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Record W4231416008 · doi:10.5194/hess-2016-260

Field-scale water balance closure in seasonally frozen conditions

2016· preprint· en· W4231416008 on OpenAlexaboutno aff
Xicai Pan, W. Helgason, A. Ireson, H. Wheater

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowmeltWater balanceGroundwater rechargeSurface runoffHydrology (agriculture)Environmental scienceSnowEvapotranspirationSnowpackPrecipitationWater contentGroundwaterGeologyAquiferGeographyEcologyGeomorphologyMeteorology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.633

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.224
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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