Comparing the impact for hydrology of the new ERA5 reanalyses dataset over ERA-Interim for 8 hydrological models in 6 catchments using the eWaterCycle community modelling environment.
Bibliographic record
Abstract
The release of the European Centre for Medium-Range Weather Forecasts (ECMWF)’s Re-Analysis 5 (ERA-5) global climate forcing dataset is expected to greatly improve the quality of hydrological modeling. Following this release there is great interest in assessing the improvements of ERA-5 relative to its predecessor ERA-Interim for hydrological modeling and predictions. In this study we compare streamflow predictions when using ERA-interim vs ERA-5 as forcing data for a suite of hydrological models from different research groups that capture the variation in modelling strategies within the hydrological modelling community. We check whether physically based models, defined as those that do not require additional parameter calibration, would lead to different conclusions in comparison to conceptual models, defined as those that require calibration. Based on the hydrological model structure we expect that conceptual models that need calibration show less difference in predicting discharge (skill) between ERA-5 and ERA-Interim, where-as the physical based (non-calibrated) models most likely will benefit from the improved accuracy of the ERA-5 input. This assessment will provide the HEPEX community with answers on how the ERA-5 dataset will improve hydrological predictions based on different hydrological modelling concepts. An additional key objective while conducting this study is compliance to the FAIR principles of data science. To achieve this we held a workshop in Leiden, the Netherlands, where multiple hydrological models were integrated into the eWatercycle II system. eWatercycle II is a hydrological model platform containing a growing number of hydrological models. The platform facilitates research and cohesivity within the hydrological community by providing an Open-Source platform built specifically to advance the state of FAIR and Open Science in Hydrological Modeling. We also use this study to demonstrate the feasibility of eWatercycle II as a platform for FAIR hydrological models. Preliminairy results from this comparison study were presented at the AGU Fall Meeting 2019. Here we will present the full results of the comparison study.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".