cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000213
title: August Standard Deviations Of Daily Reference Crop Evaporation (mm) Fao Penman-Monteith Equivalent

description: The first detailed maps for South Africa of Penman-Monteith equivalent reference crop, Erpm, were derived from monthly ratios of Erpm: A-pan using data at 174 sites (Crosby and Crosby, 1996). From these ratios, Schulze (1997) identified 14 `regions` of similar ratio patterns which were then used to map Erpm from gridded A-pan values. The maps in the present Atlas are based on a much more deterministic and more fundamental approach with daily maximum and minimum temperatures, and hence daily saturated vapour pressures, generated over South Africa on a 1` x 1` (1.7 x 1.7 km) raster for 50 years, based on research by Schulze and Maharaj (2004). Empirically determined month-by-month gridded values of actual vapour pressure for South Africa.

The FAO (1992) definition of Penman-Monteith derived reference crop evaporation (Erpm) is `The rate of evapotranspiration from a hypothetical crop with an assumed crop height of 0.12 m, a fixed canopy resistance of 70 s.m-1 and albedo of 0.23, which would closely resemble evapotranspiration from an extensive surface of green grass cover of uniform height, actively growing, completely shading the ground and not short of water`.
DOI: 10.15493/sarva.saws.10000112
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 JJA season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (JJA) rainfall (mm per month) change from the median 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 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.csag.10000270
title: Mean of Maximum 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/sarva.dws.10000009
title: River profile classification Catchment E

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/sarva.csag.10000100
title: Mean of Maximum 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/bea.data.10000093
title: Population Living in Poverty

description: * The dataset shows estimates of the total population living in poverty. The data was allocated to mesozones by combining with a base mesozone layer. The estimate is a sum of income levels below the upper bound poverty line. In a report by Stats SA in which poverty trends between 2006 and 2011 are examined it is stated that: * Approximately a third (32,9%) of all households in South Africa were living below the upper bound poverty line. * The education level of the household head is closely related with levels of poverty - two thirds (65,0%) of households where the head had no formal education were poor as compared with only 2,8% of households where the head had a post-matric qualification. * Limpopo was the only province in which the majority (50,9%) of households were living in poverty - nevertheless, although Gauteng (16,2%) had the lowest incidence of poor households, the large number of households in the province meant that more than one in ten (12,8%) poor households in South Africa were found in Gauteng. * More than half (55,2%) of all households in rural areas were poor compared to approximately a quarter (22,0%) of households in urban areas.
DOI: 10.15493/saeon.fynbos.8b9108
title: 8B manual weekly rainfall October 1991 - 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 Langrivier catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.csag.10000366
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/dea.carbon.10000005
title: Above Ground Herbaceous Biomass (gC/sq.m)

description: Above Ground Herbaceous Biomass (AGBherb) is predominantly grasses, but also forbs, restios, sedges. It is based on published relationships between rainfall and yearly grass production, reduced proportionately to take into account competition by trees (TCF). AGBherbvaries greatly through the year – reaching a peak near the end of the growing and declining to near zero by the beginning of spring, especially in the presence of fire and/or herbivory. Units: average gC/m2within 1km x 1km pixel
DOI: 10.15493/sarva.beeh.10000316
title: Standard Deviations (C) Of Daily Mean Temperature April

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: 10.15493/sarva.geoss.10000002
title: Electrical conductivity of groundwater

description: Amalgamation of Hydrogeological Map series, giving an interpolated country-wide extent of the Electrical Conductivity of groundwater .
DOI: 10.15493/saeon.egagasini.10000006
title: The ASCA July 2016 (SEAmester I) processed Niskin bottle data for station CTD001 - CTD023

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 is the processed Niskin bottle data collected by water samples from the shipborne CTD (Conductivity, Temperature, Depth) for all depths at the following stations: 001, 002, 003, 004, 005, 006, 007, 008, 009, 010, 011, 012, 013, 014, 015, 016, 017, 018, 019, 020, 021, 022 and 023.
DOI: 10.15493/dea.carbon.10000131
title: Total Herbaceous Biomass for 2018 in gC.m2

description: Total herbaceous biomass is the sum of the above ground and below ground biomass per land unit, proportionally allocated to land cover classes using the SANLC2018
DOI: 10.15493/saeon.fynbos.lb1120
title: Lambrechtbos B stream flow and stage height August 2011 - August 2020

description: Hourly average and halfhourly sample stream flow in cumec estimated from stage height measured with an OTT Orpheus Mini in the stilling well at a V-notch weir in Lambrechtbos B catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.csag.10000141
title: Rainfall days per month 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/nccis.data.10000019
title: Linear trend in Annual maximum length of wet spell (1921-2015) as defined as the maximum number of consecutive days with precipitation ≥ 1mm

description: An updated analysis of trends in South African rainfall is presented in The Third National Communication (TNC) to UNFCCC published in 2018. The analysis builds on the studies of Kruger & Sekele (2012) and Mackellar et al. (2014). 

A total of 60 weather stations were used for the rainfall trend analysis spanning the period 1921-2015. Apart from the trends in the annual rainfall totals, long-term changes in rainfall can manifest in changes in rainfall extremes. An extreme rainfall trend analysis is also performed, based on rainfall extreme indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) (Wang and Feng, 2013).  The base period, from which the annual index values of all indices are determined (except the annual maxima and minima) was selected as 1981 – 2010, which can be considered to be the present general norm for similar trend studies. The trends were tested for significance at the 95% confidence level.

Based on the data, there is strong evidence of statistically significant increases in rainfall occurring over the southern interior regions, extending from the western interior of the Eastern Cape and eastern interior of the Western Cape northwards into the central interior region of the Northern Cape, over the period 1921-2015. Extreme daily rainfall events have increased over these same areas, with these increases also being statistically significant and extending northwards into North West, the Free State and Gauteng. Over Limpopo there is strong evidence of statistically significant decreases in annual rainfall totals.
DOI: 10.15493/sarva.beeh.10000360
title: Standard Deviation (C) Of Daily Minimum Temperature November

description: Minimum (Night-Time) temperatures, there is abundant evidence for the climatic limitation of crop distributions to be described by minimum temperatures, from a perspective of crop survival (on Frost for tolerance levels of crops). Many subtropical crops may be killed at 5 degree Celsius already through chilling injury, even in the absence of frost (Chang, 1968). High minima increase respiration loss, thereby decreasing net photosynthesis.

Daily minimum temperature time series were derived from over 970 qualifying temperature stations` quality controlled daily records for the 50 year period 1950 - 1999. From these time series, 50 years of daily values of minimum temperatures were generated at each of 429 700 one arc minute (i.e. 1.7 km x 1.7 km) raster points covering South Africa, using regional and seasonal lapse rate and infilling/data extension techniques developed by Schulze and Maharaj (2004). Note that in the generation of daily minimum temperatures no account could be taken of cold air drainage into hollows or valleys, which can take place from surrounding higher-lying areas through radiative cooling during calm, stable clear night conditions. Reasons for this are provided in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as standard deviation of daily minimum temperatures for each month of the year, could be computed for purposes of mapping and production of statistics which are tabulated for Swaziland, Lesotho and for each of the nine provinces of the RSA. This approach to mapping minimum temperatures is thus fundamentally different to that used in the previous `South African Atlas of Agrohydrology and Climatology` (Schulze, 1997), in being much more physically based.
DOI: 10.5281/zenodo.8431534
title: Test_COG

description: Test for COG file
DOI: 10.5281/zenodo.8431535
title: Test_COG

description: Test for COG file
DOI: 10.5281/zenodo.8431619
title: Test_COG

description: Test for COG file
DOI: 10.5281/zenodo.8431651
title: Test_COG

description: Test for COG file
DOI: 10.15493/saeon.fynbos.15a4071
title: 15A manual weekly rainfall January 1940 - May 1971

description: Weekly rainfall data collected by a Snowdon gauge with orifice at 1.2 meters above ground in the Lambrechtbos catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.beeh.10000084
title: Heat Units (Standard Deviation) - February

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. 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/sarva.beeh.10000141
title: Monthly Means Of Daily Average Relative Humidity (%) July

description: Daily minimum and maximum relative humidity is necessary as relative humidity, defined for practical purposes as the ratio of actual to saturated vapour pressure, expressed as a percentage generally varies considerably in the course of a day because, despite actual vapour pressure, ed, remaining relatively constant, saturated vapour pressure, ea, varies curvilinearly with temperature. The minimum RH of a day is thus experienced during the hottest part of the day when ea is high and the ratio of ed : ea is therefore small. Conversely, the maximum RH of a day is experienced around sunrise when temperatures are low, thus ea is low and the ratio of ed : ea therefore approaches unity.

In computations of daily RHmin and RHmax, the uncorrected actual vapour pressure, ed , is assumed not to change significantly in the course of a day. Because uncorrected ed at any specific location has been shown to be predictable over South Africa on a month-by-month basis with a high degree of confidence when using predominantly geographical factors in multiple regression equations for each month the ed at any location is therefore held at a relatively constant value for any given month of the year, varying only slightly on a day-by-day basis within that month because temperature range is a variable in the regression equation. However, at that specific location the saturated vapour pressure, ea , can vary considerably from one day to the next, as well as within a given day between morning and midday, because it is related to temperature. Therefore, RHmin and RHmax can range considerably within a given day and vary markedly from one day to the next.
DOI: 10.15493/sarva.saws.10000266
title: Root Mean Square Difference in percentage (%) between the nine ensemble member change anomalies of the seasonal percentage rainfall for the median for 2036 - 2065 relative to 1976-2005, for the DJF 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 2036 - 2065 relative to 1976-2005, for the DJF 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: 10.5258/soton/d2817
title: Dataset supporting the publication "Linear confinement in the partially-deconfined phase"

description: This dataset is supporting the publication "Linear confienement in the partially-deconfined phase" (arxiv:2208.14402) by Gautam, Hanada, Holden, and Rinaldi and subsequently published as Gautam, V., Hanada, M., Holden, J. et al. Linear confinement in the partially-deconfined phase. J. High Energ. Phys. 2023, 195 (2023). https://doi.org/10.1007/JHEP03(2023)195 Code and data are available on GitHub at time of writing at https://github.com/erinaldi/pd-potential/tree/main
DOI: 10.15493/saeon.fynbos.2b9208
title: 2B manual weekly rainfall April 1992 - March 2008

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 Swartboschkloof weather station in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/saeon.egagasini.24000001
title: The ASCA April 2015 raw SPAR data for stations 001 - 024

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 SPAR.
DOI: 10.15493/sarva.csag.10000044
title: Rainfall days per month 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/