Articles | Volume 30, issue 11
https://doi.org/10.5194/hess-30-3439-2026
https://doi.org/10.5194/hess-30-3439-2026
Technical note
 | 
05 Jun 2026
Technical note |  | 05 Jun 2026

Technical note: Benchmarking large-domain model performance under sampling uncertainty

Gaby J. Gründemann, Wouter J. M. Knoben, Yalan Song, Katie van Werkhoven, and Martyn P. Clark

Data sets

Data for "Separating Signal from Noise in Large-Domain Hydrologic Model Evaluation: Benchmarking model performance under sampling uncertainty" G. Gründemann et al. https://doi.org/10.5281/zenodo.18028487

U.S. Geological Survey National Water Information System Database U.S. Geological Survey https://doi.org/10.5066/F7P55KJN

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Short summary
The quality of large-domain hydrologic model simulations is often quantified with so-called accuracy metrics. Here we use simple benchmarks to provide relevant context for these accuracy metrics. Results show that areas where the model cannot beat the benchmarks do not always align with areas where the accuracy metrics are low. This suggests that model improvements are possible in regions that under more typical model evaluation approaches (i.e., without benchmarks) might not be obvious.
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