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This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge.
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...
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.
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.
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...
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),...
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,
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.
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,
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...
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.
This dataset provides one shapefile of polylines for the 456 river segments in this study, and one shapefile of reservoir polygons for the Pepacton and Cannonsville reservoirs.
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: DE,
Delaware,
MD,
Maryland,
NJ,
Predictions are evaluated against observed temperatures.
This dataset includes model inputs including gridded weather data, a stream network distance matrix, stream reach attributes and metadata, and reservoir characteristics.
Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are predicted throughout the water column (known as temperature profiles). In this data release, multiple modeling approaches were used to generate predictions of daily temperature profiles for thousands of lakes in the Midwest. Predictions were generated using two modeling frameworks: a machine learning model (specifically an entity-aware long short-term memory or EA-LSTM model; Kratzert et al., 2019) and a process-based model (specifically the General Lake Model or GLM; Hipsey et al., 2019). Both the EA-LSTM and GLM frameworks were used to generate...
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...
This data release component contains model code and configurations for the LSTM models used to predict stream temperature.
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>
This data release component contains model code and configurations for the LSTM and linear regression models used to predict stream temperature.
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...
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