The impact of calibrating soil organic carbon model Yasso with multiple datasets

Abstract. Soil Organic Carbon (SOC) models are important tools in determining global SOC distributions and how carbon stocks are affected by climate change. Their performances are, however, affected by data and methods used to calibrate them. Here we study how the Yasso SOC model performs if calibrated individually or with multiple datasets and how the chosen calibration method affected the parameter estimation. We found that when calibrated with multiple datasets, the model showed a better global performance compared to a single dataset calibration. Furthermore, our results show that more advanced calibration algorithms should be used for SOC models due to the multiple local maximas in the likelihood space.