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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.
- 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 in five southern European cities: an improved GIS-based classification method based on Copernicus dataPublication . Oliveira, Ana; Lopes, António; Niza, SamuelWhile climate change projections for the Mediterranean region indicate an increased exposure to heatwaves (HW), such prospects are particularly challenging in urban areas, where thermal stress can be exacerbated by the Urban Heat Island (UHI) effect. In that regard, understanding spatial patterns of thermal performance is of the utmost importance, in order to address corresponding adaptation measures. Local Climate Zones (LCZ) have become the standard typification of Land Cover/Land Use classes, according to their climatic response. However, the corresponding satellite- based classification method from the World Urban Database and Access Portal Tools (WUDAPT) presents accuracy issues when applied to European cities. Several studies have provided alternative LCZ methodologies, but these usually require data which is not often readily available (e.g. high-resolution digital surface models), therefore rendereing them hard to replicate. This study addresses this issue by developing an alternative geographic information system (GIS)-based method, and the corresponding toolbox, to translate Copernicus datasets into LCZ maps: Urban Atlas and Corine Land Cover shapefiles are used as the baseline dataset for the reclassification. The method was proven to be accurate in the five cities used in the case study - Athens, Barcelona, Lisbon, Marseille, and Naples - 81% overall accuracy, and 0.79 Kappa coefficient, on average. Results reveal the presence of a diurnal surface UHI, with lower land surface temperatures (LST) found in tree covered areas. However, similar LST found in the other LCZ classes (e.g. between compact and sparsely built-up areas) indicates that diurnal patterns of the urban energy balance components must be considered to better characterise the UHI of these cities.
- Local climate zones datasets from five Southern European cities: Copernicus based classification maps of Athens, Barcelona, Lisbon, Marseille and NaplesPublication . Oliveira, Ana; Lopes, António; Niza, SamuelHere, we provide Local Climate Zones (LCZ) map datasets from five Southern European Mediterranean cities: Athens (Greece), Barcelona (Spain), Lisbon (Portugal), Marseille (France) and Naples (Italy). The maps were produced according to a geographic information system (GIS)-based classification method, using freely available Copernicus Land Monitoring Service (CLMS) input data. Several maps are provided: (i) five LCZv1 maps (one per city) depicting urban LCZ's aggregated by density (no building height information); (ii) five LCZv1_leaf maps (one per city), identical to the previously mentioned ones, with tree cover LCZ classes A and B reclassification according to the Dominant Leaf Type (DLT) (deciduous or coniferous); (iii) two LCZv1_BH maps (Athens and Lisbon) distinguishing urban LCZ classes 123 and 456 according to the dominant building height (BH); and (iv) two LCZv1_leaf_BH maps (Athens and Lisbon) identical to the previous ones with added DLT-based land cover classification. The LCZ classification maps are available in both ArcGIS .lyr layer and GeoTIFF raster formats (Appendix 1 and 2), with a spatial resolution of 50×50m pixels, and are suitable to urban climate-related studies, particularly at the metropolitan and city scales of analysis. The data here provided is related to the article entitled «Local Climate Zones in five Southern European cities: an improved GIS-based classification method based on free data from the Copernicus Land Monitoring Service» [1], and the corresponding method/ArcGIS based custom Toolbox is freely available in «Local Climate Zones classification from Copernicus Land Monitoring Service datasets: an ArcGIS-based Toolbox» [2].
- 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.
- Annual summaries dataset of Heatwaves in Europe, as defined by the Excess Heat FactorPublication . Oliveira, Ana; Lopes, António; Correia, EzequielThe dataset includes six yearly time series of six Heatwave (HW) aspects/metrics (or statistical summaries) calculated from the E-OBS dataset (v19eHOM, available in https://www.ecad.eu/download/ensembles/downloadversion19.0eHOM.php) following the Excess Heat Factor (EHF) methodology implemented in the ClimPACT tool, in compliance with the guidelines established by the Expert Team on Climate Change Detection and Indices (ET-SCI). These aspects correspond to annual summaries of HW frequency, duration and intensity, considering solely the events occurring during the extended summer season (from June to September). Input Daily Maximum (TX) and Minimum (TN) near-surface air temperature data were retrieved from a European gridded dataset (E-OBS) – the ensemble homogenized version ‘19.0eHOM’, at 0.1° × 0.1° spatial resolution, covering the European region, and retrieved from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service. The E-OBS dataset is based on station observations, provided by the European Climate Assessment & Dataset. The here-presented HW aspects/summaries outputs of the ClimPACT tool correspond to the gridded annual statistical summaries of HW – these are detected based on the positive Excess Heat Factor (EHF) days, an HW index based on the human health response to heat extremes. The summaries include: (i) annual Number of Heatwaves (HWN); (ii) annual Heatwave Days Frequency (HWF); (iii) annual Maximum Heatwave Duration (HWD); (iv) annual Mean Heatwave Magnitude (HWM); and (v) annual Maximum Heatwave Amplitude (HWA). In addition, the annual maximum Heatwave Severity (HWS) was calculated, by dividing HWA by the 85th percentile of the positive EHF days. These annual time series can be used in HW-related studies focusing on the European region, particularly those focusing on climatology, trends, and impacts on human health.
- Excess Heat Factor climatology, trends, and exposure across European Functional Urban AreasPublication . Oliveira, Ana; Lopes, António; Soares, AmílcarIn Europe, regional climate change prospects indicate the urgency of adapting to extreme weather events. While increasing temperature trends have already been detected, in the last decades, the adoption of a European heatwave (HW) early-warning index is not yet consensual, partially due to the significant number of alternative algorithms, in some cases adjusted to the measurement of sector-specific impacts (as per the Expert Team on Climate Risk and Sector-specific Indices (ET-SCI)). In particular, the Excess Heat Factor (EHF) has been shown to accurately predict heat-related human health outcomes, in mid-latitude climates, provided that local summer exposure to excess heat is mostly driven by extreme air temperatures, with a lower contribution from relative humidity. Here, annual summaries of EHF-based HW detection were calculated for the European region, using daily maximum and minimum temperatures from the homogenised version of the E-OBS gridded dataset. Annual HW frequencies, duration, mean magnitude, maximum amplitude, and severity were subject to climatology and trend analysis across the European biogeographical regions, considering the 1961–1990 period as the baseline reference for anomaly detection in the more recent (1991–2018) decades. As HW-dependent morbidity/mortality affects mostly the elderly, an EHF-based HW Exposure Index was also calculated, by multiplying the recent probability of severe events per the number of people aged 65, or more, in the European Functional Urban Areas (FUAs). Results show that recent historical EHF-based patterns diverge across European Biogeographical regions, with a clear latitudinal gradient. Both the historical mean and recent trends point towards the greater exposure in the southern European Mediterranean region, driven by the significant increase of HW frequency, duration and maximum severity, especially in the last 3 decades; conversely, annual maximum EHF intensities (i.e., greatest deviations from the local 90th daily mean temperature) are mostly found in the northern and/or high altitude Boreal, Alpine and Continental regions, as a consequence of the latitudinal effect of local climatology on the HWM/HWA indices (this also translates into greater magnitudes of change, in this regions). Nonetheless, by simultaneously considering the probability of Severe HW occurrence in the last three decades, together with the log transformation of people aged 65 or more, results show that greater HW Exposure Indices affect FUAs across the whole Europe, irrespective of its regional climate, suggesting that more meaningful vulnerability assessments, early warning and adaptation measures should be prioritized accordingly.
- Heatwaves and summer urban heat islands: a daily cycle approach to unveil the urban thermal signal changes in Lisbon, PortugalPublication . Oliveira, Ana; Lopes, António; Correia, Ezequiel; Niza, Samuel; Soares, AmílcarLisbon is a European Mediterranean city, greatly exposed to heatwaves (HW), according to recent trends and climate change prospects. Considering the Atlantic influence, air temperature observations from Lisbon’s mesoscale network are used to investigate the interactions between background weather and the urban thermal signal (UTS) in summer. Days are classified according to the prevailing regional wind direction, and hourly UTS is compared between HW and non‐HW conditions. Northern‐wind days predominate, revealing greater maximum air temperatures (up to 40 °C) and greater thermal amplitudes (approximately 10 °C), and account for 37 out of 49 HW days; southern‐wind days have milder temperatures, and no HWs occur. Results show that the wind direction groups are significantly different. While southern‐wind days have minor UTS variations, northern‐wind days have a consistent UTS daily cycle: a diurnal urban cooling island (UCI) (often lower than –1.0 °C), a late afternoon peak urban heat island (UHI) (occasionally surpassing 4.0 °C), and a stable nocturnal UHI (1.5 °C median intensity). UHI/UCI intensities are not significantly different between HW and non‐HW conditions, although the synoptic influence is noted. Results indicate that, in Lisbon, the UHI intensity does not increase during HW events, although it is significantly affected by wind. As such, local climate change adaptation strategies must be based on scenarios that account for the synergies between potential changes in regional air temperature and wind.
- 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.