HR: 10:20h
AN: H52C-01 INVITED    [Abstracts]
TI: Estimation of coupled water and energy balance model parameters from equilibrium constraints on temperature and moisture
AU: * Salvucci, G D
EM: gdsalvuc@bu.edu
AF: Boston University, Department of Geography and Environment, Boston, MA 02215, United States
AU: Sun, J
EM: jians@bu.edu
AF: Boston University, Department of Geography and Environment, Boston, MA 02215, United States
AU: Entekhabi, D
EM: darae@mit.edu
AF: MIT, Department of Civil and Environmental Engineering, Cambridge, MA 02139, United States
AU: Farhadi, L
EM: farhadi@mit.edu
AF: MIT, Department of Civil and Environmental Engineering, Cambridge, MA 02139, United States
AB: Salvucci [2001, WRR 37(5), 1357-1366] demonstrated that the conditionally averaged net moisture flux (dS/dt) on soil moisture storage (S) tends to zero due to equilibrating tendencies in the water balance, and that this property can be used to estimate the moisture dependence of net drainage and evaporation from precipitation measurements. Here we extend this idea to the coupled energy and water balance at the land surface. We show that, using conditional averaging and stationarity constraints, we can express a single objective function that measures the moisture and temperature dependent errors in land surface water and energy fluxes solely in terms of observed forcings (e.g. precipitation, radiation, wind speed) and surface states (moisture and temperature). It is through these conditional expectations – equivalent to the joint covariance of the states and forcing – that we can then estimate the model parameters. Here we illustrate through proof-of-concept examples and data from Ameriflux sites, that the combination of surface moisture and temperature data provides a robust, empirical basis for estimating evaporation models. Furthermore, because the method is derived only from stationarity and conservation statements (of energy and water), it is scale-free and thus derives effective land surface model parameters at the scale of the data applied in the estimation.
DE: 1816 Estimation and forecasting
DE: 1818 Evapotranspiration
DE: 1846 Model calibration (3333)
DE: 1855 Remote sensing (1640)
SC: Hydrology [H]
MN: 2007 Fall Meeting