cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.saws.10000151
title: Seasonal mean near-surface (2m) temperature (°C) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), for MAM season under the RCP 8.5 pathway

description: Seasonal (MAM) mean near-surface (2m) temperature (°C) 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 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/saeon.fynbos.7a3668
title: 7A manual daily rainfall April 1936 - April 1968

description: Daily rainfall data collected by a Snowdon gauge with orifice at 1.2 meters above ground at the Weikamp site in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/saeon.egagasini.10000078
title: The ASCA April 2016 raw MicroCAT data collected at Mooring B for all depths

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current`s volume, heat and salt transport and its variability from mesoscale (eddies), through seasonal to interannual timescales. The ASCA shelf and tall moorings extend 200 km offshore along decending TOPEX/Jason satellite ground track # 96, through the core of the Agulhas Current, with CPIES measurements extending the array to 300 km offshore of Port Elizabeth. The data collected during this project have been obtained from various oceanographic instrumentation fixed to each mooring. This raw data was collected by MicroCAT sensors at Mooring B at the following depths: 300m, 400m, 700m, 900m and 1200m.
DOI: 10.15493/sarva3.data.10000038
title: Landslide susceptibility of SA
DOI: 10.15493/saeon.fynbos.21a6470
title: 21A manual weekly rainfall November 1964 - August 1970

description: Weekly rainfall data collected by a Snowdon gauge with orifice at 1.2 meters above ground at Dasbos in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.beeh.10000073
title: Heat Units (April)

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.10000190
title: Mean 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/sarva.csag.10000450
title: Number of days for Maximum Temperature over 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.csag.10000001
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/sarva.saws.10000115
title: Seasonal total rainfall (mm per 3-month season) change from the 90% percentile projected for 2066-2095, 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 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.saws.10000063
title: Seasonal mean near-surface (2m) temperature (°C) change from the median projected for 2066 - 2095, relative to present (1976 - 2005), for JJA season under the RCP 4.5 pathway

description: Seasonal (JJA) mean near-surface (2m) temperature (°C) 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 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.csag.10000308
title: Maximum of Minimum Temperature for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.saws.10000044
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the 90% percentile for 2036 - 2065 relative to 1976-2005, for the SON season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (SON) mean near-surface (2m) temperature (°C) 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 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/bea.data.10000071
title: Feasible Extraction of Maize Residues from Formal Agriculture

description: Data was derived from the following sources:
* Extent of commercial maize farming in Gauteng, North West, Free State, and Mpumalanga was obtained from the Department of Agriculture (2014).
* On such land, production of Maize was estimated from scaled productivity (Schulze et. al. yield estimates applied to recent average (10 year) maize production per province, derived from Department of Agriculture Annual Statistics.
* Residue production was calculated based a sustainable yield-dependent formula - see discussion in  BioEnergy Atlas fact sheets.
DOI: 10.15493/saeon.fynbos.26b1517
title: 26B event rainfall September 2015 - October 2017

description: Event rainfall data collected by Davis Instruments tipping bucket gauge installed at 1.2m above ground at 416 masl in the Swartboschkloof 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 0.2 mm increments.
DOI: 10.15493/sarva.beeh.10000129
title: Monthly Means Of Daily Maximum 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/bea.data.10000027
title: AWB-NOT: All Woody Biomass - Pelleting (Catchments)

description: * Technical Challenges - Existing expertise and infrastructure in respect of `Working for Water` programmes in respect of harvesting and eradication projects required for conversion to electricity. It may be simpler and less risky to generate new sources of renewable electricity rather than converting existing power stations to co-firing. * Cost Challenges - There may be as many as 40 viable projects, all having a 20-year lifetime - with significant capital investment required. * Policy Challenges - The projects are feasible and well aligned with existing expertise and infrastructure in respect of `Working for Water` programmes. Integration with DEA `Working for Energy` required and incorporation into IPP programmes needed. * Environmental Challenges - The net impact on greenhouse gas emissions is large, despite land use change effects, given the significant reduction in GHG as CO2 equivalents in comparison to coal.
DOI: 10.15493/sarva.saws.10000014
title: Root Mean Square Difference between the nine ensemble member change anomalies of the annual total rainfall for the 90% percentile for 2036-2065 relative to 1976-2005, under the RCP 4.5 pathway

description: Root Mean Square Difference for annual total rainfall (mm per year) 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 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: 10.48331/scielo.oxpopg/bwmwzr
title: 1 Pierre Mounier.pdf

description: :unav
DOI: 10.48331/scielo.oxpopg
title: Those of us who do: Open Access Plataforms - Open Access platforms in Europe

description: Presentation by Pierre Mounier in the Those of us who do: Open Access Plataforms panel of the SciELO 25 Years Week. Event page: https://25.scielo.org/en
DOI: 10.15493/dea.carbon.10000066
title: South African Land Cover 1990 Class P Built-up Resampled to a 1km Grid

description: The 1990 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 4/5 multispectral imagery, acquired between April 1989 and October 1993. 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 4/5 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 4/5 standard map projection format as provided by the USGS. Class 42–72 of the 1990 South African National land cover dataset was clustered to derive South African land cover 1990 Class P Built-up 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.beeh.10000449
title: Days / Year With Mild Soil Water Stress In A 0.9m 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.saws.10000082
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the median for 2036 - 2065 relative to 1976-2005, for the MAM season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (MAM) rainfall (mm per month) change from the median 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.10000049
title: Seasonal mean near-surface (2m) temperature (°C) change from the 90% percentile projected for 2066 - 2095, relative to present (1976 - 2005), for DJF season under the RCP 4.5 pathway

description: Seasonal (DJF) mean near-surface (2m) temperature (°C) change from the 90% 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 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.10000454
title: Streamflow concentration

description: This dataset contains periods of highest streamflows and streamflow concentration in South Africa.  Periods of high streamflows are defined as the 3 consecutive months which have the highest cumulative median streamflows at the outflow point of a Quaternary Catchment (QC). Streamflow concentration is defined as the percentage of mean annual flow that is made up of the highest 3 month cumulative streamflow in a QC (including upstream contributions), with a low percentage indicative of a spread of streamflow and vice-versa. Distribution patterns of the 3 consecutive month period over SA of highest streamflows are the December (i.e. November, December, January).
DOI: 10.15493/nccis.data.10000028
title: Projected changes in annual mean temperature (°C) under RCP 4.5 for the 2046-2065 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.