Influence of Soil Heterogeneity and Spatial Discretization on Catchment Water Balance Modeling
Bibliographic record
Abstract
This study investigated the impacts of the spatial variability of soil hydraulic properties and the effects of spatial discretization on the water balance in a fully coupled system. The integrated surface–subsurface, three‐dimensional, finite element model HydroGeoSphere was applied to the forested Wüstebach basin (27 ha) to simulate water fluxes. The fully coupled flow simulation model was applied to the headwater catchment at two different spatial resolutions (25 and 100 m). The change in spatial resolution required an aggregation of the soil map, which influenced the water fluxes and the spatial patterns of soil moisture. The nonlinear relationship between soil moisture and transpiration caused the spatial aggregation of soil moisture to have a larger effect on the water balance than did aggregating the soil hydraulic properties. In addition to the total discharge, the effects on the spatial patterns of the simulated soil moisture were also investigated. The results show that aggregating soil hydraulic properties results in lower uncertainties than does using a coarser discretization. This can be explained by the nonlinearity of the relationship between soil moisture and evapotranspiration.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".