cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000048
title: Median First and Last Dates of Heavy Frost their Variability, and the Duration of the Frost Period

description: During a heavy frost day, temperature at recognised meteorological stations is measured in a Stevenson screen at a height of 1.5 m above the ground. Particularly with radiation frosts, a frost occurrence on the ground is therefore not always recorded as a temperature at or below 0 degree Celsius in a Stevenson screen. A temperature of ≤ 0 degree Celsius recorded in a Stevenson Screen is therefore termed a `heavy` frost, on the assumption that the temperature on the ground would be even lower.

In the mapping statistics on frost over South Africa, the temperature database contains quality controlled time series of daily maximum and minimum temperatures from > 970 qualifying temperature stations, the records of which had been infilled and extended (where necessary) to a common 50 year time period 1950 - 1999 by techniques described in Schulze and Maharaj (2004). These daily station records were then used with regionally and seasonally determined temperature lapse rates to generate 50 years of daily maximum and minimum temperatures at each of 429 700 one arc minute (1.7 x 1.7 km) raster points covering the RSA, Lesotho and Swaziland. At each raster point a minimum temperature of ≤ 0 degree Celsius in the 50 year daily time series was designated a `heavy frost day`. Any raster point where < 5 years out of the 50 experienced at least one heavy frost day, that point was deemed to be `frost free`. Frost analyses were therefore only performed on daily values at those raster points with 5 or more years recording at least one heavy frost day.
DOI: 10.15493/sarva3.data.29062022
title: South African Education District Boundaries

description: This dataset demarcates the boundaries of the education districts across South Africa. Education districts differ greatly in terms of their geographical size, number of schools served and the poverty profile of communities they encompass. There are 86 districts at present, most of which are based on district municipalities or sub-divisions thereof. The Free State and the Northern Cape have used district municipalities as the basis for education district demarcation, so they are purposely limited by the number of district municipalities defined in their respective provinces. Therefore, these two provinces have perfect alignment between education districts and district municipality boundaries. Likewise, the North West has named their education districts after district municipalities but adopted slightly different boundaries to them, creating an overlap between the education districts and district municipalities. The remaining provinces have split some of there district municipalities to create unique education districts. Mpumalanga has split one of its three district municipalities into two education districts  (creating four eductaion disstricts for the province) while Limpopo split each of its five district municipalities into two education districts to reach a total of 10 education districts. Provinces with large metropolitan areas such as Gauteng, Western Cape and KwaZulu-Natal have tended to split their metropolitan areas into two or more districts. The Eastern Cape province diverted from the norm and adopted local municipalities as the basis for demarcation of education districts, making it the province with the most education districts as local municipality boundaries are far more numerous than district municipalities. The education districts of the Eastern Cape perfectly align with the local municipality boundaries of the province. This data was originally sourced from the Department of Basic Education (DBE) https://www.education.gov.za/Programmes/EMIS/EGISInteractiveData.aspx and is being republished by SAEON's uLwazi Node.
DOI: 10.15493/sarva.beeh.10000056
title: Monthly Rainfall and its Inter-Annual Variability (October)

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.251120-19
title: 2018 Final Vegetation Map of South Africa, Lesotho and Swaziland

description: Latest update of the map of the National Vegetation Map Project and follows version VEGMAP 2012. All polygons are single-part. This file is also made available in geodatabase, shapefile format and as a cmf
DOI: 10.15493/sarva.csag.10000003
title: Median monthly precipitation (mm) 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.tierberg.10000012
title: Plant List, Tierberg Karoo Research Centre

description: The data contains a list of the 208 higher plant species recorded between 1987 and 2007 within the one square kilometre (100ha) fenced to exclude sheep in 1987 and known as the Tierberg Karoo Research Centre (TKRC), on the Farm Tierberg in the Prince Albert district of the Western Cape. The database comprises 209 lines and 8 columns. The database includes a brief description of the habitat and plant life form as well as an indication of relative abundance within the TKRC study site.

Users should cite: Milton, S.J., Dean, W.R.J. & Kerley, G.I.H. 1992. Tierberg Karoo Research  Centre: history, physical environment, flora and fauna. Transactions of the Royal Society of South Africa 48(1):15-46.  Any publications, thesis, reports etc stemming from this data must be sent to SAEON Databank for records to be kept with the data
DOI: 10.15493/sarva.beeh.10000179
title: Climatically Optimum Growth Areas Papaya Composite Of All Criteria

description: Papaya, or pawpaw (Carica papaya) is a tropical fruit grown widely between latitudes 32 degree N and S. High in Vitamin C, papaya is indigenous to tropical America (ARC, 2005). This fruit, initially brought to the attention of Europe around 1520, was introduced to South Africa in seed form by Jan van Riebeeck in 1652, but only grown commercially in the Lowveld of Mpumalanga for the first time in the early 20th century by a Captain Elphick (ARC, 2005). Papaya needs relatively little water in the rainy summer season in South Africa, but if irrigated every 2 weeks in the dry season it is well adapted to hot, dry areas as the fruit then matures early and is then highly palatable owing to its high sugar content (Smith, 1998). Papaya has high heat requirements, with average daily temperatures for optimum growth between 20 and 30 degree Celsius.

Determination of climatically optimum growth areas for Papaya in South Africa is based on the expert knowledge of Bower (2005) and Moll (2005), climatically optimum growth areas were determined according to the four basic criteria: Criterion 1: Heat units (base 12 degree Celsius) should exceed 2 000 days per annum, Criterion 2: Optimum areas should have a low frequency of 4 consecutive days with maximum temperatures > 36 degree Celsius, Criterion 3: Monthly means of daily average temperatures in December and January should be 23 degree Celsius - 30 degree Celsius and Criterion 4: Optimum growth areas should have a low frequency of 4 consecutive days with minimum temperatures < 17 degree Celsius. Using the 50 year time series of quality controlled daily maximum and minimum temperatures generated by Schulze and Maharaj (2004) at a spatial resolution of 1 arc minute (i.e. 1` x 1` of a degree latitude/longitude), the above four temperature based criteria were first mapped individually and then superimposed to determine the climatically optimum growth areas of papaya in South Africa.
DOI: 10.15493/sarva.beeh.10000445
title: Highest Annual Baseflow In 10 Years (mm)

description: This dataset contains recharge to the groundwater store and baseflow in South Africa (SA). Baseflows, consist of contributions to runoff from the intermediate/groundwater store which had been previously recharged. Reliable assessments of exploitable groundwater resources are essential, particularly for sustainable development in semi-arid areas, which are more heavily dependent on groundwater than the moister areas. In the distribution patterns of mean annual baseflow, high values occur in the eastern and southwestern areas of SA and low values in the centre-west, many more unexpected highs and lows are present.
DOI: 10.15493/sarva.saws.10000172
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the median for 2066 - 2095 relative to 1976-2005, for the DJF season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (DJF) mean near-surface (2m) temperature (°C) 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 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.10000073
title: Rainfall days per month 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.10000036
title: Projected changes in annual mean temperature (°C) under RCP 8.5 for the 2015-2035 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/saeon.egagasini.10000103
title: The ASCA July 2019 (SEAmester IV) raw Vertical bongo data, SA Agulhas Voyage 037

description: The Agulhas System Climate Array (ASCA) is a mooring array and is designed to provide long-term observations of the Agulhas Current volume, heat and salt transport of the and it`s variability from mesoscale (eddies), through seasonal to interannual timescales. The ASCA shelf and tall moorings extend 200 km offshore along decending TOPEX/Jason satellite ground track # 96, through the core of the Agulhas Current, with CPIES measurements extending the array to 300 km offshore of Port Elizabeth. The data was collected during the ASCA project have been obtained from various oceanographic instrumentation. This specific raw data was collected using a vertical bongo net.
DOI: 10.15493/sarva.csag.10000448
title: Total precipitation for the month (mm) 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.10000147
title: Moisture Growing Season Duration Of Season (Days) Based On Fao (1978) Method

description: In order to ascertain when, on average, there is enough water in the soil for sustained plant growth to take place, not only does a certain amount of precipitation have to have occurred, but simultaneously the precipitation must exceed some lower threshold of plant evaporative losses for sustained growth to continue. An average moisture growing season over South Africa was determined by adapting a simple water budgeting approach of the FAO (1978), developed originally for agro-ecological zone mapping of Africa. Using this adapted method, the start of the moisture growing season was first determined followed by the determination of the end of the moisture growing season. From these, the duration of the moisture growing season could then be calculated.

The adapted FAO approach to determining moisture growing season is assumed that during the period when P ≥ 0.3Er sustained plant growth can take place, where P is median monthly precipitation (mm) and means monthly Eapan is taken as the reference potential evaporation, Er. Average daily magnitudes of P and Eapan were derived by interpolative techniques from their respective 1` x 1` grid values, and the above assumption of P ≥ 0.3Er was tested for. Note that the use of long-term climate averages yield only a simple and very smoothed general moisture growing season, in not accounting for any differences in soils nor for the probability of individual daily rainfall events occurring; however, the averaging does eliminate possible `false starts` growth early in the season when isolated rains may fall.
DOI: 10.15493/sarva.saws.10000137
title: Annual total rainfall (mm per year) change from the 10% percentile, projected for 2036 - 2065 relative to the present (1976-2005) under the of the RCP 8.5 pathway conditions.

description: Annual total rainfall (mm per year) change from the 10% percentile 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 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/sarva.beeh.10000232
title: Monthly Means Of Daily Solar Radiation (Mj/M2/Day) June

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/sarva.beeh.10000204
title: December Means Of Reference Crop Evaporation (mm) Fao Penman-Monteith Equivalent

description: The FAO (1992) definition of Penman-Monteith derived reference crop evaporation (Erpm) is `The rate of evapotranspiration from a hypothetical crop with an assumed crop height of 0.12 m, a fixed canopy resistance of 70 s.m-1 and albedo of 0.23, which would closely resemble evapotranspiration from an extensive surface of green grass cover of uniform height, actively growing, completely shading the ground and not short of water`. 

The first detailed maps for South Africa of Penman-Monteith equivalent reference crop, Erpm, were derived from monthly ratios of Erpm: A-pan using data at 174 sites (Crosby and Crosby, 1996). From these ratios, Schulze (1997) identified 14 `regions` of similar ratio patterns which were then used to map Erpm from gridded A-pan values. The maps in the present Atlas are based on a much more deterministic and more fundamental approach with daily maximum and minimum temperatures, and hence daily saturated vapour pressures, generated over South Africa on a 1` x 1` (1.7 x 1.7 km) raster for 50 years, based on research by Schulze and Maharaj (2004). Empirically determined month-by-month gridded values of actual vapour pressure for South Africa.
DOI: 10.15493/bea.data.10000070
title: Production of Maize 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, maize potential was calculated from data published by Schulze (2007) on maize-growing potential. * Grain, Oil, and Residue (Stover) production was calculated based on grain yields, and aggregated to meso-zones for planning and feasibility analysis. * Grain, Oil and Residue ratios were derived from literature. * Exploitable fraction of residue was reported based on assessment of yields and published literature
DOI: 10.15493/sarva.beeh.10000010
title: A-Pan Equivalent Reference Potential Evaporation (July)

description: Reasons for having selected the A-Pan as a reference for potential evaporation is that the US Weather Bureau Class A evaporimeter is, universally, the most common evaporation pan in usage, having been adopted as the standard evaporation pan since the International Geophysical Year 1957/8. Also when operated properly it is accepted as a reasonably reliable, simple and inexpensive local integrator of atmospheric evaporative demand, particularly for time periods longer than one day. Problems associated with the A-Pan as a reference for potential evaporation is that they are found generally near dams, existing irrigation projects or in areas of low relative relief where commercial agriculture is practiced. These sites are not always representative of higher altitudes where water is generated.

The data from over 570 South African stations at which a minimum of three years` concurrent monthly temperature, rainfall and A-pan evaporation observations are available, were used to develop temperature based equations of A-pan equivalent evaporation for each of the 12 evaporation regions identified in South Africa. The equations, unique for each month and for each region, were used to develop the 12 rasterised images of mean monthly A-pan equivalent evaporation on a 1` x 1` of a degree latitude/longitude grid.