Recent advancements in the DayCent model

The DayCent model is a biogeochemical tool for simulating carbon (C) and nitrogen (N) cycling in terrestrial ecosystems, including soil C dynamics, greenhouse gas (GHG) emissions, crop yields. Here, we present the recent advancements in the DayCent model. Key improvements include the integration of remote sensing data, specifically solar-induced chlorophyll fluorescence (SIF), which enhances the accuracy of crop yield predictions. New algorithms have been incorporated to simulate agrivoltaic systems, allowing the model to assess interactions between crop growth, soil and water dynamics and photovoltaic energy production. DayCent has been coupled with the MODFLOW groundwater model to enable feedback between root zone processes and groundwater hydrology. Ongoing developments include extending DayCent-UV version by improving nitrogen dynamics and coupling it with CWRF model. We are also developing a knowledge-guided machine learning (KGML) version using surrogate models that are significantly faster and highly accurate, with future plans to explore data assimilation and couple it with a surrogate SWAT model. These enhancements broaden the model’s applicability and improve performance.