Most computational hydrology is not reproducible, so is it really science?

Christopher Hutton*, Thorsten Wagener, Jim Freer, Dawei Han, Christopher Duffy, Berit Arheimer

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

63 Citations (Scopus)
524 Downloads (Pure)

Abstract

Reproducibility is a foundational principle in scientific research. Yet in computational hydrology, the code and data that actually produces published results is not regularly made available, inhibiting the ability of the community to reproduce and verify previous findings. In order to overcome this problem we recommend that re-useable code and formal workflows, which unambiguously reproduce published scientific results, are made available for the community alongside data, so that we can verify previous findings, and build directly from previous work. In cases where reproducing large-scale hydrologic studies is computationally very expensive and time-consuming, new processes are required to ensure scientific rigour. Such changes will strongly improve the transparency of hydrological research, and thus provide a more credible foundation for scientific advancement and policy support.
Original languageEnglish
Pages (from-to)7548–7555
Number of pages8
JournalWater Resources Research
Volume52
Issue number10
Early online date19 Oct 2016
DOIs
Publication statusPublished - Oct 2016

Keywords

  • Hydrology
  • Reproducibility
  • Software
  • Code
  • Verification
  • Workflows

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