Study on the Uncertainty of Critical Rainfall for Flash Floods in Small Watersheds Based on the Random Rainfall Pattern

Flash floods cause great harm to people's lives and property safety. Rainfall is the key factor which induces flash floods, and critical rainfall (CR) is the most widely used indicator in flash flood early warning systems. Due to the randomness of rainfall, the CR has great uncertainty, which causes missed alarms when predicting flash floods. To improve the early warning accuracy for flash floods, a random rainfall pattern (RRP) generation method based on control parameters, including the comprehensive peak position coefficient (CPPC) and comprehensive peak ratio (CPR), is proposed and an early warning model with dynamic correction based on RRP identification is established. The rainfall-runoff process is simulated by the HEC-HMS hydrological model, and the CR threshold space corresponding to the RRP set is calculated based on the trial algorithm. Xinxian, a small watershed located in Henan Province, China, is taken as the case study. The results show that the method for generating the RRP is practical and simple, and it effectively reflects the CR uncertainty caused by the rainfall pattern uncertainty. The HEC-HMS model is proved to have good application performance in the Xinxian watershed. Through sensitivity analysis, the effect of the antecedent soil moisture condition, CPPC, and CPR are compared. The proposed early warning model is practical and effective, which increases the forecast lead time.

The Influence of Rainfall Amounts Not Exceeding Critical Rainfall on Landslide Occurrence in Japan

Critical rainfall events are used in landslide early-warning systems to predict the occurrence and severity of disasters. In this study, past landslide disasters in Japan were identified for which the critical rainfall set for each 1-km grid was exceeded using historical landslide records, radar-based rainfall data over a 1-km grid, and standard rainfall data collected over the past 17 years. It was determined that nearly equal numbers of rainfall events were identified with higher and lower rainfall amounts than the critical rainfall. The probability that a series of rainfall events would cause a landslide was approximately 1.15% when the critical rainfall was exceeded and 0.09% otherwise, a difference of approximately 10 times. It was also found that even if critical rainfall was not exceeded, in the case of debris flow and slope failures, there was rainfall that exceeded the standard rainfall one or two days before. In the case of landslides, there was rainfall that exceeded the critical rainfall one or two weeks before, and if the critical rainfall was exceeded in another rainfall event, a landslide could occur. The operational evaluation of Japanese LEWSs has a recall value of 0.486 as the accuracy of occurrence prediction, which was related to the fact that almost half of the rainfall events occurred in nonexceedance of the reference rainfall. The specificity was 0.935, known as the accuracy of nonoccurrence prediction, which was also greatly influenced by the TN (true negative) data of nonexceeding rainfall events, which accounted for most of the data.

Rainfall Warning Model for Rainfall-Triggered Channelized Debris Flow Based on Physical Model Test—A Case Study of Laomao Mountain Debris Flow in Dalian City

Debris flows are among the most frequent and hazardous disasters worldwide. Debris flow hazard prediction is an important and effective means of engineering disaster mitigation, and rainfall threshold is the core issue in debris flow prediction. This study selected the Laomao Mountain debris flow in Dalian as the research object and explored the relationship among the percentage of coarse sand content of soil, rainfall conditions and the critical rainfall values that induce debris flows on the basis of field investigation data, combined with the results of a flume test, soil suction measurement and geomechanical analysis. The new multi-parameter debris flow initiation warning models were obtained through the mathematical regression analysis method. The critical rainfall values of debris flows in this area were calculated by the previous research on the mechanism of hydraulic debris flow initiation (HIMM). Lastly, the multi-parameter debris flow initiation warning models were compared and analyzed with the critical rainfall values obtained using the HIMM method and the rainfall information available in historical rainfall data, and the reliability of the models was verified. The comparison results showed that the new multi-parameter debris flow initiation warning models can effectively modify the traditional intensity–duration model and have certain reliability and practical values. They can provide an effectual scientific basis for future work on the monitoring and prediction of debris flow disasters.

Monitoring of Rainfall and Soil Moisture at the Rebaixader Catchment (Central Pyrenees)

ABSTRACT The instrumental monitoring of torrential catchments is a fundamental research task and provides necessary information to improve our understanding of the mechanisms of debris flows. While most monitoring sites include meteorological sensors and analyze the critical rainfall conditions, very few contain soil moisture measurements. In our monitoring site, the Rebaixader catchment, 11 debris flows and 24 debris floods were detected during the last 9 years. Herein, the initiation mechanisms of these torrential flows were analyzed, focusing on the critical rainfall conditions and the soil water dynamics. Comparing the temporal distribution of both rainfall episodes and torrential flows, the Kernel density plots showed maximum values for rainfalls at the beginning of June, while the peak for torrential flows is on July 20. Thus, the antecedent rainfall, and especially the soil moisture conditions, may influence the triggering of torrential flows. In a second step, a new updated rainfall threshold was proposed that included total rainfall duration and mean intensity. The analysis of soil moisture data was more complicated, and no clear trends were observed in the data set. Therefore, additional data have to be recorded in order to quantitatively analyze the role of soil moisture on the triggering of flows and for the definition of thresholds. Some preliminary results show that the soil moisture at the beginning of a rainfall event affects the maximum increase of soil moisture, while a slight trend was visible comparing the initial soil moisture with the necessary rainfall amount to trigger a torrential flow.

Study on the Early Warning and Forecasting of Flash Floods in Small Watersheds Based on the Rainfall Pattern of Risk Probability Combination

Flash floods cause great harm to people's life and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. This paper proposes a novel rainfall pattern based on total rainfall and peak rainfall intensity, i.e., the rainfall pattern of risk probability combination (RPRPC). To determine the joint distribution function with the best fitting effect, copula functions are introduced and optimized. On this basis, the HEC-HMS hydrological model is used to simulate the rainfall-runoff process, a trial algorithm is used to calculate the critical rainfall (CR), and an optimistic-general-pessimistic (O-G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern (TRP) and that the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, the influence of antecedent soil moisture condition (ASMC) and rainfall pattern on critical rainfall varies with the change of peak rainfall intensity and rainfall duration. Finally, the O-G-P early warning mode is effective and provides a valuable reference for the early warning and forecasting of flash floods in small watersheds in hilly areas.