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The US Geological Survey Chesapeake Bay Watershed Land Cover Data Series, 2011 edition, (CBLCD-e11) consists of Level I Land Cover data for the years 1984, 1992, 2001, 2006 and 2011. It consists of a series of five 8-bit unsigned integer raster data files of 30 meter spatial resolution in Albers Conic Equal Area projection, NAD83 datum. The 1984 – 2006 data layers were created by aggregating most Level II Anderson classes of the USGS CBLCD Land Cover Data Series released in 2010 (Irani and Claggett, 2010).
This dataset provides timeseries data on water quality and quantity, as collected or computed from outside sources. The format is many tables with one row per time series observation (1 tab-delimited file per site-variable combination, 1 zip file per site). This compilation of data is intended for use in estimating or interpreting metabolism. Sites were included if they met the initial criteria of having at least 100 dissolved oxygen observations and one of the accepted NWIS site types ('ST','ST-CA','ST-DCH','ST-TS', or 'SP'). This dataset is part of a larger data release of metabolism model inputs and outputs for 356 streams and rivers across the United States (https://doi.org/10.5066/F70864KX). The complete release...
This metadata record documents 11 comma delimited tables representing the amount of reported best management practice (BMP) implementation for the years from 1985 to 2014 at three geographic scales: county or land-river modeling segment, River Input Monitoring (RIM) station drainage areas, and the entire Chesapeake Bay Watershed (CBWS). Data originated from the Chesapeake Bay Watershed jurisdictions including Maryland, Pennsylvania, Virginia, Delaware, New York, West Virginia, and the District of Columbia. Data were reported to the Chesapeake Bay Program for an annual review of progress toward meeting nitrogen, phosphorus, and sediment reduction goals.
This child item describes Python code used to estimate average yearly and monthly tourism per 1000 residents within public-supply water service areas. Increases in population due to tourism may impact amounts of water used by public-supply water systems. This data release contains model input datasets, Python code used to develop the tourism information, and output estimates of tourism. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Output from this code was used as an input feature in the public supply delivery and water use machine learning models. This page includes the following files: tourism_input_data.zip...
This child item describes a public supply delivery machine learning model that was developed to estimate public-supply deliveries. Publicly supplied water may be delivered to domestic users or to commercial, industrial, institutional, and irrigation (CII) users. This model predicts total, domestic, and CII per capita rates for public-supply water service areas within the conterminous United States for 2009-2020. This child item contains model input datasets, code used to build the delivery machine learning model, and national predictions. This dataset is part of a larger data release using machine learning to predict public-supply water use for 12-digit hydrologic units from 2000-2020. This page includes the following...
This child item describes Python code used to query census data from the TigerWeb Representational State Transfer (REST) services and the U.S. Census Bureau Application Programming Interface (API). These data were needed as input feature variables for a machine learning model to predict public supply water use for the conterminous United States. Census data were retrieved for public-supply water service areas, but the census data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the census data collector code were used as input...
Measures used to assess trends in the 10th, 50th, and 90th quantiles of annual peak streamflow from 1916-2015 at 2,683 U.S. Geological Survey stations and within 191 4-digit HUCs in the conterminous United States. Linear quantile regression was applied to the selected quantiles of log-transformed annual peak streamflow to represent trends for a range of flood frequencies from small, common floods to large, infrequent floods. Comparative trends in pairs of quantiles were characterized as coherent, convergent, or divergent by comparing the slopes of linear quantile regression equations.
Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training and evaluation are from 12,227 lakes and are included as well as daily meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes.
To estimate changes in nitrogen, phosphorus, and sediment loads from technologies and management practices (TMPs), the Chesapeake Bay Program Partnership's CAST model was used to run scenarios with and without TMPs for years between 1985 and 2020 for the Chesapeake Bay watershed. Scenarios were run using the native model segmentation of land-river segments and downscaled to the National Hydrography Dataset (NHD) plus version 2 catchments. The land-river segment loads were disaggregated to the National Hydrography Dataset Plus, Version 2.0 using methods described in "CAST Data Input Disaggregation from County and Land-River Segment Scale to National Hydrography Dataset Plus". The estimated total nitrogen (TN), total...
This data release contains relevant datasets used in the analysis for the Chesapeake Bay stressor identification project, for which results are described in Fanelli and others (2022). For this project, two existing information sources were compiled and summarized to identify key in-stream stressors reported by the scientific literature or through regulatory assessments to be likely affecting benthic macroinvertebrate community metrics in the Chesapeake Bay region: 1) the peer reviewed multi-stressor literature, in which the effects multiple in-stream stressors on benthic macroinvertebrate community metrics are simultaneously assessed; and 2) reported causes of biological impairment as listed by state or district...
This dataset presents the total estimated monthly public-supply water withdrawal by 12-digit hydrologic unit code (HUC12) in the conterminous United States for 2015. Public-supply water use was estimated by spatially and temporally downscaling available data from each state. The total represents combined groundwater and surface water withdrawals for 83,178 watersheds. Public supply refers to water withdrawn by public and private water suppliers that provide water for cities, towns, rural water districts, mobile-home parks, Native American Indian reservations, and military bases. Public-supply facilities are classified under the Standard Industrial Classification (SIC) 4941 and provide water to at least 25 people...
Freshwater salinization is an emerging issue for freshwater environments in the Chesapeake Bay, USA region. Salinization is often described by measurements of specific conductance (SC). This data release contains specific conductance observations collected by multiple regional agencies for streams within the Chesapeake Bay Watershed. This inventory compiles and harmonizes data from the Water Quality Portal (WQP), which is a data repository developed by the National Water Quality Monitoring Council and supported by the U.S. Environmental Protection Agency and U.S. Geological Survey, and the U.S. Geological Survey National Water Information System (NWIS). Both discrete measures of SC, which are single measures taken...
Categories: Data;
Tags: Chesapeake Bay watershed,
Delaware,
District of Columbia,
Hydrology,
Maryland,
The data presented here are blacklegged tick nymph densities, white-tailed deer densities, and Borrelia burgdorferi (causative agent of Lyme disease) prevalences for eight national parks in the eastern United States from 2014-2022.
The Gap Analysis Project (GAP) Analytical Database represents a synthesis of three core datasets for the conterminous U.S. Specifically 1) the GAP/LANDFIRE National Terrestrial Ecosystems_2011; 2) the Protected Areas Database of the United States (PAD-US) 1.4; and 3) the Species Ranges and Habitat Distribution Models for all terrestrial vertebrates. This database provides a mechanism to effiiently obtain summary statistics of those for a variety of spatial extents, including US states, US counties, Landscape Conservation Cooperation Network Areas, EPA's Level III-IV Ecoregions of the United States, and Level I-III Ecoregions of North America and 12-digit (6th level) hydrologic units. Disclaimer for Approved Database...
This dataset provides input data formatted for use in estimating metabolism. The format is tables of prepared time series inputs (1 tab-delimited file per site, in 1 zip file per site). This dataset is part of a larger data release of metabolism model inputs and outputs for 356 streams and rivers across the United States (https://doi.org/10.5066/F70864KX). The complete release includes: modeled estimates of gross primary productivity, ecosystem respiration, and the gas exchange coefficient; model input data and alternative input data; model fit and diagnostic information; site catchment boundaries and site point locations; and potential predictors of metabolism such as discharge and light availability.
This data set provides industrial-scale onshore wind turbine locations, corresponding facility information, and turbine technical specifications, in the United States to March 2014. The database has nearly 49,000 wind turbine records that have been collected, digitized, locationally verified, and internally quality assured and quality controlled. Turbines from the Federal Aviation Administration Digital Obstacle File, product date March 2, 2014, were used as the primary source of turbine data points. Verification of the position of turbines was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacle Repository System (FAA...
Categories: Data;
Types: ArcGIS REST Map Service,
Citation,
Map Service;
Tags: Alabama,
Alaska,
Arizona,
Arkansas,
California,
To quantify the potential for landward migration at the estuary level, we developed a geospatial dataset for the conterminous United States (CONUS) that identifies the boundaries for estuarine drainage areas. Nine estuarine drainage areas in south Florida were delineated using data developed by the South Florida Water Management District (SFWMD 2018). For the rest of CONUS, we used information contained within the National Fish Habitat Action Plan (NFHAP) - Coastal Spatial Framework (CSF) (National Centers for Coastal Ocean Science 2021). The original NFHAP-CSF data included 612 drainage areas, which were too many for our purposes. Therefore, we merged smaller drainage areas with larger, adjacent drainage areas...
We quantified the potential area available for landward migration of tidal saline wetlands and freshwater wetlands due to sea-level rise (SLR) at the estuary scale for 166 estuarine drainage areas and at the state scale for 22 coastal states and District of Columbia. We used 2016 Coastal Change Analysis Program (C-CAP) data in combination with the future wetland migration data under the 1.5 m global SLR scenario to evaluate the potential for wetland migration into all the individual C-CAP classes and into the following six land cover categories: (1) freshwater forest (wetland); (2) freshwater marsh (wetland); (3) terrestrial forest (upland); (4) terrestrial grassland (upland); (5) agricultural croplands (upland);...
The U.S. Geological Survey is developing national water-use models to support water resources management in the United States. Model benefits include a nationally consistent estimation approach, greater temporal and spatial resolution of estimates, efficient and automated updates of results, and capabilities to forecast water use into the future and assess model uncertainty. This data release contains data used in a machine learning model to estimate monthly water use for communities that are supplied by public-supply water systems in the conterminous United States for 2000-2020. This data release also contains associated scripts used to produce input features as well as model output values by 12-digit hydrologic...
This dataset was created to support the Washington D.C. StreamStats project funded by the Washington D.C. Department of Energy and Environment (DOEE). The dataset contains digital elevation model (DEM), flow direction and catchment layers that were conditioned using Washingtons D.C.’s stormwater network layer. The data are hosted online as a component of the USGS StreamStats web application (https://streamstats.usgs.gov), where users can interact with a map of Washington D.C.’s stormwater pipe system and National Hydrography Dataset (NHD) “best resolution” blue lines to delineate drainage basins that account for pipe flow. This project utilized 1-meter (high resolution) terrain products, which improves upon existing...
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