cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.csag.10000227
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.10000122
title: Root Mean Square Difference between the nine ensemble member change anomalies of the annual mean near-surface (2m) temperature for the 90% percentile for 2036 - 2065 relative to 1976-2005, under the RCP 8.5 pathway

description: Root Mean Square Difference for annual mean near-surface (2m) temperature (°C) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature averages, which are used to generate projections of annual 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.10000418
title: Gross Irrigation Demand April Schedule: 35 Mm Net, 7 Day Cycle

description: This data set contains the GROSS IRRIGATION DEMAND Schedule: 35 mm net, 7 day cycle in South Africa. Gross irrigation demand is the gross irrigation water  that needs to be abstracted from a source of water (e.g. dam) to satisfy the irrigated crop`s water requirement. Fixed Cycle / Fixed Amount Irrigation, is assumed to be by overhead irrigation with conveyance, wind/spray drift and interception losses of 10%, 12% and 1.5 mm per event thus being assumed, in each case with a 7 day cycle. The 35 mm day net represents an equivalent  "peak demand" of 5 mm/day to the plant.
DOI: 10.15493/nccis.data.10000052
title: Projected changes in annual mean temperature (°C) under RCP 4.5 for the 2046-2065 time period based on the Norwegian Earth System Model (NorESM1-M)

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.10000181
title: Passion Fruit: Optimum Growth Areas - Criterion 2: Fewer than 2 consecutive days per annum with minimum temperatures < -2 degree Celsius

description: Passion fruit are believed to have originated from southern Brazil (Kenyaweb, 2005).The most commonly commercially grown of 55 edible species of passion fruit is Passiflora edulis, which consists of perennial woody vines with each producing about 100 fruits per year (RBGK, 2005). In South Africa passion fruit ideally requires a cool subtropical climate for optimum production (Smith, 1998). The vines prefer moderate temperatures throughout the year, with monthly means of daily maxima < 29 degree Celsius. The plants are sensitive to severe frosts, and monthly means of minimum temperatures should be > 6 degree Celsius (NDA, 2005). For commercial production under rainfed conditions MAP should ideally exceed 1 200 mm and be well distributed throughout the year (NDA, 2005), as it is important to maintain the soil moist throughout the growing season to keep the vines flowering and fruiting for longer periods and prevent the fruit from shriveling and falling prematurely (Kenyaweb, 2005). Total commercial production of passion fruit in South Africa is 1 300t per season, with a range (between 1998/9 - 2003/4) in more recent from 900 - 1 700 t (NDA, 2005).The gross value is R65 million/year (NDA, 2005).

Criteria for climatically optimum growth areas for passion fruit were refined by Bower (2005). Six criteria were identified and using the 1` x 1` gridded information on temperature related parameters derived by Schulze and Maharaj (2004), the following criteria were mapped: Criterion 1: > 330 days per year with a minimum temperature exceeding 2 degree Celsius; Criterion 2: Fewer than 2 consecutive days per annum with minimum temperatures < -2 degree Celsius; Criterion 3: There should not be > 3 consecutive frost days per annum; Criterion 4: In total, there should be < 25 days per annum with frost; Criterion 5: Maximum temperatures should be < 28 degree Celsius on at least 25 days of each month; Criterion 6: Maximum temperatures should not exceed 35 degree Celsius on any two consecutive days per month. Mapped criteria were given either categorical or probabilistic weightings and when the six maps were superimposed, suitable and less suitable climatically optimum growth areas could be identified. The assumption was made that for commercial production the passion
DOI: 10.15493/sarva.csag.10000056
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/nrf.saiab.20232504
title: Bathymetry data collected from uMdloti Kob, July 2021

description: African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA
description: This dataset forms part of the South African Institute for Aquatic Biodiversity (SAIAB)'s collection of Bathymetry data for the African Coelacanth Ecosystem Programme (ACEP) Smart Zones MPA. This multi-disciplinary study will begin at the start of the implementation of two new MPAs (uThukela MPA and Protea Banks MPA, both proclaimed in 2019), and will generate the relevant data needed to underpin an evidence-based approach to adaptive management of the MPAs, within a “SMART” framework. This framework defines specific objectives for the different MPA zones; develops measurable indicators of success for each zone; sets achievable goals given the limited resources available for planning and management; ensures that the research findings are relevant (to both biodiversity and human stakeholders). This is a rare time-sensitive opportunity to conduct baseline surveys of the geology, benthic biodiversity (sessile and mobile species), reef-associated ichthyoplankton and mero-zooplankton and fish communities of mesophotic (40-150m) soft-coral and sponge-dominated reefs and submarine habitats located in three different management zones at the initiation of management of two brand new MPAs, against which long-term monitoring data can be compared to assess the effectiveness of the MPAs in achieving their objectives. The balanced sampling design is such that it will also initiate a “natural experiment” that over time will investigate the effect of no-take zones on fish diversity and abundance, benthic community structure, and trophic complexity. This will also enable the determination of the value of the MPA for fisheries sustainability support and resilience to disturbances caused by extrinsic factors such as pollution or climate change. Additionally, this study will assess whether the zonation plan provides for representation of all reef and canyon types within each zone or if there is unbalanced coverage for certain reefs and in certain zones. This is important to assess at the initiation of the MPA, especially to ensure that the interpretation of monitoring results in future, which compare ecosystems and fish on reefs and canyons in the mesophotic zone, are not flawed by natural differences from the outset due to the selection of zone boundaries. This dataset consists of XYZ points below the sea surface and relative to mean sea level. This data was collected under the ACEP SMART ZONES MPA project (2021 – 2023) using the ACEP Geophysics and Mapping Platform.
description: See metadata file for additional information.
DOI: 10.15493/sarva.251120-8
title: 2018 Dunebase to Exclusive Economic Zone (EEZ)

description: An extraction of the zone from the dune base line to the edge of South Africa's Exclusive Economic Zone (EEZ) from the first version of an integrated coast map including coastal ecosystem types from the terrestrial, marine, and estuarine realms.
DOI: 10.15493/sarva.saws.10000040
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the median for 2036 - 2065 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 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.csag.10000376
title: Rainfall days per month (> 2mm) 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.beeh.10000282
title: Extremes (1950 - 2000) Of Maximum Temperatures (C) August

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/dea.carbon.10000034
title: Tmax 1986-2015 90th Percentile

description: The current long term annual climate conditions for the 30-year period 1986-2015. The maximum daily temperature values simulated by six different global circulation models (GCMs), i.e. csiro, gfdl20, gfdl21, miroc, mpi, and ukmo were downscaled with the Cubic Conformal Atmospheric Model (CCAM) developed by CSIRO (Australia) and updated and modified by the CSIR (South Africa). The mean maximum daily temperature was calculated per year for each of the six downscaled GCMs for the 30-year period. The 90th percentile values are based on the mean 30-year maximum daily temperature per year from the six downscaled GCMs. 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.saws.10000207
title: Seasonal total rainfall (mm per 3-month season) change from the median projected for 2036-2065, relative to present (1976 - 2005), for JJA season under the RCP 8.5 pathway

description: Seasonal (JJA) rainfall (mm per month) change from the median 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/saeon.fynbos.36022022
title: Craig Doone Farm rainfall data

description: Monthly rainfall records were collected on Craig Doone farm, Howison's Poort outside Makhanda, Eastern Cape from January 1991 to October 2008. Daily rainfall data was then collected between 2009 and 2011. 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.tierberg.10000011
title: Phytophageous Insect List, Tierberg Karoo Research Centre

description: The list is one of the spreadsheet contained in the file “INVERT LIST”. Data contains a list of 38 phytophagous insect species collected on Tripteris (Osteospermum) sinuatum and Pteronia pallens at Tierberg Karoo Research Centre (33010`S, 27017`E) in the Prince Albert district of the southern Karoo in winter and spring months from 1987-1992. The objective of the collection was to compare insect communities on a plant that is palatable to mammals (Tripteris sinuata) with one that is toxic to most mammals (Pteronia pallens). Plant parts on which insects were collected are classified as leaf, stem, flower, or seed.
DOI: 10.15493/saeon.fynbos.9b1820
title: 9B event rainfall February 2018 - August 2020

description: Event rainfall data collected by Texas Electronics tipping bucket gauge installed at 1.2m above ground at 292 masl in the Tierkloof catchment of the Jonkershoek valley in the Western Cape of South Africa. Data recorded by a Pendant Event logger (UA-003-64, Onset, Massachusetts, U.S.A). Tipping bucket calibrated to 0.254 mm increments.
DOI: 10.15493/sarva.beeh.10000136
title: Monthly Means Of Daily Average Relative Humidity (%) February

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.10000273
title: Monthly Means Of Daily Maximum Temperature (C) November

description: High daytime maximum temperatures, up to 30 – 35 degree Celsius, generally increase rates of photosynthesis (i.e. the process by which `food` is manufactured in a plant), and thereby the rate of organic matter production. High maxima can be critical for some plants in both a negative context (e.g. the potential for grain production is reduced in maize when temperatures are too high during the flowering period) and a positive context (e.g. strawberries need high daytime temperatures to develop flavour). However, the rate of respiration (i.e. the processes by which `food` is used by a plant) also increases with temperature. As maximum temperatures reach the upper limits for a given crop, the rate of respiration (`food` used) may exceed that of photosynthesis (`food` manufactured). Thus, for example, for tomatoes growth peaks at 36 degree Celsius (Whiting et al., 2004).

Time series of daily maximum temperatures from over 970 qualifying temperature stations were generated from quality controlled data for common 50 year period 1950 - 1999 using infilling and record extension techniques developed by Schulze and Maharaj (2004). From these time series at point locations, 50 years of daily maximum temperature values were generated at each of 429 700 one arc minute (1.7 km x 1.7 km) raster points covering South Africa, using regionally and seasonally determined lapse rates and other physically appropriate spatial interpolation approaches. These are given in detail in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as 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 Swaziland, Lesotho and for each of the nine provinces of the RSA.
DOI: 10.15493/sarva.csag.10000364
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.10000207
title: Mean 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/