cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.080420-2
title: District Health Barometer 2019/20

description: The data are a reformatted version of the 2019/20 District Health Barometer (DHB) health indicator dataset provided by the HST. The intention of this publication was to provide an overview of the delivery of selected healthcare services in the public health sector across the provinces, districts and local municipalities/sub-districts of South Africa. The DHB has been an annual publication since 2005. The main focus of the 2019/20 publication is the Sustainable Development Goals (SDGs) and the Universal Health Coverage (UHC)b index. Data are drawn from the electronic District Health Information Software (WebDHIS), the Ideal Clinic Realisation and Maintenance system, Statistics South Africa (Stats SA) surveys, the National Treasury Basic Accounting System (BAS), the Personnel Administration (PERSAL) system, the TIER.Net for Tuberculosis (TB) and antiretroviral (ART) data, the Electronic Drug-resistant Tuberculosis Register (EDRWeb), the National Income Dynamics Study (NiDS), and other National Department of Health (NDoH) information systems. The publication seeks to highlight inequities in health outcomes, health-resource allocation and health delivery, and to track the efficiency of health processes, across all provinces and districts.
 
Modifications were made to the data table provided in line with Hadley Wickem`s `tidy data` standards for statistical research (DOI: 10.18637/jss.v059.i10). The dates of the indices were also standardised to represent a single year in the YYYY format and a column of municipal codes was allocated to each row in line with the coding from the South African Municipal Demarcation Board. Lastly, centroids of the political areas described (national, provincial, district municipal, and local municipal areas) were added as applied in a final column. The indicator units, numerators, denominators, and definitions have been added to the table to give additional additional context.
DOI: 10.15493/saeon.fynbos.20a6489
title: 20A manual daily rainfall November 1964 - September 1989

description: Daily rainfall data collected by a Snowdon gauge with orifice at 1.2 meters above ground at Uitkyk in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.beeh.10000190
title: Positive Chill Units - August

description: Certain biennial plants which have a dormant (i.e. rest) season during winter may require a period of accumulated minimum temperatures below a threshold in order to stimulate growth, develop leaves, flower or set fruit. Deciduous trees, for example, need a certain period of winter chilling for completion of their seasonal dormancy. Insufficient winter chilling may result in delayed foliation, reduced fruit set (including fruits remaining small and being misshapen as they ripen) and reduced fruit quality (including reduced firmness, and/or greener fruit coloration). The required amount of chilling for completion of the rest period varies between species, cultivars and different locations. Many chill accumulation models have been formulated, most of them requiring hourly temperatures to have either been observed or estimated. The Linsley-Noakes PCU model was used to produce maps of chill units in this Atlas. The techniques by which PCUs were computed and then mapped are described in the shaded sections which follow.

Derivation of hourly values of temperature for the determination of PCUs hourly values of temperature were determined from the 50 year time series of daily maximum and minimum temperatures generated at each of the 429 700 raster points covering South Africa, using the techniques described by Schulze and Maharaj (2004). For each daytime and night-time cycle the sine-log equations derived by Linsley-Noakes et al. (1995) were employed. In these equations the estimation of hourly temperatures is based on the observation that the daytime solar cycle follows a sine curve from sunrise to sunset, whereas night-time cooling may be described by a logarithmic equation.
DOI: 10.15493/sarva.beeh.10000368
title: Monthly mean of diurnal (daily) temperature ranges for the 50 year (1950 - 1999) period (C) - July

description: The diurnal temperature range is the difference between the maximum and minimum temperatures on a given day. Some plants respond to the rhythmic variations brought about by shifts from high temperatures during the day to low temperatures during the night. The main long term factors affecting diurnal temperature ranges over South Africa are continentality and altitude (Schulze, 1997). It nevertheless needs emphasising here that short term, irregular diurnal conditions are often controlled by the passage of cold fronts or other air masses which may be associated with strongly contrasting temperature regimes.

"Monthly means of diurnal (daily) temperature ranges were derived from the 50 year (1950 - 1999) time series of quality controlled daily  maximum and minimum temperature 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). From theoretical considerations, experience and analyses of high quality daily temperature datasets, the diurnal temperature range is very seldom < 2 degree Celsius. When, therefore, on a given day the temperature range value was computed to be < 1.5 degree Celsius, this value was `forced` to be 1.5 degree Celsius, with equal compensatory adjustments being made to that day's maximum and minimum temperature values."
DOI: 10.15493/saeon.fynbos.141220-6
title: Jonkershoek forestry stock map 1962

description: Map of forestry plantation compartments for the Jonkershoek valley, Western Cape, South Africa as of 1962.
DOI: 10.15493/sarva.beeh.10000438
title: Terrain morphology

description: Altitude can be high or low, but that by itself does not present a complete description of terrain characteristics, nor does it analyse the landforms quantitatively, nor does it examine landscape influences on, for example, hydrological responses or agricultural potential. Kruger (1983) mapped the terrain morphology of South Africa, Lesotho and Swaziland into six broad classes (A, B, . . . to F) according to relief (low to high). Five of the broad categories were subdivided further to give a total of 30 subdivisions. Terrain Morphological Attributes include Slope form, Relief, Drainage density, Stream frequency and Percentage area with a slope of < 5%.
DOI: 10.15493/sarva.beeh.10000452
title: Mean annual stormflow losses from irrigated area

description: This dataset contains the stormflow and deep percolation losses from irrigated areas under different modes of scheduling in South Africa (SA). Stormflow losses from irrigated areas are due to frequent additional water applications to crops under irrigation, over and above those from naturally occurring rainfall events while deep percolation losses is due to very different soil water budgets of irrigated fields compared with those of surrounding rain-fed land uses. Distribution patterns over SA of mean annual stormflow losses from irrigated areas range from < 10 mm in the east  to > 80 mm in the semi-arid west.
DOI: 10.15493/sarva.beeh.10000407
title: Kikuyu Yield Estimation

description: Kikuyu, Pennisetum clandestinum, native to the East African highlands, has become an important pasture grass providing palatable and highly nutritious material. Kikuyu is essentially a summer pasture (i.e. October – March) which requires a MAP of > 800 mm. It thrives best where MAT < 17 degree Celsius (Smith, 1998). Kikuyu is a relatively shallow rooted plant, extracting soil water to only 0.3 m (Smith, 1998).  

Dry matter yield of kikuyu may be estimated by Smith (1994) rule-based approach (cf. first shaded box) using the effective rainfall for the summer months October to March in conjunction with accumulated summer month heat units (base 10 degree Celsius), and assuming that base fertilizer levels have been brought up by addition of phosphates and potassium and that 250kg nitrogen per ha has been applied.
DOI: 10.15493/sarva.saws.10000048
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the 10% percentile 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) mean near-surface (2m) temperature (°C) 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 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: 10.5281/zenodo.8319607
title: FlashNet: AI framework for lightning forecasts

description: This repository contains the code and a subset of the data used in:<br> "AI vs fully-deterministic algorithms: unraveling the dilemma for lightning prediction in the medium-range forecast horizon" by Mattia Cavaiola, Federico Cassola, Davide Secchetti, Francesco Ferrari, and Andrea Mazzino
DOI: 10.5281/zenodo.8430596
title: FlashNet: AI framework for lightning forecasts

description: This repository contains the code and a subset of the data used in:<br> "AI vs fully-deterministic algorithms: unraveling the dilemma for lightning prediction in the medium-range forecast horizon" by Mattia Cavaiola, Federico Cassola, Davide Secchetti, Francesco Ferrari, and Andrea Mazzino
DOI: 10.15493/saeon.egagasini.10000088
title: The ASCA April 2016 processed ADCP data collected at Mooring D for all depths

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current`s volume, heat and salt transport and its variability from mesoscale (eddies), through seasonal to interannual timescales. The ASCA shelf and tall moorings extend 200 km offshore along decending TOPEX/Jason satellite ground track # 96, through the core of the Agulhas Current, with CPIES measurements extending the array to 300 km offshore of Port Elizabeth. The data collected during this project have been obtained from various oceanographic instrumentation fixed to each mooring. This is the processed product from data collected by an ADCP (Acoustic Doppler Current Profiler) at Mooring D.
DOI: 10.15493/sarva.saws.10000156
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the 10% percentile for 2036 - 2065 relative to 1976-2005, for the MAM season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (MAM) mean near-surface (2m) temperature (°C) 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 temperature 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.10000206
title: January Standard Deviations Of Daily Reference Crop Evaporation (mm) Fao Penman-Monteith Equivalent

description: The first detailed maps for South Africa of Penman-Monteith equivalent reference crop, Erpm, were derived from monthly ratios of Erpm: A-pan using data at 174 sites (Crosby and Crosby, 1996). From these ratios, Schulze (1997) identified 14 `regions` of similar ratio patterns which were then used to map Erpm from gridded A-pan values. The maps in the present Atlas are based on a much more deterministic and more fundamental approach with daily maximum and minimum temperatures, and hence daily saturated vapour pressures, generated over South Africa on a 1` x 1` (1.7 x 1.7 km) raster for 50 years, based on research by Schulze and Maharaj (2004). Empirically determined month-by-month gridded values of actual vapour pressure for South Africa.

The FAO (1992) definition of Penman-Monteith derived reference crop evaporation (Erpm) is `The rate of evapotranspiration from a hypothetical crop with an assumed crop height of 0.12 m, a fixed canopy resistance of 70 s.m-1 and albedo of 0.23, which would closely resemble evapotranspiration from an extensive surface of green grass cover of uniform height, actively growing, completely shading the ground and not short of water`.
DOI: 10.15493/sarva.csag.10000188
title: Number of days for Minimum Temperature over 90th percentile 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.csag.10000129
title: Median monthly precipitation (mm) 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.251120-15
title: 2018 Terrestrial ecosystem threat status and protection level

description: [NBA 2018 Terrestrial ThreatStatus Protection Level] forms a key part of the National Biodiversity Assessment. Ecosystem Threat Status and Protection level was calculated as part of the National Biodiversity Assessment process for 2018. These two metrics indicate the current status and protection level of South Africa's ecosystems as determined by the 2018 NBA Process. The data forms the foundation for many land use decision process including conservation planning.
DOI: 10.15493/sarva.saws.10000232
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the median for 2066-2095 relative to 1976-2005, for the JJA season, under the RCP 8.5 pathway

description: Root Mean Square Difference for seasonal (JJA) rainfall (mm per month) change from the median projected for 2066-2095, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily rainfall averages, which are used to generate projections of seasonal change. The projections are generated using the high (RCP8.5) pathway which associates CO2 concentrations of approximately 950ppm by the year 2100. The associated RMSD it calculated and shows the uncertainty range of the projected model simulated residual values, and gives a relative perspective of spatial areas associated with higher and lower projection uncertainties.
DOI: 10.15493/sarva.beeh.10000143
title: Monthly Means Of Daily Average Relative Humidity (%) September

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.10000411
title: Sunflower Seed Yield Estimation (T.Ha-1.Season-1) Based On SmithS (1994) Climatic Criteria

description: Comparatively speaking, sunflowers (Helianthus annus) perform well under drought conditions and they are therefore popular in the climatically more marginal areas of South Africa. Planted from November to December in the eastern parts and up to mid-January in the drier western parts of South Africa, good yields require an evenly distributed 400 - 600 mm rainfall during their growing season of about 120 days (Smith, 1998). Optimum growing temperatures are 18 - 25 degree Celsius, sub-optimally the range is 13 - 30 degree Celsius. Mean January temperatures should exceed 19 degree Celsius (Smith, 1998).

Using Smith`s (1994) climatic criteria, yields of sunflower seeds are estimated using the effective rainfall for October to March and heat units (base 10 degree Celsius) for the same period, with modifications to yield made for soil properties and management levels. Rainfall values were derived from the 1 arc minute median monthly rainfalls generated for South Africa by Lynch (2004).
DOI: 10.15493/bea.data.10000064
title: Solid Waste Disposal Sites

description: Data was digitised from addresses provided in the following sources * survey of solid waste disposal sites listed by CSIR as part of their work package 4, commissioned by the BioEnergy Atlas project. This data was obtained from the Department of Environmental Affairs. * Sites listed by the Department of Environmental Affairs in SAWIS (South African Waste Information Centre).
DOI: 10.15493/sarva.beeh.10000337
title: Extremes (1950 - 2000) Of Minimum Temperatures (C) January

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.10000300
title: Monthly Means Of Daily Average Temperature (C) January

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.