Abstract:
As the intensity and frequency of extreme weather events are projected to increase under climate change,
assessing their impact on cropping systems and exploring feasible adaptation options is increasingly critical.
Process-based crop models (PBCMs), which are widely used in climate change impact assessments, have
improved in simulating the impacts of major extreme weather events such as heatwaves and droughts but still fail
to reproduce low crop yields under wet conditions. Here, we provide an overview of yield-loss mechanisms of
excessive rainfall in cereals (i.e., waterlogging, submergence, lodging, pests and diseases) and associated
modelling approaches with the aim of guiding PBCM improvements. Some PBCMs simulate waterlogging and
ponding environments, but few capture aeration stresses on crop growth. Lodging is often neglected by PBCMs;
however, some stand-alone mechanistic lodging models exist, which can potentially be incorporated into PBCMs.
Some frameworks link process-based epidemic and crop models with consideration of different damage mech anisms. However, the lack of data to calibrate and evaluate these model functions limit the use of such frame works. In order to generate data for model improvement and close knowledge gaps, targeted experiments on
damage mechanisms of waterlogging, submergence, pests and diseases are required. However, consideration of
all damage mechanisms in PBCM may result in excessively complex models with a large number of parameters,
increasing model uncertainty. Modular frameworks could assist in selecting necessary mechanisms and lead to
appropriate model structures and complexity that fit a specific research question. Lastly, there are potential
synergies between PBCMs, statistical models, and remotely sensed data that could improve the prediction ac curacy and understanding of current PBCMs’ shortcoming
