Comparing the Impacts of Single- and Multi-Objective Optimization on the Parameter Estimation and Performance of a Land Surface Model

Abstract. In land surface models (LSMs), precise parameter specification is crucial to reduce the inherent uncertainties and enhance the accuracy of the simulation. However, due to the multi-output nature of LSMs, the impact of different optimization strategies (e.g., single- and multi-objective optimization) on the optimization outcome and efficiency remains ambiguous. In this study, we applied a revised particle evolution Metropolis sequential Monte Carlo (PEM-SMC) algorithm for both single- and multi-objective optimization of the Common Land Model (CoLM), constrained by latent heat flux (LE) and net ecosystem exchange (NEE) measurements from a typical evergreen needle-leaf forest observation site. The results reveal that the revised PEM-SMC algorithm, demonstrates a robust ability to tackle the multi-dimensional parameter optimization challenge for LSMs. The sensitive parameters for different target outputs can exhibit conflicting optimal values, resulting in single-objective optimization improving the simulation performance for a specific objective at the expense of sacrificing the accuracy for other objectives. For instance, solely optimizing for LE reduced the root-mean-square error (RMSE) of the simulated and observed LE by 20 % but increased the RMSE of the NEE by 97 %. Conversely, multi-objective optimization can not only ensure that the optimized parameter values are physically sound but also balances the simulation performance for both LE and NEE, as evidenced by the decrease in RMSE for LE and NEE of 7.2 W/m2 and 0.19 μmol m-2 s-1, respectively. In conclusion, these findings reveal that comprehensively integrating the various available observational data for multi-objective optimization is preferable for parameter calibration in complex models.