scholarly journals Hazard assessment of debris flows for Leung King Estateof Hong Kong by incorporating GIS with numericalsimulations

2004 ◽  
Vol 4 (1) ◽  
pp. 103-116 ◽  
Author(s):  
K. T. Chau ◽  
K. H. Lo
Keyword(s):  
Hong Kong ◽  
Debris Flow ◽  
Numerical Simulations ◽  
Computer Simulations ◽  
Debris Flows ◽  
Potential Hazard ◽  
Expert Opinions ◽  
Flow Parameters ◽  
Digital Elevation ◽  
Elevation Model

Abstract. As over seventy percent of the land of Hong Kong is mountainous, rainfall-induced debris flows are not uncommon in Hong Kong. The objective of this study is to incorporate numerical simulations of debris flows with GIS to identify potential debris flow hazard areas. To illustrate this approach, the proposed methodology is applied to Leung King Estate in Tuen Mun. A Digital Elevation Model (DEM) of the terrain and the potential debris-flow sources were generated by using GIS to provide the required terrain and flow source data for the numerical simulations. A theoretical model by Takahashi et al. (1992) improved by incorporating a new erosion initiation criterion was used for simulating the runout distances of debris flows. The well-documented 1990 Tsing Shan debris flow, which occurred not too far from Leung King Estate, was used to calibrate most of the flow parameters needed for computer simulations. Based on the simulation results, a potential hazard zone was identified and presented by using GIS. Our proposed hazard map was thus determined by flow dynamics and a deposition mechanism through computer simulations without using any so- called expert opinions, which are bounded to be subjective and biased.

scholarly journals Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy)

2015 ◽  
Vol 15 (8) ◽  
pp. 1785-1806 ◽  
Author(s):  
M. Cama ◽  
L. Lombardo ◽  
C. Conoscenti ◽  
V. Agnesi ◽  
E. Rotigliano
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Stochastic Modelling ◽  
Storm Events ◽  
Catchment Scale ◽  
M Cell ◽  
Small Catchment ◽  
Digital Elevation ◽  
Geologic Map ◽  
Elevation Model

Abstract. The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios. The study area is the small catchment of the Itala torrent (10 km2), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chrono-validation, based on the time partition of the landslide inventory. It was therefore possible to analyse and compare the performances both of the 2007 calibrated model in predicting the 2009 debris flows (forward chrono-validation), and vice versa of the 2009 calibrated model in predicting the 2007 debris flows (backward chrono-validation). Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chrono-validated models. These are interpreted as effects of the non-linearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.

Mitigation of Debris Flows—Research and Practice in Hong Kong

2021 ◽  
Vol 27 (2) ◽  
pp. 231-243
Author(s):  
Ken K. S. Ho ◽  
Raymond C. H. Koo ◽  
Julian S. H. Kwan
Keyword(s):  
Hong Kong ◽  
Debris Flow ◽  
Debris Flows ◽  
Geotechnical Engineering ◽  
Mitigation Strategies ◽  
Mitigation Measures ◽  
Landslide Risk ◽  
Engineering Practice ◽  
Design Methodologies ◽  
Technical Framework

ABSTRACT Dense urban development on a hilly terrain coupled with intense seasonal rainfall and heterogeneous weathering profiles give rise to acute debris-flow problems in Hong Kong. The Geotechnical Engineering Office (GEO) of the Hong Kong SAR Government has launched a holistic research and development (R&D) programme and collaborated with various tertiary institutes and professional bodies to support the development of a comprehensive technical framework for managing landslide risk and designing debris-flow mitigation measures. The scope of the technical development work includes compilation of landslide inventories, field studies of debris flows, development and calibration of tools for landslide run-out modelling, back analysis of notable debris flows, physical and numerical modelling of the interaction between debris flows and mitigation measures, formulation of a technical framework for evaluating debris-flow hazards, and development of pragmatic mitigation strategies and design methodologies for debris-flow countermeasures. The work has advanced the technical understanding of debris-flow hazards and transformed the natural terrain landslide risk management practice in Hong Kong. New analytical tools and improved design methodologies are being applied in routine geotechnical engineering practice.

scholarly journals Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster

2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Hiroyuki Miura
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
Large Scale ◽  
Satellite Images ◽  
Erosion Depth ◽  
Digital Elevation ◽  
Elevation Model ◽  
Fusion Analysis ◽  
Optical Satellite Images ◽  
Debris Flow Disaster

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.

scholarly journals Mobility characteristics of debris slides and flows triggered by Hurricane Maria in Puerto Rico

Landslides ◽  
2020 ◽  
Vol 17 (12) ◽  
pp. 2795-2809 ◽  
Author(s):  
Erin K. Bessette-Kirton ◽  
Jeffrey A. Coe ◽  
William H. Schulz ◽  
Corina Cerovski-Darriau ◽  
Mason M. Einbund
Keyword(s):  
Puerto Rico ◽  
Debris Flows ◽  
Extreme Rainfall ◽  
Source Areas ◽  
Tropical Environments ◽  
Digital Elevation ◽  
Wide Range ◽  
Hazard And Risk ◽  
Elevation Model ◽  
Landslides And Debris Flows

Abstract Mobility is an important element of landslide hazard and risk assessments yet has been seldom studied for shallow landslides and debris flows in tropical environments. In September 2017, Hurricane Maria triggered > 70,000 landslides across Puerto Rico. Using aerial imagery and a lidar digital elevation model (DEM), we mapped and characterized the mobility of debris slides and flows in four different geologic materials: (1) mudstone, siltstone, and sandstone; (2) submarine basalt and chert; (3) marine volcaniclastics; and (4) granodiorite. We used the ratio of landslide-fall height (H) to travel length (L), H/L, to assess the mobility of landslides in each material. Additionally, we differentiated between landslides with single and multiple source areas and landslides that either did or did not enter drainages. Overall, extreme rainfall contributed to the mobility of landslides during Hurricane Maria, and our results showed that the mobility of debris slides and flows in Puerto Rico increased linearly as a function of the number of source areas that coalesced. Additionally, landslides that entered drainages were more mobile than those that did not. We found that landslides in soils developed on marine volcaniclastics were the most mobile and landslides in soils on submarine basalt and chert were the least mobile. While landslides were generally small (< 100 m2) and displayed a wide range of H/L values (0.1–2), coalescence increased the mobility of landslides that transitioned to debris flows. The high but variable mobility of landslides that occurred during Hurricane Maria and the associated hazards highlight the importance of characterizing and understanding the factors influencing landslide mobility in Puerto Rico and other tropical environments.

scholarly journals Characteristics and Prevention of the Debris Flows following Wenchuan Earthquake in Jushui River Basin, An County, China

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Yonggang Ge ◽  
Jianqiang Zhang ◽  
Xiaojun Guo
Keyword(s):  
Debris Flow ◽  
Wenchuan Earthquake ◽  
River Basin ◽  
Debris Flows ◽  
Flash Flood ◽  
River Channel ◽  
Hazard Identification ◽  
Control Mode ◽  
Potential Hazard ◽  
Prevention Measures

After analysing the catastrophic debris flows on August 18, 2012, and on July 9, 2013, in Jushui River basin, An County, the Wenchuan Earthquake seriously striken areas, it was found that they were characterized by the clay soil content of 0.1~1.2%, the density of 1.68~2.03 t/m3, the discharges of 62.2 m3/s to 552.5 m3/s, and the sediment delivery modulus of 1.0~9.4 × 104 m3/km2. Due to intense rainstorm, many large debris flows produced hazard chain, involved in flash flood, debris flow, dammed lake, and outburst flood, and rose Jushui River channel about 1~4 m as well as amplified flood. The hazards and losses mainly originated from the burying and scouring of debris flows, flood inundating, and river channel rise. The prevention of debris flows is facing the intractable problems including potential hazard identification, overstandard debris flow control, control constructions destructing, and river channel rapid rise. Therefore, the prevention measures for the basin, including hazard identification and risk assessment, inhabitants relocating, monitoring and alarming network establishing, emergency plans founding, and river channel renovating, and the integrated control mode for watershed based on regulating the process of debris flow discharge, were recommended for mitigation.

scholarly journals Numerical Modelling Based on Digital Elevation Model (DEM) Analysis of Debris Flow at Rinjani Volcano, West Nusa Tenggara, Indonesia

2021 ◽  
Vol 7 (3) ◽  
pp. 279
Author(s):  
Muhammad Fatih Qodri ◽  
Noviardi Noviardi ◽  
Al Hussein Flowers Rizqi ◽  
Lindung Zalbuin Mase
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
High Speed ◽  
Human Life ◽  
Mass Movement ◽  
High Viscosity ◽  
Particle Flows ◽  
Digital Elevation ◽  
Dem Analysis ◽  
Elevation Model

Debris flow is a disaster occurring in cases where a sediment particle flows at high speed, down to the slope, and usually with high viscosity and speed. This disaster is very destructive and human life-threatening, especially in mountainous areas. As one of the world’s active volcanoes in the world, Rinjani had the capacity to produce over 3 million m3 volume material in the 2015 eruption alone. Therefore, this study proposes a numerical model analysis to predict the debris flow release area (erosion) and deposition, as well as the discharge, flow height, and velocity. The Digital Elevation Model (DEM) was analyzed in ArcGIS, to acquire the Cartesian coordinates and “hillshade” form. This was also used as a method to produce vulnerable areas in the Jangkok watershed. Meanwhile, the Rapid Mass Movement Simulation (RAMSS) numerical modeling was simulated using certain parameters including volume, friction, and density, derived from the DEM analysis results and assumptions from similar historical events considered as the best-fit rheology. In this study, the release volume was varied at 1,000,000 m3, 2,000,000 m3, and 3,000,000 m3, while the simulation results show movement, erosion, and debris flow deposition in Jangkok watershed. This study is bound to be very useful in mitigating debris flow as disaster anticipation and is also expected to increase community awareness, as well as provide a reference for structural requirements, as a debris flow prevention.

scholarly journals Numerical Simulation of Failure Behavior of Granular Debris Flows Based on Flume Model Tests

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Zhou ◽  
Ye-xun Li ◽  
Min-cai Jia ◽  
Cui-na Li
Keyword(s):  
Numerical Simulation ◽  
Debris Flow ◽  
Numerical Simulations ◽  
Failure Mode ◽  
Debris Flows ◽  
Fine Particles ◽  
Model Tests ◽  
Failure Behavior ◽  
Medium Sand ◽  
Grain Sizes

In this study, the failure behaviors of debris flows were studied by flume model tests with artificial rainfall and numerical simulations (PFC3D). Model tests revealed that grain sizes distribution had profound effects on failure mode, and the failure in slope of medium sand started with cracks at crest and took the form of retrogressive toe sliding failure. With the increase of fine particles in soil, the failure mode of the slopes changed to fluidized flow. The discrete element methodPFC3Dcan overcome the hypothesis of the traditional continuous medium mechanic and consider the simple characteristics of particle. Thus, a numerical simulations model considering liquid-solid coupled method has been developed to simulate the debris flow. Comparing the experimental results, the numerical simulation result indicated that the failure mode of the failure of medium sand slope was retrogressive toe sliding, and the failure of fine sand slope was fluidized sliding. The simulation result is consistent with the model test and theoretical analysis, and grain sizes distribution caused different failure behavior of granular debris flows. This research should be a guide to explore the theory of debris flow and to improve the prevention and reduction of debris flow.

scholarly journals Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans

2021 ◽  
Vol 13 (9) ◽  
pp. 1711
Author(s):  
Matej Babič ◽  
Dušan Petrovič ◽  
Jošt Sodnik ◽  
Božo Soldo ◽  
Marko Komac ◽  
...  
Keyword(s):  
Machine Learning ◽  
Debris Flow ◽  
Human Life ◽  
Alluvial Fans ◽  
Digital Elevation ◽  
Elevation Model ◽  
Flow Activity ◽  
Geomorphometric Parameters

Alluvial (torrential) fans, especially those created from debris-flow activity, often endanger built environments and human life. It is well known that these kinds of territories where human activities are favored are characterized by increasing instability and related hydrological risk; therefore, treating the problem of its assessment and management is becoming strongly relevant. The aim of this study was to analyze and model the geomorphological aspects and the physical processes of alluvial fans in relation to the environmental characteristics of the territory for classification and prediction purposes. The main geomorphometric parameters capable of describing complex properties, such as relative fan position depending on the neighborhood, which can affect their formation or shape, or properties delineating specific parts of fans, were identified and evaluated through digital elevation model (DEM) data. Five machine learning (ML) methods, including a hybrid Euler graph ML method, were compared to analyze the geomorphometric parameters and physical characteristics of alluvial fans. The results obtained in 14 case studies of Slovenian torrential fans, validated with data of the empirical model proposed by Bertrand et al. (2013), confirm the validity of the developed method and the possibility to identify alluvial fans that can be considered as debris-flow prone.

scholarly journals Predicting storm triggered debris flow events: application to the 2009 Ionian-Peloritan disaster (Sicily, Italy)

2015 ◽  
Vol 3 (3) ◽  
pp. 1731-1774
Author(s):  
M. Cama ◽  
L. Lombardo ◽  
C. Conoscenti ◽  
R. Rotigliano
Keyword(s):  
Debris Flow ◽  
Binary Logistic Regression ◽  
Stochastic Modelling ◽  
Storm Events ◽  
M Cell ◽  
Small Catchment ◽  
Digital Elevation ◽  
Susceptibility Model ◽  
Geologic Map ◽  
Elevation Model

Abstract. The main assumption on which landslide susceptibility assessment by means of stochastic modelling lays is that the past is the key to the future. As a consequence, a stochastic model able to classify a past known landslide scenario should be able to predict a future unknown one as well. However, storm triggered landslide events in the Mediterranean region could pose some limits on the operative validity of such expectation, as they typically result by a randomness in time recurrence and magnitude. This is the case of the 2007/09 couple of storm events, which recently hit north-eastern Sicily resulting in largely different disaster scenarios. The purpose of this study is to test whether a susceptibility model based on stepwise binary logistic regression is able to predict a storm triggered debris flow scenario. The study area is the small catchment of the Itala torrent (10 km2), which drains from the southern Peloritan Mountains eastward to the Ionian sea, in the province of the Messina territory (Sicily, Italy). The shallow landslides activated in the occasion of two close intense rainfall events have been mapped by integrating remote and field surveys, producing two event inventories which include 73 landslides, activated in 2007, and 616 landslides, triggered by the 2009 storm. The set of predictors were derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The topic of the research was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory and chrono-validation, based on the time partition of the landslide inventory. It was therefore possible to analyse and compare the performances both of the 2007-calibrated model in predicting the 2009 landslides (forward chronovalidation) and vice versa of the 2009-calibrated model in predicting the 2007 landslides (backward chronovalidation). Both the two predictions resulted in largely acceptable performances, in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors between the self-validated and the chrono-validated models which are linked to the characteristics of the two triggering storms are highlighted.

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