Developing tool to assess model uncertainty in sediment simulation

Project ID: 8680
Principal Investigator: Blair Greimann
Research Topic: Sediment Management and River Restoration
Funded Fiscal Years: 2015
Keywords: uncertainty, sediment transport

Research Question

The central question this proposal will help answer is: "What is the uncertainty of predictions from hydraulic, sediment transport, and hydrology models?"

Hydraulic, sediment transport, and hydrologic models are widely used by the Bureau of Reclamation to manage water and power infrastructure, make decisions about water delivery, evaluate control strategies for invasive species, assess impacts of system operation on endangered species, project the impacts of climate changes on water systems, and many other applications. It is well-known that the predictions made by such models include some degree of uncertainty due to simplifications that are made in modeling the real system, errors in the estimated values of model parameters, and other sources. However,the magnitude and origins of the uncertainty are not well understood in most applications.

The primary goal of this project is to develop and implement a framework to quantify and ultimately reduce the uncertainty associated with predictions from hydrologic, sediment transport, and hydraulic models.

Need and Benefit

Model uncertainty can add significant costs to Reclamation projects. In general, neither the conditions under which a project must function nor the impacts of a project on the surrounding environment can be known with certainty in advance of the project's implementation. Consequently, projects are planned and designed using scenarios that fall at the most conservative end of the range of plausible outcomes. This procedure usually results in the over-design of hydraulic structures and the over-estimation of project impacts. If the uncertainty in the model predictions can be reduced, then the range of possible outcomes would also be reduced, which would ultimately increase the efficiency of project designs. In certain cases, a better understanding of this uncertainty might also prompt more conservative designs than current practice, which might help avoid future unintended impacts or even project failures. The methodology and software tools developed in this research project would benefit three general activities related to sediment transport modeling. The methodology employed can also be readily extended to hydraulic and hydrologic simulation.

* Data collection: The software will allow Reclamation employees and other users to determine observation strategies that efficiently reduce the uncertainty in the model predictions. The software will help identify the parameters that are most important to the model forecasts and the types of data that can most efficiently reduce the uncertainty in these model parameters. For example, if sediment deposition needs to be predicted in a flood prone area, the software will help identify the data that is most important to reducing the uncertainty in the predicted deposition patterns and rates. The software will also quantify the uncertainty in the prediction caused by uncertainty in the bed material collection.

* Computer model improvement: Nearly all numerical models use simplified
representations of the actual hydrologic and hydraulic processes to make predictions about the system behavior. In sediment transport modeling, for example, the modeler must always select sediment transport formulas for use in representing a river system. Because such formulas simply the actual sediment transport process, this choice introduces uncertainty into the model forecasts and might even introduce systematic biases in the results. The proposed methodology will allow a user to assess whether the mathematical structure of the model is adequate for the particular application being considered. For example, if one is trying to predict river reaches where deposition will
occur, the tools developed under this research project will help identify the reaches that can be simulated accurately and the reaches where the model is deficient. By identifying the circumstances where the model structure is more or less certain, improvements can be made that specifically benefit Reclamation applications. These improvements might be revised computer models or simply the use of different process representations that are already available in existing computer models.

* Reclamation and its project partners' decision making: As described above, numerical models are commonly used to guide decision makers by providing forecasts of expected outcomes and/or worst-case scenarios. These forecasts are typically derived from engineering judgment rather than a probabilistic analysis of possible outcomes. The more formalized procedure developed in this project would help ensure that the actual range of possible outcomes and the likelihood of occurrence of these outcomes are understood. For example, a distributed hydrologic model of a river basin is sometimes used to predict flows into a reservoir. The uncertainty of these predictions is usually not known. This research project would develop the methodology necessary to assess that uncertainty and its implications for the project.

Contributing Partners

Contact the Principal Investigator for information about partners.

Research Products

Bureau of Reclamation Review

The following documents were reviewed by experts in fields relating to this project's study and findings. The results were determined to be achieved using valid means.

Developing tool to assess model uncertainty in sediment simulation (final, PDF, 322KB)
By Blair Greimann
Publication completed on September 30, 2015

The uncertainty in numerical model predictions can result from simplifications in the model's representations of the physical systems (model structure uncertainty), errors in the values assigned to model parameters (parameter uncertainty), and errors in the model inputs (forcing or input uncertainty). It is important to understand the nature of those uncertainties in order to guide data collection and model calibration strategies. The long-term objective of this research is to develop a formal and efficient framework to evaluate uncertainty in predictions from hydrologic, hydraulic, and sediment-transport models. In particular, this project aims to assess the uncertainty associated with parameter, forcing, and model structure using Bayesian uncertainty methods, and reduce the computational cost of the Bayesian method substantially while still providing reliable uncertainty estimates. The new approach for uncertainty will require few enough simulations to be applied to complex model applications, and retain enough formality to reliably evaluate data collection and model calibration strategies. To constrain the scope, this research focuses on applying the framework to a sediment transport model called Sedimentation and River Hydraulics - One Dimension (SRH-1D).

Developing tool to assess model uncertainty in sediment simulation (final, PDF, 3.7MB)
By Youngjai Jung, Jeff Niemann, Blair Greimann
Research Product completed on September 30, 2017

This research product summarizes the research results and potential application to Reclamation's mission.

Developing tool to assess model uncertainty in sediment simulation (final, PDF, 3.7MB)
By Youngjai Jung, Jeff Niemann, Blair Greimann
Research Product completed on September 30, 2017

This research product summarizes the research results and potential application to Reclamation's mission.


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Last Updated: 4/4/17