Simulation of Water Uptake in Maize, Using Different Levels of Process Detail

Daily crop water uptake was simulated using algorithms from three crop simulation models, CropSyst, CERES, and EPIC (listed in order of decreasing process detail). Simulated results were compared with measurements of sap flow and soil water content for maize (Zea mays L.) growing at Prosser, WA, under a wet and a dry irrigation treatment, and with soil water content measurements for nonirrigated maize ai Davis, CA. At Prosser, the dry treatment imposed only a mild stress; at Davis, the stress was severe. Simulation variables such as maximum crop evapotranspiration, root density by soil layer, and green leaf area index were provided as daily input. At Prosser, all algorithms performed similarly when simulating crop water uptake. For the wet treatment, the root mean square error (RMSE) was 0.27 to 0.28 mm d⁻¹, and the relative error [RE = 100 (RMSEIMeasured average)] was 7.0 to 7.2%. For the dry treatment, simulation accuracy decreased (RMSE = 0.33–0.38 mm d⁻¹; RE = 9.0–10.5%). The time evolution of water uptake simulated by CropSyst better depicted the measured sap flow (water uptake) difFerence between wet and dry treatments. Simulations of soil water content by layer for the wet treatment, compared with measurements available for 17 d, yielded RMSEs from 0.022 to 0.024 m³ m⁻³ and RES from 8.5 to 9.2%. For the dry treatment (12 d of measurements), the best simulations were obtained with the water uptake algorithms from CropSyst and CERES, with RMSE = 0.015 m³ m⁻³ (both models) and RE = 6.4% (CropSyst) and 6.6% (CERES), compared with RMSE = 0.019 m³ m⁻³ and RE = 8.1% for EPIC. Under the severe water stress at Davis, CropSyst had the best performance. This algorithm simulated changes in soil water content by layer (8 d of measurements availabie) with RMSE of 0.011 m³ m⁻³ and RE of 5.0%, while the RMSE and RE values for CERES and EPIC were 0.016 and 0.019 m³ m⁻³ and 7.6 and 9.0%, respectively. The more process-oriented algorithm (CropSyst) showed an increasing advantage as water stress severity increased. The EPIC algorithm had the poorest performance under water stress. This could be improved by modifying the value of the water extraction distribution parameter in EPIC, but with this change the wet treatment simulations at Prosser deteriorated substantially, indicative of Limitations in EPIC'S simple approach.