Climate change and ENSO significantly enhances seasonal flood occurrence in the Ganges-Brahmaputra-Meghna basin
Bibliographic record
Abstract
• We assessed climate change and ENSO impacts on seasonal floods in Bangladesh. • Climate change increases flood frequency by 40% across seasons and rivers. • La Niña and El Niño impacts vary by both seasons and locations. • El Niño raises pre-monsoon flood frequency by 31% in the Brahmaputra. • La Niña raises post-monsoon floods by 37% in the Ganges. Seasonal hydrological dynamics have profound socio-economic implications for communities in the Ganges-Brahmaputra-Meghna (GBM) River basin. Climate change and El Niño-Southern Oscillation (ENSO) phase are known to impact extreme flood magnitude in GBM River, however how they affect seasonal flooding pattern is not revealed. Utilizing large ensemble climate data (comprising 6000 years of non-warming and warming climate scenarios) and the global hydrodynamic model CaMa-Flood, we assess the influence of climate change and ENSO on seasonal hydrological patterns specially focusing on maximum river flow. The quantitative effects of La Niña and El Niño are calculated utilizing the Fractional Attribution Risk (FAR) method, separately for non-warming and historical climate scenarios. We assess climate change’s impact on flooding by contrasting historical and non-warming climate conditions using the FAR method. Climate change has substantially increased the maximum river flow for all seasons. In the monsoon season, climate change amplifies the likelihood of flooding with a 10-year return period of 34 %, 46 %, and 31 % at the Hardinge Bridge, Bahadurabad, and Bhairab Bazar gauge stations of the Ganges, Brahmaputra, and Meghna Rivers, respectively. The influence of ENSO still remains significant even with the influence of climate change. ENSO influence presents a nuanced picture, exhibiting variations both between seasons and across different rivers within the GBM basin. The relationship between ENSO and seasonal flood occurrence in the GBM basin can be effectively elucidated by the upward movement of moisture through vertical wind velocity, which serves as a large-scale controlling factor for flood variation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".