Long‐lead probabilistic forecasting of streamflow using ocean‐atmospheric and hydrological predictors
Bibliographic record
Abstract
A geostatistically based approach with a local regression method is used to predict the magnitude of seasonal streamflow using ocean‐atmospheric signals and the hydrological condition of a basin as predictors. The model characterizes the stochastic behavior of a forecast variable by generating a conditional distribution of the predicted value for different hydroclimatic conditions. The correlation structure between dependent and independent variables is represented by the variography of the predicted values in which the distance variable in the variogram is determined by measuring the distance between the predictors. This variogram in a virtual field constructed from the predictors makes it possible to predict variables as unmeasured points while considering historic information as measurement points of the field. Different types of kriging, as well as a generalized linear model regression, are used to predict data in interpolation and extrapolation modes. The forecast skill is evaluated using a linear error in probability space score for different combinations of predictors and different kriging methods. The method is applied to a case study of the Zayandeh‐rud River in Isfahan, Iran. The utility of the method is demonstrated for forecasting autumn‐winter and spring streamflow using the Southern Oscillation Index, the North Atlantic Oscillation, serial correlation between seasonal streamflow series, and the snow budget. The study analyzes the application of the proposed method in comparison with a K‐nearest neighbor regression method. The results of this study show that the proposed method can significantly improve the long‐lead probabilistic forecast skill for a nonlinear relationship between hydroclimatic predictors and streamflow in a region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".