EARSeL eProceedings 9, 2/2010 52 DETERMINATION OF THE AERODYNAMIC RESISTANCE TO HEAT USING MORPHOMETRIC METHODS Corinne M. Frey, and Eberhard Parlow University of Basel, Switzerland; corinne.frey(at)freenet.ch, eberhard.parlow(at)unibas.ch ABSTRACT The spatial estimation of the aerodynamic resistance to heat using morphometric methods was evaluated on the example of three different approaches using a digital surface model to calculate the roughness length for momentum and heat. The digital surface model was a result of manual digitising of a GoogleEarth image and another model retrieved from two stereoscopic SPOT im- ages. The resulting values for the building area density and frontal area index were slightly lower than comparable values found in the literature, which could be attributed to the building structure. An empirical parameter α, used for the calculation of the roughness length for heat, was fitted to observational data. α was found to be higher than suggested by literature values. The three mor- phometric methods proved to follow the same principle, the spatial analysis, however, showed that they produced different results in some very dense areas. INTRODUCTION The determination of the aerodynamic resistance to transfer of sensible heat r h , short ‘aerodynamic resistance to heat’, is necessary in the estimation of heat fluxes using bulk transfer methods applied with satellite data. In such approaches, remotely sensed surface temperatures are combined with an estimation of this parameter r h , together with the climatological variables air temperature, net radiation, and soil heat flux to derive the final product, the turbulent heat fluxes. r h thereby is a function of the roughness of the surface, described by the displacement height z d and the roughness length for mo- mentum z 0m and heat z 0h (1). The roughness of the surface is very distinct in urban areas; therefore a sound determination of these parameters is essential for successful flux modelling. Several mor- phometric methods have been summarised in (2) to determine z d and z 0m from a digital surface model, finding a distinct variability in the output of the tested approaches. They ranked the approaches by comparing their output to measurement values. They found the methods presented in (3,4,5,6) to score highest. Liu et al. (7) also verified these three methods with observational data. Their results suggest that the three methods are not very different from each other. In the above-mentioned three morphometric methods, the average roof height, the building area den- sity, and the frontal area index are used. These indices can be calculated from a digital surface model using trigonometry. The calculation of these parameters in a GIS (Geographic Information System) and their subsequent use for the estimation of the roughness parameters is described in (8). Also some other studies have reported on roughness parameters in urban areas. In (9), for example, is presented an urban roughness mapping method with the approach from (3) to localise ventilation paths in the city. (10) used the approach from (6) to extract several flow and dispersion parameters from an urban database. The parameters are the plan and frontal area densities, their function and distribution with height, their standard deviation, the aerodynamic roughness length and the sky view factor. (11) finally compared the methods of (3) and (4,5) for a portion of Rome, using cadastral data- bases. In many developed countries, digital surface models have been made available for cities by the respective authorities. In developing countries, however, this data is mostly not existing at all or not available for external researchers. For our study area, too, there was no such model available; there- fore it had to be generated manually. The resulting digital surface model does not offer the same accu- racy of up-to-date models generated from cadastral maps and provided by authorities. However, it is a good alternative and is sufficient for the needs of this study.