Estimating soil hydraulic parameters in fine-textured soils using HYDRUS-1D coupled with PEST
Bibliographic record
Abstract
Frequent flooding and drought events lead to moisture stress in crops, which leads to poor crop yield and quality. Thus, water management strategies are needed to create a conducive environment for plant growth and performance. An effective strategy requires a long-term understanding of soil moisture dynamics regulated by local soil hydraulic properties and hydrologic factors. This study focused on estimating these soil hydraulic parameters using HYDRUS-1D coupled with PEST (external calibration software) and exploring the non-unique nature of the solution for four growing seasons (2016 –2019). One hundred initial guesses of parameter sets were generated for each year for model calibration. The volumetric soil water contents determined at the site within the 0 - 10 cm, 10 - 30 cm, 30 - 70 cm, and 70 - 130 cm layers over the growing seasons were used to calibrate and validate the models. The statistical parameters used for the evaluation of models were NashSutcliff efficiency (NSE) (0 - 1), Percent bias (PBIAS) (±10%), Ratio of Root Mean Square Error to standard deviation (RSR) (< 0.7) and R2 (> 0.5). The results showed that PEST was useful in establishing the non-linearity and non-unique nature of the solution, which established that different parameter sets could result in similar performance and, therefore, ascertained the use of a more rigorous approach (maximum likelihood) based on different parameter sets to improve the reliability of the results. The model performance was satisfactory for the simulation of soil water content in 2016, 2017, and 2019 using the calibrated parameter sets. Poor model performance was observed for the 2018 validation period, which showed how the physical manipulation of soil (tillage), physiological stage of the crop, and soil response to weather (shrink-swell) could alter the soil hydraulic properties to a great extent within a season. The results also demonstrated that the saturated hydraulic conductivity varied over the years to about ten times for the topsoil (plough horizon) but was almost constant for deeper soil layers.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".