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<p>Long-term seasonal streamflow forecasting with lead times up to 12&#xa0;months is crucial for water security but remains a persistent challenge, plagued by the high uncertainty of General Circulation Model (GCM) climate projections and a lack of consensus on optimal modeling paradigms. To overcome these limitations, a systematic evaluation of more practical and reliable approaches is urgently needed. This study addresses this critical gap by developing and evaluating a comprehensive framework of autoregressive, data-driven, process-driven, and hybrid modeling approaches. As a key innovation, we evaluate these paradigms not only with standard Coupled Model Intercomparison Project (CMIP) projections but also with novel climate forcings derived from historical data using a newly developed comprehensive similarity evaluation index. The study focuses on simultaneous prediction of the monthly streamflow process for the next 12&#xa0;months in three snow-dominated catchments in the Yellow River’s source region. Our results reveal that simple autoregressive models provide a robust baseline, consistently achieving a Normalized Nash–Sutcliffe Efficiency (NNSE) exceeding 0.75 across both simulation and forecasting periods. Critically, climate data from hydrological similarity years offered a superior alternative to GCM-driven forecasting, outperforming CMIP projections with NNSE improvements of up to 26.28% and NRMSE reductions of up to 21.82%. Furthermore, data-driven and hybrid approaches consistently outperformed the standalone process-driven model, achieving superior predictive skill with an average NNSE of 0.63. These results highlight the potential to improve the accuracy and utility of long-term streamflow forecasting despite the long-standing operational use of existing methods.</p>

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Exploring viable approaches for long-term seasonal streamflow forecasting under different forcing mechanisms

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Ganggang Zuo,
  • Tingting Wang,
  • Yani Lian,
  • Ni Wang,
  • Jiancang Xie

摘要

Long-term seasonal streamflow forecasting with lead times up to 12 months is crucial for water security but remains a persistent challenge, plagued by the high uncertainty of General Circulation Model (GCM) climate projections and a lack of consensus on optimal modeling paradigms. To overcome these limitations, a systematic evaluation of more practical and reliable approaches is urgently needed. This study addresses this critical gap by developing and evaluating a comprehensive framework of autoregressive, data-driven, process-driven, and hybrid modeling approaches. As a key innovation, we evaluate these paradigms not only with standard Coupled Model Intercomparison Project (CMIP) projections but also with novel climate forcings derived from historical data using a newly developed comprehensive similarity evaluation index. The study focuses on simultaneous prediction of the monthly streamflow process for the next 12 months in three snow-dominated catchments in the Yellow River’s source region. Our results reveal that simple autoregressive models provide a robust baseline, consistently achieving a Normalized Nash–Sutcliffe Efficiency (NNSE) exceeding 0.75 across both simulation and forecasting periods. Critically, climate data from hydrological similarity years offered a superior alternative to GCM-driven forecasting, outperforming CMIP projections with NNSE improvements of up to 26.28% and NRMSE reductions of up to 21.82%. Furthermore, data-driven and hybrid approaches consistently outperformed the standalone process-driven model, achieving superior predictive skill with an average NNSE of 0.63. These results highlight the potential to improve the accuracy and utility of long-term streamflow forecasting despite the long-standing operational use of existing methods.