LAUSR

Clarifying carbon (C) cycling in small ponds is vital for understanding C transport in lowland agricultural landscape. Quantifying C flux is crucial for learning C cycling, but is challenging due to its complex cycling and significant impacts from intensive human activities. Here, we developed a process‐based model (CDP) to achieve a daily estimation of C dynamics in agricultural ponds within lowland artificial watersheds (polders), and proposed a dual evaluation approach (concentration and flux) to assess the model's performance using two data sets obtained from eight typical polders in the Lake Taihu Basin. The developed model captured pond C dynamics, achieving a Nash‐Sutcliffe efficiency of 0.44 ± 0.27. Our C flux estimations based on the newly‐developed model showed large C emissions, primarily through carbon dioxide (CO2) (497.5 g C m−2 yr−1), along with significant C burial (27.8 g C m−2 yr−1) with a hot moment in summer. Scenario simulations revealed the distinct impacts of pond C emissions and burial associated with the growth and death of phytoplankton and macrophytes. A 10% increase in macrophyte growth rates associated with a 1.8 g C m−2 yr−1 increase in CO2 emission, while a similar increase in phytoplankton growth rates related to a 12.2–16.2% increase in C burial. This study revealed a quick response of C flux to phytoplankton‐macrophyte dominance, and highlighted the high potential of the process‐based model for high‐resolution (daily) quantification of C fluxes, thereby enhancing our understanding of C cycling in lowland agricultural ponds.