Technical Note: High Nash Sutcliffe Efficiencies conceal poor simulations of interannual variance in tropical, alpine, and polar catchments
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".