cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/nrf.saiab.20232511
title: Bathymetry data collected from Zinkwazi, November 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 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 the 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/dea.carbon.10000046
title: Tmin A2 Change between 2000 and 2050 90th Percentile

description: ºC change in minimum daily temperature per year between 1986-2015 and 2036 - 2065. The mean annual minimum daily temperature for each downscaled global circulation model (GCM), i.e. csiro, gfdla20, gfdl21, miroc, mpi and ukmo using the  Cubic Conformal Atmospheric Model (CCAM) developed by CSIRO (Australia) and updated and modified by the CSIR (South Africa) was calculated for each of the 30 year periods. The absolute difference between the two periods is based on the mean annual minimum daily temperature value and is calculated as follows: (Tmin2050-Tmin2000) for each of the six downscaled GCM models. The 90th percentile is based on these differences.  Data are in raster format with a 0.5º resolution using a projected latitude / longitude coordinate reference system with `+proj=utm +zone=48 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0`.
DOI: 10.15493/sarva.251120-2
title: 2019 NMU Estuarine botanical database: Submerged

description: Collation of available estuary submerged macrophyte distribution shapefiles. Presently 97 estuaries (listed in the appendix) are captured, and new maps will be added periodically as produced. The maps collated range in date from 2004-2018. Estuaries have been mapped as per the Estuary Health Index to determine Present Ecological Status. Some estuaries were mapped as Nelson Mandela University student theses. Shapefiles were also obtained from SANParks (Swartvlei and Wilderness estuaries). Currently nine data capturers were responsible for the development of GIS estuary maps. Macrophyte habitat descriptions may have been altered from the original published maps so as to standardize macrophyte habitat types. Distribution maps differ in extent, scale and accuracy as they were produced for differing outcomes. The shapefile is formatted in accordance with SANBI specifications. More recent maps produced are delineated according to the Estuarine Functional Zone (EFZ) which is considered the 5 m topographical contour (van Niekerk and Turpie 2012).
DOI: 10.15493/sarva.beeh.10000224
title: Pinus greggii (S) Growth Areas and Yield Estimation

description: Pinus greggii from the northern populations in Mexico exist at even higher altitudes than P. greggii (S), viz. at 1 900 - 2 600 m, and P. greggii (N) is therefore even more cold tolerant than its southern counterpart (Dvorak, 2003). Additionally, in its native environment, it grows where the MAP is only 650 - 750 mm, thus making it highly drought tolerant (Dvorak, 2003). Its growth rate is, therefore, also slower than that of P. greggii (S). P. greggii (N) should, therefore, be planted where it is too cold and too dry for P. patula, judging from the accompanying matrices of climatic conditions by Kunz (2004).
DOI: 10.15493/sarva.beeh.10000292
title: Standard Deviation (C) Of Daily Maximum Temperature May

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.10000417
title: Maximum of Maximum Temperature 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.beeh.10000062
title: Monthly Rainfall and its Inter-Annual Variability (May)

description: Monthly rainfall totals are necessary to many water resources as well as agricultural problems and decisions for which MAPs, or even wet/dry season precipitation totals be they high or low, are of relatively little consequence, because an intra-year distribution of rainfall is required (Schulze, 1997). Monthly rainfall values then serve as an important tool in describing such an intra-year distribution. It should, however, be borne in mind that the use of the calendar month is but a time step of convenience for describing temporal patterns of rainfall, in that it breaks up annual precipitation into components of time long enough to smooth out many of the irregularities of daily rainfalls (Schulze, 1997). Nevertheless, large differences in rainfall can exist from one month to the next. Some of these differences result from major rainfall generating mechanisms changing from one month to the next.

How the maps of median monthly rainfall were derived since time series of monthly rainfall are more variable than those of annual rainfall, the raster surfaces of median monthly rainfall at 1 arc minute spatial resolution (i.e. at 1` x 1` latitude/longitude spacing; 1.7 x 1.7 km; with 429 700 raster points making up South Africa) were calculated by expressing the median rainfall value of a given month at each qualifying rainfall station as a ratio of the MAP surface which was generated by Lynch (2004) using Geographically Weighted Regression. These ratios were then interpolated by Inverse Distance Weighting (IDW) onto the rectangular raster of 1 arc minute. This interpolated raster was then multiplied by the raster of MAP values to give 1` x 1` values of that month's median rainfall. The procedure was then repeated for each of the 12 months of the year (Lynch, 2004).
DOI: 10.15493/sarva.beeh.10000414
title: Gross Irrigation Demand April Schedule: Demand Irrigation At 0.5 Paw

description: This data set contains the GROSS IRRIGATION DEMAND Schedule: Demand Irrigation at 0.5 PAW 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.  In demand irrigation, conveyance losses of 10% were assumed, while wind/spray drift losses were set at 12% (assuming overhead irrigation), with crop interception losses at 1.5 mm per irrigation application. The soil profile was depleted  to 50% of plant available water (PAW), i.e. by 45 mm (50% of 234 - 144 mm) before irrigation was applied to refill the soil profile to its drained upper limit (DUL).
DOI: 10.15493/sarva.dws.10000003
title: Catchments of South Africa - quaternary

description: Catchments of South Africa - quaternary
DOI: 10.15493/sarva.beeh.10000305
title: Monthly Means Of Daily Average Temperature (C) June

description: Under conditions of natural vegetation, mean temperatures are used to distinguish between three broad thermal divisions of plants, viz. mega-thermal plants, which require mean monthly temperatures above 20 degree Celsius for at least 4 months of the year; micro-thermal plants, which grow where 8 months or longer have means below 10 degree Celsius; and the meso-thermal plants of the mid-latitudes, which cover most of South Africa, and whose physiology is adapted to the strong seasonal rhythms which are experienced over most of the southern African subcontinent. Month-by-month variations of mean temperature influence the geographic range and the optimum growth areas of certain vegetation and crop species, especially those with life-cycles extending one year or less. 

Assuming a daily average temperature to be represented by the mean of that days minimum and maximum temperatures, these averages were derived from the 50 year (1950 – 1999) time series of quality controlled daily minimum and maximum temperatures which were generated from over 970 qualifying temperature stations using infilling and record extension techniques developed by Schulze and Maharaj (2004). From these point location time series, 50 years of daily mean temperature values were derived at each of 429 700 one arc minute (1.7 km x 1.7 km) grid points covering South Africa, using regionally and seasonally determined lapse rates and other physically appropriate spatial interpolation approaches (Schulze and Maharaj, 2004). From the daily values, monthly means and standard deviations of daily values for each month of the year could be computed for purposes of mapping and production of tables of statistics for each of the nine provinces of the RSA as well as for Swaziland and Lesotho.
DOI: 10.15493/dea.carbon.10000084
title: South African Land Cover 2018 Class B Wetlands Resampled to a 1km Grid

description: Raster-based land cover dataset representing the full South African landscape for the full year 2018. Derived from 20m Sentinel 2 imagery acquired between 01 January 2018 and 31 December 2018. Land cover information classes based on new Gazetted land cover standards and legend content used in 2013-14 national land cover data. All land cover and land-use classes generated using automated modelling procedures for full operational repeatability and change detection. Class 22–23,73 of the 2018 South African National land cover dataset was clustered to derive South African land cover 2018 Class B Wetlands 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/saeon.egagasini.10000071
title: The ASCA April 2016 processed 24Hz CTD profile data for stations 001 - 005

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) 24Hz sensor for the following stations: 001, 002, 003, 004 and 005.
DOI: 10.15493/sarva.beeh.10000036
title: Climatically Suitable Growth Areas for Eucalyptus dunnii

description: Dunn's White Gum, Eucalyptus dunnii is a fast-growing, tough, very dense and coarse grained. Eucalyptus dunnii grows optimally when MAP > 800 mm and MAT ranges from 15 to 19 degree Celsius (Kunz, 2004), although with higher MAPs the trees also grow well at higher temperatures (Herbert, 2000). Ideally every month should have > 40 mm rainfall with > 20 percent of MAP falling in the 4 driest months (Lee et al., 2005). E. dunnii is prone to diseasing, mainly to defoliating insects, when MAT > 19 degree Celsius (Kunz, 2004) and especially under conditions of drought (Lee et al., 2005), while snow and frost also damage this species.
DOI: 10.15493/saeon.fynbos.50022022
title: Grootvadersbosch Nature Reserve rainfall data

description: Monthly rainfall data for Grootvadersbosch Nature Reserve from August 1935 to December 1953. These data were captured from a series of handwritten notes in the Hugh Taylor collection. The data were used in a paper published by Taylor: Taylor, H.C. (1955) Forest types and floral composition of Grootvadersbosch. J. S. Afr. For. Assoc. 26:33-46. 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.10000352
title: Standard Deviation (C) Of Daily Minimum Temperature March

description: Minimum (Night-Time) temperatures, there is abundant evidence for the climatic limitation of crop distributions to be described by minimum temperatures, from a perspective of crop survival (on Frost for tolerance levels of crops). Many subtropical crops may be killed at 5 degree Celsius already through chilling injury, even in the absence of frost (Chang, 1968). High minima increase respiration loss, thereby decreasing net photosynthesis.

Daily minimum temperature time series were derived from over 970 qualifying temperature stations` quality controlled daily records for the 50 year period 1950 - 1999. From these time series, 50 years of daily values of minimum temperatures were generated at each of 429 700 one arc minute (i.e. 1.7 km x 1.7 km) raster points covering South Africa, using regional and seasonal lapse rate and infilling/data extension techniques developed by Schulze and Maharaj (2004). Note that in the generation of daily minimum temperatures no account could be taken of cold air drainage into hollows or valleys, which can take place from surrounding higher-lying areas through radiative cooling during calm, stable clear night conditions. Reasons for this are provided in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as standard deviation of daily minimum temperatures for each month of the year, could be computed for purposes of mapping and production of statistics which are tabulated for Swaziland, Lesotho and for each of the nine provinces of the RSA. This approach to mapping minimum temperatures is thus fundamentally different to that used in the previous `South African Atlas of Agrohydrology and Climatology` (Schulze, 1997), in being much more physically based.
DOI: 10.15493/sarva.csag.10000299
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.energy.10000017
title: AWB-COF: All Woody Biomass - Co-Generation (MW)

description: Biomass feasibility assesment dataset extracted from the BioEnergy Atlas for South Africa.

* Assessments are made of impacts of economy of scale, logistics, and placement on techno-economic feasibility of feedstock and processing combinations, leading to identification of a portfolio of feasible project options. These options are illustrative of areas where bioenergy is likely to be a competitive, sustainable alternative.

* Original dataset: https://dx.doi.org/10.15493/BEA.DATA.10000024

* BioEnergy Atlas Synopsis Report: https://dx.doi.org/10.15493/SAEON.BEA.DOCS.10000001
DOI: 10.15493/bea.data.10000046
title: SUN-NOT: Sunflower Oil - Biodiesel Trans-esterification

description: * Technical Challenges - Technology is relatively simple and has high conversion efficiency. * Cost Challenges - Despite efficiency, levelised costs are high, due to mainly 2 factors (1) the input cost of raw material is high, and (2) operating costs are high due to feedstock (methanol) and distillation operations. Selling oilcake has a significant effect on final product cost, with a 50% oilcake internal subsidy reducing the costs by R 6,500/ t (0.65 R/kWh). This would bring production cost into line with current range of diesel prices. * Environmental Challenges - Greenhouse gas savings are significant provided land use changes are carbon neutral. Limiting cultivation to subsistence cropland should assist with this goal.
DOI: 10.15493/sarva.beeh.10000121
title: Standard Deviations Of Daily Maximum 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/sarva.beeh.10000330
title: Monthly Means Of Daily Minimum Temperature (C) June

description: Minimum (Night-Time) temperatures, there is abundant evidence for the climatic limitation of crop distributions to be described by minimum temperatures, from a perspective of crop survival (on Frost for tolerance levels of crops). Many subtropical crops may be killed at 5 degree Celsius already through chilling injury, even in the absence of frost (Chang, 1968). High minima increase respiration loss, thereby decreasing net photosynthesis.

Daily minimum temperature time series were derived from over 970 qualifying temperature stations` quality controlled daily records for the 50 year period 1950 - 1999. From these time series, 50 years of daily values of minimum temperatures were generated at each of 429 700 one arc minute (i.e. 1.7 km x 1.7 km) raster points covering South Africa, using regional and seasonal lapse rate and infilling/data extension techniques developed by Schulze and Maharaj (2004). Note that in the generation of daily minimum temperatures no account could be taken of cold air drainage into hollows or valleys, which can take place from surrounding higher-lying areas through radiative cooling during calm, stable clear night conditions. Reasons for this are provided in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as standard deviation of daily minimum temperatures for each month of the year, could be computed for purposes of mapping and production of statistics which are tabulated for Swaziland, Lesotho and for each of the nine provinces of the RSA. This approach to mapping minimum temperatures is thus fundamentally different to that used in the previous `South African Atlas of Agrohydrology and Climatology` (Schulze, 1997), in being much more physically based.