cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000291
title: Standard Deviation (C) Of Daily Maximum Temperature April

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.10000104
title: Climatically Optimum Growth Areas Litchis Composite Of All Criteria

description: Litchis, Litchi chinensis, are indigenous to subtropical southern China and were first imported to South Africa from Mauritius in the 1870s (ARC, 2005). Of the 7 000 tons of litchis produced annually in South Africa, with a gross value exceeding R25 million, about 60 percent  come from Mpumalanga, 38 percent  from Limpopo and 2 percent from KwaZulu-Natal (Statistics SA, 2002; NDA, 2005).
DOI: 10.15493/sarva.beeh.10000363
title: Monthly mean of diurnal (daily) temperature ranges for the 50 year (1950 - 1999) period (C) - February

description: The diurnal temperature range is the difference between the maximum and minimum temperatures on a given day. Some plants respond to the rhythmic variations brought about by shifts from high temperatures during the day to low temperatures during the night. The main long term factors affecting diurnal temperature ranges over South Africa are continentality and altitude (Schulze, 1997). It nevertheless needs emphasising here that short term, irregular diurnal conditions are often controlled by the passage of cold fronts or other air masses which may be associated with strongly contrasting temperature regimes.

"Monthly means of diurnal (daily) temperature ranges were derived from the 50 year (1950 - 1999) time series of quality controlled daily  maximum and minimum temperature 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). From theoretical considerations, experience and analyses of high quality daily temperature datasets, the diurnal temperature range is very seldom < 2 degree Celsius. When, therefore, on a given day the temperature range value was computed to be < 1.5 degree Celsius, this value was `forced` to be 1.5 degree Celsius, with equal compensatory adjustments being made to that day's maximum and minimum temperature values."
DOI: 10.15493/sarva.beeh.10000448
title: Days / Year With Mild Soil Water Stress In A 0.6m Sandy Clay Loam

description: This dataset contains the soil water stress in South Africa. The importance of soil water content in agricultural applications is vital since the need to irrigate occurs when the actual soil water content is less than the critical soil water content at which plant stress commences. The objective of soil water stress mapping was to determine, for each Quaternary Catchment (QC) with its unique rainfall, temperature and potential evaporation climate, the number of days per year at which soil water content would be at critical plant stress/no stress levels.
DOI: 10.15493/sarva.csag.10000043
title: Minimum of Minimum 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/sarva3.data.10000045
title: Soils Susceptible to Wind Erosion
DOI: 10.15493/sarva.saws.10000200
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the 90% percentile for 2036 - 2065 relative to 1976-2005, for the MAM season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (MAM) mean rainfall (mm per month) from the median projected for 2036-2065, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily rainfall averages, which are used to generate projections of seasonal change. The projections are generated using the high (RCP8.5) pathway which associates CO2 concentrations of approximately 950ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.geoss.10000004
title: GRA2: Average Groundwater Exploitation Potential

description: Groundwater Resource Assessment Phase 2, 2005. Average of Groundwater Exploitation Potential for wet and dry conditions.  Units are m3/km2/a - grid with 1kmX1km cells
DOI: 10.15493/saeon.fynbos.1b5096
title: 1B manual daily rainfall January 1950 - August 1996

description: Daily rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, 1.2 meters above ground at the Herehuis building in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.gb.10000014
title: Sectoral Gross Value Added (GVA) for transport, storage and communication (SIC7) as a stock from 1995 to 2016

description: The South African Gross Geographic Value Added (GVA) data for each economic sector per year since 1995 to 2016 was supplied by Quantec to the CSIR built environment. This data was disaggregated to a 500m grid using a dasymetric mapping algorithm (https://gap.csir.co.za/gap/images/documents/developing-a-geo-data-frame-using-dasymetric-mapping-principles-to-facilitate-data-integration). and is provided for each year
DOI: 10.15493/sarva.saws.10000190
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 SON season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (SON) 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/nrf.saiab.20232503
title: Bathymetry data collected from Rosie Snowball, July 2021

description: African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA
description: This dataset forms part of 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 the 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; and develops measurable indicators of success for each zone. It also sets achievable goals given the limited resources available for planning and management. This ensures that the research findings are relevant (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 ichthyoplankton 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 the 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 plans provide 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 the initiation of the MPA, especially to ensure that the interpretation of monitoring results in future to 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 the 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/saeon.ndlovu.10000001
title: SAEON Ndlovu primary automated weather station - 30 minutes (date, time, temperature, rain), 2017-09-01 to 2018-01-31

description: This is a data set collected by the SAEON`s Ndlovu node automated weather station (AWS), located at their offices inside the Kruger National Park, Phalaborwa Gate, Limpopo Province, South Africa. The exact location of the AWS is: South: -23.945075, East: 31.165242. The make and model of the AWS is a Davis Vantage Pro 2.
DOI: 10.15493/sarva.csag.10000222
title: Number of days for Minimum 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/
DOI: 10.15493/sarva.beeh.10000378
title: Uncorrected Vapour Pressure (kPa) May

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.10000028
title: Actual Vapour Pressure (December)

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.10000324
title: Number of days for Minimum 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/
DOI: 10.15493/dea.carbon.10000067
title: South African Land Cover 2014 Class A Water Resampled to a 1km Grid

description: The 2013-14 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 8 multispectral imagery, acquired between April 2013 and March 2014. In excess of 600 Landsat images were used to generate the land cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land cover dataset, which covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. The dataset contains 72 x land cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 8 standard map projection format as provided by the USGS. Class 1–2 of the 2013-14 South African National land cover dataset was clustered to derive South African land cover 2014 Class A Water 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.csag.10000326
title: Mean of Minimum Temperature for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/dea.carbon.10000071
title: South African Land Cover 2014 Class E Commercial Agriculture Resampled to a 1km Grid

description: The 2013-14 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 8 multispectral imagery, acquired between April 2013 and March 2014. In excess of 600 Landsat images were used to generate the land cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land cover dataset, which covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. The dataset contains 72 x land cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 8 standard map projection format as provided by the USGS. Class 10–12 of the 2013-14 South African National land cover dataset was clustered to derive South African land cover 2014 Class E Commercial 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.csag.10000392
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/hcdb-8a05
title: Historical Catch Return Database

description: This database consists of a collection of historical commercial catch return datasets that were transcribed (digitised manually) from printed books and scanned copies of catch-return data sheets. Many of the latter were hand-written.

`Harbour returns` are monthly summaries (numbers) of the commercial fish species landed at harbours from Lamberts Bay to East London, as provided in the Department of Agriculture `Report of the Marine Biologist` (1897-1900) and `Report of the Government Biologist` (1900-1906) publications. The table called `Boat registration data` contains information on the numbers and types of vessels and gear used at various harbours around South Africa between 1897 and 1906. These data were digitized from the same publications as the harbour returns and provide some insight into what gear was used to yield those harbour returns.

`Linefish returns` are daily catch returns (numbers but with weekly totals in weight) for commercial line fish species at harbours ranging from Lamberts Bay to Jeffreys Bay and covering the period 1922-1935. These data were captured from catch return sheets that are lodged at the Western Cape Archives in Roeland street in Cape Town.
 
`Trawl returns` are monthly catches (by weight) from trawlers at South African Harbours (Port Nolloth to Durban) between 1921 and 1960. They were digitized from catch return sheets that are also in the Western Cape Archives.
Most tables in the database have a `RecentComment` column in which any notes pertaining to the record are included. An attempt has been made to make comments in these columns of limitations or peculiarities of the records in question. Users are advised to consult any entries in this column before performing analyses. These recently added comments are not to be confused with historical comments made in the original reports or sheets, which are included in columns headed `Remarks`.

Authors who intend to use these data are strongly encouraged to consult the series of historical reports and the catch return sheets from which these data have been digitized.
The two datasets which were digitized from original catch return sheets (Linefish Returns and Trawler Returns) contain a column titled `Image` in the appropriate tables. The numbers in this column indicate the number relating to the scanned image of the original sheet from which the data were captured (scanned images are numbered IMG0001.JPG, IMG0002.JPG...etc.). Please note, there is some overlap of image numbers for some linefish records (and between different catch return datasets, e.g. trawler returns and linefish returns), so image numbers are NOT necessarily unique identifiers of historical sheets.