cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.csag.10000082
title: Maximum of Maximum 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/sarva.csag.10000430
title: Median monthly precipitation (mm) 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/dea.carbon.10000010
title: Gross Primary Production (gC/sq.m/yr)

description: Gross primary production (GPP) is the amount of biomass (units: gC/m2/yr) that plants create in a one year by capturing sunlight and CO₂ during photosynthesis. A production efficiency model was used to estimate GPP across South Africa. Carbon fluxes were calculated to represent the long-term mean conditions. The gross and net primary production (GPP, NPP) was calculated monthly and summed to a year using climatology of monthly weather for the period 1960 to 1990, and mean monthly FAPAR and PAR for period 2000 to 2012. There are thus 12 input files for each term, corresponding to the twelve months. The climatology averaging periods for climate and satellite data are different. For Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), the MEdium Resolution Imaging Spectrometer (MERIS sensor) dataset covers the period 2000 to 2012, and the Photosynthetically Active Radiation (PAR) dataset is also for this period. Units: gC/m2/yr
DOI: 10.15493/sarva.beeh.10000231
title: Monthly Means Of Daily Solar Radiation (Mj/M2/Day) May

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.10000410
title: Soybean Yield Estimation (T.Ha-1.Season-1) Based On SmithS (1994) Climatic Criteria

description: A bushy, summer season legume, soybean (Glycine maximum) has three-fold leaves and grows to between 0.6 and 1.2 m (SPPCOM, 2005). Soybeans, planted in November - December, ideally require a MAP > 700 mm, with > 450 mm falling over the growing season. While relatively resistant to very high and low temperatures, optimum growth takes place at temperatures between 20 degree Celsius and 30 degree Celsius, with growth rates decreasing when daily maxima are > 35 degree Celsius and below 18 degree Celsius (Smith, 1994).

Using Smith`s (1998) climatic criteria, yields of soybean are estimated using the effective rainfall for October to March and heat units (base 10 degree Celsius) for the same period, with modifications to yield made for soil properties and levels of management. Rainfall values were derived from the 1 arc minute (1` x 1` latitude x longitude) median monthly rainfalls generated for South Africa by Lynch (2004).
DOI: 10.15493/saeon.fynbos.15b1820
title: 15B event rainfall February 2018 - August 2020

description: Event rainfall data collected by Texas Electronics tipping bucket gauge installed at 1.2m above ground at 310 masl in the Lambrechtbos catchment of the Jonkershoek valley in the Western Cape of South Africa. Data recorded by a Pendant Event logger (UA-003-64, Onset, Massachusetts, U.S.A). Tipping bucket calibrated to 0.254 mm increments.
DOI: 10.15493/nrf.saiab.20232512
title: Bathymetry data collected from Durnford Hats, March 2022

description: African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA
description: This dataset forms part of the the South African Institute for Aquatic Biodiversity (SAIAB)'s collection of Bathymetry data for the African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA. This multi-disciplinary study will begin at the start of implementation of two new MPAs (uThukela MPA and Protea Banks MPA, both proclaimed in 2019), and will generate the relevant data needed to underpin an evidence-based approach to adaptive management of the MPAs, within a “SMART” framework. This framework defines specific objectives for the different MPA zones; develops measurable indicators of success for each zone; sets achievable goals given the limited resources available for planning and management; ensures that the research findings are selevant (to both biodiversity and human stakeholders). This is a rare time-sensitive opportunity to conduct baseline surveys of the geology, benthic biodiversity (sessile and mobile species), reef-associated ichtyoplankton and mero-zooplankton and fish communities of mesophotic (40-150m) soft-coral and sponge-dominated reefs and submarine habitats located in three different management zones at initiation of management of two brand new MPAs, against which long-term monitoring data can be compared to assess the effectiveness of the MPAs in achieving their objectives. The balanced sampling design is such that it will also initiate a “natural experiment” that over time will investigate the effect of no-take zones on fish diversity and abundance, benthic community structure, and trophic complexity. This will also enable the determination of the value of the MPA for fisheries sustainability support and resilience to disturbances caused by extrinsic factors such as pollution or climate change. Additionally, this study will assess whether the zonation plan provides for representation of all reef and canyon types within each zone or if there is unbalanced coverage for certain reefs and in certain zones. This is important to assess at the initiation of the MPA, especially to ensure that interpretation of monitoring results in future, which compare ecosystems and fish on reefs and canyons in the mesophotic zone, are not flawed by natural differences from the outset due to the selection of zone boundaries. This dataset consists of XYZ points below the sea surface and relative to mean sea level. This data was collected under the ACEP SMART ZONES MPA project (2021 – 2023) using the ACEP Geophysics and Mapping Platform.
description: See metadata file for additional information.
DOI: 10.15493/sarva.sdg.10000006
title: Proportion of population with access to electricity

description: Sustainable development goal sdg 7.1.1 (Proportion of population with access to electricity) for the year 2011 dasymetrically mapped to a 500m grid using the 2008 spot dwelling count. The data provides a percentage of the total number of households per 500m grid cell that use electricity for Cooking, Heating and Lighting
DOI: 10.15493/sarva.csag.10000346
title: Rainfall days per month (> 10mm) 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.geoss.10000005
title: GRA2: Groundwater Recharge (mm/a)

description: Groundwater Resource Assessment Phase 2, 2005.  Recharge depth (mm/a) 1x1km grid.  Recharge figures are based on the chloride mass-balance method.  It should be noted that the coverage of chloride measurements in rainfall in South Africa is incomplete, and that the method has inherent errors.
DOI: 10.15493/dea.carbon.10000086
title: South African Land Cover 2018 Class D Natural Vegetation 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 2–4,8–13,24 of the 2018 South African National land cover dataset was clustered to derive South African land cover 2018 Class D Natural Vegetation 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/dea.carbon.10000092
title: South African Land Cover 2018 Class J Subsistence 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 41 of the 2018 South African National land cover dataset was used to derive South African land cover 2018 Class J Subsistence 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.saws.10000079
title: Seasonal total rainfall (mm per 3-month season) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), for MAM season under the RCP 4.5 pathway

description: Seasonal (MAM) mean rainfall (mm per month) 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 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.10000241
title: Annual total rainfall percentage(%) change from the median, projected for 2036 - 2065 relative to the present (1975 - 2005), under the RCP 4.5 pathway.

description: Annual total rainfall percentage(%) change from the median, projected for 2036 - 2065 relative to the present (1975 - 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/bea.data.10000052
title: High Voltage Lines

description: Eskom provided the BioEnergy Atlas with a set of data to describe their current and planned infrastructure:
* High voltage transmission lines and substations;
* Medium voltage distribution lines and substations;
* Planned new transmission lines;
* Planned new substations.
All of these were aggregated to meso-zones (planning and analysis zones used for the Atlas) in the following way:
* Distribution and transmission lines: presence or absence of lines;
* Substations: count of medium voltage substations, and proximity (5km) to high voltage substations
DOI: 10.15493/sarva.csag.10000315
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: 10.48331/scielo.geimeg/uff75e
title: 2 Malavika Legge.pptx

description: :unav
DOI: 10.15493/sarva.beeh.10000314
title: Standard Deviations (C) Of Daily Mean Temperature February

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: 10.48331/scielo.geimeg
title: Equity in Open Access: Equity in Open Science - Perspectivas dos workshops 'Equity in OA' da OASPA

description: Presentation by Malavika Legge in the Equity in Open Access: Equity in Open Science panel of the SciELO 25 Years Week. Event page: https://25.scielo.org/en
DOI: 10.15493/sarva.geoss.10000001
title: Aquifer type and yield (Groundwater classification and borehole yield)

description: Amalgamation of 1:500 000 Hydrogeological Map series, giving a country-wide extent of aquifer type (groundwater classification) and expected borehole yield. NGDB data used primarily.
DOI: 10.15493/bea.data.10000073
title: Availability of Excess Sugar Bagasse at Sugar Mills

description: Data was derived from the following sources: Eskom commissioned a study from CRSES to evaluate, inter alia, availability of excess bagasse at sugar mills after current internal energy needs have been addressed. This data has been provided to the BioEnergy Atlas by Eskom. For planning purposes, the data was assigned to meso-zones (planning units for the BioEnergy Atlas)
DOI: 10.15493/sarva.beeh.10000024
title: Actual Vapour Pressure (August)

description: Actual vapour pressure is the variable expressing atmospheric moisture content through the partial pressure exerted by the water vapour present in the atmosphere. 

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 as inputs to daily saturated vapour pressure calculations, which were in turn used with that location's monthly uncorrected actual vapour pressure.
DOI: 10.15493/sarva.csag.10000232
title: Total precipitation for the month (mm) 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/sarva.dws.10000019
title: River profile classification Catchment S

description: Longitudinal slope profiles derived for all 1:500 000 rivers of South Africa, including cross-border catchments extending into neighbouring countries. River main stems are identified by a unique reach number (REACHNUM) within each primary catchment.
DOI: 10.15493/dea.carbon.10000108
title: Above Ground Litter for 2018 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.
DOI: 10.15493/sarva.saws.10000087
title: Seasonal total rainfall (mm per 3-month season) change from the median projected for 2036-2065, relative to present (1976 - 2005), for JJA season under the RCP 4.5 pathway

description: Seasonal (JJA) rainfall (mm per month) 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 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/dea.carbon.10000090
title: South African Land Cover 2018 Class H Viticulture 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 33 of the 2018 South African National land cover dataset was used to derive South African land cover 2018 Class H Viticulture 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.saws.10000158
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the 90% percentile 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) mean near-surface (2m) temperature (°C) change from the 90% 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 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.