Extraction of recharge in variably saturated subsurface flow models
Bibliographic record
Abstract
Variably saturated subsurface flow models have been widely used in the context of water resources management as they conceptualize and simulate water flow in the unsaturated and saturated zone. By solving the Richards equation and using infiltration flux as an input, these models do not require groundwater recharge. As the models simulate the infiltration dynamics through the unsaturated zone, recharge is expected to be reliably extracted from such kinds of models. In this study, we explore to what extent variably saturated subsurface flow models can actually be used to extract groundwater recharge. In this context, we implement numerous definitions of groundwater recharge in a simple, variably saturated 1D model, extract groundwater recharge for a wide range of infiltration and groundwater dynamics imposed through boundary conditions, and assess the reliability of the extracted values. The results show that the value of recharge cannot be uniquely obtained from such kinds of models. The problem is attributed to the storage dynamics in the capillary fringe above the water table. However, it is important to keep in mind that if a variably saturated subsurface flow model of a project area is available, extracting recharge is superfluous as the model is capable of representing all the relevant flux and dynamics. Keywords: Variably saturated subsurface flow models; Groundwater recharge; unsaturated zone; Water resources management.Reference: Gong, Chengcheng, Peter G. Cook, René Therrien, Wenke Wang, and Philip Brunner. "On groundwater recharge in variably saturated subsurface flow models." Water Resources Research 59, no. 9 (2023): e2023WR034920.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".