A Systematic Evaluation of Noah‐MP in Simulating Land‐Atmosphere Energy, Water, and Carbon Exchanges Over the Continental United States. Issue 22 (24th November 2017)

• Record Type: Journal Article
• Authors: Ma, Ning; Niu, Guo‐Yue; Xia, Youlong; Cai, Xitian; Zhang, Yinsheng; Ma, Yaoming; Fang, Yuanhao
• Is Part Of: Journal of geophysical research. Volume 122:Issue 22(2017)
• Page Start: 12, 245
• Page End: 12, 268
• Publication Date: 2017-11-24
• Subjects: land surface model -- gross primary productivity -- energy fluxes -- snow cover fraction -- Noah‐MP -- HUC2 region; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2017JD027597
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Accurate 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. Key Points: Noah‐MP simulates well net radiation, runoff, and snow cover over most of the 18 HUC2 regions within CONUS Noah‐MP with a dynamic vegetation model substantially overestimates GPP and ET in highly vegetated area Adding lake water storage variations into the simulated TWS reduces the modeling biases over regions with large lakes