Effects of initial soil water content and saturated hydraulic conductivity variability on small watershed runoff simulation using LISEM
Bibliographic record
Abstract
Soil water content (θ) and saturated hydraulic conductivity (Ks) vary in space. The objective of this study was to examine the effects of initial soil water content (θi) and Ks variability on runoff simulations using the LImburg Soil Erosion Model (LISEM) in a small watershed in the Chinese Loess Plateau, based on model parameters derived from intensive measurements. The results showed that the total discharge (TD) and peak discharge (PD) were underestimated when the variability of θi and Ks was partially considered or completely ignored compared with those when the variability was fully considered. Time to peak (TP) was less affected by the spatial variability compared to TD and PD. Except for TP in some cases, significant differences were found in all hydrological variables (TD, PD and TP) between the cases in which spatial variability of θi or Ks was fully considered and those in which spatial variability was partially considered or completely ignored. Furthermore, runoff simulations were affected more strongly by Ks variability than by θi variability. The degree of spatial variability influences on runoff simulations was related to the rainfall pattern and θi. Greater rainfall depth and instantaneous rainfall intensity corresponded to a smaller influence of the spatial variability. Stronger effects of the θi variability on runoff simulation were found in wetter soils, while stronger effects of the Ks variability were found in drier soils. For accurate runoff simulation, the θi variability can be completely ignored in cases of a 1-h duration storm with a return period greater than 10 years, while Ks variability should be fully considered even in the case of a 1-h duration storm with a return period of 20 years. Editor D. Koutsoyiannis; Associate editor A. Fiori
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".