Systems and Sensors for Debris-flow Monitoring and Warning

Low-Frequency Ground Vibrations Generated by Debris Flows Detected by a Lab-Fabricated Seismometer

A lab-fabricated ocean bottom seismometer was modified and deployed terrestrially to detect low-frequency (<10 Hz) ground vibrations produced by debris flows. A frequency−response test of the new seismometer revealed that it can detect seismic signals at frequencies of 0.3−120 Hz. Its seismic ground motion detection ability was investigated by comparing its measurements of seismic signals produced by rockfalls with those of a geophone. Two new seismometers were deployed at the Aiyuzi Stream, Nantou County, Taiwan, in September 2012. Seismic signals produced by two local earthquakes, two teleseisms, and three debris flows detected by the seismometer in 2013 and 2014 were discussed. The seismic signal frequencies of the local earthquakes and teleseisms (both approximately 1800 km apart) were 0.3−30 and <1 Hz, respectively. Moreover, seismometer measurements revealed that seismic signals generated by debris flows can have minimum frequencies as low as 2 Hz. Time-matched CCD camera images revealed that debris flow surge fronts with larger rocks have lower minimum frequencies. Finally, because the seismometer can detect low-frequency seismic waves with low spatial decay rates, it was able to detect one debris flow approximately 3 min and 40 s before it arrived.

Some Considerations for Using Numerical Methods to Simulate Possible Debris Flows: The Case of the 2013 and 2020 Wayao Debris Flows (Sichuan, China)

Using a numerical simulation method based on physical equations to obtain the debris flow risk range is important for local-scale debris flow risk assessment. While many debris flow models have been used to reproduce processes after debris flow occurrence, their predictability in potentially catastrophic debris flow scenarios has mostly not been evaluated in detail. Two single-phase flow models and two two-phase models were used to reproduce the Wayao debris flow event in 2013. Then the Wayao debris flow event in 2020 was predicted by the four models with the same parameters in 2013. The depth distributions of the debris source and deposition fan were mapped by visual interpretation, electric resistivity surveys, field measurements, and unmanned aerial vehicle (UAV) surveys. The digital elevation model (DEM), rainfall data, and other simulation parameters were collected. These models can reproduce the geometry and thickness distribution of the debris flow fan in 2013. However, the predictions of the runout range and the deposition depth are quite different from the actuality in 2020. The performance and usability of these models are compared and discussed. This could provide a reference for selecting physical models to assess debris-flow risk.

Eighty Years of Data Collected for the Determination of Rainfall Threshold Triggering Shallow Landslides and Mud-Debris Flows in the Alps

Identifying the minimum rainfall thresholds necessary for landslides triggering is essential to landslide risk assessment. The Italian Alps have always been affected by shallow landslides and mud-debris flows, which caused considerable damage to property and, sometimes, casualties. We analysed information provided from different sources carrying on the most thorough research conducted for this alpine area. Thousands of documents and reports of rainfall values recorded over 80 years by rain gauges distributed in Sondrio and Brescia Provinces define the mean annual precipitation (MAP)-normalized intensity–duration thresholds for the initiation of shallow landslides and mud-debris flows. The established curves are generally lower compared to those proposed in literature for similar mountain areas in Italy and worldwide. Furthermore, we found that landslides occurred primarily at the same time or within 3 h from the maximum peak of rainfall intensity in summer events and in a period from 0 to 5 h or later in spring-autumn events. The paper provides a further contribution to the knowledge framework on the rainfall conditions required for the initiation of surficial landslides and mud-debris flows and their expected timing of occurrence. This knowledge is crucial to develop better warning strategies to mitigate geo-hydrological risk and reduce the socio-economic damage.

Development of Nomogram for Debris Flow Forecasting Based on Critical Accumulated Rainfall in South Korea

Climate change causes extreme weather events worldwide such as increasing temperatures and changing rainfall patterns. With South Korea facing growing damage from the increased frequency of localized heavy rains. In particular, its steep slope lands, including mountainous areas, are vulnerable to damage from landslides and debris flows. In addition, localized short-term heavy rains that occur in urban areas with extremely high intensity tend to lead a sharp increase in damage from soil-related disasters and cause huge losses of life and property. Currently, South Korea forecasts landslides and debris flows using the standards for forecasting landslides and heavy rains. However, as the forecasting is conducted separately for rainfall intensity and accumulated rainfall, this lacks a technique that reflects both amount and intensity of rainfall in an episode of localized heavy rainfall. In this study, aims to develop such a technique by collecting past cases of debris flow occurrences and rainfall events that accompanied debris flows to calculate the rainfall triggering index (RTI) reflecting accumulated rainfall and rainfall intensity. In addition, the RTI is converted into the critical accumulated rainfall (Rc) to use rainfall information and provide real-time forecasting. The study classifies the standards for flow debris forecasting into three levels: ALERT (10–50%), WARNING (50–70%), and EMERGENCY (70% or higher), to provide a nomogram for 6 h, 12 h, and 24 h. As a result of applying this classification into the actual cases of Seoul, Chuncheon, and Cheongju, it is found that about 2–4 h of response time is secured from the point of the Emergency level to the occurrence of debris flows.

An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network

Debris flow disasters pose a serious threat to public safety in many areas all over the world, and it may cause severe consequences, including losses, injuries, and fatalities. With the emergence of deep learning and increased computation powers, nowadays, machine learning methods are being broadly acknowledged as a feasible solution to tackle the massive data generated from geo-informatics and sensing platforms to distill adequate information in the context of disaster monitoring. Aiming at detection of debris flow occurrences in a mountainous area of Sakurajima, Japan, this study demonstrates an efficient in-situ monitoring system which employs state-of-the-art machine learning techniques to exploit continuous monitoring data collected by a wireless accelerometer sensor network. Concretely, a two-stage data analysis process had been adopted, which consists of anomaly detection and debris flow event identification. The system had been validated with real data and generated favorable detection precision. Compared to other debris flow monitoring system, the proposed solution renders a batch of substantive merits, such as low-cost, high accuracy, and fewer maintenance efforts. Moreover, the presented data investigation scheme can be readily extended to deal with multi-modal data for more accurate debris monitoring, and we expect to expend addition sensory measurements shortly.

A New Water Level Measurement Method Combining Infrared Sensors and Floats for Applications on Laboratory Scale Channel under Unsteady Flow Regime

In this paper, we studied water transport under an unsteady flow regime in an experimental channel (4 m in length; 3 cm in width). Our experiments implicated some measuring requirements, specifically, a water level (WL) detection technique that is able to measure WL in a range of 2 cm with a precision of 1 mm. The existing WL detection techniques could not meet our measurement requirements. Therefore, we propose a new measurement method that combines two approaches: An "old" water contact technique (float) with a "new" remote non-contact technique (infrared sensor). We used an extruded polystyrene (XPS Foam) that needed some adequate treatment before using it as float in experimental measurements. The combination of IR-sensors with treated float foam lead to a sensitive measurement method that is able to detect flat and sharp flow signals, as well as highly dynamic variations of water surface level. Based on the experimental measurements of WL and outflow at the channel output, we deduced a loop rating curve that is suitable with a power law adjustment. The new measurement method could be extended to larger scale applications like rivers and more complicated cross section geometry of irregular shape.

RiverCore: IoT Device for River Water Level Monitoring over Cellular Communications

Flooding is one of the most frequent and costly natural disasters affecting mankind. However, implementing Internet of Things (IoT) technology to monitor river behavior may help mitigate or prevent future disasters. This article outlines the hardware development of an IoT system (RiverCore) and defines an application scenario in a specific hydrological region of the state of Colima (Mexico), highlighting the characteristics of data acquisition and data processing used. Both fixed position and moving drifter node systems are described along with web-based data acquisition platform developments integrated with IoT techniques to retrieve data through 3G cellular networks. The developed architecture uses the Message Queuing Telemetry Transport (MQTT) protocol, along with encryption and security mechanisms, to send real-time data packages from fixed nodes to a server that stores retrieved data in a non-relational database. From this, data can be accessed and displayed through different customizable queries and graphical representations, allowing future use in flood analysis and prediction systems. All of these features are presented along with graphical evidence of the deployment of the different devices and of several cellular communication and on-site data acquisition tests.

FBG-Based Monitoring of Geohazards: Current Status and Trends

In recent years, natural and anthropogenic geohazards have occured frequently all over the world, and field monitoring is becoming an increasingly important task to mitigate these risks. However, conventional geotechnical instrumentations for monitoring geohazards have a number of weaknesses, such as low accuracy, poor durability, and high sensitivity to environmental interferences. In this aspect, fiber Bragg grating (FBG), as a popular fiber optic sensing technology, has gained an explosive amount of attention. Based on this technology, quasi-distributed sensing systems have been established to perform real-time monitoring and early warning of landslides, debris flows, land subsidence, earth fissures and so on. In this paper, the recent research and development activities of applying FBG systems to monitor different types of geohazards, especially those triggered by human activities, are critically reviewed. The working principles of newly developed FBG sensors are briefly introduced, and their features are summarized. This is followed by a discussion of recent case studies and lessons learned, and some critical problems associated with field implementation of FBG-based monitoring systems. Finally the challenges and future trends in this research area are presented.

Calibration and deployment of a fiber-optic sensing system for monitoring debris flows

This work presents a novel fiber-optic sensing system, capable of monitoring debris flows or other natural hazards that produce ground vibrations. The proposed sensing system comprises a demodulator (BraggSCOPE, FS5500), which includes a broadband light source and a data logger, a four-port coupler and four Fiber Bragg Grating (FBG) accelerometers. Based on field tests, the performance of the proposed fiber-optic sensing system is compared with that of a conventional sensing system that includes a geophone or a microphone. Following confirmation of the reliability of the proposed sensing system, the fiber-optic sensing systems are deployed along the Ai-Yu-Zi and Chu-Shui Creeks in Nautou County of central Taiwan for monitoring debris flows. Sensitivity test of the deployed fiber-optic sensing system along the creek banks is also performed. Analysis results of the seismic data recorded by the systems reveal in detail the frequency characteristics of the artificially generated ground vibrations. Results of this study demonstrate that the proposed fiber-optic sensing system is highly promising for use in monitoring natural disasters that generate ground vibrations.

Transformation of ground vibration signal for debris-flow monitoring and detection in alarm systems

Debris flows are fast mass movements formed by a mix of water and solid materials, which occur in steep torrents, and are a source of high risks for human settlements. Geophones are widely used to detect the ground vibration induced by passing debris flows. However, the recording of geophone signals usually requires storing a huge amount of data, which leads to problems in storage capacity and power consumption. This paper presents a method to transform and simplify the signals measured by geophones. The key input parameter is the ground velocity threshold, which removes the seismic noise that is not related to debris flows. A signal conditioner was developed to implement the transformation and the ground velocity threshold was set by electrical resistors. The signal conditioner was installed at various European monitoring sites to test the method. Results show that data amount and power consumption can be greatly reduced without losing much information on the main features of the debris flows. However, the outcome stresses the importance of choosing a ground vibration threshold, which must be accurately calibrated. The transformation is also suitable to detect other rapid mass movements and to distinguish among different processes, which points to a possible implementation in alarm systems.