Advancing community water resources modeling in the Cooperative Institute for Research to Operations in Hydrology (CIROH)
Bibliographic record
Abstract
The Cooperative Institute for Research to Operations in Hydrology (CIROH) is a consortium of 28 institutions to advance the National Oceanic and Atmospheric Administration’s (NOAA) science and services capabilities to provide actionable water resources intelligence. CIROH’s research aims to improve water prediction and supports four broad themes: (1) water resources prediction capabilities; (2) community water resources modeling; (3) hydroinformatics; and (4) application of social, economic and behavioral science to water resources prediction. CIROH outcomes will inform hydrological process understanding, operational forecasting techniques and workflows, community engagement in water modeling, open-source software development, translation of forecasts to actionable products, and use of predictions in decision making. This presentation will focus on CIROH’s research in community water modelling. In this theme, CIROH research focuses on advancing the predictive capabilities of the next-generation National Water Resources Modeling framework (NextGen framework) that is being developed for operational large-domain water prediction at NOAA’s National Water Center (NWC). The presentation will give examples of ongoing CIROH model development efforts to (1) integrate physical process representations into the NextGen framework across multiple levels of process granularity; (2) assess accuracy-efficiency trade-offs in the numerical solution of model equations across large spatial domains; (3) coupling process components that have hitherto been neglected in large-domain terrestrial system models (e.g., glacier hydrology, snow redistribution, connectivity of wetlands, land-atmosphere interactions over sparse forests, tile drainage, etc.); and (4) use hybrid machine learning methods to advance large-domain parameter estimation capabilities. The presentation will also highlight the establishment of research enabling infrastructure to support CIROH’s ongoing modeling advancement efforts. In summary, we will identify major challenges encountered and the high-priority research that is needed to advance capabilities in large-domain hydrologic prediction.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".