Evaluation of a surface resistance parametrization of sulphur dioxide

Pergamon Atmospheric Environment Vol. 28, No. 16, pp. 2583-2594, 1994 Copyright © 1994 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 1352-2310/94 $7.00 + 0.00

1352-2310 (94) E0098-5

EVALUATION OF A SURFACE RESISTANCE PARAMETRIZATION OF SULPHUR DIOXIDE

JAN WILLEM ERISMAN Laboratory for Air Research, National Institute of Public Health and Environmental Protection,

P.O. Box 1, 3720 BA Bilthoven, The Netherlands

(First received 10 October 1993 and in final form 3 April 1994)

Abstraet--A surface resistance parametrization for the deposition of SO 2 is evaluated using long-term measurements of SO 2 deposition over a deciduous forest, a coniferous forest, a grassland and a heathland. The parametrization was found to yield V d values generally in good agreement with the observations, the best agreement being for the coniferous forest and the heathland (more than 40% of variance accounted for). For these two types of vegetation no systematic bias was found in the parametrization. The agreement of measured and modelled values for the deciduous forest is probably influenced by errors in the measurements. For the grassland an underestimation of the time the surface was wet was found to be the cause for the disagreement between modelled and measured values during dry conditions. The parametrization is demonstrated to be representative for the Netherlands and can probably be adapted for much of Europe.

Key word index: Dry deposition, SO 2 surface resistance, measurements, parametrization.

1. I N T R O D U C T I O N

Air pollutants are transferred to soil, vegetation and water surfaces by precipitation, cloud and fog deposition and dry deposition. The amount of pollutants received by ecosystems through atmospheric deposition processes is considerable and for several reasons it is of interest to quantify it. Deposition can be studied from the atmospheric point of view, in order to determine how much is left in the atmosphere to be dispersed further, or from the receptor point of view, to determine the input of pollutants to ecosystems. For both purposes, reliable deposition monitoring or estimation results are necessary, which may be charac- terised by different accuracy and spatial resolutions.

Investigations on abatement strategies based on the critical load concept require relevant deposition data on both local and regional scales (Nilsson and Grenn- felt, 1988; L6vblad et al., 1993). On the local scale, large variations in deposition over landscape features and their variations in sensitivity make it essential to compare the critical load value for a specific ecosystem with the actual deposition so as to determine the exceedance value. On the larger regional scale, the essential parameters are dispersion and deposition which must be estimated in order to assess the relevant abatement strategies. Two different ways of calcu- lating deposition are used for these two purposes. For pollution deposition over Europe and budget estimates the regional-scale approach is required (e.g. Iver- sen et al., 1991). The local-scale approach covers the calculation of the more site-specific critical load ex- ceedances. The two approaches will be linked in order

to evaluate the complete chain from emission to deposition and to develop relevant abatement strategies (van Pul et al., 1992). This requires parametrization of the deposition processes on the ecosystem level.

The concept of critical loads has been criticised because of the difficulties in estimating dry deposition and deposition via fog on the required scales (Hicks, 1992). By comparison, wet deposition is simpler to estimate. The strong spatial variability and the many local parameters influencing dry deposition and deposition due to fog hamper the determination of local-scale fluxes. A complicating factor is that time- averaging does not smooth dry deposition patterns as with wet deposition patterns. Models for estimating local-scale fluxes must therefore contain information relevant for the appropriate time and spatial scales. These descriptions have to date not been available because parametrizations of the deposition velocity or the surface resistance to uptake of gases at different receptor surfaces are not readily available in Europe and because of lack of data of small-scale land use covering Europe. Current parametrizations used for estimating deposition over Europe do not include receptor-specific information (Sandnes and Styve, 1992).

The main objective of this paper is to test a recently developed parametrization scheme for SO2 surface resistance, formed for European pollution climates and receptor surfaces (vegetation, soils, etc.) common in Europe (Erisman et al., 1994b, this issue). The parametfization is used to compute dry deposition velocities based on routinely available input data. The

2583

2584 J .W. ERISMAN

results are eva lua ted us ing SO 2 dry depos i t i on meas - u r em e n t s over a dec iduous forest in C a n a d a (Shaw et

al., 1988; P a d r o et al., 1992, 1993), a con i fe rous forest (E r i sman et al., 1993a); a h e a t h l a n d (Er i sman, 1992; E r i s m a n et al., 1993b) a n d a g ra s s l and in the Ne t he r - l ands (Er i sman et al., 1993b). M o d e l l e d a n d m e a s u r e d depos i t i on velocit ies will be c o m p a r e d .

2. EXPERIMENTAL PROCEDURE

The data used include SO 2 dry deposition measurements over deciduous and coniferous forests, a heathland and a grassland. A short overview of the sites' characteristics, the methods used, the duration of the experiments and uncertainty estimates will be given for each data set.

2.1. Deciduous fores t

As part of two Eulerian model evaluation field studies (EMEFS), measurements were made over a deciduous forest located on Canadian Forces Base Borden (44°19'N, 80 ° 56' W). SO 2 fluxes were measured in the winter of 1990 using the eddy correlation technique (Shaw et al., 1988). Fast response SO 2 measurements were made using a modified Meloy 285 sulphur analyser with a fast response burner block. The Meloy 285 analyser employs an FPD to sense total sulphur. The FPD is negatively sensitive to water vapour, an effect for which a correction was made. Changes in SO 2 concentrations during the half hourly average measuring periods were accounted for by estimating the storage flux using the concentration measurements before and after the current period. The measured meteorological, concentration and deposition parameters were reported by Padro et al. (1994). A description of the forest and data set of fluxes and deposition velocities can also be found in Padro et aL (1994). The one-sided leaf area index (LAI) varies from 5 in summer to 1 in winter (bark area density, BAI). The average height of the trees in the forest is 18 m. The measurements above the canopy were taken at 33.4 m. The number of half hourly V d observations for SO 2 amount to 365 for the entire measurement period between 15 March 1990 and 29 April 1990 over the leafless forest. For diurnal patterns, the standard error of the mean for each half hour was estimated to be about + 0.15 cms -1 for V a and + 30% for the flux (Padro et al., 1994).

2.2. Coniferous forest

Since November 1992 continuous vertical concentration gradients of SO 2, NO 2 and NH 3 as well as relevant meteorological parameters have been measured at a Douglas fir forest site in the Netherlands. The Speulder forest site is located at the national park "Hoge Veluwe" in the central part of the Netherlands. The measuring site consists of a homogeneous 2.5 ha monoculture of mature Douglas fir, 35 years old with a stem density of 785 ha- 1. The mean tree height is approximately 20 m. The canopy is well closed with the maximum leaf area density at a height of 10-t4 m. The one-sided LAI varies throughout the year from about 8 in early spring to 11 at the end of the summer. The site is surrounded by a larger forested area of approximately 50km2; the nearest edge is at a distance of 1.5 km. Directly adjacent to the site are stands of pine, mixed beech/oak, Douglas fir and larch, with mean tree heights varying from 12 to 25 m, about roughly the same as that of the research area. A small clearing (1 ha) is situated to the north of the stand.

A sonic anemometer (Kaijo Denki DAT-310) mounted on the top of the scaffolding at a height of 36.5 m is used to measure the horizontal and vertical wind velocity, wind direction, temperature, friction velocity and the sensible heat flux. Air is sucked through five 8 m long isolated FEP Teflon

tubes (1/4" ID) to a valve system. The outlets of the valve system are connected to the gas monitors by two 20 m long isolated FEP Teflon tubes. One SO2 monitor (Thermo Environmental Instruments Inc., Model 43S) is used continuously for measurement of the concentration at the 36 m level. In this way changes of the gas concentration during a measuring cycle can be determined and corrected for. The other monitor takes samples at consecutive heights of 36, 32, 28 and 24 m above the forest to measure the vertical concentration gradient. Gradients are measured three times per hour and averaged to give hourly values. Two hourly values are used to minimise (random) measuring errors. Deposition parameters are calculated from these gradients and meteorological measurements as reported by Erisman et al. (1993a). The analysis includes measurements taken during November 1992 to June 1993. The equipment, its performance and experiments to determine measuring errors and accuracy of the measurements are described extensively in Zwart et al. (1993). The standard error for each two hourly average V a was estimated to be about _+ 40%.

2.3. Grassland

For routine monitoring of dry deposition fluxes, a system for SO 2 based on the micro-meteorological gradient technique has been developed. The SO 2 dry deposition monitoring system has been described extensively in Mennen et al. (1992) and Erisman et al. (1993b). The SO 2 concentrations are measured with two UV pulsed-fluorescence monitors (Thermo Environmental Instruments model 43W). One SO 2 monitor is used to measure the concentrations at four successive levels (heights of 4, 2, 1 and 0.5 m), while a second monitor continuously measures concentrations at the 4 m level. A change in SO s concentration during the measuring cycle can thus be detected and corrected for (Erisman et al., 1993b). A measuring cycle lasts 30 min. Selection criteria have been derived to select measuring periods satisfying the demands of the flux-profile theory. Furthermore, a scheme has been developed to calculate yearly average fluxes from the selected and rejected measuring periods.

From 1987 to 1989 the dry deposition monitoring system was placed at a rural site in the centre of the Netherlands. The undisturbed fetch over the grassland is between 700 m and 1 km for eastern, southern and western directions. The pastures surrounding the location were infrequently used for cattle grazing and were irregularly spread with manure during the growing season. The grass height was maintained at 15 cm most of the time. The soil type is so-called wet-peat with an average pH ranging from 4.8 to 5.1 and the soil surface was nearly always wet. Eight measuring cycles were averaged to yield four hour average deposition parameters, in order to minimise (random) measuring errors. The standard error for each four hourly average V a was estimated to be about _+ 50%.

2.4. Heathland

A three-year experiment involving several research groups was conducted at the Elspeetsche Veld for the determination of deposition fluxes on heathland. Micrometeorological measurements of SOy NH3 and NO2 were made using different techniques. Furthermore, throughfall and bulk precipitation fluxes of SO] - , NH~ and NO3 were measured. The +xperiment was carried out in 1989-1992 at a heathland nature reserve in the central part of the Netherlands: Elspeetsche Veld (52 ° 16' N, 5o45 ' E), near the village of Elspeet. The heathland is located in a region with moderate ambient concentrations of SOz (6 #g m - 3) and NO:, (20 ppb) (RIVM, 1989). The ammonia emission density in this area is about equal to the average NH 3 emission in the Netherlands (Erisman, 1989). The vegetation at the site belongs to the GENISTO-CALLENETUM (dry inland heath, charac terised by Calluna (Heather)). At the site, Calluna vulgaris (L.) Hull completely dominates the dwarf-shrub layer. Approx-

Surface resistance parametrization of SO 2 2585

imately 20% of the soil surface is not covered by vegetation. The Calluna plants are 4--5 yr old and the height of the canopy is 20-30 cm (Erisman et al., 1994a). Subsoil is nu- trient-poor fluvio-glacial sand with a well-developed pod- zolic soil profile. SO2 dry deposition was estimated using the monitoring system with new, more accurate SO2 monitors (Thermo Environmental Instruments, model 43S). Measuring cycles were averaged to yield two hourly averages. The standard error for each two hourly average V d was estimated as about 30%.

3. SURFACE RESISTANCE PARAMETRIZATION

The dry deposition velocities of gases are determined by turbulent transport, transport over a semi- laminar layer and reactiveness of the gas molecules at the surface. The resistance analogy (Garland, 1977; Hicks et al., 1987) and, more specifically, the surface resistance parametrization are described in Erisman et

al. (1994b). The R¢ parametrization for SO 2 will be summarized in this section. R~ is a function of the canopy stomatal gstom and mesophyll resistance Rm, the canopy cuticle or external leaf resistance Re.t, the soil resistance R~o~j and in-canopy resistance Rin ¢. In turn, these resistances are affected by leaf area, stomatal physiology, soil pH, and the presence and chemical composition of liquid drops and films. Based on values from the literature for the stomatal resistance (Wesely, 1989), and on estimated values for wet (due to rain and to an increase in relative humidity) and snow-covered surfaces, the parametrization can be applied for routinely measured components (Erisman et al., 1994b):

1 1 1 1 1 R ~ : Rstom+Rm q Ri.c+Rsoi, t-R~xt (1)

or R, = R . . . . when the surface is covered with snow. In this equation, the stomatal resistance for water vapour is estimated according to Wesely (1989):

l- 200 7qf 4o0 ) Rstom : R i 1 -~- L ~ 0 . ~ ~ ; 4 ( - T ( T ~ 4 0 i ; (2)

where T is the surface temperature in °C and Q the global radiation in W m- z. The minimal water vapour resistance R i is taken as 150, 250, 70 and 200 for deciduous forest, coniferous forest, grassland and heathland, respectively (Baldocchi et al., 1987). The stomatal resistance of SO2 is obtained from the water vapour stomatal resistance by multiplying it with the ratio of the diffusion coefficient of SO2 over that of HzO (2.1/1.1). More accurate descriptions of the stomatal flux exist but these need more input data (Jarvis, 1976; Baldocchi et al., 1987). However, the stomatal resistance parametrization has to be applicable to all areas of Europe, where these data are generally not available. The mesophyll resistance for SO2 is assumed to be zero (Wesely, 1989). The in-canopy aerodynamic resistance R~,~ is modelled tentatively

based on data from van Pul (1992):

b LAI h g i n c - - (3)

U*

where u* is the friction velocity, LA] is the one-sided leaf area index (set to one for a deciduous forest in winter), h the vegetation height and b an empirical constant taken as 14 m - 1. Equation (3) is only applied to tall vegetation. For low vegetation Rinc is assumed to be negligible. In this way the exchange caused by penetration of gusts is accounted for in a very straight- tbrward way. The soil surface resistance below forest canopies is taken to be 100 s m -1.

Erisman et al. (1994a) derived a cuticle or deli- quescent resistance R~xt parametrization for the external leaf surface of heather plants:

during or just after precipitation: Rex t = 1 s m- 1

in all other cases:

Rex t=25,000 E X P [ - 0 . 0 6 9 3 r h ] s m 1 at rh < 81.3%

(4)

Rext=0.58x1012 E X P [ - 0 . 2 7 8 r h ] s m -~ at rh > 81.3%

where rh is the relative humidity. Equation (4) is applied for air temperatures above - I°C. Below this temperature it is assumed that surface uptake de- creases and Rex t is set to 200( -- 1 < T < - 5°C) or 500(T< --5°C) s m -1. R~x ~ will be zero for some hours after precipitation has stopped. During daytime the surface was assumed to be wet for 2 h after a rain event in the warm season, and for 4 h in the winter. During nighttime, vegetation was expected to be dry after 4 h in summer and after 8 h in winter. For cases where rain chemistry does not allow for SO2 uptake, Rex t will be significantly different from zero, depending on the extent that rain is saturated with S(IV) and on the plant neutralization capacity. This effect is not included in the model.

Resistance for snow-covered surfaces is given as

Rs,o~ = 500 s m- 1 at T < -- I°C

R . . . . = 7 0 ( 2 - T) sm-1 at 1 < T < -- I°C. (5)

In this way hourly Re values can be parametrized and, accordingly, Vo values estimated (Section 4).

4. MODELLED Va COMPARED WITH MEASUREMENTS

For the comparison of modelled deposition parameters with those obtained from measurements, only data satisfying theoretical constraints and well above detection limits were employed. Data were rejected for wind speeds below 1 m s- 1, for wind direction with no ideal fetch, when one of the measured parameters was below the detection limit and when the concentration changes during a measuring cycle were too large

2 5 8 6 J . W . E R I S M A N

(Padro et al., 1993; Erisman et al., 1993a, b, 1994a). These selection criteria had little influence on the Canadian data. The Speulder forest data set was reduced by about 50%, mainly as a result of the detection limit of the SO 2 monitors and because of technical problems. It has not been estimated whether this has led to a systematic bias in the data set. For Zegveld and Elspeet about 70-80% of the data were rejected, mainly as a result of a poor fetch and because of the detection limit of the SO 2 monitors used. It was found that for the Zegveld and Elspeet data selection did not lead to a systematic bias in deposition parameters. The conditions in the remaining data set were found to be representative for the whole measuring period (Erisman et al., 1993b).

The remaining data set comprises 364 half hourly averaged deposition parameters for the deciduous forest, 652 two hourly averaged data for the coniferous forest, 1391 two hourly averaged data for the heathland and 821 four hourly averaged data for the

grassland. For each measuring period R c values were calculated according to the parametrization given in Section 3. Furthermore, R c values were calculated using parametrizations given by Wesely (1989) for comparison. The aerodynamic resistance R a and the quasi-laminar layer resistance R b were estimated according to the equations given in Erisman et al. (1994b). The inverse of the sum of the three resistances is the dry deposition velocity V a. In Figs 1A-D, modelled V a values are compared to those obtained from measurements for the deciduous forest, coniferous forest, heathland and grassland, respectively. In these figures average modelled values and their standard deviation for each class of measured V d are plotted vs class averages. Classes are formed by placing combinations of measured and modelled V d values in increasing order of measured values and averaging the combinations over an equal number of data. The standard deviation is given as a measure for the variation in each class. This cannot be considered as a

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Fig. 1. Comparison of modelled V d (m s- ]) with those obtained from measurements: (A) deciduous forest; (B) coniferous forest; (C) grassland and (D) heathland. Solid dots represent average modelled values for class average measured values. The line represents the 1 : 1 ratio. Small overbars represent measured class averages + standard deviation,

large overbars represent modelled averages _+ standard deviation.

measure for the uncertainty in class averages. In Table 1, minimum, maximum and average values of some measured parameters are given. This table also gives average deposition parameters and the standard deviation. The correlation coefficient of the modelled V d vs the measured values and the standard error therein are presented here too. Averages, standard deviations and correlation coefficients between measured and modelled V d obtained with the parametrization by Wesely (1989) are also given.

The results in Figs 1A-D show that modelled V d correlate well with the measurements made at the coniferous forest and the heathland, where 44 and 41% of the variance were accounted for, respectively. The correlation between modelled and measured velocities for the deciduous forest and grassland is lower where 15 and 21% of variance were accounted for,

respectively, The data sets are too large to use statistical tests to test the hypothesis whether overall averages or variances of measured and modelled values are equal. It can be done for subsets, e.g. for each class in Figs 1A-D. These results can be roughly extracted from the figures. It can be seen that for the coniferous forest and heathland measurements, averages are about equal for each class except for negative values. For the grassland, average measured values are significantly higher. For the deciduous forest the lowest modelled values are higher than those measured, whereas the larger values are equal. For each class, Wesely's parametrization yields significantly lower values than measured or than obtained from the parametrization presented here. Wesely's parametrization implies too large a value of Re. The correlation between modelled and measured values is lower

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2588 J.W. ER1SMAN

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Surface resistance parametrization of S O 2

Table 2. Average dry deposition velocities (m s- l) and correlation coefficients between modelled and measured V d for dry/wet and daytime and nighttime periods.

2589

Daytime Nighttime

Dry Wet Dry Wet conditions conditions conditions conditions

Deciduous forest Measured V d 0.003 (0.006) 0.006 (0.007) 0.001 (0.003) 0.001 (0.006) Modelled V d 0.006 (0.007) 0.011 (0.010) 0.003 (0.00l) 0.006 (0.010) Correlation coeff. 0.10 0.83 0.32 0.26 Standard error 0.007 0.005 0.001 0.010 Number 195 59 85 26

Coniferous forest Measured V d 0.007 (0.016) 0.023 (0.020) 0.007 (0.008) 0.025 (0.020) Modelled V a 0.006 (0.004) 0.024 (0.014) 0.006 (0.005) 0.026 (0.016) Correlation coeff. 0.47 0.57 0.56 0.60 Standard error 0.004 0.012 0.004 0.013 Number 167 142 123 220

Grassland Measured V a 0.014 (0.010) 0.015 (0.010) 0.013 (0.01 l) 0.017 (0.013) Modelled V a 0.007 (0.003) 0.015 (0.006) 0.007 (0.004) 0.014 (0.006) Correlation coeff. 0.42 0.49 0.60 0.58 Standard error 0.009 0.009 0.009 0.011 Number 260 268 121 173

Heathland Measured F o 0.008 (0.011) 0.014 (0.015) 0.009 (0.011) 0.012 (0.013) Modelled V~ 0.008 (0.005) 0.019 (0.010) 0.007 (0.006) 0.015 (0.010) Correlation coeff. 0.38 0.65 0.58 0.81 Standard error 0.005 0.012 0.006 0.008 Number 403 507 148 333

Standard deviation is given between parentheses. The estimates have been presented in more significant figures than consistent with their accuracy, for intercomparison.

for Wesely's R e parametrization than that given here (Table 1).

It is more interesting to see whether modelled and measured values agree for periods where certain processes dominate than just to compare class averages. Four periods were selected where different resistances are expected to dominate: (1) daytime dry conditions (Rstom), and (2) wet conditions (Rext) , (3) nighttime dry conditions ( R i J and (4) wet conditions (Rext). Results of averages and the correlation between modelled and measured values for these four periods are presented in Table 2. The results presented in Table 2 show the same general features as obtained for class averages. The modelled V d values for the deciduous forest are generally higher than those measured. The modelled Vd values for the coniferous forest and heathland are generally in good agreement with measurements. Grassland V d values agree well for wet periods. How- ever, for dry conditions in daytime and nighttime, modelled values are too low. It is not clear from these results which resistance from equation (1) is modelled better.

Figures 2 A - D give examples of time series of measured and modelled V d values for the four types of vegetation. These figures illustrate comparisons of the diurnal cycles between the model and measurement

under different conditions. Again, over the deciduous forest the model seems to overpredict.

5. DISCUSSION

The comparison analysis was conducted for dry deposition velocities instead of surface resistances, because it is the most important parameter and because R c values show large scatter with no general distribution which makes comparison difficult. Fur- thermore, R e values are very sensitive to measurement errors, especially at small VdS. Small VdS are the result of either large values of R a + R b or a large resistance to surface uptake. Small measurement errors might result in very large positive or negative values of Re, introducing enormous scatter in the data set. Meas- urement errors lead to a normal distribution of errors in V~, but to an unknown distribution in R c (see Section 5.1). By only comparing modelled with measured Va values there is the danger of ignoring differences between modelled and measured values which can result from surface processes occurring during periods with very high values of R, + R b (i.e. stable conditions), which yield low V d. In general, these processes do not influence agreement between model

2590 J.W. ERISMAN

and measurements and may therefore be ignored. However, in cases where they dominate, this can introduce a large systematic bias.

5.1. Uncertainties

The measured and modelled deposition parameters are subject to several uncertainties. During periods

when the fluxes or concentrations are small, measurements usually show a high uncertainty, due to the relative high error in the concentration gradient. Systematic errors are excluded as much as possible through the selection procedure. However, a random measurement error can lead to random errors in I'd, which might lead to negative values. These might be

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-0.04 - 0.035 0.03 0,025 0.02 0.015 E 0.01 0.005 > 0 -0.005 -0.01 -0.015

. . . . . rh - wetness • Vd modelled ~ Vd indicator measured (1 =wet)

Fig. 2. Examples of time series of modelled and measured V d : (A) deciduous forest; (B) coniferous forest; (C) grassland and (D) heathland.

misinterpreted as emission rather than small deposition. On the other hand, at zero resistance to uptake at the surface (Re~0), random errors can yield Vd values that are larger than the maximum possible V a. Large random errors cause frequency plots of measured V d to look like normal distributions (the distribution of the random error), while the "real" distribution of V d is logarithmic. Figures 3A-D give the frequency distributions of measured and modelled Vo. As expected from the results presented in Fig. 1, the modelled and measured distributions of V d over the coniferous forest and heathland are similar. Moreover, the distributions approach the logarithmic form, as expected. Thus, measured Va is not influenced to a large extent by a random error.

The distribution for the grassland is logarithmic, but the peaks for the modelled and measured distributions are different, suggesting an incorrect R~ parametrization. An investigation of individual measurements revealed that by using 4 h averages, the surface wetness parametrization did yield an underestimate of the time the surface was wet (see also Table 2). It was observed in the field that the surface was wet nearly all of the time, whereas the results in Table 1 suggest that this was only true for about 50% of the cases.

The distribution for the deciduous forest follows the shape of a normal distribution. This suggests that the measurement results are dominated by a random error. Concentrations during the measurements are relatively low, with an average value of 3.1/~gm -1 (Table 1). The lower the concentration, the higher the error in the concentration gradient as a result of random errors. This can be seen by introducing an extra selection criterion on concentration. If measurements at concentrations below 2/~g m- 1 are rejected, the shape of the frequency distribution becomes logarithmic. Furthermore, the correlation coefficient between modelled and measured values increases to 0.6

(36% of variance accounted for), with no systematic differences between the averages. Also, comparison between modelled and measured values for dry/wet conditions during daytime and nighttime shows im- provement.

5.2. Comparison with other parametrizations

Much early work done on parametrization of the surface resistance to gaseous dry deposition comes from the U.S.A. (Hicks et al., 1987, 1989; Wesely, 1989), the U.K. (Garland, 1977, 1978; Fowler, 1978, 1985) and Canada (Voldner et al., 1986; Padro et al., 1994). Wesely (1989) recently derived a scheme for the RADM model representative for North American conditions (see Section 4). Padro et al. (1994) used this scheme and the scheme presently used in the ADOM model to compare SO a V d values with those obtained from measurements at the Borden forest, the same data set as used here. They found that ADOM's estimates were much larger than the measured values, whereas Wesely's parametrization yielded smaller estimates than were measured. These results are com- parable to those found in the present study. Wesely's parametrization yields much lower V a values than measured for each of the four types of vegetation. This is mainly caused by the surface resistance description for periods during or after rain. Wesely assumes that R c values increase during or after rainfall as a result of sufficient S(IV) saturation of the rain, which prevents further uptake of SO 2. This assumption is based on unpublished measurements by the Argonne National Laboratory (Wesely, 1989). Before Wesely's parametrization is used, this assumption must be tested since it has serious implications, especially in those areas where the surface is frequently wetted by rain. It could lead to an underestimate of the surface uptake of SO2 by a factor of 2 (low vegetation) to 8 (forests)! Testing the assumption on S(IV) saturation can be done using short-term precipitation measurements of

2592 J.W. ERISMAN

S(IV), base cations and pH, and ambient NH s and base cation concentrations.

5.3. Representatiz~eness of parametrization for Eu- ropean pollution climates

As the surface resistance parametrization is derived from the measurements made at the heathland site Elspeetsche Veld (Erisman et al., 1994a), it might only be representative for the Dutch pollution climate. This pollution climate is different from other climates cam-

man in Europe, and might therefore not be useful for models used to estimate deposition in Europe. How- ever, from Table 1 it is obvious that the measured parameters (C, T, u, Q and rh) cover a very wide range, which is representative for large areas in Europe. The ratio of hours when vegetation was assumed to be wet to those for which the vegetation was assumed to be dry is larger than for most areas in Europe, because of the Dutch marine climate. However, there were sufficient measurements made during very dry periods to

(A) lOO

80

0 . . . . . i I:-.~:~t-~:,,,,q:.,~:--~:---.~: _ , / I.

88 0 0 0 0 0 0 0 8 0 ~ 9 9 o o o o o o d d d o d d d d o

Cla~ average Vd

I•measured ~m0delled ]

(B) 250

200

,50 E 100 I

50 I ,_ i 0 , , ,

(C) 250

. . . . . . .

8 o

0 ~ ~ ~ O 0 d 8 8 o o o o o ~ 8 8 8 8 o o 8 8 8 - - ~ ' d d d d d d d d d d d d d d d d d d d d

Cla~averageVd

• measured [ ] ~delled

200 E ~ 5 0

~ 100

0

0 0 0 o 0 0

~ ~ o" o" o," 0," ~ q~ d d d d d d d d d d d d d d d d d d d d

Class average Vd

I measured [ ] modelled

Fig. 3. A-C.


(D) 350

,,., 300 C

250

200

~ 150

"6 100

O O . . . . q . . o," o, o, o, o, o, 9 o, o o o o o o d d d o d

Class average Vd

Fig. 3. Histograms of modelled (open columns) and measured (dark columns) V e (m s-t) : (A) deciduous forest; (B) coniferous forest; (C) grassland and (D) heathland.

allow a valid study of the parametrization under such conditions. In any case, during these periods deposition velocities and fluxes tend to be very low and therefore not too important.

A major concern in the Netherlands is the relatively high neutralisation capacity caused by large ammonia emissions from intensive livestock breeding. This is not representative for large areas of Europe (and U.S.A., see the discussion on comparison to Wesely's parametrization), where emissions and concentrations are much lower (Buijsman et al., 1987; Asman and van Jaarsveld, 1992). In the Netherlands the annual average ammonia concentration is about equal to that of SO2:5 and 4 ppb, respectively. This is also the case at the three Dutch measuring sites. It has been suggested that the presence of ammonia enhances sulphur deposition (van Breeman et al., 1982; Adema et al., 1986). However, Erisman and Wyers (1993) showed that this so-called co-deposition is important only in extreme conditions, i.e. when there is a very high ratio of NH 3 concentration to that of SO2. They found that surface wetness was most important for enhancing dry deposition, but they did not demonstrate that NH 3 does not play a role under wet conditions.

6. C O N C L U S I O N S

This paper describes the testing of a surface resistance parametrization for surface uptake of SO 2 using eddy correlation measurements over a deciduous forest in Canada and vertical gradient measurements over a coniferous forest, a grassland and a heathland in the Netherlands. The surface resistance parametrization includes the stomatal resistance, the resistance for wet surfaces caused by precipitation and relative humidity, the resistance to in-canopy aerodynamic transport, the resistance to soil and to snow- covered surfaces. It is concluded that the modelled V d compare reasonably well with the measurements, yielding no systematic differences for the coniferous

forest and the heathland (more than 40% of variance accounted for). There is much less agreement with the measurements for the deciduous forest and grassland, showing systematic differences. However, it was concluded that the errors were not due to the surface resistance parametrization, but rather to possible random errors in the (low) concentration measurements over the deciduous forest and for the grassland measurements to underestimating the time the surface was assumed wet. The parametrization was tested for four different classes (dry and wet conditions for day and night). No systematic error could be detected from this comparison other than what is described above.

The surface resistance parametrization developed by Wesely (1989) was also tested using the same data set. Results show a systematic underestimation of Va values when compared to measured values. The underestimate is mainly the result of the parametrization of Rc during and after precipitation. Wesely assumes an increase in Ro, whereas measurements show a decrease to values close to zero. This can lead to an underestimation of V a S O / b y a factor of 2 for low vegetation to a factor of 8 for forests.

The parametrization is demonstrated for the Netherlands and may be representative for large areas in Europe. Other types of vegetation can be modelled by adjusting the stomatal resistance. The parametrization might not be representative for those areas which have a completely different pollution climate from that occurring during the measurements. This might be the ease for very dry areas or areas where ammonia concentrations are negligibly small compared to those for SO2. Unfortunately, there is a lack of long-term measurements for such areas. It is therefore recommended that such measurements be col- lected over these areas to evaluate and/or improve surface resistance parametrizations.

Acknowledgement~Jacob Padro is gratefully acknowledged for providing the Canadian data and reviewing the manuscript. Mrs Ruth de Wijs is gratefully acknowledged for editing the manuscript.

2594 J.W. ERISMAN

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Documents

Evaluation of a surface resistance parametrization of sulphur dioxide