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Location Folder Show Tree ROOT _ScienceBase Catalog __National and Regional Climate Adaptation Science Centers Alaska CASC Midwest CASC National CASC North Central CASC Northeast CASC More... Filters Date Range Past year (8) Past month (1) Past week (0) Past day (0) Custom range... To   Search Extensions Shapefile (1) Types Citation (6) Map Service (3) OGC WFS Layer (3) OGC WMS Layer (3) OGC WMS Service (2) More... Contacts Jordan S. Read Gretchen J.A. Hansen Alison P. Appling Anuj Karpatne Jared Willard More... Categories Data (33) Data Release - Revised (2) Tag Types Theme (40) Place (40) Science Themes (21) USGS Scientific Topic Keyword (10) Water, Coasts and Ice (21) Organization (21) Wildlife and Plants (20) More... Tag Schemes none (21) ISO 19115 Topic Category (29) U.S. Department of Commerce, 1987, Codes for the identification of the States, the District of Columbia and the outlying areas of the United States, and associated areas (Federal Information Processing Standard 5-2): Washington, D. C., NIST (21) None (6) Department of Commerce, 1995, Countries, Dependencies, Areas of Special Sovereignty, and Their Principal Administrative Divisions, Federal Information Processing Standard (FIPS) 10-4, Washington, D.C., National Institute of Standards and Technology (21) https://www.sciencebase.gov/vocab/category/NCCWSC/ScienceThemes (21) USGS Thesaurus (10) More... View Results as: JSON ATOM CSV Browse Folder

Output Data by HUC4 Sub-basin for Hydrologic Simulations of the CONUS using the NHM-PRMS, 1950-2010, Maurer Calibration, Dynamic Parameters This data release contains inputs for and outputs from hydrologic simulations for the conterminous United States (CONUS) using the Precipitation Runoff Modeling System (PRMS) version 5.1.0 and the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018). Historical simulations using the Maurer forcings (Maurer and others, 2002) were conducted for the period 1950-2010. This metadata record documents the simulation output files for simulations ran using the dynamic parameters file. The output files are aggregated at the HUC4 level and are grouped and downloadable by HUC2 hydrologic region. Each zip folder contains identical information, just for a different region and set of hydrologic response... Categories: Data; Tags: United States, inlandWaters, model, streamflow, streamflow modeling FiCli: Fish and Climate Change Database (ver. 2.0, Oct 2023) Inland fishes provide important ecosystem services to communities worldwide and are especially vulnerable to the impacts of climate change. Fish respond to climate change in diverse and nuanced ways which creates challenges for practitioners of fish conservation, climate change adaptation, and management. Although climate change is known to affect fish globally, a comprehensive online, public database of how climate change has impacted inland fishes worldwide and adaptation or management practices that may address these impacts does not exist. We conducted an extensive, systematic primary literature review to identify peer-reviewed journal publications describing projected and documented examples of climate change... Categories: Data; Tags: Africa, Americas, Asia, Europe, Middle East, All tags... Oceania, USGS Science Data Catalog (SDC), USGS:63eff8edd34efa0476b039b7, climate adaptation, climate change, climate change, database, fish, fisheries management, inland, inland fishery resources, utilitiesCommunication, Fewer tags Daily HOBO Pro V.2 soil temperature measurements at the Great Dismal Swamp National Wildlife Refuge (2015-2017) Daily HOBO Pro V.2 soil temperature measurements at the Great Dismal Swamp National Wildlife Refuge (2015-2017). Data collected in Great Dismal Swamp National Wildlife Refuge in Southern VA and Northern NC from 9 plot sites representing three general forest types: Atlantic White Cedar, Pocosin Pine, and Maple and Gum. Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: Climate, Great Dismal Swamp National Wildlife Refuge, Soil Sciences, USGS Science Data Catalog (SDC), Wetland One Meter Topobathymetric Digital Elevation Model for Majuro Atoll, Republic of the Marshall Islands, 1944 to 2016 Located in the northern tropical Pacific Ocean, Majuro is the capital of the Republic of the Marshall Islands. Majuro Atoll consists of a large, narrow landmass and a set of smaller perimeter islands surrounding a lagoon that is over 100 square miles in size. The waters surrounding the Majuro Atoll land areas are relatively shallow with poorly mapped bathymetry. However, the Pacific Ocean on the exterior of the coral atoll and the lagoon within its interior consist of deep bathymetry with steep slopes. The highest elevation of the Majuro Atoll is estimated at only 3-meters above sea level, which is the island community of Laura located on the western part of the atoll. At the eastern edge of the atoll lies the capital... Categories: Data; Tags: 3D Elevation Program, 3DEP, American Society of Photogrammetry and Remote Sensing, Base Maps, Bathymetric, All tags... Bathymetry, Calalin, Calalin Channel, CoNED, Coastal National Elevation Database, Coastal Zone, DEM, DTM, Digital Elevation Model, Digital Terrain Model, Djarrit, Earth Cover, Geography, Geomorphology, Hydrologic, Hydrologic Modeling, Information Sciences, Laura, Lidar Bathymetry, Light Detection and Ranging, Majuro, Majuro, Majuro Atoll, Majuro Atoll, Majuro Atoll, Majuro Atoll, Multi-beam Sonar, National Standards for Spatial Digital Accuracy, Pacific Islands, Pacific Ocean, RMI, Remote Sensing, Republic of the Marshall Islands, Republic of the Marshall Islands, Republic of the Marshall Islands, Republic of the Marshall Islands, Rita, SDB, Satellite-derived bathymetry, SfM, Single-beam Sonar, Structure from Motion, TBDEM, Topobathy, Topobathymetric, Topobathymetric Digital Elevation Model, Topobathymetric Lidar, U.S., U.S. Geological Survey, USA, USGS, USGS Science Data Catalog (SDC), United States, Water Resources, Western Pacific, Western Pacific Islands, lidar, Fewer tags Water Balance Model Inputs and Outputs for the Conterminous United States, 1900-2015 This metadata record describes monthly input and output data covering the period 1900-2015 for a water-balance model described in McCabe and Wolock (2011). The input datasets are precipitation (PPT) and air temperature (TAV) from the PRISM group at Oregon State University. The model outputs include estimated potential evapotranspiration (PET), actual evapotranspiration (AET), runoff (RUN) (streamflow per unit area), soil moisture storage (STO), and snowfall (SNO). The datasets are arranged in tables of monthly total or average values measured in millimeters or degrees C and then multiplied by 100. The data are indexed by the identifier PRISMID, which refers to an ASCII raster of cells in an associated file named... Types: Citation; Tags: Conterminous United States, National Water Quality Program, USGS Science Data Catalog (SDC), Water Resources, evaporation, All tags... precipitation, streamflow, temperature, Fewer tags Monthly Water Balance Model Futures A monthly water balance model (MWBM) was driven with precipitation and temperature using a station-based dataset for current conditions (1949 to 2010) and selected statistically-downscaled general circulation models (GCMs) for current and future conditions (1950 to 2099) across the conterminous United States (CONUS) using hydrologic response units from the Geospatial Fabric for National Hydrologic Modeling (Viger and Bock, 2014). Six MWBM output variables (actual evapotranspiration (AET), potential evapotranspiration (PET), runoff (RO), streamflow (STRM), soil moisture storage (SOIL), and snow water equivalent (SWE)) and the two MWBM input variables (atmospheric temperature (TAVE) and precipitation (PPT)) were summarized... Categories: Data, Data Release - Revised; Types: Map Service, NetCDF OPeNDAP Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: USGS Science Data Catalog (SDC), United States, Water Resources, hydrology, inlandWaters, All tags... precipitation, runoff, streamflow, water cycle, Fewer tags Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added... Categories: Data; Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 3c All lakes historical inputs This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs... Categories: Data; Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD). Categories: Data; Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of... Categories: Data; Tags: 007, 012, Fish, Northeast CASC, Rivers, Streams and Lakes, All tags... US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Data for climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes This data release is comprised of tidal marsh biomass data and spatial predictions of peak biomass and Julian day of peak biomass using data from the Landsat archive. Aboveground biomass dry weight of mixed-species plots (25x50 cm) at a tidal marsh in Willapa Bay, Washington were used to establish a relationship between biomass and tasseled cap greeness (TCG). The julian day of annual peak greenness and the value of annual peak greenness for 32 years at Bandon National Wildlife Refuge (NWR), Grays Harbor NWR, and Nisqually NWR was calculated by fitting a Gaussian function to the TCG values for a given year. The value of each 30 meter pixel is the Julian day of maximum predicted TCG or the maximum predicted TCG.... Categories: Data; Tags: Bandon Marsh National Wildlife Refuge, Billy Frank Jr Nisqually National Wildlife Refuge, Coos County, Ecology, Grays Harbor County, All tags... Grays Harbor National Wildlife Refuge, Information Sciences, Land Use Change, Remote Sensing, Thurston County, USGS Science Data Catalog (SDC), Willapa Bay, Washington, annual peak greenness, biomass, environment, inlandWaters, landsat, location, plant production, remote sensing, tasseled cap greenness, tidal marsh, Fewer tags Output Data by HUC4 Sub-basin for Hydrologic Simulations of the CONUS using the NHM-PRMS, 1950-2010, Maurer Calibration, Static Parameters This data release contains inputs for and outputs from hydrologic simulations for the conterminous United States (CONUS) using the Precipitation Runoff Modeling System (PRMS) version 5.1.0 and the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018). Historical simulations using the Maurer forcings (Maurer and others, 2002) were conducted for the period 1950-2010. This metadata record documents the simulation output files for simulations ran using the static parameters file. The output files are aggregated at the HUC4 level and are grouped and downloadable by HUC2 hydrologic region. Each zip folder contains identical information, just for a different region and set of hydrologic response units... Categories: Data; Tags: United States, inlandWaters, model, streamflow, streamflow modeling Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD). Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Data Quality Assurance - Field Replicates This dataset contains replicate samples collected in the field by community technicians. No field replicates were collected in 2012. Replicate constituents with differences less than 10 percent are considered acceptable. Categories: Data; Types: Citation; Tags: Water Quality, Yukon River Basin, Alaska, USA and Canada Data Quality Assurance - Instrument Detection Limits This dataset includes laboratory instrument detection limit data associated with laboratory instruments used in the analysis of surface water samples collected as part of the USGS - Yukon River Inter-Tribal Watershed Council collaborative water quality monitoring project. Categories: Data; Types: Citation; Tags: Water Quality, Yukon River Basin in Alaska, USA and Canada Process-guided deep learning predictions of lake water temperature Climate change has been shown to influence lake temperatures in different ways. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we focused on improving prediction accuracy for daily water temperature profiles in 68 lakes in Minnesota and Wisconsin during 1980-2018. The data are organized into these items: Spatial data - One shapefile of polygons for all 68 lakes in this study (.shp, .shx, .dbf, and .prj files) Model configurations - Model parameters and metadata used to configure models (1 JSON file, with metadata for each of 68 lakes, indexed by "site_id") Model inputs - Data formatted as model inputs for predicting temperature a. Lake... Tags: 007, 012, Ecology, Fish, Limnology, All tags... MN, Minnesota, Northeast CASC, Rivers, Streams and Lakes, US, USGS Science Data Catalog (SDC), United States, WI, Water Quality, Water Resources, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North Temperate Lakes Long-TERM Ecological Research Program (NTL-LTER; https://lter.limnology.wisc.edu/). The buoy is supported by both the Global Lake Ecological Observatory Network (gleon.org) and the NTL-LTER. This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD). Categories: Data; Tags: 007, 012, Fish, Northeast CASC, Rivers, Streams and Lakes, All tags... US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Supporting data for two MWBMglacier applications to the Copper River basin in Alaska These data can be used to replicate the application of MWBMglacier as described in two journal articles: 1) Enhancement of a parsimonious water balance model to simulate surface hydrology in a glacierized watershed (in review), and 2) Hydrologic regime changes in a high-latitude glacierized watershed under future climate conditions (doi:10.3390/w10020128). These simulations provide results from historical and 12 future general circulation model scenarios for the period 1949-2099 to determine the potential effects of climate change on the hydrology and water quality of a snow-dominated mountainous environment. In addition to the inputs and outputs, this Data Release includes summaries of the input and output data... Categories: Data; Tags: Climatology, Glaciers and Permafrost, Million Dollar Bridge, Northwest CASC, Rivers, Streams and Lakes, All tags... USGS Science Data Catalog (SDC), Water Resources, Water, Coasts and Ice, glaciology, hydrology, mathematical modeling, snow and ice cover, Fewer tags Process-guided deep learning water temperature predictions: 6c All lakes historical evaluation data This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for... Categories: Data; Tags: 007, 012, Fish, MN, Minnesota, All tags... Northeast CASC, Rivers, Streams and Lakes, US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Process-guided deep learning water temperature predictions: 3a Lake Mendota inputs This dataset includes model inputs that describe local weather conditions for Lake Mendota, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake... Categories: Data; Tags: 007, 012, Fish, Northeast CASC, Rivers, Streams and Lakes, All tags... US, United States, WI, Water, Coasts and Ice, Wildlife and Plants, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags 1 2 »