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«Water 2015, 7, 1808-1824; doi:10.3390/w7051808 OPEN ACCESS water ISSN 2073-4441 Article Detecting Flood Variations in ...»

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Water 2015, 7, 1808-1824; doi:10.3390/w7051808

OPEN ACCESS

water

ISSN 2073-4441

www.mdpi.com/journal/water

Article

Detecting Flood Variations in Shanghai over 1949–2009 with

Mann-Kendall Tests and a Newspaper-Based Database

Shiqiang Du 1,†,*, Honghuan Gu 1,†, Jiahong Wen 1, Ke Chen 2 and Anton Van Rompaey 3

1

Department of Geography, Shanghai Normal University, Guilin Road 100, Shanghai 200234, China; E-Mails: guhonghuan@hotmail.com (H.G.); jhwen@shnu.edu.cn (J.W.) 2 Department of Management, Shanghai University of Engineering Science, Longteng Road 333, Shanghai 201620, China; E-Mail: chen_ke_1984@126.com 3 Geography Research Group, Department of Earth and Environmental Sciences, Katholieke Universiteit Leuven, Celestijnenlaan 200E, Heverlee 3001, Belgium;

E-Mail: Anton.VanRompaey@ees.kuleuven.be † These authors contributed equally to this work.

* Author to whom correspondence should be addressed; E-Mail: shiqiangdu@shnu.edu.cn;

Tel./Fax.: +86-21-6432-3927.

Academic Editor: Miklas Scholz Received: 10 March 2015 / Accepted: 17 April 2015 / Published: 27 April 2015 Abstract: A valuable aid to assessing and managing flood risk lies in a reliable database of historical floods. In this study, a newspaper-based flood database for Shanghai (NFDS) for the period 1949–2009 was developed through a systematic scanning of newspapers. After calibration and validation of the database, Mann-Kendall tests and correlation analysis were applied to detect possible changes in flood frequencies. The analysis was carried out for three different flood types: overbank flood, agricultural waterlogging, and urban waterlogging.

The compiled NFDS registered 146 floods and 92% of them occurred in the flood-prone season from June to September. The statistical analyses showed that both the annual flood and the floods in June–August increased significantly. Urban waterlogging showed a very strong increasing trend, probably because of insufficient capacity of urban drainage system and impacts of rapid urbanization. By contrast, the decrease in overbank flooding and the slight increase in agricultural waterlogging were likely because of the construction of river levees and seawalls and the upgrade of agricultural drainage systems, respectively. This Water 2015, 7 1809 study demonstrated the usefulness of local newspapers in building a historical flood database and in assessing flood characterization.

Keywords: newspapers; disaster database; flood; waterlogging; Mann-Kendall tests

1. Introduction Natural disasters are considered to be an increasing threat for sustainable development and the security of humankind [1]. Each year natural disasters take a huge toll in deaths, injuries, property damage, and economic loss [2]. In the decade 2005–2014, 3979 natural disasters were registered by the EM-DAT that in total caused more than 0.8 million deaths, affected 1.7 billion people, and brought about $1.4 trillion in economic damages [3]. Among the registered natural disasters, flood is the most frequent disaster (41% of all registered natural disasters) and responsible for 49% of the total affected population, for 7% of all deaths, and for 22% of the economic loss due to natural disasters [3]. It is therefore clear that disaster risk reduction, in particular flood risk reduction, will remain high on the agenda of both scientists and policy makers in the coming decades [4].

A fundamental element in analyzing and managing natural disasters is reliable databases on their causes, frequencies, and locations [5–7]. In databases of historical natural disasters lies the opportunity for uncovering the relationships amongst the occurrence of natural events (typhoons, rainstorms, etc.), the exposure of vulnerable elements (people, assets, etc.), and countermeasures implemented by authorities (upgrading drainage system, building river levees, etc.) [8–10]. They are also vital to insurance business, governmental decision-making and public awareness [11,12]. Databases have been established in an increasing number of regions [13–18].

In establishing disaster databases, reliable and continuous data is amongst the most difficult tasks [19,20]. The global-scale disaster databases of EM-DAT, Nat Cat Service, and Sigma mainly employ data from governments, UN agencies, NGOs, research institutions, insurance business, and press agencies [19]. The DesInventar uses pre-existing data, newspapers, and institutional reports to establish national and regional disaster databases in Latin America, the Caribbean, Asia, and Africa [19,21]. The UNDP’s GRIP (Global Risk Identification Program) also helps developing countries to develop their databases, mainly using media news and official reports [19]. The GRIP gives a profile of 51 disaster databases, among which 41 (80%) used newspapers or online media data and 33 (65%) used newspapers or online media data as a major data source [13].

Newspapers have substantial advantages over other disaster data sources [21,22]. Firstly, newspapers provide continuous and nearly complete disaster information for a wide time range [7,23] as they play an essential role in hazard warning before disaster, relief, and rescue communication during disaster, and damage inventory reporting after disaster [24–30]. Secondly, newspapers’ information is relatively reliable and convincing as they typically try to obtain disaster information from authoritative sources [29,30]. Thirdly, newspapers can give more detailed information of small- and medium-scale disasters that are often not represented in larger-scale disaster databases [5,19]. Finally, newspaper archives are usually more accessible than other data sources [21,22]. Newspaper archives are therefore considered as a critical data source for analyzing natural disasters, especially for local and regional scale studies.





Water 2015, 7 1810 Despite these advantages, relatively few studies used newspaper archives for long-term analysis of natural disasters mainly because of the time-consuming procedures to develop the databases and because of the difficulties in calibrating and validating them. Shi [31] built a newspaper-based Chinese disaster database and compiled the Natural Disaster System Atlas of China. Hilker, Badoux, and Hegg [11] characterized Swiss flood and landslide risks by exploring 3000 Swiss newspapers and magazines between 1972 and 2007. Miah et al. [32] investigated newspapers’ coverage of climate change issues in Bangladesh. Papagiannaki et al. [33] investigated the seasonal and spatial variations of high-impact weather events in Greece from 2001 to 2011 by employing meteorological observations and newspaper reports. Barnolas and Llasat [5] analyzed the spatial distribution of Catalonia’s floods spanning the 20th century based on documentary, instrumental information, and newspaper reports. Newspapers were also used to analyze the development of social risk perception in Catalonia from 1982 to 2007 [34,35]. The newspaper-based Portugal disaster database supported the assessment of temporal trends and the spatial distribution of recorded hydro-geomorphologic events for the period 1865–2010 [7,22].

In this study, new procedures for developing and calibrating a natural disaster database based on newspaper archives will be introduced and possible trends in disaster frequency will be investigated.

The floods in Shanghai will be taken as an example application as this worldwide metropolis faces great flood risk because of (1) its geographic location; (2) economic status; and (3) the expected consequences of global climate change [36,37]. This contribution aims to develop a newspaper-based flood database of Shanghai (NFDS) and to quantify the flood characteristics. Following a description of the study area, Section 2 describes the data source, classification, and calibration methods in developing NFDS and presents the statistical methods in analyzing the database. Section 3 shows the statistical results of the NFDS including floods’ seasonal distribution and temporal variation. Section 4 discusses the knowledge earned in developing and analyzing NFDS. A brief conclusion follows those discussions.

2. Materials and Methods

2.1. Study Area Shanghai is amongst the largest seaports in the world and the largest cities in China. The city has a population of 24 million and a terrestrial area of 6340 km2. It borders the Jiangsu Province and the Zhejiang province in the west. On the other three sides, it is surrounded by water, namely the Yangtze River Estuary (North), the East China Sea (East), and the Hangzhou Bay (South) (Figure 1). The Huangpu River runs through the city. Shanghai has a subtropical monsoon climate. The rainfall averages 1123 mm per year, and is unevenly distributed over time. The wet season, from April to September, is expected to have 70% of the annual precipitation. Rainstorms are very common in this area due to the influence of typhoons, plum rains, strong convective weather systems, and urban heat island [38].

Occupying the coastal part of the Yangtze River alluvial plain, Shanghai is topographically flat, with exception of some hills to its west. Altitude is mainly about 3–5 m above the sea level. However, the average tidal amplitude of the estuary system ranges from 2.4 to 4.6 m [39] and typhoons can essentially enhance the tide level. On 18 August 1997, the highest tide record of 5.99 m occurred in Shanghai.

The combination of the flat alluvial plain, the subtropical monsoon climate, the tidal amplitude, typhoons, and upstream discharges causes serious flood risk to Shanghai and threats to its sustainable Water 2015, 7 1811 development [36,37]. The situation is exacerbated by land subsidence and sea level rising [39,40].

Shanghai has constructed massive seawalls and river levees since 1950 to reduce flood risk caused by over-flowing of rivers and seawaters. It has built pumping stations and dredged river-based agricultural drainage system to prevent agricultural waterlogging. Meanwhile, it has also upgraded the urban drainage system to prevent urban waterlogging by dredging river-based drainage systems, installing underground pipes, and building pumping stations [38,41–43].

–  –  –

2.2. Data and Methods for Developing the Flood Database for Shanghai The development of NFDS has two major steps as described in Figure 2, namely (1) disaster information registration and (2) calibration and validation. The major source of disaster information was 22,645 copies of newspapers from Jiefang Daily and Xinmin Evening News that are prominent newspapers in Shanghai. These newspapers were freely accessible in hard copy at Shanghai Library.

The database’s calibration and validation employed the following documentary data: (1) government reports, which were acquired from Shanghai Water Authority; (2) literature [38,43–45]; (3) 12-hour precipitation (1949–2009) and typhoon series (1949–1999), which were downloaded from China Meteorological Data Sharing Service Network; and (4) astronomical tide information, which was provided by the Shanghai Water Authority.

The disaster information registration converted the newspapers’ unstructured disaster information into structured database of NFDS. It primarily registered 163 floods through manually analyzing the newspapers. For a report to be primarily entered into the flood database we must have both inundation and damage information [4]. Five major items for each event were registered, namely time, location, Water 2015, 7 1812 damages, triggering factors, and types. The time item registered when a flood event starts and ends (year, month, date, and hour). It would be used for analyzing flood frequency and relating flood events to precipitation and tide for calibrating the primary database. Location registered the districts or counties that were affected by a flood event. Damages comprised the area of inundated farmlands; the number of killed, injured, missing, evacuated, or homeless people; the number of damaged and destroyed buildings;

and the number of inundated or interrupted roads. However, location and damage information was inconsistent and incomplete over time because many reports only gave vague descriptions of where the floods occurred and how much damage occurred. Triggering factors included names and types of triggering events, e.g., typhoons, plum rains, and strong convective weather.

Figure 2. Methodology of developing NFDS (1949–2009).

Typically, flood types were not directly given in newspaper reports. They were inferred based on damage details and triggering factors. In Chinese, floods are typically classified into two subtypes, namely overbank flood (Hong-Zai) and waterlogging (Lao-Zai) (Table 1). Overbank flood results from the over-flowing of rivers, lakes, and seawaters. It includes both fluvial floods and coastal storm-surge induced floods because the two categories are difficult to distinguish in deltaic cities like Shanghai that border both river and sea. Waterlogging (pluvial flood) occurs when the accumulation of precipitation exceeds evapotranspiration, infiltration, and the capacity of rainstorm drainage system. Waterlogging happens both in farmlands and in urban areas. Waterlogging that affects crops’ growth and causes yield losses was registered as agricultural waterlogging. Waterlogging that causes inundation of roads and Water 2015, 7 1813 buildings’ ground floor in urban areas was registered as urban waterlogging. A waterlogging event that happens in countryside and cities simultaneously was registered as mixed waterlogging in addition to urban waterlogging and agricultural waterlogging. It is often impossible to classify a given event into a specific subtype because a combination of subtype floods may occur. For instance, a typhoon event can cause agricultural waterlogging, urban waterlogging, and overbank flood [46]. Such events were registered as all of the categories if they agreed with the definitions of perspective categories. The classification was mainly implemented by investigating the flood record details.

–  –  –



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