Estimating land-use change impacts on direct runoff and non-point source pollutant loads in the Richland Creek basin (Illinois, USA) by applying the L-THIA model
Bibliographic record
Abstract
An export coefficient approach to hydrological and non-point source (NPS) pollution modeling enables quick and simple assessment of long-term impacts for planning purposes. An export coefficient and geographic information system based L-THIA (Long-Term Hydrologic Impact Assessment) model was applied to the Richland Creek basin (Illinois, USA) to assess the impacts of future urban growth on direct runoff, NPS total nitrogen (TN), total suspended particles (TSP), and total phosphorous (TP) loads. The model predicted that mean annual direct runoff and TSP loading would increase by around 7% and 4% respectively by 2030 with moderate and rapid urban growth simulated by a land-use change model, while TN and TP loads would change little. Such changes are due to the projected land-use change patterns, mainly from agriculture to commercial/industrial or low-intensity residential, and to the different contributions of land-uses to runoff and NPS pollutant loads. At a subbasin scale, the most developed subbasin is projected to experience the greatest increase in commercial/industrial land at the expense of agricultural land and thus notable increases in runoff and TSP load. The changes in runoff and TSP load in other subbasins and the changes in TN and TP loads in all the subbasins show little spatial variability even though the range of per cent increases in low-intensity residential is extremely wide. This study reveals the effect of different ‘urban’ land-use types on water quality and suggests that proper simulation or planning of different urban land-use types must be carried out for impact assessments.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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".