons were given for the selection of a respective variable.
DOI: 10.15493/sarva.beeh.10000264
title: Monthly Means Of Daily Maximum Temperature (C) February

description: High daytime maximum temperatures, up to 30 – 35 degree Celsius, generally increase rates of photosynthesis (i.e. the process by which `food` is manufactured in a plant), and thereby the rate of organic matter production. High maxima can be critical for some plants in both a negative context (e.g. the potential for grain production is reduced in maize when temperatures are too high during the flowering period) and a positive context (e.g. strawberries need high daytime temperatures to develop flavour). However, the rate of respiration (i.e. the processes by which `food` is used by a plant) also increases with temperature. As maximum temperatures reach the upper limits for a given crop, the rate of respiration (`food` used) may exceed that of photosynthesis (`food` manufactured). Thus, for example, for tomatoes growth peaks at 36 degree Celsius (Whiting et al., 2004).

Time series of daily maximum temperatures from over 970 qualifying temperature stations were generated from quality controlled data for common 50 year period 1950 - 1999 using infilling and record extension techniques developed by Schulze and Maharaj (2004). From these time series at point locations, 50 years of daily maximum temperature values were generated at each of 429 700 one arc minute (1.7 km x 1.7 km) raster points covering South Africa, using regionally and seasonally determined lapse rates and other physically appropriate spatial interpolation approaches. These are given in detail in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as 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 Swaziland, Lesotho and for each of the nine provinces of the RSA.
DOI: 10.15493/sarva.beeh.10000239
title: Standard Deviation Of Daily Solar Radiation (Mj/M2/Day) January

description: These daily estimates of solar radiation were derived using modifications for South African conditions of the Bristow and Campbell (1984) equation. The daily values can be used as input in other equations such as the Penman-Monteith equation for potential crop evaporation.

The method of estimation of daily solar radiation loadings over South Africa and its verification are as follows: based on the premise that clear sky solar radiation is determined essentially by extraterrestrial solar radiation modulated by an atmospheric extinction function, and dependent on high maximum temperatures associated with a high diurnal range in temperature, while cloudy/rainy conditions are associated with a low diurnal temperature range, the Bristow and Campbell (1984) equation was modified for South African conditions by regionally optimised expressions of clear sky depletion for atmospheric water vapour content, and by month-by-month regionalised expressions for solar radiation extinction by cloudy/rainy conditions. Other than the optimised regional/monthly coefficients and the computation of extraterrestrial radiation, which is undertaken with trigonometrical geometry functions, the only other variables used were daily maximum and minimum temperatures. The latter were determined for the 50 year period 1950 - 1999 at each of 429 700 one arc minute (1.7 x 1.7 km) raster points over South Africa by techniques outlined in Schulze and Maharaj (2004).
DOI: 10.15493/dea.carbon.10000033
title: Tmax 1986-2015 10th Percentile

description: The current long term annual climate conditions for the 30-year period 1986-2015. The maximum 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 maximum daily temperature was calculated per year for each of the six downscaled GCMs for the 30-year period. The 10th percentile values are based on the mean 30-year maximum 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/dea.carbon.10000100
title: Above ground herbaceous biomass for 1990 in t/ha

description: The above ground herbaceous biomass is a very small fraction of the total terrestrial organic carbon stocks and it was calculated with the same model as in the 2014 NTCSA dataset. The equation incorporates Rain Use Efficiency, amount of rain required to enable production per year and tree cover fraction. A mean herbaceous layer is modelled based on mean long term rainfall. The above ground herbaceous biomass was based on harvest factors obtained from the 2002 Agricultural Census. A proportional approach per land use, based on 1990 National Land Cover (NLC) data was used for the soil organinc carbon (SOC) analysis.
DOI: 10.15493/sarva.csag.10000111
title: Consecutive Dry Days 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.10000062
title: South African Land Cover 1990 Class L Sugarcane Dry Resampled to a 1km Grid

description: The 1990 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 4/5 multispectral imagery, acquired between April 1989 and October 1993. 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 4/5 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 4/5 standard map projection format as provided by the USGS. Class 28–31 of the 1990 South African National land cover dataset was clustered to derive South African land cover 1990 Class L Sugarcane Dry 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.context.10000002
title: District Councils

description: Polygons created to show boundaries of districts councils within South Africa. District councils and metropolitan areas are a level above the DMAs (District management areas) and local municipalities in the geography hierarchy. Metropolitan areas are conurbations featuring high population density; intense movement of people, goods and services; extensive development; and multiple business districts and industrial areas. Other features include a complex and diverse economy, a single area where integrated development is desirable, and strong interdependent social and economic linkages between its constituent units. There are eight metros in South Africa: Cape Town, EThekwini (Durban), Ekurhuleni (East Rand), Johannesburg, Tshwane (Pretoria), Nelson Mandela Bay (Port Elizabeth), Buffalo City (East London) and Mangaung (Bloemfontein). 
*Original data was obtained from the Municipal Demarcation Board.
DOI: 10.15493/sarva.saws.10000020
title: Root Mean Square Difference between the nine ensemble member change anomalies of the annual total rainfall for the 90% percentile for 2066 - 2095 relative to 1976-2005, under the RCP 4.5 pathway

description: Root Mean Square Difference for annual total rainfall (mm per year) 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 annual 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.231120-3
title: 2017 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 2017 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.10000268
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/