A Numerical Water Tracer Model for Understanding Event-Scale Hydrometeorological Phenomena
Bibliographic record
Abstract
Abstract We develop and implement a novel numerical water tracer model within the Noah LSM with multiparameterization options (WT-Noah-MP) that is specifically designed to track individual hydrometeorological events. This approach provides a more complete representation of the physical processes beyond the standard land surface model output. Unlike isotope-enabled LSMs, WT-Noah-MP does not simulate the concentration of oxygen or hydrogen isotopes, or require isotope information to drive it. WT-Noah-MP provides stores, fluxes, and transit time estimates of tagged water in the surface–subsurface system. The new tracer tool can account for the horizontal and vertical heterogeneity of tracer transport in the subsurface by allowing partial mixing in each soil layer. We compared model-estimated transit times at the H. J. Andrews Experimental Watershed in Oregon with those derived from isotope observations. Our results show that including partial mixing in the soil results in a more realistic transit time distribution than the basic well-mixed assumption. We then used WT-Noah-MP to investigate the regional response to an extreme precipitation event in the U.S. Pacific Northwest. The model differentiated the flood response due to direct precipitation from indirect thermal effects and showed that a large portion of this event water was retained in the soil after 6 months. The water tracer addition in Noah-MP can help us quantify the long-term memory in the hydrologic system that can impact seasonal hydroclimate variability through evapotranspiration and groundwater recharge.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".