The North-American Land Data Assimilation System (NLDAS) is a multi-institutional collaboration project which has been sponsored by the NOAA Office of Global Program (GEWEX American Prediction Project - GAPP), NASA Terrestrial Hydrology Program, and Climate Program Office (Climate Prediction Program of the Americas –CPPA, Modeling, Analysis, Prediction, and Projection –MAPP). NLDAS is a quasi-operational system that supports U.S. operational drought monitoring and seasonal hydraulic prediction, in particular for the National Integrated Information System including U.S. Drought Monitor (USDM) and NCEP Climate Prediction Center Monthly Drought Briefing. Detailed information about NLDAS can be found at NOAA (http://www.emc.ncep.noaa.gov/mmb/nldas) and NASA (http://ldas.gsfc.nasa.gov/nldas/) websites. The system consists of a retrospective 29-year (1979-2008) historical execution and a near real-time daily update execution using four land surface models (NCEP/Noah, NASA/Mosaic, NWS/OHD/SAC, and VIC developed by Princeton University and University of Washington) on a common 1/8th degree grid using common hourly land surface forcing. The 29-year NLDAS retrospective run is used to derive the climatology of each of the four land models. Then the current near real-time (past week, past month) land states (e.g. soil moisture, snowpack), and water fluxes (e.g. evaporation, total runoff, streamflow) of each of the four models from daily executions are depicted as anomalies and percentiles with respect to their own model climatology. One key application of the near real-time updates is drought monitoring over CONUS, shown at the “NLDAS Drought” tab of the NLDAS website. NLDAS has become mature enough for NCEP operational implementation (planned for the near future). At the same time, we recognize that the current NLDAS is not an “actual” land data assimilation system because remotely-sensed estimates of land-surface states such as soil moisture and snowpack, and in-situ observations such as streamflow and soil moisture, are not yet assimilated into current version of NLDAS. The NCEP/EMC NLDAS team is collaborating with the NASA Goddard Hydrological Sciences Laboratory to add their Land Information System (LIS) to the current NLDAS system which would allow assimilation of remotely-sensed data and in-situ observations, e.g. via an ensemble Kalman filter approach.
This study will present an introduction of NLDAS, overall evaluation of NLDAS products, in particular for total runoff, evapotranspiration, top 1m, and total column soil moisture, and application of USDM statistics to help improve and evaluate NLDAS drought monitoring products. Based on USDM statistics and an optimization approach, we developed an Optimal Blended NLDAS Drought Index (OBNDI). Several drought events 2000, 2002, 2011, and 2012 are used to demonstrate OBNDI ability. Finally we use reconstructed OBNDI to discuss 1988 U.S. drought.