U.S. Department of EnergyOffice of Scientific and Technical Information
CLM5 CAMELS Basins Ensemble
Abstract
Land surface models such as Community Land Model Version 5 (CLM5) are essential tools for simulating the behaviors of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrologic parameters and the implications that these uncertainties have on water resources applications. To address this long-standing issue, we conduct a comprehensive hydrologic parameter uncertainty characterization (UC) of CLM5 over the hydroclimatic gradients of the Conterminous United States using five meteorological datasets. Key datasets produced from the UC experiment include a benchmark dataset of CLM5 default hydrological performance, parameter sensitivity identified for 28 hydrological metrics, and large ensemble outputs for hydrological predictions. The presented datasets can assist CLM5 calibration and to support broad applications such as evaluating vulnerabilities to droughts and floods. The dataset can be used to identify under what hydroclimate conditions parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how land runoff uncertainties interact with other Earth system processes. Please refer to the included README.pdf for a description of the included files. Note that raw CLM5 model outputs for each forcing dataset are hosted on a Globus endpoint: https://app.globus.org/file-manager?destination_id=d22ef858-27b0-11ed-a910-fd3165076336.
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- Pacific Northwest National Laboratory; Pacific Northwest National Laboratory
- Pacific Northwest National Laboratory
- Cornell University
- National Center for Atmospheric Research
- Publication Date:
- Research Org.:
- MultiSector Dynamics - Living, Intuitive, Value-adding, Environment
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1884563
- DOI:
- https://doi.org/10.57931/1884563
Citation Formats
Yan, Hongxiang, Sun, Ning, Eldardiry, Hisham, Thurber, Travis, Reed, Patrick M., Malek, Keyvan, Gupta, Rohini S., Kennedy, Daniel, Swenson, Sean, Vernon, Chris R., Burleyson, Casey D., and Rice, Jennie S. CLM5 CAMELS Basins Ensemble. United States: N. p., 2022.
Web. doi:10.57931/1884563.
Yan, Hongxiang, Sun, Ning, Eldardiry, Hisham, Thurber, Travis, Reed, Patrick M., Malek, Keyvan, Gupta, Rohini S., Kennedy, Daniel, Swenson, Sean, Vernon, Chris R., Burleyson, Casey D., & Rice, Jennie S. CLM5 CAMELS Basins Ensemble. United States. doi:https://doi.org/10.57931/1884563
Yan, Hongxiang, Sun, Ning, Eldardiry, Hisham, Thurber, Travis, Reed, Patrick M., Malek, Keyvan, Gupta, Rohini S., Kennedy, Daniel, Swenson, Sean, Vernon, Chris R., Burleyson, Casey D., and Rice, Jennie S. 2022.
"CLM5 CAMELS Basins Ensemble". United States. doi:https://doi.org/10.57931/1884563. https://www.osti.gov/servlets/purl/1884563. Pub date:Tue Aug 30 04:00:00 UTC 2022
@article{osti_1884563,
title = {CLM5 CAMELS Basins Ensemble},
author = {Yan, Hongxiang and Sun, Ning and Eldardiry, Hisham and Thurber, Travis and Reed, Patrick M. and Malek, Keyvan and Gupta, Rohini S. and Kennedy, Daniel and Swenson, Sean and Vernon, Chris R. and Burleyson, Casey D. and Rice, Jennie S.},
abstractNote = {Land surface models such as Community Land Model Version 5 (CLM5) are essential tools for simulating the behaviors of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrologic parameters and the implications that these uncertainties have on water resources applications. To address this long-standing issue, we conduct a comprehensive hydrologic parameter uncertainty characterization (UC) of CLM5 over the hydroclimatic gradients of the Conterminous United States using five meteorological datasets. Key datasets produced from the UC experiment include a benchmark dataset of CLM5 default hydrological performance, parameter sensitivity identified for 28 hydrological metrics, and large ensemble outputs for hydrological predictions. The presented datasets can assist CLM5 calibration and to support broad applications such as evaluating vulnerabilities to droughts and floods. The dataset can be used to identify under what hydroclimate conditions parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how land runoff uncertainties interact with other Earth system processes. Please refer to the included README.pdf for a description of the included files. Note that raw CLM5 model outputs for each forcing dataset are hosted on a Globus endpoint: https://app.globus.org/file-manager?destination_id=d22ef858-27b0-11ed-a910-fd3165076336.},
doi = {10.57931/1884563},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Aug 30 04:00:00 UTC 2022},
month = {Tue Aug 30 04:00:00 UTC 2022}
}