Regionalization of Hydrologic Behavior and Pothole Water Storage Dynamics in Prairie Pothole Region
Bibliographic record
Abstract
In the Prairie Pothole Region (PPR), potholes govern catchment hydrologic behavior through complex and dynamic fill-spill-connection mechanisms. This complexity—combined with predominantly ungauged catchments and limited fine-resolution pothole inventories—challenges the hydrologic models and purely data-driven deep learning approaches. To address this, we developed the δHBV-Pot model within differentiable modeling framework (δ). This physics-informed deep learning model integrates the conceptual HBV model with a probabilistic algorithm that emulates the aggregated effects of pothole fill-spill-connection processes. Applied to 98 PPR catchments, δHBV-Pot demonstrates stronger predictive accuracy and physical realism than purely data-driven Long Short-Term Memory (LSTM) model and two conceptual hydrology models. PPR-scale regional δHBV-Pot model successfully simulates the hydrologic behavior of the majority of pseudo-ungauged (or test) catchments withheld during model development, effectively regionalizing (1) high-flow magnitude and inter-annual variability, (2) intra-annual flashiness of high-flow and normal flow, and (3) inter-annual variability in pothole water storage dynamics. Moreover, the model identifies vulnerable catchments with large high-flow magnitude and variability—even where no streamflow data exist—and delineates catchments with varying temporal variability in pothole water storage without relying on detailed pothole inventories. Our analysis reveals a negative correlation between pothole storage extent and high-flow metrics, suggesting that greater pothole storage reduces both high-flow magnitude and variability. Our findings underscore the value of integrating conceptual hydrology with data-driven approaches in pothole-dominated regions. This combined strategy uncovers new patterns from big data while enabling the regionalization of high-flow and pothole storage characteristics to ungauged catchments—critical for vulnerability assessment and designing sustainable water/ecological strategies in these 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".