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

Tropical land-surface processes play a key role in Earth system dynamics, yet evaluation and calibration of land surface models in these regions remain limited. This study addresses this gap through site-specific calibration of the Noah with Multi-Parameterizations (Noah-MP) land surface model at two tropical forest sites: the Panama site with a tropical monsoon climate and the Malaysia site with a tropical rainforest climate. We develop an efficient machine learning-based calibration framework that combines emulator construction with parameter optimization. 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. The emulator-based framework enables efficient calibration and shows robust performance during independent validation periods at each site. While the calibrated parameters are not directly transferable between the two sites with different climate regimes, they perform effectively during unseen validation periods at their respective sites, suggesting the potential transferability of parameters across similar climates. Remaining challenges include simulating nighttime sensible heat fluxes, balancing the optimization of latent and sensible heat fluxes, and capturing seasonal soil moisture dynamics. These limitations may stem from structural simplifications in Noah-MP, such as the lack of multi-species vegetation modeling, soil organic layer representation, and detailed tropical subsurface hydrology. Our results demonstrate how the calibration framework can effectively improve Noah-MP performance in tropical forests and provide guidance for future development priorities, which can support broader efforts to generalize model calibration strategies and improve Earth system model fidelity in data-scarce, climatically distinct regions.