What is Missing from the Prescription of Hydrology for Land Surface Schemes?
Bibliographic record
Abstract
Abstract Land surface schemes (LSSs) are of potential interest both to hydrologists looking for innovative ways to simulate river flow and the land surface water balance and to atmospheric scientists looking to improve weather and climate predictions. This paper discusses three ideas, which are grounded in hydrological science, to improve LSS predictions of streamflow and latent heat fluxes. These three possibilities are 1) improved representation of lateral flow processes, 2) the appropriate representation of surface heterogeneity, and 3) calibration to streamflow as a way to account for parameter uncertainty. The current understanding of lateral hydrological processes is described along with their representation of a selected group of LSSs. Issues around spatial heterogeneity are discussed, and calibration in hydrologic models and LSSs is examined. A case study of an evapotranspiration-dominated basin with over 10 years of extensive observations in central Canada is presented. The results indicate that in this particular basin, calibration of streamflow presents atmospheric modelers with a unique opportunity to improve upon the current practice of using lookup tables to define parameter values. More studies are needed to determine if model calibration to streamflow is an appropriate method for generally improving LSS-modeled heat fluxes around the globe.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".