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Record W3000434086 · doi:10.5194/hess-2019-640

Diagnosis of future changes in hydrology for a Canadian Rocky Mountain headwater basin

2020· article· en· W3000434086 on OpenAlexafffundabout
Xing Fang, John W. Pomeroy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Saskatchewan
FundersBiogeoscience Institute, University of CalgaryCanada Research ChairsAlberta InnovatesCanadian Foundation for Climate and Atmospheric SciencesGovernment of Alberta
KeywordsSnowmeltSnowpackHydrology (agriculture)Environmental scienceStreamflowWeather Research and Forecasting ModelEvapotranspirationSnowMeltwaterClimate changeStructural basinDrainage basinClimatologyGeologyGeographyEcologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract. Climate change is anticipated to have impacts on the water resources of the Saskatchewan River, which originates in the Canadian Rocky Mountains. To better understand the climate change impacts in the mountain headwaters of this basin, a physically based hydrological model was developed for this basin using the Cold Regions Hydrological Modelling platform (CRHM) for Marmot Creek Research Basin (~9.4 km2), located in the Front Ranges of the Canadian Rocky Mountain. Marmot Creek is composed of ecozones ranging from montane forests to alpine tundra and alpine exposed rock and includes both large and small clearcuts. The model included blowing and intercepted snow redistribution, sublimation, energy-balance snowmelt, slope and canopy effects on melt, Penman-Monteith evapotranspiration, infiltration to frozen and unfrozen soils, hillslope hydrology, streamflow routing and groundwater components and was parameterised without calibration from streamflow. Near-surface outputs from the 4-km Weather Research and Forecasting (WRF) model were bias corrected using the quantile delta mapping method with respect to meteorological data from five stations located from montane forest to mountaintop during October 2005–September 2013. The bias corrected WRF outputs during current period (CTRL, 2005–2013) and future period (PGW, 2091–2099) were used to drive model simulations to assess changes in Marmot Creek's hydrology. Under a "business as usual" forcing scenario: representative concentration pathway 8.5 (RCP8.5) in PGW, the basin warms up by 4.7 °C and receives 16 % more precipitation, which leads to a 40 mm decline in seasonal peak snowpack, 84 mm decrease in snowmelt volume, 0.2 mm day-1 slower melt rate, and 49 days shorter snowcover duration. The alpine snow season will be shortened by almost one and half month, but at some lower elevations there are large decreases in peak snowpack (~45 %) as well as a shorter snow season. Losses of peak snowpack will be much greater in clearcuts than under forest canopies. In alpine and treeline ecozones blowing snow transport and sublimation will be suppressed by higher threshold wind speeds for transport, in forest canopies sublimation losses from intercepted snow will decrease due to faster unloading and drip, and for all ecozones, evapotranspiration will increase due to longer snow-free seasons and more rainfall. Runoff will begin earlier in all ecozones, but, as result of variability in surface and subsurface hydrology, forested and alpine ecozones generate larger runoff volumes, ranging from 12 % to 25 %, whereas the treeline ecozone has a small (2 %) decrease in runoff volume due to decreased melt volumes from smaller snowdrifts. The shift in timing in streamflow is notable, with 236 % higher flows in spring months and 12 % lower flows in summer and 13 % higher flows in early fall. Overall, Marmot Creek basin annual streamflow discharge will increase by 18 % with PGW without a change in its streamflow generation efficiency, despite the basin shifting from snowmelt runoff towards rainfall-dominated runoff generation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.024
GPT teacher head0.209
Teacher spread0.185 · 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".

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Citations6
Published2020
Admission routes3
Has abstractyes

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