Standardized data sets produced from the Massachusetts Coast Flood Risk Model (MC-FRM)include annual exceedance probability (AEP) ARC-GIS based rasters across all modeled climatetime horizons, and depth of flooding at three AEP levels across all modeled climate timehorizons (present day, 2030, 2050, and 2070). However, additional non-standardized datasetswere requested by the Massachusetts Executive Office of Energy and Environmental Affairs(EEA) to be utilized in the development of climate resiliency tools for the State ofMassachusetts. These datasets were produced using the same probabilistic modelingframework created and utilized for the original MC-FRM and Boston Harbor Flood Risk Model(BH-FRM) datasets. These new non-standardized data consist of (1) Water Surface Elevations,(2) Maximum Wave Heights, and (3) calculations of Design Flood Elevations (DFEs). These weredetermined for six (6) selected annual exceedance probabilities (AEPs) and three (3) timehorizons. Additionally, EEA also requested (4) development of Tidal Benchmarks across thestate for 2030, 2050, and 2070 projected future sea level rise conditions. For these requestedoutputs, Woods Hole Group created a set of geographic data features and raster datasets.These datasets were calculated directly from the MC-FRM model simulation results butrequired additional calculation and processing efforts to create the required GIS based outputs.

These data are from the output of a basin scale storm surge and wave model simulation where the analysis of the data was focused on the coastal areas adjacent to four different National Park units (Acadia National Park, Boston Harbor Islands, Boston National Historic Park, and Cape Cod National Seashore)

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation layer (AE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the forecast elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.

This Zenodo archive contains essential input datasets utilized in our research study titled "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution". This archive contains only input data. The Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the ISIMIP repository. Datasets Included: CoDEC (Coastal Dataset for the Evaluation of Climate Impact):

This dataset is described in Muis et al. (2020) cf_esl folder: Contains data representing total CoDEC water levels. Individual NetCDF files store data for each grid point. cf_tides folder: This folder holds data related to tidal elevation. coor_coastal.nc: A NetCDF file featuring the spatial grid utilized in CoDEC. This dataset comprises only coastal grid points.

HR (Hybrid Reconstructions):

HybridRec_Upd0422.mat: This file contains data from the Hybrid Reconstructions dataset (Dangendorf et al 2019), aligned to the CoDEC grid, and includes satellite altimetry integral to producing the Hybrid Reconstructions dataset. Each row corresponds to one grid point on the CoDEC grid. For ease of use in our applications, we offer a preprocessing script in our source code named split_hr_dataset_to_stations.py. We here provide the specific versions of HR and CoDEC that are used in our study to ensure accurate replication.

To illustrate and evaluate the impact of the higher 100-year coastal floods in the future, we produced a dataset representing stillwater flood elevations over land for flood heights of seven-and-one-half-feet above mean higher high water (MHHW, the average of the higher high water elevation of each tidal day.) The data includes horizontal spatial extent of seven-and-one-half-foot coastal floods above mean higher high water in the City of Boston.

Title: Boston Archdioceses 5 ft Sea Level InundationThese data are for planning, educational, and awareness purposes only and should not be used for site-specific analysis, navigation, or permitting.App and Map Development: “Boston Archdioceses 5 ft Sea Level Inundation”. Scale not given. Version 1.0. CT, USA: GoodLands Inc. 2019.Catholic Data: “USCCB Institution Extraction from Parcel Properties”. Scale not given. Version 1.0. CT, USA: GoodLands Inc. 2017.Sea Level Rise Data:Title:NOAA Office for Coastal Management Sea Level Rise Data: 1-6 ft Sea Level Rise Inundation ExtentShort Name:NOAA_OCM_SLR_1to6ft_metadataStatus:CompletedPublication Date:2016Abstract:These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an onlinemapping viewer depicting potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping vieweris to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is ascreening-level tool that uses nationally consistent data sets and analyses.Data and maps provided can be used at several scales to helpgauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at:https://www.coast.noaa.gov/slrThese data depict the potential inundation of coastal areas resulting from a projected 1 to 6 feet rise in sea level above currentMean Higher High Water (MHHW) conditions. The process used to produce the data can be described as a modified bathtub approach that attemptsto account for both local/regional tidal variability as well as hydrological connectivity. The process uses two source datasets to derive thefinal inundation rasters and polygons and accompanying low-lying polygons for each iteration of sea level rise: the Digital Elevation Model (DEM)of the area and a tidal surface model that represents spatial tidal variability. The tidal model is created using the NOAA National GeodeticSurvey's VDATUM datum transformation software (http://vdatum.noaa.gov) in conjunction with spatial interpolation/extrapolation methods andrepresents the MHHW tidal datum in orthometric values (North American Vertical Datum of 1988).The model used to produce these data does not account for erosion, subsidence, or any future changes in an area's hydrodynamics. It is simplya method to derive data in order to visualize the potential scale, not exact location, of inundation from sea level rise.Purpose:The purpose of these data is to show potential sea level rise inundation ranging from 1 to 6 feet above current Mean Higher High Water (MHHW)for the area. Although the water surface mapped represents a particular increase in sea level in feet above MHHW, the actual cell values inthe raster dataset represent depth in meters.Notes:10963Supplemental Information:A detailed methodology for producing these data can be found via the following url:https://coast.noaa.gov/data/digitalcoast/pdf/slr-inundation-methods.pdfSpatial_Reference_Information:Horizontal_Coordinate_System_Definition:Geographic:Latitude_Resolution: 0.0000001Longitude_Resolution: 0.0000001Geographic_Coordinate_Units: Decimal degreesGeodetic_Model:Horizontal_Datum_Name: North American Datum of 1983Ellipsoid_Name: Geodetic Reference System 80Semi-major_Axis: 6378137.000000Denominator_of_Flattening_Ratio: 298.257222101Vertical_Coordinate_System_Definition:Altitude_System_Definition:Altitude_Datum_Name: North American Vertical Datum of 1988Altitude_Resolution: 0.001Altitude_Distance_Units: metersAltitude_Encoding_Method: Explicit elevation coordinate included with horizontal coordinatesKeil Schmid, Brian Hadley, and Kirk Waters (2014) Mapping and Portraying Inundation Uncertainty of Bathtub-Type Models. Journal of Coastal Research: Volume 30, Issue 3: pp. 548 – 561.Documentationhttps://coast.noaa.gov/digitalcoast/tools/slr.htmlBoston Archdioceses High Resolution Boundary, derived from:MassGIS Data: County Boundaries: https://docs.digital.mass.gov/dataset/massgis-data-county-boundariesContributorMassGIS (Bureau of Geographic Information)Modified Date2018-12-05Release Date2018-12-05Identifier2f6f9906-5088-47d7-917a-fee2e4ab1db1Spatial / Geographical Coverage LocationMassachusettsLicenseCreative Commons AttributionAuthorMassGISContact NameMassGISContact Emailmassgismail@mass.govPublic Access LevelPublicData QualityFalseContent TypeDataLanguageEnglish (United States)

These data were created as part of the National Oceanic and Atmospheric Administration Office for Coastal Management's efforts to create an online mapping viewer called the Sea Level Rise and Coastal Flooding Impacts Viewer. It depicts potential sea level rise and its associated impacts on the nation's coastal areas. The purpose of the mapping viewer is to provide coastal managers and scientists with a preliminary look at sea level rise (slr) and coastal flooding impacts. The viewer is a screening-level tool that uses nationally consistent data sets and analyses. Data and maps provided can be used at several scales to help gauge trends and prioritize actions for different scenarios. The Sea Level Rise and Coastal Flooding Impacts Viewer may be accessed at: http://www.coast.noaa.gov/slr This metadata record describes the Boston Weather Forecast Office (BOX WFO) digital elevation model (DEM), which is a part of a series of DEMs produced for the National Oceanic and Atmospheric Administration Office for Coastal Management's Sea Level Rise and Coastal Flooding Impacts Viewer described above. The DEMs created for this project were developed using the NOAA National Weather Service's Weather Forecast Office (WFO) boundaries. The DEM includes the best available lidar known to exist at the time of DEM creation that met project specifications for the Boston WFO, which includes the coastal counties of Massachusetts and Rhode Island. The DEM was produced from LiDAR datasets acquired by the U.S. Geological Survey (USGS) under the LiDAR for the Northeast Project along with LiDAR datasets for Dukes County, Nantucket, and the City of Boston. Hydrographic breaklines were delineated from LiDAR intensity imagery generated from the LiDAR datasets. The final DEM is hydro flattened such that water elevations are less than or equal to -0.5 meters. The DEM is referenced vertically to the North American Vertical Datum of 1988 (NAVD88) with vertical units of meters and horizontally to the North American Datum of 1983 (NAD83). The resolution of the DEM is approximately 5 meters.

Five inundation scenarios from STORMTOOLS were brought together such that each polygon would depict one inundation scenario. The layer depicts the unique inundation scenarios at current Mean Higher High Water (MHHW), MHHW Plus One Foot of Sea Level Rise, MHHW Plus Three Feet of Sea Level Rise, MHHW Plus Five Feet of Sea Level Rise, and MHHW Plus Seven Feet of Sea Level Rise. These scenarios, and all attendant modeling, originated with CRMC. This layer was created by the Rhode Island Statewide Planning Program (RISPP) as part of the 2016 Municipal Transportation Assets Vulnerable to Sea Level Rise and Storm Surge Project using inundation data from the STORMTOOLS Dataset prepared by the Coastal Recourses Management Council (CRMC). Five sea level rise inundation scenarios from STORMTOOLS were brought together such that each polygon would depict one inundation scenario.

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation prediction layer (PAE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the likelihood of elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.

Standardized data sets produced from the Massachusetts Coast Flood Risk Model (MC-FRM)include annual exceedance probability (AEP) ARC-GIS based rasters across all modeled climatetime horizons, and depth of flooding at three AEP levels across all modeled climate timehorizons (present day, 2030, 2050, and 2070). However, additional non-standardized datasetswere requested by the Massachusetts Executive Office of Energy and Environmental Affairs(EEA) to be utilized in the development of climate resiliency tools for the State ofMassachusetts. These datasets were produced using the same probabilistic modelingframework created and utilized for the original MC-FRM and Boston Harbor Flood Risk Model(BH-FRM) datasets. These new non-standardized data consist of (1) Water Surface Elevations,(2) Maximum Wave Heights, and (3) calculations of Design Flood Elevations (DFEs). These weredetermined for six (6) selected annual exceedance probabilities (AEPs) and three (3) timehorizons. Additionally, EEA also requested (4) development of Tidal Benchmarks across thestate for 2030, 2050, and 2070 projected future sea level rise conditions. For these requestedoutputs, Woods Hole Group created a set of geographic data features and raster datasets.These datasets were calculated directly from the MC-FRM model simulation results butrequired additional calculation and processing efforts to create the required GIS based outputs.

This dataset is used for status assessment, habitat conservation, and planning for coastal areas of Maine. This subset of data published by the Department of Agriculture, Conservation and Forestry represents low lying areas of the non-tidal landscape that are adjacent to tidal estuaries that could be inundated at highest annual tide if sea level is increased by 3.3 feet. Tidal marshes are ecologically and economically significant natural systems. Planning for their continued functional existence given various sea level rise scenarios is beneficial to both society and wildlife. Predictions for the amount of sea level rise in the next 50 to 100 years vary, but the fact that sea level is rising has been documented. This dataset is intended to be used to identify areas of the landscape where existing tidal marshes could migrate or expand to given a 3.3 foot increase in sea level. Identifying these areas creates the opportunity for government agencies, towns, private conservation organizations, and land managers to plan for compatible uses of the lands before they become inundated. This data can be paired with similarly created data that provides for scenarios with 1, 2, and 6 foot increases in sea level. Together, these datasets provide frames of reference for incremental increases of predicted sea level rise, to better serve planning purposes at different time frames.

The Coastal Flood Resilience Zoning Overlay District goes beyond the areas identified in FEMA flood maps, applying to areas of the City that could be inundated during a major coastal storm event, known as a 1 percent chance flood event with 40-inches of sea level rise. The zoning overlay promotes resilient planning and design, provides consistent standards for the review of projects, and maximizes the benefits of investments in coastal resilience.

Standardized data sets produced from the Massachusetts Coast Flood Risk Model (MC-FRM)include annual exceedance probability (AEP) ARC-GIS based rasters across all modeled climatetime horizons, and depth of flooding at three AEP levels across all modeled climate timehorizons (present day, 2030, 2050, and 2070). However, additional non-standardized datasetswere requested by the Massachusetts Executive Office of Energy and Environmental Affairs(EEA) to be utilized in the development of climate resiliency tools for the State ofMassachusetts. These datasets were produced using the same probabilistic modelingframework created and utilized for the original MC-FRM and Boston Harbor Flood Risk Model(BH-FRM) datasets. These new non-standardized data consist of (1) Water Surface Elevations,(2) Maximum Wave Heights, and (3) calculations of Design Flood Elevations (DFEs). These weredetermined for six (6) selected annual exceedance probabilities (AEPs) and three (3) timehorizons. Additionally, EEA also requested (4) development of Tidal Benchmarks across thestate for 2030, 2050, and 2070 projected future sea level rise conditions. For these requestedoutputs, Woods Hole Group created a set of geographic data features and raster datasets.These datasets were calculated directly from the MC-FRM model simulation results butrequired additional calculation and processing efforts to create the required GIS based outputs.