Examining differences in streamflow estimation for gauged and ungauged catchments using evolutionary data assimilation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data assimilation has allowed hydrologists to account for imperfections in observations and uncertainties in model estimates. Typically, updated members are determined as a compromised merger between observations and model predictions. The merging procedure is conducted in decision space before model parameters are updated to reflect the assimilation. However, given the dynamics between states and model parameters, there is limited guarantee that when updated parameters are applied into measurement models, the resulting estimate will be the same as the updated estimate. To account for these challenges, this study uses evolutionary data assimilation (EDA) to estimate streamflow in gauged and ungauged watersheds. EDA assimilates daily streamflow into a Sacramento soil moisture accounting model to determine updated members for eight watersheds in southern Ontario, Canada. The updated members are combined to estimate streamflow in ungauged watersheds where the results show high estimation accuracy for gauged and ungauged watersheds. An evaluation of the commonalities in model parameter values across and between gauged and ungauged watersheds underscore the critical contributions of consistent model parameter values. The findings show a high degree of commonality in model parameter values such that members of a given gauged/ungauged watershed can be estimated using members from another watershed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it