ORI GIN AL PA PER

A dual effect of urban expansion on flood risk in the PearlRiver Delta (China) revealed by land-use scenariosand direct runoff simulation

Shiqiang Du • Anton Van Rompaey • Peijun Shi • Jing’ai Wang

Received: 7 June 2014 / Accepted: 29 December 2014� Springer Science+Business Media Dordrecht 2015

Abstract Since the late 1970s, the Pearl River Delta (PRD) in China has undergone a

rapid transition from an agricultural landscape to a metropolitan area. The rapid urbani-

zation has not only increased the area of impervious surfaces in the central plain; it has

displaced farmland to the hilly peripheral areas. The objectives of this study are to: (1)

analyze how these changes in land use in the PRD have influenced flood incidence over the

past 20 years and (2) explore possible changes in flood incidence in the coming two

decades. An integrated simulation of the land-use changes and the hydrological processes

is employed to investigate the impact of urbanization on the volume of direct runoff.

Historical flood records are used for validation. The simulation results indicate that land-

use change in the PRD has markedly increased direct runoff over the past two decades.

Changes in direct runoff generation and in land use are significantly correlated with his-

torical floods at the county level. These results suggest that the increase in floods stems

from the dual effect of urbanization on land use through the expansion of impervious

surfaces and the displacement of farmlands to the hilly outskirts. The simulations suggest

Electronic supplementary material The online version of this article (doi:10.1007/s11069-014-1583-8)contains supplementary material, which is available to authorized users.

S. Du � P. Shi (&) � J. WangState Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University,Xinjiekouwai Street 19, Beijing 100875, People’s Republic of Chinae-mail: spj@bnu.edu.cn

S. DuDepartment of Geography, Shanghai Normal University, Guilin Road 100, Shanghai 200234,People’s Republic of China

S. Du � A. Van RompaeyGeography Research Group, Department of Earth and Environmental Sciences, Katholieke UniversiteitLeuven, Celestijnenlaan 200E, Heverlee 3001, Belgium

J. WangKey Laboratory of Regional Geography, College of Geography and Remote Sensing Science, BeijingNormal University, Xinjiekouwai Street 19, Beijing 100875, People’s Republic of China

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Nat HazardsDOI 10.1007/s11069-014-1583-8

that the dual effect would continue in a future of business as usual. Stopping farmland

replacement would likely reduce the increase in direct runoff generation, and this reduction

would be augmented if changes in farmland replacement were combined with compact city

development.

Keywords Urbanization � Farmland replacement � Scenario analysis � Cellular automata �Hydrological model

1 Introduction

Urbanization is a process of urban land-use expansion to hold a growing number of urban

dwellers (Bai et al. 2012). Rapid urbanization takes place when the internal processes

through which cities develop are accelerated by immigration or industrial expansion (Bai

et al. 2014; Grimm et al. 2008). Accelerated land-use change in the process of rapid

urbanization is often blamed for increasing threats to ecosystems and human health

(Alberti et al. 2007; MEA 2005; Mileti 1999; Vermeiren et al. 2012). With respect to

hydrological processes, urban expansion is also believed to reduce a landscape’s capacity

to control floodwaters because paved surfaces, such as roads and parking lots, create large

expanses of impervious ground (Gilroy and McCuen 2012; Solin et al. 2011). Impervious

surfaces reduce infiltration during storm events and increase direct runoff that, in turn,

increases flooding in urban areas (Bosch and Hewlett 1982; Richert et al. 2011). The

growing incidence of urban flooding is a major challenge to urban sustainability efforts that

calls for integrated land–water management solutions in urban planning and development

(Mileti 1999; Shi et al. 2005).

The impact of urbanization on flood risk in more developed countries has been studied

using field measurements, experimental watersheds, and statistical modeling (Bosch and

Hewlett 1982; DeFries and Eshleman 2004). In less developed countries, however, urban

flooding is more common and more complicated than the flooding experienced in the cities

of developed countries (Cheng 2005; Zhang and Wang 2007; Zhou et al. 2011). One

explanation for this situation is the concentrated increase of the global urban population in

developing countries (Balica et al. 2012; Hallegatte et al. 2013). In addition, most

developing countries have relatively inadequate flood risk mitigation and they generally

lag behind on sustainable land-use practices, such as best storm management (Battiata et al.

2010), green roof (Mentens et al. 2006), and green infrastructure (European-Commission

2007). Furthermore, and perhaps most fundamentally, developing countries generally face

great challenges in resolving the trade-offs between the demands of city development and

the need to reduce flood risk in the midst of rapid urbanization (Cheng 2005; Xiang et al.

2011).

China has been experiencing a process of rapid and massive urbanization since the late

1970s. China added 500 million residents to its urban population between 1980 and 2011,

increasing the urbanization rate from 20 to 51 % (NBSC 2012; Yusuf and Saich 2008).

This trend is expected to continue in the coming decades: China’s urban population is

projected to add another 300 million residents by 2050 (UN 2012). This rapid urbanization

has brought with it intimidating challenges to the sustainability of China’s cities. These

challenges relate to not only expanding city size and the infrastructure needed to hold the

rapidly growing population; they also point to the need to ameliorate the unwelcome

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effects of urbanization on the incidence of natural disasters, such as floods (Cheng 2005;

Xiang et al. 2011).

In China, rapid urban expansion usurps valuable farmland. Thus, China also faces

potential food shortages for its population and for export (Brown 1995; Liu et al. 2010). To

protect the food supply and promote the efficient use of urban land, China instituted the

New Land Administration Law (NLAL) in 1999 that permits urban development of

farmland only when an equivalent area of new farmland is developed elsewhere (LAL

2004). This policy of ‘‘no net loss’’ of farmland intends to ensure that the area of farmland

remains the same despite urbanization (Lichtenberg and Ding 2008; Wang et al. 2012).

The implementation of NLAL introduced a new dimension to the relationship of

urbanization to flood incidence. Since it took effect, urbanization has been argued to

influence flood incidence in two ways. First, it directly increases the incidence of flooding

by increasing the area of impervious surfaces that block the ground absorption of rainwater

(Shi et al. 2007; Weng 2001). Second, urbanization compensates for displaced farmland

with forested land, which reduces the land’s ability to retain rainfall and thereby increases

the generation of direct runoff (Cheng 2005; Zhang et al. 2008). Between 2000 and 2005,

China converted about 42 % of its unused land that was located mainly on the periphery

into farmland (Liu et al. 2010). In the Pearl River Delta (PRD) of Southern China, urban

expansion in the central PRD has pushed farmlands into its hilly peripheral areas (Du et al.

2014). Thus, farmland replacement and urban expansion are linked as a dual influence of

urbanization on direct runoff generation and flood risk. Understanding this still largely

overlooked dual effect of urbanization on flood risk is undoubtedly important for guiding

urban planning and minimizing conflicts between economic development and environ-

mental protection.

To gain a better understanding of the dual effect of urbanization on flood incidence, we

developed an integrated analysis by combining a land-use model with a hydrological

model using the PRD area in Southern China as a case study. Following a description of the

study area, Sect. 2 describes the development of three land-use scenarios and the employed

hydrological model. Section 3 shows the detailed impacts of land-use changes on direct

Fig. 1 Location, elevation, and rivers (a) and the administrative boundaries (b) of the Pearl River Delta(PRD)

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runoff and flood incidence over the past 20 years and in predicted future scenarios. Dis-

cussion and conclusions in Sects. 4 and 5, respectively, follow those results.

2 Materials and methods

2.1 Study area

The PRD area was chosen for this case study because of its key characteristics of rapid

urbanization and a high incidence of floods (Hallegatte et al. 2013). The PRD is an

emerging coastal metropolitan area, bordering Hong Kong and Macau (two special

administrative districts of China). It covers approximately 45,000 km2 and consists of nine

municipalities (Fig. 1). Its elevation ranges from \5 m in the central alluvial plain to

1,300 m in the hills that surround the plain. The delta’s dominant soil types are red earth

and paddy soils, and it has a subtropical climate, with a mean annual temperature of 22 �C

and a mean annual precipitation of 1,700 mm. Because of the impact of the East Asian

monsoonal circulation, about 80 % of the rainfall occurs between April and September and

the delta is most prone to flooding during those months (Shi et al. 2007; Weng 2001; Zhou

et al. 2011).

The PRD had long been an important base of China’s food production. Since the late

1970s, however, the PRD has been one of China’s most rapidly industrializing regions

(Weng 2001). The area’s rapid industrialization and economic growth have led to an

unprecedented rate of urban expansion and farmland loss (Li 1998). Built-up lands

increased from 1,600 to 6,800 km2 between 1990 and 2010, with the ratio of built-up land

to total land use increasing from \4 to 15 % over that period (Du et al. 2014). The rapid

urbanization resulted in the seizing of large tracts of farmland (Li 1998) and the dis-

placement of this farmland to the hilly peripheral areas (Du et al. 2014; Zhang and Wang

2007) in accordance with NLAL policy (DLRGP 2008; LAL 2004).

The land-use changes in the PRD are substantially affecting the area’s hydrological

processes (Shi et al. 2007; Weng 2001; Zhang et al. 2008; Zhang and Wang 2007; Zheng

et al. 2009). Weng (2001) analyzed the impacts of urbanization on direct runoff in the

central low-lying alluvial plain of the PRD and found that the increase in annual direct

runoff was positively related to the urban growth rate. Shi et al. (2007) found that rapid

urbanization in Shenzhen between 1980 and 2000 was correlated with a distinct increase in

peak discharge and a decrease in runoff confluence time. Zhang and Wang (2007) and

Zhang et al. (2008) used historical hydrological and land-use data to analyze the impacts of

urbanization-related activities, such as dredging floodways and constructing water facili-

ties, on the flood risk in Foshan. The results suggested that human urbanization activities

had impaired the rivers’ abilities to buffer floods. Similarly, Zhou et al. (2011) found that

the rapid urbanization in Shenzhen had substantially changed the landscape geomorphol-

ogy and had caused the disappearance of many small rivers between 1980 and 2005. The

previous research has consistently found that urbanization affects flood risk. However, the

ways in which urbanization interacts with other land-use changes, particularly farmland

replacement, on flood risk are less clear.

2.2 Land-use data and land-use scenarios

Land-use scenarios have become popular tools for bridging scientific land modeling and

real-world planning (Brown et al. 2013; Reenberg 2011). They provide plausible

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descriptions of possible future states of land systems and the associated consequences of

land-use changes for the sustainability of the relevant societies (Xiang and Clarke 2003).

This study applied land-use data and land-use model to investigate the impacts of farmland

displacement and urbanization on direct runoff. The land-use data for 1990, 2000, and

2010 were derived from Du et al. (2014), which were monitored with the classification tree

method using Landsat TM/ETM imagery. A land-use model was constructed to investigate

the possible future land-use conditions for 2020 and 2030. The modeling procedure con-

sisted of: (1) developing the scenarios of the sizes of new built-up lands and new farm-

lands, and (2) calculating the allocation of the new built-up lands and new farmlands.

Three scenarios of new built-up lands and new farmlands were developed (Table 1).

The first scenario is ‘‘business-as-usual’’ (BAU). It assumes that (1) built-up land will

increase linearly on the trajectory observed between 1990 and 2010, and (2) all farmland

that is displaced by new built-up land will be replaced from somewhere else (in compliance

with NLAL). The second scenario is ‘‘no farmland replacement’’ (NFR). It retains BAU’s

first assumption regarding new built-up land but it does not retain BAU’s second

assumption regarding farmland replacement. The third scenario is ‘‘compact development

and no farmland replacement’’ (CDNFR). It also has no assumptions about farmland

replacement. With respect to new built-up land, in the CDNFR scenario, it is 25 % less

than in the BAU scenario. The CDNFR scenario is reasonable because the Chinese central

government requires local officials to use urban lands efficiently (Bai et al. 2014).

The three scenarios were allocated at a spatial resolution of 30 m, which is the original

resolution of the land-use maps. The procedure is based primarily on logistic regression

and cellular automata rules (Fig. 2, Poelmans and Van Rompaey 2009; Wu 2002), which

were implemented via MATLAB. First, new built-up land was allocated on the possibility

that non-built-up lands will be built up (BU possibility). The BU possibility was assessed

using a binary logistic regression that considered the following variables: biophysical

environment, accessibility, regional differences, and neighborhood properties. According

to the assessed possibility, the requested built-up lands were iteratively assigned to the

pixels ranking the highest. In each iteration, 5,000 pixels of new built-up lands were

allocated. The values of the neighborhood variables were updated after each iteration to

generate a new BU possibility map for the next iteration. The second step in the BAU

scenario was computing farmland replacement. The amount of farmland replacement was

set as equal to the amount of farmlands lost in assigning lands for new built-up lands.

These farmlands were then allocated based on the farmland possibility map using the same

procedure that was implemented for allocating the built-up lands.

The allocation procedures for new built-up lands and farmlands were calibrated and

validated separately by simulating the observed land-use changes between 2000 and 2010.

The calibration for new built-up lands employed a random sample of 80,000 pixels. Half of

Table 1 Land-use scenarios

Scenario Assumption 1: built-up land growth Assumption 2: farmland replacement

BAU Linear trend Continue

NFR Linear trend Stop

CDNFR 25 % reduction compared with the linear trend Stop

BAU business as usual, NFR no farmland replacement, CDNFR compact development and no farmlandreplacement

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the pixels changed from non-built-up land to built-up land, while the other half retained

non-built-up land between 2000 and 2010. The calibration for farmland reclamation also

employed a random sample, in which 40,000 pixels changed between 2000 and 2010 from

non-farmland and non-built-up land to farmland while 40,000 pixels retained non-farmland

and non-built-up land.

The allocation procedure was validated for the probability maps and the result maps.

First, the probability maps were validated using the total operating characteristic (TOC)

procedure, which compares the location of the multi-threshold probability of land-use

change to the observed land-use change (Pontius and Schneider 2001; Pontius and Par-

mentier 2014). Second, a three-map comparison was performed to validate the simulated

land-use map for 2010 (Pontius et al. 2011). This method compares the differences and

similarities among the observed 2000, observed 2010, and simulated 2010 land-use maps

and provides data on the sizes of hits, misses, false alarms, wrong hits, and correct

rejections (Brown et al. 2013; Pontius et al. 2011).

2.3 Assessing the impact of land-use changes on direct runoff and historical floods

A distributed hydrological model was used to assess the impacts of past and future

(simulated) land-use changes on direct runoff and the associated flood risk. This model was

developed by the USDA soil conservation service (SCS) (now known as the Natural

Resources Conservation Service) based on curve numbers and empirical equations (USDA

1972). The SCS model has been widely used to estimate storm runoff depth because of its

relatively low demand for input data and highly reliable simulation results (Du et al. 2012;

Weng 2001). In this model, the depth of direct runoff is mathematically expressed as

follows:

Q ¼ P� 0:2Sð Þ2

Pþ 0:8S; P� 0:2S

Q ¼ 0; P� 0:2S

8<

:ð1Þ

where Q (mm) is the depth of direct runoff, P (mm) is the precipitation, and S is the

potential maximum retention or infiltration. The value of S, which describes the capacity of

Fig. 2 Land-use model for urban expansion and farmland reclamation

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a pixel to retain storm precipitation, can be estimated by a dimensionless runoff curve

number (CN) via S = (25,400/CN) - 254, whereby the CN is determined by the ante-

cedent moisture, soil, and land-use properties.

The inputted precipitation was that of a 2-year return storm of 24-h duration. The

precipitation volume was first calculated by the Pearson-III method (Liu et al. 2012) for

each of the 14 weather stations for which the daily rainfall data were available from 1951

to date. Those point data were subsequently interpolated to each pixel using a point

Kriging method via geographic information system (GIS) software. Soil properties at the

pixel level were derived from the soil maps produced by Liu (1993) and Shi et al. (2004).

The applied CN value was derived from Shi et al. (2007) in a case study of Shenzhen

(Table S1).

Historical flood data were applied to validate the relationship between land-use changes

and direct runoff. This dataset was derived from the China disaster database (CDD)

managed by Beijing Normal University (Wang et al. 2008). The Pearson correlation was

applied to the relationship of recorded flood frequency to changes in land use (i.e., built-up

land, forested land, and farmland) and to the relationship of recorded flood frequency to

simulated direct runoff. Changes to land use in the land-use dataset were calculated via

spatial analysis (Du et al. 2014). Before performing the Pearson correlation, flood fre-

quencies and changes in land use and direct runoff were normalized to 0–1 using the range

standardization method as follows:

Fig. 3 Observed land-use maps for 1990 (a), 2000 (b), and 2010 (c) and simulated land-use map for 2010(d)

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Xi ¼xi �minðxiÞ

maxðxiÞ �minðxiÞð2Þ

where Xi refers to the normalized value of the specific variable x for county i, xi is the value

of variable x for county i, and min(xi) and max(xi) are the minimum and maximum values

of variable x for all counties.

3 Results

3.1 Land-use change simulation and future scenarios

Rapid urban expansion and marked farmland replacement occurred in the PRD between

2000 and 2010, as shown by Fig. 3 and in support of Du et al. (2014) and Zhang et al.

(2008). Logistic regression analyses produced the coefficients of the explanatory factors

(Table 2) relating to the likelihoods of the presence of new built-up land and new

Table 2 Logistic regression results for built-up land and farmland

Variablename

Type Source Coefficient

Built-upland

Farmland

Bio-physical

Elevation 0–1 SRTMa -15.63*** -5.44***

Slope 0–1 -3.59*** -2.56***

Fertile soil 0 or 1 Liu (1993) 0.20*** -0.42***

Accessibility

Dist. to freeway 0–1 Chinese geo-database

of Beijing Normal

University

(BNU)

– –

Dist. to freeway drop-points 0–1 -1.68*** 1.14***

Dist. to main roads 0–1 1.16*** -0.57*

Dist. to railway 0–1 -0.43*** –

Dist. to metropolitan centers 0–1 -1.79*** –

Dist. to municipality centers 0–1 – 0.50***

Dist. to county centers 0–1 – -0.98***

Neighborhood

Built-up proportion in 3 9 3 neighborhood 0–1 Observed land-use maps 6.80*** -6.79***

Farmland proportion in 3 9 3

neighborhood

0–1 0.66*** 2.25***

Regional difference

Shenzhen 0 or 1 Chinese geo-database of BNU 2.57*** -0.48***

Dongguan 0 or 1 2.11*** –

Huizhou, Zhaoqing, and Jiangmen 0 or 1 2.05*** 0.47***

Zhuhai, Zhongshan, and Foshan 0 or 1 1.97*** –

Guangzhou 0 or 1 1.78** 0.33***

Intercept -0.65* 0.55***

– not significant, * p \ 0.05, ** p \ 0.01, *** p \ 0.001; two-tailed tests of significancea These data are available at http://www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1

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farmland. Elevation and slope were particularly significant fitted variables of land-use

transformation because new built-up lands and farmlands both avoided the higher eleva-

tions and steeper slopes in the PRD. Their coefficients were much larger for built-up land

than for farmland. Built-up land appears to have pushed farmland outward from the central

plain to the less accessible areas. Moreover, built-up lands typically covered areas of

relatively greater fertility, whereas new farmlands were developed on relatively less fertile

land (Liu 1993). As shown by the accessibility variables, built-up lands appear to have

usurped the land with high accessibility scores. The coefficients for the effects of the

neighborhood variables indicate that built-up lands pushed the farmlands toward the

periphery of the PRD. Shenzhen and Dongguan experienced the most extensive urban

expansion, and the peripheral counties of Guangzhou, Huizhou, Zhaoqing, and Jiangmen

were relatively more likely to be transformed into new farmland (Table 2).

The calibrated coefficients produced probabilities that the available lands would become

either built-up land or farmland. Both probabilities were assessed using the TOC proce-

dure. The calculated probability curves for built-up land and farmland were much higher

than the random line (Fig. 4). They were much closer to the maximum boundary lines than

to the minimum boundary lines. Areas under the curve (AUC) were 88 and 80 % for built-

up land and farmland, respectively. The results show a positive association between the

probability of lands becoming new built-up land or new farmland (in other words, lands

that experience a change in type of use) and the observed presence of new built-up land or

farmland. The simulated land-use map for 2010 (Fig. 3d) was derived from these proba-

bilities. The three-map comparison produced the image shown in Fig. 5. The allocation and

quantity disagreements between the simulated 2010 and observed 2010 maps were 13 and

2 %, respectively. The allocation disagreement was mainly caused by false alarms and

misses.

The BAU land-use scenarios produced for 2020 and 2030 (Fig. 6a, b) suggest large

increases in built-up lands in the future. In this scenario, built-up lands are expected to

increase by 5,100 km2 between 2010 and 2030, representing a proportional increase in the

entire PRD from 15 to 26 %. Spatially, the built-up land in the central plain of the PRD is

Fig. 4 TOC curves for built-up land (a) and farmland (b) horizontal axis of Hits ? False alarms means thetotal area under a multi-threshold probability. Vertical axis of hits means the presence of new land use in thearea under a certain threshold probability. Hits ? Misses is the upper boundary of the TOC space.Maximum and minimum define the mathematically possible space where the TOC curve appears. Randomindicates the statistically expected result of a random allocation procedure

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predicted to develop into a megacity by 2030, swallowing up counties in Shenzhen,

Dongguan, Guangzhou, Foshan, Zhongshan, and Zhuhai (Fig. 1). Under this scenario,

enormous farmlands of 2,800 km2 would disappear and, by 2030, new farmland replace-

ment would deforest about 700 km2 of the hills surrounding the flat plain.

In contrast, according to the CDNFR scenario, the PRD would experience 25 % less

urban growth than in the BAU scenario and there would be no replacement of farmland in

the hilly areas (Fig. 6c, d). As a result, the decrease in forests would be 91 % less than in

the BAU scenario. The results from the NFR scenario closely resembled the results of the

BAU scenario; however, the NFR scenario reduces the extent of BAU-estimated defor-

estation by 90 % (Fig. 6e, f).

3.2 Impacts of land-use change on direct runoff

Figure 7 shows the changes in direct runoff from 1990 to 2010 using observed land-use

data (Table S2). Direct runoff did not noticeably increase in the decade between 1990 and

2000 (Fig. 7a); in fact, it decreased by more than 5 % in the peripheral counties of

Zhaoqing, Jiangmen, and Huizhou. In contrast, in the 10 years between 2000 and 2010,

direct runoff increased remarkably across the entire area (Fig. 7b). In the PRD as a whole,

direct runoff decreased by 0.2 % between 1990 and 2000 but increased by more than 6 %

Fig. 5 Map of five-type agreement and disagreement based on a three-map comparison (2000 and 2010observed maps and 2010 simulated map)

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between 2000 and 2010. The land-use changes appear to have had a marked influence on

direct runoff generation between 2000 and 2010.

The impact of urban expansion and farmland displacement on direct runoff and asso-

ciated flood incidence was also validated by the Pearson correlation analyses between (1)

flood frequency and changes in land use and between (2) flood frequency and direct runoff

generation. Recorded flood frequencies increased in all of the PRD counties from the 1980s

and the 1990s to the 2000s (Fig. 8). Figure 9 shows statistically significant correlations

between changes in land use and the frequency of flooding. Each sub-figure includes 54

data points, and each point represents a county record either from 1990 to 2000 or from

2000 to 2010. Flood frequency was significantly correlated with the changes in built-up

land and forested land at the county level (Fig. 9a, b; r = 0.55 and 0.44, respectively).

Fig. 6 Simulated land-use maps for 2020 and 2030 under three future scenarios

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Flood frequency had a relatively small but still statistically significant correlation with the

change in farmland (Fig. 9c; r = 0.22). The normalized changes in simulated direct runoff

significantly correlated with the recorded flood frequency at the county level (Fig. 9d;

r = 0.52), verifying, to a certain extent, the usefulness of the direct runoff simulation.

If urban expansion were to grow linearly and farmland replacement continued (the BAU

scenario), the simulated increase in the volume of direct runoff between 2000 and 2010

would likely continue into the future unless compact development and land-use policy

adjustments were implemented (Fig. 10a, b; Table S2). In the BAU scenario, direct runoff

in the PRD would increase by 9 % between 2010 and 2030. All of the counties would

experience noticeable increases in direct runoff (see Fig. 1). Alternatively, under the

assumptions of the NFR (Fig. 10c, d) and CDNFR scenarios (Fig. 10e, f), the increase in

direct runoff would be modest. The NFR and CDNFR increases in direct runoff in the PRD

between 2010 and 2030 would be 31 and 34 % less, respectively, than the estimations of

the BAU scenario. Spatially, the increase in direct runoff would be modest in all of the

counties.

The largest difference in future outcomes among the three scenarios would occur in

Longmen, located in the northernmost county of Huizhou. The simulated direct runoff (in

Longmen) declined by 5 % from 1990 to 2000 (Fig. 7a) and then increased by 21 %

between 2000 and 2010 (Fig. 7b). Under the BAU assumptions, the direct runoff would

increase by more than 12 % between 2010 and 2030. By contrast, in the CDNFR scenario,

Fig. 7 Variation in direct runoff over the periods of 1990–2000 and 2000–2010

Fig. 8 Decadal number of flood incidences recorded in counties

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the direct runoff would increase by only 0.3 % between 2010 and 2030, which is 98 %

lower than the 12 % increase in direct runoff in the BAU scenario. The NFR scenario

results for 2030 were very similar to the CDNFR results, producing only 0.02 % more

direct runoff than the CDNFR scenario. Taken together, these results suggest that farmland

replacement (simulated only in the BAU scenario) would play a crucial role in increasing

direct runoff, particularly in the counties outside of the central plain where farmland

replacement is expected to be concentrated.

4 Discussion

The results of this study suggest that urbanization has a dual effect on hydrological pro-

cesses. In the context of current land-use policy (NLAL), urban expansion and farmland

replacement are linked to increases in direct runoff generation. The farmland protection

policies oblige land developers to claim new land for farmland to compensate for the

farmland that is lost to urbanization (DLRGP 2008; LAL 2004). In the PRD, where the

economy and the population are rapidly growing, deforestation appears to be an

unavoidable consequence of new farmland creation (Du et al. 2014; Zhang et al. 2008).

The two land-use changes, from farmland to built-up land and from forest to farmland, can

individually increase the volume of direct runoff (Bosch and Hewlett 1982; DeFries and

Eshleman 2004). These two processes are found in this study to combine their effects to

influence direct runoff. Urbanization in the PRD influences direct runoff through its

expansion of built-up land that is resistant to rainwater absorption (i.e., impervious

Fig. 9 Relationships between flood frequency and changes in a built-up land, b forested land, c farmland,and d simulated runoff * two-tailed tests of significance, ** one-tailed tests of significance

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surfaces) and by pushing farmland into the hilly areas, which causes deforestation. This

study’s integrated analysis of land-use scenarios and direct runoff generation clearly point

to this dual effect of urbanization on direct runoff.

One of this study’s strengths is its application of historical flood records to verify the

findings of the direct runoff simulations. Variation in flooding incidence is far more

complex than its link to direct runoff generation because flood risk is not determined only

by hydrological processes; it is also influenced by social factors such as exposure and

vulnerability to waterlogging and inundation (Balica et al. 2012; Hallegatte et al. 2013).

Fig. 10 Variation in future direct runoff for 2020 and 2030 under three land-use scenarios compared to2010 direct runoff

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Therefore, the associations between direct runoff generation and flooding incidence found

by this study should be interpreted with caution, although they provide strong evidence that

urban expansion and farmland replacement combine to increase direct runoff generation.

This study employed historical flood records to verify the findings of the direct runoff

simulations. The results found that flood incidence is significantly correlated with changes

in direct runoff generation and in land use, strongly implying that direct runoff simulation

is a useful tool and that the combined effects of land-use changes in the PRD definitely

influence flood incidence.

The results also highlight the usefulness of employing land-use change scenarios to

predict the future outcomes of these impacts. This study did not aim to accurately predict

the future state of land use, and it did not consider changes in farmland and other non-

urban lands that were not related to farmland replacement. Rather, it simulated land-use

changes that were related only to urban expansion and farmland replacement. Not

accounting for other land-use changes contributed to the disagreement between the sim-

ulated and the observed land-use maps for 2010 (Fig. 5), in addition to model and data

uncertainty as argued by Pontius and Petrova (2010). However, the land-use change

probabilities that were applied to future land-use scenarios were relatively reliable (Fig. 4).

The three land-use scenarios indicated farmland replacement as a statistically significant

factor in the increase of direct runoff generation (Figs. 9, 10). The method of applying the

scenarios used in this study can be replicated in other locations around the world where

cities are rapidly growing at the expense of adjacent farmland. The within results can

provide a broad guide to decision makers who are struggling to find ways to expand their

cities efficiently without exacerbating urban flooding.

Future research can apply a wider range of plausible scenarios that include other factors.

For example, the array of scenarios can be expanded to include the plausible future states

of all land-use types using methods such as system dynamic analysis (He et al. 2005).

However, there may be critical uncertainty about the land-use quantity and allocation

because of the unreliability of the reference data and the problems related to land-use

models (Pontius et al. 2011). Land reclamation from the sea and changes to fishponds

(Fig. 3a–c) may also markedly influence flood incidence (Cheng 2005; Zhang and Wang

2007; Zhou et al. 2011). The SCS model employed in this study was unable to model the

extent of these impacts on flood incidence, which should be simulated by a hydraulic

model (Thompson et al. 2004). Additionally, precipitation with various return periods may

be important to variation in flood incidence. We used a constant precipitation scenario,

even though variation in precipitation may mediate the effects of different land-use sce-

narios on direct runoff (Mejı́a and Moglen 2010). The result was a direct and clear rep-

resentation of the effects of different land development scenarios. However, we encourage

researchers to investigate the impact of various rainfall scenarios on the relationship of

land use to direct runoff.

The results imply that the land-use policy of claiming new farmland to compensate for

the farmland displaced by urbanization (NLAL) is having harmful side effects and should

be radically adapted or abandoned. In the context of rapid urbanization in China, the

official ‘‘no net loss’’ policy of the past 10 years neither protects farmland nor increases the

efficiency of urban land use. It is estimated that approximately 10,000 km2 of extant urban

space can be developed in China without displacing farmland by using unused land in

cities and towns, adjusting the industrial structure, and heightening land-use intensity (Lu

and Huang 2010). It is likely that the displacement of farmlands would remarkably

decrease were urban lands used more efficiently. A revision of the NLAL should therefore

focus on stimulating more efficient urban land use. Policymakers should consider the

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‘‘green way’’ and ‘‘green infrastructure’’ (Ahern 1995; Yu 2005) as possible ways to

control urban expansion and achieve a sustainable landscape.

5 Conclusions

Data on direct runoff simulations and historical floods were applied in this study to

investigate the relationship of land-use changes to flood occurrence in the PRD area. The

results strongly suggest that urbanization in the PRD area has a dual effect on direct runoff

and flood incidence by expanding impervious built-up land and reclaiming farmland in

forested hills. Compared with the period of 1990–2000, simulated direct runoff and

recorded floods remarkably increased between 2000 and 2010 at the county level. Increases

in simulated direct runoff, built-up land, and farmland were significantly and positively

correlated with recorded floods. However, a significant negative correlation was found

between flood incidence and increases in forested land.

These findings are likely linked to China’s ‘‘no net loss’’ policy that has been in place

since 1999 because it requires compensation for the farmland that has been lost to

urbanization. Rapid urbanization is continuing in the PRD, and urbanization’s dual effect

on flood incidence is expected to continue and increase in intensity in the coming years.

Abandoning farmland replacement as a remedy for the loss of agricultural land might

ameliorate the anticipated increases in direct runoff and, thus, decrease flood incidence.

Combined with innovative urban planning, more improvement might be realized.

Acknowledgments Editor Professor T.S. Murty and the anonymous referees who reviewed this paper aregreatly appreciated for their thoughtful comments and suggestions that ultimately improved its quality. Weare also grateful for the contributions made by Professor Jiahong Wen, Professor Guoyi Han, and theEditage Language Service for their efforts toward improving the language of the manuscript. This researchis supported by the National Basic Research Program of China (973 Program) (Grant No. 2012CB955404)and National Natural Science Foundation of China (Grant Nos. 41401603 and 41201548).

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