Analysis of water resources in the Mahanadi River Basin, India under projected climate conditions
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Bibliographic record
Abstract
Abstract The paper presents the outcomes of a study conducted to analyse water resources availability and demand in the Mahanadi River Basin in India under climate change conditions. Climate change impact analysis was carried out for the years 2000, 2025, 2050, 2075 and 2100, for the months of September and April (representing wet and dry months), at a sub‐catchment level. A physically based distributed hydrologic model (DHM) was used for estimation of the present water availability. For future scenarios under climate change conditions, precipitation output of Canadian Centre for Climate Modelling and Analysis General Circulation Model (CGCM2) was used as the input data for the DHM. The model results show that the highest increase in peak runoff (38%) in the Mahanadi River outlet will occur during September, for the period 2075–2100 and the maximum decrease in average runoff (32·5%) will be in April, for the period 2050–2075. The outcomes indicate that the Mahanadi River Basin is expected to experience progressively increasing intensities of flood in September and drought in April over the considered years. The sectors of domestic, irrigation and industry were considered for water demand estimation. The outcomes of the analysis on present water use indicated a high water abstraction by the irrigation sector. Future water demand shows an increasing trend until 2050, beyond which the demand will decrease owing to the assumed regulation of population explosion. From the simulated future water availability and projected water demand, water stress was computed. Among the six sub‐catchments, the sub‐catchment six shows the peak water demand. This study hence emphasizes on the need for re‐defining water management policies, by incorporating hydrological response of the basin to the long‐term climate change, which will help in developing appropriate flood and drought mitigation measures at the basin level. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it