Calibrating the soil organic carbon model Yasso20 with multiple datasets

Autor: T. Viskari, J. Pusa, I. Fer, A. Repo, J. Vira, J. Liski
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Geoscientific Model Development, Vol 15, Pp 1735-1752 (2022)
Druh dokumentu: article
ISSN: 1991-959X
1991-9603
DOI: 10.5194/gmd-15-1735-2022
Popis: Soil organic carbon (SOC) models are important tools for assessing global SOC distributions and how carbon stocks are affected by climate change. Their performances, however, are affected by data and methods used to calibrate them. Here we study how a new version of the Yasso SOC model, here named Yasso20, performs if calibrated individually or with multiple datasets and how the chosen calibration method affects the parameter estimation. We also compare Yasso20 to the previous version of the Yasso model. 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 multiple local maxima in the likelihood space. The comparison showed that the resulting model performed better with the validation data than the previous version of Yasso.
Databáze: Directory of Open Access Journals
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