Implementing a Dynamic Root Distribution Scheme Responsive to Soil Environment Into Noah-MP-Crop: Improving Simulations of Soil Water, Crop Growth, and Energy Fluxes in Agro-Ecosystems

Reliable simulation of land–atmosphere interactions in land surface models (LSMs) requires realistic root distribution modeling, yet conventional static root parameterizations often fail to capture seasonal root dynamics in cropland ecosystems. In this study, we developed a soil–environment–responsive dynamic root distribution scheme (SE_root) within the Noah-MP-Crop framework that explicitly accounts for soil moisture (SM), temperature, aeration, bulk density, and soil texture. Evaluations against in situ observations in the North China Plain demonstrated that SE_root substantially outperformed the default static (fixed_root) and exponential dynamic (Exp_root) parameterizations. Site-scale simulations exhibited improved accuracy in capturing SM dynamics, leaf area index (LAI), and latent heat flux (LHF), yielding R² values consistently above 0.56. The simulated vertical root biomass distribution, with approximately 70% of root biomass concentrated in the upper 40 cm and declining with depth, closely matched field observations. Relative to the fixed_root and Exp_root schemes, the site-scale mean absolute errors were reduced by 10%–12% for SM and 4%–10% for LAI. Regional simulations further revealed that by capturing the dynamic feedback between root growth and local soil constraints, SE_root better represented the spatial heterogeneity of SM and LAI, alongside modest LHF improvements. Overall, incorporating this soil-responsive root parameterization improves the representation of SM dynamics, root allocation, crop growth, and land–atmosphere exchanges. These findings underscore the importance of explicitly representing root–soil interactions in agricultural LSMs, offering a robust pathway for coupling with climate models to capture crop–climate feedbacks and support sustainable management.