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Data for "Streamflow prediction in poorly gauged watersheds in the United States through data-driven sparse sensing"
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| 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 |
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| Views: | 613 |
| Downloads: | 27 |
| +1 Votes: | 1 other +1 this |
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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
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Content
readme.txt
This archive contains several MATLAB data and script files:
1) DataDSS.mat:
This MATLAB data file contains the data used in this study. It contains the following variables:
A) Data (18x6 cell array): CAMELS daily streamflow time-series in 543 watersheds across CONUS
B) Attributes (543x60 table): CAMELS watershed attributes in 543 watersheds across CONUS
C) regions (543x1 matrix): Index representing the classification of watersheds into nine regions classified by US Census Bureau
2) DSS.m:
This MATLAB script is a function that can perform data-driven sparse sensing given appropriate input
Input: x -- time-series for training (e.g., streamflow in nearby watersheds)
y -- time-series for prediction (e.g., streamflow in target watershed)
R -- number of columns and samples to be considered in prediction
Output: yQR -- predicted time-series
yPivot -- optimal times for sampling
nse -- NSE between the measured and predicted time-series
nsem -- modified NSE between the measured and predicted time-series
3) DSSmaster.m:
This MATLAB script is used to demonstrate how to use the data-driven sparse sensing to predict streamflow time-series in ungauged watersheds using CAMELS dataset as an example. Sample figures (different from the paper) can be plotted.
Credits
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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