Coupling Nitrogen Transport and Transformation Model with Land Surface Scheme SABAE-HW and its Application on the Canadian Prairies
Bibliographic record
Abstract
The main goal of this research is to contribute to the understanding of nutrient transport and transformations in soil and its impact on groundwater on a large scale. This thesis specifically integrates the physical, chemical and biochemical nitrogen transport processes with a spatial and temporal Land Surface Scheme (LSS). Since the nitrogen biotransformation kinetics highly depends on soil moisture and soil temperature, a vertical soil nitrogen transport and transformations model was coupled with SABAE-HW. The model provides an improved interface for groundwater modeling to simulate soil moisture and soil temperature for a wide range of soil and vegetation. It is assumed that the main source of organic N is from animal manure. A-single-pool nitrogen transformation is designed to simulate nitrogen dynamics. Thus, the complete mathematical model (SABAE-HWS) is able to investigate the effects of nitrogen biochemical reactions in all seasons. This thesis reports the first field comparison of SABAE-HW using an extensive ten-year data set from BOREAS/BERMS project located in Saskatchewan, Canada. The performance of SABAE-HWS is calibrated and verified using 3 years (2002-2004) data from Carberry site in Canada, Manitoba. The effects of three rates of hog manure application, 2500, 5000, and 7500 gal/acre, was investigated to study the distribution of soil ammonium and soil nitrate within the 120 cm of soil profile. The results clearly showed that there is a good agreement between observed and simulated soil ammonium and nitrate for all treatment at the first two years of study. However, it was found a significant difference between observations and simulations at lower depths for 7500 gal/acre by the end of growing season of 2004. Also, 10 years climate data from OJP site was used to evaluate the effect of manure rates on the distribution of soil nitrate at Carberry site. The results indicated that to minimize the risk of nitrate leaching, the rate of manure application, accumulated soil nitrogen from earlier applications and the atmospheric conditions should be all taken into account at the same time. Comparing the results of SABAE-HWS and SHAW model also showed the importance of the crop growth model in simulating soil NH4-N and NO3-N.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".