Field-scale water balance closure in seasonally frozen conditions
Bibliographic record
Abstract
Abstract. Hydrological water balance closure is a simple concept, yet in practice it is uncommon to measure every significant term independently in the field. Here we demonstrate the degree to which the field-scale water balance can be closed using only routine field observations in a seasonally frozen prairie pasture field site in Saskatchewan, Canada. Arrays of snow and soil moisture measurements were combined with a precipitation gauge and flux tower evapotranspiration estimates. We consider three hydrologically distinct periods: the snow accumulation period over the winter, the snowmelt period in spring, and the summer growing season. In each period, we attempt to quantify the residual between net precipitation (precipitation minus evaporation) and the change in field-scale storage (snow and soil moisture), while accounting for measurement uncertainties. When the residual is negligible, a simple 1-D water balance with no net drainage is adequate. When the residual is non-negligible, we must find additional processes to explain the result. We identify the hydrological fluxes which confound the 1-D water balance assumptions during different periods of the year, notably blowing snow and frozen soil moisture redistribution during the snow accumulation period, and snowmelt runoff and soil drainage during the melt period. Challenges associated with quantifying these processes, as well as uncertainties in the measurable quantities, caution against the common use of water balance residuals to estimate fluxes and constrain models in such a complex environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".