Tropical wetlands are releasing methane faster than scientists can track, and a new digital twin aims to close that gap. Methane traps heat far more powerfully than carbon dioxide, and its concentration in the atmosphere has jumped sharply—by 15 parts per billion in 2020 and 18 in 2021, compared to the usual 5–12. Satellite data points to tropical wetlands as the likely culprit, but current models disagree wildly on how much methane these wetlands produce. They also cannot map where the wetlands actually are, because the flooded areas shrink and expand with rainfall and river flow. Without knowing the source, policymakers cannot design effective mitigation strategies. This fellowship builds a Wetland Digital Twin—a machine-learning system that fuses satellite observations with land surface models to estimate methane emissions in real time. It will also use high-resolution satellite imagery and advanced hydrology models to map changing wetland extent far more accurately than before. If successful, the system could become a climate service that informs international pledges, such as the COP26 Methane Pledge to cut emissions 30% by 2030. It would give governments and agencies like the IPCC and UNEP a concrete tool to track natural methane sources, rather than relying on uncertain estimates.
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Methane (CH4) is a major greenhouse gas. Its short atmospheric lifetime (~9 years) means we can mitigate its emissions and warming effects. At COP26, countries signed up to the Methane Pledge, strengthened at COP27, committing to reduce emissions in 2030 by 30%, eliminating over 0.2C of global warming by 2050. The challenge is that methane has many sources, man-made and natural. Man-made emissions include significant contributions from fossil fuels (111 Tg CH4 yr-1) and agriculture/waste (217 Tg CH4 yr-1), with natural signals dominated by wetland emissions (181 Tg CH4 yr-1, >30% of total emissions). Estimates suggest tropical wetlands contribute >65% of all wetland emissions, over 20% of the total global methane budget. However, these estimates are hugely uncertain. To fully understand the methane budget, we must monitor these natural emissions and understand how, when and where they are produced and how they might change under future climate scenarios. Failure to do so would restrict capability to inform policy and take mitigation action. The problem is becoming more urgent. Recent years have seen a rapid and surprising increase in atmospheric methane. Global values increased by 15 ppb in 2020 and 18 ppb in 2021, compared to 5-12 ppb in recent years. This acceleration is alarming and points to significant climate-feedbacks that are not fully understood nor expected. Studies using satellite data generated by my work (e.g. Qu et al., 2022, Feng et al., 2022) have reached the conclusion that tropical wetlands are the likely source of this new, and as of yet unexplained, increase in the methane growth rate. We know methane is produced in wetlands by microbes but questions remain on the effect of factors such as temperature, water level and soil type. State-of-the-art process-based land surface models can produce wetland methane emissions but huge discrepancies between model estimates limit their utility and assessing these models against observations is key. Importantly, we also do not know how large these methane-producing wetland areas are, as they continually change in size in response to rainfall and riverflow. Therefore, even if models capture the correct wetland methane climate-response, the wetland extent itself will limit ability to accurately estimate emissions. The problem therefore is two-fold: 1) Can we reconcile large discrepancies in our ability to model the wetland methane emission response to climate feedbacks? 2) Can we dramatically improve our estimates of wetland extent to constrain the spatial/temporal changes in methane emissions? This fellowship will use satellite observations and land surface models to build an innovative and dedicated Wetland Digital Twin; a machine-learning system capable of estimating methane produced by wetlands, transforming our understanding of the causes of methane emissions and responses to the changing climate. In parallel, we need much better knowledge of wetland locations and how they change over time. By applying new machine-learning methods to very-high-resolution satellite imagery and combining with advanced hydrological modelling, I will better map these wetland areas and understand their dynamics. To achieve this, I will work closely with Project Partners, specialising in land surface modelling (GCP, UKCEH, UK Met Office), machine learning and artificial intelligence (ESA Phi-Lab, NEODAAS), IT infrastructure (NEODAAS, JASMIN, CGI), high-resolution remote sensing (Planet) and climate modelling (UK Met Office) while also engaging with a range of Stakeholders from wetland ecosystem specialists to policymakers (e.g. COP/IPCC, UNEP, RAMSAR, CIFOR, CEOS/GCOS). This new Wetland Digital Twin capability, driven by Earth Observation data and powered by machine learning, will allow us to develop climate services that are capable of providing decision-support for policymakers and enable better understanding of the climate response of these critical ecosystems.
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