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doi:10.3808/jei.202500530
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Assessment of Uncertainty Propagation from Climate Modeling to Hydrologic Forecasting under Changing Climatic Conditions

H. J. Wu1, X. D. Ye2 *, B. Y. Zhang1, and B. Chen1 *

  1. Northern Region Persistent Organic Pollution Control (NRPOP) Laboratory, Memorial University of Newfoundland, Faculty of Engineering and Applied Science, St. John’s, NL A1B 3X5, Canada
  2. Key Laboratory for Coastal Marine Eco-Environment Process and Carbon Sink of Hainan province, Yazhou Bay Innovation Institute, College of Ecology and Environment, Hainan Tropical Ocean University, Sanya 572000, China

* Corresponding author. Tel: +86 17602406401. E-mail address: owkwork@163.com (X. D. Ye).
* Corresponding author. Tel: +1 (709) 864-8958; Fax: +1 (709) 864-4042. E-mail address: bchen@mun.ca (B. Chen).

Abstract


The changing climate has a profound impact on the hydrological cycle and water balance, complicating water resources management. General circulation models (GCMs) and downscaling methods have been widely employed to reflect and quantify climate change effects in hydrological studies. The uncertainties associated with GCMs, downscaling methods, and hydrological modeling mutually interact, significantly amplifying the complexity of uncertainty analysis. To address this challenge, we proposed the Integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) method, specifically designed to assess the uncertainty propagation effect from statistical downscaling and hydrological modeling. This study aims to utilize ISES-UPA to inves-tigate the effects and contributions of different uncertainty components to the total uncertainty in hydrological modeling under changing climatic conditions. Successfully applied to a real case study in Sichuan, China, the results reveal that the total propagated uncertainty significantly surpasses the simple addition of other sources (e.g., about 2.15 times from statistical downscaling and about 4.44 times from hydrological modeling on average). By using ISES-UPA, individual and combined uncertainties from statistical downscaling and hydrological modeling can be compared and quantified, thereby enhancing the reliability of hydrological studies under changing climate conditions.

Keywords: climate change, hydrological modeling, statistical downscaling, uncertainty propagation effects, uncertainty analysis


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