Assessing and enhancing Noah-MP land surface modeling over tropical forests using machine learning techniques

Yanyan Cheng, Yaomin Wang, Kalli Furtado, Cenlin He, Fei Chen, Alan D. Ziegler, Song Chen, Matteo Detto, Yuna Mao, Baoxiang Pan, Yoshiko Kosugi, Marryanna Lion et al.

Geoscientific model development · 17 March 2026 · Institute of Atmospheric Physics, Chinese Academy of Sciences, NSF NCAR / UCAR, Princeton University

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In one sentence. Machine-learning-guided site calibration of Noah-MP at tropical forest and urban sites improves latent and sensible heat flux and soil moisture, but nighttime fluxes remain hard.

Abstract

Abstract. Despite the critical role of tropical land-surface processes in Earth system dynamics, large gaps persist in model evaluation and calibration for these regions. This study addresses this disparity through site-specific calibration of the Noah with Multi-Parameterizations (Noah-MP) land surface model at two tropical forest sites in Panama and Malaysia and one urban tropical site in Singapore. The site-specific calibration improves the model’s ability to simulate key variables, including latent and sensible heat fluxes as well as soil moisture, particularly at daily and seasonal scales. Sensitivity analyses identify consistently influential parameters across land cover types, offering guidance for model tuning in other tropical contexts. Nevertheless, challenges persist, particularly in estimating nighttime sensible heat fluxes, balancing the optimization of latent and sensible heat fluxes, and capturing seasonal soil moisture dynamics. These insufficiencies may be due to a lack of realistic complexity in Noah-MP’s land-surface physics, including multi-species vegetation modeling, soil organic layer treatments, subsurface hydrological processes, and permeable urban surfaces. Our results demonstrate how targeted parameter refinement can improve Noah-MP’s performance in the tropics and inform future development priorities. These findings contribute to broader efforts to generalize model calibration strategies and improve Earth system model fidelity in data-scarce, climatically distinct regions.

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