Technical note: Separating signal from noise in large-domain hydrologic model evaluation – Benchmarking model performance under sampling uncertainty

Abstract. Large-domain hydrologic modeling studies are becoming increasingly common. The evaluation of the resulting models is however often limited to the use of aggregated performance scores that show where model accuracy is higher and lower. Moreover, the inherent uncertainty in such scores, stemming from the choice of time periods used for their calculation, often remains unaccounted for. Here we use a collection of simple benchmarks whilst accounting for this sampling uncertainty to provide context for the performance scores of a large-domain hydrologic model. The benchmarks suggest that there are considerable constraints on the model's performance in approximately one-third of the basins used for model calibration and in approximately half of the basins where model parameters are regionalized. Sampling uncertainty has limited impact: in most basins the model is either clearly better or worse than the benchmarks, though accounting for sampling uncertainty remains important when the performance of different models is more similar. The areas where the benchmarks outperform the model only partially overlap with areas where the model achieves lower performance scores, and this suggests that improvements may be possible in more regions than a first glance at model performance values may indicate. A key advantage of using these benchmarks is that they are easy and fast to compute, particularly compared to the cost of configuring and running the model. This makes benchmarking a valuable tool that can complement more detailed model evaluation techniques by quickly identifying areas that should be investigated more thoroughly.