Application of the SAFE model to a Norway spruce stand at Ballyhooly, Ireland

Foresk;;ology Management ELSEVIER

Forest Ecology

and Management

101 (1998)

331-338

Application of the SAFE model to a Norway spruce stand at B allyhooly , Ireland Julian Aherne a,* , Harald Sverdrup b, Edward P. Farrell a, Thomas Cummins a ’ Department

of Enuironmental b Department

Resource

of Chemical

Management,

Engineering

Unicbersity

II. P.O.

Accepted

11 March

Box

124,

College S-221

Dublin,

Dublin,

00 Lund.

Sweden

Ireland

1997

Abstract Acid rain results in qualitative and quantitative changes in terrestrial ecosystem boundary conditions. Typically these changes exceed the range of variation observed or inferred from past states. Methods of predicting future states are required, such as process-based models or experimental treatments that mimic future scenarios. The dynamic biogeochemical model SAFE was applied to Ballyhooly intensive forest monitoring plot located in southern Ireland. SAFE is a dynamic, process-oriented soil chemistry model developed with the objective of studying the effects of acid deposition on soils and groundwaters. It calculates the values of different chemical state variables as a function of time. The model requires input data on soil mineralogy, soil texture, CEC and base saturation, together with time-series data for atmospheric deposition, nutrient uptake and cycling, and hydrology. Model results indicate that the basic principles in the model are capable of describing the present soil-solution chemistry at Ballyhooly without extensive calibration. However, the gibbsite equilibrium model does not describe the observed concentrations of aluminium at Ballyhooly, and the assumption that sulphur adsorption is insignificant appears also to be incorrect. Inclusion of processes such as sulphur adsorption, aluminium complexation with organic matter, or a kinetic-based aluminium model may improve model results. 0 1998 Elsevier Science B.V. Keywords;

SAFE;

Soil; Acidification;

Atmospheric

deposition;

Forest ecosystems

1. Introduction The phenomenon of acid rain has been known for over a century but its possibleenvironmental impacts are still being debated today. While the concept of forest decline has become widely accepted a recent assessmentof forest health has argued against there being evidence of any new, widespread decline of European forests. Nevertheless, there have been widespreadchangesin the chemical status of forest

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soils over the past 100 years, and there are very real threats of a deterioration in forest condition as a result of climate change (Schlaepfer, 1993). Ireland, being situated on the north western seaboardof Continental Europe, receives a relatively low level of anthropogenic acidifying substancesvia atmospheric deposition compared to the rest of Europe. However, recent Irish work has clearly shown that anthropogenic atmospheric deposition of measurable amounts occurs. The loads measuredhave the potential to acidify poorly-buffered surface waters (Bowman, 1991), to accelerate acidification in forest ecosystems(Farrell et al., 1993) and to exceed

critical loads of acidity for sensitive soils (Aheme and Farrell, 1995a). Knowledge about future states of ecosystems can be basedon managementexperience but only within the range of past variations in boundary conditions (Hauhs. 1990). Acid rain results in qualitative and quantitative changes in ecosystem boundary conditions, typically exceeding the range of variation observed or inferred from past states. Predictive methods are required such as process-basedmodels or zxperimental treatments that mimic future scenarios. Process-basedmodels require identification of key processesfor successfulpredictions. The successof models dependson their ability to describe the observationsat appropriate space-and time-scales. The application of the dynamic biogeochemical model SAFE to the Ballyhooly intensive forest monitoring plot is described in this paper. SAFE has been applied to the other EXMAN sites (Jiinsson et al.. 199.5: Walse et al.. 1995 and Warfvinge et al.. 1998b1,providing a common platform of comparison [Warfvinge et al., 1998a).

2. Methods

seen hy comparison of precipitation and throughfall pH values. 4.95-5.28. However. the site receives :I relatively low level of anthropogenic acidifying inputs. The dominant soil type is an orthic podzol (Spodosol: Typic Haplorthod). Sandstonetill is obcrlain by periglacial colluvium. in which the soil is developed. The soil is deep and relatively free-rlrainix, stones are common and fine roots OCCLII throughout. The soil-solution in the upper mineral horizons is in the aluminium buffer range. with a tendency to enter iron buffering. In the deeper hori-” zons. cation-exchange processescontrol pH, with a tendency for the aluminium buffering to be itnportant during acid- or salt-stress.The principal ions of marine origin, sodium. chloride and to a lesserextent magnesiumand sulphate reflect in their soil-solution concentration the degree of oceanity of the site Regular sampling of soil water at three depths, 0 cm (zero tension). 2.5 cm and 75 cm (600 hPa underpressure,ceramic cups), along with continuous monitoring of open-land bulk precipitation and forest bulk throughfall have been carried out since 1989. Detailed site descriptions are given by Farrell and Boyle ( 1991) and Farrell et al. (1996).

2.I. Site description

2.2. Model descriptiorl

The Ballyhooly intensive forest monitoring plot is located in a mixed intensive farming area in the south of Ireland (8”25’W, 52”08’N). The region has sub-oceanic temperatures, with mild winters, and autumn and winter maxima of precipitation. Precipitation is mostly frontal with frequent. light, windblown rain. The altitude is 70 m. annual precipitation averages 1140 mm and the mean temperature is

SAFE is a dynamic, process-oriented soil chemistry model developed with the objective of studying the effects of acid deposition on soils and groundwaters. It calculates the values of different chemical state variables as a function of time. SAFE is structured into different compartments to represent the natural layering (hor11ons ‘_, ) in soilc. Several basic assumptionshave been employed in the model development. Sulphate adsorption is considered insignificant. Cation exchange capacity (CEC) is kept constant throughout the simulation. as are the selectivity coefficients of the exchangeable cations. The amount of organic material in the soil is assumedto be at or nearly at steady-state. Gibbsite equilibrium is used to describe the concentrations of aluminium. SAFE contains the following chemical suhsysterns: (i) deposition. leaching and accumulation of dissolved chemical components;(ii) chemical weathering reactions of soil minerals with the soil solution. depending on mineralogy, texture, soil moisture. pH,

9.9”C.

The monitoring plot is in a plantation of Norway Spruce (Pica ubies CL.) Karst.) established on a former oak woodland site, supporting a relatively rich woodland flora. The sprucewas planted in 1939. with periodic thinning between 1950- 1985 and was clearfelled in 1995. The oceanic influence is reflected in the ionic composition of precipitation, approximately 50% of sulphatedeposition is of non-marine origin. Ammonium deposition is significant, the resulting deacidification due to local emissionsof ammonia can be

J. Aherne

et al. /Forest

Ecology

aluminium, organic acids and base cation concentration; (iii) cation exchange reactions between aluminium and base cation; (iv) nitrification and denitrification; (v) cycling of nitrogen and base cations in the canopy, using input data for canopy exchange, litterfall, uptake, mineralisation and immobilisation; and (vi) solution equilibrium reactions involving carbon dioxide, aluminium and organic acids. The outstanding difference between SAFE and other comparable soil chemistry models is that the weathering rate is calculated from site-measured geophysical properties. Detailed model descriptions are given by Warfvinge and Sverdrup (19921, Warfvinge et al. (1993) and Sverdrup et al. (1995). The SAFE model requires input data on soil mineralogy, soil texture, CEC and base saturation, together with time-series data for atmospheric deposition, nutrient uptake and cycling, and hydrology. 2.3. Soil data The soil profile in Ballyhooly was divided into four horizons to keep modelling methodology consistent with the other EXMAN sites (Jonsson et al., 1995; Walse et al., 1995 and Warfvinge et al., 1998b). This resulted in the following stratification: one organic horizon 0 ( - 10-O cm> and three mineral horizons E (0- 10 cm), B (lo-40 cm) and C (40-75 cm). Although the available data to a large extent meet the requirements of the SAFE model, some default input values had to be used. Bulk density, CEC, base saturation and mineralogy are site-measured. Mineralogy was determined by X-ray diffraction analysis carried out by the Czech Geological Survey. Sitemeasured soil texture and bulk density were used to estimate the exposed mineral surface area according to Sverdrup et al. (1990). The values for the CO, pressure at different levels in the soil, expressed as multiples of ambient pressure, were based on default values. The potential error introduced by assigning an incorrect value is quite limited as the soil matrix does not contain minerals highly susceptible to CO, weathering. The gibbsite equilibrium coefficients are also based on default values taken from Sverdrup et al. (I 9901. DOC is based on mean annual soil-solution values for the 0 and C horizons; default values were used for the other horizons. The distribution of

and Management

101 f 1998) 331-338

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nutrient uptake was initially based on measured fine-root distribution, but was adjusted to achieve the best possible fit with soil-solution base cation and nitrogen concentrations. Nutrient uptake was estimated from annual volume increment, stemwood density and stem nutrient concentrations according to De Vries (1991). The vertical water flux- and water content have been modelled using the FORHYD model (Aheme et al., 1995). The model takes precipitation (open-land and throughfall), soil water retention characteristics and fine root distribution as field inputs. The model’s central module is a finite difference model of unsaturated vertical soil water flow based on the Richards equation (Richards, 193 1). Simulated and measured soil moisture tensions were used for calibration and validation. There is strong evidence of occasional lateral soil-water flow on the Ballyhooly plot, however the model does not take lateral flow in the unsaturated zone into account. Details of the model are given by Tiktak et al. (1990) and Tiktak and Bouten (1992). Mean soil moisture content and mean annual vertical water flux, 1989-1995, were used as model inputs. These parameters are held constant by the model throughout the simulation. A summary of all the soil parameter values and mineralogy are given in Table 1. 2.4. Time series data Time series data have been developed for a landuse history which begins in 1850 with a mature oak forest present. Gradual clearfell of the oak was assumed between 1927-1937, followed by a planting with Norway spruce in 1939. Periodic thinning occurred between 1950- 1985 and the site was clearfelled during 1995. It is assumed that a planting of another spruce forest will take place within two years. A simplified approach was used to construct the time series trends due to the extremely complex landuse history and the limited historical data available. During the monitoring period 1989-1995, constant long-term average values were used to construct the time series tends instead of specifying the year-to-year annual variation. This was carried out to be consistent with parameters such as water content and vertical water flux which are held constant by the model.

334

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et cd. / Forest

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and

The open-land and throughfall deposition of SO:-, NO;, NH:, Cl- and the base cations were measured during 1989- 1995. Throughfall data collected in forest stands can, if interpreted with care, be used to approximate dry deposition of some ions (Hultberg and Grennfelt, 1992; Ferm and Hultberg, 199.5). The dry deposition for SO:--, Na+ and Cl-- were estimated as the difference between throughfall and bulk deposition. The Ulrich method (Ulrich, 1983) was used to estimate the dry deposition of the remaining base cations. The interaction of nitrogen with the forest canopy is extremely complex (Sutton et al., 1995). There is considerable evidence that nitrogen is taken up by the canopy at Ballyhooly (Van den Beuken, 1994) so the difference between throughfall and bulk deposition can not be used as an estimate of Table 1 Soil characteristics

based on four horizons

characteristics:

morphology

Soil layer thickness Soil bulk density Specific surface area Moisture content Inflow outflow Pm+&

dry deposition. The canopy budget model (Draaijers and Erisman, 1995) was used to estimated dry deposition of NH:. It was assumed that NO; does not interact with the canopy, though this may be incorrect. Using the ratios of ions to Nap’ in seawater. depositions were divided into four fractions: dry marine, wet marine, dry anthropogenic and wet anthropogenic. Cl-. Na+ and Mg” ions were considered to be solely of marine origin. It was assumed that wet marine inputs were constant over time. The dry marine component of these ions was scaled in proportion to the forest growth. Historic deposition trends to the oak forest were scaled using data from an intensive oak forest monitoring plot located in the west of Ireland (Farrell et al., 1993. 1994) and data

E

B

c

hydroiog?

m kg m ’ m? m- I m’ tn. ’ % of throughfall Q of throughfall

0.1 500 100 0.4

100 80

0.1 1330

0.3

Ii.35

1453

’ 33 x IO” -..0.36 80 70

1727

2.71 0.3 70

x IOh

7.97 il.2 AS

65

60

IO 2s 5

211 IO i!

size

c/r of fine earth % of fine earth ?t of tine earth

Fine sand Silt Clay Chemical

and

331-338

Soil layer 0

Physical

101 (IYYX)

for Ballyhooly

Units

Parameter

Managenrenf

29 I6 8

churucteristics

CO, pressure Mg + Ca + K uptake Nitrogen uptake Dissolved organic carbon Log gibbsite equilibrium constant Cation exchange capacity Base saturation (1989)

Times ambient B of total % of total mg 1-l kmol’ me ’ mmol. kg-’ 5%

2 35 70 20 6.5

110 87

5 30 20 I0 7.5 30 37

5 8.5

Cl.5 9.0

40

70

16

20

Mineralogy

K-Feldspar Oligoclase Muscovite Chlorite Apatite Kaolinite Calcite Quartz

%, of % of o/o of o/c of % of % of % of % of

mineral mineral mineral mineral mineral mineral mineral mineral

1.486 1.132 4.378 0.400

0.0 10 0.092 0.006 62.73

1.293 0.313 4.797 0.990

I.366 1 .S?l LO.27 I.523

0.007 0.060 0.017 64.82

0.0 16 0.054 0.000 60.75

x 10”

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canopy uptake. Litterfall fluxes were estimate using litterfall necromass and litterfall nutrient concentrations. Release of nutrients after clearfell and thinnings were described using an exponential decay equation and scaled to present values calculated from litter flux and needle nutrient concentration.

3. Results and discussion -‘k VE

120

-$*

80

2 I

6n

Eg 8 9 B x

-

100

Toml amonium deposition Total base cation dqxxition

40 20 0

1850

1900

1950

2cca

2050

Fig. 1. Time series of total atmospheric deposition of sulphate, nitrate. ammonium and base cations (mmol, mm2 yr- ’ ).

for European forests (De Vries, 1991). The dry marine fraction of SOi- deposition was similarly scaled using forest growth. The trend of anthropogenic SO:- deposition was estimated from work by Mylona (1993) and RAINS model (Alcamo et al., 1990) calculations, scaled to present day values. The trend of anthropogenic SO:- deposition was also used to scale the dry and wet anthropogenic fractions of Ca’+ and K+. Nitrogen deposition originates solely from anthropogenic sources; the trend of NH: deposition was estimated from work by Asman and Janssen (1987) and RAINS model calculations. RAINS model calculations were also used to estimate NO; deposition. The future deposition trends for all ions were scaled from IIASA RAINS model predictions. Total depositions were estimated by summing the relevant fractions for each ion. The constructed time series of total depositions, 18502050, used as model inputs are shown in Fig. 1. Net nutrient storage in forest stems was described using a Michaels-Mentum equation which was scaled to current uptake values calculated from annual volume increment, stemwood density and stem nutrient concentrations. Nutrient uptake by canopy exchange was estimated as the difference between throughfall and total deposition. Positive values indicate canopy leaching and negative values indicate

The SAFE model is calibrated on base saturation by fitting calculated base saturation to site-measured data. As with all dynamic models, SAFE must be triggered from an equilibrium situation. This ensures that the simulation reflects changes because of changes in external and internal loads, rather than just unstable initial conditions. Figs. 2-4 show the results of the simulation of soil-solution concentrations of major ions. The simulated concentrations in soil 0, E, B and C horizons are compared with measuredmean concentrations (1989- 1995) for humus water and soil water (75 cm depth), which correspond to modelled 0 and C horizons. Fig. 2 shows modelled and measuredvalues for base saturation and soil-solution pH. As mentioned above, SAFE is calibrated on present basesaturation

01 0.40 1 19 0.20 i

-E -c

0

0 D

B ::

0.0 1850 4.8

1900

1950

2cca

2050

1900

1950

2ooo

I 2050

I

4.6

3.6

’ 1850

Fig. 2. Modelled and measured base saturation and soil-solution pH. Curves are modelled time-series, symbols are measured values.

by adjustment of the initial base saturation. The model results suggest that base saturation has been relatively constant over time. There is good agreement between modelled and measured present day pH, the gradual decline and increase in pH appears to be mainly explained by the changing landuse history. Fig. 3 shows modelled and measured concentrations of base cations and nitrogen. There is also good agreement between modelled and measured present day base cation concentrations. This is to be expected because the model can be calibrated with respect to base cation concentration by adjusting the base cation uptake. Nitrogen concentrations for the C horizon agree well, which indicates that the mass balance for nitrogen is correct. However. the model overestimates the nitrogen concentration in the 0 horizon which would suggest that the internal nitrogen cycling has been parameterised incorrectly. This is further complicated by the uptake of nitrogen by the forest canopy and the uncertainties associated with its estimation. Modelled and measured concentrations of sulphur and aluminium are shown in Fig. 4. Sulphur concentrations for the 0 horizon agree well. However, there is a large overestimation for the C horizon. This

1850

1900

lY50

zoo0

2050

0

..: 1850

Fig.

3.

Modelled

minium concentrations time-series. symbols

1900

and

19?ll

measured

( wmol are observed

soil-solution k

Iocx’

?OS!l

sulphur

L.-l). Curves mean concentratwns.

are

and

ulu-

modelled

would seem to indicate that there is a retention of sulphur in the mineral soil at Ballyhooly which is not modelled by SAFE. The gibbsite equilibrium model used in SAFE to describe the concentrations of aluminium is generally viewed as an over simplification (Neal, 1988). The gibbsite model does not take interactions between aluminium and organic matter into account, which may explain the overestimation in the 0 horizon. The overestimation in the C horizon may be due to an incorrect choice of gibbsite coefficients. it is very difficult to tune the different gibbsite coefficients to yield both good pH and aluminium values. In this simulation, the values used were not subject to any adjustment.

4. Conclwhns

18.50

1900

19.50

2cm

Fig. 3. Modelled and measured soil-solution base cations norganic nitrogen concentrations ( pm01 * L-’ ). Curves nodelled time-series, symbols are observed mean concentrations.

2050

and are

The aim of the application of SAFE to the Ballyhooly intensive forest monitoring plot was to provide a common platform of comparison between EXMAN sites (Warfvinge et al., 1998a). The SAFE model is data demanding compared to many other biogeochemical models, however the data produced by the

J. Aherne

et al. / Forest Ecology

and Management

monitoring plot is detailed enough to parameterise the model and generate reasonable results without extensive calibration. The modelling scenario indicates that the basic principles in the model are capable of describing the present soil-solution chemistry. However, results indicate the gibbsite equilibrium model does not describe the observed concentrations of aluminium at Ballyhooly. Inclusion of complexation with organic matter, or a kinetic-based model similar to that described by Alveteg et al. (1995) may improve this. The assumption that sulphur adsorption is insignificant appears also to be incorrect for Ballyhooly. Again, inclusion of sulphur adsorption may bring model results closer to the observations. Limitations in the data can be attributed to uncertainties in the estimatesof nitrogen uptake by the canopy, default literature values used for model input parameters,the distribution of nutrient uptake within the soil and the vertical water flux and water content. The model calculations serve as a partial model validation and background for the applicability of further scenario analysis. The observed changes in soil chemistry are explained by the model mainly as a result of soil acidification effects from atmosphericdeposition inputs and landusechanges. The Ballyhooly plot has undergone considerable disturbance of nutrient cycles since the early 1800s. Base cations have accumulated in biomassand have been removed in harvest. This is accompaniedby increasedambient levels and forest-mediated scavenging of acidifying substances.In combination, thesetrends are strongly associatedwith the modelled decline in soil-solution pH. Forest health assessed as a combination of defoliation and discolouration has deteriorated over the period of the project (Farrell et al., 1996). However, the degree of deterioration was small and may not indicate any long-term deterioration. The site is not subject to acutely damaging levels of air-borne chemicals when compared to central European sites, although it is still possible that critical loads are exceeded(Aherne and Farrell, 1995b). Acknowledgements Financial support for this work was provided by C.E.C. DGVI and DGXII, and the Swedish Institute.

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The authorswould like to thank Charlotta and Jonatan Walse, Mattias Alveteg and Per Warfvinge at the Department of Chemical Engineering II, Lund University for their help in the application of the SAFE model.

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ing of forest ecosystems in Ireland, Final Report. Forest Ecosystem Research Group Internal Report Number 13. Department of Environmental Resource Management, University College Dublin. Farrell. E.P., Boyle, G.M.. Cummins. T.. Aherne. J., van den Beuken. R.. 1996. Continued monitoring of a forest ecosystem in Ireland, Ballyhooly Final Report. Forest Ecosystem Research Group Internal Report Number 17. Department 01 Environmental Resource Management. University College Dublin, 122 pp. Ferm. M., Hultberg. H.. 1995. Method to estimate atmospheric deposition of base cations in coniferous throughfall. Water AiiSoil Pollut. 85. 2229-2234. Hauhs, M., 1990. Ecosystem modelling: science or technology?. J. Hydrol. 116. 25-33. Hultberg, H., Grennfelt, P., 1992. Sulphur and seasalt deposition as reflected by throughfall and runoff chemistry in forested catchments. Environ. Pollut. 75, 215-222. Jonsson, C., Warfvinge, P., Sverdrup. H., 1995. Application of the SAFE model to the Soiling spruce site. Ecol. Model. 83, 85-96. Neal. C.. 1988. Aluminium solubility relationships in acid waters - A practical example of the need for a radical reappraisal. J. Hydrol. 104. 141-159. Mylona, S., 1993. Trends of sulphur dioxide emissions, air concentrations and depositions of sulphur in Europe since 1880. Technical report, EMEP/MSC-W. Schlaepfer, R. (Ed.). 1993. Long-term implications of climate change and air pollution on forest ecosystems. Vienna, IUFRO: Birmensdorf. WSLIUFRO Word Series Vol. 4, I32 pp. Sutton, M.A., Fowier, D.. Burkhardt. J.K.. Milford, C., 1995. Vegetation atmosphere exchange of ammonia: canopy cycling and the impacts of elevated nitrogen inputs. Water Air Soil Pollut. 85, 2057-2063. Sverdrup, H., de Vries, W., Henriksen. A.. 1990. Mapping critical loads. Miljiirapport 1990:14. Nordic Council of Ministers, Copenhagen, 125 pp. Sverdrup, H.. Warfvinge, P., Blake. L., Goulding. K.. 1995.

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