An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin
Bibliographic record
Abstract
Abstract As one of the most pressing issues in the world, climate change has already caused evident impacts on natural and human systems (e.g., hydrological cycle, eco‐environment and socio‐economy) in recent decades. In this study, an integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis (MBHA) method is developed for quantifying climate change impacts on runoff. MBHA incorporates multiple global climate models (multi‐GCMs), hydrological model (HBV‐light), and Bayesian neural network (BNN) within a general framework. MBHA can provide the reliable prediction for runoff as well as reflect the impact of climate change on data scarcity catchments. MBHA is applied to the Amu Darya River basin in Central Asia. Climate data are derived from multiple GCMs (i.e., GFDL‐ESM2G, HadGEM2‐AO and NorESM1‐M) under RCP4.5 and RCP8.5. Several findings can be summarized: (1) during 2021–2100, both precipitation and temperature would increase, with more precipitation falling as rain instead of snow; (2) by 2100, glacier areas are predicted to reduce by 62.3% (RCP4.5) and 71.9% (RCP8.5); (3) under RCP8.5, monthly runoff would increase by 11.2% in 2021–2060 and reduce by 5.0% in 2061–2100; this is because the glaciers would rapidly disappear with the rising temperature after 2060. The findings suggest that the shrinked glacier and the reduced runoff threaten the water availability especially in summer seasons as well as affect the agricultural irrigation in the downstream of the Amu Darya River.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".