Multi-Satellite Remote Sensing of Global Surface Water Extent and Volume Change. Fabrice PAPA (1), Catherine PRIGENT (2), William B. ROSSOW (1), Elaine MATTHEWS (3), Andreas GUNTNER (4), Frederic FRAPPART (5) et al (6). (1) NOAA-CREST-CCNY, New York, USA (2) LERMA-Observatoire de Paris, Paris, France (3) NASA-GISS, New York, USA (4) GFZ, Postdam, Germany (5) CESBIO, Toulouse, France (6) LSCE, Paris/ IRD, Brasilia/ LEGOS, Toulouse/ UCI, Irvine …. Mail to:

Continental Surface Waters and their Roles They play a crucial role in the global biochemical and hydrological cycles The largest methane source (~ 20-40%), a powerful greenhouse gas The only CH 4 source dominated by short-term climate variations Important compartment of continental water storage, regulate the local river hydrology Part of the fresh water input in the ocean via river discharges Sources for recharching ground water supplies. Role in present sea level rise? Surface Water extent and storage is crucial to measure However: incomplete knowledge of seasonal and inter-annual variability at regional to global scales

What before SWOT? We are currently trying to develop new methods which match with SWOT goals: 1) Multi-year global dataset of surface water extent using multi-satellite methods 2) Dataset of surface water volume change combining multi-satellite methods First for specific area: Rio Negro, Ganges… But with an ultimate goal to do so at global scale Applications: - dynamic of surface water extent, roles in the water/energy cycle - evaluation of hydrological models/ input for hydrological models - methane emissions studies Applications: - large scale hydrology, decomposition of GRACE components - contribution of continental water to sea level rise

1) Global surface water extent dynamic

1)Global surface water extent from multi-satellite method Dynamic of surface water extent at global scale Merging of satellite data with different wavelengths (surface classification, NN, vegetation) Passive microwave SSM/I emissivities at 19, 37 GHz, H and V polarizations Active microwave ERS scatterometer backscattering coefficient at 5.25 GHz Visible and near infrared AVHRR NDVI (visible and near-infrared reflectances) [Prigent et al, 2001; Prigent et al, 2007 Papa et al., 2006, 2007,2008] Mean fractional surface water extent at annual maximum  Data mapped on equal-area grid with 0.25°x0.25° resolution at equator (773 km²)  Monthly resolution for (soon 5 days) and at least extended to 2012 and longer %

Global and zonal temporal variations of inundated surfaces extent Dynamic of surface water extent at global scale Need of validation, comparison, evaluation of these results Global results: maximum extent: ~6.7 million km², strong seasonal cycle and inter-annual variability Deseasonalized anomalies: decrease of surface water extent especially over the Tropics

Surface water extent at global scale: the Amazon case study SAR estimates (100m) Multi-Satellites derived estimates (~25 km) Good agreement between the SAR-derived estimates and the Multi-Satellites derived estimates Some differences at higher and lower stage for small and large extents ( 90%) [Prigent et al, 2007, JGR] But comparison only for 2 months in SWOT will provide direct comparisons over longer period and different environments

GPCC rain Surface water extent In-Situ River discharge Surface water extent River height from altimeter In-situ river level height Now with current altimeters and in-situ gauges, evaluation is possible only for few points over the Amazon. SWOT will provide more data to compare with and with much more details. Surface water extent at global scale: the Amazon basin case study Only 1 point of discharge available to us

Over the Tropics, comparison with the trend in the density of population for coastal regions South Mexico Madras, India Hanoi, Vietnam Trend in surface water extent Trend in the population density Good spatial agreement between the decrease in SW extent and the increase in the density of population (this has been checked for other locations), at least from 1990 to 2005 Surface water extent, the coastal regions case The SWOT high spatial resolution will help better understand in details what we are currently observing on the coast at ~25km Interval

Global Surface water extent dynamic: high demand from the “methane” community Wetlands are the bigger contributors to the interannual variability in methane emissions Since 1999, compensation between an increase in anthropogenic emission and a decrease in CH4 emissions from wetlands CH4 emissions from wetlands estimated from multi-sat. method SWOT will help characterizing wetland dynamics for CH4 model emission Bousquet et al, Nature, 2006

2) Surface water volume change

Surface water volume change Using the surface water extent dataset to get surface water volume change Surface water extent GPCC rain WGHM surface storage GRACE total storage

Surface water volume change Good agreement between GRACE, SWE, WGHM, altimeter river height

Test area Rio Negro basin ( km²) for altimetry-based approach Altimeter track (T/P) In situ gauge station Altimeter station Surface water volume change

1)Surface water volumes by combination of inundation extent with water levels from altimetry Surface water storage 17  Inundation map Bilinear interpolation Water levels from Topex/Poseidon and ENVISAT RA-2 and in situ gauges data Surface water volumes Water level maps [Frappart et al., JGR, 2008]

2)Surface water volumes by combination of inundation extent with topographic data Surface water storage

Surface water storage change: Rio Negro basin case study Mean seasonal amplitude of water storage change DEM Alti. WGHM GRACE When developed at global scale, this approach could be an opportunity to evaluate SWOT products at regional/global scale We could also construct 2 decades of surface water volume to complete backward the SWOT measurements Surface water volume change from multi-sat/alti is ~ 38% of Grace total storage

Given what we are observing at large scale with the “crude” 25 km interval sampling surface water extent dataset, SWOT will provide opportunities to better understand: Why SWOT would be great: Why surface water extent is declining at global scale and especially over the Tropics, at least from 1993 to 2005 The decrease in coastal regions thanks to its high resolution The interannual variability in methane emissions (and trends?) The up-coming surface water volume change at global scale will provide a dataset to compare with SWOT measurements at least at the large scale.