General overestimation of ERA5 precipitation in flow simulations for High Mountain Asia basins
Bibliographic record
Abstract
Abstract Precipitation is one of the most important input to hydrological models, although obtaining sufficient precipitation observations and accurate precipitation estimates in High Mountain Asia (HMA) is challenging. ERA5 precipitation is the latest generation of reanalysis dataset that is attracting huge attention from various fields but it has not been evaluated in hydrological simulations in HMA. To remedy this gap, we first statistically evaluated ERA5 precipitation with observations from 584 gauges in HMA, and then investigated its potential in hydrological simulation in 11 HMA basins using the Variable Infiltration Capacity (VIC) hydrological model. The ERA5 precipitation generally captures the seasonal variations of gauge observations, and the broad spatial distributions of precipitation in both magnitude and trends in HMA. The ERA5 exhibits a reasonable flow simulation (RB of 5%–10%) at the Besham hydrological station of the upper Indus (UI) basin when the contribution from glacier runoff is added to the simulated total runoff. But it overestimates the observations in other HMA basins by 33%–106% without considering glacier runoff, mostly due to the overestimates in the ERA5 precipitation inputs. Therefore, a bias correction is definitely needed before ERA5 precipitation is used for hydrological simulations in HMA basins.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".