Weighting climate models for hydrological projections: effects on contrasting hydroclimatic regions
Bibliographic record
Abstract
Abstract Weighting climate models has recently become a more accepted approach. However, it remains a topic of ongoing discussion, especially for analyses needed at regional scales, such as hydrological assessments. Various studies have evaluated the weighting approaches for climate simulations. Yet, few case studies have assessed the impacts of weighting climate models on streamflow projections. Additionally, the methodological and location limitations of previous studies make it difficult to extrapolate their conclusions over regions with contrasting hydroclimatic regimes, highlighting the need for further studies. Thus, this study evaluates the effects of different climate model’s weighting approaches on hydrological projections over hydrologically diverse basins. An ensemble of 24 global climate model (GCM) simulations coupled with a lumped hydrological model is used over 20 North American basins to generate 24 GCM-driven streamflow projections. Six unequal-weighting approaches, comprising temperature-, precipitation-, and streamflow-based criteria, were evaluated using an out-of-sample approach during the 1976–2005 reference period. Moreover, the unequal-weighting approaches were compared against the equal-weighting approach over the 1976–2005, 2041–2070, and 2070–2099 periods. The out-of-sample assessment showed that unequally weighted ensembles can improve the mean hydrograph representation under historical conditions compared to the common equal-weighting approach. In addition, results revealed that unequally weighting climate models not only impacted the magnitude and climate change signal, but also reduced the model response uncertainty spread of hydrological projections, particularly over rain-dominated basins. These results underline the need to further evaluate the adequacy of equally weighting climate models, especially for variables with generally larger uncertainty at regional scale.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".