ABSTRACT Large-sample hydrology (LSH) relies on data from large sets (tens to thousands) of catchments to go beyond individual case studies and derive robust conclusions on hydrological processes and models. Numerous LSH datasets have recently been released, covering a wide range of regions and relying on increasingly diverse data sources to characterize catchment behaviour. These datasets offer novel opportunities, yet they are also limited by their lack of comparability, uncertainty estimates and characterization of human impacts. This article (i) underscores the key role of LSH datasets in hydrological studies, (ii) provides a review of currently available LSH datasets, (iii) highlights current limitations of LSH datasets and (iv) proposes guidelines and coordinated actions to overcome these limitations. These guidelines and actions aim to standardize and automatize the creation of LSH datasets worldwide, and to enhance the reproducibility and comparability of hydrological studies.

中文翻译：

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大样本水文学：最新进展、新数据集指南和重大挑战

摘要 大样本水文学 (LSH) 依赖于来自大型集水区（数万到数千）的数据，以超越个别案例研究，并得出关于水文过程和模型的可靠结论。最近发布了许多 LSH 数据集，涵盖了广泛的地区，并依靠日益多样化的数据源来表征流域行为。这些数据集提供了新的机会，但它们也受到缺乏可比性、不确定性估计和人类影响特征的限制。本文 (i) 强调了 LSH 数据集在水文研究中的关键作用，(ii) 回顾了当前可用的 LSH 数据集，(iii) 强调了 LSH 数据集的当前局限性，以及 (iv) 提出了克服这些局限性的指南和协调行动.