<i>Towards improving the DNDC model for simulating soil hydrology and tile drainage</i>
Bibliographic record
Abstract
Abstract The Denitrification Decomposition (DNDC) model is a widely used process-based model for simulating greenhouse gas emissions and soil carbon change. The model, however, has known limitations for simulating soil hydrology, which is a crucial driver influencing biogeochemical processes. The purpose of this study was to improve DNDC for simulating hydrology, including a new sub-model for mechanistic tile drainage, and then to compare its performance to the Root Zone Water Quality model (RZWQM2) using datasets of runoff and drainage in eastern Canada and the US Midwest. In DNDC, we incorporated a heterogeneous soil profile, extended the soil depth from 50 to 200 cm, and included root penetration and density functions to improve water and N uptake by plants. A fluctuating water table and mechanistic tile drainage were incorporated, including the ability to simulate sub-irrigation and controlled drainage. In order to keep model input and calibration requirements manageable, a cascade water flow approach was maintained, however, a mechanism was included to slow drainage above field capacity. Results indicated that the performance of DNDC for simulating soil water storage and water and N flow to tile drains were greatly improved, with the performance being at least equal to RZWQM2. After the developments, the DNDC model was able to capture the differences in water and N losses that occurred between conventional drainage and controlled drainage management with sub-irrigation. The model improvements should increase the performance of DNDC for simulating biogeochemical processes and for assessing drainage design implications on water quality and GHG emissions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".