2016
DOI: 10.1016/j.jhydrol.2016.05.055
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Stemflow of a xerophytic shrub (Salix psammophila) in northern China: Implication for beneficial branch architecture to produce stemflow

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Cited by 49 publications
(56 citation statements)
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“…Significantly correlated variables were further tested with a partial correlation analysis for their separate effects on SF b . Then, the qualified variables were fed into a stepwise regression with forward selection to identify the most influential bio-/abiotic factors (CarlyleMoses and Schooling, 2015; Yuan et al, 2016). Similar to a principal component analysis and ridge regression, stepwise regression was commonly used because it got a limited effect of multicollinearity (Návar and Bryan, 1990;Honda et al, 2015;Carlyle-Moses and Schooling, 2015).…”
Section: Discussionmentioning
confidence: 99%
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“…Significantly correlated variables were further tested with a partial correlation analysis for their separate effects on SF b . Then, the qualified variables were fed into a stepwise regression with forward selection to identify the most influential bio-/abiotic factors (CarlyleMoses and Schooling, 2015; Yuan et al, 2016). Similar to a principal component analysis and ridge regression, stepwise regression was commonly used because it got a limited effect of multicollinearity (Návar and Bryan, 1990;Honda et al, 2015;Carlyle-Moses and Schooling, 2015).…”
Section: Discussionmentioning
confidence: 99%
“…However, the FR does not provide a good connection between hydrological processes (e.g., rainfall redistribution) and the plant growth processes (e.g., biomass accumulation and allocation). Recently, Yuan et al (2016) introduced the parameter of stemflow productivity (SFP), expressed as the volume of stemflow yield per unit of branch biomass. The SFP describes the efficiency by comparing the stemflow yield of unit biomass increment at different sized branches.…”
Section: Yuan Et Al: Comparisons Of Stemflow and Its Bio-/abioticmentioning
confidence: 99%
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