Integration of Satellite Estimates of Daily Inundation Extent into a Land Surface Ecosystem-Atmosphere Gas Exchange Model: Impacts on Methane Modeling
Abstract
Soil moisture and the spatial extent of soil saturation, transient inundation, and wetland ecosystems are key determinants of greenhouse gas (GHG, e.g., methane) emissions from the land surface to the atmosphere. We are investigating how near-daily surface water and soil moisture observations such as those expected from NASA's planned Soil Moisture Active-Passive (SMAP) mission could be integrated into an ecosystem-atmosphere gas exchange model to improve its estimates of GHG fluxes. SMAP, to be launched in November 2014, will combine ~3-km resolution synthetic aperture radar (SAR), ~40-km-resolution L-band radiometry, and 3-day revisit period to make a novel dataset expected to provide inundation and soil moisture estimates superior to alternative methods at that temporal-spatial scale. We test the potential impact of this new data source using the Dynamic Land Surface Ecosystem Model (DLEM). DLEM quantifies regional fluxes of methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O) given atmospheric forcing data, with soil saturation as a prognostic variable. In this presentation, we discuss the results of integrating DLEM CH4 emission model products with time-varying subgrid inundation extent estimates from satellite remote sensing observations of North America. To emulate SMAP observations, we have derived a new daily inundation fraction dataset for 2008-2010 using data from NASA's Advanced Microwave Scanning Radiometer-EOS (AMSR-E). To test data-model integration, we created a testbed composed of two separate multi-year DLEM runs in which subgrid land cover conditions were artificially prescribed: one run with maximum wetlands coverage and one with no wetlands. We can combine CH4 products from the two runs using our daily inundation fraction estimates or other inundation representations such that the combination approximates CH4 flux results from a model with explicit inundation forcing. The testbed allows us to simulate a larger array of mixed-grid cases than would be possible with individual model runs explicitly forced by different daily inundation data inputs. Here, we compare CH4 flux results representing two model-data integration realizations: one in which the saturated wetlands coverage is held constant (i.e., representing persistent wetlands) and one in which it is allowed to vary with daily SMAP-like inundation estimates. We show how the impacts of inundation inputs on model CH4 emission vary regionally, seasonally, and year-to-year. We also assess the relative impact on atmospheric CH4 concentration using atmospheric transport results from the Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF/STILT) Lagrangian particle dispersion model.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFM.B51E0344G
- Keywords:
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- 0497 BIOGEOSCIENCES Wetlands;
- 1855 HYDROLOGY Remote sensing;
- 0466 BIOGEOSCIENCES Modeling;
- 0315 ATMOSPHERIC COMPOSITION AND STRUCTURE Biosphere/atmosphere interactions