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Record W3165147366 · doi:10.5194/egusphere-egu21-10186

Multi-model projections of evaporation in a sub-tropical lake

2021· article· en· W3165147366 on OpenAlexaff
Sofia La Fuente, Iestyn Woolway, Eleanor Jennings, Gideon Gal, Georgiy Kirillin, Tom Shatwell, Robert Ladwig, T. L. Moore, Raoul‐Marie Couture, Marianne Côté, Love Råman Vinnå

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsClimate changeEnvironmental scienceCoupled model intercomparison projectLake ecosystemEvaporationPotential evaporationClimate modelHydrology (agriculture)Pan evaporationEcosystemRepresentative Concentration PathwaysAtmospheric sciencesClimatologyMeteorologyEcologyGeologyGeographyOceanography

Abstract

fetched live from OpenAlex

Evaporation of surface water is critical to the basic functioning of lakes. It directly and, in some cases, substantially modifies the hydrologic, chemical, and energy budgets, making evaporation one of the most important physical controls on lake ecosystems. Predicting lake evaporation response to climate change is, therefore, of paramount importance. Most studies that simulate climate change impacts on lake evaporation have utilised only a single mechanistic model. Whilst such studies have merit, the advantage of applying multiple, independently developed models (i.e., an ensemble approach), is that some of the inherent uncertainties in the individual lake models due to, for example, different model structures, can be reduced thus enabling increased robustness of historic and future projections. In this study, we present results from the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP) Lake Sector, where lake evaporation responses to 20th and 21st century (1901-2099) climate change has been simulated with a suite of independently developed lake models under different climate change scenarios (Representative Concentration Pathways, RCP, 2.6, 6.0 and 8.5). Our study focuses on Lake Kinneret (Israel), a sub-tropical monomictic lake of socioeconomic importance. Our simulations are validated during the historic period with bulk evaporation estimates calculated from high frequency meteorological and in-lake observations. Our results demonstrate that the lake models provide an accurate representation of historical variability in lake evaporation, with promising comparisons of the magnitude, timing and seasonality of evaporative water loss. Future evaporation projections at Lake Kinneret show that evaporation anomalies will increase by the end of the century. We show that multi-model projections of lake evaporation can accurately represent the historic period and hence provide reliable future projections that will be vital for water management.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.022
GPT teacher head0.253
Teacher spread0.231 · 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 designSimulation or modeling
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

Citations2
Published2021
Admission routes1
Has abstractyes

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