a, , and are reflectance of blue, red, and NIR band in the HJ-1A CCD optical image, respectively.Calculation for vegetation indices a.

An estimation of RMSE corresponding to interpolation methods.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.

Layers in this dataset represent Public Land Survey System subdivisions for Canadian County. Included are Townships, Sections, Quarter Sections and Government Lots. This data was created from 2019 to 2021 as part of a project to update county parcel data in partnership with ProWest & Associates (https://www.prowestgis.com/) and CEC Corporation (https://www.connectcec.com/). Corners were located to the quarter section level and additional corners were determined for the South Canadian River meanders based on the original government surveys. Quarter section corners were located using Certified Corner Records ( filed by Oklahoma licensed professional surveyors with the Oklahoma Department of Libraries where those records included coordinates. When a corner record could not be found or did not include coordinates, other interpolation methods were employed. These included connecting known corner record locations to unknown corners using data from filed subdivisions or from highway plans on record with the Oklahoma Department of Transportation. Where no corner records with coordinates were available and no interpolation methods could be used, aerial inspection was used to locate corners as the last option.Corner location accuracy varies as the method of locating the corner varies. For corners located using Certified Corner Records, accuracy is high depending on the age of the corner record and can possibly be less than 1 U.S. Foot. For corners located using interpolation methods, accuracy depends on the additional material used to interpolate the corner. In general, newer subdivisions and highway plans yield higher accuracy. For meander corners located using original government surveys, accuracy will be low due to the age of those surveys which date to the 1870's at the earliest. Additionally, corners that were located with aerials as the last available option cannot be assumed to be accurate.The data was built at the quarter section level first by connecting located corners and larger subdivisions were created from the quarter sections. For townships that extend into Grady County, township lines were only roughly located outside sections not in Canadian County.

Results of the evaluation criterions of spatial interpolation method for single factor pollution index and comprehensive pollution indices.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

Sediment samples were collected in the summer of 2017 over the entire study area at 500 m spacing and in the channels. Grain size analysis of the collected sediment samples was conducted using a Malvern Mastersizer 2000© laser beam grain sizer. The median grain size (d50 in µm) at each sample location was then interpolated over the entire study area. Interpolation was made with the SAGA-GIS software (version 7.9.0). According to the cross-validation error, the best method for the D50mm interpolation was the Modified Quadratic Shepard. The 10-fold validation provided an R² of 0.93, an NMRSE of 24.5, an RMSE of 83.9, an MRE of 7039 with the fit set to “node”, the quadratic neighbours and weighting neighbours set to 50 and the spatial resolution was set to 10 m. The resultant interpolation map was then categorized following the nomenclature of Blott and Pye (2001) provided in the file style_sediment_map.qml.

Library of Wroclaw University of Science and Technology scientific output (DONA database)

Price inflation has outbalanced the income of residents and buyers in major post-industrial city-regions, and real estate has become an important driver of these inequalities. In a context of a resilient inflation of home values during the last two decades in the greater Paris Region, it is critical to examine housing price dynamics to get a better understanding of socioeconomic segregation. This paper aims at presenting spatial analysis of the dynamics of segregation pertaining to inflation, analyzing price and sellers and buyers data. Using interpolation techniques and multivariate analysis, the paper presents a spatial analysis of property-level data from the Paris Chamber of Notaries (1996-2012) in a GIS (159,000 transactions in suburban areas, single family homes only). Multivariate analysis capture price change and local trajectories of occupational status, i.e. changes in balance between inward and outward flows of sellers and buyers. We adopt a method that fits the fragmented spatial patterns of suburbanization. To do so, we remove the spatial bias by means of a regular 1-km spatial grid, interpolating the variables within it, using a time-distance matrix. The main results are threefold. We document the spatial patterns of professionalization (a rise of executives, intermediate occupation and employees) to describe the main trends of inward mobility in property ownership in suburbs, offsetting the outward mobility of retired persons. Second, neighborhood trajectories are related the diverging patterns of appreciation, between local contexts of accumulation with a growth of residential prices, and suburbs with declining trends. The maturity of suburbanization yields a diversified structure of segregation between the social groups, that do not simply oppose executives vs. blue collar suburbs. A follow-up research agenda is finally outlined.

Basic Geographic Data Set of Resources and Environment in Central and Western Asia Region, includes six parts: administrative divisions map, topographic and geomorphological map, river system maps, precipitation map, temperature map and potential evapotranspiration map. The precipitation and temperature datasets are interpolated based on the ground observations, while the potential evapotranspiration dataset is calculated based on the Penman-Monteith equation. The precipitation, temperature and potential evapotranspiration datasets are resampled from the original 0.5° CRU dataset by using the linear interpolation method in ArcGIS software. This dataset is made based a large number of gauge observations with good quality control and homogeneity check. The results of the related studies (Deng and Chen, 2017; Li et al., 2017; Li et al., 2016) suggested that this dataset is applicable and satisfactory for the climatological studies. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.

This resource is 3 GIS layers in a web service related to the geology of Merrimack Basin, New Hampshire. It consists of GIS geostatistical Interpolation of a surface that models the depth to bedrock. It is derived from known georeferenced locations where depths to bedrock have been observed. These primarily include bedrock outcrops and well or boring locations. Interpolation method was ordinary kriging, using a lag size of 347 ft, 18 lags, a nugget of 42.24, partial sill of 647.508, and a 4633 ft range. A maximum of 20 neighbors and a minimum of 8 neighbors in a single circular sector were used in interpolation. The data are provided in the following formats: a web map service, a web feature service, and an ERSI Service Endpoint. It was compiled by the New Hampshire Geological Survey and made available for distribution through the National Geothermal Data System.

1) Merrimack Basin Bedrock Depth: This layer displays a GIS geostistical Interpolation of a surface that models the depth to bedrock. It was derived from known georeferenced locations where depths to bedrock have been observed. These primarily include bedrock outcrops and well or boring locations. Interpolation method was ordinary kriging, using a lag size of 448.6 ft. A maximum of 20 neighbors and a minimum of 8 neighbors were used in interpolation. Layer in the web service is "merri_bdkd".

2) Merrimack Basin Bedrock Depth Standard Error: This layer displays the Predicted Standard Error of GIS geostatistical interpolation of a surface that models the depth to bedrock. The predicted standard error is the standard deviation of the predicted surface, and is a function of distance from the nearest data point. Layer in web service is "merri_error".

3) Merrimack Basin Bedrock Depth Contours: This layer displays10 foot contours of a GIS geostatistical interpolation of a surface that models the depth to bedrock. Derived from known georeferenced locations where depths to bedrock have been observed. Contours were smoothed and topology was enforced to ensure that contours did not cross. Features with length less than 100 ft were removed. Layer in web service is "merri_bdkd_contour".

This data provides an interpolated raster surface of dissolved oxygen values across a region covered by an August 25, 2016 AUV survey. The raster was generated by using a natural neighbors interpolator within a GIS on the empirical data set. This interpolator was chosen due to the non-normal distribution observed among the data, and its ability to produce smoother approximations than alternative interpolation methods. During the August 24 survey, 13,910 data points were collected. A subset of 4452 (25%) random points were removed prior to interpolation to check for the accuracy of the interpolated surface.

Open AccessTo examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.

NOTE: The authors believe that this dataset is likely to be LESS accurate than using a clip of the larger San Francisco Bay groundwater dataset, here: https://dash.berkeley.edu/stash/dataset/doi:10.6078/D1W01Q. The reason is that additional datapoints and editing were used to improve the San Francisco Bay groundwater map after this Alameda County map was produced. We expect that the most recent San Francisco Bay map is the most accurate, even for smaller geographic scales.

This dataset contains a comparison of four interpolation methods used to estimate a minimum depth to groundwater surface for Alameda County, within one kilometer/0.6 mi of San Francisco Bay. The interpolation is based on well data from the CA State Water Board GAMA GeoTracker database, and the depth to water was calculated using a 2m USGS Digital Elevation Model.

Methods Well data - California State Water Control Board: GAMA GeoTracker http://geotracker.waterboards.ca.gov/data_download_by_county. Ground elevation data - U.S. Geological Survey https://topotools.cr.usgs.gov/coned/sanfrancisco.php. 2 meter DEM. SF Bay extent (includes open water and tidal wetlands) San Francisco Estuary Institute (SFEI): Bay Area Aquatic Resource Inventory (BAARI) http://www.sfei.org/baari. GIS FILE PROPERTIES: File format: ESRI Layer Package. Cellsize: 6.56. Linear unit: Feet. Z unit: Feet. Projected Coordinate System: NAD_1983_2011_StatePlane_California_III_FIPS_0403_Ft_US. Geographic Coordinate System: GCS_NAD_1983_2011.

METHODS: We subtracted the minimum depth to water at each well point from the ground elevation (extracted from the 2m DEM) to determine groundwater elevation at each well point. These elevations represent the maximum measured groundwater table height in the past 20 years. We then performed the interpolation on this groundwater elevation dataset, a total of 3,183 individual well points. Wells within one mile of the coast were included in the interpolation; results are shown within one kilometer (0.6 miles) of the coast, a distance used in previous studies of sea level rise-induced groundwater inundation. Wells within one-half mile north and south of the county borders were included in the interpolation to ensure continuity, but results are shown only for area within Alameda County.

We tested a variety of methods available in ArcGIS and used cross-validation to determine which method minimized prediction error most. We compared root mean square error (RMSE) to see how accurately each model predicted values at non-sampled locations, and examined mean error (ME), or the averaged difference between actual and predicted values, to see if each model was skewed in one direction or another. For each interpolation technique, we chose the input parameters (e.g. power, number of neighbors included) that minimized RMSE most.

After performing the groundwater table interpolation, we subtracted the output from the original elevation surface to display estimated minimum depth to water values. The interpolation and subtraction method we used produced some negative values for depth to water, indicating water above the ground surface, especially in areas where there were no well sample points at the base of a slope or in a valley. In the provided data files, we have changed these negative values to zero for clarity.

This data package contains the minimum depth to water results obtained by using each of the four interpolation methods, as well as files showing the minimum and maximum of the four methods for comparison. Also included are files showing the bay edge file used and no data areas (greater than 1km/0.6mi from the nearest well point).

The rapid growth of urban development, industrialization, mining, farming, and biological activities has resulted in potentially toxic metal pollution of the soil all over the world. This has caused degradation of soil quality, lower crop production, and risk to human health. For this work, two study sites were selected to evaluate metal concentrations in the agricultural as well as the recreational soil around the Cerrito Blanco in Matehuala, San Luis Potosi, Mexico. The concentrations of eight metals, namely As, Ca, Mg, Na, K, Sr, Mn, and Fe were analysed in order to determine the level of contamination risk as well as their spatial distributions. However, this study is mainly focused on toxic metals, e.g. As, Sr, Mn, and Fe. The contamination indices techniques were used to evaluate the risk assessment of soil. Additionally, the positive matrix factorization (PMF) model as well as the geostatistical analysis was used to identify the contamination sources based on 64 surface soil samples. After implementing PMF to analyze the soils, it was possible to differentiate the variations in factors linked to the contaminants, farming impacts, and the reference soil geochemistry. The soil in the two studied locations included high concentrations of As, Ca, Mg, K, Sr, Mn, and Fe, including variations in their spatial compositions, which were caused by direct mining activities, the movement and deposition of smelting waste, and the extensive use of irrigated contaminated groundwater for irrigation. The four possible factors were identified for soil pollution including industrial, transportation, agricultural, and naturogenic based on the PMF and geostatistical analysis. The spatial distribution of metal concentrations in the soil was also presented using a geographical information system (GIS) interpolation technique. The identification of metal sources and contamination risk mapping presents a significant role in minimizing pollution sources, and it may be performed in regions with high levels of soil contamination risk.

Due to the uneven distribution of meteorological stations in the Sanjiang River Basin, most of them are along the traffic trunk lines, and there are many areas without observation data, it is difficult to obtain accurate spatial distribution characteristics by ordinary spatial interpolation methods. Based on worldclim v2 1. For the air temperature data in the spatial data set, the MATLAB language is used to read the air temperature data in the study area of Sanjiang River Basin, calculate and output the data in GIS format, and the ArcGIS software is used to realize the spatial distribution data set of the average air temperature in Sanjiang River Basin from 2007 to 2018. Through this data set, the problem of uneven distribution of meteorological stations in Sanjiang Basin due to complex terrain and many mountains and valleys is effectively solved, and the long-term average distribution of air temperature in Sanjiang Basin from 2007 to 2018 can be better reflected.

Abstract

This dataset was derived by the Bioregional Assessment Programme. The parent 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 contains raster representations of Total Dissolved Solid (TDS) measurement trends in groundwater samples for each hydrogeological formation in the Galilee Basin subregion.

The dataset also contains supplementary polygon Feature Classes for each formation, to be used in the visualisation of the rasters. For each formation this includes:

a) A rectangular data extent polygon feature class - created based on the distribution of data points for each formation and used to define the extent of the each raster

b) Data extent mask - further defines the extent of data distribution as well as the spatial extent of the formation, used to visualise the TDS trends for each formation only within the formation boundary and near the spread of point data.

Purpose

Provides a visual representation for use in maps, of TDS measurement trends in groundwater for each hydrogeological formation in the Galilee Basin subregion.

Dataset History

The raster layers within this dataset were created using the 'Topo to Raster' interpolation method in ArcGIS. Topo to Raster uses an iterative finite difference interpolation technique. This method is preferred for map and visualisation purposes, especially in sparse data regions, as surface continuity is not compromised at a global level. This results in raster layers with smooth surfaces and trends for any level of data density, and surface continuity between areas of varying density.

Raster layers and polygon Feature Classes were created from the source point Feature Classes (dataset: GAL Hydrochemistry Formations QC for TDS v02 GIS - GUID: 109a21cd-a167-4320-84be-ab56cfc12cee)

Formation Data Extent polygons: An arbitrary rectangular polygon was created around the extent of points contained in each source point Feature Class

Formation Data Extent Mask: a hole was clipped from the Formation Data Extent polygon. The Eastern boundary of each hole was traced from the equivalent formation polygon found within the Galilee Groundwater Model, Hydrogeological Formation Extents v01 dataset (GUID: 5afbf7f1-1ee0-444b-9f77-dbad8d8de95b), while the western, northern and southern extent was defined by the distribution of point data or the Galilee subregion boundary (Bioregional Assessment areas v03, GUID: 96dbf469-5463-4f4d-8fad-4214c97e5aac).

Topo to Raster parameters

Input feature data = respective point feature class from source dataset

Field = TDS

Type = Point Elevation

Output cell size = 0.001

Output extent = Formation data extent polygon Feature Class

Smallest z value to be used in interpolation = smallest TDS value of input point Feature Class

Largest z value to be used in interpolation = largest TDS value of input point Feature Class

Drainage enforcement = NO_ENFORCE

Primary type of input data = SPOT

All other parameters left as default.

Dataset Citation

Bioregional Assessment Programme (XXXX) GAL Hydrochemistry Formations QC for TDS v02 Surfaces. Bioregional Assessment Derived Dataset. Viewed 11 April 2016, http://data.bioregionalassessments.gov.au/dataset/ff165a41-f7f3-4922-870e-6837fd40f228.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From GAL Hydrochemistry Formations QC for TDS v02

• Derived From GAL Aquifer Formation Extents v01

• Derived From GAL Aquifer Formation Extents v02

• Derived From Carmichael Coal Mine and Rail Project Environmental Impact Statement

• Derived From QLD Hydrochemistry QA QC GAL v02

• Derived From Bioregional Assessment areas v03

• Derived From GAL Hydrochemistry Formations QC for TDS v01

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From RPS Galilee Hydrogeological Investigations - Appendix tables B to F (original)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Phanerozoic OZ SEEBASE v2 GIS

• Derived From QLD Department of Natural Resources and Mining Groundwater Database Extract 20131111

• Derived From QLD DNRM Hydrochemistry with QA/QC

• Derived From GAL Hydrochemistry Formations QC for TDS v02 GIS

• Derived From Galilee Groundwater Model, Hydrogeological Formation Extents v01

• Derived From Queensland petroleum exploration data - QPED

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Three-dimensional visualisation of the Great Artesian Basin - GABWRA

• Derived From QLD Department of Natural Resources and Mining Groundwater Database Extract 20142808

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From Queensland Geological Digital Data - Detailed state extent, regional. November 2012

• Derived From Geoscience Australia, 1 second SRTM Digital Elevation Model (DEM)

These data were created as part of the National Oceanic and Atmospheric Administration Coastal Services Center's efforts to create an online mapping viewer depicting potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses.Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: http://www.csc.noaa.gov/slr These data depict the potential inundation of coastal areas resulting from a projected 1 to 6 feet rise in sea level above current Mean Higher High Water (MHHW) conditions. The process used to produce the data can be described as a modified bathtub approach that attempts to account for both local/regional tidal variability as well as hydrological connectivity. The process uses two source datasets to derive the final inundation rasters and polygons and accompanying low-lying polygons for each iteration of sea level rise: the Digital Elevation Model (DEM) of the area and a tidal surface model that represents spatial tidal variability. The tidal model is created using the NOAA National Geodetic Survey's VDATUM datum transformation software (http://vdatum.noaa.gov) in conjunction with spatial interpolation/extrapolation methods and represents the MHHW tidal datum in orthometric values (North American Vertical Datum of 1988). The model used to produce these data does not account for erosion, subsidence, or any future changes in an area's hydrodynamics. It is simply a method to derive data in order to visualize the potential scale, not exact location, of inundation from sea level rise.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information