Water and element fluxes calculated in a sandy forest soil taking spatial variability into account

Fores;;;ology Management ELSEVIER

Forest Ecology and Management 101 ( 1998) 269-280

Water and element fluxes calculated in a sandy forest soil taking spatial variability into account Claus Beier * Environmental

Science and Technology

Department, RIS0 National Laboratory/Danish POB 49, DK-4000 Roskilde, Denmark

Forest and Landscape

Research

Institute.

Accepted 11 March 1997

Abstract Water and element fluxes in the unsaturated zone of soils are most often calculated on the basis of average water fluxes and average soil solution concentrations. However, if the input of water and elements exhibit a strong systematic variability, this will most likely be reflected in the flow of water and elements in the soil. In such cases the ‘average’ based calculations will be subjected to significant errors. In order to overcome this problem, the present paper describes a method to calculate the water and element fluxes in the soil of a Norway spruce plantation taking into account the known stem-distance related variability in both water and element fluxes. The calculations are based on studies and previous findings of spatial variability in a Norway spruce plantation in Denmark. The suggested method leads to an improved Cl-balance when calculated for 6 years. The possible factors responsible for the errors in water and element fluxes are discussed and preferential flow paths, sampling under big trees, errors in modelling evapotranspiration and incorrect weighing between subareas are concluded to be most important. 0 1998 Elsevier Science B.V. Keywords:

Hydrology; Modelling; Unsaturated zone; Forest soil; Flux estimation; Spatial variability; Norway spruce

1. Introduction Increasing emissions of air pollutants have led to increasing environmental problems in many forest ecosystems, such as soil acidification, nitrate contamination of groundwater, unbalanced nutrition of forest trees, etc. (Ulrich, 1987; Johnson and Ball, 1990/9 1). A number of these environmental problems are linked to the overall balance of water and elements in the ecosystem. Scientific and political concepts based on input/output balances exist and are widely used. Soil acidification in a soil horizon

* Tel.: +45-46-774161; fax: +45-42-370025; e-mail: [email protected].

or a soil profile is often calculated by the input/output balancesof H+, NH:, NO; and SOi- (de Vries and Breeuwsma, 1987) and the ‘critical load’ concept is basedon such balancesfor nitrogen (Gundersen, 1992) and acidity (e.g., Warfvinge et al., 1992). Furthermore, a large number of researchprogrammes and projects have beenperformed over the last lo- 15 years which encompassstudies of ion balancesand element cycling in forest ecosystems. In all such casesit is crucial that the input and output fluxes are calculated or estimated accurately. The input of water and elementsto the forest soil is most often measuredusing throughfall collectors placed on the forest floor beneath the canopy. The water flux through the soil profile (percolation) is

0378-l 127/98/$19.00 0 1998 Elsevier Science B.V. All rights reserved PII SO378-1127(97)00142-4

estimated assuming that elements like chloride or iodine behave as a biogeochemically inert tracer (Jury and Fliihler, 1993), or by use of hydrological models which calculate an average percolation for the specific site. The element fluxes are subsequently estimated by multiplying the average soil solution concentrations with the average water fluxes. However, the use of average water fluxes and average soil solution concentrations may in some cases lead to errors in the balances. This is most likely the case in areas where the input of water and elements exhibit a systematic variability, as has been reported for a Norway spruce stand in Denmark. Here the throughfall exhibited a large systematic variability related to the distance from the tree (Beier et al.. 1993a; Gundersen et al., 19951. Such large variability in the input pattern may be reflected in the soil solution concentrations and therefore calculations of water and element fluxes based on average values will most likely lead to erroneous figures. This has been shown for a European beech stand and to some extent a Norway spruce stand in Solling, Germany (Koch and Matzner, 1993). Instead, the spatial vatiability pattern should be accounted for when water and element fluxes in the soil are calculated. The present paper describes the application of a new method for such calculations.

2. Site description

and measurements

2. I. Stand and site characteristics The calculations are based on studies in a 76 year old (1995) Norway spruce (Picea abies, L. Karst) plantation located at Klosterhede, Lemvig in Western Table 1 Stand and site characteristics

at Kiosterhede,

Canopy LA1 (m’ mm2 1 Surface res.(s/m)

6 200

Root fractions

2.2. Soil chartrcteristics The soil is classified as a Typic Haplorthod (podzol) developed on a homogeneoussandy, nutrient poor deposit of a glacio fluvial outwash plain. The mineral soil is coarse sand with a low clay content ( < 5%‘). An organic layer (moi- humus) of 7 cm has developed during the current rotation. Below the organic layer the mineral soil profile is layered in various horizons with an A/AE horizon tea. 10 cm>, Bh horizon (ca. 3 cm), Bs horizon (ca. 20 cm). BC

Denmark Height(m) Intercept.

‘0 (mm/LAI)

Trees pr. ha.

x00

1s-10 50-75

I I c/i 4’4

0.225

(cm)

o-7 10-35

Meteorology Air temp.

Jutland (X”24’E. 56”29’N). Denmark. The study area was completely flat and 27 m above sea level. The stand was homogeneous, consisting of even-aged trees planted in rows, and managed to be of nearly the same size. The canopy was ‘closed’ with >ingle tree canopy diameters being 3-6 m. Leaf area index (LA11 was measured by a portable shoot area measuring instrument (LICOR LA1 2000) subsequently adjusted to leaf area for Norway spruce by a multiplication factor of 1.6 as suggestedby Gower and Norman (1991). The interception capacity per unit LA1 was estimatedfrom monthly amountsof precipitation and throughfall water collected in funnels through the 6 year study period assuming that the difference in water was due to interception and subsequentevaporation. Surface resistancewas ohtained from eddy correlation measurementsin a Norway spruce stand of the same height and density situatedat Ulborg 50 km from Klosterhede(Pileg%rd. unpublished data). Root distribution was measured by 5 soil cores (IO cm diameter) divided into 10 cm intervals (Hansen and Thomsen. 19911. The most important site and stand characteristics are given in Table I.

(Yearly

28% 14%

7-15 35-50

35% 8%

9.O”C

Precip.

860 mm

mean)

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horizon (ca. 40 cm) and C horizon. The mineral soil layers are homogeneous with very few stones (Beier et al., 1993b; Raulund-Rasmussen, 1994). The hydrological properties expressed by pF curves, saturated conductivity, porosity and residual water content at wilting point were measured for the A and B horizons. Hydrological properties were measured by sampling of small undisturbed soil cores from each layer (diameter 6.1 cm, height 5 cm) according to Schonning (1985) and Jacobsen (1989). Porosity was estimated from the density of the soil sample and a theoretical density of 2.65 g cmp3 for minerals). For the C horizon, the water content at pF 1, 2 and 3 was measured as for the A and B horizons and pF curves and hydrological properties were generated by the PLOTPF programme (Jansson, 1993). The p F curves for the organic top layer were obtained from a comparable Swedish spruce stand included in the soil database delivered by the PLOTPF programme. The pF curves are shown in Fig. 1. 2.3. Spatial mriability Previously, Beier et al. (1993a) found that the input fluxes of water and elements to the forest floor of a similar Norway spruce plantation exhibited a strong pattern governed by the distance to the nearest stem. The water fluxes were small close to the stems and increased at higher distances. In contrast, the element concentrations and the element fluxes were highest close to the stem. A similar pattern has been found in the plantation at Klosterhede (unpublished data) and the variability in input is still reflected in the soil solution concentrations for several substances after percolation through the upper soil layers (Fig. 2) (Gundersen et al., 1995). Furthermore, the area of the forest floor situated at different distances to the stems account for different fractions of the total forest floor area. Therefore, in the present study, the forest floor area has been ‘divided’ into three subareas dependent on the distance to the stems. The subareas are denoted ‘close’ (distance O-O.5 m), ‘in between’ (distance 0.5-1.5 m) and ‘far’ (distance > 1.5 m) and the relative fractions of these subareas have been calculated (Table 2). From throughfall measurements it has been

0

10 20 30 40 50 Watercontent (~01%)

Fig. 1. p F curves for the different

60

soil depths at Klosterhede.

calculated that the input of water to the ‘in between’ subareaequalsthe average throughfall for the whole stand, whereasthe water flux to the ‘far’ subareais

272

C. Beier

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Eco1og.v

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Management

I01

f IYYK) 26Y-280

I 2000

100 1000 2500

0

jan-88

jan-90

jan-92

jam94 ---_

jan-88 - -close

jan-90 -in

jan-92

jan-94

between

0 jan-88 w

jan-90 far

jam92

jan-94

1

Fig. 2. Concentrations of Cl, SO, and Ca (pmol< I -’ ) in the B-ho rtzon at Klosterhede in the period 198% 1993. The concentrations shown for each of the 3 subareas ‘close’, ‘in between’ and ‘far’. which refer to the distance to the nearest stem.

25% higher and to the ‘close’ subarea 25% lower compared to the average throughfall (Table 2). In subsequent calculations the spatial variability in water fluxes and element concentrations and the relative area of each of the subareas are taken into account.

2.4. Sampling

procedures

The input of water and elementsto the forest floor was measuredmonthly by 4 integrating throughfall collectors (Rasmussenand Beier, 1986) designedto take into account the spatial variability described above.

Table 2 Area fractions and scaling factors for water input to the three subareas at Klosterhede. The scaling factor for water indicates the relative water input to the subarea compared to the average for the stand Distance

Area fraction Scaling factor water input

Subarea ‘close’

‘in between’

‘far’

0.28

0.48

0.75

1.00

0.24 I .25

arc

Soil solutions were collected monthly by 9 tension regulated PTFE suction cups (PRENART type) placed in the B horizon at 5.5 cm depth. The samplers were installed 3 in each of the subareaspreviously described and the three samplerswithin each subareawere connected to one sampling bottle. Root growth was negligible below the depth of the samplers and the soil solutions are therefore assumedto represent the chemical content of the percolating water leaving the rooting zone. Soil tension was measuredcontinuously at various depths (15, 35, 55 and 75 cm) by tensiometers (IMKO Gmbh type). Soil temperature was measured continuously at the same depths. Soil water content was measured at 15 and 25 cm depth for a three month period by the TDR (Time Domain Reflectometry) technique. 6 TDR probes were installed horizontally at each depth from small soil pits. Fach probe consistedof 2 parallel electrodes(25 cm length, 2 mm diameter, distance between electrodes 2 cm). The 6 probes at each depth were installed to cover the variability in water input determined by the stems. Meteorological input variables (daily averages) were measuredon location (daily precipitation, air temperature and radiation) or obtained by interpolation between measurementsfrom the nearest (2- 15 km) meteorological monitoring stations (humidity and wind speed).

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3. Flux calculations

and hydrological

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300.0

modelling

3.1. Input jluxes

200.0

Water and ion inputs were calculated directly from the input to the throughfall collectors which were designed to account for both the systematic differences in input at the different distances to the stems as well as the relative area at the different distances (Beier and Rasmussen, 1989). 3.2. Hydrological

101 (IY98)

100.0

modelling

Water fluxes in the soil were modelled using the soil water and heat model SOIL (Jansson and Halldin, 1979). This is a one dimensional model based on two coupled differential equations describing heat transport (derived from Fourier’s law) and water transport (derived from Darcy’s law) in a layered soil profile. The model includes a description of the hydrological and thermal properties for each soil layer. The model also includes a description of the vegetation (rooting depths, LAI, height, displacement height, roughness length, etc.). The model is driven by daily meteorological data of precipitation, air temperature, global radiation, relative humidity, and wind speed. The SOIL model is described and documented in Jansson (1991a,b). Soil hydrological parameters for the model were generated with the PLOTPF programme (Jansson, 1993) as previously described. Soil thermal properties were based on standard coefficients for sandy, clay and organic soils (frozen as well as unfrozen) for subsequent calculation of thermal conductivity according to Kersten (1949). 3.3. Model calibration

and validation

The SOIL model was run for the period 19891994 based on daily average values of the above mentioned meteorological parameters. The model was calibrated to measured values of soil tension and soil temperature at the different depths for the periods available (Fig. 3). The calibration involved small adjustment in thickness of the thin and very variable humic Bh layer in the range of 2-6 cm and the surface resistance. The model describes the measured soil tempera-

200.0

100.0 C 3 e

0.0

E 0 --

3oo.o-

i-b-

0.0! v sep-91

okt-91

I nov-91

dec-91

jam92

Fig. 3. Modelled (SOILN) and measured soil tension for different depths at Klosterhede. Bold line indicates modelled values and thin lines indicates parallel measurements with tensiometers.

ture well with discrepancies between modelled and measured values generally below 1°C. On the contrary, discrepancies between modelled and measured soil tension can be large for single probes in some

IO-

:

1 bh-horizon

1 /

t

00

401 ‘;r apr-93

.j:

; aug-93

p’“y-[j dec-93

apr-94

Fig. 4. Modelled (SOIL) and TDR-measured soil water contents (a) at 3 different soil layers at Klosterhede April 1993 to June 1994. TDR-measurements were performed by horizontally installed 25 cm long TDR probes. Solid lines indicate modelled values and dashed lines indicates average water content measured by 2-S TDR probes.

periods, especially at high tensions. However, the tensiometersoperate only at low water tensions ( < 1000 cm water), which is also the reason for the limited number of data available. The model is able to predict dynamic changes in soil tension due to infiltration. After calibration the model runs were validated by comparison to measured water contents by TDRtechnique for a 14 month period following a drought period in the summer 1993 (Fig. 4). Measured and

modelled soil water content match well except that the model overestimated the effect of the spring 1994 drought on water content. The slight difference in responsetime between the model and the me:+ surements may be caused by the horizontally installed TDR probesintegrating the water content in a 2.5 cm path along the probes which may include different soil characteristics as the soil horizons are variable in depth in the superficial soil horizons. In order to assessthe uncertainty in the water output from the model and its dependency on the uncertainty in input parameters, comparable runs were performed with varying parameter setting,s,As the focus in this study has been on the water and element output. these tests were limited to parameters influencing the percolation below the rooting zone rather than the internal distribution of water among the soil layers. The climatic parameters (mainly temperature and precipitation) and canopy parameters (surface resistance) have the strongest influence on the overall water budget, whereas changesin soil layering and hydraulic conditions in the soil mainly influences the timing and distribution of the water within the soil profile. Table 3 shows the influence of a -t 1°C change in air temperature. and a + 10% change in precipitation and canopy resistanceon the percolation. In general changes in water input have a significant effect on the percolation of water whereastemperature and surface reaistance have relatively small effects. On the other hand the uncertainty in the measurementsof precipitation and throughfall is relatively small and has been verified by comparison to precipitation data from a nearby meteorological station. A further source of uncertainty is the modelling of transpiration. There IS

Table 3 Influence Change

of changes in input parametera in parameter

10% reduced water input 10% increased water input 10% reduced canopy resistans lO’Sincreased canopy resistans I “C reduced temperature 1°C increased temperature

Relative

on the percolation

flux”

change in output

(%?,)

-~ 17

+??I -3 43

“The change is given in ‘/c relative with the calibrated dataset.

4-7

-. 4 to the percolation

calculated

C. Beier/

Forest Ecology

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a more or less direct connection between a possible error in the modelled transpiration and a corresponding error in the percolation. If for example transpiration is overestimated by 50 mm of water the percolation will be underestimated by almost the same amount. The accuracy in transpiration modelling is therefor important but independent measurements of transpiration or evapotranspiration has not been available in this study. On the basis of the calibration and validation results, it is concluded that the model describes the water flow well and that the model is suitable for further application at Klosterhede. 3.4. Modelling element fluxes

soil water fluxes

and calculating

101 (1998)

215

269-280

of each subarea, Percolation (SOIL),,,,,,, x was calculated and subsequently scaled by the percentage of area occupied by that subarea (Eq. (2)). x = Percolation( SOIL) quhareaX

Percolation,,,,,,, X

Area fractionsubarea X

(2)

3.4.3. The total Jzux of element Y in each subarea (Outiux Lburra x) This was calculated by multiplying the water percolation calculated in Eq. (2) with the soil solution concentration below the root zone, Cone qubarea x, obtained by the soil solution samplers placed within that area (Eq. (3)). Outflux qubareaX = Percolation,,,,,,,

X

x Cone Lbarea x The above standard application of the SOIL model implies running the model by use of the average water input by precipitation thereby obtaining an average soil water flux within and out of the soil profile. However, as stated above, the input of water by throughfall varies according to the distance from the nearest stem. In order to account for this variability the forest floor was separated into 3 subareas as previously described and the water and solute fluxes were calculated for each subarea independently according to the following four steps. 3.4.1. The input of water to each subarea X f Waterinput,l,h,,,,u x 1 This was calculated from the mean waterinput measured in the stand, Waterinput,,,,, and a scaling factor for water expressing the amount of throughfall to the subarea relative to the average input for the stand (Table 2) (Eq. (1)). Hereby, three flux ‘scenarios’ were obtained according to the three subareas: Waterinput,,,,,,, X

(Water scaling factorhubareax )

3.4.4. The totalflux of element Y out of the soil with percolation (0utjlu.x YToro,) This was calculated by adding the outflux calculated for each of the subareas ‘far’, ‘in between’ and ‘close’ (Eq. (4)). Outflux I&, = Outflux Yclose+ Outflux Yi” beLwee” + outflux

Y,,

(4)

3.4.5. ‘Average approach’for chloride For comparison the chloride budget was calculated for the same period according to a simple ‘average’ approach. Here, the daily precipitation is used to generate a daily average percolation of water for the whole area. Subsequently, the daily water percolation was summed for the periods in which soil solutions were sampled (Percolation,,,,) and multiplied by the mean Cl concentration for that period (Cone Cl,,,,) (Eq. (5)). Outflux Cl total= Percolation,,,,

x = Waterinput,,,,

(3)

X

Cone Cl mean (5)

(1)

4. Calculated water and element fluxes 3.4.2. The total soil water percolation out of each subarea (Percolation,s,,h,,,,u x) The SOIL model was run separately for each of the three subareas based on the water input calculated above (Eq. (1)). The percolation per unit area

The input of water and elements and the percolation out of the rooting zone were calculated for each of the 6 years 1989-1994 (Table 4) according to the above procedure (Eqs. (l)-(4)).

216

C. B&r/

Table 4 Water and element

fluxes

Water (mm1 Input output Balance Cl- (meqtn’ yr-‘1 Input Litterfall (maximum) output Balance Nat fmeqm-” Input Ou1put Balance

/J

M,g L ’ cmeq ItI Input output Balance

‘I

? ?‘r

and percolation

water from

101 (1998)

26Y-2X0

the soil (output)

at Klosterhede

1989- 1994

1989

1990

1991

1Y92

1993

1994

563 261 302

006 521 37’)

554 262 292

700 403 297

625 291 334

708 394 314

JO56 3137 191’)

846 IS 921 -3

1163 22 2063 - 880

875 10 834 61

7Y5 18 1641 - 826

76X 20 1126 - 338

x71 17 105-l - 163

531x I 01 7638 L218

127 679 48

960 1619 - 658

699 695 4

67h 1346 - 770

673 Y60 - 287

686 Y49 .- 36’

-1323 i,!li 1925

72 23 49

9s 31 68

7x 12 66

6’) I6 53

64 I1 53

74 11 64

is2 100 352

183 158 75

242 314 -72

182 98 84

171 197 - 26

(63 13.3 31

I64 126 38

I 106 1026 80

68 40 28

04 58 36

75 75 51

72 33 30

82 36 4h

70 36 34

461 237 225

21 23 4

23 38 - 15

35 17 18

3x ‘7 II

1x 17 17

37 22 15

lxx 144 1-t

225 139 86

276 319 - 103

218 208 10

202 341 - 145

203 2.55 -. 52

198 392 - 194

1322 1719 3Y7

104 2 102

1’6 3 123

85 2 83

83 3 80

66 1 65

78 1 77

54’ 11 5.31

81 2 79

70 3 76

84 1 83

94 2 92

68 0 6X

7s (I 7s

4x0 X 172

Total

yr-‘1

H ’ (mrq m - ’ yr Input output Balance

‘I

SO:(meqm-’ Input output Balance

y-‘i

NH,’ (meqm-’ Input output Balance

yr-‘i

output Balance

(input)

and Management

yr-‘i

ca’+ (meq m - ? ?“Input output Balance

K’ cmeqw-Input output Balance

in throughfall

Forest Ecology

The water budget showed a strong fluctuation over the years with a throughfall input ranging from 554 to 906 and an output ranging from 261 to 527

mm per year. In years with moderate water input the evapotranspiration loss was fairly constant in the range of 292-334 mm. In 1990 the input of through-

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269-280

5. Discussion and conclusions

fall was strongly elevated, which resulted in a slightly increased percolation, and especially, in increased evapotranspiration. The interception loss for these 6 years amounted to 23-29% of the precipitation (data not shown) and the transpiration loss amounted to 42-54% of the water input to the soil. In order to make a rough assessment of the calculated element outputs, the Cl budget was calculated including inputs by throughfall and litterfall. Throughfall was assumed to equal total deposition (wet + dry, no canopy leaching) at this sea salt influenced site (Beier et al., 1992). Litterfall of Cl was not measured specifically but it was assumed that Cl in litterfall was mainly attached to the litter and derived from sea salt in a 1:1 relationship with Na. Consequently, Nalitterra,, was assumed to determine the upper limit for Cl in litterfall. The total Cl-budget over the 6 year period showed an output exceeding the input by ca. 4 1%. For comparison the Cl output calculated by the ‘average’ approach (Eq. (5)) exceeded the input by 61% in the six year total budget (Table 5). The Cl budget exhibited large variability from year to year. All elements, except Na, SO, and Cl, showed positive 6 year balances. However, it has to be remembered that these balances only consider throughfall input and output by leaching and do not include weathering, internal cycling and accumulation in the biomass. Consequently, the elements most strongly influenced by accumulation (NO,, NH,, Ca and K) and by circulation (leaching contributing to throughfall, K and Ca) had the most positive balances. All elements showed strong fluctuations in output and in the balances from year to year, except for NO, and NH, which were totally removed or immobilised from the soil solution by the trees and micro-organisms.

The present study strongly illustrates the problems connected to spatial variability when calculating water and element fluxes in areas with a strong systematic variability. A similar conclusion was recently drawn by Koch and Matzner (1993) based on a study in a beech stand where a large amount of stemflow led to strong underestimation of the outflow of Ca, Mg, and K. At the present site with a well known variability pattern the inclusion of the spatial variability in the water fluxes and element concentrations in the element flux calculations reduced the overestimation of the fluxes calculated compared to the ‘average’ approach. However, if the assumption of chloride and to some extent sulphate being inert in the soil matrix is correct the six year balances for chloride and sulphate should be balanced and the results therefor indicate that the output fluxes calculated by the suggested procedure are still too high. There may be several explanations for the discrepancies between input and output for Cl. First of all, the estimated input to the soil may be too small. However, the error in the above ground inputs is assumed to be small since the measurements of both water and element inputs are not subject to large uncertainties compared to the estimates for the soil. The inputs from weathering or desorption is assumed to be zero which is in fact the basic assumption when Cl is considered inert. Unfortunately, we have no measurements from the site to confirm or falsify this assumption. Second, there is a strong year to year variation in the Cl balance. These differences may be caused by a ‘delay’ in input/output response meaning that deposition inputs in the late part of one year will not

Table 5 Chloride budget

approach

Cl-

(meq m-’

Input Litterfall output Balance

for Klosterhede yr-‘)

(maximum)

1989-1994

calculated

by the ‘average’

(see text, Eq. (5))”

1989

1990

1991

1992

1993

1994

846 15 925 -79

1163 22 2393 - 1230

875 10 985 - 110

795 18 1868 - 1073

768 20 1303 - 535

871 17 1228 - 357

a Input fluxes are calculated from the throughfall and average soil solution concentrations.

measurements.

Output

fluxes

are calculated

multiplying

soil modelled

Total 5318 102 8701 -3281 water

percolation

occur in the output until the following year meaning that the assumption of a balanced Cl budget may not be valid. On the other hand this problem is assumed to be reduced in this study, since a 6 year period should be sufficient to reduce the problem of delays in the first or the last year. Furthermore, the difference between the input and the output equals ca. 2 years of Cl-input which is too much to be caused by leaching of previously deposited Cl. Anyhow, the variation in the Cl-budget shows that calculations of water and element budgets based on short term Cl-budgets should be made with great caution. A third point of uncertainty is that other sources of variability exist apart from the stem distance. It is known from other studies in Norway spruce plantations in Denmark that the tree size also influences the water and element fluxes. Larger fluxes and higher solute concentrations are observed under bigger trees (Beier et al., 1993a). Since the element fluxes in the soil are based on measured soil solution concentrations, the calculations are sensitive to the place of sampling. If for example. soil samplers are placed under trees bigger than the average for the stand, the soil solution concentrations will tend to be higher than the average, and so will the calculated element fluxes. In the present study, the soil solution samplers were placed under two trees having a mean diameter at breast height of 26 cm compared to the 24 cm average for the stand. Therefore, it was expected that the concentrations in the soil solutions would be higher than the average for the stand leading to an overestimation of the output from the soil. Consequently, the tree size as well as other factors like direction of sampling from the nearest stem, gaps, edge effects, root distribution, etc. could be included. In the present study the soil water samplers only allowed for stem distance related calculations. A fourth factor that may explain the unbalanced result is the possibility of variability in plant parameters between the different subareas. The method and the hydrological modelling assume each plant parameter to be equal in the different subareas. Since all above ground parameters are linked to the soil through the stem it is probably only the root distribution. that might be changed by the differences in water and element input to the subareas. If the root distribution adapts to the difference in water supply

among the subareas, this would cause a difference in water uptake by roots among the subareas. Consequently, the hydrological modelling and the overall water and element budgets would be influenced. However. root sampling was performed to highlight this problem, and no differences in root distribution were found between the subareas. although the number of samples in each subarea was too small to make a statistical comparison. Furthermore. root adaptation would most likely lead to increased root growth and higher water uptake in the subarea ‘far’ with the biggest water input. This would cause a smaller water and element flux out of this subarea which already has the lowest element concentrations. Consequently the overestimation of the output fluxes would be even greater. It is therefore concluded that this factor is of minor importance. Fifth, the output of water may be overestimated by the SOIL model if the transpiration is underestimated. The uncertainty in the canopy parameters are large and therefor the uncertainty in the ET estimates may be quite large as well. If an underestimated transpiration alone should explain the unbalanced Cl budget, this implies that the water percolation should be reduced by ca. 100 mm (28%) from 356 to ca. 250 mm yr ~’ meaning that the transpiration would amount to ca. 426 mm yr- ’ (48% of total water input) compared to 320 mm yr- ’ (33% of total water input) in the present calibration. Finally, variability in the soil parameters may cause errors in the calculations. The hydrological modelling by the SOIL model. like most hydrological models, assumes a ‘homogeneous’ water tlux over the area according to average soil hydraulic conditions without accounting for preferential flow paths or ‘funnelling’ effects (e.g., Jury and Fliihler. 19931. In the sandy soil at Klosterhede. the assumption of homogeneity is likely to be valid, but still preferential tlow paths cannot be neglected. if a part of the water is transported through preferential flow paths, this water would be less concentrated in Cl than the soil water collected by the soil water iamplers. Consequently, the percolation would he a mixture of the small pore water represented by the present calculation and the less concentrated water flowing in preferential flow paths. This would reduce the calculated flux of Cl out of the soil system and lead to a more balanced Cl budget.

C. Beier/

Forest Ecology

and Management

Conclusively, the problem of preferential flow paths, soil water collection under bigger trees, errors in ET modelling and problems connected to weighing of the water flow from the different subareas are suggested to be the main factors influencing the element fluxes. In this discussion I have focused on the Cl-budget as an indication of the validity of the calculations but if Cl is unbalanced because of errors in the weighing procedure or preferential flow paths the other elements will be subject to errors as well. The inclusion of the known spatial variability pattern in the calculation procedure for water and element fluxes in the unsaturated zone improved the calculations compared to the more simple ‘average’ approach. The inclusion of the variability thereby improves the quality of the results and reduces the number of sampling points needed and the risk of errors caused by single errors in the sampling points or the samples. This also stresses the importance of including measurements covering known systematic variability patterns in ecosystem studies. However, the proposed procedure was generated for a relatively uniform area with a well defined variability pattern. In other more complex systems, a similar well defined variability pattern may not exist or the effort needed to identify and include it in the procedure may be too high to be justified by the result. In such cases, a large number of sampling points may still be the only practical way to obtain a reasonable flux estimate. On the basis of the results of this study, it is clear that problems connected to flow paths and the link between the water flowing in the soil system and the water sampled need more attention in studies dealing with element budgets.

Acknowledgements The study was performed at the Danish Forest and Landscape Research Institute and financed by the Groundwater Research Centre at Technical University of Denmark and by the Nordic Council of Ministers within the NORN project. I want to thank Preben Frederiksen. Preben Jorgensen. Lisbet Thomassen and Andreas Harder for their responsible work in the field and the laboratory. I am very grateful to Per Gundersen for his contribution and calculations on the spatial variability pattern for wa-

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ter and elements and Kirsten Schelde and Anton Thomsen for their support and help with the SOIL modelling and the TDR system. Finally I am grateful to Karin Hansen, Lennart Rasmussen and Bjam R. Andersen for valuable comments and discussions.

References Beier, C., Rasmussen, L., 1989. Problems related to collection of rain and throughfall water. Comm. Eur. Commun. Brussels Air Pollut. Res. Report 21, 101-110. Beier, C., Gundersen, P., Rasmussen. L., 1992. A new method for estimation of dry deposition of particles based on throughfall measurements in a forest edge. Atm. Env. 26A, 1553-1559. Beier. C., Hansen, K., Gundersen, P., 1993a. Spatial variability of throughfall fluxes in a spruce forest. Environ. Poilut. 81, 251-261. Beier. C.. Bredemeier, M., Collins, J.F.. Cummins. T.. de Visser, P.H.B.. Farrell, E.P.. Kreutzer, K., Prijbstle, P., Raben, G.H., Rasmussen, L., Schierl. R., Steinberg. N., Zuleger, M., 1993b. EXMAN-Experimental Manipulation of Forest Ecosystems in Europe-Project period 1988-1991. In: Beier, C.. Rasmussen, L. (Eds.). Ecosyst. Res. Report, 7, Commission of the European Communities, Brussels. pp. 124. de Vries, W., Breeuwsma, A., 1987. The relation between soil acidification and element cycling. Water, Air. Soil Pollut. 35, 293-3 10. Gower. S.T., Norman, J.M., 1991. Rapid estimation of leaf area index in conifer and broad-leaf plantations. Elcology 72, 18961900. Gundersen, P., 1992. Mass balance approaches for establishing critical loads for nitrogen in Terrestrial ecosystems. NORD 41, 55-1 IO. Gundersen, P.. Andersen, B.R., Beier. C., Rasmussen, L.. 1995. Experimental manipulation of water and nutrient input to a Norway spruce plantation at Klosterhede, Denmark: 1) Unintentional induced physical and chemical changes. Plant Soil 168/69, 601-611. Hansen, H.B.R., Thomsen. L.. 1991. Root investigations and biomass measurements at three Danish Norway spruce sites (In Danish, English summary). Lab. of Env. Sci. Ecology, Techn. Uni. Denmark., Lyngby, pp. 105. Jacobsen. O.H., 1989. Unsaturated hydraulic conductivity for some Danish soils-Methods and characterization of soils (In Danish, English summary). Statens Planteavlsfors@g, Beretning nr. S 2030. pp. 60. Jansson. P.-E.. 1991a. Simulation model for soil water and heat conditions-Description of the SOIL model. Swedish Univ. Agric. Sci.. Dep. Soil Sci.. Report 165. Uppsala, Sweden. pp. 72. Jansson, P.-E.. 1991b. SOIL model-User’s manual. Swedish Univ. Agric. Sci., Dep. Soil Sci., Communications 91: 7. Uppsala. Sweden. pp. 59. Jansson. P.-E.. 1993. PLOTPF - User’s manual. Swed. Univ.

280

C. Beier/

Forest

Ecology

and

Agric. Sci., Dep. Soil Sci., Commun. 93: 6, Uppsala. Sweden. pp. 33. Jansson, P.-E.. Halldin, S., 1979. Model for annual water and energy flow in a layered soil. In: Halldin S. (Ed.), Comparison of forest and energy exchange models, Sot. of Ecol. Modell.. Copenhagen. 145-163. Johnson, D.W.. Ball. J.T., 1990/91. Environmental pollution and impacts on soils and forests nutrition in North America. Water. Air Soil Pollut. 54. 3-20. Jury. W.A., Fliihler, H., 1993. Transport of chemicals through soil: Mechanics, models, and field applications. Adv. Agron. 47, 141-201. Kersten, M.S., 1949. Thermal properties of soils. Inst. of Technology, Eng. Exp. Station, Bull. No. 28. Minneapolis Univ.. Minnesota. USA. pp. 26. Koch, AS., Matzner. E., 1993. Heterogeneity of soil and soil solution chemistry under Norway spruce t P~CXYI a&es Karst.)

Manugement

101CI

998)

269-280

and European beech ( Faps sihticu L.i ;is intluenced by distance from the stem basis. Plant Soil 151, 227-237. Rasmussen, L.. Beier, C.. 1986. Improved Methods for Throughfall and Stemflow collection. Comm. Eur. Commun. Brussels Air Pollut. Res. Report 4. 92-97. Raulund-Rasmussen. K.. 1994. Pedological and mineralogical characterisation of five Danish forest soils, For. Landsc. Res. 1. o--33. Schonning, P., 1985. Equipment for drainage of soil samples. (Ln Danish, English summary). Statens Planteavfsforsog. Beretning No. S 1762. 25 pp. Warfvinge. P., Holmberg, M., Posch. M., Wright. R.F., IY92 The use of dynamic models to set target loads. AMBJO 2 I. 369-376. Ulrich, B.. 1987. Stability, elasticity, and resilience ol terrestrial ecosystems with respect to matter balance. Ecol. Stud. 61. I I-49.