cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000099
title: Heat Units (Winter)

description: The concept of the heat unit (or degree day), known since the mid-18th century already, revolves around the development of a plants or organism`s being dependent upon the accumulated heat to which it was subjected during its lifetime, or else during a certain developmental stage. This measure of accumulated heat is known as physiological time. In general it holds that the lower the temperature, the slower the rate of growth and development of plants and invertebrate animals and the amount of heat required to complete a given organism's development does not vary. Physiological time is usually expressed and approximated in units of degree days, also popularly called heat units, where these are an accumulation of mean temperatures above a certain lower threshold value and below an upper developmental threshold (above which growth is considered to remain static or even decline), over a period of time. The degree day concept is not without its limitations. These include that, a linear relationship is assumed between growth and temperature, threshold temperatures may change during the life cycle of a crop or Pest, temperatures exceeding the upper threshold may, in fact, have a detrimental/negative effect on development.

Derivation over South Africa of heat units were done by calculating degree days for the daily temperature fluctuations that occur in nature is a difficult task1 and several methods of differing complexities, all approximations of the actual degree days accumulated, are available. These range from degree hours, sine and triangular methods based on hourly temperatures, as well as so-called cut-off methods when the upper threshold of temperature is reached. To estimate heat units (HUs) over South Africa, the 50 year time series of daily maximum and minimum temperatures (Tmxd, Tmnd), generated at each of the 429 700 raster points covering the area at 1` x 1` latitude/longitude, was used (Schulze and Maharaj, 2004). The generation of this time series is described in Section 2a. HUs was computed for each day for a threshold temperature of 10 degree Celsius, i.e. HU = [(Tmxd + Tmnd) / 2 - 10] for HU ≥ 0.0 This method is a considerable advance on the computation of HUs in the previous version of this Atlas (Schulze, 1997), in which HUs were calculated from regression derived monthly means of Tmx and Tmn. From these daily values, monthly as well as seasonal and annual HUs and their standard deviations, could be calculated and mapped.
DOI: 10.15493/deff.10000003
title: Wave power atlas for South Africa

description: The spatial dataset is provided as a point shapefile and indicates the average annual and seasonal wave power (kW/m of wave crest). Model outputs were produced on a 500m resolution (nested numerical grid approach) and written out at the 7m and 15m bathymetry contour lines. False Bay is the exception, with output on a 200m resolution. The numerical code, Simulating WAves in the Nearshore (SWAN) was used for the present study. Averages are based on stationary, coastal, spectral wave model computations. The simulation period is at a three-hourly resolution spanning from 1997 until 2013. Saturated, wind wave computations and swell computations were executed separately. The combined product presented here were thus the result of total wave energy addition.
  
The wave power averages are also presented in terms of coastal wave exposure classifications. Through this classification the South African coastline can thus readily be understood in terms of total and seasonal experienced wave exposure. These range from fully sheltered to fully exposed and is based on widely accepted literature. 

These data were originally produced during a Research and Development contract between the Council for Scientific and Industrial Research (CSIR) and the Department of Environmental Affairs (DEA). The scope of the project was to determine the coastal wave run-ups for the entire South African coastline (Theron et al., 2014*). These data were then used as input for post-processing and thus producing the wave power atlas data for South Africa, as presented here.  *Theron, A., Rossouw, M., Rautenbach, C., Page, P., von Saint Ange, U., van Niekerk, L., & Luck-Vogel, M. (2014). DEA-CSIR Coastal Vulnerability Phase 2 Wave Report.
DOI: 10.15493/saeon.egagasini.10000014
title: The ASCA July 2018 (SEAmester III) raw oblique Bongo data for unit 1911

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 was collected during the ASCA project have been obtained from various oceanographic instrumentation. This specific raw data was collected using an oblique Bongo net for unit 1911.
DOI: 10.15493/sarva.beeh.10000180
title: Passion Fruit: Optimum Growth Areas - Criterion 1: > 330 days per year with a minimum temperature exceeding 2 degree Celsius

description: Passion fruit are believed to have originated from southern Brazil (Kenyaweb, 2005).The most commonly commercially grown of 55 edible species of passion fruit is Passiflora edulis, which consists of perennial woody vines with each producing about 100 fruits per year (RBGK, 2005). In South Africa passion fruit ideally requires a cool subtropical climate for optimum production (Smith, 1998). The vines prefer moderate temperatures throughout the year, with monthly means of daily maxima < 29 degree Celsius. The plants are sensitive to severe frosts, and monthly means of minimum temperatures should be > 6 degree Celsius (NDA, 2005). For commercial production under rainfed conditions MAP should ideally exceed 1 200 mm and be well distributed throughout the year (NDA, 2005), as it is important to maintain the soil moist throughout the growing season to keep the vines flowering and fruiting for longer periods and prevent the fruit from shriveling and falling prematurely (Kenyaweb, 2005). Total commercial production of passion fruit in South Africa is 1 300t per season, with a range (between 1998/9 - 2003/4) in more recent from 900 - 1 700 t (NDA, 2005).The gross value is R65 million/year (NDA, 2005).

Criteria for climatically optimum growth areas for passion fruit were refined by Bower (2005). Six criteria were identified and using the 1` x 1` gridded information on temperature related parameters derived by Schulze and Maharaj (2004), the following criteria were mapped: Criterion 1: > 330 days per year with a minimum temperature exceeding 2 degree Celsius; Criterion 2: Fewer than 2 consecutive days per annum with minimum temperatures < -2 degree Celsius; Criterion 3: There should not be > 3 consecutive frost days per annum; Criterion 4: In total, there should be < 25 days per annum with frost; Criterion 5: Maximum temperatures should be < 28 degree Celsius on at least 25 days of each month; Criterion 6: Maximum temperatures should not exceed 35 degree Celsius on any two consecutive days per month. Mapped criteria were given either categorical or probabilistic weightings and when the six maps were superimposed, suitable and less suitable climatically optimum growth areas could be identified. The assumption was made that for commercial production the passion
DOI: 10.15493/bea.data.10000015
title: Pinus taeda yield estimates allocated to mesozones

description: *The dataset shows climatically optimum growth areas and yield estimates of Pinus taeda allocated to mesozones. Yield estimates were derived from Schulze R.E. and Maharaj M. (2007) and then allocated to mesozones by combining with a base mesozone dataset obtained from the CSIR Geospatial Analysis Platform (GAP). *The map shows the climatically optimum growth areas to be along the north coast of the Eastern Cape, significant tracts in the midlands of KwaZulu-Natal, and parts of Mpumalanga. Major climatic growth constraints are drought related. Highest Mean Annual Increments (MAIs), according to Smith's (1994) equations, are 20 - 22 t/ha/annum and these coincide with the climatically optimum growth areas.
DOI: 10.15493/saeon.egagasini.10000038
title: The ASCA June 2018 raw CTD profile data collected for station 001 - 011

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 the ASCA project have been obtained from various oceanographic instrumentation. This raw data was collected by the ship-borne CTD (Conductivity, Temperature, Depth) sensor for stations 001, 001B, 002, 003, 004, 005, 006, 007, 008, 009, 0010, 0011.
DOI: 10.15493/sarva.beeh.10000451
title: Drainage Rate

description: This dataset contains the soils: hydrological attributes in South Africa. Crop yield and hydrological models require soils information such as thicknesses of the various horizons modeled, soil water contents at critical retention levels and the erodibility of the soil for input. These variables have been derived for all soil series identified in South Africa via the AUTOSOILS decision support tool and their attributes are area averaged at the level of Land types for purposes of mapping.
DOI: 10.4121/5e40ecd6-98c4-423c-bd1c-d24f0f7a7a59.v1
title: 20211109 20211215 Main deployment - Nearshore - RealDune/REFLEX

description: Nearshore field measurements of the main November - December deployment of the RealDune/REFLEX experiments at the Dutch coast in Autumn/Winter 2021/2022. The lidar and GoPro data are stored separately. This dataset is part of the data collection 'Nearshore coastal measurements of calm, moderate, and storm conditions at two artificial dunes along the Dutch Coast during the RealDune/REFLEX experiments'.
DOI: 10.15493/sarva.saws.10000276
title: Root Mean Square Difference in percentage (%) between the nine ensemble member change anomalies of the seasonal percentage rainfall for the median for 2066 - 2095 relative to 1976-2005, for the JJA season, under the RCP 8.5 pathway

description: Root Mean Square Difference in percentage (%) between the nine ensemble member change anomalies of the seasonal percentage rainfall for the median for 2066 - 2095 relative to 1976-2005, for the JJA season, 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.10000035
title: Climatically Suitable Growth Areas for Eucalyptus benthamii

description: Commonly named the Camden White Oak, Eucalyptus benthamii, is a tall, smooth-barked. It is a cool climate species, growing optimally at MATs between 14 and 18 degree Celsius. E. benthamii is less tolerant of low MAPs than E. badjensis, with > 850 mm MAP required for optimal growth (Kunz, 2004).This species is susceptible to snow damage (Kunz, 2004).
DOI: 10.15493/sarva.saws.10000074
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 DJF season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (DJF) rainfall (mm per month) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily 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.10000206
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 JJA season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (JJA) rainfall (mm per month) 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 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.csag.10000336
title: Number of days for Maximum Temperature over 90th percentile 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.saws.10000131
title: Annual mean near-surface (2m) temperature (°C) change from the 10% percentile projected for 2066-2095, relative to present (1976 - 2005), under the RCP 8.5 pathway

description: Annual mean near-surface (2m) temperature (°C) change from the 10th percentile projected for 2066-2095, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature 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/saeon.egagasini.10000102
title: The ASCA July 2019 (SEAmester IV) raw Oblique 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 an oblique bongo net.
DOI: 10.15493/bea.data.250320-21
title: BioEnergy Atlas Model Outputs - Forestry Residue

description: Optimised Facility Locations, Transport Routes and Feedstock Catchment Areas, and modelled cost of Loading, Transport, Unloading and Processing for the production of BioEnergy products from Forestry Residue. These datasets were compiled in support of the Forestry Residue case study published in the second edition of the BioEnergy Atlas for South Africa.
DOI: 10.15493/sarva.saws.10000006
title: Root Mean Square Difference between the nine ensemble member change anomalies of the annual mean near-surface (2m) temperature for the 10% percentile for 2036 - 2065 relative to 1976-2005, under the RCP 4.5 pathway

description: Root Mean Square Difference for annual mean near-surface (2m) temperature (°C) change from the 10% percentile projected for 2036-2065, relative to present (1976 - 2005), under the RCP 4.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature averages, which are used to generate projections of annual change. The projections are generated using the medium to low (RCP4.5) pathway which associates CO2 concentrations of approximately 560ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.231120-7
title: The distribution of Quintile 3 Secondary Schools per District Municipality

description: School Quintiles are an indication of the socio-economic status of the school. Quintile 1 schools are the poorest, while quintile 5 schools are the least poor .Schools are ranked according to poverty of the school community . This dataset indicates the distribution of schools according to quintiles using the data from the master list of schools obtained from the Department of Basic Education.
DOI: 10.15493/saeon.fynbos.13b4408
title: 13B manual weekly rainfall January 1944 - February 2008

description: Weekly rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, 1.2 meters above ground in the Tierkloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.saws.10000150
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the 10% percentile for 2036 - 2065 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 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 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.beeh.10000382
title: Uncorrected Vapour Pressure (kPa) September

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: 10.4121/d629b478-b9ce-49ff-b0b2-12e1cc56bfdd.v1
title: 20211106 20211108 Storm deployment - Nearshore - RealDune/REFLEX

description: Nearshore field measurements of the November storm deployment of the RealDune/REFLEX experiments at the Dutch coast in Autumn/Winter 2021/2022. This dataset is part of the data collection 'Nearshore coastal measurements of calm, moderate, and storm conditions at two artifical dunes along the Dutch Coast during the RealDune/REFLEX experiments'.