From Earth Observation to Artificial Intelligence: a journey towards urban climate adaptation.

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CERENA organized the “Webinar Series in Spatial Data Sciences …think spatially about your data science problems”, from 11th May to 29th June 2022.
Ana Oliveira, Vasco Leal, Manuel Galamba, Rita Cunha, António Lopes, Ezequiel Correia, Amilcar Soares and Samuel Niza.
11 de Maio de 2022

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As climate change prospects point towards the pressing need for local-scale adaptation measures, heat exposure becomes one of the key aspects in determining the health of the urban environment. In addition, many western metropolises are characterized by an ageing population which may lead to an increased community-level sensitivity to heat extremes – that is the case in many European Functional Urban Areas (FUAs), including in the Greater Lisbon (hereinafter Lisbon) area. Lisbon has already a track record of being regularly exposed to severe heatwaves (HW), and regional climate change prospects point to its aggravation in coming decades (frequency, duration, and severity), as with other Southern European cities. Accordingly, there is a pressing need to pinpoint the urban locations where people are relatively more exposed to the excess heat, which can lead to dehydration, cerebrovascular accidents or thrombogenesis.
In this study, air temperature measurements from citizen-owned meteorological stations is retrieved from open data platforms, quality controlled and co-located with Earth Observation (EO) data and products to downscale the official air temperature forecasts (from deterministic numerical weather predictions, NWP) from the native regional scale (2.5km) up to a metric spatial resolution (200m). As the NWP model resolves the regional physical processes, the Machine Learning (ML) high-resolution output is able to adjust its bias to the specificities of the urban location, by accurately predicting the local contribution of the urban heat island (UHI) effect, quantifying the heat anomaly at the neighbourhood scale. The cooling effect of the urban green infrastructure is also detected, providing mensurable scenarios to support future urban greening initiatives. In addition, with these results, the identification of short-term critical areas during heatwave events becomes possible, supporting the local public health stakeholders in their decision-making – i.e., regarding where and when to act.

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