Hydro-Meteorological Trends in Southwest Coastal Bangladesh: Perspectives of Climate Change and Human Interventions
Bibliographic record
Abstract
The southwest coastal region of Bangladesh, being under tidal influence and dependent on sweet water supplies from upstream, has a unique brackish water ecosystem. The region, having vast low-lying areas enclosed by man-made polders, is considered to be highly vulnerable to climate change induced hazards. In this study, linear trends in hydro-climatic variables, such as temperature, rainfall, sunshine, humidity, sweet water inflow and tidal water level in the region are assessed using secondary data and following both parametric and nonparametric statistical techniques. Correlation between the sweet water flow from the Gorai River, a major distributary of the Ganges River, and the salinity level in the Rupsa-Pasur River near Khulna, a southern metropolis, is also investigated. The results reveal that the temperature in the Khulna region is increasing at a significant rate, particularly in recent years. The number of extremely cold nights is decreasing and the heat index is increasing. The sunshine duration has a decreasing trend and the humidity has an increasing trend. Rainfall is increasing in terms of both magnitude and number of rainy days. However, the annual maximum rainfall and the number of days with high intensity rainfall have remained almost static. The annual maximum tidal high water level is increasing and the annual minimum low water level is decreasing at a rate of 7 - 18 mm and 4 - 8 mm per year, respectively. There is a negative correlation between the Gorai flow and the river water salinity around Khulna. Dredging of the Gorai during 1998-2001 resulted in an improvement of the salinity situation in the Khulna region. The variation in water salinity, tidal water level and sweet water flows in different time periods indicates that the human interventions through upstream diversion and coastal polders have contributed more in hydro-morphological changes in the southwest than the climate change. However, there are some evidences of climate change in the meteorological variables at Khulna.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".