An experiment was conducted to concentrate on the rainfall interception process of individual trees for four common species in Beijing, China, which included needle species (Platycladus orientalis and Pinus tabulaeformis) and broadleaf species (Quercus variabilis and Acer truncatum). Two types of interception storages, the maximum (Cmax) and the minimum interception storage (Cmin), were examined at four simulated rainfall intensities (from 11.7 to 78.5 mm h−1). Results showed that an average of 91% of Cmax for all the species was intercepted during the first 10 minutes of rainfall, while 45% of Cmax drained off after rainfall cessation. Leaf area index (LAI) and leaf area (LA) were significantly correlated (p < 0.05) with Cmax and Cmin, while such significant correlations were not found between rainfall intensity and Cmax and Cmin. Average Cmax and Cmin across all the species corresponded to 3 and 1% of gross rainfall. Mean Cmax and Cmin of the needle species were 3.0 and 1.8 times larger than that for the broadleaf ones. Results revealed that interception was a dynamic process which encompassed three phases. In addition, LAI and LA were valid predictors of interception in small trees, and deserve further test in forest stands.

INTRODUCTION

Rainfall interception refers to the proportion of rainwater that is temporarily intercepted and stored on vegetation surfaces, and subsequently evaporates to the atmosphere or drains to the ground as throughfall (TF) or stemflow (SF) (Muzylo et al. 2009). Acting as a rainfall mitigating buffer, interception usually accounts for 10–60% gross precipitation (Llorens & Domingo 2007; Gerrits et al. 2010), and also is widely accepted to affect the subsequent hydrologic processes, such as evaporation, transpiration, infiltration, and surface runoff (Savenije 2004; Janeau et al. 2015).

Interception has usually been studied on three scales: the single branch level (with foliage) (Keim et al. 2006; Xiao & McPherson 2015), the individual tree level (Asadian & Weiler 2009; Xiao & McPherson 2011), and the forest stand level (Dietz et al. 2006; Safeeq & Fares 2014). In most of these studies, interception has been indirectly calculated by the difference between gross rainfall and the sum of TF and SF, indicating that only the intercepted water for evaporation has been estimated as a flux or stock. The mechanism of the interception process which includes wetting, saturation, and post-rainfall drainage has thus far been overlooked. However, a process-based interception is critical to clarify the rainfall-mitigating and time-lagging effect during rainfall, and thereby helps to control peak flow, flood, and erosion (Keim & Skaugset 2004). Limited results from previous studies indicated that almost 30% of intercepted rainwater tended to drip off after rainfall (Aston 1979; Pitman 1989; Keim et al. 2006). As a result, interception would be better characterized as a dynamic process.

According to previous investigations, interception is governed by both biotic and abiotic factors (Xiao et al. 2000a; Gerrits & Savenije 2011). Abiotic factors such as rainfall intensity shapes the way raindrops interact with leaf and branch surfaces: Price & Carlyle-Moses (2003) and Wang et al. (2007) reported that high rainfall intensity reduced interception amount because it caused splashing and shaking of the crown. Keim et al. (2006) and Xiao & McPherson (2015) found the opposite: interception increased with rainfall intensity given that the leaves were beyond saturated. In addition, rainfall frequency as well as air temperature, wind speed, and net radiation impacts evaporation and interception (Xiao et al. 2000b). On the other hand, biotic factors such as leaf morphology, roughness, inclination, and hydrophobicity tended to affect interception by modifying the intercepted-rainwater pathways inside the crown (Holder 2013; Nanko et al. 2013). Other factors that played a role included branch inclination, surface property, and bark texture (Herwitz 1987; Levia et al. 2015). Crown gap fraction controls interception as it represents the proportion of free TF that has no contact with the crown (Rutter et al. 1971). Among the biotic factors, leaf area index (LAI) and leaf area (LA) have been extensively used in previous analyses (Carlyle-Moses & Gash 2011). LAI, calculated as the total LA divided by crown projection area (CPA) (Watson 1947), has been measured indirectly in forest stands based on the transmission of light mechanism (Fleischbein et al. 2005). However, the overlapping canopy from different trees may have resulted in an underestimation of LAI (Breda 2003). On the other hand, few studies have quantified interception on an LA basis due to the labor-intensive and destructive measurement, although it has been recognized as an accurate indicator, and easy to scale up from single trees to forest stand level in several studies (van Dijk & Bruijnzeel 2001a, 2001b).

Therefore, the objective of this research is to quantify interception on a process basis. First, we aim to depict the wetting–saturation–drainage phases in interception. Second, we examine the influence of different biotic (LAI, LA, and leaf type) and abiotic factors (rainfall intensity) on interception.

MATERIALS AND METHODS

Tree characteristics

Four common tree species in Northern China including two conifer species (Platycladus orientalis and Pinus tabulaeformis) and two broadleaf deciduous species (Quercus variabilis and Acer truncatum) were selected for this study (China Forest Editorial Committee 2001). Two four-year-old trees for each species were chosen from the field in Jiufeng National Forestry Park, Beijing, China (116°28′E, 39°34′N). The trees were then transplanted into a 2-m-diameter plastic cylinder and were transported from the field to a rainfall simulation laboratory, where several tree characteristics were measured before rainfall simulation began (Table 1). LAI was measured using an LAI-2200 Plant Canopy Analyzer (LICOR Inc., Lincoln, NE, USA). CPA was determined from photos taken from a height of 15 m above the tree, and was further analyzed using Photoshop software (Adobe System Inc., San Jose, CA, USA). No wilting phenomenon was observed before or during the experimental procedures.

Table 1

Characteristics of the experimental trees

Tree species Height (m) Basal diameter (cm) Crown height (m) Crown diameter (m) LAI CPA (m2LA (m2
PO-1 1.75 1.6 1.28 0.73 2.61 0.89 2.32 
PO-2 1.81 1.5 1.09 0.85 2.02 0.76 1.54 
PT-1 1.52 2.4 0.96 0.94 2.35 0.99 2.33 
PT-2 1.77 2.5 1.05 0.96 2.01 1.12 2.25 
QV-1 1.63 2.1 1.06 0.66 1.74 1.04 1.81 
QV -2 2.24 1.9 1.64 0.75 1.32 1.22 1.61 
AT-1 2.12 2.2 1.55 1.12 2.05 1.48 3.03 
AT -2 1.96 2.0 1.35 0.97 1.95 1.33 2.59 
Tree species Height (m) Basal diameter (cm) Crown height (m) Crown diameter (m) LAI CPA (m2LA (m2
PO-1 1.75 1.6 1.28 0.73 2.61 0.89 2.32 
PO-2 1.81 1.5 1.09 0.85 2.02 0.76 1.54 
PT-1 1.52 2.4 0.96 0.94 2.35 0.99 2.33 
PT-2 1.77 2.5 1.05 0.96 2.01 1.12 2.25 
QV-1 1.63 2.1 1.06 0.66 1.74 1.04 1.81 
QV -2 2.24 1.9 1.64 0.75 1.32 1.22 1.61 
AT-1 2.12 2.2 1.55 1.12 2.05 1.48 3.03 
AT -2 1.96 2.0 1.35 0.97 1.95 1.33 2.59 

LAI, leaf area index; CPA, crown projected area.

PO-1 and PO-2 represent two Platycladus orientalis trees, PT-1 and PT-2 represent two Pinus tabulaeformis trees, QV-1 and QV-2 represent two Quercus variabilis trees, and AT-1 and AT-2 represent two Acer truncatum trees.

Rainfall simulator

The infrequency and unpredictability of natural rainfall makes it difficult for us to study interception on a process basis. To address this challenge, a rainfall simulator was used. Over the past few decades, a wide variety of rainfall simulators have been used to conveniently and rapidly obtain diverse rainfall intensities and interception rates (Moore et al. 1983). In general, at least three criteria should be taken into consideration when designing a simulator (Wilson et al. 2014): (1) the simulated rainfall should have physical characteristics similar to natural rainfall with respect to raindrop terminal velocity and kinetic energy; (2) the simulator should produce various rainfall intensities, and can be easily moved and operated to fit the study area; and (3) the cost must be low. Based on these principles, two types of rainfall simulators have been developed: drip tank and spray nozzles. The drip tank simulator with hypodermic needles usually operates at low pressure and thereby generates light rainfall with small raindrop diameter. The raindrop kinetic energy is mainly determined by the hanging height of the simulator (Battany & Grismer 2000). In contrast, simulators with spray nozzles, working under high pressure, can provide a wide range of raindrop diameters. Also, most of the simulated raindrops can reach 90% of the kinetic energy of natural rainfall. As a result, spray nozzle simulators have been extensively used in many studies (Fister et al. 2012).

The spray nozzle simulator used in this study was jointly manufactured in 2006 by Beijing Jiaotong University and Beijing Normal University. A more detailed description is presented by Zhang et al. (2007). The simulator is composed of a water supply system with a pump (YCGT90W, Chuanji Co. Ltd, Shanghai, China), a rainfall output system including a Veejet 80150 sprinkler with three rotation nozzles (Spraying Systems Co., Wheaton, IL, USA) and a computer-control system. The simulator is able to spray water over an area of 2.2 m × 1.5 m at a height of 4.5 m. A wide range of rainfall intensities (20 to 200 mm h−1) can be simulated by a combination of different nozzle sizes and swing frequencies. Simulated raindrops have a 2.3 ± 0.3 diameter and can reach terminal velocity. The simulated rainfall uniformity is about 80%, which is in accordance with traits of natural rainfall. Since rainfall intensities over the past 50 years in Beijing have ranged from 2.6 to 80 mm h−1 (Zhong et al. 2013), a range of low to moderate simulated rainfall intensities of 11.7, 25.2, 48.4, and 78.5 mm h−1 were used in the experiment for 40 minutes.

Experimental procedure

The experiments were conducted at an average temperature of 25.8 °C in the laboratory from June to September, 2012. Prior to the rainfall simulation, experimental trees were positioned on an electronic weighing balance (EP-500, E&C Co. Ltd, Shanghai, China; minimum graduation: 0.1 g) and an aluminum cover was set above the balance to exclude the effect of TF and SF on rainfall interception measurements (Figure 1). The lower edge of the aluminum cover was placed on the ground surface and the gradient of the cover was 10–20° from horizontal level. This was done to accelerate TF flowing down to the ground through the cover rather than allow it to infiltrate into the cylinder. A plastic reverse-funnel was cohered on the tree bole to allow SF to drip down to the cover. As a result, cumulative rainfall interception was recorded as the change in tree weight.

Figure 1

Schematic diagram of the experiment.

Figure 1

Schematic diagram of the experiment.

To fully depict the interception process, two different interception storages were measured: the maximum interception storage (Cmax) and the minimum interception storage (Cmin). Cmax is the amount of rainwater intercepted and detained by the tree crown immediately before rainfall cessation; Cmin, taken as the amount of rainwater detained on the crown when drainage ceases after rainfall, can only be removed by evaporation (Li et al. 2013).

After the rainfall began, tree weight was recorded every 10 seconds in the first minute, then every 30 seconds in the next 9 minutes and every minute in the remaining 30 minutes. Cmax was thereby calculated as the amount of intercepted water when simulated rainfall ceased. After the cessation, change in tree weight due to the leaf and branch drip was recorded every 10 seconds for the first minute, 30 seconds for the next minute, and each minute thereafter until a weight change of <0.1 g was obtained. Consequently, Cmin was calculated as the difference in tree weight before the rainfall simulation and after the drainage process. After these primary interception experiments, tree crown was divided into three vertical layers from the bottom to the top. The leaves and branches of the bottom layer were initially removed and preserved for LA measurement. Similarly, tree traits such as crown depth, crown diameter, LAI, and CPA were remeasured. Afterwards, each tree was repositioned on the electronic weighing balance to be resubjected to the simulated rainfall. Likewise, rainfall simulation was repeated after the middle layer was removed. Total LA of broadleaves was determined by tracing the outline of every leaf, and then calculating the area using Adobe Photoshop software (Adobe System Incorporated). For needles, LA was calculated by measuring needle length and diameter to determine the two-dimensional area, and then scaled to column, three-dimensional, and four-dimensional cones based on needle shape.

It should be mentioned that evaporation (E) was not taken into consideration in the simulation process because the amount of wind and sunlight radiation in the laboratory was considered negligible. To minimize the impact of E on the drainage process, some water was poured on the wall of the laboratory to keep a relatively stable humidity. All the examined trees were moved outside the laboratory to naturally dry the crown for 24 hours. Each test run was repeated once daily, resulting in a total of 192 rainfall events.

Statistical analysis

Pearson's correlation method was used to test whether Cmax and Cmin were significantly correlated with rainfall intensity, LAI, and LA. Parameters were considered to be significantly correlated when they were at or above the 95% confidence level (p ≤ 0.05). Meanwhile, linear, polynomial, and non-linear regressions were used to analyze the variation trend between Cmax and Cmin and those factors. One-way analysis of variance (ANOVA) with the Fisher LSD (least significant difference) test at p ≤ 0.05 was used to determine if there were significant differences in Cmax, Cmin between the broadleaf and coniferous species. Related data were grouped according to leaf morphology (e.g., broadleaf species were represented by PO and PT, while coniferous species were represented by QV and AT). All statistical analyses were performed using IBM SPSS Statistics 20.0 software.

RESULTS AND DISCUSSION

Rainfall interception process

The rainfall interception process by an entire tree crown over time is shown in Figure 2. Generally, the interception process included three phases: the rapid-dampening phase, the stable-saturation phase, and the drainage phase. The rapid-dampening phase usually took place in the very first 10 minutes of the rainfall, when the cumulative interception (IC) amount accounted for 91% (STD = 8%) of Cmax, and 175% of Cmin regardless of species and rainfall intensities, showing that the crown could temporarily hold water in excess of its storage capacity. As the rainfall continued for an additional 20 minutes, IC was relatively stable because the crown was too wet (even saturated) to retain more rainwater. After rainfall, an average of 45% (STD = 6%) of Cmax drained off for all the species, ranging from the largest drainage of 63% (STD = 5%) of Cmax for P. tabulaeformis, to the lowest drainage of 15% (STD = 2%) for A. truncatum. The results are consistent with the drainage percentage of 10 to 70% reported in previous studies (e.g., Aston 1979; Pitman 1989; Calder et al. 1996a). As Xiao et al. (2000b) implied, in theory, there should be no drainage until the interception storage capacity is exceeded during rainfall. However, drips from the crown were observed even at the beginning of rainfall (less than 10 minutes) probably due to the steep leaf angle, leaf hydrophobicity, and downward wind created by intense rainfall, indicating that the rainfall-lagging effect of interception may have already occurred although the crown was not saturated. Given that our experiment was conducted on a small and single tree level, we would expect that the drainage, rainfall-lagging, and peak flow delaying effect will be more considerable in mature forests, and thereby warrants further research.

Figure 2

Time series of mean crown interception storage for P. orientalis (a), P. tabulaeformis (b), Q. variabilis (c), and A. truncatum (d) at four rainfall intensities. Closed symbols indicate Cmax and open symbols indicate Cmin. Error bars indicate STD. The Cmax and Cmin data are presented as mm per crown projected area (m2), the same expressions are shown in Figure 37.

Figure 2

Time series of mean crown interception storage for P. orientalis (a), P. tabulaeformis (b), Q. variabilis (c), and A. truncatum (d) at four rainfall intensities. Closed symbols indicate Cmax and open symbols indicate Cmin. Error bars indicate STD. The Cmax and Cmin data are presented as mm per crown projected area (m2), the same expressions are shown in Figure 37.

Mean IC at the first minute was 38% (STD = 18%) of the cumulative precipitation for all the species, but decreased dramatically with time in each single rainfall event thereafter (Figure 3). In the end, Cmax and Cmin corresponded to only 3% (STD = 2%) and 1% (STD = 1%) of the total precipitation (Figure 3). This result implied that the interception storage capacity decreased considerably with continuous wetting by rainfall.

Figure 3

Time series of proportion of mean interception storage in accumulative precipitation for P. orientalis (a), P. tabulaeformis (b), Q. variabilis (c), and A. truncatum (d) at four rainfall intensities. Closed symbols indicate Cmax and open symbols indicate Cmin.

Figure 3

Time series of proportion of mean interception storage in accumulative precipitation for P. orientalis (a), P. tabulaeformis (b), Q. variabilis (c), and A. truncatum (d) at four rainfall intensities. Closed symbols indicate Cmax and open symbols indicate Cmin.

Impact of rainfall intensity on rainfall interception

As expected, higher RIs (48.4 and 78.5 mm h−1) tended to wet and saturate the crown more rapidly than lower RIs (11.7 and 25.2 mm h−1) (Figure 2). However, RI showed no statistically significant correlations with Cmax and Cmin (Figures 4 and 5). Further, the polynomial regression analysis revealed an increase–decrease trend of Cmax and Cmin with increasing RI; the largest values of Cmax and Cmin were not obtained at the highest RI of 78.5 mm h−1. This phenomenon can result from two mechanisms. First, as we had observed, raindrops with huge kinetic energy generated by the intense rainfall (48.4 and 78.5 mm h−1) splashed when hitting the leaf surface. This force, in combination with the hydrophobicity of juvenile leaves (Crockford & Richardson 1990), likely caused the interception amount to decrease. Second, the temporarily remaining droplets on leaves were partly removed by the downward wind from the intense rainfall (Dunkerley 2009).

Figure 4

Relationships between Cmax and rainfall intensity for all the examined trees of P. orientalis (a), P. tabulaeformis (b), Q. variabilis (c), and A. truncatum (d). N = 8.

Figure 4

Relationships between Cmax and rainfall intensity for all the examined trees of P. orientalis (a), P. tabulaeformis (b), Q. variabilis (c), and A. truncatum (d). N = 8.

Figure 5

Relationships between Cmin and rainfall intensity for P. orientalis (a), P. tabulaeformis (b), Q. variabilis (c), and A. truncatum (d). N = 8.

Figure 5

Relationships between Cmin and rainfall intensity for P. orientalis (a), P. tabulaeformis (b), Q. variabilis (c), and A. truncatum (d). N = 8.

Contradicting relationships between RI and interception storages were reported in previous studies. Price & Carlyle-Moses (2003) found that interception storage capacity when RI < 7 mm h−1 was larger than when RI > 7 mm h−1. Calder (1996b) drew a similar conclusion when comparing the storage capacity at RIs of 36 and 45 mm h−1. However, several researchers found that interception storage capacity was larger at higher RIs than lower RIs (e.g., Aston 1979; Humbert & Najjar 1992; Keim et al. 2006). These outcomes also indicated that additional light rainfall experiments should be conducted to determine the threshold RI in the interception process.

Impact of tree characteristics on rainfall interception

The effect of crown structure parameters (particularly LAI and LA) on rainfall interception was examined. LAI showed statistically significant positive correlations (p = 0.000 and 0.000) with both average Cmax and Cmin across all species (Figure 6). Both mean Cmax and Cmin escalated with increasing LAI (R > 0.9). (Note: the presented mean values included Cmax and Cmin of the entire crown, and the remaining crowns after the first and second defoliations.) Previous studies have mainly reported two linear relationships between LAI and Cmin: Cmin = a × LAI, where ‘a’ was a coefficient, ranging from 0.15 to 0.5 (Pitman 1989; Kondo et al. 1992; van Dijk & Bruijnzeel 2001a; Deguchi et al. 2006; Carlyle-Moses & Price 2007), and Cmin = a × LAI + b (Gómez et al. 2001; Fleischbein et al. 2005), where ‘a’ and ‘b’ were coefficients. Both the polynomial model from our study and linear models from previous studies suggest that more rainwater will be intercepted with increasing LAI. However, Cmin was greater in the linear equation than in the polynomial equation at the same LAI. This is probably because the small and low ranges of LAI in our study (LAI = 0.57–2.61, Tables 1 and 2) compared with those in previous studies (LAI = 3–10). To be able to scale up the polynomial relationship, more tests are needed in large and mature trees spanning a wide range of LAI. Similarly, as previous studies have primarily examined single species in mature forests, additional experiments and observations are needed on various species to determine whether the linear relationships can be extrapolated to mixed species forests.

Table 2

List of LAI and LA after first and second defoliation for all the experimental trees

Tree species LAIAD1 CPAAD1 (m2LAAD1 (m2LAIAD2 CPAAD2 (m2LAAD2 (m2
PO-1 2.32 0.58 1.35 1.77 0.33 0.58 
PO-2 1.65 0.51 0.84 1.14 0.34 0.39 
PT-1 1.78 0.72 1.28 1.22 0.45 0.55 
PT-2 1.68 0.77 1.29 1.33 0.41 0.54 
QV-1 1.35 0.76 1.03 0.89 0.45 0.40 
QV -2 0.88 0.82 0.72 0.57 0.48 0.27 
AT-1 1.48 1.05 1.55 0.95 0.66 0.63 
AT -2 1.23 0.84 1.03 0.77 0.55 0.42 
Tree species LAIAD1 CPAAD1 (m2LAAD1 (m2LAIAD2 CPAAD2 (m2LAAD2 (m2
PO-1 2.32 0.58 1.35 1.77 0.33 0.58 
PO-2 1.65 0.51 0.84 1.14 0.34 0.39 
PT-1 1.78 0.72 1.28 1.22 0.45 0.55 
PT-2 1.68 0.77 1.29 1.33 0.41 0.54 
QV-1 1.35 0.76 1.03 0.89 0.45 0.40 
QV -2 0.88 0.82 0.72 0.57 0.48 0.27 
AT-1 1.48 1.05 1.55 0.95 0.66 0.63 
AT -2 1.23 0.84 1.03 0.77 0.55 0.42 

AD1 and AD2 indicates after first and second defoliation, respectively.

Figure 6

Mean Cmax (a) and Cmin (b) regardless of rainfall intensities in relation to leaf area index (LAI) for all the examined trees. The data included the entire crown, and remaining crowns after first and second defoliation, N = 24. Cross (×) symbols indicate P. orientalis, cross (+) symbols indicate tabulaeformis, closed circles indicate Q. variabilis, closed stars indicate A. truncatum. Error bars indicate STD.

Figure 6

Mean Cmax (a) and Cmin (b) regardless of rainfall intensities in relation to leaf area index (LAI) for all the examined trees. The data included the entire crown, and remaining crowns after first and second defoliation, N = 24. Cross (×) symbols indicate P. orientalis, cross (+) symbols indicate tabulaeformis, closed circles indicate Q. variabilis, closed stars indicate A. truncatum. Error bars indicate STD.

Beyond the role of leaves in intercepting rainfall, studies have recognized the importance of the role of branch and bark in interception and evaporation (Keim et al. 2006), particularly in leafless periods (Herbst et al. 2008). For instance, an index including both leaf and branch area, such as woody area index and plant area index, has been introduced in several studies (Livesley et al. 2014; Safeeq & Fares 2014). These expanded indices could provide stronger correlations with interception than we have observed with LA. Along those lines, it is likely that there is a positive relationship between interception rates and crown height (CH). This is because the number of overlapping leaves and branches will increase with increasing CH, which forms a multi-layer interception effect, and time delay. CH is an easily measurable metric that deserves further investigation.

Mean Cmax and Cmin per LA (m2) were 0.13–0.38 and 0.09–0.13 mm across species (Table 3), which was in the range reported by Aston (1979), who found that Cmin ranged from 0.03 to 0.18 mm for seven broadleaf and one needle species. Keim et al. (2006) reported that Cmax ranged from 0.01 to 0.75 mm when testing the single branch of five broadleaf and three needle species. A larger Cmin of 0.7 mm per LA was reported by Alavi et al. (2001) in a Norway spruce stand. Further, our results showed that Cmax and Cmin increased significantly with LA (Figure 7, p < 0.05, R > 0.6). This is mainly due to the fact that more rainwater was re-intercepted as a denser crown was formed by increasing LA. Cmax and Cmin showed similar linear correlation with LA in previous studies (Aston 1979; Keim et al. 2006), which indicates that interception can be estimated even in mixed-species forests by using the linear equation. However, the estimation should be verified in forest stand, where overlapping branches and leaves, and other factors such as branch inclination, flexibility, and above-canopy energy are different with single small trees (Pugh & Small 2013).

Table 3

Values of mean Cmax and Cmin for each species and leaf types

Tree species Mean Cmax /CPA (mm) Mean Cmin /CPA (mm) Mean Cmax /LA (mm) Mean Cmin /LA (mm) 
P. orientalis 0.60 ± 0.09 0.27 ± 0.04 0.26 ± 0.03 0.12 ± 0.01 
P. tabulaeformis 0.83 ± 0.18 0.30 ± 0.07 0.38 ± 0.07 0.13 ± 0.02 
Q. variabilis 0.21 ± 0.04 0.13 ± 0.03 0.13 ± 0.02 0.09 ± 0.02 
A. truncatum 0.27 ± 0.03 0.19 ± 0.03 0.14 ± 0.02 0.09 ± 0.01 
Needle species 0.71 ± 0.18 0.28 ± 0.06 0.32 ± 0.06 0.12 ± 0.01 
Broadleaf species 0.24 ± 0.10 0.16 ± 0.05 0.13 ± 0.02 0.09 ± 0.01 
Tree species Mean Cmax /CPA (mm) Mean Cmin /CPA (mm) Mean Cmax /LA (mm) Mean Cmin /LA (mm) 
P. orientalis 0.60 ± 0.09 0.27 ± 0.04 0.26 ± 0.03 0.12 ± 0.01 
P. tabulaeformis 0.83 ± 0.18 0.30 ± 0.07 0.38 ± 0.07 0.13 ± 0.02 
Q. variabilis 0.21 ± 0.04 0.13 ± 0.03 0.13 ± 0.02 0.09 ± 0.02 
A. truncatum 0.27 ± 0.03 0.19 ± 0.03 0.14 ± 0.02 0.09 ± 0.01 
Needle species 0.71 ± 0.18 0.28 ± 0.06 0.32 ± 0.06 0.12 ± 0.01 
Broadleaf species 0.24 ± 0.10 0.16 ± 0.05 0.13 ± 0.02 0.09 ± 0.01 

CPA, crown projected area; LA, leaf area.

All the values are shown with STD.

Figure 7

Mean Cmax (a) and Cmin (b) regardless of rainfall intensities in relation to leaf area (LA) for all the examined trees. The data included the entire crown, and remaining crowns after first and second defoliation, N = 24. Cross (×) symbols indicate P. orientalis, cross (+) symbols indicate tabulaeformis, closed circles indicate Q. variabilis, closed stars indicate A. truncatum. Error bars indicate STD.

Figure 7

Mean Cmax (a) and Cmin (b) regardless of rainfall intensities in relation to leaf area (LA) for all the examined trees. The data included the entire crown, and remaining crowns after first and second defoliation, N = 24. Cross (×) symbols indicate P. orientalis, cross (+) symbols indicate tabulaeformis, closed circles indicate Q. variabilis, closed stars indicate A. truncatum. Error bars indicate STD.

Leaf type also played a relevant role in determining interception. The needle crown of P. tabulaeformis had the highest mean Cmax and Cmin on both a CPA and LA basis due to its dense clusters (Table 3). This resulted in raindrops that were intercepted multiple times by the overlapped branches and needles and thereby gradually accumulated in the crown. By comparison, Q. variabilis with its broadleaf crown showed the lowest mean Cmax and Cmin because its crown was relatively open and rainwater could pass through directly. Additionally, the smooth and hairless leaves likely failed to prevent rainwater from dripping. Generally, average Cmax and Cmin for the needle species were 0.71 (STD = 0.18) and 0.28 (STD = 0.10) mm on a CPA basis, which was 2.96 and 1.75 times larger than that for broadleaf species regardless of rainfall intensities. Moreover, ANOVA analysis suggested that there were significant differences (p = 0.000 and 0.000) in Cmax and Cmin between the two types. These results were in line with Barbier et al. (2009), who in reviewing 28 articles found that needle species intercepted more rainwater than broadleaf species. Therefore, the proportion of broadleaf and needle trees (i.e., LAI ratio) in mixed forests should be taken into consideration to precisely estimate interception.

CONCLUSIONS

The present study highlighted the interception process to address some critical challenges that have seldom been investigated. The first challenge was how to depict the rainfall interception process. Generally, the complete interception consisted of three phases, the rapid-dampening phase (0–10 minutes, 90% of Cmax was reached), the stable-saturated phase (10–40 minutes, no evident IC fluctuation), and the draining phase (40–70 minutes, 45% of Cmax dripped). Almost 90% of the Cmax amount was intercepted in the rapid-dampening phase. In the subsequent stable-saturated phase, the interception storage showed no evident fluctuation as the crown was gradually wetted and saturated. Approximately 45% of Cmax drained off in the draining phase due to gravity. The second challenge was to evaluate the effect of rainfall characteristics and tree traits on interception. The major findings showed that LAI and LA were relevant indicators for predicting interception as they were found not only to be significantly correlated with Cmax and Cmin, but were adequately explained using polynomial and linear relationships. By comparison, rainfall intensity was not significantly correlated with Cmax and Cmin, but the co-variation trends implied that there should be a threshold rainfall intensity to influence interception. Last, needle crowns with dense branch and needle foliage distribution intercepted more rainfall water than the relatively open broadleaf crowns. Further studies are warranted to accurately quantify interception on a process basis, and establish related models with more easily measurable parameters such as CH, branch number, length, density, and inclination.

ACKNOWLEDGEMENTS

We thank the working staff from Beijing Normal University for their careful inspection on the rainfall simulator and the help of postgraduates Changjiang Xie, Yong Liu, Jingping Tan, Xiaoli Wu, and Yinghu Zhang. We also deeply thank the editor and two anonymous reviewers for their thoughtful recommendations on this manuscript. This study was supported by Fundamental Research Funds for the Central Universities (No. TD2011-03, No. BLYJ201406); Chinese Scholarship Council Fund; National Natural Science Fund of China (41171028); National Advanced Project of the 12th Plan, China (2011BAD38B05); and National Forestry Public Welfare Industry Research Project (201104005). All the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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