Catchment scale runoff time-series generation and validation using statistical models for the Continental United States
Abstract
We developed statistical models to generate runoff time-series at National Hydrography Dataset Plus Version 2 (NHDPlusV2) catchment scale for the Continental United States (CONUS). The models use Normalized Difference Vegetation Index (NDVI) based Curve Number (CN) to generate initial runoff time-series which then is corrected using statistical models to improve accuracy. We used the North American Land Data Assimilation System 2 (NLDAS-2) catchment scale runoff time-series as the reference data for model training and validation. We used 17 years of 16-day, 250-m resolution NDVI data as a proxy for hydrologic conditions during a representative year to calculate 23 NDVI based-CN (NDVI-CN) values for each of 2.65 million NHDPlusV2 catchments for the Contiguous U.S. To maximize predictive accuracy while avoiding optimistically biased model validation results, we developed a spatio-temporal cross-validation framework for estimating, selecting, and validating the statistical correction models. We found that in many of the physiographic sections comprising CONUS, even simple linear regression models were highly effective at correcting NDVI-CN runoff to achieve Nash-Sutcliffe Efficiency values above 0.5. However, all models showed poor performance in physiographic sections that experience significant snow accumulation.
- Publication Date:
- Research Org.:
- Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1841797
- Alternate Identifier(s):
- OSTI ID: 1981603
- Grant/Contract Number:
- DW8992298301; SC0014664
- Resource Type:
- Published Article
- Journal Name:
- Environmental Modelling and Software
- Additional Journal Information:
- Journal Name: Environmental Modelling and Software Journal Volume: 149 Journal Issue: C; Journal ID: ISSN 1364-8152
- Publisher:
- Elsevier
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Computer Science; Engineering; Environmental Sciences & Ecology; Water Resources
Citation Formats
Patton, Douglas, Smith, Deron, Muche, Muluken E., Wolfe, Kurt, Parmar, Rajbir, and Johnston, John M. Catchment scale runoff time-series generation and validation using statistical models for the Continental United States. United Kingdom: N. p., 2022.
Web. doi:10.1016/j.envsoft.2022.105321.
Patton, Douglas, Smith, Deron, Muche, Muluken E., Wolfe, Kurt, Parmar, Rajbir, & Johnston, John M. Catchment scale runoff time-series generation and validation using statistical models for the Continental United States. United Kingdom. https://doi.org/10.1016/j.envsoft.2022.105321
Patton, Douglas, Smith, Deron, Muche, Muluken E., Wolfe, Kurt, Parmar, Rajbir, and Johnston, John M. Tue .
"Catchment scale runoff time-series generation and validation using statistical models for the Continental United States". United Kingdom. https://doi.org/10.1016/j.envsoft.2022.105321.
@article{osti_1841797,
title = {Catchment scale runoff time-series generation and validation using statistical models for the Continental United States},
author = {Patton, Douglas and Smith, Deron and Muche, Muluken E. and Wolfe, Kurt and Parmar, Rajbir and Johnston, John M.},
abstractNote = {We developed statistical models to generate runoff time-series at National Hydrography Dataset Plus Version 2 (NHDPlusV2) catchment scale for the Continental United States (CONUS). The models use Normalized Difference Vegetation Index (NDVI) based Curve Number (CN) to generate initial runoff time-series which then is corrected using statistical models to improve accuracy. We used the North American Land Data Assimilation System 2 (NLDAS-2) catchment scale runoff time-series as the reference data for model training and validation. We used 17 years of 16-day, 250-m resolution NDVI data as a proxy for hydrologic conditions during a representative year to calculate 23 NDVI based-CN (NDVI-CN) values for each of 2.65 million NHDPlusV2 catchments for the Contiguous U.S. To maximize predictive accuracy while avoiding optimistically biased model validation results, we developed a spatio-temporal cross-validation framework for estimating, selecting, and validating the statistical correction models. We found that in many of the physiographic sections comprising CONUS, even simple linear regression models were highly effective at correcting NDVI-CN runoff to achieve Nash-Sutcliffe Efficiency values above 0.5. However, all models showed poor performance in physiographic sections that experience significant snow accumulation.},
doi = {10.1016/j.envsoft.2022.105321},
journal = {Environmental Modelling and Software},
number = C,
volume = 149,
place = {United Kingdom},
year = {Tue Mar 01 00:00:00 EST 2022},
month = {Tue Mar 01 00:00:00 EST 2022}
}
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