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Record W2040905301 · doi:10.1002/hyp.6962

Analysis of water resources in the Mahanadi River Basin, India under projected climate conditions

2008· article· en· W2040905301 on OpenAlexaboutno aff
Shilpa M. Asokan, Dushmanta Dutta

Bibliographic record

VenueHydrological Processes · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceSurface runoffClimate changeDrainage basinHydrology (agriculture)Water resourcesPrecipitationIrrigationWater resource managementPopulationFlood mythClimate modelGeographyMeteorologyGeology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.235
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations112
Published2008
Admission routes1
Has abstractyes

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