Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges. Issue 5 (3rd April 2020)

• Record Type: Journal Article
• Authors: Addor, Nans; Do, Hong X.; Alvarez-Garreton, Camila; Coxon, Gemma; Fowler, Keirnan; Mendoza, Pablo A.
• Is Part Of: Hydrological sciences journal. Volume 65:Issue 5(2020)
• Page Start: 712
• Page End: 725
• Publication Date: 2020-04-03
• Subjects: streamflow records -- data standardization -- reproducibility of hydrological experiments -- data uncertainties -- human interventions -- cloud computing; Hydrology -- Periodicals; 551.4805
• DOI: 10.1080/02626667.2019.1683182
• Languages: English
• ISSNs: 0262-6667

Abstract

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.