The August 2024 Bangladesh Flood: Examining Meteorological Triggers, Forecasting Limitations, and Cross-Border Water Dynamics.
Bibliographic record
Abstract
Bangladesh, the eighth-most populated country with around 180 million people, is highly vulnerable to flooding due to its location in the Bengal Delta and its low-lying, flat topography. The country faces two main types of flooding: flash floods during the pre-monsoon season (March to mid-June), and seasonal flooding during the South Asian monsoon (June to October). In August 2024, an unprecedented flash flood hit Bangladesh’s northeastern and southeastern regions, diverging from typical flood patterns. The flood, occurring between August 21 and 28, killed 71 people, affected six million, and displaced half a million residents who took shelter in over 3,400 emergency locations. More than 7,000 schools closed, interrupting education for 1.7 million students. Initial estimates place the economic impact at around $1.2 billion USD.Despite these impacts, the Bangladesh Meteorological Department and flood forecasting agencies failed to predict the flood, leading to a severe humanitarian and public health crisis across 11 districts. This study aims to investigate the meteorological factors that contributed to this catastrophic flood. Using satellite, radar, and ERA5 reanalysis data, we identified favorable local, regional, and large-scale conditions that led to extreme rainfall over eastern Bangladesh and India in the third week of August. From August 17 to 22, a low-pressure system was positioned over Bangladesh and the Indian states of Tripura, Assam, and West Bengal due to downstream atmospheric blocking. This coincided with a sea surface temperature anomaly exceeding 2°C in the northeastern Bay of Bengal, an MJO amplitude over 2 in Phases 2 and 3, a westerly wind burst near 70°E, and a strong extratropical jet over the Indian subcontinent. These factors together produced around 700 mm of rain in six days, with 24-hour totals over 300 mm, leading to widespread flooding. Uncoordinated water releases from upstream dams in India’s Tripura state further intensified transboundary river flooding. The August 2024 flood underscores the urgent need for improved flood forecasting and cross-border water management protocols. Enhanced regional cooperation, particularly regarding dam water release notifications, and investment in predictive technology could help reduce the impact of future floods.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".