An Evaluation of the Impacts of Land Surface Modification, Storm Sewer

Nat Hazards (2012) 63:305–323 DOI 10.1007/s11069-012-0153-1

ORIGINAL PAPER

An evaluation of the impacts of land surface modiﬁcation, storm sewer development, and rainfall variation on waterlogging risk in Shanghai

Xiaodan Wu • Dapeng Yu • Zhongyuan Chen • Robert L. Wilby

Received: 7 November 2011 / Accepted: 15 March 2012 / Published online: 3 April 2012 Springer Science+Business Media B.V. 2012

Abstract Despite continuing efforts to upgrade the urban storm sewer system since the late 1950s, the City of Shanghai is still vulnerable to persistent rainstorm waterlogging due to excess surface runoff and sewer surcharge, which frequently cause significant damage to buildings and disruption to traffic. Rapid urbanization and associated land cover changes are the major factors contributing to waterlogging. However, it is unclear to what extent changes in rainfall variability over the past few decades are also involved. This paper investigates the combined impacts of land use and land cover change, storm sewer development, and long-term variations in precipitation. Evidence of persistent waterlogging is presented first. We then give an account of land surface modifications during the process of urbanization and the development of the city’s urban storm sewer system. Statistical analysis suggests that the increase in runoff coefficient due to conversion of lands from agricultural to industrial, commercial, and residential uses is a major factor driving greater waterlogging risk. In particular, historical analysis of aerial photographs reveals the rate and extent of modification to river networks in the past few decades. The natural drainage network has shrunk by 270 km, significantly reducing the city’s capacity to transport excess surface flow. In line with other studies, we find no significant overall trends in annual rainfall totals (at Baoshan and Xujiahui). However, seasonal and monthly rainfall intensities have increased. At the daily scale, we find that compared to pre-1980s: (i) there has been an increase in the number of wet days with precipitation exceeding 25 mm (Heavy Rainfall) and decrease in those below 25 mm and (ii) the number of consecutive wet days with precipitation maximum and average exceeding the threshold known to cause waterlogging shows an increasing trend. Since rainfall intensity is expected

X. Wu Department of Geography, East China Normal University, Shanghai 200062, China

D. Yu (&) R. L. Wilby Department of Geography, Centre for Hydrological and Ecosystem Science, Loughborough University, Leicestershire LE11 3TU, UK e-mail: [email protected]

Z. Chen State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China 123 306 Nat Hazards (2012) 63:305–323 to increase under climate change, this could further compound the impacts of land use changes and place even greater pressure on the existing storm sewer system.

Keywords Waterlogging Flood risk Urban drainage Land use change Precipitation extremes Climate change

1 Introduction

Rainstorm waterlogging due to excess surface runoff and insufficient capacity of storm sewer systems is one of the most frequent and serious natural hazards in many big cities around the world, especially those subject to frequent torrential rainfall. Such events can pose problems for cities undergoing rapid urbanization where land surface characteristics have been modified in ways that favour increased surface runoff. The volume and flow rate may exceed the capacity of existing storm sewer systems, leading to more frequent sur- charging, surface flooding, property damage, and traffic disruption (e.g. Butler et al. 2007). Although inundation depths brought about by waterlogging are generally shallower compared to those caused by fluvial or coastal floods, the damage and disruption can be substantial. It is not only during urbanization that a city could be adversely affected by waterlogging. Rainstorm waterlogging may also pose widespread problems for developed cities with well-established sewer systems. For example, it was reported that the majority of the 2007 floods in the UK originated from overloaded rainstorm sewer in developed areas, and among those buildings affected, about 25 % were built during the last 25 years (Pitt 2008). Recent research on urban waterlogging has focused on three aspects: (i) assessing waterlogging risks, particularly in terms of damages to buildings (e.g. Huang et al. 2008; Shi et al. 2010), and in some cases disruption to traffic (e.g. Gao et al. 2009); (ii) modelling the extent and magnitude of waterlogging either within a GIS environment (e.g. Boyle et al. 1998; Zhang et al. 2005; Chen et al. 2009) or using 1D–2D coupled numerical models (e.g. Hsu et al. 2000; Mark et al. 2004; Schmitta et al. 2004); or (iii) evaluating impacts of land use and land cover change on waterlogging risks (e.g. Quan et al. 2010), sometimes in combination with downscaled climate change scenarios (e.g. Xia 1990; Zhu 1990, 1999; Deng 1998; Jin 2000). Some risk assessments have focused on the impacts of waterlogging on traffic disruption and damage to buildings. For example, Shi et al. (2010) investigated the potential impacts of rainstorm waterlogging on old-style residence exposure risks in Shanghai based on distributed water depths derived from a series of scenario-based simulations using a waterlogging simulation model. Han et al. (2006) assessed the risks of storm waterlogging on traffic in the City of Tianjin using a coupled 1D/2D unsteady flow model partitioned on an irregular grid. These studies demonstrate the potential magnitude of waterlogging damage to buildings and traffic infrastructure. The advent of computational methods for simulating both subsurface pipe flow within the urban storm sewer system and runoff on the urban surface has enabled research into the extent of waterlogging (e.g. Hsu et al. 2000; Yin et al. 2006; Dong and Lu 2008). Studies typically couple a 1D model of flow through storm sewer system and 2D model of surface inundation, with a treatment of flow exchange at the common boundary (such as manholes). For example, Hsu et al. (2000) used a 1D model of drainage flow (SWWM) and a 2D diffusion-based solution of the Saint-Venant equations for surface runoff, taking into account pumping stations at the outlets of the sewer system. The model was verified 123 Nat Hazards (2012) 63:305–323 307 against inundation observations collected during a 28-h rainstorm event in 1998 over the City of Taipei, simulated with recorded rainfall data and a 120-m resolution DEM. The model was subsequently used to predict waterlogging risks based on precipitation scenarios. Other studies have also been undertaken to investigate the causes of waterlogging. For example, land use changes associated with urbanization are often considered to be one of the major contributory factors (e.g. Veldkamp and Verburg 2004; Quan et al. 2010). There has been relatively little research into interactions between land surface modification, development of urban storm sewer systems, variation of local rainfall regimes, and waterlogging characteristics at the city scale. In particular, precipitation is thought to be the major factor affecting waterlogging in urban locations as it is the primary source of surface runoff in the absence of fluvial or tidal surges. Impacts of precipitation on waterlogging have been predominately investigated via frequency analysis, with synthetic rainfall hy- etographs (generated from historical data) linked to indicators of waterlogging. This is especially important since rising concentrations of greenhouse gases are expected to increase the intensity of precipitation at the regional level (IPCC 2007). Over the last few decades, numerous studies have been carried out to evaluate the impacts of global and regional climate warming on precipitation regimes (Zhao et al. 2009; Kioutsioukis et al. 2010). However, most have focused on relatively large regions and timescales that are unlikely to reveal potential links between precipitation and local waterlogging. Therefore, in the context of climate variability and change, there is a need to investigate the rela- tionship between daily to multi-day precipitation characteristics and occurrence of waterlogging. High-intensity precipitation events have become more frequent in South China (Zhai et al. 2005). Shanghai, a major city in the region, has experienced frequent waterlogging problems, and there is growing public awareness of the associated damage and disruption. Hence, the authorities are keen to better understand the underlying drivers. This paper evaluates three interrelated factors that affect waterlogging in the City of Shanghai, namely land surface modification, changes in the rainfall regime, and development of the drainage system during the course of urbanization over the last six decades. Section 2 describes the study site, data availability, and methods of data processing and analysis. This is followed by the results and discussion in Sect. 3. Conclusions are drawn, and research questions for future studies are raised in Sect. 4.

2 Methodology

2.1 Study site

The City of Shanghai has developed on the floodplain of the Huangpu River, which originally comprised of many tributaries. The city has undergone rapid development during the past few decades and is now the most populated in China. The administrative divisions of Shanghai are shown in Fig. 1a, with the inner-city districts shown in Fig. 1b. Lying on the west coast of the East China Sea, Shanghai has a northern subtropical monsoon climate, with an annual average precipitation of 1,122 mm and an annual mean temperature of 15.8C. With the influence of the East Asian monsoon and frequent typhoons plus high flows from the upstream catchments of the Huangpu River and storm surges from the East China Sea, the city is vulnerable to coastal and fluvial flood risk. With the construction of flood defences along the coast (Fig. 2a) and major watercourses 123 308 Nat Hazards (2012) 63:305–323

Fig. 1 a Location of the study area; and b extent and magnitude of waterlogging after the 10 June 1999 rainfall event in the inner-city area

Fig. 2 Typical ﬂood defences in Shanghai: a along the coast; and b along the Huangpu River

(Fig. 2b), most of the inner-city area of Shanghai is now protected against 1,000-year fluvial flood events (Yin 2011). However, Shanghai is still subject to frequent flooding related to urban rainstorm waterlogging, which is typically associated with torrential downpours, rapid surface runoff, and incapacity of the storm sewer systems to drain the excess flow in some parts of the city. Between 1980 and 1993, there were on average 251 road sections and 52,700 buildings suffering from waterlogging every year (Yuan 1999). More recent data are available from the Shanghai Water Resources Bureau in the form of the spatial distribution of waterlogging and recorded depth at each site following major events. These data are based on reports from district administrations before 2003 and automatic measurements obtained from sensors within manholes and digital meters installed at road overpasses since 2003 (Zheng 2011). For example, Fig. 1b shows the spatial extent of waterlogging and maximum water depth for each section after a major rainstorm event on 10th June 1999. Data obtained herein were collated with those reported by Shi et al. (2010) and are summarized 123 Nat Hazards (2012) 63:305–323 309

Table 1 Occurrence of waterlogging in the inner-city of Shanghai since 1997 Event Rainfall (max) Number of road sections Number of houses waterlogged (water depth) inundated (water depth)

10 July 1997 51.5 mm/24 h *60 (10–30 cm) *1,500 10 June 1999 93.4 mm/24 h 163 (5–45 cm) *2,500 30 June 1999 139.5 mm/24 h *170 (5–30 cm) *2,850 31 August 2000a 86 mm/24 h More than 100 More than 3,000 23 June 2001a 200 mm/24 h *50 More than 570 5–9 August 2001a 275 mm/24 h 476 47,797 27–28 August 2002 54.4 mm/24 h *60 (5–20 cm) More than 1,300 (5–10 cm) 22 August 2004a 83 mm/h 30 *700 (15–30 cm) 5–8 August 2005a 292 mm/24 h 187 *20,000 18 September 2007a 164 mm/24 h 128 (10–30 cm) *8,035 25 August 2008a 117 mm/h 100 (10–40 cm) *10,000 (5–10 cm) 10 July 2009 59.9 mm/24 h *95 (10–40 cm) More than 3,000 (5–10 cm) 1 September 2010 144.6 mm/24 h More than 80 (10–35 cm) *500 (10–20 cm) 12 August 2011 98.7 mm/h *50 (5–40 cm) *800 (10–35 cm) a Data taken from Shi et al. (2010) in Table 1. These statistics suggest that there has been a persistent problem of waterlogging since 1980 when records began. Shanghai’s vulnerability to waterlogging risks can, in the first place, be attributed to the local topography, exacerbated by land subsidence during urban expansion. First, the natural terrain of the city is characterized by low relief, with elevations ranging from just 4.8 to 5.0 m above sea level in the higher terrain on the outskirts of the inner-city area, to around 3.2–4.8 m in the low-lying floodplains and inner-city area (Fig. 1b). Most of the inner-city is below the water surface elevation of the Huangpu River during high tides. Partially as a result of this, storm-induced surface runoff during the flood season (June to September) tends to accumulate in the inner-city area where the old-style residential buildings are typically located (Shi et al. 2010). Second, with the increasing number of high-rise buildings and abstraction of groundwater, Shanghai has experienced significant land subsidence, at rate of 7 mm/year since 1996 (Shanghai Geological and Environmental Bulletin 2007). This is also thought to have exacerbated the waterlogging risks, especially in the inner-city area (Fig. 1b). Sea level rise and the associated increase in high tidal levels is another factor to consider. The Chinese State Oceanic Administration reports that Shanghai’s sea level is rising the fastest in China. The combined effects of these two factors are the lowering of land levels relative to sea level. This favours higher base water levels for the storm sewer system, rendering drainage systems progressively less effective. Increased surface runoff due to land surface modification during urbanization is also considered to be another factor favouring waterlogging. Finally, frequent torrential rainstorms are often blamed by the public and media for the waterlogging. However, it is not clear whether multi-decadal climate variations have adversely affected the incidence of waterlogging in Shanghai. The statistics shown in Table 1 and Fig. 1b, therefore, reflect the net effect of any changes in the drivers of flooding (rainfall patterns, subsidence, land cover changes) and mitigation measures (expansion and upgrading of the sewer system). 123 310 Nat Hazards (2012) 63:305–323

The primary focus of this case study is to: (i) describe modiﬁcations to land surface characteristics and the development of the urban storm sewer system during the process of urbanization; (ii) analyse variations in rainfall over the past few decades; and (iii) evaluate the combined impacts of these factors on waterlogging risk in Shanghai in the context of past and projected climate change.

2.2 Data collation

Land surface modifications over the last few decades in Shanghai are captured by a series of aerial photographs (1964, 1979, 1984, 1989, 1994, 1999, and 2006) courtesy of the East China Normal University and Centre for Remote Sensing and Surveying of Shanghai. In combination with field observation and historical maps (1840–1950s), these enable land uses at discrete time slices to be derived through digitization and image processing with reasonable accuracy. Statistics describing the urban drainage system over the last 60 years were provided by the Shanghai Municipal Water Affairs Bureau and Shanghai Municipal Statistics Bureau. These include annual reports of the length of drainage sewer pipes and the number of pumping stations. These provide evidence of the development of the urban storm sewer system of Shanghai. Precipitation records for Shanghai were obtained from two meteorological stations, namely Xujiahui (31120N,121260E, 5.0 m) and Baoshan (31240N, 121270E, 4.6 m), separated by a distance of *23 km. Annual rainfall data from 1873 to 1951, as well as daily observations between 1952 and 2009, were recorded at the Xujiahui station, but data are missing between 1999 and 2006 (Fig. 3a). Comparison of cumulative daily rainfall totals at the two stations over the period 1991–1998 demonstrates a highly significant correlation (Fig. 3b). A quantile-to-quantile plot (Fig. 3c) of the daily rainfall at the two stations during the same period suggests that: (i) at the lower end of the spectrum (c. \40 mm), the distribution of the daily rainfall at the two stations is very similar; however, (ii) when the high rainfall values are considered (c. [40 mm), the distribution does not resemble each other. Given the relatively short length of the analysis window (8 years) and the rareness of daily rainfall exceeding 40 mm (1.4 % in Baoshan and 1.6 % in Xujiahui), this difference is attributed to sampling uncertainty. Moreover, the percentile of the 25 and 50 mm daily rainfall events (classified as Heavy Rainfall and Very Heavy Rainfall in Shanghai) are calculated for the Xujiahui and Baoshan stations, with and without dry days considered (Table 2). Results suggest that: (i) when all the data are considered, there is a 0.31 and 0.27 % positive difference between the Baoshan and Xujiahui stations for the 25 and 50 mm, respectively, suggesting that Baoshan has slightly fewer raindays than Xujiahui and (ii) with only wet days considered, there is only a marginal difference of 0.093 and 0.077 % in the percentiles for the 25 and 50 mm, respectively. It is therefore reasonable to assume that combining the daily rainfall of the Xujiahui station with those of the Baoshan gives a homogenous record of daily rainfall records for 1952–2009 in Shanghai. Therefore, daily rainfall recorded in the Baoshan station was used to supplement the data recorded in the Xujiahui station for the period between 1999 and 2006.

2.3 Methods of analysis

Land use classiﬁcation was carried out by the GIS laboratory in East China Normal University through digitization and image processing. Three types of land use were derived 123 Nat Hazards (2012) 63:305–323 311

(a)

(b)10000 (c) 125 9000 Daily Rainfall (mm) Linear 8000 100 y=x 7000 6000 y = 1.0896x - 170.37 75 5000 R 2 = 0.9991

4000 50 Xujiahui (mm) 3000 Baoshan (mm)

2000 25 1000

0 0 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 0 25 50 75 100 125 Baoshan (mm) Xujiahui (mm)

Fig. 3 a Data availability at the Xujiahui and Baoshan stations; b comparison of cumulative sum of daily rainfall during 1991–1998 at Xujiahui and Baoshan stations; and c quantile to quantile plot of the wet day daily rainfall in Xujiahui and Baoshan during 1991 and 1998. Integer quantiles are presented

Table 2 Percentiles of the 25- Baoshan Xujiahui and 50-mm rainfall in Baoshan and Xujiahui stations during All data (%) Wet days (%) All data (%) Wet days (%) 1991 and 1998, with and without dry days included in the analysis 25 mm 97.06 91.09 96.75 90.16 50 mm 99.11 97.37 98.84 96.60

from the available data: agricultural; residential and industrial; and transport. Apart from the conversion of agricultural land into less permeable surface for residential and industrial uses, another modification to the land surface characteristics immediately evident from the data collected is the modification to watercourses. Historically, Shanghai had a dense network of watercourses. However, many of the streams have since diminished, and this has significantly reduced the capacity of draining surface water by natural means. Based on the data collected, the density and extent of watercourses was investigated. River networks for different periods were derived from the aerial photographs and maps by manual digitization, verified by field survey and anecdotal evidence. Since rivers are linear features readily detectable from aerial photographs and well represented on paper maps, the results are considered to be reliable over the period of interest. 123 312 Nat Hazards (2012) 63:305–323

The rainfall records were analysed at three different timescales. Annual and seasonal/ monthly rainfall totals were investigated using linear trend analysis. In addition, seasonal and monthly precipitation was investigated using the Mann–Kendall (M–K) statistic. This nonparametric test is widely used to evaluate signiﬁcance of trends in seasonal and monthly precipitation series (e.g. Lins and Slack 1999; Yue et al. 2002; Wilby 2006; Cao et al. 2008; Li and Su 2009). The M–K test can be analysed in two steps. For a precipitation time series Xm ¼ðx1; x2; ...; xtÞ, the Mann–Kendall statistic value can be calculated from:

Xt1 Xt S ¼ Sgnðxn xmÞð1Þ m¼1 n¼mþ1 where Sgn is the sign function: 2 3 1 ðxn xmÞ\0 6 7 Sgnðxn xmÞ¼4 0 ðxn xmÞ¼0 5 ð2Þ

þ1 ðxn xmÞ [ 0 The standard normal statistical variable is given by: 2 3 pffiffiffiffiffiffiffiffiffiffiffiffiSþ1 S\0 6 VarðSÞ 7 Z ¼ 4 0 S ¼ 0 5 ð3Þ pffiffiffiffiffiffiffiffiffiffiffiffiS1 S [ 0 VarðSÞ where variance Var(S) is calculated with: tðt 1Þð2t þ 5Þ VarðÞ¼S ð4Þ 18 A Z-value above zero suggests an increasing trend and vice versa. Given a certain confidence level a (here taken as 95 %), jjZ Z1a=2 indicates a significant increasing or decreasing trend in the time series. Finally, daily precipitation series were analysed in terms of the annual and seasonal frequencies of wet days and occurrence of daily totals exceeding thresholds known to cause waterlogging in Shanghai. Maximum and average accumulations were also calculated for wet-spells spanning 2–16 days. The N-day indices are used to detect changes in the annual incidence of lower intensity, but persistent wet-spells with large accumulations.

3 Results and discussion

3.1 Land use and Land cover change

Figure 4 shows the changing area (km2) of the three major types of land use in the inner- city. The increase in area occupied by residential and industrial land, and the corresponding decrease in the agricultural land throughout the period are noteworthy. Furthermore, there is a step change in both land use categories shortly after China’s ‘‘Open Door Policy’’ in 1978, indicating the rapid conversion of land use from agricultural to urban use. The direct impact of the land modification is assumed to reduce the capacity for draining excess surface water by natural means such as infiltration and river networks. The reduced infiltration rate of the land surface during urbanization, and the corresponding increase in 123 Nat Hazards (2012) 63:305–323 313 surface runoff have been well documented and are considered to be a major factor attributing to waterlogging risks in cities (e.g. Veldkamp and Verburg 2004; Quan et al. 2010). One common metric used to describe the rate of surface runoff for a particular land use/land cover type is the runoff coefficient, representing the ratio of surface runoff to total rainfall amount. The average runoff coefficient of Shanghai over time was calculated based on the land use statistics shown in Fig. 4 and standard runoff coefficients for each land use type (according to Standard of Storm Sewer Systems in Shanghai). Figure 4 shows a steady increase since 1947. Reduction in the surface drainage network is also thought to be a major factor that has increased the risk of waterlogging in Shanghai. Infilling and conversion of watercourses into urban lands will not only result in higher runoff volume, but also hinder the drainage capacity of the floodplain. To investigate this effect, river networks during different periods were derived from the data sets available, and the results are presented in Fig. 5. Figure 5 shows a rapid decrease in the density of river networks, especially to the west of the Huangpu River where the City of Shanghai has developed. In the 1840s, the Huangpu River and Suzhou Creek were joined by a multitude of tributaries (Fig. 5a). Following the rapid increase in industrial and commercial activities, as well as population growth, and associated demands for infrastructure, the river network was heavily modified during the 1950s (Fig. 5b) especially in the inner-city area. However, on the outskirts of the city, namely the eastern, northwest, and southwest, relatively dense river networks were still present. Between the 1950s and 1964 (Fig. 5c), there was no major change except the loss of tributary Zhaojiabang which straddled the boundary between Xuhui and Changning districts in the 1950s. Changes from 1964 to 1994 were less than in previous decades and occurred mainly in the northern and southeast part of the city (Fig. 5d). Notable during this period is the steady decrease in the river network density in the Pudong New Area where initiatives favoured development the eastern part of the city in the early

Fig. 4 Temporal evolution of: (i) three major land use types in Shanghai; and (ii) composite runoff coefﬁcient over time for the City of Shanghai, calculated with the standard runoff coefﬁcients for different land use types in Shanghai (Standard of Storm Sewer Systems in Shanghai): agricultural, 0.1; urban, 0.8; and road, 0.45 123 314 Nat Hazards (2012) 63:305–323

(d) 1979 (e) 1984 (f) 1989

(g)1994 (h)1999 (i) 2006

Fig. 5 Spatial distribution of surface river network during: a pre-1950s (historical data with no records in Pudong New Area); b 1950s; c 1964; d 1979; e 1984; f 1989; g 1994; h 1999; and i 2006 in Shanghai (b–h) after Chen et al. 2002)

1990s. Between 1994 and 1999 (Fig. 5g, h), most of the streams in the northwest of the city disappeared, and those in the Pudong New Area were increasingly being converted due to the rapid development of the district. The latest data set obtained in 2006 (Fig. 5i) shows that most of the original tributaries and surface channels have now disappeared apart from the Suzhou Creek and two streams located in the Yangpu and Hongkou districts. These watercourses have more or less maintained their footprints throughout the periods shown in Fig. 5. Indeed, as mentioned previously, most of the flood risk management measures in Shanghai have focused on fluvial and coastal flood defence along the Huangpu River and Suzhou Creek. Using GIS tools, it is calculated that, over the past six decades, the total length of natural surface drainage has decreased by *270 km in Shanghai. Mismanagement and, in some cases, total neglect of river systems, during the process of urbanization, is the major cause of diminution of small tributaries shown in Fig. 5. More specifically, rivers were usually filled to create space for construction, especially after a long period of degradation in conveyance capacity due to sedimentation and excessive vegetation growth. In many cases, this also coincided with declining water quality because of domestic and industrial effluents (Li et al. 2002).

123 Nat Hazards (2012) 63:305–323 315

3.2 Development of storm sewer system

The loss of river networks, combined with conversion of agricultural land into less permeable urban surface, significantly reduced the capacities for draining surface runoff via natural means, thus increasing the risk of waterlogging in Shanghai. Storm sewer systems have since been built, usually reactively rather than proactively after repeated waterlogging incidents. The urban sewers of Shanghai transport waste and storm water flow in separate drainage systems. A typical urban storm sewer system has three components: a surface runoff capture system, an underground pipe system, and pumping stations. Between 1963 and 1976, the design capacity of the systems in terms of rainfall return period was for the one in a 0.5-year event (27 mm/h) with typical runoff coefficient ranging between 0.4 and 0.5. Following several extreme rainstorms in 1977, the design event was increased to the one in 1.0 year (36 mm/h) with runoff coefficients raised to be between 0.5 and 0.6. The length of storm sewer system shows a steady increase in the length of storm sewer pipes since 1947, with a much faster rate of increase from the early 1990s (Fig. 6). The number of pumping stations displays a similar upward trend, with marked increase from the late 1950s. However, the development of storm sewer system does not seem to have kept pace with the demands of the city. As reported previously (Table 1), waterlogging is still a frequent hazard that causes widespread damage and disruption to the city. The city centre is particularly vulnerable as over half of the system is designed for a rain event with

(a) 12000

10000

8000

6000

4000

2000 Length of Sewer (km)

0 1940 1950 1960 1970 1980 1990 2000 2010 yr

(b) 250

200

150

100 Stations (unit)

Number of Pumping 50

0 1940 1950 1960 1970 1980 1990 2000 2010 yr

Fig. 6 Temporal evolution of: a length of urban storm sewer system; and b number of pumping stations in Shanghai 123 316 Nat Hazards (2012) 63:305–323 return period of just 6 months, compounded by low relief, high runoff coefﬁcients, and rising base water levels.

3.3 Rainfall regime variation

The rainfall records were analysed at three different timescales (annual, seasonal/monthly, and daily) in order to assess temporal variations that have a relevance to waterlogging risks. The analysis also provides insight into how the city’s sewer system might be impacted by climate change.

3.3.1 Annual and ﬂood season rainfall

First, linear trend analysis was carried out to investigate whether there has been any significant change in the annual and flood season precipitation totals over the period investigated. Figure 7 shows the best-fit trend lines for annual and flood season rainfall amounts from 1873 to 2009. It is apparent that, although there is strong inter-annual variability and a weak upward trend, no statistically significant change in annual and flood season precipitation amount is present. To investigate rainfall variability, the standard deviation of both annual and flood season rainfall totals was calculated from an 11-year moving average window, and the results are presented in Fig. 8. The pattern is consistent with earlier studies showing multi- decadal variations in annual and flood season precipitation totals (e.g. Zhou and Yang 2001).

3.3.2 Seasonal and monthly rainfall

Monthly total precipitation is plotted against time in two dimensions in Fig. 9. Two features emerge from this graph. First, during 1952–2009, precipitation in ﬂood seasons (the area encompassed by sold lines) contributes most to the total annual precipitation. Second, monthly precipitation totals during ﬂood seasons appear to have increased since the early 1980s (indicated by the darker contour rings). This has implications for waterlogging risks as increased precipitation totals may signal increased frequency of precipitation events with magnitudes that favour waterlogging.

2000 Annual Rainfall Flood Season Rainfall 1800 Linear (Annual) Linear (Flood season)

1600 y = 0.3881x + 1132 1400 R2 = 0.0051

1200

1000

800 Rainfall (mm) y = 0.4027x + 577.67 600 R2 = 0.0072 400

200

0 1873 1881 1889 1897 1905 1913 1921 1929 1937 1945 1953 1961 1969 1977 1985 1993 2001 2009 yr

Fig. 7 Annual and ﬂood season rainfall totals (mm) in Shanghai 1873–2009 123 Nat Hazards (2012) 63:305–323 317 ) 4 3 (a) 2 1 0 -1 -2 -3 -4

Standardization ( 1873 1881 1889 1897 1905 1913 1921 1929 1937 1945 1953 1961 1969 1977 1985 1993 2001 2009 yr

) 4 3 (b) 2 1 0 -1 -2 -3 -4 1873 1881 1889 1897 1905 1913 1921 1929 1937 1945 1953 1961 1969 1977 1985 1993 2001 2009 yr Standardization (

Fig. 8 Standard deviation of: a annual; and b ﬂood season rainfall, between 1873 and 2009. Bars are standard deviations of the annual and ﬂood season (June–September) rainfall, while solid lines are moving average calculated from an 11-year window

12 11 600mm

10 500mm 9 8 400mm 7 6 300mm

5 200mm 4 3 100mm 2 Time Scale (month) 1 0mm 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 Year

Fig. 9 Time series of monthly rainfall in Shanghai 1952–2009

Seasonal and monthly precipitation was further investigated using the Mann–Kendal (M–K) statistic. In Shanghai, spring spans from March to May, summer from June to August, autumn from September to November, and winter from December to February. Seasonal and monthly rainfall M–K tests were undertaken from 1952 to 2009. Figure 10a suggests an increase in summer and winter rainfall from c. 1985 and 2001, respectively, with summer rainfall reaching the 95 % confidence level at around 1998, followed by a slight dip *2003 and a continued upward trend passing the confidence cut-off again *2008, signalling a significant increasing trend in summer precipitation. On the other hand, spring and autumn precipitation demonstrates a decreasing trend since c. 1994, with the spring trend becoming significant at around 2008. Trend analysis of precipitation from May to September (Fig. 10b) confirms the findings from Fig. 10a on a monthly scale. May and September precipitation displays a clear decreasing trend since c. 1994 and 1970, respectively. However, the most notable trend is the August precipitation, which shows a clear increase since the early 1980s, with significant increase confirmed at c. 1997. Con- versely, June and July precipitation decreased from 1952 to 1996. Since then, there has been no clear trend in June precipitation, while July precipitation displays an increasing trend. 123 318 Nat Hazards (2012) 63:305–323

(a) 4 3

0 Z-score -1

-2

-3 Spring Summer Autumn Winter -4 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 198 8 1991 1994 1997 2000 2003 2006 2009 yr

(b) 4 3

0 Z-score -1

-2

-3 May June July August September -4 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 yr

Fig. 10 Mann–Kendall test for signiﬁcant trend (Zscore) in: a seasonal; and b monthly 1952–2009 rainfall variables in Shanghai. Dotted lines show the range of 95 % conﬁdence level

3.3.3 Analysis of daily rainfall

Daily precipitation was analysed with reference to the standard classification of precipitation thresholds in China. According to the Office of State Flood Control and Drought Relief Headquarters, a precipitation event with high magnitude can be classified as heavy rain (25–49.9 mm), rainstorm (50–99.9 mm), and heavy rainstorm (C100 mm). In Shanghai, an hourly precipitation total exceeding 20 mm is likely to cause widespread waterlogging (Zhou and Yuan 1993; Yang and Yuan 1997). Figure 9 has already dem- onstrated an increasing trend in the clustering of heavy rainfall between May and Sep- tember on a monthly scale. We now describe daily rainfall series in terms of both the frequency and temporal clustering of days with precipitation exceeding 25 mm. First, annual and flood season wet day frequencies were calculated (Fig. 11). This shows a clear reduction in both the annual and nonflood season frequency of wet days from 1952 to 2009. However, there is no noticeable trend in the number of wet days during the flood season although linear regression suggests a slight decrease. The number of wet days with rainfall above and below 25 mm was calculated for the flood season. Figure 11b shows that there have been a decrease in the number of wet days with daily rainfall below 25 mm and an increase in events exceeding 25 mm. This is in line with the regional scale findings by: (i) Zhai et al. (2005), who reported an overall decrease in wet days but an increase in days with heavy rain over South China for the period 1951–2000; and (ii) Guan et al. (2011), who found an increasing trend in daily precipitation extremes over the last 123 Nat Hazards (2012) 63:305–323 319

(a) 220 Annual Wet Days Flood Season Wet Days 170 Non Flood Season Wet Days

120

70 Number of Wet Days

20 1950 1960 1970 1980 1990 2000 2010 yr

(b) 200 40 <25mm 25mm 160 30

120 20

80 10 Wet Days Below 25mm Wet Days Exceeding 25mm 40 0 1950 1960 1970 1980 1990 2000 2010 yr

Fig. 11 a Number of annual, ﬂood season, and nonﬂood season wet days; and b annual total number of wet days with precipitation below and exceeding 25 mm, from 1952 to 2009

20 years. Similarly, frequency analysis was undertaken to compare the magnitude of the one in 1-year daily rainfall event between 1951 and 1980 to that of 1981 and 2010. Using Pearson III frequency analysis, the one in 1-year daily rainfall event (27 mm/day) between 1951 and 1980 becomes a one in 0.7-year event between 1981 and 2010. Due to the lack of hourly rainfall data, no attempt was made to relate the return period of the daily rainfall with that of the hourly rainfall, although it would be expected that the level of performance of the storm sewer system has effectively degraded since it was constructed due to the increase in the more extreme events. Second, in relation to waterlogging, another important factor to consider is the clustering of successive wet days and their individual as well as aggregate precipitation amounts. To investigate this, rainfall instances comprising consecutive wet days ranging from 2 to 16 days (R2D, R3D, …, R16D) were calculated for each year. The maximum and average daily precipitation within each consecutive set of daily rainfall events is shown in Fig. 12. This conﬁrms what is found from Fig. 11b in terms of the decrease in below- 25 mm rainfall events and increase in those exceeding 25 mm. Moreover, Fig. 12 also suggests that events with precipitation exceeding 50 mm (regions enclosed by dashed black lines) appear to have occurred more frequently since the early 1980s. In particular, there is a marked increase in the number of consecutive wet days with a maximum daily rainfall exceeding 100 mm (regions enclosed by solid black lines) since the early 1980s. Similarly, the average rainfall amount during each consecutive period demonstrates an identical pattern (Fig. 12). However, no signiﬁcant trend was found for precipitation events 123 320 Nat Hazards (2012) 63:305–323

(a) 16 15 300mm 14 13 250mm 12 11 200mm 10 9 150mm 8 7 6 100mm 5 Consecutive Days 4 50mm 3 2 0mm 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 Year

(b) 16 15 300mm 14 13 250mm 12 11 200mm 10 9 150mm 8 7 6 100mm 5 Consecutive Days 4 50mm 3 2 0mm 1952 1955 1958 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 Year

Fig. 12 a Maximum; and b average daily rainfall in Shanghai during 1952–2009 for clustering wet days (R2D–R16D). Maximum (average) consecutive daily rainfall amounts exceeding 50, 100, and 150 mm are the regions enclosed by dashed, solid, and yellow lines, respectively

with maximum daily rainfall exceeding 150 mm (regions enclosed by solid yellow lines), but it is acknowledged that the sample size is too small for reliable analysis. The occurrence of prolonged rainfall with relatively high magnitude favours waterlogging as the runoff coefficient will be higher for wet surfaces. This has social and economic implications, as it is expected that the frequency of damage to buildings caused by waterlogging from successive heavy rainstorms implied by Fig. 12 would show pro- portionate increases. Similarly, traffic disruptions would be more severe and emergency responses might also be stretched in the events of repeated waterlogging. Another factor to consider is the possibility of greater deterioration of sewers due to more frequent sur- charging and increased flow rates (Butler and Davies 2004).

4 Conclusions and further studies

Although much more localized than fluvial or coastal flooding, waterlogging due to rapid surface runoff arising from heavy rainfall and incapacity of the storm sewer system to drain excess water has been a frequent problem for the City of Shanghai, causing widespread disruption to traffic and damage to buildings. The construction of storm sewer system since the 1950s and their subsequent expansion during a phase of rapid urbanization does not seem to have alleviated the problem. Due to the lack of pre-1980 record and the potential bias in data collection, it is unclear whether the occurrence of waterlogging has been 123 Nat Hazards (2012) 63:305–323 321 increasing. However, available records suggest that waterlogging has been a persistent problem despite major efforts to construct new and upgrade existing storm sewer systems. This study has investigated some of the driving factors of waterlogging in Shanghai. Interpretation of historical maps suggests that there has been significant modification to land surface characteristics during the process of urbanization, in ways that favour increased surface runoff and decreased capacity of natural drainage due to reduced infiltration rate and loss of watercourses. In particular, statistical analysis suggests that there has been a significant increase in the overall runoff coefficient, from 0.25 in 1950 to 0.7 in 2010 (Fig. 4). Historical analysis of river networks illustrates the rate and extent of modification to surface water steams in the past few decades. The natural drainage network has shrunk by 270 km, significantly reducing the city’s capacity to transport excess surface flow. No significant overall trends in annual rainfall totals (at Baoshan and Xujiahui) were found. However, seasonal and monthly rainfall intensities have increased. At the daily scale, compared to pre-1980s, there has been an increase in the number of wet days with precipitation exceeding 25 mm (Heavy Rainfall) and decrease in those below 25 mm, suggesting that there has been an increase in the number of rainfall events that could potentially cause waterlogging. Moreover, the number of consecutive wet days with precipitation maximum and average exceeding the threshold known to cause waterlogging shows an increasing trend. It can be concluded that, in the past few decades, land cover changes, compounded by shifts in rainfall regime, have outpaced the development of the urban storm sewer system, increasing the risk of waterlogging in Shanghai. Intense precipitation events are expected to increase in East Asia (IPCC 2007), consistent with the historical trend in the region (Zhai et al. 2005) and thereby place further pressure on the city’s storm sewer system. Compared with European countries where the design capacity of urban storm sewer system is typically between 10 and 50 years (Schmitta et al. 2004; Zhou and Li 2008), the design capacity of the storm sewer system (between one in 0.5 year and one in 1.0 year) is very low in Shanghai. A major retrofit of the whole system would be unrealistic. However, given the severity and persistence of the problem, city authorities should at least consider increasing design capacity of the new system. Likewise, extra capacity could be installed during routine maintenance and repair of the drainage system to better prepare the city for anticipated increases in heavy rainfall and associated waterlogging risk. Apart from the major drivers of waterlogging investigated herein, sea level rise and land subsidence are also factors that need to be considered in Shanghai, especially in the longer term. Changes in land level relative to the sea level could lead to higher base water levels for the drainage system, making the system less effective. The Chinese State Oceanic Administration projects an 80–130 mm rise in national mean sea level plus an additional 11–13 mm in Shanghai due to local effects, yielding a total rise of 91–143 mm by 2040. Land subsidence and compaction of sediments due to natural conditions and human activities have also been recorded in Shanghai. Estimated rates of land subsidence are relatively constant at nearly 1 mm/year since the Pliocene (Qian 1996), while subsidence due to compaction varies because of extraction of groundwater, construction of high-rising buildings, and underground projects (Gong et al. 2008). The average rate of compaction subsidence was estimated to be 7 mm/year between 2007 and 2010, and this is expected to stabilize at 5 mm/year beyond 2010 (Yin 2011). Under these projections, land subsidence in Shanghai could reach 240 mm by 2050. Further research on waterlogging in Shanghai could explore two aspects. First, the consequences of improved urban drainage systems, with or without climate change and 123 322 Nat Hazards (2012) 63:305–323 further urban development (Auld 2008; Semadeni-Davies et al. 2008a, b), could be investigated for Shanghai to inform decision-making. Moreover, the risk of pluvial flooding due to rapid surface runoff and overcharged sewer drainage flow investigated herein and those from other sources, such as coastal flooding due to relative sea level rise and fluvial flooding from extreme tidal events, are expected to grow under climate change projections for Shanghai (Yin 2011). Therefore, there is an urgent need to investigate flood risks from multiple sources and their potential physical as well as socio-economic impacts. In terms of the methodological approaches that can be used for such investigations, hydraulic and hydrological modelling offers great potential. High-resolution coupled modelling (\5 m) of surface flood inundation and urban storm sewer system is becoming increasingly feasible because of improving computational power and data availability. Scenario-based modelling with multiple projections of urban development with climate change could be undertaken to evaluate the potential impacts of flooding from various sources, as well as effectiveness of adaptation responses.

Acknowledgments This study was supported by an East China Normal University Overseas Study Fel- lowship (52YB2030) awarded to Xiaodan Wu, which enabled her to visit the Centre for Hydrological and Ecosystem Science in the Department of Geography, Loughborough University, for 6 months between October 2010 and March 2011. The authors thank the anonymous referees for their constructive remarks.

References

Auld HE (2008) Adaptation by design: the impact of changing climate on infrastructure. J Public Works Infrastructure 3:276–288 Boyle SJ, Tsanis IK, Kanaroglou PS (1998) Developing geographic information systems for land use impact assessment in flooding conditions. Water Resour Plan Manag 124:89–98 Butler D, Davies JW (2004) Urban drainage, 2nd edn. Spon, London Butler D, McEntee B, Onof C, Hagger A (2007) Sewer storage tank performance under climate change. Water Sci Technol 56:29–35 Cao JP, Chi DC, Wu LQ, Liu L, Li SY, Yu L (2008) Mann-Kendall examination and application in the analysis of precipitation trend. Agric Sci Technol Equip 179:35–40 (In Chinese) Chen J, Arleen AH, Lensyl DU (2009) A GIS-based model for urban flood inundation. J Hydrol 373:184–192 Chen DC, Li XP, Yang JS, Chen ZY, Wu CJ (2002) Evolution of river networks in Shanghai during urbanization. City Issues 5:1002–2031 (In Chinese) Deng DP (1998) Ones again on problems in urban stormy statistic. Water Wastewater Eng 24:15–19 (In Chinese) Dong ZY, Lu JR (2008) Numerical simulation of urban waterlogging disaster due to plum storm. In: Proceedings of 16th IAHR-APD congress and 3rd symposium of IAHR-ISHS. Hohai University Press, Nanjing Gao B, Zhao XH, Sun X, Peng CR (2009) Study on risk assessment of urban waterlogging. 2009 Third international symposium on intelligent information technology application, vol 3, pp 352–355 Gong SL, Li C, Yang SL (2008) Land subsidence and urban flood prevention safety in Shanghai. Hydrogeol Eng Geol 35(4):96–101 (In Chinese) Guan ZY, Han J, Li MG (2011) Circulation patterns of regional mean daily precipitation extremes over the middle and lower reaches of the Yangtze River during the boreal summer. Clim Res 50:171–185 Han SQ, Xie YY, Li DM, Li PY, Sun ML (2006) Risk analysis and management of urban rainstorm water logging in Tianjin. J Hydrodyn 18:552–558 Hsu MH, Chen SH, Chang TJ (2000) Inundation simulation for urban drainage basin with storm sewer system. J Hydrol 234:21–37 Huang DP, Liu C, Fang HJ, Peng SF (2008) Assessment of waterlogging risk in Lixiahe region of Jiangsu Province based on AVHRR and MODIS image. Chin Geogr Sci 18:178–183 IPCC (2007) Climate change 2007: the summary for policymakers. The AR4 synthesis report. Cambridge University Press, Cambridge, vol 13 Jin GY (2000) The problems of frequency compunction on the urban design storm. Hydrology 20:14–18 (In Chinese) 123 Nat Hazards (2012) 63:305–323 323

Kioutsioukis I, Melas D, Zerefos C (2010) Statistical assessment of changes in climate extremes over Greece (1952–2002). Int J Climatol 30:1723–1737 Li Z, Su YX (2009) The analysis on precipitation variation characteristic in Guangxi from 1961 to 2004. Chin Agric Sci Bull 25:268–272 (In Chinese) Li XP, Chi JG, Wu CJ, Chen ZY (2002) Management of water environment and storm sewer system in Shanghai: an investigation of river network evolution during the last 50 years and its impacts. Report to Shanghai Urban Drainage Co. Ltd (In Chinese) Lins HF, Slack JR (1999) Streamflow trends in the United States. Geophys Res Lett 26:227–230 Mark O, Weesakul S, Apirumanekul C, Aroonnet SB, Djordjevic S (2004) Potential and limitations of 1D modeling of urban flooding. J Hydrol 299:284–299 Pitt M (2008) Learning lessons from the 2007 floods (The Pitt Review). Final report, June 2008. Cabinet Office, London. http://archive.cabinetoffice.gov.uk/pittreview/thepittreview/final_report.html Qian ZH (1996) Determination of the crustal vertical motion at Sheshan area, Shanghai by VLBI. Ann Shanghai Obs Acad Sin 17:52–56 (In Chinese) Quan RS, Liu M, Lu M, Zhang LJ, Wang JJ, Xu SY (2010) Waterlogging risk assessment based on land use/ cover change: a case study in Pudong New Area, Shanghai. Environ Earth Sci 61:1113–1121 Schmitta TG, Thomasa M, Ettrichb N (2004) Analysis and modelling of flooding in urban drainage systems. J Hydrol 299:300–311 Semadeni-Davies A, Hernebring C, Svensson G, Gustafsson L-G (2008a) The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: combined sewer system. J Hydrol 350(1–2):100–113 Semadeni-Davies A, Hernebring C, Svensson G, Gustafsson L-G (2008b) The impacts of climate change and urbanisation on drainage in Helsingborg, Sweden: combined sewer system. J Hydrol 350(1–2):100–113 Shanghai Housing and Land Resources Administration (2007) Shanghai geological environment bulletin, Shanghai Shi Y, Shi C, Xu SY, Sun AL, Wang J (2010) Exposure assessment of rainstorm waterlogging on old-style residences in Shanghai based on scenario simulation. Nat Hazards 53:259–272 Veldkamp A, Verburg PH (2004) Modeling land use change and environmental impact. J Environ Manag 72:1–3 Wilby RL (2006) When and where might climate change be detectable in UK river flows. Geophys Res Lett 33(L19407):1–5 Xia ZY (1990) The comparison between the application of P-III curve and exponential curve in deriving the storm intensity formulae. China Water Wastewater 6:32–38 (In Chinese) Yang K, Yuan W (1997) The causes of Shanghai urban storm water and the exploring of control measures. Shanghai Constr Sci Technol 2:12–13 (In Chinese) Yin J (2011) Study on the risk assessment of typhoon storm tide in China coastal area. Phd thesis, School of Resource and Environmental Sciences, East China Normal University, China (in Chinese) Yin JM, Gu XQ, Cai Z, Zeng HQ (2006) Study and application of rainstorm waterlogging mathematical simulation in Nanchang city. Proc SPIE Int Soc Opt Eng 6421. doi:10.1117/12.697800 Yuan ZL (1999) Flood and drought disasters in Shanghai. Hohai University Press, Nanjing (In Chinese) Yue S, Pilon P, Cavadias G (2002) Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological data. J Hydrol 259:254–271 Zhai PM et al (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18:1096–1108 Zhang XN, An R, Zhang WT (2005) Development of flood and waterlogging risk map for Shanghai City. J Hohai Univ (Natural Sciences) 33:251–254 (In Chinese) Zhao F, Deng H, Zhao X (2009) Rainfall regime in three Gorges area in China and the control factors. Int J Climatol 30:1396–1406 Zheng XY (2011) Personal communication. Shanghai Flood Protection Information Centre Zhou YC, Li T (2008) Case study: the performance and design outline of a buffering storm water drainage system for a low-lying area. Water Environ J 22:199–205 Zhou LY, Yang K (2001) Variation of precipitation in shanghai during the last one hundred years and precipitation differences between city and suburb. Acta Geographica Sinica 56:467–476 Zhou NC, Yuan W (1993) The study on storm-born surface water accumulation in downtown area of Shanghai. Acta Geographica Sinica 48:262–271 (In Chinese) Zhu YY (1990) Rational formula based on nonlinear concentration area-distance relation and its application. J Fuzhou Univ (Natural Science) 18:92–97 (In Chinese) Zhu YY (1999) The method of parameter rate of storm intensity formula. China Water Wastewater 15:32–33 (In Chinese)

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