Assessing Impacts of Land Use and Land Cover (LULC) Change on Stream Flow and Runoff in Rur Basin, Germany
Bibliographic record
Abstract
Understanding the impact of land use/land cover (LULC) change on hydrology is the key to sustainable water resource management. In this study, we used the Soil and Water Assessment Tool (SWAT) to evaluate the impact of LULC change on the runoff in the Rur basin, Germany. The SWAT model was calibrated against the observed data of stream flow and runoff at three sites (Stah, Linnich, and Monschau) between 2000 and 2010 and validated between 2011 and 2015. The performance of the hydrological model was assessed by using statistical parameters such as the coefficient of determination (R2), p-value, r-value, and percentage bias (PBAIS). Our analysis reveals that the average R2 values for model calibration and validation were 0.68 and 0.67 (n = 3), respectively. The impacts of three change scenarios on stream runoff were assessed by replacing the partial forest with urban settlements, agricultural land, and grasslands compared to the 2006 LULC map. The SWAT model captured, overall, the spatio-temporal patterns and effects of LULC change on the stream runoffs despite the heterogeneous runoff responses related to the variable impacts of the different LULC. The results show that LULC change from deciduous forest to urban settlements, agricultural land, or grasslands increased the overall basin runoff by 43%, 14%, and 4%, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".