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

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

Research output: Contribution to journalComment/debatepeer-review

69 Scopus citations

Abstract

Reproducibility is a foundational principle in scientific research. Yet in computational hydrology the code and data that actually produces published results are 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 reuseable 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 rigor. 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 (US)
Pages (from-to)7548-7555
Number of pages8
JournalWater Resources Research
Volume52
Issue number10
DOIs
StatePublished - Oct 1 2016

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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