Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Bibliographic record
Abstract
Abstract. Robust large-domain predictions of water availability and threats require models that work well across different basins in the model domain. It is currently common to express a model's accuracy through aggregated efficiency scores such as the Nash-Sutcliffe Efficiency and Kling-Gupta Efficiency, and these scores often form the basis to select among competing models. However, recent work has shown that such scores are subject to considerable sampling uncertainty: the exact selection of time steps used to calculate the scores can have large impacts on the scores obtained. Here we explicitly account for this sampling uncertainty to determine the number of models that are needed to simulate hydrologic processes across large spatial domains. Using a selection of 36 conceptual models and 559 basins, our results show that model equifinality, the fact that very different models can produce simulations with very similar accuracy, makes it very difficult to unambiguously select one model over another. If models were selected based on their validation KGE scores alone, almost every model would be selected as the best model in at least some basins. When sampling uncertainty is accounted for, this number drops to 4 models being needed to cover 95% of investigated basins, and 10 models being needed to cover all basins. We obtain similar conclusions for an objective function focused on low flows. These results suggests that, under the conditions typical of many current modeling studies, there is limited evidence that using a wide variety of different models leads to appreciable differences in simulation accuracy compared to using a smaller number of carefully chosen models.
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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 | 0.012 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".