State data assimilation as a tool for forecasting soil carbon stocks
Article: Improving Yasso15 soil carbon model estimates with ensemble adjustment Kalman filter state data assimilation
Authors: Viskari, Toni et al.
Publication: Geoscientific Model Development
Model-based forecasts of soil organic carbon (SOC) are important for assessing the soil carbon pools and their changes. However, the reliability and applicability of these forecasts is limited due to the lack of detailed observations. This study aimed to figure out whether the SOC model and forecast based on available measurement data can be updated by using state data assimilation (SDA). In this method, which is used for example in weather forecasting, the information from several sources is combined to create more precise estimations. The results of this study showed that SDA is a beneficial tool for forecasting changes in soil organic carbon stocks.