New Global Datasets for Methane Modeling: Natural Wetlands, Lakes and Reservoirs

Natural wetlands, lakes and reservoirs (WLR) are the major natural sources of global methane (CH4) emissions. Most methane emission models are applied to externally-defined distributions of these sources. To define wetlands, current methane models rely on surface inundation data which captures not only flooded wetlands but also lakes and other surface-waters while missing non-flooded wetlands. It has been shown that methane emissions vary between these different sources as well as among the sources with different characteristics. For example, observations confirm that per unit area CH4 fluxes are higher in carex-dominated wetlands than in shrub tundra, and higher for small, shallow lakes than for large, deep lakes. However, often lake and reservoir datasets are incomplete, do not include depth information and ignore small lakes which we estimate may add 40% to the total global lake area. In addition, few CH4 models include wetland or lake types consistent with those represented in the flux literature which makes validation difficult. These findings confirm the critical need for source data that describe CH4-relevant classes within WLRs, in addition to accurate and mutually exclusive spatial distributions of each source.

We have developed new spatially explicit 0.25° global datasets of natural wetlands, lakes and reservoirs critical to methane emission models. For each new dataset, we developed and applied hierarchical methane-centric classification systems: wetland criteria include vegetation, flooding/non-flooding, permafrost state, and soil organic carbon content; lake and reservoir criteria comprise permafrost state, ground-ice content, organic substrate, size and depth. Each source-specific classification yielded classes that can be used to stratify the methane-flux measurements thus enabling all flux measurements to be used for model development and evaluation. Our new suite of data constitutes a unique framework for modeling methane emissions by identifying methane-relevant WLR types and creating mutually exclusive datasets enabling emissions from each source to be modeled and evaluated individually and in combination.