Hydrologic Impacts of Municipal Wastewater Irrigation to a Temperate Forest Watershed
Bibliographic record
Abstract
Land application of municipal wastewater to managed forests is an important treatment and water reuse technology used globally, but the hydrological processes of these systems are not well characterized for temperate areas with annual rainfall of 1200 mm or greater. This study evaluated the impact of municipal wastewater irrigation to the local water balance at a 3000-ha land application facility where secondary-treated wastewater is land applied to a mixed hardwood-pine forest over 900 ha. Stable isotopes of hydrogen (H) and oxygen (O), chloride concentrations, and specific conductance were used in combination with hydrometric measurements to estimate the wastewater composition in groundwater, surface water, and at the watershed outlet during dry and wet seasonal periods and during one large rainfall event. Wastewater and water bodies receiving irrigation were found to have significantly higher δH, δO, specific conductance, and chloride concentrations. Using these tracers, a two-component, three-end member geochemical mixing model estimated mean wastewater compositions in the surficial aquifer receiving irrigation from 47 to 73%. Surface water onsite was found to reflect the high wastewater composition in groundwater. Land-applied wastewater contributed an estimated 24% of total streamflow, with the highest wastewater compositions in surface water observed during major storm events and at low-flow conditions. Groundwater and surface water within the watershed were found to have proportionally higher wastewater compositions than expected based on the proportion of irrigation to rainfall received by these areas.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".