Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility

2021 ◽  
Vol 106 (1) ◽  
pp. 881-912
Author(s):  
Jingbo Sun ◽  
Shengwu Qin ◽  
Shuangshuang Qiao ◽  
Yang Chen ◽  
Gang Su ◽  
...  
Keyword(s):  
Debris Flow ◽  
Physical Model ◽  
Statistical Methods ◽  
Regional Scale ◽  
The Impact ◽  
Debris Flow Susceptibility

Why are some alpine catchments debris-flow active and others not? - the influence of geomorphology on debris-flow initiation

2020 ◽  
Author(s):  
Philipp Aigner ◽  
Leonard Sklar ◽  
Markus Hrachowitz ◽  
Roland Kaitna
Keyword(s):  
Debris Flow ◽  
Debris Flows ◽  
Regional Scale ◽  
Alpine Regions ◽  
Order Of Magnitude ◽  
Elevation Changes ◽  
Alpine Catchments ◽  
Flow Activity ◽  
The Impact ◽  
Debris Flow Susceptibility

<p>Processes like flash floods or debris flows, which typically occur in small headwater catchments, represent a substantial natural hazard in alpine regions. Due to the entrainment of sediment, the discharge of debris flows can be up to an order of magnitude larger compared to 100-year fluvial flood events in the same channel, which poses a great threat to affected communities. Besides the triggering rainfall, the initiation of debris flows depends on the watershed’s hydrological and geomorphological susceptibility, which makes it hard to predict and understand where and when debris flows occur.</p><p>In this study we aim to quantify the influence of geomorphologic characteristics and long-term sediment dynamics on debris flow activity in the Austrian Alps. Based on a database of debris-flow events within the last 60+ years, a geomorphological assessment of active and non-active sub-catchments in different study regions is carried out. In a first step, we derive geomorphological characteristics, such as terrain roughness, Melton number as well as weathering potential of geological units found within the watersheds. Based on the findings of the terrain shape analysis, a set of representative watersheds will be selected for systematic monitoring of surface elevation changes over the project period of three years. This will be achieved by comparing digital surface models based on photogrammetric UAV surveys and monitoring of channel reaches with cameras.</p><p>In order to project these findings onto a larger regional scale, the derived terrain parameters will be used to integrate and extend a previously designed hydro-meteorological debris-flow susceptibility model (Prenner et al., 2018) with a sediment-disposition-model. This will form the basis for an advanced debris flow forecasting tool and help to better assess the impact of climate change on the magnitude and frequency of future debris flows.</p><p> </p><div><span>References:</span></div><div><span>Prenner, D.</span>, <span>Kaitna, R.</span>, <span>Mostbauer, K.</span>, & <span>Hrachowitz, M.</span> ( <span>2018</span>). <span>The value of using multiple hydrometeorological variables to predict temporal debris flow susceptibility in an Alpine environment</span>. <em>Water Resources Research</em>, <span>54</span>, <span>6822</span>– <span>6843</span>. </div><p> </p>

scholarly journals Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2079
Author(s):  
Yang Chen ◽  
Shengwu Qin ◽  
Shuangshuang Qiao ◽  
Qiang Dou ◽  
Wenchao Che ◽  
...  
Keyword(s):  
Neural Networks ◽  
Debris Flow ◽  
Statistical Methods ◽  
Convolutional Neural Networks ◽  
Debris Flows ◽  
Susceptibility Mapping ◽  
Jilin Province ◽  
Support Vector ◽  
Validation Set ◽  
Debris Flow Susceptibility

Debris flows are a major geological disaster that can seriously threaten human life and physical infrastructures. The main contribution of this paper is the establishment of two–dimensional convolutional neural networks (2D–CNN) models by using SAME padding (S–CNN) and VALID padding (V–CNN) and comparing them with support vector machine (SVM) and artificial neural network (ANN) models, respectively, to predict the spatial probability of debris flows in Jilin Province, China. First, the dataset is randomly divided into a training set (70%) and a validation set (30%), and thirteen influencing factors are selected to build the models. Then, multicollinearity analysis and gain ratio methods are used to quantify the predictive ability of factors. Finally, the area under the receiver operatic characteristic curve (AUC) and statistical methods are utilized to measure the accuracy of the models. The results show that the S–CNN model gets the highest AUC value of 0.901 in the validation set, followed by the SVM model, the V–CNN model, and the ANN model. Three statistical methods also show that the S–CNN model produces minimum errors compared with other models. The S–CNN model is hailed as an important means to improve the accuracy of debris–flow susceptibility mapping and provides a reasonable scientific basis for critical decisions.

Debris-flow susceptibility assessment at regional scale: Validation on an alpine environment

Landslides ◽  
2014 ◽  
Vol 12 (3) ◽  
pp. 437-454 ◽  
Author(s):  
Francesco Bregoli ◽  
Vicente Medina ◽  
Guillaume Chevalier ◽  
Marcel Hürlimann ◽  
Allen Bateman
Keyword(s):  
Debris Flow ◽  
Scale Validation ◽  
Regional Scale ◽  
Susceptibility Assessment ◽  
Alpine Environment ◽  
Debris Flow Susceptibility

scholarly journals Impact of spatial data uncertainty in debris flow susceptibility analysis

2021 ◽  
Author(s):  
Laurie Jayne Kurilla ◽  
Giandomenico Fubelli
Keyword(s):  
Debris Flow ◽  
Maximum Entropy ◽  
Spatial Data ◽  
Data Uncertainty ◽  
Susceptibility Map ◽  
Contributing Factors ◽  
European Continent ◽  
Value Differences ◽  
The Impact ◽  
Debris Flow Susceptibility

Abstract. In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows, demonstrates the impact of uncertainty in defining appropriate predisposing factors, and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented, and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Five Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points, and localized distribution, and hence susceptibility results likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent and context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility of the continent of Europe. A novel representation of the uncertainty is expressed, and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.

scholarly journals Regional Scale Mapping of Debris-Flow Susceptibility in the Southern French Alps

2017 ◽  
Author(s):  
Mélanie Bertrand ◽  
Frédéric Liébault ◽  
Hervé Piégay
Keyword(s):  
Debris Flow ◽  
Regional Scale ◽  
French Alps ◽  
Debris Flow Susceptibility

Deriving sediment transport information from debris-flow impact force signals

2020 ◽  
Author(s):  
Hui Tang ◽  
Yan Yan ◽  
Kaiheng Hu
Keyword(s):  
Debris Flow ◽  
Physical Model ◽  
Ground Motion ◽  
Debris Flows ◽  
Impact Force ◽  
Sediment Flux ◽  
Seismic Ground Motion ◽  
Inversion Model ◽  
Force Signal ◽  
The Impact

<p>Runoff-generated debris flow has hazardous implications for downstream communities and infrastructure in alpine landscapes. Our understanding of fluid mechanisms of debris flows is very limited, in part, by a lack of direct observations and measurements. Seismic ground motion-based observations provide new constraints on debris flow physics, but it is still not widely applied due to the missing of validated inversion models for interpreting the impact force which generates seismic ground motion. Here we propose a physical model for the high-frequency spectral distribution of impact force signal generated by debris flows. Then we present a new inversion model based on the physical model for the impact force signal and apply this to the devastating debris flows in Dongchuang, China, on 25 August 2004. The amplitude and frequency characteristics of the impact force data can enable the estimation of grain size, sediment concentration, and sediment flux. Results suggest that in-situ data from three sensors could have provided a reconstruction of sediment flux profile in the vertical direction. Meanwhile, an inversion model designed for debris flows impact force would potentially provide hydrodynamics information as well.</p>

Using high-resolution DEMs for debris flow detection based on topographic signatures: A case study in the Quebrada del Toro, NW Argentina

2020 ◽  
Author(s):  
Ariane Mueting ◽  
Bodo Bookhagen ◽  
Manfred R. Strecker
Keyword(s):  
High Resolution ◽  
Debris Flow ◽  
Spatial Resolution ◽  
Debris Flows ◽  
Regional Scale ◽  
High Elevation ◽  
Transport Processes ◽  
Drainage Area ◽  
Sparse Vegetation ◽  
The Impact

<p>Mountainous high-relief terrains in climatically sensitive regions are often subjected to natural extreme events such as debris flows and landsliding. With people and infrastructure at risk, it is important to identify, measure, and comprehend the driving forces and mechanisms of slope movements in these environments at regional scale. Geomorphologic analyses and hazard assessments in these regions are, however, often limited by the availability of good-quality high-resolution digital elevation models (DEMs). Publically available data often have lower spatial resolution and are distorted in high-relief areas. In contrast, airplane-based lidar (light detection and ranging) data provide highly accurate information on 3D structure, yet, acquisition is costly and limits the size of the respective study area. Finding adequate, economical alternatives for creating high-resolution DEMs is therefore essential to study Earth-surface processes at regional scale, which may enable the detection of spatial variations, clusters and trends.</p><p>In areas with sparse vegetation, stereogrammetry has proven to be a viable tool for creating high-resolution DEMs. Here, we use SPOT-7 tri-stereo satellite imagery to create DEMs at 3 m spatial resolution for the Quebrada del Toro (QdT) in the Eastern Cordillera of NW Argentine Andes, an area with extreme gradients in topography, rainfall and erosion. Over 5000 GPS points collected during fieldwork ensure the spatial coherence of our DEMs.</p><p>Field observations in this high-elevation area show that the hillslopes of the deeply incised QdT gorge are characterized by debris flow deposits of various extent. Debris flows have a specific slope-drainage area relationship that curves in log-log space. Using high-resolution topographic data, we are able to provide further evidence for this phenomenon and characterize the distinct topographic signature of debris flows. We specifically focus on the transition zone between debris-flow and fluvial processes, which is variable in the different catchments. The transition is characterized by a pronounced kink revealed in slope-drainage plots, as well as an increase of slope scatter in the drainage area logbins. We propose that the presence and location of this kink reflects the nature of the dominating transport processes in the corresponding catchments. In light of these observations we discriminate between debris-flow and fluvially dominated catchments in the QdT and identify regions that primarily exhibit slope movement. Our new results reveal a cluster of fluvial catchments to the SE of our study area – an area that receives significantly more moisture than upstream regions. In contrast, debris flows are prominent in areas of sparse vegetation, where occasional extreme rainfall events are efficient in transporting large amounts of talus downhill. These observations are key to a better understanding of the relationships between the impact of extreme rainfalls at high elevation and the formation of large volumes of sediment in the arid highlands of the Andes.</p>

Impact of spatial data uncertainty in global debris flow susceptibility analysis

2021 ◽  
Author(s):  
Laurie Kurilla ◽  
Giandomenico Fubelli
Keyword(s):  
Debris Flow ◽  
Spatial Data ◽  
Debris Flows ◽  
Predisposing Factors ◽  
Susceptibility Analysis ◽  
Factors Associated ◽  
Event Location ◽  
The Impact ◽  
Debris Flow Susceptibility

<p>There are many types and degrees of uncertainty associated with spatial data and processes. </p><p>There are many factors and attributes associated with debris flow analyses which are prone to uncertainty.  For simplicity, in this presentation, only two attributes of debris flow events are investigated along with the impact of their uncertainty on the determination of environmental predisposing factors.    These two attributes, critical to debris flow susceptibility analyses, are landslide classification and event location.  The associated predisposing factors studied herein are lithology, soils, climate, ecophysiographic units, topography, hydrology, and tectonics.</p><p>In a landslide susceptibility analysis, landslide event location accuracy is paramount yet often inaccurately known.  Landslide inventories are often constructed based on mapping from aerial imagery, media reports, and field work by third party sources; and in a data-driven approach to debris flow susceptibility analysis the landslide type is important in modeling the relevant predisposing factors distinctive to each landslide type. </p><p>In a study of global debris flow susceptibility an analysis of the impact between known location and a location accuracy offset, and landslide categorization uncertainty demonstrates the impact of uncertainty in defining the appropriate predisposing factors associated with debris flows.</p><p>This analysis is part of a larger debris flow global susceptibility determination which trains on known debris flow events and the predisposing factors associated with them to identify potential areas that may be susceptible to debris flows.  This study looks at the impact/differences that mis-categorization or location uncertainty have on the determination of predisposing factors, along with methods of conveying uncertainty information. </p>

AN EVALUATION OF POST-WILDFIRE DEBRIS FLOW SUSCEPTIBILITY AND HAZARD MODELS

2016 ◽  
Author(s):  
Jordan Carey ◽  
◽  
Nicholas Pinter ◽  
Andrew L. Nichols
Keyword(s):  
Debris Flow ◽  
Hazard Models ◽  
Debris Flow Susceptibility
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