Revising a process‐based biogeochemistry model (DNDC) to simulate methane emission from rice paddy fields under various residue management and fertilizer regimes

A comprehensive biogeochemistry model, DNDC, was revised to simulate crop growth and soil processes more explicitly and improve its ability to estimate methane (CH4) emission from rice paddy fields under a wide range of climatic and agronomic conditions. The revised model simulates rice growth by tracking photosynthesis, respiration, C allocation, tillering, and release of organic C and O2 from roots. For anaerobic soil processes, it quantifies the production of electron donors [H2 and dissolved organic carbon (DOC)] by decomposition and rice root exudation, and simulates CH4 production and other reductive reactions based on the availability of electron donors and acceptors (NO3−, Mn4+, Fe3+, and SO42−). Methane emission through rice is simulated by a diffusion routine based on the conductance of tillers and the CH4 concentration in soil water. The revised DNDC was tested against observations at three rice paddy sites in Japan and China with varying rice residue management and fertilization, and produced estimates consistent with observations for the variation in CH4 emission as a function of residue management. It also successfully predicted the negative effect of (NH4)2SO4 on CH4 emission, which the current model missed. Predicted CH4 emission was highly sensitive to the content of reducible soil Fe3+, which is the dominant electron acceptor in anaerobic soils. The revised DNDC generally gave acceptable predictions of seasonal CH4 emission, but not of daily CH4 fluxes, suggesting the model's immaturity in describing soil heterogeneity or rice cultivar‐specific characteristics of CH4 transport. It also overestimated CH4 emission at one site in a year with low temperatures, suggesting uncertainty in root biomass estimates due to the model's failure to consider the temperature dependence of leaf area development. Nevertheless, the revised DNDC explicitly reflects the effects of soil electron donors and acceptors, and can be used to quantitatively estimate CH4 emissions from rice fields under a range of conditions.