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Metabolism estimates for 356 U.S. rivers (2007-2017): 3. Timeseries data 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... Tags: 007, 012, AK, AL, AR, All tags... Aerobic respiration, Alabama, Alaska, Arkansas, CA, CO, CT, California, Carbon, Carbon dioxide, Colorado, Connecticut, DC, DE, Delaware, Dissolved oxygen, District of Columbia, Energy metabolism, FL, Florida, GA, Gas exchange, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, LA, Louisiana, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NJ, NM, NY, Nebraska, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, Oxygen, PA, PR, Pennsylvania, Photosynthesis, Primary production, Puerto Rico, Rivers, SC, SD, South Carolina, South Dakota, Streams, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, Water courses, West Virginia, Wisconsin, Wyoming, environment, inlandWaters, Fewer tags Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 2 Water temperature observations Observed water temperatures from 1980-2019 were compiled for 2,332 lakes in the US. These data were used as training, test, and error-estimation data for process-guided deep learning models and the evaluation of process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological Research Project, and digitized temperature records from the MN Department of Natural Resources. This dataset is part of a larger data release of lake temperature model inputs and outputs for these same lakes... Tags: 007, 012, IA, IL, Illinois, All tags... Iowa, MI, MN, Michigan, Minnesota, ND, North Dakota, OH, Ohio, SD, South Dakota, US, United States, WI, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates Using predicted lake temperatures from uncalibrated, process-based models (PB0) and process-guided deep learning models (PGDL), this dataset summarized a collection of thermal metrics to characterize lake temperature impacts on fish habitat for 881 lakes. Included in the metrics are daily thermal optical habitat areas and a set of over 172 annual thermal metrics. Categories: Data; Tags: MN, Minnesota, SD, South Dakota, US, All tags... United States, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal habitat, thermal profiles, water, water resources, Fewer tags Metabolism estimates for 356 U.S. rivers (2007-2017): 4. Model inputs 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. Tags: 007, 012, AK, AR, Aerobic respiration, All tags... Alaska, Arkansas, CA, CO, CT, California, Carbon, Carbon dioxide, Colorado, Connecticut, DC, DE, Delaware, Dissolved oxygen, District of Columbia, Energy metabolism, FL, Florida, GA, Gas exchange, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, LA, Louisiana, MA, MD, MI, MN, MO, Maryland, Massachusetts, Michigan, Minnesota, Missouri, NC, ND, NE, NJ, NM, NY, Nebraska, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, Oxygen, PA, Pennsylvania, Photosynthesis, Primary production, Rivers, SC, SD, South Carolina, South Dakota, Streams, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, Water courses, West Virginia, Wisconsin, Wyoming, environment, inlandWaters, Fewer tags Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 2 Water temperature observations Observed water temperatures from 1980-2018 were compiled for 877 lakes in Minnesota (USA). There were four lakes included in this data release that did not have temperature observations available at the time of compilation or these data existed elsewhere and were unknown to the compilation team. These data were used as training, test, and error-estimation data for process-guided deep learning models and the evaluation of process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological... Tags: MN, Minnesota, SD, South Dakota, US, All tags... United States, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, water resources, Fewer tags Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling approach includes process-guided deep learning and data assimilation (Zwart et al., 2023). The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7). In combination with data provided in Oliver et al. (2022),... Tags: NY, New York, PA, Pennsylvania, US, All tags... USGS Science Data Catalog (SDC), United States, Water Resources, deep learning, environment, forecast, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, streams, temperature, water, water resources, Fewer tags Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6 model evaluation Water temperature estimates from multiple models were evaluated by comparing predictions to observed water temperatures. The performance metric of root-mean square error (in degrees C) is calculated for each lake and each model type, and matched values for predicted and observed temperatures are also included to support more specific error estimation methods (for example, calculating error in a particular month). Errors for the process-based model are compared to predictions as shared in Model Predictions data since these models were not calibrated. Errors for the process-guided deep learning models were calculated from validation folds and therefore differ from the comparisons to Model Predictions because those... Categories: Data; Tags: MN, Minnesota, SD, South Dakota, US, All tags... United States, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, water resources, Fewer tags Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes This dataset provides shapefile outlines of the 2,332 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is included, which includes lake metadata and all features that were considered for the meta transfer model (not all meta features were used). This dataset is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. (https://doi.org/10.5066/P9I00WFR). Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: 007, 012, IA, IL, Illinois, All tags... Iowa, MI, MN, Michigan, Minnesota, ND, North Dakota, OH, Ohio, SD, South Dakota, US, United States, WI, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 1 Lake information for 881 lakes This dataset provides shapefile outlines of the 881 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is also included. This dataset is part of a larger data release of lake temperature model inputs and outputs for 881 lakes in the U.S. state of Minnesota (https://doi.org/10.5066/P9PPHJE2). Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: MN, Minnesota, SD, South Dakota, US, All tags... United States, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, water resources, 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 Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022) This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for the analysis, Experiment A, Experiment B, Experiment C, Experiment D, and Experiment AuxIn. Experiment A tested multi-task learning for predicting streamflow with 25 years of training data and using a different model for each of 101 sites. Experiment B tested multi-task learning for predicting streamflow with 25 years of training data and using a single model for all 101 sites. Experiment C tested multi-task learning for predicting streamflow with just 2 years of training data. Experiment D tested multi-task learning for predicting water temperature with... Categories: Data, Data Release - Revised; Tags: US, USGS Science Data Catalog (SDC), United States, deep learning, environment, All tags... hybrid modeling, inlandWaters, machine learning, modeling, streamflow, temperature, water, water resources, Fewer tags 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 Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 6 model evaluation Predictions are evaluated against observed temperatures. Categories: Data; Tags: 007, 012, IA, IL, Illinois, All tags... Iowa, MI, MN, Michigan, Minnesota, ND, North Dakota, OH, Ohio, SD, South Dakota, US, United States, WI, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 4 Model inputs This dataset includes model inputs (specifically, meteorological inputs to the predictive models and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. states of North Dakota, South Dakota, Minnesota, Wisconsin, and Michigan (https://doi.org/10.5066/P9PPHJE2). Categories: Data; Tags: 007, 012, US, United States, climate change, All tags... deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, Fewer tags Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 5 Model predictions 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 South Dakota, North Dakota, Minnesota, Wisconsin, and Michigan. 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. Process-Guided Deep Learning (PGDL) models were deep learning models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training... Categories: Data; Tags: 007, 012, IA, IL, Illinois, All tags... Iowa, MI, MN, Michigan, Minnesota, ND, North Dakota, OH, Ohio, SD, South Dakota, US, United States, WI, Wisconsin, climate change, deep learning, environment, hybrid modeling, inlandWaters, machine learning, modeling, reservoirs, temperate lakes, temperature, thermal profiles, water, 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 Metabolism estimates for 356 U.S. rivers (2007-2017): 1. Site data This dataset provides site identifiers, details, and quality indicators. The format is a table with 1 row per site (tab-delimited file). 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. Tags: 007, 012, AK, AL, AR, All tags... Aerobic respiration, Alabama, Alaska, Arkansas, CA, CO, CT, California, Carbon, Carbon dioxide, Colorado, Connecticut, DC, DE, Delaware, Dissolved oxygen, District of Columbia, Energy metabolism, FL, Florida, GA, Gas exchange, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, LA, Louisiana, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NJ, NM, NY, Nebraska, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, Oxygen, PA, PR, Pennsylvania, Photosynthesis, Primary production, Puerto Rico, Rivers, SC, SD, South Carolina, South Dakota, Streams, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, Water courses, West Virginia, Wisconsin, Wyoming, environment, inlandWaters, Fewer tags Metabolism estimates for 356 U.S. rivers (2007-2017): 2a. Site coordinates This dataset provides site locations as shapefile points. The format is a shapefile for all sites combined (.shp, .shx, .dbf, and .prj files). 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. Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: 007, 012, AK, AL, AR, All tags... Aerobic respiration, Alabama, Alaska, Arkansas, CA, CO, CT, California, Carbon, Carbon dioxide, Colorado, Connecticut, DC, DE, Delaware, Dissolved oxygen, District of Columbia, Energy metabolism, FL, Florida, GA, Gas exchange, Georgia, IA, ID, IL, IN, Idaho, Illinois, Indiana, Iowa, KS, KY, Kansas, Kentucky, LA, Louisiana, MA, MD, ME, MI, MN, MO, MS, MT, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, NC, ND, NE, NJ, NM, NY, Nebraska, New Jersey, New Mexico, New York, North Carolina, North Dakota, OH, OK, OR, Ohio, Oklahoma, Oregon, Oxygen, PA, PR, Pennsylvania, Photosynthesis, Primary production, Puerto Rico, Rivers, SC, SD, South Carolina, South Dakota, Streams, TN, TX, Tennessee, Texas, US, UT, United States, Utah, VA, Virginia, WA, WI, WV, WY, Washington, Water courses, West Virginia, Wisconsin, Wyoming, environment, inlandWaters, Fewer tags Examining the influence of deep learning architecture on generalizability for predicting stream temperature in the Delaware River Basin This data release and model archive provides all data, code, and modelling results used in Topp et al. (2023) to examine the influence of deep learning architecture on generalizability when predicting stream temperature in the Delaware River Basin (DRB). Briefly, we modeled stream temperature in the DRB using two spatially and temporally aware process guided deep learning models (a recurrent graph convolution network - RGCN, and a temporal convolution graph model - Graph WaveNet). The associated manuscript explores how the architectural differences between the two models influence how they learn spatial and temporal relationships, and how those learned relationships influence a model's ability to accurately predict... Categories: Data; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: DE, Delaware, Hydrology, MD, Maryland, All tags... NJ, NY, New Jersey, New York, PA, Pennsylvania, US, USGS Science Data Catalog (SDC), United States, Water Quality, Water Resources, climate change, domain shift, environment, explainable AI (XAI), generalization, hybrid modeling, inlandWaters, machine learning, modeling, process guided deep learning, stream temperature, temperature, water, water resources, Fewer tags Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four approaches for improving groundwater process representation: 1) a custom loss function that leverages the unique patterns of air and water temperature coupling resulting from different temperature drivers, 2) inclusion of additional groundwater-relevant catchment attributes, 3) incorporation of additional process model outputs, and... Categories: Data; Tags: DE, Delaware, Hydrology, MD, Maryland, All tags... NJ, NY, New Jersey, New York, PA, Pennsylvania, US, USGS Science Data Catalog (SDC), United States, Water Quality, Water Resources, deep learning, environment, explainable AI (XAI), groundwater, hybrid modeling, inlandWaters, machine learning, modeling, process guided deep learning, stream temperature, temperature, water, water resources, Fewer tags 1 2 »