Publication: Automatic Differentiation: A Tool for Variational Data Assimilation and Adjoint Sensitivity Analysis for Flood Modeling
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Automatic Differentiation: A Tool for Variational Data Assimilation and Adjoint Sensitivity Analysis for Flood Modeling

- incollection -
 

Area
Flood Modeling

Author(s)
William Castaings , Denis Dartus , Marc Honnorat , François-Xavier Le Dimet , Youssef Loukili , Jérôme Monnier

Published in
Automatic Differentiation: Applications, Theory, and Implementations

Editor(s)
H. M. Bücker, G. Corliss, P. Hovland, U. Naumann, B. Norris

Year
2005

Publisher
Springer

Abstract
This paper illustrates the potential of automatic differentiation (ad) for very challenging problems related to the modeling of complex environmental systems prone to floods. Numerical models are driven by inputs (initial conditions, boundary conditions and parameters) which cannot be directly inferred from measurements. For that reason, robust and efficient methods are required to assess the effects of inputs variations on computed results and estimate the key inputs to fit available observations. We thus consider variational data assimilation to solve the parameter estimation problem for a river hydraulics model, and adjoint sensitivity analysis for a rainfall-runoff model, two essential components involved in the generation and propagation of floods. Both applications require the computation of the gradient of a functional, which can be simply derived from the solution of an adjoint model. The adjoint method, which was successfully applied in meteorology and oceanography, is described from its mathematical formulation to its practical implementation using the automatic differentiation tool TAPENADE.

Cross-References
Bucker2005ADA

AD Tools
TAPENADE

Related Applications
- Modeling of Complex Environmental Systems prone to Floods

BibTeX
@INCOLLECTION{
         Castaings2005ADA,
       author = "William Castaings and Denis Dartus and Marc Honnorat and Le Dimet,
         Fran\c{c}ois-Xavier and Youssef Loukili and J\'{e}r\^{o}me Monnier",
       title = "Automatic Differentiation: {A} Tool for Variational Data Assimilation and Adjoint
         Sensitivity Analysis for Flood Modeling",
       editor = "H. M. B{\"u}cker and G. Corliss and P. Hovland and U. Naumann and B.
         Norris",
       booktitle = "Automatic Differentiation: {A}pplications, Theory, and Implementations",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       year = "2005",
       abstract = "This paper illustrates the potential of automatic differentiation (AD) for very
         challenging problems related to the modeling of complex environmental systems prone to floods.
         Numerical models are driven by inputs (initial conditions, boundary conditions and parameters) which
         cannot be directly inferred from measurements. For that reason, robust and efficient methods are
         required to assess the effects of inputs variations on computed results and estimate the key inputs
         to fit available observations. We thus consider variational data assimilation to solve the parameter
         estimation problem for a river hydraulics model, and adjoint sensitivity analysis for a
         rainfall-runoff model, two essential components involved in the generation and propagation of
         floods. Both applications require the computation of the gradient of a functional, which can be
         simply derived from the solution of an adjoint model. The adjoint method, which was successfully
         applied in meteorology and oceanography, is described from its mathematical formulation to its
         practical implementation using the automatic differentiation tool TAPENADE.",
       crossref = "Bucker2005ADA",
       ad_area = "Flood Modeling",
       ad_tools = "Tapenade",
       pages = "249--262",
       doi = "10.1007/3-540-28438-9_22"
}


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