The United States has an average elevation of roughly 2,500 feet (763m) above sea level, however there is a stark contrast in elevations across the country. Highest states Colorado is the highest state in the United States, with an average elevation of 6,800 feet (2,074m) above sea level. The 10 states with the highest average elevation are all in the western region of the country, as this is, by far, the most mountainous region in the country. The largest mountain ranges in the contiguous western states are the Rocky Mountains, Sierra Nevada, and Cascade Range, while the Appalachian Mountains is the longest range in the east - however, the highest point in the U.S. is Denali (Mount McKinley), found in Alaska. Lowest states At just 60 feet above sea level, Delaware is the state with the lowest elevation. Delaware is the second smallest state, behind Rhode Island, and is located on the east coast. Larger states with relatively low elevations are found in the southern region of the country - both Florida and Louisiana have an average elevation of just 100 feet (31m) above sea level, and large sections of these states are extremely vulnerable to flooding and rising sea levels, as well as intermittent tropical storms.

At 282 feet below sea level, Death Valley in the Mojave Desert, California is the lowest point of elevation in the United States (and North America). Coincidentally, Death Valley is less than 85 miles from Mount Whitney, the highest point of elevation in the mainland United States. Death Valley is one of the hottest places on earth, and in 1913 it was the location of the highest naturally occurring temperature ever recorded on Earth (although some meteorologists doubt its legitimacy). New Orleans Louisiana is the only other state where the lowest point of elevation was below sea level. This is in the city of New Orleans, on the Mississippi River Delta. Over half of the city (up to two-thirds) is located below sea level, and recent studies suggest that the city is sinking further - man-made efforts to prevent water damage or flooding are cited as one reason for the city's continued subsidence, as they prevent new sediment from naturally reinforcing the ground upon which the city is built. These factors were one reason why New Orleans was so severely impacted by Hurricane Katrina in 2005 - the hurricane itself was one of the deadliest in history, and it destroyed many of the levee systems in place to prevent flooding, and the elevation exacerbated the damage caused. Highest low points The lowest point in five states is over 1,000 feet above sea level. Colorado's lowest point, at 3,315 feet, is still higher than the highest point in 22 states or territories. For all states whose lowest points are found above sea level, these points are located in rivers, streams, or bodies of water.

The National Ecological Framework for Canada's "Elevation by Ecodistrict” dataset provides elevation information for ecodistrict framework polygons, in meters. It includes codes and descriptions for minimum elevation, maximum elevation, mean elevation and the difference in elevation.

A bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from LiDAR data using triangulated irregular network (TIN) processing of the ground point returns. Hydro-flattened Bare Earth DEMs represent water bodies in a cartographically and aesthetically pleasing manner, and are not intended to accurately map water surface elevations. In a Hydro-flattened DEM, water surfaces are flat and level for lakes with a greater area than two acres, and gradated for rivers or other long impoundments (e.g., reservoirs) that are wider than 100 feet, and tidal areas. Any existing island larger than one acre was be delineated. Water surface edge elevations were at or below the immediately surrounding terrain. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average elevation for that area. The specified coordinate system for this dataset is California State Plane Zone II (FIPS 0402), NAD83 (2011), with units in US Survey Feet for horizontal, and vertical units are NAVD88 (12A) US Survey Feet. The dataset encompasses a portion of Sonoma County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Consortium.

The National Ecological Framework for Canada's "Elevation by Ecozone” dataset provides elevation information for ecozone framework polygons, in meters. It includes codes and descriptions for minimum elevation, maximum elevation, mean elevation and the difference in elevation.

This dataset contains projections of coastal cliff-retreat rates and positions for future scenarios of sea-level rise (SLR). Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical and statistical models based on field observations such as historical cliff retreat rate, nearshore slope, coastal cliff height, and mean annual wave power, as part of Coastal Storm Modeling System (CoSMoS) v.3.0 Phase 2 in Southern California. Details: Cliff-retreat position projections and associated uncertainties are for scenarios of 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, and 5 meters of SLR. Projections were made at CoSMoS cross-shore transects (CST) spaced 100 m alongshore using a baseline sea-cliff edge from 2010 (included in the dataset). Within each zip file, there are two separate datasets available: one that ignores coastal armoring, such as seawalls and revetments, and allows the cliff to retreat unimpeded (“Do Not Hold the Line”); and another that assumes that current coastal armoring will be maintained and 100% effective at stopping future cliff erosion ("Hold the Line"). Eight numerical models synthesized from literature (Trenhaile, 2000; Walkden and Hall, 2005; Trenhaile, 2009; Trenhaile, 2011; Ruggiero and others, 2011; Hackney and others, 2013) were used to make projections. All models relate breaking-wave height and period to cliff rock or unconsolidated sediment erosion. Models range in complexity from 2-D models in which the entire profile evolves, from below water to the cliff edge, to simple 1-D empirical or statistical models in which only the cliff edge evolves as a function of wave impact intensity and frequency. The projections are a robust average of all models, and the uncertainties are proportional to 1) underlying uncertainties in the model input data, such as historical cliff retreat rates, and 2) the differences between individual model forecasts at each CST so that uncertainty is larger when the models do not agree. As sea level rises, waves break closer to the sea cliff, more wave energy impacts the cliffs, cliff erosion rates accelerate. Model behavior also includes wave run-up (Stockdon and others, 2006), wave set-up that raises the water level during big-wave events, and tidal levels. The more complex 2-D models were run on idealized cliff profiles extending from about 10 m water depth to 1 kilometer inland from the cliff edge. Profiles were extracted by overlaying the cross-shore transects on a high-resolution digital elevation model (DEM) covering the Southern California study area. For all models, the presence of a beach was recorded (yes or no) for all transects using aerial photography, and the cliff toe elevation (or beach/cliff junction) was digitized from the DEM profiles. Using historic cliff edge retreat rates by Hapke and Reid (2007), unknown coefficients within the cliff-profile models were calibrated using a Monte Carlo simulation (in other words, coefficients were tuned until the modeled mean retreat rate equaled the observed mean retreat rate for a given transect). Uncertainty was tallied using a root mean squared error (RMSE) approach. The RMSE represents cumulative uncertainty from multiple sources and assumes that different sources of error will, at times, cancel each other out. It is therefore not a 'worst-case uncertainty' (in other words, a straight sum of errors) but instead an average uncertainty. Total RMSE increased with SLR rate and varied between +/- 2-3 m to a maximum of +/- 50 m for the extreme 5 m SLR scenario. For more information on model details, data sources, and integration with other parts of the CoSMoS framework, see CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf).

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and warning efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of North American Vertical Datum of 1988 (NAVD 88) or Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Cell size for the DEMs ranges from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).The DEM Global Mosaic is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), along with the global GEBCO_2014 grid: http://www.gebco.net/data_and_products/gridded_bathymetry_data. NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service is a general-purpose global, seamless bathymetry/topography mosaic. It combines DEMs from a variety of near sea-level vertical datums, such as mean high water (MHW), mean sea level (MSL), and North American Vertical Datum of 1988 (NAVD88). Elevation values have been rounded to the nearest meter, with DEM cell sizes going down to 1 arc-second. Higher-resolution DEMs, with greater elevation precision, are available in the companion NAVD88: http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042 and MHW: http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799 mosaics. By default, the DEMs are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Please see NCEI's corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. In this visualization, the elevations/depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png.A map service showing the location and coverage of land and seafloor digital elevation models (DEMs) available from NOAA's National Centers for Environmental Information (NCEI). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. Layers available in the map service: Layers 1-4: DEMs by Category (includes various DEMs, both hosted at NCEI, and elsewhere on the web); Layers 6-11: NCEI DEM Projects (DEMs hosted at NCEI, color-coded by project); Layer 12: All NCEI Bathymetry DEMs (All bathymetry or bathy-topo DEMs hosted at NCEI).This is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), with vertical units referenced to mean high water (NAVD88). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service provides data from many individual DEMs combined together as a mosaic. By default, the rasters are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Alternatively, a single DEM or group of DEMs can be isolated using a filter/definition query or using the 'Lock Raster 'mosaic method in ArcMap. This is one of three services displaying collections of DEMs that are referenced to common vertical datums: North American Vertical Datum of 1988 (NAVD88): http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042, Mean High Water (MHW): http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799, and Mean Higher High Water: http://noaa.maps.arcgis.com/home/item.html?id=9471f8d4f43e48109de6275522856696. In addition, the DEM Global Mosaic is a general-purpose global, seamless bathymetry/topography mosaic containing all the DEMs together. Two services are available: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff Elevation Values: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff and Color Shaded Relief: http://noaa.maps.arcgis.com/home/item.html?id=feb3c625dc094112bb5281c17679c769. Please see the corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. This service has several server-side functions available. These can be selected in the ArcGIS Online layer using 'Image Display ', or in ArcMap under 'Processing Templates '. None: The default. Provides elevation/depth values in meters relative to the NAVD88 vertical datum. ColorHillshade: An elevation-tinted hillshade visualization. The depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png. GrayscaleHillshade: A simple grayscale hillshade visualization. SlopeMapRGB: Slope in degrees, visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/SlopeMapLegend_V7b.png. SlopeNumericValues: Slope in degrees, returning the actual numeric values. AspectMapRGB: Orientation of the terrain (0-360 degrees), visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/AspectMapLegendPie_V7b.png. AspectNumericValues: Aspect in degrees, returning the actual numeric values.

Climate change is projected to cause extensive plant range shifts, and in many cases such shifts already are underway. Most long-term studies of range shifts measure emergent changes in species distributions but not the underlying demographic patterns that shape them. To better understand species’ elevational range shifts and their underlying demographic processes, we use the powerful approach of rephotography, comparing historical (1978-82) and modern (2015-16) photographs taken along a 1000 m elevational gradient in theColorado Desert of Southern California. This approach allowed us to track demographic outcomes for 4263 individual plants of 11 long-lived, perennial species over the past ~36 years. All species showed an upward shift in mean elevation (average = 45 m), consistent with observed increasing temperature and severe drought in the region. We found that varying demographic processes underlaid these elevational shifts, with some species showing higher recruitment and some showing higher survival with increasing elevation. Species with faster life history rates (higher background recruitment and mortality rates) underwent larger elevational shifts. Our findings emphasize the importance of demography and life history in shaping range shift responses and future community composition, as well as the sensitivity of desert systems to climate change despite the typical ‘slow motion’ population dynamics of perennial desert plants. Methods We utilized photos originally taken by Dr. Wilbur Mayhew between 1977 and 1982 (Mayhew 1981), which we digitized from 35 mm slides stored at Philip L. Boyd Deep Canyon Desert Research Center (doi:10.21973/N3V66D). We relocated permanently marked sites where historical photos had been taken and rephotographed them using a Canon 5D Mark II camera and tripod in 2015 and 2016. We took one additional set of photos in April 2017 after the end of a multi-year drought, so that we could distinguish dormant from dead individuals of two drought-deciduous species (brittlebush, Encelia farinosa and white bursage, Ambrosia dumosa). We approximated the original view of the original photos as closely as possible in modern photos. For each photo view, we chose a single historical and modern photo for analysis based on resolution, contrast and coloration. The mean timespan between paired historical and modern photos was 36 years. We perfected the alignment between the paired historical and modern photos in Photoshop by making one photo semi-transparent, then rotating and resizing it while maintaining original aspect ratios. Data extraction We extracted data on 11 perennial species that appeared in 5+ sites. We extracted data from the photos in ArcGIS, arranging the paired photos as map layers. We created polygons to delimit a survey area close enough to the camera to identify species; these polygons serve as the “sites” in our subsequent analysis. In some cases, we collected data on larger-bodied or particularly conspicuous species, such as ocotillo (Fouquieria splendens) and creosote (Larrea tridentata), in a larger area including locations farther from the camera than for smaller, less conspicuous species. We recorded whether each plant underwent recruitment (absent historical, alive modern), mortality (alive historical, dead modern) or survival (alive both). We excluded plants that were dead in the historical period or with main stems outside the site polygon. In some cases we consulted other historical and modern photos of the same site to determine species identity or assess whether an individual was alive. We counted and measured clusters of agave (Agave deserti) and Mojave yucca (Yucca schidigera) as single individuals. Rarely, we may have misidentified pygmy cedar (Peucephyllum schottii) for creosote where these species co-occur on steep slopes, since they have similar morphology and are difficult to distinguish from a distance. We measured individual relative change in plant size by measuring the height (perpendicular to the ground) and width (the largest horizontal extent of the plant perpendicular to the camera, i.e. canopy width) of surviving plants in both time periods, using the ruler tool in ArcGIS and focusing on woody biomass. When dead agave rosettes were surrounded by live rosettes, we did not include the width that was dead if it was >20% the total width. We calculated the relative change in height of each plant as (H1–H0) / H0, where H indicates plant height and the subscripts 0 and 1 indicate the historical and modern period, respectively. We used an equivalent equation for relative change in width. For some species at some sites, we could not track the fate of individuals between the two time periods. This most often occurred for narrow-bodied and relatively short-lived species (e.g. teddy bear cholla, Cylindropuntia bigelovii) in photo pairs that were difficult to perfectly align, thereby making it difficult to tell whether plants either survived, or died and were replaced by recruits. It also occurred when a large plant died and a new plant “appeared” in a spot that was previously hidden, such that we were unable to determine whether the second plant was a recruit or a surviving plant. We therefore designated two site types for each species: “trackable” sites – those where we could track the fate of at least one third of individuals of a given species over time, and “count-only” sites – those where we could track fewer than one third of individuals, and instead only counted individuals. Count-only sites were retained for analyses of mean elevation shifts but not demographic rates. Geophysical data We used Google Earth Pro “ground level view” to draw polygons matching the extent of the site polygons outlined in the photos. To do so, we first “stood” at the camera’s locality and angle, then used corresponding features (e.g. washes, large creosote, hills) to find the exact site, and finally dropped pins to mark polygon vertices. We used these polygons to extract data on each site’s size, as well as its mean elevation, aspect, slope and annual solar radiation (“insolation”) using USGS NED Contiguous US 1/3 arc-second digital elevation model (2013) in ArcGIS. We took the cosine of aspect to create linear values ranging from -1 (South) to 1 (North). Additional details Additional details on how these data were collected and processed can be found in the Methods and Supplementary Materials of Skikne et al. 2024. Contrasting demographic processes underlie uphill shifts in a desert ecosystem.

This bathymetric dataset provides an update to the stage-storage relation for Little Rock Reservoir located in the Angeles National Forest, California. Bathymetric data was collected using a multibeam echo sounder to provide near-complete coverage and was merged with U.S. Geological Survey 3D Elevation Project lidar to compute a digital elevation model (DEM) of the reservoir and surrounding watershed. The DEM was used to computed storage and surface area for a range of stage elevations. Results show that the mean cross-spillway elevation was 3273 feet above the North American Vertical Datum 1988 (NAVD88) and the mean dam crest elevation was 3277 feet (NAVD88). At the spillway elevation the storage was 3335.8 acre-feet with a surface area of 101.7 acres. At the crest elevation the storage was 3765.6 acre-feet with a surface area of 112.7 acres.

No description is available. Visit https://dataone.org/datasets/%7B5996BD4C-F74F-4699-96AF-B655BAFE1F42%7D for complete metadata about this dataset.

This repository contains supporting material for a journal article being submitted to one of the journals published by the American Geophysical Union, titled Earth’s Future. The repository contains the following items: 1. README file of what is in the repository including methods associated with the geodatabase 2. File Geodatabase 1. README file The files collected here relate to a study being submitted to the American Geophysical Union’s journal, Earth’s Future. The title of the paper being submitted is, “The contribution of Microrefugia to landscape thermal inertia for climate-adaptive conservation and adaptation strategies.” The study was conducted across 40,250 km2 of complex mountainous terrain in Northern California. The objective of the study was to consider whether it was possible to identify the relative strength of microrefugia systematically in order to provide conservation and climate-adaptation strategies with information that could help with prioritizing actions. We selected an operational scale of 10 ha (25 acres) as a scale that is suitable for various types of landscape planning exercises, and created a hexagon grid for the region. We calculated the mean value for multiple variables and appended them into the hexagons. For thermal inertia, we calculated the mean elevation per hexagon and then its coolest (highest) point using an environmental lapse rate. We also calculated solar energy loading, calculated the mean solar load per hexagon, and calculated its effect on air temperature. We combined these two temperature metrics to identify how much thermal buffering capacity each hexagon contains, as measured by how much warming it could experience before the mean temperature, as determined from a baseline time period, is no longer found anywhere within the hexagon. We tied the mean annual temperature from 1981–2010 to the mean elevation in each hexagon, as well as a temperature from an earlier period, and from several future periods, based on global circulation models. The study shows how long current (baseline) climate conditions found in each hexagon may persist and shows how the resulting map of landscape thermal inertia can be used when considering natural vegetation types for conservation, identifying which parts of high-priority wildlife corridors have the greatest capacity to retain their current climate conditions, and what the potential for retaining baseline climate conditions is for areas with late-seral forest conditions as represented by forest canopy height. The methods section below describes the data used in the study to create the data in the geodatabase that is posted here. The Geodatabase itself provides all the data needed to replicate the various results presented in the paper. Further information can be found in Thorne et al. 2020. That report is more extensive than the results in our associated paper, but it contains more information on the calculation of various metrics associated with and was the foundation from which we developed this study. The report is provided here in order to keep all the relevant materials compiled for potential use by others. 2. File Geodatabase The geodatabase is provided as a separate file. Name: ThermalInertiaIndex.gdb Contents:

AllHexagons

A feature class containing all 408,948 hexagon grids used in this study Fields within the feature class:

Id

A unique ID for each hexagon

Watershed

Watershed the hexagon falls within

DomWHR

Habitat type (WHR) that had the majority coverage within the hexagon

WHR_Name

Descriptive name of the habitat type

WHR_GroupName

Major vegetation type

CanopyHt_Score

Canopy Height Score ranging from 1 (under 1m) to 5 (over 25m)

CanopyHt_m

Average canopy height within the hexagon (m)

Conn_Score

Connectivity Score ranging from 1 (low) to 5 (high)

dem10m

Average elevation within the hexagon (m)

dem10m_min

Minimum elevation within the hexagon (m)

dem10m_max

Maximum elevation within the hexagon (m)

SRtemp_min

The lowest Solar Radiation load within the hexagon (degree C)

ElevLR_NegEff2

Effect of elevation on air temperature (degree C)

Thermal_Inertia

Hexagon buffering capacity (degree C)

tave_5180

Average temperature 1951-1980

tave_8110

Average temperature 1981-2010

tave_1039mi8

Average temperature 2010-2039 (MIROC-ESM RCP 8.5)

tave_4069mi8

Average temperature 2040-2069 (MIROC-ESM RCP 8.5)

tave_7099mi8

Average temperature 2070-2099 (MIROC-ESM RCP 8.5)

tave_1039cn8

Average temperature 2010-2039 (CNRM-CM5 RCP 8.5)

tave_4069cn8

Average temperature 2040-2069 (CNRM-CM5 RCP 8.5)

tave_7099cn8

Average temperature 2070-2099 (CNRM-CM5 RCP 8.5)

Connectivity_Scores

90m raster containing all 3 connectivity scores Fields within the raster:

TNC_Conn_Score

Connectivity Score from reclassed TNC/Omniscape

CEHC_Score

Connectivity Score from reclassed California Essential Habitat Connectivity

Combined_Score

Overall Connectivity Score

Methods These methods describe the steps taken to calculate the attribute columns in the associated database. Compilations were done on publicly available data such as digital elevation models, climate data and others. For references to the public base data used, please see references in Table 1. There are two sections a. How we processed material into the hexagon framework b. The sequence of steps for each of the analyses presented in the results section of the main report a. How we processed material into the hexagon framework We created a geodatabase of 10 ha hexagons for the region in order to summarize the spatial data in this study into spatial units that are comparable across the region but that also represent an area size that is relevant for site-level plans such as landscape connectivity or forest conservation. The hexagon geodatabase covers 28,269 km2 in within the 5 watersheds in northern California, and 40,895 km2 in the 5 watersheds plus a 10 km buffer area. Integrating data into the hexes Data from a variety of grid scales, including 10, 30, 90, and 270m was added using the ArcGIS sample tool with the Hexagon centroids to sample the 270m resolution data, and the zonal statistics tool within Hexagon boundaries for raster data with smaller grid cell sizes.

This study used four types of data (Table 1):

Air temperature & topographic – Topographic data was used to calculate microrefugia buffering capacity for each hexagon. Temperature data was used to evaluate the effect of historical and projected future warming on the ability of local sites to retain baseline temperature conditions. Habitats / Dominant Vegetation Types – Habitat data was used to profile the presence and extent of microrefugia by habitat type for the region Landscape Connectivity Models – were used to find microrefugia in areas that are highly ranked for landscape connectivity Forest Structure data – was used to identify where large, late seral trees occupy microrefugia sites.

Microrefugia – Air temperature & topographic

National Elevation Dataset

www.usgs.gov/core-science-systems/ngp/tnm-delivery

Raster - 10m

Solar Radiation Model

Developed at UC Davis for this study from 25m DEM

Raster - 25m

Environmental Lapse Rate Model

Developed at UC Davis for this study from 10m DEM

Raster - 10m

Linking Temperature to Hexagons

Downscaled PRISM Tmax & Tmin – BCM – current & historical

http://climate.calcommons.org/dataset/2014-CA-BCM

Raster – 270 m

Downscaled future climate projections MIROC & CNRM RCP8.5

http://climate.calcommons.org/dataset/2014-CA-BCM

Raster – 270 m

Habitats / Dominant Vegetation Types

FVEG - CalFire (FRAP)

https://frap.fire.ca.gov/mapping/gis-data/

Raster - 30m

Vegetation and Climate Refugia

Vegetative Climate Exposure (UCD Modeling)

Raster - 270m

Landscape Connectivity Models

California Essential Connectivity

https://wildlife.ca.gov/Conservation/Planning/Connectivity/CEHC

Polygon

Omniscape Climate Connectivity

https://omniscape.codefornature.org/

90 m

Forest Structure

Canopy Height - SALO Sciences

https://forestobservatory.com/

Raster - 10m

Table 1: Data sources b. The sequence of steps for each of the analyses presented in the results section of the main report Microrefugia – thermal buffering capacity Thermal buffering capacity combined two metrics that represent potential modifications to the air temperature in each 10-ha hexagon. First, a 10m digital elevation model was used to calculate the variation in air temperature within each hexagon due to variations in elevation, using a standard environmental lapse rate. Second, the influence of solar radiation on air temperature was calculated. These two metrics were combined. Elevational Effect on Air Temperature Column: ElevLR_NegEff2 Zonal Statistics was performed on a 10m DEM for each hex. The range of elevation was used with environmental lapse rate to calculate “buffering capacity” within each Hexagon. We used an environmental lapse rate of 0.00649606 C⁰/ meter (International Civil Aviation Organization, 1993) to calculate the range of temperatures within the hexagon. To calculate the effect of elevation on air temperature within each hexagon we used the following equation: (Average Elevation – Maximum Elevation) x 0.00649606

Solar Radiation Effect on Air Temperature: – Column: SRtemp_min We ran the analysis on a 25 m-resolution DEM. We calculated annualized solar radiation via the r.sun model available in GRASS 7.8 (https://grass.osgeo.org/grass70/manuals/r.sun.html) which calculates direct, diffuse, and reflected solar irradiation for a given day, location, topography, and atmospheric conditions. We assumed clear-sky conditions to run this model, and ran the model for 2 days in each month, from which we calculated solar

Seamless unconfined groundwater heads for coastal California groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (i.e. land surface less than approximately 10 m above mean sea level) areas. In areas where coastal elevations increase rapidly (e.g., bluff stretches), the model boundary was set approximately 1 kilometer inland of the present-day shoreline. Steady-state MODFLOW groundwater flow models were used to obtain detailed (10-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for both current and future sea-level rise (SLR) scenarios (0 to 2 meters (m) in 0.25 m increments, 2.5 m, 3 m, and 5 m) using a range of horizontal hydraulic conductivity (Kh) scenarios (0.1, 1, and 10 m/day). For each SLR/Kh combination, results are provided for two marine boundary conditions, local mean sea level (LMSL) and mean higher-high water (MHHW), and two model versions. In the first model version, groundwater reaching the land surface is removed from the model, simulating loss via natural drainage. In the second model version, groundwater reaching the land surface is retained, simulating the worst-case "linear" response of groundwater head to sea-level rise. Modeled groundwater heads were then subtracted from high-resolution topographic digital elevation model (DEM) data to obtain the water table depths. Additional details about the groundwater model and data sources are outlined in Befus and others (2020) and in Groundwater_model_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5b8ef008e4b0702d0e7ec72b?name=Groundwater_model_methods.pdf). Methods specific to groundwater head and water table depth products are outlined in Groundwater_head_and_water_table_depth_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5bda1563e4b0b3fc5cec39b4?name=Groundwater_head _and_water_table_depth_methods.pdf). Please read the model details, data sources and methods summaries, and inspect model output carefully. Data are complete for the information presented. Users should note that while the metadata Spatial Reference Information/UTM Zone Number in this document is 10, some files in southern California are in UTM Zone 11, as noted in the Format Specification for individual downloadable files. As a result users may need to modify the metadata for automated import and display of Zone 11 datafiles.

A bare earth digital elevation model (DEM) represents the earth's surface with all vegetation and human-made structures removed. The bare earth DEMs were derived from LiDAR data using triangulated irregular network (TIN) processing of the ground point returns. Each image corresponds to a 37,800-square-foot tile. Each pixel is 3 feet and represents an average elevation for that area. The specified coordinate system for this dataset is California State Plane Zone II (FIPS 0402), NAD83 (2011), with units in U.S. Survey Feet for horizontal, and vertical units are NAVD88 (12A) US Survey Feet. The dataset encompasses a portion of Sonoma County. WSI collected the LiDAR and created this data set for the Sonoma County Vegetation Mapping and LiDAR Consortium.Data hosted by Sonoma County Information Systems Department (ISD).

Forest Elevation(Ht) Mean 2015Mean height of lidar first returns (m). Represents the mean canopy height. Products relating the structure of Canada's forested ecosystems have been generated and made openly accessible. The shared products are based upon peer-reviewed science and relate aspects of forest structure including: (i) metrics calculated directly from the lidar point cloud with heights normalized to heights above the ground surface (e.g., canopy cover, height), and (ii) modelled inventory attributes, derived using an area-based approach generated by using co-located ground plot and ALS data (e.g., volume, biomass). Forest structure estimates were generated by combining information from lidar plots (Wulder et al. 2012) with Landsat pixel-based composites (White et al. 2014; Hermosilla et al. 2016) using a nearest neighbour imputation approach with a Random Forests-based distance metric. These products were generated for strategic-level forest monitoring information needs and are not intended to support operational-level forest management. All products have a spatial resolution of 30 m. For a detailed description of the data, methods applied, and accuracy assessment results see Matasci et al. (2018). When using this data, please cite as follows: Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., Bolton, D.K., Tompalski, P., Bater, C.W., 2018b. Three decades of forest structural dynamics over Canada's forested ecosystems using Landsat time-series and lidar plots. Remote Sensing of Environment 216, 697-714. Matasci et al. 2018) Wulder et al. 2018)Geographic extent: Canada's forested ecosystems (~ 650 Mha)Time period: 1985–2011

Bathymetry for Tomales Bay was derived from three surveys containing22,020 soundings. Five older, less accurate, overlapping surveys wereomitted before tinning. The average separation between soundings was 37meters. The three surveys used dated from 1993 and 1994. The total range ofsounding data was 1.5 meters to -18.9 meters at mean low water. A mean highwater value of 1.1 meters was assigned to the shoreline. Two points were foundthat were not consistent with the surrounding data and were removed priorto tinning. DEM grid values outside the shoreline (on land) wereassigned null values (-32676).Tomales Bay has four 7.5 minute DEMs and a single one degree DEM. The1 degree DEMs were generated from the higher resolution 7.5 minuteDEMs which covered the estuary. A Digital Elevation Model (DEM)contains a series of elevations ordered from south to north with theorder of the columns from west to east. The DEM is formatted as oneASCII header record (A- record), followed by a series of profilerecords (B- records) each of which include a short B-record headerfollowed by a series of ASCII integer elevations (typically in unitsof 1 centimeter) per each profile. The last physical record of theDEM is an accuracy record (C-record).The 7.5-minute DEM (30- by 30-m data spacing) is cast on theUniversal Transverse Mercator (UTM) projection. It provides coveragein 7.5- by 7.5-minute blocks. Each product provides the samecoverage as a standard USGS 7.5-minute quadrangle but the DEMcontains over edge data. Coverage is available for many estuaries ofthe contiguous United States but is not complete.

Seamless unconfined groundwater heads for coastal California groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (i.e. land surface less than approximately 10 m above mean sea level) areas. In areas where coastal elevations increase rapidly (e.g., bluff stretches), the model boundary was set approximately 1 kilometer inland of the present-day shoreline. Steady-state MODFLOW groundwater flow models were used to obtain detailed (10-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for both current and future sea-level rise (SLR) scenarios (0 to 2 meters (m) in 0.25 m increments, 2.5 m, 3 m, and 5 m) using a range of horizontal hydraulic conductivity (Kh) scenarios (0.1, 1, and 10 m/day). For each SLR/Kh combination, results are provided for two marine boundary conditions, local mean sea level (LMSL) and mean higher-high water (MHHW), and two model versions. In the first model version, groundwater reaching the land surface is removed from the model, simulating loss via natural drainage. In the second model version, groundwater reaching the land surface is retained, simulating the worst-case "linear" response of groundwater head to sea-level rise. Additional details about the groundwater model and data sources are outlined in Befus and others (2020) and in Groundwater_model_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5b8ef008e4b0702d0e7ec72b?name=Groundwater_model_methods.pdf). Methods specific to groundwater head and water table depth products are outlined in Groundwater_head_and_water_table_depth_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5bda1563e4b0b3fc5cec39b4?name=Groundwater_head _and_water_table_depth_methods.pdf). Please read the model details, data sources and methods summaries and inspect model output carefully. Data are complete for the information presented. Users should note that while the metadata Spatial Reference Information/UTM Zone Number in this document is 10, some files in southern California are in UTM Zone 11, as noted in the Format Specification for individual downloadable files. As a result users may need to modify the metadata for automated import and display of Zone 11 datafiles.

Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected water levels for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in

Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, San Diego, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected ocean current velocities for the 100-year storm and 0.0 m sea-level rise scenario. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Bathymetry for San Francisco Bay was derived from thirty surveys containing417,452 soundings. Older, less accurate, overlapping surveys wereeither partially or entirely omitted. The average separation between soundingswas 53 meters. Four surveys in the southern part of the bay dated from1956. The remaining surveys dated from 1971 to 1993. The total range ofsounding data was 12.1 meters to -12.7 meters at mean low water. Mean highwater values between 1.2 and 2.3 meters were assigned to the shoreline.Twenty-five points were found that were not consistent with the surroundingdata. These were removed prior to tinning. DEM grid values outside the shoreline (on land) wereassigned null values (-32676).San Francisco Bay has twenty-four 7.5 minute DEMs and three onedegree DEMs. The 1 degree DEMs were generated from the higherresolution 7.5 minute DEMs which covered the estuary. A DigitalElevation Model (DEM) contains a series of elevations ordered fromsouth to north with the order of the columns from west to east. TheDEM is formatted as one ASCII header record (A- record), followed bya series of profile records (B- records) each of which include ashort B-record header followed by a series of ASCII integerelevations (typically in units of 1 centimeter) per each profile.The last physical record of the DEM is an accuracy record(C-record).The 7.5-minute DEM (30- by 30-m data spacing) is cast on theUniversal Transverse Mercator (UTM) projection. It provides coveragein 7.5- by 7.5-minute blocks. Each product provides the samecoverage as a standard USGS 7.5-minute quadrangle but the DEMcontains over edge data. Coverage is available for many estuaries ofthe contiguous United States but is not complete.