Meteorologically measured fluxes of energy and matter between the surface and the atmosphere originate from a source area of certain extent, located in the upwind sector of the device. The spatial representativeness of such measurements is strongly influenced by the heterogeneity of the landscape. The footprint concept is capable of linking observed data with spatial heterogeneity. This study aims at upscaling eddy covariance derived fluxes to a grid size of 1 km edge length, which is typical for mesoscale models or low resolution remote sensing data.
Here an upscaling strategy is presented, utilizing footprint modelling and SVAT modelling as well as observations from a target land use area. The general idea of this scheme is to model fluxes from adjacent land use types and combine them with the measured flux data to a grid representative flux according to the land use distribution within the grid cell. The performance of the upscaling routine is evaluated with real datasets, which are considered to be land use specific fluxes in a grid cell. The measurements above rye and maize fields stem from the LITFASS experiment 2003 in Lindenberg, Germany and the respective modelled timeseries were derived by the SVAT model SEWAB. Contributions from each land-use type to the observations are estimated using a forward lagrangian stochastic model. A representation error is defined as the error in flux estimates made, when accepting the measurements unchanged as grid representative flux and ignoring flux contributions from other land use types within the respective grid cell. Results show, that this representation error can be reduced up to 56% when applying the spatial integration. This shows the potential for further application of this strategy, although the absolute differences between flux observations from rye and maize were so small, that the spatial integration would be rejected in a real situation. Corresponding thresholds for this decision have been estimated as a minimum mean absolute deviation in modelled timeseries of the different land use types with 35Wm−2 for the sensible heat flux and 50Wm−2 for the latent heat flux. Finally a quality flagging scheme to classify the data with respect to representativeness for a given grid cell is proposed, based on an overall flux error estimate. This enables the data user to infer the uncertainty of mesoscale models and remote sensing products with respect to ground observations. Major uncertainty sources remain the lack of an adequate method for energy balance closure correction as well as model structure and parameter estimation, when applying the model for surfaces without flux measurements.
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