  A Telemac study is a computationally intensive application for the real cases
and in the context of quantifying or optimizing uncertainties, the running
times can be too long. This paper is an example of an approximation of the
Telemac results by a more abstract but significantly faster model. It shows how
a metamodel can be easily built with low computational costs, and how it can
help to understand and improve some global results of Telemac. I. INTRODUCTION
Many sources of uncertainty lie in the real-world problems. Telemac as any
model (i.e. approximation of reality) is error prone since uncertainties appear
in the initial or boundary conditions, the system parameters, the modelling
simplification or the numerical calculations themselves. Therefore, it is
difficult to say with confidence if the design of a Telemac model has met all
the requirements to be optimal. Calibration consists of tuning the model
parameters so that the results are in better agreement with a set of
observations. This phase is crucial before any further study can be conducted
by avoiding a meaningless analysis or prediction based on false or too
inaccurate results. This paper presents a statistical calibration of a Telemac
2D model (Gironde Estuary in France) with the learning of Telemac errors by a
metamodel (i.e. a model of the simulation errors) to make the best use of
limited observations data over a short time period. The metamodel here is a
simplified version of Telemac behaving the same for all the locations where
observation points are available. If the metamodel is correct, it will be able
to compute as Telemac would do but with a highly reduced computational cost.
