Climate Change, High-Temperature Stress, Rice Productivity, and Water Use in Eastern China: A New Superensemble-Based Probabilistic Projection

AbstractThe impact of climate change on rice productivity in China remains highly uncertain because of uncertainties from climate change scenarios, parameterizations of biophysical processes, and extreme temperature stress in crop models. Here, the Model to Capture the Crop–Weather Relationship over a Large Area (MCWLA)-Rice crop model was developed by parameterizing the process-based general crop model MCWLA for rice crop. Bayesian probability inversion and a Markov chain Monte Carlo technique were then applied to MCWLA-Rice to analyze uncertainties in parameter estimations and to optimize parameters. Ensemble hindcasts showed that MCWLA-Rice could capture the interannual variability of the detrended historical yield series fairly well, especially over a large area. A superensemble-based probabilistic projection system (SuperEPPS) coupled to MCWLA-Rice was developed and applied to project the probabilistic changes of rice productivity and water use in eastern China under scenarios of future climate chang...