RCP8.5-Based Future Flood Hazard Analysis for the Lower Mekong River Basin
Bibliographic record
Abstract
Climatic variations caused by the excessive emission of greenhouse gases are likely to change the patterns of precipitation, runoff processes, and water storage of river basins. Various studies have been conducted based on precipitation outputs of the global scale climatic models under different emission scenarios. However, there is a limitation in regional- and local-scale hydrological analysis on extreme floods with the combined application of high-resolution atmospheric general circulation models’ (AGCM) outputs and physically-based hydrological models (PBHM). This study has taken an effort to overcome that limitation in hydrological analysis. The present and future precipitation, river runoff, and inundation distributions for the Lower Mekong Basin (LMB) were analyzed to understand hydrological changes in the LMB under the RCP8.5 scenario. The downstream area beyond the Kratie gauging station, located in the Cambodia and Vietnam flood plains was considered as the LMB in this study. The bias-corrected precipitation outputs of the Japan Meteorological Research Institute atmospheric general circulation model (MRI-AGCM3.2S) with 20 km horizontal resolution were utilized as the precipitation inputs for basin-scale hydrological simulations. The present climate (1979–2003) was represented by the AMIP-type simulations while the future (2075–2099) climatic conditions were obtained based on the RCP8.5 greenhouse gas scenario. The entire hydrological system of the Mekong basin was modelled by the block-wise TOPMODEL (BTOP) hydrological model with 20 km resolution, while the LMB area was modelled by the rainfall-runoff-inundation (RRI) model with 2 km resolution, specifically to analyze floods under the aforementioned climatic conditions. The comparison of present and future river runoffs, inundation distributions and inundation volume changes were the outcomes of the study, which can be supportive information for the LMB flood management, water policy, and water resources development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".