Deriving catchment-scale water and energy balance parameters using data assimilation based on extended Kalman filtering

Important catchment-scale water and energy balance parameters are derived for a small catchment in southeastern Australia by assimilation in a catchment-scale soil-vegetation-atmosphere transfer (SVAT) model of subcatchment-scale soil water content observations and land surface temperature measurements. In order to incorporate the subcatchment-scale soil moisture variability and its time evolution in a data assimilation scheme, an extended Kalman filter (EK.F) method is used in combination with a cost function minimization approach to derive effective parameters for the catchment as a whole. These parameters are the minimum surface resistance to evaporation and the soil hydrodynamic parameters. This method provides a balanced assessment of all the uncertainties regarding the description of the catchment hydrological behaviour. Moreover, these uncertainties are propagated forward in time in a single framework, which combines soil moisture correction with effective parameter estimation. Two issues are addressed in this paper: (a) the applicability of the method for scaling purposes (effective parameterization) and (b) the relationship between effective parameters obtained by this method and parameters obtained by a classical minimization routine that ignores soil moisture correction and observation uncertainty. Effective parameters are found to be consistent with the available local parameter measurements. Derived parameter sets with and without EKF have been found to be very similar and this has been explained in a number of ways.