Data for "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing"
| Authors: | |
|---|---|
| Owners: | Kun Zhang |
| Type: | Resource |
| Storage: | The size of this resource is 106.1 MB |
| Created: | Feb 24, 2023 at 11:25 p.m. |
| Last updated: | Feb 27, 2023 at 1:11 p.m. (Metadata update) |
| Published date: | Feb 27, 2023 at 1:11 p.m. |
| DOI: | 10.4211/hs.fc8455652d1044218f3046b7dd56e5ea |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
|---|---|
| Views: | 839 |
| Downloads: | 32 |
| +1 Votes: | 1 other +1 this |
| Comments: | No comments (yet) |
Abstract
This archive includes data used in Zhang et al.'s WRR paper "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing", which is under review currently. The archive contains 1) raw data (daily-scale CAMELS streamflow data and watershed attributes) and 2) MATLAB scripts used to perform data-driven sparse sensing and generate sample figures. The streamflow data used in this study was retrieved from the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset (https://ral.ucar.edu/solutions/products/camels) The MATLAB code used for data-driven sparse sensing was retrieved from the Github repository by Krithika Manohar (https://github.com/kmanohar/SSPOR_pub) and customized for this study.
Subject Keywords
Coverage
Spatial
Temporal
| Start Date: | 01/01/1981 |
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| End Date: | 12/31/2010 |
Content
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| 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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