Reconstruction of Inundation and Greenhouse Gas Emissions from Siberian Wetlands over the Last 60 Years T.J. Bohn 1, E. Podest 2, R. Schroeder 2, K.C. McDonald 2, L. C. Bowling 3, and D.P. Lettenmaier 1 1 Dept. of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA 2 JPL-NASA, Pasadena, CA, USA, 3 Purdue University, West Lafayette, IN, USA Steve Burges Retirement Symposium University of Washington Seattle, WA, 2010-Mar-24

West Siberian Lowlands (Gorham, 1991) Western Siberian Wetlands 30% of world’s wetlands are in N. Eurasia High latitudes experiencing pronounced climate change Response to future climate change uncertain Wetlands: Largest natural global source of CH 4

Climate Factors Water Table Living Biomass Acrotelm Catotelm Aerobic R h CO 2 Anaerobic R h CH 4 Precipitation Temperature (via evaporation) Temperature (via metabolic rates) NPP CO 2 Water table depth not uniform across landscape - heterogeneous Relationships non-linear Note: currently not considering export of DOC from soils

Modeling Framework VIC hydrology model –Large, “flat” grid cells (e.g. 100x100 km) –On hourly time step, simulate: Soil T profile Water table depth Z WT NPP Soil Respiration Other hydrologic variables… Link to CH4/CO2 emissions model (Walter & Heimann 2000) How to represent spatial heterogeneity of water table depth?

Distributed Water Table 01 Cumulative Area Fraction кiкi к max к min Topographic Wetness Index CDF Topographic Wetness Index к(x,y) DEM (e.g. GTOPO30 or SRTM) 01 Cumulative Area Fraction Water Table Saturated Soil Soil Storage Capacity CDF(mm) = f(к i ) S max 0 VIC Soil Moisture (t) Water Table Depth Z wt (t,x,y) Resolution = 1 km Summarize for One 100km VIC grid cell

High-Resolution Lake Observations Need multi-temporal observations Remote Sensing products: (courtesy JPL Carbon and Water Cycles Group (E. Podest, R. Schroeder, and K. McDonald)) LANDSAT open water classifications –30m –Repeat cycle: decadal – PALSAR open water, inundation, and saturated soil –30m –Repeat cycle: sporadic – AMSR inundation –25km –Repeat cycle: 10 days – km Landcover Classification (Bartalev et al 2003): 0.1% Open Water 30m LANDSAT Open Water Class : 1.1% Open Water 30m LANDSAT Open Water Class : 2.4% Open Water 30m LANDSAT Open Water Class : 4.1% Open Water

Study Domain: W. Siberia Wetness Index from GTOPO-30 and SRTM3 Ural Mtns Yenisei R. Ob’ R. Vasuygan Wetlands Chaya/Bakchar/ Iksa Basin Close correspondence between: wetness index distribution and observed inundation of wetlands from satellite observations GLWD Wetland delineation (Lehner and Doll, 2004)

Chaya/Bakchar/ Iksa Basin 100km Grid Cells Close correspondence between wetness index and wetland vegetation 4 focus areas (see next slide)

Comparison with PALSAR Observed Inundated Fraction (PALSAR Classification) Simulated Inundated Fraction (at optimal Zwt) ROI ROI ROI ROI Observed Inundated Fraction (PALSAR Classification) Simulated Inundated Fraction (at optimal Zwt) Spatial distribution of inundation compares favorably with remote sensing This offers a method to calibrate model soil parameters Approx. 30 km

How do resulting emissions differ between uniform water table and distributed water table? Experiment: Calibrate methane model to match in situ emissions at a point (Bakchar site, Friborg et al, 2003) Distributed case: calibrate distributed model water table depth to match observed inundation Water Table CH4 Uniform case: select water table timeseries from single point in the landscape having same long-term average methane emissions as the entire grid cell in the distributed case; apply this water table to entire grid cell Inundated Area (matching remote sensing)

Interannual Variability, Uniform Water TableDistrib Water Table Uniform Water Table: Shallower than average of distributed case But never reaches surface; no inundation Resulting CH4 has higher variability than for distributed case Distributed case is buffered by high- and low-emitting regions Possible trend in temperature, also in CH4 Impact on trends?

Net Greenhouse Warming Potential NPP RhCO2 RhCH4 RhCO2 - NPP NET GHWP NPP and RhCO2 approximately cancel Net GHWP essentially follows GHWP(CH4) Uniform water table: CH4 has larger interannual variability So does net GHWP Impact on trend assessment? CH4 makes up small part of C budget, but large contribution to greenhouse warming potential On 100-year timescale, GHWP(CH4) = approx. 23 * GHWP(CO2)

Conclusions Advantages of distributed water table: –Facilitates comparison with satellite measurements and point measurements –More realistic representation of hydrologic and carbon processes Spatial distribution of water table has large effect on estimates of greenhouse gas emissions and their trends

Thank You This work was carried out at the University of Washington and the Jet Propulsion Laboratory under contract from the National Aeronautics and Space Administration. This work was funded by NASA grant NNX08AH97G.

Calibration – Bakchar Bog, 1999 Soil T Z WT (water table depth) CH 4 VBM = VIC-BETHY-Methane (Bohn et al., 2007)