Browsing by Author "Killeen, A."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
- Global hydrological dataset of daily streamflow data from the Reference Observatory of Basins for international hydrological climate change detection (ROBIN), 1863 - 2022Publication . Turner, S.; Hannaford, J.; Barker, L.J.; Suman, G.; Armitage, R.; Killeen, A.; Griffin, A.; Davies, H.; Kumar, A.; Dixon, H.; Albuquerque, Maria Teresa; Ribeiro, N. Almeida; Alvarez-Garreton, C.; Amoussou, E.; Arheimer, B.; Asano, Y.; Berezowski, T.; Bodian, A.; Boutaghane, H.; Capell, R.; Dakhaoui, H.; Daňhelka, J.; Do, H.X.; Ekkawatpanit, C.; El Khalki, E.M.; Fleig, A.K.; Fonseca, R.; Giraldo-Osorio, J.D.; Goula, A.B.T.; Hanel, M.; Hodgkins, G.; Horton, S.; Kan, C.; Kingston, D.G.; Laaha, G.; Laugesen, R.; Lopes, W.; Mager, S.; Markonis, Y.; Mediero, L.; Midgley, G.; Murphy, C.; O'Connor, P.; Pedersen, A.I.; Pham, H.T.; Piniewski, M.; Rachdane, M.; Renard, B.; Saidi, M.E.; Schmocker-Facker, P.; Stahl, K.; Thyler, M.; Toucher, M.; Tramblay, Y.; Uusikivi, J.; Venegas-Cordero, N.; Vissesri, S.; Watson, A.; Westra, S.; Whitfield, P.HData were provided by national measuring agencies following a selection process from their wider networks. Following submission of data and metadata to the ROBIN Network, a quality control process was conducted centrally to assess the quality and suitability of the station's inclusion in the dataset. Daily streamflow records were visually screened for change points, visually anomalous conditions indicating methodology changes or infilled data gaps and obvious errors in the data. Stations were removed if they showed signs of not having a sufficiently ‘near-natural' regime, and edits were made over datasets to remove obviously erroneous data periods
- ROBIN: Reference observatory of basins for international hydrological climate change detectionPublication . Turner, S.; Hannaford, J.; Barker, L. J.; Suman, G.; Killeen, A.; Armitage, R.; Chan, W.; et al.; Albuquerque, M.T.D.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.