Advancing the science and practice of community hydrologic modeling: Development of open-source models, methods, and datasets to enable process-based hydrologic prediction across North America (and beyond)
Bibliographic record
Abstract
Many hydrologic modelling groups face similar challenges, with untapped opportunities to share code and concepts across different model development groups. An active community of practice is emerging, where the focus is not so much on developing a community hydrologic model, but more on advancing the science and practice of community hydrologic modeling. This presentation will summarize our recent efforts to develop open-source models, methods, and datasets to enable process-based hydrologic prediction across North America (and beyond). The contributions include (1) developing ensemble meteorological datasets for North America and the globe; (2) developing modular approaches to hydrologic modeling through a hierarchal approach that separates different model sub-domains (vegetation, snow, soil, groundwater) and separates the physical representations from the numerical solution; (3) implementing third-party numerical solvers (sundials) to improve the robustness and efficiency of the numerical solutions; (4) developing agile parallelization methods capable of handling heterogeneous computing loads and bottlenecks in the downstream reaches of large river networks; (5) implementing flexible model configuration toolbox to accelerate the implementation of large-domain hydrologic models; (6) advancing methods for river lake routing, including development of integrated river-lake hydrography datasets and development of large-domain reservoir management models; (7) advancing methods for large-domain parameter estimation; (8) advancing methods for ensemble data assimilation; and (9) advancing methods for probabilistic hydrologic prediction on time scales from seconds to seasons. We will discuss some of the major challenges encountered and the high-priority research that is necessary to advance capabilities in large-domain hydrologic prediction.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".