Integration of hydrological models with entropy and multi-objective optimization based methods for designing specific needs streamflow monitoring networks

Ursulak, Jacob and Coulibaly, Paulin, (2020). Integration of hydrological models with entropy and multi-objective optimization based methods for designing specific needs streamflow monitoring networks. Journal of Hydrology, 593 n/a-n/a

Document type:
Article
Collection:
UNU-INWEH Journal Articles

Metadata
Links
Versions
Statistics
  • Sub-type Journal article
    Author Ursulak, Jacob
    Coulibaly, Paulin
    Title Integration of hydrological models with entropy and multi-objective optimization based methods for designing specific needs streamflow monitoring networks
    Appearing in Journal of Hydrology
    Volume 593
    Publication Date 2020-12-13
    Place of Publication Amsterdam
    Publisher Elsevier
    Start page n/a
    End page n/a
    Language eng
    Abstract Water resource managers depend on the collection of accurate hydrometric data for various modeling and planning projects. An essential use of hydrometric data includes hydrologic modelling and forecasting to support decision making in water resources planning and management. It is, therefore, essential to design hydrometric monitoring networks while considering the relationship between data collection and model application. A new model-based network design strategy is proposed that embeds hydrological models into a multi-objective evolutionary algorithm, facilitating direct optimization according to the model-based design objectives. This method is compared to the traditional model-based approach used to design hydrometric monitoring networks. The traditional approach is to first conduct optimization using secondary design objectives, that are not model based, to identify a set of optimal networks. Hydrological models are then applied as a post-processing mechanism to identify which of the optimal networks best satisfy the model orientated design objectives or users’ needs. In this investigation, the well-established dual-entropy multi-objective optimization (DEMO) approach was employed to conduct the initial network design based on the principles of information theory, followed by post-processing with rainfall-runoff models. Two case studies are evaluated, a monitoring network reduction in the Fraser River basin and a network augmentation in an upstream subsection of the Churchill River basin. Results show that embedding models in the optimization algorithm consistently yields better network configurations compared to those identified using the traditional method. It is shown that a smaller size optimal network that outperforms larger size networks can be identified directly by the proposed method. The models and model performance criteria used in the design process can be readily adapted, allowing for a user-directed design capable of addressing problem-specific objectives on a case by case basis.
    Copyright Holder Elsevier B. V.
    Copyright Year 2020
    Copyright type All rights reserved
    DOI 10.1016/j.jhydrol.2020.125876
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 202 Abstract Views  -  Detailed Statistics
    Created: Sat, 29 Jan 2022, 06:58:07 JST by Anderson, Kelsey on behalf of UNU INWEH