Applying footprint models to investigate MO-dissimilarity over heterogeneous areas

Monin Obukhov Similarity Theory (MOST) is one of the cornerstones of surface layer meteorology and as such it is widely applied in models and for data analysis. One major disadvantage of using MOST for describing land-atmosphere interactions is that all turbulence properties are described as a function of the same limited set of key parameters. Therefore, MOST is in particular not valid over heterogeneous terrain (Andreas et al., 1998). To study the limits of MOST, deviations of similarity functions from their homogeneous versions need to be linked to the degree of heterogeneity (which may be different for different scalars, i.e. water vapor, CO2 and heat (Moene and Schüttemeyer, 2008)). In particular, the degree to which observations at a given point are influenced by different sources should be known. Footprint models can be used to partition EC (eddy covariance) measurements in scalar fluctuations originating from the different land use types in the measurement area. Calculations are made within footprint models to estimate the contribution of each unit element of the upwind surface area to a measured vertical flux. However, validations of footprint models with natural tracer experiments using several EC-stations and land use types are still needed. We will therefore present validation results of two analytical footprint models, using two different validation methods and several eddy covariance stations in two different landscapes. The first landscape is flat, with very small spatial variability in roughness lengths and soil properties. This mosaic-like land use pattern, together with entrainment, are expected to be the only challenge to MOST-based footprint models. The second landscape, in contrast, additionally includes slight slopes, spatial patterns in soil moisture, and some tree groups. The two sites should thus enable a characterization of the importance of different types of MOST violation. The analytical model of Kormann and Meixner (2001) and the approximately analytical model of Hsieh et al. (2000) were validated first using flux data from the Transregio experiment in Merken in August 2009. The eddy covariance observations were obtained in three adjacent fields with contrasting fluxes, a wheat, barley and a sugar beet field, as well as close to the edge between two of the fields. Two validation methods were applied: forward validation and validation by inversion. For the forward method, fluxes from the wheat, barley and sugar beet field were used together with the footprint model to estimate the flux measured at an EC-station in the barley field near the border with the sugar beet field. The inverted validation method is based on decomposed fluxes of seven eddy covariance stations in the three fields, to estimate the surface fluxes of the three underlying land use types and therewith perform a cross validation with left-out eddy covariance measurements. We secondly used EC-measurements from a more heterogeneous and complex campaign called BLLAST (in southern France in June 2011) to validate both footprint models using flux measurements from a wheat and a grass field, and from measurements at the border between these fields. Validation results will be shown and compared for both models, validation methods and datasets. Using the validated footprint models in a later stage of our study, the degree of heterogeneity at the point of observation can be quantified. Subsequently, deviations from published MOST relationships can be linked to the degree of heterogeneity.

Bibliographic Details

Main Authors:	van de Boer, A., Graf, A., Moene, A.F., Schüttemeyer, D.
Format:	Article in monograph or in proceedings biblioteca
Language:	English
Published:	American Meteorological Society
Subjects:	Life Science,
Online Access:	https://research.wur.nl/en/publications/applying-footprint-models-to-investigate-mo-dissimilarity-over-he
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