Parameter allocation approach for runoff simulation in an arid catchment using the KINEROS2 hydrological model
Bibliographic record
Abstract
Abstract The KINEROS2 model was utilized for runoff simulation in the Dehgin catchment situated in the Hormozgan province of Iran. A parameter allocation procedure was used in lieu of parameter optimization. After parameter allocation, the model was able to adequately simulate hydrographs associated with high‐magnitude peak discharge events with the efficiency values between 0.011–0.83 for Nash–Sutcliffe and 0.36–0.98 for Kling–Gupta, but the model did not accurately simulate hydrographs corresponding to low‐magnitude peak discharge events. Although calibration after parameter allocation improved model performance with respect to the simulation of low‐magnitude discharge events, numerical values of the hydraulic conductivity and net capillary pressure as the most sensitive model parameters did not agree with parameters known to be reasonable in the region. So that the value of hydraulic conductivity was decreased from 61 to 55 mm/h in channels and from 3.7 to 1.7 mm/h in planes. The new values are physically reasonable but are not approximately the same as physical values associated with the regional and environmental context of the Dehgin catchment. In this case, the values of the evaluation criteria were obtained between −2.5 and 0.78 for Nash–Sutcliffe and 0.17 and 0.98 for Kling–Gupta. The results of using the HydroPSO package in R to automated calibration of the model, with the value of Nash–Sutcliffe between −0.63 and 0.43, indicated that autocalibration without intelligent and deliberate selection of parameters cannot accurately represent hydrological processes, and therefore should be avoided. Also, the results show that an understanding of the catchment environmental conditions and appropriate allocation of parameters is initially more effective as a first step of the modeling process and thereby contributes to a first‐order characterization of environmental conditions in the catchment.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".