1. A B C D The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of climate-active gases are key global change questions. Surface inundation is a crucial state variable that affects the rate of land-atmosphere carbon exchange and the partitioning of carbon between CO2 and CH4. Ground observation networks of large-scale inundation patterns are sparse because they require large fiscal, technological and human resources. Thus, satellite remote sensing products for global inundation dynamics, as well as total water storage and atmospheric carbon, can provide a complete synoptic view of past and current carbon - surface water dynamics over large areas that otherwise could not be assessed. Here we present results from a correlative analysis between spaceborne measurements of CO2, CH4, water storage (derived from gravity anomalies), inundated water fraction. A general assessment is conducted globally, and further analysis is focused on four regions of interest: north Amazon, Congo, Ob, and Ganges-Brahmaputra river basins. Surface Water Global maps of inundation extent at ~25 km grid spacing are derived from combined passive-active microwave remote sensing data sets from the SSM/I, QuikSCAT and ASCAT instruments. We apply these data with ancillary land cover maps from MODIS to: This is the Inundated Wetlands Earth Science Data Record (IW_ESDR) This analysis was supported by a grant from the NASA Terrestrial Ecology Program, and the development of the inundation datasets was supported by the NASA MEaSUREs program. The GRACE and AIRS data were obtained from the Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA (http://podaac.jpl.nasa.gov).http://podaac.jpl.nasa.gov Datasets Global Correlations between Atmospheric Carbon and Surface Water Products Atmospheric Carbon (CO 2 and CH 4 ) The AIRS (Atmospheric Infrared Sounder) instrument aboard EOS/Aqua is a nadir cross-track scanning infrared spectrometer with 2378 channels covering from 649 to 2674 cm-1 at high spectral resolution and is most sensitive to CH 4 in the middle to upper troposphere. A gridded 2.5˚ x 2˚ total column average CO 2 mole fraction product is available. No column average product is available for methane, so we use the 1˚ x 1˚ mid-tropospheric (~309 hPa) volume mixing ratio for CH 4 measurements. Maximum surface inundation fraction appears to demonstrate stronger correlation with atmospheric carbon than monthly average Correlation coefficients (R) between atmospheric carbon and surface water, Jan 2004 – Dec 2011. CO2 column volume mixing ratios (VMRs) (ppm), and CH4 VMRs (ppm) – versus: 1) Total water storage (GRACE) 2) Monthly average surface inundation fraction (IW AVG) 3) Monthly maximum surface inundation fraction(IW MAX). All data has been converted to monthly anomalies and detrended. Regional Analysis A) North Amazon B) Congo C) Ob, SiberiaD) Ganges-Brahmaputra Scatter plots illustrate the atmospheric carbon volume mixing ratio anomalies (left: CO2 VMR, right: CH4 VMR) in the x-axis, versus maximum surface inundation fraction anomalies (IW MAX) along the y- axis, spatially averaged over (A) Amazon, (B) Congo, (C) Ob, and (D) Ganges-Brahmaputra river basins, Jan 2004 – Dec 2011. These four river basins were selected because they demonstrated high correlation between the surface inundation and total water storage (GRACE) datasets, as well as high variability in atmospheric CH4 concentration. Correlation coefficient (R) is reported. 1)Define the potential global domain of land inundation 1)Establish land cover driven predictive equations for implementing a dynamic mixture model adjusted to total column water vapor obtained from NASA’s Modern Era Retrospective-Analysis (MERRA) 1)Construct a continuous, global record of daily surface water fraction dynamics Large-scale atmospheric carbon and surface water dynamics inferred from satellite-based observations Katherine Jensen 1,2, Ronny Schroeder 2,3,4, Kyle C. McDonald 2,3 1 The City University of New York Graduate Center, 365 5 th Ave, New York, NY, USA 2 The City College of New York, 160 Convent Avenue, New York, NY, USA 3 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, USA 4 University of Hohenheim, D-70593 Stuttgart, Germany Total Water Storage The Gravity Recovery and Climate Experiment (GRACE) mission consists of twin satellites whose orbits measure the temporal change in the Earth’s gravitational field. The global gravity field is described as a geoid height, the deviation of the gravitational equipotential surface from a reference, “Equivalent water thickness” (or total water storage) is derived as a weighted sum of the geoid spherical harmonics with respect to spherical degree and the Earth’s load deformation coefficients. We use the monthly, 1˚ x 1˚ “equivalent water thickness” product. All datasets were resolved to a common 1˚ x 1˚ grid, spanning Jan 2004 – Dec 2011 Conclusions GRACEIW AVG IW MAX GRACEIW AVG IW MAX CH4 CO2 Correlation coefficient (r) between monthly average IW_ESDR and GRACE anomalies, Jan 2004 – Dec 2011. Lowest agreement between the two products is found generally in arid areas (majority of Africa, Australia) – but exhibits high agreement near river basins. R=-0.5095 R=0.6147 R=0.4103 R=-0.2673 R=0.7416R=-0.1699 Standard deviation of monthly CO2 (left) and CH4 (right) atmospheric concentrations, Jan 2004 – Dec 2011. Both significantly positive and negative correlations are observed globally between atmospheric carbon concentrations and surface water For all 4 river basins regions selected we observe: -Moderately strong, positive correlations between CH4 and IW MAX -Weak negative relationship between CO2 and IW_MAX Future work: - Incorporate a methane model and investigate relationship between methane fluxes and surface inundation. - Validate surface inundation product over regions where IW_ESDR and GRACE exhibited negative correlation. ] ] R=-0.1878R=0.6497