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Harnessing Backcasting to Identify Drivers of Critical Warming at Hoover Dam Using Hydrodynamic and Machine Learning Models
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| Type: | Resource | |
| Storage: | The size of this resource is 216.7 MB | |
| Created: | May 12, 2026 at 4:07 p.m. (UTC) | |
| Last updated: | May 21, 2026 at 11:40 p.m. (UTC) | |
| Citation: | See how to cite this resource | |
| Content types: | File Set Content Single File Content CSV Content |
| Sharing Status: | Public |
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| Views: | 376 |
| Downloads: | 57 |
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Abstract
This HydroShare resource supports the journal article “Harnessing Backcasting to Identify Drivers of Critical Warming at Hoover Dam Using Hydrodynamic and Machine Learning Models.” The repository provides the complete computational and data framework used to conduct the analyses presented in the publication, with the goal of enabling transparency, reproducibility, and reuse by the hydrologic research community.
The resource includes curated input datasets, Python-based workflows, and supporting scripts used to develop surrogate machine learning models that emulate hydrodynamic simulation outputs. These models are leveraged within a backcasting framework to identify the physical and operational drivers associated with critical thermal conditions at Hoover Dam. The repository also contains code for applying explainable artificial intelligence techniques to interpret model behavior and quantify the relative influence of key drivers, as well as scripts for generating all figures and visualizations presented in the article.
All materials are organized to facilitate replication of the study results and to support adaptation of the methods for related hydrologic systems and climate-impact analyses. This resource is intended for researchers, practitioners, and students interested in coupled hydrodynamic–machine learning modeling, explainable AI, and scenario-based assessment of extreme environmental conditions.
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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