scholarly journals Epistemic uncertainties and natural hazard risk assessment. 1. A review of different natural hazard areas

2017 ◽  
Author(s):  
Keith J. Beven ◽  
Susana Almeida ◽  
Willy P. Aspinall ◽  
Paul D. Bates ◽  
Sarka Blazkova ◽  
...  
Keyword(s):  
Debris Flows ◽  
Volcanic Ash ◽  
Natural Hazard ◽  
Epistemic Uncertainty ◽  
Probability Distributions ◽  
Good Practice ◽  
Pyroclastic Flows ◽  
Dam Safety ◽  
Landslides And Debris Flows ◽  
Hazard Risk Assessment

Abstract. This paper discusses how epistemic uncertainties are considered in a number of different natural hazard areas including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. In each case it is common practice to treat most uncertainties in the form of aleatory probability distributions but this may lead to an underestimation of the resulting uncertainties in assessing the hazard, consequences and risk. It is suggested that such analyses might be usefully extended by looking at different scenarios of assumptions about sources of epistemic uncertainty, with a view to reducing the element of surprise in future hazard occurrences. Since every analysis is necessarily conditional on the assumptions made about the nature of sources of epistemic uncertainty it is also important to follow the guidelines for good practice suggested in the companion Part 2.

scholarly journals Epistemic uncertainties and natural hazard risk assessment – Part 2: Different natural hazard areas

2016 ◽  
Author(s):  
K. J. Beven ◽  
S. Almeida ◽  
W. P. Aspinall ◽  
P. D. Bates ◽  
S. Blazkova ◽  
...  
Keyword(s):  
Debris Flows ◽  
Volcanic Ash ◽  
Natural Hazard ◽  
Epistemic Uncertainty ◽  
Probability Distributions ◽  
Good Practice ◽  
Pyroclastic Flows ◽  
Dam Safety ◽  
Landslides And Debris Flows ◽  
Hazard Risk Assessment

Abstract. This paper discusses how epistemic uncertainties are considered in a number of different natural hazard areas including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. In each case it is common practice to treat most uncertainties in the form of aleatory probability distributions but this may lead to an underestimation of the resulting uncertainties in assessing the hazard, consequences and risk. It is suggested that such analyses might be usefully extended by looking at different scenarios of assumptions about sources of epistemic uncertainty, with a view to reducing the element of surprise in future hazard occurrences. Since every analysis is necessarily conditional on the assumptions made about the nature of sources of epistemic uncertainty it is also important to follow the guidelines for good practice suggested in the companion Part 1 by setting out those assumptions in a condition tree.

scholarly journals Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas

2018 ◽  
Vol 18 (10) ◽  
pp. 2741-2768 ◽  
Author(s):  
Keith J. Beven ◽  
Susana Almeida ◽  
Willy P. Aspinall ◽  
Paul D. Bates ◽  
Sarka Blazkova ◽  
...  
Keyword(s):  
Natural Hazards ◽  
Natural Hazard ◽  
Epistemic Uncertainty ◽  
Good Practice ◽  
Companion Paper ◽  
Expert Elicitation ◽  
Pyroclastic Flows ◽  
Probabilistic Uncertainty ◽  
Many Sources ◽  
Landslides And Debris Flows

Abstract. This paper discusses how epistemic uncertainties are currently considered in the most widely occurring natural hazard areas, including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. Our aim is to provide an overview of the types of epistemic uncertainty in the analysis of these natural hazards and to discuss how they have been treated so far to bring out some commonalities and differences. The breadth of our study makes it difficult to go into great detail on each aspect covered here; hence the focus lies on providing an overview and on citing key literature. We find that in current probabilistic approaches to the problem, uncertainties are all too often treated as if, at some fundamental level, they are aleatory in nature. This can be a tempting choice when knowledge of more complex structures is difficult to determine but not acknowledging the epistemic nature of many sources of uncertainty will compromise any risk analysis. We do not imply that probabilistic uncertainty estimation necessarily ignores the epistemic nature of uncertainties in natural hazards; expert elicitation for example can be set within a probabilistic framework to do just that. However, we suggest that the use of simple aleatory distributional models, common in current practice, will underestimate the potential variability in assessing hazards, consequences, and risks. A commonality across all approaches is that every analysis is necessarily conditional on the assumptions made about the nature of the sources of epistemic uncertainty. It is therefore important to record the assumptions made and to evaluate their impact on the uncertainty estimate. Additional guidelines for good practice based on this review are suggested in the companion paper (Part 2).

Sediment Induced Disasters in the World and 1999-Debris Flow Disasters in Venezuela

2010 ◽  
Vol 5 (3) ◽  
pp. 229-235 ◽  
Author(s):  
Takahisa Mizuyama ◽  
◽  
Shinji Egashira ◽  
Keyword(s):  
Debris Flows ◽  
Pyroclastic Flows ◽  
Hazard Maps ◽  
Strong Earthquakes ◽  
Control Structures ◽  
The World ◽  
Hazard Education ◽  
And Control ◽  
Landslides And Debris Flows ◽  
Sediment Disaster

Many sediment related disasters have occurred in many areas of the world. The table of sediment related disasters from 1997 to 2006 is shown. It shows strong earthquakes and super hurricanes/typhoons cause large landslides and debris flows. Climate change may trigger larger disasters more frequently in the future. Stratovolcanoes are geologically weak and cause huge landslides and debris avalanches. Active volcanoes release lava flows and pyroclastic flows, which cause serious damages. As an example of a typical sediment disaster, a disaster which occurred in Venezuela, in 1999 is briefly reported. The disaster was caused by unusual heavy rainfall. Many people were killed by many debris flows and shallow landslides. The disaster shows information on hazards such as hazard maps and rainfall is necessary and control structures may reduce damages if they had existed. Proper land-use and hazard education are needed.

scholarly journals Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of the issues

2015 ◽  
Vol 3 (12) ◽  
pp. 7333-7377 ◽  
Author(s):  
K. J. Beven ◽  
W. P. Aspinall ◽  
P. D. Bates ◽  
E. Borgomeo ◽  
K. Goda ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Boundary Conditions ◽  
Significant Influence ◽  
Natural Hazard ◽  
Good Practice ◽  
Simulation Models ◽  
Hazard Risk ◽  
Hazard Risk Assessment ◽  
Lack Of Knowledge

Abstract. Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. There is a lack of knowledge about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities, even as elicited probabilities rationalized on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions that are made for risk management, so it is important to communicate the meaning of an uncertainty estimate and to provide an audit trail of the assumptions on which it is based. Some suggestions for good practice in doing so are made.

scholarly journals Epistemic uncertainties and natural hazard risk assessment – Part 2: What should constitute good practice?

2018 ◽  
Vol 18 (10) ◽  
pp. 2769-2783 ◽  
Author(s):  
Keith J. Beven ◽  
Willy P. Aspinall ◽  
Paul D. Bates ◽  
Edoardo Borgomeo ◽  
Katsuichiro Goda ◽  
...  
Keyword(s):  
Natural Hazard ◽  
Good Practice ◽  
Simulation Models ◽  
Decision Makers ◽  
Sources Of Uncertainty ◽  
Uncertainty Estimates ◽  
Feasible Sets ◽  
Hazard Assessments ◽  
The Impact ◽  
Hazard Risk Assessment

Abstract. Part 1 of this paper has discussed the uncertainties arising from gaps in knowledge or limited understanding of the processes involved in different natural hazard areas. Such deficits may include uncertainties about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities, even as elicited probabilities rationalized on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions made, say, for risk management, so it is important to examine the sensitivity of such decisions to different feasible sets of assumptions, to communicate the meaning of associated uncertainty estimates, and to provide an audit trail for the analysis. A conceptual framework for good practice in dealing with epistemic uncertainties is outlined and the implications of applying the principles to natural hazard assessments are discussed. Six stages are recognized, with recommendations at each stage as follows: (1) framing the analysis, preferably with input from potential users; (2) evaluating the available data for epistemic uncertainties, especially when they might lead to inconsistencies; (3) eliciting information on sources of uncertainty from experts; (4) defining a workflow that will give reliable and accurate results; (5) assessing robustness to uncertainty, including the impact on any decisions that are dependent on the analysis; and (6) communicating the findings and meaning of the analysis to potential users, stakeholders, and decision makers. Visualizations are helpful in conveying the nature of the uncertainty outputs, while recognizing that the deeper epistemic uncertainties might not be readily amenable to visualizations.

scholarly journals Epistemic uncertainties and natural hazard risk assessment. 2. What should constitute good practice?

2017 ◽  
Author(s):  
Keith J. Beven ◽  
Willy P. Aspinall ◽  
Paul D. Bates ◽  
Eduardo Borgomeo ◽  
Katsu Goda ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Significant Influence ◽  
Natural Hazard ◽  
Good Practice ◽  
Simulation Models ◽  
Hazard Risk ◽  
Uncertainty Estimates ◽  
Feasible Sets ◽  
Hazard Risk Assessment

Abstract. Part 1 of this paper has discussed the uncertainties arising from gaps in knowledge or limited understanding of the processes involved in different natural hazard areas. Such deficits may include uncertainties about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities, even as elicited probabilities rationalised on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions made, say, for risk management, so it is important to examine the sensitivity of such decisions to different feasible sets of assumptions, to communicate the meaning of associated uncertainty estimates and to provide an audit trail for the analysis. A conceptual framework for good practice in dealing with epistemic uncertainties is outlined and implications of applying the principles to natural hazard science are discussed.

RESEARCH OF LANDSLIDES AND DEBRIS FLOWS IN BAILONG RIVER BASIN:PROGRSS AND PROSPECT

2013 ◽  
Vol 33 (4) ◽  
pp. 1 ◽  
Author(s):  
Xingmin MENG ◽  
Guan CHEN ◽  
Peng GUO ◽  
Muqi XIONG ◽  
Wasowski Janusz
Keyword(s):  
Debris Flows ◽  
Landslides And Debris Flows

scholarly journals Comparison of length and dynamics of wood pieces in streams covered with coniferous and broadleaf forests mapped using orthophotos acquired by an unmanned aerial vehicle

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Haruka Tsunetaka ◽  
Slim Mtibaa ◽  
Shiho Asano ◽  
Takashi Okamoto ◽  
Ushio Kurokawa
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Debris Flows ◽  
Rain Gauge ◽  
Japan Meteorological Agency ◽  
Landslide Occurrence ◽  
Direct Measurements ◽  
Wood Piece ◽  
Aerial Vehicle ◽  
Main Components ◽  
Landslides And Debris Flows

AbstractAs wood pieces supplied by landslides and debris flows are one of the main components of ecological and geomorphic systems, the importance of quantifying the dimensions of the wood pieces is evident. However, the low accessibility of disturbed channels after debris flows generally impedes accurate and quick wood-piece investigations. Thus, remote-sensing measurements for wood pieces are necessitated. Focusing on sub-watersheds in coniferous and broadleaf forests in Japan (the CF and BF sites, respectively), we measured the lengths of wood pieces supplied by landslides (> 0.2 m length and > 0.03 m diameter) from orthophotos acquired using a small unmanned aerial vehicle (UAV). The measurement accuracy was analyzed by comparing the lengths derived from the UAV method with direct measurements. The landslides at the CF and BF sites were triggered by extremely heavy rainfalls in 2017 and 2018, respectively. UAV flights were operated during February and September 2019 at the CF site and during November 2018 and December 2019 at the BF site. Direct measurements of wood pieces were carried out on the date of the respective second flight date in each site. When both ends of a wood piece are satisfactorily extracted from an orthophoto acquired by the UAV, the wood-piece lengths at the CF site can be measured with an accuracy of approximately ±0.5 m. At the BF site, most of the extracted lengths were shorter than the directly measured lengths, probably because the complex structures of the root wad and tree crown reduced the visibility. Most wood pieces were discharged from landslide scars at the BF site, but at the CF site, approximately 750 wood pieces remained in the landslide scars approximately 19 months after the landslide occurrence. The number of wood pieces in the landslide scars of the CF site increased with increasing landslide area, suggesting that some wood pieces can be left even if large landslides occur. The lengths and locations of the entrapped wood pieces at both sites were not significantly changed between the two UAV flight dates. However, during this period, the rainfall intensities around the CF site measured by the closest rain-gauge of the Japan Meteorological Agency reached their second highest values from 1976 to 2019, which exceeded the 30-year return period. This suggests that most of the entrapped wood pieces rarely migrated even under intense rainfall.

scholarly journals Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

2021 ◽  
Vol 10 (5) ◽  
pp. 315
Author(s):  
Hilal Ahmad ◽  
Chen Ningsheng ◽  
Mahfuzur Rahman ◽  
Md Monirul Islam ◽  
Hamid Reza Pourghasemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Debris Flows ◽  
Regional Scale ◽  
Indus Basin ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Susceptibility Maps ◽  
Landslides And Debris Flows

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

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