Fuzzy clustering-based climatic zoning method for building climate responsiveness in Qinba region, China

Abstract

Accurate climate zoning is crucial for enhancing energy efficiency. This study aims to further subdivide microclimate zones to enhance the precision of climate zoning and subsequently propose climate response strategies for the refined areas. The Qinba region, a climatic transition zone between northern and southern China, serves as the focal point of this study. The research employs a fuzzy clustering method, utilizing integrated weights on seven meteorological indicators spanning 40-year period, to delineate climate zones in the Qinba region. The results indicate that parts of Qinba region can be categorized into three clusters significantly correlated with altitude, which are closely consistent with the distribution of climate data and previous researches. Lower altitude areas experience hot summers and warm winters, while higher altitude regions have warm summers and cold winters with a larger increase in average annual temperature. Furthermore, this research confirms the alignment of climate zones with climate response strategies through investigation of over 30 traditional villages with climatic experience and simulation of three typical cities using climate software. Both sources emphasize the significance of sun shading, ventilation, and solar gain as key strategies applicable across all three zones. The proposed method accurately synthesizes various dimensions of meteorological data and is suitable for small-scale climate zoning. It can provide valuable insights for advancing climate-complex small-area zoning and climate-responsive strategies.