cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/bea.data.10000030
title: AWB-REF: All Woody Biomass - Chipping and Pelleting

description: * Technical Challenges - The primary product from a wood pelleting or wood conversion technology needs to be incorporated into refinery feedstock, either as a coal or gas supplement in the case of synfuels refineries, or as a biocrude product in the case of crude oil refineries. In both instances such flexibility may not exist. * Cost Challenges - Costs are acceptable comparable to current coal costs. * Policy Challenges - The projects are feasible and well aligned with existing expertise and infrastructure in respect of `Working for Water` programmes. Integration with DEA `Working for Energy` required. * Environmental Challenges - The net impact on greenhouse gas emissions is sizable, despite land use change effects, given the significant reduction in GHG as CO2 equivalents in comparison to coal. If natural vegetation replaces invasives at more or less the same annual increment, LUC effects are near zero.
DOI: 10.5519/qd.ahcqbp9x
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (14 records)
DOI: 10.15493/sarva.030921-1
title: Mean annual precipitation across South African municipalities from 1983 - 2020

description: This is a subset of the University of East Anglia Climatic Research Unit (CRU)`s Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate containing precipitation (mm/year) readings from South Africa`s weather stations across local municipalities for the years 1983-2020. 

The CRU TS 4.05 dataset was developed and has been subsequently updated, improved and maintained with support from a number of funders, principally the UK`s Natural Environment Research Council (NERC) and the US Department of Energy. Long-term support is currently provided by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre. Current gridded products (CRU TS) are presented either as ASCII grids, or in NetCDF format. The gridding process used in Brohan et al.. (2006) and earlier publications assigns each station to the 5 degree latitude/longitude box within which it is located. The gridding then simply averages all available station temperatures (as anomalies from 1961-90) within each grid box for each month from 1851. No account is taken of the station`s elevation or location within the grid box (anomalies show little consistent dependence on altitude). A more up-to-date location for a station is not important for the gridding, unless a site change were to move the station to an adjacent grid box. 

In this instance, the data was derived as a subset of the original dataset. CRU publishes the data in a NetCDF file format, however for data visualisation purposes the datasets were transformed into tidy tables and represented in the South African Risk and Vulnerability Atlas (SARVA) by the South African Environmental Observation Network`s uLwazi Node. 

Citation: University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2021): CRU TS4.05: Climatic Research Unit (CRU) Time-Series (TS) version 4.05 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2020). NERC EDS Centre for Environmental Data Analysis, 2021. https://catalogue.ceda.ac.uk/uuid/c26a65020a5e4b80b20018f148556681
DOI: 10.57830/2300501
title: Bats and Owls Inventory at Devil's Postpile National Monument - Open Format Dataset

description: For its size, Devils Postpile National Monument supports a diverse assemblage of nocturnal fauna. Our summer 2022 surveys recorded 15 bat species within the monument. Five of these species had not been previously confirmed in the monument, including two of the three sensitive species targeted: pallid bat (Antrozous pallidus) and fringed myotis (Myotis thysanodes). No California Spotted Owls (Strix occidentalis occidentalis) were confirmed, but a Great Horned Owl (Bubo virginianus) was recorded. Across the disturbance categories of interest identified by monument leadership (e.g., high, moderate, and low levels of fire and blowdown disturbance), there was little variability in bat species richness, total activity, or individual species occurrence. Sites with moderate levels of disturbance recorded the most species and highest levels of activity compared to high and low disturbance, and both priority species were recorded within all three disturbance categories.
DOI: 10.15493/sarva.csag.10000333
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.270121-1
title: South African hazardous events mortalities data

description: Supporting data for - Davis-Reddy, Claire & Hilgart, Amelia. (2021). Towards an interoperable National Hazards Events Database for South Africa. Frontiers in Climate.

South Africa's National Disaster Policy and international reporting requirements including the Sendai Framework for Disaster Risk Reduction 2015-2030 and the Sustainable Development Goals require reporting databases on disaster losses in order to facilitate evidence-based decisions and actions. Data fragmentation, gaps in data availability, spatial and temporal aggregations, poor data quality,  and lack of metadata are South Africa's most significant challenges to improved risk management planning, mitigation, and disaster loss reporting. Researchers need to discover and access scale- and task-appropriate data to integrate into their models or analyses. Although there is a growing number of international and national initiativesaimed at publishing disaster loss data, open access, integration and reuse remains difficult due to interoperability barriers to working with datasets. The lack of systematic and standardised data reporting on disasters forms a major weakness for any long-term disaster planning. In this paper, we consider the current sources of disaster data for South Africa and present our recommended solutions built into an online disaster reporting prototype system.
DOI: 10.15493/saeon.egagasini.10000057
title: The ASCA June 2018 raw microcat data at Mooring E 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 raw data collected by MicroCAT sensors at Mooring E for the following depths: 100m, 200m, 300m, 400m, 550m, 750m, 1000m, 1500m, 2000m and 3000m.
DOI: 10.15493/sarva.beeh.10000074
title: Heat Units (May)

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/saeon.fynbos.27022022
title: Koppieskraal Farm rainfall data

description: Monthly rainfall data for Koppieskraal Farm, on the Nuweveld escarpment between Fraserburg and Beaufort West. The data was collected using a standard rain gauge. This record forms part of a collection of historical rainfall data submitted to SAEON. The data were generated by farmers on a voluntary basis and have been published as submitted.
DOI: 10.15493/sarva.beeh.10000025
title: Actual Vapour Pressure (September)

description: Actual vapour pressure is the variable expressing atmospheric moisture content through the partial pressure exerted by the water vapour present in the atmosphere. 

The monthly uncorrected vapour pressure, ed, at any given location in South Africa was subsequently held constant for that entire month and derivatives of ed, such as daily maximum and minimum relative humidity or daily vapour pressure deficit which depend on ratios of uncorrected actual to saturated vapour pressure, were then computed using daily maximum/minimum temperatures as inputs to daily saturated vapour pressure calculations, which were in turn used with that location's monthly uncorrected actual vapour pressure.
DOI: 10.15493/sarva.beeh.10000260
title: Saturated Vapour Pressure (kPa) October

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/sarva.beeh.10000201
title: September Means Of Reference Crop Evaporation (mm) Fao Penman-Monteith Equivalent

description: 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`. 

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.
DOI: 10.15493/dea.carbon.10000095
title: South African Land Cover 2018 Class M Plantation Forest Resampled to a 1km Grid

description: Raster-based land cover dataset representing the full South African landscape for the full year 2018. Derived from 20m Sentinel 2 imagery acquired between 01 January 2018 and 31 December 2018. Land cover information classes based on new Gazetted land cover standards and legend content used in 2013-14 national land cover data. All land cover and land-use classes generated using automated modelling procedures for full operational repeatability and change detection. Class 5–7 of the 2018 South African National land cover dataset was clustered to derive South African land cover 2018 Class M Plantation Forest for the current product and resampled to a 1000 x 1000m grid resolution Albers Equal Area map projection +proj=aea +lat_1=-22 +lat_2=-38 +lat_0=-30 +lon_0=25 +x_0=1400000 +y_0=1300000 +datum=WGS84 +units=m +no_defs
DOI: 10.15493/bea.data.10000021
title: AOW-NOT: All Serviced Organic Waste - Biogas Digesters (Catchments)

description: * Technical Challenges - Proper utilisation of domestic organic waste requires development of biogas installations, based on digesters, or a large scale in many urbanised areas of the country. It may be useful to develop a standard unit (20,000 tons/annum) that can be replicated as required - there appears to be little economy of scale beyond the size of a large digester. The feedstock considered here includes currently unserviced households, as these come on stream expansion can be undertaken.
* Cost Challenges - There may be as many as 100 viable projects, with significant capital investment required. Costs are comparable to new coal-based electricity.
* Policy Challenges - The projects are feasible, require little or no subsidy, and can be implemented incrementally both at individual sites and across the country. Enabling policy, permitting, and regulation will be required to ensure safe handling of waste products, and IPP contributions to the grid allowed.
DOI: 10.15493/saeon.egagasini.24000003
title: The ASCA April 2015 raw Bongo data

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 bongo nets deployed off the stern of the vessel
DOI: 10.15493/sarva.csag.10000048
title: Consecutive Dry Days 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/dea.carbon.10000043
title: Tmax A2 Change between 2000 and 2050 90th Percentile

description: ºC change in maximum daily temperature per year between 1986-2015 and 2036 - 2065. The mean annual maximum daily temperature for each downscaled global circulation model (GCM), i.e. csiro, gfdla20, gfdl21, miroc, mpi and ukmo using the  Cubic Conformal Atmospheric Model (CCAM) developed by CSIRO (Australia) and updated and modified by the CSIR (South Africa) was calculated for each of the 30 year periods. The absolute difference between the two periods is based on the mean annual maximum daily temperature value and is calculated as follows: (Tmax2050-Tmax2000) for each of the six downscaled GCM models. The 90th percentile is based on these differences.  Data are in raster format with a 0.5º resolution using a projected latitude / longitude coordinate reference system with `+proj=utm +zone=48 +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0`.
DOI: 10.15493/dea.carbon.10000120
title: Soil loss from ISRIC reference data (2018)

description: The total carbon per land unit in South Africa was determined with ISRIC Africa SoilGrids data and vegetation biomass. By using the South Africal National Land Cover 2018 product the estimated soil loss for the country could be calculated. The ISRIC data was released in 2015
DOI: 10.15493/sarva.beeh.10000281
title: Extremes (1950 - 2000) Of Maximum Temperatures (C) July

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.10000176
title: Rainfall days per month (> 10mm) 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.10000305
title: Minimum of Maximum Temperature 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/saeon.fynbos.43022022
title: Jan Kempdorp House rainfall data

description: Monthly rainfall data recorded at Jan Kempsdorp Farm (latitude: -28.140526, longitude: 24.500114) and house (latitude: -27.906003, longitude: 24.614614), near Christiana in North West, South Africa. The data was collected using a Snowden standard rain gauge. 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.10000202
title: Root Mean Square Difference between the nine ensemble member change anomalies of the seasonal total rainfall for the median 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 rainfall (mm per month) from the median 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 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.