Spatial and Temporal Scale Effect in Simulating Hydrologic Processes in a Watershed
Bibliographic record
Abstract
Small-scale variations of hydrologic processes both in space and time have a significant impact on the simulation of hydrologic processes at different scales in the watershed. The objectives of this study were to investigate: (1) how the spatial and temporal grid scales affect the results of hydrologic process simulations; and (2) how the variability of input driving parameters (e.g., elevation and precipitation intensity) at different grid scales is related to the simulated discharge response. A hydrologic model system (HMS) was used to simulate hydrologic processes at different spatial and temporal grid scales in a small watershed. The spatial distributions of various hydrologic properties, such as soil and land-use/land-cover data, were included in the simulations along with a digital elevation model (DEM) and precipitation data. 5-min precipitation records collected from four gauge stations within the watershed were used to drive fifty model simulations, which were designed to examine the effects of grid size change and different parameterization schemes on hydrologic responses. Simulation results showed that small-scale variations in elevation and precipitation both in space and time have significant impacts on the streamflow hydrograph and the discharge volume. Results illustrated that the grid scale effect on the hydrologic response is highly correlated to the variability of elevation and precipitation at the corresponding scales; and therefore, the variations in elevation and precipitation are strong indications of the hydrologic response in the watershed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".