Statistical prediction of the nocturnal urban heat island intensity based on urban morphology and geographical factors - An investigation based on numerical model results for a large ensemble of French cities

Gardes, Thomas; Schoetter, Robert; Hidalgo, Julia; Long, Nathalie; Marquès, Eva; Masson, Valéry

doi:10.1016/j.scitotenv.2020.139253

Cited by 40 publications

(25 citation statements)

References 55 publications

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“…Nedkov et al [63] employed the three-level MAES (mapping and assessment of ecosystems and their services) typology, where the third level included LCZ classes that contributed to more detailed spatial representation of the study area by applying GIS-based analyses and visual interpretation of orthophoto images. Using the combination of building and surface databases from France's national administration sources, such as the MApUCE project, BD TOPO, GENIUS, and INSEE, Gardes et al [62] obtained detailed object and land-cover features in order to calculate the LCZ patterns of French cities.…”

Section: Combined Methodsmentioning

confidence: 99%

“…In a number of studies, analyses took in ten or more cities at once, in one or more European countries, and some did not address all the cities in detail, but showed only general LCZ patterns. This was the situation for 42-100 cities in France [59][60][61], 235 cities in Bulgaria [62], from ten to a few dozen cities across Europe [52,[63][64][65][66]. Cities from those articles do not, therefore, appear in Figure 5.…”

Section: Context and Content Of Lcz Studiesmentioning

confidence: 98%

“…Basset et al [144] used 29 UMN weather stations in Birmingham (UK) for a spatial interpolation of UHI and observed a UHI advection process in the urban area, while Wicki et al [138] modelled and evaluated UHI intensity within the LCZs of Basel (Switzerland), also using UMN datasets. Straub et al [140] employed a statistical modelling approach to the assessment of spatial patterns of UHI intensity, while Gardes et al [62] focused on the night-time UHI intensity in French cities in terms of urban morphology parameters. Emmanuel and Loconsole [49] introduced a low data-intensity and easy-to-use method for classifying urban areas into LCZs, and made an assessment of the performance of green infrastructure in the mitigation of urban overheating in various LCZs of the Clyde Valley, Glasgow, Scotland.…”

Section: Thermal Analysis Based On Modelling Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Mapping Local Climate Zones and Their Applications in European Urban Environments: A Systematic Literature Review and Future Development Trends

Lehnert

Savić

Milošević

et al. 2021

IJGI

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In the light of climate change and burgeoning urbanization, heat loads in urban areas have emerged as serious issues, affecting the well-being of the population and the environment. In response to a pressing need for more standardised and communicable research into urban climate, the concept of local climate zones (LCZs) has been created. This concept aims to define the morphological types of (urban) surface with respect to the formation of local climatic conditions, largely thermal. This systematic review paper analyses studies that have applied the concept of LCZs to European urban areas. The methodology utilized pre-determined keywords and five steps of literature selection. A total of 91 studies were found eligible for analysis. The results show that the concept of LCZs has been increasingly employed and become well established in European urban climate research. Dozens of measurements, satellite observations, and modelling outcomes have demonstrated the characteristic thermal responses of LCZs in European cities. However, a substantial number of the studies have concentrated on the methodological development of the classification process, generating a degree of inconsistency in the delineation of LCZs. Recent trends indicate an increasing prevalence of the accessible remote-sensing based approach over accurate GIS-based methods in the delineation of LCZs. In this context, applications of the concept in fine-scale modelling appear limited. Nevertheless, the concept of the LCZ has proven appropriate and valuable to the provision of metadata for urban stations, (surface) urban heat island analysis, and the assessment of outdoor thermal comfort and heat risk. Any further development of LCZ mapping appears to require a standardised objective approach that may be globally applicable.

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Section: Combined Methodsmentioning

confidence: 99%

Section: Context and Content Of Lcz Studiesmentioning

confidence: 98%

Section: Thermal Analysis Based On Modelling Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Mapping Local Climate Zones and Their Applications in European Urban Environments: A Systematic Literature Review and Future Development Trends

Lehnert

Savić

Milošević

et al. 2021

IJGI

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“…Several algorithms can be used for this purpose. A widely used technique for modelling urban temperatures is using Multiple Linear Regression (MLR), which has been tested for both temporal [52][53][54][55] and spatial predictions [56][57][58][59][60]. However, the increasing availability of machine learning and big data solutions is boosting the widespread use of other algorithms which, although potentially harder to interpret, are likely to improve their accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…However, the increasing availability of machine learning and big data solutions is boosting the widespread use of other algorithms which, although potentially harder to interpret, are likely to improve their accuracy. Popular machine learning techniques include Support Vector Machines [61][62][63][64], Random Forest [58,60,62,65,66], or Artificial Neural Networks (ANN).…”

Section: Introductionmentioning

confidence: 99%

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

et al. 2021

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In the last decades, urban climate researchers have highlighted the need for a reliable provision of meteorological data in the local urban context. Several efforts have been made in this direction using Artificial Neural Networks (ANN), demonstrating that they are an accurate alternative to numerical approaches when modelling large time series. However, existing approaches are varied, and it is unclear how much data are needed to train them. This study explores whether the need for training data can be reduced without overly compromising model accuracy, and if model reliability can be increased by selecting the UHI intensity as the main model output instead of air temperature. These two approaches were compared using a common ANN configuration and under different data availability scenarios. Results show that reducing the training dataset from 12 to 9 or even 6 months would still produce reliable results, particularly if the UHI intensity is used. The latter proved to be more effective than the temperature approach under most training scenarios, with an average RMSE improvement of 16.4% when using only 3 months of data. These findings have important implications for urban climate research as they can potentially reduce the duration and cost of field measurement campaigns.

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The Trend of Heat-Related Mortality in European Cities

Ascione¹,

Masi²,

Mastellone³

et al. 2022

Advances in Sustainability Science and Technology

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Statistical prediction of the nocturnal urban heat island intensity based on urban morphology and geographical factors - An investigation based on numerical model results for a large ensemble of French cities

Cited by 40 publications

References 55 publications

Mapping Local Climate Zones and Their Applications in European Urban Environments: A Systematic Literature Review and Future Development Trends

Mapping Local Climate Zones and Their Applications in European Urban Environments: A Systematic Literature Review and Future Development Trends

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

The Trend of Heat-Related Mortality in European Cities

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