Promoting Open and Transparent Hydrologic Modeling: Workflows, Tools and Self-Contained Modules
Bibliographic record
Abstract
Configuring process-based hydrologic models can be a cumbersome task, especially for larger domains. In the past model inputs (data), configuration and analysis code, as well as the source code of the models themselves were only rarely openly available. More recently, the hydrology community is moving toward a more open culture, focused on shareable data, tools and code. Here we present various recent open-source advances along the entire modeling chain. These include:Workflows for model configuration of large-domain hydrologic models, data-driven seasonal streamflow forecasting and forcing data processing; Tools for the remapping of forcing variables from one set of spatial elements to another; Tools for adjusting and correcting baseline geofabrics for internal consistency and efficient routing; Computationally frugal sensitivity analysis methods; Independent hydrologic process modules for specific geographic landscape features and routing through reservoirs; Improved numerical methods for model solving and parallelization. These tools are publicly available with the specific aim to make them useful to others. During this PICO, we welcome discussion about the tools, as well as general discussion about the opportunities and pitfalls surrounding open-source science.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".