On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation

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dc.contributor.authorWagener, Thorsten
dc.contributor.authorGleeson, Tom
dc.contributor.authorCoxon, Gemma
dc.contributor.authorHartmann, Andreas
dc.contributor.authorHowden, Nicholas
dc.contributor.authorPianosi, Francesca
dc.contributor.authorMostaquimur, Rahman
dc.contributor.authorRosolem, Rafael
dc.contributor.authorStein, Lina
dc.contributor.authorWoods, Ross
dc.date.accessioned2022-04-01T13:17:42Z
dc.date.available2022-04-01T13:17:42Z
dc.date.copyright2021en_US
dc.date.issued2021
dc.description.abstractOur ability to fully and reliably observe and simulate the terrestrial hydrologic cycle is limited, and in-depth experimental studies cover only a tiny fraction of our landscape. On medieval maps, unexplored regions were shown as images of dragons—displaying a fear of the unknown. With time, cartographers dared to leave such areas blank, thus inviting explorations of what lay beyond the edge of current knowledge. In hydrology, we are still in a phase where maps of variables more likely contain hydrologic dragons than blank areas, which would acknowledge a lack of knowledge. In which regions is our ability to extrapolate well developed, and where is it poor? Where are available data sets informative, and where are they just poor approximations of likely system properties? How do we best identify and acknowledge these gaps to better understand and reduce the uncertainty in characterizing hydrologic systems? The accumulation of knowledge has been postulated as a fundamental mark of scientific advancement. In hydrology, we lack an effective strategy for knowledge accumulation as a community, and insufficiently focus on highlighting knowledge gaps where they exist. We propose two strategies to rectify these deficiencies. Firstly, the use of open and shared perceptual models to develop, debate, and test hypotheses. Secondly, improved knowledge accumulation in hydrology through a stronger focus on knowledge extraction and integration from available peer-reviewed articles. The latter should include metadata to tag journal articles complemented by a common hydro-meteorological database that would enable searching, organizing and analyzing previous studies in a hydrologically meaningful manner. This article is categorized under: Engineering Water > Planning Water Science of Water > Hydrological Processes Science of Water > Methodsen_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipPartial support to Thorsten Wagener was provided by a Royal Society Wolfson Research Merit Award (WM170042) and by the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship endowed by the German Federal Ministry of Education and Research. Partial support for Tom Gleeson was provided by a Benjamin Meaker Distinguished Visiting Professorship at the University of Bristol. Andreas Hartmann was supported by the Emmy-Noether-Program of the German Research Foundation (HA 8113/1-1). Rafael Rosolem was partially supported by the International Atomic Energy Agency of the United Nations (IAEA/UN) coordinated research project (CRP D12014). Francesca Pianosi was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through a “Living with Environmental Uncertainty” fellowship [EP/R007330/1]. Lina Stein was funded as part of the WISE CDT under a grant from the Engineering and Physical Sciences Research Council (EPSRC) (EP/L016214/1).en_US
dc.identifier.citationWagener, T., Gleeson, T., Coxon, G., Hartmann, A., Howden, N., Pianosi, F., Rahman, M., Rosolem, R., Stein, L., & Woods, R. (2021). On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation. Wiley Interdisciplinary Reviews: Water, 8(6). https://doi.org/10.1002/wat2.1550en_US
dc.identifier.urihttps://doi.org/10.1002/wat2.1550
dc.identifier.urihttp://hdl.handle.net/1828/13824
dc.language.isoenen_US
dc.publisherWiley Interdisciplinary Reviews: Wateren_US
dc.subjectlarge-scale hydrologyen_US
dc.subjectuncertaintyen_US
dc.subjectperceptual modelen_US
dc.subjectmetadataen_US
dc.subjectmachine learningen_US
dc.titleOn doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulationen_US
dc.typeArticleen_US

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