A128-13
Sensitivity of lake and wetland methane emissions upscaling to littoral zone area using airborne synthetic aperture radar
Friday, 11 December 2020: 11:06
Virtual
Ethan D. Kyzivat1, Laurence C Smith2,3, Tamlin Pavelsky4, David E Butman5, Colin J Gleason6, Fenix Garcia Tigreros7, Chang Huang8,9, Chao Wang10, Theodore Langhorst4, Jessica V. Fayne11, Catherine Kuhn12, Merritt Harlan13, Yuta Ishitsuka14, Dongmei Feng15, Rob Spencer16, Kimberly Wickland17, Mark Dornblaser18 and Robert G Striegl19, (1)Brown University, Department of Earth, Environmental and Planetary Sciences, Providence, RI, United States, (2)Brown University, Providence, RI, United States, (3)University of California Los Angeles, Geography, Los Angeles, CA, United States, (4)University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, (5)University of Washington, School of Environmental and Forest Sciences, Seattle, WA, United States, (6)University of Massachusetts, Amherst, MA, United States, (7)University of Washington, Seattle, WA, United States, (8)Northwest University, Xi'an, China, (9)Brown University, Providence, United States, (10)University of North Carolina at Chapel Hill, Chapel Hill, United States, (11)University of California Los Angeles, Department of Geography, Los Angeles, CA, United States, (12)University of Washington Seattle Campus, School of Environmental and Forestry Sciences, Seattle, WA, United States, (13)UMass-Amherst, Amherst, United States, (14)University of Massachusetts Amherst, Civil & Environmental Engineering, Amherst, MA, United States, (15)University of Massachusetts Amherst, Amherst, United States, (16)Department of Earth, Ocean and Atmospheric Sciences, Florida State University, Tallahassee, FL, United States, (17)US Geological Survey, Water Resources Mission Area, Boulder, CO, United States, (18)US Geological Survey, Boulder, CO, United States, (19)USGS WRD, Boulder, CO, United States
Abstract:
Wetlands are the largest environmental source of methane, yet global wetland maps, a key component of methane models, are in disagreement and do not have the spatial resolution to resolve the smallest water bodies. These unmapped water bodies emit methane disproportionately to their size, and inaccurate water body accounting may contribute to the gap between bottom-up and top-down model methane emission estimates. Furthermore, the littoral zone, defined as the near-shore and often-vegetated shallow region of a lake, takes up proportionately more area in these small water bodies than in large ones. The littoral zone is a known hotspot for methane flux, but is rarely mapped systematically across many water bodies.
To facilitate better understanding of the littoral/pelagic partitioning in lakes and wetlands, we develop a supervised classification based on emergent aquatic vegetation. We use L-band synthetic aperture radar (SAR) from NASA’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne imaging system, flown during the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) during 2017-2019. With a wavelength of 23.8 cm and a ground sample distance of ~5m, UAVSAR L-band returns are ideal for mapping littoral areas due to double-bounce backscatter between vegetation and the water surface. We focus on three study sites: (1) The Peace-Athabasca Delta (PAD), a 5,000 km2 inland delta in northern Alberta; (2) Canadian Shield lakes, NWT; and (3) Yukon Flats National Wildlife Refuge, AK. Preliminary results show a major variation in emergent vegetation coverage, from ~2% in the Canadian Shield to ~15% in the PAD. In contrast, emergent vegetation within the most hydrologically dynamic site (PAD) only ranges from ~14-20%. Thus, this geographic variation is greater than the seasonal variation within any single site.
Finally, we perform a simple methane emissions upscaling calculation for these study areas. We use the mapped emergent vegetation areas to test the sensitivity of the upscaling estimate to both changes in littoral zone area and the magnitude of the flux attributed to these areas. These results will help identify probable sources of error in bottom-up methane models and show how remotely-sensed littoral zone area can be used as a predictor for water body methane emissions.
