Urban wind field calculation through the Röckle based method: the basics for a GIS implementation

Abstract

<p>Wind speed is one of the key parameter affecting human thermal comfort: high wind speed during winter and low wind speed during summer may exacerbate respectively cold and heat stress. In urban areas, where more than 50% of the world population is currently living, the wind field is strongly affected by the size and the organization of the obstacles (mainly buildings and trees). Simple and quick estimation of the wind speed and direction in an urban setting could then be an interesting information for urban planning purpose. To calculate a high resolution three-dimensional wind field in an urban setting, Computational Fluid Dynamic (CFD) models are mostly used. They usually solve advection and turbulence equations by an iterative process which is too long for most of the urban planning applications. To reduce this time, Röckle (1990) proposed:</p><ul><li>to decrease the number of iteration by initializing the wind field around buildings: this is done by modeling empirically the wind speed and direction using results from wind tunnel observations,</li> <li>to solve only the advection equation from this initial wind field since the turbulence is supposed roughly “solved” by the initialization.</li> </ul><p>At our knowledge, at least two models have been developed using this approach: QUIC-URB (Brown et al. 2018) and the second is part of the SkyHelios software (Fröhlich and Matzarakis, 2018). However: (i) none of these softwares are open-source (i.e. source code is not freely available), it is then rather complicated to propose scientific improvements and (ii) none of them are integrated in a commonly used GIS-based urban planning tool which would popularize their use by urban planners.</p><p>Our presentation will focus on the development of our tool called URock, an open-source application of the Röckle methodology. If the results produced by this tool are consistent with observation, it should be included in QGIS (a commonly used urban planning GIS) through the plug-in UMEP (Lindberg et al. 2017).</p><p><strong>References</strong></p><p>Brown, Michael John. Quick Urban and Industrial Complex (QUIC) CBR Plume Modeling System: Validation-Study Document. No. LA-UR-18-29993. Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2018.</p><p>Fröhlich, Dominik, and Andreas Matzarakis. "Spatial estimation of  thermal indices in Urban Areas—Basics of the SkyHelios Model." Atmosphere 9.6 (2018): 209.</p><p>Lindberg F, Grimmond CSB, Gabey A, Huang B, Kent CW, Sun T, Theeuwes N, Järvi L, Ward H, Capel-Timms I, Chang YY, Jonsson P, Krave N, Liu D, Meyer D, Olofson F, Tan JG, Wästberg D, Xue L, Zhang Z (2018) Urban Multi-scale Environmental Predictor (UMEP) - An integrated tool for city-based climate services. Environmen tal Modelling and Software.99, 70-87 https://doi.org/10.1016/j.envsoft.2017.09.020</p><p>Röckle, R., 1990: Bestimmung der Strömungsverhältnisse im Bereich komplexer Bebauungsstruk-turen. PhD thesis Fachbereich Mechanik der Technischen Hochschule Darmstadt Darmstadt.</p>