Long-term effects of land-use change on water resources in urbanizing watersheds
Bibliographic record
Abstract
The changes in energy balance resulting from land-use change may significantly affect the amount and timing of water loss to the atmosphere as evapotranspiration (ET). Also, these will impact water fluxes in the watershed system, influencing runoff rate, flow volume, intensity, and frequency of floods. During the past century, land-use change in the SuAsCo (Sudbury-Assabet and Concord) watershed has altered basin hydrology, sediment, and nutrient load that is detrimental to water resources in SuAsCo. This study uses an integrated physically-based model Hydrological Simulation Program-FORTRAN (HSPF), along with Land Transformation Model (LTM), to assess predicted temporal and spatial changes in water, nutrient, and sediment yields for future land-use scenarios of 2035, 2065, and 2100. Results showed that a 75% increase in effective impervious area and a 50% decrease in forest area in 2100 (from 2005 baseline levels) are projected to cause a 3% increase in annual streamflow and a 69% increase in total yearly mean surface runoff. The average annual total suspended solid (TSS) yield at the watershed outlet is estimated to increase by 54% in 2100. An increase of 12% and 13% concentrations of average annual total phosphorus (TP) and total nitrogen (TN) are predicted by 2100 due to urban expansion and increased runoff volume. This integrated modeling approach will inform watershed managers and landowners about critical areas of the SuAsCo watershed to apply best management practices (BMPs) to mitigate the effects of land-use land cover (LULC) change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".