Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges

Nans Addor, Hong Do, Gemma Coxon, Camila Alvarez-Garreton, Keirnan Fowler, Pablo Mendoza

Research output: Contribution to journalArticle (Academic Journal)peer-review

58 Citations (Scopus)
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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.
Original languageEnglish
JournalHydrological Sciences Journal
Early online date3 Dec 2019
Publication statusE-pub ahead of print - 3 Dec 2019


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