High-resolution downscaled climate variables spatiotemporal variation and drought projected in the Sanjiang Plain, Northeast China
Bibliographic record
Abstract
Abstract Drought is greatly impacted by climate variables, and high-resolution downscaled climate variables are valuable for precisely identifying drought characteristics. Due to Sanjiang Plain’s crucial strategic position and drastic climate changes, we analyze its spatiotemporal variation in climate variables and standardized precipitation evapotranspiration index (SPEI). Two sharing economy pathway scenarios (SSP245 and SSP585) during the early (2023–2030), middle (2050–2060), and late periods (2090–2100) are projected. The Weather Research and Forecasting (WRF) and Statistical Downscaling Model (SDSM) are used for downscaling to simulate temperature and precipitation, respectively. WRF model is driven by the bias-corrected CMIP6 dataset, the ensemble of CMIP6 daily predictor variables are applied to SDSM, which generate high-resolution downscaled data named SSP-DS scenario. The SPEI computed from precipitation and reference evapotranspiration (ET0) is adopted to identify drought characteristics. The results indicate that downscaled results accurately reflect the CMIP6 original outputs change trend, but increase ET0 and reduce precipitation. The average temperature, total ET0, total precipitation manifests an increasing trend over time, and SSP585-DS scenario increases more significantly. High radiative forcing contributes to increasing temperature and ET0. Seven stations dry and wet characteristics have no obvious spatial heterogeneity; accumulated16 to 23 (17 to 24) drought events are captured, mild drought is the most frequent and extreme drought is the least under the SSP245-DS and SSP585-DS scenario. This study predicts the spatiotemporal variation in climate variables and drought characteristics based on high-resolution downscaled data, which contributes to Sanjiang Plain management strategy against drought risk and climate change.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".