cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8431310
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: 10.15493/sarva.saws.10000197
title: Seasonal total rainfall (mm per 3-month season) change from the 10% percentile projected for 2036-2065, relative to present (1976 - 2005), for DJF season under the RCP 8.5 pathway

description: Seasonal (DJF) rainfall (mm per month) change from the 10% 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 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/dea.carbon.10000063
title: South African Land Cover 1990 Class M Plantation Forest 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 32–34 of the 1990 South African National land cover dataset was clustered to derive South African land cover 1990 Class M Plantation Forest 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.energy.10000009
title: Ruggedness Index - Eastern Cape

description: Purpose: 
This data set was created for the WASA project and the Department of Energy, South Africa. The wind resource maps were designed specifically for inclusion in GIS-based strategic environmental assessments (SEA) for wind power in Western Cape and parts of Northern and Eastern Cape.

Methodology:
Reference is made to the information and documentation available from www.wasaproject.info and www.wasa.csir.co.za.

Limitations:
The data set is limited by the operational envelopes of the wind atlas methodology and the WAsP models. The accuracy depends on a) the accuracy of the VNWA, which has been verified against the data from 10 WASA measurement masts, b) the WAsP microscale modelling and c) the input topographical data.

In complex terrain (RIX > 5%), the wind resources may be significantly over-estimated by the WAsP microscale modelling. Above and close to built-up areas like cities, towns and villages, the results are less reliable. Close to and above forested areas, the results are also less reliable and should be interpreted and used accordingly.

The data set was designed specifically for planning purposes and should be used with utmost care for design, development and detailed assessments of actual wind farms; where local, on-site measurements are strongly recommended. The wind resource maps are subject to change without notice if and when more accurate and reliable data, models and procedures become available.

Available documentation:
The wind atlas methodology is described in the European Wind Atlas (1989); the application of WAsP in the program documentation, see www.wasp.dk. The Verified Numerical Wind Atlas for South Africa is a product of the Wind Atlas for South Africa project and is described further on the WASA download pages http://wasadata.csir.co.za/wasa1/WASAData.

Acknowledgements:
WASA project for Frogfoot application and provision of wind-climatological and topographical data. WAsP development teams at DTU Wind Energy and World in a Box Oy for Frogfoot development.
DOI: 10.15493/saeon.fynbos.21b1720
title: 21B event rainfall October 2017 - August 2020

description: Event rainfall data collected by Texas Electronics tipping bucket gauge installed at 1.2m above ground at 490 masl at the Dasbos station in 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.10000328
title: Rainfall days per month 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/bea.data.10000053
title: High Voltage Stations

description: Eskom provided the BioEnergy Atlas with a set of data to describe their current and planned infrastructure:
* High voltage transmission lines and substations;
* Medium voltage distribution lines and substations;
* Planned new transmission lines;
* Planned new substations.
All of these were aggregated to meso-zones (planning and analysis zones used for the Atlas) in the following way:
* Distribution and transmission lines: presence or absence of lines;
* Substations: count of medium voltage substations, and proximity (5km) to high voltage substations
DOI: 10.15493/saeon.fynbos.7b1820
title: 7B event rainfall February 2018 - August 2020

description: Event rainfall data collected by Texas Electronics tipping bucket gauge installed at 1.2m above ground at 278 masl at Weikamp station in 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/saeon.egagasini.10000083
title: The ASCA April 2016 processed ADCP data collected at Mooring C 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 processed data was collected by an ADCP (Acoustic Doppler Current Profiler) at Mooring C.
DOI: 10.15493/bea.data.10000076
title: Production of Soybeans on Subsistence and Underutilised Farmland

description: Data was derived from the following sources: * Extent of underutilised and subsistence farmland, data obtained from Department of Agriculture, Forestry, and Fisheries. * On such land, soybean potential was calculated from data published by Schulze and Maharaj (2007) on soybean-growing potential. * Grain, Oil, Oilcake and Residue production was calculated based on grain yields, and aggregated to meso-zones for planning and feasibility analysis. * Grain, Oil, Oilcake, and Residue ratios were derived from literature
DOI: 10.15493/sarva.saws.10000230
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 JJA season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (JJA) 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/saeon.egagasini.10000082
title: The ASCA April 2016 raw Current Meter data collected at Mooring C 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 Current Meters at Mooring C at the following depths: 1000m, 1500m and 2000m.
DOI: 10.15493/sarva.beeh.10000172
title: Monthly Means Of Daily Minimum Relative Humidity (%) November

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.10000049
title: RUR-NOT: Unserviced Domestic Organic Waste - Biogas Digesters (Catchments)

description: * Technical Challenges - Proper utilisation of unserviced domestic organic waste requires deployment of household or community digesters, on a large scale in mostly rural areas of the country. It may be useful to develop a standard unit (1.5-5 tons/annum) that can be replicated as required - there appears to be little economy of scale beyond the size of a 10 t/a digester. The feedstock considered here focuses on currently unserviced households, and supply of biomass and operation can be improved by addition of cattle dung. Data is being prepared and awaited for estimates of cattle dung availability and its spatial distribution. * Cost Challenges - There will be a large number of viable projects, with significant capital investment required. Costs are comparable to new coal-based electricity. Subsidy is in lieu of grid electricity. * Policy Challenges - The projects are feasible, requiring little or no subsidy, but households are unlikely to be able to afford the capital investment required. Alignment with the housing subsidy scheme should be investigated. * Environmental Challenges - The net positive impact on greenhouse gas emissions is large, since there are no negative land use change impacts, given the significant reduction in GHG as CO2 equivalents in comparison to coal.
DOI: 10.15493/sarva.csag.10000403
title: Number of days for Minimum 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/sarva.csag.10000451
title: Maximum 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/sarva.beeh.10000106
title: Acacia mearnsii (timber) Growth areas and Yield estimation

description: A native of southeastern Australia, Acacia mearnsii is an evergreen tree growing to 15 m in height. A. mearnsii has proved to be the most drought resistant of the commercial hardwoods grown in South Africa (Herbert, 1993). Acacia mearnsii requires a MAP of 750 mm in colder areas and 850 mm where it is warmer (Smith, 1998). Ideally, mean annual temperature should be in the range 16 - 19 degree Celsius, but with A. mearnsii being sensitive to warm humid conditions, means of January (i.e. mid-summer) temperatures should be > 22 degree Celsius.

Determinations of climatically optimum growth areas of Acacia Mearnsii have been defined in the ICFR`s Forestry Toolbox (Kunz, 2004) by mean annual precipitation (MAP) and mean annual temperature (MAT). Acacia meanrsii: Timber yield and bark yield estimates were dune using Smith`s (1994) rule-based approach for estimating mean annual increment (MAIs) of A. mearnsii, and considering only his climatic criteria and not soil or management factors. Results show that areas of highest yields of A. mearnsii timber correspond with those of bark, with significant tracts of the northeast of the Eastern Cape displaying considerable potential for future Acacia mearnsii plantations.
DOI: 10.15493/saeon.fynbos.34022022
title: Dandyslaagte Farm rainfall data

description: Monthly rainfall data recorded at Dandyslaagte Farm, 19km from Griekwastad on the road towards Postmasburg. 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/sarva.030921-2
title: Mean annual temperature across South African municipalities from 1983 - 2020

description: This is a subset of the University of East Anglia Climatic Research Unit (CRU)`s Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate containing temperature [˚C] readings from South Africa`s weather stations across local municipalities for the years 1983-2020. 

The CRU Time Series 4.05 dataset was developed and has been subsequently updated, improved and maintained with support from a number of funders, principally the UK`s Natural Environment Research Council (NERC) and the US Department of Energy. Long-term support is currently provided by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre. Current gridded products (CRU TS) are presented either as ASCII grids, or in NetCDF format. The gridding process used in Brohan et al. (2006) and earlier publications assigns each station to the 5 degree latitude/longitude box within which it is located. The gridding then simply averages all available station temperatures (as anomalies from 1961-90) within each grid box for each month from 1851. No account is taken of the station`s elevation or location within the grid box (anomalies show little consistent dependence on altitude). A more up-to-date location for a station is not important for the gridding, unless a site change were to move the station to an adjacent grid box.  

In this instance, the data was derived as a subset of the original dataset. CRU publishes the data in NetCDF file format, however for data visualisation purposes the datasets was transformed into tidy tables, represented in the South African Risk and Vulnerability Atlas (SARVA) by the South African Environmental Observation Network`s uLwazi Node. 

Citation: University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, 2021. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681
DOI: 10.15493/sarva.csag.10000201
title: Maximum 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.45022022
title: Opraap West Farm rainfall data

description: Monthly rainfall data from Opraap West Farm, near Klerksdorp. 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/sarva.beeh.10000160
title: Standard Deviations Of Daily Minimum Relative Humidity (%) November

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/saeon.egagasini.10000076
title: The ASCA April 2016 raw ADCP data collected at Mooring B

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 an ADCP (Acoustic Doppler Current Profiler) at Mooring B.