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Filters: Tags: {"scheme":"https://www.sciencebase.gov/vocab/category/NCCWSC/WaterCoastsandIce"} (X) > partyWithName: U.S. Geological Survey (X)

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Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional HEC-RAS hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1...
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This dataset includes electrical resistance data from a network of 50 data loggers that was installed throughout the Willow-Whitehorse watershed of SE Oregon in September 2014. Data loggers were downloaded in August 2015 and September 2016. These data loggers were used as “electrical resistance” (ER) sensors, following Chapin et al. 2014. The sensors were Onset HOBO Pendant temperature data loggers that were modified to monitor streamflow intermittency and determine the timing of stream drying.
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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).
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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...
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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...
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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...
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The files in the sub-folder "1. Juvenile coho salmon abundance and survival" consist of fish survey data and the associated analysis. The file "02_Fish survey data_all events_2019-11-27.csv" contains the actual fish survey data that was collected in Mason Creek, tributary of East Fork Lewis River, SW Washington, during summer of 2017. The protocol for the fish surveys are outline in the file "Fish Rescue Field Protocol_2017_FINAL VERSION_2017-06-01.pdf". The abundance and survival analysis can be found in the file "Juvenile MR abundance_Coho_02Contraints_2019-12-02.R". This file should be loaded through the .Rproj file "Fish Abundance.Rproj". There are many files needed to run the analysis that consist of summaries...
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This data set includes bi-monthly data on submerged aquatic vegetation species composition, percent cover, above and below ground biomass and environmental data at coastal sites across the fresh to saline gradient in Barataria Bay, LA. This project was co-funded by the South Central Climate Adaptation Science Center and the Gulf Coast Prairie and the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperatives. An alternate reference to this product can be found here.
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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).
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The Sea Surface Temperature (SST) data of the nearshore region of the North Pacific show temperature ranges in degrees C using points whose locations correspond to the centroids of AVHRR Pathfinder version 5 monthly, global, 4 km data set (PFSST V50). The pathfinder rasters are available from the Physical Oceanography Distributed Active Archive Center (PO.DAAC), hosted by NASA JPL. The data points in this dataset lie within a 20 km buffer from the GSHHS (Global Self-consistent, Hierarchical, High-resolution Shoreline) coastline. The GSHHS vector data are available from the National Geophysical Data Center (NGDC). Furthermore, each point in the SST dataset is categorized by the ecoregion in which it is located. This...
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This dataset contains inputs for a numerical groundwater-flow model of the Upper San Pedro Basin in southeastern Arizona and Northern Sonora, Mexico.
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Within large-river ecosystems, floodplains serve a variety of important ecological functions. A recent survey of 80 managers of floodplain conservation lands along the Upper and Middle Mississippi and Lower Missouri Rivers in the central United States found that the most critical information needed to improve floodplain management centered on metrics for characterizing depth, extent, frequency, duration, and timing of inundation. These metrics can be delivered to managers efficiently through cloud-based interactive maps. To calculate these metrics, we interpolated an existing one-dimensional HEC-RAS hydraulic model for the Lower Missouri River, which simulated water surface elevations at cross sections spaced (<1...
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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...
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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).
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This dataset includes spatial location information from 167 temperature data loggers that were installed throughout the Willow/Rock/Frazer watersheds of northern Nevada between July 30 and August 14, 2015. One hundred twelve data loggers were installed in stream channels (some of which were dry), 50 data loggers were installed outside the stream channel to measure air temperature, and 5 data loggers were installed on ridgetops to measure air temperature across the watershed.
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The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the...
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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...
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This dataset provides bi-monthly data on seed biomass collected in shallow water habitats across the fresh to saline gradient at coastal sites in Barataria Bay, Louisiana. This project was co-funded by the South Central Climate Adaptation Science Center and the Gulf Coast Prairie and the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperatives. An alternate reference to this product can be found here.
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This dataset includes stream temperatures from a network of 112 data loggers that was installed in stream channels throughout the Willow/Rock/Frazer watersheds of northern Nevada between July 31 and August 15, 2015. Ninety-seven data loggers were recovered and downloaded in late July 2016.


map background search result map search result map Probability of Predicted Elevation with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83) Groundwater flow model of Northern Arizona by Pool and others, 2011 Submerged aquatic vegetation and environmental data along a salinity gradient in Barataria Bay, Louisiana (2015) Seed biomass from shallow coastal water areas along a salinity gradient in Barataria Bay, Louisiana (2015) Electrical resistance data from the Willow-Whitehorse watersheds of southeast Oregon, USA, 2014-2016 Stream temperature data in Willow/Rock/Frazer watersheds of northern Nevada, 2015-16 Temperature data loggers in Willow/Rock/Frazer watersheds of northern Nevada, 2015 Climate Change Scenario Inundation Metrics along the Upper and Middle Mississippi and Lower Missouri Rivers Quantify Depth of Inundation for Floodplains on the Missouri River for a Calculated Return Interval of 5 Years Supporting data for two MWBMglacier applications to the Copper River basin in Alaska Sea Level Rise Systematic Mapping Literature Review Process-guided deep learning predictions of lake water temperature Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Juvenile coho salmon stream survey data and associated analysis to estimate abundance and survival in Mason Creek, tributary of East Fork Lewis River, SW Washington, during summer of 2017 Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Juvenile coho salmon stream survey data and associated analysis to estimate abundance and survival in Mason Creek, tributary of East Fork Lewis River, SW Washington, during summer of 2017 Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Electrical resistance data from the Willow-Whitehorse watersheds of southeast Oregon, USA, 2014-2016 Temperature data loggers in Willow/Rock/Frazer watersheds of northern Nevada, 2015 Stream temperature data in Willow/Rock/Frazer watersheds of northern Nevada, 2015-16 Quantify Depth of Inundation for Floodplains on the Missouri River for a Calculated Return Interval of 5 Years Climate Change Scenario Inundation Metrics along the Upper and Middle Mississippi and Lower Missouri Rivers Groundwater flow model of Northern Arizona by Pool and others, 2011 Supporting data for two MWBMglacier applications to the Copper River basin in Alaska Submerged aquatic vegetation and environmental data along a salinity gradient in Barataria Bay, Louisiana (2015) Seed biomass from shallow coastal water areas along a salinity gradient in Barataria Bay, Louisiana (2015) Sea Level Rise Systematic Mapping Literature Review Process-guided deep learning predictions of lake water temperature Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Probability of Predicted Elevation with respect to projected sea levels for the Northeastern U.S. from Maine to Virginia for the 2020s, 2030s, 2050s and 2080s (Albers, NAD 83)