Application of L‐moments and Bayesian inference for low‐flow regionalization in Sefidroud basin, Iran
Bibliographic record
Abstract
Abstract Reliable estimation of low flows at ungauged catchments is one of the major challenges in water‐resources planning and management. This study aims at providing at‐site and ungauged sites low‐flow frequency analysis using regionalization approach. A two‐stage delineating homogeneous region is proposed in this study. Clustering sites with similar low‐flow L‐moment ratios is initially conducted, and L‐moment‐based discordancy and heterogeneity measures are then used to detect unusual sites. Based on the goodness‐of‐fit test statistic, the best‐fit regional model is identified in each hydrologically homogeneous region. The relationship between mean annual 7‐day minimum flow and hydro‐geomorphic characteristics is also constructed in each homogeneous region associated with the derived regional model for estimating various low‐flow quantiles at ungauged sites. Uncertainty analysis of model parameters and low‐flow estimations is carried out using the Bayesian inference. Applied in Sefidroud basin located in northwestern Iran, two hydrologically homogeneous regions are identified, i.e. the east and west regions. The best‐fit regional model for the east and west regions are generalized logistic and Pearson type III distributions, respectively. The results show that the proposed approach provides reasonably good accuracy for at‐site as well as ungauged‐site frequency analysis. Besides, interval estimations for model parameters and low flows provide uncertainty information, and the results indicate that Bayesian confidence intervals are significantly reduced when comparing with the outcomes of conventionalt‐distribution method. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".