Using Local Climate Zone scheme for UHI assessment: Evaluation of the method using mobile measurements

Leconte, François; Bouyer, Julien; Claverie, Rémy; Pétrissans, Mathieu

doi:10.1016/j.buildenv.2014.05.005

Cited by 251 publications

(142 citation statements)

References 19 publications

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“…(Table 2). The scheme has been applied most to the study of the Urban Heat Island (UHI), for which it was developed -see Fenner et al (2014), Leconte et al (2014), and Stewart et al (2014). As an example, Fig.…”

Section: Urban Energy Budget (Ueb) Modelsmentioning

confidence: 99%

Using LCZ data to run an urban energy balance model

2015

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a b s t r a c tIn recent years a number of models have been developed that describe the urban surface and simulate its climatic effects. Their great advantage is that they can be applied in environments outside the cities in which they have been developed and evaluated. Thus, they may be applied to cities in the economically developing world, which are growing rapidly, and where the results of such models may have greatest impact with respect to informing planning decisions. However, data requirements, particularly for the more complex urban models, represent a major obstacle to their employment. Here, we examine the potential for running the Surface Urban Energy and Water Balance model (SUEWS) using readily obtained data. SUEWS was designed to simulate energy and water balance terms at a neighbourhood scale (P1 km 2 ) and requires site-specific meteorological data and a detailed description of the surface. Here, its simulations are evaluated by comparison with measurements made over a seven month (approximately 3 seasons) period (April-October) at two flux tower sites (representing urban and suburban landscapes) in Dublin, Ireland. However, the main purpose of this work is to test the performance of the model under 'ideal' and 'imperfect' circumstances in relation to the input data required to run SUEWS. The ideal case uses detailed urban land cover data and meteorological data from the tower sites. The imperfect cases use parameters derived from the Local Climate Zone (LCZ) classification scheme and meteorological data from a standard weather station located beyond the urban area. For the period of record examined, the simulations show good E-mail address: paul.alexander@nuim.ie (P.J. Alexander).Urban Climate 13 (2015) 14-37 Contents lists available at ScienceDirectUrban Climate journ al homepage: www.elsevier.com/locate/uclim agreement with the observations in both ideal and imperfect cases, suggesting that the model can be used with data that is more easily derived. The comparison also shows the importance of including vegetative cover and of the initial moisture state in simulating the urban energy budget.

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Section: Urban Energy Budget (Ueb) Modelsmentioning

confidence: 99%

Using LCZ data to run an urban energy balance model

2015

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“…Most recent studies investigating urban temperature fields utilize the LCZ classification and largely support the correspondence of LCZs with air temperature fields in cities and their surroundings (e.g., [6,[8][9][10]). However, a number of authors working with LCZs have pointed out that the influence of thermal, radiative, metabolic and surface-cover properties on the formation of local climate may significantly vary with respect to the geographical location of the zone, the size of the city, the position within the city (distance from the city center, neighboring zones) and relief [11][12][13]. Simultaneously, Bechtel and Daneke [14] and Lelovics et al [9] created the first LCZ mapping methods and applied the LCZ concept to secondary uses.…”

Section: Introductionmentioning

confidence: 99%

Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities

2016

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Abstract:The main factors influencing the spatiotemporal variability of urban climate are quite widely recognized, including, for example, the thermal properties of materials used for surfaces and buildings, the mass, height and layout of the buildings themselves and patterns of land use. However, the roles played by particular factors vary from city to city with respect to differences in geographical location, overall size, number of inhabitants and more. In urban climatology, the concept of "local climate zones" (LCZs) has emerged over the past decade to address this heterogeneity. In this contribution, a new GIS-based method is used for LCZ delimitation in Prague and Brno, the two largest cities in the Czech Republic, while land surface temperatures (LSTs) derived from LANDSAT and ASTER satellite data are employed for exploring the extent to which LCZ classes discriminate with respect to LSTs. It has been suggested that correctly-delineated LCZs should demonstrate the features typical of LST variability, and thus, typical surface temperatures should differ significantly among most LCZs. Zones representing heavy industry (LCZ 10), dense low-rise buildings (LCZ 3) and compact mid-rise buildings (LCZ 2) were identified as the warmest in both cities, while bodies of water (LCZ G) and densely-forested areas (LCZ A) made up the coolest zones. ANOVA and subsequent multiple comparison tests demonstrated that significant temperature differences between the various LCZs prevail. The results of testing were similar for both study areas (89.3% and 91.7% significant LST differences for Brno and Prague, respectively). LSTs computed from LANDSAT differentiated better between LCZs, compared with ASTER. LCZ 8 (large low-rise buildings), LCZ 10 (heavy industry) and LCZ D (low plants) are well-differentiated zones in terms of their surface temperatures. In contrast, LCZ 2 (compact mid-rise), LCZ 4 (open high-rise) and LCZ 9 (sparsely built-up) are less distinguishable in both areas analyzed. Factors such as seasonality and thermal anisotropy remain a challenge for future research into LST differences.

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“…The thermal differentiation of urban areas using LCZ as a basis has already been demonstrated both empirically and through modeling (Fenner et al, 2014;Stewart et al, 2014;Leconte et al, 2015;Colunga et al, 2015). Moreover, the ability to identify LCZ across entire urban areas i.e.…”

Section: Lcz-suews Approachmentioning

confidence: 98%

“…LCZ were designed to better describe the site characteristics of urban temperature sensors, hence can aid in the placement of instruments, the interpretation and evaluation of model simulations based on these observations; 4. The scheme was designed to be universally understood and since they employ standard building forms are likely to coincide (either in part or entirely) with other land use land cover classifications that have already been established (Alexander and Mills, 2014;Leconte et al, 2015). Fig.…”

Section: Lcz-suews Approachmentioning

confidence: 99%

Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation

et al. 2016

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There are a number of models available for examining the interaction between cities and the atmosphere over a range of scales, from small scales -such as individual facades, buildings, neighbourhoods -to the effect of the entire conurbation itself. Many of these models require detailed morphological characteristics and material properties along with relevant meteorological data to be initialised. However, these data are difficult to obtain given the heterogeneity of built forms, particularly in newly emerging cities. Yet, the need for models which can be applied to urban areas (for instance to address planning problems) is increasingly urgent as the global population becomes more urban. In this paper, a modeling approach which derives the required land cover parameters for a mid-complex urban energy budget and water budget model (SUEWS) in a consistent manner is evaluated in four cities (Dublin, Hamburg, Melbourne and Phoenix). The required parameters for the SUEWS model are derived using local climate zones (LCZs) for land cover, and meteorological observations from off-site synoptic stations. More detailed land cover and meteorological data are then added to the model in stages to examine the impact on model performance with respect to observations of turbulent fluxes of sensible (Q H ) and latent (Q E ) heat. Replacing LCZ land cover with detailed fractional coverages was shown to marginally improve model performance, however the performance of model coupled with 'coarse' LCZ data was within

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Using Local Climate Zone scheme for UHI assessment: Evaluation of the method using mobile measurements

Cited by 251 publications

References 19 publications

Using LCZ data to run an urban energy balance model

Using LCZ data to run an urban energy balance model

Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities

Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation

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