Forest canopy interception loss exceeds wet canopy evaporation in Japanese cypress (Hinoki) and Japanese cedar (Sugi) plantations

Journal of Hydrology 507 (2013) 287–299

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Forest canopy interception loss exceeds wet canopy evaporation in Japanese cypress (Hinoki) and Japanese cedar (Sugi) plantations Takami Saito a,c,⇑, Hiroki Matsuda a, Misako Komatsu a, Yang Xiang a, Atsuhiro Takahashi c, Yoshinori Shinohara b, Kyoichi Otsuki a a b c

Kasuya Research Forest, Kyushu University, Fukuoka 811-2415, Japan Faculty of Agriculture, Kyushu University, Fukuoka 812-8581, Japan Hydrospheric Atmospheric Research Center (HyARC), Nagoya University, Nagoya 464-8601, Japan

a r t i c l e

i n f o

Article history: Received 15 October 2012 Received in revised form 26 March 2013 Accepted 30 September 2013 Available online 26 October 2013 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of David J. Gochis, Associate Editor Keywords: Interception loss Throughfall Stemflow Rainfall intensity Splash droplet Penman–Monteith equation

s u m m a r y The aim of this study is to evaluate rainfall partitioning at the forest canopy and reveal the physical process of canopy interception loss. Observations were conducted for 19 months in neighboring stands of Chamaecyparis obtusa Sieb. et Zucc. (Hinoki) and Cryptomeria japonica D. Don (Sugi). Cumulative amounts for the period showed that portions of throughfall (TF), stemflow (SF) and interception (IC) to rainfall (RF) for Hinoki were 65.3%, 9.1%, and 25.5%, respectively. Corresponding values for Sugi were 67.9%, 6.6%, and 25.5%. The smaller TF and larger SF in Hinoki than those in Sugi were induced by greater mean funneling ratio of a tree and greater tree density in Hinoki. Similar IC/RF would result from similar leaf area index. In analyses for rainfall events, rainfall period (RP) was defined as the period excluding short no-rainfall periods within an event, and rainfall intensity (RFI) was as RF/RP. In events with canopy saturation (RF P 10 mm), IC/RF was insensitive to RP and RFI. This was related to an increasing rate of IC with RFI. Evaporation for IC estimated by the model, based on the Penman–Monteith equation, was approximately 40% of cumulative IC observed. Underestimation was great in events with long RP, but not with large RFI. We suggest that large amount of IC occurred during rainfall, which is induced by splash droplets transport (SDT) by canopy ventilation. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction Canopy interception loss (IC) is essential in the water budget of forest catchments in montane regions. Many such forest catchments are in river headwaters and are composed of plantations. Plantations occupy 27% of land area in Japan, and 19% were occupied by two coniferous species as Japanese cypress: Chamaecyparis obtusa (Hinoki), and Japanese cedar: Cryptomeria japonica (Sugi) (Japan Forest Agency, 2012). Therefore, accurate evaluation and understanding of IC physical processes in those plantations takes precedence over that of others. In temperate coniferous plantations or forests, rainfall portioning to interception at canopy (IC/RF) was reported as 22% in 27year-old Douglas-fir (Pseudotsuga menzsii) plantation in Chile (Iroumé and Huber, 2002), 22% in 25-year-old and 25% in 500-yearsold Douglas-fir forest in WA, USA (Pypker et al., 2005), 24% in 33-years-old Pinus sylvestris stand in Spain (Llorens et al., 1997), and 26.5% in 15-year-old Pinus radiata orchard in NSW, Australia (Pook et al., 1991). In Japan, Tanaka et al. (2005) showed that IC/ ⇑ Corresponding author. Present address: Hydrospheric Atmospheric Research Center (HyARC), Nagoya University, Nagoya 464-8601, Japan. Tel.: +81 52 789 3473. E-mail address: [email protected] (T. Saito). 0022-1694/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jhydrol.2013.09.053

RF was 14–26% in Hinoki and16–26% in Sugi plantations from observations and literatures, although there are few Sugi plantation results. Based on these studies, rough estimations of IC/RF were attained for practical purposes. However, it is still difficult to estimate IC with sufficient accuracy for evaluating its impact on the water budget of forest stands. Although the empirical IC/RF had a range of 10%, we cannot improve accuracy because of insufficient understanding of the process of canopy interception loss. Forest structure and meteorology are involved in IC/RF determination, but their interconnection and dominant factors are still under debate. Regarding forest structure, Leaf Area Index (LAI) are potential factors (Fleischbein et al., 2005; Toba and Ohta, 2005), as is canopy closure (Valente et al., 1997). Regarding meteorology, parameters in the physical equation for evaporation (e.g., Penman–Monteith) are closely related to IC/RF (Carlyle-Moses and Gash, 2011). Rainfall intensity (RFI), the amount of rainfall divided by its period, is a basic meteorological factor, but is not included in typical models of evaporation and canopy IC. Hattori et al. (1982) first demonstrated increasing canopy interception with increasing RFI in a Hinoki stand. Nonetheless, this and later studies (Hashino et al., 2002; Toba and Ohta, 2005) included durations of no rainfall periods within rainfall events, which potentially caused RFI

T. Saito et al. / Journal of Hydrology 507 (2013) 287–299

The study site is in Yayama Experimental Catchment (2.98 ha; 33°310 N and 130°390 E; 300–400 m a.s.l.) in Fukuoka Prefecture in

)

Max 20 Min 10

800

0

600

400

200

Rainfall (mm month-1)

2.1. Study site

30

0 Dec Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan 2011 Dec Nov Oct Sep Aug Jul Jun 2010

2. Materials and methods

western Japan. The catchment is on a slope facing northeast, and is one of the headwaters of the Onga River. The catchment was a coniferous plantation of Ch. obtusa and Cr. japonica. The trees were planted in 1969, and were 41 years old at the start of observation in June 2010. Some portions of the forest floor in the catchment were covered by crowns of evergreen broad-leaved shrubs, such as Eurya japonica Thunb. Those shrubs were cleared before observation commencement. Regarding meteorology of the site, air temperature and precipitation had clear seasonal changes (Fig. 1). In 2011, the monthly mean temperature for daily maximum and minimum were 28.6 °C in August and 2.7 °C in January. Annual rainfall was 2469 mm, and the pronounced rainfall season was June and July. Snowfall was frequent in January and February. Observation was conducted from 5 June 2010 to 31 December 2011 (19 months), which included two of the high-rainfall seasons. Total rainfall during this period was 4284 mm. At the Iizuka meteorological observatory, 15.5 km north of our site at 37 m a.s.l., annual rainfalls in 2010 and 2011 (2002 and 1858 mm yr1, respectively) were greater than the 30-yr average of 1971–2010 (1790 mm yr1; Japan Meteorological Agency). Annual rainfall at our site was greater than that at the observatory, probably because of the higher altitude of the former (390 m a.s.l.) The meteorological data indicate that the site is in a warm, temperate climate area, and is clearly affected by the Asian monsoon. Two study plots for observing interception were sited at mid slope in the catchment. Details of the two plots are presented in Table 1. The Hinoki plot was 8.4  8.2 m on the northwestward slope with inclination approximately 20°. The Sugi plot was 8.4  10.9 m on the northeastward slope with inclination approximately 25°. Comparing trees between the plots, mean diameter at breast height (DBH) and mean tree height in Hinoki trees were smaller than those in Sugi trees, but tree density was greater.

Dec Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan 2011 Dec Nov Oct Sep Aug Jul Jun 2010

underestimation. Murakami (2006) showed dependence of canopy interception on rainfall intensity (DOCIORI), based on hourly IC datasets within a rainfall event. However, the hourly IC, calculated from the difference between hourly precipitation outside the forest and that on the forest floor (i.e., between gross and net precipitation), was potentially contaminated by changing the amount of canopy water storage. Therefore, we should first examine IC in a unit of event that is independent of canopy water storage fluctuation. Then, we should define RFI before assessing the relationship between IC and RFI. Murakami (2006) explained the mechanism of DOCIORI by splash droplet evaporation (SDE). A raindrop striking the canopy splashes and produces numerous small droplets, which evaporate as they fall through the air. The production and evaporation of splash droplets increases with RFI. This SDE concept was based on simulation under a fixed relative humidity (RH) of 95%, irrespective of RFI. Continuous evaporation under fixed RH must be supported by continuous energy supply to the canopy. However, available energy in the canopy during rainfall was not examined enough. Thus, we should estimate the limit of evaporation by the energy budget in the canopy. Comparison of IC calculated by a physical model with observed IC is an effective approach to reveal the IC process. The physical model of IC can determine the amount of evaporation for it, which is constrained by available energy at the forest canopy. However, IC models in earlier studies are not suitable to detect the amount of evaporation in IC. The Rutter model (Rutter et al., 1971) is based on the equation as; (1–p) RR = RD + RE ± DC, where (1–p) is the proportion of rain striking the canopy. RR, RD, and RE are the sums of rainfall, drainage to the forest floor, and evaporation from the canopy in a given time step. DC is change in the amount of water stored on the canopy (C). The Rutter model is inadequate, because water loss from the canopy is explained only by RD and RE. If a process such as SDE is prominent, C will include not only water attached on the canopy surface but also detaches from the surface. The process of dissipating detached water is not assumed. In another approach, the Gash model (Gash, 1979) requires the precipitation necessary to saturate the canopy (P 0G ), which is an indispensable parameter to determine IC before and after canopy saturation. The Gash model is not suitable in this study because calculation of P 0G al  ready includes E = R, which is the mean rate of evaporation divided by that of rainfall under saturated canopy conditions. The mechanism   of establishing E = R is not clear (Carlyle-Moses and Gash, 2011), and   E = R corresponds to IC/RF after canopy saturation during a rainfall   event. Many studies have used empirical relationships of E = R (Deguchi et al., 2006; Murakami, 2007; Muzylo et al., 2009), but results based on this use must not be applied in the examinations for IC/RF determination process. Therefore, we should construct a physical model for IC that clearly shows the theoretical limit of evaporation. The aims here are to examine the factor implied by observed IC/RF and to reveal the physical process of interception loss. (1) Observations were made in neighboring Hinoki and Sugi stands over 19 months. Despite their different forest structures, the stand canopies had identical meteorological conditions. (2) The sampling design was determined statistically, to ensure observation accuracy. (3) Observed IC/RF was examined with RFI defined by an improved method. (4) The model predicts the theoretical amount of IC explained by evaporation. Comparing this to observed IC elucidates the range of observed IC, where the process is not known. These approaches elucidate the process of IC in conifer plantations.

Air temperature (

288

Fig. 1. Monthly meteorological data from weather station in an open area beside Yayama Experimental Catchment. Monthly mean values for daily maximum and minimum air temperature and monthly cumulative amount of rainfall are shown. Observation was conducted from June 2010 through December 2011 (19 months).

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T. Saito et al. / Journal of Hydrology 507 (2013) 287–299 Table 1 Propeties of interception plots (numbers with ± operator indicate Mean ± SD). Species

Plot horizontal area (m2)

Number of rain gauge (effective)

Number of trees

Age (years)

Tree density (trees ha2)

Basal area (m2 ha1)

Mean DBH (cm)

Mean height (m)

Leaf biomass (t ha1)

LAI (direct) (m2 m2)

LAI (LAI-2000) (m2 m2)

Canopy openess (%)

Ch. obtusa (Hinoki) Cr. japonica (Sugi)

59.0 72.3

19 19

9 8

41 41

1526 1107

45.4 66.6

19.2 ± 3.5 27.4 ± 4.1

16.8 ± 1.3 22.6 ± 0.8

8.0 10.3

3.7 2.8

3.59 ± 0.13 3.87 ± 0.12

3.1 ± 0.3 2.6 ± 0.3

Chamaecyparis obtusa (Hinoki)

Cryptomeria japonica (Sugi)

(B)

(A) (3)

(4)

(4)

(3) (1)

(2)

(2)

(D) N

Tree Tree (with stemflow gauge)

N

(C)

(1)

2m

Rain gauge Rain gauge (unusual output) Fig. 2. Photos of (A) Hinoki and (B) Sugi stands. White arrows indicate (1) funnel-type rain gauge with 0.2 mm tipping bucket, (2) data logger for rain gauges, (3) stemflow gauging system, and (4) rain collector (not used in this study). Schematic diagrams of (C) Hinoki and (D) Sugi plots. Locations of trees, rain gauges and stemflow gauging systems are shown. Rain gauges of unusual output were not used for estimating mean throughfall.

The forest canopy was completely closed on both plots as indicated by small canopy openness. Photos and tree positions for the Hinoki and Sugi plots are shown in Fig. 2. 2.2. Meteorology A weather station was installed in an open area, 320 m distant from the site at nearly the same altitude as the study plots (390 m a.s.l.) Rainfall was observed with a funnel-type rain gauge with 0.5mm tipping bucket (TK-1, Takeda Keiki, Tokyo, Japan) with a inlet of 20 cm in diameter. Other observations were solar radiation (LP PYRA 03, Delta Ohm, Padova, Italy), air temperature and RH (HMP155, Vaisala, Helsinki, Finland), and wind speed and direction (Model 03002-5, Young, Traverse City, MI). The anemometer was installed at 3 m in height, and the other sensors were placed at

1–2 m in height. Data were recorded with a data logger (CR1000, Campbell Scientific, Logan, UT) every 10 min. For missing meteorological data, data from an alternate system were used after calibration with the main system. Backup sensors were a 0.5 mm tipping bucket rain gauge (OW-34, Ota Keiki, Tokyo, Japan) with data logger (UA-003-64, Onset Computer, Bourne, MA) and temperature and humidity sensors (U23-0002, Onset Computer; TR-71Ui, T&D, Nagano, Japan). On the forest floor, heat flux through the ground surface was measured every 10 min by heat flux plates (MF-180M, EKO Instruments, Tokyo, Japan) embedded approximately 5 cm beneath the surface. Mean values of the two plots were used as soil heat flux in further calculation. Atmospheric pressure was measured by a water level logger (U-20, Onset Computer), which was placed on the forest floor near the plots.

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2.3. Throughfall Throughfall (TF) was observed using funnel-type rain gauges with 0.2 mm tipping bucket (Davis Instruments, Hayward, CA). An inlet area of the funnel was 214 cm2. Twenty gauges each were installed on the forest floor, approximately 50 cm above ground at the Hinoki and Sugi plots. The gauges were arranged in a latticework of 4 rows  5 gauges at an interval of approximately 2 m, covering the entire plot areas (Fig. 2). Data were recorded by a data logger (OWL2pe, EME Systems, Berkeley, CA) every 10 min. The mean value of 19 rain gauges was used for TF of each plot, because one of the 20 gauges did not perform well in both plots. At the Sugi plot, one gauge had a problem connecting to the logger. At the Hinoki plot, data from one gauge was 231% the average of the other 19 gauges. This gauge was under an inclined stem with rough bark, only 30 cm from the stem bottom. Water drops detached from SF might have contributed to TF of this gauge (Levia et al., 2011), thus its data were not treated as TF or SF. Alternatively, the data was converted to rainfall amount per plot area, and then added to net rainfall (nRF = TF + SF) of the Hinoki plot for each event. The cumulative amount of water dropping into this gauge contributed only 0.053% of the RF to the nRF. The sampling strategy for TF was examined by statistical tests with a computer program. The program showed the effect of increasing the number of rain gauges on the coefficient of variation (CV), and associated errors for mean value of TF. (1) Input data were the cumulative amounts of TF at each rain gauge for whole study period, when all 19 gauges performed well. (2) The program made a group of combinations of rain gauges for predetermined sample size (from 2 to 19 gauges). (3) Each combination was made

40

(A) CV max (%)

30

Hinoki Sugi

20

by random selection from 19 gauges, without duplication. (4) Redundant combinations were excluded. The combinations were constructed as many as possible for each sample size, but their maximum number was set to 1000. (5) CV was calculated for each combination, and the maximum value (CVmax) in a group of combinations for each sample size was output. The maximum error of mean TF (emax) for each sample size was estimated as follows (Houle et al., 1999):

emax ¼

t ða;n1Þ  CV max pffiffiffi ; n

ð1Þ

where n is the number of rain gauges, and t is Student’s t-value with confidence level a set to 5% and n1 degrees of freedom. Fig. 3(A) shows that CVmax decreased exponentially with increasing number of rain gauges (n). When n > 17, CVmax < 10%, and when n = 19, CVmax = 9.6% and 9.4% in the Hinoki and Sugi plots, respectively. Fig. 3(B) shows that emax decreased exponentially with increasing n. When n > 17, emax < 5%, and when n = 19, emax = 4.6% and 4.5%, respectively. The slope of decreasing CVmax with increasing n (i.e., dCVmax/dn) was only < 0.26% when n > 18, and the slope for emax (demax/dn) was < –0.28%. The area of the 19 gauges was 0.41 m2, which was 0.69% and 0.56% of the Hinoki and Sugi plot areas, respectively. The gauge inlets were clogged by sediment during some rainfall events, but the minimum number of working gauges was 13 and 11 in Hinoki and Sugi plot, respectively. 2.4. Stemflow Stemflow (SF) was collected by a collar-type gauge wound around the stem, approximately 1.2 m above the ground (Fig. 4). A garden hose of 20 mm diameter was fastened to the tree bole at a moderate slope. A transparent plastic sheet 20 cm in width was wound around the hose. Small gaps at the bottom of the collar were filled by silicone sealant. SF flowed down along the stem surface was thereby captured by the collar, and was led into 90-L buckets placed at the bottom of the gauging tree. The transparent wall of the collar facilitated maintenance of the gauge, because it allowed litter and water leakage in the collar to be seen. Five gauging trees were selected based on DBH, including all tree sizes in the plots. Water accumulation in a 90-L bucket

10

(A)

0 0

2

4

6

8

10

12

14

16

18

20

200

(B)

190

e max (%)

50

(B)

Transparent plastic sheet Stem

Stem

Silicone sealant

Hinoki Sugi

40 30 20 10 0 0

2

4

6

8

10

12

14

16

18

20

Number of rain gauge to 90 L bucket Fig. 3. (A) Maximum CV (CVmax) plotted against combination of given number of rain gauges to estimate mean throughfall. The gauge combinations were made by random selection from 19 gauges, without duplication. (B) Maximum error (emax) plotted against number of rain gauges. emax was calculated with CVmax.

Garden hose (φ 20 mm)

Fig. 4. Schematic diagrams of stemflow collecting system. (A) External appearance and (B) vertical section are shown.

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V bucket þ V over Ntree  ; ntree Aplot

ð2Þ

P where Vbucket is the sum of Vbucket for gauging trees, ntree and Ntree are the numbers of gauging and total trees in the plot, and Aplot (m2) is plot horizontal area. The number of gauging trees was normally five, but in some events decreased to two gauges because of troubles in data logger. The equipment was maintained after rainfall events or at least twice a month. Accumulated water level in the SF buckets was measured manually for calibration, and the water was then flushed away. The tipping buckets for TF were cleaned. 2.5. Leaf biomass and Leaf Area Index Destructive measurement was conducted in the plots for biomass measurement which horizontal area was 256.0 m2 and 285.2 m2, and the number of trees was 42 and 35 in Hinoki and Sugi stand, respectively. Those plots included the interception plots, respectively. Five trees were harvested in January 2012 in each plot, including two or three in the interception plots, with a wide range of DBH. Stems with branches and leaves were cut at vertical intervals of 1 m from the base. A unit of shoot composed of leaves and small branches with green surfaces was treated as a leaf in both species (Tadaki, 1976). In each vertical section, leaf dry weight (DWsct) were obtained as DWsct = FWsct  (DWsmp1/FWsmp1), where FWsct was leaf fresh weight, FWsmp1 and DWsmp1 was leaf fresh and dry weight for first sample leaves, respectively. FWsmp1 was >9% of FWsct, and DWsmp1 was obtained after dried at 80 °C for >4 days. Leaf dry weight for P each tree (DWtree) was determined as DW tree ¼ ni¼1 DW sct i , where n was the number of sections in each tree (from 3 to 8). In each section, leaf area (LAsct) was obtained as LAsct = DWsct  (LAsmp2/ DWsmp2), where LAsmp2 and DWsmp2 was project leaf area and leaf dry weight of second sample leaves. To measure LAsmp2, the fresh leaves was placed on an image scanner, and arranged to avoid mutual shading within the shoot as much as possible. Leaf area for P each tree (LAtree) was determined as LAtree ¼ ni¼1 LAsct i . Allometry equations were obtained for leaf biomass as DWtree = 0.0007(DBH2)1.4762 for Hinoki (R2 = 0.81), and DWtree = 0.0503(DBH2)0.7831 for Sugi (R2 = 0.51). For leaf area, these were LAtree = 0.0031(DBH2)1.4833 for Hinoki (R2 = 0.89), and LAtree = 0.2521(DBH2)0.6912 for Sugi (R2 = 0.54). Those allometry equations were applied to all trees in each plot, and total leaf dry mass and total leaf area (LMstand and LAstand, respectively) were obtained. Leaf biomass (ton ha–1) or direct LAI (m2 m2) was from LMstand or LAstand divided by the plot horizontal area. Indirect estimates of LAI were produced optically by two Plant Canopy Analyzers (LAI-2000, Li-COR, Lincoln, NE). Fisheye photos of the stand crown were taken from the forest floor. These and LAI were obtained in December 2011 at 20 points in each plot, just above each rainfall gauge. Image analyzing software (Gap Light Analyzer ver. 2) gave canopy openness. Results are shown in Table 1.

ð4Þ

SF ¼ aS  RF þ bS

ð5Þ

IC ¼ aI  RF þ bI ;

ð6Þ

where aT, aS, and aI are regression line slopes of TF, SF, and IC against RF. Also, bT, bS, and bI are intercepts of those lines, respectively. Eq. (6) was applied only to events with more than the threshold value of RF (10 mm). Rainfall intensity for each event was evaluated by two methods. First, storm intensity for a rainfall event was STI = RF/SP. Storm period (SP) was that between the initial and final tipping recorded by the rain gauge. Second, rainfall intensity for a rainfall event was RFI = RF/RP. Here, rainfall period (RP) for each event was evaluated as the sum of 10-min periods for which tipping was recorded during the event. Fig. 5 shows a schematic diagram for calculating SP and RP. Two rainfall events with the same cumulative RF have the same SP, but different RP. STI could not detect an intensity difference between the two events, whereas RFI indicates greater intensity in event (A) than event (B). 2.7. The model Our canopy interception model was established on a basic equation similar to Horton (1919) and Gash (1979). The model assumes that the canopy has water storage capacity (Sc), and calculates canopy IC as evaporation from canopy water storage. The

4

Event (A)

3 2 1 0

0

Interception (IC; mm) was calculated from RF, TF and SF, which were recorded every 10 min as follows;

ð3Þ

12

6

start

18

stop (6 h) end

Time

4

Event (B)

3 2 1 0

0

start

2.6. Data analysis

IC ¼ RF  ðTF þ SFÞ;

TF ¼ aT  RF þ bT

Rainfall (mm 10 min-1)

P SF ¼

The data of RF, TF, SF and IC every 10 min were integrated, respectively, for each rainfall event, and used in following analyses. One rainfall event was defined as a series of rainfalls over 10 min (P0.5 mm), which were distinguished from other events by a minimum no-rainfall period of 6 h (Hattori et al., 1982; Link et al., 2004). Events suitable for analysis were selected as follows. The events with rainfall 60.5 mm and snowfall were excluded, to eliminate data error. Also, the events with missing TF or SF data were not used. Those data processing resulted in 131 and 121 effective events for the Hinoki and Sugi plots, respectively. The relationships of TF, SF and IC against RF were established, respectively, using empirical models of liner regression as follows (Hattori et al., 1982).

Rainfall (mm 10 min-1 )

(Vbucket; units L) was recorded by capacitance water-level probes (Odyssey, Dataflow Systems, Christchurch, New Zealand) every 10 min. Overflow from all 90-L buckets (Vover; L) was collected by a garden hose network, and led to a water flow gauge with a 100 ml tipping bucket and recorded every 10 min. SF (mm) for each stand was calculated every 10 min as follows;

6

12

Time

18

stop

(6 h) end

Fig. 5. Schematic diagrams showing calculation of rainfall period for each rainfall event. Events (A) and (B) have the same cumulative amount of rainfall (RF) during same storm period (SP). SP is the period between initial and final tipping recorded by rain gauge. RP is the sum of horizontal black bands, which indicate periods of rain gauge tipping.

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output is IC for each event (ICmdl), composed by summing evaporation during and after rainfall. The equation is as follows.

IC mdl ¼

Z

ts

ic  dt þ Sstop;

ð7Þ

0

Rt where 0s ic  dt indicates integral of ic from rainfall start (t = 0) to stop (t = ts) in each event, and ic is the rate of IC. Sstop is canopy water storage remaining at rainfall cessation. In Eq. (7), ic is calculated as

ic ¼



Epot  Dt

for S > Epot  Dt

S=Dt

for S 6 Epot  Dt



Spre þ rf  Dt

forðSpre þ rf  DtÞ 6 Sc

Sc

forðSpre þ rf  DtÞ > Sc

D ðRn P

  2 zd ln ; 2 z0 k u 1

ð11Þ

where k is the von Karman constant (0.4), z is reference height (=h + 3; m), z0 is roughness length (=0.1  h; m), d is zero plane displacement (=0.75  h; m), and u is wind speed (m s1) observed at the weather station. Mean tree heights (h) were 16.8 and 22.6 m for the Hinoki and Sugi plots, respectively. 2.8. Canopy water storage capacity

ð9Þ

In actual calculation, ICmdl, S, Sc, and Sstop were in the unit of mm. ic and running balance of S were obtained at the intervals of 10 min (i.e. Dt = 10 min). Epot (mm s1) in Eq. (8) was converted from potential evaporation in molar units (epot; mol m2 s1) by Epot = epot(Mw/qw)103, where Mw (18 g mol1) is molecular mass of water, and qw (106 g m3) is density of water. epot is calculated by the Penman–Monteith equation (Monteith, 1965; Campbell and Norman, 1998):

epot ¼

ra ¼

ð8Þ

;

where Epot is potential evaporation rate, described later. S is the current amount of canopy water storage, and Dt is time interval. A running balance of S in Eq. (8) depends on S of the previous period (Spre) plus RF at current time interval (Spre + rfDt), where rf is intensity of RF. If (Spre + rfDt) > Sc, water in the storage overflows as TF and SF.

S¼

temperature and RH at the weather station. ra was calculated by the following (Monteith, 1965; Rutter et al., 1971).

 GÞ þ C p  qa  DP  r1a   ; c k DP þ c rarþr a

ð10Þ

where D (kPa K1) is slope of the saturation vapor pressure function, and P (kPa) is atmospheric pressure. Rn and G (W m2) are net radiation and heat flux through the soil surface, respectively. Cp (29.3 J mol1 K1) is the specific heat of air, qa (41.6 mol m3) is density of air at 20 °C, D (kPa) is vapor pressure deficit. k (4.4  104 J mol1) is latent heat of water vaporization, and c (=Cp/k; k1) is the psychrometric constant. rc and ra (m2 s m3) are canopy and aerodynamic resistance for water transfer from vegetation to atmosphere, respectively. rc can be set to zero on a completely wet canopy (e.g. Rutter et al., 1971). Rn was estimated from solar radiation (Rs) at the weather station, based on Rn = 0.87Rs  30.2 (R2 = 0.98). It was obtained in a nearby mixed Hinoki and Sugi forest of similar tree heights (Kumagai et al., 2008). G and P were from measured values, and correction was done during missing periods. D was estimated from

Canopy water storage capacity (Sc) was estimated indirectly, using regression lines of nRF vs. RF of selected rainfall events (Leyton method; Rutter et al., 1971; Hattori et al., 1982; Link et al., 2004). In the events suitable for this regression, canopy water storage should be saturated, and evaporation rate is negligible. Points from those events are at the upper envelope of the plot. Slope of the regression line is near 1 and negative intercept on ordinate indicates Sc. The relationship for entire events provides a consistent Sc throughout the observation period. Evergreen coniferous forests have small seasonal change of LAI relative to deciduous forests. 2.9. Statistics Regression lines were calculated by the least squares method. The significance of regression coefficient was examined by t-test. Analysis of covariance (ANCOVA) was applied for detecting significant differences in slopes or intercepts of the lines between the species. All statistical tests were conducted according to Sokal and Rohlf (1995). 3. Results 3.1. Throughfall, stem flow and interception loss in observation Table 2 shows a summary of observation results. Total rainfall in events suitable for analysis was 2940 and 2772 mm in the Hinoki and Sugi stands, respectively. The maximum RF among effective events was 212.0 mm. Rainfall on the canopy was partitioned into either TF or SF. For cumulative amounts of RF, TF/RF of Hinoki was smaller (2.6%) and SF/RF larger (+2.6%) than those of Sugi. The difference of nRF was slight between species, which resulted in similar IC/RF (25.5%). Fig. 6(A) and (B) shows TF or SF versus RF for each event. In regression lines for TF and SF, the coefficients of determination were high (R2 > 0.87), and the regression coefficients were

Table 2 Summaries of stand structure of study sites and observed rainfall partitioning. Species Age Tree density Mean Mean Plot Basal area Duration RF TF SF IC TF (years) (trees ha1) height DBH area (m2 ha1) (month) (mm) (mm) (mm) (mm) (%) (cm) (m2) (m)

SF (%)

IC (%)

aT (TF) aS (SF) Reference (mm (mm mm1) mm1)

Chamaecyparis obtusa (Hinoki) 41 1526 29 2051 70 923

17 11 19

19 16 34

59 29 195

45 42 91

19 12 30

2940 1543 5132

1919 1044 3810

268 169 585

751 329 738

65.3 9.1 25.5 0.631 67.7 11.0 21.3 0.690 74.2 11.4 14.4 0.825

0.112 0.147 0.114

This study Hattori et al. (1982) Tanaka et al. (2005)*

Cryptomeria japonica (Sugi) 41 1107 30 1467 70 513

23 15 27

27 23 39

72 225 195

67 62 66

19 12 41

2772 1584 7873

1882 1009 6189

182 162 445

707 413 1240

67.9 63.7 78.6

0.087 0.124 0.064

This study Sato et al. (2003) Tanaka et al. (2005)*

Italicized values are calculated from original articles. * Data for upper and lower canopy trees.

6.6 25.5 0.677 10.2 26.1 0.744 5.7 15.8 0.877

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Throughfall or Stemflow (mm)

Chamaecyparis obtusa (Hinoki) 140

(A)

120

Throughfall Stemflow

100

Cryptomeria japonica (Sugi)

(B)

TF = 0.631RF + 0.481 R2 = 0.99

TF = 0.677RF + 0.050 R2 = 0.99

80 60 SF = 0.112RF - 0.470 R2 = 0.94

40

SF = 0.087RF - 0.482 R2 = 0.87

20 0 0

50

100

150

200

0

50

Rainfall (mm) 70

(C)

Interception (mm)

60

100

150

200

Rainfall (mm)

(D)

50 40 30 IC = 0.271RF - 1.109 R2 = 0.90 (RF ≥10 mm)

20

IC = 0.249RF - 0.586 R2 = 0.92 (RF ≥ 10 mm)

10 0

0

50

100

150

200

0

50

Rainfall (mm)

100

150

200

Rainfall (mm)

Fig. 6. Relationship between rainfall (RF) and throughfall (TF) or stemflow (SF), for each rainfall event in (A) Hinoki and (B) Sugi stands. Relationship between RF and interception (IC) is shown for (C) Hinoki and (D) Sugi. Only events with RF P 10 mm are plotted.

Chamaecyparis obtusa (Hinoki)

Cryptomeria japonica (Sugi)

1.0 RFI (mm h-1) 20 15 10 5

IC/RF

0.8 0.6

(A)

(B)

0.4 0.2 0.0 0

50

100

150

200

0

50

Rainfall (mm)

100

150

200

Rainfall (mm)

40

Net rainfall (mm)

(C)

(D)

y = 1.00x - 2.03 R2 = 1.00

30

y = 1.01x - 2.22 R2 = 0.99

20

10

0 0

10

20

Rainfall (mm)

30

40 0

10

20

30

40

Rainfall (mm)

Fig. 7. Ratio of interception to rainfall (IC/RF) vs. rainfall in (A) Hinoki and (B) Sugi. Circle size indicates rainfall intensity (RFI). Plots of net rainfall (throughfall + stemflow) vs. rainfall in (C) Hinoki and (D) Sugi. Crosses (+) indicate events selected for regression, which are in upper envelope of plots between 9.5 and 31 mm of rainfall.

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statistically significant (P < 0.01). The slopes of regression lines were slightly different from the ratios of cumulative values due to the variation of the data points. But, the tendencies between the species were similar. Also, Fig. 6(C) and (D) show IC vs. RF for events with RF P 10 mm. Close liner relationships were found in both species (R2 P 0.90, P < 0.01). The statistical tests (ANCOVA) for regression lines between Hinoki and Sugi revealed significant differences in slopes of RF vs. TF and RF vs. SF (P < 0.01). However, there was no significant difference in either slope (P > 0.05) or intercept (P > 0.05) of RF vs. IC. In addition, we confirmed the extrapolation of the regression lines for RF vs. TF in two events with RF > 212 mm. Those events began on 25 June (RF = 335.5 mm) and 11 July (575.5 mm) of 2010. Fig. 7(A) and (B) display the ratio of IC to RF (IC/RF) against RF for each event. For events with RF < 10 mm, there were many plots with greater IC/RF than the mean value. The upper envelope declined severely with increasing RF for both species. On the other hand, there was no clear tendency of IC/RF against RF for events with P10 mm RF. Circle size for a plot is proportional to RFI. There was no clear tendency of IC/RF among RFI. The number of rainfall events with RF < 10 mm was 49% and 50% of all events in Hinoki and Sugi, respectively. But, the cumulative amount of RF in those events was only 9% that of total RF. Thus, properties in events with RF < 10 mm did not have a large impact on total amounts of RF, IC and IC/RF over the entire study period. Fig. 7(C) and (D) show the plots of nRF against RF for evaluating Sc. We chose the points for the regression between 9.5 6 RF 6 31 mm. This range was set because canopy water storage did not reach saturation in events with small RF and interception loss from the canopy will be significant in events with large RF. Six points were selected for both plots. Sc was estimated at 2.03 and 2.22 mm for Hinoki and Sugi, respectively.

3.2. Rainfall intensity and interception loss Fig. 8(A) and (B) show the relationship between IC and RP. Data were categorized into three groups of RFI, low (0 6 RFI < 5 mm h1); middle (5 6 RFI < 8 mm h1); and high (8 mm h1 6 RFI). Fig. 8(C) and (D) show IC vs. SP. Data for all events were categorized into three groups according to STI, low (0 6 STI < 1 mm h1); middle (1 6 STI < 3 mm h1); and high (3 mm h1 6 STI), according to Hattori et al. (1982) and Toba and Ohta (2005). Parameters of the regression lines are shown in Table 3. Linear relationships were clear for three RFI groups in both stands, as indicated by high coefficients of determination (R2 P 0.85). Also, R2 values were high for STI groups except for lowest STI group. Regression line slope increased with RFI or STI for each stand. Slopes of the three lines among STI or RFI groups were significantly different within the stands (P < 0.01). In addition, norainfall period within an event (noRP = SP – RP) had weak positive relationship with RP (R2 < 0.56). But, the relationships between noRP and IC in the three groups of RFI were not strong in both species (R2 < 0.7, data not shown). Fig. 9(A) and (B) show plots of IC/RP against RFI (=RF/RP). IC/RP presents the rate of IC obtained with RP. Events with RF P 10 mm were used for analysis, because of a great variety of IC/RF in events with RF < 10 mm (Fig. 7(A) and (B)). Positive liner relationships were found for both plots. Scale in the right side of the Fig. indicates latent heat (lE) for evaporation equivalent to IC/RF. The maximum values of IC/RF were more than 6 mm h–1 which corresponded with 4076 W m–2 of lE. Fig. 9(C) and (D) show plots of IC/SP against STI (=RF/SP) for events with RF P 10 mm. IC/SP increased linearly with increasing STI for both species. The maximum values of IC/SP were more than 2.5 mm h–1 which corresponded with 1698 W m–2 of lE. The regression coefficients for the four lines were statistically significant (P < 0.01).

Chamaecyparis obtusa (Hinoki)

Cryptomeria japonica (Sugi)

70

(A)

Interception (mm)

60

(B) 8 ≤ RFI

50

5 ≤ RFI < 8 0 ≤ RFI < 5

40 30 20 10 0 0

5

10

15

20

25 0

5

Rainfall Period (h)

10

15

20

25

Rainfall Period (h)

70

(C)

Interception (mm)

60

(D)

3 ≤ STI

50

1 ≤ STI < 3 0 ≤ STI < 1

40 30 20 10 0 0

10

20

30

Storm Period (h)

40

50

0

10

20

30

40

50

Storm Period (h)

Fig. 8. Interception (IC) vs. rainfall period (RP) for each rainfall event, in (A) Hinoki and (B) Sugi. Data were categorized into three groups with varying ranges of rainfall intensity (RFI; mm h1). IC vs. storm period (SP) for each rainfall event in (C) Hinoki, and (D) Sugi. RP and SP are referred in Fig. 5. Data were categorized into three groups of varying ranges of storm intensity (STI; mm h1).

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T. Saito et al. / Journal of Hydrology 507 (2013) 287–299 Table 3 Parameters of regression lines on the relationship between interception and rainfall intensity or storm intensity. Chamaecyparis obtusa (Hinoki) N Rainfall intensity (RFI; mm h 0 6 RFI < 5 73 5 6 RFI < 8 35 8 6 RFI 23

Cryptomeria japonica (Sugi) 2

Slope

Intercept

R

N

Slope

Intercept

R2

1.11 1.52 2.49

0.07 0.06 0.07

0.86 0.92 0.95

64 34 23

0.84 1.32 2.28

0.75 0.64 1.29

0.85 0.90 0.95

0.14 0.50 1.00

0.78 0.48 0.32

0.42 0.80 0.84

31 52 38

0.13 0.44 0.95

1.06 0.36 0.88

0.40 0.81 0.82

1

)

Storm intensity (STI; mm h1) 0 6 STI < 1 34 1 6 STI < 3 58 3 6 STI 39

Chamaecyparis obtusa (Hinoki)

8

Cryptomeria japonica (Sugi) 5000

(B)

6

4000 3000

4

2000 2 y = 0.188x + 0.237 R2 = 0.67 (RF ≥ 10 mm)

0

0

10

15

20

25 0

Rainfall Intensity (mm h ) -1

5

10

15

20

25

Rainfall Intensity (mm h ) -1

(D)

0

2000 1500

2

1000 1 y = 0.182x + 0.106 R2 = 0.76 (RF ≥ 10 mm)

500

y = 0.216x + 0.029 R2 = 0.83 (RF ≥ 10 mm)

0

equivalent lE (W m-2)

(C)

3

IC/SP (mm h-1)

5

1000

y = 0.219x + 0.092 R2 = 0.71 (RF ≥ 10 mm)

equivalent lE (W m-2)

IC/RP (mm h-1)

(A)

0 0

2

4

6

8

10

Storm Intensity (mm h )

12

14 0

-1

2

4

6

8

10

Storm Intensity (mm h )

12

14

-1

Fig. 9. Plots of interception divided by rainfall period (IC/RP) vs. rainfall intensity (RFI = RF/RP) for each rainfall event in (A) Hinoki, and (B) Sugi. Plots of interception divided by storm period (IC/SP) vs. storm intensity (STI = RF/SP) for each rainfall event in (C) Hinoki, and (D) Sugi. Only events with rainfall (RF) P 10 mm are plotted. Left abscissa indicates latent heat (lE) equivalent to IC/RP or IC/SP.

3.3. Observed and model estimation of interception loss

80

Σ ICmdl / Σ ICobs (%)

Fig. 10 shows the cumulative amount of IC estimated by the model (ICmdl), relative to that from observation (ICobs). This integration was done for events in which ICmdl could be calculated (i.e., no missing data of Rs and u). Total amounts of ICobs were 728 and 689 mm for Hinoki and Sugi, respectively. In ICobs, parts explained by ICmdl after rainfall were 33.8% and 35.5%, and those during rainfall were only 5.0% and 5.4%. As a result, unexplained parts in Hinoki and Sugi were 61.2% and 59.0%, respectively. Fig. 11 shows plots of ICobs vs. ICmdl for each event. The same dataset was used in Fig. 11(A) and (C) for Hinoki, and (B) and (D) for Sugi. There was great variation in the plots of ICobs against ICmdl; thus, it was impossible to explain ICobs of all events by a single linear relationship with ICmdl. Events with ICobs approximately <6 mm were simulated by ICmdl. The model successfully explained the events with small IC. Such events had a smaller portion of IC during rainfall than that after rainfall, which was approximately Sc (2 mm). In contrast, the events with P6 mm ICobs were not explained by ICmdl. These events had a larger portion of IC during rainfall than that after rainfall. In Fig. 11(A) and (B), circle size

100

60

40

20

0 Hinoki

Sugi

unexplained

ΣIC mdl during rainfall ΣIC mdl after rainfall Fig. 10. Cumulative amount of interception estimated by the model (RICmdl) relative to that of observation (RICobs) over entire study period. Numbers in parentheses denote actual amounts of RICobs for events in which ICmdl could be calculated.

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Chamaecyparis obtusa (Hinoki)

Cryptomeria japonica (Sugi)

70

(A)

60

20 15

50

ICobs (mm)

(B)

RFI (mm h-1)

40

10 5

30 20 10 0 0

1

2

3

4

5

6

0

1

2

3

4

5

6

4

5

6

ICmdl (mm)

ICmdl (mm) 70

(C)

ICobs (mm)

60

(D)

RP (h)

50

20 15

40

10 5

30 20 10 0 0

1

2

3

4

5

ICmdl (mm)

6

0

1

2

3

ICmdl (mm)

Fig. 11. Plots of observed interception (ICobs) vs. interception calculated by the model (ICmdl) in (A and C) Hinoki, and (B and D) Sugi for each rainfall event. Circle size indicates (A and B) rainfall intensity (RFI), or (C and D) rainfall period (RP). Lines are 1:1.

indicates RFI. There were some events near the 1:1 line, irrespective of RFI, and events with large distances from the line did not have large RFIs. In Fig. 11(C) and (D), circle size represents RP. There was a clear tendency for events near the line to have small RP, and for those distant from the line to have greater RP. In addition, this tendency with RP was not found when circle size represents noRP (data not shown). 4. Discussion Clear positive relationships were found between the rate of IC and RFI in both Hinoki and Sugi plots (Fig. 9). Moreover, our heat budget model indicates that large amount of IC during rainfall was not explained by theoretical amount of evaporation from the canopy (Fig. 10). 4.1. Forest structure Our study stands have been under a standard level of forest management in the Kyushu area. Basal areas (Table 1) were similar to the ideal values of a 40-year-old stand, which were 44.7 m2 ha–1 for Hinoki and 60.4 m2 ha–1 for Sugi in the stand yield tables of that area (Japan Forest Agency, 1957; Inoue and Miyahara, 1954). Leaf biomass and LAI in our Hinoki plot were smaller than those reported as 11.9 t ha–1 and 5.1 ha ha–1 in 45-years-old Hinoki stand in the Kyushu area (Tadaki et al., 1966), but were in the range of previous studies (Saito and Furumoto, 1982). Those values in Sugi stand with similar age were not in literature. Tadaki (1976) reported 19.6 ± 4.4 t ha–1 and 5–7 ha ha–1 as mean values in Japan, though the values included many stands with younger age. Thus, leaf biomass and LAI of our sites were rather smaller than mean values, but those were within the range of the plantations in Japan. The canopy was completely closed as indicated by small canopy openness (Table 1).

4.2. Sampling strategy for throughfall TF is spatially heterogeneous (Kimmins, 1973). Adequate sampling strategy was required for accurate estimation of mean TF. According to statistical examinations (Fig. 3), 19 rain gauges satisfied the requirement to reduce CV to <10% and error to <5%, at the a = 5% confidence level of t value. Thus, the level of accuracy was suitable for estimating precise TF, compared with previous studies (Houle et al., 1999; Carlyle-Moses et al., 2004). Increasing the total area of rain gauges relative to plot area might improve the accuracy of TF. However, increasing n to greater than 19 does not decrease CV and e effectively as indicated by small absolute values of dCVmax/dn and demax/dn (Fig. 3). Using a troughtype gauge (Hattori et al., 1982) or a roof-type gauge (Konishi et al., 2006) with funnel-type gauges might be valuable, but there is no general consensus on improving accuracy. Random relocation of gauges would produce more accurate TF than fixed gauges (Lloyd and Marques, 1988). However, inter-storm differences of TF would be more difficult to explain (Levia and Frost, 2006), because the canopy structure above the gauges varies with every relocation pattern. Therefore, we determined that 19 fixed gauges in the plot represented a valid sampling strategy to obtain accurate TF. 4.3. Partitioning rainwater at forest canopy Rainfall partitioning to TF in Hinoki was significantly smaller than that in Sugi, and the partitioning to SF was larger vice versa (Fig. 6 (A) and (B)). Tree density of the Hinoki was 1.4 times that of Sugi (Table 1). Moreover, the tree structure of Hinoki collected SF effectively relative to Sugi, which was evaluated by comparing funneling ratio (F; Herwitz, 1986; Levia et al., 2010) between the species. F is calculated for each tree by F = Sf/(RF  B), where Sf is stemflow volume (unit; L), RF is rainfall (mm), and B is trunk basal area

T. Saito et al. / Journal of Hydrology 507 (2013) 287–299

(m2). Sf and RF were summed over 21 events for Hinoki and over 18 events for Sugi, in which all SF gauges in each plot functioned and SF was collected in the 90-L bucket without overflow (see Fig. 2). F of Hinoki trees (mean ± SD: 22.2 ± 4.4) was significantly larger than that of Sugi trees (8.2 ± 6.2) (ANOVA; P < 0.01). Results suggests that tree architecture and tree density in Hinoki cause larger SF/ RF, which gives smaller TF/RF than Sugi. Rainfall portioning to IC was similar between the species (Fig. 6(C) and (D)). LAI was similar in both stands, approximately 3.5 m2 m2 (Table 1). These results suggest similar IC/RF among the stands with comparable LAI and closed canopy within the same catchment. This may be true even if the stands are composed of Hinoki or Sugi, with a certain variation of tree density. Our results on RF partitioning were compared with those in three selected studies, which made reliable observations for more than 12 months under closed canopies of mature Hinoki and Sugi stands. Comparison can be made between Hinoki and Sugi of younger (30 yr) and older (70 yr) ages than that of our site (41 yr). Our results of TF/RF and SF/RF were in the ranges of previous studies, although IC/RF was slightly greater than others in Hinoki. Our data were also within the range of studies in a temperate ecoregion, where TF/RF and SF/RF were roughly 75% and 10% (Levia and Frost, 2003; Levia and Frost, 2006). Tanaka et al. (2005) was the only study with simultaneous observation of neighboring Hinoki and Sugi stands. TF/RF was smaller and SF/RF larger in Hinoki than in Sugi. IC/RF was similar between the species. The tendency toward species difference is similar to our results. 4.4. Canopy water storage capacity There have been few studies on Sc in Hinoki and Sugi plantations, thus the results shown in Fig. 7(C) and (D) are essential. Sc values of 2.03 and 2.22 mm for Hinoki and Sugi, respectively, are larger than the 1.24 mm of Hattori et al. (1982) for a Hinoki stand, but similar to 2 mm of Suzuki et al. (1979) for a mixed pine-Hinoki stand. Our results are in the range of other conifer forests: 1.2– 4.3 mm in Douglas fir, 0.5–1.0 mm in Pinus spp., 1.2–2 mm in Picea sitchensis, and 3.6 mm in old Douglas fir (Link et al., 2004). Direct measurement methods gave 2–3 mm in Sitka spruce (P. sitchensis) and 2.4 mm in Douglas fir (Klaassen et al., 1998). Therefore, our results provide a range of Sc in common Hinoki and Sugi forests, which is comparable to coniferous forests in other countries. 4.5. Rate of canopy interception loss increase with rainfall intensity The relationship between RF and IC had a small range in both stands (Fig. 6(C) and (D)). IC/RF for each event varied between 0.1–0.3 in events with RF P 10 mm, but a tendency with RFI was not found (Fig. 7(A) and (B)). Results suggest that IC/RF was insensitive to variability of rainfall properties such as RP and RFI. Toba and Ohta (2005) reported that IC/RF in Siberia and Japan remained near 0.2 in events with RF P 10 mm, regardless of mean rainfall intensity (equal to STI in this study). This insensitivity of IC/RF did not exist in events with RF < 10 mm. Such RF would not be sufficient for wetting the entire canopy surface. There was a positive linear relationship between IC and RP within the three groups of different RFI range, in both stands (Fig. 8(A) and (B)). This indicates that the amount of IC in an event increases with RF duration. This suggests that the major portion of IC occurs during rainfall. Moreover, the line slopes increased with RFI for both stands. This indicates that the rate of IC increased with RFI. A positive linear relationship was also found between IC and SP (Fig. 8(C) and (D)), and the line slopes increased with STI. These STI results support the findings of Hattori et al. (1982). STI was calculated with SP including short no-rainfall periods, which possibly affected the properties of IC. However, RFI was calculated with RP

297

excluding short no-rainfall periods within an event (see Fig. 5). Results suggest that IC rate increases with RFI, and this property is mainly determined during rainfall. The rate of IC linear increase with RFI is shown in plots of IC/RP vs. RFI (Fig. 9(A) and (B)). The plot for Hinoki in event base corresponds with that in hourly base in Murakami (2006). Linear relationship also existed between IC/SP and STI (Fig. 9(C) and (D)). The plot for Sugi is similar to that in Hashino et al. (2002). This study is the first report of increasing IC rate with RF rate for both Hinoki and Sugi stands in simultaneous observation. The accumulated evidence (Hattori et al., 1982; Hashino et al., 2002; Toba and Ohta, 2005; Murakami, 2006) suggests that the rate of IC generally increases with RFI and STI in coniferous forests with closed canopies in Japan. This dependence of IC on RFI is a clue to the process of IC in forests. 4.6. Mechanism of increasing rate of interception loss The rate of IC exceeded effective radiant energy. Provided that the rate of IC indicates evaporation rate at the canopy, lE of evaporation strongly exceeds 700 W m–2 (1 mm h–1) under high RFI or STI (right abscissa of Fig. 9). This indicates the lE equivalent to the IC rate exceeded Rn in midday in summer (Suzuki et al., 1979). Nonetheless, Rn during rainfall was much smaller than this value. Moreover, it is difficult to assume that radiation energy input to the forest increased with RFI. Therefore, we suggest that IC during rainfall is not principally driven by radiant energy. Energy supply during rainfall does not largely induced by net radiation but downward sensible heat flux by advection (Carlyle-Moses and Gash, 2011). The process of increasing IC rate with RFI is assumed as follows. First, canopy water storage (C) and evaporation area increase with RFI on the canopy, as predicted by the Rutter model (Rutter et al., 1971; Levia et al., 2011). Nevertheless, increasing C is not important for increasing the IC rate in events with RF P 10 mm. It is assumed that Epot occurs under the given meteorological conditions during event, when all canopy surface were wet (i.e., C > Sc) (Rutter et al., 1971; Carlyle-Moses and Gash, 2011). Second, evaporation from raindrops within the canopy is accelerated by increasing the number of small splash droplets (SDE; Murakami, 2006). This hypothesis is theoretically possible in the atmosphere with constant RH, irrespective of RFI. The constant RH during rainfall must be supported by sufficient sensible heat flux (H) supply to the evaporation site by advection. Downward H during rainfall was evaluated by eddy correlation technique, and was reported approximately 200 W m–2 in maximum in Japanese forests (Mizutani et al., 1997; Machimura, 1998; Takanashi et al., 2003). That value was observed during low RFI during daytime, and H under high RFI was smaller than that value. H would be insufficient for lE correspond with observed IC rate under typical RFI. Therefore, we suggest that the process for increasing IC rate with RFI is independent of evaporation. 4.7. Estimation of canopy interception loss by the model The model estimates the theoretical constraint for IC that can be induced by evaporation. The constraint comes from the energy budget at the canopy in any time scale. In our results, the amount of ICmdl relative to that of ICobs was approximately 40%, where 37% was after rainfall (Fig. 10). 60% of ICobs was not explained by ICmdl. This degree of underestimation corresponds with that reported in Murakami (2007). Our results implies 15% of annual rainfall was returned to the atmosphere by unknown processes before reaching the forest floor. The great underestimation of IC by the model indicates that ICobs exceeds the theoretical limit of wet canopy evaporation. The

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Penman–Monteith equation (i.e., Eq. (10)) estimates evaporation from the surface of all physical bodies in the forest such as foliage, stems, forest floor vegetation and soil. But, Eq. (10) does not assume the evaporation from water detached from the canopy surface such as raindrops and splash droplets. Thus, Murakami (2007) claimed that SDE explains the underestimation of observed IC by the heat budget model. However, SDE requires vaporization heat (=lE). Observed energy (Rn–G and H) was already changed to lE for evaporation on the canopy surface. Other source of energy for SDE would not be prominent. Therefore, we suggest that SDE is not vital for IC during rainfall. The processes of the unexplained part of ICobs are suggested by the relationship between ICmdl and ICobs for each event (Fig. 11). Values of ICobs were as much as 15 times larger than those of ICmdl. This substantial discrepancy indicates the magnitude of the unknown process for IC during rainfall. The events with P6 mm ICobs had a larger portion of IC during rainfall than that of IC after rainfall. Therefore, we suggest that the large discrepancy of ICmdl and ICobs is caused by failure of the model to estimate IC during rainfall. By the definition RF = RFI  RP, a larger RF is induced by higher RFI or greater RP in each event. It is valuable to examine which is responsible in situations with the large discrepancy between ICobs and ICmdl. The events with large distances from the 1:1 line did not necessarily have high RFI (Fig. 11(A) and (B)). This implies that RFI itself is not a critical factor in the discrepancy. On the other hand, there was a clear tendency for events with large distances to have larger RP and events close to the line to have smaller RP (Fig. 11(C) and (D)). This implies that RP is critical in the discrepancy between ICobs and ICmdl. Greater RP would increase the period of opportunity for the unknown process to act. This suggests that that process occurred not only under high RFI but under typical RFI, and that the cumulative effect of the process is a positive function of RP during an event. 4.8. Mechanism of interception loss during rainfall It is difficult to explain the primary process of IC by evaporation in the events with RF > 10 mm. The first difficulty is represented by the tendency for the IC rate to increase with RFI. The driving force for evaporation (i.e., Rn–G and D) had a tendency to decrease with RFI, which means evaporation at the canopy tends to be suppressed by increasing RFI. The second difficulty is that the IC rate during rainfall was high. The lE equivalent to typical IC/RP was greater than available energy at the canopy including radiation and downward H. This is true even for IC/SP including short non-rainfall periods. The third difficulty is that the amount of ICmdl relative to that of ICobs was only 40%. Moreover, ICobs was more than 10 times greater than ICmdl for some events. The discrepancy was clear in the events with large RP, owing to the difficulty to estimate IC during rainfall. It is difficult to assume Rn, D or u values 10 times greater than observed, even considering measurement accuracy. There is a consideration that the difference between observed IC and calculated evaporation reflects rainfall storage on the canopy surface (Iida et al., 2005). However, this requires an unrealistically large storage at rainfall cessation such as 6.5 mm event–1. The three findings above suggest that the IC process during rainfall is largely controlled by processes other than evaporation at the canopy. We hypothesize the process of IC after canopy saturation as follows. A raindrop striking plant structures produces a large number of small splash droplets. Larger and faster incident drops generate more numerous splash droplets, and their size distribution is very wide (Stow and Stainer, 1977). Small droplets increase under high rainfall intensities in forests (Nanko et al., 2006). Small droplets remain suspended in air for long periods. The terminal velocity of a small droplet is described by Stokes’ law as t = 2r2g(qw–qa)/(9g),

where t is the terminal velocity of raindrops (m s1), r is raindrop radius (m), g is gravitational acceleration (9.81 m s–2), qw and qa are densities of water and air (qw–qa106 g m–3), and g is viscosity of air (0.018 g m–1 s–1) at 20 °C. Estimated t of a small droplet (<10 lm) is less than 0.012 m s–1. The chance for transport increases with the period of suspension of the droplet. We propose that the splash droplets transport (SDT) by canopy ventilation is the primary process for IC during rainfall. Humid air that includes small droplets is transported by ventilation from the space including the canopy where micrometeorology is presented by the values at the weather station to outside. Some works indicate the importance of canopy ventilation for IC (Crockford and Richardson, 1990). However, the amount of IC from this transport was not directly evaluated, because a method has not been established. Production of splash droplets on the forest canopy may be given physical expression in the future. Results of laboratory experiments provide clues to this expression at a branch scale (Stow and Stainer, 1977; Herwitz, 1987). Recent cloud resolving models (CRMs; e.g. Moeng and Arakawa, 2012) have potential to simulate transporting moisture from the surface to the cloud layer. 5. Conclusions Rainfall partitioning to TF and SF differed between the species, because of variation in tree and stand structure. However, partitioning to IC was similar (26%) because of similar canopy structure. IC/RF was insensitive to RF properties in the events with canopy saturation, which was related to increasing IC rate with RFI. The model for IC produced only 40% of observed IC in their cumulative amounts, which indicates the constraint of energy budget on evaporation during rainfall. We suggest that SDT by canopy ventilation is essential for IC during rainfall. Acknowledgments We are grateful to Drs. Y. Onda, H. Kato, K. Nanko and A. Hirata of Tsukuba University for organizing this research project. We thank members of the Eco-hydrology Laboratory in the Kasuya Research Forest of Kyushu University for their help with fieldwork and discussion. We also appreciate Drs. T. Kumagai, A. Kume, N. Tanaka, and N. Kobayashi for critical advice on this study. Associate editor and two anonymous reviewers gave valuable comments on a manuscript. This study was financially supported by CREST: ‘‘Development of Innovative Technologies for Increasing in Watershed Runoff and Improving River Environment by the Management Practice of Devastated Forest Plantation.’’ References Campbell, G.S., Norman, J.M., 1998. An introduction to environmental biophysics, second ed. Springer, New York, p. 233. Carlyle-Moses, D.E., Gash, J.H.C., 2011. Rainfall interception loss by forest canopies. In: Levia, D.F., Carlyle-Moses, D, Tanaka, T. (Eds.), Forest Hydrology and Biogeochemistry: Synthesis of Past Research and Future Directions, Ecological Studies, vol. 216. Springer, New York, pp. 407–423. Carlyle-Moses, D.E., Flores Laureano, J.S., Price, A.G., 2004. Throughfall and throughfall spatial variability in Madrean oak forest communities of northeastern Mexico. J. Hydrol. 297, 124–135. Crockford, R.H., Richardson, D.P., 1990. Partitioning of rainfall in a eucalyput forest and pine plantation in southeastern Australia: IV the relationship of interception and canopy storage capacity, the interception of these forests, and the effect on interception of thinning the pine plantation. Hydrol. Process. 4, 169–188. Deguchi, A., Hattori, S., Park, H.-T., 2006. The influence of seasonal changes in canopy structure on interception loss: application of the revised Gash model. J. Hydrol. 318, 50–102. Fleischbein, K., Wilcke, W., Goller, R., Boy, J., Valarezo, C., Zech, W., Knoblich, K., 2005. Rainfall interception in a lower mantane forest in Ecuador: effects of canopy properties. Hydrol. Process. 19, 1355–1371. Gash, J.H.C., 1979. An analytical model of rainfall interception by forests. Quart. J. Roy. Met. Soc. 105, 43–55.

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