Assessing and Reducing the Uncertainty of Predictions from Hydraulic and Hydrologic Models
Project ID: 9320
Principal Investigator: Blair Greimann
Research Topic: Sediment Management and River Restoration
Priority Area Assignments: 2011 (Climate Change Adaptation Research), 2012 (Climate Change Adaptation Research)
Funded Fiscal Years: 2011, 2012 and 2013
The central question this proposal will help answer is: "What is the uncertainty of predictions from hydraulic, sediment transport, and hydrology models?"
Hydraulic and hydrologic models are widely used by the Bureau of Reclamation (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 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 overdesign of hydraulic structures and the overestimation 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 hydrologic and hydraulic modeling:
* 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 are 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 are relatively simple representations of 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' decisionmaking: As described above, numerical models are commonly used to guide decisionmakers 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.
Independent Peer Review
The following documents were reviewed by qualified Bureau of Reclamation employees. The findings were determined to be achieved using valid means.
Method for Assessing Impacts of Parameter Uncertainty in sediment transport models (interim, PDF,
By Jeff Niemann and Blair Greimann
Publication completed on June 01, 2011
the degree to which parameter values are constrained by calibration data and the impacts of the remaining parameter uncertainty on model
forecasts. The method uses a new multiobjective version of generalized likelihood uncertainty estimation. The likelihoods of parameter values
are assessed using a function that weights different output variables on the basis of their first-order global sensitivities.
EVALUATION OF PARAMETER AND MODEL UNCERTAINTY IN SIMPLE APPLICATIONS OF A 1D SEDIMENT TRANSPORT MODEL (interim, PDF,
By Ms. Shaina Sabatine
Report completed on January 17, 2012
This information was last updated on May 18, 2013
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