Comparing impact of ERA5 vs ERAInterim on hydrology using the eWaterCycle Open Hydrological Platform
Bibliographic record
Abstract
Model comparisons are an important exercise to gain new hydrological insight from the diversity in our communities hydrological models. Current practice in model comparison studies is to have each model be run by the creator/representative of that model and combine the results of all these model runs in a single analysis. In this work we present the first major model comparison done within the eWaterCycle Open Hydrological Platform. eWaterCycle is a platform for doing hydrological experiments where hydrological models are accessed as objects from an (online) Jupyter notebook experiment environment. Through the use of GRPC4BMI and containers, (pre-existing and newly made) models in any programming language can be used, without diving into the code of those models. This makes eWaterCycle ideally suited to compare (and couple) models with widely different model setups: conceptual versus distributed for example. eWaterCycle is FAIR by design: any eWaterCycle experiment should be reproducible by anyone without the support of the original model developer. This will make it easier for hydrologists to work with each other's models and speed up the cycle of hydrological knowledge generation. In this comparison we’re looking at the impact of the new ERA5 dataset over the older ERA-Interim dataset as a forcing for hydrological models. A key component in making hydrological experiments reproducible and transparent in eWaterCycle is the use of EMSValTool as a pre-processor for hydrological experiments. Using EMSValTool’s recipes structure ensures that model specific input files based on ERA5 or ERA-Interim are all handled identically where possible and that model specific operations are clearly and transparently defined. We have run 7 models or model-suites (LISFlood, MARRMoT, WFLOW, HYPE, PCRGlobWB 2.0, SUMMA, HBV) for 6 basins forced with both ERA5 and ERA-Interim and compared model outputs against GRDC discharge observations. From this broad comparison we will conclude what the impact of ERA5 over ERA-Interim will be for hydrological modelling in the foreseeable future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| 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".