Hydrological modeling in the Manas River Basin using soil and water assessment tool driven by CMADS
Bibliographic record
Abstract
Hydrological simulation in meteorological ungauged areas has always been a difficult problem for the study on atmospheric and hydrological coupling; meanwhile, it is also one of the important factors that restrict model development and basin water resource knowledge. To analyze the mechanism of atmospheric and hydrological coupling, this study quantitatively evaluated water cycle situation in basins covered with glaciers and snow, and chose the Manas River Basin (MRB) in China as the typical research area to verify the adaptability of the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool model (CMADS). The SWAT model was firstly built to simulate water resources, then we calibrated the model with CMADS dataset and started localization in the Manas River Basin (MRB), China, and finally calibrated simulated runoff with observed data SWAT-CUP (SWAT Calibration and Uncertainty Programs). In addition, parameter sensitivity analysis, and parameter calibration and validation were also included in the present study. Results showed that the SWAT model could well reproduce the runoff process of two stations (Kenswat and Hongshanzui) in the research area by using data from CMADS. The simulation performed well on monthly scale in both stations, where R 2 = (0,5560,999) and NSE = (0,9370,998), and also showed satisfactory effects, where R 2 = (0,9270,993) and NSE = (0,8360,997).Our research suggests that the SWAT model can show satisfactory results through parameter calibration in areas with high glacial recharge rate. Moreover, CMADS can provide necessary meteorological data for SWAT simulations, and support parameter calibration and historical surface data analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".