A Stepwise Clustered Hydrological Model for Addressing the Temporal Autocorrelation of Daily Streamflows in Irrigated Watersheds
Bibliographic record
Abstract
Abstract Streamflow simulations at daily time steps are vital to water resources management, especially in arid regions. Previously, data‐driven models have been used as an effective tool for daily streamflow simulation. However, the accuracy of conventional data‐driven approaches is affected by the temporal autocorrelation of daily streamflow, especially in irrigated watersheds where the persistence of saturated flows dominates irrigation seasons. This study presents a Stepwise Clustered Regression Tree Ensemble (SCRTE) to address the streamflow autocorrelation. With the provision of a state‐of‐the‐art data‐driven model Stepwise Cluster Analysis (SCA), the SCRTE enables both single‐ and multi‐output settings (i.e., model predictand can be either a scalar or a vector), which can thus address interactions among streamflow values over multiple consecutive days. The autocorrelation effect of daily streamflow is evaluated based on single‐ and multi‐output SCA ensembles, which can then be aggregated according to their performance for various streamflow quantile ranges. To facilitate the irrigation scheduling decision‐making under rigorous transboundary water regulations, the SCRTE is applied to three interconnected watersheds with mixed land use, located in a floodplain of the Yellow River basin in China. The results show that the SCRTE outperforms seven well‐known benchmark models across seven evaluation metrics. Our findings reveal that the SCRTE can reflect the varying effects of autocorrelation over different streamflow quantile ranges, thereby improving the streamflow simulation. The multi‐output SCA ensembles are more capable of addressing the medium flows, while the single‐output one can better simulate the low and high flows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".