Data for "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing"

Authors:
Kun ZhangMitul LuharManuela BrunnerAnthony Parolari
Owners: Kun Zhang
  • Contributions
    Joined

Type: Resource
Storage: The size of this resource is 38.7 MB
Created: Mar 29, 2023 at 2:53 a.m.
Last updated: Apr 04, 2023 at 1:04 p.m. (Metadata update)
Published date: Apr 04, 2023 at 1:04 p.m.
DOI: 10.4211/hs.49b0f3b0f6924b2d917b3659fb03926b
Citation: See how to cite this resource
Sharing Status: Published
Views: 851
Downloads: 21
+1 Votes: Be the first one to 
 this.
Comments: No comments (yet)

Abstract

This archive includes data used in Zhang et al.'s paper "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing", which has been published in Water Resources Research (WRR) (https://doi.org/10.1029/2022WR034092) 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

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
49.4163°
East Longitude
-66.4453°
South Latitude
24.7344°
West Longitude
-125.5078°

Temporal

Start Date: 01/01/1981
End Date: 12/31/2010
Leaflet Map data © OpenStreetMap contributors

Content

    No files to display.

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

Zhang, K., M. Luhar, M. Brunner, A. Parolari (2023). Data for "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing", HydroShare, https://doi.org/10.4211/hs.49b0f3b0f6924b2d917b3659fb03926b

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
CC-BY

Comments

There are currently no comments

New Comment

required