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Trend analysis of dam inflow data using the trend accuracy index and the potential-evapotranspiration correction factor

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Abstract

Climate change intensifies rainfall and runoff variability, complicating long-term dam inflow simulation. Long-term runoff models often optimize parameters using error-based metrics such as NSE or RMSE, which reduce deviations but may fail to reproduce long-term trends. We optimize the four-tank model using the trend accuracy index (TAI) and the potential-evapotranspiration correction factor (PET-CF) to better capture inflow trends. We validate the approach by assessing trends in simulated inflows. We evaluate monthly, seasonal, and annual rainfall trends at 101 stations, examine their consistency with inflow trends, and apply K-means + + clustering. Using rainfall in the Nakdong river basin and inflow at Hapcheon Dam, we conduct clustering and trend analyses for 2000–2019 and two future periods (2021–2050, 2051–2100) under SSP2-4.5 and SSP3-7.0. Three clusters capture spatial differences in rainfall linked to station location and elevation. Across time scales, rainfall and inflow trends are not consistently aligned in either the historical or future periods. In dam inflow simulation, TAI + PET-CF achieves the highest concordance with observed inflow trends (76.4%), outperforming NSE-based calibration. Combined with rainfall trend and clustering information, the framework can support efficient water-resources management.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C2091773). This work was supported by INHA UNIVERSITY Research Grant.

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Authors and Affiliations

  1. Gyeonggi Research Institute, Suwon, South Korea

    Won-joon Wang

  2. Department of Civil Engineering, Inha University, Incheon, South Korea

    Hung Soo Kim

Authors

  1. Won-joon Wang
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  2. Hung Soo Kim
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Contributions

Won-joon Wang: Writing—Original Article, Data Processing, ValidationHung Soo Kim: Supervision, Writing—Review and Editing.

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Correspondence to
Hung Soo Kim.

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Wang, Wj., Kim, H.S. Trend analysis of dam inflow data using the trend accuracy index and the potential-evapotranspiration correction factor.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-40225-y

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  • DOI: https://doi.org/10.1038/s41598-026-40225-y

Keywords

  • Cluster analysis
  • Modified Mann−Kendall test
  • Homogeneity test
  • Tank model
  • Trend accuracy index

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