Simulation of crop yield using the global hydrological model H08 (crp.v1)

Abstract. Food and water are essential for life. A better understanding of the food–water nexus requires the development of an integrated model that can simultaneously simulate food production and the requirements and availability of water resources. H08 is a global hydrological model that considers human water use and management (e.g., reservoir operation and crop irrigation). Although a crop growth sub-model has been included in H08 to estimate the global crop-specific calendar, its performance as a yield simulator is poor, mainly because a globally uniform parameter set was used for each crop type. Here, through country-wise parameter calibration and algorithm improvement, we enhanced H08 to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. The simulated crop yield was compared with the Food and Agriculture Organization (FAO) national yield statistics and the global data set of historical yield for major crops (GDHY) gridded yield estimates with respect to mean bias (across nations) and time series correlation (for individual nations). The improved simulations showed good consistency with FAO national yield. The mean biases of the major producer countries were considerably reduced to −4 %, 3 %, −1 %, and 1 % for maize, wheat, rice, and soybean, respectively. The corresponding coefficients of determination (R²) of the simulated and FAO statistical yield increased from 0.01 to 0.98, 0.21 to 0.99, 0.06 to 0.99, and 0.14 to 0.97 for maize, wheat, rice, and soybean, respectively; the corresponding root mean square error (RMSE) decreased from 7.1 to 1.1, 2.2 to 0.6, 2.7 to 0.5, 2.3 to 0.3 t/ha. Comparison with the reported performances of other mainstream global crop models revealed that our improved simulations have comparable ability to capture the temporal yield variability. The grid-level analysis showed that the improved simulations had similar capacity to GDHY yield, in terms of reproducing the temporal variation over a wide area, although substantial differences were observed in other places. Using the improved model, we confirmed that an earlier study on quantifying the contributions of irrigation on global food production can be reasonably reproduced. Overall, our improvements enabled H08 to estimate crop production and hydrology in a single framework, which will be beneficial for global food–water–land–energy nexus studies.