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

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.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2025-04-18
DOI
https://doi.org/10.1038/s41597-025-04907-y
Citations
4
Primary Topic
Hydrology and Watershed Management Studies
Type
article
Field-Weighted Citation Impact
3.27

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Hélio Lopes, Mohamed Elmehdi Saidi, Hung T. Pham, Adam Watson et al.
4 citations
Scientific Data
Hydrology and Watershed Management Studies
3.27
article

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

Hélio Lopes, Mohamed Elmehdi Saidi, Hung T. Pham, Adam Watson, J. Hannaford, Yannis Markonis, Jan Daňhelka, Napoleão S. Ribeiro, Richard Laugesen, Ernest Amoussou, Amit Kumar, Juan Diego Giraldo‐Osorio, Stephen Turner, Teresa Albuquerque, Guy F. Midgley, Ansoumana Bodian, Seth Westra, Chaiwat Ekkawatpanit, Berit Arheimer, Sophie L. Horton, Yves Tramblay, J. Uusikivi, Adam Griffin, Anne Fleig, Martin Hanel, Hamouda Boutaghane, Nelson Venegas‐Cordero, Mark Thyer, El Mahdi El Khalki, M. Piniewski, Sarah Mager, Alannah Killeen, Paul H. Whitfield, Richard Armitage, Wing‐Le Chan, Hong Xuan, Mariame Rachdane, Kerstin Stahl, Lucy Barker, Benjamin Renard, Harry Dixon, P. Schmocker-Fackel, Paul O’Connor, Conor Murphy, Tomasz Berezowski, Camila Álvarez-Garretón, Daniel G. Kingston, Gregor Laaha, René Capell, Supattra Visessri, Michele Toucher, C. E. Kan, Rita Fonseca, Huw C. Davies, Arne Pedersen, Asano Yasuyoshi, G. Suman, A. B. T. Goula, H Dakhaoui, L. Medeiro
article en
4 citations

Abstract

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.

Scientific DataVol. 12(1)
Tunis University (TN), Badji Mokhtar-Annaba University (DZ), Université Gaston Berger (SN), Tokyo Institute of Technology (JP), Cadi Ayyad University (MA), Bureau of Meteorology (AU), Oak Ridge National Laboratory (US), Swedish Meteorological and Hydrological Institute (SE), Norwegian Water Resources and Energy Directorate (NO), Universidade Politécnica de Castelo Branco (PT), National University of Ireland, Maynooth (IE), Chulalongkorn University (TH), University of Freiburg (DE), Gdańsk University of Technology (PL), Warsaw University of Life Sciences (PL), Université Nangui Abrogoua (CI), University of Carthage (TN), Czech Hydrometeorological Institute (CZ), University of Canterbury (NZ), The Open University (GB), Czech University of Life Sciences Prague (CZ), Aix-Marseille Université (FR), University of Évora (PT), Pontificia Universidad Javeriana (CO), Stellenbosch University (ZA), Finnish Environment Institute (FI), University of Science and Technology - The University of Da Nang (VN), University of Saskatchewan (CA), Centre d'Etude Regional pour l'Amelioration de l'Adaptation a la Secheresse (SN), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), UK Centre for Ecology & Hydrology (GB), Animal, Santé, Territoires, Risques et Ecosystèmes (FR), Acteurs, Ressources et Territoires dans le Développement (FR), Centre de Recherche et d’Enseignement de Géosciences de l’Environnement (FR), Université de Parakou (BJ), National Engineering School of Tunis (TN), Université Mohammed VI Polytechnique (MA), South African Environmental Observation Network (ZA), Center for Climate and Resilience Research (CL), Federal Office for the Environment (CH), National Water Agency (BR), Institut Sénégalais de Recherches Agricoles (SN), Institut de Recherche pour le Développement (FR), UMR Espace-Dev, University of Salford (GB), The University of Adelaide (AU), King Mongkut's University of Technology Thonburi (TH), Tunis El Manar University (TN), The University of Tokyo (JP), University of Birmingham (GB), Nong Lam University Ho Chi Minh City (VN), University of Otago (NZ), Universidad Politécnica de Madrid (ES), BOKU University (AT)
U.S. Geological Survey, Natural Environment Research Council
Climate action
Openalex Percentile: Top 9%
Hydrology and Watershed Management Studies
3.27
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.