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| Type: | Resource | |
| Storage: | The size of this resource is 5.0 GB | |
| Created: | Aug 15, 2024 at 3:55 p.m. (UTC) | |
| Last updated: | Sep 03, 2024 at 5:21 p.m. (UTC) (Metadata update) | |
| Published date: | Sep 03, 2024 at 5:20 p.m. (UTC) | |
| DOI: | 10.4211/hs.8da6ebf2ee9a491490bb09a6349e70fe | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 1352 |
| Downloads: | 70 |
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| Comments: | No comments (yet) |
Abstract
This dataset offers a comprehensive collection of water quality data for approximately 500 stations across the Continental United States (CONUS). It includes 20 common water quality parameters, along with meteorological, hydrological, and land use variables such as streamflow, precipitation, temperature, evapotranspiration, and vegetation indices. To support water quality modeling research, we provide model outputs from both conventional statistical (WRTDS) and advanced deep learning (LSTM) approaches. This dataset is designed to facilitate model development, validation, and application, and to promote reproducible research.
Subject Keywords
Coverage
Spatial
Temporal
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Content
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| United States Department of Energy | DE-SC0018155 | |
| Stanford University | Human-Centered AI (HAI) program and Data Science fellowship |
Contributors
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
| Name | Organization | Address | Phone | Author Identifiers |
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| Kate Maher |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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