Hydrologic Memory Patterns Assessment over a Drought-Prone Canadian Prairies Catchment
Bibliographic record
Abstract
Understanding the persistence in land surface processes, such as that in the deep subsurface moisture storage, has great implications for seasonal weather prediction over a drainage basin. The Canadian Prairies is a region of intense and recurrent drought outbreaks with myriad negative impacts on the regional ecosystem as well as on all sectors of the Prairies’ economy due to high mitigation costs associated with these frequent outbreaks. Given that there are neither physical observations of soil moisture at depths of hydrological importance nor measurements of the total water storage over drought-prone Canadian Prairies subcatchments, this places constraints on studies that focus on the assessments of the interrelationship between the land surface and atmospheric processes. This study focuses on the estimation of the memory in the simulated deep soil moisture and total water storages over the 406,000 km2 Saskatchewan River Basin (SRB) in the Canadian Prairies using a physically based land surface model. The variable infiltration capacity (VIC) hydrological model was developed and deployed in simulating the deep soil moisture in conjunction with the total water storage over this large catchment. In developing a suitable hydrological model for the SRB, parameters estimated from the calibrated and validated hydrological model for the adjacent Upper Assiniboine River Basin (UARB) were transferred. Subsequently, the memory in the anomalies associated with the meteorological variables, simulated deep moisture and total water storage components in conjunction with the computed terrestrial storage deficit indices (TSDIs) estimated from the gravity recovery and climate experiment (GRACE) remote-sensing satellite system and the VIC model were assessed. Given the degree of agreement in the estimated memory associated with the hydrologic model-based terrestrial storage deficit indices with those estimated for the simulated deep moisture storage anomalies over this catchment, this study concludes that the latter could also be used in characterizing the severity of frequent Canadian Prairies droughts.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".