NCA-LDAS: An Integrated Terrestrial Water Analysis System for Development, Evaluation, and Dissemination of National Climate Indicators

An Integrated Terrestrial Water Analysis System, known as NCA-LDAS, has been created as an end-to-end enabling tool for the development, evaluation, and dissemination of terrestrial hydrologic indicators in support of the National Climate Assessment (NCA). The goal is to develop and provide public accessibility to, i) gridded time series terrestrial water and energy balance states and fluxes over the continental U.S. focusing on the satellite era, and ii) quantifiable indicators and estimated trends in our nation's water stores and fluxes over a wide range of scales and locations. The ultimate goal is to support sustained assessment of national climate for improved understanding, adaptation and management of water resources and related water and energy sectors.

The NCA-LDAS project has produced, for the first time, a multivariate land surface analysis over the contiguous United States (CONUS) that includes water cycle observations from the satellite-era (1979-present). This analysis includes a unique snow depth Environmental Data Record (EDR), in addition to other standard EDRs such as soil moisture and terrestrial water storage from GRACE. NCA-LDAS models include both Noah Ver. 3.3 and Catchment Ver. Fortuna 2.5 at 1/8th degree resolution. Work builds upon the legacy of the Land Information System (LIS) modeling framework (Kumar et al, 2006; Peters-Lidard et al, 2007). NCA-LDAS also provides public access to all the NCA-LDAS multivariate data assimilation (DA) data products including input data, output fields and standard water indicators through the NCA-LDAS website and the NASA GES-DISC. Sample water indicators computed from these instances will be shown, along with trend evaluations using the Mann-Kendall test.

Results indicate that there were significant improvements in snow fields for Noah and marginal improvements for CLSM. Domain-mean average seasonal cycle of RMSE and bias for the snow depth fields, which are improved through multivariate assimilation, will be shown. Improvements in snow and soil moisture fields were found to translate to downstream improvements in stream flow. For Noah, multivariate DA leads to streamflow improvements in parts of the East coast, and Upper Mississippi and Missouri basins but degradation in the Western US. For CLSM, the results show degradation in the Eastern US with improvements in the Missouri and some Northwest basins.