Related Concept Videos
01:30Typical Model Studies
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.
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
00:59Propagation of Uncertainty from Random Error
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...
01:21Design Example: Creating a Hydraulic Model of a Dam Spillway
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
00:54Uncertainty: Confidence Intervals
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
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11:19
Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
Published on: October 21, 2016
A model-averaging method for assessing groundwater conceptual model uncertainty.
Ming Ye1, Karl F Pohlmann, Jenny B Chapman
1Department of Scientific Computing,Florida State University, Tallahassee, FL 32306, USA. mye@fsu.edu
Ground Water
|October 1, 2009
Summary
Model uncertainty significantly impacts groundwater predictions more than parameter uncertainty. Geological interpretations are a larger source of this uncertainty than recharge estimates in the Death Valley Regional Flow System.
Area of Science:
- Hydrogeology
- Environmental Modeling
- Geoscience
Background:
- Groundwater flow systems are complex and require robust modeling for accurate predictions.
- Uncertainty in model components and parameters is a significant challenge in hydrogeological studies.
- The Death Valley Regional Flow System (DVRFS) presents unique challenges due to data limitations and geological complexity.
Purpose of the Study:
- To evaluate alternative groundwater flow models for the northern Yucca Flat area within the DVRFS.
- To quantify and compare the contributions of model uncertainty and parametric uncertainty to predictive uncertainty.
- To identify the primary sources of uncertainty affecting groundwater flow predictions in the study area.
Main Methods:
- Development of 25 plausible groundwater flow models by combining different recharge and geological interpretations.
- Assessment of parametric uncertainty using Monte Carlo simulation within each model.
- Evaluation of model uncertainty using model averaging techniques, including information criteria and Generalized Likelihood Uncertainty Estimation (GLUE).
Main Results:
- Model uncertainty was found to contribute significantly more to predictive uncertainty than parametric uncertainty.
- Uncertainty stemming from geological interpretations had a greater impact on predictions than uncertainty from recharge estimates.
- Weighted residuals showed greater variation across different geological models compared to different recharge models.
Conclusions:
- Geological interpretations represent a critical source of uncertainty in DVRFS groundwater modeling.
- Model averaging techniques are essential for managing uncertainty in complex hydrogeological systems.
- Further research should focus on refining geological models to reduce predictive uncertainty in groundwater flow assessments.