Deep learning modeling framework for multi-resolution streamflow generation
Bibliographic record
Abstract
ABSTRACT Generating continuous streamflow information through integrated climate-hydrology modeling at fine spatial scales of the order of a few kilometers is often challenged by computational costs associated with running high-resolution (HR) climate models. To address this challenge, the present study explores deep learning approaches to generate HR streamflow information from that at low resolution (LR), based on runoff generated by climate models. Two sets of daily streamflow simulations spanning 10 years (2011–2020), at LR (50 km) and HR (5 km), for the Ottawa River basin, Canada, are employed. The proposed deep learning model is trained using upscaled features derived from LR streamflow simulation for the 2011–2018 period as input and the corresponding HR streamflow simulation as the target; data for 2019 are used for validation. The model estimates for the year 2020, when compared with unseen HR data for the same year, suggest good performance, with differences in monthly mean values for different accumulation area categories in the −0.7–5% range and correlation coefficients for streamflow values for the same accumulation area categories in the 0.92–0.96 range. The developed framework can be ported to other watersheds for generating similar information, which is required in climate change adaptation studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".