通过水文气象驱动因素的自我监督学习，为未计量流域的径流预测提供一个具有制度意识的框架
A regime-aware framework for runoff prediction in ungauged basins via self-supervised learning of hydrometeorological drivers.
摘要(中文译文)
无资料流域的径流预测仍然是一项根本性挑战，其原因在于缺乏流量观测资料，且数据驱动模型在异质水文条件下的可迁移性有限。本研究提出了一种基于水文情势感知的框架，从共享驱动因素诱发的水文情势视角对水文过程进行建模。该框架不依赖径流观测资料，而是采用基于视图的对比自监督学习策略，从水文气象驱动因素中提取可迁移特征。具体而言，通过时间掩蔽和特征扰动生成增强的时间窗口视图，并对这些视图进行对比学习，以获得与径流无关的表征，从而通过无监督聚类识别跨流域水文情势。随后构建情势特异性预测模型，并基于情势相似性为目标流域进行自适应组合。实验结果表明，所提方法在无资料测试流域上取得了0.5的纳什-萨特克利夫效率中位数，证明其在无资料条件下具有更好的预测性能。这些发现凸显了从驱动因素空间学习共享水文情势的有效性，并为无资料流域的稳健径流预测提供了新视角。
Abstract
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.
如何引用
AMA
Jiaxing Zhang, Xuemei Liu, Hairui Li, Jiakang Du. A regime-aware framework for runoff prediction in ungauged basins via self-supervised learning of hydrometeorological drivers.. Scientific reports. 2026; doi:10.1038/s41598-026-61333-9.
APA
Jiaxing Zhang, Xuemei Liu, Hairui Li, Jiakang Du (2026). A regime-aware framework for runoff prediction in ungauged basins via self-supervised learning of hydrometeorological drivers.. Scientific reports. https://doi.org/10.1038/s41598-026-61333-9
GB/T 7714
Jiaxing Zhang, Xuemei Liu, Hairui Li, Jiakang Du. A regime-aware framework for runoff prediction in ungauged basins via self-supervised learning of hydrometeorological drivers.[J]. Scientific reports, 2026 doi:10.1038/s41598-026-61333-9.
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