Linking forest cover to water quality: A multivariate analysis of large monitoring datasets

Delphine Brogna, Adrien Michez, Sander Jacobs, Marc Dufrêne, Caroline Vincke, Nicolas Dendoncker

Résultats de recherche: Contribution à un journal/une revueArticle

Résumé

Forested catchments are generally assumed to provide higher quality water. However, this hypothesis must be validated in various contexts as interactions between multiple land use and land cover (LULC) types, ecological variables and water quality variables render this relationship highly complex. This paper applies a straightforward multivariate approach on a typical large monitoring dataset of a highly managed and densely populated area (Wallonia, Belgium; 10-year dataset), quantifying forest cover effects on nine physico-chemical water quality variables. Results show that forest cover explains about one third of the variability of water quality and is positively correlated with higher quality water. When controlling for spatial autocorrelation, forest cover still explains 9% of water quality. Unlike needle-leaved forest cover, broad-leaved forest cover presents an independent effect from ecological variables and explains independently 4.8% of water quality variability while it shares 5.8% with cropland cover. This study demonstrates clear independent effects of forest cover on water quality, and presents a method to tease out independent LULC effects from typical large multivariate monitoring datasets. Further research on explanatory variables, spatial distribution effects and water quality datasets could lead to effective strategies to mitigate pollution and reach legal targets.

langueAnglais
Numéro d'article176
journalWater
Volume9
Numéro3
Les DOIs
étatPublié - 2017

Empreinte digitale

forest cover
multivariate analysis
water quality
monitoring
Water Quality
Multivariate Analysis
Datasets
Forests
Water quality
Monitoring
water
effect
land cover
land use
Land use
forested catchment
broad-leaved forest
autocorrelation
spatial distribution
pollution

mots-clés

    Citer ceci

    Brogna, Delphine ; Michez, Adrien ; Jacobs, Sander ; Dufrêne, Marc ; Vincke, Caroline ; Dendoncker, Nicolas. / Linking forest cover to water quality : A multivariate analysis of large monitoring datasets. Dans: Water. 2017 ; Vol 9, Numéro 3.
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    Linking forest cover to water quality : A multivariate analysis of large monitoring datasets. / Brogna, Delphine; Michez, Adrien; Jacobs, Sander; Dufrêne, Marc; Vincke, Caroline; Dendoncker, Nicolas.

    Dans: Water, Vol 9, Numéro 3, 176, 2017.

    Résultats de recherche: Contribution à un journal/une revueArticle

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    AU - Brogna,Delphine

    AU - Michez,Adrien

    AU - Jacobs,Sander

    AU - Dufrêne,Marc

    AU - Vincke,Caroline

    AU - Dendoncker,Nicolas

    PY - 2017

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    N2 - Forested catchments are generally assumed to provide higher quality water. However, this hypothesis must be validated in various contexts as interactions between multiple land use and land cover (LULC) types, ecological variables and water quality variables render this relationship highly complex. This paper applies a straightforward multivariate approach on a typical large monitoring dataset of a highly managed and densely populated area (Wallonia, Belgium; 10-year dataset), quantifying forest cover effects on nine physico-chemical water quality variables. Results show that forest cover explains about one third of the variability of water quality and is positively correlated with higher quality water. When controlling for spatial autocorrelation, forest cover still explains 9% of water quality. Unlike needle-leaved forest cover, broad-leaved forest cover presents an independent effect from ecological variables and explains independently 4.8% of water quality variability while it shares 5.8% with cropland cover. This study demonstrates clear independent effects of forest cover on water quality, and presents a method to tease out independent LULC effects from typical large multivariate monitoring datasets. Further research on explanatory variables, spatial distribution effects and water quality datasets could lead to effective strategies to mitigate pollution and reach legal targets.

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