Simulating daily soil water under foothills fescue grazing with the soil and water assessment tool model (Alberta, Canada)
Bibliographic record
Abstract
Abstract Grazing is common in the foothills fescue grasslands and may influence the seasonal soil‐water patterns, which in turn determine range productivity. Hydrological modelling using the soil and water assessment tool (SWAT) is becoming widely adopted throughout North America especially for simulation of stream flow and runoff in small and large basins. Although applications of the SWAT model have been wide, little attention has been paid to the model's ability to simulate soil‐water patterns in small watersheds. Thus a daily profile of soil water was simulated with SWAT using data collected from the Stavely Range Sub‐station in the foothills of south‐western Alberta, Canada. Three small watersheds were established using a combination of natural and artificial barriers in 1996–97. The watersheds were subjected to no grazing (control), heavy grazing (2·4 animal unit months (AUM) per hectare) or very heavy grazing (4·8 AUM ha −1 ). Soil‐water measurements were conducted at four slope positions within each watershed (upper, middle, lower and 5 m close to the collector drain), every 2 weeks annually from 1998 to 2000 using a downhole CPN 503 neutron moisture meter. Calibration of the model was conducted using 1998 soil‐water data and resulted in Nash–Sutcliffe coefficient (EF or R 2 ) and regression coefficient of determination ( r 2 ) values of 0·77 and 0·85, respectively. Model graphical and statistical evaluation was conducted using the soil‐water data collected in 1999 and 2000. During the evaluation period, soil water was simulated reasonably with an overall EF of 0·70, r 2 of 0·72 and a root mean square error (RMSE) of 18·01. The model had a general tendency to overpredict soil water under relatively dry soil conditions, but to underpredict soil water under wet conditions. Sensitivity analysis indicated that absolute relative sensitivity indices of input parameters in soil‐water simulation were in the following order; available water capacity > bulk density > runoff curve number > fraction of field capacity (FFCB) > saturated hydraulic conductivity. Thus these data were critical inputs to ensure reasonable simulation of soil‐water patterns. Overall, the model performed satisfactorily in simulating soil‐water patterns in all three watersheds with a daily time‐step and indicates a great potential for monitoring soil‐water resources in small watersheds. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".