cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.csag.10000411
title: Precipitation 90th percentile for Near Future (2046-2065)

description: Model Run: Near future (2046 - 2065) (Near future (2046 - 2065)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000429
title: Consecutive Dry Days for Far Future (2080 - 2100)

description: Model Run: Far future (2080 - 2100) (Far future (2080 - 2100)). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.saws.10000145
title: Seasonal mean near-surface (2m) temperature (°C) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), for DJF season under the RCP 8.5 pathway

description: Seasonal (DJF) mean near-surface (2m) temperature (°C) change from the 90% percentile projected for 2036-2065, relative to present (1976 - 2005), under the RCP 8.5 pathway for the southern African region. To generate the image, nine coarse General Circulation Models (GCM) are downscaled to a finer spatial resolution (0.44°x 0.44°) using the Rossby Centre regional model (RCA4) forcing its lateral boundaries. The model simulated daily temperature averages, which are used to generate projections of 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.10000072
title: Minimum 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.csag.10000287
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.tierberg.10000006
title: Cicada Counts

description: This package contains four spreadsheets documenting the emergence of Cicadas (Quintillia spp) in 1989, 1990 and 1991 on the Tierberg Karoo Research Centre and on nearby farms. It include information on the mass and density of the cicadas, their density  in relation to landuse and vegetation structure, and changes in density over 30 days after emergence of the adults.
DOI: 10.15493/sarva.sdg.10000005
title: Total number of houses from the 2011 census that utilise solar energy as a fuel source, as well as number of households that use solar energy for cooking, heating and lighting

description: Total number of houses from the 2011 census that utilise solar energy for fuel, dasymetrically mapped to a 500m grid using the 2008 spot dwelling count. The data provides an indication of the total number of households per 500m grid cell that use solar energy for Cooking, Heating and Lighting
DOI: 10.15493/sarva.saws.10000102
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the 10% percentile for 2066-2095 relative to 1976-2005, for the DJF season, under the RCP 4.5 pathway

description: Root Mean Square Difference for seasonal (DJF) rainfall (mm per month) 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 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.fynbos.33022022
title: Holscher rainfall data

description: Monthly rainfall data collected at 77 Wagenaar Street, Monte Vista, Cape Town between 1985 and 2008 by the Holscher family.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/nccis.data.10000008
title: Linear trend in annual number of warm nights (1931-2015) defined as the annual number of days when minimum temperature (TN) > 90th percentile

description: An updated analysis of trends in South African temperatures 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 27 weather stations was used for for temperature trends analysis spanning the period 1931-2015. Apart from the trends in the annual mean minimum, maximum and average temperatures, and average diurnal temperature range, the analyses also includes the determination of trends in extreme temperatures, of which the indices considered are based on those 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.

The analysis demonstrates that, South Africa has been warming significantly over the period 1931-2015. Over the western parts of the country, including much of the Western and Northern Cape, and also in the east over Gauteng, Limpopo and the east coast of KwaZulu-Natal, the observed rate of warming has been 2 °C/century or even higher – in the order of twice the global rate of temperature increase.  Associated increases in the annual number of hot days have also occurred, but there have been decreases in the annual number of cold nights over most of the country.
DOI: 10.15493/bea.data.10000047
title: SUN-NOT: Sunflower Oil - Biodiesel Trans-esterification (Catchments)

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/saeon.fynbos.5b4408
title: 5B manual monthly rainfall January 1944 - February 2008

description: Monthly rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, with orifice at 1.2 meters above ground in the Bosboukloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.beeh.10000006
title: A-Pan Equivalent Reference Potential Evaporation (March)

description: Reasons for having selected the A-Pan as a reference for potential evaporation is that the US Weather Bureau Class A evaporimeter is, universally, the most common evaporation pan in usage, having been adopted as the standard evaporation pan since the International Geophysical Year 1957/8. Also when operated properly it is accepted as a reasonably reliable, simple and inexpensive local integrator of atmospheric evaporative demand, particularly for time periods longer than one day. Problems associated with the A-Pan as a reference for potential evaporation is that they are found generally near dams, existing irrigation projects or in areas of low relative relief where commercial agriculture is practiced. These sites are not always representative of higher altitudes where water is generated.

The data from over 570 South African stations at which a minimum of three years` concurrent monthly temperature, rainfall and A-pan evaporation observations are available, were used to develop temperature based equations of A-pan equivalent evaporation for each of the 12 evaporation regions identified in South Africa. The equations, unique for each month and for each region, were used to develop the 12 rasterised images of mean monthly A-pan equivalent evaporation on a 1` x 1` of a degree latitude/longitude grid.
DOI: 10.15493/saeon.context.10000010
title: 2016 District Councils

description: Polygons created to show boundaries of districts councils within South Africa. District councils and metropolitan areas are a level above the DMAs (District management areas) and local municipalities in the geography hierarchy. Metropolitan areas are conurbations featuring high population density; intense movement of people, goods and services; extensive development; and multiple business districts and industrial areas. Other features include a complex and diverse economy, a single area where integrated development is desirable, and strong interdependent social and economic linkages between its constituent units. There are eight metros in South Africa: Cape Town, EThekwini (Durban), Ekurhuleni (East Rand), Johannesburg, Tshwane (Pretoria), Nelson Mandela Bay (Port Elizabeth), Buffalo City (East London) and Mangaung (Bloemfontein). 
*Original data was obtained from the Municipal Demarcation Board.
DOI: 10.15493/sarva.csag.10000394
title: Precipitation 90th percentile for Current Climate (1950-1999)

description: Model Run: Control - Current Climate (Observed/current climate as modelled by climate model). The Self-Organizing Map Downscaling (SOMD) was developed at the Climate Systems Analysis Group (CSAG)[1], University of Cape Town. This is a leading empirical downscaled technique and provides meteorological station level response to global climate change forcing (See Hewitson and Crane (2006) for methodological details and Wilby et al. (2004) for a review of this and other statistical downscaling methodologies). Downscaling of a General Circulation Model (GCM) is accomplished by deriving the normative local response from the atmospheric state on a given day, as defined from historical observed data.  [1] http://www.csag.uct.ac.za/
DOI: 10.15493/sarva.csag.10000386
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/sarva.dws.10000015
title: River profile classification Catchment N

description: Longitudinal slope profiles derived for all 1:500 000 rivers of South Africa, including cross-border catchments extending into neighbouring countries. River main stems are identified by a unique reach number (REACHNUM) within each primary catchment.
DOI: 10.15493/sarva.saws.10000253
title: Seasonal percentage (%) change in rainfall from the median projected for 2036-2065, relative to present (1976 - 2005), for MAM season under the RCP 4.5 pathway

description: Seasonal percentage (%) change in rainfall from the median projected for 2036-2065, relative to present (1976 - 2005), for MAM season 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: 10.25345/c5s756w3j
title: MassIVE MSV000093081 - PHF6 cooperates with SWI/SNF complexes to facilitate transcriptional progression
title: Genes encoding subunits of SWI/SNF (BAF) chromatin remodeling complexes are mutated in nearly 25% of cancers. To gain insight into the mechanisms by which SWI/SNF mutations drive cancer, we contributed ten rhabdoid tumor (RT) cell lines mutant for SWI/SNF subunit SMARCB1 to a genome-scale CRISPR Cas9 depletion screen performed across 896 cell lines. We identify PHF6 as specifically essential for RT cell survival and demonstrate that dependency on Phf6 extends to Smarcb1-deficient cancers in vivo. As mutations in either SWI/SNF or PHF6 can cause the neurodevelopmental disorder Coffin-Siris syndrome, our findings of a dependency suggest a previously unrecognized functional link. We demonstrate that PHF6 co-localizes with SWI/SNF complexes at promoters, where it is essential for maintenance of an active chromatin state. We show that in the absence of SMARCB1, PHF6 loss disrupts the recruitment and stability of residual SWI/SNF complex members, collectively resulting in the loss of active chromatin at promoters and stalling of RNA Polymerase II progression. Our work establishes a mechanistic basis for the shared syndromic features of SWI/SNF and PHF6 mutations in CSS and the basis for selective dependency on PHF6 in SMARCB1-mutant cancers.
DOI: 10.15493/sarva.csag.10000439
title: Maximum of Minimum 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/dea.carbon.10000104
title: Below ground herbaceous biomass for 2014 in t/ha

description: Below ground herbaceous values were calculated with the same set of equations as used for above ground herbaceous layers. Agricultural biomass is  reduced with a factor of 0.2  of the above ground biomass while a ratio of 1:1 is assumed between above ground and below ground herbaceous biomass for natural vegetation. National Land Cover 2014 (NLC1990) was used to calculate biomass per land cover class for 2014.
DOI: 10.15493/sarva.csag.10000431
title: Total precipitation for the month (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.10000221
title: Pinus elliottii Growth Areas and Yield Estimation

description: Pinus elliottii a native of the southern USA also goes by the name of slash pine. It is an evergreen conifer with very long needles, an extensive root system and a moderate taproot (Hill, 2001). P. elliottii is the hardiest of the three main species of pine grown in South Africa (the other two being P. patula and P. taeda) and produces a relatively hard, durable wood which, because of its high resin content, however, limits its marketability (Smith, 1998). Normally planted in pits, it is weed sensitive (Smith, 1998).

Climatically optimum growth areas for Pinus elliottii have been defined in the ICFR`s Forestry Toolbox (Kunz, 2004), by mean annual precipitation (MAP) and mean annual temperature (MAT), as illustrated in This figure also shows the climatic conditions under which suboptimum growth would take place because conditions may at times be too cold and damage from snow occurs, or because occasional drought may occur. Optimum and sub-optimum growth areas of P. elliottii were mapped by applying these criteria to the one arc minute (1` x 1` of a degree latitude/longitude) databases of MAP (Lynch, 2004) and MAT (Schulze and Maharaj, 2004) for South Africa. Using Smith`s (1994) rule based approach and considering only his climatic criteria and not soil or management factors, Pinus elliottii yield estimates were done.
DOI: 10.15493/sarva.251120-13
title: NBA2018 national wetland map 5

description: National Wetland Map 5 was released on 2019/10/03. It is associated with the third and final version of the South African Inventory of Inland Aquatic Ecosystems (SAIIAE) Report of 2018 or in short, the Inventory Report. National Wetland Map 5 includes inland wetlands, some rivers and estuaries, but is associated with river line data and many other data sets within the South African Inventory of Inland Aquatic Ecosystems (SAIIAE). National Wetland Map 5 should be used together with the artificial wetlands layer.

Please ensure you obtain the full geodatabase and relevant reports to understand the appropriate use of the data. If the Estuaries are used, please cite Van Niekerk et al., 2019. Technical Report of the Estuarine Ecosystems for the NBA 2018.
DOI: 10.15493/sarva.beeh.10000275
title: Extremes (1950 - 2000) Of Maximum 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.csag.10000045
title: Rainfall days per month (> 10mm) 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/