cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/saeon.egagasini.10000028
title: The ASCA January 2018 raw 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 raw data, collected by a S-ADCP (Shipborne-Acoustic Doppler Current Profiler).
DOI: 10.15493/bea.data.10000083
title: Serviced Organic Waste - Solid Waste and Waste Water

description: Data was derived from two other data sets generated by the BioEnergy Atlas: * organic component of solid waste (serviced), from households; * organic component of waste water (serviced), also from households.
DOI: 10.15493/sarva.beeh.10000003
title: Altitude and Relative Relief (standard deviation)

description: Relative relief (RR) is an index of the variability of the topography of an area, irrespective of whether that area is at a high or low altitude. As such, RR is also an index of the hilliness of the area. Relative relief, RR, in the context of this Atlas RR is the standard deviation (m) of the 9 point values of altitudes from a 3` x 3` block of altitudes from the 1 arc minute database of altitudes covering South Africa, with that value of RR representing the middle pixel of the block of 9.
DOI: 10.15493/sarva.beeh.10000162
title: Monthly Means Of Daily Minimum Relative Humidity (%) January

description: Daily minimum and maximum relative humidity is necessary as relative humidity, defined for practical purposes as the ratio of actual to saturated vapour pressure, expressed as a percentage generally varies considerably in the course of a day because, despite actual vapour pressure, ed, remaining relatively constant, saturated vapour pressure, ea, varies curvilinearly with temperature. The minimum RH of a day is thus experienced during the hottest part of the day when ea is high and the ratio of ed : ea is therefore small. Conversely, the maximum RH of a day is experienced around sunrise when temperatures are low, thus ea is low and the ratio of ed : ea therefore approaches unity.

In computations of daily RHmin and RHmax, the uncorrected actual vapour pressure, ed , is assumed not to change significantly in the course of a day. Because uncorrected ed at any specific location has been shown to be predictable over South Africa on a month-by-month basis with a high degree of confidence when using predominantly geographical factors in multiple regression equations for each month the ed at any location is therefore held at a relatively constant value for any given month of the year, varying only slightly on a day-by-day basis within that month because temperature range is a variable in the regression equation. However, at that specific location the saturated vapour pressure, ea , can vary considerably from one day to the next, as well as within a given day between morning and midday, because it is related to temperature. Therefore, RHmin and RHmax can range considerably within a given day and vary markedly from one day to the next.
DOI: 10.15493/sarva.beeh.10000115
title: Standard Deviations Of Daily Maximum Relative Humidity (%) May

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.10000094
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 SON season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (SON) 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.csag.10000262
title: Rainfall days per month (> 2mm) for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000248
title: Median monthly precipitation (mm) 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.10000215
title: Seasonal total rainfall (mm per 3-month season) change from the 10% percentile projected for 2036-2065, relative to present (1976 - 2005), for SON season under the RCP 8.5 pathway

description: Seasonal (SON) 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/sarva.beeh.10000044
title: Climatically Suitable Growth Areas for Eucalyptus macarthurii

description: Eucalyptus macarthurii also known as Paddy`s River Box or Camden Woollybutt, with dull green leaves, produces a moderately dense fibrous timber which is, however, unsuitable for sawn timber (Australiaplants, 2004). This hardy, fast growing large tree is frost tolerant (except when young) and can produce commercially viable yields with relatively low risk on marginal sites (Herbert, 1993). For optimum growth it requires a MAP > 800 mm when MAT is around 15.5 degree Celsius , but needs an additional 17 mm annual rainfall for every degree Celsius  above 15.5  (Kunz, 2004). January (hottest month) means of daily maximum temperatures should be < 25 degree Celsius  while July (coldest) means of minima for this cold tolerant species should exceed -3 degree Celsius (Herbert, 1993).
DOI: 10.15493/saeon.egagasini.10000011
title: The ASCA July 2016 (SEAmester I) raw Expendable Bathythermograph (XBT) profile 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. This is the raw data collected by Expendable Bathythermographs (XBTs) which are individual probes that get deployed from a ship and collect temperature measurements as it falls through the water column.
DOI: 10.15493/sarva.beeh.10000114
title: Standard Deviations Of Daily Maximum Relative Humidity (%) April

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.10000456
title: Biomass Increment (T/Ha/An) per 100 mm Gross Irrigation Schedule: 15 mm Net, 7 Day Cycle

description: This dataset contains the yield increment with irrigation in South Africa, which is defined in this Atlas as (Irrigated Yield - Dryland Yield) / (100 mmGross Irrigation Demand). In light of increasing costs of irrigation water, which may vary from region to region according to its availability/scarcity, and the role played in irrigation scheduling by naturally occurring rainfall, yield increases need to be expressed with respect to the amount of irrigation water required by the plant for maximum yield over and above the natural rainfall utilised by the plant.
DOI: 10.15493/saeon.egagasini.181122-1
title: SkiMonkey mounted MicroCAT CTD data from around Prince Edward Island on SA Agulhas II Voyage 024, April 2017

description: With the declaration on 9 April 2013 of the Prince Edward Islands (PEIs) as South Africa’s first offshore Marine Protected Area (MPA), the outcomes of this cruise will further contribute toward an integrated view and a better understanding of the functioning of the combined island/marine PEI ecosystem.This accession contains raw and processed SkiMonkey mounted SBE-37 MicroCAT CTD data collected from part of the Marion Island Relief Voyage on the SA Agulhas II Voyage 024. At each station the SkiMonkey III towed camera system, with the MicroCAT CTD attached, was deployed off the stern of the ship, from the plankton towing winch at a speed of 1 m/s until contact with the seafloor was made. Once on the seafloor, the system was towed behind the vessel at a speed of 1 knot for approximately 20 minutes. Following this, the system was retrieved and the data were downloaded and backed-up appropriately. Note that latitude and longitude information were only recorded at two points per station, when the system touched down on the seafloor and when it was retrieved from the seafloor (i.e. Start_Lat_DD, Start_Lon_DD, End_Lat_DD, End_Lon_DD). The download is divided into station level folders, within which the user can find ReadME documents describing the variables recorded in the data, a _StationLevelData sheet describing the instrument deployment, a _RawData file with data extracted as is from the instrument, and a _CleanData file with processed data as described in the metadata lineage statement.
description: During deployments, data were stored on the system's internal memory and subsequently downloaded upon retrieval (ASCII engineering units). Quality control processes and procedures were then conducted on the raw data (filenames including _RawData). This included removing unused columns, deleting data collected while the system was out-of-water, and then saving the data in a useable comma delimited format (filenames including _CleanData).
DOI: 10.15493/sarva.csag.10000375
title: Rainfall days per month (> 10mm) 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.10000057
title: Seasonal mean near-surface (2m) temperature (°C) change from the median projected for 2066 - 2095, relative to present (1976 - 2005), for MAM season under the RCP 4.5 pathway

description: Seasonal (MAM) 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.saws.10000064
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 JJA season, under the RCP 4.5 pathway

description: Root Mean Square Difference for 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/dea.carbon.10000026
title: Savanna Woody Fractional Cover percentage (1km)

description: Woody fractional cover is defined as the percentage area covered by a canopy surface over a particular pixel resolution and is expressed in percent (%). The product was developed through the integration of 2010 ALOS PALSAR-1 Synthetic Aperture Radar images (SAR), ancillary digital elevation model (DEM) derived variables and airborne LiDAR data. Commercial plantations of very high AGB, permanent water bodies and urban areas were masked out since it was not captured by the LiDAR tracks. The mapped product was clipped to the savannah biome (Mucina and Rutherford 2006) and the SAR AGB woody map was resampled to 1km pixel resolutions using a mean aggregation method.
DOI: 10.15493/bea.data.10000099
title: Bush Encroachment and Land Cover Change

description: * The dataset shows landcover change between 1990 (lcov_1990) and 2013 (lcov_2013). * Attribute information includes ; pagenumber, mapcode12 (vegetation type), dn_new (aboveground carbon values-DMt/ha), lcov_1990, lcov_2013, parent1990 (general landcover name), parent2013, old_new (landcover change), parent_old_new (landcover change by name), dn_ave_1990 (average for each landcover in 1990), dn_ave_2013, dn_ave_change (average carbon change between 1990 and 2013); and boolean information for the following: bush encroachment, grass encroachment, perturbation, degradation, restoration, habitat loss. * Landcover data entries were obtained from  Landsat satellite images used to generate landcover information into 72 landcover types; vegetation type is collected by SANBI; Aboveground woody biomass (carbon values) collected from high resolution data in the Savannah (CSIR- DMt/ha at 100m resolution) or from lower resolution data for the rest of the country (1km).
DOI: 10.15493/sarva.beeh.10000241
title: Standard Deviation Of Daily Solar Radiation (Mj/M2/Day) March

description: These daily estimates of solar radiation were derived using modifications for South African conditions of the Bristow and Campbell (1984) equation. The daily values can be used as input in other equations such as the Penman-Monteith equation for potential crop evaporation.

The method of estimation of daily solar radiation loadings over South Africa and its verification are as follows: based on the premise that clear sky solar radiation is determined essentially by extraterrestrial solar radiation modulated by an atmospheric extinction function, and dependent on high maximum temperatures associated with a high diurnal range in temperature, while cloudy/rainy conditions are associated with a low diurnal temperature range, the Bristow and Campbell (1984) equation was modified for South African conditions by regionally optimised expressions of clear sky depletion for atmospheric water vapour content, and by month-by-month regionalised expressions for solar radiation extinction by cloudy/rainy conditions. Other than the optimised regional/monthly coefficients and the computation of extraterrestrial radiation, which is undertaken with trigonometrical geometry functions, the only other variables used were daily maximum and minimum temperatures. The latter were determined for the 50 year period 1950 - 1999 at each of 429 700 one arc minute (1.7 x 1.7 km) raster points over South Africa by techniques outlined in Schulze and Maharaj (2004).
DOI: 10.15493/sarva.dws.10000008
title: River profile classification Catchment D

description: Longitudinal slope profiles derived for all 1:500 000 rivers of South Africa, including cross-border catchments extending into neighbouring countries. River main stems are identified by a unique reach number (REACHNUM) within each primary catchment.
DOI: 10.15493/sarva.beeh.10000238
title: Monthly Means Of Daily Solar Radiation (Mj/M2/Day) December

description: These daily estimates of solar radiation were derived using modifications for South African conditions of the Bristow and Campbell (1984) equation. The daily values can be used as input in other equations such as the Penman-Monteith equation for potential crop evaporation.

The method of estimation of daily solar radiation loadings over South Africa and its verification are as follows: based on the premise that clear sky solar radiation is determined essentially by extraterrestrial solar radiation modulated by an atmospheric extinction function, and dependent on high maximum temperatures associated with a high diurnal range in temperature, while cloudy/rainy conditions are associated with a low diurnal temperature range, the Bristow and Campbell (1984) equation was modified for South African conditions by regionally optimised expressions of clear sky depletion for atmospheric water vapour content, and by month-by-month regionalised expressions for solar radiation extinction by cloudy/rainy conditions. Other than the optimised regional/monthly coefficients and the computation of extraterrestrial radiation, which is undertaken with trigonometrical geometry functions, the only other variables used were daily maximum and minimum temperatures. The latter were determined for the 50 year period 1950 - 1999 at each of 429 700 one arc minute (1.7 x 1.7 km) raster points over South Africa by techniques outlined in Schulze and Maharaj (2004).