Waterfront Public Access Areas (WPAAs)

Waterfront Public Access Areas (WPAAs) are privately owned waterfront zoning lots where publicly accessible open space is provided to and along the shoreline for public enjoyment, as shown on the <a href="https://waterfrontaccess.planning.nyc.gov/">Waterfront Access Map (WAM)</a>.

GRU Customer Reclaimed Water Consumption

Monthly reclaimed water consumption in Kilo-gallons (kgals) by service address for all customers in the GRU Service Area. Reclaimed water is also known as sewer or wastewater. (Potable water use can be found in another dataset)

Stormwater_Features

For NPDES Stormwater sewer system enhanced mapping project. Contains a GIS polygon feature class of stormwater basins in Gainesville, FL as a result of the NPDES stormwater system mapping project. This feature does not participate in the GIS network, and is for cartographic purposes only. This file is current only up to 02/04/08 and may be incomplete, and only covers those areas of Gainesville, FL that have been mapped up to 02/04/08. The file is also subject to constant updating as project progresses. This feature class is for informational purposes only. Do not rely on this file for accuracy of dimensions, size or location. The City of Gainesville does not assume responsibility to update this information for any error or omission in this file. This shapefile may indicate the zoning/land use on the properties as shown. Do not rely on this file for accuracy of dimensions. For specific information, contact the City of Gainesville, Florida.

Environmental Sensitivity Project (2015)

Historically, the City of Edmonton has managed ‘natural areas’ within the North Saskatchewan River Valley and the Tablelands separately, guided by inventories such as the Ribbon of Green and Geowest (1993). Over the past decade, City policy has shifted to manage natural areas with consideration of their role within an ecological network. Today, a goal of the City is to protect, preserve and enhance a functioning ecological network throughout the city limits. This network should include lands in both the river valley and the Tablelands. To further this goal, a model was developed in 2015 for determining environmental sensitivity scores across the entirety of the city. This model guided the collection of several digital data layers with coverage across the entire study area (including several ecological assets, threats to assets, and development and cultural constraints). Data layers were then used to develop spatial outputs that summarized the distribution of these assets, threats and constraints. These base layers have been compiled into this dataset to help inform planning, development and conservation throughout Edmonton. Environmental sensitivity analysis incorporated recent mapping of the ecological network of native and non-native vegetation, streams, wetlands and other waterbodies as much as possible, with practical limitations. The City’s urban Primary Land and Vegetation Inventory (uPLVI) and remote sensing data used for this assessment were completed in 2015 and 2013 respectively, which is relatively recent, but not current. Similarly, infrastructure data (roads, subdivision development and stormwater facilities) provided varied in month of acquisition from 2015. Some discrepancy between mapped and actual features may result, due to loss and changes from ongoing development activities.

Horseshoe Crab Spawning Survey

Delaware Bay shore survey data starting with 1999 which denotes peak spawning occurrences by day and lunar period, proportion of spawning in May (coinciding with shorebird stopovers), average water temperature, index values for female and male crabs per square meter by beach and bay-wide, the annual sex ratio, and index of abundance per beach.

Parcel

RSBS SMO: Part 2 of 2, New York State Residential Statewide Baseline Study: Single and Multifamily Occupant Telephone or Web Survey

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. This is part 2 (contains: Clothes Washing and Drying; Water Heating; Home Lighting; Pool and Spa; Small Household Appliances; and Miscellaneous Equipment) of 2; part 1 (https://data.ny.gov/d/3m6x-h3qa) contains: Behavior and Demographics; Building Shell; Kitchen Appliances; and Heating and Cooling. The New York State Energy Research and Development Authority (NYSERDA), in collaboration with the New York State Department of Public Service (DPS), conducted a statewide residential baseline study (study) from 2011 to 2014 of the single-family and multifamily residential housing segments, including new construction, and a broad range of energy uses and efficiency measures. This dataset includes 2,982 single-family and 379 multifamily occupant survey completes for a total of 3,361 responses. The survey involved 2,285 Web, 1,041 telephone, and 35 mini-inspection surveys. The survey collected information on the following building characteristics: building shell, kitchen appliances, heating and cooling equipment, water heating equipment, clothes washing and drying equipment, lighting, pool and spa equipment, small household appliances, miscellaneous energy consuming equipment, as well as behaviors and characteristics of respondents.

Land Use_data

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.

SLR Annual High Wave Flooding - 2.0 Ft. Scenario

Hawaii is exposed to large waves annually on all open coasts due to its location in the Central North Pacific Ocean. The distance over which waves run-up and wash across the shoreline will increase with sea level rise. As water levels increase, less wave energy will be dissipated through breaking on nearshore reefs and waves will arrive at a higher elevation at the shoreline. Computer model simulations of future annual high wave flooding were conducted by the University of Hawaii Coastal Geology Group using the XBeach (for eXtreme Beach behavior) numerical model developed by a consortium of research institutions. The model propagates the maximum annually recurring wave, calculated from offshore wave buoy data, over the reef and to the shore along one-dimensional (1D) cross-shore profiles extracted from a 1-meter DEM. Profiles are spaced 20 meters apart along the coast. This approach was used to model the transformation of the wave as it breaks across the reef and includes shallow water wave processes such as wave set-up and overtopping. The IPCC AR5 RCP8.5 sea level rise scenario was used in modeling exposure to annual high wave flooding from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet. This particular layer depicts annual high wave flooding using the 2.0-ft (0.5991-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2075, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Historical data used to model annual high wave flooding include hourly measurements of significant wave height, peak wave period, and peak wave direction, and was acquired from offshore wave buoy data from PacIOOS. Maximum surface elevation and depth of the annual high wave flooding is calculated from the mean of the five highest modeled water elevations at each model location along each profile. Output from the simulations is interpolated between transects and compiled in a 5-meter map grid. Depth grid cells with values less than 10 centimeters are not included in the impact assessment. This was done to remove very thin layers of water excursions that (1) are beyond the accuracy of the model and (2) might not constitute a significant impact to land and resources when only occurring once annually. Any low-lying flooded areas that are not connected to the ocean are also removed. Annual high wave flood modeling covered wave-exposed coasts with low-lying development on Maui, Oahu, and Kauai. Annual high wave flooding was not available for the islands of Hawaii, Molokai, and Lanai, nor for harbors or other back-reef areas throughout all the islands. Additional studies would be needed to add the annual high wave flooding for those areas. The maximum annually recurring wave parameters (significant wave height, period, direction) were statistically determined using historical wave climate records and do not include potential changes in future wave climate, the effects of storm surge, or less-frequent high wave events (e.g., a 1-in-10 year wave event). In some locations, the extent of flooding modeled was limited by the extent of the 1-meter DEM. Changes in shoreline location due to coastal erosion are not included in this modeling. As shorelines retreat, annual high wave flooding will reach farther inland along retreating shorelines. Waves are propagated along a "bare earth" DEM which is void of shoreline structures, buildings, and vegetation, and waves are assumed to flow over an impermeable surface. The DEM represents a land surface at one particular time, and may not be representative of the beach shape during the season of most severe wave impact, particularly for highly variable north and west-exposed beaches. Undesirable artifacts of 1D modeling include over-predicted flooding along some transects with deep, shore-perpendicular indentations in the sea bottom such as nearshore reef channels. The 1D modeling does not account for the pres

SLR Coastal Erosion - 1.1 Ft. Scenario

UPDATED - Nov. 2020. Studies of historical shoreline change using aerial photographs and survey maps show that 70% of beaches on Kauai, Oahu, and Maui are eroding (receding landward) (Fletcher et al. 2012). Beaches exist in a delicate balance between existing water levels, wave energy, and sand supply. Coastal erosion modeling was conducted for sandy shorelines of Kauai, Oahu, and Maui by the University of Hawaii Coastal Geology Group. The methods are described in Anderson et al. (2015) and combine historical shoreline change data with a model of beach profile response to sea level rise from Davidson-Arnott (2005) in order to estimate probabilities of future exposure to erosion at transects (shore-perpendicular measurement locations) spaced approximately 20 meters apart along the shoreline. The model accounts for localized alongshore variability in shoreline change by incorporating trends from the historical erosion mapping studies. Historical data used to model coastal erosion consisted of: (1) historical shoreline positions and erosion rates measured from high-resolution (0.5 meters) ortho-rectified aerial photographs and NOAA topographic charts dating back to the early 1900s (Fletcher et al. 2012, Romine et al. 2013), and (2) beach profile field survey data (Gibbs et al. 2001, Fletcher et al. 2012). The vegetation line was identified in the most recent aerial photography dating from 2006 to 2008. The output of the modeling is the estimated exposure zone to future erosion hazards. Based on the model and IPCC AR5 RCP8.5 sea level rise scenario, there is an 80% probability that land impacted by erosion would be confined within the exposure zone at that particular time. The exposure zones extend landward from the current-day shoreline (vegetation line) up to the 80% cumulative probability contour from sea level rise at 0.5, 1.1, 2.0, and 3.2 feet, which incorporates the uncertainty (upper and lower bounds) of the IPCC RCP8.5 sea level rise projection. This particular layer depicts coastal erosion using the 1.1-ft (0.3224-m) sea level rise scenario. While the RCP8.5 predicts that this scenario would be reached by the year 2050, questions remain around the exact timing of sea level rise and recent observations and projections suggest a sooner arrival. Assumptions and Limitations: Historical shoreline change data and beach profiles needed to model coastal erosion are available only for sandy shores of Kauai, Oahu, and Maui. Exposure was not modeled for less-erodible rocky coasts and bluffs, though the latter can be prone to sudden failure in some areas. In addition, modeling did not account for: (1) existing seawalls or other coastal armoring in the backshore; (2) increasing wave energy across the fringing reef with sea level rise; (3) possible changes in reef accretion and nearshore sediment processes with sea level rise; and (4) possible changes to sediment supply from future shoreline development and engineering, such as construction or removal of coastal armoring or other coastal engineering. Data compiled by the Pacific Islands Ocean Observing System (PacIOOS) for the Hawaii Sea Level Rise Viewer hosted at https://pacioos.org/shoreline/slr-hawaii/. Users of these data should cite the following publication: Anderson, T.R., Fletcher, C.H., Barbee, M.M., Frazer, L.N., and B.M. Romine (2015). Doubling of Coastal Erosion Under Rising Sea Level by Mid-Century in Hawaii, Natural Hazards, doi:10.1007/s11069-015-1698-6. For further information, please see the Hawaii Sea Level Rise Vulnerability and Adaptation Report: https://climateadaptation.hawaii.gov/wp-content/uploads/2017/12/SLR-Report_Dec2017.pdf