用集合模型+机器学习修正误差，无需校准就能精准预测无观测流域的河流流量。
Multi-Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins
- 用47个概念水文模型集合，通过贝叶斯平均融合输出，再用储层计算模型线性修正误差。
- 数据丰富时性能接近LSTM（NSE 0.59 vs 0.64），但计算时间仅为3%；数据稀缺时仍保持NSE 0.51。
- 无需逐个校准模型，适合缺乏观测数据的地区，兼具高效、可解释与可推广性。
尽管准确洪水预测至关重要，许多地区仍缺乏足够的河流流量观测数据。虽然已有大量日尺度河流流量预测模型，但在数据稀疏条件下实现高精度、可解释性与高效性仍是重大挑战。本文提出一种新方法：多模型集成与储层计算结合的水文预测框架（HYPER）。该方法对47个未校准的流域相关概念水文模型进行贝叶斯模型平均（BMA）集成。随后，采用储层计算（RC）模型——一种机器学习方法——通过线性回归训练以修正BMA输出误差，该过程非迭代，确保计算效率。对于无观测流域，通过将已知有观测流域的BMA与RC权重映射到流域属性，构建通用化预测框架。在87个日本流域上评估：数据丰富场景下，HYPER中位数纳什-萨特克利夫效率（NSE）为0.59，与基准LSTM（NSE 0.64）相当，但仅需其3%的计算时间；数据稀缺场景（仅约20%流域有观测）下，HYPER仍保持稳健性能（NSE 0.51），得益于集成模型的物理结构；而LSTM因数据不足性能大幅下降（NSE -0.61）。结果表明，使用足够大的模型集合并结合机器学习偏差修正，无需校准单个概念模型即可实现高精度预测。HYPER提供了一种鲁棒、高效且可推广的流量预测方案，尤其适用于无观测流域。通过消除流域特异性校准，该框架为多样数据稀疏区域提供了可扩展、可解释的水文预测解决方案。
原文摘要 · Abstract (English)
Despite the necessity for accurate flood prediction, many regions lack sufficient river discharge observations. Although numerous models for daily river discharge prediction exist, achieving high accuracy, interpretability, and efficiency under data-scarce conditions remains a major challenge. We address this with a novel method, HYdrological Prediction with multi-model Ensemble and Reservoir computing (HYPER). Our approach applies Bayesian model averaging (BMA) to 47 "uncalibrated" catchment-based conceptual hydrological models. A reservoir computing (RC) model, a type of machine learning model, is then trained via linear regression to correct BMA output errors, a non-iterative process ensuring computational efficiency. For ungauged basins, we infer the required BMA and RC weights by mapping them to catchment attributes from gauged basins, creating a generalizable framework. Evaluated on 87 Japanese basins, in a data-rich scenario, HYPER (median Nash Sutcliffe Efficiency, NSE, of 0.59) performed comparably to a benchmark LSTM (NSE 0.64) but required only 3 % of its computational time. In a data-scarce scenario (where only ~20 % of basins are gauged), HYPER maintained robust performance (NSE 0.51) by leveraging the physical structure of the ensemble. In contrast, the LSTM's performance degraded substantially (NSE -0.61) due to data insufficiency. These results demonstrate that calibrating individual conceptual hydrological models is unnecessary when using a sufficiently large ensemble that is assembled and combined with machine-learning-based bias correction. HYPER provides a robust, efficient, and generalizable solution for discharge prediction, particularly in ungauged basins. By eliminating basin-specific calibration, HYPER offers a scalable, interpretable framework for accurate hydrological prediction in diverse data-scarce regions.
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