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Title: Definition and solution of a stochastic inverse problem for the Manning’s n parameter field in hydrodynamic models

Journal Article · · Advances in Water Resources
 [1];  [2];  [3];  [2];  [4]
  1. Univ. of Colorado, Denver, CO (United States)
  2. Univ. of Texas, Austin, TX (United States)
  3. Colorado State Univ., Fort Collins, CO (United States)
  4. Univ. of Notre Dame, South Bend, IN (United States)
+ Show Author Affiliations

The uncertainty in spatially heterogeneous Manning’s n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented in this paper. Technical details that arise in practice by applying the framework to determine the Manning’s n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of “condition” for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. Finally, this notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning’s n parameter and the effect on model predictions is analyzed.

Research Organization:
Colorado State Univ., Fort Collins, CO (United States); Univ. of Colorado, Denver, CO (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
SC0009279; FG02-04ER25620; FG02-05ER25699; FC02-07ER54909; SC0001724; SC0005304; DGE-1110007; INL00120133; DE0000000SC9279; PO672TO001; 00069249; 00115474; B573139; B584647; B590495
OSTI ID:
1343755
Alternate ID(s):
OSTI ID: 1249698
Journal Information:
Advances in Water Resources, Vol. 78; ISSN 0309-1708
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 17 works
Citation information provided by
Web of Science

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Cited By (5)

A research on the estimation of coefficient roughness in open channel applying entropy concept journal September 2018
Uncertainty quantification of two-phase flow problems via measure theory and the generalized multiscale finite element method journal November 2016
Specification of Additional Information for Solving Stochastic Inverse Problems journal January 2019
Experimental Design : Optimizing Quantities of Interest to Reliably Reduce the Uncertainty in Model Input Parameters preprint January 2016
Forward and backward uncertainty quantification: methods, analysis, and applications text January 2019

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Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
Manning’s n coefficient
measure theory
parameter estimation
set-valued inverse solutions
shallow water equations
stochastic inverse problems