Assessment of the Impacts of Climate Change on Hydrological Characteristics of the Mbarali River Sub Catchment Using High Resolution Climate Simulations from CORDEX Regional Climate Models
Bibliographic record
Abstract
This study assesses the impacts of climate change on water resources over Mbarali River sub-catchment using high resolution climate simulations from the Coordinated Regional Climate Downscaling Experiment Regional Climate Models (CORDEX_RCMs). Daily rainfall, minimum and maximum temperatures for historical climate (1971-2000) and for the future climate projection (2011-2100) under two Representative Concentration Pathways RCP 8.5 and RCP 4.5 were used as input into the Soil and Water Assessment Tool (SWAT) hydrological model to simulate stream flows and water balance components for the Mbarali River sub-catchment. The impacts of climate change on hydrological conditions over Mbarali river catchment were assessed by comparing the mean values of stream flows and water balance components during the present (2011-2040), mid (2041-2070) and end (2071-2100) centuries with their respective mean values in the baseline (1971-2000) climate condition. The results of the study indicate that, in the future, under both RCP 4.5 and RCP 8.5 emission scenarios, the four main components that determine change in catchment water balance (rainfall, ground water recharge, evaporation and surface runoff) over Mbarali river catchment are projected to increase. While the stream flows are projected to decline in the future by 13.33% under RCP 4.5 and 13.67% under RCP 8.5 emission scenarios, it is important to note that simulated surface runoff under RCP8.5 emission scenario is higher than that which is obtained under the RCP4.5 emission scenario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".