Note: This dataset version has been superseded by a newer version. It is highly recommended that users access the current version. Users should only use this version for special cases, such as reproducing studies that used this version. The Special Sensor Microwave Imagers (SSM/I) are a series of six satellite radiometers that have been in operation since 1987 under the Defense Meteorological Satellite Program (DMSP). The six SSM/Is (aboard F08, F10, F11, F13, F14, and F15) have a seven channel linearly polarized passive microwave radiometer that operate at frequencies of 19.36 (vertical and horizontal polarized), 22.235 (vertical polarized), 37.0 (vertical and horizontal polarized), and 85.5 GHz (vertical and horizontal polarized). The Remote Sensing Systems (RSS) Version-6 SSM/I Fundamental Climate Data Record (FCDR) dataset has incorporated all past geolocation corrections, sensor calibration (including cross-scan biases), and quality control procedures in a consistent way for the entire 24-year SSM/I brightness temperature period of record. Version-5 was relatively short lived due to subtle calibration problems that caused small spurious trends in the climate retrievals (the SSM/I record had become long enough at this point to detect such errors). The problem was due to subtle correlations in the derivation of the target factors for the F10 and F14 SSM/I. Like the Microwave Sounding Unit (MSU), some of the SSM/I exhibit errors that are correlated with the hot-load target temperatures, and we removed these errors using the target multiplier approach. Application of the solutions described herein provided the current V6 SSM/I TA and TB dataset. RSS Version-6 SSM/I FCDR data are stored as netCDF-4 files that have been internally compressed at the maximum GZIP utility level. A typical file will have a size of 6.4 megabytes.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of 9am vapour pressure across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables). All data was interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude. 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance: Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly vapour pressure: RMSE = 0.38 hPA Daily vapour pressure: RMSE = 1.24 hPa

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This geodatabase contains the climate data used in the Gippsland Basin Groundwater Model (Beverly, 2015). The data was extracted from the national SILO data sets for 199 climate stations in the Gippsland region. The geodatabase includes the following datasets (average daily values): climate zones, evaporation, radiation, rainfall, evapotranspiration, maximum temperature and minimum temperature. The data was produced using ANUClim software (Hutchinson, 2001), using the interpolation methods discussed in Jeffery et al., 2001.

This dataset extends beyond the boundary of the Bioregional Assessment Programme and is included here without any modification.

Purpose

Illustrate climate parameters (temp, rain, solar radiation etc) across the Gippsland area and for use within the Gippsland Groundwater model

Dataset History

For a given climate station daily climate data is a combination of original measurements and rectified data to remove any gaps in the record using interpolation methods discussed in Jeffery et al. (2001).

To account for sparsely located climate stations within Gippsland, average daily maximum temprature,were scaled to each solution point within the study area according to interpolated average daily maximum temprature spatial layer created using the ANUClim software (Hutchinson 2001).

This data set was originally sourced from the SILO national climate data for the Gippsland Groundwater Model (Beverly, 2015). It has been reused in the Bioregional Assessment Programme without modification. The data is provided as an output from ANUClim.

Dataset Citation

Victorian Department of Environment, Land, Water and Planning (2015) Gippsland Product 2.1 climate data geodatabase v01. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/aa6108b8-a1d8-464c-9562-7d6a69304685.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the national SILO data sets of climate station records. The parent dataset(s) is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Gippsland Product 2-1 Climate Data Geodatabase v01

This geodatabase contains the climate data used in the Gippsland Basin bioregion. The data was extracted from the national SILO data sets for 199 climate stations in the Gippsland region. The geodatabase includes the following datasets (average daily values): climate zones, evaporation, radiation, rainfall, evapotranspiration, maximum temperature and minimum temperature. The data was produced using ANUClim software (Hutchinson, 2001), using the interpolation methods discussed in Jeffery et al., 2001.

Purpose

To spatially represent interpolated average daily minimum temprature (ºC) for the period 1957-2012- in Gippsland

Dataset History

For a given climate station daily climate data is a combination of original measurements and rectified data to remove any gaps in the record using interpolation methods discussed in Jeffery et al. (2001).

To account for sparsely located climate stations within Gippsland, average daily minimum temperature were scaled to each solution point within the study area according to an interpolated average daily minimum temperature spatial layer, created using the ANUClim software (Hutchinson 2001).

Dataset Citation

Bioregional Assessment Programme (XXXX) Gippsland average daily minimum temperature. Bioregional Assessment Derived Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/26bd3c85-2262-4e6d-ae91-299672ff4526.

Dataset Ancestors

• Derived From Gippsland Product 2.1 climate data geodatabase v01

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of minimum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using trivariate splines (latitude, longitude and elevation) with partial dependence upon a topographic index of relative elevation. Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables) with partial dependence upon a topographic index of relative elevation. All models were fit and interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance (3DS-T): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly minimum temperature RMSE = 0.80 °C Daily minimum temperature RMSE = 1.73 °C

Please refer to the linked manuscript for further details.

Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-minute temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records. Methods To monitor canopy climate eight canopy climate stations were established in a stratified sampling design targeting ridges (four stations) and valleys (four stations). A nearby valley and a ridge correspond to a site. Air temperature, relative humidity, photosynthetic active radiation, and leaf wetness were recorded by all canopy climate stations, while precipitation, wind, and global radiation were recorded by selected canopy climate stations. All canopy climate stations collected data every 30 seconds and recorded data every 10 minutes between April 2019 and February 2020. To monitor canopy climate with a high temporal resolution, we installed eight climate stations, each in the crown of a tree, using adapted climbing techniques (Perry 1978) in the second half of April 2019. These trees belonged to seven species distributed in 6 botanical families and averaged 64 + 0.6 cm (mean + SE) for DBH, 26 + 0.2 m of height, and 7.2 + 0.2 m of crown radius (Table 1); we targeted canopy trees, i.e., those immersed in canopy strata of the forest, therefore we excluded emergent trees. In each tree, the sensor set (Fig. 2) was established in the medium section and upper side of crown branches (i.e., in the middle canopy sensu Johansson 1974). We verified the performance of the canopy climate stations before installation in the lab, and after installation from the ground via WIFI, using the interphase provided by the data logger maker (Device Configuration Utility, 2.21.16 by Campbell scientific). Verifications from the ground were performed one week, four months, and ten months after installation. After the last verification, we removed the climate stations. Data verification Data were checked in by the authors. In each data series, a variable of each climate parameter was plotted to identify record gaps and suspicious data, suggesting partial or definitive sensor failure (Fig. 3), this data exploration was enriched with fieldnotes taken at the time of removing stations. To ease comparisons among stations and sites, data series were aligned, and suspicious data were labeled as 999 (R script Data_Manipulation.R). There is no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. The original data series went through a data verification process while the derived data series resulted from a data compilation process. Data verification consisted of four steps: i) visualization and diagnosis of suspicious data, i.e., those occurring the possible values for the climate parameter in the locality or those with cumulative error (for instance, PAR that was above cero during night hours), resulting from malfunctioning of specific sensors; ii) replacement of suspicious data with NA; iii) calculation of derived climate variables, average relative humidity as the mean value of the recorded relative humidity variables (maximum and minimum), vapor pressure deficit (VPD) derived from average relative humidity and average air temperature following Frego and Fenton (2005), calculated dew point (DewPtC) derived from average air temperature and average relative humidity following Lawrence (2005), and transformation of solar radiation data, recorded in kWm2 to W/m2, because this is the most commonly used unit), and; and iv) creation of fields used to filter data, i.e., Date, Hour, and DateHour, by using functions of R base (trunc; R Core Team 2020), and “lubridate”(hour; Grolemund & Wickham 2011) and “chron”(as.chron; James & Hornik 2020) R packages. Data compilation consisted of three steps: i) aligning series for each climate parameter; ii) assigning a value for each climate parameter to each timestep, by either copying the value recorded by a single station or calculating a mean value when two or more stations registered that parameter; absent data were filled with NA; and iii) writing the aggregated data series as an output (in format csv file).

Current Abstract These tabular data are summaries of climate related variables within catchments of the Chesapeake Bay watershed at the 1:24,000 scale using the xstrm methodology. Variables being counted as climate related include temperature and precipitation by both annual and monthly timesteps. Outputs include tabular comma-separated values files (CSVs) and parquet files for the local catchment and network summaries linked to the National Hydrography Dataset Plus High-Resolution (NHDPlus HR) catchments by NHDPlus ID. Local catchments are defined as the single catchment the data is summarized within. Network accumulation summaries were completed for each of the local catchments and their network-connected upstream catchments for select variables. The summarized data tables are structured as a single column representing the catchment id values (ie. NHDPlus ID) and the remaining columns consisting of the summarized variables. The downstream network summary type is not present within the dataset as no summaries were conducted using that summary type. Additionally, for a full description of the variables included within these summaries see xstrm_nhdhr_climate_chesapeake_baywide_datadictionary.csv in the attached files. The xstrm local summary methodology takes either raster or point data as input then summarizes those data by "zones", in this case the NHDPlus HR catchments. The network summaries then take the results from the local summaries and calculates the desired network summary statistic for the local catchment and its respective upstream or downstream catchments. As a note concerning use of these data, any rasters summarized within this process only had their cells included within a catchment if the center of the raster cell fell within the catchment boundary. However, the resolution of the input raster data for these summaries was considered to provide completely adequate coverage of the summary catchments using this option. If a confirmed complete coverage of a catchment is desired (even if a raster cell only is minimally included within the catchment) then it is recommended to rerun the xstrm summary process with the "All Touched" option set to “True”. These data were updated in September of 2024 where several variables unnecessary to the use of the data summaries were removed, incorrectly calculated area variables and all dependent variables were corrected, and several new variables were added to the dataset. See xstrm_nhdhr_climate_chesapeake_baywide_versionhistory.txt for further details. Further information on the Xstrm summary process can be found at the Xstrm software release pages: Xstrm: Wieferich, D.J., Williams, B., Falgout, J.T., Foks, N.L. 2021. xstrm. U.S. Geological Survey software release. https://doi.org/10.5066/P9P8P7Z0. Xstrm Local: Wieferich, D.J., Gressler B., Krause K., Wieczorek M., McDonald, S. 2022. xstrm_local Version-1.1.0. U.S. Geological Survey software release. https://doi.org/10.5066/P98BOGI9.

CSIRO, through the Water for a Healthy Country National Research Flagship, was contracted by the National Water Commission to assess the current and future water availability in 18 regions across the Murray-Darling Basin (MDB) considering climate change and other risks to water resources. Three climate scenarios were used for the hydrological modelling in the project: historical climate, recent climate and future climate. The historical climate scenario, is the baseline against which other scenarios are compared. This dataset is used to run rainfall-runoff models and is based on observed SILO Drill climate data from 1895-2006. The original SILO data has been removed due to licensing restrictions. The areal potential evapotranspiration (APET) and point potential evapotranspiration (PPET) were both derived. Further details are in the published report: Chiew FHS, Teng J, Kirono D, Frost AJ, Bathols JM, Vaze J, Viney NR, Young WJ, Hennessy KJ and Cai WJ (2008) Climate data for hydrologic scenario modelling across the Murray-Darling Basin. A report to the Australian Government from the CSIRO Murray-Darling Basin Sustainable Yields Project. CSIRO, Australia. https://publications.csiro.au/rpr/pub?pid=procite:88ee61de-92b9-4acc-b9b1-bf6de5d45ca9 Format Name : netCDF Format Version : 3.0 Dataset creation date: 2007

Lineage: The data set published here is incomplete. Due to license conditions associated with the SILO dataset, we are unable to publish the SILO component of the data set. The APET and PPET are estimated from the climate data using Morton's ET algorithm. The relative humidity (actual vapour pressure divided by saturation vapour pressure) is a by-product of the above calculations. P-T-ET is estimated using Mike Raupach's computer program - this variable is not used in the project. The rainfall, minimum temperature, maximum temperature, incoming solar radiation and actual vapour pressure come from SILO Data Drill from the Queensland Department of Environment and Resource Management. The SILO Data Drill provides surfaces of daily rainfall and other climate data interpolated from point measurements made by the Australian Bureau of Meteorology. Details at: http://www.derm.qld.gov.au/services_resources/item_list.php?category_id=8&page=2 \r Credit: The Murray-Darling Basin Sustainable Yields project was undertaken by CSIRO under the Australian Government's Raising National Water Standards Program, administered by the National Water Commission. Process Step:\r The original .csv data was processed along with the cell index data to produce a set of netCDF files. The conversion programs were written by CSIRO Information Management & Technology staff, with the output reviewed and tested by a range of stakeholders. Each netCDF file is stamped with the version of the software used to do the conversion and the code is available from https://netcdftools.svn.sourceforge.net/svnroot/netcdftools/trunk

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of minimum temperature (approx. 1.2 m from ground) across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Surfaces are developed using bivariate splines (latitude and longitude) with partial dependence upon elevation. Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using bivariate splines (latitude and longitude as spline variables) with partial dependence upon elevation. All models were fit and interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Daily anomalies were calculated by subtracting daily observations from climate normals and interpolated with full spline dependence upon latitude and longitude 5. Interpolated anomalies were added to interpolated climate normals to obtain the final daily surfaces. 6. Monthly surfaces are calculated as an aggregation of the daily product. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance (2DS-E): Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly minimum temperature RMSE = 0.96 °C Daily minimum temperature RMSE = 1.81 °C

Please refer to the linked manuscript for further details.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the national SILO data sets of climate station records. The parent dataset(s) is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Gippsland Product 2-1 Climate Data Geodatabase v01

This geodatabase contains the climate data used in the Gippsland Basin bioregion. The geodatabase includes the following datasets (average daily values): climate zones, evaporation, radiation, rainfall, evapotranspiration, maximum temperature and minimum temperature.

Purpose

To spatially represent interpolated mean annual rainfall (mm/yr) for the period 1957-2012- in Gippsland

Dataset History

The data was extracted from the national SILO data sets for 199 climate stations in the Gippsland region (see Lineage). The data was produced using ANUClim software (Hutchinson, 2001), using the interpolation methods discussed in Jeffery et al., 2001.

For a given climate station daily climate data is a combination of original measurements and rectified data to remove any gaps in the record using interpolation methods discussed in Jeffery et al. (2001).

To account for sparsely located climate stations within Gippsland, mean daily rainfall were scaled to each solution point within the study area according to interpolated mean annual rainfall spatial layer created using the ANUClim software (Hutchinson 2001).

Dataset Citation

Bioregional Assessment Programme (XXXX) Gippsland mean annual rainfall. Bioregional Assessment Derived Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/033319be-8058-48b5-ba2d-23f3931a25d4.

Dataset Ancestors

• Derived From Gippsland Product 2.1 climate data geodatabase v01

Abstract

The dataset was derived by the Bioregional Assessment Programme from the national SILO data sets of climate station records. The parent dataset(s) is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Gippsland Product 2-1 Climate Data Geodatabase v01

This geodatabase contains the climate data used in the Gippsland Basin bioregion. The data was extracted from the national SILO data sets for 199 climate stations in the Gippsland region. The geodatabase includes the following datasets (average daily values): climate zones, evaporation, radiation, rainfall, evapotranspiration, maximum temperature and minimum temperature. The data was produced using ANUClim software (Hutchinson, 2001), using the interpolation methods discussed in Jeffery et al., 2001.

Purpose

To spatially represent interpolated average daily solar radiation (MJ/m2) for the period 1957-2012- in Gippsland

Dataset History

For a given climate station daily climate data is a combination of original measurements and rectified data to remove any gaps in the record using interpolation methods discussed in Jeffery et al. (2001).

To account for sparsely located climate stations within Gippsland, average daily solar radiation ,were scaled to each solution point within the study area according to interpolated average daily solar radiation spatial layer created using the ANUClim software (Hutchinson 2001).

Dataset Citation

Bioregional Assessment Programme (XXXX) Gippsland average daily solar radiation. Bioregional Assessment Derived Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/3dfaf28b-1e7a-4e44-a8c4-d86c3b2e2f1d.

Dataset Ancestors

• Derived From Gippsland Product 2.1 climate data geodatabase v01

This point layer contains monthly summaries of daily temperatures (means, minimums, and maximums) and precipitation levels (sum, lowest, and highest) for the period January 1981 through December 2010 for weather stations in the Global Historical Climate Network Daily (GHCND). Data in this service were obtained from web services hosted by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc. The data were compiled at Esri from publicly available sources hosted and administered by NOAA. Because the ACIS data is updated and corrected on an ongoing basis, the date of collection for this layer was Jan 23, 2019. The following process was used to produce this dataset:Download the most current list of stations from ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt. Import this into Microsoft Excel and save as CSV. In ArcGIS, import the CSV as a geodatabase table and use the XY Event layer tool to locate each point. Using a detailed U.S. boundary extract the points that fall within the 50 U.S. States, the District of Columbia, and Puerto Rico. Using Python with DA.UpdateCursor and urllib2 access the ACIS Web Services API to determine whether each station had at least 50 monthly values of temperature data for each station. Delete the other stations. Using Python add the necessary field names and acquire all monthly values for the remaining stations. Thus, there are stations that have some missing data. Using Python Add fields and convert the standard values to metric values so both would be present. Thus, there are four sets of monthly data in this dataset: Monthly means, mins, and maxes of daily temperatures - degrees Fahrenheit. Monthly mean of monthly sums of precipitation and the level of precipitation that was the minimum and maximum during the period 1981 to 2010 - mm. Temperatures in 3a. in degrees Celcius. Precipitation levels in 3b in Inches. After initially publishing these data in a different service, it was learned that more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer these most precise coordinates are used. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. If your study area includes areas outside of the U.S., use the World Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer. The data in this layer come from the same source archive, however, they are not curated by the ACIS staff and may contain errors. Revision History: Initially Published: 23 Jan 2019 Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 3,222 of 9,636 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR data is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as: Esri, 2019: U.S. Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

SILO is a Queensland Government database containing continuous daily climate data for Australia from 1889 to present. Gridded datasets are constructed by spatially interpolating the observed point data. Continuous point datasets are constructed by supplementing the available point data with interpolated estimates when observed data are missing.

Purpose

SILO provides climate datasets that are ready to use. Raw observational data typically contain missing data and are only available at the location of meteorological recording stations. SILO provides point datasets with no missing data and gridded datasets which cover mainland Australia and some islands.

Dataset History

Lineage statement:

(A) Processing System Version History

* Prior to 2001

The interpolation system used the algorithm detailed in Jeffrey et al.1

* 2001-2009

The normalisation procedure was modified. Observational rainfall, when accumulated over a sufficient period and raised to an appropriate fractional power, is (to a reasonable approximation) normally distributed. In the original procedure the fractional power was fixed at 0.5 and a normal distribution was fitted to the transformed data using a maximum likelihood technique. A Kolmogorov-Smirnov test was used to test the goodness of fit, with a threshold value of 0.8. In 2001 the procedure was modified to allow the fractional power to vary between 0.4 and 0.6. The normalisation parameters (fractional power, mean and standard deviation) at each station were spatially interpolated using a thin plate smoothing spline.

* 2009-2011

The normalisation procedure was modified. The Kolmogorov-Smirnov test was removed, enabling normalisation parameters to be computed for all stations having sufficient data. Previously parameters were only computed for those stations having data that were adequately modelled by a normal distribution, as determined by the Kolmogorov-Smirnov test.

* January 2012 - November 2012

The normalisation procedure was modified:

o The Kolmogorov-Smirnoff test was reintroduced, with a threshold value of 0.1.

o Data from Bellenden Ker Top station were included in the computation of normalisation parameters. The station was previously omitted on the basis of having insufficient data. It was forcibly included to ensure the steep rainfall gradient in the region was reflected in the normalisation parameters.

o The elevation data used when interpolating normalisation parameters were modified. Previously a mean elevation was assigned to each station, taken from the nearest grid cell in a 0.05° 0.05° digital elevation model. The procedure was modified to use the actual station elevation instead of the mean. In mountainous regions the discrepancy was substantial and cross validation tests showed a significant improvement in error statistics.

o The station data are normalised using: (i) a power parameter extracted from the nearest pixel in the gridded power surface. The surface was obtained by interpolating the power parameters fitted at station locations using a maximum likelihood algorithm; and (ii) mean and standard deviation parameters which had been fitted at station locations using a smoothing spline. Mean and standard deviation parameters were fitted at the subset of stations having at least 40 years of data, using a maximum likelihood algorithm. The fitted data were then spatially interpolated to construct: (a) gridded mean and standard deviation surfaces (for use in a subsequent de-normalisation procedure); and (b) interpolated estimates of the parameters at all station locations (not just the subset having long data records). The parameters fitted using maximum likelihood (at the subset of stations having long data records) may differ from those fitted by the interpolation algorithm, owing to the smoothing nature of the spline algorithm which was used. Previously, station data were normalised using mean and standard deviation parameters which were taken from the nearest pixel in the respective mean and standard deviation surfaces.

* November 2012 - May 2013

The algorithm used for selecting monthly rainfall data for interpolation was modified. Prior to November 2012, the system was as follows:

o Accumulated monthly rainfall was computed by the Bureau of Meteorology;

o Rainfall accumulations spanning the end of a month were assigned to the last month included in the accumulation period;

o A monthly rainfall value was provided for all stations which submitted at least one daily report. Zero rainfall was assumed for all missing values; and

o SILO imposed a complex set of ad-hoc rules which aimed to identify stations which had ceased reporting in real time. In such cases it would not be appropriate to assume zero rainfall for days when a report was not available. The rules were only applied when processing data for January 2001 and onwards.

In November 2012 a modified algorithm was implemented:

o SILO computed the accumulated monthly rainfall by summing the daily reports;

o Rainfall accumulations spanning the end of a month were discarded;

o A monthly rainfall value was not computed for a given station if any day throughout the month was not accounted for - either through a daily report or an accumulation; and

o The SILO ad-hoc rules were not applied.

* May 2013 - current

The algorithm used for selecting monthly rainfall data for interpolation was modified. The modified algorithm is only applied to datasets for the period October 2001 - current and is as follows:

o SILO computes the accumulated monthly rainfall by summing the daily reports;

o Rainfall accumulations spanning the end of a month are pro-rata distributed onto the two months included in the accumulation period;

o A monthly rainfall value is computed for all stations which have at least 21 days accounted for throughout the month. Zero rainfall is assumed for all missing values; and

o The SILO ad-hoc rules are applied when processing data for January 2001 and onwards.

Datasets for the period January 1889-September 2001 are prepared using the system that was in effect prior to November 2012.

Lineage statement:

(A) Processing System Version History

No changes have been made to the processing system since SILO's inception.

(B) Major Historical Data Updates

* All observational data and station coordinates were updated in 2009.

* Station coordinates were updated on 26 January 2012.

Process step:

The observed data are interpolated using a tri-variate thin plate smoothing spline, with latitude, longitude and elevation as independent variables.4 A two-pass interpolation system is used. All available observational data are interpolated in the first pass and residuals computed for all data points. The residual is the difference between the observed and interpolated values. Data points with high residuals may be indicative of erroneous data and are excluded from a subsequent interpolation which generates the final gridded surface. The surface covers the region 112˚E - 154˚E, 10˚S - 44˚S on a regular 0.05˚ × 0.05˚grid and is restricted to land areas on mainland Australia and some islands.

Gridded datasets for the period 1957-current are obtained by interpolation of the raw data.

Gridded datasets for the period 1957-current are obtained by interpolation of the raw data. Gridded datasets for the period 1889-1956 were constructed using an anomaly interpolation technique. The daily departure from the long term mean is interpolated, and the gridded dataset is constructed by adding the gridded anomaly to the gridded long term mean. The long term means were constructed using data from the period 1957-2001. The anomaly interpolation technique is described in Rayner et al.6

The observed and interpolated datasets evolve as new data becomes available and the existing data are improved through quality control procedures. Modifications gradually decrease over time, with most datasets undergoing little change 12 months after the date of observation.

Dataset Citation

"Queensland Department of Science, Information Technology, Innovation and the Arts" (2013) SILO Patched Point data for Narrabri (54120) and Gunnedah (55023) stations in the Namoi subregion. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/0a018b43-58d3-4b9e-b339-4dae8fd54ce8.

This dataset contains the daily Arctic sea ice area (SIA) and sea ice extent (SIE) data for all CMIP6 models and the historical period based on the NOAA/NSIDC Climate Data Record (CDR) created for Heuzé and Jahn, The first ice-free day in the Arctic Ocean could occur before 2030, accepted, Nature Communications. This is a derived dataset based on publicly available underlying data: - For the CMIP6 data, the SIA and SIE data included here is based on the daily siconc and siconca CMIP6 model output freely available on the CMIP6 data portals (https://pcmdi.llnl.gov/CMIP6/). These pan-Arctic daily SIA and SIE were calculated north of 30N, on each model's native grid, using each models grid area data (areacello or areacella). SIA was defined as sea ice concentration multiplied by the grid cell area and summed over all grid cells. SIE was defined as the sum of the grid cell area for all grid cells where the sea ice concentration was larger than 0.15. All processed SIA and SIE data is included in this dataset, even if the model was later excluded from the analysis for one reason or another (see Heuzé and Jahn 2024, Methods section). All data included has the same number of days as the underlying model. The historical data spans 1980-2014 and can be found in the CMIP6_historical_data.zip file, and the scenario data spans 2015 to the end of the 21st century simulation, for multiple scenarios (SSPs), and can be found in CMIP6_ssp_data.zip. Files are provided as .zip files to make it easy to download all data at once, as the SIA and SIE data is saved in one file per model and ensemble member, and for the scenario simulations, also per ssp. - For the NOAA/NSIDC Climate Data Record (CDR), the SIA and SIE data included here is based on the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4, doi:10.7265/efmz-2t65, Meier et al 2021. The sea ice concentration is multiplied by the grid size of each grid box, for this data, 25x25 kilometers (km) = 625 kilometers squared (km2), and then summed over the full domain. In doing that, we include the interpolated data in the pole hole as included in the sea ice concentration data, but exclude all land/coastal grid points (i.e., values > 2.5 in the underlying data). As the filename indicates, we removed all leap year data from this data (dropped every Feb 29th) so that all years have 365 days. Note that while the file name says this data is for 19790101 to 20231231, it does indeed include 1978 as first year (so 1978-01-01-2023-12-31), with daily data starting on 1978-10-25 (nan before then). We did not change the name of the data file to still allow all archived scripts using this datafile to run. Scripts that work on this data associated with Heuzé and Jahn (2024) can be found at: https://zenodo.org/records/14008665, doi:10.5281/zenodo.14006059 References: Meier, W. N., F. Fetterer, A. K. Windnagel, and S. Stewart. 2021. NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 4. Boulder, Colorado, USA. NSIDC: National Snow and Ice Data Center https://doi.org/10.7265/efmz-2t65

Climate reanalysis and climate projection datasets offer the potential for researchers, students and instructors to access physically informed, global scale, temporally and spatially continuous climate data from the latter half of the 20th century to present, and explore different potential future climates. While these data are of significant use to research and teaching within biological, environmental and social sciences, potential users often face barriers to processing and accessing the data that cannot be overcome without specialist knowledge, facilities or assistance. Consequently, climate reanalysis and projection data are currently substantially under-utilised within research and education communities. To address this issue, we present two simple “point-and-click” graphical user interfaces: the Google Earth Engine Climate Tool (GEEClimT), providing access to climate reanalysis data products; and Google Earth Engine CMIP6 Explorer (GEECE), allowing processing and extraction of CMIP6 projection data, including the ability to create custom model ensembles. Together GEEClimT and GEECE provide easy access to over 387 terabytes of data that can be output in commonly used spreadsheet (CSV) or raster (GeoTIFF) formats to aid subsequent offline analysis. Data included in the two tools include: 20 atmospheric, terrestrial and oceanic reanalysis data products; a new dataset of annual resolution climate variables (comparable to WorldClim) calculated from ERA5-Land data for 1950-2022; and CMIP6 climate projection output for 34 model simulations for historical, SSP2-4.5 and SSP5-8.5 scenarios. New data products can also be easily added to the tools as they become available within the Google Earth Engine Data Catalog. Five case studies that use data from both tools are also provided. These show that GEEClimT and GEECE are easily expandable tools that remove multiple barriers to entry that will open use of climate reanalysis and projection data to a new and wider range of users.

[ Derived from parent entry - See data hierarchy tab ]

This is the Baltic and North Sea Climatology (BNSC) for the Baltic Sea and the North Sea in the range 47 ° N to 66 ° N and 15 ° W to 30 ° E. It is the follow-up project to the KNSC climatology. The climatology was first made available to the public in March 2018 by ICDC and is published here in a slightly revised version 2. It contains the monthly averages of mean air pressure at sea level, and air temperature, and dew point temperature at 2 meter height. It is available on a 1 ° x 1 ° grid for the period from 1950 to 2015. For the calculation of the mean values, all available quality-controlled data of the DWD (German Meteorological Service) of ship observations and buoy measurements were taken into account during this period. Additional dew point values were calculated from relative humidity and air temperature if available. Climatologies were calculated for the WMO standard periods 1951-1980, 1961-1990, 1971-2000 and 1981-2010 (monthly mean values). As a prerequisite for the calculation of the 30-year-climatology, at least 25 out of 30 (five-sixths) valid monthly means had to be present in the respective grid box. For the long-term climatology from 1950 to 2015, at least four-fifths valid monthly means had to be available. Two methods were used (in combination) to calculate the monthly averages, to account for the small number of measurements per grid box and their uneven spatial and temporal distribution: 1. For parameters with a detectable annual cycle in the data (air temperature, dew point temperature), a 2nd order polynomial was fitted to the data to reduce the variation within a month and reduce the uncertainty of the calculated averages. In addition, for the mean value of air temperature, the daily temperature cycle was removed from the data. In the case of air pressure, which has no annual cycle, in version 2 per month and grid box no data gaps longer than 14 days were allowed for the calculation of a monthly mean and standard deviation. This method differs from KNSC and BNSC version 1, where mean and standard deviation were calculated from 6-day windows means. 2. If the number of observations fell below a certain threshold, which was 20 observations per grid box and month for the air temperature as well as for the dew point temperature, and 500 per box and month for the air pressure, data from the adjacent boxes was used for the calculation. The neighbouring boxes were used in two steps (the nearest 8 boxes, and if the number was still below the threshold, the next sourrounding 16 boxes) to calculate the mean value of the center box. Thus, the spatial resolution of the parameters is reduced at certain points and, instead of 1 ° x 1 °, if neighboring values are taken into account, data from an area of 5 ° x 5 ° can also be considered, which are then averaged into a grid box value. This was especially used for air pressure, where the 24 values of the neighboring boxes were included in the averaging for most grid boxes. The mean value, the number of measurements, the standard deviation and the number of grid boxes used to calculate the mean values are available as parameters in the products. The calculated monthly and annual means were allocated to the centers of the grid boxes: Latitudes: 47.5, 48.5, ... Longitudes: -14.5, -13.5, … In order to remove any existing values over land, a land-sea mask was used, which is also provided in 1 ° x 1 ° resolution. In this version 2 of the BNSC, a slightly different database was used, than for the KNSC, which resulted in small changes (less than 1 K) in the means and standard deviations of the 2-meter air temperature and dew point temperature. The changes in mean sea level pressure values and the associated standard deviations are in the range of a few hPa, compared to the KNSC. The parameter names and units have been adjusted to meet the CF 1.6 standard.

Identifying environmental characteristics that limit species’ distributions is important for contemporary conservation and inferring responses to future environmental change. The Tasmanian native hen is an island-endemic flightless rail and a survivor of a prehistoric extirpation event. Little is known about the regional-scale environmental characteristics influencing the distribution of native hens, or how their future distribution might be impacted by environmental shifts (e.g., climate change). Using a combination of local fieldwork and species distribution modelling, we assess environmental factors shaping the contemporary distribution of the native hen, and project future distribution changes under predicted climate change. We find 37.2% of Tasmania is currently suitable for the native hens, owing to low summer precipitation, low elevation, human-modified vegetation, and urban areas. Moreover, in unsuitable regions, urban areas can create ‘oases’ of habitat, able to support populations with high breeding activity by providing resources and buffering against environmental constraints. Under climate change predictions, native hens were predicted to lose only 5% of their occupied range by 2055. We conclude that the species is resilient to climate change and benefits overall from anthropogenic landscape modifications. As such, this constitutes a rare example of a flightless rail to have adapted to human activity. Methods Local-scale factors measurements (fieldwork) We selected geographically distant populations presenting different rainfall profiles during the late-autumn to spring period, April-November 2019, as rainfall is an important factor for native hens’ survival and reproduction (Ridpath, 1972a; Lévêque, 2022): ‘East’ (wukaluwikiwayna/Maria Island National park; 42°34'51"S 148°03'56"E), ‘North’ (Narawntapu National park; 41°08'53"S 146°36'52"E), and ‘West’ (adjacent to the town of Zeehan [712 inhabitants]; 41°53'03"S 145°19'56"E). The period April-November corresponds to the six-month period preceding the middle point of the breeding season, generally used for native hens’ surveys (Goldizen et al., 1998; Lévêque, 2022). All three populations were surveyed between the 10th and the 22nd of November 2019 (late spring, in the middle point of the breeding season) to determine population structure (total number of groups, group composition [number of adults and young], and breeding activity). Each population was monitored over two to five days, depending on habitat complexity and extent of the population area, until all native hens in the area had been surveyed, i.e., when the territories’ structure was found identical at least four times for populations with no previous data (‘North’ and ‘West’), and at least two times in well-known populations (‘East’; Lévêque, 2022), over two different half-day. To align with methods used by Lévêque (2022), we used territory mapping (Bibby et al., 2000; Gibbons & Gregory, 2006) as native-hens maintain year-round territories, and population sizes were measurable with our survey methodology. Territory mapping consists of establishing the location of birds over a number of visits to obtain distinct clusters representing each territory. Boundaries are determined by vocal disputes between neighbours, which are frequent in native hens. During each survey, a minimum of two observers conducted repeated group identification, based on location, neighbours’ location, and number of individuals per group (from two to five individuals per group in this study). The number of individuals and their age category (fledgling, juvenile, or adult) were recorded per territory. The total pasture area surveyed per population, and the total pasture area occupied by native hens were: North population: 2.0 km2 (1.3 km2 occupied); West population: 1.5 km2 (0.7 km2 occupied); East population: 1.5 km2 (0.6 km2 occupied). We measured environmental characteristics in the native-hens’ territory following methods established by Goldizen et al. (1998) to obtain quantitative measures of i) protection cover, ii) water availability, and iii) food availability; these parameters are important for native hen reproduction (Goldizen et al., 1998).

Protection cover was determined as the length (m) of the interface between dense patches of bushes and pasture, used by native hens for hiding and protecting chicks against predators (Lévêque, 2022). It is an important parameter for breeding success (Goldizen et al., 1998). We measured the total protection cover available to native hens in each population using satellite data from Google Maps (www.google.com/maps, accessed on 09/12/2019). For measures of food availability (grass) on territories, we selected random transects of a total length of 1 m across all territories (East: n = 15, North: n = 26, West: n = 22). Measurements of vegetation characteristics were measured and recorded every 2 cm along each transect, including the percentage of i) total vegetation cover, ii) green vegetation, iii) vegetation cover that was grass, iv) vegetation cover that was moss, and v) the grass height (average length of grass blades). The same observer (LL) recorded all measures. Water availability on territories was recorded as territories that had access to water (running or stagnant) at the time the surveys were undertaken. Rainfall data was collected from the Bureau of Meteorology (B.O.M.; www.bom.gov.au/climate/data) at the three population sites: North population at Port Sorell (Narawntapu National Park – 4km away from the population site), West population at Zeehan (West Coast Pioneers Museum), East population at Maria Island (Darlington). Rainfall was reported as the amount of rainwater that had accumulated i) during the six months prior to breeding season midpoint (31/10/2019); following Goldizen et al. (1998)) and ii) during summer [December-February]. Information on recent droughts (on a 3- to 11-month period prior to 31/10/2019) was assessed using values on rainfall percentile deficiency (below the 10th percentile) from B.O.M. (http://www.bom.gov.au/climate/drought/#tabs=Rainfall-tracker). The 6-, 7-, and 12- month-periods were not accessible. B.O.M. defines the category ‘Serious deficiency’ as rainfall that “lies above the lowest five percent of recorded rainfall but below the lowest ten percent (decile range 1) for the period in question”, and ‘severe deficiency’ as “rainfall is among the lowest five percent for the period in question”.

Species Distribution Modelling Data preparation We collected presence-point data for native hens across Tasmania from the Atlas of Living Australia (ALA: www.ala.org.au; accessed 19 February 2021). We additionally included data from BirdLife Tasmania, the Department of Primary Industries, Water and Environment (DPIPWE) reports, and our personal observations, resulting in a total of 23,923 occurrences. Our study area included the Tasmanian mainland and nearby islands, however a large area from the south-west of Tasmania was removed where native hen distribution is not well documented, however, they are thought to be rare or absent in this region due to large proportion of button grass vegetation creating unsuitable habitat (Fig. S2). All subsequent analyses were undertaken in Program R v4.0.4 (R Core Team, 2021). Duplicates were removed by converting presence points into grid presences at 1 km2 resolution and retaining one native hen observation per grid (n = 2447 grid points after this step). Occurrences were visually inspected for any potential errors/outliers from outside Tasmania and Tasmanian islands: this removed seven false occurrences on King and Flinders islands and two observations in freshwater inland lakes (Lake Crescent and Great Lake). As true-absence records were mostly unavailable, we generated pseudoabsences for sites where other land-bird species had been recorded (indicating observation effort at that point), but without native hen detections (Hanberry et al., 2012; Amin et al., 2021; Barlow et al., 2021). Native hens are large-bodied, ground-dwelling, active in the day, and have a loud, distinct call, all of which accounts for a high detectability, if present at a location. We extracted these data from ALA, with 780,499 possible observations on the Tasmanian mainland and all nearby islands. We then excluded all grid cells with a native hen presence and removed any records within 3 km of native hen records: this value was chosen because it is the dispersal distance under which a native hen can naturally move outside of its territory (Ridpath, 1972a). This process resulted in 3,222 pseudoabsence grid points. Citizen-science datasets offer unique opportunities to study a species distribution using ‘crowd-sourced’ effort, however, they tend to be access-biased and have non-random, clustered observations, leading to overrepresentation of certain regions and biases towards some environmental conditions (usually near urban areas; Steen et al., 2021). One way to reduce spatial autocorrelation is to selectively de-cluster occurrences in biased areas using a pre-defined (minimum linear) Nearest Minimum-neighbour Distance NMD (Pearson et al. 2007). As un-urbanised, sparsely populated areas have the least spatial point clustering (and hence spatial bias), the average number of observations in low human densities areas provides the threshold number of records that can be used to tune and select the optimal NMD (Amin et al., 2021). Therefore, we subdivided our data on a grid of 25 km2 cells to be relevant to the metric of human density and used the median of population density index (excluding cells < 1 human/km2) to define thresholds for low and high density. Population density was extracted from the ‘2011 Census of Population and Housing across Australia’ (bit.ly/3bth7W9). ‘Low density’ was defined as < 6 people/km2 and ‘High density’ as

[NOTE - 11/24/2021: this dataset supersedes an earlier version https://doi.org/10.15482/USDA.ADC/1518654 ] Data sources. Time series data on cattle fever tick incidence, 1959-2020, and climate variables January 1950 through December 2020, form the core information in this analysis. All variables are monthly averages or sums over the fiscal year, October 01 (of the prior calendar year, y-1) through September 30 of the current calendar year (y). Annual records on monthly new detections of Rhipicephalus microplus and R. annulatus (cattle fever tick, CFT) on premises within the Permanent Quarantine Zone (PQZ) were obtained from the Cattle Fever Tick Eradication Program (CFTEP) maintained jointly by the United States Department of Agriculture (USDA), Animal Plant Health Inspection Service and the USDA Animal Research Service in Laredo, Texas. Details of tick survey procedures, CFTEP program goals and history, and the geographic extent of the PQZ are in the main text, and in the Supporting Information (SI) of the associated paper. Data sources on oceanic indicators, on local meteorology, and their pretreatment are detailed in SI. Data pretreatment. To address the low signal-to-noise ratio and non-independence of observations common in time series, we transformed all explanatory and response variables by using a series of six consecutive steps: (i) First differences (year y minus year y-1) were calculated, (ii) these were then converted to z scores (z = (x- μ) / σ, where x is the raw value, μ is the population mean, σ is the standard deviation of the population), (iii) linear regression was applied to remove any directional trends, (iv) moving averages (typically 11-year point-centered moving averages) were calculated for each variable, (v) a lag was applied if/when deemed necessary, and (vi) statistics calculated (r, n, df, P<, p<). Principal component analysis (PCA). A matrix of z-score first differences of the 13 climate variables, and CFT (1960-2020), was entered into XLSTAT principal components analysis routine; we used Pearson correlation of the 14 x 60 matrix, and Varimax rotation of the first two components. Autoregressive Integrated Moving Average (ARIMA). An ARIMA (2,0,0) model was selected among 7 test models in which the p, d, and q terms were varied, and selection made on the basis of lowest RMSE and AIC statistics, and reduction of partial autocorrelation outcomes. A best model linear regression of CFT values on ARIMA-predicted CFT was developed using XLSTAT linear regression software with the objective of examining statistical properties (r, n, df, P<, p<), including the Durbin-Watson index of order-1 autocorrelation, and Cook’s Di distance index. Cross-validation of the model was made by withholding the last 30, and then the first 30 observations in a pair of regressions. Forecast of the next major CFT outbreak. It is generally recognized that the onset year of the first major CFT outbreak was not 1959, but may have occurred earlier in the decade. We postulated the actual underlying pattern is fully 44 years from the start to the end of a CFT cycle linked to external climatic drivers. (SI Appendix, Hypothesis on CFT cycles). The hypothetical reconstruction was projected one full CFT cycle into the future. To substantiate the projected trend, we generated a power spectrum analysis based on 1-year values of the 1959-2020 CFT dataset using SYSTAT AutoSignal software. The outcome included a forecast to 2100; this was compared to the hypothetical reconstruction and projection. Any differences were noted, and the start and end dates of the next major CFT outbreak identified. Resources in this dataset: Resource Title: CFT and climate data. File Name: climate-cft-data2.csv Resource Description: Main dataset; see data dictionary for information on each column Resource Title: Data dictionary (metadata). File Name: climate-cft-metadata2.csv Resource Description: Information on variables and their origin Resource Title: fitted models. File Name: climate-cft-models2.xlsx Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel; XLSTAT,url: https://www.xlstat.com/en/; SYStat Autosignal,url: https://www.systat.com/products/AutoSignal/

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

The dataset contains shapefiles of the the total combined foot prints of all polygons for each of Baseline, CRDP polygons, and the spatial difference between the two (i.e. ACRD). These shapefiles are used in the surface water modelling to define the spatial extent of the mine workings for the three scenarios.

Purpose

Used only for report map display and area calculation purposes

Dataset History

Polygons from the source dataset were selected as follows:

for the total surface water modelling baseline footprint polygons were selected where SW_BL - 'yes'.

for the CRDP footprint polygons were selected where any of SW_BL, SW_CRDP or SW_ACRD = "yes"

The Baseline total footprint was removed (ERASE command) from the CRDP total footprint to create a ACRD total footprint.

Respective selections were dissolved to obtain overall total footprints.

Dataset Citation

Bioregional Assessment Programme (2016) HUN SW modelling total mine footprint v01. Bioregional Assessment Derived Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/b78597ba-4781-4b95-80c3-d6c11cb2e595.

Dataset Ancestors

• Derived From HUN Groundwater footprint polygons v01

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From HUN Historical Landsat Images Mine Foot Prints v01

• Derived From Historical Mining footprints DTIRIS HUN 20150707

• Derived From HUN Mine footprints for timeseries

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From HUN Historical Landsat Derived Mine Foot Prints v01

• Derived From HUN SW footprint shapefiles v01

• Derived From Climate model 0.05x0.05 cells and cell centroids

[ Derived from parent entry - See data hierarchy tab ]

This is the Baltic and North Sea Climatology (BNSC) for the Baltic Sea and the North Sea in the range 47 ° N to 66 ° N and 15 ° W to 30 ° E. It is the follow-up project to the KNSC climatology. The climatology was first made available to the public in March 2018 by ICDC and is published here in a slightly revised version 2. It contains the monthly averages of mean air pressure at sea level, and air temperature, and dew point temperature at 2 meter height. It is available on a 1 ° x 1 ° grid for the period from 1950 to 2015. For the calculation of the mean values, all available quality-controlled data of the DWD (German Meteorological Service) of ship observations and buoy measurements were taken into account during this period. Additional dew point values were calculated from relative humidity and air temperature if available. Climatologies were calculated for the WMO standard periods 1951-1980, 1961-1990, 1971-2000 and 1981-2010 (monthly mean values). As a prerequisite for the calculation of the 30-year-climatology, at least 25 out of 30 (five-sixths) valid monthly means had to be present in the respective grid box. For the long-term climatology from 1950 to 2015, at least four-fifths valid monthly means had to be available. Two methods were used (in combination) to calculate the monthly averages, to account for the small number of measurements per grid box and their uneven spatial and temporal distribution: 1. For parameters with a detectable annual cycle in the data (air temperature, dew point temperature), a 2nd order polynomial was fitted to the data to reduce the variation within a month and reduce the uncertainty of the calculated averages. In addition, for the mean value of air temperature, the daily temperature cycle was removed from the data. In the case of air pressure, which has no annual cycle, in version 2 per month and grid box no data gaps longer than 14 days were allowed for the calculation of a monthly mean and standard deviation. This method differs from KNSC and BNSC version 1, where mean and standard deviation were calculated from 6-day windows means. 2. If the number of observations fell below a certain threshold, which was 20 observations per grid box and month for the air temperature as well as for the dew point temperature, and 500 per box and month for the air pressure, data from the adjacent boxes was used for the calculation. The neighbouring boxes were used in two steps (the nearest 8 boxes, and if the number was still below the threshold, the next sourrounding 16 boxes) to calculate the mean value of the center box. Thus, the spatial resolution of the parameters is reduced at certain points and, instead of 1 ° x 1 °, if neighboring values are taken into account, data from an area of 5 ° x 5 ° can also be considered, which are then averaged into a grid box value. This was especially used for air pressure, where the 24 values of the neighboring boxes were included in the averaging for most grid boxes. The mean value, the number of measurements, the standard deviation and the number of grid boxes used to calculate the mean values are available as parameters in the products. The calculated monthly and annual means were allocated to the centers of the grid boxes: Latitudes: 47.5, 48.5, ... Longitudes: -14.5, -13.5, … In order to remove any existing values over land, a land-sea mask was used, which is also provided in 1 ° x 1 ° resolution. In this version 2 of the BNSC, a slightly different database was used, than for the KNSC, which resulted in small changes (less than 1 K) in the means and standard deviations of the 2-meter air temperature and dew point temperature. The changes in mean sea level pressure values and the associated standard deviations are in the range of a few hPa, compared to the KNSC. The parameter names and units have been adjusted to meet the CF 1.6 standard.