An Integrated Modeling Approach for Evaluation of Phosphorus Loading in Rural Nova Scotia Watersheds
Bibliographic record
Abstract
Residential on-site wastewater systems (OWS) are a potential source of phosphorus (P) which can negatively impact surface water quality in rural watersheds. The magnitude of P loading from OWS is typically not monitored, and is further complicated when agricultural land-uses are intermixed with residential dwellings. Watershed-scale computer simulations are commonly used tools for evaluating the impacts of land-use changes on P loading. Existing models simulate OWS P treatment via vertical flow transport in native soils. However, in Nova Scotia (NS) OWS designs rely pre-dominantly on lateral flow and imported sand filter media.\nIn this thesis, a watershed-scale computer modeling framework for simulating P loads from agriculture and lateral flow OWS designs was developed and tested. The framework consists of the P on-site wastewater simulator (POWSIM), designed specifically for this study, which is used in conjunction with the Soil and Water Assessment Tool (SWAT) model. The POWSIM loading tool has three computational components: (i) OWS disposal field design type selection and treatment media mass calculation; (ii) disposal field P treatment dynamics; and (iii) soil subsurface plume P treatment dynamics. The active P treatment media mass and dynamics equations were developed from numerical modeling (HYDRUS-2D) and lateral flow sand filter (LFSF) OWS disposal field experiments. A 2-part piecewise linear model was found to best represent LFSF P treatment processes.\nTesting of the modeling framework in the mixed land-use Thomas Brook Watershed (TBW) in NS demonstrated improved simulation of baseflow total P (TP) loads in both a predominantly residential subcatchment and one dominated by agriculture over the SWAT model without POWSIM. Different residential and agricultural development and beneficial management practice (BMP) scenarios were evaluated in the TBW. Agricultural BMPs were most effective at reducing cumulative TP loads while OWS BMPs were best at mitigating in-stream eutrophication impacts. The 50 year simulation period for the various scenarios found peak OWS TP loading occurring between 25 and 50 years, suggesting that modeling for many decades is required for proper evaluation. This study highlights the importance in identifying specific water quality issues that need to be targeted prior to implementing a BMP strategy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".