Modelling near ground wind speed in urban environments using high-resolution digital surface models and statistical methods

University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

Abstract: Wind is a complex phenomenon and a critical factor in assessing climatic conditions and pedestrian comfort within cities. This master’s thesis attempts to quantify and model the relationship between near ground wind speed and urban geometry using two-dimensional raster data and variable selection methods based on Multiple Linear Regression. A simple regression model can be implemented in a Geographic Information System (GIS) to assess spatial distribution of wind speed at the street scale in complex urban environments. Wind speed data two meters above ground is obtained from simulations by computer fluid mechanics modelling (CFD) and used as a response variable. Utilizing a shadow-casting raster algorithm, four measures of urban geometry are derived from high-resolution surface models (DSM): Sky View Factor (SVF), Fetch, Frontal Area Index (FAI) and Angular Frontal Area Index (αFAI). To compute Fetch, FAI and αFAI, the shadow-casting algorithm needs a search angle and search distance as input parameters. To evaluate the effect of these parameters, a number of settings were tested resulting in a total of 53 different predictors. A sequential variable selection algorithm followed by all-possible subset regression was used to select predictors and candidate models for further evaluation. Models including SVF and Fetch are found to 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). The RMSE of predicted wind speed for city models ranges from between 0.01 (dense building geometry) to 0.22 (wide street aligned in the wind direction with air flow channelling). The differences between the selected candidate models are less than 0.02, and no model consistently performs “best” over all city models. Models that included FAI and αFAI did not improve on these results. Predictors adding information on width and height ratio and alignment of street canyons with respect to wind direction are possible developments to improve model performance. To assess the applicability of any derived model, the results of the CFD model should be thoroughly evaluated against field measurements.

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