Estimation of Basin Water Balance Components Using SWAT Model, In South Africa
Bibliographic record
Abstract
This study is aimed at evaluating the impacts of land use land cover (LULC) changes on the availability of water balance components for a period of 28 years .To realize this objective, the Soil and Water Assessment Tool (SWAT) was utilized for the simulation of streamflow.The inputs used in SWAT were the digital elevation model (DEM), LULC maps for 1990 and 2018, soil maps, and slope data created using the ArcGIS spatial analysis tool.A three-year warm-up period was used.The results show that there was a rise in LULC within 28 years.The analysed LULC changes in 1990 showed that the least dominant classes were water and barren land, the most dominant were pasture and residential, followed by forest mixed, which accounted for 71.44%, 16.73%, and 5.50% of the total area, respectively.However, the analysed LULC changes for 2018 showed the most dominant to be pasture, followed by residential, and agricultural land-generic, comprising 65.08%, 18.82%, and 7.96%, respectively.In addition, the hydrology model outputs from 1990 and 2018 were compared, resulting in changes in evapotranspiration, surface runoff, lateral flow, and percolation of -0.7mm, 4.24mm, -3.39mm, and -0.13mm, respectively.This study identifies that LULC changes result in heightened surface runoff due to impervious surfaces, deforestation, and drainage, leading to increased flood risk and soil erosion.The reduction in evapotranspiration, lateral flow, and percolation impacts modified climate, diminished cooling, decreased baseflow in rivers and streams, and lower groundwater recharge.These results suggest that there's a relation between LULC changes and their impact on the water balance of the catchment.Therefore, more studies should be done on this catchment to assess the potential effects of other water balance components, such as a possible increase in abstraction, groundwater, and climate change, on the runoff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".