Improve the Performance of the Noah‐MP‐Crop Model by Jointly Assimilating Soil Moisture and Vegetation Phenology Data

• Main Authors: Tongren Xu, Fei Chen, Xinlei He, Michael Barlage, Zhe Zhang, Shaomin Liu, Xiangping He
• Format: Article
• Language: English
• Published: American Geophysical Union (AGU) 2021-07-01
• Series: Journal of Advances in Modeling Earth Systems
• Subjects: Ensemble Kalman filter; land data assimilation; leaf area index; Noah‐MP‐Crop; soil moisture; solar‐induced chlorophyll fluorescence
• Online Access: https://doi.org/10.1029/2020MS002394

Abstract The interactions between crops and the atmosphere significantly impact surface energy and hydrology budgets, climate, crop yield, and agricultural management. In this study, a multipass land data assimilation scheme (MLDAS) is proposed based on the Noah‐MP‐Crop model. The ensemble Kalman filter (EnKF) method is used to jointly assimilate the leaf area index (LAI), soil moisture (SM), and solar‐induced chlorophyll fluorescence (SIF) observations to predict sensible (H) and latent (LE) heat fluxes, gross primary productivity (GPP), etc. Such joint assimilation is demonstrated to be effective in constraining the model state variables (i.e., leaf biomass and SM) and optimizing key crop‐model parameters (i.e., specific leaf area [SLA], and maximum rate of carboxylation, Vcmax). The performance of the MLDAS is evaluated against observations at two AmeriFlux cropland sites, revealing good an agreement with the observed H, LE, and GPP. When using optimized model parameters (SLA and Vcmax) and jointly assimilating LAI, SM, and SIF observations, the MLDAS produces 34.28%, 26.90%, and 51.82% lower root mean square deviations for daily H, LE, and GPP estimates compared with the Noah‐MP‐Crop open loop simulation. Our findings also indicate that the H and LE predictions are more sensitive to SM measurements, while the GPP simulations are more affected by LAI and SIF observations. The results indicate that performances of physical models can be greatly improved by assimilating multi‐source observations within MLDAS.