cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.beeh.10000214
title: September 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/bea.data.10000019
title: Sunflower Seed Yield Estimates per mesozone

description: *The dataset shows sunflower seed yield estimates allocated to mesozones. Yield estimates were derived from Schulze R.E. and Maharaj M. (2007) and then allocated to mesozones by combining with a base mesozone layer obtained from the CSIR Geospatial Analysis Platform (GAP). *The map shows that the majority of production areas of sunflower seed in the Free State and North West should, according to Smith's (1994;1998) climatic criteria, yield 1.0-1.5 t/ha annually. The map furthermore shows that there are patches in Mpumalanga, Swaziland, KwaZulu-Natal and the Eastern Cape where in excess of 3 t/ha could, climatically, be achieved. *Using Smith's (1994; 1998) climatic criteria, yields of sunflower seed are estimated using the effective rainfall for October to March and heat units (base 10°C) for the same period, with modifications to yields made for soil properties and levels of management.
DOI: 10.15493/sarva.251120-20
title: NBA 2018 Rivers

description: This spatial rivers dataset is part of the South African Inventory of Inland Aquatic Ecosystems (SAIIAE) which was released with the National Biodiversity Assessment (NBA) 2018. In the NBA 2018 the National Freshwater Ecosystem Priority Area (NFEPA) rivers GIS layer was used to represent the diversity of rivers nationally. It used the 1:500 000 river network coverage available from the Department of Water and Sanitation (DWS) and can be downloaded from the following website: http://www.dwaf.gov.za/iwqs/gis_data/river/rivs500k.aspx. This GIS layer summarizes the river ecosystem types, river condition, Ecosystem Threat Status (ETS) and Ecosystem Protection Level (EPL) as well as the free-flowing (62 identified by the NFEPA project) and flagship river information.
DOI: 10.15493/sarva.beeh.10000119
title: Standard Deviations Of Daily Maximum Relative Humidity (%) September

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.csag.10000196
title: Consecutive Dry Days 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.quartets.10000004
title: CCAB - Medium Risk - Climate 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/saeon.fynbos.38022022
title: Toitskraal rainfall data

description: Monthly rainfall data from Toitskraal, near Marble Hall, Limpopo. The data was collected using a Snowden standard rain gauge. This record forms part of a collection of historical rainfall data submitted to SAEON. The data were generated by farmers on a voluntary basis and have been published as submitted.
DOI: 10.15493/saeon.egagasini.10000073
title: The ASCA April 2016 processed shipborne-ADCP data

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. This is the processed data, collected by a S-ADCP (Shipborne-Acoustic Doppler Current Profiler).
DOI: 10.15493/sarva.csag.10000458
title: Minimum 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.csag.10000370
title: Number of days for Maximum Temperature over 90th percentile 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/saeon.egagasini.05132022
title: Nansen Sediment Data 2019

description: Sediment data collected during 107 bottom trawls on the continental shelf off the west coast of South Africa. Data was collected during the Dr Fridtjof Nansen 2019-402 cruise in March 2019. Analyses were done through the Council for Scientific and Industrial Research (CSIR).
DOI: 10.15493/saeon.egagasini.10000032
title: The ASCA April 2015 processed CTD profile 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 is the processed product from data collected by the shipborne CTD (Conductivity, Temperature, Depth) sensor for the following stations: 1, 2, 3, 4, 5, 7, 8a, 8b, 9a, 9b, 10a, 10b, 11a, 11b, 12a, 12b, 13a, 13b, 14a, 14b, 15a, 15b, 16a, 16b, 17a, 17b, 18a, 18b, 19a, 19b, 20a, 20b, 21a, 21b, 22a, 22b, 23a, 23b, 24a and 24b.
DOI: 10.4121/b3cc08f1-cc3e-4b16-b007-72a421435f2c.v1
title: 20220105 20220107 Storm deployment - Nearshore - RealDune/REFLEX

description: Nearshore field measurements of the January storm deployment of the RealDune/REFLEX experiments at the Dutch coast in Autumn/Winter 2021/2022. This dataset is part of the data collection 'Nearshore coastal measurements of calm, moderate, and storm conditions at two artificial dunes along the Dutch Coast during the RealDune/REFLEX experiments'.
DOI: 10.15493/sarva.beeh.10000120
title: Standard Deviations Of Daily Maximum Relative Humidity (%) October

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.csag.10000355
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/saeon.fynbos.10b4491
title: 10B manual monthly rainfall April 1944 - March 1991

description: Monthly 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 Lambrechtbos catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.beeh.10000170
title: Monthly Means Of Daily Minimum Relative Humidity (%) September

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.10000226
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 MAM season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (MAM) mean rainfall (mm per month) from the median 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.080420
title: District Health Barometer 2018/19

description: The data are a reformatted version of the 2018/19 District Health Barometer (DHB) health indicator dataset provided by the HST. The intention of this publication was to provide an overview of the delivery of selected healthcare services in the public health sector across the provinces, districts and local municipalities/sub-districts of South Africa. The DHB has been an annual publication since 2005. The main focus of the 2018/19 publication is the Sustainable Development Goals (SDGs)a and the Universal Health Coverage (UHC)b index. Data are drawn from the electronic District Health Information Software (WebDHIS), the Ideal Clinic Realisation and Maintenance system, Statistics South Africa (Stats SA) surveys, the National Treasury Basic Accounting System (BAS), the Personnel Administration (PERSAL) system, the TIER.Net for Tuberculosis (TB) and antiretroviral (ART) data, the Electronic Drug-resistant Tuberculosis Register (EDRWeb), the National Income Dynamics Study (NiDS), and other National Department of Health (NDoH) information systems. The publication seeks to highlight inequities in health outcomes, health-resource allocation and health delivery, and to track the efficiency of health processes, across all provinces and districts.

Modifications were made to the data table provided in line with Hadley Wickem`s `tidy data` standards for statistical research (10.18637/jss.v059.i10). The dates of the indices were also standardised to represent a single year in the YYYY format and a column of municipal codes was allocated to each row in line with the coding from the South African Municipal Demarcation Board. Lastly, centroids of the political areas described (national, provincial, district municipal, and local municipal areas) were added as applied in a final column.
DOI: 10.15493/sarva.csag.10000387
title: Number of days for Minimum 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/