PhD Thesis

The PhD thesis embarks on a comprehensive exploration of urban climate adaptation, commencing with a review of Urban Climate Adaptation Indicators (UCAI) to identify research gaps and evaluate indicators addressing heat stress and heavy precipitation reduction. Subsequently, it employs micro-scale modelling through the ENVI-met software. Our ongoing review delves into the intricate landscape of urban climate adaptation, investigating how cities grapple with a spectrum of challenges, including heat stress, air pollution, water scarcity, and heavy rainfall. Traditionally, these challenges have been addressed in isolation, necessitating a more holistic approach. Our investigation seeks to answer two pivotal questions: (1) How can frameworks optimize climate adaptation measures across multiple hazards? (2) What methods can be employed to assess the suitability of nature-based solutions across diverse climate zones and urban scales? Furthermore, the study explores the potential transferability of findings to different urban contexts, emphasizing transparency in urban planning. The research then transitions to the analysis and implementation of green and blue infrastructure (GBI) within Local Climate Zones (LCZs) to combat heat stress and heavy precipitation. ENVI-met simulations facilitate this analysis, allowing us to identify the most vulnerable LCZs to changing climate conditions and explore diurnal and seasonal variations, while also considering factors such as compactness, openness, ventilation potential, wind flow, and the role of green infrastructure in mitigating heat stress and heavy precipitation/flash floods. Ultimately, the research seeks to determine resilient structural configurations for climate adaptation, drawing from previous studies and utilizing the ENVI-met microscale model to simulate the intricate dynamics of urban spaces, thus contributing invaluable insights to enhance urban climate resilience.

The PhD research project will be compiled as cumulative thesis in the form of 3- 4 published scientific research papers that will demonstrate the main sections of the whole PhD project. It will demonstrate the utility of sensors in urban weather observation stations, their setup and testing for comparisons. The research project of Kassel urban climate observation network (KUCON) is fundamentally depending on the type and number of weather sensors that will collect weather data from various defined location across the urban and sub urban areas of Kassel city. The PhD project has two main objectives, 1) to  demonstrate the utility and comparative analysis of compact weather stations, which will be installed at defined locations in urban and rural areas across Kassel and 2) High spatial resolution dataset of air temperature within urban canopy layer (UCL) for Kassel city. The thesis will investigate the following research questions:

1. Comparison of accuracy of All-In-One (AIO) and Low-Cost Sensors (LCS) focusing on air temperature parameters in an urban environment.
2. What is the optimal setup regarding the placement of sensors for urban climate observation networks?
3. To what extent can the RS techniques estimate air temperature to compensate missing ground based air temperature stations?

In the first step, the selection process of weather sensors for urban weather observation is divided into two stages. The first stage selection of sensors is based on parameters like accuracy, power consumption, weight and cost. In the second stage selection, different AIO and LC air temperature sensors will be tested first in a controlled environment climate chamber and then in an open environment in-situ testing. Once the appropriate sensors are selected through the comparative analysis process then a systematic approach for sensor placement for urban climate observation networks will be developed as the second step. These maps will show locations of existing sensors and areas for required sensors to be installed. Furthermore local climate zone (LCZ) maps and differential maps for number of existing and required sensors will be developed. During the process, analysis of globally available data sources from satellite data, such as LST, ERA5 reanalysis data, LCZ classification and digital surface models (DSM), along with locally available data sources including urban climatic maps and crowd sourcing data will be utilized to improve the statistically driven sensor placement. The results will demonstrate the observed air temperature variability and its defined methodology for sensor placements in Kassel. The observed data will be available to be shared to different urban climate network platforms and stakeholders.