Karst spring recession curve analysis: efficient, accurate methods for both fast and slow flow components
Bibliographic record
Abstract
Abstract. Analysis of karst spring recession hydrographs is essential for determining hydraulic parameters, geometric characteristics and transfer mechanisms that describe the dynamic nature of karst aquifer systems. The extraction and separation of different fast and slow flow components constituting karst spring recession hydrograph typically involve manual and subjective procedures. This subjectivity introduces bias, while manual procedures can introduce errors to the derived parameters representing the system. To provide an alternative recession extraction procedure that is automated, fully objective and easy to apply, we modified traditional streamflow extraction methods to identify components relevant for karst spring recession analysis. Mangin’s karst-specific recession analysis model was fitted to individual extracted recession segments to determine matrix and conduit recession parameters. We introduced different parameters optimisation approaches of the Mangin’s model to increase degree of freedom thereby allowing for more parameters interaction. The modified recession extraction and parameters optimisation approaches were tested on 3 karst springs in different climate conditions. The results show that the modified extraction methods are capable of distinguishing different recession components and derived parameters reasonably represent the analysed karst systems. We recorded an average KGE > 0.7 among all recession events simulated by recession parameters derived from all combinations of recession extraction methods and parameters optimisation approaches. While there are variability among parameters estimated by different combinations of extraction methods and optimisation approaches, we find even much higher variability among individual recession events. We provide suggestions to reduce the uncertainty among individual recession events and to create a more robust analysis by using multiple pairs of recession extraction method and parameters optimisation approach.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".