As cities continue to expand, it has become crucial to describe their evolution in time and space (e.g. Verbavatz & Barthelemy, 2020). Urban growth often occurs at a faster-than-exponential rate, which may result in innovation cycles, finite-time singularities, or even collapses (as observed in finance and biological systems).
This project aims at (i) reviewing the literature on the space-time evolution of urban areas, (ii) explore the feedbacks between population growth and the development of urban transport networks in selected cities/regions, (iii) test and adapt an existing urban growth model and (iv) verify whether simple spatial rules can explain complex urban dynamics (e.g. Li et al. 2017). A good knowledge of mathematical modelling (i.e. partial differential equations) and coding (e.g., Matlab/Python) is required for this project.
If you are interested, please contact Prof. Gabriele Manoli ([email protected]).
Urban vegetation can provide several ecosystem services – such as storm water regulation and heat mitigation – and green infrastructures are promoted worldwide to create healthier and more sustainable urban environments (e.g. Willis & Petrokofski 2017). Yet, despite recent progress, it remains challenging to quantify and incorporate the benefits of urban vegetation into planning and decision making (Hamel et al, 2021).
This project aims to (i) review the literature on modeling vegetation in urban contexts, (ii) run simulations at selected study sites using state-of-the-art urban ecosystem models, (iii) explore scenarios of change and quantify the resulting benefits. The overall objective is to assess the effects of different vegetation strategies on urban climate and hydrology. A good knowledge of mathematical modeling and coding (e.g., Matlab/Python) is required for this project.
If you are interested, please contact Prof. Gabriele Manoli ([email protected]).
Climate change is anticipated to increase both temperatures and rainfall extreme events, posing relevant challenges for urban areas. In response, green roofs, also known as vegetated rooftops, have gained increasing attention due to their multifaceted advantages, including stormwater management, heat mitigation, and biodiversity enhancement. However, assessing these multifunctional benefits often involves complex evaluations, as stormwater management and heat mitigation are typically analyzed separately. The objective of this thesis is to quantify the combined benefits of an existing green roof in Mendrisio, Switzerland, by integrating and coupling two distinct open-source models: SWMM (hydrological model) and UT&C (microclimate model).
The first task of the student will be to set-up a SWMM model of the green roof, and validated with the outflow measurements of the roof.
In a second task, the student will couple SWMM inputs with an existing UT&C model, in order to streamline the simulation of both model with a single set of inputs.
Finally, the student will use downscaled and bias corrected climate projections for the Mendrisio region (already available), in order to quantify the climate change adaptation potential of the green roof, performing a sensitivity analysis to understand which design parameters are relevant in face of climate change.
If you are interested, please contact Prof. Gabriele Manoli ([email protected]).
Urban areas modify the surface energy balance, generally increasing surface and air temperatures but they also affect atmospheric humidity, wind, and air quality. This project aims at testing urban climate sensors (e.g., a flux tower installed at the UNIL campus, thermal cameras, drones), analyse the collected data, and compare observations with theoretical results
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Cities modify the surface energy balance, generally increasing surface and air temperatures. While the physical drivers of urban warming and its diurnal evolution are well understood, the high spatial heterogeneity of urban surfaces make it difficult to properly characterise the space-time variability of the temperature fields. To overcome this problem, this project aims at developing a stochastic description of urban climate. A strong background in mathematics (e.g., stochastic differential equations) and physics (e.g., climate) is required for this project.
If you are interested, please contact Prof. Gabriele Manoli ([email protected]).
On June 2023 Switzerland passed a new climate and innovation law that aims at accelerating the country transition from fossil fuel to renewable energy and attaining the zero-emission goal by 2050. As urban environments continue to grow, so does their energy demand and because of this, it is crucial that Swiss cities be at the frontline of the shift in energy production towards renewable sources.
At the URBES lab we use a combination of mesoscale and microscale numerical models to explore the urban wind energy potential across major Swiss conurbations. In this context, we are looking for a motivated student to help with the ingestion of the swissBUILDING3D 3.0 Beta high resolution building morphology dataset into the PALM-4U (Maronga et al. 2020) computational fluid dynamics (CFD) model.
A good knowledge of the AutoCAD or (Arc/Q)GIS softwares is required for this project.
If you are interested, please contact Prof. Gabriele Manoli ([email protected]). or Dr. Aldo Brandi ([email protected]).
Extreme climatic events are becoming more and more frequent and they are both driving human migrations and impacting refugees across the world (Issa et al., 2023; McMichael, 2023). Humanitarian settlements have to cope with such climate change impacts but also population pressure and poor infrastructures (e.g., lack of drainage systems, waste management) and there is an urgent need for sustainable and affordable solutions to improve the living conditions of these vulnerable populations. Nature-based solutions (NBS) can provide multiple benefits to migrants and refugees but there is lack of guidance on how to develop and implement NBS in different humanitarian contexts. This project aims at (i) reviewing the literature on existing case studies, guidelines, and best practices for humanitarian actors on the development of NBS for humanitarian applications and (ii) map the main refugee camps, disasters, and/or global migratory fluxes and assess the local climatic conditions, risks, and potential benefits of NBS.
If you are interested, please contact Prof. Gabriele Manoli ([email protected]).
The project consists in the use of Machine Learning (ML) approaches for applications to urban climate, land use change and urban growth modelling, urban mobility, etc. Knowledge of ML is a prerequisite for this project (e.g., CS-433 Machine Learning course).
If you are interested, please contact Prof. Gabriele Manoli ([email protected]).
Climate change is a pressing global concern that poses significant challenges to urban areas. Local Climate Zones (LCZs) are microclimatic zones within cities, characterized by distinct thermal, morphological, and land use properties. Understanding how LCZs evolve over time can provide crucial insights into the urban heat island effect, energy consumption patterns, and susceptibility to natural hazards. The research is focused on predicting changes in LCZ distribution over time and assessing their implications for future urban resilience planning.
The research involves data processing of historical LCZ distributions and relevant remote sensing data. It utilizes machine learning spatio-temporal models to predict LCZ changes and estimate their corresponding impact on urban areas. The analysis aims to investigate the associations between LCZ changes and natural hazard susceptibility in cities, ultimately providing strategies and guidance for resilient urban development.
If you are interested, please contact Prof. Gabriele Manoli ([email protected]).