Estimating the Urban Heat Island Contribution to Urban and Rural Air Temperature Differences over Complex Terrain: Application to an Arid City

摘要

This study proposes a method for estimating the canopy-layer net urban heat island (UHI) in regions with complex terrain that lack preurban observations. The approach is based on a linear relationship between the urban-rural temperature difference (DELTA T_(u-r)), measured via screen-level air temperature, and the population of the city, which was found to have the highest correlation with observations. The linear relation is extrapolated to zero population to yield the desired preurban value. The difference between the zero population DELTA T_(u-r) and the current one is proposed to represent the net UHI. Given the uncertainties of the population method, the relatively short time period of the temperature record, and possible inhomogeneity in the data, the results should be regarded as a first-order approximation of the net UHI contribution. The UHI was evaluated for an arid city, Beer Sheba, Israel, for the minimum and maximum air temperatures for the summer and the winter. The study region resembles the combined effect of complex terrain (i.e., the concave topography of the city in contrast with the plateau landscape surrounding it), the UHI, and the regional warming trend. The study assumes that the regional warming does not affect theDELTA T_(u-r). The concave topography of the city dominates over the UHI contribution during nighttime, resulting in an average lower minimum temperature in the city relative to the rural area. This difference has decreased considerably during the study period and has even reversed for the summer nights toward the end of the period. The estimated net UHI contribution in Beer Sheba varies between +0.8° and +3.1°c, with the highest values during the night hours. The high positive UHI during the night is in linewith previous studies. The positive UHI in the summer implies further aggravation of heat stress beyond that occurring, and that predicted to increase, over the region.