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.

scholarly journals Development of Korean Nomograms for Forecasting and Alerting Debris Flows Based on Critical Accumulated Rainfall

2019 ◽  
Author(s):  
Dong-Ho Nam ◽  
Suk-Ho Lee ◽  
Byung-Sik Kim
Keyword(s):  
South Korea ◽  
Urban Areas ◽  
Debris Flows ◽  
Rainfall Intensity ◽  
Short Term ◽  
Rainfall Events ◽  
Accumulated Rainfall ◽  
Landslides And Debris Flows ◽  
Climate Events ◽  
Heavy Rains

Ongoing climate change causes abnormal climate events worldwide such as increasing temperatures and changing rainfall patterns. With South Korea facing growing damage from the increased frequency of localized heavy rains, the country is not an exception. 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 predicts 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. This study, therefore, 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 precipitation 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 hr, 12 hr, and 24 hr. As a result of applying this classification into the actual cases of Seoul, Chuncheon, and Cheongju, it is found that about 2–4 hr of response time is secured from the point of the Emergency level to the occurrence of debris flows.

scholarly journals Numerical Simulation of Debris Flows of the Catastrophic Event of February 2019 in Mirave – Peru

2019 ◽  
Vol 14 (6) ◽  
pp. 1
Author(s):  
Alexandre Almeida Del Savio ◽  
Samuel Ismael Quisca Astocahuana ◽  
Leonardo Franco Castillo Navarro
Keyword(s):  
Numerical Simulation ◽  
Debris Flow ◽  
Debris Flows ◽  
Disaster Risk ◽  
Extreme Weather Events ◽  
Emergency Plans ◽  
Weather Events ◽  
Adequate Representation ◽  
Management Policies ◽  
Deposition Processes

Debris flows are geophysical phenomena, caused by torrential rain, which occur in mountainous areas, characterized by the detachment of slope and riverbed materials and their consequent dislodge through watersheds and streams. Debris flows usually carry sludge, water and rocks, and can destroy everything in their path. On February 8th, 2019, an event of this type occurred and destroyed Mirave’s community in Peru, which was located in the areas of transport and deposition of debris flows. This research presents the modeling and numerical simulation to reproduce the transport and deposition processes of the debris flow that occurred in Mirave. The initiation process of the debris flow in streams was represented by hydrographs obtained from the estimated rain runoff volumes and solid materials found at each evaluated micro watershed. The numerical simulation results show acceptable results in terms of reproduction of the extension of the affectation and deposition areas of solids related to the studied debris flow. The resulting velocity field shows an adequate representation of the erosion zones observed in the area. The model used for evaluating the disaster risk by debris flows can predict and delimit, with acceptable accuracy, the potentially dangerous areas for a mudslide event. The application of the proposed methodology for assessing the disaster risk due to debris flows at watersheds and streams is useful to understand the extent of debris flow affectation during extreme weather events, as well as to develop emergency plans, and to formulate disaster management policies in Peru or in other countries with similar conditions.

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.

scholarly journals Debris-flow susceptibility assessment through cellular automata modeling: an example from 15–16 December 1999 disaster at Cervinara and San Martino Valle Caudina (Campania, southern Italy)

2003 ◽  
Vol 3 (5) ◽  
pp. 457-468 ◽  
Author(s):  
G. Iovine ◽  
S. Di Gregorio ◽  
V. Lupiano
Keyword(s):  
Cellular Automata ◽  
Debris Flow ◽  
Urban Areas ◽  
Debris Flows ◽  
Soil Cover ◽  
Southern Italy ◽  
Landslide Risk ◽  
Campania Region ◽  
Optimal Values ◽  
Debris Flow Susceptibility

Abstract. On 15–16 December 1999, heavy rainfall severely stroke Campania region (southern Italy), triggering numerous debris flows on the slopes of the San Martino Valle Caudina-Cervinara area. Soil slips originated within the weathered volcaniclastic mantle of soil cover overlying the carbonate skeleton of the massif. Debris slides turned into fast flowing mixtures of matrix and large blocks, downslope eroding the soil cover and increasing their original volume. At the base of the slopes, debris flows impacted on the urban areas, causing victims and severe destruction (Vittori et al., 2000). Starting from a recent study on landslide risk conditions in Campania, carried out by the Regional Authority (PAI –Hydrogeological setting plan, in press), an evaluation of the debris-flow susceptibility has been performed for selected areas of the above mentioned villages. According to that study, such zones would be in fact characterised by the highest risk levels within the administrative boundaries of the same villages ("HR-zones"). Our susceptibility analysis has been performed by applying SCIDDICA S3–hex – a hexagonal Cellular Automata model (von Neumann, 1966), specifically developed for simulating the spatial evolution of debris flows (Iovine et al., 2002). In order to apply the model to a given study area, detailed topographic data and a map of the erodable soil cover overlying the bedrock of the massif must be provided (as input matrices); moreover, extent and location of landslide source must also be given. Real landslides, selected among those triggered on winter 1999, have first been utilised for calibrating SCIDDICA S3–hex and for defining "optimal" values for parameters. Calibration has been carried out with a GIS tool, by quantitatively comparing simulations with actual cases: optimal values correspond to best simulations. Through geological evaluations, source locations of new phenomena have then been hypothesised within the HR-zones. Initial volume for these new cases has been estimated by considering the actual statistics of the 1999 landslides. Finally, by merging the results of simulations, a deterministic susceptibility zonation of the considered area has been obtained. In this paper, aiming at illustrating the potential for debris-flow hazard analyses of the model SCIDDICA S3–hex, a methodological example of susceptibility zonation of the Vallicelle HR-zone is presented.

scholarly journals Debris Flow Damage Assessment by Considering Debris Flow Direction and Direction Angle of Structure in South Korea

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 328 ◽  
Author(s):  
Dong Nam ◽  
Man-Il Kim ◽  
Dong Kang ◽  
Byung Kim
Keyword(s):  
South Korea ◽  
Debris Flow ◽  
Debris Flows ◽  
Impact Force ◽  
Mountainous Areas ◽  
Mountainous Regions ◽  
Impact Forces ◽  
The Impact

Recently, human and property damages have often occurred due to various reasons—such as landslides, debris flow, and other sediment-related disasters—which are also caused by regional torrential rain resulting from climate change and reckless development of mountainous areas. Debris flows mainly occur in mountainous areas near urban living communities and often cause direct damages. In general, debris flows containing soil, rock fragments, and driftwood temporarily travel down to lower parts along with a mountain torrent. However, debris flows are also often reported to stream down from the point where a slope failure or a landslide occurs in a mountain directly to its lower parts. The impact of those debris flows is one of the main factors that cause serious damage to structures. To mitigate such damage of debris flows, a quantitative assessment of the impact force is thus required. Moreover, technologies to evaluate disaster prevention facilities and structures at disaster-prone regions are needed. This study developed two models to quantitatively analyze the damages caused by debris flows on structures: Type-1 model for calculating the impact force, which reflected the flow characteristics of debris flows and the Type-2 model, which calculated the impact force based on the topographical characteristics of mountainous regions. Using RAMMS a debris flow runoff model, the impact forces assessed through Type-1 and Type-2 models were compared to check reliability. Using the assessed impact forces, the damage ratio of the structures was calculated and the amount of damage caused by debris flows on the structures was ultimately assessed. The results showed that the Type-1 model overestimated the impact force by 10% and the Type-2 model by 4% for Mt. Umyeon in Seoul, compared to the RAMMS model. In addition, the Type-1 model overestimated the impact force by 3% and Type-2 by 2% for Mt. Majeok in Chuncheon, South Korea.

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 GIS-based modelling of landslide and debris flow hazard in mountainous terrain of Agra Khola watershed, central Nepal

2006 ◽  
Vol 34 ◽  
pp. 117-128
Author(s):  
P. B. Thapa ◽  
T. Esaki ◽  
B. N. Upreti
Keyword(s):  
Debris Flow ◽  
Prediction Model ◽  
Debris Flows ◽  
Flow Distribution ◽  
Spatial Prediction ◽  
Distribution Map ◽  
Central Nepal ◽  
Landslides And Debris Flows ◽  
Debris Flow Hazard ◽  
Hazard Levels

A comprehensive GIS-based analytical approach was followed to derive a spatial database of landslides and debris flows in the Agra Khola watershed of central Nepal which suffered from the hydrological disaster of 1993. For this purpose, the landslides and debris flows occurring in that area between 1993 and 2006 were delineated. From the database, the influence of geological and geomorphic variables was quantified and a spatial prediction model for landslide and debris flow hazard was worked out. In this process, quantitative statistical analysis (bivariate, multivariate) as applied to predict elements or observations between stable and unstable zones. The predicted results were classified into various hazard levels m a hazard map and were validated by comparing it with the landslide and debris flow distribution map of the Agra Khola watershed. Also the GIS-based hazard prediction model has objectivity in the procedure and reproducibility of the results in the mountainous terrains.

scholarly journals Underground Cities : Sustainable Urbanism of the Future

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1754-1757
Keyword(s):  
Climate Change ◽  
Urban Areas ◽  
Design Guidelines ◽  
Extreme Weather Events ◽  
Natural Catastrophes ◽  
Weather Events ◽  
Sustainable Urbanism ◽  
The World ◽  
High Precipitation ◽  
Impacts Of Climate Change

Cities across the world are the main contributors to climate change but at the same time they are also the most vulnerable to its consequences. Some of the disastrous impacts of climate change include extreme weather events, periods of extreme heat and cold, high precipitation, floods, strong cyclones and storms. There is a need for urban design guidelines to effectively address the issues of climate chanbe and increase the resilience of cities. One way to adapt to this is through engineered infrastructure. Today nearly 70% of the world live in urban areas and in the next 20 years two billion more people are expected to move to the cities. With increasing urban densification land and buildable areas are going to become increasingly scarce. One possible solution is to build downwards instead of upwards. Underground areas are less susceptible to external influences and have the ability to better withstand natural catastrophes and hence can be sustainable solution for an unpredictable future. This paper will analyze the viability of underground cities through examples from history and existing case studies along with new upcoming proposals and probe how using underground spaces can increase the resilience of future cities

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 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.

Sign in / Sign up

Export Citation Format

Share Document