Using HydroGeoSphere in a Forested Catchment: How does Spatial Resolution Influence the Simulation of Spatio-temporal Soil Moisture Variability?
Bibliographic record
Abstract
Soil moisture is a key variable in the soil-plant-atmosphere system because it interacts with various system components. Both the measurement and the simulation of the soil moisture pattern and its spatio-temporal variability are current challenges in hydrology. This study applies the model HydroGeoSphere in a natural forest ecosystem to assess whether the model can simulate the spatio-temporal variability and pattern of soil moisture. The assessment is performed by comparing the simulation results with soil moisture measurements. The model is used at two different model resolutions to reveal the scale dependency of the calibrated model parameters, the water balance, the discharge components, and the spatial distribution of soil moisture and its variogram parameters. Discharge simulation results show that the model is capable of reproducing the discharge characteristics. A weak correlation is found between simulated and measured soil moisture dynamics in the topsoil, but the correlation is stronger in 20 cm depth. In 50 cm depth, the model is able to simulate the seasonal trend but not the short-term dynamics because preferential flow is not simulated. Furthermore, a decrease in soil moisture variance during continued drying is observed for both simulations and the measurements at both resolutions. In addition, the pattern of measured soil moisture shows a patchy character that does not show in the simulated pattern indicating that using uniform soil properties in the topsoil makes the soil moisture simulation inaccurate.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".