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A decision-centric impact assessment of operational performance of the Yongdam Dam, South Korea

  • Daeha Kim*
  • , Eunhee Kim
  • , Seung Cheol Lee
  • , Eunji Kim
  • , June Shin
  • *Corresponding author for this work
  • Jeonbuk National University

Research output: Contribution to journalJournal articlepeer-review

Abstract

Amidst the global climate crisis, dam operation policies formulated under the stationary climate assumption could lead to unsatisfactory water management. In this work, we assessed status-quo performance of the Yongdam Dam in Korea under various climatic stresses in flood risk reduction and water supply reliability for 2021-2040. To this end, we employed a decision-centric framework equipped with a stochastic weather generator, a conceptual streamflow model, and a machine-learning reservoir operation rule. By imposing 294 climate perturbations to dam release simulations, we found that the current operation rule of the Yongdam dam could redundantly secure water storage, while inefficiently enhancing the supply reliability. On the other hand, flood risks were likely to increase substantially due to rising mean and variability of daily precipitation. Here, we argue that the current operation rules of the Yongdam Dam seem to be overly focused on securing water storage, and thus need to be adjusted to efficiently improve supply reliability and reduce flood risks in downstream areas.

Original languageEnglish
Pages (from-to)205-215
Number of pages11
JournalJournal of Korea Water Resources Association
Volume55
Issue number3
DOIs
StatePublished - 2022.03

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 6 - Clean Water and Sanitation
    SDG 6 Clean Water and Sanitation
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • Dam operation
  • Decision-centric impact assessment
  • Flood risk
  • Water supply and storage reliability

Quacquarelli Symonds(QS) Subject Topics

  • Environmental Sciences
  • Engineering - Civil & Structural

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