The modelling of rainfall interception in growing and dormant seasons for apine plantation and a black locust plantation in semi-arid Northwest China
Changkun Maa,c, Xiangdong Lib,c, Yi Luoa,b,⁎, Mingan Shaoa,b,⁎, Xiaoxu Jiaa,b
a Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing100101, Chinab College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, Chinac State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, Shaanxi, China
A R T I C L E I N F O
This manuscript was handled by Marco Borga,Editor-in-Chief, with the assistance of DanielePenna, Associate Editor
Interception loss can remove a significant portion of rainwater from forested ecosystems. Therefore, the quan-tification and modelling of interception loss are of significant importance if human and ecosystem water de-mands are to be balanced under a future changing climate. This is particularly true for semi-arid/arid regions,where afforestation has become an important ecological restoration measure to tackle desertification, povertyand climate change. However, quantification and modelling of interception loss of plantations in these regionshave rarely been reported. In the present study, rainfall interception loss was quantified and modelled over aone-year period (January-December 2016) for a deciduous broad-leafed R. pseudoacacia plantation and anevergreen needle-leaf P. tabuliformis plantation (common afforestation tree species) situated in the semi-aridLoess Plateau of China. The stand age, density, canopy cover and leaf area index of R. pseudoacacia during thestudy period were 15 years, 2000 tree ha−1, 0.48 and 1.41m2m−2, respectively. The corresponding values forPinus tabuliformis were 17 years, 1200 tree ha−1, 0.62 and 2.53m2m−2. The measured throughfall, stemflowand derived estimates of interception loss for R. pseudoacacia were 81.1%, 1.3% and 17.6%, respectively. Thecorresponding values for P. tabuliformis were 75.4%, 0.7% and 23.9%. Given that the weather conditions ex-perienced by the two forest stands were similar, the observed higher interception loss for P. tabuliformis can beexplained by the higher canopy storage capacity and wet canopy evaporation rate of this species. The revisedGash analytical model of rainfall interception was well calibrated and validated against field measurements andwas able to simulate the cumulative interception loss at two forest stands accurately, and it also effectivelycaptured the seasonal variation (leafed growing and leafless dormant seasons), provided that the optimized wet-canopy evaporation rates were used. The revised model was highly sensitive to the canopy storage capacity andchanges in the ratio of mean wet canopy evaporation to mean rainfall intensity and less sensitive to canopycover, but it was found to be fairly insensitive to the trunk storage capacity.
1. Introduction
Arid and semi-arid ecosystems constitute more than 30% of theEarth’s terrestrial surface and are among the world’s most fragile eco-systems due to periodic droughts and increasing overexploitation of thelimited water resources (Malagnoux et al., 2007). Moreover, theseecosystems are facing extraordinary challenges, including desertifica-tion, biodiversity loss, poverty and climate change (FAO, 2015). Totackle such challenges, large-scale afforestation and reforestation ef-forts have been implemented in these regions to convert farm-, grass-and shrub-land into forest plantations (Cao et al., 2011; Malagnouxet al., 2007; FAO, 2015; Sadeghi et al., 2016) because trees and forests
are vital for averting desertification and increasing the resilience ofecosystems in the face of global change. Such restoration measures,however, have raised concerns about the effects of the new forestplantations on water resources (Farley et al., 2005; Wang et al., 2011)because canopies of plantations may reduce water yield from water-sheds through transpiration, interception and evaporation (Farley et al.,2005), creating potentially conflicting demands for water betweenecosystems and humans (Feng et al., 2016). In these water-scarce aridand semiarid regions, where limited precipitation is the main source ofwater, gross rainfall plays an important role in regulating plant growthand survival, net primary productivity, and ecosystem C, nutrient, andwater fluxes (e.g., Niu et al., 2007; Sadeghi et al., 2016). Hence, a good
https://doi.org/10.1016/j.jhydrol.2019.06.021Received 16 January 2019; Received in revised form 2 May 2019; Accepted 9 June 2019
understanding of the amount of rainfall (NR, net rainfall) reaching theground is essential for water resource managers to develop effectivewater resources management and water plan strategies.
When rain falls on forest canopies, a portion reaches the forest flooras throughfall (Tf) and stemflow (Sf), and the remainder is retained onthe canopy and subsequently evaporated back to the atmosphere (in-terception loss, I). The actual amount of rainfall that reaches the forestfloor (Tf+ Sf) is net rainfall (NR), calculated as the difference betweenthe gross rainfall and interception loss. Interception loss is the sink termin the water balance of a watershed and is recognized as a considerablyimportant hydrological process in water resource management (Muzyloet al., 2009). The importance of interception loss has been repeatedlydemonstrated in different tree species under various ecological systems(Crockford and Richardson, 2000; Dunkerley, 2000; Llorens andDomingo, 2007). A literature review has shown that a significant por-tion of gross rainfall is lost to interception loss (Limousin et al., 2008;Carlyle-Moses and Gash, 2011). For example, interception losses havebeen reported to range from 9% of the gross rainfall in an Amazonianrainforest (Llorens, 1998) to as high as 60% in a coniferous forest in theMediterranean mountain area (Forgeard et al., 1980). The largevariability in interception loss is strongly dependent on the canopystructure (e.g., density, leaf area index, canopy cover and canopy sto-rage capacity) and climate variables (e.g., evaporation rate, wind andrainfall characteristics) (Crockford and Richardson, 2000; Gash, 1979;Staelens et al., 2008). Among all these factors, canopy structure is al-ways identified as the most important influential factor in most rainfallinterception models (Deguchi et al., 2006). Canopy structure may bealtered by changes in tree species composition, gap fraction, leaf spatialdistribution, and temporal changes in foliage amounts (Franklin et al.,2002; He et al., 2014). Within forests, more attention has been paid tothe influence of tree species composition on interception loss, whichtended to be higher in coniferous forests than in broad-leaved forests(Carlyle-Moses and Gash, 2011). Moreover, most of these studies haveonly included measurements from the leafed growing season (Price andCarlyle-Moses, 2003), and relatively little work has sought to in-vestigate the effects of seasonal changes on the foliage amount (i.e.,both leafed growing and leafless dormant seasons) on interception loss(Deguchi et al., 2006). Additionally, interception loss within deciduoustrees is more affected by seasonal changes in canopy structure than thatwithin evergreen trees (Augusto et al., 2002).
Interception loss models allow measurement results to be extra-polated in space and time, and they also provide insight into the me-chanisms of interception process (Rutter et al., 1975; Gash et al., 1995).Hence, interception models are particularly needed by forest managersas a component of hydrological predication tools. To accurately predictinterception loss by different types of vegetation, many interceptionloss models have been developed (Muzylo et al., 2009). These modelsare largely based on either the Rutter (Rutter et al., 1971, 1975) or Gash(Gash, 1979; Gash et al., 1995) models. The original Gash (1979) modelis a simplified version of the Rutter model, features an empirical ap-proach, and requires fewer data. However, it overestimates interceptionloss for sparse forests since it predicts overall (whole plot area) eva-poration rather than evaporation per canopy area (Gash et al., 1995;Valente et al., 1997). Therefore, Gash et al. (1995) revised the originalmodel by introducing a canopy cover fraction. The revised Gash ana-lytical model was more robust and provided more accurate estimates ofinterception loss for sparse forests (Carlyle-Moses and Price, 2007;Deguchi et al., 2006; Muzylo et al., 2009; Valente et al., 1997), as ittook into account forest sparseness and improved forest boundaryconditions. The revised Gash model has been successfully applied toforests across different climatic conditions, including temperate forests(Deguchi et al., 2006; Gash et al., 1995), Mediterranean forests(Limousin et al., 2008) and tropical rainforests (Ghimire et al., 2017).However, to the knowledge of the authors, only a few studies havetested the revised Gash model in arid and semi-arid forests (Dunkerley,2000), and this model has rarely been validated in arid and semi-arid
plantations (e.g., Motahari et al., 2013; Sadeghi et al., 2015).The revised Gash model has been applied less frequently in decid-
uous forests than in evergreen forests. Moreover, modelling studies ofdeciduous forests have mostly been focused on measurements from theleafed growing season (e.g., Carlyle-Moses and Price, 1999; Price andCarlyle-Moses, 2003), with only a few studies focusing on both theleafed growing and leafless dormant seasons (e.g., Deguchi et al., 2006;Muzylo et al., 2012). The limited number of interception loss modellingstudies in deciduous forests is possibly a consequence of difficulties inmodel application in this kind of forests. To correctly apply the revisedmodel, separate parameterization and validation datasets are requiredfor both the leafed and leafless seasons (Muzylo et al., 2012), which willprolong the observation period (extend from the leafed season to theleafless season) and is also a time-consuming and labour-intensiveprocess. Although interception loss takes a much lower fraction of grossrainfall in the leafless period than in the leafed period for deciduousforests, it is still a critical component of the water balance (e.g., Deguchiet al., 2006; Šraj et al., 2008). Therefore, to better understand the ef-fects of deciduous canopies on water resources, interception loss mea-surement or/and modelling should be applied in both leafed growingand leafless dormant seasons.
Two of the most commonly selected tree species for afforestation inthe arid and semi-arid regions of Asia (e.g., Afghanistan, Iran, Pakistan,Iraq, Lebanon and China) are Roinia pseudoacacia and Pinus tabuliformis(e.g., Guo et al., 2008; Jia et al., 2017; Sadeghi et al., 2016). These twospecies were chosen due to their great tolerance to drought, infertilityand low/high temperature, as well as their superior growth comparedwith some native tree species (Ma et al., 2014; Vitkova et al., 2017).However, the phenological and morphological traits of these two spe-cies are distinct from each other: R. pseudoacacia is a broad-leaveddeciduous tree species with a narrow crown and smooth bark, while P.tabuliformis is a needle-leaved evergreen tree species with a flat-toppedcrown and rough bark. Furthermore, these two species are also sig-nificantly different in canopy thickness, branching architecture and leafshedding patterns, all of which will be differentially affected by rainfalland other climate variables (e.g., wind and solar radiation) and lead todistinct rainfall partitioning (throughfall, stemflow and canopy inter-ception) patterns (e.g., Carlyle-Moses and Gash, 2011; Park andCameron, 2008). Hence, quantification, comparison and modellingstudies of rainfall partitioning between these two species in arid andsemi-arid regions may benefit watershed and forest managers by pro-viding valuable information about canopy hydrological processes.However, such a comparison and modelling study has rarely been re-ported (Jian et al., 2015). Therefore, the objectives of this research areto (i) measure and compare interception loss, throughfall and stemflowbetween afforested stands of R. pseudoacacia and P. tabuliformis duringdifferent measurement periods (e.g., annual, leafed growing and leaflessdormant seasons) in the semi-arid region of China, (ii) calibrate andvalidate the revised Gash analytical model for R. pseudoacacia and P.tabuliformis forest stands during the leafed growing and leafless dor-mant seasons, and (iii) explore the underlying causes of differences ininterception loss between the two forest stands. An advantage of thisstudy is that the two studied forests experienced similar climatic con-ditions during the experimental period, potentially minimizing the ef-fects of meteorological differences on interception loss and allowingcomparison primarily due to structural differences.
2. Materials and methods
2.1. Site description
The study was conducted within the Yeheshan watershed (YHS,34°31.76′N, 107°54.67′E) in the Provincial Natural Reserve of FufengCounty in Shaanxi Province, China (Fig. 1). The YHS is in a hilly andgully region with an elevation ranging from 449m to 1662m above sealevel and an area of 10,996 ha. Forest covers approximately 90% of the
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watershed and is made up of a mosaic of pure stands of different treevarieties, including both coniferous and broad-leaved species. R. pseu-doacacia is the dominant tree species in this region and accounts forapproximately 75% of the canopy trees. P. tabuliformis is the second-most abundant species, constituting approximately 15% of the canopytrees. The other tree species—Platycladus orientalis, Populus davidiana,Quercus wutaishansea and Quercus variabili—are usually found as in-dividual trees or in small groups.
The YHS experiences a semi-arid continental climate characterizedby a hot humid summer (June-August) and cold dry winter (December-February). Based on climate data (1958–2016) from the Fufeng Bureauof Meteorology, the nearest meteorology station to YHS (≤10 km), theaverage annual precipitation is 580mm (SD=139mm), and theaverage annual temperature is 12.7 °C (SD=0.6 °C), with an absolutemaximum of 42.2 °C in June and an absolute minimum of −21.2 °C inJanuary. Precipitation mainly occurs during the leafed growing season(May-October) and has a large inter-annual variation with a variationcoefficient of 0.3. The average annual reference evapotranspiration was1217mm, which was calculated as the product of pan evaporation andthe pan coefficient. Depending on the average annual wind speed(1.5 m s−1) and relative humidity (72%) over the study region, the pancoefficient was set to 0.80 (FAO, 1998). The dominant wind directionvaries seasonally, with southeast winds in the growing season andnorthwest winds in the dormant season (November-April).
Two representative experimental plots (50×50m2) were estab-lished in the adjacent R. pseudoacacia and P. tabuliformis woodlands,approximately 200m apart from each other. The two plots are locatedon a relatively flat slope (5–10°), underlain by deep silt loam soil (over50m deep). The deciduous R. pseudoacacia stand was established in2002 with a dense understory of Stipa bungeana, Artemisia sacrorum andArtemisia scoparia. The evergreen P. tabuliformis stand was establishedin 2000 with a sparse understory of grass species. The R. pseudoacaciastand had a stem density of 2000 tree ha−1, a mean diameter at breastheight (DBH, 1.3m) of 11 cm and a mean tree height of 9.6 m in 2016,
while the corresponding values for the P. tabuliformis stand were 1200tree ha−1, 15 cm and 7.2 m, respectively. The leaf area index (LAI) wasestimated using digital hemispherical photography and CAN-EYE soft-ware (version 6.3) (Baret and Weiss, 2004). Hemispherical photographswere taken with a Nikon D100 equipped with a 4.5mm Sigma circularfisheye lens. For the best representation of canopy gap fraction, 12photos were taken within each stand (6 taken between tree rows andanother 6 within trees rows), and the camera was oriented carefullysuch that the edge of the image was perpendicular to the tree row ineach stand. Hemispherical photographs were then processed accordingto the CAN-EYE tutorial document (Baret and Weiss, 2004). Theaverage LAI value of R. pseudoacacia was 2.40m2 m−2 during thegrowing season (including leaf burst and senescence, May-October) and0.41m2 m−2 during the dormant season (wood area index, November-April). The average LAI value of P. tabuliformis was 2.55m2 m−2 and2.50m2 m−2 for the growing and dormant seasons, respectively, in-dicating small seasonal variations. Details of other forest structuralfeatures of the study plots are summarized in Table 1.
2.2. Measurements of gross rainfall, throughfall and stemflow
Measurements at the R. pseudoacacia and P. tabuliformis plots wereconducted from 1 January 2016 until 31 December 2016. Gross rainfall(Pg, mm) was measured for each study plot with a weighing-bucket raingauge (T-200B, Geonor, Eiksmarka, Norway) connected to a CR1000data logger (Campbell Scientific Inc., Logan, Utah, USA) and onemanual rain gauge (30 cm diameter) in a nearby clearing (< 30maway). The manual gauges were used to check the weighing gauge andwere read immediately after each rainfall event. A rain event was de-fined as a period with more than 0.2mm of the total Pg, separated by atleast 6 h without rain. Field observations with the leaf wetness sensor(237L, Campbell Scientific, Logan, UT, USA) at each forest stand con-firmed that the time interval was sufficient for residual rainwater toevaporate from the tree crown in this climate.
Fig. 1. Location of the study site (Yeheshan watershed) in semi-arid Northwest China.
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Throughfall (Tf, mm) was measured under the canopy with 30 rain-gauges (identical to the manual Pg rain-gauge) in each study plot. Toovercome the spatial variability of Tf, rain gauges were installed ran-domly along three 30m long transects (10m apart) underneath thecanopy, with an average distance of 2.5m between neighbouringgauges. The average Tf can be accurately estimated using a combinationof stationary and manual roving gauges to provide representativesamples (Crockford and Richardson, 2000; Holwerda et al., 2012; Ritterand Regalado, 2014); therefore, two-thirds of the rain gauges wererelocated to new random positions after every three rainfall events. Toavoid ground splash effects, the orifices of all rain-gauges were placedapproximately 1m above the ground.
Stemflow (Sf, mm) was measured on thirteen representative trees ateach study plot. The Sf was sampled using spiral-type Sf collars con-structed from plastic hoses. Each Sf collar was attached to the stems andsealed in an upward spiral pattern with silicon sealant to divert rainwater into a Sf collector. The measured data were then scaled to theunit ground area by the average basal area per unit ground area foreach study plot. Therefore, interception loss (I, mm) was calculated asthe difference between Pg and the sum of Tf and Sf.
2.3. Meteorological measurements
Meteorological variables were measured by an automatic weatherstation mounted on a 13-metre-high tower (∼2 m above the black lo-cust canopy) located at the centre of the R. pseudoacacia woodland.Relative humidity and air temperature were measured with anHMP155A (Vaisala, Finland)-type thermohydrometer. Net radiationwas measured using a 4-component radiometer (CNR4, Kipp & Zonen,Netherlands). Wind speed was measured with a 3-D sonic anem-ometers/thermometer (CAST3; Campbell Scientific Inc. Logan, UT,USA). Close to the station, two soil heat flux plates (HFP01, HuksefluxThermal Sensors, the Netherlands) were installed 5 cm below theground surface. Meteorological data were automatically sampled at 10 sintervals and recorded at 10-min intervals by using a CR1000 datalogger (Campbell Scientific Inc. Logan, Utah, USA). As the two studyplots were close to each other (< 200m apart), the recorded meteor-ological variables were considered to be the same for the two studyplots.
2.4. Revised Gash analytical model
The revised Gash analytical model (Gash et al. 1995) was used tomodel interception loss at the R. pseudoacacia and P. tabuliformis plots.The model operates under the assumption that Pg is intercepted as aseries of individual rainfall events, with enough time to completely drythe canopy and trunk between two successive events. Each rainfallevent can be distinguished by three sequential phases: (i) a wetting-upphase, during which Pg is less than the threshold value required to
saturate the canopy (Pg’, mm); (ii) a saturation phase, during which thecanopy achieves and maintains a saturation state, when rainfall in-tensity ( −R, mmh 1) exceeds the mean wet-canopy evaporation rate( −E, mmh 1) of the saturated canopy; and (iii) a drying phase afterrainfall has ceased. Interception losses through evaporation take placeduring each phase, and the total interception loss for a particular eventis obtained as the sum of different components.
To calculate interception loss, the revised analytical model requirestwo types of data: canopy parameters and atmospheric variables. Therequired canopy parameters are the free throughfall coefficient (ρ, hereassumed to be one minus canopy cover fraction, c), the canopy storagecapacity (S), the trunk storage capacity (St), and the rainfall fractiondiverted into Sf (pt). The required atmospheric variables are Pg, E an-dRduring each rainfall event. The amounts of rainwater required toentirely saturate the canopy (Pg’) and trunk (Pt’) are calculated usingEqs. (1) and (2), respectively:
′ = − −P R E S( ¯/ ¯ ) ln(1 (E /R))g c c c (1)
′ =P S p/t t t (2)
whereEcis the mean evaporation rate from the saturated canopy duringa rainfall event and is defined as =E E c¯ ¯/c ; Sc is the canopy storagecapacity per unit area of cover and is calculated as Sc= S/c. The revisedanalytical model divides interception loss into five components, and theequation used to calculate each component is presented in Table 2.
2.5. Estimation of model parameters
Following Wallace and McJannet (2008), S for R. pseudoacacia andP. tabuliformis stands were obtained as the negative intercept of theregression of Pg against the sum of Tf and Sf. The free throughfallcoefficient ρreg was estimated in accordance with Jackson (1975), as theslope of the linear regression between Pg and Tf for small rainfall thatwas too small to saturate the canopy. An alternative method to estimatethis parameter, ρLAI, was also applied during the growing and dormantseasons using hemispherical photographs (taken by a Nikon D100 di-gital camera with a fish-eye lens, Baret and Weiss, 2004; Muzylo et al.,2012) and processed using CAN-EYE software (version 6.3). To achievethis goal, 12 photos were taken within each stand, and the camera wasoriented carefully such that the edge of the image was perpendicular tothe tree row in each stand (Macfarlane et al., 2007). All photos weretaken each month on cloudy days or at dusk under uniform sky con-ditions. The rainfall fraction diverted to the trunks pt and the trunkstorage capacity St were estimated by the method of Gash and Morton(1978) as the slope and the negative intercept of the linear regressionbetween Pg and Sf.
E was estimated using three approaches. First, the evaporation ratewas estimated using the Penman-Monteith equation (hereafter referredto asEPM), assuming a canopy conductance of zero (Gash et al. 1995; Shiet al. 2010):
Table 1Forest structural characteristics of the Roinia pseudoacacia and Pinus tabuliformisstudy plots.
DBH denotes diameter at breast height and LAI leaf area index.a Data are given as mean ± standard deviation.
Table 2Equations describing the five components of interception loss in the revisedGash analytical model.
Components of interception loss Formula
For m rainfall events insufficient to saturate the canopy(Pg < Pg’)
(1) Evaporation from unsaturated canopy ∑ =c Pim
g i1 ,
For n rainfall events sufficient to saturate the canopy(Pg > Pg’)
(1) Wetting up the canopy ′ −nc p S( )g c
(2) Evaporation from saturated canopy during rainfall ∑ − ′=cE R¯ / ¯ (P P )c i
n1 g,i g
(3) Evaporation after rainfall has ceased ncSc(4) Evaporation from trunks for q events, which saturate
the trunks (Pg > Pt’)+ ∑ =
−qS P Pt t in q
g i1 ,
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=− +
+E
Rn G ρ c Dgλ γ
¯ (Δ )ΔPM
a p a
(3)
where △ is the slope of the curve relating the saturation vapor pressureto temperature (kPa k−1), Rn is the net radiation (W m−2), G is the soilheat flux (W m−2), ρa is the air density (kg m−3), cpis the specific heatof the air (J kg−1 k−1), D is the vapor pressure deficit (kPa), λ is thelatent heat of vaporization of water (kPa k−1), γ is the psychometricconstant (J kg−1), andgais the aerodynamic conductance (m s−1),which is calculated using the method provided by Gash et al. (1995):
=−
g k uln((z d)/z )a
2
02 (4)
where k is von Karman’s constant (0.41), u (m s−1) is the wind speed, zis the height at which wind speed was measured (m), d is the zero planedisplacement height (m, here assumed to be 0.7 h), and z0 is theroughness length for momentum (m, here assumed to be 0.1 h). Second,the mean evaporation rate (ETF) was derived from the value ofE R¯/ ¯asestimated from the linear regression relationship between Pg and in-terception loss (Gash, 1979). Third, the mean evaporation rate (EOPT)was estimated through optimization ofE R¯/ ¯by minimizing the root meansquare error between the simulated and observed interception loss forall rainfall events (Marquardt, 1963).
To investigate the effects of seasonal changes in structural para-meters on interception loss, all parameters were estimated separatelyfor two vegetation periods: (1) the leafed growing season (May-October) and (2) the leafless dormant season (November-April). Theperiods were determined according to the phenological measurementsand LAI data of the R. pseudoacacia stand.
2.6. Sensitivity analysis
To identify the relative importance of the parameters in the revisedanalytical model, sensitivity analyses were performed separately for theleafed growing season and leafless dormant season. In this analysis,parameters S, c, E R¯/ ¯ , pt and St were considered, their values were in-creased or decreased by up to 50% of their original values, and thesimulated results were compared to their field data.
3. Results
3.1. Rainfall characteristics
The mean relative error between four rainfall measurements amongthe two forest sites was 3.1%, which indicates that Pg over the studyarea did not vary considerably, and the mean value was thus used as Pgin this study. During the study period, the total measured rainfall was510mm generated by 78 discrete rainfall events, with 464.8 mm(91.1%) and 45.2mm (8.9%) occurring during the leafed growingseason and leafless dormant season, respectively. However, the fre-quency distribution of the event size, duration and intensity were si-milar between the growing and dormant seasons (Fig. 2). Generally,smaller events (< 5 mm) were more frequent but contributed a lowerpercentage of total gross rainfall than larger events (especially duringthe dormant season), and vice versa (Fig. 2A). Rainfall duration dis-tribution was less positively skewed (Fig. 2B), with events in shorter(≤2h) or longer (≥20 h) duration ranges corresponding to a lowerevent frequency (11.5% and 5.1%, respectively). In contrast to rainfallduration, the frequency distribution of rainfall intensity was highlypositively skewed (Fig. 2C), suggesting that low-intensity rainfall(≤0.5mmh−1) had a higher frequency of occurrence than high in-tensity rainfall (≥5 mm h−1). However, rainfall events with low in-tensity contributed a lower percentage of gross rainfall than high in-tensity rainfall. Statistical analysis indicated that the frequencydistribution of rainfall intensity between growing and dormant seasonswas not significant (p=0.064), except for low-intensity events
(≤0.5mmh−1), a significant difference (p < 0.01) was detected.
3.2. Rainfall partitioning
The cumulative annual Tf was 413.6 mm for the R. pseudoacaciastand and 384.6 mm for the P. tabuliformis stand, which accounted for81.1% and 75.4% of Pg, respectively (Table 3). The mean standard er-rors (SE) of the mean Tf for average individual rainfall events were10.3% and 7.6% for the R. pseudoacacia and P. tabuliformis stands, re-spectively. The total Tf for R. pseudoacacia in the growing and dormant
Fig. 2. Percentage of gross rainfall and frequency distributions of the (A)amount, (B) duration, and (C) intensity of rainfall events at the R. pseudoacaciaand P. tabuliformis forest sites during the measurement period (annual, leafedgrowing and leafless dormant seasons) from 1 January to 31 December 2016.Different lower-case letters indicate significant differences (p < 0.05) in thepercentage of gross rainfall between the leafed growing and leafless dormantseasons. Each column represents the mean value (n= 4) with standard devia-tion bars.
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seasons was 373.4 mm (80.3% of corresponding rainfall, P) and40.2 mm (89% of P), respectively. The corresponding values for P. ta-buliformis were 350.2 mm (75.3% of P) and 34.4 mm (76.1% of P).
The cumulative annual Sf was larger in the R. pseudoacacia stand(1.3% of Pg) than in the P. tabuliformis stand (0.7% of Pg). The calcu-lated mean SE values of Sf were much higher: 18.5% for R. pseudoacaciaand 26.3 for P. tabuliformis. In the R. pseudoacacia stand, the cumulativeSf was 6.0mm (1.35% of P) and 0.8 mm (1.8% of P) for the growing anddormant seasons, respectively. The corresponding values for P. tabuli-formis were 3.3 mm (0.7% of P) and 0.3mm (0.7% of P).
Interception loss was derived by subtracting the measured Tf and Sffrom Pg. During the study period, the estimated interception loss waslarger in the P. tabuliformis stand (23.9% of Pg) than in the R. pseu-doacacia stand (17.6% of Pg). The mean SE values of interception loss,which was estimated as the root sum of the variances of Tf and Sf, were9.9% for R. pseudoacacia and 15.6% for P. tabuliformis. In the R. pseu-doacacia stand, the estimated interception loss during the growingseason (85.4mm) was much larger than that in the dormant season(4.2 mm), representing 18.4% and 9.2%, respectively of the corre-sponding rainfall (P). In the P. tabuliformis stand, the growing seasoninterception loss was 111.3mm vs. 10.5mm for the dormant season,representing 23.9% and 23.2% of P, respectively.
3.3. Model parameterization
The total number of rainfall events measured at the two forest sitesduring the growing season (May-October) and dormant season(November-April) of 2016 were each divided into two subsets, i.e.,dataset 1 (n=35) in the growing season and dataset 1* (n= 11) in thedormant season for model calibration and dataset 2 (n=25) and da-taset 2* (n=7) in the growing and dormant seasons, respectively, formodel validation. To account for seasonal variation in rainfall intensity,the data from June, August and October in dataset 1 and fromNovember, December, February and March in dataset 1* were used formodel calibration. The data from May, July and September in dataset 2and January and from April in dataset 2* were used for model valida-tion. Total gross rainfall for dataset 1 and dataset 1* was 267.9mm and28.1 mm, respectively. The corresponding values for dataset 2 and da-taset 2* were 196.9 mm and 17.1 mm.
The values for canopy parameters to be used in the revised Gashanalytical model were obtained from the calibration datasets (dataset 1and dataset 1*), which ensured an independent validation. The meancanopy parameters during the growing and dormant seasons for R.pseudoacacia and P. tabuliformis stands are presented in Table 4. Thefree throughfall coefficients, ρ, estimated from the fisheye photographs(ρLAI) and from regression fitting (ρreg) were similar to each other, withno significant differences (p greater than 0.05) for both forest sites(Fig. 3 and Table 4). In the R. pseudoacacia stand, the ρ values weresignificantly (p < 0.001) higher in the dormant season than in thegrowing season, while in the P. tabuliformis stand, there were no sig-nificant differences (p=0.302) in ρ between the two study seasons. Formodelling purpose, the mean value of ρLAI and ρreg was used to estimate
the canopy cover c (Table 4).In the R. pseudoacacia stand, the canopy storage capacity, S, was
1.34mm in the growing season, nearly seven times higher than that inthe dormant season (0.20 mm), while in the P. tabuliformis stand, S wasslightly higher in the growing season (1.43 mm) than in the dormantseason (1.38 mm) (Fig. 3c and 3d). The trunk storage capacity, St, washigher in the growing season (0.074mm) than in the dormant season(0.052mm) for R. pseudoacacia, while the values (0.041mm) were si-milar in the two seasons for P. tabuliformis (Fig. 3e and 3f). Similarresults were found for the threshold rainfall to entirely saturate thecanopy (Pg′) and the trunk (Pg′′) in the growing season (Pg′: 1.97 mm forR. pseudoacacia and 2.52mm for P. tabuliformis; Pg′′: 5.69 mm for R.pseudoacacia and 5.86mm for P. tabuliformis) and the dormant season(Pg′:1.05mm for R. pseudoacacia and 2.49mm for P. tabuliformis; Pg′′:3.06 mm for R. pseudoacacia and 5.86mm for P. tabuliformis) (Table 4).For the fraction of rainfall contributing to stemflow (Pt), the estimatedvalues (0.007) were similar between the two seasons for P. tabuliformis,while this value was higher in the dormant season (0.017) than in thegrowing season (0.013) for R. pseudoacacia (Fig. 3).
3.4. Model results
Measured and simulated total interception loss using differentevaporate rates for the calibration dataset (dataset 1) from R. pseu-doacacia and P. tabuliformis stands during the growing season aresummarized in Table 5 and presented in Fig. 4. Generally, simulatedinterception loss was underestimated to different extents for the twoforest sites, and the underestimation was larger in the R. pseudoacaciastand than in the P. tabuliformis stand. The predicted interception lossusingEPMvalues was underestimated by 18.9% in the growing season forR. pseudoacacia. The corresponding value for P. tabuliformis was 16.3%.The simulated interception loss usingETFwas closer to the measuredvalues, with an underestimation of 5.4% and 4.5%, respectively, for theR. pseudoacacia and P. tabuliformis stands (Table 5). The predicted in-terception loss using EOPT agreed well with the measured values forboth forest sites, resulting in an underestimation ranging from 2.6% forthe R. pseudoacacia stand to 2.3% for the P. tabuliformis stand (Table 5and Fig. 4). In total, the performance of the revised analytical Gashmodel greatly improved through the usage ofEOPT , with the relativeerror decreasing from 18.9% to 2.3% and the Nash-Sutcliffe coefficientincreasing from 0.64 to 0.93 (Table 5). The performance of the modelwas validated using evaporation rates ETF and EOPT and the estimatedcanopy parameters from the calibration datasets (dataset 1). The si-mulated interception losses using ETF and EOPT for the validation da-tasets (dataset 2) from the R. pseudoacacia and P. tabuliformis standswere also in good agreement with the field measurements, with relativeerrors of< 7.6% and Nash-Sutcliffe coefficients above 0.74 for bothforest stands (Table 5 and Fig. 4).
The observed and simulated interception loss using different eva-poration rates during the dormant season for calibration dataset (da-taset 1*) are presented in Table 6 and Fig. 5. The model run with EPMwas largely underestimated for the two forest sites, with an
Table 3Measured gross rainfall, throughfall, stemflow and derived estimates of interception losses for the Roinia pseudoacacia and Pinus tabuliformis forest stands during thestudy period of January-December 2016.
Experimental plot Study period Gross rainfall (mm) Throughfall (mm) Stemflow (mm) Interception (mm)
R. pseudoacacia Growing season 464.8 373.4 (80.3%) 6.0 (1.3%) 85.4 (18.4%)Dormant season 45.2 40.2 (89.0%) 0.8 (1.8%) 4.2 (9.2%)Annual 510.0 413.6 (81.1%) 6.8 (1.3%) 89.5 (17.6%)
P. tabuliformis Growing season 464.8 350.2 (75.3%) 3.3 (0.7%) 111.3 (23.9%)Dormant season 45.2 34.4 (76.1%) 0.3 (0.7%) 10.5 (23.2%)Annual 510.0 384.6 (75.4%) 3.6 (0.7%) 121.8 (23.9%)
Values in parentheses are the percentage to corresponding gross rainfallLeafed growing season is May-October and Leafless dormant season is November-April.
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underestimation of 21.7% and 18.3% for the R. pseudoacacia and P.tabuliformis stands, respectively. A much better fit between simulatedand measured interception losses was obtained usingETF , with a modelefficiency greater than 0.78 for all forest sites. However, the modelunderestimated the interception loss by 8.7% for R. pseudoacacia, whichwas larger than the underestimation of 5.0% for P. tabuliformis. Theperformance of the revised Gash model using EOPT was slightly betterthan that of the model using ETF for both forest sites, resulting in anunderestimation of only 4.3% and 3.3% for R. pseudoacacia and P. ta-buliformis, respectively. Use of the optimized canopy evaporation rateimproved the model efficiency for both plantations during the dormantseason, with improvements of the Nash-Sutcliffe coefficient from 0.56to 0.84 for R. pseudoacacia and from 0.60 to 0.87 for P. tabuliformis.UsingEOPT , the simulated interception loss was also in very goodagreement with the measured values for both plantations for the vali-dation dataset (dataset 2*), with underestimations of only 5% and4.6%, respectively (Table 6 and Fig. 5). Similar to the results obtainedby usingEOPT , the predicted total interception loss for the validationdatasets also agreed well with those derived from the throughfall andstemflow measurements, with a relative error< 6.8% and modellingefficiency greater than 0.72 (Table 6 and Fig. 5).
Table 7 summarizes different interception components predicted bythe revised analytical Gash model usingEOPT . The results revealed thatmost of the interception loss occurred in the stage of “after rainfall hadceased” in the growing season for both forest sites: 41.2% for R. pseu-doacacia and 42.5% for P. tabuliformis (Table 7). The second-largestterm occurred in the stage of “during saturated rainfall conditions”,with the P. tabuliformis stand exhibiting a slightly greater loss of thiskind (40.1%) than R. pseudoacacia (37.5%). The contribution of smallrainfall to the total evaporation loss was also larger, 9.2% for R. pseu-doacacia and 8.4% for P. tabuliformis, whereas evaporation losses duringcanopy wetting for R. pseudoacacia and from the trunks for P. tabuli-formis were minor (< 6%). In the dormant season, the simulationsuggested that 39.3% and 30% of total interception occurred in thestages of “during saturated rainfall conditions” and “after rainfall hadceased”, respectively, for the R. pseudoacacia stand. The correspondingvalues for P. tabuliformis were 18% and 60%. Evaporation losses fromsmall rainfall events also played a larger role in total evaporation lossfor both forest sites (12% for R. pseudoacacia and 12.7% for P. tabuli-formis), while evaporation loss from trunks was minor (< 10%).
3.5. Sensitivity analysis
A sensitivity analysis of the canopy and climatic variables to inter-ception loss is presented in Fig. 6. Generally, the results indicated thatthe revised analytical Gash model was highly sensitive to variations inthe canopy parameter S and the climatic variable E R¯/ ¯ for both forest
sites. A decrease of 50% in S resulted in a decrease of 24.3% and 9.0%in simulated interception loss for the R. pseudoacacia stand during thegrowing and dormant seasons, respectively (Fig. 6a, b). For the P. ta-buliformis stand, a 50% decrease of S reduced the predicted interceptionloss by 23.6% for the growing season and 29.2% for the dormant season(Fig. 6c, d). A decrease in E R¯/ ¯ of 50% reduced the simulated inter-ception loss by 16.6% and 31.6% for R. pseudoacacia during thegrowing and dormant seasons, respectively. The corresponding valuesfor P. tabuliformis stand were 21.3% and 16.8%. The revised analyticalmodel was less sensitive to the canopy parameter c and trunk parameterpt (Fig. 6). A decrease in c of 50% resulted in a 4.5% and 6.5% decreasein interception loss for the growing and dormant seasons, respectively,in the R. pseudoacacia stand. For the P. tabuliformis stand, the corre-sponding values were 7.0% and 6.2%. Likewise, a 50% decrease in ptreduced interception loss by 2.7% in the growing season and 9.2% inthe dormant season for R. pseudoacacia. The corresponding values for P.tabuliformis were 2.3% and 2.1%. The revised analytical model wasfairly insensitive to the trunk parameter St for both forest sites (Fig. 6).A decrease of 50% in St resulted in a decrease of 1.6% and 3.8% ininterception loss for the growing and dormant seasons, respectively, inthe R. pseudoacacia stand, whereas a similar decrease in St in the P.tabuliformis stand only reduced the interception loss by<0.2%.
4. Discussion
4.1. Interception, throughfall and stemflow
The interception loss in this study for the R. pseudoacacia stand(17.6% of Pg) was similar to the value (20% of Pg) reported by Sadeghiet al. (2016) but much higher than the value (8.6% of Pg) observed byWang et al. (2013). The reason for this large difference between ourstudy and that of Wang et al. (2013) is likely caused by the lowerrainfall intensity (R=1.83mmh−1) and higher evaporation rates(E=0.18mmh−1) during rainfall in the study area compared to that inthe central Loess Plateau, where most rainfall events are short and in-tense (R=2.82mmh−1) and evaporation rates are lower(E=0.11mmh−1) (Wang et al., 2013). The measured interception lossfor the P. tabuliformis stand (23.9% of Pg) was slightly higher than thevalues already reported for other pine forests with a low stem density,such as 22.9% in a Pinus elliottii and Pinus caribaea plantation reportedby Fan et al. (2014), 22.3% in a mature pine forest reported by Bryantet al. (2005), 19.2% in a Pinus pseudostrobus forest reported by Silvaet al. (2001), 16.5% in a planted Pinus roxburghii forest reported byGhimire et al. (2012), and 14.2% in a pure and natural Pinus armandiiforest reported by Shi et al. (2010). It is possible that the higher in-terception loss was due to the higher tree density, LAI and canopy coverin our study, which may have resulted in a higher E during rainfall
Table 4Summary of canopy and climatic parameters used in the revised analytical model for the R. pseudoacacia and P. tabuliformis forest stands. Canopy-related parameterswere estimated using calibration datasets (June, August and October in the leafed growing season; November, December, February and March in the leafless dormantseason).
Forest type Canopy-related parameters a Climate-related parameters b
S (mm) ρreg ρLAI ‾ρ c St (mm) pt R EPM ETF EOPT E/R Pg′ Pg″
a Canopy parameters: S, canopy storage capacity; ρreg, free through coefficient estimated from regression between throughfall and gross rainfall; ρLAI, free throughcoefficient estimated from digital hemispherical photography; ρ, mean free through coefficient of ρreg and ρLAI; c, canopy cover; St, trunk storage capacity; pt, theproportion of rain diverted into stemflow.
b Climatic parameters: R (mm h−1), average rainfall intensity; ETF (mm h−1), throughfall-based estimate of wet-canopy evaporation; EPM (mm h−1), wet-canopyevaporation estimated based on Penman-Monteith equation; EOPT (mm h−1), optimized wet-canopy evaporation; E/R, the ratio of wet-canopy evaporation to rainfallintensity; pg′ (mm), the amount of rain to saturate the canopy; pg″(mm), the amount of rain to saturate the trunk.
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Fig. 3. The relationship between gross rainfall and rainfall partitioning components: throughfall (a, b), throughfall + stemflow (c, d), and stemflow (e, f) for R.pseudoacacia and P. tabuliformis plantations during the growing season (a, c and e) and dormant season (b, d and f).
Table 5A comparison of the total observed and modelled interception loss (mm) by the revised Gash analytical model using different wet-canopy evaporation rates for thecalibration and validation datasets during the growing season for the R. pseudoacacia and P. tabuliformis forest stands.
Calibration (dataset 1) Validation (dataset 2)
Model 1 (EPM) Model 2 (ETF) Model 3 (EOPT) Model 2 (ETF) Model 3 (EOPT)
R. pse P. tab R. pse P. tab R. pse P. tab R. pse P. tab R. pse P. tab
R. pse denotes R. pseudoacacia and P. tab denotes P. tabuliformis.Dataset 1: June, August and October.Dataset 2: May, July and September.
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(Gash et al., 1995; Šraj et al., 2008). Overall, the observed interceptionloss was higher in P. tabuliformis than in R. pseudoacacia, which is ex-pected, as interception loss tends to be higher in evergreen coniferousforests than in broad-leafed deciduous forests (Fan et al., 2014; Sadeghiet al., 2016), and these findings are in good agreement with previouswork synthesizing interception loss studies (Carlyle-Moses and Gash,2011). Since the weather conditions experienced by the two forest
stands were similar, the higher interception loss observed for the P.tabuliformis stand thus can be largely explained by its higher S andEduring rainfall (Table 4).
The interception loss percentage was significantly higher during theleafed period (18.4%) than the leafless period (9.2%) for deciduous R.pseudoacacia, which agrees well with findings for other deciduous for-ests (Deguchi et al., 2006; Muzylo et al., 2012; Park et al., 2000). The
Fig. 4. Cumulative measured and modelled interception losses for the R. pseudoacacia (a and b) and P. tabuliformis (c and d) stands during the growing season usingthe calibration and validation datasets. Modelled interception losses were calculated for each forest stand using wet-canopy evaporation rates estimated with thePenman-Monteith equation (EPM), a throughfall-based wet-canopy evaporation rate (ETF), and an optimized wet-canopy evaporation (EOPT). The modelled inter-ception loss for the validation datasets was calculated for each forest stand using ETFandEOPT .
Table 6A comparison of the total observed and modelled interception loss (mm) by the revised Gash analytical model using different wet-canopy evaporation rates for thecalibration and validation datasets during the dormant season for the R. pseudoacacia and P. tabuliformis forest stands.
Calibration (dataset 1*) Validation (dataset 2*)
Model 1 (EPM) Model 2 (ETF) Model 3 (EOPT) Model 2 (ETF) Model 3 (EOPT)
R. pse P. tab R. pse P. tab R. pse P. tab R. pse P. tab R. pse P. tab
R. pse denotes R. pseudoacacia and P. tab denotes P. tabuliformis.Dataset 1*: November, December, February and March.Dataset 2*: January and April.
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decreased interception loss (increased net precipitation received at thesoil surface) in the leafless season may depend on the reduction in leafamounts and canopy cover (Table 4), as shown in the positive re-lationship between the leaf area index and interception loss that hasalready been reported elsewhere (e.g., between seasons, Deguchi et al.,2006; Park et al., 2000; by thinning, Molina and del Campo, 2012;
Shinohara et al., 2015; by beetle infestation, Bearup et al., 2014). In-creased net precipitation during the leafless dormant season will in-crease the soil water content in the unsaturated soil zone, which couldincrease tree growth during the next leafed growing season. For ex-ample, Worbes (1999) reported that the precipitation amount duringthe dormant season has a close relationship with tree diameter growth
Fig. 5. Cumulative measured and modeled interception losses for R. pseudoacacia (a and b) and P. tabuliformis (c and d) stands during the dormant season usingcalibration and validation datasets. Modeled interception losses for calibration were calculated for each forest stand using wet-canopy evaporation rates estimatedwith the Penman-Monteith equation (EPM), a throughfall-based wet-canopy evaporation rate (ETF), and an optimized wet-canopy evaporation (EOPT). The modelledinterception loss for the validation datasets were calculated for each forest stand using ETFandEOPT .
Table 7Components of interception loss simulated by the revised Gash analytical model with the optimized wet-canopy evaporation rate for the Roinia pseudoacacia and Pinustabuliformis forest stands.
Components of interception Simulated interception (mm)
Growing season Dormant season
R. pseudoacacia P. tabuliformis R. pseudoacacia P. tabuliformis
For m rainfall events insufficient to saturate the canopy (Pg < Pg’)(1) Evaporation from unsaturated canopy 7.9 (9.2%) 9.1 (8.4%) 0.5 (12.0%) 1.3 (12.7%)For n rainfall events sufficient to saturate the canopy (Pg > Pg’)(1) Wetting up the canopy 4.6 (5.3%) 6.0 (5.5%) 0.3 (8.5%) 0.5 (4.9%)(2) Evaporation from saturated canopy during rainfall 32.4 (37.5%) 43.2 (40.1%) 1.6 (39.3%) 1.8 (18.0%)(3) Evaporation after rainfall has ceased 35.6 (41.2%) 45.8 (42.5%) 1.2 (30.0%) 6.0 (60.0%)(4) Evaporation from trunks for q events, which saturate the trunks (Pg > Pt’) 5.9 (6.8%) 3.6 (3.3%) 0.4 (9.6%) 0.4 (4.3%)Simulated interception loss (mm) 86.5 107.7 4.0 10.0Measured interception loss (mm) 85.4 111.3 4.2 10.5
Values in parentheses are the percentage to corresponding total interception
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during the growing season for deciduous forests. Hence, in water-lim-ited regions (e.g., arid and semi-arid regions), where water resourceavailability may have a substantial impact on forest growing, forestmanagers are highly recommended to implement hydrology-orientedsilviculture (e.g., reduce leaf amount and cover) to reduce stand inter-ception loss and transpiration and, at the tree level, to improve growthand water use efficiency.
The observed throughfall Tf percentage of the R. pseudoacacia stand(81.1% of Pg) was much lower than that previously reported for other R.pseudoacacia forests with a low basal area in the central Loess Plateau,e.g., 89.1% of Pg reported by Wang et al. (2013). Likewise, the Tf for theP. tabuliformis stand (75.4% of Pg) was also much lower than theamounts reported in other pine forests with a low basal area, e.g., 85%in a natural Pinus armandii forest reported by Shi et al. (2010) and83.8% in a thinned Pinus halepensis forest reported by Molina and delCampo (2012), but it was comparable to the values (78.3% of Pg) re-ported by Fan et al. (2014) in a pine plantation with a similar basalarea. This result indicates that the lower Tf fraction may possibly beexplained by the relatively high basal area in the two forest stands.Since the basal area was similar between the two forests, the lower Tffraction in P. tabuliformis relative to R. pseudoacacia may well have beencaused by their thicker crown depth and denser leaves. The thickercrown depth and denser leaves likely increased the length of interactionbetween raindrops and the crown surface, resulting in more rainwaterretention and a reduced Tf fraction (Park and Cameron, 2008; Sadeghiet al., 2016).
In this study, the observed stemflow percentage of R. pseudoacacia(1.3% of the gross rainfall) was obviously higher than that of P. tabu-liformis (0.7% of the gross rainfall, p greater than 0.05). The steeplyangled branches and smooth bark of R. pseudoacacia would favourably
channel rainwater to Sf compared with the low-angled branches andrough bark of P. tabuliformis, thus potentially generating a higher Sf forR. pseudoacacia (Fan et al., 2014). The measured Sf for P. tabuliformis(0.7% of Pg) was comparable to those observed for other pine forestwith a similar basal area, e.g., 0.9% of Pg in a natural Pinus armandiiforest reported by Shi et al. (2010) and 1% of Pg in a Pinus elliottii andPinus caribaea plantation reported by Fan et al. (2014), but it wasslightly lower than the observations (1.5% of Pg) in a natural Pinushalepensis forest with a high basal area reported by Molina and delCampo (2012). This finding may indicate that the lower Sf can be ex-plained by the lower basal area in our forest stand. Concerning decid-uous R. pseudoacacia, the measured Sf was slightly higher in the leaflessdormant season (1.8% of Pg) than in the leafed growing season (1.3% ofPg), which is in good agreement with reported findings for other de-ciduous forests (Herbst et al., 2008; Muzylo et al., 2012; Staelens et al.,2008). The increased Sf in the leafless dormant season relative to theleafed growing season was likely caused by the reduction in leafamounts and the low evaporation rates during rainfall (Table 3 and 4),as previously reported by Herbst et al. (2008) and Andre et al. (2008).
4.2. Wet-canopy evaporation
The average annual wet-canopy evaporation rates EPM calculatedusing the Penman-Monteith equation were 0.05mmh−1 (0.05mmh−1
in growing season, 0.04mmh−1 in dormant season) and 0.04mmh−1
(0.06 mmh−1 in growing season, 0.03mmh−1 in dormant season) forthe R. pseudoacacia and P. tabuliformis stands, respectively, fallingwithin the range of estimates for other temperate forests(0.02–1.11mmh−1; Herbst et al., 2006; Muzylo et al., 2012; Shinoharaet al., 2015). Estimates (ETF) based on the regression method were
Fig. 6. Sensitivity analyses of canopy parameters S, c, pt and St and climatic parametersE R¯/ ¯of the revised Gash analytical model for the predicted interception lossesfor R. pseudoacacia (a and b) and P. tabuliformis (c and d) stands during the growing and dormant seasons.
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scaled to the canopy cover fraction to allow a direct comparisonwithEPM and provided average annual values of 0.25mmh−1 and0.26mmh−1 for the R. pseudoacacia stand (0.25 mmh−1 for both thegrowing and dormant seasons) and the P. tabuliformis stand(0.38 mmh−1 in the growing season; 0.15mmh−1 in the dormantseason), which were approximately six (R. pseudoacacia) and four (P.tabuliformis) times higher than the corresponding values ofEPM . Theoptimized wet-canopy evaporation rates EOPT were very close to the ETF
values (Table 4), but they were much lower than the optimized valuesin (sub-) tropical forests (Ghimire et al., 2017; Fan et al., 2014; Wallaceand McJannet, 2008).
Significant differences between EPM andETF , as in this study, havealso been reported in other interception modelling studies (Ghimireet al., 2012, 2017; Muzylo et al., 2012; Wallace and McJannet, 2008),and the possible reasons for this larger discrepancy are discussed below.First, the Penman-Moentich equation considers vegetation as a single“big leaf”, which may no longer be valid for the tall and sparse forestsbecause their complicated vertical structure tends to enhance eddyturbulence and evaporation (Holwerda et al., 2012). Second, the zeroplane displacement heights and roughness heights used to calculate theaerodynamic conductance ga in the Penman-Monteith equation can bequestionable and may tend to underestimate ga and thus EPM whenapplied in areas with a rugged topography like this study area (Ghimireet al., 2012; Holwerda et al., 2012). Finally, the larger discrepancybetween EPM and ETF can also be caused by the evaporation of smallsplashed rainfall droplets (Murakami, 2007), the possible existence of alarger-scale biotic pump of atmospheric moisture (Makarieve andGorshkov, 2007) and the difficulty associated with accurately mea-suring the very high relative humidity levels during rainfall (Wallanceand McJannet, 2008).
4.3. Canopy parameters
The average canopy storage capacity S was significantly higher inthe growing season (1.34mm) than the dormant season (0.2 mm) for R.pseudoacacia, which agrees well with the findings for other temperateforests (Fathizadeh et al., 2018; Herbst et al., 2008; Muzylo et al.,2012). The S values for P. tabuliformis were similar between thegrowing season (1.43 mm) and the dormant season (1.38 mm), fallingwithin the range reported in other pine forests (0.5–3.0 mm, Llorensand Gallart, 2000). Since the local climate and basal area were similarbetween the two forest stands, the differences in S could thus be at-tributed to the differences in canopy structure. However, it is difficult todraw a generalization for S because it is controlled by many factors,such as rainfall characteristics (e.g., raindrop size distribution and in-tensity), climate conditions (e.g., wind speed), canopy traits (e.g., basalarea, height and wood area index) and measurement errors (Carlyle-Moses and Gash, 2011).
The free throughfall coefficient, a structure parameter assumed tobe one minus the canopy cover c, is a sensitive parameter for modellinginterception loss, and it is also an important parameter that is usuallyconsidered in forest water resource management. In this study, themeasured average annual free throughfall coefficient was 0.52 for theR. pseudoacacia stand and 0.38 for the P. tabuliformis stand, indicatingthat the canopy interception fraction in P. tabuliformis was higher thanthat in R. pseudoacacia. Hydrologically, the free throughfall coefficienthas an important effect on soil water content and nutrient cycling, as itreflects the fraction of rainfall passes through the canopy withoutcontacting the canopy surface and removing dry deposition in the ca-nopy (Asadian, 2007). Thus, the lower and higher S values in the P.tabuliformis stand could result in less throughfall reaching the forestfloor and, hence, show a potential to enhance soil water scarcity in thisarid/semi-arid region undergoing re/afforestation. In contrast, thelower and higher S values can protect the forest floor from raindropsplash erosion and delay the peaks in storm runoff (Asadian, 2007).Therefore, the free throughfall coefficient and canopy storage capacity
should be considered when selecting trees for re/afforestation wheresoil erosion is serious.
The measured Sf was relatively low in comparison to other rainfallfractions for the two forest stands (Table 3), introducing a risk inmaking comparisons of the Sf-related parameters St and pt with otherstudies. The observed average annual value of pt for R. pseudoacacia(0.015) was much lower than the only reported value of 0.032 by Wanget al. (2013), but the value for P. tabuliformis (0.007) was comparable tothat reported for other pine forests with a similar basal area(0.009–0.014, Fan et al., 2014; Shi et al., 2010). In this study, the ob-served mean value of St was slightly higher in the R. pseudoacacia stand(0.063) than in the P. tabuliformis stand (0.041), which is in goodagreement with the much higher trunk and longer branches of R.pseudoacacia than of P. tabuliformis, potentially counteracting the effectof the rougher bark of P. tabuliformis.
4.4. Performance of the revised analytical model
The revised Gash analytical model of rainfall interception sa-tisfactorily estimated the total interception loss in both R. pseudoacaciaand P. tabuliformis stands (Tables 5 and 6), and it also captured well theseasonal (growing and dormant seasons) variation. In terms of model-ling error, the revised model performed better for P. tabuliformis thanfor R. pseudoacacia, and the modelling errors between observed andsimulated interception loss were acceptable compared to the reports inother temperate forest interception modelling studies (e.g., Deguchiet al., 2006; Fathizadeh et al., 2018; Herbst et al., 2006, 2008; Muzyloet al., 2012). In the present study, the model slightly overestimatedinterception loss for some smaller rainfall events, while the interceptionloss for larger rainfall events tended to be underestimated. The mod-elling errors resulting from an underestimation of most large rainfallevents were considered to be responsible for the underestimation of thetotal interception loss, as reported previously (Fathizadeh et al., 2018;Shi et al., 2010). Therefore, careful attention should be paid whenapplying this revised model to changing rainfall conditions towardslarger rainfall sizes (Sadeghi et al., 2015). The derived Nash-Sutcliffemodel efficiency for the revised model was higher in the P. tabuliformisstand (0.6–0.93) than in the R. pseudoacacia stand (0.56–0.90), and itwas comparable to previous findings in other temperate rainfall inter-ception studies, generally ranging from 0.44 to 0.94 (Bryant et al.,2005; Fan et al., 2014). In this study, the use of EOPT greatly improvedthe overall model performance compared with the use ofEPM , but EOPTprovided little improvement effect on the overall model performancecompared with ETF (Table 5 and Table 6).
The application of different parameterization sets to shorter timeperiods (e.g., seasonal or monthly) for modelling interception loss isnecessary because many model parameters (e.g., canopy storage capa-city, canopy cover and evaporation rate) change with varying canopystructures and shifting climatic conditions (Deguchi et al., 2006; Herbstet al., 2008; Šraj et al., 2008). Moreover, using constant parametervalues for annual interception modelling could lead to significantunder- or over-estimations. Therefore, to avoid these problems,monthly or seasonal parameter values have been recommended for usein interception loss modelling (Murakami et al., 2007; Muzylo et al.,2012). In this study, the revised analytical model was therefore runseparately for the leafed growing and leafless dormant seasons. Com-pared with the growing season, the underestimation of interception lossin the dormant season was much higher by the revised Gash analyticalmodel (Table 5 and Table 6). The higher underestimation of intercep-tion loss in the dormant season was probably introduced by the un-derestimation of rainfall intensity (Table 4), when small rainfall eventsoccur more frequently (Fig. 2) and more actual evaporation is supposedto occur. Previous studies have demonstrated that the revised analyticalmodel better estimates rainfall events when interception loss is de-pendent on S, which means that for rainfall events sufficiently large tosaturate S, E R¯/ ¯has more control over interception loss, which could
C. Ma, et al. Journal of Hydrology 577 (2019) 123849
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lead to greater bias between modelled and measured values (Fathizadehet al., 2018; Sadeghi et al., 2015). In the present study, the measure-ments indicated that the S value was lower in the dormant season thanin the growing season (Table 4), and there were more rainfall eventsthat were sufficiently large to saturate the canopy during the dormantseason (Fig. 2). Therefore, accordingly, interception loss was influencedmore by E R¯/ ¯(and less controlled by S) in the dormant season, and thus,a much greater underestimation of interception loss can be expected.
Meteorological records based on high-density gauge observationshave indicated that precipitation has shifted towards fewer and higherrainfall events over most area of the study region (Sun et al., 2015).Therefore, if the future climate in the semi-arid area shifts towards afrequency of few and intense rainfall events, then use of the revisedanalytical model for interception loss modelling can be more appro-priate for the P. tabuliformis forest than for the R. pseudoacacia forest interms of model efficiency (Tables 5 and 6). For other arid and semi-aridafforestation sites with different climatic condition and tree species,application of the revised Gash model may have different canopy andrainfall characteristics to be considered.
5. Conclusion
Rainfall interception losses were quantified and modelled for a de-ciduous broad-leafed R. pseudoacacia stand and an evergreen needle-leaf P. tabuliformis stand situated in the semi-arid Loess Plateau ofChina. Over the one-year (January-December 2016) study period, themeasured throughfall, stemflow and derived estimates of interceptionloss for R. pseudoacacia were 81.1%, 1.3% and 17.6%, respectively. Thecorresponding values for P. tabuliformis were 75.4%, 0.7% and 23.9%.Given that the weather conditions experienced by the two forest standswere similar, the observed higher interception loss for P. tabuliformiscan be explained by the higher canopy storage capacity and wet-canopyevaporation rates of this species. The revised analytical model was wellcalibrated and validated against the field measurements, and it was ableto simulate the cumulative interception loss at two forest stands accu-rately and effectively capture the seasonal variations (leafed growingand leafless dormant seasons), provided that the gross rainfall-throughfall regression method based wet-canopy evaporation rate andthe optimized wet-canopy evaporation rate were used. Analysis of themagnitude of the different interception components distinguished bythe model revealed that most of the total interception loss occurredfrom canopies in the stages of “during saturated rainfall conditions” and“after rainfall had ceased”. Small rainfall events played a larger role intotal interception loss, while interception loss from trunks was minor.The revised model was highly sensitive to canopy storage capacity andchanges in the ratio of mean wet canopy evaporation to mean rainfallintensity, and less sensitive to canopy cover, but it was found to befairly insensitive to trunk storage capacity. As interception loss canremove a significant portion of rainwater from soil moisture, tran-spiration, surface and groundwater recharge, quantification and mod-elling tools of interception loss are of significant importance to semi-/arid regions undergoing re/afforestation because the measurements andmodelling measures allow water resources and forest managers to ef-ficiently predict the effects of re/afforestation on water inputs under afuture shifting climate.
Declaration of Competing Interest
None
Acknowledgements
This study was sponsored by the National Natural ScienceFoundation of China (No. 41571130081) and the National KeyResearch and Development Plan of China (No. 2016YFC0501603). Weare indebted to the editors and reviewers for their constructive
comments and suggestions on the work.
Author Contribution
Changkun Ma and Yi Luo, Conception and design, Acquisition ofdata, Analysis and Interpretation of data, Drafting the articale; MinganShao, Conception and design; Xiangdong Li and Xiaoxu Jia, Acquisitionof data. All authors revised the Manuscript and read and approved thefinal manuscript.
References
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: guidelinesfor computing crop water requirements, FAO Irrigation and Drainage Paper No. 56.FAO, Rome.
André, F., Jonard, M., Ponette, Q., 2008. Influence of species and rain event character-istics on stemflow volume in a temperate mixed oak-beech stand. Hydrol. Process. 22(22), 4455–4466.
Asadian, Y., 2007. Rainfall interception in an urban environment. Univeristiy of BritishColumbia, Canada.
Augusto, L., Ranger, J., Binkley, D., Rothe, A., 2002. Impact of several common treespecies of European temperate forests on soil fertility. Annals of Forest Science 59 (3),233–253.
Baret, F., Weiss, M., 2004. Can-Eye: processing digital photographs for canopy structurecharacterization.
Bryant, M.L., Bhat, S., Jacobs, J.M., 2005. Measurements and modeling of throughfallvariability for five forest communities in the southeastern US. J. Hydrol. 312 (1–4),95–108.
Cao, S.X., Chen, L., Shankman, D., Wang, C.M., Wang, Y.B., Zhang, H., 2011. Excessivereliance on afforestation in China's arid and semi-arid regions: Lessons in ecologicalrestoration. Earth Sci. Rev. 104 (4), 240–245.
Carlyle-Moses, D.E., Gash, J.H.C., 2011. Rainfall interception loss by forest canopies.Forest Hydrology and Biogeochemistry: Synthesis of Past Research and FutureDirections. Springer Ecological Studies.
Carlyle-Moses, D.E., Price, A.G., 1999. An evaluation of the Gash interception model in anorthern hardwood stand. J. Hydrol. 214 (1–4), 103–110.
Carlyle-Moses, D.E., Price, A.G., 2007. Modelling canopy interception loss from aMadrean pine-oak stand, northeastern Mexico. Hydrol. Process. 21 (19), 2572–2580.
Crockford, R.H., Richardson, D.P., 2000. Partitioning of rainfall into throughfall, stem-flow and interception: effect of forest type, ground cover and climate. Hydrol.Process. 14 (16–17), 2903–2920.
Deguchi, A., Hattori, S., Park, H.T., 2006. The influence of seasonal changes in canopystructure on interception loss: Application of the revised Gash model. J. Hydrol. 318(1–4), 80–102.
Dunkerley, D., 2000. Measuring interception loss and canopy storage in dryland vege-tation: a brief review and evaluation of available research strategies. Hydrol. Process.14 (4), 669–678.
Fan, J., Oestergaard, K.T., Guyot, A., Lockington, D.A., 2014. Measuring and modelingrainfall interception losses by a native Banksia woodland and an exotic pine plan-tation in subtropical coastal Australia. J. Hydrol. 515, 156–165.
FAO, 2015. Global guidelines for the restoration of degraded forests and landscapes indrylands: building resilience and benefiting livelihood. Forestry Paper No. 175.Rome, Food and Agricultrual Orgnization of the Uniited Nations.
Farley, K.A., Jobbagy, E.G., Jackson, R.B., 2005. Effects of afforestation on water yield: aglobal synthesis with implications for policy. Glob. Change Biol. 11 (10), 1565–1576.
Fathizadeh, O., Hosseini, S.M., Keim, R.F., Boloorani, A.D., 2018. A seasonal evaluation ofthe reformulated Gash interception model for semi-arid deciduous oak forest stands.For. Ecol. Manage. 409, 601–613.
Forgeard, F., Gloaguen, Y.C., Touffet, J., 1980. Interception des précipitations et apportau sol d'élèments mineraux par les eaux de pluie et les pluviolessivats dans unehêtraie atlantique et dans quelques peuplements résineux en Bretagne. Annales desSciences Forestières 37, 53–71.
Franklin, J.F., Spies, T.A., Van Pelt, R., Carey, A.B., Thornburgh, D.A., Berg, D.R.,Lindenmayer, D.B., Harmon, M.E., Keeton, W.S., Shaw, D.C., Bible, K., Chen, J.Q.,2002. Disturbances and structural development of natural forest ecosystems withsilvicultural implications, using Douglas-fir forests as an example. For. Ecol. Manage.155 (1–3), 399–423.
Gash, J.H.C., 1979. An analyitical model of rainfall interception by forests. Q. J. R.Meteorolog. Soc. 105, 43–55.
Gash, J.H.C., Lloyd, C.R., Lachaud, G., 1995. Estimating sparse forest rainfall interceptionwith an analytical model. J. Hydrol. 170 (1–4), 79–86.
Gash, J.H.C., Morton, A.J., 1978. An application of the Rutter model to the estimation ofthe interception loss from Thetford forest. J. Hydrol. 38, 49–58.
Ghimire, C.P., Bruijnzeel, L.A., Lubczynski, M.W., Bonell, M., 2012. Rainfall interceptionby natural and planted forests in the Middle Mountains of Central Nepal. J. Hydrol.475, 270–280.
C. Ma, et al. Journal of Hydrology 577 (2019) 123849
Ghimire, C.P., et al., 2017. Measurement and modeling of rainfall interception by twodifferently aged secondary forests in upland eastern Madagascar. J. Hydrol. 545,212–225.
Guo, H., Wang, B., Ma, X., Zhao, G., Li, S., 2008. Evaluation of ecosystem services ofChinese pine forests in China. Science China-Life Sciences 51 (7), 662–670.
He, Z.B., Yang, J.J., Du, J., Zhao, W.Z., Liu, H., Chang, X.X., 2014. Spatial variability ofcanopy interception in a spruce forest of the semiarid mountain regions of China.Agric. For. Meteorol. 188, 58–63.
Herbst, M., Roberts, J.M., Rosier, P.T.W., Gowing, D.J., 2006. Measuring and modellingthe rainfall interception loss by hedgerows in southern England. Agric. For. Meteorol.141 (2–4), 244–256.
Herbst, M., Rosier, P.T.W., McNeil, D.D., Harding, R.J., Gowing, D.J., 2008. Seasonalvariability of interception evaporation from the canopy of a mixed deciduous forest.Agric. For. Meteorol. 148 (11), 1655–1667.
Holwerda, F., Bruijnzeel, L.A., Scatena, F.N., Vugts, H.F., Meesters, A.G.C.A., 2012. Wetcanopy evaporation from a Puerto Rican lower montane rain forest: The importanceof realistically estimated aerodynamic conductance. J. Hydrol. 414–415, 1–15.
Jackson, I.J., 1975. Relationships between rainfall parameters and interception by tro-pical fores. J. Hydrol. 24, 215–238.
Jia, X.X., Shao, M.A., Zhu, Y.J., Luo, Y., 2017. Soil moisture decline due to afforestationacross the Loess Plateau, China. J Hydrol 546, 113–122.
Jian, S., Zhao, C., Fang, S., Yu, K., 2015. Effects of different vegetation restoration on soilwater storage and water balance in the Chinese Loess Plateau. Agric. For. Meteorol.206, 85–96.
Limousin, J.-M., Rambal, S., Ourcival, J.-M., Joffre, R., 2008. Modelling rainfall inter-ception in a mediterranean Quercus ilex ecosystem: Lesson from a throughfall ex-clusion experiment. J. Hydrol. 357 (1–2), 57–66.
Llorens, P., 1998. Rainfall interception by a Pinus sylvestris forest patch overgrown in aMediterranean mountainous abandoned area II: Assessment of the applicability ofGash's analytical model. J. Hydrol. 199, 346–359.
Llorens, P., Domingo, F., 2007. Rainfall partitioning by vegetation under Mediterraneanconditions. A review of studies in Europe. J. Hydrol. 335 (1–2), 37–54.
Llorens, P., Gallart, F., 2000. A simplified method for forest water storage capacitymeasurement. J. Hydrol. 240 (1–2), 131–144.
Ma, F., Xu, T.T., Ji, M.F., Zhao, C.M., 2014. Differential drought tolerance in tree po-pulations from contrasting elevations. Aob Plants, 6.
Macfarlane, C., et al., 2007. Estimation of leaf area index in eucalypt forest with verticalfoliage, using cover and fullframe fisheye photography. For. Ecol. Manage. 242 (2–3),756–763.
Makarieva, A.M., Gorshkov, V.G., 2007. Biotic pump of atmospheric moisture as driver ofthe hydrological cycle on land. Hydrol. Earth Syst. Sci. 11, 1013–1033.
Malagnoux, M., Sene, E.H., Atzmon, N., 2007. Forests, trees and water in arid lands: adelicate balance. Unasylva 58, 24–29.
Marquardt, D.W., 1963. An algorithm for least-square estimation of non-linear para-meters. J. Soc. Ind. Appl. Math. 11 (2), 431–441.
Molina, A.J., del Campo, A.D., 2012. The effects of experimental thinning on throughfalland stemflow: A contribution towards hydrology-oriented silviculture in Aleppo pineplantations. For. Ecol. Manage. 269, 206–213.
Motahari, M., Attarod, P., Pypker, T.G., Etemad, V., Shirvany, A., 2013. RainfallInterception in a Pinus eldarica Plantation in a Semiarid Climate Zone: AnApplication of the Gash Model. Journal of agricultural and technology 15, 981–994.
Murakami, S., 2007. Application of three canopy interception models to a young stand ofJapanese cypress and interpretation in terms of interception mechanism. J. Hydrol.342 (3–4), 305–319.
Muzylo, A., Llorens, P., Valente, F., Keizer, J.J., Domingo, E., Gash, J.H.C., 2009. A reviewof rainfall interception modelling. J. Hydrol. 370 (1–4), 191–206.
Muzylo, A., Valente, F., Domingo, F., Llorens, P., 2012. Modelling rainfall partitioningwith sparse Gash and Rutter models in a downy oak stand in leafed and leaflessperiods. Hydrol. Process. 26 (21), 3161–3173.
Niu, S., et al., 2008. Water-mediated responses of ecosystem carbon fluxes to climaticchange in a temperate steppe. The New phytologist 177 (1), 209–219.
Park, A., Cameron, J.L., 2008. The influence of canopy traits on throughfall and stemflowin five tropical trees growing in a Panamanian plantation. For. Ecol. Manage. 255(5–6), 1915–1925.
Park, H.T., Hattori, S., Kang, H., 2000. Seasonal and inter-plot variations of stemflow,throughfall and interception loss in two deciduous broad-leaved forests. JapanSociety of Hydrology and Water Resource 13, 17–30.
Price, A.G., Carlyle-Moses, D.E., 2003. Measurement and modelling of growing-seasoncanopy water fluxes in a mature mixed deciduous forest stand, southern Ontario.Canada. Agricultural and Forest Meteorology 119 (1–2), 69–85.
Ritter, A., Regalado, C.M., 2014. Roving revisited, towards an optimum throughfallsampling design. Hydrol. Process. 28 (1), 123–133.
Rutter, A.J., Kershaw, K.A., Robins, P.C., Morton, A.J., 1971. A predictive model ofrainfall interception in forests, 1. Derivation of the model from observations in aplantation of Corsican pine. Agric. Meteorol. 9, 367–384.
Rutter, A.J., Morton, A.J., Robins, P.C., 1975. A Predictive Model of Rainfall Interceptionin Forests. II. Generalization of the Model and Comparison with Observations in SomeConiferous and Hardwood Stands. J. Appl. Ecol. 12 (1), 367–380.
Sadeghi, S.M.M., Attarod, P., Van Stan, J.T., Pypker, T.G., 2016. The importance ofconsidering rainfall partitioning in afforestation initiatives in semiarid climates: Acomparison of common planted tree species in Tehran, Iran. Sci. Total Environ. 568,845–855.
Sadeghi, S.M.M., Attarod, P., Van Stan, J.T., Pypker, T.G., Dunkerley, D., 2015. Efficiencyof the reformulated Gash's interception model in semiarid afforestations. Agric. For.Meteorol. 201, 76–85.
Shi, Z.J., Wang, Y.H., Xu, L.H., Xiong, W., Yu, P.T., Gao, J.X., Zhang, L.B., 2010. Fractionof incident rainfall within the canopy of a pure stand of Pinus armandii with revisedGash model in the Liupan Mountains of China. J. Hydrol. 385 (1–4), 44–50.
Shinohara, Y., Levia, D.F., Komatsu, H., Nogata, M., Otsuki, K., 2015. Comparativemodeling of the effects of intensive thinning on canopy interception loss in aJapanese cedar (Cryptomeria japonica D.Don) forest of western Japan. Agric. For.Meteorol. 214–215, 148–156.
Silva, I.C., Rodriguez, H.G., 2001. Interception loss, throughfall and stemflow chemistryin pine and oak forests in northeastern Mexico. Tree Physiol. 21 (12–13), 1009–1013.
Šraj, M., Brilly, M., Mikoš, M., 2008. Rainfall interception by two deciduousMediterranean forests of contrasting stature in Slovenia. Agric. For. Meteorol. 148(1), 121–134.
Staelens, J., De Schrijver, A., Verheyen, K., Verhoest, N.E.C., 2008. Rainfall partitioninginto throughfall, stemflow, and interception within a single beech (Fagus sylvatica L.)canopy: influence of foliation, rain event characteristics, and meteorology. Hydrol.Process. 22 (1), 33–45.
Sun, Q., Miao, C., Duan, Q., Wang, Y., 2015. Temperature and precipitation changes overthe Loess Plateau between 1961 and 2011, based on high-density gauge observations.Global Planet. Change 132, 1–10.
Valente, F., David, J.S., Gash, J.H.C., 1997. Modelling interception loss for two sparseeucalypt and pine forests in central Portugal using reformulated Rutter and Gashanalytical models. J. Hydrol. 190 (1–2), 141–162.
Vitkova, M., Mullerova, J., Sadlo, J., Pergl, J., Pysek, P., 2017. Black locust (Robiniapseudoacacia) beloved and despised: a story of an invasive tree in Central Europe. ForEcol Manage 384, 287–302.
Wallace, J., McJannet, D., 2008. Modelling interception in coastal and montane rain-forests in northern Queensland. Australia. Journal of Hydrology 348 (3–4), 480–495.
Wang, L., Zhang, Q., Shao, M.a., Wang, Q., 2013. Rainfall interception in a Robiniapseudoacacia forest stand: estimates using Gash’s analytical model. J. Hydrol. Eng. 18(4), 474–479.
Wang, Y.H., Yu, P.T., Feger, K.-H., Wei, X.H., Sun, G., Bonell, M., Xiong, W., Zhang, S.L.,Xu, L.H., 2011. Annual runoff and evapotranspiration of forestlands and non-forest-lands in selected basins of the Loess Plateau of China. Ecohydrology 4, 277–287.
Worbes, M., 1999. Annual growth rings, rainfall-dependent growth and long-term growthpatterns of tropical trees from the Caparo Forest Reserve in Venezuela. J. Ecol. 87 (3),391–403.
C. Ma, et al. Journal of Hydrology 577 (2019) 123849
