HR: 1340h
AN: A13B-0122    [Abstracts]
TI: Land surface model evaluation using a new soil moisture dataset from Kamennaya Steppe, Russia
AU: * Atkins, T
EM: tatkins@envsci.rutgers.edu
AF: Rutgers University, Dept. of Env. Sci. 14 College Farm Rd., New Brunswick, NJ 08817 United States
AU: Robock, A
EM: robock@envsci.rutgers.edu
AF: Rutgers University, Dept. of Env. Sci. 14 College Farm Rd., New Brunswick, NJ 08817 United States
AU: Speranskaya, N
EM: speran@mail.rcom.ru
AF: State Hydrological Institute, V.O.,23, St. Petersburg, 199053 Russian Federation
AB: The land surface affects the atmosphere through the transfer of energy and moisture and serves as the lower boundary in numerical weather prediction and climate models. To obtain good forecasts, these models must therefore accurately portray the land surface. Actual in situ measurements are vital for testing and developing these models. It is with this in mind that we have obtained a dataset of soil moisture, soil temperature and meteorological measurements from Kamennaya Steppe, Russia. The meteorological dataset spans the time period 1965-1991, while the soil moisture dataset runs from 1956-1991. The soil moisture dataset contains gravimetric volumetric total soil moisture measurements for 10 layers taken from forest, agricultural and grassland soils. The meteorological dataset contains 3-hourly measurements of precipitation, temperature, wind speed, pressure and relative humidity. We obtained longwave and shortwave radiation data from standard formulae. The data will be made available to the public via the Rutgers University Center for Environmental Prediction Global Soil Moisture Data Bank. Soil temperature is important in determining the timing, duration and intensity of runoff and snowmelt, particularly at the beginning and end of the winter when the ground is only partially frozen. Soil temperature can in turn be affected by the vertical distribution of roots. The soil temperature data are for 1969-1991. The data are daily averaged for every 20 cm to 1.2 meters in depth. These data are used to investigate the natural sensitivity of soil temperature to vegetation type and root distribution. We also use the temperature data, as well as water balance and snowfall data to test the sensitivity of the Noah land surface model (LSM) soil temperature to vertical root distribution, and what effect that has on the hydrology of the site. In addition to soil temperature data, we also have soil moisture data for several vegetation types. We compare the soil moisture time series for different vegetation types, in order to ascertain the effect of different root distributions on natural soil moisture variations, as well as provide information on the root distribution itself, which was not measured at the site. We then run several Noah LSM simulations, which include different vegetation parameters, as well as three different vertical root distributions. These distributions are the standard Noah LSM uniform root distribution, a non-uniform root distribution in which root mass density decreases exponentially with depth, using standard parameters based on the Zeng model, and a non-uniform distribution with parameters based on information about the actual root distribution at the site. The in situ soil moisture data was then used to validate the Noah LSM results. The results of those validation experiments are presented here.
DE: 3322 Land/atmosphere interactions
DE: 1866 Soil moisture
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting