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Machine-learning synthesis reveals non-linear interactions between water table depth and temperature in northern peatlands, highlighting hydrology as a critical lever for climate mitigation.
Peatlands are the largest terrestrial stores of organic carbon, but drainage has turned them into substantial sources of CO 2 . While water level is widely recognized as the primary control of CO 2 emissions from peatlands, effective future management requires understanding its interaction with rising temperatures under a warming climate. Using 276 site-years of annual CO 2 flux observations across temperate and boreal peatlands, we apply explainable machine-learning to disentangle the combined effects of water table depths and temperature on ecosystem CO 2 exchange at annual scales. Across peatlands spanning diverse land-cover—including natural fens and bogs, croplands, grasslands, and extraction sites—CO 2 emissions exhibit a non-linear response to water table depth. Emissions decline when water tables are raised above 60-75 cm depth. Optimal mitigation requires water tables of 20 cm or higher. We find that deep water tables interact with temperatures to increase emissions at temperate sites. On a subset of 113 site-years of daily CO 2 flux data, we show that at warm temperatures, higher water tables suppress, whereas deeper water tables enhance temperature-driven CO 2 emissions from peatlands. We demonstrate that hydrology regulates the temperature sensitivity of peatland carbon release, revealing a key control on carbon–climate feedbacks under future warming.
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Behrens et al. (2026) studied this question.
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