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Record W3032838543 · doi:10.5194/hess-24-2731-2020

Diagnosis of future changes in hydrology for a Canadian Rockies headwater basin

2020· article· en· W3032838543 on OpenAlexafffundabout
Xing Fang, John W. Pomeroy

Bibliographic record

VenueHydrology and earth system sciences · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Saskatchewan
FundersGlobal Water FuturesAlberta InnovatesBiogeoscience Institute, University of CalgaryCanada Research ChairsCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaGovernment of AlbertaUniversity of Saskatchewan
KeywordsSnowmeltHydrology (agriculture)Environmental scienceSnowpackStreamflowEvapotranspirationWeather Research and Forecasting ModelClimate changeMeltwaterSnowWater balanceDrainage basinClimatologyGeologyGeographyGeomorphologyEcology

Abstract

fetched live from OpenAlex

Abstract. Climate change is anticipated to impact the hydrology of the Saskatchewan River, which originates in the Canadian Rockies mountain range. 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 Rockies. 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 low-elevation montane forests to alpine ridgetops and running over October 2005–September 2013. The bias-corrected WRF outputs during a current period (2005–2013) and a future pseudo global warming 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 will warm up by 4.7 ∘C and receive 16 % more precipitation, which will lead to a 40 mm decline in seasonal peak snowpack, 84 mm decrease in snowmelt volume, 0.2 mm d−1 slower melt rate, and 49 d shorter snow-cover duration. The alpine snow season will be shortened by almost 1.5 months, but at some lower elevations there will be large decreases in peak snowpack (∼45 %) in addition to a shorter snow season. Declines in the peak snowpack will be much greater in clearcuts than under mature forest canopies. In alpine and treeline ecozones, blowing snow transport and sublimation will be suppressed by higher-threshold wind speeds for transport, in forest ecozones, sublimation losses from intercepted snow will decrease due to faster unloading and drip, and throughout the basin, evapotranspiration will increase due to a longer snow-free season and more rainfall. Runoff will begin earlier in all ecozones, but, as a result of variability in surface and subsurface hydrology, forested and alpine ecozones will generate the greatest runoff volumetric increases, ranging from 12 % to 25 %, whereas the treeline ecozone will have a small (2 %) decrease in runoff volume due to decreased melt volumes from smaller snowdrifts. The shift in timing in streamflow will be notable, with 236 % higher flows in spring months and 12 % lower flows in summer and 13 % higher flows in early fall. Overall, Marmot Creek's annual streamflow discharge will increase by 18 % with PGW, without a change in its streamflow generation efficiency, despite its basin shifting from primarily 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.945

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)0.0000.000

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 head0.027
GPT teacher head0.209
Teacher spread0.182 · 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.

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

Citations41
Published2020
Admission routes3
Has abstractyes

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