# Update of Early Warning Indicators of Flash Floods: A Case Study of Hunjiang District, Northeastern China

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## Abstract

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## 1. Introduction

^{2}and 0.5 million administrative villages with a total population of 900 million [17], mainly including the distribution law and risk zoning, the storm flood law in small watersheds, the early warning indicators, the prediction model, the monitoring and early warning system and the group monitoring and prevention system [18,19]. However, the research and application started relatively late, and the research is still in the exploratory stage. Moreover, the errors in the statistical data of the grassroots are obvious, resulting in insufficient timeliness and inaccurate warnings. Therefore, it is urgent to take reasonable measures to check and verify early warning indicators, thereby improving the accuracy of flash flood monitoring and forecasting.

## 2. Material and Methods

#### 2.1. Study Area

#### 2.2. Methodology

#### 2.2.1. Design Rainstorm Calculation

_{tij}is the maximum rainfall of the j-th flash flood at the i-th rain station in t period, and the smallest of the N statistical values is initially considered as the critical rainfall, and the calculation formula is as follows:

_{tij}is the maximum rainfall of the j-th flash flood at the i-th rain station in t period.

_{tj}is the maximum average rainfall in the corresponding rainfall process of the j-th flash flood in the t period (obtained by moving average), and there are N (one for each disaster) maximum surface average rainfall in each period.

#### 2.2.2. Runoff Generation Calculation

_{0}, and the maximum point storage capacity corresponding to a

_{0}is α.

#### 2.2.3. Confluence Calculation

_{m}is the peak flow under design standards, S

_{P}is the rain force of design rainstorm, $\tau $ is the basin confluence time, $\overline{f}$ is the loss parameter and is mainly determined according to local experience, F is the watershed area, and L is the longest runoff confluence path in the watershed.

_{c}is the duration of net rainfall, and the calculation of flood peak runoff coefficient φ is theoretically divided into two parts:

_{c}≥ τ, full confluence occurs:

_{c}< τ, partial confluence occurs:

_{m}can be calculated by introducing the above parameters into the Equations (10) and (11) according to the trial algorithm and graphic method.

_{m}is the flow rate in m/s, A is flow area of the section in m

^{2}, n is the roughness, R is the hydraulic radius in m, and J is the hydraulic gradient of the river. Refer to HEC-RAS to divide the control section into the left bank, for more details on HEC-RAS, please see Pappenberger et al. (2005) [23]. The discharges of the main channel and the right bank were calculated separately. It is assumed that the surface shear force exists at the interface between the beach and the channel. According to the equilibrium relationship of force and the momentum transport theory of Prandtl, the relationship of shear stress at the interface between the beach and the channel is determined, and then the discharge of the three parts is determined. The calculation formula is as follows:

^{3}/s; ${V}_{1}$, ${V}_{2}$, ${V}_{3}$ are the velocity of the main channel, left shore, and right shore in m/s; ${A}_{1}$, ${A}_{2}$, ${A}_{3}$ are effective cross-section area of main cthe hannel, left bank and right bank, respectively, in m

^{2}.

- (i)
- The water–surface ratio determined by the flood marks of the latest floods should be selected.
- (ii)
- The comprehensive gradient (no sudden change at the bottom of the river) or the partial gradient (sudden change, for example, the gradient of the upstream section of the dam must be the gradient of the upstream water–surface, and the additional gradient caused by the river congestion should be eliminated.

^{3}/s, F is watershed area in km

^{2}, S(t) is the S curve obtained from the instantaneous unit hydrograph.

_{i}is the unit hydrograph of small watershed after storage in m

^{3}/s, I is the unit hydrograph of small watershed before storage in m

^{3}/s, and c is the storage coefficient.

#### 2.3. Data Sources

## 3. Results and Discussion

#### 3.1. Determine Updating Object

- (1)
- Meteorological and hydrological data. Due to the flash flood watershed is mostly less than 200 km
^{2}, one of the screening conditions is the watershed area is less than 1000 km^{2}(considering the original disaster prevention objects, hydrometeorological data, and station distribution). Moreover, taking into account the short-duration characteristics of heavy rainfall, and the rapid convergence of the slopes, and the rapid convergence of the river on the slope, both the areas with smaller upstream watersheds and better rainfall data are considered. - (2)
- Typical historical flash flood. Since the convergence time of flash flood is usually less than 12 h, considering the spatial–temporal distribution, frequency, the average value and variation coefficient of heavy rain, the northern Hunjiang district is the key prevention area, which mainly distributes in the central region from west to east. In the northeast hilly area, the upstream is prone to trigger flash flood disasters due to the small catchment area. Combined with the recent flash flood disaster data and expert scoring, Figure 4 is the Distribution map of the review objects in Hunjiang. Table 1 is the watershed attribute information. Among them, Xiangmu-1 and Sanchahe-3 are located in the lower reaches of the river, mainly affected by upstream rainfall, and are typical villages that cause floods due to the fluctuation of river floods.

#### 3.2. Design Flood

#### 3.2.1. Water Level-Discharge Curves

#### 3.2.2. Inference Formula

#### 3.3. Early Warning Indicators

_{c}is critical rainfall, mm; P

_{e}is disaster-causal rainfall, mm.

## 4. Conclusions

- (1)
- Based on the survey data, such as recent flash flood data, meteorological hydrological data, and topographical population data, the three typical riverside villages of Shangqing-4, Xiangmo-1, and Sanchahe-3 were comprehensively screened by expert scoring method.
- (2)
- Using the inference formula method and the water level-discharge curves respectively, the maximum error of the flood peak is 10.6%, which has high reliability and can be applied to the inspection and verification of early warning indicators. The measurement results along the river section are consistent with the actual measurement results, but the roughness of the main channel and the two banks are high.
- (3)
- The calculation method of the design rainstorm and the relevant parameters of the early warning indicator are reasonable. Both the gradient and the roughness in CFFIED are larger than the measured. When the same water level condition, the peek flow value is smaller than the rechecked value.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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Prevention Object | Rainfall Collection Area (km^{2}) | Slope (‰) | Roughness | Design Peak Flow (m^{3}/s) | Runoff Concentration-Time (h) | Peak Flow/10^{4} m^{3} | Flood Duration (h) | Flood Stage (m) | Cause Disaster (m) | Earling Warning Period (h) | Soil Moisture (%) | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Left Bank | Main Channel | Right Bank | |||||||||||

Shangqing-4 | 1.2 | 51 | 0.05 | 0.035 | 0.05 | 18.1 | 0.5 | 4.8 | 10 | 728.8 | 728.4 | 1 January | 0.2 Wm |

Xiangmo-1 | 80.5 | 11 | 0.06 | 0.04 | 0.06 | 280 | 7.4 | 339.3 | 47 | 472.8 | 471.8 | 12 January | 0.5 Wm |

Sanchahe-3 | 51.4 | 42 | 0.05 | 0.035 | 0.05 | 266.7 | 6.3 | 323.7 | 53 | 477.8 | 477.2 | 6 January | 0.8 Wm |

Name of Village | Roughness of the Left Bank | Rroughness of the Right Bank | Roughness of the Main Channel | Slope (‰) | Diagram of Water Level-Flow Relation |
---|---|---|---|---|---|

Shangqing-4 | 0.043 | 0.043 | 0.032 | 51 | |

Xiangmo-1 | 0.048 | 0.048 | 0.035 | 11 | |

Sanchahe-3 | 0.045 | 0.045 | 0.035 | 42 | |

Prevention Object | Confluence Time (h) | Peak Flow (10^{4} m^{3}) | Flood Duration (h) | Flood-Peak Stage (m) | Design Peak Flow (m^{3}/s) | ||
---|---|---|---|---|---|---|---|

Inference Formula | Water Level-Flow Relation | PB (%) | |||||

Shangqing-4 | 0.5 | 4.8 | 10 | 728.75 | 18.1 | 16.19 | 10.6 |

Xiangmo-1 | 7.4 | 339.3 | 47 | 472.84 | 170.1 | 168.66 | 0.8 |

Sanchahe-3 | 6.3 | 323.7 | 53 | 477.76 | 266.7 | 274.02 | 2.6 |

Reviewing Object | Early Warning Period (h) | Antecedent Precipitation Index (Wm) | Characteristic Rainfall (mm) | Updated Critical Rainfall (mm) | PB (%) | Early Warning Index (mm) | |
---|---|---|---|---|---|---|---|

Prepare to Transfer | Transfer Now | ||||||

Shangqing-4 | 1 | 0.8 | 30 | 26 | 13.3 | 19 | 26 |

Xiangmo-1 | 1 | 26 | 45 | 15.4 | 32 | 45 | |

2 | 28 | 59 | 7.1 | 42 | 59 | ||

3 | 34 | 69 | 5.8 | 51 | 69 | ||

Sanchahe-3 | 1 | 62 | 80 | 29 | 56 | 80 | |

2 | 81 | 88 | 8.6 | 62 | 88 | ||

3 | 104 | 97 | 6.5 | 69 | 97 |

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**MDPI and ACS Style**

Ma, M.; Zhang, J.; Su, H.; Wang, D.; Wang, Z.
Update of Early Warning Indicators of Flash Floods: A Case Study of Hunjiang District, Northeastern China. *Water* **2019**, *11*, 314.
https://doi.org/10.3390/w11020314

**AMA Style**

Ma M, Zhang J, Su H, Wang D, Wang Z.
Update of Early Warning Indicators of Flash Floods: A Case Study of Hunjiang District, Northeastern China. *Water*. 2019; 11(2):314.
https://doi.org/10.3390/w11020314

**Chicago/Turabian Style**

Ma, Meihong, Jingnan Zhang, Huidong Su, Dacheng Wang, and Zhongliang Wang.
2019. "Update of Early Warning Indicators of Flash Floods: A Case Study of Hunjiang District, Northeastern China" *Water* 11, no. 2: 314.
https://doi.org/10.3390/w11020314