Modeling wheat development under extreme weather with WOFOST-EW v1

Abstract. Extreme weather events pose significant challenges to crop production, making their assessment essential for developing effective climate adaptation strategies. Process-based crop models are valuable for evaluating climate change impacts on crop yields but often struggle to simulate the effects of extreme weather accurately. To fill this knowledge gap, this study introduces WOFOST-EW v1, an enhanced version of the World Food Studies Simulation Model (WOFOST), which integrates extreme weather indices and deep learning algorithm to improve simulations of winter wheat growth under extreme conditions. Deep learning offers powerful nonlinear fitting capabilities, enabling it to capture subtle and intricate interactions between extreme weather events and crop development, thereby significantly improving simulation accuracy under extreme scenarios. We validate WOFOST-EW using phenological, yield, and extreme weather data from agricultural meteorological stations in the North China Plain. The results show that WOFOST-EW improves simulation accuracy. The RRMSE for heading and maturity decreases from 4.61 % to 3.73 % and from 4.74 % to 3.98 %, respectively (with RMSE reductions of 10.64 % and 12.86 %). The R2 value for yield simulations increases from 0.67 to 0.76. In addition, we further validate the WOFOST-EW model in years affected by extreme weather and find that, compared to the original WOFOST model (R2 ranging from 0.61 to 0.71), WOFOST-EW achieves more accurate results (R2 ranging from 0.80 to 0.86). WOFOST-EW effectively captures the impacts of extreme weather, offering a reliable tool for agricultural planning and climate adaptation. As extreme weather events become increasingly frequent, WOFOST-EW can assist decision-makers in more accurately evaluating crop yields, providing technical support for agricultural systems in the context of global climate change.