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Our 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 > Methods.
Bibliographical noteFunding Information:
Thanks to Sina Leipold from the University of Freiburg for helpful comments on the philosophy of science. We further thank Keith Beven, Conrad Jackisch, Eric Wood, Marc Bierkens, Alberto Viglione, Jan Seibert, Hoshin Gupta, Bodo Bookhagen, and two anonymous reviewers for constructive criticism on earlier versions of the manuscript. Especially the debates with Keith Beven in the context of this commentary are highly appreciated. Thanks to Marc Bierkens for sharing the drawing of the early PCR‐GLOBWB perceptual model. Partial 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).
Alexander von Humboldt‐Stiftung, Grant/Award Number: Alexander von Humboldt Professorship; Deutsche Forschungsgemeinschaft, Grant/Award Number: HA 8113/1‐1; Engineering and Physical Sciences Research Council, Grant/Award Numbers: EP/L016214/1, EP/R007330/1; International Atomic Energy Agency, Grant/Award Number: CRP D12014; Royal Society, Grant/Award Number: WM170042 Funding information
© 2021 The Authors. WIREs Water published by Wiley Periodicals LLC.
- large-scale hydrology
- machine learning
- perceptual model
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Pianosi, F. & Penuela-Fernandez, A.
1/07/17 → 23/12/22