ROBIN: reference observatory of basins for international hydrological climate change detection

Turner, S. ORCID: https://orcid.org/0000-0001-8358-8775; Hannaford, J. ORCID: https://orcid.org/0000-0002-5256-3310; Barker, L.J. ORCID: https://orcid.org/0000-0002-2913-0664; Suman, G.; Killeen, A.; Armitage, R. ORCID: https://orcid.org/0009-0007-5338-4756; Chan, W. ORCID: https://orcid.org/0000-0003-4296-3203; Davies, H.; Griffin, A. ORCID: https://orcid.org/0000-0001-8645-4561; Kumar, A. ORCID: https://orcid.org/0000-0001-6444-4047; Dixon, H. ORCID: https://orcid.org/0000-0002-7415-063X; Albuquerque, M.T.D.; Almeida Ribeiro, N.; Alvarez-Garreton, C. ORCID: https://orcid.org/0000-0002-5381-4863; Amoussou, E.; Arheimer, B.; Asano, Y.; Berezowski, T. ORCID: https://orcid.org/0000-0003-4074-0866; Bodian, A.; Boutaghane, H.; Capell, R.; Dakhaoui, H.; Daňhelka, J.; Do, H.X. ORCID: https://orcid.org/0000-0001-9169-579X; Ekkawatpanit, C.; El Khalki, E.M.; Fleig, A.K.; Fonseca, R.; Giraldo-Osorio, J.D.; Goula, A.B.T.; Hanel, M.; Horton, S. ORCID: https://orcid.org/0000-0001-5879-1464; Kan, C.; Kingston, D.G.; Laaha, G.; Laugesen, R. ORCID: https://orcid.org/0000-0002-3811-5845; Lopes, W.; Mager, S. ORCID: https://orcid.org/0000-0001-6610-5874; Rachdane, M.; Markonis, Y. ORCID: https://orcid.org/0000-0003-0144-8969; Medeiro, L.; Midgley, G.; Murphy, C.; O’Connor, P.; Pedersen, A.I.; Pham, H.T. ORCID: https://orcid.org/0000-0001-9331-9384; Piniewski, M.; Renard, B.; Saidi, M.E. ORCID: https://orcid.org/0000-0001-8949-2547; Schmocker-Fackel, P.; Stahl, K. ORCID: https://orcid.org/0000-0002-2159-9441; Thyer, M.; Toucher, M.; Tramblay, Y. ORCID: https://orcid.org/0000-0003-0481-5330; Uusikivi, J.; Venegas-Cordero, N.; Visessri, S.; Watson, A.; Westra, S.; Whitfield, P.H. ORCID: https://orcid.org/0000-0001-6937-9459. 2025 ROBIN: reference observatory of basins for international hydrological climate change detection. Scientific Data, 12, 654. 13, pp. 10.1038/s41597-025-04907-y

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Abstract/Summary

Human-induced warming is modifying the water cycle. Adaptation to posed threats requires an understanding of hydrological responses to climate variability. Whilst these can be computationally modelled, observed streamflow data is essential for constraining models, and understanding and quantifying emerging trends in the water cycle. To date, the identification of such trends at the global scale has been hindered by data limitations – in particular, the prevalence of direct human influences on streamflow which can obscure climate-driven variability. By removing these influences, trends in streamflow data can be more confidently attributed to climate variability. Here we describe the Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) – the first iteration of a global network of streamflow data from national reference hydrological networks (RHNs) – comprised of catchments which are near-natural or have limited human influences. This collaboration has established a freely available global RHN dataset of over 3,000 catchments and code libraries, which can be used to underpin new science endeavours and advance change detection studies to support international climate policy and adaptation.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1038/s41597-025-04907-y
UKCEH and CEH Sections/Science Areas:	Water and Climate Science (2025-) Directors, SCs
ISSN:	2052-4463
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Hydrology Computer Science Data and Information
Related URLs:	Dataset Code
Date made live:	25 Apr 2025 10:22 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539318

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