A robust initialization method for accurate soil organic carbon simulations

[摘要] Changes in soil organic carbon (SOC) stocks are a majorsource of uncertainty for the evolution of atmospheric CO 2 concentration during the 21st century. They are usually simulated bymodels dividing SOC into conceptual pools with contrasted turnover times.The lack of reliable methods to initialize these models, by correctlydistributing soil carbon amongst their kinetic pools, strongly limits theaccuracy of their simulations. Here, we demonstrate that PARTY SOC , amachine-learning model based on Rock-Eval ® thermal analysis,optimally partitions the active- and stable-SOC pools of AMG, a simple and well-validated SOC dynamics model, accounting for effects of soil managementhistory. Furthermore, we found that initializing the SOC pool sizes of AMGusing machine learning strongly improves its accuracy when reproducing theobserved SOC dynamics in nine independent French long-term agriculturalexperiments. Our results indicate that multi-compartmental models of SOCdynamics combined with a robust initialization can simulate observed SOCstock changes with excellent precision. We recommend exploring theirpotential before a new generation of models of greater complexity becomesoperational. The approach proposed here can be easily implemented on soilmonitoring networks, paving the way towards precise predictions of SOC stockchanges over the next decades.

[学科分类] 大气科学