Impact of spatial data uncertainty in debris flow susceptibility analysis

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.

Impact of spatial data uncertainty in global debris flow susceptibility analysis

10.5194/egusphere-egu21-628 ◽

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 ◽

There are many types and degrees of uncertainty associated with spatial data and processes.&#160;There are many factors and attributes associated with debris flow analyses which are prone to uncertainty.&#160; 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.&#160;&#160;&#160; These two attributes, critical to debris flow susceptibility analyses, are landslide classification and event location.&#160; The associated predisposing factors studied herein are lithology, soils, climate, ecophysiographic units, topography, hydrology, and tectonics.In a landslide susceptibility analysis, landslide event location accuracy is paramount yet often inaccurately known.&#160; 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.&#160;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.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.&#160; 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.&#160;

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

Natural Hazards ◽

10.1007/s11069-020-04498-4 ◽

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 Map for Mount Rainier, Washington Based on Debris Flow Initiation Zone Characteristics from the November, 2006 Climate Event in the Cascade Mountains

10.15760/etd.2650 ◽

2000 ◽

Author(s):

Kassandra Lindsey

Keyword(s):

Debris Flow ◽

Susceptibility Map ◽

Cascade Mountains ◽

Initiation Zone ◽

Mount Rainier ◽

Climate Event ◽

Debris Flow Susceptibility ◽

Debris Flow Initiation

A Global Debris Flow Susceptibility Map Based on a Comparative Analysis of a Single Global Model Versus a Continent-by-continent Approach

10.21203/rs.3.rs-1189090/v1 ◽

2021 ◽

Author(s):

Laurie Jayne Kurilla ◽

Giandomenico Fubelli

Keyword(s):

Comparative Analysis ◽

Debris Flow ◽

Flow Behavior ◽

Model Performance ◽

Global Model ◽

Susceptibility Map ◽

World Population ◽

Continental Scale ◽

Susceptibility Modeling ◽

Abstract Debris flows, and landslides in general, are worldwide catastrophic phenomena. As world population and urbanization grow in magnitude and geographic coverage, the need exists to extend focus, research, and modeling to a continental and global scale.Although debris flow behavior and parameters are local phenomena, sound generalizations can be applied to debris flow susceptibility analyses at larger geographic extents based on these criteria. The focus of this research is to develop a global debris flow susceptibility map by modeling at both a continental scale for all continents and by a single global model and determine whether a global model adequately represents each continent. Probability Density, Conditional Probability, Certainty Factor, Frequency Ratio, and Maximum Entropy statistical models were developed and evaluated for best model performance using fourteen environmental factors generally accepted as the most appropriate debris flow predisposing factors. Global models and models for each continent were then developed and evaluated against verification data. The comparative analysis demonstrates that a single global model performs comparably or better than individual continental models for a majority of the continents, resulting in a debris flow susceptibility map of the world useful in international planning, and future debris flow susceptibility modeling for determining societal impacts.

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

10.5194/egusphere-egu2020-7805 ◽

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 ◽

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&#8217;s hydrological and geomorphological susceptibility, which makes it hard to predict and understand where and when debris flows occur.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.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.&#160;<div>References:</div><div>Prenner, D., Kaitna, R., Mostbauer, K., & Hrachowitz, M. ( 2018). The value of using multiple hydrometeorological variables to predict temporal debris flow susceptibility in an Alpine environment. Water Resources Research, 54, 6822&#8211; 6843. </div>&#160;

Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-1471-2015 ◽

2015 ◽

Vol 15 (2) ◽

pp. 1471-1522

Author(s):

J. Struzewska ◽

M. Zdunek ◽

J. W. Kaminski ◽

L. Lobocki ◽

M. Porebska ◽

...

Keyword(s):

Pearson Correlation ◽

Phase 1 ◽

Surface Ozone ◽

Data Uncertainty ◽

Mean Bias Error ◽

Bias Error ◽

European Continent ◽

Statistical Measures ◽

The Mean ◽

Abstract. In the scope of the AQMEII Phase 1 project the GEM-AQ model was run over Europe for the year 2006. The modelling domain was defined using a global variable resolution grid with a rotated equator and uniform resolution of 0.2° × 0.2° over the European continent. Spatial distribution and temporal variability of the GEM-AQ model results were analysed for surface ozone and PM10 concentrations. Model results were compared with measurements available in the ENSEMBLE database. Statistical measures were used to evaluate performance of the GEM-AQ model. The mean bias error, the mean absolute gross error and the Pearson correlation coefficient were calculated for the maximum 8 h running average ozone concentrations and daily mean PM10 concentrations. The GEM-AQ model performance was characterised for station types, European climatic regions, and seasons. The best performance for ozone was obtained at suburban stations and the worst performance was obtained for rural stations where the model tends to underestimate. The best results for PM10 were calculated for urban stations, while over most of Europe concentrations at rural sites were too high. Discrepancies between modelled and observed concentrations were discussed in the context of emission data uncertainty as well as the impact of large scale dynamics and circulation of air masses. Presented analyses suggest that interpretation of modelling results is enhanced when regional climate characteristics are ta ken into consideration.

Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182211987 ◽

2021 ◽

Vol 18 (22) ◽

pp. 11987

Author(s):

Patricia Arrogante-Funes ◽

Adrián G. Bruzón ◽

Fátima Arrogante-Funes ◽

Rocío N. Ramos-Bernal ◽

René Vázquez-Jiménez

Keyword(s):

Risk Assessment ◽

Spatial Data ◽

Risk Model ◽

Assessment Tools ◽

Landslide Risk ◽

Susceptibility Map ◽

Natural Ecosystem ◽

Economic Vulnerability ◽

Ecological Vulnerability ◽

Among the numerous natural hazards, landslides are one of the greatest, as they can cause enormous loss of life and property, and affect the natural ecosystem and their services. Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life, globally. The landslide risk assessed by the integration of susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools that enable them to address these disasters, starting with their prevention, with free spatial data and appropriate models. Our study shows a heuristic risk model by integrating a susceptibility map made by AutoML and a vulnerability one that is made considering ecological vulnerability and socio-economic vulnerability. The input data used in the State of Guerrero (México) approach uses spatial data, such as remote sensing, or official Mexican databases. This aspect makes this work adaptable to other parts of the world because the cost is low, and the frequency adaptation is high. Our results show a great difference between the distribution of vulnerability and susceptibility zones in the study area, and even between the socio-economic and ecological vulnerabilities. For instance, the highest ecological vulnerability is in the mountainous zone in Guerrero, and the highest socio-economic vulnerability values are found around settlements and roads. Therefore, the final risk assessment map is an integrated index that considers susceptibility and vulnerability and would be a good first attempt to challenge landslide disasters.

Evaluation of the GEM-AQ model in the context of the AQMEII Phase 1 project

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-3971-2015 ◽

2015 ◽

Vol 15 (8) ◽

pp. 3971-3990 ◽

Cited By ~ 5

Author(s):

J. Struzewska ◽

M. Zdunek ◽

J. W. Kaminski ◽

L. Łobocki ◽

M. Porebska ◽

...

Keyword(s):

Pearson Correlation ◽

Phase 1 ◽

Surface Ozone ◽

Data Uncertainty ◽

Mean Bias Error ◽

Bias Error ◽

European Continent ◽

Statistical Measures ◽

The Mean ◽

Abstract. In the scope of the AQMEII Phase 1 project the GEM-AQ model was run over Europe for the year 2006. The modelling domain was defined using a global variable resolution grid with a rotated equator and uniform resolution of 0.2° × 0.2° over the European continent. Spatial distribution and temporal variability of the GEM-AQ model results were analysed for surface ozone and PM10 concentrations. Model results were compared with measurements available in the ENSEMBLE database. Statistical measures were used to evaluate performance of the GEM-AQ model. The mean bias error, the mean absolute gross error and the Pearson correlation coefficient were calculated for the maximum 8 h running average ozone concentrations and daily mean PM10 concentrations. The GEM-AQ model performance was characterized for station types, European climatic regions and seasons. The best performance for ozone was obtained at suburban stations, and the worst performance was obtained for rural stations where the model tends to underestimate. The best results for PM10 were calculated for urban stations, while over most of Europe concentrations at rural sites were too high. Discrepancies between modelled and observed concentrations were discussed in the context of emission data uncertainty as well as the impact of large-scale dynamics and circulation of air masses. Presented analyses suggest that interpretation of modelling results is enhanced when regional climate characteristics are taken into consideration.

Efficacy of Crisis Intervention

Crisis ◽

10.1027//0227-5910.20.2.78 ◽

1999 ◽

Vol 20 (2) ◽

pp. 78-85 ◽

Cited By ~ 6

Author(s):

Thomas Reisch ◽

Petra Schlatter ◽

Wolfgang Tschacher

Keyword(s):

Crisis Intervention ◽

Intervention Program ◽

Suicide Ideation ◽

Indirect Evidence ◽

Well Being ◽

Term Therapy ◽

Contributing Factors ◽

Step Therapy ◽

Long Term Therapy ◽

This study assesses the efficacy of the treatment approach implemented in the Bern Crisis Intervention Program, where particular emphasis is placed on the remediation of suicide ideation and suicidal behavior, and depression, fear, and phobia are generally considered to be contributing factors. Four questionnaires addressing psychopathology, emotional well-being, social anxiety, and personality were administered prior to and after the treatment of 51 patients over a period of 2 to 3 weeks. The reduction of symptoms contributing to suicidal ideation and behavior was interpreted as indirect evidence of an antisuicidal effect of the program. Significant improvements were found in the psychopathology ratings, with depression and anxiety showing the largest reductions. The impact on personality and social phobia, however, was only moderate, and on average patients still exhibited symptoms after attending the program. This residual symptomatology points to the necessity of introducing a two-step therapy approach of intensive intervention targeted at the precipitating causes of the crisis, augmented by long-term therapy to treat underlying problems.

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

10.1130/abs/2016cd-274669 ◽

2016 ◽

Author(s):

Jordan Carey ◽

◽

Nicholas Pinter ◽

Andrew L. Nichols

Keyword(s):

Debris Flow ◽

Hazard Models ◽