Comparison and Integrated Application for Runoff Simulation Models in Small and Medium-Sized River Basins of Southeast China Coastal Area
Bibliographic record
Abstract
Runoff simulation is of fundamental importance for hydrological research. This study evaluated the applicability of multiple hydrological models and their ensembles for simulating runoff in small and medium-sized river basins of southeastern coastal China, focusing on the Xixi tributary of Jinjiang River and the Songxi and Chongyang tributaries of Minjiang River in Fujian Province. Four lumped hydrological models were selected for analysis: GR4J, IHACRES, TVGM, and MISDc-2L. The Bayesian model averaging method was utilized to compare the performance of each individual model and the multi-model ensemble in runoff simulation. Results: (1) For the calibration and validation periods of four hydrological stations, the mean values of KGE, NS, and R2 for the models GR4J, IHACRES, TVGM, and MISDc-2L were all above 0.7, and the mean values of |RE| were below 8.3%, without significant simulation accuracy variations when basin size changes, demonstrating strong regional applicability for runoff simulation; (2) The multi-model ensemble simulations using Bayesian model averaging of GR4J, TVGM, and MISDc-2L exhibited higher accuracy than individual models; (3) The MISDc-2L model demonstrated strong applicability in daily runoff simulations for both small and medium-sized river basins in Fujian Province and the large-sized Dongting Lake basin, showing that it is worthy of further application in other river basins across China. The findings of this study provide a reference for the selection and application of hydrological models for runoff simulation in small and medium-sized river basins of southeastern coastal China.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".