The representation of rivers in operational ocean forecasting systems: a review
Bibliographic record
Abstract
Abstract. The connection between the ocean and the land is made possible thanks to rivers, which are a vital component of the Earth's system. They govern the hydrological and biogeochemical contributions to the coastal ocean through surface and subsurface water discharge and influence local circulation and the distribution of water masses, modulating processes such as upwelling and mixing. This paper provides an overview of recent approaches to representing coastal river discharges and processes in operational ocean forecasting systems (OOFSs), with a particular focus on estuaries. The methods discussed include those currently adopted in coarse-resolution ocean forecasting systems, where mixing processes are primarily parameterized, as well as more advanced modelling and coupling approaches tailored to high-resolution coastal systems. A review of river data availability is also presented, illustrating various sources of freshwater discharge and salinity, from observational data to climatological datasets, alongside operational river discharge products that enhance the representation of water discharges in operational systems. New satellite-derived datasets and emerging river modelling techniques are also introduced. In addition, responses from a survey of existing OOFS providers are synthetized, with a focus on how river forcing is treated, from global to coastal scales. Challenges such as data accuracy, standardization, and model coupling are discussed, highlighting the need for improved interfaces between monitoring and modelling systems. Finally, some recommendations and ways forward are formulated in relation to identified limitations in current OOFSs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".