cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.energy.10000008
title: Mean Power Density (W/sq.m) - Western 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/nccis.data.10000011
title: Linear trend in annual total precipitation when daily precipitation ≥1 mm

description: An updated analysis of trends in South African rainfall is presented in The Third National Communication (TNC) to UNFCCC published in 2018. The analysis builds on the studies of Kruger & Sekele (2012) and Mackellar et al. (2014). 

A total of 60 weather stations were used for the rainfall trend analysis spanning the period 1921-2015. Apart from the trends in the annual rainfall totals, long-term changes in rainfall can manifest in changes in rainfall extremes. An extreme rainfall trend analysis is also performed, based on rainfall extreme indices developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) (Wang and Feng, 2013).  The base period, from which the annual index values of all indices are determined (except the annual maxima and minima) was selected as 1981 – 2010, which can be considered to be the present general norm for similar trend studies. The trends were tested for significance at the 95% confidence level.

Based on the data, there is strong evidence of statistically significant increases in rainfall occurring over the southern interior regions, extending from the western interior of the Eastern Cape and eastern interior of the Western Cape northwards into the central interior region of the Northern Cape, over the period 1921-2015. Extreme daily rainfall events have increased over these same areas, with these increases also being statistically significant and extending northwards into North West, the Free State and Gauteng. Over Limpopo there is strong evidence of statistically significant decreases in annual rainfall totals.
DOI: 10.15493/sarva.beeh.10000439
title: Coefficient Of Variation (%) Of Annual Net Primary Production

description: Primary production is a quantitative expression of vegetative matter (e.g. harvestable yield) which can be produced (e.g. in tons) by the natural environment at a location per unit area (e.g. per hectare) over a given period of time (e.g. in a season or a year). Primary production is useful in overall agricultural planning of a country or region, in objectively assessing the intrinsic environmental biomass production capability between one region and the next. Rosenzweig Equation was used for estimating Primary Production over South Africa. the equation takes into account rainfall, energy status, vegetation and soil characteristics.
DOI: 10.15493/saeon.fynbos.51022022
title: Rietpoort Farm rainfall data

description: Monthly rainfall data collected on Rietpoort Farm near Graaff-Reinet in the Eastern Cape.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.saws.10000110
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 4.5 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 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/saeon.tierberg.10000010
title: Plant Phenology

description: The data contains information on the phonological stages of plant species recorded between 1987 and 1993 within the one square kilometre (100ha) fenced to exclude sheep in 1987 and known as the Tierberg Karoo Research Centre (TKRC), on the Farm Tierberg in the Prince Albert district of the Western Cape. The database comprises 5929 rows and 10 columns. The database includes a list of dates on which phenology records were collected, a list of plant species examined on those dates, the habitat in which the plant was growing, and a description of the conditions of the growth stages of the flower, fruit, leaf and shoot.
DOI: 10.48331/scielo.w1fbbz/9lsp0x
title: 1-Ivo_Milton_Raimundo_Junior.pptx

description: :unav
DOI: 10.15493/sarva.energy.10000011
title: Ruggedness Index - Western 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: 10.48331/scielo.w1fbbz
title: Rumando para ciência aberta: em busca de visibilidade e impacto - Reflexões sobre o JBCS

description: Apresentação de Ivo M. Raimundo Junior no painel Rumando para ciência aberta: em busca de visibilidade e impacto da Semana SciELO 25 Anos. Página do evento: https://25.scielo.org/
DOI: 10.15493/sarva.beeh.10000161
title: Standard Deviations Of Daily Minimum Relative Humidity (%) December

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.10000251
title: Saturated Vapour Pressure (kPa) January

description: The average energy of the molecules in a liquid such as water is governed by temperature. The higher the temperature, the higher the average energy. Some molecules have higher energies than the average, and if they are on the surface of the liquid, can move fast enough to escape the inter-molecular attractive forces holding the liquid together. They then evaporate from the surface of the liquid. This process can continue until the space above the liquid is saturated with water molecules. Equilibrium is then reached with as many molecules leaving the surface of water as are re-entering it. At this point the pressure exerted by that vapour is called the saturated vapour pressure. The symbol used for saturated vapour pressure is ea and the unit of pressure is kPa. Since the molecular kinetic energy is greater at higher temperature, more molecules can escape the surface and the saturated vapour pressure is correspondingly higher. This relationship is illustrated in the shaded box.

Daily values of saturated vapour pressure were computed from the 50 year daily time series of temperature generated for South Africa at each one of 429 700 raster points at one arc minute interval, using techniques developed by Schulze and Maharaj (2004).
DOI: 10.15493/saeon.fynbos.9b4491
title: 9B manual daily rainfall April 1944 - October 1991

description: Daily rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, 1.2 meters above ground in the Tierkloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.saws.10000089
title: Seasonal total rainfall (mm per 3-month season) change from the 10% percentile projected for 2036-2065, relative to present (1976 - 2005), for JJA season under the RCP 4.5 pathway

description: Seasonal (JJA) rainfall (mm per month) change from the 10% percentile 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.10000184
title: Maximum of Maximum Temperature 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.10000265
title: Monthly Means Of Daily Maximum Temperature (C) March

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.beeh.10000341
title: Extremes (1950 - 2000) Of Minimum Temperatures (C) May

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/sarva.beeh.10000175
title: Papaya: Optimum Growth Areas - Criterion 1: Heat units (base 12 degree Celsius) should exceed 200 days per annum

description: Papaya, or pawpaw (Carica papaya) is a tropical fruit grown widely between latitudes 32 degree N and S. High in Vitamin C, papaya is indigenous to tropical America (ARC, 2005). This fruit, initially brought to the attention of Europe around 1520, was introduced to South Africa in seed form by Jan van Riebeeck in 1652, but only grown commercially in the Lowveld of Mpumalanga for the first time in the early 20th century by a Captain Elphick (ARC, 2005). Papaya needs relatively little water in the rainy summer season in South Africa, but if irrigated every 2 weeks in the dry season it is well adapted to hot, dry areas as the fruit then matures early and is then highly palatable owing to its high sugar content (Smith, 1998). Papaya has high heat requirements, with average daily temperatures for optimum growth between 20 and 30 degree Celsius.

Determination of climatically optimum growth areas for Papaya in South Africa is based on the expert knowledge of Bower (2005) and Moll (2005), climatically optimum growth areas were determined according to the four basic criteria: Criterion 1: Heat units (base 12 degree Celsius) should exceed 2 000 days per annum, Criterion 2: Optimum areas should have a low frequency of 4 consecutive days with maximum temperatures > 36 degree Celsius, Criterion 3: Monthly means of daily average temperatures in December and January should be 23 degree Celsius - 30 degree Celsius and Criterion 4: Optimum growth areas should have a low frequency of 4 consecutive days with minimum temperatures < 17 degree Celsius. Using the 50 year time series of quality controlled daily maximum and minimum temperatures generated by Schulze and Maharaj (2004) at a spatial resolution of 1 arc minute (i.e. 1` x 1` of a degree latitude/longitude), the above four temperature based criteria were first mapped individually and then superimposed to determine the climatically optimum growth areas of papaya in South Africa.
DOI: 10.15493/sarva.csag.10000436
title: Minimum of Maximum Temperature 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.10000266
title: Monthly Means Of Daily Maximum Temperature (C) April

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.