Technical Note: High Nash Sutcliffe Efficiencies conceal poor simulations of interannual variance in tropical, alpine, and polar catchments

Abstract. Streamflow time series can be decomposed into interannual, seasonal, and irregular components, with regionally varying contributions of each component. Seasonal variance dominates in many tropical, alpine, and polar regions, while irregular variance dominates in most other regions. Interannual variability in streamflow is known to strongly influence human and ecological systems and is likely to increase under the influence of climate change, though we find that historical interannual variance is usually only a small fraction of the total variance. We show that hydrologic models often simulate one component well while failing to simulate the others, a fact that is hidden by popular performance metrics such as the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE) which aggregate performance to a single number. We analyse 18 regional and global hydrologic models and find that in highly seasonal catchments where the NSE and KGE are consistently the highest, the models are almost always worse at simulating interannual variability. The NSE of the interannual component is lower in highly seasonal catchments, and simulated year-to-year changes in ecologically relevant hydrologic signatures are less accurate. This is concerning because it indicates that these hydrologic models may struggle to predict long-term responses to climate change, especially in tropical, alpine, and polar regions, which are some of the most vulnerable regimes regarding climate change.