Interpreting carbon-water trade-offs in Daisy crop model using Pareto-based calibration

Abstract. Improving the simulation of carbon and water exchanges is crucial for reliable crop modelling under changing climate conditions. Although model calibration is a key step, optimising multiple outputs can be challenging and often reveals trade-offs between calibration objectives. We applied a Pareto-based multi-objective calibration with the Speed-constrained Multi-objective Particle Swarm Optimisation (SMPSO) algorithm to the Daisy soil–plant–atmosphere model, targeting dry matter (DM), net ecosystem exchange (NEE), and latent heat flux (LE) of winter wheat crops.

The optimal parameter set achieved good accuracy for all objectives (RMSE = 0.948 t ha^-1 for DM, 1.49 gC m^-2 day^-1 for daily NEE and 30.7 W m^-2 for daily LE) but revealed singular trade-offs. The strong compromise between dry matter and NEE likely suggests wrong parameterisation and measurement bias, while the trade-off between NEE and LE reflects equifinality issues from evapotranspiration partitioning. Lastly, this analysis also pointed out limitations in simulating stomatal regulation during heatwaves conditions, supporting the decoupling between transpiration and carbon assimilation. These findings show that Pareto-based calibration can also serve as a diagnostic tool, identifying structural weaknesses and guiding targeted improvements in process representation for more robust crop model evaluation.