Parametric Inference in Large Water Quality River Systems

Conference Paper (2019)
Author(s)

Antonio Moreno-Rodenas (TU Delft - Sanitary Engineering)

J.G. Langeveld (TU Delft - Sanitary Engineering)

Francois Clemens (Deltares, TU Delft - Sanitary Engineering)

Research Group
Sanitary Engineering
DOI related publication
https://doi.org/10.1007/978-3-319-99867-1_51
More Info
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Publication Year
2019
Language
English
Research Group
Sanitary Engineering
Pages (from-to)
307-311
ISBN (print)
978-3-319-99866-4
ISBN (electronic)
978-3-319-99867-1

Abstract

Environmental models often contain parameters, which are not measurable, yet conceptual descriptions of some physical process. The value of such parameters is often derived by measuring internal state model variables in the system and indirectly tuning/calibrating the value of the parameters so some degree of match is achieved. Bayesian inference is a widely used tool in which the modeller can transfer some prior beliefs about the parameter space, which is updated when additional knowledge on the system is acquired (e.g. more measurements are available). However, the amount of simulations required to perform a formal inference becomes prohibitive when using computationally expensive models. In this work the inference of the hydraulic and dissolved oxygen processes is presented for a large scale integrated catchment model. Two emulator structures were used to accelerate the sampling of the river flow and dissolved oxygen dynamics. Posterior parameter probability distributions were computed using one year of measured data in the river.

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