A model for integration of urban heat island effect mitigation into planning processes: Local climate zone based morphological approach

According to the Global Risk Report, extreme weather events have become the most important risk factor that humanity faces till 2017 (WEF, 2020). The WHO reveals that approximately 30 % of the world’s population are currently living under climatic conditions where temperatures have a lethal effect for at least 20 days of a year (WMO, 2020). Urban heat island (UHI) effect has become a substantial local problem since it increases the maximum temperatures and the effect of extreme heat. Due to the fact that the UHI effect is becoming much more serious issue day by day in terms of public health, the growing interest in increasing the adaptive capacity by reducing the local risks caused by extreme temperatures in the process of climate change adaptation, and reducing the energy consumption caused by increasing use of air conditioners due to UHI effect, the need for the new tools and methods to interrelate climate change, urban climate, UHIs, and planning and design is increasing. 

In this context, Local Climate Zone (LCZ) classification has a significant importance in interrelating these concepts because of its parametric and systematic approach to the built environment.The aim of the project is to reveal the UHI formation by using the LCZ classification, and to create a tool at the interface of climatology-urbanism to develop design, planning and policy proposals to reduce the UHI effect by quantitatively defining the relationship between UHI formation and the urban morphology. In the scope of the project, in three urban areas, Mardin-(Csa), Eskişehir-(Bsk) and Sivas-(Dsb), which have been chosen based on climate classification in Turkey, annual mean temperature, annual mean temperature change, energy consumption and population data; (i)the local climate zone approach will be used to classify the characteristics of the urban built environment and topography, (ii)the relationship between LCZ, urban morphology, planning parameters and land surface temperatures in these cities with different and similar climate types will be revealed quantitatively, (iii)the effects of different planning and design strategies to decrease the UHI effect in these cities will be revealed through modeling. 

The project will contribute to both national and international UHI literature, also it will contribute to the LCZ classification system with a theoretically and empirically original study.The study revealing the UHI effect of the land surface temperature by using LCZ classification in the whole urban area, comparing the UHI effect observed in cities with different climate class, examining the applicability of Stewart's 17 LCZs in Turkish cities, quantitatively revealing the relationship between UHI effect and the built and natural environment in the city, the opportunity to use the findings of quantitative assessment as an input for planning processes and policies, and defining a process/tool that can project climate change policies to local level constitute the original value of the project.Within the scope of the project, first, the UHI effect of the land surface temperature observed in different seasons in the selected city centers will be determined through satellite images during day and night hours; the relationship between the built environment of the city and the natural environment will be examined through statistical analysis and by using the formation of the UHI effect and the LCZ classification. 

Temperature, humidity, precipitation, wind speed, wind direction, solar radiation measurements will be performed by establishing mini climate stations in LCZ to be determined in the cities; the data obtained from the stations will be examined both in terms of their suitability to the parameters determined for LCZ and will be the basis for thermal comfort modeling. By using the ENVI-met program, simulations of the built environment will be made, the relationship between design parameters and thermal comfort will be revealed, and models will be made for the integration of these data into planning and artificial intelligence-based forward projection. Thus; on one side instead of seeking solutions after urban development is completed and the UHI effect is formed, land use decisions can be enabled in the early stages of planning and design; prediction of the thermal load makes it possible to prevent the formation of the heat island effect that may arise, while determining construction conditions, on the other side reducing the UHI effects in the urban built-up area can be possible.