cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000013
title: A-Pan Equivalent Reference Potential Evaporation (October)

description: Reasons for having selected the A-Pan as a reference for potential evaporation is that the US Weather Bureau Class A evaporimeter is, universally, the most common evaporation pan in usage, having been adopted as the standard evaporation pan since the International Geophysical Year 1957/8. Also when operated properly it is accepted as a reasonably reliable, simple and inexpensive local integrator of atmospheric evaporative demand, particularly for time periods longer than one day. Problems associated with the A-Pan as a reference for potential evaporation is that they are found generally near dams, existing irrigation projects or in areas of low relative relief where commercial agriculture is practiced. These sites are not always representative of higher altitudes where water is generated.

The data from over 570 South African stations at which a minimum of three years` concurrent monthly temperature, rainfall and A-pan evaporation observations are available, were used to develop temperature based equations of A-pan equivalent evaporation for each of the 12 evaporation regions identified in South Africa. The equations, unique for each month and for each region, were used to develop the 12 rasterised images of mean monthly A-pan equivalent evaporation on a 1` x 1` of a degree latitude/longitude grid.
DOI: 10.15493/sarva.saws.10000070
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the median for 2066 - 2095 relative to 1976-2005, for the SON season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (SON) mean near-surface (2m) temperature (°C) 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 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.10000169
title: Monthly Means Of Daily Minimum Relative Humidity (%) August

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.beeh.10000280
title: Extremes (1950 - 2000) Of Maximum Temperatures (C) June

description: When days with very low or very high temperatures are experienced, the question is invariably asked as to whether that `extreme` minimum or maximum temperature was a record, either for the country or province, or for a specific location or month of the year. It is for that reason that `extremes` of temperatures, at both ends of the spectrum, were extracted from the daily temperature database generated by Schulze and Maharaj (2004) for the 51 year period (in this case) from 1950 to 2000, and analysed in tabular and mapped form. While these maps/tables are informative and interesting, there are nevertheless some limitations as to their accuracy, for reasons which are discussed in the shaded blocks containing some scientific background to the analyses.Limitations over South Africa to mapping extremes of temperatures, the generation of daily temperature time series at one arc minute spatial resolution at the 429 700 grid points covering South Africa depends on the selection of "best" control stations, extrapolating to altitudes above that of the highest-lying station in a particular region, as well as computational procedures regarding initial and final daily temperature values (Schulze and Maharaj, 2004). With respect to extreme minimum temperatures, no account has been taken of cold air drainage into valleys, which may aggravate minimum extremes.

Extremes of temperatures were derived from over 970 qualifying stations' records for the 51 year (1950 - 2000) time series of quality controlled daily maximum and minimum values generated at each of the 429 700 one arc minute (i.e. 1.7 x 1.7 km) raster points covering South Africa, using the regional/seasonal lapse rate and infilling techniques developed by Schulze and Maharaj (2004). In order to avoid analysing and mapping anomalous temperature extremes resulting from incorrect recordings, the extremes which were derived are therefore taken as the means of the two highest or lowest values found in the temperature time series at each grid point.
DOI: 10.15493/sarva.beeh.10000306
title: Monthly Means Of Daily Average Temperature (C) July

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/nccis.data.10000043
title: Projected changes in annual mean temperature (°C) under RCP 8.5 for the 2046-2065 time period based on the Geophysical Fluid Dynamics Laboratory Coupled Model (GFDL-CM3)

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.10000318
title: Standard Deviations (C) Of Daily Mean Temperature June

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.beeh.10000441
title: Mean Number Of Days With Rainfall greater than 10 mm April

description: This dataset contains the daily rainfall: A Threshold Analysis of South Africa (SA). The hydrological and agricultural analyses at daily time steps was because diurnality encapsulates many hydrologically and agriculturally related processes (e.g. evaporation, and transpiration), furthermore many operational decisions are made according to daily conditions (e.g. irrigation, tillage, pest control). Distribution patterns over SA of the mean number of days per month with rainfall exceeding 10 mm shows the higher threshold of 10 mm per day, a mean of 4 - 5 such events are measured per summer month in those Quaternary Catchments (QCs) located in the higher-lying mountainous areas of KwaZulu-Natal and Mpumalanga, but this drops  to fewer than a single day per month with >10 mm in the western third of SA.
DOI: 10.15493/sarva.csag.10000083
title: Mean 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/dea.carbon.10000044
title: Tmin A2 Change between 2000 and 2050 Median

description: ºC change in minimum daily temperature per year between 1986-2015 and 2036 - 2065. The mean annual minimum daily temperature for each downscaled global circulation model (GCM), i.e. csiro, gfdla20, gfdl21, miroc, mpi and ukmo using the  Cubic Conformal Atmospheric Model (CCAM) developed by CSIRO (Australia) and updated and modified by the CSIR (South Africa) was calculated for each of the 30 year periods. The absolute difference between the two periods is based on the mean annual minimum daily temperature value and is calculated as follows: (Tmin2050-Tmin2000) for each of the six downscaled GCM models. The 50th percentile (median value) is based on these differences.  Data are in raster format with a 0.5º resolution using a projected latitude / longitude coordinate reference system with `+proj=utm +zone=48 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0`.
DOI: 10.15493/sarva.csag.10000441
title: Minimum of Minimum Temperature for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000081
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.10000005
title: 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

description: 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.csag.10000280
title: Precipitation 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.10000236
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the 90% percentile for 2066-2095 relative to 1976-2005, for the SON season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (SON) rainfall (mm per month) change from the 90% percentile projected for 2066-2095, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily 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.quartets.10000003
title: CCAB - High Risk Scenarios - Biome Delineations 2011

description: Over the last decade, the science of climate change has evolved rapidly. Nevertheless, there is considerable uncertainty about the evolution of climate over 50 or 100 year time-scales, and while confidence in global circulation models is growing there is greater appreciation of the uncertainties involved, especially in ‘downscaling' the global models to produce climate projections at the regional and local scales. Acknowledging this unavoidable uncertainty, we have developed a statistical approach to incorporate a wide range of possible climate scenarios in impacts models that uses median, and 90th and 10th percentile changes in temperature and rainfall from a number of general circulation models, from which future scenarios are developed. These scenarios were compiled by SANBI (Guy Midgley and Danni Guo). Highest temperature increases and largest rainfall decreases were combined to generate a ‘high-risk' or ‘worst case' scenario, and vice-versa for a ‘low-risk' or ‘best case' scenario. Likewise the median projected changes in rainfall and temperature were combined to generate an intermediate scenario.

Based on outputs from 15 global circulation models that were spatially interpolated, we developed three climate scenarios for South Africa for approximately 2050 as follows:1
- Best case scenario: smallest predicted increases in temperature and changes in rainfall,
- Intermediate scenario: middle of the range (median) predicted increases in temperature increases and changes in rainfall,
- Worst case scenario: greatest predicted increases in temperature and changes in rainfall.
-This means that the results presented are not dependent on any particular global circulation model but hold under a very wide range of possible climate futures, enhancing the robustness of conclusions as climate changes and as climate science revises outputs and projections.

Note that the study was based on medium term data (for 2050) as this represented a compromise between the uncertainty associated with the very long time horizon data (2100) and the very small changes predicted by the shorter duration data (e.g. 2020). This 50 year time horizon also represents a reasonable long term planning horizon as it is within the lifetimes of most people currently living. When this study is updated based on new climate data, if possible (we are dependent on work done by a third party), we will include both the medium term and long term predictions. In the interim, as climate change occurs gradually over time, it is useful to conceptualize the worst case scenarios for 2050 as being likely to represent the intermediate case scenarios in 2100. 

Available documentation:
Driver A., Sink, K.J., Nel, J.N., Holness, S., Van Niekerk, L., Daniels, F., Madjiet, P.A., Jonas, Z. and Maze, K. 2012. National Biodiversity Assessment 2011: An assessment of South Africa's biodiversity and ecosystems. Synthesis Report. South African National Biodiversity Institute and Department of Environmental Affairs, Pretoria.
DOI: 10.15493/dea.carbon.10000077
title: South African Land Cover 2014 Class K Sugarcane Irrigated Resampled to a 1km Grid

description: The 2013-14 South African National land cover dataset produced by (© GEOTERRAIMAGE - 2014) as a commercial data product was generated from digital, multi-seasonal Landsat 8 multispectral imagery, acquired between April 2013 and March 2014. In excess of 600 Landsat images were used to generate the land cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land cover dataset, which covers the whole of South Africa, is presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. The dataset contains 72 x land cover / use information classes, covering a wide range of natural and man-made landscape characteristics. The original land cover dataset was processed in UTM (north) / WGS84 map projection format based on the Landsat 8 standard map projection format as provided by the USGS. Class 26–27 of the 2013-14 South African National land cover dataset was clustered to derive South African land cover 2014 Class K Sugarcane Irrigated 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