cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.csag.10000422
title: Maximum of Minimum 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.10000405
title: Eragrostis Curvula Yield (T.Ha-1.Season-1) Based On SmithS (1994) Climatic Criteria

description: Eragrostis curvula, also known as Weeping Love Grass or Oulandsgras, is a tufted perennial with culms varying in height from 0.5 to 1.5 m and long, narrow bright green to bluegreen leaf blades up to 3 mm wide (Van Oudtshoorn,1992). Eragrostis curvula has a summer (September to March) growth period in areas where MAP > 600 mm and preferably > 700 mm (Smith, 1998), although it is difficult making hay from it when rainfall is too high (cf. also the effective rainfall fraction component of the yield equation in the first shaded box). It grows best where MAT is in the range 13 – 16 degree Celsius (Smith, 1994).

Dry matter yield of Eragrostis curvula may be estimated by Smith`s (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 appropriate applications of phosphates and potassium (with applications of K increasing as N application is increased) and that at least 250 kg nitrogen per ha has been applied.
DOI: 10.15493/saeon.fynbos.7b4491
title: 7B 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 at the Weikamp site in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/saeon.egagasini.10000004
title: The Agulhas System Climate Array Phase 1 project extending across the Agulhas Current at 34 degree South

description: The Agulhas System Climate Array (ACSA) is a mooring array and is designed to provide long-term observations of the Agulhas Current volume, heat and salt transport of the and it`s 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.
DOI: 10.15493/sarva.csag.10000134
title: Mean 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/saeon.egagasini.10000030
title: The ASCA April 2015 analysed salinity data for stations CTD008 - CTD025

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 raw data was collected by the shipborne CTD (Conductivity, Temperature, Depth) sensor, from which water samples were collected and analysed further using an Autosal Salinometer for the following CTD stations 008, 009, 010, 011, 012, 013, 014, 015, 016, 017, 018, 019, 020, 021, 022, 023, 024 and 025.
DOI: 10.15493/sarva.saws.10000176
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal mean near-surface (2m) temperature for the 90% percentile for 2066 - 2095 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 90% percentile 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 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.csag.10000425
title: Rainfall days per month 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.10000450
title: Highest Annual Sediment Yield (T/Ha) In 10 Years

description: Soil erosion is a serious problem in Southern Africa (Lorentz and Schulze,1995). Rooseboom (1992) estimated that the average annual sediment yield in southern Africa varied between 0.3 and 3.3 t/ha. To date a simple method has not yet been found to estimate sediment yield from a catchment. Complex deterministic models are available to estimate erosion processes and sediment transport. However, these models are limited in their application owing to their reliance on calibration.

The Universal Soil Loss Equation, USLE (Wischmeier and Smith, 1978), is an equation which has received recognition as an empirical method useful for planning and design purposes. This method is the foundation for other empirical equations which is then applied at a catchment scale to estimate sediment yield, such as the daily stormflow event based Modified Universal Soil Loss Equation, MUSLE (Williams, 1975), which has been widely verified world-wide and in South Africa (Kienzle et al., 1997).
DOI: 10.15493/sarva.csag.10000340
title: Number of days for Minimum Temperature under 10th 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/bea.data.250320-11
title: Forestry Residue Availability - Harvest residue assessment methodology

description: A report describing the methodology used to develop estimations of the volume of residue available within different forestry regions.
DOI: 10.15493/sarva.beeh.10000122
title: Standard Deviations Of Daily Maximum 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.10000037
title: Climatically Suitable Growth Areas for Eucalyptus elata

description: Known for its narrow, bright leaves with a peppermint smell, Eucalyptus elata typically grows in narrow belts along water courses and valleys and is known as the River Peppermint (CSIRO, 2005). E. elata thrives across a wide range of temperatures (MAT 14 - 19 degree Celsius ), but is susceptible to pest infestations when MAT > 19 degree Celsius (Kunz, 2004). It generally favours cool to warm, humid to sub-humid climates, ideally with daily means of maximum temperatures of 24 - 27 degree Celsius  in the hottest month (January), and with daily means of minimum temperatures in the range of 0 - 5 degree Celsius  in July, the coolest month (CSIRO, 2005). Eucalyptus elata is, however, intolerant of droughts, and is best grown with MAPs > 850 mm (Kunz, 2004).
DOI: 10.15493/sarva.csag.10000180
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.beeh.10000093
title: Heat Units (Standard Deviation) - November

description: The concept of the heat unit (or degree day), known since the mid-18th century already, revolves around the development of a plants or organism`s being dependent upon the accumulated heat to which it was subjected during its lifetime, or else during a certain developmental stage. This measure of accumulated heat is known as physiological time. In general it holds that the lower the temperature, the slower the rate of growth and development of plants and invertebrate animals and the amount of heat required to complete a given organism`s development does not vary. Physiological time is usually expressed and approximated in units of degree days, also popularly called heat units, where these are an accumulation of mean temperatures above a certain lower threshold value and below an upper developmental threshold (above which growth is considered to remain static or even decline), over a period of time. The degree day concept is not without its limitations. These include that, a linear relationship is assumed between growth and temperature, threshold temperatures may change during the life cycle of a crop or Pest, temperatures exceeding the upper threshold may, in fact, have a detrimental/negative effect on development.
Derivation over South Africa of heat units were done by calculating degree days for the daily temperature fluctuations that occur in nature. These range from degree hours, sine and triangular methods based on hourly temperatures, as well as so-called cut-off methods when the upper threshold of temperature is reached. To estimate heat units (HUs) over South Africa, the 50 year time series of daily maximum and minimum temperatures (Tmxd, Tmnd), generated at each of the 429 700 raster points covering the area at 1` x 1` latitude/longitude, was used (Schulze and Maharaj, 2004). The generation of this time series is described in Section 2a. HUs was computed for each day for a threshold temperature of 10 degree Celsius, i.e. HU = [(Tmxd + Tmnd) / 2 - 10] for HU ≥ 0.0 This method is a considerable advance on the computation of HUs in the previous version of this Atlas (Schulze, 1997), in which HUs were calculated from regression derived monthly means of Tmx and Tmn. From these daily values, monthly as well as seasonal and annual HUs and their standard deviations, could be calculated and mapped.
DOI: 10.15493/sarva.beeh.10000133
title: Monthly Means 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.10000070
title: Heat Units (January)

description: The concept of the heat unit (or degree day), known since the mid-18th century already, revolves around the development of a plants or organism`s being dependent upon the accumulated heat to which it was subjected during its lifetime, or else during a certain developmental stage. This measure of accumulated heat is known as physiological time. In general it holds that the lower the temperature, the slower the rate of growth and development of plants and invertebrate animals and the amount of heat required to complete a given organism's development does not vary. Physiological time is usually expressed and approximated in units of degree days, also popularly called heat units, where these are an accumulation of mean temperatures above a certain lower threshold value and below an upper developmental threshold (above which growth is considered to remain static or even decline), over a period of time. The degree day concept is not without its limitations. These include that, a linear relationship is assumed between growth and temperature, threshold temperatures may change during the life cycle of a crop or Pest, temperatures exceeding the upper threshold may, in fact, have a detrimental/negative effect on development.

Derivation over South Africa of heat units were done by calculating degree days for the daily temperature fluctuations that occur in nature. These range from degree hours, sine and triangular methods based on hourly temperatures, as well as so-called cut-off methods when the upper threshold of temperature is reached. To estimate heat units (HUs) over South Africa, the 50 year time series of daily maximum and minimum temperatures (Tmxd, Tmnd), generated at each of the 429 700 raster points covering the area at 1` x 1` latitude/longitude, was used (Schulze and Maharaj, 2004). The generation of this time series is described in Section 2a. HUs was computed for each day for a threshold temperature of 10 degree Celsius, i.e. HU = [(Tmxd + Tmnd) / 2 - 10] for HU ≥ 0.0 This method is a considerable advance on the computation of HUs in the previous version of this Atlas (Schulze, 1997), in which HUs were calculated from regression derived monthly means of Tmx and Tmn. From these daily values, monthly as well as seasonal and annual HUs and their standard deviations, could be calculated and mapped.
DOI: 10.15493/sarva.beeh.10000001
title: Acacia mearnsii Growth areas and Yield estimation

description: A native of southeastern Australia, Acacia mearnsii is an evergreen tree growing to 15 m in height. A. mearnsii has proved to be the most drought resistant of the commercial hardwoods grown in South Africa (Herbert, 1993). Acacia mearnsii requires a MAP of 750 mm in colder areas and 850 mm where it is warmer (Smith, 1998). Ideally, mean annual temperature should be in the range 16 - 19 degree Celsius, but with A. mearnsii being sensitive to warm humid conditions, means of January (i.e. mid-summer) temperatures should be > 22 degree Celsius.

Determinations of climatically optimum growth areas of Acacia Mearnsii have been defined in the ICFR's Forestry Toolbox (Kunz, 2004) by mean annual precipitation (MAP) and mean annual temperature (MAT). Acacia meanrsii: Timber yield and bark yield estimates were dune using Smith's (1994) rule-based approach for estimating mean annual increment (MAIs) of A. mearnsii, and considering only his climatic criteria and not soil or management factors. Results show that areas of highest yields of A. mearnsii timber correspond with those of bark, with significant tracts of the northeast of the Eastern Cape displaying considerable potential for future Acacia mearnsii plantations.
DOI: 10.15493/sarva.231120-8
title: The distribution of Quintile 4 Secondary Schools per District Municipality

description: School Quintiles are an indication of the socio-economic status of the school. Quintile 1 schools are the poorest, while quintile 5 schools are the least poor .Schools are ranked according to poverty of the school community . This dataset indicates the distribution of schools according to quintiles using the data from the master list of schools obtained from the Department of Basic Education.
DOI: 10.15493/sarva.csag.10000372
title: Mean 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.saws.10000012
title: Root Mean Square Difference between the nine ensemble member change anomalies of the annual mean near-surface (2m) temperature for the 10% percentile for 2066-2095 relative to 1976-2005, under the RCP 4.5 pathway

description: Root Mean Square Difference for annual mean near-surface (2m) temperature (°C) change from the 10% 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 temperature averages, which are used to generate projections of annual 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.10000112
title: Median monthly precipitation (mm) 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/