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Record W1587182852

Snow hyydrology of Canadian prairie droughts : model development and application

2007· article· en· W1587182852 on OpenAlexaboutno aff
Xing Fang

Bibliographic record

VenueUniversity Library - University of Saskatchewan (University of Saskatchewan) · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSnowClimatologyEnvironmental scienceMeteorologySnowmeltGeographyGeology
DOInot available

Abstract

fetched live from OpenAlex

Hydrological models have been developed to estimate snow accumulation, snowmelt and snowmelt runoff on the Canadian Prairies; however, their proper scale of application is unknown in the Prairie environment. The first objective of this thesis is to examine the proper scale for pre-melt snow accumulation as snow water equivalent (SWE) and snowmelt in a Prairie first order basin. Spatially distributed and spatially aggregated approaches were used to calculate SWE and snowmelt at St. Denis National Wildlife Area (SDNWA). Both approaches used models with similar physics, but differed in the model scale at which calculations were carried out. The simulated pre-melt SWE, cumulative seasonal SWE, and daily snowmelt from the two modelling approaches were compared to field observations of pre-melt SWE, cumulative seasonal SWE, and daily snowmelt; comparisons of areal cumulative seasonal SWE, areal snowmelt, snowmelt duration, and snow-covered area were also conducted between two modelling approaches. Results from these comparisons showed that both approaches had reasonable and similar accuracy in estimation of SWE and snowmelt. The spatially aggregated approach was more computationally efficient and was selected as a modelling scale for small-sized prairie basins. Another objective of this thesis is to derive a snow hydrology model for the Canadian Prairies. Physically-based hydrological models were assembled in the Cold Regions Hydrological Model Platform (CRHM) using the aggregated approach. Tests of pre-melt SWE and surface snowmelt runoff were conducted at two basins in Saskatchewan – Creighton Tributary of Bad Lake and Wetland 109, St. Denis. Results showed that the snow hydrology model had a reasonable capability to simulate SWE and snowmelt runoff to the stream and wetland. Droughts are natural hazards that develop frequently on the Canadian Prairies. Analyzing the impact of drought on hydrological processes and water supply is another objective of this thesis. Synthetic drought scenarios were proposed for the Creighton Tributary of Bad Lake and the corresponding impacts on the snowmelt runoff-related processes were examined. Results indicated that wind redistribution of snow was very sensitive to drought conditions, sublimation of blowing snow and snow-covered period were sensitive to drought, but winter evaporation and infiltration did not show strong trend. The results also showed that drought conditions had magnified effects on the snowmelt runoff and could cause cessation of streamflow. Also, the impacts of the recent 1999-2005 drought on the snowmelt hydrology were investigated at St. Denis. Results illustrated that three-years (1999-2002) of severe winter drought were followed by a normal year (2002-03) and then a two-year (2003-05) recovery period, and then returning to normal (2005-06). Results showed that both snowfall and rainfall during hydrological winter were consistently low for severe drought and surface snowmelt runoff was very much lower during severe drought, about 45-65 mm less compared to that in the normal periods.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.092
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.005
GPT teacher head0.143
Teacher spread0.138 · 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

Citations1
Published2007
Admission routes1
Has abstractno

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