Wildfire Suppression Difficulty Index (terrestrial) (SDIt) is a quantitative rating of relative difficulty in performing fire control work. In its original formulation for use in Spain, SDI included aerial resource use, however for development and application in the United States we removed the aerial resource component due to a lack of consistent data. We note this distinction of “terrestrial only” calculations with the inclusion of “t” in the acronym. SDIt factors in topography, fuels, expected fire behavior under severe fire weather conditions, firefighter line production rates in various fuel types, and accessibility (distance from roads/trails) to assess relative suppression effort. For this dataset severe fire behavior is modeled with 15 mph up-slope winds and fully cured fuels. SDI has a continuous value distribution from 1-10. Here it is binned to six classes from lowest to highest difficulty.

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).

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.

Percentiles were calculated separately for the baseline and reference site data (combined) and for near dredge sites (<2 km) during dredging. Shown are the median, mean and range (min–max) for the 100th (maximum), 99th, 95th, 80th, 50th (median) percentiles and mean for one hour, one day, 14 d and 30 d running mean periods.Turbidity (NTU) percentile values for various running mean time periods for the Barrow Island, Cape Lambert and Burrup Peninsula dredging projects.

All day and peak transit 20th, 50th, and 80th percentile speeds on stop segments estimated on a single day for all CA transit operators that provide GTFS real-time vehicle positions data.

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.

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

The U.S. Geological Survey (USGS) Water Resources Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand and an understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use. This data release contains Mann-Kendall monotonic trend analyses for 18 observed annual and monthly streamflow metrics at 6,347 U.S. Geological Survey streamgages located in the conterminous United States, Alaska, Hawaii, and Puerto Rico. Streamflow metrics include annual mean flow, maximum 1-day and 7-day flows, minimum 7-day and 30-day flows, and the date of the center of volume (the date on which 50% of the annual flow has passed by a gage), along with the mean flow for each month of the year. Annual streamflow metrics are computed from mean daily discharge records at U.S. Geological Survey streamgages that are publicly available from the National Water Information System (NWIS). Trend analyses are computed using annual streamflow metrics computed through climate year 2022 (April 2022- March 2023) for low-flow metrics and water year 2022 (October 2021 - September 2022) for all other metrics. Trends at each site are available for up to four different periods: (i) the longest possible period that meets completeness criteria at each site, (ii) 1980-2020, (iii) 1990-2020, (iv) 2000-2020. Annual metric time series analyzed for trends must have 80 percent complete records during fixed periods. In addition, each of these time series must have 80 percent complete records during their first and last decades. All longest possible period time series must be at least 10 years long and have annual metric values for at least 80% of the years running from 2013 to 2022. This data release provides the following five CSV output files along with a model archive: (1) streamflow_trend_results.csv - contains test results of all trend analyses with each row representing one unique combination of (i) NWIS streamgage identifiers, (ii) metric (computed using Oct 1 - Sep 30 water years except for low-flow metrics computed using climate years (Apr 1 - Mar 31), (iii) trend periods of interest (longest possible period through 2022, 1980-2020, 1990-2020, 2000-2020) and (iv) records containing either the full trend period or only a portion of the trend period following substantial increases in cumulative upstream reservoir storage capacity. This is an output from the final process step (#5) of the workflow. (2) streamflow_trend_trajectories_with_confidence_bands.csv - contains annual trend trajectories estimated using Theil-Sen regression, which estimates the median of the probability distribution of a metric for a given year, along with 90 percent confidence intervals (5th and 95h percentile values). This is an output from the final process step (#5) of the workflow. (3) streamflow_trend_screening_all_steps.csv - contains the screening results of all 7,873 streamgages initially considered as candidate sites for trend analysis and identifies the screens that prevented some sites from being included in the Mann-Kendall trend analysis. (4) all_site_year_metrics.csv - contains annual time series values of streamflow metrics computed from mean daily discharge data at 7,873 candidate sites. This is an output of Process Step 1 in the workflow. (5) all_site_year_filters.csv - contains information about the completeness and quality of daily mean discharge at each streamgage during each year (water year, climate year, and calendar year). This is also an output of Process Step 1 in the workflow and is combined with all_site_year_metrics.csv in Process Step 2. In addition, a .zip file contains a model archive for reproducing the trend results using R 4.4.1 statistical software. See the README file contained in the model archive for more information. Caution must be exercised when utilizing monotonic trend analyses conducted over periods of up to several decades (and in some places longer ones) due to the potential for confounding deterministic gradual trends with multi-decadal climatic fluctuations. In addition, trend results are available for post-reservoir construction periods within the four trend periods described above to avoid including abrupt changes arising from the construction of larger reservoirs in periods for which gradual monotonic trends are computed. Other abrupt changes, such as changes to water withdrawals and wastewater return flows, or episodic disturbances with multi-year recovery periods, such as wildfires, are not evaluated. Sites with pronounced abrupt changes or other non-monotonic trajectories of change may require more sophisticated trend analyses than those presented in this data release.

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 gro ...

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

Dredging poses a potential risk to tropical ecosystems, especially in turbidity-sensitive environments such as coral reefs, filter feeding communities and seagrasses. There is little detailed observational time-series data on the spatial effects of dredging on turbidity and light and defining likely footprints is a fundamental task for impact prediction, the EIA process, and for designing monitoring projects when dredging is underway. It is also important for public perception of risks associated with dredging. Using an extensive collection of in situ water quality data (73 sites) from three recent large scale capital dredging programs in Australia, and which included extensive pre-dredging baseline data, we describe relationships with distance from dredging for a range of water quality metrics. Using a criterion to define a zone of potential impact of where the water quality value exceeds the 80th percentile of the baseline value for turbidity-based metrics or the 20th percentile for the light based metrics, effects were observed predominantly up to three km from dredging, but in one instance up to nearly 20 km. This upper (~20 km) limit was unusual and caused by a local oceanographic feature of consistent unidirectional flow during the project. Water quality loggers were located along the principal axis of this flow (from 200 m to 30 km) and provided the opportunity to develop a matrix of exposure based on running means calculated across multiple time periods (from hours to one month) and distance from the dredging, and summarized across a broad range of percentile values. This information can be used to more formally develop water quality thresholds for benthic organisms, such as corals, filter-feeders (e.g. sponges) and seagrasses in future laboratory- and field-based studies using environmentally realistic and relevant exposure scenarios, that may be used to further refine distance based analyses of impact, potentially further reducing the size of the dredging footprint.

This dataset is a set of raster tidal statistics for the Australian region at a 1/32 degree resolution derived from the EOT20 global tidal model. This dataset provides rasters for Lowest Predicted Tide (LPT), Highest Predicted Tide (HPT), Mean Low Spring Water (MLSW), Mean High Spring Water (MHSW), tidal range (HPT-LPT), tidal percentiles (1, 2, 5, 10, 20, 50, 80, 90, 95, 98, 99) and monthly climatologies (median over all simulated years for a given month) of LPT, Mean, and HPT.

Lowest Predicted Tide is a proxy for Lowest Astronomical Tide (LAT) and Highest Predicted Tide is a proxy for Highest Astronomical Tide (HAT) estimated over a shorter simulation period of typically 5 years.

The tidal modelling statistics are all represented as GeoTiff images to allow easy use in subsequent analysis and visualisation in GIS tools. The monthly climatology datasets (LPT, Mean and HPT) are stored as multi-band images with one band per month. The tidal percentiles are also stored as multi-band images with one band per percentile value.

This dataset also includes a comparison between the EOT20 tidal model and 70 tide gauges around Australia. This includes a comparison of the monthly min, mean and maximum over the last 19 years of data, and a monthly climatology over the full tidal record. Generated plots of the 70 stations comparisons are available in the data download section.

All dataset products and validation analysis were performed using Python scripts, allowing this dataset to be fully reproduced. The source code is available from GitHub.


Limitations:

This modelling was limited to the accuracy of the EOT20 global model. Tidal statistics were calculated using a 30 minute time increment over a 5-year period (2020-2025) (Note: the initial release data only covered a single year 2023). A simulation period of 19 years is needed to capture the full lunar cycle. The calculation time period was limited due to the high computational cost of processing the full 19 year lunar cycle. Based on reviewing the tide gauge data and the matching tidal predictions we find that in the last 19 years the largest tidal ranges occurred in 2004 - 2006 and 2021 - 2024 and the lowest tidal range were in the middle of this period from 2012 - 2014. By modelling from 2020-2025 we the statistics are based on the period of the cycle with the highest tidal ranges.

The EOT20 tidal model provides tidal constituents on a grid that is 1/8 degree in resolution. Land areas pixels are excluded from the model grid. This means that nearshore areas, particularly those associated with river mouths or bays, are excluded from the model. In this dataset we infill these areas based on model parameters extrapolated using nearest neighbour interpolation. This will result in increased error in the tidal estimates in the locations.

This dataset is not suitable as a tidal datum for administrative and jurisdictional extents. This dataset has not gone through enough validation for its use in critical decisions. It was developed to assist in understanding tidal conditions experienced by shallow marine environments.


Format:

GeoTiff raster files (EPSG:4326). One file per statistic. Percentiles contain 11 bands corresponding to 1, 2, 5, 10, 20, 50, 80, 90, 95, 98, and 99 percentile of time exposure. Monthly_LPT, Monthly_Mean, and Monthly_HPT each have 12 bands corresponding to months of the year, where band 1 is January, band 2 is February, etc.


Dataset relevance:

This section aims to improve the discoverability of the dataset by highlighting key areas where this dataset is relevant and what it shows.

This dataset provides a plot of the monthly lowest tide, mean tide and highest tide as a time series and a climatology (where all years are overlaid on each other and the monthly results are taken from the median value of all the results for each month) based on 70 tide gauges (based on data made available through the BOM website) around Australia, with a comparison with the same tidal values predicted from the EOT20 tidal model. This includes the following locations:
- Queensland: Cape Ferguson, Rosslyn Bay, Booby Island, Bowen, Brisbane Bar, Bundaberg (Burnett Heads), Cairns, Gladstone, Gold Coast Operations Base, Goods Island, Hay Point, Ince Point, Karumba, Lucinda (Offshore), Mackay Outer Harbour, Mooloolaba, Nardana Patches, Mourilyan Harbour, Port Alma, Port Douglas, Shute Harbour, Townsville, Turtle Head, Urangan, Weipa (Humbug Point), Thursday Island
- New South Wales: Port Kembla, Botany Bay, Eden, Lord Howe Island, Newcastle, Norfolk Island, Fort Denison (Sydney), Yamba
- Victoria: Portland, Stony Point, Lorne
- South Australia: Port Stanvac, Thevenard, Port Adelaide (Outer Harbor), Port Giles, Port Lincoln, Port Pirie, Victor Harbor, Wallaroo, Whyalla
- Western Australia: Esperance, Hillarys, Broome, Albany, Bunbury (Inner), Cape Lambert, Carnarvon, Exmouth, Fremantle, Geraldton, King Bay, Onslow, Port Hedland, Wyndham
- Tasmania: Burnie, Spring Bay, Low Head, Mersey River (Devonport)
- Northern Territory: Darwin, Milner Bay - Groote Eylandt
- Indian Ocean: Cocos - Keeling Islands (Home Island)

The tidal range product shows that the strongest tides occur in the Kimberley, in the northern portions of the Pilbara along Eighty Mile Beach, in Joseph Bonaparte Gulf, between Darwin and the Tiwi Islands, and in Broad Sound in Queensland. Areas that have a low tidal range include the coast south of Ningaloo Reef and much of the Gulf of Carpentaria.

References:

Bishop-Taylor, R., Sagar, S., Phillips, C., & Newey, V. (2024). eo-tides: Tide modelling tools for large-scale satellite earth observation analysis. https://github.com/GeoscienceAustralia/eo-tides

Sutterley, T. C., Alley, K., Brunt, K., Howard, S., Padman, L., Siegfried, M. (2017) pyTMD: Python-based tidal prediction software. 10.5281/zenodo.5555395

Hart-Davis Michael, Piccioni Gaia, Dettmering Denise, Schwatke Christian, Passaro Marcello, Seitz Florian (2021). EOT20 - A global Empirical Ocean Tide model from multi-mission satellite altimetry. SEANOE. https://doi.org/10.17882/79489

Hart-Davis Michael G., Piccioni Gaia, Dettmering Denise, Schwatke Christian, Passaro Marcello, Seitz Florian (2021). EOT20: a global ocean tide model from multi-mission satellite altimetry. Earth System Science Data, 13 (8), 3869-3884. https://doi.org/10.5194/essd-13-3869-2021


Change log:

As updates to this dataset are published, the changes will be recorded here.
- 2025-02-27 v1: Initial release of the dataset that is based on the northern-au-test.yaml. This has a limited spatial extent and 1-year simulation. This is only a spatial subset of the full dataset. This initial release has been archived (https://nextcloud.eatlas.org.au/apps/sharealias/a/AU_NESP-MaC-3-17_AIMS_EOT20-tidal-stats_v1).
- 2025-03-18 v1-1: Release of the full Australian geographic scope calculated over 5 years.

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 hig ...

Because the vast majority of species are well-diverged, relatively little is known about the genomic architecture of speciation during the early stages of divergence. Species within recent evolutionary radiations are often minimally diverged from a genomic perspective, and therefore provide rare opportunities to address this question. Here, we leverage the hamlet radiation (Hypoplectrus spp, brightly colored reef fishes from the tropical western Atlantic) to characterize genomic divergence during the early stages of speciation. Transect surveys and spawning observations in Belize, Honduras, and Panama confirm that sympatric barred (H. puella), black (H. nigricans) and butter (H. unicolor) hamlets are phenotypically distinct and reproductively isolated, although hybrid spawnings and individuals with intermediate phenotypes are seen on rare occasions. A survey of approximately 100,000 restriction-site associated SNPs in 126 samples from the three species across the three replicate populat...

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.

Note: All continuous data are described in median with quintiles (Q1-4, 20th and 80th percentile). ICU, intensive care unit.#P values generated by Chi square or Mann-Whitney test.*426 individuals did not have driver’s licence prior to admission related to motor vehicle trauma.**Analysis included only those admitted to the ICU (n = 1360); length of ICU stay was significantly longer among those with prior traffic offences if all patients were included in this analysis.Differences in characteristics and outcomes between those with and without prior traffic offences (N = 10,330).

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.

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.