João P. Leitão

Not all SuDS are created equal: Impact of different approaches on combined sewer overflows

Publisher: Elsevier BV

Date: 03-2021

DOI: 10.1016/J.WATRES.2020.116780

Stormwater management impacts of small urbanising towns: The necessity of investigating the ‘devil in the detail’

Publisher: Elsevier BV

Date: 02-2021

DOI: 10.1016/J.SCITOTENV.2020.143835

Blue Green Systems for urban heat mitigation: mechanisms, effectiveness and research directions

Publisher: IWA Publishing

Date: 12-2022

DOI: 10.2166/BGS.2022.028

Abstract: Reflected in the growing body of literature, urban heat mitigation is increasingly relevant as cities experience extreme heat, exacerbated by climate change and rapid urbanisation. Most studies focus on urban–rural temperature differences, known as the Urban Heat Island, which does not provide insight into urban heat dynamics. Here, we synthesise current knowledge on spatio-temporal variations of heat sources and sinks, showing that a targeted and absolute understanding of urban heat dynamics rather than an urban–rural comparison should be encouraged. We discuss mechanisms of heat sinks for microclimate control, provide a clear classification of Blue Green Systems and evaluate current knowledge of their effectiveness in urban heat mitigation. We consider planning and optimisation aspects of Blue Green Infrastructure (greenery and water bodies/features), interactions with hard surfaces and practices that ensure space and water availability. Blue Green Systems can positively affect urban microclimates, especially when strategically planned to achieve synergies. Effectiveness is governed by their dominant cooling mechanisms that show diurnal and seasonal variability and depend upon background climatic conditions and characteristics of surrounding urban areas. Situationally appropriate combination of various types of Blue Green Systems and their connectivity increases heat mitigation potential while providing multiple ecosystem services but requires further research.

Sewer asset management – state of the art and research needs

Publisher: Informa UK Limited

Date: 21-10-2019

DOI: 10.1080/1573062X.2020.1713382

Flood Uncertainty Estimation Using Deep Ensembles

Publisher: MDPI AG

Date: 22-09-2022

DOI: 10.3390/W14192980

Abstract: We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted water depth. Efficient, accurate, and trustworthy methods for urban flood management have become increasingly important due to higher rainfall intensity caused by climate change, the expansion of cities, and changes in land use. While physically based flood models can provide reliable forecasts for water depth at every location of a catchment, their high computational burden is hindering their application to large urban areas at high spatial resolution. While deep learning models have been used to address this issue, a disadvantage is that they are often perceived as “black-box” models and are overconfident about their predictions, therefore decreasing their reliability. Our deep learning model learns the underlying phenomena a priori from simulated hydrodynamic data, obviating the need for manual parameter setting for every new rainfall event at test time. The only inputs needed at the test time are a rainfall forecast and parameters of the terrain such as a digital elevation model to predict the maximum water depth with uncertainty estimates for complete rainfall events. We validate the accuracy and generalisation capabilities of our approach through experiments on a dataset consisting of catchments within Switzerland and Portugal and 18 rainfall patterns. Our method produces flood hazard maps at 1 m resolution and achieves mean absolute errors as low as 21 cm for extreme flood cases with water above 1 m. Most importantly, we demonstrate that our approach is able to provide an uncertainty estimate for every water depth within the predicted hazard map, thus increasing the model’s trustworthiness during flooding events.

Universidade Técnica De Lisboa Instituto Superior Técnico

Location: Portugal

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Imperial College London

Location: United Kingdom of Great Britain and Northern Ireland

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Eawag: Swiss Federal Institute Of Aquatic Science And Technology

Location: Switzerland

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EAPS (currently Safemode)

Location: Portugal

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LNEC: Laboratório Nacional De Engenharia Civil

Location: Portugal

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Discovery Projects - Grant ID: DP210103704

Start Date: 2022

End Date: 12-2024

Amount: $390,000.00

Funder: Australian Research Council