Wood, E., D. Lettenmaier, P. Houser, C. Kummerow, R. Pinker; 06/15/2008-06/14/2013; Developing consistent Earth System Data Records for the global terrestrial water cycle. NASA ROSES-2006 NNH06ZDA001N-MEASURES; $277,940; 0.2 FTE/yr (GMU). Sponsor POC: Ms. Martha Maiden (202/358-1078, firstname.lastname@example.org), Award #NNX08AN40A
Summary: We propose 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 (lat-long) 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 we propose to include are a) surface meteorology (precipitation, air temperature, humidity and wind), b) surface downward radiation (solar and longwave) and c) 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. We intend to construct data records for all variables back to 1950, recognizing that the post-satellite data will be of higher quality than pre-satellite (a reasonable compromise given the need for long-term records to define interannual and interdecadal variability of key water cycle variables). A distinguishing feature will be inclusion of two variables that reflect the massive effects of anthropogenic manipulation of the terresrtial water cycle, specifically reservoir storage, and irrigation water use. The overall goal of the proposed project is to develop long term, consistent ESDRs for terrestrial water cycle states and variables by updating and extending previously funded Pathfinder data set activities to the investigators, and by making available the data set to the scientific community and data users via a state-of-the-art internet web-portal. The ESDRs will utilize algorithms and methods that are well documented in the peer reviewed literature. The proposed 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. 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). There currently exists no comprehensive effort to integrate data sets from remote-sensing, in-situ and models on global scales, and a major focus of our proposal will be to do so. Such a data set is needed to address the NASA strategic goal 3A, which is to study Earth from space to advance scientific understanding and meet societal needs. In particular, the developed ESDRs will help NASA meet its research outcomes in three areas related to this strategic goal; namely Outcome 3A.2, Weather and extreme weather events, by providing more accurate land states (surface soil moisture and snow extent) from which to initialize weather and seasonal climate prediction models; Outcome 3A.4 Progress in quantifying reservoirs and fluxes in the global water cycle, though improved understanding of water cycle variability obtained from the long-term consistent ESDRs; and Outcome 3A.7 Societal benefits from Earth system science, by providing ESDRs that support determination of drought and flood risks globally and advances the understanding of freshwater sustainability. Furthermore,the project will participate in the Data Systems Working Groups, in the areas of managing distributed data (Standards and Interfaces WG), collaborating for the optimal use of existing tools (Reuse WG), and integrating methods for merging heterogeneous datasets (Technology Infusion WG).