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  Modelling the penetration of cool air from a park into urban street canyons


   School of Geography, Earth and Environmental Sciences

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  Dr X Cai, Dr L Chapman  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Overview:
Risks associated with extreme weather in cities, such as heat waves, droughts, flooding and wind damage, are all projected to increase with global climate change. One example is the 2003 European heat wave which caused more than 30,000 excessive deaths and was listed as the deadliest natural disaster in the last 50 years (UNEP, 2004). As a consequence governments need to direct considerable effort to determine feasible urban planning plans and climate mitigation strategies. Specific measures may include using scientific evidence to guide the design of new developments (e.g. optimising the size and locations of parks, and density and geometries of buildings), and to improve current urban built-forms (e.g. adopting green infrastructure, reducing energy consumption). Among these approaches, green technologies (infrastructure) are of popular choices due to their multiple fold benefits (biodiversity, biophilic, sustainability, natural resources etc). These measures and strategies must be supported by reliable scientific investigation into the underlying processes and mechanisms that control or determine the state of urban climate. This proposal attempts to advance the knowledge in this discipline so that informed decisions can be made regarding mitigating the impacts of urban heat effectively. The aim of this research is to answer a question: how effective (distance and degree) can an urban park cool down the temperature inside an urban street network next to the park under a given meteorological condition?

Methodology:
Air flows inside the urban canopy layer are highly complex and spatially varying in character. A three-dimensional microclimate model will be adopted in the study. This model is designed to simulate the surface-plant-air interactions in urban environment with a typical resolution of metre. Typical areas of application are Urban Climatology, Architecture, Building Design or Environmental Planing.
Model validation will be conducted using the urban climate data from the Birmingham Urban Climate Laboratory (BUCL), which has been functioning since 2013 in collecting data for the high density network in Birmingham. The stations with nearby urban parks will be selected for the validation. Another dataset for London is provided by Dr Kieron Doick (the project partner, see below). Senario simulations will then be conducted in the parametric space of (1) wind conditions; (2) atmospheric stability; and (3) building morphometric parameters (e.g. P, F).

Training and skills:
CENTA students will attend 45 days training throughout their PhD including a 10 day placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to the student’s projects and themes.
The goal of this project is to train the student to become a mature researcher with the expertise and skills in the areas of urban climate modelling. The University’s IT Services provide subject specific training, e.g. UNIX, parallel computation, computing languages (Matlab, R, and FORTRAN). The student will also receive training on numerical meteorological / climate modelling. In addition, frequent visits to the HiTemp sites will provide hands-on experience of observation.
Partners and collaboration (including CASE):
Dr Kieron Doick, the Acting Head of Land Regeneration and Urban Greenspace Research Group, Centre for Sustainable Forestry and Climate Change, Forest Research, will be a partner of the research. His group is particularly interested in urban trees and greenspace in a changing climate. They have monitored temperature in London’s urban streets near 10 parks for 5 months and are planning to extend the monitoring programmes over the next few years. This dataset would be valuable for the model validation of this project.

Where will I study?

 About the Project