Abstract

Croplands significantly contribute to land-atmosphere coupling processes and can notably influence land surface heat, moisture, and momentum exchanges, alter mesoscale boundary convergence/convection, and ultimately modify local and regional weather and climate via the bio-geophysical characteristics such as albedo, soil moisture, surface roughness, canopy height, and leaf area index. Simultaneously from a reverse perspective, the environmental factors and changing climate can impact crop productivity and threaten food security at a regional and global scale. Nearly 20% of continental United States (and ~13% of the global land) are cover with croplands and further cropland expansion to produce more food is untenable due to lack of fertile land. In this situation, land management and agricultural practices will intensify to alleviate the negative impacts of climate variability on crop productivity. Crop models have been employed as the primary tool to assess the trade-off effect between climate and croplands, generate high spatiotemporal resolution regional agro-climatic related products, and evaluate adaptive strategies to increase crop productivity and mitigate the risk inherent in the food security. With the advancement of land surface models (LSM), the representation of croplands was incorporated in these models. Despite considerable signs of progress in LSM models, the majority of the models neglected growth characteristics and agricultural practices (e.g., planting dates, water absorption patterns, irrigation, fertilization) and assumed single cultivar for the whole research region. This resulted in large uncertainties in regional crop modeling simulations, particularly in simulating crop yield under the projected changing climate. In response to these needs, dynamic corn (Zea mays) and soybean (Glycine max) growth simulations were introduced into Noah-MP (named as the Noah-MP-Crop model). The primary evaluations of the model showed promising enhancements in capturing spatiotemporal heterogeneity of crop leaf area index (LAI), simulated yield, surface energy fluxes, and the overall ability of the coupled version with WRF model in simulating the land-atmosphere interactions and mesoscale convection. . Nevertheless, the model has not been evaluated over different climatic conditions. We tested the model under differing water stress conditions over the Midwest US from 2011 to 2013 using the High-resolution land surface model (HRLDAS) configured with the Noah LSM, the default Noah-MP, the Noah-MP with dynamic vegetation, and Noah-MP-Crop. Preliminary results of testing the model showed various results correspond to different landscapes and climatic conditions. The analysis is underway, and we expect by identifying the source of uncertainty, we can move forward toward disentangling the sources of bias.