Scholarlink

<p>Runoff prediction in ungauged basins remains a fundamental challenge due to the absence of discharge observations and the limited transferability of data-driven models across heterogeneous hydrological conditions. This study proposes a regime-aware framework that models hydrological processes from the perspective of shared driver-induced regimes. Instead of relying on runoff observations, a view-based contrastive self-supervised learning strategy is employed to extract transferable features from hydrometeorological drivers. Specifically, augmented temporal-window views generated through temporal masking and feature perturbation are contrasted to learn runoff-independent representations, enabling the identification of cross-basin hydrological regimes via unsupervised clustering. Regime-specific prediction models are then constructed and adaptively combined for target basins based on regime similarity. Experimental results show that the proposed method achieves a median Nash–Sutcliffe efficiency of 0.5 across ungauged test basins, demonstrating improved prediction performance under ungauged conditions. These findings highlight the effectiveness of learning shared hydrological regimes from driver space and provide a new perspective for robust runoff prediction in ungauged basins.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A regime-aware framework for runoff prediction in ungauged basins via self-supervised learning of hydrometeorological drivers

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

查看全文
  • Jiaxing Zhang,
  • Xuemei Liu,
  • Hairui Li,
  • Jiakang Du

摘要

Runoff prediction in ungauged basins remains a fundamental challenge due to the absence of discharge observations and the limited transferability of data-driven models across heterogeneous hydrological conditions. This study proposes a regime-aware framework that models hydrological processes from the perspective of shared driver-induced regimes. Instead of relying on runoff observations, a view-based contrastive self-supervised learning strategy is employed to extract transferable features from hydrometeorological drivers. Specifically, augmented temporal-window views generated through temporal masking and feature perturbation are contrasted to learn runoff-independent representations, enabling the identification of cross-basin hydrological regimes via unsupervised clustering. Regime-specific prediction models are then constructed and adaptively combined for target basins based on regime similarity. Experimental results show that the proposed method achieves a median Nash–Sutcliffe efficiency of 0.5 across ungauged test basins, demonstrating improved prediction performance under ungauged conditions. These findings highlight the effectiveness of learning shared hydrological regimes from driver space and provide a new perspective for robust runoff prediction in ungauged basins.