Parameter estimation in surface exchange models using nonlinear inversion:how many parameters can we estimate and which measurements are most useful?

Models of mass and energy exchanges between the biosphere and the atmospher e generally contain a nonlinear dependence between fluxes and model paramet ers, and,thus estimation of these parameters from measurements in a heterog eneous landscape depends on the scale of the observations. The scale-depend ence of a typical surface-exchange model (the CSIRO Biospheric Model, CBM) is examined using the diurnal variation of hourly fluxes of CO2, latent hea t, sensible heat and soil heat. The fluxes were measured using micrometeoro logical techniques over six sites in a grazing/pasture system in SE Austral ia during a period of three weeks in 1995. Nonlinear parameter inversion wa s used to determine model parameters. Analysis of the covariance of the estimates of the parameters and the unexp lained residuals of the model showed that a maximum of three or four parame ters could be determined independently from the observations for all six si tes. Estimates of a key model parameter, j(max), the mean of maximum potent ial electron transport rate of all leaves within the canopy, was best deter mined by the measurements of net CO2 flux at all sites examined. Measuremen ts of ground heat flux provide little information about any of the model pa rameters in CBM. Because of nonlinearities in the surface exchange model, calculated fluxes will be in error if parameters for the component vegetation types are simpl y averaged in proportion to their areal fraction. The magnitude of these er rors was examined for CBM using a hypothetical land surface consisting of t wo surface types, each with different parameter values. Predictions of net CO2, latent heat and ground heat fluxes using a linear combination of model parameters for the two surface types were quite similar with those found u sing optimal estimates of the parameters for the landscape, but were signif icantly poorer for sensible heat fluxes.