A Systematic Evaluation of Noah-MP in Simulating Land-Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United States
AffiliationUniv Arizona, Dept Hydrol & Atmospher Sci
Keywordsland surface model
gross primary productivity
snow cover fraction
MetadataShow full item record
PublisherAMER GEOPHYSICAL UNION
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: Atmospheres
Rights©2017. American Geophysical Union. All Rights Reserved.
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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.
Note6 month embargo; published online: 24 November 2017
VersionFinal published version
SponsorsNational 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 Grants