Hydrological performance of ERA5 and MERRA-2 precipitation products over the Great Lakes Basin
Bibliographic record
Abstract
The Laurentian Great Lakes Basin (GLB) of North America. Precipitation is often the paramount driver in hydrological systems and dynamics. Gridded precipitation data from the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) are playing a key role in hydrological modelling and hydro-climatological analysis activities. This study explores the discrepancies between ERA5 total precipitation (TP) and MERRA-2 bias-corrected total precipitation (PRECTOTCORR) products and their impacts on streamflow simulation over the GLB. ERA5 TP and MERRA-2 PRECTOTCORR perform differently, with substantial discrepancies in the eastern and mid-southern regions of the GLB. MERRA-2 PRECTOTCORR severely underestimates precipitation along the eastern shores of the Great Lakes within the eastern GLB, where lake-effect snowfall is common. Accordingly, the MERRA-2 PRECTOTCORR-driven hydrological modelling typically significantly underpredicts streamflow in those areas. In contrast, MERRA-2 PRECTOTCORR performs very well for precipitation estimation and stream discharge simulations in the mid-southern portion of the basin (i.e., the southern portion of the U.S. State of Michigan). However, the northernmost region of the GLB exhibits streamflow overprediction from the PRECTOTCORR-driven hydrological modelling. On average, ERA5 TP overestimates precipitation, especially in winter and spring. Accordingly, ERA5 TP typically results in higher simulated streamflow when compared to PRECTOTCORR. The quantified hydrological performance for the two state-of-the art precipitation products provides critical guidance for hydrological modelling applications in the GLB or other similar regions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".