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Record W3018678353

Lake-river and lake-atmosphere interactions in a changing climate over Northeast Canada

2016· article· en· W3018678353 on OpenAlexaffabout
O. Huziy, Laxmi Sushama

Bibliographic record

VenueEGUGA · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEnvironmental scienceClimate changeClimate modelClimatologyPrecipitationStreamflowForcing (mathematics)InterimHydrology (agriculture)GeographyDrainage basinMeteorologyGeology
DOInot available

Abstract

fetched live from OpenAlex

Lakes influence the regional climate and hydrology in a number of ways and therefore they should be represented in climate models in a realistic manner. Lack of representation of lakes in models can lead to errors in simulated energy and water fluxes, for lake-rich regions. This study focuses on the assessment of the impact of climate change on lakes and hydrology as well as on the influence of lakes on projected changes to regional climate and surface hydrology, particularly streamflows, for Northeast Canada. To this end, transient climate change simulations spanning the 1950–2100 period are performed, with and without lakes, with the fifth generation of the Canadian Regional Climate Model (CRCM5), driven by the Canadian Earth System Model (CanESM2) at the lateral boundaries for Representative Concentration Pathway 8.5. An additional CRCM5 simulation, driven by European Centre for Medium-Range Weather Forecasts Re-Analysis Interim (ERA-Interim) for the 1980–2010 period, is performed in order to assess performance and boundary forcing errors. Performance errors are assessed by comparing the ERA-Interim-driven simulation with available observation datasets, for the 1980–2010 period, for selected variables: 2-m air temperature, total precipitation, snow water equivalent and streamflow. The validation results indicate reasonable model performance over the study region. Boundary forcing errors are studied by comparing ERA-Interim-driven simulation with the one driven by CanESM2 for the current 1980–2010 period, to identify regions and seasons for which projected changes should be interpreted with extra caution. Comparison of projected changes from the CRCM5 simulations with and without lakes suggest that the presence of lakes results in a dampening of projected increases to 2-m air temperature for all seasons almost everywhere in the study domain, with maximum dampening of the order of 2 °C occurring during winter, mostly in the vicinity of the lakes. As for streamflows, projected increases to spring streamflows, based on the simulation with lakes, are found to be smaller than that without lakes and this is due to the storage effect of lakes. Similarly, lower decreases in summer streamflows in future climate are noted in the simulation with lakes due to the gradual release of snowmelt water stored in lakes. An additional CRCM5 transient climate change simulation with lakes and interflow, i.e. lateral flow in the soil layers, is compared with the simulation with lakes, but without interflow, to assess the impact of interflow on projected changes to regional climate and hydrology. Maximum interflow is projected to shift earlier in spring and the maximum interflow rate is expected to decrease by around 25 % in future. Results suggest that the impact of interflow on projected changes to precipitation, soil moisture and humidity are modest, even though the interflow intensity is changing noticeably in future climate. The impact of the interflow on projected changes to streamflows is in the range of ±50 m3/s. This study thus for the first time demonstrates the impact of lakes and interflow on projected changes to the regional climate and hydrology for the study region using a single regional modelling system.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.197
Teacher spread0.192 · 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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes2
Has abstractyes

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