Most computational hydrology is not reproducible, so is it really science?. Issue 10 (19th October 2016)

• Record Type: Journal Article
• Authors: Hutton, Christopher; Wagener, Thorsten; Freer, Jim; Han, Dawei; Duffy, Chris; Arheimer, Berit
• Is Part Of: Water resources research. Volume 52:Issue 10(2016:Oct.)
• Page Start: 7548
• Page End: 7555
• Publication Date: 2016-10-19
• Subjects: hydrology -- reproducibility -- software -- code -- verification -- workflows; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2016WR019285
• Languages: English
• ISSNs: 0043-1397

Abstract

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. Key Points: Articles that rely on computational work do not provide sufficient information to allow published scientific findings to be reproduced We argue for open reuseable code, data, and formal workflows, allowing published findings to be verified Reproducible computational hydrology will provide a more robust foundation for scientific advancement and policy support