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An explainable artificial intelligence approach to deciphering groundwater depth responses to climate variability and human activities in the Western United States
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| Type: | Resource | |
| Storage: | The size of this resource is 639.8 MB | |
| Created: | May 06, 2026 at 10:05 p.m. (UTC) | |
| Last updated: | May 26, 2026 at 6:31 p.m. (UTC) | |
| Citation: | See how to cite this resource |
| Sharing Status: | Private (Accessible via direct link sharing) |
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| Views: | 62 |
| Downloads: | 5 |
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Abstract
This resource provides machine learning–based predictions of monthly depth-to-water (DTW) at ~4 km spatial resolution across the Western United States for 2000 - 2020, developed to improve understanding of groundwater dynamics under the combined influences of climate variability and human activities. The dataset includes spatially continuous DTW estimates generated using advanced machine learning models trained on climatic, geological, hydrological variables, groundwater use and land use information, and groundwater observations.
Reference:
Dai, Q., Siegel, D., & Xu, T. (2026). An explainable artificial intelligence approach to deciphering groundwater depth responses to climate variability and human activities in the Western United States. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2026.181852
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Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| U.S. Army Engineer Research and Development Center | Design and deployment of Engineering with Nature (EWN) solutions for western resilience. | W912HZ-21-2-0040 |
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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| Tianfang Xu | Arizona State University | Tempe, AZ | ||
| Daniel Siegel | The Earth Genome | San Francisco, CA |
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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