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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...
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climate change,
deep learning,
environment,
hybrid modeling,
inlandWaters,
machine learning,
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reservoirs,
temperate lakes,
temperature,
thermal profiles,
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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.
Categories: Data;
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climate change,
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inlandWaters,
machine learning,
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reservoirs,
surface temperature,
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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).
Categories: Data;
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Northeast CASC,
Rivers, Streams and Lakes,
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United States,
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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,
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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...
Categories: Data;
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US,
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Water, Coasts and Ice,
Wildlife and Plants,
Wisconsin,
climate change,
deep learning,
environment,
hybrid modeling,
inlandWaters,
machine learning,
modeling,
reservoirs,
temperate lakes,
temperature,
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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...
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
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