LAWS: A new crop–water coupling model integrating an agricultural module into a process-based hydrological model (CWatM v1.08)

Abstract. It is essential to jointly represent crop growth, water availability, and human water management in an integrated framework to understand future water-food interactions. Although existing large-scale coupling models have improved the representation of crop-water interactions, few integrate detailed crop growth and hydrological processes, together with their interactions, within a unified process-based framework. Here, we developed the Large-scale Agriculture Water System model (LAWS) by integrating a process-based agricultural module into the Community Water Model (CWatM). We evaluated its performance in simulating crop growth dynamics and yields across six experimental sites and at global, national, and grid-cell levels for maize, wheat and soybean. Results showed that, at the site level, LAWS reproduced the main seasonal dynamics of leaf area index and biomass for different crops and regions. Pearson temporal correlations between simulated and reference global yield-anomaly series were 0.68, 0.14, and 0.54 for maize, wheat, and soybean, respectively. Across the top 20-producing countries, the correlations between simulated and reference long-term mean yields were 0.74, 0.79, and 0.32 for maize, wheat, and soybean, respectively. At the grid-cell level, the simulated long-term mean yields showed broad spatial patterns consistent with those in the global gridded yield dataset. Overall, the yield performance of LAWS was broadly comparable to mainstream global crop models, supporting its application to large-scale assessments of crop production. Furthermore, LAWS can represent the interactions between crop growth and the water cycle, providing a framework for sustainable agricultural planning under water constraints. Through a case study, we demonstrated this capability by assessing the contribution of nonrenewable groundwater to irrigated maize production globally. In addition, its open-source and modular architecture facilitates further development to improve the simulation of agricultural systems, the water cycle, and human water management.