Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland)

Feigenwinter, Christian; Vogt, Roland; Parlow, Eberhard; Lindberg, Fredrik; Marconcini, Mattia; Frate, Fabio Del; Chrysoulakis, Nektarios

doi:10.1109/jstars.2018.2807815

Cited by 33 publications

(26 citation statements)

References 19 publications

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“…Their impact in absolute terms is limited in the urban core, as Q E was found to have low values (fewer than 30 W m −2 ); however, for the vegetated suburbs, the estimation sensitivity is expected to be higher. The sensitivity analysis findings are in agreement with those reported in [67,81] (in terms of percentage variations), where T s was also considered to be the most important parameter in the resulting sensitivity of the fluxes.…”

Section: Resultssupporting

confidence: 90%

“…In the literature, several studies have modeled the urban energy fluxes using a remotely-sensed approach [66][67][68][132][133][134][135][136][137][138]; however, only few of them made synchronous measurements of the turbulent fluxes available for validation purposes. Here, the obtained accuracy is considered to be consistent with the reported errors in recent studies of long-term, remotely-sensed turbulent fluxes [68,81,86]. Furthermore, the present work extends the current knowledge of the applicability of the downscaled satellite T s in the urban Q H estimation, which to the authors' knowledge has only be examined in [68].…”

Section: Discussionsupporting

confidence: 87%

“…where w [ms −1 ], θ [K], and q [kgkg −1 ] represent the short-term fluctuations of vertical wind, potential temperature, and specific humidity respectively. The adjacent half-hour intervals to the corresponding satellite overpasses were used, in order to reduce the large fluctuations of EC measurements [81]. Turbulent flux source areas were estimated from the Korman and Meixner footprint model [82] using the Urban Multi-scale Environmental Predictor (UMEP) GIS tool [83].…”

Section: Bowen Ratiomentioning

confidence: 99%

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Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Agathangelidis

Cartalis

Santamouris

2019

Climate

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Cities worldwide are getting warmer due to the combined effects of urban heat and climate change. To this end, local policy makers need to identify the most thermally vulnerable areas within cities. The Local Climate Zone (LCZ) scheme highlights local-scale variations; however, its classes, although highly valuable, are to a certain extent generalized in order to be universally applicable. High spatial resolution indicators have the potential to better reflect city-specific challenges; in this paper, the Urban Heat Exposure (UHeatEx) indicator is developed, integrating the physical processes that drive the urban heat island (UHI). In particular, the urban form is modeled using remote sensing and geographical information system (GIS) techniques, and used to estimate the canyon aspect ratio and the storage heat flux. The Bowen ratio is calculated using the aerodynamic resistance methodology and downscaled remotely sensed surface temperatures. The anthropogenic heat flux is estimated via a synergy of top-down and bottom-up inventory approaches. UHeatEx is applied to the city of Athens, Greece; it is correlated to air temperature measurements and compared to the LCZs classification. The results reveal that UHeatEx has the capacity to better reflect the strong intra-urban variability of the thermal environment in Athens, and thus can be supportive for adaptation responses. High-resolution climate projections from the EURO-CORDEX ensemble for the region show that the adverse effects of the existing thermal inequity are expected to worsen in the coming decades.These four decisive parameters for the urban climate (H/W, β, ∆Q S , Q F ) are estimated in this work using meteorological, earth observation, and geographical information system (GIS) data; the Climate 2019, 7, 75 3 of 28 different variables are subsequently incorporated in the UHeatEx composite indicator via principal component analysis (PCA). An additional mapping of the study area using the LCZ scheme is applied, and similarities and differences between UHeatEx and LCZs are discussed. Both results are correlated with air temperature measurements from a network of automatic weather stations (AWSs) in the area under examination, for a six-year period (the warm period months). A qualitative and quantitative analysis examines the presence of thermal inequity in Athens and its relationship with socioeconomic characteristics. The growing importance of thermal indicators in sustainable urban planning, due to climate change, is demonstrated by presenting the simulated future conditions of the city. To this end, high-resolution climate projections from an ensemble of regional climate models (RCMs) were used.

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Section: Resultssupporting

confidence: 90%

Section: Discussionsupporting

confidence: 87%

Section: Bowen Ratiomentioning

confidence: 99%

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Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Agathangelidis

Cartalis

Santamouris

2019

Climate

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“…The rapid growth of cities, in combination with fast technological advancement, has created many challenges for the management of urban environments. Today's cities consume natural resources, generate heat, and produce pollution in amounts that far exceed those of past decades (Feigenwinter et al, 2018;Ng & Ren, 2018). The world's population and energy consumption have been steadily increasing since the Industrial Revolution.…”

Section: Introductionmentioning

confidence: 99%

Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship

Faroughi

Karimimoshaver

Aram

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively.

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“…Further details on the site and data processing are available inSchmutz et al (2016);Lietzke and Vogt (2013);Feigenwinter et al (2018).Six surface stations are used to evaluate the model performance inside the urban canopy layer. The sites of Basel Feldbergstrasse (BFEL) and Basel St. Johannplatz (BSJO) are operated by the local environmental agency (Lufthygieneamt beider 5 Basel).…”

mentioning

confidence: 99%

COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees

Mussetti¹,

Brunner²,

Henne³

et al. 2019

Preprint

Self Cite

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Abstract. Street trees are more and more regarded as an effective measure to reduce excessive heat in urban areas. However, the vast majority of mesoscale urban climate models do not represent street trees in an explicit manner and for example do not take the important effect of shading by trees into account. In addition, urban canopy models that take interactions of trees and urban fabrics directly into account are usually limited to the street or neighbourhood scale and, hence, cannot be used to analyse the citywide effect of urban greening. In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEP-Tree and the mesoscale weather and climate model COSMO. The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June–July 2015. Overall, the model compared well with measurements of individual components of the surface energy balance and with air and surface temperatures obtained from a flux tower, surface stations and satellites. Deficiencies were identified for night-time air temperature and humidity, which can mainly be traced back to limitations in the simulation of the night-time stable boundary layer in COSMO. The representation of street trees in the coupled model generally improved the agreement with observations. Street trees produced large changes in simulated sensible and latent heat flux, and wind speed. Within the canopy layer, the presence of street trees resulted in a slight reduction in daytime air temperature and a very minor increase in night-time air temperature. The model was found to realistically respond to changes in the parameters defining the street trees: leaf area density and stomatal conductance. Overall, COSMO-BEP-Tree demonstrated the potential of (a) enabling city-wide studies on the cooling potential of street trees and (b) further enhancing the modelling capabilities and performance in urban climate modelling studies.

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Spatial Distribution of Sensible and Latent Heat Flux in the City of Basel (Switzerland)

Cited by 33 publications

References 19 publications

Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation

Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship

COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees

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