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Peer-reviewed

Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating Model‐Agnostic and Model‐Specific Configuration Steps in Applications of Large‐Domain Hydrologic Models

Wouter Knoben, Martyn Clark, Jerad Bales, Andrew Bennett, Shervan Gharari, Christopher B. Marsh, Bart Nijssen, Alain Pietroniro, Raymond J. Spiteri, Guoqiang Tang, David G. Tarboton, Andrew W. Wood

Water Resources Research · 2022

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Summary

Abstract Despite the proliferation of computer‐based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor reused. Given the commonalities between existing process‐based hydrologic models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here, we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model‐agnostic

Source type
Peer-reviewed study
DOI
10.1029/2021wr031753
Catalogue ID
SNmokyl7if-tjnxgb
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