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2 Journal of Hydrology (27) 347, available at journal homepage: Evaluation of canopy interception schemes in land surface models Dagang Wang *, Guiling Wang, Emmanouil N. Anagnostou Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 6269, USA Received 12 October 26; received in revised form 16 August 27; accepted 3 September 27 KEYWORDS Canopy interception; Evaluation; Rain type Summary Field measurements of interception loss ratio published in the literature for different locations over the globe are used in this study to evaluate four canopy interception schemes incorporated into the Community Land Model version 3 (CLM3). Those are the CLM3 default scheme, the Shuttleworth scheme, a scheme that considers the sub-grid variability of both precipitation and canopy water storage (WW7 scheme), and a herein revised WW7 scheme (rain type-distinguished WW7) that distinguishes for rain type (convective versus stratiform). With the exception of Shuttleworth scheme that exhibits significant temporal resolution sensitivity in simulating the canopy interception loss, the rest three schemes are validated against observations. The rain type-distinguished WW7 scheme exhibits the best performance in producing the annual rainfall interception loss ratio over the globe with an overall mean bias error (MBE) of.7 and root mean square error (RMSE) of.121. This scheme also produces a realistic seasonality of the rainfall interception loss ratio in tropical areas, which is verified against field measurements from a site in the Amazon rainforest. ª 27 Elsevier B.V. All rights reserved. Introduction Land surface is an important component of the climate system and its parameterization is a challenging task in climate modeling (Dickinson et al., 26). To realistically represent the land surface processes, many land surface models (LSMs) of varying sophistication level have been developed * Corresponding author. Current address: Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 854, USA. Tel.: ; fax: address: dagangw@princeton.edu (D. Wang). (Sellers et al., 1986, 1996; Dickinson et al., 1993; Bonan, 1996; Dai et al., 23) since Manabe (1969) incorporated LSM into a climate model for the first time. Over vegetated areas, precipitation interception by vegetation canopy is the first of a sequence of land surface hydrological processes, and it is a significant component of surface water balance. The interception loss (loss of precipitation due to evaporation of intercepted water) varies with vegetation types, canopy density, and meteorological conditions and accounts for 1 48% of the gross precipitation (Hörmann et al., 1996). The parameterization of canopy hydrological processes is therefore an important aspect of land surface /$ - see front matter ª 27 Elsevier B.V. All rights reserved. doi:1.116/j.jhydrol

3 Evaluation of canopy interception schemes in land surface models 39 modeling. The representation of canopy interception substantially impacts the partitioning of precipitation between evapotranspiration and runoff, and could even cause the hydrological regime to shift between evaporation-dominated and runoff-dominated regimes (Pitman et al., 199). The partitioning of evapotranspiration between canopy interception loss and plant transpiration is also significantly influenced by the canopy interception representation (e.g., Wang and Eltahir, 2; Wang et al., 25). A wide range of surface water budgets result from differences in canopy interception scheme. For example, Wang and Wang (27) showed that a canopy interception scheme considering precipitation sub-grid variability in the Community Land Model version 3 (CLM3) leads to an interception loss ratio of.24 in the Amazon region, compared to.45 in the default interception scheme of CLM3. However, with few exceptions (Liu, 21), the performance of canopy interception scheme at large scale has not been evaluated against observations. The canopy hydrology representation therefore becomes a major source of uncertainty in land surface modeling. Over the years, an increasing number of observations of canopy interception loss at various regions of the globe have been reported in the literature, which provides a good opportunity to evaluate the parameterization of canopy hydrological processes in LSMs. This paper compiles such published observations and evaluates canopy hydrology schemes in the framework of the CLM3 against the observational data. The schemes to be evaluated include, in sequence of increasing complexity, the default scheme in CLM3, Shuttleworth (1988b) scheme incorporated into CLM by Wang et al. (25) that considers the rainfall sub-grid variability, an improved scheme developed by Wang and Wang (27) (WW7 hereafter) to include the sub-grid variability of canopy storage and reduce the sensitivity to model temporal resolution, and the rain type-distinguished WW7 scheme that treats convective and stratiform rain differently. Canopy interception schemes The water balance equation for the reservoir on vegetation canopy is os ¼ Ic Dr Ew ot ð1þ where S is the canopy water storage, Ic is the canopy interception rate, Dr the canopy drip rate, and Ew is the evaporation rate from wet foliage. This equation is independent of the approach used to model the canopy hydrological processes. Different approaches differ in how to estimate the three terms on the right hand side of Eq. (1) described as follows. The default interception scheme in CLM3 The precipitation intercepted by vegetation canopy Ic is considered as an exponential function of canopy density: Ic ¼ P m ½1 expð :5ðLAI þ SAIÞÞŠ ð2þ where P m is the grid-averaged precipitation intensity (simulated by atmosphere models or derived from reanalysis data), LAI is the one-sided leaf area index, and SAI is the one-sided stem area index. The canopy dripping occurs when canopy water storage S exceeds the water holding capacity C: S C Dt Dr ¼ ; S > C ð3þ ; S 6 C where Dt is the model time step. The Shuttleworth scheme Shuttleworth (1988b) proposed a canopy interception scheme that accounts for the rainfall sub-grid variability for GCM. This scheme assumes that rainfall only occurs over a fraction l of each grid cell and follows an exponential distribution within the rain-covered area. What stays on canopy (i.e., the difference between canopy interception rate Ic and drip rate Dr) at a grid cell can be estimated as: Ic Dr ¼ P m P m exp li max ð4þ P m The maximum canopy infiltration rate I max in Eq. (4) is estimated as: I max ¼ C S ð5þ Dt S in Eq. (5) is the grid-averaged canopy water storage at the end of the previous time step or the beginning of the current time step. Note that the canopy water is redistributed uniformly across each grid cell between time steps. Wang et al. (25) adopted this scheme but used spatially and temporally varying l estimated as the ratio of the model predicted rainfall intensity P m to the conditional mean rainfall intensity P o derived from satellite-based observation, as in Eltahir and Bras (1993): l ¼ P m P o The Shuttleworth scheme with reduced sensitivity to temporal resolution (WW7 scheme) Wang et al. (25) study used the Shuttleworth scheme supplemented by Eq. (6) to simulate the canopy hydrological processes in CLM at the model time step of 6 min. In a subsequent study by Wang and Wang (27) it was shown that the interception loss simulated with a time step of 2 min is very different than that with a time step of 6 min. This sensitivity had also been reported by Dolman and Gregory (1992) when the Shuttleworth scheme was applied into a 1-D GCM. WW7 identified the physical basis of this sensitivity and applied two physically based treatments to improve the parameterization of the canopy hydrological processes: (1) the canopy interception and the following vegetation evaporation within the rain-covered area are treated separately from those within the non-rain area, and the relative rain location between adjacent time steps is tracked; (2) within the raincovered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain a somewhat wet canopy during any rain time step. With treatment (1), the canopy interception and drip occurs in the rain-covered area only: ð6þ

4 31 D. Wang et al. ( Ic rain Dr rain ¼ Pm Pm expð limax Þ Pm l l ð7þ Ic norain ¼ Dr norain ¼ : where the subscript rain ( norain ) stands for rain-covered (non-rain) areas. The maximum canopy infiltration rate is estimated based on the canopy storage at the beginning of the current time step within the rain-covered area (Sb rain ): I max ¼ C Sb rain ð8þ Dt During interception, the canopy water storage within the rain-covered area obtains replenished from rainfall. Canopy within the non-rain area gets no newly intercepted water. These different canopy water storages give rise to different wetted fractions of canopy therefore different interception losses within these two types of areas. Quantifying Sb rain in Eq. (8) requires tracking the relative locations of the rain between adjacent time steps. A typical thunder storm event often includes rain cells that occur over different places at different time. As a result, rainfall tends to stay over the same area for a certain period of time before shifting to other areas. To account for this fact, WW7 assumed typical rain duration of one hour according to Cosgrove (1999) study. With treatment (2), during each rainy time step in the model after new interception is added to the canopy, S rain is adjusted according to: S adjusted rain ¼ max min fwet rain E p Dt; P m l Dt ; S rain ð9þ where fwet rain is the fraction of wetted canopy within the rain-covered area, E p is the potential evaporation rate. When these two treatments are applied to CLM3, the simulation results over the Amazon region indicate that the new canopy interception scheme is fairly stable under different temporal resolutions. If both convective and stratiform rains occur simultaneously within a grid cell at any time step, Eq. (7) in Section The Shuttleworth scheme with reduced sensitivity to temporal resolution (WW7 scheme) changes to: 8 Ic rainc Dr rainc ¼ Pc þ P l l 1 exp I max;rainc >< Pc l þp l ð1þ >: Ic norainc Dr norainc ¼ P l 1 exp I max;norainc P l where the subscript rainc ( norainc ) denotes the convective rain-covered (non-convective rain) area, P c is the convective rain rate, and P l is the stratiform rain rate. The maximum canopy infiltration rates I max,rainc and I max,norainc are estimated using Eq. (8) based on Sb rainc and Sb norainc, respectively. Similar to Eq. (9), Sb rainc and Sb norainc are estimated as: 8 < : S adjusted rainc S adjusted norainc P ¼ max min fwet rainc E p Dt; c þ P l l Dt ; S rainc ¼ max min fwet norainc E p Dt;P l Dt ;Snorainc ð11þ Not that if only convective rain occurs, the scheme reduces to the WW7 scheme; if only stratiform occurs (i.e., P c is zero), the scheme reduces to the WW7 scheme with l equal to 1.. The canopy interception loss constitutes the rainfall and snow interception. The developed schemes described in Sections The Shuttleworth scheme, The Shuttleworth scheme with reduced sensitivity to temporal resolution (WW7 scheme), and Rain type-distinguished WW7 focus on the rainfall interception by canopy. Consequently, when these schemes applied to land surface models, the snowfall interception is still represented as in Eq. (2) of Section The default interception scheme in CLM3 that is applied to both the rainfall and snowfall interception in the publicly available version CLM3 model. Rain type-distinguished WW7 In WW7, stratiform and convective rain types are treated identically: they fall into the same fraction of each model grid cell and have same probability density functions (pdfs) within the rain-covered area. This is not realistic. In areas dominated by convective rain, such as the Amazon region, this treatment may not substantially impact the model simulation results. However, in areas where stratiform rain contributes significant portion of the total precipitation, it is necessary to distinguish stratiform from convective rain, since the former has a much larger coverage within a model grid cell than the latter. To treat these two types of rain differently, we assume that stratiform rain covers the whole grid cell and convective rain covers a fraction l of the grid cell with l estimated using Eq. (6) with P m (all rain) replaced by P c (convective rain). The canopy interception is then estimated within the convective rain-covered area (i.e., the fraction l of the grid cell) differently from within the non-convective rain area (the rest of the grid cell) under different conditions, as described in the following. Note that the non-convective rain area could be either the stratiform rain-covered or non-rain area. Data Two types of observational data are used in this study. One is the interception loss observations used to evaluate model performance. The other is the high-resolution rain rate observations for rainfall coverage fraction estimate, as described in Section Canopy interception schemes. This study focuses on evaluating the model simulation of rainfall interception loss ratio (interception loss as a fraction of rain) against observations. Field measurements of rainfall interception loss ratio published in the literature are compiled for tropical and extratropical areas, respectively, as shown in Tables 1 and 2. The locations of these measurements are shown in Fig. 1. The measured rainfall interception loss ratio is averaged to match the model s spatial resolution (1 1 in this study) if there are multiple measurements within one model grid cell. The global high-resolution rain rates used in this study are derived from the SSM/I.25-degree rain rate products ( Products/6_Ancillary/2_GPROF6/index.html) on the basis of 4 years (22 25) of satellite observations. The retrievals are based on the most recent version of the operational overland passive microwave rainfall algorithms

5 Evaluation of canopy interception schemes in land surface models 311 Table 1 Published interception loss ratio observations in tropics Source Location Period of measurement Vegetation type Interception ratio (%) 6 26 S, 17 2E December 1977 April 1978 Acacia auriculiformis 11.2 Bruijnzeel and Wiersum (1987) 6 26 S, 17 2 E December 1978 March 1979 Acacia auriculiformis 17.9 Bruijnzeel and Wiersum (1987) Between 4,79 12 and 4 5 S, W April 1998 April 21 Epiphyte 36.4 Fleischbein et al. (25) 1 33 S, 37 8 E November 1994 June1997 G. Robusta 1.7 Jackson (2) 1 17 S, E November 1993 April1994 Dipterocarpaceae, 11.4 Asdak et al. (1998) 1 17 S, E June 1994 June 1995 Dipterocarpaceae, 6.2 Asdak et al. (1998) 2 57 S, W September 1983 September 1985 Tropical rain forest 12.4 Shuttleworth (1988) 4 51 N, 75 3 W August 1986 August 1987 Guttiferae, Cunoniaceae 12.4 Veneklaas and Van EK (199) 4 51 N, W August 1986 August 1987 Cunoniaceae 18.3 Veneklaas and Van EK (199) 4 51 N, W October 1982 January 1984 Cyatheaceae 11.4 Vis (1986) 4 51 N, W October 1982 January 1984 Weinmannia 15.1 Vis (1986) 4 51 N, W October 1982 January 1984 Weinmannia 24.6 Vis (1986) 4 51 N, W August 1982 January 1984 Eugenia biflora 21.8 Vis (1986) 8 43 N, W July 1988 July 1989 Epiphyte 37.2 Cavelier et al. (1997) 9 35 N, W September 1999 August 2 Epiphyte 28 Hölscher et al. (24) N, W July 1995 June 1996 Epiphyte 35. Holder (24) N, W July 1995 June 1996 Epiphyte 4.3 Holder (24) (McCollum and Ferraro, 23). This algorithm is calibrated using coincident Tropical Rainfall Measuring Mission Precipitation Radar rainfall estimates and is used to produce the latest versions of global TRMM, SSM/I, and Advanced Microwave Sounding Radiometer-Earth Observing System (AMSR- E) rain products. The study by McCollum and Ferraro (23) has shown regional dependence on the overland rain retrieval error (particularly, a significant negative bias over the continental Indian monsoon). The authors attributed this underestimation to the more maritime air mass that produces less ice, and consequently less scattering. Studies have also identified storm microphysical variations within smaller scale areas of the same convective region triggered by changes in the low-level wind direction, and/or modulations originated by tropical easterly waves. Rain retrievals from passive microwave observations (like SSM/I) are also questionable in distinguishing cold surface from areas of precipitation, which results in overestimation of precipitation over high-latitude area and part of mid-latitude area during the winter season (Negri et al., 1995). This overestimation in winter storms however does not impact the results of this study that focuses on rainfall interception. A recent error study for SSM/I rain retrievals over those regions by Dinku and Anagnostou (26) have shown that correlations of the SSM/I to the TRMM Precipitation Radar rainfall estimates range between.67 (for Africa) to.64 (Amazon basin) and.58 (South Asia region). The corresponding overall regional systematic differences for summer season are 1.64, 1.74, and Method and experimental design Statistical analyses are performed to provide quantitative measures for the model performance. The following statistics are used for this purpose: (a) Mean bias error (MBE): P N i¼1 MBE ¼ ðm i O i Þ N (b) Root mean square error (RMSE): sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P N i¼1 RMSE ¼ ðm i O i Þ 2 N (c) Mean absolute percent deviation (MAPD) MAPD ¼ 1 X N jm i O i j N O i i¼1 where M stands for the model simulated value, O stands for the observed value, and N is the number of measurements. The four canopy hydrology schemes described in Section Canopy interception schemes are evaluated in the framework of the Community Land Surface Model version 3 (CLM3) in its stand-alone mode: the CLM default scheme, the Shuttleworth scheme, the WW7 scheme, and the rain type-distinguished WW7 scheme. Correspondingly, four types of simulations are designed: the Control simulation using the CLM default scheme (labeled as CTL), the Experiment simulation 1 using the Shuttleworth scheme (labeled

6 Table 2 Same as Table 1, but for extratropics Location Period of measurement Vegetation type Interception ratio (%) Source S, E Annual Astrebla Lappacea 32 Dunkerley and Booth (1999) S, E Annual Atriplex Vesicaria 5.8 Dunkerley and Booth (1999) S, E Annual Maireana Sedifolia 1.1 Dunkerley and Booth (1999) N, W May 1987 August 1987 Acacia Farnesiana Navar and Bryan (1994) 42 1 S, E October 1976 September 1978 Nothofagus spp Pearce and Rowe (1981) 35 2 N, E April 2 December 22 Quercus serrata 16.8 Deguchi et al. (26) 36 7 N, 14 6 E Pinus densiflora 17.2 Iida et al. (25) 36 7 N, 14 6 E Pinus densiflora 9.2 Iida et al. (25) 44 5 N, 5 W February 1986 January 1987 Pinus pinaster 11.9 Gash et al. (1995) N, W December 1996 February 1998 Pyrus calleryana 15 Xiao et al. (2) N, W December 1996 February 1998 Quercus suber 27 Xiao et al. (2) 38 5 N, 8 51 W January 1992 July 1994 Pinus pinaster 17.1 Valente et al. (1997) N, 8 36 W January 1992 July 1994 Eucalytus globulus 1.8 Valente et al. (1997) 42.2 N, E July 1993 December 1995 Pinus sylvestris 17.4 Llorens et al. (1997) N, 46 W June 1987 April 1989 Pine forest Loustau et al. (1992) N, W April November 1999, March December 2 Pseudotsuga menziesii 24.1 Link et al. (24) N, W June 22 November 22 Pseudotsuga menziesii 21.4 Pypker et al. (25) N, W March 2 December 2 Pseudotsuga menziesii 21.2 Pypker et al. (25) N, 7 12 E May November 1988, April October 1989, May November 199 Picea abies 34.2 Viville et al. (1993) 52 2 N, 42 W Pinus silvestris 27.3 Gash (1979) N, 3 41 W September 1975 June 1978 Pinus silvestris 26.7 Gash et al. (198) 54 6 N, 1 15 E December 199 April 1991 Asperulo Fagetum 1.9 Hörmann et al. (1996) N, 3 16 W May 1988 October 1988 Picea sitchensis 29 Teklehaimanot et al. (1991) N, 2 24 W August 1977 September 1978 Pinus silvestris 31.7 Gash et al. (198) Around 56 N, 4 W October 1983 June 1986 Picea sitchensis 28 Johnson (199) N, 3 29 W January 1977 December 1977 Pinus silvestris 42.4 Gash et al. (198) 312 D. Wang et al.

7 Evaluation of canopy interception schemes in land surface models N 3 N 3 S 6 S 18 Figure 1 12 W 6 W 6 E 12 E Location of rainfall interception loss observations over globe. 18 Table 3 Experiment types Parameterization Highlights CTL Eqs. (2) and (3) The default CLM3 scheme EXP1 Eqs. (4) (6) The scheme that accounts for the rainfall sub-grid variability based on Shuttleworth scheme (1988b) but with the temporally and spatially varied rainfall coverage fraction facilitated by the high-resolution rain rate observation EXP2 Eqs. (7) (9) The scheme that treats the canopy interception within rain-covered areas and within non-rain areas separately and updates the canopy interception to sustain the wet canopy within raincovered areas during the rainy time step based on the scheme in EXP1 EXP3 Eqs. (1) and (11) The scheme that treats the canopy interception of the convective rainfall differently from that of the stratiform rainfall based on the scheme in EXP2 as EXP1), the Experiment simulation 2 using the WW7 scheme (labeled as EXP2), and the Experiment simulation 3 using the rain type-distinguished WW7 scheme (labeled as EXP3), summarized in Table 3. The model simulation period is from January 1, 2 to December 31, 25 for each type of simulation and the model spatial resolution is 1 by 1. To obtain the initial values of model-state variable (e.g., vegetation temperature, soil temperature, soil moisture, and so on), 5 year of spinup is conducted before running the model. The NCEP/NCAR reanalysis data (Kalnay et al., 1996) is used as a surrogate for the forcing simulated by atmosphere models. Different from the publicly available version of CLM3 model that uses AVHRR-based LAI, the LAI in this study is derived from MODIS observations (Tian et al., 24). Results Our study focuses on the rainfall interception loss. The snowfall intercepted by vegetation canopy is excluded from the total interception loss in the result analysis. As a result, the interception loss ratio in this study refers to the ratio of rainfall interception to total rainfall instead of total precipitation in the following sections. Temporal resolution sensitivity As reported by Dolman and Gregory (1992) and WW7, the simulated interception loss is very sensitive to model temporal resolution when Shuttleworth (1988) scheme is applied into land surface models. To reduce this sensitivity, WW7 applied two physically-based treatments into the canopy hydrological processes based on the Shuttleworth scheme, as briefly described in Section The Shuttleworth scheme with reduced sensitivity to temporal resolution (WW7 scheme). Fig. 2 shows the annual rainfall interception loss ratio at the temporal resolutions of 2 min and 6 minin CTL, EXP1, EXP2, and EXP3 and their differences. It is apparent that in CTL, EXP2, and EXP3 the difference in modeled interception loss ratio between 2-min and 6- min temporal resolution is negligible compared to the simulated values at either temporal resolution. By contrast, such difference is much more significant in EXP1: The globally averaged difference is.117, which is about half of the global averaged interception loss ratio simulated with a 2-min resolution and is even larger than the simulated with a 6- min resolution. The sensitivity problem appears more severe in the densely vegetated areas, such as the Amazon basin, Central Africa, and some parts of Europe. Given that the temporal resolution sensitivity is sufficiently small in CTL, EXP2 and EXP3, we will proceed validating the canopy interception schemes used in only these three types of simulations against observations. For simplicity, the simulations at the temporal resolution of 6 min for CTL, EXP2 and EXP3 are used in the following sections. Difference between EXP2 and EXP3 A color bar with finer scale is used in Fig. 3 to show the difference between EXP2 and EXP3. As mentioned in Sections

8 Author's personal copy 314 D. Wang et al. a) CTL (2min).338 d) EXP1 (2min).215 g) EXP2 (2min).176 j) EXP3 (2min) N 45S b) CTL (6min) e) EXP1 (6min) h) EXP2 (6min) k) EXP3 (6min) N 45S c) CTL (2min6min).28 f) EXP1 (2min6min).117 i) EXP2 (2min6min).2 l) EXP3 (2min6min).17 45N 45S 9W 9E 9W E 9W E W 9E Figure 2 The annual rainfall interception loss ratio (rainfall interception loss as a fraction of total rainfall) at the temporal resolutions of 2 min and 6 min in the control simulation (CTL), the experiment simulation 1 (EXP1), the experiment simulation 2 (EXP2) and the experiment simulation 3 (EXP3) and their differences. The number in each title is globally averaged value. EXP3 EXP2 9N.9.8 6N.7 3N S.3 6S 9S 18 Figure W 6W 6E 12E 18 The differences in the annual rainfall interception loss ratio between EXP3 and EXP2. The Shuttleworth scheme with reduced sensitivity to temporal resolution (WW7 scheme) and Rain type-distinguished WW7, the difference between the WW7 scheme (used in EXP2) and the rain type-distinguished WW7 scheme (used in EXP3) is that the latter treats convective and stratiform rain differently when the rainfall sub-grid variability is considered in the canopy hydrological processes. Fig. 3 shows the comparison between EXP3 and EXP2 in annual rainfall interception loss ratio at global scale. Little difference is found over the globe except in the western coastal area of America, northeastern US, western and northern Europe, northeastern Russia, and southern Australia where stratiform rain accounts for a substantial portion of the total rainfall. In these areas, the interception loss ratio in EXP3 is larger than that in EXP2 by.4-.1, which can explain a performance difference between EXP2 and EXP3 in reproducing observations in the next section.

9 Evaluation of canopy interception schemes in land surface models a) CTL b) EXP2 c) EXP3 MBE =.156 RMSE =.228 MAPD = MBE =.56 RMSE =.144 MAPD =.576 MBE =.7 RMSE =.121 MAPD =.523 Simulation Observation Observation Observation Figure 4 The model simulated versus observed annual rainfall interception loss ratio in CTL, EXP2 and EXP3. The cross points stand for tropics, and the circle points stand for extratropics. The values of MBE, RMSE, and MAPD are for all areas combined. Simulations versus observations Fig. 4 shows the modeled versus observed annual rainfall interception loss ratio in CTL, EXP2 and EXP3 over places where observational data is available. Evidently, the interception loss ratio at most sites is overestimated in CTL compared to observations (Fig. 4a). Such overestimation is severe for both tropical and extratropical areas. When the scheme is applied that accounts for the rainfall sub-grid variability in the representation of the canopy hydrological processes, but does not distinguish the rain types (i.e., convective and stratiform rain) in EXP2, the simulation of the interception loss ratio over tropical areas is substantially improved overall, but exhibiting with a severe underestimation of the interception loss ratio in extratropical areas (Fig. 4b). Convective rain, which exhibits significant sub-grid variability and usually covers a small fraction of the model grid cell (for example, 1 1 in this study), is dominant in tropical areas. Due to the use of the grid-averaged rainfall intensity in CTL, rainfall becomes drizzling leading to overestimation of interception loss, especially in tropical areas where convective rainfall is dominant and vegetation is dense. Such overestimation in tropical areas is dramatically reduced by accounting for the rainfall subgrid variability in the scheme used in EXP2. The interception loss ratio is however underestimated by EXP2 over extratropical areas. In extratropical areas, stratiform rain, which has greater spatial coverage, makes a substantial contribution to the total rainfall amount. By treating the rainfall as if it was all convective, as done in EXP2, the model underestimates the rainfall coverage fraction and overestimates rain intensity in the rain-covered area thus underestimate the interception loss ratio. In EXP3, which considers that convective rainfall covers a fraction of each model grid cell, while stratiform rain covers the whole grid cell, the simulated interception loss ratio is larger than that in EXP2 (Fig. 3). One of the areas where the difference between EXP3 and EXP2 is most significant was shown in Fig. 3 to be Western Europe where there are many available observations of interception loss values and included in this study. It is therefore not surprising that the underestimation of interception loss ratio in extratropical areas is reduced from EXP2 to EXP3, as shown comparing the scatter plots of Fig. 4b and c. In tropical areas, there is little difference in the model performance between EXP3 and EXP2. Overall, the simulated results in EXP3 are closest to observations among the three types of simulations, even though the interception loss ratio in tropical areas is still somewhat overestimated and in extra-tropical areas underestimated. Table 4 shows the quantitative comparison in simulating the annual rainfall interception loss ratio among CTL, EXP2 and EXP3 using mean bias error (MBE), root mean square error (RMSE), and mean absolute percentage deviation (MAPD) described in Section Method and experimental design. Consistent with Fig. 4, Table 4 demonstrates that considering the rainfall sub-grid variability in EXP2 (EXP3) improves the model simulation compared with CTL. This improvement is more significant in tropical areas than extra-tropical areas. The magnitude of the negative bias over extra-tropical areas in EXP3 is smaller than that in EXP2. Seasonality difference between CTL and EXP3 Here we examine the model performance in simulating the seasonality of interception loss ratio. Given that it does not usually rain in winter over high-latitude and mid-latitude Table 4 Statistical analysis on model simulation against observation in CTL, EXP2, and EXP3 over all areas combined, tropical areas, and extratropical areas (MBE: Mean bias error; RMSE: Root mean square error; MAPD: Mean absolute percentage deviation, defined in Section Method and experimental design ) All areas Tropical areas Extratropical areas CTL EXP2 EXP3 CTL EXP2 EXP3 CTL EXP2 EXP3 MBE RMSE MAPD

10 316 D. Wang et al. 1N 1S 2S a) MAM (CTL) e) MAM (EXP3) 1N 1S 2S b) JJA (CTL) f) JJA (EXP3) 1N 1S 2S c) SON (CTL) g) SON (EXP3) 1N 1S d) DJF (CTL) 2S 8W 6W 4W 2W 2E 4E 6E h) DJF (EXP3) 8W 6W 4W 2W 2E 4E 6E Figure 5 The rainfall interception loss ratio for March April May (MAM), June July August (JJA), September October November (SON) and December January February (DJF) in CTL and EXP3 over tropical areas. areas, we present the seasonal cycle only over two major tropical areas: the Amazon basin and Central Africa, as shown in Fig. 5. In addition, Fig. 5 shows the seasonality only in CTL and EXP3, since there is little difference between EXP2 and EXP3 in tropical areas. As shown in Fig. 5a d, the interception loss ratio in CTL does not vary much with season. This is attributed to the formulation of the interception scheme used in CTL in which the interception loss ratio is a function of LAI only, and seasonal variation of LAI over most of the tropical areas is small (not shown here). However, the field measurement at a site (2 57 S, W) in the Amazon basin (Shuttleworth, 1988a) shows that the interception loss ratio in the dry season (i.e., June, July, and August) is approximately twice as large as that in the wet season (i.e., December, January, and February). The high interception loss ratio in the dry season is due to the extremely low rainfall amount falling over dense evergreen tree canopy. The interception scheme used in EXP3 captures such seasonality: the value of the interception loss ratio tends to be smaller in areas and seasons of larger rain amount. Fig. 5e h (EXP3) shows that as the rainfall regime moves from north to south with season, the interception loss ratio becomes greater in northern areas and less in southern areas. Conclusions This paper evaluated four canopy hydrology schemes: the Community Land Model (CLM) default scheme, the Shuttleworth scheme (Shuttleworth, 1988b), the scheme proposed by Wang and Wang (27) (WW7 scheme), and the rain type-distinguished WW7 scheme using the CLM in its stand-alone mode. The CLM default scheme does not consider the rainfall sub-grid variability. The WW7 scheme was developed based on the Shuttleworth scheme, but treats the processes within rain-covered and non-rain areas separately, and track the relative location of the rain between adjacent time steps. The rain type-distinguished WW7 scheme further distinguishes stratifrom from convective rain based on WW7 scheme. Due to the severe sensitivity of the Shuttleworth scheme to model temporal resolution, we only validated the CLM default scheme, the WW7 scheme, and the rain type-distinguished WW7 scheme against observations of rainfall interception loss that we compiled based on published records. Without considering the rainfall sub-grid variability in the canopy hydrological processes, the CLM default scheme overestimates the rainfall interception loss ratio at most sites where we compared against observations, especially in tropical areas. The WW7 scheme reduces the positive bias by accounting for the rainfall sub-grid variability in the canopy hydrological processes, but underestimates interception loss ratio in extratropical areas. The rain type-distinguished WW7 scheme alleviates this underestimation, while it performs as well as the WW7 scheme in tropical areas. Among the three schemes, the rain typedistinguished WW7 scheme is the best in reproducing the

11 Evaluation of canopy interception schemes in land surface models 317 annual rainfall interception loss ratio with a mean bias error (MBE) value of.7 and a root mean square error (RMSE) value of.121 considering all the observation sites (both tropical and extratropical) combined. The rain type-distinguished WW7 scheme was also shown to perform better in producing the seasonality of rainfall interception loss ratio compared to the CLM default scheme. The simulated interception loss ratio with the rain type-distinguished WW7 scheme changes with the seasonal variation of rainfall amount in tropical areas, which is verified by observation at a densely vegetated site in the Amazon basin (Shuttleworth, 1988a). The interception loss ratio in the dry season for this area is approximately twice as large as that in wet season. By contrast, the interception loss ratio with the CLM default scheme remains almost the same values for the different seasons. Note that the evaluation in this study is conducted by comparing the interception loss ratios derived from model grided values and site observations. Observations at a point scale may not be representative of grid-averaged value at larger scale. However, the observed mean values at large scale is difficult to obtain. Consequently, site observations are the best we can get to evaluate canopy hydrology schemes in land surface models. Acknowledgements This study was supported by NASA Earth System Science Fellowship (NNG5GP38H) (to Mr. Dagang Wang), Georgia Institute of Technology (NNG4GB89G) (to Prof. G. Wang), and NASA Water and Energy Cycle program (to Prof. E.N. Anagnostou). References Asdak, C., Jarvis, P.G., van Gardingen, P., Fraser, A., Rainfall interception loss in unlogged and logged forest areas of Central Kalimantan, Indonesia. Journal of Hydrology 26, Bonan, G.B., A land surface model (LSM version 1.) for ecological, hydrological, and atmospheric studies: technical description and user guide. NCAR Technical Note NCAR/TN- 417+STR, Boulder, Colorado, p. 15. Bruijnzeel, L.A., Wiersum, K.F., Rainfall interception by a young Acacia auriculiformis (A. Cunn.) plantation forest in West Java, Indonesia: application of Gash s analytical model. Hydrological processes 1, Cavelier, J., Jaramillo, M., Solis, D., Leon, D., Water balance and nutrient inputs in bulk precipitation in tropical montane cloud forest in Panama. Journal of Hydrology 193, Dai, Y.J., Zeng, X., Dickinson, R.E., Baker, I., Bonan, G.B., Bosilovich, M.G., Denning, A.S., Dirmeyer, P.A., Houser, P.R., Niu, G.Y., Oleson, K.W., Schlosser, C.A., Yang, Z.L., 23. The common land model. Bulletin of the American Meteorological Society 84, Cosgrove, C.M., Volumetric and spatial dimensions of convective rain events. Ph.D. dissertation, University of Virginia, p Deguchi, A., Hattori, S., Park, H., 26. The influence of seasonal changes in canopy structure on interception loss: application of the revised Gash model. Journal of Hydrology 318, Dickinson, R.E., Henderson-Sellers, A., Kennedy, P.J., Biosphere-Atmosphere Transfer Scheme (BATS) version 1e as coupled to the NCAR Community Model. NCAR Technical Note NCAR/TN-387+STR, Boulder, Colorado, p. 72. Dickinson, R.E., Oleson, K.W., Bonan, G.B., Hoffman, F., Thornton, P., Vertenstein, M., Yang, Z.L., Zeng, X., 26. The community land model and its climate statistics as component of the community climate system model. Journal of Climate 19, Dinku, T., Anagnostou, E.N., 26. TRMM calibration of SSM/I algorithm for overland rainfall estimation. Journal of Applied Meteorology and Climatology 45, Dolman, A.J., Gregory, D., The parameterization of rainfall interception in GCMs. Quarterly Journal of the Royal Meteorological Society 118, Dunkerley, D.L., Booth, T.L., Plant canopy interception and its significance in a banded landscape, arid western New South Wales, Australia. Water Resources Research 35, Eltahir, E.A.B., Bras, R.L., Estimation of the fractional coverage of rainfall in climate models. Journal of Climate 6, Fleischbein, K., Wilcke, W., Goller, R., Boy, J., Valarezo, C., Zech, W., Knoblich, K., 25. Rainfall interception in a lower montane forest in Ecuador. Hydrological processes 19, Gash, J.H.C., An analytical model of rainfall interception by forests. Quarterly Journal of the Royal Meteorological Society 15, Gash, J.H.C., Wright, I.R., Lloyd, C.R., 198. Comparative estimates of interception loss from three coniferous forests in Great Britain. Journal of Hydrology 48, Gash, J.H.C., Lloyd, C.R., Lchaud, G., Estimating sparse forest rainfall interception with an analytical model. Journal of Hydrology 17, Holder, C.D., 24. Rainfall interception and fog precipitation in a tropical montane could forest of Guatemala. Forest Ecology and Management 19, Hörmann, G., Branding, A., Clemen, T., Herbst, M., Hinrichs, A., Thamm, F., Calculation and simulation of wind controlled canopy interception of a beech forest in Northern Germany. Agricultural and Forest Meteorology 79, Hölscher, D., Kohler, L., van Dijk, A., Bruijnzeel, L.A., 24. The importance of epiphytes to total rainfall interception by a tropical montane rain forest in Costa Rica. Journal of Hydrology 292, Iida, S., Tanaka, T., Sugita, M., 25. Change of interception process due to the succession from Japanese red pine to evergreen oak. Journal of Hydrology 315, Jackson, N.A., 2. Measured and modeled rainfall interception loss from an agroforestry system in Kenya. Agricultural and Forest Meteorology 1, Johnson, R.C., 199. The interception, throughfall and stemflow in a forest in Highland Scotland and the comparison with other upland forests in the UK. Journal of Hydrology 118, Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Wollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., Joseph, D., The NCEP/NCAR 4-year analysis project. Bulletin of the American Meteorological Society 77, Link, T.E., Unsworth, M., Marks, D., 24. The dynamics of rainfall interception by a seasonal temperate rainforest. Agricultural and Forest Meteorology 124, Liu, S., 21. Evaluation of the Liu model for prediction rainfall interception in forests world-wide. Hydrological processes 15, Llorens, P., Poch, R., Latron, J., Gallart, F., Rainfall interception by a Pinus sylvestris forest patch overgrown in a Mediterranean mountainous abandoned area. I. Monitoring design and results down to the event scale. Journal of Hydrology 199, Loustau, D., Berbigier, P., Granier, A., Moussa, F.E.H., Interception loss, throughfall and stemflow in a maritime pine

12 318 D. Wang et al. stand. I. Variability of throughfall and stemflow beneath the pine canopy. Journal of Hydrology 138, Manabe, S., Climate and the ocean circulation-1. The atmospheric circulation and the hydrology of the earth s surface. Monthly Weather Review 97, McCollum, J.R., Ferraro, R.R., 23. Next generation of NOAA/ NESDIS TMI, SSM/I, and AMSR-E microwave land rainfall algorithms. Journal of Geophysical Research 18, doi:1.129/ 21JD1512. Navar, J., Bryan, R., Fitting the analytical model of rainfall interception of Gash to individual shrubs of semi-arid vegetation in northeastern Mexico. Agricultural and Forest Meteorology 68, Negri, A.J., Nelkin, E.J., Adler, R.F., Huffman, G.J., Kummerow, C., Evaluation of passive microwave precipitation algorithms in wintertime midlatitude situations. Journal of Atmospheric and Oceanic Technology 12, Pearce, A.J., Rowe, L.K., Rainfall interception in a multistoried, evergreen mixed forest: estimates using Gash s analytical model. Journal of Hydrology 49, Pitman, A.J., Henderson-Sellers, A., Yang, Z.L., 199. Sensitivity of regional climates to localized precipitation in global models. Nature 346, Pypker, T.G., Bond, B.J., Link, T.E., Marks, D., Unsworth, M.H., 25. The importance of canopy structure in controlling the interception loss of rainfall: examples from a young and an oldgrowth Douglas-fir forest. Agricultural and Forest Meteorology 13, Sellers, P.J., Mintz, Y., Sud, Y.C., Dalcher, A., A simple biosphere model (SiB) for use within General Circulation Models. Journal of the Atmospheric Sciences 43, Sellers, P.J., Randall, D.A., Collatz, G.J., Berry, J., Field, C., Dazlich, D.A., Zhang, C., Collelo, G.D., Bounoua, L., A revised land surface parameterization (SiB2) for Atmospheric GCMs. Part 1: model formulation. Journal of Climate 9, Shuttleworth, W.J., 1988a. Evaporation from Amazonian rainforest. Proceedings of Royal Society of London. Series B, Biological Sciences 233, Shuttleworth, W.J., 1988b. Macrohydrology the new challenge for process hydrology. Journal of Hydrology 1, Teklehaimanot, Z., Jarvis, P.G., Ledger, D.C., Rainfall interception and boundary layer conductance in relation to tree spacing. Journal of Hydrology 123, Tian, Y., Dickinson, R.E., Zhou, L., Zeng, X., Dai, Y., Myneni, R.B., Knyazikhin, Y., Zhang, X., Friedl, M., Yu, H., Wu, W., Shaikh, M., 24. Comparison of seasonal and spatial variations of LAI/FPAR from MODIS and Common Land Model. Journal of Geophysical Research 19, D113. doi:1.129/23jd3777. Valente, F., David, J.S., Gash, J.H.C., Modeling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models. Journal of Hydrology 19, Veneklaas, E.J., van EK, R., 199. Rainfall interception in two tropical montane rain forests, Columbia. Hydrological processes 4, Vis, M., Interception, drop size distribution and rainfall kinetic energy in four colombian forest ecosystems. Earth Surface Processes and Landforms 11, Viville, D., Biron, P., Graner, A., Dambrine, E., Probst, A., Interception on a mountainous declining spruce stand in the Strengbach catchment (Voges, France). Journal of Hydrology 144, Wang, D.G., Wang, G.L., Anagnostou, E.N., 25. Use of satellitebased precipitation observation in improving the parameterization of canopy hydrological processes in land surface models. Journal of Hydrometeorology 6, Wang, D.G., Wang, G.L., 27. Towards a robust canopy hydrology scheme with precipitation sub-grid variability. Journal of Hydrometeorology 8, Wang, G.L., Eltahir, E.A.B., 2. Impact of rainfall subgrid variability on modeling the biosphere atmosphere system. Journal of Climate 13, Xiao, Q., McPherson, E.G., Ustin, S.L., Grismer, M.E., Simpson, J.R., 2. Winter rainfall interception by two mature opengrown trees in Davis, California. Hydrological Processes 14,