A diagnostic approach to constraining flow partitioning in hydrologic models using a multiobjective optimization framework
Bibliographic record
Abstract
Abstract Hydrologic models are often tasked with replicating historical hydrographs but may do so without accurately reproducing the internal hydrological functioning of the watershed, including the flow partitioning, which is critical for predicting solute movement through the catchment. Here we propose a novel partitioning‐focused calibration technique that utilizes flow‐partitioning coefficients developed based on the pioneering work of L'vovich (1979). Our hypothesis is that inclusion of the L'vovich partitioning relations in calibration increases model consistency and parameter identifiability and leads to superior model performance with respect to flow partitioning than using traditional hydrological signatures (e.g., flow duration curve indices) alone. The L'vovich approach partitions the annual precipitation into four components (quick flow, soil wetting, slow flow, and evapotranspiration) and has been shown to work across a range of climatic and landscape settings. A new diagnostic multicriteria model calibration methodology is proposed that first quantifies four calibration measures for watershed functions based on the L'vovich theory, and then utilizes them as calibration criteria. The proposed approach is compared with a traditional hydrologic signature‐based calibration for two conceptual bucket models. Results reveal that the proposed approach not only improves flow partitioning in the model compared to signature‐based calibration but is also capable of diagnosing flow‐partitioning inaccuracy and suggesting relevant model improvements. Furthermore, the proposed partitioning‐based calibration approach is shown to increase parameter identifiability. This model calibration approach can be readily applied to other models.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".