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Record W2120561153 · doi:10.5194/hess-18-4407-2014

Inundation and groundwater dynamics for quantification of evaporative water loss in tropical wetlands

2014· article· en· W2120561153 on OpenAlexaff
J. Schwerdtfeger, Mark S. Johnson, Eduardo Guimarães Couto, Ricardo Santos Silva Amorim, Luciana Sanches, José Holanda Campelo, Markus Weiler

Bibliographic record

VenueHydrology and earth system sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoBundesministerium für Bildung und ForschungInstituto Nacional de Ciência e Tecnologia em Áreas Umidas
KeywordsWetlandEnvironmental scienceEvaporationGroundwaterDry seasonStage (stratigraphy)Hydrology (agriculture)Potential evaporationTropicsTropical climateWet seasonWater balanceSurface waterAtmospheric sciencesEcologyGeographyEnvironmental engineeringGeologyMeteorology

Abstract

fetched live from OpenAlex

Abstract. Characterizing hydrological processes within tropical wetlands is challenging due to their remoteness, complexity and heterogeneity. In particular, estimates of evaporative water loss are inherently uncertain. In view of the large influence on the local and regional climate, the quantification of evaporation is essential for the determination of the water balance of permanent and intermittent water bodies. Data for tropical wetlands are scarce where their remoteness impedes direct evaporation measurements. Seasonal inundation dynamics affect evaporation processes in tropical wetlands, which can be analysed in two stages: the first stage during the wet season and the second stage during the dry season. As yet no adequate method exists for determining second-stage evaporation in a data-scarce environment that additionally allows for a transfer of simulated actual evaporation (AET) to other locations. Our study aimed at developing a process-based model to simulate first- and second-stage evaporation in tropical wetlands. We selected a set of empirical potential evaporation (PET) models of varying complexity, each based on different assumptions and available data sets, and evaluated the models with pan evaporation observations in the Pantanal of South America, one of the largest tropical wetlands in the world. We used high-resolution measurements of surface and groundwater levels at different locations to determine the water available for evaporation. AET was derived by constraining simulated PET based on available water. The model of best fit was applied to different types of water bodies with varying hydroperiods to capture first- and second-stage evaporation across a range of wetland types. With our new model we could quantify evaporative water loss in the dry and the wet season for different locations in the Pantanal. This new spatially explicit approach represents an improvement in our understanding of the role of evaporation in the water balance of the Pantanal. We recommend the application of this model in other remote tropical wetlands, since only a minimum of input data is necessary.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.229
Teacher spread0.217 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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