Methane emissions from inland waters: Quantifying the largest uncertainty in the global methane budget

Methane (CH4) is the second most important greenhouse gas after carbon dioxide, accounting for 35% of the greenhouse gas-driven warming in 2010-2019 relative to 1850-19001. Methane emissions continue to increase annually at a rate of 18.1 ppb/yr. Globally, aquatic ecosystems account for approximately half of methane sources, with inland water emissions among the most, if not the most, uncertain worldwide methane source2. Sources of uncertainty associated with the methane emissions in inland waters include a poor understanding of how the empirical drivers of emissions change across rivers of varying sizes and stream orders in response to different river flows, river management regimes (e.g. damming), seasonal changes including temperature and light availability, and land use types, which influence nutrient concentrations and in turn, ecosystem metabolism3. Addressing the whole-system drivers of methane emissions across space and time has previous been limited by the absence of large-scale river network data products that include flow data at the reach scale. The recent publication of the global reach-scale, river flow model MERIT-Hydro4 and 35 years of associated flow data (GRADES)5 now allows for the development of biogeochemical models that track the cascading fluxes and transformations of dissolved constituents such as methane through inland waters worldwide at previously impossible resolutions, enabling local conclusions to be generated as well as global.

Project goals
The primary goal of this research is to use a combined model- and field-based approach to quantify the large-scale (national, continental, and/or global) controls and drivers of methane emissions from inland waters, including rivers, reservoirs, lakes, wetlands, and estuaries, and forecast how emissions will change in the future. The modelling component will rely on the high-resolution hydrological and GIS data products now available as the system backbone, with methane emission mechanisms constrained using a combination of empirical observation field data collected by the student, mechanistic or kinetic data, and machine learning approaches. Field data will include the use of floating greenhouse gas flux chambers installed on rivers and water bodies at strategic sampling points. An interdisciplinary approach will allow the student to develop a project that integrates elements of (1) hydrological modelling, (2) biogeochemical modelling, and (3) climate modelling. The project outcome will be directly relevant to IPCC and Global Carbon Project stakeholders (https://www.globalcarbonproject.org/), as well as local and national governments aiming to meet climate emissions goals.