Statistical Downscaling of Daily Precipitation Process at an Ungaged Location in the Context of Climate Change
Bibliographic record
Abstract
Downscaling methods have been proposed for establishing the linkages between the large-scale climate variables given by GCMs and the observed characteristics of hydrologic variables at a local site. These downscaling methods, however, are not suitable for dealing with cases where the hydrologic data at the location of interest are limited (a partially gauged site) or not available (an ungauged site). The downscaling of a hydrologic process such as the daily precipitation for such cases remains still a crucial challenge for water resources planning and management in practice. The present study proposes therefore a statistical downscaling (SD) approach to establishing accurately the linkages between the climate variables given by GCM outputs and the “estimated” daily precipitation characteristics at a location of interest where the precipitation data are limited or unavailable. More specifically, the suggested SD procedure is based on a combination of two components: (i) a regional stochastic method for reconstructing the unmeasured daily precipitation series at the ungauged site; and (ii) a SD model for describing the linkage between the constructed daily precipitation series and the climatic predictors given by GCM outputs. The feasibility and accuracy of the proposed SD procedure was assessed based on the NCEP re-analysis and observed daily precipitation data available from a network of 62 raingages in South Korea. Results of this assessment have indicated that the proposed procedure could provide comparable results as those given by the downscaling using real observed precipitation data at the same local site.
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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".