Impact of Climate Change on Precipitation and Temperature Changes in the Northwest Region of Bangladesh Using SDSM: A Comparison of CanESM2 and HadCM3 Models
Bibliographic record
Abstract
Assessment of climate change-induced precipitation and temperature changes is crucial for the adaptive and sustainable management of water resources in a country. The objective of this study is to explore the impact of climate change on future precipitation and temperature changes in the northwest region of Bangladesh using the statistical downscaling model (SDSM). In this study, Rajshahi station is taken as the case study area, and two widely applied general circulation models (GCMs), namely the Canadian Earth System Model (CanESM2) and the Hadley Center Coupled Model (HadCM3), are used for the climate change analysis. The results demonstrate that after bias correction, the CanESM2-based downscaling model performs better compared to the HadCM3-based downscaling model. The bias-corrected models for both GCMs are then employed for the projection of future precipitation and temperatures for the 2040s and 2090s, considering climate change scenarios. The precipitation trend is found to be negative for both GCMs in all scenarios. Considering the worst climate change scenarios for both GCMs (i.e., the RCP8.5 scenario in the CanESM2 and the A2 scenario in the HadCM3), the mean annual precipitation will be decreased by 9.3% and 4.5% in the 2040s and 12.1% and 4.1% in the 2090s. Furthermore, the mean annual maximum temperature will be increased by 0.233°C and 0.245°C in the 2040s and 0.468°C and 0.633°C in the 2090s, whereas the mean annual minimum temperature will be increased by 0.394°C and 0.188°C in the 2040s and 0.394°C and 0.357°C in the 2090s. Thus, the current study comes to the conclusion that decreased precipitation and increased temperatures will have an effect on the water resources in the study region, leading to a reduction in the overall supply of surface water and groundwater storage. It is expected that the study findings will help water managers and policymakers in developing a framework for sustainable and adaptive water management in the face of climate change. Journal of Engineering Science 14(2), 2023, 127-136
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".