Downscaling Climate Variables to River Basin Scale in India for IPCC SRES Scenarios Using Support Vector Machine
Bibliographic record
Abstract
Realistic assessments of the local impacts of natural climate variability and projected climate change in the future are important to make independent judgements about actions required to mitigate and manage natural disasters; manage the natural environment and their water resources in a sustainable manner. A river basin which integrates some of the important systems like ecological and socio-economic systems can be ideal to study the impact of climate change on the water cycle at a local scale. General circulation models (GCMs) are among the most advanced tools to simulate climatic conditions on earth hundreds of years into the future. The GCMs are generally run at coarser scale to cover the whole globe and as a result they are inherently unable to represent local scale features. Consequently, there is a continuing need for new and improved techniques for obtaining effective projections of hydrological and meteorological variables at the river basin scale. Downscaling is one such technique, which is gaining popularity in estimating these variables at regional and local scales by translating information simulated by GCMs at global scale. This paper emphasises the importance of downscaling to a river basin scale and presents a methodology to downscale monthly climate output from GCM to this scale using Support Vector Machine (SVM). Implementation of the methodology is demonstrated by downscaling maximum temperature to Malaprabha reservoir catchment in India (which is considered to be a climatically sensitive region), using simulations from the third generation Canadian Global Climate Model (CGCM3) for IPCC SRES scenarios A1B, A2, B1 and COMMIT.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".