Towards a synthesis of perceptual models of dominant hydrologic processes across North America
Bibliographic record
Abstract
There is increasing recognition that providing robust assessments of future water resource availability and water-related risks requires the use of the right models in the right places. Traditionally, selecting or developing an appropriate model for a given basin was possible based on thorough understanding of the dominant hydrologic processes in the basin under consideration. On national, continental, and global scales however, the commonly used method so far has been a “one model fits all” approach. This is in part due to the lack of a comprehensive overview of how dominant hydrologic processes vary across large geographical domains.Here we introduce a community-driven synthesis effort to address this large-scale hydrologic challenge, focusing on North America as a test case. Over the past half year, we have convened multiple virtual workshops and organized several in-person opportunities to bring together water science experts working in various regions across the continent. The workshops covered five key parts of the continent (the densely populated East and West coasts, the center region used for agriculture, the northern regions that are particularly vulnerable to climate change, and the tropical islands). Invited speakers shared their knowledge, experience, and expertise around the dominant hydrologic processes and existing modeling efforts in these regions. These were followed by structured discussion among the workshop attendees, as well as during dedicated further interactions later, to divide the continent into a manageable number of hydrologic landscapes and to define representative perceptual models of hydrologic behavior for the various parts of each larger region. Here we present an overview of these resulting perceptual models and invite further discussion. Ultimately, these perceptual models can be mapped onto computational models, modules and individual equations, and so support a theory-based large-scale effort to develop the most appropriate hydrologic model for any location in the wider North American domain. The methodology used is not unique to the North American context and similar approaches could be used elsewhere where large-scale synthesis of hydrologic process understanding is desired. 
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".