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- An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: a heatwave event in NaplesPublication . Oliveira, Ana; Lopes, António; Niza, Samuel; Soares, AmílcarSouthern European functional urban areas (FUAs) are increasingly subject to heatwave (HW) events, calling for anticipated climate adaptation measures. In the urban context, such adaptation strategies require a thorough understanding of the built-up response to the incoming solar radiation, i.e., the urban energy balance cycle and its implications for the Urban Heat Island (UHI) effect. Despite readily available, diurnal Land Surface Temperature (LST) data does not provide a meaningful picture of the UHI, in these midlatitudes FUAs. On the contrary, the mid-morning satellite overpass is characterized by the absence of a significant surface UHI (SUHI) signal, corresponding to the period of the day when the urban-rural air temperature difference is typically negative. Conversely, nocturnal high-resolution LST data is rarely available. In this study, an energy balance-based machine learning approach is explored, considering the Local Climate Zones (LCZ), to describe the daily cycle of the heat flux components and predict the nocturnal SUHI, during an HW event. While the urban and rural spatial outlines are not visible in the diurnal thermal image, they become apparent in the latent and storage heat flux maps – built-up infrastructures uptake heat during the day which is released back into the atmosphere, during the night, whereas vegetation land surfaces loose diurnal heat through evapotranspiration. For the LST prediction model, a random forest (RF) approach is implemented. RF results show that the model accurately predicts the LST, ensuring mean square errors inferior to 0.1 K. Both the latent and storage heat flux components, together with LCZ classification, are the most important explanatory variables for the nocturnal LST prediction, supporting the adoption of the energy balance approach. In future research, other locations and time-series data shall be trained and tested, providing an efficient local urban climate monitoring tool, where in-situ air temperature observations are not available.
- Local climate zones classification method from Copernicus land monitoring service datasets: an ArcGIS-based toolboxPublication . Oliveira, Ana; Lopes, António; Niza, SamuelLocal Climate Zones (LCZ) have become a worldwide standard for identifying land cover classes, according to their climate-relevant morphological parameters. The LCZ's are mostly used to evaluate urban climate performance, particularly the relationship between the urban heat island effect (UHI) and the characteristics of the built-up environment. The World Urban Database and Access Portal Tools (WUDAPT) has provided a supervised LCZ classification method based only on moderate resolution free satellite imagery, mostly Landsat 7 or 8 (30 m pixel size, in the visible spectrum brands); however, its' results are less accurate for European cities. Conversely, alternative geographic information system (GIS)-based methods developed so far require information that is hardly available to all, such as building footprints or heights. Here, the ArcGIS based LCZ from Copernicus Toolbox (LCZC) provides an alternative classification method that uses only freely accessible information from the Copernicus Land Monitoring Service (CLMS), being possible to replicate it in 800 European urban locations. The method combines Urban Atlas (UA) and Corine Land Cover (CLC) with Tree Cover Density, Dominant Leaf Type and Grassland information, to produce a higher-resolution baseline shapefile that is classified according to each feature's dominant characteristics. The LCZC toolbox output is a LCZ raster map. It has been validated in five European cities: Athens, Barcelona, Lisbon, Marseille, and Naples.•The LCZC toolbox provides an alternative LCZ GIS-based classification, based on freely accessible CLMS datasets.•The use of CLMS shapefile higher-resolution inputs, particularly the UA and CLC datasets, ensures an output LCZ map that has greater detail and higher accuracy.•The availability of CLMS information in 800 European urban areas guarantees that the method can be replicated in those locations.
- An urban climate-based empirical model to predict present and future patterns of the Urban Thermal SignalPublication . Oliveira, Ana; Lopes, António; Correia, Ezequiel; Niza, Samuel; Soares, AmílcarAir temperature is a key aspect of urban environmental health, especially considering population and climate change prospects. While the urban heat island (UHI) effect may aggravate thermal exposure, city-level UHI regression studies are generally restricted to temporal-aggregated intensities (e.g., seasonal), as a function of time-fixed factors (e.g., urban density). Hence, such approaches do not disclose daily urban-rural air temperature changes, such as during heatwaves (HW). Here, summer data from Lisbon's air temperature urban network (June to September 2005-2014), is used to develop a linear mixed-effects model (LMM) to predict the daily median and maximum Urban Thermal Signal (UTS) intensities, as a response to the interactions between the time-varying background weather variables (i.e., the regional/non-urban air temperature, 2-hours air temperature change, and wind speed), and time-fixed urban and geographic factors (local climate zones and directional topographic exposure). Results show that, in Lisbon, greatest temperatures and UTS intensities are found in 'Compact' areas of the city are proportional to the background air temperature change. In leeward locations, the UTS can be enhanced by the topographic shelter effect, depending on wind speed - i.e., as wind speed augments, the UTS intensity increases in leeward sites, even where sparsely built. The UTS response to a future urban densification scenario, considering climate change HW conditions (RCP8.5, 2081-2100 period), was also assessed, its results showing an UTS increase of circa 1.0 °C, in critical areas of the city, despite their upwind location. This LMM empirical approach provides a straightforward tool for local authorities to: (i) identify the short-term critical areas of the city, to prioritise public health measures, especially during HW events; and (ii) test the urban thermal performance, in response to climate change and urban planning scenarios. While the model coefficient estimates are case-specific, the approach can be efficiently replicated in other locations with similar biogeographic conditions.