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 usually impossible to measure every significant term independently in the field. Here we explore field scale water balance closure in a prairie pasture field site in Saskatchewan, Canada. The area is cold, flat and semi-arid, with snowmelt-dominated runoff. 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. Over two years studied (1 November 2012 to 31 October 2014), we saw similar snowpacks develop each winter result in markedly different runoff responses during melt. This was attributed to different soil moisture conditions prior to the snow accumulation period in each year. In the more typical year (2013), the snow pack mostly infiltrates into the soil, and the water balance is dominated by vertical land-atmosphere exchanges. However, in the wetter year (2013–2014), the snowpack was not absorbed as soil moisture, and significant losses (i.e. deep or lateral fluxes) occurred in response to rainfall in the early growing season. As a result, we were unable to close the water balance. In particular, we were unable to quantify how the excess melt water was partitioned between lateral runoff and vertical soil drainage leading to groundwater recharge. Shallow piezometers suggest groundwater recharge was significant in the wet year, and was depression focused. It is concluded that models which use physically-based process representations to partition the melt cannot be rigorously validated using conventional field-scale measurements based on water balance residuals. Rather, models should be constrained using direct observations, accounting for uncertainty, and there is a need to establish which observations (what, where and when) are most effective at constraining the uncertainties in the water balance components.
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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 | 0.002 |
| 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 | 0.001 |
| 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".