Physical-based hydrological modelling to predict soil moisture in a mesoscale catchment in cold climates
Bibliographic record
Abstract
Knowledge of soil moisture is significant for supporting agricultural production and other ecosystem services in cold climates. Climate change is expected to produce more fluctuations in precipitation across the globe and cause more frequent extremes in soil moisture, including floods and drought which have major impacts on agriculture and infrastructure. Forecasting can help mitigate the impacts of soil moisture extremes by providing warnings about upcoming extreme events and prompt mitigation measures. This study constructed a physically-based groundwater-surface water model for an agriculturally dominated watershed in the Red River Valley, Manitoba, to determine the soil moisture variability in a cold climate in deeper soil layers. A 1D replica of the main watershed model was additionally used for sensitivity analysis of soil hydraulic parameters that influenced moisture at different depths. Historically available soil moisture data and additional data from installed Sentek probes in observational fields were used for calibration. Statistical analysis was performed by comparing simulated and measured soil moisture. At the surface (5 cm), the sand series in the 1D model had an excellent match and the 3D results produced a good correlation at the surface during calibration and forecasting. The model results of the deeper layers in the clay soils also showed a good fit during calibration and forecasting while the sand series showed poor correlation at lower depths. The modelling framework in this study provides valuable insights into different hydrological processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".