On doing large-scale hydrology with Lions: Realising the value of perceptual models and knowledge accumulation

This is a Preprint and has not been peer reviewed. This is version 4 of this Preprint.

Downloads

Download Preprint

Authors

Thorsten Wagener , Tom Gleeson , Gemma Coxon, Andreas Hartmann , Nicholas Howden, Francesca Pianosi, Shams Rahman, Rafael Rosolem, Lina Stein, Ross Woods

Abstract

Moving the study domain in hydrology to larger and larger regions leaves us with significant knowledge gaps because we are unable to observe the hydrology of many parts of the world, while in-depth hydrologic studies cover only a fraction of our landscape. On medieval maps, knowledge gaps were shown as images of lions. How do we best acknowledge and reduce these gaps in hydrology, i.e. our hydrologic lions? The accumulation of knowledge has been postulated as the fundamental mark of scientific advancement by some philosophers of science. In hydrology, knowledge accumulation has been somewhat fragmented, left as a pursuit for (often brilliant) individuals rather than emphasised as a necessary focus for the research community. Our knowledge of a region’s hydrology originates from available observations. However, the ability of observations to reliably characterise hydrological phenomena is limited, and large areas of the globe lack detailed observations. In this commentary we propose two strategies to rectify these deficiencies. First, the use of shared perceptual models as ways to capture, debate and test our experience with different hydrologic systems. Second, improved knowledge accumulation in hydrology by more strongly focusing on knowledge extraction from available historical articles. This effort should include the addition of meta-data to tag hydrologic journal articles and by developing a related hydrological database that would enable searching, organizing and analysing previous studies in a hydrologically meaningful manner.

DOI

https://doi.org/10.31223/osf.io/zdy5n

Subjects

Earth Sciences, Engineering, Environmental Sciences, Hydrology, Physical Sciences and Mathematics

Keywords

machine learning, uncertainty, hydrology, Global Hydrology, Knowledge, Metadata

Dates

Published: 2020-05-08 17:30

Last Updated: 2020-05-10 13:17

Older Versions
License

GNU Lesser General Public License (LGPL) 2.1

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.