CPUC_Fire-Threat_Map_Tier_3

In 2012, the CPUC ordered the development of a statewide map that is designed specifically for the purpose of identifying areas where there is an increased risk for utility associated wildfires. The development of the CPUC -sponsored fire-threat map, herein "CPUC Fire-Threat Map," started in R.08-11-005 and continued in R.15-05-006.

A multistep process was used to develop the statewide CPUC Fire-Threat Map. The first step was to develop Fire Map 1 (FM 1), an agnostic map which depicts areas of California where there is an elevated hazard for the ignition and rapid spread of powerline fires due to strong winds, abundant dry vegetation, and other environmental conditions. These are the environmental conditions associated with the catastrophic powerline fires that burned 334 square miles of Southern California in October 2007. FM 1 was developed by CAL FIRE and adopted by the CPUC in Decision 16-05-036.

FM 1 served as the foundation for the development of the final CPUC Fire-Threat Map. The CPUC Fire-Threat Map delineates, in part, the boundaries of a new High Fire-Threat District (HFTD) where utility infrastructure and operations will be subject to stricter fire‑safety regulations. Importantly, the CPUC Fire-Threat Map (1) incorporates the fire hazards associated with historical powerline wildfires besides the October 2007 fires in Southern California (e.g., the Butte Fire that burned 71,000 acres in Amador and Calaveras Counties in September 2015), and (2) ranks fire-threat areas based on the risks that utility-associated wildfires pose to people and property.

Primary responsibility for the development of the CPUC Fire-Threat Map was delegated to a group of utility mapping experts known as the Peer Development Panel (PDP), with oversight from a team of independent experts known as the Independent Review Team (IRT). The members of the IRT were selected by CAL FIRE and CAL FIRE served as the Chair of the IRT. The development of CPUC Fire-Threat Map includes input from many stakeholders, including investor-owned and publicly owned electric utilities, communications infrastructure providers, public interest groups, and local public safety agencies.

The PDP served a draft statewide CPUC Fire-Threat Map on July 31, 2017, which was subsequently reviewed by the IRT. On October 2 and October 5, 2017, the PDP filed an Initial CPUC Fire-Threat Map that reflected the results of the IRT's review through September 25, 2017. The final IRT-approved CPUC Fire-Threat Map was filed on November 17, 2017. On November 21, 2017, SED filed on behalf of the IRT a summary report detailing the production of the CPUC Fire-Threat Map(referenced at the time as Fire Map 2). Interested parties were provided opportunity to submit alternate maps, written comments on the IRT-approved map and alternate maps (if any), and motions for Evidentiary Hearings. No motions for Evidentiary Hearings or alternate map proposals were received. As such, on January 19, 2018 the CPUC adopted, via Safety and Enforcement Division's (SED) disposition of a Tier 1 Advice Letter, the final CPUC Fire-Threat Map.

Additional information can be found here.

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.

This GIS dataset contains the results of wind-wave parameter modelling in the area of the Gulf of Gdańsk (Southern Baltic). For the simulations, a high resolution SWAN model was used. The dataset consists of the significant wave height, the direction of the wave approaching the shore and the wave period during 21 historical, extreme storms (rasters). The storms were selected by an automatic search over the 44-year-long significant wave height time series.

This dataset displays Risk Areas which help illustrate the geographic distribution of coastal risk along the shoreline of Westchester, Nassau, Suffolk, and New York City Counties. The objective of the Risk Assessment (and resulting mapped Risk Areas) is to define areas at risk from coastal hazards. Data were collected from sources accurate enough to differentiate geographic areas according to the likelihood of flooding, erosion, waves and storm surge. To the extent allowed by source data, areas where flood water can extend up streams and under culverts and bridges are reflected in mapping. The mapping process and data sources used are described below. Mapped Risk Areas are classified into three categories: Extreme, High, and Moderate. Extreme Risk Areas: These are areas currently at risk of frequent inundation, vulnerable to erosion in the next 40 years, or likely to be inundated in the future due to sea level rise. Criteria and source data used to define these Extreme Risk Areas include: FEMA V zone. Areas subject to Shallow Coastal Flooding per NOAA NWS’s advisory threshold. Areas prone to erosion, natural protective feature areas susceptible to erosion. Added 3 feet to the MHHW shoreline and extended this elevation inland over the digital elevation model (DEM) to point of intersection with ground surface. These four criteria were overlaid and polygons were generated that included the maximum spatial extend of the above-listed criteria. These polygons represent Extreme Risk Areas. High Risk Areas: These are areas that fall outside of the Extreme Risk Areas and are currently at infrequent risk of inundation or are at risk in the future from sea level rise. Criteria and source data used to define these High Risk Areas include: Area bounded by the 1% annual flood risk zone (FEMA V and A zones). Added 3 feet to NOAA NWS coastal flooding advisory threshold and extended this elevation inland over the DEM to point of intersection with ground surface. Polygons were created that were upland of the Extreme Risk Area boundary and included the maximum spatial extent of the above-listed criteria. These polygons represent High Risk Areas. Moderate Risk Areas: These are areas that fall outside of the Extreme and High Risk Areas, but are currently at moderate risk of inundation from infrequent events or are at risk in the future from sea level rise. Criteria and source data used to define these High Risk Areas include: Area bounded by the 0.2% annual risk (500 year) flood zone, where available; Added 3 feet to the Base Flood Elevation for the current 1% annual risk flood event and extended this elevation inland over the DEM to point of intersection with ground surface; Area bounded by SLOSH category 3 hurricane inundation zone. Polygons were created that were upland of the Extreme and High Risk Area boundaries and included the maximum spatial extent of the above-listed criteria. These polygons represent Moderate Risk Areas. (7/1/13)View Dataset on the Gateway

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

The National Weather Service (NWS) Storm Prediction Center (SPC) routinely collects reports of severe weather and compiles them with public access from the database called SeverePlot (Hart and Janish 1999) with a Graphic Information System (GIS). The composite SVRGIS information is made available to the public primarily in .zip files of approximately 50MB size. The files located at the access point have organized severe weather data by County Warning Area (CWA). A CWA is a grouping of counties for which severe weather information is distributed. Although available to all, the data provided may be of particular value to weather professionals and students of meteorological sciences. An instructional manual is provided on how to build and develop a basic severe weather report GIS database in ArcGis and is located at the technical documentation site contained in this metadata catalog.

This metadata record is for AfA product AfA 010. Extreme Sea Levels Estuary values is part of Coastal Design/Extreme Sea Levels,a GIS dataset and supporting information providing design / extreme sea level and typical surge information around the coastline of the UK, including England, Wales, Scotland, Northern Ireland, Isle of Man and Jersey. The information is relevant under present day (year 2018) conditions and does not account for future changes due to climate change, such as sea level rise. This is a specialist dataset which informs on work commenced around the coast ranging from coastal flood modelling, scheme design, strategic planning and flood risk assessments.

Extreme Sea Level values describes the extreme sea levels for 16 different annual probabilities of exceedance. Confidence levels relating to the 5% and 95% lower and upper bounds of confidence are included. Mean High Water Spring (MHWS) and Highest Astronomical Tide (HAT) predicted tide levels are also included in the dataset for some sites but may not be used for navigational purposes. This dataset provides level this level information for sites in estuaries, tidal rivers and harbours. Levels for open coastal areas are provided separately in Extreme Sea Levels.

This 2018 update to the Coastal Design Sea Levels dataset was carried out in partnership for the UK Coastal Flood Forecasting partnership, which includes the Environment Agency (EA), Scottish Environment Protection Agency (SEPA), Natural Resources Wales (NRW) and the Department for Infrastructure Northern Ireland (DfINI).

A bundle download of all Coastal Design Sea Levels datasets is available from this record. Please see individual records for full details and metadata on each product.

The Severe Weather Data Inventory (SWDI) is an integrated database of severe weather records for the United States. SWDI enables a user to search through a variety of source data sets in the NCDC (now NCEI) archive in order to find records covering a particular time period and geographic region, and then to download the results of the search in a variety of formats. The formats currently supported are Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML. The current data layers in SWDI are: Storm Cells from NEXRAD (Level-III Storm Structure Product); Hail Signatures from NEXRAD (Level-III Hail Product); Mesocyclone Signatures from NEXRAD (Level-III Meso Product); Digital Mesocyclone Detection Algorithm from NEXRAD (Level-III MDA Product); Tornado Signature from NEXRAD (Level-III TVS Product); Preliminary Local Storm Reports from the NOAA National Weather Service; Lightning Strikes from Vaisala NLDN.

This story map journal highlights some apps, web maps, and databases to understand and prepare for extreme heat. Some of the apps contained in this story map are:

This study is the first comprehensive publication of tidal datums and extreme tides for San Francisco Bay (Bay) since the United States Army Corps of Engineers (USACE) published itsSan Francisco Bay Tidal Stage vs. Frequency Study in 1984 (USACE 1984). The USACE study was groundbreaking at the time of publication, presenting tidal datums and the “100-year tide” elevation for 53 locations around the Bay. The purpose of this study is to update and expand on the USACE study and to present daily and extreme tidal information for more than 900 locations along the Bay shoreline. Tidal datums, described further in Section 2 , are standard elevations defined by a certain phase of the tide (e.g., mean high tide, mean low tide). A tidal datum is used as a reference to measure and define local water levels, and as such is specific to local hydrodynamic processes and is not easily extended from one area to another without substantiating measurements or analysis. Many industries and activities rely on tidal datums, including shipping and navigation, coastal flood management, coastal development, and wetland restoration. Extreme tidal elevations are estimated for less-frequent extreme tides (e.g., 2-year tides to 500-year tides [tides with a 50.0 percent to 0.2 percent annual chance of occurrence, respectively]). Knowledge of the 100-year tide, or the water elevation with a 1 percent annual chance of occurrence, is critical for shoreline planning, floodplain management, and sea level rise (SLR) adaptation efforts. This study presents detailed daily and extreme tide information for the entirety of the Bay shoreline. This data set will support floodplain management efforts; shoreline vulnerability and risk analyses; shoreline engineering, design, and permitting; ecological studies; and appropriate sea level rise adaptation planning. The goal of this study is to provide data that support a wide-range of planning efforts around the Bay, particularly as communities seek to understand—and begin to adapt to—rising sea levels. You can access the full report at: http://www.adaptingtorisingtides.org/wp-content/uploads/2016/05/20160429.SFBay_Tidal-Datums_and_Extreme_Tides_Study.FINAL_.pdf.

This data resource is a layer in a map service. To download it, please go to the "Layers" section of this page and click the name of the dataset. This will open a new page that features a download button. Open the Map Service: https://gis.chesapeakebay.net/ags/rest/services/ChesapeakeProgress/cpClimate_High_Temp/MapServer This Chesapeake Bay Program indicator of progress toward the Climate Monitoring and Assessment Outcome shows trends in the number of unusually hot days per year at weather stations across the Chesapeake Bay watershed. An unusually hot day is defined as one on which the maximum temperature observed at a particular weather station is among the top five percent of daily highs observed at that weather station across all years of measurement.

This dataset contains the maps of flood hazard of an extreme flood. The dataset is structured according to the INSPIRE Annex III Theme - Natural Risk Zones.

This Geographic Information System (GIS) dataset is part of a comprehensive effort designed to facilitate analysis and understanding of sea-level-rise exposure in the United States and outlying territories. The dataset is derived from sea-level-rise projections published in two National Oceanic and Atmospheric Administration (NOAA) technical reports: 1) Global and Regional Sea Level Rise Scenarios for the United States (2017; https://tidesandcurrents.noaa.gov/publications/techrpt83_Global_and_Regional_SLR_Scenarios_for_the_US_final.pdf) and 2) Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean projections and Extreme Water Level Probabilities Along U.S. Coastlines (2022; https://sealevel.globalchange.gov/internal_resources/756/noaa-nos-techrpt01-global-regional-SLR-scenarios-US.pdf).

Each of the NOAA technical reports includes multiple sets of point projections based on mean global sea-level-rise scenarios. Global mean sea-level-rise scenarios provide an overall estimate of how sea level could change in the future. However, local effects can produce sea level changes that are substantially different than the global average. To capture those effects, the sea-level-rise projections produced for these reports utilized a 1-degree grid (approximately 111 km by 89 km at 38° north latitude) covering the coastlines of the U.S. mainland, Alaska, Hawaii, and the Caribbean and Pacific Island territories as well as the precise location of tide gauges along these coastlines. Adjustments to sea level projections at each point location include 1) shifts in oceanographic factors such as circulation patterns, 2) changes in the Earth’s gravitational field and rotation, and flexure of the crust and upper mantle, due to melting of land-based ice, 3) vertical land movement (subsidence or uplift) due to glacial isostatic adjustment (ongoing changes in elevation due to the retreat of ice sheets at the end of the last Ice Age), sediment compaction, groundwater and fossil fuel withdrawals and other non-climatic factors.

The 2017 report included six scenarios: 0.3, 0.5, 1.0, 1.5, 2.0 and 2.5 meters of global mean sea-level rise; the 2022 report reassessed the projections for the first five scenarios and eliminated the extreme (2.5-m) scenario from consideration based on its very low probability of occurrence. The projections in these reports are provided at approximately decadal time scales and include a year 2000 baseline and the following time horizons: 2010 (2017 dataset only), 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100, 2110 (2022 dataset only), 2120, 2130 (2022 dataset only), 2140 (2022 dataset only), 2150, and 2200 (2017 dataset only). GIS visualizations for each of these 149 combinations is available as polygons that show areal extent of mean sea level and rasters that include a water depth component for each pixel at 30-m resolution. Data files are grouped by dataset (2017 or 2022) and geography, with the continental United States divided along regional boundaries used by the US Environmental protection Agency.

These datasets are intended to provide users with GIS data layers linked to time horizons that are useful to programmatic or project-based planning processes, thus providing critical insight for policymakers, researchers, planners, and others concerned with climate adaptation practices addressing sea-level rise in coastal areas.

Populations of many cold-water species are likely to decline this century with global warming, but declines will vary spatially and some populations will persist even under extreme climate change scenarios. Especially cold habitats could provide important refugia from both future environmental change and invasions by non-native species that prefer warmer waters. The Climate Shield website hosts geospatial data and related information that describes specific locations of cold-water refuge streams for native Cutthroat Trout (Oncorhynchus clarkii) and Bull Trout (Salvelinus confluentus) across the American West. Forecasts about the locations of refugia could enable the protection of key watersheds, inform support among multiple stakeholders, and provide a foundation for planning climate-smart conservation networks that improve the odds of preserving native trout populations through the 21st century. The Northern Rockies Adaptation Partnership provided a valuable forum that accelerated this work. The Great Northern and North Pacific Landscape Conservation Cooperatives generously funded the NorWeST project, which serves as the foundation for Climate Shield. The Climate Shield Cutthroat Trout and Bull Trout models were developed from fish surveys conducted at more than 4,500 locations in over 500 streams, as described in the cited peer-reviewed studies and agency reports. Resources in this dataset:Resource Title: Digital Maps and ArcGIS Shapefiles. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects/ClimateShield/maps.html Information is available here to download as easy-to-use digital maps (.pdf files) and ArcGIS shapefiles for all streams within the historical ranges of native trout across the northwestern U.S. The geographic areas match the NorWeST production units because those stream temperature scenarios are integral to Climate Shield.

This is a collection of 124 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to Lindersson et al. (accepted february 2020 in WIREs Water) for further information about review methodology. The collection is a descriptive list, holding the following information for each dataset: Category - as structured in Lindersson et al. (in preparation). Sub-category- as structured in Lindersson et al. (in preparation). Abbreviation - official or as specified in Lindersson et al. (in preparation). Title - full title of dataset. Product(s) - type of product(s) offered by the dataset. Period - time period covered by the dataset, not defined for all datasets. Temporal resolution - not defined for static datasets. Angular spatial resolution - only defined for gridded datasets. Metric spatial resolution - only defined for gridded datasets. Map scale Extent - geographic coverage of dataset given in latitude limits. Description Creating institute(s) Data type - raster, vector or tabular. File format Primary EO type - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data. Data sources - lists the data sources behind the dataset, to the extent this is feasible. Data sources also in this table - data sources that are also included as datasets in this collection. Intentionally compatible with - defines other datasets in this collection that the dataset is intentinoally compatible with. Citation - dataset reference or credit. Documentation - dataset documentation. Web address - dataset access link. NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

This Geographic Information System (GIS) dataset is part of a comprehensive effort designed to facilitate analysis and understanding of sea-level-rise exposure in the United States and outlying territories. The dataset is derived from sea-level-rise projections published in two National Oceanic and Atmospheric Administration (NOAA) technical reports: 1) Global and Regional Sea Level Rise Scenarios for the United States (2017; https://tidesandcurrents.noaa.gov/publications/techrpt83_Global_and_Regional_SLR_Scenarios_for_the_US_final.pdf) and 2) Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean projections and Extreme Water Level Probabilities Along U.S. Coastlines (2022; https://sealevel.globalchange.gov/internal_resources/756/noaa-nos-techrpt01-global-regional-SLR-scenarios-US.pdf).

Each of the NOAA technical reports includes multiple sets of point projections based on mean global sea-level-rise scenarios. Global mean sea-level-rise scenarios provide an overall estimate of how sea level could change in the future. However, local effects can produce sea level changes that are substantially different than the global average. To capture those effects, the sea-level-rise projections produced for these reports utilized a 1-degree grid (approximately 111 km by 89 km at 38° north latitude) covering the coastlines of the U.S. mainland, Alaska, Hawaii, and the Caribbean and Pacific Island territories as well as the precise location of tide gauges along these coastlines. Adjustments to sea level projections at each point location include 1) shifts in oceanographic factors such as circulation patterns, 2) changes in the Earth’s gravitational field and rotation, and flexure of the crust and upper mantle, due to melting of land-based ice, 3) vertical land movement (subsidence or uplift) due to glacial isostatic adjustment (ongoing changes in elevation due to the retreat of ice sheets at the end of the last Ice Age), sediment compaction, groundwater and fossil fuel withdrawals and other non-climatic factors.

The 2017 report included six scenarios: 0.3, 0.5, 1.0, 1.5, 2.0 and 2.5 meters of global mean sea-level rise; the 2022 report reassessed the projections for the first five scenarios and eliminated the extreme (2.5-m) scenario from consideration based on its very low probability of occurrence. The projections in these reports are provided at approximately decadal time scales and include a year 2000 baseline and the following time horizons: 2010 (2017 dataset only), 2020, 2030, 2040, 2050, 2060, 2070, 2080, 2090, 2100, 2110 (2022 dataset only), 2120, 2130 (2022 dataset only), 2140 (2022 dataset only), 2150, and 2200 (2017 dataset only). GIS visualizations for each of these 149 combinations is available as polygons that show areal extent of mean sea level and rasters that include a water depth component for each pixel at 30-m resolution. Data files are grouped by dataset (2017 or 2022) and geography, with the continental United States divided along regional boundaries used by the US Environmental protection Agency.

These datasets are intended to provide users with GIS data layers linked to time horizons that are useful to programmatic or project-based planning processes, thus providing critical insight for policymakers, researchers, planners, and others concerned with climate adaptation practices addressing sea-level rise in coastal areas.

GIS U.S. Monthly Extremes is a web based product that is extracted from the U.S. COOP Summary of the Month dataset (DSI-3220). This is meteorological data from the U.S. Cooperative Observer Network (COOP), which consists of stations operated by state universities, state or federal agencies, and also private individuals whose stations are managed and maintained by the National Weather Service (NWS). The network includes regular NWS offices, and airports with weather stations operated by the NWS or the Federal Aviation Administration (FAA). The Network also includes U.S. military bases. There are typically about 8,000 stations operating in any one year. The earliest data is from 1886 and is organized by month. Attributes included for the GIS application are COOP ID, WBAN ID, Station Name, State, Year, Latitude, Longitude, Station Elevation, Precipitation and Temperature Extremes, and Snowfall/Snowdepth Extremes. Data is updated on a monthly basis.