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

Academic Article

Prediction of the impacts of urbanization using a new assessment
system combining an urban expansion model and WRF-Case study for Guangzhou in ChinaYuan Li*1Tsubasa Okaze*2

Akashi Mochida*2*1 Architecture Department, Hitachi Architects & Engineers* Dept. of Architecture and Building Science, Graduate School of Engineering, Tohoku University
2Corresponding author: Yuan Li, liy@hae.co.jp
ABSTRACTThis study presents a new urban environment assessment system that allows for a dynamic special appraisal
by integrating Weather Research and Forecasting (WRF), a numerical simulation model for mesoscale climate andCellular Automata (CA) Model, the urban growing prediction model as a whole. Changes in land-use informationin WRF can strongly influence simulation results, this system first predicts the growth of urban areas over severalyears by using remote sensing (RS) images, and a modified CA model. This modified CA model is an evolution of
the CA-Markov model with improved flexibility and variability, and with control conditions designed to dominate
urban expansion patterns. The predicted urban land-use results are then imported into the WRF simulation for anenvironment assessment, to evaluate the impact on the urban environment by a given pattern of expansion, as wellas control conditions. A study based in the Guangzhou area, in China, is cited as an example to clarify the workingsof this system.
Key Words : Mesoscale urban environment, Urban sprawl, WRF simulation, CA model

the year 2000 to 2012, under various scenarios of differing urban

1. Introduction

planning and environment protection strategies, were predicted
China has been undergoing a period of economic reform andby the modified CA model developed in this study, and theirexpansion since the 1970s, accompanied by rapid andimpacts on the regional environment in Guangzhou were thenwidespread

analyzed using the WRF results.

urbanization,

andadeterioration

ofurbanenvironments caused by urban heat islands(1).
In this study, a new urban environment assessment system

2. Extraction of land-use maps from remote sensing data andwas developed by combining prediction models for urban

prediction of land-use maps with a standard CA-Markov

expansion and mesoscale climates. The developed system

model

contains two components: a land-use prediction model and amesoscale meteorological model used for regional environment

By default, the geogrid program in WRF interpolates land-use

simulations. In this study, urban expansion was simulated by acategories from USGS data derived from the early 1990s, and is

modified Cellular Automata (CA) model, based on land-use data

hardly appropriate for the situation of Guangzhou in the year

extracted from remote sensing data, while Weather Research and2000 when compared to RS data from the same year, as theForecasting (WRF) was used to simulate the regional

urban area is too small (Fig.1(1)). As well, the urban area in theenvironment. For simulations using WRF, we rewrote the default

alternative MODIS geo model(2) provided in WRF after version

land-use data in WRF with land-use information as predicted by

3.1 is too large(Fig1.(2)).
So, the land-use map for this region in 2000 was extracted by

the CA model.
This assessment system was used to investigate the impacts

the present authors using a combination of nine Landsat-7 RSof urbanization in Guangzhou city, the capital of Guangdong

data bands in IDRISI(3), and was classified into MODIS

Province, located in southern China. The land-use changes from24-categories. This map was re-sampled in GIS into a resolution- 133 -Evergreen Needleleaf Forest
ShrublandEvergreen Broadleaf Forest
Permanent wetlands

Mixed Forests
Croplands

Urban and Built-Up

Barren/Sparsely VegetatedWater(1) USGS
Defaulted WRF land-use datafrom early 1990s(2) MODISOne of the WRF land-use afterversion 3.1(3) RS2000
Extracted land-use datain 2000 from RS datasets(4) RS2005
Extracted land-use data
in 2005 from RS datasets(5) CA2005
Predicted map by standard
CA-Markov model(6) RS2012
Extracted land-use datain 2012 from RS datasets(7) CA2012
Predicted map by standard
CA-Markov model

Figure 1: Default geographic models in WRF and land-use maps derived from the standard CA-Markov modelof 1 km to match that of the calculation mesh in WRF, andprocess controls temporal change among land-use classes, based

renamed as RS2000 (Fig.1 (3)). The extracted RS2000 dataseton the transition matrix, Pij, that describes the probabilities ofprovided by the present authors was an improved fit for the city,each land-use category changing from a certain class i to anotherespecially for the urban area. Similarly, the land-use maps inclass j. In this study, this matrix Pij was at first calculated from2005 and 2012 were also extracted from RS datasets fromthe initial state in the starting year of RS2000 to mid-year of

separate years, and were renamed as RS2005(Fig.1(4)) andRS2005 by the Markov chain.

RS2012(Fig.1(6)), respectively.

Simultaneously, the CA model in a CA-Markov model

In order to predict urban expansion, the CA model was taken(4)

controls spatial pattern changes through local rules considering

into consideration . Based on the land-use maps of RS2000 andneighborhood configuration and transition to potential maps. AsRS2005, a standard Cellular Automata Markov (CA-Markov)

shown in Fig.2, the changing rule of land-use was defined as: ifmodel that reflects actual urban growth was established for thethe target point (x,y) is not water and there is developed lands inland-use simulations. In a CA-Markov model, the Markov chain

the vicinity of the target point , the probability of this pointchanging into urban land in this step will be the mean of theprobability of its neighborhood changing into urban land, and if

Target point (x,y)
Neighborhood this mean is larger than a certain number , the point (x,y)
changes into an urban classification. The change in land-use ineach year was considered as a single step.
By using the standard CA-Markov model, land-use maps in2005 (CA2005) and 2012 (CA2012) were predicted (Fig.1 (5)St+1 (x,y) = f St (x,y), Nand (7)) as based on RS2000, with the  value set to 0.5. This

St(x,y): State of point (x,y) at time tvalue was determined so that the percentage of urban areas in

N: Vicinity of target point

the entire region in CA2005 and CA2012 were almost identicalFigure 2: The changing rule at the target point

to those in RS2005 and RS2012, respectively.
- 134 -3. WRF simulations using predicted and extracted land-use

2, when northerly wind was observed at the observation site.
Here, SET* index is defined as the equivalent temperature of andata

isothermal environment at 50% RH in which a subject, while
In order to validate the prediction results of land-use data withwearing clothing standardized for the activity concerned, would

the CA model in 2005 and 2012, as well as to clarify the effecthaveof urban expansion on the thermal environment, numericalthermo-regulatory strain (skin wettedness) as in the actual test

experiments with WRF were performed via the three steps

environment(5). The results show that the standard CA-Markovshown in Table 1. The nesting of the three calculation domains

model provides a satisfactory urban sprawl prediction thatare shown in Fig.3. The grid size in the horizontal direction in

accurately reflects the actual urban expansion. It is also clear thatdomain 3 was 1 km × 1 km, and the side length was 120 km. For

the spatial SET* distribution is quite different from that of T2 incomparison purposes, an assessment area that covers seven main

the southern area. For the suburban region in the southern area,

districts in Guangzhou and two comparison points wereeven though the air temperature in this region is higher than theseparately selected in suburban and urban areas, as marked inurban area in the middle from west to east, SET* is lower. ThisFig.3. In this figure, comparison point 1 is a weather station of

result was caused by the different surface temperature,

the global observation dataset established by NOAA.

reflectance, and humidity in between the urban and suburban

WRF simulations were performed for a 1-month period, andthesameheatstress(skintemperature)

andareas.

results for August 1 to August 5, which were rainless days,
were obtained through validation with weather records in thisstudy. WRF simulations in step A, using the default
geographic models and the extracted RS2000 land-use data,
separatelyprovedthatsimulation

results

candiffersignificantly with different land-use models, and that theresult obtained from the extracted RS2000 model is muchcloser to observations as compared with the default models at
comparison point 1.

Figure 3: Configuration of the calculation domains
and assessment area

Simulation results from the extracted and predicted maps
in 2005 and 2012 were clearly consistent with each other atObservation
40

results agreed well with observations at comparison point 138(see Fig. 4). The spatial distribution results of T2 (temperature36at 2 m height) and SET* (the standard effective temperature)in step C are shown in Figures 5 (1) to (4) for 15:00 on August

Average T2 [C]both comparison points in steps B and C, and all simulation

RS2012(Point1)

CA2012(Point1)

34
32
30
28

Microphysics Option
Shortwave Radiation
OptionLongwave Radiation
OptionLand-surface Option
Boundary Layer
Cumulus Option

260:00

3:00

6:00

12:00

15:00

18:00

21:00

TimeRS2012(Point2)

CA2012(Point2)

40
38
36
34
32
30
2826

WDM 6-class scheme

0:00

3:00

6:00

Dudhia scheme
rrtm scheme

9:00(1) Point 1(Suburban area)

Average T2 [C]

Table 1: Analytical conditions
Step A
Case No.
A1
A2
A3
Simulation
2000.07.20, 8:00~2000.08.20,8:00
Period(GMT)
Geo model
USGS
MODIS
RS2000
Step B
Case No.
B1
B2
B3
Simulation
2005.07.20, 8:00~2005.08.20,8:00
Period(GMT)
Geo model
RS2000RS2005
CA2005
Step C
Case No.
C1
C2
Simulation
2012.07.20, 8:00~2012.08.20,8:00
Period(GMT)
Geo model
RS2012
CA2012

9:00

12:00

15:00

18:00

21:00

Time

(2) Point 2(Urban area)

Figure 4: Average T2 on sunny days (August 1 -5, 2012)
The error bars show the standard deviations ofUnified Noah land-surface model
YSU scheme
Kain-Fritsch (new Eta) scheme

observation at point 1 with dot line and CA2012 withsolid line.
- 135 -(1) T2 (RS2012)(2) T2 (CA2012)(3) SET* (RS2012)(4) SET* (CA2012)

Figure 5: Spatial distribution of T2 and SET* in step C (15:00 on August 2)

4. Modified CA model and protection proposal for urban
expansion
Based on the Standard CA model, a new coefficient D wasadded to control the expansion method of the original city, inorder to create different urban development modes for theassessment of future years. According to existing research (e.g.,Li and Yeh(6)), this controlled CA model was modified into thefollowing formula:
St+1 (x,y)=f St (x,y), D x,y , N(1) South-North
expanded citymode (SN_12)(2) East-West
expanded citymode (EW_12)(3) Centered mode
(Center_12)

Figure 6: Three different urban sprawl typologies(1)Here, the new parameter, D(x,y), is the extension coefficientof point ( ,y). By using this new coefficient, a controlled
expansion of the urban areas can be used by the CA model toregulate urban development in various ways. The coefficient D
can also be modified to obtain a feedback of the environmental
assessment results. In this case, the formula can be rewritten as:
St+1 (x,y)=f Pts (x,y)
Pts (x,y)=f D x,y ×P x,y(2)(3)

This modified CA model has been used in previous studies to(1) SN_12(2) EW_12(3) Center_12

create urban expansion modes under differing control conditions

Figure 7: Spatial distributions of SET* on periods ofnortherly wind

(7)

(e.g., White and Engelen ). In the present study, this model wasused to introduce environmental effects into considerations ofurban expansion.

5. Investigation of the effect of urban expansion patterns on
the thermal environment
In order to evaluate the influence of various urban expansion
patterns, three different urban sprawl typologies were defined inthis study: (1) A South-North expanded city mode (SN_12),
Guangzhou city (Fig.6 (1)); (2) An East-West expansion pattern(1) SN_12(2) EW_12(3) Center_12
 CA2012
 CA2012
 CA2012
Figure 8: Spatial distributions of SET* difference between(EW_12) to create a linearly extended urban pattern (Fig. 6 (2));

each urban expansion case (SN_12, EW_12 andand (3) A Centered mode (Center_12), which is a common

Center_12) and actual situation predicted by CAsprawl mode in Chinese cities such as Beijing (Fig.6 (3)). The

model (CA2012)

which is most similar to the existing urban planning of- 136 -regional environments in these three land-use conditions weresimulated in WRF.
Spatial distributions of averaged SET* during periods ofnortherly wind (Fig.7) suggest that urban forms can exert
various influences on air temperature distributions. Fig.8
illustrates the spatial distributions of SET* difference between
each urban expansion case (SN_12, EW_12, and Center_12) andthe actual situation case predicted by the standard CA-Markov
model (CA2012). In case SN_12 (Fig.8(1)), SET* in thesouthern part of the expanded city area clearly increased
compared to that in case CA2012. However, SET* in this area isstill not as high as the other city areas because this area is

Figure 9: Probability densities of T2 during the period ofsurrounded by cropland and located near the sea. In case EW_12

northerly wind(Fig.8(2)), SET* decreased from the north west region to thesouth west region. In case Center_12 (Fig.8(3)), SET* in someareas in the middle of the western part increased even thoughReferences

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The authors are grateful for the support provided by JSPS
KAKENHI Grant-in-Aid for Scientific Research (B) (Grant No.
26289200).The assistance provided by Mr. Chen G. (South(Received Feb. 9, 2015, Accepted Feb. 10, 2015)

China University of Technology) in preparing some figures inthis manuscript is gratefully acknowledged.

- 137 -