Journal of Heat Island Institute International Vol. 9-2 (2014)

Academic Article

Urban Environmental Climate Maps for Urban Planning
Considering Urban Heat Island Mitigation in Hiroshima
Kaoru Matsuo*1

Takahiro Tanaka*1*1 Graduate School of Engineering, Hiroshima University
Corresponding author: Kaoru Matsuo, d133596@hiroshima-u.ac.jp
ABSTRACTIn recent years, urban heat island phenomena caused by land cover change and increase of anthropogenic heatrelease occur in many cities throughout Japan. Temperature rises in urban areas because of this urban heat island
phenomenon and global climate change. In Hiroshima, the severe urban heat island phenomenon reportedlyengenders high temperatures. Therefore, urban planning incorporating mitigation of the urban heat island
phenomenon is also necessary in Hiroshima. Urban Environmental Climate Maps (UECMs) are made for urban
planning, architectural design, and environmental policy-making in considation of urban heat island mitigation. For
producing UECMs, first, it is necessary to elucidate the summer temperature distribution patterns and theircontributing factors in urban area. Therefore, this study aims at analyzing the temperature distribution according topatterns by observed data in Hiroshima, and classifying all observation points based on the daily temperature
distribution.
Key Words : Urban environment climate maps, Climate zoning, Urban heat island phenomenon, Coastal cityurban heat island mitigation have been produced in Japan, their

1. Background and objective

definitions differ slightly. Therefore, the authors supply a
In recent years, urban heat island phenomena caused by landdefinition for UECMs from a reference in the literature(1) ascover change and increased anthropogenic heat release arefollows. UECMs are made for urban planning, architectural

occuring in many cities throughout Japan. Temperatures rise indesign, and environmental policy-making in consideration ofurban areas because of this urban heat island phenomenon andurban heat island mitigation. The role of this map is to provide

global warming. Accordingly, various problems have occurred

some information from the view of urban climate to the place ofsuch as uncomfortable outdoor environments in summer,

decision making (including public involvement). Therefore, theincreased energy consumption, effects on urban ecosystems, andpurpose of creating these maps is to support design. The essencedamage to human health. In Hiroshima, the severe urban heatof climate research results and recommendations by experts are

island phenomenon reportedly engenders high temperatures.

described on UECMs. When stakeholders (e.g., regidents,

Therefore, urban planning incorporating mitigation of the urban

planner, architects, specialists) make decisions about urban

heat island phenomenon is needed for Hiroshima. The authors

planning, architecture design, and environmental policy-making,intend to produce Urban Environmental Climate Maps (UECMs)

they and experts can use these maps as communication tools.

for Hiroshima as the final goal of this study.

Actually, UECMs consist of a Climate Analysis Map (CAM)

Few reports in the relevant literature describe studies in which

and a Hint Map for Urban Planning and Design (HM). The roleplannersof CAM is representing actual climate conditions. That of HM is(orstakeholders)examine

urban

heat

islandphenomenon mitigation based on scientific knowledge in theirrepresenting recommendations for urban planning and design.

planning processes. That is true apparently because the urban

In Japan, CAMs have been produced for Tokyo(2) and Osaka(3).

climate phenomenon is difficult for stakeholders (e.g., residents,

Some HMs have also been made(4)(5). Some HMs presenting

local government officials, designers, and planners) torecommendations have been produced at the district level,

understand. UECMs are therefore proposed as a tool supportingpresenting some proposals from a climatic environment

urban planning decision. Although trial UECMs incorporating

perspective. However, such maps showing recommendations for- 61 -

entire city areas are also needed. Outside Japan, HMs have been

・Area: 905.41 [km2]produced at the city level(6) in Hong Kong, but Hiroshima and・Total population: 1,174.209 (2012)

Hong Kong differ in their urban structures and factors of・Population density: 1,310 [persons/km2]

underlying the temperature distribution, wind distribution, and・Number of households: 535,241 [household]so on. Given that background, HM must be made at the citylevel based on analyses of the urban heat island phenomenon in

2.2 Method

Hiroshima. In addition, as for HM at the whole city area,

This study progressed according to the following steps.

indicating issues to be considered in planning processes and

1) Observing temperatures in Hiroshima (64 points).

climatic resources in each place is expected to be effective. For

2) Extracting fine weather days.

production of UECMs, first, it is necessary to understand the3) Daily wind pattern classification.

summer temperature distribution patterns and their contributing4) Analysis of temperature distributions.

factors in urban area. Therefore, this study aims at analyzing5) Classifying all observation points based on the daily

temperature distribution according to patterns using observed
data in Hiroshima, and classifying all observation points based

temperature change patterns.
6) Considering local characteristic of the temperatureon the daily temperature distribution.

distribution in Hiroshima.

2. Research outline

3. Temperature distribution analysis according to the pattern

2.1 Outline of study area

3.1 Outline of meteorological observations

For this study, Hiroshima is selected as a targeted area (Fig. 1).

First, 64 temperature sensors in instrument screens were set in

In Hiroshima, a plain is formed around the Ootagawa Delta.

Hiroshima. Basic information related to observation points is

Hilly areas spread to the surrounding plain. Additionally, sea andgiven below.

land breeze circulation are observed in the south area facing the・Period: From July 20, 2013 to September 23, 2013.

sea. Especially in this area located between the Shikoku

・Observation interval: 10 min

Mountains and Chugoku Mountains, sea and land breeze often・Instrumental error: correctedblow because seasonal winds blow only slightly(7). BasicIn this study, the temperature distribution was analyzed using

information related to Hiroshima is presented below.

these observed data and local meteorological station data
(temperature and wind). Observation points and meteorologicalstations are presented in Fig. 1.
3.2 Wind pattern classification of fine weather days

Hiroshima

First, typical summer fine weather days (35 days) were extracted
Hiroshima
prefecturefrom all data (66 days) of the observation period using thefollowing criteria.
・Daily precipitation is within 1 [mm].
・Daylight hours are 40% or more of the possible duration ofsunshine.
・Daily maximum temperature is 30 °C or higher.
・The weather is not rainy.
By the following classification, the data of these typical summer
fine weather days (35 days) are used. That is true because thetarget day of this study is typical summer fine weather days.
Second, wind blowing pattern classification was performed
using only the wind direction and speed data of the local
meteorological station. Some measurement data (wind speed andwind direction) are presented in Fig. 2, a wind rose is depicted inFig. 3. It is apparent that several different types of the daily winddirection

Fig. 1 Location of Hiroshima and observation points

changepatterns

exist.

Wind

blowing

patternclassification was performed using a method proposed in areport of an early study(7). In the classification, a “sea breeze” is- 62 -

defined as “wind blowing from the direction, SE, SSE, S, SSW,
SW, WSW, and W” because the major sea breeze direction is

July 24

SSW. Additionally, a “land breeze” is defined as “wind blowing

July 25

from the direction, NW, NNW, N, NNE, NE, ENE and E”. They
Aug. 16

do not include wind from the ESE or WNW. As a consequence

Aug. 17of classification, the typical summer fine weather days (35 days)

Sep. 5are classifiable into three patterns, “pattern A (sea breeze doesn’t
1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Wind direction: SSW
Wind Speed: 3.0m/s

Timeblow)”, “pattern B (sea breeze blows, but land breeze doesn’t
blow)” and “pattern C (land and sea breeze blow)” (Table 1). In
addition, in the following analysis, the data of Pattern C (29

Fig. 2 Part of local meteorological station datadays) are used because the majority of the typical summer fineweather days are of “pattern C”.

N
NNW
NW

20%

NNE
NE

15%
10%

WNW

ENE

4. Classifying all observation points based on dailyEtemperature change patterns

5%

W

0%

WSW

ESE
SW

Two steps are used for classifying all observation points based

SE
SSW

SSE

on the daily temperature change patterns. In step 1, principal

S

component analysis is performed. In step 2, cluster analysis isFig. 3 Wind rose

conducted using principal component scores (PC scores).
Table 1 Criteria of classification and results
Criteria

4.1 Principal component analysis (PCA)

Classification result

PCA was conducted using six variables showing patterns ofPattern A
Sea breeze doesn’t blow

1) Not match the Pattern B or C.

Sep. 5, Sep. 17

Pattern B
Sea breeze blows,
but land breeze doesn’t
blow1) Sea breeze blows the majorityof daytime (from 1 p.m. to 4 p.m.).
2) Land breeze doesn’t blow the
majority of nighttime (from 3 a.m.
to 6 a.m. and from 0 a.m. to 3 a.m.
in the next day ).

July 24, July 25, July 31, Aug. 9

Pattern C
Land and sea breeze blow

1) Sea breeze blows the majorityof daytime (from 1 p.m. to 4 p.m.).
2) Land breeze blows the majorityof nighttime (from 3 a.m. to 6 a.m.
and from 0 a.m. to next day 3 a.m.
in the next day).

July 20, July 21, July 22, July 26,
Aug. 6, Aug. 7, Aug. 8, Aug. 10,
Aug. 11, Aug. 12, Aug. 13, Aug.14,
Aug. 15, Aug.16, Aug. 17, Aug. 18,
Aug. 19, Aug. 20, Aug. 21, Aug. 27,
Aug.28, Sep. 10, Sep. 13, Sep. 14,
Sep.19, Sep. 20, Sep.21, Sep. 22,
Sep. 23

daily temperature change(8). Table 2 presents variables. Table 3
presents PCA results. Because the PC loadings of “averagetemperature,” “minimum temperature,” “daily range,” and“degree-hour in the nighttime” are high, the first PC isapparently the “nighttime temperature characteristic.” The PC
loadings of “maximum temperature” and “degree-hour in thedaytime” are high scores. Therefore the second PC is apparently
the “daytime temperature characteristic”.

Table 2 Variables of PCA
4.2 Cluster analysis

Variables

For step 2, cluster analysis was performed using PC scores with

1) Average temperature (degree C.)
2) Average of daily maximum temperatures (degree C.)

Wards method. Fig. 4 shows results of cluster analysis.

3) Average of daily minimum temperatures (degree C.)

Furthermore, all observation points were classified into four4) Average daily range of temperatures (degree C.)

clusters (C1–C4). Regarding the first PC (nighttime temperature

5) Integration of degree-hour in the daytime (degree C.*h)characteristic), C1 and C3 are located on the positive side, and

6) Integration of degree-hour in the nighttime (degree C.*h)

C2 and C4 on the negative side. The temperature of C1 and C3
tend to be high. Those of C2 and C4 tend to be low during
temperature characteristic), C1 and C2 are located on the

Table 3 Result of PCA
Principal component

1st PCpositive side, and C3 and C4 on the negative side. The

2nd PC

temperatures of C1 and C2 tend to be high. Those of C3 and C4

Average temperature

0.772

0.630

Maximum temperature-0.4160.896

Minimum temperature

0.973

0.198

Daily range-0.9190.391

5. Considering the local characteristic of temperature

Degree-hour in the daytime-0.234

0.961

distribution

Degree-hour in the nighttime

0.946

0.293

tend to be low during the daytime.

Eigenvalue

3.51

2.40

Fig. 5 shows the four clusters on the map. In addition, Fig. 6

Contribution ratio (%)

58.51

40.03

shows the average temperature of four clusters. C3 is located in

Cumulative contribution ratio (%)

58.51

98.54

- 63 -

High 2.5
2

C2

C1

Second PC （daytime temperature characteristic）

1.5

1

0.5

0

C4
-0.5

-1

-1.5

-2

Low -2.5

C3
-2.5

-2

-1.5

Low

-1

-0.5

0

0.5

1

1.5

2

First PC （nighttime temperature characteristic）

2.5

High

Fig. 4 Results of cluster analysis

Fig. 5 4 clusters on the map

Temperature(℃）

36
34

C1

C2

32

C3

C4

30
28
26
24
22
20
0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

Fig. 6 Hourly average temperature of 4 clusters
the southwest area; C1 is located in the northeast area of theplain. In addition, C2 is in areas surrounding the plain, C4 is inhigh elevation areas. Then, local characteristic of temperature
are found for daytime and nighttime in Figs. 5 and 6. These
considerations are presented below.

- 64 -

Fig. 7 The numerical simulation results of WRF(Direction and speed)Fig. 8 Temperature distribution
Table 4 Characteristic of daily temperature change patterns
and geographical
Cluster

Characteristic of daily
temperature change patterns

Geographicalcharacteristic

PointsC1

The temperature is relatively high at
daytime and nighttime.

Northeast area of the plain

21

C2

The temperature is relatively high at
daytime, the temperature is relatively
low at nighttime.

Areas along the plain

17

C3

The temperature is relatively low atnighttime, the temperature is relatively
high at nighttime.

Southwest area of the plain

17

C4

The temperature is relatively low at
daytime and nighttime.

High elevation area

5(1) Daytime
The C3 and C4 temperature tend to be lower. Those of C1 andC2 area tend to be higher. Temperature characteristics of C3 areconsidered to differ from C1 and C2 in the same plain because
the distribution is affected by sea breezes. In addition, thedifference of temperatures between C3 and C1/C2 is about
2.0 °C during the daytime because the temperature rise isapparently mitigated by sea breezes in coastal areas. In otherwords, the C3 temperature is mitigated by sea breezes but thoseFig. 7 The numerical simulation results of WRF(Temperature)of C1, C2 can not be mitigated by sea breezes. Moreover, thedistribution of C3, C1 and C2 apparently show that sea breezes
blow from the southwest to the northeast.
(2) Nighttime- 65 -

The C2 and C4 tempperatures tend to be lower. Those of C1 and(2004)

C3 tend to be higher. In addition, the C1 and C2 temperatures(4) Working Group for Practical Use of Klimaatlas, Environment

become lower than that of C3 after 6 p.m., which differs from

Planning Sub-committee in Architectural Institute of Japan,

the temperature distribution in the daytime because theStudy Group of Klimaatlas, Study of Practical Use of

temperature of C1 and C2 is apparently lower as a result of theKlimaatlas: A Trial to make the Recommendation Map byeffects of green or cold air drainage, but the C3 is apparently not

Workshop, AIJ Journal of Technology and Design, 14, 207-210

affected by them.

(2001)In addition, Fig. 7 shows numerical simulation results by the(5) Working Group for Practical Use of Klimaatlas Environment

meso-scale meteorological model WRF. One might infer from

Planning Sub-committee in Architectural Institute of Japan.

these figures that results of the numerical simulation show

Study of Practical Use of Klimaatlas (Part 2): A Trial to make thesimilar patterns of temperature distribution (Fig. 8) and cluster

Climate Analysis Map and the Recommendation Map by

analysis. For example, they have the same tendency by which

Kitakyushu workshop, AIJ Journal of Technology and Design, 18,

the temperatures in coastal areas tended to be lower and those in203-206 (2003)

the northeast area of the plain tended to be higher during(6) E. Ng, Towards Planning and Practical Understanding of the

daytime. As subjects for future works, simulation results must be

Need for Meteorological and Climatic Information in the Design

confirmed through comparison with meteorological observationof High-density Cities: A Case-based Study of Hong Kong,data and by analyses of the results of the numerical simulation to

International Journal of Climatology 32 (4), 582-598 (2012)

elucidate the wind distribution patterns.

(7) K. Miyata, The sea and land Breeze in Hiroshima Prefecture,
Keisuisya (1982)(8) M. Nabeshima, M. Nishioka, M. Nakao, Horizontal

6. Summary

temperature variation in the Osaka Plain in summer,
In this study, classification of all observation points was done

Transactions of the Society of Heating, Air-Conditioning andbased on the daily temperature change patterns and local

Sanitary Engineers of Japan, 140, 1-10 (2008)

characteristics of the temperature distribution using observation
data. Table 4 presents characteristic of daily temperature change(Received Nov. 30, 2014, Accepted Dec. 27, 2014)

patterns and geographical characteristics for each cluster.
Finally, the future works will be undertaken as described below.
(1) Climate zoning based on the classification of allobservations.
(2) Analyzing the factors of the temperature distribution using a
meso-scale meteorological model (for sea breezes), andsatellite remote sensing data (green), and so on.
(3) Examining the urban planning method incorporating
mitigation of the urban heat island based on the results of (1)and (2); and finally producing UECMs.
References(1) T. Tanaka, K. Yamazaki, and M. Moriyama, Urban
Environmental Climate Maps for Supporting Environmental and
Urban Planning Works in Local Government: Case Study in Cityof Sakai, Osaka, Summaries of Technical Papers of Annual
Meeting Architectural Institute of Japan, D-1, 207-210 (2008)(2) T. Ojima; Learning Community for Promoting Cool-City
Eco-City, THE COOL CITY strategy of heat island mitigation:
Architecture and Urban planning using Green, Water and Wind,
Building Technology (2010)(3) T. Tanaka and M. Moriyama, Making and Using of "Urban
Environmental Climate Map" for Supporting Urban Planningwhich is Adapted for Climate, Papers and Proceedings of theGeographic Information Systems Association, 13, 391-394- 66 -