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Record W3004037657 · doi:10.5194/hess-2019-671

The Spatial Extent of Hydrological and Landscape Changes across the Mountains and Prairies of the Saskatchewan and Mackenzie Basins

2020· article· en· W3004037657 on OpenAlexafffundabout
Paul H. Whitfield, Philip Kraaijenbrink, Kevin Shook, John W. Pomeroy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of SaskatchewanSimon Fraser UniversityEnvironment and Climate Change Canada
FundersGlobal Water FuturesCanada Research ChairsEnvironment and Climate Change CanadaNational Aeronautics and Space Administration
KeywordsStreamflowClimate changePhysical geographyHydrographEnvironmental scienceClimatologyDrainage basinStructural basinSnowmeltNormalized Difference Vegetation IndexVegetation (pathology)SnowHydrology (agriculture)GeographyGeologyMeteorologyCartographyGeomorphology

Abstract

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Abstract. East of the Continental Divide, the cold interior of Western Canada has one of the world's most extreme and variable climates and is experiencing rapid environmental change. In the large Saskatchewan and Mackenzie River basins, the warming climate is changing the landscape, vegetation, cryosphere, and water. This study of a large number (395) of gauged basins in these large river basins provides the basis for a large-scale analysis of observed hydrological and landscape changes. In this region, the existing data sets are complex; observed streamflow records are available for differing series of years and streamflow measurements consist of both continuous and seasonal records. This diversity has been compensated for using novel analytical approaches: [1] a Streamflow Regime classification using dynamic time-warping that covers only the common period of the calendar year amongst all stations, and which is restricted to a time window of seasonal observations, [2] a classification of seasonal Streamflow Regime change using k-means clustering of the year divided into five-day bins. An assessment of landscape and hydrological storage change for each gauged basin was conducted using Landsat 5 TM imagery of Normalized Difference Vegetation, Water, and Snow Indices (NDVI, NDWI, and NDSI) for 1985 to 2010. Therefore, this analysis is for a different time period than the streamflow regime and trend patterns. Twelve Streamflow Regime Types were identified using dynamic time-warping to overcome the problem of timing differences producing flow clusters due to latitude or elevation, rather than from the shape of the hydrograph resulting from differing processes. The success of this approach suggests that there is sufficient information in the time window to adequately resolve regions; Streamflow Regime Types exhibit a strong connection to location; the spatial distribution follows ecoregions and shows a strong distinction between mountains and plains. Clustering of seasonal trends resulted in six Trend Patterns. The Trend Patterns also have a strong and distinct spatial organization. The Trend Patterns include one with decreasing streamflow, four with different seasonal increasing streamflows, and one without any apparent trend structure. Trends in the mean, minimum, and maximum of three satellite indices were determined; the spatial patterns of NDWI and NDSI were similar to each other, but NDVI patterns were generally dissimilar. Streamflow Regime Types, the Trend Patterns, and satellite indices showed spatial coherence. The overlap between hydrological and landscape change was not perfectly coherent, suggesting that landscape changes may have a different domain from the existing hydrological regimes and from the observed trend patterns. Three particular areas of change are identified: [i] north of 60° where streamflow and greenness are increasing while wetness and snowcover are decreasing, [ii] in the western Boreal Plains where streamflows and greenness are decreasing but wetness and snowcover are not changing, and [iii] across the Prairies where there are three patterns of increasing streamflow and an increase in the wetness index, the largest changes occur in the eastern portion of the Canadian Prairies, with only few increases in greenness and snow indices. The results demonstrate the spatial extent of these changes.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.192
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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 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

Citations1
Published2020
Admission routes3
Has abstractyes

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