Impacts of hydrological model calibration on projected hydrological changes under climate change—a multi-model assessment in three large river basins
Bibliographic record
Abstract
Abstract This study aimed to investigate the influence of hydrological model calibration/validation on discharge projections for three large river basins (the Rhine, Upper Mississippi and Upper Yellow). Three hydrological models (HMs), which have been firstly calibrated against the monthly discharge at the outlet of each basin (simple calibration), were re-calibrated against the daily discharge at the outlet and intermediate gauges under contrast climate conditions simultaneously (enhanced calibration). In addition, the models were validated in terms of hydrological indicators of interest (median, low and high flows) as well as actual evapotranspiration in the historical period. The models calibrated using both calibration methods were then driven by the same bias corrected climate projections from five global circulation models (GCMs) under four Representative Concentration Pathway scenarios (RCPs). The hydrological changes of the indicators were represented by the ensemble median, ensemble mean and ensemble weighted means of all combinations of HMs and GCMs under each RCP. The results showed moderate (5–10%) to strong influence (> 10%) of the calibration methods on the ensemble medians/means for the Mississippi, minor to moderate (up to 10%) influence for the Yellow and minor (< 5%) influence for the Rhine. In addition, the enhanced calibration/validation method reduced the shares of uncertainty related to HMs for three indicators in all basins when the strict weighting method was used. It also showed that the successful enhanced calibration had the potential to reduce the uncertainty of hydrological projections, especially when the HM uncertainty was significant after the simple calibration.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".