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dc.contributor.authorMa, Ning
dc.contributor.authorNiu, Guo-Yue
dc.contributor.authorXia, Youlong
dc.contributor.authorCai, Xitian
dc.contributor.authorZhang, Yinsheng
dc.contributor.authorMa, Yaoming
dc.contributor.authorFang, Yuanhao
dc.date.accessioned2018-01-31T15:51:05Z
dc.date.available2018-01-31T15:51:05Z
dc.date.issued2017-11-27
dc.identifier.citationA Systematic Evaluation of Noah-MP in Simulating Land-Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United States 2017, 122 (22):12,245 Journal of Geophysical Research: Atmospheresen
dc.identifier.issn2169897X
dc.identifier.doi10.1002/2017JD027597
dc.identifier.urihttp://hdl.handle.net/10150/626444
dc.description.abstractAccurate simulation of energy, water, and carbon fluxes exchanging between the land surface and the atmosphere is beneficial for improving terrestrial ecohydrological and climate predictions. We systematically assessed the Noah land surface model (LSM) with mutiparameterization options (Noah-MP) in simulating these fluxes and associated variations in terrestrial water storage (TWS) and snow cover fraction (SCF) against various reference products over 18 United States Geological Survey two-digital hydrological unit code regions of the continental United States (CONUS). In general, Noah-MP captures better the observed seasonal and interregional variability of net radiation, SCF, and runoff than other variables. With a dynamic vegetation model, it overestimates gross primary productivity by 40% and evapotranspiration (ET) by 22% over the whole CONUS domain; however, with a prescribed climatology of leaf area index, it greatly improves ET simulation with relative bias dropping to 4%. It accurately simulates regional TWS dynamics in most regions except those with large lakes or severely affected by irrigation and/or impoundments. Incorporating the lake water storage variations into the modeled TWS variations largely reduces the TWS simulation bias more obviously over the Great Lakes with model efficiency increasing from 0.18 to 0.76. Noah-MP simulates runoff well in most regions except an obvious overestimation (underestimation) in the Rio Grande and Lower Colorado (New England). Compared with North American Land Data Assimilation System Phase 2 (NLDAS-2) LSMs, Noah-MP shows a better ability to simulate runoff and a comparable skill in simulating R-n but a worse skill in simulating ET over most regions. This study suggests that future model developments should focus on improving the representations of vegetation dynamics, lake water storage dynamics, and human activities including irrigation and impoundments.
dc.description.sponsorshipNational Key Research and Development Program of China [2017YFA0603101]; China Postdoctoral Science Foundation [2017LH032, 2017M620069]; National Natural Science Foundation of China [41661144025, 41430748]; NASA MAP Program [80NSSC17K0352]; University of Arizona Germinating Research Program Success: Faculty Seed Grantsen
dc.language.isoenen
dc.publisherAMER GEOPHYSICAL UNIONen
dc.relation.urlhttp://doi.wiley.com/10.1002/2017JD027597en
dc.rights©2017. American Geophysical Union. All Rights Reserved.en
dc.subjectland surface modelen
dc.subjectgross primary productivityen
dc.subjectenergy fluxesen
dc.subjectsnow cover fractionen
dc.subjectNoah-MPen
dc.subjectHUC2 regionen
dc.titleA Systematic Evaluation of Noah-MP in Simulating Land-Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United Statesen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien
dc.identifier.journalJournal of Geophysical Research: Atmospheresen
dc.description.note6 month embargo; published online: 24 November 2017en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionKey Laboratory of Tibetan Environment Changes and Land Surface Processes; Institute of Tibetan Plateau Research, and Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences; Beijing China
dc.contributor.institutionDepartment of Hydrology and Atmospheric Sciences; University of Arizona; Tucson AZ USA
dc.contributor.institutionEnvironmental Modeling Center, National Centers for Environmental Prediction; College Park MD USA
dc.contributor.institutionDepartment of Civil and Environmental Engineering; Princeton University; Princeton NJ USA
dc.contributor.institutionKey Laboratory of Tibetan Environment Changes and Land Surface Processes; Institute of Tibetan Plateau Research, and Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences; Beijing China
dc.contributor.institutionKey Laboratory of Tibetan Environment Changes and Land Surface Processes; Institute of Tibetan Plateau Research, and Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences; Beijing China
dc.contributor.institutionDepartment of Hydrology and Water Resources; Hohai University; Nanjing China
refterms.dateFOA2018-05-24T00:00:00Z
html.description.abstractAccurate simulation of energy, water, and carbon fluxes exchanging between the land surface and the atmosphere is beneficial for improving terrestrial ecohydrological and climate predictions. We systematically assessed the Noah land surface model (LSM) with mutiparameterization options (Noah-MP) in simulating these fluxes and associated variations in terrestrial water storage (TWS) and snow cover fraction (SCF) against various reference products over 18 United States Geological Survey two-digital hydrological unit code regions of the continental United States (CONUS). In general, Noah-MP captures better the observed seasonal and interregional variability of net radiation, SCF, and runoff than other variables. With a dynamic vegetation model, it overestimates gross primary productivity by 40% and evapotranspiration (ET) by 22% over the whole CONUS domain; however, with a prescribed climatology of leaf area index, it greatly improves ET simulation with relative bias dropping to 4%. It accurately simulates regional TWS dynamics in most regions except those with large lakes or severely affected by irrigation and/or impoundments. Incorporating the lake water storage variations into the modeled TWS variations largely reduces the TWS simulation bias more obviously over the Great Lakes with model efficiency increasing from 0.18 to 0.76. Noah-MP simulates runoff well in most regions except an obvious overestimation (underestimation) in the Rio Grande and Lower Colorado (New England). Compared with North American Land Data Assimilation System Phase 2 (NLDAS-2) LSMs, Noah-MP shows a better ability to simulate runoff and a comparable skill in simulating R-n but a worse skill in simulating ET over most regions. This study suggests that future model developments should focus on improving the representations of vegetation dynamics, lake water storage dynamics, and human activities including irrigation and impoundments.


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