Tree traits and meteorological factors influencing the initiation and rate of stemflow from isolated deciduous trees

Abstract

Tree canopy processes affect the volume and biogeochemistry of inputs to the hydrological cycle in cities. From June 2012 to November 2013, we studied stemflow production from 37 isolated deciduous park trees in a semi‐arid climate dominated by small precipitation events. To clarify the effects of canopy traits on stemflow metrics, we analysed branch angles, bark relief (one component of roughness), tree size, canopy and wood cover fraction, median leaf size, and branch and leader counts. High branch angles contributed to stemflow production in both single‐leader and multi‐leader trees. While bark relief was negatively correlated with stemflow rates in multi‐leader trees, it was positively correlated with rates for single‐leader trees, possibly reflecting the conducive role of linear furrows once bark of single‐leader trees is saturated. The association between numerous leaders, low stemflow initiation thresholds, and high rates deserves further study. Among meteorological variables, rain depth was strongly correlated with stemflow yields; rainfall inclination angle and wind speed were positively correlated with yields, while total intra‐storm break duration and vapour pressure deficit were inversely related. For rain depths <3 mm, greater stemflow was generally associated with leafless canopies. In support of integrated stormwater management, our results can inform climate‐sensitive selection and siting of urban trees with traits that tend to either promote or minimize stemflow, depending on infiltration potential. Copyright © 2015 John Wiley & Sons, Ltd.

Variation in leaf area density drives the rainfall storage capacity of individual urban tree species

“…Despite being an important part of rainfall partitioning for mature isolated trees (Carlyle‐Moses & Schooling, ), water loss via stem flow (SF) has not being counted in the calculations because the example assumes that the SF water is running directly onto an impervious surface, and counting as run‐off water. However, in a realistic setting, further research could integrate information on the presence or absence of permeable surfaces at or near tree bases, so as to acknowledge another significant ecosystem service that trees provide in streetscapes (Carlyle‐Moses & Schooling, ).…”

Comparisons of stemflow and its bio-/abiotic influential factors between two xerophytic shrub species

“…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). Moreover, we excluded variables that had a variance inflation factor (VIF) greater than 10 to minimize the effects of multicollinearity (O'Brien, 2007), and kept the regression model having the least AIC (Akaike information criteria) values and largest R 2 .…”

“…However, as to biotic mechanisms, although the canopy structure (Mauchamp and Janeau, 1993;Crockford and Richardson, 2000;Pypker et al, 2011) and branch architecture (Herwitz, 1987;Murakami, 2009;Carlyle-Moses and Schooling, 2015) have been studied for years, the most important plant traits vary with location and shrub species and have not yet been determined. The effects of the leaves have been studied more recently at a smaller scale, e.g., leaf orientation (Crockford and Richardson, 2000), shape (Xu et al, 2005), arrangement pattern (Owens et al, 2006), pubescence (Garcia-Estringana et al, 2010), area (Sellin et al, 2012), epidermis microrelief (RothNebelsick et al, 2012), amount , or biomass (Yuan et al, 2016).…”

“…The raindrop attributes (D, V and A) were calculated on the basis of the best-fit equations developed from rainfall intensity and wind speed (Laws and Parsons, 1943;Gunn and Kinzer, 1949;Herwitz and Slye, 1995;Van Stan II et al, 2011;Carlyle-Moses and Schooling, 2015).…”

Stemflow of a xerophytic shrub (Salix psammophila) in northern China: Implication for beneficial branch architecture to produce stemflow

“…Bayes information criterion (BIC) was used to assess the regression models with forward and backward selection methods. The step-wise regression was commonly used to identify the most influential indicator (Návar and Bryan, 1990;Yang et al, 2008;Honda et al, 2015;Carlyle-Moses and Schooling, 2015), which got limited influence by correlation as other analysis methods did, i.e., principal component analysis, ridge regression, etc. In this study, the rainfall characteristics tested were P, RD, RI, I, I 5 , I 10 and I 30 .…”