Effects of topography and snowmelt on hydrologic simulation in the Yellow River’s source region
Bibliographic record
Abstract
To quantify the effect of topography and snowmelt on hydrologic simulation,the(Soil and Water Assessment Tool(SWAT) model is employed for the hydrological simulation in the Yellow River’s source region for the period 1960—1990.The topographical effect is determined through the partitioning of subbasins into elevation bands.While,the snowmelt effect is simulated using a snowmelt module.A series of simulations is conducted.The result shows that a satisfactory result of model simulations can be obtained when the snowmelt module applied alone or jointly with the consideration of elevation bands.The model will have a better performance if the topography effect is considered,indicating that topography plays a dominant role in water balance simulations.The model temperature is more sensitive to the partitioning of subbasins than precipitation.A reduced temperature value will lead to the reduction of evapotranspiration from subbasins,and hence increases the water yield of subbasins.Groundwater will get the most yield increase and followed by surface water and lateral flow.The influence of topography and snowmelt is likely to change to the source of groundwater recharges.An excellent simulation result can be obtained through calibrating model groundwater parameters that consider the effect of topography and snowmelt.The study provides valuable information for other hydrologic simulation in mountainous watersheds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".