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Record W4239216054 · doi:10.5194/tc-2019-286

The surface energy balance in a cold-arid permafrost environment,Ladakh Himalaya, India

2020· preprint· en· W4239216054 on OpenAlexaff
John Mohd Wani, Renoj J. Thayyen, C. S. P. Ojha, Stephan Gruber

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsCarleton University
FundersDivision of Human Resource DevelopmentScience and Engineering Research BoardNational Institutes of HealthMinistry of Education, India
KeywordsPermafrostLatent heatSensible heatEnergy balanceEnvironmental scienceSnowHeat fluxAridCryosphereAtmospheric sciencesFlux (metallurgy)ClimatologyHydrology (agriculture)GeologyHeat transferGeographyGeomorphologyMeteorologyPhysicsSea iceChemistry

Abstract

fetched live from OpenAlex

Abstract. Cryosphere of the cold-arid trans-Himalayan region is unique with its significant permafrost cover. While the information on the permafrost characteristics and its extent started emerging, the governing energy regimes of this cryosphere region is of particular interest. This paper present the results of Surface Energy Balance (SEB) studies carried out in the upper Ganglass catchment in the Ladakh region of India, which feed directly to the River Indus. The point SEB is estimated using the one-dimensional mode of GEOtop model from 1 September 2015 to 31 August 2017 at 4727 m a.s.l elevation. The model is evaluated using field monitored radiation components, snow depth variations and one-year near-surface ground temperatures and showed good agreement with the respective simulated values. The study site has an air temperature range of −23.7 to 18.1 °C with a mean annual average temperature (MAAT) of −2.5 and ground surface temperature range of −9.8 to 19.1 °C. For the study period, the surface energy balance characteristics of the cold-arid site show that the net radiation was the major component with mean value of 28.9 W m−2 followed by sensible heat flux (13.5 W m−2) and latent heat flux (12.8 W m−2), and the ground heat flux was equal to 0.4 W m−2. The partitioning of energy balance during the study period shows that 47 % of Rn was converted into H, 44 % into LE, 1 % into G and 7 % for melting of seasonal snow. Both the study years experienced distinctly different, low and high snow regime. Key differences due to this snow regime change in surface energy balance characteristics were observed during peak summer (July–August). The latent heat flux was higher (lower) during this period with 39 W m−2 (11 W m−2) during high (low) snow years. The study also shows that the sensible heat flux during the early summer season (May, June) of the high (low) snow was much smaller (higher) −3.4 W m−2 (36.1 W m−2). During the study period, snow cover builds up in the catchment initiated by the last week of December facilitating the ground cooling by almost three months (October to December) of sub-zero temperatures up to −20 °C providing a favourable environment for permafrost. It is observed that the Ladakh region have a very low relative humidity in the range of 43 % as compared to, e.g., ~ 70 % in the Alps facilitating lower incoming longwave radiation and strongly negative net longwave radiation averaging ~ −90 W m−2 compared to −40 W m−2 in the Alps. Hence, the high elevation cold-arid region land surfaces could be overall colder than the locations with more RH such as the Alps. Further, it is apprehended that high incoming shortwave radiation in the region may be facilitating enhanced cooling of wet valley bottom surfaces as a result of stronger evaporation.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.194
Teacher spread0.177 · 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".

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Citations3
Published2020
Admission routes1
Has abstractyes

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