Blueprint conceptualization for a river basin's digital twin

Debasish Pal, Hannu Marttila, Pertti Ala-Aho, Eliisa Lotsari, Anna-Kaisa Ronkanen, Carlos Gonzales-Inca, Danny Croghan, Marie Korppoo, Maria Kamari, Erik van Rooijen, Linnea Blafield, Jari Silander, Aziza Baubekova, Joy Bhattacharjee, Ali Torabi Haghighi, Cintia Bertacchi Uvo, Harri Kaartinen, Mehdi Rasti, Bjorn Klove, Petteri Alho

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

To achieve a good ecological status of water resources, we are exploring new frontiers by envisioning river basin planning through the newly promoted digital twin perspective. In river basin management, a digital twin is an innovative virtual paradigm – a holistic living replica of the river basin achieved by seamless integration of real-time monitoring, historical observations, data analytics, predictive modeling, and high-performance computing within a framework of interoperable software and scalable hardware – leveraging nuanced understanding of complex environmental, social, and economic interactions, discerning uncertainties, and bridging critical knowledge gaps for progressive improvement in system understanding, optimization of operational efficiency, and continuous advancements in decision-making. This perspective paper lays the groundwork in transforming the futuristic vision of a river basin's digital twin into reality. The proposed blueprint outlines the processes for integrating digital twin components, creating dynamic replicas of river basin systems, and conducting virtual what-if analyses. Aligning with digital transformation, this work segments the river basin into distinct systems to effectively manage diverse objectives and ensure adaptability across various river basin types with spatiotemporal scalability. Supporting sustainable management, the digital twin holds immense potential to surpass existing decision-support systems through continuous bi-directional feedback loops with the river basin.
Original languageEnglish
Pages (from-to)197-212
Number of pages16
JournalHydrology Research
Volume56
Issue number3
DOIs
Publication statusPublished - Mar 2025
MoE publication typeA1 Journal article-refereed

Keywords

  • Data management
  • Decision-making
  • Digital twin
  • Hybrid modeling
  • River basin management
  • data management
  • decision-making
  • river basin management
  • digital twin
  • hybrid modeling

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