Case Study Kleine Nete: Observation Error and Uncertainty

Wouter Zijl, Florimond De Smedt, Mustafa El-Rawy, Okke Batelaan

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter deals with a realistic case study in which a time series of conductivity observations by the double constraint methodology (DCM) is combined with the Kalman filter to determine the observation uncertainty from the spread of the observation noise.

Original languageEnglish
Title of host publicationThe Double Constraint Inversion Methodology
Subtitle of host publicationEquations and Applications in Forward and Inverse Modeling of Groundwater Flow
EditorsWouter Zijl, Florimond De Smedt, Mustafa El-Rawy, Okke Batelaan
PublisherSpringer
Chapter5
Pages75-86
Number of pages12
Edition9783319713410
ISBN (Electronic)978-3-319-71342-
ISBN (Print)9783319713410
DOIs
Publication statusPublished - 2018

Publication series

NameSpringerBriefs in Applied Sciences and Technology
Number9783319713410
ISSN (Print)2191-530X
ISSN (Electronic)2191-5318

Bibliographical note

©The Author(s) 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

Keywords

  • Porous media
  • Double Constraint Method
  • observation error
  • uncertainty
  • inverse modeling

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  • Cite this

    Zijl, W., De Smedt, F., El-Rawy, M., & Batelaan, O. (2018). Case Study Kleine Nete: Observation Error and Uncertainty. In W. Zijl, F. De Smedt, M. El-Rawy, & O. Batelaan (Eds.), The Double Constraint Inversion Methodology: Equations and Applications in Forward and Inverse Modeling of Groundwater Flow (9783319713410 ed., pp. 75-86). (SpringerBriefs in Applied Sciences and Technology; No. 9783319713410). Springer. https://doi.org/10.1007/978-3-319-71342-7_5