Articles | Volume 13, issue 12
Methods for assessment of models
01 Dec 2020
Methods for assessment of models |  | 01 Dec 2020

Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation

Toni Viskari, Maisa Laine, Liisa Kulmala, Jarmo Mäkelä, Istem Fer, and Jari Liski


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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Toni Viskari on behalf of the Authors (22 Sep 2020)
ED: Publish subject to technical corrections (09 Oct 2020) by Carlos Sierra
AR by Toni Viskari on behalf of the Authors (16 Oct 2020)  Author's response   Manuscript 

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Short summary
The research here established whether a Bayesian statistical method called state data assimilation could be used to improve soil organic carbon (SOC) forecasts. Our test case was a fallow experiment where SOC content was measured over several decades from a plot where all vegetation was removed. Our results showed that state data assimilation improved projections and allowed for the detailed model state be updated with coarse total carbon measurements.