Model archive component 2, Model Code, in: Machine learning methods for downscaling stream temperature predictions, tested in eight watersheds of the conterminous United States
This model archive component contains model codes used in the methods experiments of Barclay and others (2026). Code files are archived within a single zip file, code.zip, that preserves the necessary file structure to reproduce simulations. Contents of code.zip are described in the metadata file (2_code.xml) and also in 2_code_file_dictionary.csv. Notes on use of the code files are in 2_code_README_model_code.md.
This component is part of a model archive that provides all data, code, and model outputs used in the corresponding manuscript (Barclay and others, 2026) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream temperatures at fine spatial resolution in a watershed. The models were tested in eight watersheds across the conterminous United States for the period of 1979 through 2021.
The data are organized into these components:
1. Model Inputs (https://www.sciencebase.gov/catalog/item/687127e6d4be026e1750edaf) - Meteorological data, river network matrices, and stream temperature observations
2. [THIS ITEM] Model Code (https://www.sciencebase.gov/catalog/item/687128f8d4be026e1750ee0d) - Python files and README for reproducing model training and evaluation
3. Coarse Model (https://www.sciencebase.gov/catalog/item/6871290dd4be026e1750ee0f) - Trained coarse stream temperature model to be downscaled
4. Model Outputs (https://www.sciencebase.gov/catalog/item/6871292ad4be026e1750ee13) - Model simulation outputs and evaluation metrics
The publication associated with this model archive is: Barclay, J.R., Koenig, L.E., Fan, Y., Jia, X., Appling, A.P., (2026). Big data for small streams: Leveraging a coarse national model to enhance fine-resolution stream temperature predictions, http://doi.org/10.1029/2025WR041874.
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Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[ "010:12" ] |
| contactPoint |
{ "fn": "Janet Barclay", "@type": "vcard:Contact", "hasEmail": "mailto:jbarclay@usgs.gov" } |
| description | This model archive component contains model codes used in the methods experiments of Barclay and others (2026). Code files are archived within a single zip file, code.zip, that preserves the necessary file structure to reproduce simulations. Contents of code.zip are described in the metadata file (2_code.xml) and also in 2_code_file_dictionary.csv. Notes on use of the code files are in 2_code_README_model_code.md. This component is part of a model archive that provides all data, code, and model outputs used in the corresponding manuscript (Barclay and others, 2026) to test machine learning (ML) methods for downscaling and multi-scale modeling of stream temperature to combine an ML model and/or input data at coarse spatial resolution with an ML model and/or input data at fine spatial resolution to predict stream temperatures at fine spatial resolution in a watershed. The models were tested in eight watersheds across the conterminous United States for the period of 1979 through 2021. The data are organized into these components: 1. Model Inputs (https://www.sciencebase.gov/catalog/item/687127e6d4be026e1750edaf) - Meteorological data, river network matrices, and stream temperature observations 2. [THIS ITEM] Model Code (https://www.sciencebase.gov/catalog/item/687128f8d4be026e1750ee0d) - Python files and README for reproducing model training and evaluation 3. Coarse Model (https://www.sciencebase.gov/catalog/item/6871290dd4be026e1750ee0f) - Trained coarse stream temperature model to be downscaled 4. Model Outputs (https://www.sciencebase.gov/catalog/item/6871292ad4be026e1750ee13) - Model simulation outputs and evaluation metrics The publication associated with this model archive is: Barclay, J.R., Koenig, L.E., Fan, Y., Jia, X., Appling, A.P., (2026). Big data for small streams: Leveraging a coarse national model to enhance fine-resolution stream temperature predictions, http://doi.org/10.1029/2025WR041874. |
| distribution |
[ { "@type": "dcat:Distribution", "title": "Digital Data", "format": "XML", "accessURL": "https://doi.org/10.5066/P13TRUEM", "mediaType": "application/http", "description": "Landing page for access to the data" }, { "@type": "dcat:Distribution", "title": "Original Metadata", "format": "XML", "mediaType": "text/xml", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.687128f8d4be026e1750ee0d.xml" } ] |
| identifier | http://datainventory.doi.gov/id/dataset/USGS_687128f8d4be026e1750ee0d |
| keyword |
[ "CA", "CO", "California", "Colorado", "GA", "Georgia", "MA", "Massachusetts", "NY", "New York", "OR", "Oregon", "TX", "Texas", "US", "USGS:687128f8d4be026e1750ee0d", "United States", "WI", "Wisconsin", "deep learning", "downscaling", "environment", "inlandWaters", "machine learning", "mathematical simulation", "modeling", "streams", "water resources", "water temperature" ] |
| modified | 2026-10-20T00:00:00Z |
| publisher |
{ "name": "U.S. Geological Survey", "@type": "org:Organization" } |
| spatial | -122.29435803014, 32.6258846826916, -72.4125519980412, 44.1608195676367 |
| theme |
[ "geospatial" ] |
| title | Model archive component 2, Model Code, in: Machine learning methods for downscaling stream temperature predictions, tested in eight watersheds of the conterminous United States |
