Multivariate analysis of the low-flow regimes in eastern Canadian rivers

Daigle, Anik; St‐Hilaire, André; Beveridge, Dan; Caissie, Daniel; Benyahya, Loubna

doi:10.1080/02626667.2010.535002

Cited by 28 publications

(22 citation statements)

References 26 publications

(41 reference statements)

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“…PCA in many ways forms the basis for multivariate data analysis and it was first formulated in statistics by Pearson in 1901 [48]. There are many possibilities of using PCA; representation of spatial cause-effect data, in investigation of causal connections between spatial rainfall-runoff and rainfall-drought [49], as well as description of regional characteristics and differences among low-flow regimes at multiple hydrological stations, have been demonstrated [50].…”

Section: Multi-criteria Analyses Methodsmentioning

confidence: 99%

The Application of Multi-Criteria Analysis Methods for the Determination of Priorities in the Implementation of Irrigation Plans

Karleuša

Hajdinger

Tadić

2019

Water

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Irrigated agriculture has considerable impacts on the environment. To minimize negative effects and maximize positive effects, it is necessary to provide comprehensive analyses beyond the strictly technical domain. In this study, we apply a methodology for determining priorities in implementing irrigation plans using multi-criteria analysis methods on a specific case study area in the sub-catchment area of the Orljava River in Požega-Slavonia County, Croatia. Five potential irrigation areas (Orljava-Londža, Pleternica, Ovčare, Treštanovci, and Venje-Hrnjevac) were analyzed according to five selected criteria: environmental protection, water-related (four sub-criteria), social, economic, and time criteria with different criteria importance (weight). The aim of this study was to confirm the adequacy of using six multi-criteria analysis (MCA) methods (mostly used: PROMETHEE, AHP, ELECTRE TRI, and the less used: DEXi, PRIME, and PCA) in determining priorities for fulfilling irrigation plans, present models for preparation of the input data, apply certain methods, and compare the results on the selected case study area. The methods' adequacy was confirmed during the research. Five of the six MCA methods identified the Ovčare area as the most appropriate for irrigation development (i.e., it has priority in implementing the irrigation plan). According to one (AHP) of the six methods, Orljava-Londža has more advantages over other areas. All MCA methods, except PCA, chose Venje-Hrnjevac as the least advisable (last to be implemented) alternative. Conclusions from this research confirm findings from recently published research regarding the application of MCA on water management problems.

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Section: Multi-criteria Analyses Methodsmentioning

confidence: 99%

The Application of Multi-Criteria Analysis Methods for the Determination of Priorities in the Implementation of Irrigation Plans

Karleuša

Hajdinger

Tadić

2019

Water

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“…Hydrometric stations were sorted by their low flow characteristics using a Principal Component Analysis (PCA), based on a methodology used by Daigle et al (2011). PCA is a multivariate statistical approach computing linear combinations (principal components, or PC) of original variables to maximize the explained variance while maintaining orthogonality between PCs.…”

Section: Frequency Analysis Statistical Non-parametric Tests and Multivariate Analysismentioning

confidence: 99%

Southern Quebec environmental flow assessments: spatial and temporal scales sensitivity

Berthot

St‐Hilaire

Caissie

et al. 2020

Canadian Water Resources Journal / Revue canadienne des ressour

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Faced with increasing demands for water withdrawals and a changing climate, the Quebec Department of Environment and Fight Against Climate Change is reviewing its water withdrawal guidelines to protect riverine ecosystems. For Southern Quebec, guidelines currently limit water withdrawals to a maximum of 15% of the 7Q2 (mean 7day low flow with a return period of two years) during low flow periods. In this context, one of the issues raised is to investigate measures that help to preserve riverine ecosystems during low flow periods by establishing cut-off flow restrictions. This study compared eight low flow metrics to investigate which can be considered useful metrics to assess environmental flow in Southern Quebec rivers. Using 98 hydrometrics stations with a minimum of 20 years of daily flow data from eight hydrological regions, those low flow metrics were compared to three thresholds based on Tennant Method for monthly and annual temporal scales. The relevance of current hydrological regions delineation was investigated by looking at results within these regions, compared to six groups of stations defined using multivariate analyses. This study emphasizes that assessing environmental flows is linked to the hydrological context of the area of interest, the temporal scale of the historical data available, and the catchment size. The results showed that (1) winter low flows were lower than summer low flows; (2) 23% to 26% of the values were under the conservative thresholds for all the metrics depending of the time scale; and (3) the 7Q2, 7Q10 (mean 7-day low flow with a return period of ten years), Q95 and Q90 (95th and 90th percentile on the flow duration curve) are the less

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“…Six low flow indices characterizing different aspects of the low flow regime of the three rivers were considered. These indices were selected based on the study presented in Daigle et al (2011) which dealt with the characterization of low flow regimes of rivers in Eastern Canada using data from 175 discharge stations. Seventy-one indices considered relevant from a low flow perspective were identified and categorized into five groups describing different aspects of the low flow regime: magnitude, frequency, duration, variability and timing (Richter et al 1996).…”

Section: Selection Of Low Flow Indicesmentioning

confidence: 99%

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices

Joshi

St‐Hilaire

Ouarda

et al. 2016

Hydrological Sciences Journal

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This work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.

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Multivariate analysis of the low-flow regimes in eastern Canadian rivers

Cited by 28 publications

References 26 publications

The Application of Multi-Criteria Analysis Methods for the Determination of Priorities in the Implementation of Irrigation Plans

The Application of Multi-Criteria Analysis Methods for the Determination of Priorities in the Implementation of Irrigation Plans

Southern Quebec environmental flow assessments: spatial and temporal scales sensitivity

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices

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