@article{fdi:010093472,
  title = {{ROBIN} : reference observatory of basins for international hydrological climate change detection},
  author = {{T}urner, {S}. and {H}annaford, {J}. and {B}arker, {L}. {J}. and {S}uman, {G}. and {K}illeen, {A}. and {A}rmitage, {R}. and {C}han, {W}. and {D}avies, {H}. and {G}riffin, {A}. and {K}umar, {A}. and {D}ixon, {H}. and {A}lbuquerque, {M}. {T}. {D}. and {R}ibeiro, {N}. {A}. and {A}lvarez-{G}arreton, {C}. and {A}moussou, {E}. and {A}rheimer, {B}. and {A}sano, {Y}. and {B}erezowski, {T}. and {B}odian, {A}. and {B}outaghane, {H}. and {C}apell, {R}. and {D}akhaoui, {H}. and {D}anhelka, {J}. and {D}o, {H}. {X}. and {E}kkawatpanit, {C}. and {E}l {K}halki, {E}. {M}. and {F}leig, {A}. {K}. and {F}onseca, {R}. and {G}iraldo-{O}sorio, {J}. {D}. and {G}oula, {A}. {B}. {T}. and {H}anel, {M}. and {H}orton, {S}. and {K}an, {C}. and {K}ingston, {D}. {G}. and {L}aaha, {G}. and {L}augesen, {R}. and {L}opes, {W}. and {M}ager, {S}. and {R}achdane, {M}. and {M}arkonis, {Y}. and {M}edeiro, {L}. and {M}idgley, {G}. and {M}urphy, {C}. and {O}'{C}onnor, {P}. and {P}edersen, {A}. {I}. and {P}ham, {H}. {T}. and {P}iniewski, {M}. and {R}enard, {B}astien and {S}aidi, {M}. {E}. and {S}chmocker-{F}ackel, {P}. and {S}tahl, {K}. and {T}hyer, {M}. and {T}oucher, {M}. and {T}ramblay, {Y}ves and {U}usikivi, {J}. and {V}enegas-{C}ordero, {N}. and {V}isessri, {S}. and {W}atson, {A}. and {W}estra, {S}. and {W}hitfield, {P}. {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{H}uman-induced warming is modifying the water cycle. {A}daptation to posed threats requires an understanding of hydrological responses to climate variability. {W}hilst these can be computationally modelled, observed streamflow data is essential for constraining models, and understanding and quantifying emerging trends in the water cycle. {T}o 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. {B}y removing these influences, trends in streamflow data can be more confidently attributed to climate variability. {H}ere we describe the {R}eference {O}bservatory of {B}asins for {IN}ternational hydrological climate change detection ({ROBIN}) - the first iteration of a global network of streamflow data from national reference hydrological networks ({RHN}s) - comprised of catchments which are near-natural or have limited human influences. {T}his 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.},
  keywords = {{MONDE}},
  booktitle = {},
  journal = {{S}cientific {D}ata},
  volume = {12},
  numero = {1},
  pages = {654 [13 p.]},
  year = {2025},
  DOI = {10.1038/s41597-025-04907-y},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093472},
}