cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15493/sarva.saws.10000225
title: Seasonal total rainfall (mm per 3-month season) change from the median projected for 2066-2095, relative to present (1976 - 2005), for MAM season under the RCP 8.5 pathway

description: Seasonal (MAM) mean rainfall (mm per month) 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/saeon.egagasini.10000025
title: The ASCA January 2018 processed CTD profile data for station 001 - 007

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 is the processed product from data collected by the shipborne CTD (Conductivity, Temperature, Depth) sensors for the following stations: 001, 002, 003, 004, 005, 006 and 007.
DOI: 10.48331/scielo.odf7q1/6diqmr
title: 5-Solange Santos.pptx

description: :unav
DOI: 10.48331/scielo.odf7q1
title: Modelo SciELO de Publicación - Repositorio de las revistas en SciELO Data: como implementar

description: Presentación de Solange Santos en el panel Modelo SciELO de Publicación de la Semana SciELO 25 Años. Página del evento: https://25.scielo.org/es
DOI: 10.15493/saeon.fynbos.10000008
title: Dwarsberg weather station (1214m), event rainfall, 03/2013 - 09/2019

description: Hourly rainfall data collected by a Texas Electronics (2013/03/03 - current) tipping bucket gauge installed at 1.2m above ground at 1214 masl in the Jonkershoek valley in the Western Cape of South Africa. Data recorded by CR1000 logger (Campbell Scientific, Utah, U.S.A).  Tipping bucket calibrated to a 0.254 mm per tip event.  (Collection continuing, contact SAEON for updated datasets)
DOI: 10.15493/saeon.tierberg.10000003
title: Tierberg Karoo Research Centre: Overview of Contributions

description: The original objective of TKRC was to understand landuse and weather patterns on the plant and animal communities, interactions and processes in natural Karoo rangeland (Milton et al. 1992).  The site is only 100 ha in area, and the station was established in 1987 before climate change was recognised as a major driver of change (Milton et al. 2007), with the result that few of the bigger picture response variables and drivers of environmental, social and ecosystem change have been investigated. The data collected on TKC from 1988 to 2008 are summarised together with names of worksheets and explanatory files, data publication status and accessibility.
DOI: 10.48331/scielo.0nszbf/2ovfwo
title: 4-Alex Mendonca.pptx

description: :unav
DOI: 10.48331/scielo.0nszbf
title: Modelo SciELO de publicação - Preprints e avaliação por pares informada

description: Apresentação de Alex Mendonça no painel Modelo SciELO de publicação da Semana SciELO 25 Anos. Página do evento: https://25.scielo.org/
DOI: 10.15493/sarva.beeh.10000283
title: Extremes (1950 - 2000) Of Maximum Temperatures (C) September

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.saws.10000255
title: Seasonal percentage (%) change in rainfall from the median projected for 2066-2095, 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 2066-2095, 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: 10.15493/bea.data.10000094
title: Main Economic Sector maps

description: * Economic data was supplied by Quantec for the year 2009. For each sector Gross Value Added (GVA) was used as it is a measure of the value of goods and services produced in an area, industry or sector of an economy. The concept of Gross Geographic Value Added (GVA) - is used as a basis for making estimates of regional economic activity - is broadly similar to what is more generally known as Gross Geographic Product (GGP).
* The source for the economic sector category descriptions used here is ‘Statistical Papers, Series M, No. 4, Rev. 3, Department of International Economic and Social Affairs, Statistical Office of the United Nations, New York, 1990' provided by Statistics South Africa.
DOI: 10.15493/saeon.fynbos.9b9108
title: 9B manual weekly rainfall October 1991 - February 2008

description: Weekly rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, 1.2 meters above ground in the Tierkloof catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.48331/scielo.wweboi/hii2oc
title: 3-Denise Peres.pptx

description: :unav
DOI: 10.48331/scielo.wweboi
title: Modelo SciELO de Publicación - Critérios SciELO: actualizaciones

description: Presentación de Denise Peres en el panel Modelo SciELO de Publicación de la Semana SciELO 25 Años. Página del evento: https://25.scielo.org/es
DOI: 10.15493/sarva.beeh.10000446
title: Days / Year With Mild Soil Water Stress In A 1.2m Sandy Soil

description: This dataset contains the soil water stress in South Africa. The importance of soil water content in agricultural applications is vital since the need to irrigate occurs when the actual soil water content is less than the critical soil water content at which plant stress commences. The objective of soil water stress mapping was to determine, for each Quaternary Catchment (QC) with its unique rainfall, temperature and potential evaporation climate, the number of days per year at which soil water content would be at critical plant stress/no stress levels.
DOI: 10.15493/saeon.fynbos.8b4491
title: 8B manual daily rainfall January 1944 - October 1991

description: Daily rainfall data collected by a Snowdon gauge fitted with a Nipher shield. Gauge orifice parallel to the slope, 1.2 meters above ground in the Langrivier catchment in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/sarva.csag.10000427
title: Rainfall days per month (> 2mm) 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: 10.48331/scielo.f6y6a0/lfslqo
title: 2-Renata Postalli.pptx

description: :unav
DOI: 10.15493/sarva.saws.10000119
title: Seasonal total rainfall (mm per 3-month season) change from the 10% percentile projected for 2066-2095, relative to present (1976 - 2005), for SON season under the RCP 4.5 pathway

description: Seasonal (SON) 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: 10.48331/scielo.f6y6a0
title: Modelo SciELO de Publicación - SciELO Publishing Schema (SciELO PS): Ciencia Abierta

description: Presentación de Renata Postalli en el panel Modelo SciELO de Publicación de la Semana SciELO 25 Años. Página del evento: https://25.scielo.org/es
DOI: 10.15493/saeon.context.10000011
title: 2016 Local Municipalities

description: Boundaries captured to depict the extent of municipalities in South Africa and allow for easier management. 
Original data was obtained from the Municipal Demarcation Board. 
Attribute code construction: The first digit is the provincial code. The second and third digits indicate sequential numbering that forms a unique number within each province.
DOI: 10.15493/sarva.beeh.10000090
title: Heat Units (Standard Deviation) - August

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/bea.data.250320-10
title: Harvest residue potential

description: A dataset describing the volume of residue available within different forestry regimes according to the forestry regime, age class and species.
DOI: 10.15493/saeon.fynbos.2b4492
title: 2B manual daily rainfall April 1944 - April 1992

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 Swartboschkloof weather station in the Jonkershoek valley, Western Cape, South Africa.
DOI: 10.15493/dea.carbon.10000098
title: South African Land Cover 2018 Class P Built-up 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 47–67 of the 2018 South African National land cover dataset was clustered to derive South African land cover 2018 Class P Built-up 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/sarva.beeh.10000331
title: Monthly Means Of Daily Minimum Temperature (C) July

description: Minimum (Night-Time) temperatures, there is abundant evidence for the climatic limitation of crop distributions to be described by minimum temperatures, from a perspective of crop survival (on Frost for tolerance levels of crops). Many subtropical crops may be killed at 5 degree Celsius already through chilling injury, even in the absence of frost (Chang, 1968). High minima increase respiration loss, thereby decreasing net photosynthesis.

Daily minimum temperature time series were derived from over 970 qualifying temperature stations` quality controlled daily records for the 50 year period 1950 - 1999. From these time series, 50 years of daily values of minimum temperatures were generated at each of 429 700 one arc minute (i.e. 1.7 km x 1.7 km) raster points covering South Africa, using regional and seasonal lapse rate and infilling/data extension techniques developed by Schulze and Maharaj (2004). Note that in the generation of daily minimum temperatures no account could be taken of cold air drainage into hollows or valleys, which can take place from surrounding higher-lying areas through radiative cooling during calm, stable clear night conditions. Reasons for this are provided in Schulze and Maharaj (2004). From these daily values, monthly means of daily minimum temperatures, as well as standard deviation of daily minimum temperatures for each month of the year, could be computed for purposes of mapping and production of statistics which are tabulated for Swaziland, Lesotho and for each of the nine provinces of the RSA. This approach to mapping minimum temperatures is thus fundamentally different to that used in the previous `South African Atlas of Agrohydrology and Climatology` (Schulze, 1997), in being much more physically based.
DOI: 10.48331/scielo.me2r3c/0vnf4a
title: 3-Ritsuko_Nakajima.pdf

description: :unav
DOI: 10.48331/scielo.me2r3c
title: Those of us who do: Open Access Plataforms - J-STAGE for Japanese Scholarly Journals: Collaborative Efforts and Challenges in advancing open access and visibility

description: Presentation by Ritsuko Nakajima in the Those of us who do: Open Access Plataforms panel of the SciELO 25 Years Week. Event page: https://25.scielo.org/en
DOI: 10.15493/sarva.beeh.10000123
title: Monthly Means Of Daily Maximum Relative Humidity (%) January

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.10000424
title: Highest Annual Stormflows (mm) In 10 Years

description: This data set contains Stormflow in South Africa (SA). Stormflow is defined as the water which is generated from a specific rainfall event, either at or near the surface in a catchment. Important statistics on stormflows include annual means, inter-annual variabilities, and magnitudes in wet and dry years and the number of stormflow events per annum exceeding critical thresholds. Distribution patterns over SA show that the highest mean annual stormflows, in excess of 100 mm and > 200 mm, occur in the humid eastern third of SA with its high number of  rainfall events.
DOI: 10.15493/sarva.saws.10000219
title: Seasonal total rainfall (mm per 3-month season) change from the median projected for2066-2095, relative to present (1976 - 2005), for DJF season under the RCP 8.5 pathway

description: Seasonal (DJF) 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.csag.10000456
title: Maximum of Minimum 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.egagasini.10000062
title: The ASCA June 2018 processed microcat data at Mooring F 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 MicroCAT sensors at Mooring F at the following depths: 500m, 650m, 800m, 1000m, 1500m, 2000m and 3500m.