Application of SWAT and WEAP Models for Sustainable Management of Water Resources in the Two Rivers Dam Catchment, Uasin Gishu County, Kenya
Bibliographic record
Abstract
Kaptagat Forest, the main source of the Ellegerini River, which feeds the Ellegerini Dam and Two Rivers Dam, is under threat of extinction due to human activity. The Two Rivers Dam catchment that has over the years been a source of water in Uasin Gishu County is slowly depleting and urgent measures are required to restore it. Activities including commercial logging, charcoal burning and firewood harvesting have exerted a lot of pressure on the catchment, posing a great threat to the livelihoods of the people of Eldoret town who depend on the reservoirs for water supply. The main objective of this study was to customise SWAT and WEAP models for the sustainable management of water resources in the Two Rivers Dam catchment. The specific objectives were to set up and apply the SWAT model to generate simulated river flows draining to the Two Rivers and Ellegerini Reservoirs as an input to the WEAP model to determine the impact of land use change on the hydrological function of the Two Rivers Dam catchment, to set up, calibrate and validate a WEAP model for the Two Rivers Dam catchment and to apply the WEAP model in analyses of various management and infrastructural development projects scenarios to enhance river flow and water storage in the Two Rivers and Ellegerini Reservoirs. The goodness of fit SWAT model statistical evaluation indices attained during the calibration period was R2 = 0.854, NSE = 0.822 and Bias = 0.392. Additionally, for the validation period, the R2 = 0.786, NSE = 0.815 and Bias = 0.381. The modelled results indicate that the land use change resulted in decreased baseflow and increased surface runoff hence the high fluctuations of water levels in the Two Rivers and Ellegerini reservoirs. The WEAP model results for actual and simulated water demand in the calibration period of 2019, the R2 = 0.88, while during the validation period in the year 2020, the R2 = 0.85. The results of the model simulation indicated that the management option that had the most impact on all the scenarios was the reduction of unaccounted-for water, while the one with the least impact was increased water use efficiency. It was concluded that the models were able to simulate the observed conditions reasonably well and can therefore be used to effectively manage water resources and assist the relevant stakeholders in decision-making. The study recommended that forested areas need to be properly conserved in order to restore the hydrological function of the catchment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".