Application of Different Weighting Schemes and Stochastic Simulations to Parameterization Processes Considering Observation Error: Implications for Climate Change Impact Analysis of Integrated Watershed Models
Bibliographic record
Abstract
We investigated the potential impact of observation error on the calibration performance of an integrated watershed model. A three-dimensional integrated model was constructed using HydroGeoSphere and applied to the Sabgyo watershed in South Korea to assess the groundwater–surface water interaction process. During the model calibration, three different weighting schemes that consider observation error variances were applied to the parameter estimation tool (PEST). The applied weighting schemes were compared with the results from stochastic models, in which observation errors from surface discharges were considered a random variable. Based on the calibrated model, the interactions between groundwater and surface water were predicted under different climate change scenarios (RCP). Comparisons of calibration performance between the different models showed that the observation-error-based weighting schemes contributed to an improvement in the model parameterization. Analysis of the exchange flux between groundwater and surface water highlighted the significance of groundwater in delaying the hydrological response of integrated water systems. Predictions based on different RCP scenarios suggested the increasing role of groundwater in watershed dynamics. We concluded that the comparison of different weighting schemes for the determination of error covariance could contribute to an improved characterization of watershed processes and reduce the model uncertainty arising from observation errors.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".