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Record W7047794695

Improving and Testing the Prairie Hydrological Model at Smith Creek Research Basin

2014· report· en· W7047794695 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2014
Typereport
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsHydrology (agriculture)WetlandSnowmeltSurface runoffDrainage basinDrainageWater balanceStreamflowStructural basinWater year
DOInot available

Abstract

fetched live from OpenAlex

The 2010 Prairie Hydrological Model configuration of the Cold Regions Hydrological Model was developed to include improved snowmelt and evaporation physics and a hysteretic relationship between wetland storage and runoff contributing area. The revised model was used to simulate the snow regimes on and the streamflow runoff from the five sub-basins and main basin of Smith Creek, Saskatchewan for six years (2007-2013) with good performance when compared to field observations. Smith Creek measured streamflows over this period included the highest annual flow volume on record (2011) and high flows from heavy summer rains in 2012. Smith Creek basin has undergone substantial drainage from 1958 when it contained 96 km2 of wetlands covering 24% of the basin area to the existing (2008 measurement) 43 km2 covering 11% of the basin. The Prairie Hydrological Model was run over the 2007-2013 period for various wetland extent scenarios that included the 1958 historical maximum, measured extents in 2000 and 2008, a minimum extent that excluded drainage of conservation lands and an extreme minimum extent involving complete drainage of all wetlands in Smith Creek basin. Overall, Smith Creek total flow volumes over six years increase 55% due to drainage of wetlands from the current (2008) state, and decrease 26% with restoration to the 1958 state. This sensitivity in flow volume to wetland change is crucially important for the water balance of downstream water bodies such as Lake Winnipeg. Whilst the greatest proportional impacts on the peak daily flows are for dry years, substantial impacts on the peak daily discharge of record (2011) from wetland drainage (+78%) or restoration (-32%) are notable and important for infrastructure in and downstream of Smith Creek. For the flood of record (2011), the annual flow volume and the peak daily discharge are estimated to increase from 57,317 to 81,227 dam3 and from 19.5 to 27.5 m3/s, respectively, due to wetland drainage that has already occurred in Smith Creek. Although Smith Creek is already heavily drained and its streamflows have been impacted, the annual flow volumes and peak daily discharge for the flood of record can still be strongly increased by complete drainage from the 2008 wetland state, rising to 103,669 dam3 and 49 m3/s respectively. This model simulation exercise shows that wetland drainage can increase annual and peak daily flows substantially, and that notable increases to estimates of the annual volume and peak daily flow of the flood of record have derived from wetland drainage and will proceed with further wetland drainage.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.883
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.233
Teacher spread0.189 · 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

Citations5
Published2014
Admission routes1
Has abstractyes

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