On the accuracy of crop production and water requirement calculations: Process-based crop modeling at daily, semi-weekly, and weekly time steps for integrated assessments.

Integrated models are crucial for evaluation of the complex interactions and trade-offs among policy choices and socioeconomic, technical, and environmental processes. The use of process-based crop models as components of integrated models offers the possibility of significantly improving such analyses; however, challenges exist in terms of simulation scales and degree of integration. Therefore, this study evaluates the applicability of coarser-than-daily simulation time steps to simulate long-term crop yields in integrated models, and the impacts of aggregated weather input data on yields for a water-driven crop-process model based on the FAO AquaCrop model. We ran simulations at daily, semi-weekly, and weekly time steps in conjunction with coarser temporal resolution (weekly) weather input data for three crops in four locations over ten years to represent a range of crops and growing environments. Simulation results were compared to a reference case from AquaCrop using daily time step with daily weather data. Model skill for simulating crop biomass and yield and water demands was assessed statistically for each of these four hypothetical farms. Visual representations were also used to compare simulated soil moisture, crop canopy, and actual evapotranspiration values. Weekly climate data led to overestimation of crop biomass and yield regardless of the time step used. High agreements and low bias errors were realized for crop production and water estimates at daily and semi-weekly time steps, whereas weekly simulations showed poorer performance. Longer time steps intensified the impacts of weather input data aggregation, and overestimation became more pronounced with increases in time step length. The findings have important implications for integrated assessments that couple crop models with other socioeconomic, environmental, or hydrologic models, and provide guidance for modelers involved in interdisciplinary agricultural and water resources applications, including policy assessments, evaluation of water and food security, and resource use and efficiency under climate change.