The Desna River Daily Multi-Site Streamflow Modeling Using SWAT with Detail Snowmelt Adjustment
Bibliographic record
Abstract
The Ukrainian Government started the process of EU water Directives implementation aimed at developing of the River Basin Management Plan for 9 main river catchments. The program SWAT was tested to simulate the water flow of a large plain river with a predominant snow supply. River discharges (12 gauges), snow cover depth (13 stations), and the soft data, including graphically defined surface runoff and MODIS evapotranspiration, were used to calibrate the model. The calibration flowchart, along with a detailed source data description, is proposed to aid with streamflow simulation for the snowmelt-driven watersheds and fill the existing gap of distributed hydrological modeling in the region. The main issues with snow simulation and climate data coverage are discussed. The program SUFI-2 in SWAT-CUP package was used for performing calibration/validation and uncertainty analysis. The statistics (R2, NSE, PBIAS) showed good agreement (NS > 0.7, R2 > 0.75, PBIAS < ±10%) for a major part of the gauges and satisfactory for all of them (NS > 0.5, R2 > 0.6, PBIAS < ±15%), except of two linked upstream outlets. This study provides the basis for further research on water resources, the impacts of climate change, and water quality issues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".