cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/nccis.data.10000041
title: Projected changes in annual mean temperature (°C) under RCP 4.5 for the 2080-2099 time period based on the Geophysical Fluid Dynamics Laboratory Coupled Model (GFDL-CM3).

description: The analyses of future climate change over South Africa as described in the Third National Communication, are from the projections of the Coupled Global Climate Models (CGCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Assessment Report (AR) 5 of the Intergovernmental Panel on Climate Change (IPCC). These projections are used to inform on the uncertainty range of the large-scale climate change futures over the southern African region. At the Council for Scientific Industrial Research (CSIR), a dynamic regional climate model CCAM (conformal-cubic atmospheric model) of the Commonwealth Scientific and Industrial Research Organisation (CSIRO) was used to downscale CMIP5 CGCM projections to 50 km resolution over Africa. These downscalings were for both Representative Concentration Pathway (RCP) 8.5 and Representative Concentration Pathway (RCP) 4.5 of AR5 of the IPCC. RCP 4.5 describes a future with relatively ambitious emission reductions whereas RCP 8.5 describes a future with no reductions in emissions. Emissions in RCP 4.5 peak around 2040, then decline and in RCP 8.5 emissions continue to rise throughout the 21st century. The change in temperature is expressed as an anomaly, the difference between the average climate over a period of the last several decades (1971-2000), and the projected climate (short to medium term 2021 to 2050). The simulations were performed on supercomputers of the CSIRO and on the Centre for High Performance Computing (CHPC) of the Meraka Institute of the CSIR in South Africa.
DOI: 10.15493/dea.carbon.10000110
title: Total Soil Organic Carbon for 1990 in t/ha

description: ISRIC Africa SoiGrids data were used to calculate a reference layer of soil organic carbon (SOC). It was done by using the organic carbon content and bulk density data sets available at 250m resolution and for 6 different depths. The data was aggregated to a depth of 30cm for topsoil and 100cm for subsoil. The impact of land use on the SOC is included by incorportating the 1990 National Land Cover data set for South Africa.
DOI: 10.15493/saeon.fynbos.4a3691
title: 4A manual monthly rainfall January 1936 - September 1991

description: Monthly rainfall data collected by a Snowdon gauge with orifice at 1.2 meters above ground in the Langrivier catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.csag.10000107
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/sarva.beeh.10000219
title: Pineapples: Optimum Growth Areas (Rainfall Requirements)

description: The pineapple, i.e. Ananas comosus is a frost sensitive tropical and subtropical plant. Pineapples can grow well under a wide range of rainfall from MAP 600 - 1 200 mm (Smith, 1998). However, irrigation is necessary when MAP is < 500 mm or when consecutive months of low rainfall occur. Daily mean temperatures around 23 – 24 degree Celsius are considered optimum for both acid and sugar content of pineapples. South Africa accounts for 6 percent of the world`s pineapple production in a market dominated by Thailand, Indonesia, the Phillipines and Kenya (SA Yearbook, 2005). Of the 6 350 ha planted to pineapple in South Africa, 69 percent is in the Eastern Cape and 30 percent in KwaZulu-Natal (Statistics SA, 2002). Two varieties are grown, viz. the smoother, larger and juicier Cayenne and the smaller, but sweeter Queen variety, with production shifting more and more towards the Cayenne variety. In South Africa pineapples flower within 14 - 20 months of planting, with summer fruit in the Eastern Cape taking 5 months to mature and winter fruit up to 7 months, implying that the plant crop is harvested at 18 - 24 months (Bathurst, 2005).

For pineapple production potential, precipitation is evaluated as follows: Monthly precipitation is divided by 10, with any monthly amount in excess of 100 mm scoring 10. The largest possible annual precipitation score would thus be 120, areas with MAP less than 500 mm are considered unsuitable for pineapple production, except under irrigation, areas with more than 3 consecutive months less than 15 mm or 4 consecutive months less than 25 mm or 5 consecutive months less than 40 mm precipitation per month are also considered unsuitable without supplementary irrigation.
DOI: 10.15493/sarva.140823
title: South African Sustainable Development Goals Index Scores

description: This data is an Excel spreadsheet containing the Sustainable Development Goal (SDG) Index Scores of South Africa, as published in the 2023 Sustainable Development Report. The dataset covers a 10-year period from 2012 to 2022. The SDG Index Score, measured on a scale of 0 to 100, represents the country's overall progress in achieving all 17 SDGs, as proposed by Sachs et al. (2023). The data encompasses both the aggregate index score for the entire nation and the individual index scores for each of the 17 SDGs. This dataset provides valuable insights into South Africa's sustainable development performance and its progress towards meeting the global goals outlined in the SDGs. Researchers, policymakers, and stakeholders can utilise this dataset to assess and strategise sustainable development initiatives in the country.
DOI: 10.15493/saeon.egagasini.10000091
title: The ASCA April 2016 raw MicroCAT data collected at Mooring C3

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current`s volume, heat and salt transport and its 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 collected during this project have been obtained from various oceanographic instrumentation fixed to each mooring. This raw data was collected by a MicroCAT sensor at Mooring C3.
DOI: 10.15493/sarva.csag.10000156
title: Mean 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.beeh.10000023
title: Actual Vapour Pressure (July)

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/saeon.fynbos.4b4491
title: 4B manual monthly rainfall February 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 Langrivier catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/saeon.fynbos.17b4591
title: 17B manual monthly rainfall February 1945 - September 1991

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 at Jonkersnek in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.csag.10000266
title: Total precipitation for the month (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/
DOI: 10.15493/sarva.030921-3
title: Mean annual vapour pressure across South African municipalities from 1991 - 2019

description: This is a subset of the University of East Anglia Climatic Research Unit (CRU)`s Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate containing Vapour Pressure [KPa] readings from South Africa`s weather stations across local municipalities for the years 1991-2019. 

The CRU Time Series 4.05 dataset was developed and has been subsequently updated, improved and maintained with support from a number of funders, principally the UK`s Natural Environment Research Council (NERC) and the US Department of Energy. Long-term support is currently provided by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre. Current gridded products (CRU TS) are presented either as ASCII grids, or in NetCDF format. The gridding process used in Brohan et al. (2006) and earlier publications assigns each station to the 5 degree latitude/longitude box within which it is located. The gridding then simply averages all available station temperatures (as anomalies from 1961-90) within each grid box for each month from 1851. No account is taken of the station`s elevation or location within the grid box (anomalies show little consistent dependence on altitude). A more up-to-date location for a station is not important for the gridding, unless a site change were to move the station to an adjacent grid box.  

In this instance, the data was derived as a subset of the original dataset. CRU  publishes the data in NetCDF file format,  however for data visualisation purposes the datasets was transformed into tidy tables, represented in the South African Risk and Vulnerability Atlas (SARVA) by the South African Environmental Observation Network`s uLwazi Node. 

Citation: University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, 2021. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681
DOI: 10.15493/sarva.saws.10000238
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the median for 2066-2095 relative to 1976-2005, for the SON season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (SON) rainfall (mm per month) 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 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.beeh.10000435
title: Inter-Annual Cv (%), Maize Yield Short Season Hybrid Plant Date: 15 December

description: This data set contains Maize, Zea mays L. in South Africa and is the country`s most important field and grain crop. Objectives of the study was to simulate maize yields, and their inter-annual, at a spatial resolution of Quaternary Catchments for 12 different combinations of three plant dates, viz. 15 October, 15 November and 15 December. This was done to evaluate which hybrid lengths and plant dates give the highest yields irrespective of plant dates and hybrid lengths respectively and also which hybrid lengths and plant dates give the lowest coefficients of variation (%) irrespective of plant dates and hybrid lengths respectively.
DOI: 10.15493/sarva.beeh.10000442
title: Mean Number Of Days With Rainfall greater than 2 mm April

description: This dataset contains the daily rainfall: A Threshold Analysis of South Africa (SA). The hydrological and agricultural analyses at daily time steps was because diurnality encapsulates many hydrologically and agriculturally related processes (e.g. evaporation, and transpiration), furthermore many operational decisions are made according to daily conditions (e.g. irrigation, tillage, pest control).Distribution patterns over SA of the mean number of days per month with rainfall > 2 mm shows that on average, 2 mm per day is exceeded on more than 10 days in the summer months November to February in parts  KwaZulu-Natal and Eastern Cape.  During the winter months May to August most of SA records fewer than 2 days per month with rainfall >2 mm, except in the southwestern corner of the winter rainfall region, where up to 8 and more such occurrences are recorded.
DOI: 10.15493/sarva.beeh.10000380
title: Uncorrected Vapour Pressure (kPa) July

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.csag.10000065
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/sarva.beeh.10000011
title: A-Pan Equivalent Reference Potential Evaporation (August)

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.
DOI: 10.15493/sarva.energy.10000029
title: SUG-MOL: Sugar Cane - Ethanol Fermentation (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.10000044

* BioEnergy Atlas Synopsis Report: https://dx.doi.org/10.15493/SAEON.BEA.DOCS.10000001
DOI: 10.15493/sarva.energy.10000021
title: IAP-BIC: Invasive Alien Plants - Biomass Integrated Combined Gasification Cycle (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.10000032

* BioEnergy Atlas Synopsis Report: https://dx.doi.org/10.15493/SAEON.BEA.DOCS.10000001
DOI: 10.15493/sarva.beeh.10000340
title: Extremes (1950 - 2000) Of Minimum Temperatures (C) April

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.