Estimating Soil Hydraulic Properties During Constant Flux Infiltration Inverse Procedures
Bibliographic record
Abstract
There is a need for accurate and cost‐effective methods to estimate the hydraulic properties of soils. Past work indicated measurements of a single hydraulic response will not necessarily result in unique and stable estimates of hydraulic parameters when the number of unknowns is more than two. Prior information regarding the parameters or additional measurements are needed for the estimation problem to be well posed. However, accurate prior information is seldom available due to variations of the hydraulic properties in space and time. This paper presents a method for estimating hydraulic properties from simultaneous measurements of soil water storage to a fixed depth as a function of time during constant flux infiltration, and steady‐state pressure head readings using vertically installed multi‐purpose time domain reflectometry probes (MTDR). Multi‐purpose TDR probes have a porous steel cup at their ends allowing soil water storage and ψ to be simultaneously measured at the same location. Our parameter estimation is formulated by an inverse procedure which combines a weighted nonlinear least square method with analytical solutions for soil water content and pressure head as functions of depth and time during one dimensional infiltration. We analyze the possibility of using water storage data combined with the initial and steady‐state pressure head readings for the purpose of estimating soil hydraulic properties. The uniqueness problem was analyzed by studying the behavior of response surfaces. The combination of water storage measurements during constant flux infiltration with an initial and a steady‐state pressure head reading yielded unique and stable solutions of the inverse problem. The utility of the parameter estimation procedure is demonstrated using experimental and theoretical data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".