Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region |
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Authors: | Zhang Kai Oswald Evan M Brown Daniel G Brines Shannon J Gronlund Carina J White-Newsome Jalonne L Rood Richard B O'Neill Marie S |
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Institution: | aDepartment of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA;bDepartment of Epidemiology, University of Michigan, Ann Arbor, MI, USA;cDepartment of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor, MI, USA;dSchool of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, USA |
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Abstract: | BackgroundBecause of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scale.ObjectiveThis study examined whether satellite-derived impervious surface and meteorological conditions from multiple sites can improve characterization of spatial variability of temperature within an urban area.MethodsTemperature was measured at 17 outdoor sites throughout the Detroit metropolitan area during the summer of 2008. Kriging and linear regression were applied to daily temperatures and secondary information, including impervious surface and distance-to-water. Performance of models in predicting measured temperatures was evaluated by cross-validation. Variograms derived from several scenarios were compared to determine whether high-resolution impervious surface information could capture fine-scale spatial structure of temperature in the study area.ResultsTemperatures measured at the sites were significantly different from each other, and all kriging techniques generally performed better than the two linear regression models. Impervious surface values and distance-to-water generally improved predictions slightly. Restricting models to days with lake breezes and with less cloud cover also somewhat improved the predictions. In addition, incorporating high-resolution impervious surface information into cokriging or universal kriging enhanced the ability to characterize fine-scale spatial structure of temperature.ConclusionsMeteorological and satellite-derived data can better characterize spatial variability in temperature across a metropolitan region. The data sources and methods we used can be applied in epidemiological studies and public health interventions to protect vulnerable populations from extreme heat events. |
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Keywords: | Geostatistics Impervious surface Kriging Spatial interpolation Temperature |
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