Relation between Heat Island Intensity and City Size Indices/Urban Canopy Characteristics in Settlements of Nagano Basin, Japan

Sakakibara, Yasushi; Matsui, Emi

doi:10.4157/grj.78.812

Cited by 37 publications

(27 citation statements)

References 3 publications

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“…Most of the existing studies conclude that the heat island intensity in coastal cities is reduced during the daytime of the warm period, because of the presence of the sea breeze that transfers relatively cooler air into the city and enhances wind speeds (Sakakibara and Emi, 2005). Several numerical and observational studies have investigated the specific interaction of the sea breeze system with the urban heat island.…”

Section: Heat Island In Coastal Citiesmentioning

confidence: 97%

Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions

Santamouris

2015

Science of The Total Environment

390

154

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Section: Heat Island In Coastal Citiesmentioning

confidence: 97%

Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions

Santamouris

2015

Science of The Total Environment

390

154

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“…The main causes of the UHI are the higher heat capacity of urban surfaces, the trapping of radiation in street canyons, the reduced vertical exchange due to a reduced wind speed, and anthropogenic heat release (Yow, 2007). The UHI intensity varies with the morphology and the size of the city (Oke, 1973;Sakakibara and Matsui, 2005) and with meteorological conditions (Arnfield, 2003). It decreases with higher wind speeds, cloud cover and relative humidity (e.g.…”

Section: Introductionmentioning

confidence: 99%

A statistical model for the urban heat island and its application to a climate change scenario

Hoffmann

Krueger

Schlünzen

2011

Intl Journal of Climatology

106

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A linear statistical model relating the nocturnal urban heat island (UHI) intensity of Hamburg with meteorological conditions is constructed from observations taken by the German Meteorological Service (DWD). To find the appropriate predictors the relationship between different meteorological variables and the UHI of Hamburg is analyzed. Results and physical plausibility suggest that cloud cover, wind speed and relative humidity are the relevant variables and can be used to construct a statistical model. The parameters for the statistical model are determined with the generalized least square method. With the help of the statistical model up to 42% of the UHI variance can be explained. The statistical model is then used to statistically downscale results from climate runs of the regional climate models (RCM) REMO and CLM. Both RCMs were driven with the A1B SRES emission scenario runs of the global climate model ECHAM5/MPI-OM. The resulting values for the future UHI are analyzed with respect to monthly averages and the frequency distribution. Results show that changes in the UHI are different for the different months. Significant change (decrease of UHI) in the results of both RCMs and for both realizations of the A1B scenario can be found for April in at the middle and the end of the century and in December at the end of the century. For the summer months which are most relevant to the development of adaption strategies the results differ between the RCMs. REMO results show no significant changes for the summer, while analyses of CLM suggest significant increase in July and August. The frequency distribution of the summer UHI shows no significant changes for REMO and only in one realization of CLM a significant increase in moderate and strong UHI days can be found for the end of the century.

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“…As a result, a little less than 100 m around the buildings, on average, was taken into account in the calculations. Sakakibara & Matsui (2005) used 250 m grid cells in assessing the effects of urban canopy characteristics and city size indices on UHI intensity, while Costa et al (2007) used a buffer with a 350 m radius to the predominant wind direction and 150 m radius to the downwind direction.…”

Section: Introductionmentioning

confidence: 99%

Effects of scale on modelling the urban heat island in Turku, SW Finland

Suomi¹,

Hjort²,

Käyhkö³

2012

Clim. Res.

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Scale has a central role in the modelling of an urban heat island (UHI). In the present study, the effects of the spatial extent of explanatory variables and modelling approach were explored in Turku (179 000 inhabitants), SW Finland, based on the average temperatures recorded by 50 temperature loggers over a 2 yr period. First, the optimal monthly circle of influence around temperature measurement points was determined for 6 urban and 6 non-urban explanatory variables. Second, the independent effect of the intercorrelated urban variables was tested using hierarchical partitioning (HP), a method for tackling multicollinearity. Third, the relative importance of the strongest urban variable was explored with 6 non-urban variables utilizing HP and multiple linear regression (LR). The mode of the optimal monthly buffer size was 1000 m for the urban variables and 2000 m for the non-urban variables. The optimal buffer size of the urban variables was largest in spring and smallest in autumn and winter. A clear seasonal trend was not observed with the non-urban variables. Based on HP, the strongest urban variable had a more independent effect on temperatures than the non-urban variables. The importance of the 'best' urban variable was also revealed in LR. Of the non-urban variables, the importance of the Landsat ETM+ derived 'greenness' variable was highlighted in HP and 'water bodies' in LR. In conclusion, the circle of influence of the explanatory variables varied substantially. Scale should be considered before doing multivariate analyses in UHI studies. Methodologically, the HP technique complements traditional UHI studies in multivariate model settings.

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Relation between Heat Island Intensity and City Size Indices/Urban Canopy Characteristics in Settlements of Nagano Basin, Japan

Abstract: Abstract:This paper demonstrates the relation

Cited by 37 publications

References 3 publications

Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions

Analyzing the heat island magnitude and characteristics in one hundred Asian and Australian cities and regions

A statistical model for the urban heat island and its application to a climate change scenario

Effects of scale on modelling the urban heat island in Turku, SW Finland

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