Wildfire Suppression Difficulty Index (SDI) 80th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft). SDI factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The maximum, and 80th, 90th, and 96th percentiles of the annual maximums of daily water levels recorded at monitoring sites in and near Miami-Dade County, Florida, during the 1974-2009 water years. [≥, greater than or equal to; %, percent; GW, groundwater; NPS, National Park Service; SFWMD, South Florida Water Management District; USGS, U.S. Geological Survey. All data adjusted to the North American Vertical Datum of 1988. Latitude and longitude are in decimal degrees]

This dataset contains the geographic data used to create maps for the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt

Demographic data from the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Demographics/q9ix-kfws

Filter by the Indicator column to select data for a particular indicator map.

Export notes: Dataset may not automatically open correctly in Excel due to geospatial data. To export the data for geospatial analysis, select Shapefile or GEOJSON as the file type. To view the data in Excel, export as a CSV but do not open the file. Then, open a blank Excel workbook, go to the Data tab, select “From Text/CSV,” and follow the prompts to import the CSV file into Excel. Alternatively, use the exploration options in "View Data" to hide the geographic column prior to exporting the data.

USER NOTES: 4/7/2025 - The maps and data have been removed for the Health Professional Shortage Areas indicator due to inconsistencies with the data source leading to some missing health professional shortage areas. We are working to fix this issue, including exploring possible alternative data sources.

5/21/2025 - The following changes were made to the 2023 report data (Equity Report Year = 2023). Self-Sufficiency Wage - a typo in the indicator name was fixed (changed sufficienct to sufficient) and the percent for one PUMA corrected from 56.9 to 59.9 (PUMA = San Diego County (Northwest)--Oceanside City & Camp Pendleton). Notes were made consistent for all rows where geography = ZCTA. A note was added to all rows where geography = PUMA. Voter registration - label "92054, 92051" was renamed to be in numerical order and is now "92051, 92054". Removed data from the percentile column because the categories are not true percentiles. Employment - Data was corrected to show the percent of the labor force that are employed (ages 16 and older). Previously, the data was the percent of the population 16 years and older that are in the labor force. 3- and 4-Year-Olds Enrolled in School - percents are now rounded to one decimal place. Poverty - the last two categories/percentiles changed because the 80th percentile cutoff was corrected by 0.01 and one ZCTA was reassigned to a different percentile as a result. Low Birthweight - the 33th percentile label was corrected to be written as the 33rd percentile. Life Expectancy - Corrected the category and percentile assignment for SRA CENTRAL SAN DIEGO. Parks and Community Spaces - corrected the category assignment for six SRAs.

5/21/2025 - Data was uploaded for Equity Report Year 2025. The following changes were made relative to the 2023 report year. Adverse Childhood Experiences - added geographic data for 2025 report. No calculation of bins nor corresponding percentiles due to small number of geographic areas. Low Birthweight - no calculation of bins nor corresponding percentiles due to small number of geographic areas.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

As of 5/18/2023 this dataset will be updated weekly on Tuesdays with a weekly granularity.

This dataset includes the VA health planning region, sewershed (i.e., wastewater treatment facilityservice area), start of collection week, percentile, percentile groups (Highest: 80-100th, Higher: 60-79.9th, Middle: 40-59.9th, Lower: 20-39.9th, Lowest: 0-19.9th), and report date. This dataset was first published on 05/18/2023. The data set increases in size weekly and as a result, the dataset may take longer to update; however, it is expected to be available by 12:00 noon. When you download the data set, the sewersheds will be sorted in ascending alphabetical order by health region. The sample collection dates will be sorted in ascending order, meaning that the earliest date will be at the top. The most recent date will be at the bottom of each sewershed’s data.

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.

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.

These data tables provide the latest percentile rents and pooled small centre statistics from our 2022 Rental Market Survey. This data is useful for both affordability analysis and for determining affordability criteria for current and legacy affordable housing products and programs. The data tables include: vacancy rates average rents percentile rents (30th, 50th, 65th and 80th) The housing data are arranged by bachelor, 1, 2 and 3+ bedroom units for privately initiated apartments of 3 units and over.

This dataset represents the average of the relative nutrient loss rates due to water erosion for the three nutrients total nitrogen, total phosphorus and soil organic carbon. The dataset is masked to cropping and grazing lands. The units are percentage/year. Relative nutrient loss is calculated as the annual loss of nutrient from the top 5 cm of soil relative to the total stock of each nutrient in the full depth of the soil profile. Annual erosion rate data are from Teng et al. (2016) and soil nutrient data are from the Soil and Landscape Grid of Australia. For a full description of the methods used to generate this datset see McKenzie et al. (2017).For raster data download follow link: Hillslope Erosion download To present the average relative nutrient loss rate data in Figure 4.5 in McKenzie et al. (2017), the data were divided into seven classes using percentiles as the class breaks. That is, 20 % of the grid cells fell into each of the first four classes, 10 % of the grid cells into the fifth class, and 5 % into each of the sixth and seventh classes. The actual average nutrient loss rate values which represent those class breaks are listed below:0-20th percentile: < 0.003 %/y20-40th percentile: 0.003 - 0.005 %/y40-60th percentile: 0.005 - 0.009 %/y60-80th percentile: 0.009 - 0.019 %/y80-90th percentile: 0.019 - 0.045 %/y90-95th percentile: 0.045 - 0.098 %/y95-100th percentile: > 0.098 %/yNOTE: The associated dataset is available on request to geospatial@dcceew.gov.au

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.

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.

This dataset represents the average of the relative nutrient loss rates due to water erosion for the three nutrients total nitrogen, total phosphorus and soil organic carbon. The dataset is masked to cropping and grazing lands. The units are percentage/year. Relative nutrient loss is calculated as the annual loss of nutrient from the top 5 cm of soil relative to the total stock of each nutrient in the full depth of the soil profile. Annual erosion rate data are from Teng et al. (2016) and soil nutrient data are from the Soil and Landscape Grid of Australia. For a full description of the methods used to generate this datset see McKenzie et al. (2017).To present the average relative nutrient loss rate data in Figure 4.5 in McKenzie et al. (2017), the data were divided into seven classes using percentiles as the class breaks. That is, 20 % of the grid cells fell into each of the first four classes, 10 % of the grid cells into the fifth class, and 5 % into each of the sixth and seventh classes. The actual average nutrient loss rate values which represent those class breaks are listed below:0-20th percentile: < 0.003 %/y20-40th percentile: 0.003 - 0.005 %/y40-60th percentile: 0.005 - 0.009 %/y60-80th percentile: 0.009 - 0.019 %/y80-90th percentile: 0.019 - 0.045 %/y90-95th percentile: 0.045 - 0.098 %/y95-100th percentile: > 0.098 %/y

This upload contains aligned IPA transcriptions for the THCHS-30 dataset from OpenSLR. Thereby, punctuation is added, silence marked and duration markers for each phoneme are assigned. Furthermore, the silence on the beginning and ending of each file is marked.

The words were transcribed using pypinyin (v0.47.1) via dict-from-pypinyin (v0.0.1) and mapped to IPA using the pinyin-ipa-map-TONE3-all.json mapping from here. The alignment was done using Montreal Forced Aligner (v2.0.5) and the acoustic model Mandarin MFA (v2.0.0a).

Phoneme duration markers:

• ˘ -> [0, 20) percentile (speaker-wise), e.g., a˥˩˘
• (none) -> [20, 80) percentile (speaker-wise), e.g., a˥˩
• ˑ -> [80, 90) percentile (speaker-wise), e.g., a˥˩ˑ
• ː -> [90, inf) percentile (speaker-wise), e.g., a˥˩ː

Thereby each phoneme (including tones) was considered on its own, i.e., phonemes with different tones were not considered together for the percentile calculation.

Silence markers:

• SILX -> silence at start/end of a recording (aligned on all tiers)
• SIL0 -> no silence
• SIL1 -> [0, 33.33333333) percentile of all silences (speaker-wise)
• SIL2 -> [33.33333333, 66.66666666) percentile of all silences (speaker-wise)
• SIL3 -> [66.66666666, inf) percentile of all silences (speaker-wise)

Files:

• grids.zip
  • contains TextGrids for all audio files containing three tiers words, phonemes and transcription
    • words contains the aligned Chinese words
    • phonemes contains the IPA pronunciations including silence markers at start and end (SILX)
    • transcription contains unaligned phonemes including punctuation and word boundary labels (SIL0)
  • the folder structure is equal to one from the dataset
• grids-sdp.zip
  • same as grids.zip except the folder structure is the one from speech-dataset-parser (v0.0.4)
• preview-start/middle/end.png
  • preview of the first TextGrid from speaker A2 opened in Praat in different positions
• words-vocabulary.txt
  • contains all Chinese words from tier words
• phonemes-vocabulary.txt
  • contains all phonemes from tier phonemes
• transcription-vocabulary.txt
  • contains all phonemes/punctuation from tier transcription
• phonemes-durations.pdf
  • contains the plotted phoneme duration distribution of tier phonemes
• phonemes-durations-simple.pdf
  • contains the plotted phoneme duration distribution of tier phonemes if all duration markers are ignored
• phonemes-durations-simple-toneless.pdf
  • contains the plotted phoneme duration distribution of tier phonemes if all duration markers and tones are ignored
• pronunciations-broad.dict
  • contains the broad pronunciations for each word including punctuation but not duration markers
  • e.g., 一下。 i˥ ɕ j a˥˩ 。
• pronunciations-narrow.dict
  • contains the narrow pronunciations for each word including punctuation, duration markers and weights (= occurrence) over all speakers
  • e.g., 一下。 3 i˥ ɕ j a˥˩ː 。
• pronunciations-narrow-speakers.zip
  • contains the narrow pronunciations separated for each speaker
• script.sh
  • contains the script to reproduce all results
  • error in line 32: replace with speech-dataset-parser==0.0.4

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.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_9834f6cdf1982e201c69c2bca45ad1c6/view

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.

Antarctic krill is a key component of Southern Ocean ecosystems and there is significant interest in identifying regions acting as sources for the krill population. We develop a mechanistic model combining thermal and food requirements for krill egg production, with predation pressure post-spawning, to predict regions that could support high larval production (spawning habitat). We optimise our model on regional data using a maximum likelihood approach and then generate circumpolar predictions …Show full descriptionAntarctic krill is a key component of Southern Ocean ecosystems and there is significant interest in identifying regions acting as sources for the krill population. We develop a mechanistic model combining thermal and food requirements for krill egg production, with predation pressure post-spawning, to predict regions that could support high larval production (spawning habitat). We optimise our model on regional data using a maximum likelihood approach and then generate circumpolar predictions of spawning habitat quality. The uploaded datasets represent model predictions of seasonal circumpolar spawning habitat quality of Antarctic krill as well as composite data of the circumpolar mean annual number of weeks in which modelled spawning habitat quality is higher than the summer 80th percentile.

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission is a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.The purpose of G-LiHT’s Metrics data product (GLMETRICS) is to provide extensive lidar height and density metrics and return statistics in more than 80 science data set layers. Included in the product are mean, standard deviation, and percentile information for ground, tree, and shrub data. Some flights also contain Canopy Height Model (CHM) and Digital Terrain Model (DTM) returns. The total number of metrics layers varies by flight or campaign.GLMETRICS data are processed as a raster data product (GeoTIFF) at a 13 meter spatial resolution over locally defined areas. Known Issues* Science Data Layers do not currently reflect valid Fill Value, No Data Value, Valid Range, or Scaling Factor. These will be updated when more information is available.

This compilation data release is a selection of remotely sensed imagery used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Datasets include: • Mosaic 5 m digital elevation model (DEM) with shaded relief • Normalised Difference Vegetation Index (NDVI) percentiles • Tasselled Cap exceedance summaries • Normalised Difference Moisture Index (NDMI) • Normalised Difference Wetness Index (NDWI)

The 5m spatial resolution digital elevation model with associated shaded relief image were derived from the East Kimberley 2017 LiDAR survey (Geoscience Australia, 2019b).

The Normalised Difference Vegetation Index (NDVI) percentiles include 20th, 50th, and 80th for dry seasons (April to October) 1987 to 2018 and were derived from the Landsat 5,7 and 8 data stored in Digital Earth Australia (see Geoscience Australia, 2019a). Tasselled Cap Exceedance Summary include brightness, greenness and wetness as a composite image and were also derived from the Landsat data. These surface reflectance products can be used to highlight vegetation characteristics such as wetness and greenness, and land cover.

The Normalised Difference Moisture Index (NDMI) and Normalised Difference Water Index (NDWI) were derived from the Sentinel-2 satellite imagery. These datasets have been classified and visually enhanced to detect vegetation moisture stress or water-logging and show distribution of moisture. For example, positive NDWI values indicate waterlogged areas while waterbodies typically correspond with values greater than 0.2. Waterlogged areas also correspond to NDMI values of 0.2 to 0.4.

Geoscience Australia, 2019a. Earth Observation Archive. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/57D9DCA3910CD

Geoscience Australia, 2019b. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 https://pid.geoscience.gov.au/dataset/ga/129985

Description: This dataset contains layers of predicted occurrence for 65 groundfish species as well as overall species richness (i.e., the total number of species present) in Canadian Pacific waters, and the median standard error per grid cell across all species. They cover all seafloor habitat depths between 10 and 1400 m that have a mean summer salinity above 28 PSU. Two layers are provided for each species: 1) predicted species occurrence (prob_occur) and 2) the probability that a grid cell is an occurrence hotspot for that species (hotspot_prob; defined as being in the lower of: 1) 0.8, or 2) the 80th percentile of the predicted probability of occurrence values across all grid cells that had a probability of occurrence greater than 0.05.). The first measure provides an overall prediction of the distribution of the species while the second metric identifies areas where that species is most likely to be found, accounting for uncertainty within our model. All layers are provided at a 1 km resolution. Methods: These layers were developed using a species distribution model described in Thompson et al. 2023. This model integrates data from three fisheries-independent surveys: the Fisheries and Oceans Canada (DFO) Groundfish Synoptic Bottom Trawl Surveys (Sinclair et al. 2003; Anderson et al. 2019), the DFO Groundfish Hard Bottom Longline Surveys (Lochead and Yamanaka 2006, 2007; Doherty et al. 2019), and the International Pacific Halibut Commission Fisheries Independent Setline Survey (IPHC 2021). Further details on the methods are found in the metadata PDF available with the dataset. Abstract from Thompson et al. 2023: Predictions of the distribution of groundfish species are needed to support ongoing marine spatial planning initiatives in Canadian Pacific waters. Data to inform species distribution models are available from several fisheries-independent surveys. However, no single survey covers the entire region and different gear types are required to survey the range of habitats that are occupied by groundfish. Bottom trawl gear is used to sample soft bottom habitat, predominantly on the continental shelf and slope, whereas longline gear often focuses on nearshore and hardbottom habitats where trawling is not possible. Because data from these two gear types are not directly comparable, previous species distribution models in this region have been limited to using data from one survey at a time, restricting their spatial extent and usefulness at a regional scale. Here we demonstrate a method for integrating presence-absence data across surveys and gear types that allows us to predict the coastwide distributions of 66 groundfish species in British Columbia. Our model leverages the use of available data from multiple surveys to estimate how species respond to environmental gradients while accounting for differences in catchability by the different surveys. Overall, we find that this integrated method has two main benefits: 1) it increases the accuracy of predictions in data-limited surveys and regions while having negligible impacts on the accuracy when data are already sufficient to make predictions, 2) it reduces uncertainty, resulting in tighter confidence intervals on predicted species occurrences. These benefits are particularly relevant in areas of our coast where our understanding of habitat suitability is limited due to a lack of spatially comprehensive long-term groundfish research surveys. Data Sources: Research data was provided by Pacific Science’s Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2020 for all species which had at least 150 observations, across all gear type and survey datasets available. Uncertainties: These are modeled results based on species observations at sea and their related environmental covariate predictions that may not always accurately reflect real-world groundfish distributions though methods that integrate different data types/sources have been demonstrated to improve model inference by increasing the accuracy of the predictions and reducing uncertainty.