Streamflow predictability in the Saskatchewan/Nelson River basin given macroscale estimates of the initial soil moisture status
Bibliographic record
Abstract
Soil moisture estimates obtained over large spatial areas will become increasingly available through current and upcoming satellite missions and from numerous land surface parameterization schemes run at global- and continental-scale resolutions. The goal of this research was to evaluate the potential for using macroscale estimates of soil moisture for enhancing streamflow forecasts. Towards this research objective, monthly streamflow estimates were obtained from over 50 gauge locations within the Nelson basin, Canada, for the period 1979–1999. For each streamflow record, multiple linear regression models were used to remove components of the streamflow signal related to previous streamflow, climate teleconnections (e.g. ENSO and AO) and snow water equivalence. Correlations were then assessed between the macroscale soil moisture estimates and the residuals of the multiple linear regression analysis over lead times of one, two and three months. At the one- and two-month lead time, statistically significant relationships between soil moisture and the residuals of streamflow are observed over a large proportion of the gauging locations. The number of catchments with statistically significant relationships decreases significantly after two months and particularly in the months of April—June. This study demonstrates that available macroscale estimates of soil moisture have the potential to enhance streamflow prediction, although further study is suggested to improve upon the soil moisture estimates and their application in a forecast system.
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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 | 0.000 |
| 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".