Transcript Slide 1
IN51B-1150, AGU 2008 Fall Meeting Developing Consistent Earth System Data Records for the Global Terrestrial Water Cycle Alok Sahoo1, Ming Pan2, Huilin Gao3, Eric Wood2, Paul Houser1, Dennis Lettenmaier3, Rachel Pinker4 and Christian Kummerow5 GMU/CREW; Princeton University2; University of Washington3, University of Maryland4; Colorado State University5 Project Goals Data Records to Produce Methodology The overall goal is to develop consistent, long-term Earth System Data Records (ESDRs) for the major components (storages and fluxes) of the terrestrial water cycle at a spatial resolution of 0.5 degrees (latitude-longitude) and for the period 1950 to near-present. The resulting ESDRs are intended to provide a consistent basis for estimating the mean state and variability of the land surface water cycle at the spatial scale of the major global river basins. The ESDRs to produce include: The ESDR’s will be created by updating and extending previously funded Pathfinder data set activities to the investigators. The ESDRs will utilize algorithms and methods that are well documented in the peer reviewed literature. The ESDRs will merge satellite-derived products with predictions of the same variables by LSMs driven by merged satellite and in situ forcing data sets (most notably precipitation), with the constraint that the merged products will close the surface water budget. Left Figure: Simulated (using the VIC model) mean seasonal cycle and variability, and observations for four large global rivers. Pathfinder – the Pilot Project Reanalysis Observations High temporal/low spatial resolution Generally low temporal resolution surface meteorology (precipitation, air temperature, humidity and wind), surface downward radiation (solar and longwave) and derived and/or assimilated fluxes and storages such as surface soil moisture storage, total basin water storage, snow water equivalent, storage in large lakes, reservoirs, and wetlands, evapotranspiration, and surface runoff. Schematic of the Elements of the Water Budget ESDRs Satellite ReP, Rn analysis In-situ P, Ta Reprocessed ISCCP Net radiation, Rn (UMD) Reprocessed satellite Precipitation, P (CSU) Princeton 50yr Pathfinder Forcing data set (P, Rn, Ta, q, w) (PU, UW) SMMR, TMI, AMSR-E surface moisture (GMU/CREW, PU) Updated and Reprocessed data set AVHRR, MODIS Snow Extent (UW) CRU Precipitation Temperature SW Radiation 1901-2000, Monthly, 0.5deg P, T, GPCP Tmin, Tmax, Cld 1997-, Daily, 1.0de P UW 1979-2000, Daily, 2.0deg P TRMM 2002-, 3hr, 0.25deg P SRB 1985-2000, 3hr, 1.0deg Lw, Sw Precipitation Surface soil moisture Snow Extent/SWE Evapotranspiration VIC Land Surface Model (PU, UW) Irrigation water use Irrigation Model River discharge Reservoir storage Bias Correct and Downscale • corrected rainday statistics, undercatch • • • • removal of biases in monthly values removal of spurious trends in SW adjustment for elevation effects downscale in time and space ISCCP-based Latent heat/ET (PU) River routing and Reservoir model Merging/Data Assimilation, for water budget consistency (GMU/CREW, PU, UW) GRDC river basin Runoff data set SW Radiation Temperature Diurnal Cycle Princeton George Mason Univ. University of University Ctr. for Res. in Env. & Water Washington University of Maryland Colorado State Univ. Global Forcing Dataset High temporal/high spatial resolution, bias corrected, trend corrected, etc… (PU) (GMU/CREW) (UW) (UMD) Data Sources The primary land surface forcing variable, precipitation, will be formed by merging model (reanalysis) and in situ data with satellite-based precipitation products such as TRMM, GPCP, and CMORPH. Derived products will include surface soil moisture (from TRMM, AMSR-E, SMMR, SSM/I passive microwave and ERS microwave scatterometers), snow extent (from MODIS and AVHRR), evapotranspiration (model- derived using ISCCP radiation forcings from geostationary and LEO satellites), and runoff (from LSM predictions and in-situ measurements). Data records will be produced at a number of different levels – each of them will have different sources and levels of processing, for example: Project Team Precipitation Figure above: Monthly mean water budgets for selected basins, 1950-2000. P = precipitation, E = evapotranspiration, Qs = surface runoff, Qb = baseflow, dS/dt = change in storage (soil moisture + snow). (CSU) • • • • • • • Downscaled Pathfinder Water Cycle Forcing Data; Merged Multi-sensor Precipitation; VIC Model Derived Water Budget Variables; Reservoir Storage and Irrigation Water Use; Radiative Fluxes; Satellite Soil Moisture, Evapotranspiration and Snow; Merged model derived and satellite products. Supported by NASA Grant NNX08AN40A “NRA/Research Opportunities in Space and Earth Sciences-2006”