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Filters Date Range Past year (0) Past month (0) Past week (0) Past day (0) Custom range... To   Search Extensions Shapefile (1) Types Downloadable (1) Map Service (1) OGC WFS Layer (1) OGC WMS Layer (1) Shapefile (1) Contacts Farshid Rahmani Farshid (Contractor) Felfelani U.S. Geological Survey U.S. Geological Survey - ScienceBase Samantha K Oliver More... Categories Data (8) Tag Types Place (13) Theme (13) USGS Scientific Topic Keyword (2) Harvest Set (2) Tag Schemes 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 (13) none (13) 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 (13) ISO 19115 Topic Category (13) View Results as: JSON ATOM CSV

Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 3 Model inputs This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge. Categories: Data; Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags 2 Observations: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30. Categories: Data; Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani et al. (2020): locally-fitted linear regression, LSTM-noQ, LSTM-obsQ, and LSTM-simQ. Categories: Data; Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags 4 Model Code: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release component contains model code and configurations for the LSTM models used to predict stream temperature. Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 1 Spatial information This data release component contains a shapefile of monitoring site locations coincident with the outlets of the 118 river basins modeled by Rahmani et al. (2020).<\p> Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 4 Models This data release component contains model code and configurations for the LSTM and linear regression models used to predict stream temperature. Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release provides all data and code used in Rahmani et al. (2021b) to model stream temperature and assess results. Briefly, we modeled stream temperature at sites across the continental United States using deep learning methods. The associated manuscript explores the prediction challenges posed by reservoirs, the value of additional training sites when predicting in gaged vs ungaged sites, and the value of an ensemble of attribute subsets in improving prediction accuracy. The data are organized into these child items: Site Information - Attributes and spatial information about the monitoring sites and basins in this study Observations - Water temperature observations for the sites used in this study Model... Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, USGS Science Data Catalog (SDC), UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, none, streams, water resources, water temperature, Fewer tags Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 6 Model evaluation This data release component contains evaluation metrics used to assess the predictive performance of each stream temperature model. For further description, see the metric calculations in the supplement of Rahmani et al. (2020), equations S1-S7. Categories: Data; Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 2 Observations This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30. Categories: Data; Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags 1 Site Information: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release component contains shapefiles of river basin polygons and monitoring site locations coincident with the outlets of those basins. A table of basin attributes is also supplied. Attributes, observations, and weather forcing data for these basins were used to train and test the stream temperature prediction models of Rahmani et al. (2021b).<\p> Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags 3 Model Forcings: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge. Categories: Data; Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted discharge from a separate model when no discharge measurements are available. The data are organized into these items: Spatial Information - Locations of the 118 monitoring sites used in this study Observations - Water temperature observations for the 118 sites used in this study Model Inputs - Model inputs, including basin... Tags: AL, Alabama, DE, Delaware, GA, All tags... Georgia, Hydrology, IA, ID, Idaho, Iowa, KS, Kansas, MA, MD, ME, MI, MS, Maine, Maryland, Massachusetts, Michigan, Mississippi, NC, NJ, NM, NV, NY, Nevada, New Jersey, New Mexico, New York, North Carolina, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, South Carolina, TN, TX, Tennessee, Texas, US, USGS Science Data Catalog (SDC), UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, Water Quality, Water Resources, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags 5 Model Predictions: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins This data release item contains water temperature predictions for 455 river sites across the U.S. Predictions are from the models described by Rahmani et al. (2021b). Categories: Data; Tags: AL, AR, AZ, Alabama, Arizona, All tags... Arkansas, CA, CO, CT, California, Colorado, Connecticut, DC, DE, Delaware, District of Columbia, GA, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NH, NJ, NM, NV, NY, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, PA, Pennsylvania, RI, Rhode Island, SC, SD, South Carolina, South Dakota, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, West Virginia, Wisconsin, Wyoming, deep learning, environment, inlandWaters, machine learning, modeling, streams, water resources, water temperature, Fewer tags