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Data for "Reconstruction of sparse stream flow and concentration time-series through compressed sensing"
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| Type: | Resource | |
| Storage: | The size of this resource is 8.5 MB | |
| Created: | Sep 12, 2022 at 1:55 p.m. | |
| Last updated: | Feb 25, 2023 at 12:05 a.m. (Metadata update) | |
| Published date: | Dec 05, 2022 at 7:36 p.m. | |
| DOI: | 10.4211/hs.a70e86faebe448128ecb0e208626ba4d | |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 845 |
| Downloads: | 19 |
| +1 Votes: | 1 other +1 this |
| Comments: | No comments (yet) |
Abstract
This archive includes data used in Zhang et al.'s GRL paper "Reconstruction of sparse stream flow and concentration time-series through compressed sensing", which has been published and is available at https://doi.org/10.1029/2022GL101177. There are two directories that contain 1) daily-scale streamflow and water quality data in multiple gages during 2015-2021, and 2) 15-min-scale stream flow and water quality data in one gage during 2015-2021. The variables include streamflow, temperature, specific conductance, turbidity, dissolved oxygen, nitrate concentration, and phosphorous concentration. The number of gages for different variables varies in the daily-scale data. All the data was retrieved from the USGS National Water Information System (https://waterdata.usgs.gov/nwis)
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Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
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| Army Corps of Engineers (USACE) Engineer Research and Development Center (ERDC) | Novel Technologies to Mitigate Water Contamination for Resilient Infrastructure | W9132T2220001 |
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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