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. These shapefiles are used to create the maps in NAM2.6.2. They are mostly derived from the input files for the groundwater model. The shape files infclude: ag_extraction: These are points …Show full descriptionAbstract 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. These shapefiles are used to create the maps in NAM2.6.2. They are mostly derived from the input files for the groundwater model. The shape files infclude: ag_extraction: These are points that represent the location of groundwater bores used for agricultural extraction. boundaries: These are line shape files used for defining the location and extent of lateral boundary conditions of different stratigraphic layers of the groundwater model coal extraction: These are polygon shape files providing the areal extent of the baseline and ACRD coal mines in the Namoi subregion that are including in the groundwater model. grid: Polygon shape file representing the mesh of the groundwater model. It also include points that represent the midpoints of each model cell and the suset that represents the model nodes that outcrop. obs: Shape file of observation bores, the data from which is used for constraining the groundwater model. River: Set of shape files containing the AWRA catchments, AWRA-R nodes, network of rivers and creeks classified into important reaches and non important reaches based on the distance form the CRDP areas, extent of flood and irrigation recharge Purpose The purpose of this dataset is to create pretty pictures. The actual model inputs files are archived separately. These shapefiles are used along with the software ALGOMESH to generate inputs for the models including model initial and boundary conditions. Thease are also used to generate maps in the product 2.6.2 Dataset History Some of the components of this dataset are source data. These include the locations of groundwater and observation bores, river and creek network. Other components are derived: The groundwater model mesh and model cell centres are generated in the ALGOMESH software and exported as shape file. The coal mine extents are derived from digitized mine footprints. Dataset Citation Bioregional Assessment Programme (2016) Namoi groundwater model input shapefiles. Bioregional Assessment Derived Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/fb22671f-8b47-48e2-9fcd-232543fb8ad6. Dataset Ancestors Derived From Murray-Darling Basin floodplain inundation 1 in 100 year extent Derived From Bioregional_Assessment_Programme_Catchment-scale Land Use Management (CLUM) Derived From NSW Office of Water - National Groundwater Information System 20141101v02 Derived From Namoi groundwater observation bores Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From GEODATA TOPO 250K Series 3 Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Detailed street boundaries for Baltimore City. No metadata was provided with this dataset; the UVM Spatial Analysis Lab has attempted to evaluate this dataset and generate metadata. This dataset depicts the linear boundaries for street and paved areas in Baltimore City and has an extremely high degree of positional accuracy. For the best available transportation data use the Roads_GDT_MSA dataset.

   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.


   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Single subway route for Baltimore City that extends into Baltimore County. No metadata was provided with this dataset; the UVM Spatial Analysis Lab has attempted to evaluate this dataset and generate metadata. There are no attributes associated with this dataset. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Shapefiles of canopy disturbances for the 50-ha Smithsonian ForestGEO plot on Barro Colorado Island, Panama, for 46 successive time intervals (47 dates) between 2 October 2014 and 28 November 2019. We defined a canopy disturbance as a substantial decrease in canopy height in a contiguous patch of canopy occurring over one measurement interval. We identified canopy disturbances through a combination of analysis of the canopy surface model changes and visual interpretation of the orthomosaics. We first differenced surface elevation models for successive dates to obtain a raster of the canopy height changes for the associated interval. We then pre-delineated major canopy disturbances by filtering for areas in which canopy height decreased more than 10 m in contiguous areas of at least 25 m2, and that had an area-to-perimeter ratio greater than 0.6. We note that 25 m2 is the minimum gap area used in previous studies of this site by Brokaw (1982) and Hubbell et al. (1999). The area-to-perimeter condition removes artifacts associated with slight shifts in the measured positions of individual trees from one image set to another, whether due to wind or alignment errors (note that this criterion involves a combination of shape and size). Finally, we systematically examined 1-ha square subplots for each pair of successive dates and edited the pre-delineated polygons, removed false positives, and added visible new canopy disturbances that were not previously delineated (whether because they were too small in area or in canopy height drop). We also classified disturbances as being due to treefalls (a whole previously live tree fell, creating a clearly visible gap on the forest floor, or the whole live crown disappeared), branchfalls (a portion of a live crown broke), or standing dead trees disintegrating based on visual inspection of the orthomosaics. Before and after orthomosaic classifications are shown in Figure S2 of the associated Biogeosciences article by Araujo et al. These data are licensed under CC BY, meaning use of the data is allowed so long as attribution is given via citation. These data should be cited either as an individual dataset or as part of the larger collection: Araujo, Raquel F., Samuel Grubinger, Milton Garcia, Jonathan P. Dandois, and Helene C. Muller-Landau. 2021. Shapefiles of canopy disturbances for the 50-ha plot on Barro Colorado Island, Panama, for 2014-2019. Smithsonian Figshare. DOI:10.25573/data.14417915orAraujo, Raquel F., Samuel Grubinger, Milton Garcia, Jonathan P. Dandois, and Helene C. Muller-Landau. 2021. Collection of datasets: Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Smithsonian Figshare. DOI: 10.25573/data.c.5389043These datasets were used in the following peer-reviewed journal article:Araujo, R. F., S. Grubinger, C. H. S. Celes, R. I. Negrón-Juárez, M. Garcia, J. P. Dandois, and H. C. Muller-Landau. 2021. Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Biogeosciences.The code used to analyze these data for this article are available in GitHub, at https://github.com/Raquel-Araujo/gap_dynamics_BCI50haAuthor contribution for datasets for 2014-2015: Helene C. Muller-Landau conceived the research, wrote the grant proposal that funded the research, and designed data collection. Jonathan Dandois constructed the drones, led drone data collection, performed photogrammetry processing, and did preliminary horizontal alignment. Samuel Grubinger finalized horizontal and vertical alignment and identified canopy disturbances. Raquel F. Araujo revised canopy disturbances and classified them as branchfalls, treefalls, or standing dead trees. Author contribution for datasets for 2016-2019: Helene C. Muller-Landau conceived the research and designed the data collection. Milton Garcia led drone data collection and processed drone imagery. Raquel F. Araujo performed horizontal and vertical alignment, identified canopy disturbances, and classified disturbances as branchfalls, treefalls, or standing dead trees. Acknowledgments: We thank Marino Ramirez, Pablo Ramos, Paulino Villareal and others for assistance with drone data collection; and Milton Solano for assistance with data processing and organization. We gratefully acknowledge the financial support of the Smithsonian Institution Competitive Grants Program for Science; the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; and the Smithsonian Tropical Research Institute fellowship program. Kristina Anderson-Teixeira, Stephanie Bolman, Richard Condit, Stuart Davies, Matteo Detto, Jefferson Hall, Patrick Jansen, Stefan Schnitzer, Edmund Tanner, and S. Joseph Wright were co-PIs on the original Smithsonian proposal, and we thank them for their contributions to the proposal and input on the research.

This feature class/shapefile represents electric power control areas. Control Areas, also known as Balancing Authority Areas, are controlled by Balancing Authorities, who are responsible for monitoring and balancing the generation, load, and transmission of electric power within their region, often comprised of the retail service territories of numerous electric power utilities. Each control area is interconnected with neighboring ones to facilitate emergency support, coordinated operations, and power purchases and sales. The following updates have been made since the previous release: 1 feature removed.

Brownfields_BACI File Geodatabase Feature Class Thumbnail Not Available Tags Biophysical Resources, Air, Land, Water, BES, Brownfields, Pollution Summary BES Analysis Description Brownfield parcels in Baltimore City. No metadata was provided with this dataset; the UVM Spatial Analysis Lab has attempted to evaluate this dataset and generate metadata. This dataset appears to have a high degree of positional accuracy based on comparisons with high resolution imagery. Credits Maryland Department of the Environment Use limitations BES research only. Extent West -76.661468 East -76.530941 North 39.334224 South 39.235433 Scale Range There is no scale range for this item.

Geospatial datasets were developed to estimate the depth to the top of the Dakota Sandstone in feet below land surface datum within the Ute Mountain Ute Reservation in Colorado. This study was completed by the U.S. Geological Survey (USGS) in cooperation with the Ute Mountain Ute Tribe. One dataset was created for the contours showing the altitude (in feet) of the top of the Dakota Sandstone (shapefile Kd_talt_hand), and a second dataset was created for polygons representing the outcrops of the Dakota Sandstone (shapefile Dakota_outcrop_poly). These two datasets were used in combination with USGS digital elevation models (DEM) to create a dataset for the depth of the top of the Dakota Sandstone below the land surface contoured at a 100-foot interval (shapefile kd_depth_ci100). The kd_depth_ci100 dataset was used to generate a figure showing the generalized depth to the top of the Dakota Sandstone in feet below land surface in Bauch and Arnold (2019).

Parking lots along with related tax and owner information for Baltimore City. No metadata was provided with this dataset; the UVM Spatial Analysis Lab has attempted to evaluate this dataset and generate metadata. When compared to high-resolution imagery and other transportation datasets positional inaccuracies were observed. As a result caution should be taken when using this dataset. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

This layer contains the heat flow sites and data of the State of Colorado compiled from the International Heat Flow Commission (IHFC) of the International Association of Seismology and Physics of the Earth's Interior (IASPEI) global heat flow database (www.heatflow.und.edu/index2.html). The data include different items: Item number, descriptive code, name of site, latitude and longitude, elevation, depth interval, number of temperature data, temperature gradient, number of conductivity measurement, average conductivity, number of heat generation measurements, average heat production, heat flow, number of individual sites, references, and date of publication.

Abstract The dataset was derived by the Bioregional Assessment Programme from the NSW Office of Water surface water database. 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. This dataset shows the location of stream gauges within the Gloucester subregion. Site details such as the period of data collection, catchment area and elevation are …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from the NSW Office of Water surface water database. 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. This dataset shows the location of stream gauges within the Gloucester subregion. Site details such as the period of data collection, catchment area and elevation are attached as attributes. Timeseries streamflow data for these gauges are stored in separate datasets. Purpose The dataset was used to identify locations of streamflow gauges. Dataset History The dataset was created using the NSW Office of Water surface water data. The data were imported into Arcgis using location information and a shapefile created from the selection of gauges identified as meeting the criteria. The processing steps are as follows Generate a shapefile using the gauge data obtained from the NSW Office of Water Select those gauges with streamflow data length more than 10 years since 1980 and save new shapefile Dataset Citation Bioregional Assessment Programme (XXXX) Selected streamflow gauges within and near the Gloucester subregion. Bioregional Assessment Derived Dataset. Viewed 14 June 2016, http://data.bioregionalassessments.gov.au/dataset/06cb7374-03be-48c4-846c-ba56e1e42932. Dataset Ancestors Derived From NSW Streamflow data obtained from Office of Water for the Gloucester subregion

A US Geological Survey Next Generation Water Observing Systems (NGWOS) Research and Development 'testbed' site was established near the Kankakee River in LaPorte County, IN for the purpose of quantifying the effects of groundwater and surface water interactions on the fate and transport of excess nutrients, specifically nitrate. A Fiber Optic Distributed Temperature Sensing (FO-DTS) survey was conducted within the stream reach adjacent to a well cluster to pinpoint areas of discrete groundwater discharge to the river. DTS data were used to identify locations where longer-term deployments of vertical temperature profilers (VTP) were conducted to indirectly estimate vertical groundwater discharge (m/d) at the shallow streambed interface through time using collected temperature time-series. Point measurements of streambed thermal properties were made using a Tempos thermal property analyzer where the DTS survey suggested discrete groundwater seepage was occurring. This was done in efforts to support the future modeling of groundwater discharge rates (1D, i.e., L/T) using collected vertical temperature profiles from installed VTP's. VTP data from Kankakee are still actively being collected so these data are not included in this data release, however; thermal properties of the sediments are included. Seepage meter measurements were made at select locations on the streambed to better understand flux occurring at the streambed interface using direct measurements of discharge. When these meters were emplaced piezometers were installed next to them for determining vertical hydraulic gradients. This information was used to determine estimates of vertical hydraulic conductivity when combined with measured groundwater discharge rates from the seepage meters. Piezometers were pumped and instantaneous water quality information was collected using water quality sondes (YSI EXO2) in the field. Continuous water level information was also collected (In-Situ LevelTroll and AquaTroll) in some of the piezometers to monitor diurnal changes that might occur during the deployment period. This data release includes 5 compressed folders: 1) FO_DTS_PROCESSED_KANKAKEE.zip includes all processed FO-DTS data including a local readme.txt file that explains the contents in detail. 2) FO_DTS_RAW_KANKAKEE.zip includes all raw FO-DTS data including a local readme.txt file that explains the contents in detail. 3) SEEPAGE_AND_GRADIENTS_KANKAKEE.zip includes all the information related to piezometers and seepage meters including a local readme.txt file that explains the contents in detail. This folder also contains data on the thermal properties of the streambed sediments. 4) KANKAKEE_DTSCABLELINE.zip includes an ESRI ArcGIS shapefile and associated files that show the FODTS line layout as interpolated between GPS points collected along the line for georeferencing 5) KANKAKEE_MEASUREMENT_LOCATIONS includes an ESRI ArcGIS shapefile and associated files that show locations where measurements contained in this data release were made. References for methods and data contained in this data release: Barclay, J. R., Briggs, M. A., Moore, E. M., Starn, J. J., Hanson, A. E. H., & Helton, A. M. (2022). Where groundwater seeps: Evaluating modeled groundwater discharge patterns with thermal infrared surveys at the river-network scale. Advances in Water Resources, 160. https://doi.org/10.1016/j.advwatres.2021.104108 Briggs, M. A., Jackson, K. E., Liu, F., Moore, E. M., Bisson, A., & Helton, A. M. (2022). Exploring Local Riverbank Sediment Controls on the Occurrence of Preferential Groundwater Discharge Points. Water, 14(1). https://doi.org/10.3390/w14010011 Tyler, S.W., Selker, J.S., Hausner, M.B., Hatch C.E., Torgersen, T., Thodal C.E., Schladow, G.S. (2008). Environmental temperature sensing using Raman spectra DTS fiber-optic methods. Water Resources Research, Vol. 45. doi:10.1029/2008WR007052, 2009 Rosenberry & Hayashi (2013). Assessing and Measuring Wetland Hydrology, Chpt. 3. Cunningham, W.L., and Schalk, C.W., comps., 2011, Groundwater Technical Procedures of the U.S. Geological Survey: U.S. Geological Survey Techniques and Methods, book 1, chap. A1, 151 p.https://doi.org/10.3133/tm1A1

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.

Baltimore City Highways. No metadata was provided with this dataset; the UVM Spatial Analysis Lab has attempted to evaluate this dataset and generate metadata. When compared to high-resolution imagery and detailed street data offsets as great as 50m were observed. Due to positional accuracy errors this dataset should be used with caution. There are no attributes associated with this dataset. For the best available transportation data use the Roads_GDT_MSA dataset. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Global map of open permanent water bodies at 300m spatial resolution derived from the full ENVISAT-ASAR dataset between 2005 and 2010.

In an attempt to improve the characterization of inland water bodies in global LC products, a SAR-based approach has been implemented. Multi-temporal acquisitions of Envisat ASAR Wide Swath Mode with local gap fillers based on Image Mode and Global Monitoring Mode from the years 2005 to 2010, MERIS data and auxiliary datasets have been used to generate a single epoch map of permanent open water bodies at 300 m.

Static map of stable open water bodies at 300m spatial resolution resulting from a land/water classification based on Envisat ASAR, SRTM-SWBD and MERIS data. The water pixels of this map correspond to the class "Water Bodies" of the CCI-LC Maps.

The product consists of 3 layers:

Map land/permanent water classification at 300m spatial resolution. Legend : 1-Land, 2-Water,
NObsImsWS number of observations originating from the ASAR Wide Swath Mode + Image Monitoring Mode imagery,
NObsImsGM number of observations originating from the ASAR global monitoring mode imagery.

Subway Stations for a single subway route that runs from Baltimore City to Baltimore County. No metadata was provided with this dataset; the UVM Spatial Analysis Lab has attempted to evaluate this dataset and generate metadata. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

This feature class/shapefile is for the Homeland Infrastructure Foundation Level Database (HIFLD) (https://gii.dhs.gov/HIFLD) as well as the Energy modelling and simulation community. This feature class/shapefile represents electric power plants. Power plants are all the land and land rights, structures and improvements, boiler or reactor vessel equipment, engines and engine-driven generators, turbo generator units, accessory electric equipment, and miscellaneous power plant equipment are grouped together for each individual facility. Included are the following plant types: hydroelectric dams, fossil fuel (coal, natural gas, or oil), nuclear, solar, wind, geothermal, and biomass.View Dataset on the Gateway

This directory contains files related to the scientific research project of Luc van Dijk at the Department of Earth, Energy, and Environment, University of Calgary. The project title is "High-resolution, Decadal to Weekly Geomorphic Change Analysis of the Elbow River in Calgary, using Multi-temporal Lidar and Repeat Terrestrial Laser Scanning". This project in the field of geomorphology was a collaboration between the University of Calgary and Utrecht University in the Netherlands. The project was completed on October 27, 2023. Below is a description of the files in this directory.

DisplacementVolumeDistributions_TLS.xlsx

Excel file containing tabular data of the normalized sediment displacement volumes that were obtained using TLS. Each tab in the Excel file represents a period of interest in 2023. The data in this file were used to generate the 'histogram-like' figures in the report.

DoD_rasters.zip

Folder containing the aerial lidar DEMs of Difference (DoDs) for each period of interest. The DoDs are 'waterless', i.e. the water surface is masked. The suffix of the file name before the file extension (e.g., ..._10cm.tif) indicates the maximum REM value that was used for the automated masking of the water surface extent (see report section 3.1.2). If the file name contains "large", it refers to the upstream greater area (see report section 3.1.3).

Within this folder is another folder called 'Clipped2AOIs'. This folder contains the same DoDs, but covering only the extents of the sites of interest ('AOIs' = Areas Of Interest).

FilteredPointClouds_TLS.zip

Folder containing the processed and filtered point clouds that were acquired throughout the summer of 2023 using TLS. These point clouds have been pre-processed and filtered to remove vegetation (see report section 3.2). They are grouped in sub-folders per acquisition date. The filenames are numbered to location, i.e. 'elbow1', 'elbow2', 'elbow3' and 'elbow4'. These correspond to the sites of interest: Glenmore Dam, golf club, Sandy Beach and Riverdale, respectively.

PythonScripts_Discharge_Rainfall.zip

Folder containing the Python scripts that were made to process the discharge and rainfall data that were sourced from Environment Canada and The City of Calgary (see report section 3.3). The scripts themselves contain descriptions of their purpose.

PythonScripts_DisplacementVolumeAnalysis.zip

Folder containing the Python scripts that were made to process and analyze the aerial lidar DoDs and the TLS rasterized difference point clouds (M3C2 output). The 'convert2pickle' scripts converted the sizable rasters to smaller pickle files, which were easier and faster to work with. The 'chart' scripts load the data from the pickle files, analyze them and produce the 'histogram-like' figures in the report. The scripts themselves contain descriptions of their purpose.

RainfallDischargeData.xlsx

Excel file containing the discharge and rainfall data from Environment Canada and The City of Calgary. The data came from different sources in different formats and were combined into this single table.

RasterizedDifferencedPointClouds_M3C2.zip

Folder containing the rasterized results of the differenced TLS point clouds (M3C2 output) (see report section 3.2.4). The filenames are numbered to location, i.e. 'Elbow1', 'Elbow2', 'Elbow3' and 'Elbow4'. These correspond to the sites of interest: Glenmore Dam, golf club, Sandy Beach and Riverdale, respectively. The numeric sequence in the file name indicates the start and end date of the change analysis in a 'mm-dd' format. The suffixes '_dist', '_unc' and '_sig' refer to the three output layers of the M3C2 algorithm: distance, uncertainty and significance of change. The main files of interest are the '.tif' files. Files sharing the same name, but with different extensions (.tfw, .tif.aux.xml, .tif.xml) are supplementary/auxiliary files for the '.tif' file, generated by ArcGIS Pro.

ScarpsOfInterest_shapefile.zip

Folder containing a polygon shapefile describing the extents and locations of the sites of interest. The main file of interest is the '.shp' file. The other files with the same name, but different extensions (.cpg, .dbf, .prj, .sbn, .sbx, .shp.xml, .shx) are supplementary/auxiliary files for the '.shp' file, generated by ArcGIS Pro.

Power Plants in the U.S.This feature layer, utilizing data from the Energy Information Administration (EIA), depicts all operable electric generating plants by energy source in the U.S. This includes plants that are operating, on standby, or short- or long-term out of service. The data covers all plants with a combined nameplate capacity of 1 MW (Megawatt) or more.Per EIA, "The United States uses many different energy sources and technologies to generate electricity. The sources and technologies have changed over time, and some are used more than others. The three major categories of energy for electricity generation are fossil fuels (coal, natural gas, and petroleum), nuclear energy, and renewable energy sources. Most electricity is generated with steam turbines using fossil fuels, nuclear, biomass, geothermal, and solar thermal energy. Other major electricity generation technologies include gas turbines, hydro turbines, wind turbines, and solar photovoltaics."Madison Gas & Electric Company, Sycamore Power PlantData currency: This cached Esri service is checked monthly for updates from its federal source (Power Plants)Data modification: NoneFor more information, please visit:Electricity ExplainedEIA-860, Annual Electric Generator ReportEIA-860M, Monthly Update to the Annual Electric Generator ReportEIA-923, Power Plant Operations ReportSupport documentation: MetadataFor feedback: ArcGIScomNationalMaps@esri.comEnergy Information AdministrationPer EIA, "The U.S. Energy Information Administration (EIA) collects, analyzes, and disseminates independent and impartial energy information to promote sound policymaking, efficient markets, and public understanding of energy and its interaction with the economy and the environment."

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 New South Wales Office of Water (NOW) provides to the Bureau of Meteorology a copy of their Hydstra surface water database. This dataset is a shapefile generated from the locction data supplied as apart of the the Hydstra data.

Dataset History

The dataset was initially created by the BoM in 15 January 2015. This data has been extracted by the Water Data Support team in the Bureau of Meteorology for selected gauges in the Sydney Basin from Bureau internal systems. Data has been originally supplied to the Bureau by NSW office of Water through the Water Act Regulations, 2008.

Data is supplied by BoM in CSV format and location data used the generate a point shapefile to display monitoring sites in several maps.

Dataset Citation

Bioregional Assessment Programme (XXXX) SSB Hydstra gauges v01. Bioregional Assessment Derived Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/187b3e86-4fa3-4f47-8cba-48af7dfa50fd.

Dataset Ancestors

• Derived From SYD ALL Raw Stream Gauge Data BoM v01

• Derived From SYD ALL Unified Stream Gauge Data v01