The soil moisture maps displayed on this web page are part of drought monitoring efforts in the United States. The North American Land Data Assimilation System (NLDAS) drought monitor is an experimental drought monitoring tool that is developed from near real-time soil moisture model forecasts from the National Aeronautics and Space Administration (NASA) Mosaic land surface model (LSM), the National Centers for Environmental Prediction (NCEP) Noah LSM, the Sacramento Soil Moisture Accounting (SAC) LSM, and the Variable Infiltration Capacity (VIC) LSM.
The maps presented on the NLDAS Drought Monitor Soil Moisture page display the forecast soil moisture difference from normal based on 28 years of climatology data (1980-2007) with the soil moisture differences in categories of percent of normal. Available products in the drop down menu under “Ensemble Mean LSM Output” are current, past week, and past month top 1 meter and total column soil moisture maps for the Continental United States.
An Ensemble mean forecast is a forecast that is the average of numerous forecasts generated for the same time period. The average of all model runs is computed to reduce forecast model error and develop the most likely forecast. Upon viewing the webpage, the top-most map is titled “Ensemble-Mean – Current Total Column Soil Moisture Anomaly (mm) NCEP NLDAS Products.” This map is a composite, or an ensemble mean map, of the 4 maps beneath it. In this case, the Noah LSM, the Mosaic LSM, the SAC LSM, and the VIC LSM are the numerous forecasts over the same time frame that are averaged together to produce the most likely forecast.
The Noah LSM, Mosaic LSM, SAC LSM, and VIC LSM are all initialized with NDLAS soil moisture data. NLDAS takes surface observation data and puts it into land surface models so that forecasts are developed from near real-time soil moisture data. The NLDAS soil moisture data, along with other weather surface observation data is fed into the land surface models at a 1/8th degree spatial resolution which is about 13.8 square kilometers.
SAC LSM (right): Forecast models must take into account when the ground freezes because it prevents rainfall and snow melt from entering the ground. This is most important to winter and spring forecasts. When vegetation is sparse in winter time or there is very little snow on the ground, very cold temperatures can result in deep frost depths. The SAC model accounts for such frozen ground when it computes soil moisture. It was designed to address the frozen soil moisture issues, thus is not highly detailed to account for the role of vegetation in runoff processes and energy transfers between earth’s surface and the atmosphere.
Visit http://www.nws.noaa.gov/oh/hrl/frzgrd/index.html for more detailed information.
VIC LSM (left): This LSM is a highly detailed model that is developed mainly for hydrological applications (river channel and stream flow). In addition to modeling surface interactions between the atmosphere, it takes into account snowfall at a detailed level.
Visit http://vic.readthedocs.org/en/master/Overview/ModelOverview/ for more detailed information.
Mosaic LSM (right): This model is mostly known for its ability to handle snowfall interactions on the land surface. Land surface interactions focuses on the exchange of water and energy between the overlying atmosphere and the land surface, which is comprised of vegetation and soils – both of which respond differently to water and energy. For more detailed information see: http://gmao.gsfc.nasa.gov/pubs/docs/Koster130.pdf
Noah LSM (below): This is the land surface model used in most meteorological forecast models. It has the most dynamic vegetation components of all the models. It differs from the SAC, VIC, and MOSAIC models mainly in that it is not designed for hydrological applications (soil moisture and run off to streams/rivers), but is designed for a highly detailed representation of land surface interactions applicable to meteorological forecasts. For more information, visit: http://www.ral.ucar.edu/research/land/technology/lsm.php