Regionalization of Hydrologic Behavior and Pothole Water Storage Dynamics in the Prairie Pothole Region
Bibliographic record
Abstract
In pothole-dominated catchments, such as those in the Prairie Pothole Region (PPR), potholes strongly influence catchment hydrologic behavior through complex and dynamic fill-spillconnection mechanisms. This complexity-combined with the predominance of ungauged catchments and the lack of high-resolution pothole inventories-poses challenges for both traditional hydrologic models and purely data-driven deep learning approaches. To address this, we developed the δHBV-Pot model within a differentiable modeling framework (δ). This physics-informed deep learning model integrates the conceptual HBV model with a probabilistic algorithm that emulates the aggregate effects of pothole fill-spill-connection processes. Applied to 98 PPR catchments, δHBV-Pot achieves stronger predictive accuracy and physical realism than a purely data-driven Long Short-Term Memory (LSTM) model and two conceptual hydrologic models. The PPR-scale regional δHBV-Pot model successfully simulates hydrologic behavior for the majority of pseudo-ungauged (test) catchments withheld during model development, effectively regionalizing (1) high-flow magnitude and interannual variability, (2) intra-annual flashiness of high-flow and normal flow conditions, and (3) interannual variability in pothole water storage dynamics. Moreover, the model identifies vulnerable catchments with large high-flow magnitude and variability-even in the absence of streamflow data-and delineates catchments with varying temporal variability in pothole water storage without requiring detailed pothole inventories. Our findings highlight the value of combining conceptual hydrology with data-driven deep learning in pothole-dominated regions. This integrated approach uncovers new hydrologic patterns from large datasets and enables the regionalization of high-flow and pothole storage characteristics to ungauged catchments, providing critical insights for vulnerability assessment and the design of sustainable water and ecological management strategies in pothole-dominated landscapes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".