Towards the modelling of pedestrian wind speed using high-resolution digital surface models and statistical methods

Abstract

Wind is a complex phenomenon and a critical factor in assessing climatic conditions and pedestrian comfort within cities. To obtain spatial information on near-ground wind speed, 3D computational fluid dynamics (CFD) modelling is often used. This is a computationally intensive method which requires extensive computer resources and is time consuming. By using a simpler 2D method, larger areas can be processed and less time is required. This study attempts to model the relationship between near-ground wind speed and urban geometry using 2.5D raster data and variable selection methods. Such models can be implemented in a geographic information system (GIS) to assess the spatial distribution of wind speed at street level in complex urban environments at scales from neighbourhood to city. Wind speed data, 2 m above ground, is obtained from simulations by CFD modelling and used as a response variable. A number of derivatives calculated from high-resolution digital surface models (DSM) are used as potential predictors. A sequential variable selection algorithm followed by all-possible subset regression was used to select candidate models for further evaluation. The results show that the selected models explain general spatial wind speed pattern characteristics but the prediction errors are large, especially so in areas with high wind speeds. However, all selected models did explain 90 % of the wind speed variability (R2 ≈ 0.90). Predictors adding information on width and height ratio and alignment of street canyons with respect to wind direction are suggested for improving model performance. To assess the applicability of any derived model, the results of the CFD model should be thoroughly evaluated against field measurements.