**Flash Flood Early Warning Research in China**

**Flash Flood Early Warning Research in China**

DOI: 10.5772/intechopen.69784

Haichen Li, Tao Qin and Xiaohui Lei Haichen Li, Tao Qin and Xiaohui Lei Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.69784

#### **Abstract**

**Author details**

**References**

Reshad Md. Ekram Ali1

Mohammad Zohir Uddin1

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\*Address all correspondence to: reshadekram@gmail.com

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and Md. Mahmood Hossain Khan1

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, Salma Akter1

,

Along with global climate change, extreme rainfall causes severe flash flood disasters, especially in mountainous areas. As about 67% of the terrestrial part of the whole country is mountain area with frequent heavy rainfall, China suffers from flash flood disasters throughout its history. As flash floods are distributed extensively and its influence sphere highly concentrated, it is unreasonable and uneconomical to prevent flash flood disasters mainly via engineering measures. Then, China starts exploring about flash flood early warning, which is optimal for developing country with dense populations, since the 1990s. Based on the literature research, a systematic framework for Chinese flash flood early warning research has been developed. In this frame, flash flood early warning is classified into long-term warning and real-time warning. This chapter presents the Chinese achievements in analysis methods for long-term warning, computational methods for real-time warning indicators, improving data sources used for real-time warnings and the information construction of real-time warning systems. In addition, the suggestions for future study are presented.

**Keywords:** flash flood, risk, early warning, China, review

#### **1. Introduction**

In China, flash floods are defined as floods which break out in mountain environments where villagers intensively inhabit, especially occurred in small watershed under 100 km<sup>2</sup> [1, 2]. Flash floods usually rise up and down sharply, with high velocity and cause great losses [3]. As China is located in East Asian monsoon region, severe rainfall occurs frequently here. That often causes flash flood disasters, threatening villagers' life and restricting economic development of mountain areas [4]. Over the past years, the increase of extreme rainfall in China is generally attributed to the global climate change [5]. The increase of heavy rain leads to the

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© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons

increase of flash floods. The casualties of flash flood disasters are continually mounting those years. Thus, flash flood disasters become one of the most life-threatening water disasters in China [6].

and control situation, real-time warning indicators would be calculated by integrating multisources data to provide scientific basis for drawing up contingency plan and improving mass

Flash Flood Early Warning Research in China http://dx.doi.org/10.5772/intechopen.69784 149

Although the entire research framework of flash flood real-term warning is shown in **Figure 2**, but some sectors, such as indicator system and simulation of disaster, draw little attention of scholars. Besides this, increasing researchers turn their attention to multi-sources data acquiring and applying in calculation of flash flood real-time warning indicators to struggle with data deficient. Furthermore, some experts in software engineering start taking part in the construction of flash flood early warning system platforms, including data management system,

In this section, an assumption is believed to be correct that the probability of flash flood disasters is a constant. Then, the recurrence cycle of historical disasters could be used for predicting the occurrence trend of future disasters [10]. Specifically, scholars collect historical disaster information of the target area to get one value via subtract one from the times of disasters and then divide the recording interval of disasters by this value. Then, the result reflects the activity degree of disaster and can be used for speculating the long-term probability of disaster. Using this method, the key is getting reliable, precise and adequate data of historical disasters [9].

This method used to analyse the risk of debris flow initially. For example, according to recurrence cycle of debris flow disaster, the developed phase of debris flow is divided into developing stage, active stage and decline stage [11]. As debris flows tend to occur along with flash flood, this probability analysis method was subsequently introduced into flash flood prediction. Applied to the flash flood research in Beijing, its mountain areas are divided into several zones of different level of debris flow and flash flood risk [12]. Afterwards, by this probability analysis method, plenty of works has been done in China to divide mountainous areas into

information transmission system and application visualization system.

**3. Research into long-term flash flood warning**

**Figure 2.** The research framework of flash flood real-time warning.

**3.1. Statistical analysis based on historical data**

several zones of risk for debris flows and flash flood.

prediction and disaster prevention and so on.

As flash floods are distributed extensively and its influence sphere highly concentrated, it is unreasonable and uneconomical to prevent flash flood disasters mainly via engineering measures. To cope with this, the government of China draws up a guidance, prevention in the first place and integrating prevention with control, non-engineering measure as the main and combining it with engineering measures [7, 8]. Since then flash flood early warning draws wide attention of China scholars. Against this background, it is essential to get an overall review of China research about flash flood early warning in the past years. Drawing lessons from the past, which is the main objective of this chapter, would provide effective references for engineering practice and outline the future prospects.
