cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000447
title: Days / Year With Mild Soil Water Stress In A 1.2m 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.beeh.10000092
title: Heat Units (Standard Deviation) - October

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.231120-9
title: The distribution of Quintile 5 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/bea.data.10000085
title: Extent of Crop Cultivation in South Africa

description: Data on cropland was obtained from the global data set produced by the UN Food and Agriculture Organisation (FAO). Data set was obtained as a raster image, and clipped to the boundaries of South Africa, before being converted to a vector layer. The FGGD land cover occurrence maps are global raster data layers with a resolution of 5 arc-minutes. Each pixel in each map contains a value representing the percentage of the area belonging to the land cover type concerned. The method is described in FAO and IIASA, 2007, Mapping biophysical factors that influence agricultural production and rural vulnerability, by H. von Velthuizen et al
DOI: 10.15493/saeon.egagasini.10000019
title: The ASCA July 2018 (SEAmester III) processed oblique Bongo data for unit 6412

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 is the processed product of data collected using an oblique Bongo net for unit 6412.
DOI: 10.15493/sarva.csag.10000047
title: Precipitation 90th percentile 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.saws.10000091
title: Seasonal total rainfall (mm per 3-month season) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), for SON season under the RCP 4.5 pathway

description: Seasonal (SON) 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/dea.carbon.10000130
title: Total Herbaceous Biomass for 2014 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 NLC2014
DOI: 10.15493/sarva.csag.10000337
title: Maximum of Maximum Temperature for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/saeon.fynbos.11b1820
title: 11B event rainfall February 2018 - September 2020

description: Event rainfall data collected by Texas Electronics tipping bucket gauge installed at 1.2m above ground at 400 masl in the Bosboukloof catchment of the Jonkershoek valley in the Western Cape of South Africa. Data recorded by a Pendant Event logger (UA-003-64, Onset, Massachusetts, U.S.A). Tipping bucket calibrated to 0.254 mm increments.
DOI: 10.15493/sarva.csag.10000159
title: Rainfall days per month (> 10mm) 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.10000194
title: February Means Of Reference Crop Evaporation (mm) Fao Penman-Monteith Equivalent

description: 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`. 

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.
DOI: 10.15493/saeon.fynbos.6a3668
title: 6A manual monthly rainfall January 1936 - April 1968

description: Monthly rainfall data collected by a Snowdon gauge with orifice at 1.2 meters above ground in the Tierkloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/nrf.saiab.20232516
title: Bathymetry data collected from Mobile Epibenthic Invertebrate Stations, November 2022

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 dataset is used for seabed analysis, the interpretation and creation of derivatives for geomorphological and statistical analysis in comparison with biological metrics. This dataset consists of locations below the surface of the water where the depth of water is precisely known relative to MSL. Sessile and mobile epibenthic invertebrates are organisms that are part of the benthic community. These organisms perform crucial roles and ecosystem services that benefit the benthic community and marine environment as a whole. These organisms’ distribution is affected by many abiotic and biotic factors. This study aims to determine the distribution of both the mobile and sessile epibenthic invertebrates and to investigate the relationship between the mobile and sessile epibenthic invertebrates. The objectives are to measure the abundance and composition of both the mobile and sessile epibenthic invertebrates found on the inner continental shelf of Umhlanga, KwaZulu-Natal and to analyze the statistical relationship between the sessile epibenthic assemblage data and the mobile epibenthic assemblage data. These aims and objectives will be satisfied by sampling on the Umhlanga coast using a drop camera and a baited drop camera. Multivariate tests were run on the abundance data and a Pearson’s correlation test for the relationship between the mobile and sessile epibenthic invertebrates. This study will provide new data on the presence and abundance of understudied mobile invertebrates inhabiting unconsolidated sediments of the KZN inner continental shelf and contribute to our understanding of the relationship between sessile and mobile invertebrate epifauna on the unconsolidated inner continental shelf. It will also test and establish the usefulness of a baited drop-camera for sampling epibenthic invertebrates.
description: See metadata file for additional information.
DOI: 10.15493/sarva.saws.10000030
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 4.5 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 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 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.beeh.10000088
title: Heat Units (Standard Deviation) - June

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.csag.10000398
title: Number of days for Maximum Temperature under 10th percentile 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.10000027
title: Projected changes in annual mean temperature (°C) under RCP 4.5 for the 2015-2035 time period based on the Community Climate System Model (CCSM4)

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/sarva.beeh.10000135
title: Monthly Means Of Daily Average Relative Humidity (%) January

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: 10.5281/zenodo.7687760
title: Predicting Methane Formation Rates of Freshwater Sediments in Different Biogeographic Regions

description: These datasets uploaded here contain: CH4 formation rates of sediment layers incubated from nine Swedish lakes (CH4Formation_SwedishLakes.csv), their sediment characteristics measured in different layers and land use data CH4 formation rates retrieved from Isidorova et al. (2019,urn:nbn:se:uu:diva-387547). From this dataset we extracted only the maximum CH4 formation rates and are reported in the file CH4Formation_BrazilianReservoirs.csv CH4 formation rates used to calculate CH4 formation decay rate with age for Swedish lakes (CH4Formation_SwedishLakes_forDecayRate.csv) Supplementary information SupplementaryInfo.pdf related to CH4 formation rates and sediment age Extraction of PLS analysis results PLS_List.xlsx
DOI: 10.5281/zenodo.7687761
title: Predicting Methane Formation Rates of Freshwater Sediments in Different Climates

description: These datasets uploaded here contain: CH4 formation rates of sediment layers incubated from nine Swedish lakes (CH4Formation_SwedishLakes.csv), their sediment characteristics measured in different layers and land use data CH4 formation rates retrieved from Isidorova et al. (2019,urn:nbn:se:uu:diva-387547). From this dataset we extracted only the maximum CH4 formation rates and are reported in the file CH4Formation_BrazilianReservoirs.csv CH4 formation rates used to calculate CH4 formation decay rate with age for Swedish lakes (CH4Formation_SwedishLakes_forDecayRate.csv) Extraction of PLS analysis results PLS_List.xlsx
DOI: 10.5281/zenodo.7688977
title: Predicting Methane Formation Rates of Freshwater Sediments in Different Climates

description: These datasets uploaded here contain: CH4 formation rates of sediment layers incubated from nine Swedish lakes (CH4Formation_SwedishLakes.csv), their sediment characteristics measured in different layers and land use data CH4 formation rates retrieved from Isidorova et al. (2019,urn:nbn:se:uu:diva-387547). From this dataset we extracted only the maximum CH4 formation rates and are reported in the file CH4Formation_BrazilianReservoirs.csv CH4 formation rates used to calculate CH4 formation decay rate with age for Swedish lakes (CH4Formation_SwedishLakes_forDecayRate.csv) Supplementary information SupplementaryInfo.pdf related to CH4 formation rates and sediment age Extraction of PLS analysis results PLS_List.xlsx