scholarly journals Uji Laboratorium Pengaruh Kemiringan Lereng Terhadap Kejadian Longsoran Aliran Debris Pasir Merapi

2020 ◽  
Vol 16 (1) ◽  
pp. 23-34
Author(s):  
Bayu Seto Waseso Utomo ◽  
Jati Iswardoyo ◽  
Ruzardi Ruzardi
Keyword(s):  
Debris Flow ◽  
Laboratory Test ◽  
Early Warning ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Laboratory Scale ◽  
Artificial Rainfall ◽  
Rain Simulator

The debris flow that happen on the of Mount Merapi is really hard to be seen, therefore, it is necessary to conduct laboratory-scale simulations to know when debris flows will happen as regard to rainfall intensity and the slope of Mount of Merapi. This research examines the correlation between the slope and the potential for debris flow at 25 mm/h rainfall intensity. This will be a reference for early warning of landslides on Mount of Merapi. This research uses a tool such as flume that sized 3 x 5 x 0,15 m as a model of slope of Mount of Merapi, and artificial rainfall apparatus as the rain simulator. The simulation is conducted using five years rainfall intensity of 25 mm/h in combination of slope i.e. 15, 20, 25, 30 and 35 degrees whereas the material used to represent the sediment is in form of sand taken from Gendol River upstream with 4,75 mm passing mesh sieves. The result of this simulation is the steeper the slope is, the faster the duration for the rain to cause debris flow. This research can be continued with change variation of rainfall intensity to understand the debris flows behavior. Keywords: Debris flow, Mount of Merapi, laboratory test, rainfall intensity, flume model

scholarly journals Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 750
Author(s):  
Antonio Pasculli ◽  
Jacopo Cinosi ◽  
Laura Turconi ◽  
Nicola Sciarra
Keyword(s):  
Debris Flow ◽  
Shallow Water ◽  
Early Warning ◽  
Debris Flows ◽  
Early Warning System ◽  
Warning System ◽  
Extreme Weather Events ◽  
Water Model ◽  
Computational Time ◽  
Processing Unit

The current climate change could lead to an intensification of extreme weather events, such as sudden floods and fast flowing debris flows. Accordingly, the availability of an early-warning device system, based on hydrological data and on both accurate and very fast running mathematical-numerical models, would be not only desirable, but also necessary in areas of particular hazard. To this purpose, the 2D Riemann–Godunov shallow-water approach, solved in parallel on a Graphical-Processing-Unit (GPU) (able to drastically reduce calculation time) and implemented with the RiverFlow2D code (version 2017), was selected as a possible tool to be applied within the Alpine contexts. Moreover, it was also necessary to identify a prototype of an actual rainfall monitoring network and an actual debris-flow event, beside the acquisition of an accurate numerical description of the topography. The Marderello’s basin (Alps, Turin, Italy), described by a 5 × 5 m Digital Terrain Model (DTM), equipped with five rain-gauges and one hydrometer and the muddy debris flow event that was monitored on 22 July 2016, were identified as a typical test case, well representative of mountain contexts and the phenomena under study. Several parametric analyses, also including selected infiltration modelling, were carried out in order to individuate the best numerical values fitting the measured data. Different rheological options, such as Coulomb-Turbulent-Yield and others, were tested. Moreover, some useful general suggestions, regarding the improvement of the adopted mathematical modelling, were acquired. The rapidity of the computational time due to the application of the GPU and the comparison between experimental data and numerical results, regarding both the arrival time and the height of the debris wave, clearly show that the selected approaches and methodology can be considered suitable and accurate tools to be included in an early-warning system, based at least on simple acoustic and/or light alarms that can allow rapid evacuation, for fast flowing debris flows.

Simulating debris flows triggered by rainfall in Shiyang gully, China

2020 ◽  
Author(s):  
Jiaoyang Li
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Intensity Increase ◽  
Short Term ◽  
Rain Area ◽  
Cumulative Rainfall ◽  
Event Flow ◽  
Warning Time ◽  
Main Factor

<p>A debris flow occurred in Shiyang gully, located between Hebei Province and Beijing, on 8 June 2017, resulting in 6 people dead or injured. Short-term heavy rainfall is the main factor that triggered this event, however, the meteorological agency didn’t forecast this event very well. In this study, numerical simulation using FLO-2D was performed to reproduce the debris flow event (flow depths, flow velocities, and sediment depositions)occurred in 2017. The results of the field survey showed that the influential range of debris flow is consistent with the simulation results. Simulated depth accuracy is greater than 70%. Then, we used FLO-2D is calibrated to simulate debris flows disasters under different rainfall scenarios. The results showed that, the Beijing needs to be warned when the accumulated precipitation is 40mm at the rainfall intensity of 1mm/min. As cumulative rainfall and rainfall intensity increase, the risk of Shiyang gully is increasing.  This study used FLO-2D simulated process of debris flows triggered by rainfall. The results showed the early warning time and influential range for different intensity ,accumulated precipitation, and rain area, which is beneficial to the debris flow management in the western mountainous areas of Beijing.</p>

scholarly journals Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment

2021 ◽  
Vol 21 (9) ◽  
pp. 2773-2789
Author(s):  
Jacob Hirschberg ◽  
Alexandre Badoux ◽  
Brian W. McArdell ◽  
Elena Leonarduzzi ◽  
Peter Molnar
Keyword(s):  
Random Forest ◽  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Multiple Scales ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Early Warning Systems ◽  
Antecedent Rainfall ◽  
Data Set

Abstract. The prediction of debris flows is relevant because this type of natural hazard can pose a threat to humans and infrastructure. Debris-flow (and landslide) early warning systems often rely on rainfall intensity–duration (ID) thresholds. Multiple competing methods exist for the determination of such ID thresholds but have not been objectively and thoroughly compared at multiple scales, and a validation and uncertainty assessment is often missing in their formulation. As a consequence, updating, interpreting, generalizing and comparing rainfall thresholds is challenging. Using a 17-year record of rainfall and 67 debris flows in a Swiss Alpine catchment (Illgraben), we determined ID thresholds and associated uncertainties as a function of record duration. Furthermore, we compared two methods for rainfall definition based on linear regression and/or true-skill-statistic maximization. The main difference between these approaches and the well-known frequentist method is that non-triggering rainfall events were also considered for obtaining ID-threshold parameters. Depending on the method applied, the ID-threshold parameters and their uncertainties differed significantly. We found that 25 debris flows are sufficient to constrain uncertainties in ID-threshold parameters to ±30 % for our study site. We further demonstrated the change in predictive performance of the two methods if a regional landslide data set with a regional rainfall product was used instead of a local one with local rainfall measurements. Hence, an important finding is that the ideal method for ID-threshold determination depends on the available landslide and rainfall data sets. Furthermore, for the local data set we tested if the ID-threshold performance can be increased by considering other rainfall properties (e.g. antecedent rainfall, maximum intensity) in a multivariate statistical learning algorithm based on decision trees (random forest). The highest predictive power was reached when the peak 30 min rainfall intensity was added to the ID variables, while no improvement was achieved by considering antecedent rainfall for debris-flow predictions in Illgraben. Although the increase in predictive performance with the random forest model over the classical ID threshold was small, such a framework could be valuable for future studies if more predictors are available from measured or modelled data.

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

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2181
Author(s):  
Nam ◽  
Lee ◽  
Kim
Keyword(s):  
South Korea ◽  
Debris Flow ◽  
Urban Areas ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Accumulated Rainfall ◽  
Landslides And Debris Flows ◽  
Heavy Rains

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.

Multi-parametric observations of debris-flow initiation at the headwaters of the Gadria catchment (eastern Italian Alps)

2020 ◽  
Author(s):  
Velio Coviello ◽  
Matteo Berti ◽  
Lorenzo Marchi ◽  
Francesco Comiti ◽  
Giulia Marchetti ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Debris Flow ◽  
Early Warning ◽  
Debris Flows ◽  
Lead Time ◽  
Early Warning Systems ◽  
Force Balance ◽  
Italian Alps ◽  
Alarm Systems ◽  
Debris Flow Initiation

<p>The complete understanding of the mechanisms controlling debris-flow initiation is still an open challenge in landslide research. Most debris-flow models assume that motion suddenly begins when a large force imbalance is imposed by slope instabilities or the substrate saturation that causes the collapse of the channel sediment cover. In the real world, the initiation of debris flows usually results from the perturbation of the static force balance that retains sediment masses in steep channels. These perturbations are primarily generated by the increasing runoff and by the progressive erosion of the deposits. Therefore, great part of regional early warning systems for debris flows are based on critical rainfall thresholds. However, these systems are affected by large spatial-temporal uncertainties due to the inadequate number and distribution of rain gauges. In addition, rainfall analysis alone does not explain the dynamics of sediment fluxes at the catchment scale: short-term variations in the sediment sources strongly influence the triggering of debris flows, even in catchments characterized by unlimited sediment supply.</p><p>In this work, we present multi-parametric observations of debris flows at the headwaters of the Gadria catchment (eastern Italian Alps). In 2018, we installed a monitoring network composed of geophones, three soil moisture probes, one tensiometer and two rain-triggered videocameras in a 30-m wide steep channel located at about 2200 m a.s.l. Most sensors lie on the lateral ridges of this channel, except for the tensiometer and the soil moisture probes that are installed in the channel bed at different depths. This network recorded four flow events in two years, two of which occurred at night. Specifically, the debris flows that occurred on 21 July 2018 and 26 July 2019 produced remarkable geomorphic changes in the monitored channel, with up to 1-m deep erosion. For all events, we measured peak values of soil water content that are far from saturation (<0.25 at -20 cm, <0.15 at -40 cm, <0.1 at -60 cm). We derived the time of occurrence and the duration of these events from the analysis of the seismic signals. Combining these pieces of information with data gathered at the monitoring station located about 2 km downstream, we could determine the flow kinematics along the main channel.</p><p>These results, although still preliminary, show the relevance of a multi-parametric detection of debris-flow initiation processes and may have valuable implications for risk management. Alarm systems for debris flows are becoming more and more attractive due the continuous development of compact and low-cost distributed sensor networks. The main challenge for operational alarm systems is the short lead-time, which is few tens of seconds for closing a transportation route or tens of minutes for evacuating settlements. Lead-time would significantly increase installing a detection system in the upper part of a catchment, where the debris flow initiates. The combination of hydro-meteorological monitoring in the source areas and seismic detection of channelized flows may be a reliable approach for developing an integrated early warning - alarm system.</p>

Consideration of the Validity of Debris-flow Bulking Factors

2017 ◽  
Vol 23 (4) ◽  
pp. 291-298
Author(s):  
Holly Brunkal ◽  
Paul Santi
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Peak Discharge ◽  
Data Set ◽  
Burned Areas ◽  
The Given ◽  
Basin Area ◽  
Bulking Factor ◽  
Significant Underestimation

Abstract Compilation of a database of debris-flow peak discharges (Q) allowed for a comparison with the expected basin discharge, as computed using the rational equation, Q=CIA. The observed values of Q for debris flows in unburned and burned areas were divided by the computed Q values of runoff using the rational method. This ratio is the bulking factor for that debris-flow event. Unburned and burned basins constitute two distinct populations; analysis shows that the bulking factors for burned areas are consistently higher than those for unburned basins. Previously published bulking factors for unburned areas fit the data set in about 50 percent of the observed cases in our compiled data set. Bulking factors for burned areas that were found in the published literature were well below the observed increases in peak discharge in over 50 percent of the cases investigated. If used for design purposes, these bulking factors would result in a significant underestimation of the peak discharge from a burned basin for the given rainfall intensity. Peak discharge bulking rates were found to be inversely related to basin area.

Debris-flow hazards in the Blue Ridge of central Virginia

2000 ◽  
Vol 6 (1) ◽  
pp. 3-23 ◽  
Author(s):  
G. F. Wieczorek ◽  
B. A. Morgan ◽  
R. H. Campbell
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Granitic Rocks ◽  
Blue Ridge ◽  
Eastern United States ◽  
Storm Events ◽  
Gis Technology ◽  
Madison County ◽  
Physical Examinations

Abstract The June 27, 1995, storm in Madison County, Virginia produced debris flows and floods that devastated a small (130 km 2 ) area of the Blue Ridge in the eastern United States. Although similar debris-flow inducing storm events may return only approximately once every two thousand years to the same given locale, these events affecting a similar small-sized area occur about every three years somewhere in the central and southern Appalachian Mountains. From physical examinations and mapping of debris-flow sources, paths, and deposits in Madison County, we develop methods for identifying areas subject to debris flows using Geographic Information Systems (GIS) technology. We examined the rainfall intensity and duration characteristics of the June 27, 1995, and other storms, in the Blue Ridge of central Virginia, and have defined a minimum threshold necessary to trigger debris flows in granitic rocks. In comparison with thresholds elsewhere, longer and more intense rainfall is necessary to trigger debris flows in the Blue Ridge.

scholarly journals EVALUATION ON THE IMPLEMENTATION OF EARLY WARNING SYSTEM FOR LAHAR IN MERAPI AREA (CASE STUDY AT BOYONG RIVER)

2015 ◽  
Vol 1 (3) ◽  
pp. 77
Author(s):  
Ali Cahyadi Achmad
Keyword(s):  
Debris Flow ◽  
Qualitative Analysis ◽  
Volcanic Activity ◽  
Early Warning ◽  
Early Warning System ◽  
Rainfall Intensity ◽  
Warning System ◽  
Actual Condition ◽  
Telemetry System ◽  
Debris Flood

One of disasters caused by volcanic activity of Mount Merapi is secondary disaster. The disaster usually occurs after eruption and this volcanic activity produces volcanic and pyroclastic material deposit around the top of the mountain as a result of previous eruption. This material might collapse downward in the form of debris flow as it is affected by natural event such as high intensity rainfall. Therefore, a research is needed to analyze whether existing forecasting and early warning system are capable to provide information for the people living in hazardous area before the debris flood occur. This research was carried out using field survey, observation and interview method. Data analysis used qualitative descriptive method by making description of actual condition of the researched location general condition and qualitative analysis of telemetry system installed on Mount Merapi. The qualitative analysis of telemetry system covers network, hardware, software, power supply, security system, operation and maintenance, also human resources. Research analysis used primary and secondary data. Research results revealed that mean rainfall intensity above of 60 mm/hour might trigger debris flood. Early warning should be given at the rainfall intensity level of 50-55 mm/hour, and debris flood time travel from the upstream to the observed location in Pulowatu Village is 45 minute. Based on the analysis of the present forecasting and early warning system, it is known that some of the equipment is not well functioned, so that debris flow cannot be predicted and detected. This is caused by the lack of human resource quality of the officers in operating and maintaining the equipment. Concerning that matter, it is necessary to conduct some improvement to achieve better forecasting and early warning system in order to give information regarding occurrence of debris flow.

scholarly journals Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds

2017 ◽  
Author(s):  
Francesco Marra ◽  
Elisa Destro ◽  
Efthymios I. Nikolopoulos ◽  
Davide Zoccatelli ◽  
Jean Dominique Creutin ◽  
...  
Keyword(s):  
Debris Flow ◽  
Early Warning ◽  
Debris Flows ◽  
Spatial Organization ◽  
Radar Data ◽  
Rain Gauge ◽  
Spatial Aggregation ◽  
Scale Parameters ◽  
The Impact ◽  
Triggering Rainfall

Abstract. The systematic underestimation observed in debris flows early warning thresholds has been associated to the use of sparse rain gauge networks to represent highly non-stationary rainfall fields. Remote sensing products permit concurrent estimates of debris flow-triggering rainfall for areas poorly covered by rain gauges, but the impact of using coarse spatial resolutions to represent such rainfall fields is still to be assessed. This study uses fine resolution radar data for ~ 100 debris flows in the eastern Italian Alps to (i) quantify the effect of spatial aggregation (1–20-km grid size) on the estimation of debris flow triggering rainfall and on the identification of early warning thresholds and (ii) compare thresholds derived from aggregated estimates and rain gauge networks of different densities. The impact of spatial aggregation is influenced by the spatial organization of rainfall and by its dependence on the severity of the triggering rainfall. Thresholds from aggregated estimates show up to 8 % and 21 % variations in the shape and scale parameters respectively. Thresholds from synthetic rain gauge networks show > 10 % variation in the shape and > 25 % systematic underestimation in the scale parameter, even for densities as high as 1/10 km−2.

scholarly journals Quantitative Analysis of the Effects of an Earthquake on Rainfall Thresholds for Triggering Debris-Flow Events

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuang Liu ◽  
Kaiheng Hu ◽  
Qun Zhang ◽  
Shaojie Zhang ◽  
Xudong Hu ◽  
...  
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Focal Depth ◽  
Rainfall Threshold ◽  
Effective Rainfall ◽  
Rainfall Thresholds ◽  
Jiuzhaigou Earthquake ◽  
The Wenchuan Earthquake ◽  
The Moment

The impacts of destructive earthquakes on rainfall thresholds for triggering the debris flows have not yet been well investigated, due to lacks of data. In this study, we have collected the debris-flow records from the Wenchuan, Lushan, and Jiuzhaigou earthquake-affected areas in Sichuan Province, China. By using a meteorological dataset with 3 h and 0.1° resolutions, the dimensionless effective rainfall and rainfall intensity-duration relationships were calculated as the possible thresholds for triggering the debris flows. The pre- and post-seismic thresholds were compared to evaluate the impacts of the various intensities of earthquakes. Our results indicate that the post-quake thresholds are much smaller than the pre-seismic ones. The dimensionless effective rainfall shows the impacts of the Wenchuan, Lushan, and Jiuzhaigou earthquakes to be ca. 26, 27, and 16%, respectively. The Wenchuan earthquake has the most significant effect on lowering the rainfall intensity-duration curve. Rainfall threshold changes related to the moment magnitude and focal depth are discussed as well. Generally, this work may lead to an improved post-quake debris-flow warning strategy especially in sparsely instrumented regions.

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