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<p>Traditional methods for identifying hydrologically similar years, such as Euclidean Distance (ED) and Dynamic Time Warping (DTW), often fail to balance magnitude consistency with temporal pattern alignment. To address this, we propose a comprehensive Similarity Index (SI) that dynamically integrates six methodologies: ED, DTW, Fréchet Distance, Hausdorff Distance, Longest Common Subsequence Similarity, and Cosine Similarity (CS). Using monthly data from 15 Yellow River Basin stations, we systematically compared ten identification schemes, including weighted SI variants and a hybrid CS + ED model. Performance was assessed via a dual-dimensional framework: pattern similarity (based on Kendall’s τ and Theil-Sen slope) and magnitude similarity (based on Normalized Root Mean Square Error (NRMSE), Nash-Sutcliffe Efficiency (NSE), and Q-Q plot correlation). Results indicate that while the CS + ED hybrid achieved the highest similarity score (0.8347), the SI family demonstrated superior cross-station stability, yielding the lowest coefficient of variation (4.2415). Practical value analysis using a simple linear runoff prediction model demonstrated that the proposed similarity indices achieved significant improvements in runoff forecasting. The SI method showed a 13.47% RMSE improvement over the worst-performing method, with SI(PAC) and SI(EWM) achieving 13.14% and 11.20% improvements respectively, while traditional distance/sequence-based methods showed limited performance.</p>

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Identifying Hydrological Analogous Year from Magnitude and Temporal Patterns

  • 期刊论文

  • 发表日期：

    2025年12月26日

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  • Shiqi Nie,
  • Ganggang Zuo,
  • Ni Wang,
  • Jiancang Xie

摘要

Traditional methods for identifying hydrologically similar years, such as Euclidean Distance (ED) and Dynamic Time Warping (DTW), often fail to balance magnitude consistency with temporal pattern alignment. To address this, we propose a comprehensive Similarity Index (SI) that dynamically integrates six methodologies: ED, DTW, Fréchet Distance, Hausdorff Distance, Longest Common Subsequence Similarity, and Cosine Similarity (CS). Using monthly data from 15 Yellow River Basin stations, we systematically compared ten identification schemes, including weighted SI variants and a hybrid CS + ED model. Performance was assessed via a dual-dimensional framework: pattern similarity (based on Kendall’s τ and Theil-Sen slope) and magnitude similarity (based on Normalized Root Mean Square Error (NRMSE), Nash-Sutcliffe Efficiency (NSE), and Q-Q plot correlation). Results indicate that while the CS + ED hybrid achieved the highest similarity score (0.8347), the SI family demonstrated superior cross-station stability, yielding the lowest coefficient of variation (4.2415). Practical value analysis using a simple linear runoff prediction model demonstrated that the proposed similarity indices achieved significant improvements in runoff forecasting. The SI method showed a 13.47% RMSE improvement over the worst-performing method, with SI(PAC) and SI(EWM) achieving 13.14% and 11.20% improvements respectively, while traditional distance/sequence-based methods showed limited performance.