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| Type: | Resource | |
| Storage: | The size of this resource is 1.2 MB | |
| Created: | Aug 07, 2024 at 10:18 p.m. (UTC) | |
| Last updated: | Sep 10, 2024 at 2:31 p.m. (UTC) (Metadata update) | |
| Published date: | Sep 10, 2024 at 2:31 p.m. (UTC) | |
| DOI: | 10.4211/hs.5ee190b481c749fb8398f182742720f1 | |
| Citation: | See how to cite this resource | |
| Content types: | Single File Content |
| Sharing Status: | Published |
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| Views: | 1829 |
| Downloads: | 54 |
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Abstract
This dataset contains Acoustic Doppler Current Profiler (ADCP) data collected near Erie, PA, in Lake Erie during the summer of 2019. The ADCP was deployed in an upward-looking configuration from August 9 to September 9, 2019, at a depth of 5.80 meters, and it recorded 3D water velocities and temperatures. The data collection aimed to support assessments of Cladophora and benthic algae and to validate hydrodynamic models in the region. The deployment location is specified at 42.18863 degrees north latitude and -79.98218 degrees west longitude. The U.S. Environmental Protection Agency funded this project under the Great Lakes Restoration Initiative, with the U.S. Geological Survey (USGS) and Michigan State University (MSU) conducting the data collection, processing, and quality assurance. Data quality was ensured through rigorous checks, with velocity measurement accuracy at 1% of the measured value, and temperature sensor accuracy at ±0.1 ℃. This dataset provides valuable insights for environmental and hydrodynamic studies in Lake Erie, contributing to a better understanding of water flow patterns and temperature variations essential for ecological assessments and management practices.
The ADCP data was used in the following paper:
Saeed Memari, Mantha S. Phanikumar, Vishnu Boddeti, Narendra Das; Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing. Journal of Hydroinformatics 2024; jh2024110. doi: https://doi.org/10.2166/hydro.2024.110
Subject Keywords
Coverage
Spatial
Temporal
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Related Resources
| This resource is referenced by | Saeed Memari, Mantha S. Phanikumar, Vishnu Boddeti, Narendra Das; Turbidity assessment in coastal regions combining machine learning, numerical modeling, and remote sensing. Journal of Hydroinformatics 2024; jh2024110. doi: https://doi.org/10.2166/hydro.2024.110 |
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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