A New Standpipe Lysimeter Design for the Measurement of Soil Matric Suction
Bibliographic record
Abstract
Long‐term monitoring of matric suction and in situ soil water chemistry is critical to investigating vadose zone processes. This paper presents a new method of making these measurements, based on the concept of a sealed, high‐walled zero‐tension lysimeter referred to as a standpipe lysimeter (SPL). We present the design and operational principles of the SPL and provide proof of concept and operation from experiments and modeling. The SPL, designed to measure matric suction primarily in the range of 0 to 30 kPa, provides an alternate measurement strategy to in situ tensiometers or electronic sensors. It requires no calibration or maintenance, is suited to installation at any depth using a drilling rig, and is constructed of readily available, inexpensive, materials. A prototype design was tested in a laboratory column (0.56 m diam., 3.6 m height) over a range of infiltration fluxes encompassing two to three orders of magnitude. Laboratory testing indicated the preliminary SPL design underestimated matric suction by <4 to 12%. Further improvements to the design were developed using numerical modeling studies of unsaturated water flow. The modeling of the improved SPL design was used to assess its accuracy in matric suction measurements compared with the laboratory tested and modeled preliminary design. The modeling studies indicate improvement in measurement accuracy with the improved SPL design to a range of accuracy (<± 2.5% error) similar to the modeled accuracy achieved by installations of tensiometers or other matric suction sensors in typical borehole configurations. The SPL's response time may be days to months and is a function of construction materials, the properties of the surrounding soil, and the magnitude of infiltration fluxes. The SPL is therefore best suited to specific uses requiring long‐term monitoring (years to decades) of deep vadose zones where response times are similar to those of the SPL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".