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

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

150 Engineering and Mathematical Topics in Rainfall

flow disaster zoning in the upper Yangtze river, etc. [14–18].

through mathematical operation of the hazard and the vulnerability.

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

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

Long-term warning prediction (disaster risk) = hazard × vulnerability (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 reason-

able and reliable result of flash flood risk zoning in Jiangxi province is obtained [26].

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

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

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

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

critical rainfall, frequency analysis of rain and disaster, correlation analysis, analogy method and interpolation method [27].

The case survey method is to get critical rainfall amounts via statistic of rainfall amounts in the historical disasters. To be specific by taking the minimum rainfall amounts of each time interval as the initial value of early warning rainfall and comparing it with the value of adjacent areas to determine the final rainfall amounts for flash flood early warning. If sufficient historical data are available from existing hydrological observation network or meteorological observation network, then critical rainfall for each station and region would be calculated (Eq. (2)).

$$\mathcal{R}\_{\boldsymbol{\imath}} = \text{Min} \, \mathcal{R}\_{\boldsymbol{\imath}|\boldsymbol{\jmath}} \left( \boldsymbol{j} = \mathbf{1}, \ldots, \boldsymbol{N} \right) \tag{2}$$

*R* = *aF* + *bJ* + *cL* + *d* (3)

Flash Flood Early Warning Research in China http://dx.doi.org/10.5772/intechopen.69784 153

In Eq. (3), *a, b, c* and *d* are constants calibrated by using pre-existing critical rainfall values; *F* is the drainage area; *J* is the gradient of main stream; *L* is the length of main stream and *R* is

Liu et al. and Ye et al. proposed a flash flood early warning method based on dynamic critical precipitation, which closely correlated with soil moisture saturation [36, 37]. This approach was applied in Suichuanjiang river basin and Pihe river basin for flash flood forecasting and

Besides these methods, there are two special methods, analogy method and interpolation method, which could be used to infer objective critical rainfall from nearby ones calculated by other methods [38]. Based on pre-existing early warning rainfall values of flash flood disasters in Yunnan province, Zhang et al. carried out the research to the variety regulation of the critical rainfall by the application of the Kriging of special gridding method, the inverse distance to a power method, the radial basis function method and then drew up each isoline maps [39].

The kernel of the mechanism-driven methods is searching critical rainfall responding to the water level at which flash flood disasters will be caused. A technical route of these methods is 'based on the correlation between water level and discharge, and the correlation between rainfall and discharge, calculating disaster-causing discharge according to disaster-causing water level, then calculating disaster-causing rainfall according to disaster-causing discharge'. Follow this path, Ye et al. came up with the anti-water method for calculating critical rainfall

Furthermore, these mechanism-driven methods can be classified into empirical method and numerical model method. But for the lacking of data, even these methods are driven by the disaster mechanism, the major solution for measuring the correlation between water level and

The empirical method also called the black box method, which could be subdivided into frequency analysis of rain and flood, empirical formula method and rational formula method. Based on the assumption that rainfall and flash flood disasters have the same frequency, Liu et al. and Zhang et al. calculated the critical frequency corresponding to the critical flow rate, analysed the cumulative distribution interval points of probability and determined the method for calculating the critical rainfall in the data-deficient region [41, 42]. Empirical formula and rational formula are similar in calculating design rain and floods with different principle. Based on the correlation analysis, empirical formula is concluded from practical experience and embodies the regional characteristics. This kind of method concludes china institute of water resources and hydropower research (IWHR) hydrology empirical formula, research institute of highway ministry of transport (RIOH) empirical formula, regional unit hydrographs and empirical unit hydrographs summarized by each hydrographic office of provinces or cities. By simplifying and generalizing the processes of runoff formation, the rational formula is derived for calculating the discharge of specific river cross-section (Eq. (4)). The research of flash flood early warning precipitation in Jiangxi province showed that the

the critical rainfall to be calculated.

early warning.

in Zhejiang province [40].

discharge is still rely on experiences in practice.

In Eq. (2), *t* is the time period; *i* is the order number of precipitation station; *j* is the order number of historical flash flood disaster; *Rti* refers to the critical rainfall amount and *Rtij* is the maximal precipitation amount of station '*i*' during time period '*t*' in flash flood disaster '*j*'.

If the number of precipitation station in analysis is 1, the *Rti* means the single station critical rainfall. When the number of precipitation station increased, the regional critical rainfall can be analysed. With sufficient historical disaster data, Wang et al. calculated the single station critical rainfall and the regional critical rainfall for flash flood early warning in Chengde city [28].

Based on the assumption that rainfall and flash flood disasters have the same frequency, Bin and Dou calculated the frequency of flash flood disasters in Urumqi city and then inferred the early warning rainfall amounts in the same frequency [29].

Duan analysed the flash flood rainfall values of typical small watershed in the Yellow river by multiple methods, such as single station critical rainfall method, regional critical rainfall method, frequency analysis of rain and disaster method, etc., and the difference of computed results is contrasted and analysed [30].

Wang et al. proposed a compositive computational method about mountain mud rock flow critical rainfall, which successively corrects the intermediate result assisted by single station critical rainfall and frequency analysis of rain and disaster and applied this method to make the rainfall zoning of Hubei province [31]. Follow this, Zhao et al. calculated early warning rainfall in Linqu county by the determination method which combines the single station critical rainfall method and P-III frequency analysis method [32]. Yan et al. utilized 24 h precipitation and former 10 days rainfall as a factor for predicting, and combined differentiating and analytic approach to predict flash flood real-time warning in Jiangxi province [33].

With considerable correlation between precipitation and basin parameters, Fan et al. built a statistical model concerned critical precipitation, basin areas, main river length and main river slope (Eq. (3)) and by using this model, the early warning rainfall amounts for 1045 small basins in Jiangxi province has been calculated [34]. What's more, by analysing 1101 cases of mountain torrent and geological hazard data in Yunnan province, Hu et al. calculated the critical rainfall on which five grades for early warning of mountain torrent and geological hazard are based [35].

$$R = aF + bJ + cL + d\tag{3}$$

In Eq. (3), *a, b, c* and *d* are constants calibrated by using pre-existing critical rainfall values; *F* is the drainage area; *J* is the gradient of main stream; *L* is the length of main stream and *R* is the critical rainfall to be calculated.

critical rainfall, frequency analysis of rain and disaster, correlation analysis, analogy method

The case survey method is to get critical rainfall amounts via statistic of rainfall amounts in the historical disasters. To be specific by taking the minimum rainfall amounts of each time interval as the initial value of early warning rainfall and comparing it with the value of adjacent areas to determine the final rainfall amounts for flash flood early warning. If sufficient historical data are available from existing hydrological observation network or meteorological observation network, then critical rainfall for each station and region would be calculated (Eq. (2)).

*Rti* = Min *Rtij* (*j* = 1, …,*N* ) (2)

In Eq. (2), *t* is the time period; *i* is the order number of precipitation station; *j* is the order number of historical flash flood disaster; *Rti* refers to the critical rainfall amount and *Rtij* is the maximal precipitation amount of station '*i*' during time period '*t*' in flash flood disaster '*j*'.

If the number of precipitation station in analysis is 1, the *Rti* means the single station critical rainfall. When the number of precipitation station increased, the regional critical rainfall can be analysed. With sufficient historical disaster data, Wang et al. calculated the single station critical rainfall and the regional critical rainfall for flash flood early warning in Chengde city [28].

Based on the assumption that rainfall and flash flood disasters have the same frequency, Bin and Dou calculated the frequency of flash flood disasters in Urumqi city and then inferred the

Duan analysed the flash flood rainfall values of typical small watershed in the Yellow river by multiple methods, such as single station critical rainfall method, regional critical rainfall method, frequency analysis of rain and disaster method, etc., and the difference of computed

Wang et al. proposed a compositive computational method about mountain mud rock flow critical rainfall, which successively corrects the intermediate result assisted by single station critical rainfall and frequency analysis of rain and disaster and applied this method to make the rainfall zoning of Hubei province [31]. Follow this, Zhao et al. calculated early warning rainfall in Linqu county by the determination method which combines the single station critical rainfall method and P-III frequency analysis method [32]. Yan et al. utilized 24 h precipitation and former 10 days rainfall as a factor for predicting, and combined differentiating and

With considerable correlation between precipitation and basin parameters, Fan et al. built a statistical model concerned critical precipitation, basin areas, main river length and main river slope (Eq. (3)) and by using this model, the early warning rainfall amounts for 1045 small basins in Jiangxi province has been calculated [34]. What's more, by analysing 1101 cases of mountain torrent and geological hazard data in Yunnan province, Hu et al. calculated the critical rainfall on which five grades for early warning of mountain torrent and geological

analytic approach to predict flash flood real-time warning in Jiangxi province [33].

early warning rainfall amounts in the same frequency [29].

results is contrasted and analysed [30].

hazard are based [35].

and interpolation method [27].

152 Engineering and Mathematical Topics in Rainfall

Liu et al. and Ye et al. proposed a flash flood early warning method based on dynamic critical precipitation, which closely correlated with soil moisture saturation [36, 37]. This approach was applied in Suichuanjiang river basin and Pihe river basin for flash flood forecasting and early warning.

Besides these methods, there are two special methods, analogy method and interpolation method, which could be used to infer objective critical rainfall from nearby ones calculated by other methods [38]. Based on pre-existing early warning rainfall values of flash flood disasters in Yunnan province, Zhang et al. carried out the research to the variety regulation of the critical rainfall by the application of the Kriging of special gridding method, the inverse distance to a power method, the radial basis function method and then drew up each isoline maps [39].

The kernel of the mechanism-driven methods is searching critical rainfall responding to the water level at which flash flood disasters will be caused. A technical route of these methods is 'based on the correlation between water level and discharge, and the correlation between rainfall and discharge, calculating disaster-causing discharge according to disaster-causing water level, then calculating disaster-causing rainfall according to disaster-causing discharge'. Follow this path, Ye et al. came up with the anti-water method for calculating critical rainfall in Zhejiang province [40].

Furthermore, these mechanism-driven methods can be classified into empirical method and numerical model method. But for the lacking of data, even these methods are driven by the disaster mechanism, the major solution for measuring the correlation between water level and discharge is still rely on experiences in practice.

The empirical method also called the black box method, which could be subdivided into frequency analysis of rain and flood, empirical formula method and rational formula method. Based on the assumption that rainfall and flash flood disasters have the same frequency, Liu et al. and Zhang et al. calculated the critical frequency corresponding to the critical flow rate, analysed the cumulative distribution interval points of probability and determined the method for calculating the critical rainfall in the data-deficient region [41, 42]. Empirical formula and rational formula are similar in calculating design rain and floods with different principle. Based on the correlation analysis, empirical formula is concluded from practical experience and embodies the regional characteristics. This kind of method concludes china institute of water resources and hydropower research (IWHR) hydrology empirical formula, research institute of highway ministry of transport (RIOH) empirical formula, regional unit hydrographs and empirical unit hydrographs summarized by each hydrographic office of provinces or cities. By simplifying and generalizing the processes of runoff formation, the rational formula is derived for calculating the discharge of specific river cross-section (Eq. (4)). The research of flash flood early warning precipitation in Jiangxi province showed that the rational formula method usually has more stable results with smaller error than data-driven methods [43]. In order to raise the accuracy of early warning precipitation, a method for calculating the geographic distribution rules of flow concentration parameters and the spatial distributing rules of rainstorm parameters is put forward [44].

$$Q = 0.278 \times \frac{aS}{l^\*} \times F \tag{4}$$

tried to extend forecast period via introducing advanced weather forecast technology. According to the needs of flash flood disaster survey and valuation, Liu et al. used the data of laser point cloud to automatically measure the elevation of households along the river and vertical and horizontal sections of river channel and proved that could save time and cost with more abundant data [57]. Liao tried to use the data from radar with high timespace resolution and the data from automatic rainfall station to monitor the real-time strong rainfall, which is significant for the early warning of flash flood disaster [58]. Furthermore, Liu et al. showed that how forecaster use the monitoring data of TWR01A weather radar to warn about flash flood disaster [59]. But it is still difficult to forecast flash flood just rely on real-time monitoring data. Facing this, Li et al. tried to use the ensemble forecast approach, which constructed by various physical processes of a mesoscale model, to extend forecast period for flash flood early warning [60]. Qiu and Zi and Zi et al. also studied the application of quantified precipitation forecast technology in mountain areas and proved its applicability in flash flood early warning [61, 62]. Furthermore, Chen and Li simulated the historic flash flood occurred in Yangtze river basin on the basis of the weather research and forecasting (WRF) model to explore the optimal combination of physical schemes for

Flash Flood Early Warning Research in China http://dx.doi.org/10.5772/intechopen.69784 155

Informatization is quite necessary for shortening the delay time of each step for flash flood real-time warning. Flash flood early warning information construction, covering data acquisition, transmission, early warning analysis, disaster simulation and warning broadcasting could greatly enhance the capability of flash flood real-time warning. Considering particularity of environment in mountain areas, Meng designed a data acquisition unit with low power consumption, stable performance and high precision [64]. Zhong developed a river water level remote monitoring and early warning instruments by using the water level data collection which is based on static pressure sensor of input style and the general packet radio service (GPRS) and short messaging service (SMS) technology [65]. With the aid of code division multiple access (CDMA)/GPRS communication product, Fei and Huang designed an intelligence transmission system, which could provide unimpededly, fast, reliable and stable communication channel for transmission of flash flood real-time warning signals [66]. Zhang et al. and Hao et al. designed a flash flood geological disaster early warning system based on the Internet of Things (ToH) which combine the information sensor subsystem with data acquisition subsystem [67, 68]. Based on the distributed hydrological model and dynamic critical precipitation method, Hu and Liu developed a prototype medium and small river flash floods forecasting and warning systems and did a case study in Suichuan river basin [69]. To improve operational capacity of county flood control department in monitoring and early warning of flash floods, Zhang put forward an event-driven county-level flash flood monitoring and early warning system and applied it in Luanchuan country [70]. Moreover, Zhang designed a flash flood early warning information system based on the technology of GIS, spatial database and computer networking [71]. Jin and Wang summarized the implementation methods of the WebGIS and rich Internet application (RIA) technology solutions and developed the country-level flash flood early warning system separately by Silverlight technology or Flex technology [72, 73]. Furthermore, Li, Xiu, Lu and Lin designed their own

the flash flood early warning [63].

**4.3. Information construction of warning systems**

Eq. (4) is the fundamental form of rational formula, *Q* is the design flood, *a* is a constant reflecting the losses of flood peak, *S* is the maximum of hourly rainfall, *t* is the basin flow concentration time, *n* is decline exponent of rainstorm and *F* is the drainage area.

Since the data of flash flood disasters are continuously increasing, more and more scholars start taking part in researching numerical model for calculating flash flood real-time warning indicators. These numerical models are usually built that rely on water balance, principles of hydrology or hydrodynamics. Based on the water balance equation, Jiang and Shao put forward a concept and proposed a calculation method of minimum critical rainfall with awareness that criteria warning standard should take both rainfall amount and intensity into account [45]. For warning in ungauged basins, Ye et al. proposed an approach that using an antecedent precipitation index method and a Nash model for runoff forecasting [46]. Furthermore, considering the soil moisture and observed antecedent rainfall, Chen et al. established the calculation functions for dynamic critical precipitation under different soil moisture content levels by using the fitted function relation between antecedent rainfall and critical rainfall by the least square method [47]. Guo et al. designed a flash flood warning system based on a distributed hydrological model and evaluated its practical application in Henan province [48]. Li et al. introduced the basic concepts and methods of using a distribution hydrological model technique with detailed sub-basin delineation to analyse indicators for flash flood early warning [49]. Based on the full hydrodynamic model, Wang et al. proposed a new approach to calculate the distributed threshold rainfall for flash flooding, which constitutes the basis for effective flash flood warning [50]. Wen and Zhang et al. used a 2-D dynamic flood evolution model 'FloodArea' to simulate flash flood inundation caused by different rainfall amount for refined flash flood early warning in mountain area with small watershed and no hydrological data [51–53].

#### **4.2. Improving data sources used for warning**

The main restriction to flash flood real-time early warning is the lack of real-time rainfall data, discharge data and water level data. The direct and efficient approach to solve this problem is to increase monitoring network for weather, rainfall and river situation. For guiding hydro-meteorological monitoring network layout in mountain flood prevention areas, Chen et al. and Yuan et al. put forward a technical principle and index for the layout of hydro-meteorological stations according to the needs of mountain flood prevention in China [54, 55]. In addition, Shu and Han also analysed the density of rainfall monitoring network in Suichuanjiang river basin by using the method of extracting stations and watershed model method [56].

Along with the construction of traditional hydro-meteorological monitoring network, scholars also explored the application of remote sensing technology in obtaining data and tried to extend forecast period via introducing advanced weather forecast technology. According to the needs of flash flood disaster survey and valuation, Liu et al. used the data of laser point cloud to automatically measure the elevation of households along the river and vertical and horizontal sections of river channel and proved that could save time and cost with more abundant data [57]. Liao tried to use the data from radar with high timespace resolution and the data from automatic rainfall station to monitor the real-time strong rainfall, which is significant for the early warning of flash flood disaster [58]. Furthermore, Liu et al. showed that how forecaster use the monitoring data of TWR01A weather radar to warn about flash flood disaster [59]. But it is still difficult to forecast flash flood just rely on real-time monitoring data. Facing this, Li et al. tried to use the ensemble forecast approach, which constructed by various physical processes of a mesoscale model, to extend forecast period for flash flood early warning [60]. Qiu and Zi and Zi et al. also studied the application of quantified precipitation forecast technology in mountain areas and proved its applicability in flash flood early warning [61, 62]. Furthermore, Chen and Li simulated the historic flash flood occurred in Yangtze river basin on the basis of the weather research and forecasting (WRF) model to explore the optimal combination of physical schemes for the flash flood early warning [63].

#### **4.3. Information construction of warning systems**

rational formula method usually has more stable results with smaller error than data-driven methods [43]. In order to raise the accuracy of early warning precipitation, a method for calculating the geographic distribution rules of flow concentration parameters and the spatial

Eq. (4) is the fundamental form of rational formula, *Q* is the design flood, *a* is a constant reflecting the losses of flood peak, *S* is the maximum of hourly rainfall, *t* is the basin flow

Since the data of flash flood disasters are continuously increasing, more and more scholars start taking part in researching numerical model for calculating flash flood real-time warning indicators. These numerical models are usually built that rely on water balance, principles of hydrology or hydrodynamics. Based on the water balance equation, Jiang and Shao put forward a concept and proposed a calculation method of minimum critical rainfall with awareness that criteria warning standard should take both rainfall amount and intensity into account [45]. For warning in ungauged basins, Ye et al. proposed an approach that using an antecedent precipitation index method and a Nash model for runoff forecasting [46]. Furthermore, considering the soil moisture and observed antecedent rainfall, Chen et al. established the calculation functions for dynamic critical precipitation under different soil moisture content levels by using the fitted function relation between antecedent rainfall and critical rainfall by the least square method [47]. Guo et al. designed a flash flood warning system based on a distributed hydrological model and evaluated its practical application in Henan province [48]. Li et al. introduced the basic concepts and methods of using a distribution hydrological model technique with detailed sub-basin delineation to analyse indicators for flash flood early warning [49]. Based on the full hydrodynamic model, Wang et al. proposed a new approach to calculate the distributed threshold rainfall for flash flooding, which constitutes the basis for effective flash flood warning [50]. Wen and Zhang et al. used a 2-D dynamic flood evolution model 'FloodArea' to simulate flash flood inundation caused by different rainfall amount for refined flash flood early

concentration time, *n* is decline exponent of rainstorm and *F* is the drainage area.

warning in mountain area with small watershed and no hydrological data [51–53].

The main restriction to flash flood real-time early warning is the lack of real-time rainfall data, discharge data and water level data. The direct and efficient approach to solve this problem is to increase monitoring network for weather, rainfall and river situation. For guiding hydro-meteorological monitoring network layout in mountain flood prevention areas, Chen et al. and Yuan et al. put forward a technical principle and index for the layout of hydro-meteorological stations according to the needs of mountain flood prevention in China [54, 55]. In addition, Shu and Han also analysed the density of rainfall monitoring network in Suichuanjiang river basin by using the method of extracting stations and water-

Along with the construction of traditional hydro-meteorological monitoring network, scholars also explored the application of remote sensing technology in obtaining data and

**4.2. Improving data sources used for warning**

shed model method [56].

*tn* × *F* (4)

distributing rules of rainstorm parameters is put forward [44].

*Q* = 0.278 × \_\_*aS*

154 Engineering and Mathematical Topics in Rainfall

Informatization is quite necessary for shortening the delay time of each step for flash flood real-time warning. Flash flood early warning information construction, covering data acquisition, transmission, early warning analysis, disaster simulation and warning broadcasting could greatly enhance the capability of flash flood real-time warning. Considering particularity of environment in mountain areas, Meng designed a data acquisition unit with low power consumption, stable performance and high precision [64]. Zhong developed a river water level remote monitoring and early warning instruments by using the water level data collection which is based on static pressure sensor of input style and the general packet radio service (GPRS) and short messaging service (SMS) technology [65]. With the aid of code division multiple access (CDMA)/GPRS communication product, Fei and Huang designed an intelligence transmission system, which could provide unimpededly, fast, reliable and stable communication channel for transmission of flash flood real-time warning signals [66]. Zhang et al. and Hao et al. designed a flash flood geological disaster early warning system based on the Internet of Things (ToH) which combine the information sensor subsystem with data acquisition subsystem [67, 68]. Based on the distributed hydrological model and dynamic critical precipitation method, Hu and Liu developed a prototype medium and small river flash floods forecasting and warning systems and did a case study in Suichuan river basin [69]. To improve operational capacity of county flood control department in monitoring and early warning of flash floods, Zhang put forward an event-driven county-level flash flood monitoring and early warning system and applied it in Luanchuan country [70]. Moreover, Zhang designed a flash flood early warning information system based on the technology of GIS, spatial database and computer networking [71]. Jin and Wang summarized the implementation methods of the WebGIS and rich Internet application (RIA) technology solutions and developed the country-level flash flood early warning system separately by Silverlight technology or Flex technology [72, 73]. Furthermore, Li, Xiu, Lu and Lin designed their own flash flood disaster early warning system separately in Shandong province, Xinjiang province, Dinghu city or Yueyang city [74–77].

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