Related Experiment Video
Updated: Aug 22, 2025
06:55
Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling
Published on: August 5, 2016
8.3K
Probabilistic prediction of rock avalanche runout using a numerical model.
Jordan Aaron1,2, Scott McDougall3, Julia Kowalski4,5
1Geological Institute, ETH Zürich, Zurich, Switzerland.
Landslides
|November 7, 2022
Summary
Rock avalanche predictions are improved by accounting for uncertainties in calibration data and movement. Understanding basal resistance is key to reducing uncertainty and achieving more precise rock avalanche runout forecasts.
Area of Science:
- Geosciences
- Natural Hazards
- Risk Assessment
Background:
- Rock avalanches pose significant threats to mountainous communities.
- Accurate probabilistic predictions of rock avalanche impact areas are essential for risk assessment.
Purpose of the Study:
- To assess uncertainties in rock avalanche runout models arising from calibration data and governing mechanisms.
- To develop and evaluate a method for accounting for both calibration and mechanistic uncertainties.
Main Methods:
- A back-analysis of 31 rock avalanche case histories was performed.
- Semi-empirical, calibration-based numerical runout models were utilized.
- A novel method was developed to incorporate expert judgment for uncertainty reduction.
Main Results:
- Uncertainties in forecasting are primarily driven by the bulk basal resistance of the path material.
- The proposed method effectively accounts for both calibration and mechanistic uncertainties.
- Pseudo-forecasts demonstrated that expert judgment reduces mechanistic uncertainty.
Conclusions:
- Accurate rock avalanche risk assessment requires addressing uncertainties in numerical models.
- Expert judgment in assessing bulk basal resistance significantly enhances the precision of rock avalanche runout predictions.
- The developed method offers a more robust approach to probabilistic rock avalanche forecasting.
Related Concept Videos
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
98
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
98
01:18Steps in Outbreak Investigation
170
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
170
01:30Typical Model Studies
425
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
425
01:12Conservation of Energy: Application
7.0K
When solving problems using the energy conservation law, the object (system) to be studied should first be identified. Often, in applications of energy conservation, we study more than one body at the same time. Second, identify all forces acting on the object and determine whether each force doing work is conservative. If a non-conservative force (e.g., friction) is doing work, then mechanical energy is not conserved. The system must then be analyzed with non-conservative work. Third, for...
7.0K
00:59Propagation of Uncertainty from Random Error
854
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
854
01:04Random Error
1.3K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
1.3K