Physical-based hydrological modelling for real-time forecasting of soil moisture in a mesoscale catchment
Bibliographic record
Abstract
Soil moisture is highly variable in space and time. Climate change is expected to increase the variation in precipitation that may cause more frequent extremes in soil moisture. This will have major impacts on agriculture and infrastructure. Hence, forecasting can help mitigate the impacts of soil moisture extremes by providing warning about upcoming extreme events. Accurate soil moisture forecasting will provide policymakers, farmers and other stakeholders more reliable information on crop yield potential and flood risk to improve decision making. Real-time soil moisture monitoring and forecasting can be accomplished by utilizing a numerical modelling approach that consolidates various sources of weather and hydrological data to simulate soil moisture levels. Soil water movement is difficult to describe numerically for fine-textured soils. Additionally, soil water behaviour during freeze/thaw events are generally weakly described by numerical tools. This study addresses both problems and evaluates how soil moisture can be forecasted under the hydrologically challenging conditions of the Red River Valley using the Brunkild catchment within the Red River basin. The Brunkild catchment represents a highly variable landscape cross-section that includes heavy clay soils of the Red River Valley through to the coarse-textured soils of the adjacent escarpment. Soil moisture levels were continuously monitored from June – August 2020 using Sentek sensors which were installed at depths of 10 to 90 cm with 10 cm spacing, and with POGO sensors that were used to manually measure surface soil moisture levels at monthly intervals from June to August 2020. Climate variables were obtained from the RISMA (Real-time In-situ Soil Monitoring for Agriculture) stations present inside the catchment. In addition to soil moisture data, surface water flow and groundwater data will also be used to aid with calibration and validation of a fully-integrated HydroGeoSphere (HGS) surface water – groundwater model of the catchment. Preliminary results using MERRA 2 data as climate forcing showed a strong fit for all observations in sandy soils and a good fit for all observation in clay. The next simulations will use the observed weather data. The model will be recalibrated and then being used to forecast soil moisture in the Brunkild catchment for the coming 14 days for the 2021 growing season.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".