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Reconstruction of Hydrometric Data Using a Network Optimization Model

8 pagesPublished: September 20, 2018

Abstract

Water resource managers need to implement precise and efficient water management methods particularly in the context of low flow water management. The management objectives are complex because managers must satisfy both water demands for human activities and environmental goals. More often the flow objectives are defined at specific strategic points in which hydrometric stations are based. In order to allow the manager to better understand the hydrographical network behavior, in particular for inter-basin water transfer, these strategic hydrometric stations must be reinforced by some intermediate hydrometric stations, by modeling the network behavior, and by introducing weather forecast data in order to simulate the evolution in time and space of the river. For an efficient management, it is essential that the data collected and the output of the models (the natural flow and the withdrawals) must be reliable. For this purpose, a network optimization model was developed for analyzing the consistency of the available data set (measurements and model outputs) on a hydraulic system. Herein, a reconstruction of hydrometric data using this network optimization model is applied to the Arrats watershed management.

Keyphrases: data reconstruction, network flow model, water management

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 139-146.

BibTeX entry
@inproceedings{HIC2018:Reconstruction_Hydrometric_Data_Using,
  author    = {Tahiri Ayoub and David Ladevèze and Pascale Chiron and Bernard Archimède},
  title     = {Reconstruction of Hydrometric Data Using a Network Optimization Model},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/cCrr},
  doi       = {10.29007/p6gn},
  pages     = {139-146},
  year      = {2018}}
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