CLIMATE EXTREMES AND PRECIPITATION TRENDS IN KELANI RIVER BASIN, SRI LANKA AND IMPACTS ON STREAMFLOW VARIABILITY UNDER CLIMATE CHANGE
Bibliographic record
Abstract
This paper focuses on and analyses the potential hydrological impacts of climate change on the hydrologic regime of Kelani River Basin, Sri Lanka. The basin wide future hydrology is simulated by using downscaled temperature and precipitation outputs from the Canadian Earth System Model -Version 2 (CanESM2) and the Hydrologic Engineering Centre's Hydrologic Modeling System (HEC-HMS). The research further evaluates the long-term behavior and trends of the climate extremes based on the observed historical temperature and precipitation data. The findings suggest that the temperature and precipitation extremes are on the rise while the annual average precipitation in the basin is declining. It is also predicted with the application of statistical downscaling that temperature may rise annually for representative concentration pathways of RCP2.6, RCP4.5 and RCP8.5. A detailed modelling approach is incorporated to Hanwella subwatershed (1799.67 km 2 ) of the Kelani River basin to study the subsequent water resource management options required to ensure sustainable development alternatives with the varying streamflow of the Kelani River basin under the effect of the future (2020's, 2050's and 2080's) rainfall and temperature as impending climate change impacts. The paper reviews the current state of the catchment as well as the suitability of applying the GCM's rather than RCM's to Sri Lanka to assess this river basin, according to monthly, seasonal and annual variations of the climate data. Further, a quantitative analysis of the change of amount of surface water in the selected river basin with respect to the expected variations in precipitation and temperature is also carried out. The findings of this research and overall outcome will be useful in identifying possible sustainable water management scenarios with best management options and mitigation measures like land use and carbon emission management, etc. in a more meaningful manner in the future. This study will set the baseline for commencing and continuing quantitative studies incorporating the behavior of the basinwide climatology and streamflow variability with the use of GCM's to achieve sustainable development goals.
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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 | 0.000 | 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 | 0.001 |
| 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 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".