Machine Learning-Aided Groundwater Level and CO 2 Emission Estimations in Tropical Peatlands using Global and Regional Scenarios

Tropical peatlands store a disproportionately large fraction of global soil carbon and emit large amounts of soil CO ₂ from peat decomposition and root respiration. Accurate quantification of peatland carbon emissions requires a prediction that is sensitive to the space-time dynamics of the environmental factors. This study estimated a biweekly peat CO ₂ emission representing total soil respiration (Rs) representing the whole peatland ecosystem in Rupat Island, Indonesia. The Rs was estimated using nonlinear CO ₂ to groundwater level/GWL response curves reviewed from Southeast Asian chamber studies. The GWL dataset was predicted using Extreme Gradient Boosting (XGBoost), integrating dense GWL measurements (Jan-2019 to Apr-2025) against dynamic and static predictors. Spatial upscaling of predicted GWL (− 32.19 ± 5.58 cm) resulting in estimated annual Rs of 4.02 to 5.67 Mt CO ₂ and totaling 28.15 to 39.66 Mt CO ₂ cumulative, using general and land use-based response curves. Our study reported relatively low interannual and seasonal variations (< 1 Mt CO ₂ y-1), although cultivated and drained shallow peats consistently act as emission hotspots during dry years. Our Rs estimates exceeded the global and national emission factors (EFs) and other Rs-GWL prediction methods by 1.5 to 6 times, suggesting potential for implementation in CO2 modelling at the ecosystem scale.