Year: 2019
Type: article
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.
Source: Hydrological Sciences Journal
Authors: Nans Addor, Hong X. Do, Camila Alvarez-Garreton, Gemma Coxon, Keirnan Fowler, Pablo A. Mendoza
Institutions: University of East Anglia, University of Michigan, The University of Adelaide, Nong Lam University Ho Chi Minh City, Austral University of Chile, Center for Climate and Resilience Research, University of Bristol, The University of Melbourne, Advanced Mining Technology Center, University of Chile
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: Exploring and constraining uncertainties in climate change impacts on hydrology, G1298381314, G231532280, G2522673491, G2876919490, A systematic comparison of hydrological model structures for use in climate impact studies, G4481024449, G4868669596, G5523190568, MaRIUS: Managing the Risks, Impacts and Uncertainties of droughts and water Scarcity, G6467823797
OpenAlex ID: W2994582025