Parameterizing a Coupled Surface–Subsurface Three‐Dimensional Soil Hydrological Model to Evaluate the Efficiency of a Runoff Water Harvesting Technique
Bibliographic record
Abstract
Tools are needed to quantitatively evaluate the efficiency of water harvesting techniques in dryland environments under a wide range of climatic and soil physical conditions. In a case study for the arid zone of Chile, a detailed water balance was calculated using a coupled surface–subsurface hydrological model (HydroGeoSphere). In a first step, the model was parameterized with detailed runoff and soil water content data collected during simulated rainfall to calibrate surface and subsurface flow processes simultaneously, using six responsive parameters identified by a global sensitivity analysis. The calibrated model accurately reproduced observed soil moisture contents ( R 2 = 0.92) and runoff amounts ( R 2 = 0.97), and represented the overflowing infiltration trench, which is a clear improvement over existing frameworks that do not consider surface‐subsurface flow interactions. A comparative analysis with a natural slope demonstrated that the trench was efficient in capturing runoff under high rainfall intensities, such as the one simulated, resulting in a significant decrease (46%) of runoff. In the final section, a detailed water balance of the trench was calculated for four characteristic years with increasing precipitation. Significant differences in the water balance components were only observed for the very wet year (with a return period of 67 yr), where 64% of the potential runoff was effectively harvested and stored in the soil profile. As such, this test case shows the ability of HydroGeoSphere to adequately represent the water balance components of a runoff water harvesting technique and shows its potential to become an effective tool for optimal water harvesting design, while taking both soil physical and climatic constraints into account.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".