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

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

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 several zones of risk for debris flows and flash flood.

#### **3.2. Risk analysis based on disaster-causing mechanism**

Risk analysis based on disaster-causing mechanism focuses on spatial forecast without exact time prediction. In this long-term warning method, identification of flash floods and debris flow gully, risk assessment and risk zoning are main contents for estimating the location and danger level of flash floods and debris flow. Different in spatial scale of analysis, there are three types of conventional methods [13]. The first, researchers determine whether a gully is in danger of flash flood or not, then assess its potential degree of danger by a comprehensive index. The second, the scope broadens into a larger area, and the risk zones are delineated according to the distribution and risk assessment of flash floods. The third, the scope of attention focuses on the detail in one gully, and the hazardous part can be separated from safe spaces by using an appropriate model which is selected by the type of the gully. Many scholars work in studying of flash floods and debris flow risk zoning in China since 1985 with significant achievement, such as 1991 version of China debris flow disaster distribution and risk zoning map, discussion about debris flow disaster zoning in China, research on debris flow disaster zoning in the upper Yangtze river, etc. [14–18].

**4. Research into real-term flash flood warning**

**4.1. Computational methods for real warning indicators**

nism driven, as shown in **Figure 3**.

**Figure 3.** The methods of calculating early warning rainfall.

Up to now, there still do not have a comprehensive indicator system that could take broader factors into account, such as wind direction and speed, velocity and quantity of flow, water level, rainfall intensity and quantity, etc. Instead, most scholars have been attracted to rainfall indicator, while a few other scholars focused on water-level warning indicator. The methods of calculating early warning rainfall can be divided into two classes: data driven and mecha-

Flash Flood Early Warning Research in China http://dx.doi.org/10.5772/intechopen.69784 151

The data-driven method is the most primary way to calculate early warning rainfall amounts in practice. On the premise that flash floods must have certain correlation with rainfall amount, this sort of method calculates early warning precipitation by analysing historical disaster data without considering disaster mechanism. Chen and Yuan found an overall review of those methods and classified them into case survey method, single station critical rainfall, regional

Along with rapid development of geographic information system (GIS) and remote sensing (RS) technologies, a new class of flash flood long-term warning methods based on GIS and RS have been tried by some scholars [19]. And by using GIS and RS data, flash flood disaster long-term warning prediction, or called risk assessment at that time, was calculated basing on conceptual formula (Eq. (1)). In this equation, the implication of long-term warning prediction covers the losses caused by disasters, the outburst probability of disasters and other consequence. The hazard comes from disaster danger zoning and represents the natural property of disasters. The vulnerability represents the social property influenced by disasters and is a financial analysis for measuring the disasters' destruction to human beings. Based on the positive correlation with the hazard and the vulnerability, the disaster risk can be calculated through mathematical operation of the hazard and the vulnerability.

$$\text{Long-term warming prediction (disaster risk)} = \text{hazard} \times \text{vulnerability} \tag{1}$$

Zhao assessed the risk of flash flood disasters on the upper reach area of Minjiang river by strength and frequency analysis [20]. Furthermore, Tang and Shi put forward an integrated technical route and method system, which covers data collection by GIS, spatial database construction, chosen of evaluation index system, forecast, risk assessment and zoning [21]. According to this technical route and method system, Guan and Chen drew up flash flood disasters risk assessment map of Jiangxi province, which based on geographical map and analysis of climate, rainfall, topography, gradient and river network and then overlaid this map with vulnerability assessment map for flash flood disaster risk zoning [22]. During analysing flash flood vulnerability in Wenshan city, range and depth of flash flood are considered as important indicators to improve accuracy of assessment [23, 24]. Lin et al. established a flash flood hazard zoning index system based on the micro-landforms, topography and slope position, flow accumulation and vegetation coverage and applied it to flash flood risk zoning in Tiaoshi town [25]. Latterly, by introducing land utilization as a new indicator into flash flood risk assessment, a more reasonable and reliable result of flash flood risk zoning in Jiangxi province is obtained [26].
