5th Annual Report 1996: UN ECE Convention on Long-Range Transboundary Air Pollution. International Co-operative Programme on Integrated Monitoring of Air Pollution Effects on Ecosystems

The Finnish Envi ron ment

INTERNATIONAL COOPERATION

Sirpa Kleemola and Martin Forsius (eds)

5ffi Ännual Report 1996

UN ECE Convention on Long-Range Transboundary Äir Pollution

International Co-operative Programme on Integrated Monitoring of Äir Pollution

Effects on Ecosystems

, ,

.

.

.

. .

HNNISH ENVIR0NMENTi

The Finnish Environment

Sirpa Kleemola and Martin Forsius (eds)

5ffi Ännual Report 1996

UN ECE Convention on Long-Range Transboundary Äir Pollution

International Co-operative Programme on Integrated Monitoring of Äir Pollution

Effects on Ecosystems

HELSINKI 1996

i

. . . . . . . . . . . . .

a

Piease refer te individual chopters in this report as shown in the following exampie:

Liu,

Q.

Evaluation of data with regard teeffhctsorN and 5 deposition.

In: Kleemola, 5. Forsius, M. (ecls,), Sth Annua/ Report 1996.

UN ECE lKP Integrated Monitonng. The Finnish Environment 27:58-82.

kinnish Environment lnstitute, Helsinki, Finland.

ISBN 952-1 1-0045-1 ISSN 1238-7312

Cover photo: Vegeta tien plot (0.25 m2) in the integrated monitoring area ofVuoskojärvi

Photo: Finnish Environment Insbtute Printing: Edita

HELSINKI 1 996

TheFinnish Environment27

Cøntents

lntroduction .5

1 Sites and monitoring actwities 7

2 Dynamic model apphcations at selected ICP IM sites 10

2.1. Introducfion 10

2.2 Impiementafion of the project 11

2.3 Model descripfions: MÄGIC, SAFE and SMART 12

2.4 Site descripfions 12

2.5 Methods 13

2.5.1 Deposition scenarios 13

2.5.2 Nutrient uptake scenarios 14

2.5.3 Model calibrations: MAGIC, SAFE and SMART 15

2.6 Results and discussion 15

2.6.1 Deposition and uptake scenarios 15

2.6.2 MAGIC, SAFE and SMART results 18

2.7 Conclusions 19

Acknowledgements 23

References 23

3 Assessment ofnitrogen processes at ICP IM sites 25

3.1 Materiais and methods 25

3.1.1 Catchment-scale input-output budgets 25

3.1.2 Plot-scale input-output budgets 26

3.1.3 Correlation analysis 26

3.2 Results and discussion 30

3.2.1 Input-output budgets for N 30

3.2.2 Relationships between N-pools and fluxes 35

3.4 Conclusions 38

References 38

The Fnnrsh Envwonment 27

4 Evaluation of the EDACS model results using throughfall data of

ICP IM and other sites in Europe 39

Abstract 39

4.1 Introduction 39

4.2 Methods 40

4.2.1 General description of EDACS 40

4.2.2 Wet deposifion 42

4.2.3 Dry deposition 42

4.2.3.1 Dry deposifion velocity 42

4.2.3.2 Air concenfrafions 42

4.2.4 Evaluation with throughfali data 43

4.3 Comparison between modeiled and measured ftuxes 46

4.4 Uncertainty estimates 49

4.4.1 Wet deposifion 49

4.4.2 Dry deposition 49

4.4.3 Total deposition 50

4.4.4 Throughfali measurements 50

4.5. Conclusions 51

Acknowiedgements 51

References 52

5 Vegetation monitoring in the ICP IM programme: Evaluation of data with regard to effects of N and S deposition 55

5,1 Introduction 55

5,2 Method 56

5.2.1 Data 56

5.2.2 Terminology and coding 5$

5.2.3 Data treatment and analysis 58

5.3 Resuits 62

5.3.1 Distribution pattern of epiphytic lichens on free stems and in responses to.

suiphur and nitrogen deposifion 62

5.3.2 Understorey vegetafion in response to sulphur and nitrogen deposifion ^. 69

5.4 Discussion 76

5.4.1 Conclusions regarding vegetation 77

5.4.2 Suggestions to data reporting and storage 77

Acknowledgements 78

References 78

Documentation pages 80

0

Introduction

Martin Forsius and Sirpa Kleemola ICP IM Programme Centre

Finnish Environment Institute Impacts Research Division PO.Box 140

FIN-00251 Helsinki Finland

The Integrated Monitoring Prograrnme (ICP IM) is part of the Effects Monitoring Strategy under the

ECE Convention on Long-Range Transboundanj Air Pollution. The main aim ofICP IM is to provide aframework to observe and understand the cornplex changes occuring

the external environment. The monitoring and prediction of complex ecosystem effects on undisturbed reference areas require a continuos effort to improve the collection and assessment of data on the international sade.

This report presents results ftom assessment activities carried out by the Programme Centre and collaborating institutes during the programme year 1995/96:

Section 1 of the report summarises the present monitoring activities and the content of the ICP IM database.

Section

presents main results of the calibration of three dynamic modeis (MAGIC, SAFE, SMART to dataftomfive selected IM sites. The main aim of the project is to assess the dynamic response of different deposition scenarios, including the effects of the Second sulphur protocol, and two scenarios for NO emissions. These site-specfic model applications also provide a reality checkfor the pianneä regional-scale modelling exercise of the Coordination Center for Effects (CCE/RIVM). The project has been fimnded by the Nordic Council of Ministers and carried out as joint project betzveen the Prograrnme Centre and four modelling centres (CCE/RIVM, Institute ofHydrology, Lund University,

Norwegian Institute for Water Research),

In Section 3 an update of the assessment of the effects of N-deposition on IM sites is presented. This work has been carried out at the Programme Centre. The inain aim has been to derive empirical critical tresholdsfor N-deposition, and to.

identify different ecosystern variables associated zvith N-saturation and leaching.

Section 4 presents results fto;n the testing of the EDACS deposition model using throughfall dataftorn IM-sites and other sites in Europe. Iii EDACS dry deposition is estimated with the inference rnethod (i.e. inferred from the concentration and deposition velocity). The EDACS model is used for

The Fneh Envrenment 27

0

calculations of small scale deposition estimates

Europe. The model testing and development has been carried out at the RIVM in the Netherlands (van Leeuwen et aL).

Finally, in Section 5 an assessment of vegetation monitoring data with regard to effects ofN and S deposition is presented. This assessment has been carried out at the Department of Environmental Assessrnent, SLU, Sweden (Qinghong Liii). The effects of pollutant deposition on natural vegetation, including both trees and understorey vegetation, is one of the centra concerns in the impact assessrnent and prediction.

The Fnneh Environment 27

Finland

The Jntegrated monitoring network presently covers twenty-three countries. Most of these are European countries, out of the NorthAmerican countries only Canada is taking part in the programme.

The monitoring sites are divided into two categories:

A. At the Intensive monitoring sites (A-sites) sampies are collected and observations made for many compartments in the ecosystem for the application of complex modeis. Intense investigafions of dose-response relationships are also carried out. Strict siting criteria have been set for the A-sites. These sites are normally located in protected areas.

B. Biomonitoring sites (B-sites) have the objective to quantify the variation between sites concerning some of the more important features like input output mass balance modeis of elements and modeis for bioindicators on thespatialbasis. Biomonitoring for detecting naturalchanges, effects of air pollutants and climate change, is a particular aim of these sites.

Fifteen countries in the IM network have at least one intensive monitoring site. Seven countries are running one or several biomonitoring sites. Ofthefifteen countries with intensive monitoring sites, seven have additionai biomonitoring sites. One country has chosen a monitoring site where monitoring wiii start in the near future (C-site), and another country is intending to widen the network with several new sites.

Ali in total, integrated monitoring data is at present avaiiable from 57 mostly European sites. Location of the IM monitoring sites is presented in Figure 1.1.

The performance of monitoring activities at the sites are presented in the 3rd Annual Synoptic report, 1994.An overview of the data reported internationally to the ICP IM Programme Centre and presentiy held in the IM database is given in Table 1.1. The data requirements for both intensive and biomonitoring sites are also indicated in the table. In spite of the wide coverage of the monitoring network, many National Focai Points (NfP) have, due to financial or organizationai reasons, had probiems in carrying out ali the subprogrammes.

Some of the NFPs have also had probiems in reporting complete parameter sets to the Programme Cenfre. Due to these facts, there is a definite need to improve the coverage of internationally reported data.

The Fnn!sh Envwonent 27

0

Sites and monitorin9 ativities

Sirpa Kleemola

ICP IM Programme Centre Finnish Environment Institute Impacts Research Division EO.Box 140

F1N-00251 Helsinki

Geographical Iocation of the Integrated Monitoring sites

O

O

O

O

0

Figure Ii. Geographical Iocation and categorization of the integrated monitoring sites. A-sites are suitable for complex modeUing, B-sites are suitable only for monitoring, and on Csites the activities have not yet started.

The Fnreh Envwonment 27

Table .1. Internationally reported data held presentiy jo the IC IM database.

BYO2 CAOI CIII)) CZO1 DE0I DKOI DKO3 bEll EE02 PItI

LTO3 LVI)) LV02 NL0I NOlI N012 AREA SUBPJ

AM

RUI3 93 93-94 93

RUI4 94 94 94

RUI5 90-94 RUI6 EI 1%

SE(16 SEO7 SEO%

SEI9 SEItI SEn SEI2 SE)3 UAI7

69-91 89-9 1 88-91 89-91

r93-95

93-95 94-95

- 91-94 86

- 92-94 89

ahIake, d,mne

x x 9

9 X

93 93 93 94

90 90 9))

93-94 93-94 91-94 89-94 93

93 94 93

87-92 82-93 83-92

88-92 82-94 83-92 94 83-94 87-92 84-91 84-9)) 85-94

-Subprogramme no) possib)e to carry out

*or totesI hea)th parameters in tormer subprogrammes Forest stands/Trees Xl: inc)uded f a forest cause/effect site

X2: p1015: SW (soi) waterfiow incL), catchments. RW (runoff inct.)+ RB

The Rnnh Envronment 27

0

AREA SU8PR0GRAMis(jAM AC DC MC

1—

89-94 69-94 88-92

88-94 %8-9f 19I-94

LF EN

89-95 88-92 88-94 89-94 911-94

94 68-94

ULTG

86-92 90-94 88-94 89-94

9(1-94

94 94

89-94 89 89-94

90-94j1ii

190-94 90-941

92,94

1

T

EP[AL

189-94

9,

90-94

P102 P1113

8-94

94 94 1 ^{94 94}

%6-94%9-91T%5-94+6

95

95 94 94 66-89 88-94 93

94 94 69-94

- 90-94

94 68-94

F 104 68-94 69-94 68-94 89-91 69-94 89-94

87-941 88-9)

P1115 08(11 0802 1101

88 88-94

88-94 86-93 88-93

94 9(1-91 69-94

92-93 68-93

- 94

88-94

88-94 86-93 88-94 88-93

89-94 89-94

89 69-94

95 94

88-94 91

92-93

67-94 68-9 1

lTD) 91

8% 89-93

94 9(1-93 86-9) 86-92

88-94 86-94 69-9 1

93 93-94

90

89-94

90-93T-93

90-9TF 90 86-91 90-92

9(1-91 88-93 87-94 90-91

11(2 01)3 111)4 LTOI LT02

94-95 68-94

9(1-941

93-94193-94 68

94

88-91 90-91 91 88-941 ^-

93 93-94 93

93-94 93-94 91-94

89-94

93-94 93 93-94 93 93 93

93-95 93-95 93-94 93-94 93-94 94 90-94

93 i•LL

2H-

93-94

93-95 93-95 95 93-94 93-94 911-94

93 93 94 94 93-94

93-94

94 94 93-94

93

93 93

93

92-94

:•

-

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—3-—t

92-94

93 93

92

94 94 94

87-94 87-91

67-91

92

93

67-94 87-91

94

92

94 94 94

93-94 93

95 92

92

93-94

89-94 87-94 88 89-94 OGRAMME

94 93-94 86

93

AC DC

-

93-94 94 94

89-94 87-88 87-94

MC

89-94

90-94 93-94 93-94

TF

87-94 SF

86

94 94

PL0I 68-94 88-94 88-94 88-90 93-94

89

92-94 84-94

SC SW GW RW

PL02 91

LC

PL03 P1114 P110

FC

93 88-93

92-94 93 69-94

88 1 93-94 93-94

93-94y 94

LF RB LII 01

93-94 93-991

93-94 90-91

88-94 88-94 88-9(1 91-94

89-94 89-94

yo

RUO3 RU))4 RU0S RU 12

EP

69-94 89-94 89-93 93-94

89-94 89-94

89-90 90-91

93-94 -

AL MII Into

90

93-94y

89-93 93-94 93-94

90-91 90-94

89-93

9(1-91 91

9(1-9 1 89-93 93

90 90-94 94 90-94 9(1-94

93-94

89-90 SE0I

SE02 SEO3

90 92-94 92 92-94

83-91 83-91 83-91

89 92-94 93

89 83-94

83-94 63-94

90-94 90-94 89 94

SEO4 67-91 %8-94jj17-93

92 82-90 84-94 84-93 84-94 92-93

92-93 92-93 87-93

82-90

93 -

92-94 92 85-941^{84-94 84-94}

8% 87-94 65-94 64-94

91 94

91-92J 86-94

87-93

91-921^90-94

94 93 83-94

68-94

i4

92t

— 87-8% 79-91

-

t••4

83-92 84-94 83-93 63-93

65-9482-9466-941-

4• ^1-

82-93

1-

1 f

84-9484-94

%-J-I•

86-92 88-94 67-94 j 88-94 86-94 86-911 90-94 87-93

88 94 86 94 85 88 94 84 94 87 9• 89 94

82-94 84ij% 68-94 82-94 %7-929-993

82•9%4 J ^{68-94182-94 82-92 89-94 83-94}

___

89-94

M. Forsius’, M. Alveteg2, A. Jenkins3, M. Johansson’, S, Kleemola’, A. Lfikewille4, M. Posch5, H. Sverdrup2,S, Syri’, C. Walse2

‘Finnish Environment Jnstittite, Impacts Research Division, P.O.Box 140, FJN-00251 Helsinki, Finland

‘Lund University, Chemical Engineering II, PO.Box 124, 5-22 100 Lund, Sweden 3lnstitute of Hydrology, Wallingford, Oxfordshire ^{OX1O 8BB,}United Kingdom 4Norwegian Jnstitute for Water Research, P.O.Box 173 Kjelsås, N-0411 Oslo, Norway

‘National Institute of Public Health and Environment, Coordination Center for Effects, RIVM/CCE, P.O.Box 1, NL-3720 BA Bilthoven, The Netherlands

2.! Introduction

Both steady-state and dynamic modeis have been developed to predict the acidificafion of soils, lakes, streams and groundwater (e.g. Cosby et at, 1985; dc Vries et at, 1989; Sverdrup et al, 1995). While the former are used to estimate the steady state of a system for a given ioad by negiecting time-dependent processes and finite pools, dynamic models are used to predict the gradual chemical response of a receptor to changing depositions by including the various buffer and adsorption/desorption mechanisms. The time development of acidification is important for determining the timing of necessary measures for emission control, and for assessing the dynamic response of possible critical load exceedance.

At the meeting of the UN/ECE Working Group on Effects in July 1994, dynamic modelling was considered to be a key activity for the WGE programme development. It was proposed that the activities on dynamic modelling should he carried out on two different scales of coverage: (i) the ICP IM will he responsible for dynamic modelling on selected sites, in cooperation with national data centers and invited modelling experts; and (ii) the Coordination Center for Effects (CCE) will take the responsibility for the model applications on a regional basis. These two projects are complementary; the catchment scale applications provide a reality check of the regional behaviour of the models.

A pilot study testing the suitability of the ICP IM database for dynamic modelling, using the SMART model, was carried out during 1994 (Bleeker et al, 1994 and 1995). This study showed that successful model calibrations can be

0

Dynamic model appIicatdois at

selected ICP IM sites

carried out using data from ICP IM sites, and several recommendations for improvements for the continuation of modelling exercises were given.

Ä new project for the coordinated application of three dynamic modeis (MAGIC, SAFE, SMÄRT) on selected ICP IM sites was therefore started in 1995.

The project has been funded by the Nordic Council of Ministers. The main aims of the project were:

(i) The three modeis are calibrated to the observed conditions at present-day, using consistent input data, model parameters and historical deposition scenarios for the selected ICP IM sites;

(ii) The calibrated modeis are used to predict the long-term acidiflcation of soils and runoff water, given different scenarios of future deposition of S and N. These scenarios are based on agreed measures for emission reductions;

(iii) The dynamic response of possible critical load exceedances are assessed;

(iv) Model results are compared and uncertainties assessed;

(v) The site-specific model applica%ons are used as a reality check for the regional-scale modelling exercise of the CCE.

The present paper summarises some main results of the project. Ä more detailed description is given in Forsius et al (1996),

2.2 Impiementation of the project

The modeis were applied by the same groups which have participated in the actual model development. The different tasks were carried out as follows:

Project management; data Finnish Environment Institute gathering; assessment and (Forsius, Kleemola, Johansson,

reporting Syri)

Derivation of deposition and Finnish Environment Institute nutrient uptake scenarios (Johansson)

Lund University, Sweden (Sverdrup, Älveteg, Walse) MÄGIC applications Institute of Hydrology, UK

(Jenkins)

Norwegian Institute for Water Research (Ltikewille)

SÄFE applications Lund University, Sweden (Sverdrup, Alveteg, Walse) SMÄRT applications RIVM/CCE, The Netherlands

(Posch)

The Finnish Environment 27

2.3 Model descriptions: MAGIC, SAFE and SMART

The three modeis are ali process-oriented dynamic modeis that attempt to describe the long-term impact of atmospheric deposition, net uptake by vegetation, weathering and cation exchange on the chemical composition of soil and the outflowing water. Solution chemistry is governed by charge and mass balance principles, using iumped process descriptions.

MAGIC is generaliy used as a one-box model (Cosbyetal, 1985). It calculates a separate Gaines-Thomas equilibrium for the exchange of each of the cations Ca, Mg, K and Na. Sulphate adsorption is described by a Langmuir isotherm.

The model keeps track of mass budgets and chemical equilibria of ali major ions including organic acids.

SAFE (Sverdrupetat, 1995) is a multilayer model which calculates weathering rates from measurements of soil texture, minerology and moisture. The model uses a mass transfer equafion for the jon exchange of base cations, which converges upon an equiiibrium described by a Gapon equation.

SMART (de Vii et at, 1989, Posch et iii, 1993) uses Gaines-Thomas equilibrium equations for the various exchange reactions. N-immobilisation is modeiled as a function of the C:N-ratio. A simpie iake module, describing retention of sulphate, nitrate and ammonia, has recently been added.

Ali modeis have been used in numerous studies on both catchment and regional scale (e.g. Jenkinsettai, 1990, Kämäri ettai, 1994, Sverdrup ettai, 1995).

2.4 Site descriptions

After screening of the avaiiable data, five ICP 1M sites were seiected for modei application: Afon Hafren (CB02), Birkenes (NOOl), Foreilenbach (DEO1), Gårdsjön (SEO4) and Hietajärvi (F103) (see Figure 1.1 in Section 1). The size range of the catchments is 3.7-464 ha. The sites receive varying deposition loads and have different catchment characteristics. Therefore these sites represent a wide range of possible future responses to atmospheric loadings. Some major features of the sites are listed in Tabies 2.1 and 2.2.

Table 2.1 Catchment and soil characteristks ol the modelled catchments.

Area Area Coordnates Catchment Lake Forest Forest age Domnant code name area (ha) area

(%)

(%)

DEOI orellenbach N48°56’E13°25’ 68.8 0 95 90 Spruce

E103 Kietajärvi N63°l0’E30°43’ 464 24 51 50-100 Scots pine

GBO2 Aion Kafren N52°29’W03°41’ 358 0 50 <50 Sitka-Norwayspruce

NOOl Birkenes N5823E08°I5’ 41.6 0 90 80 Norway spruce

5E04 Gårdsjön EI N58°03’E12°0I’ 3.? 0

00

Area Area Dominant Mean soil Bulk density CEC Base saturation

code name soiltype thickness (m) (glcm3)

(%)

DE0I Forellenbach Dystriccambisols 1 0.981 65.4 4.6

E103 Hietajärvi Eibric histosols 0.85 1.291 3.8 47.3

G802 Afon Hafren Peaty podzols 0.88 1.256 32.2 11.6

NOOl Birkenes Thin humus 0.22 0.713 48.1 19.1

5E04 Gårdsjön EI Orthic podzol 0.63 0.869 68.3 15.8

0

Table 2.2 Ännual precipitation and runoff (mm), and annual average deposWon and runoff fluxes for the period 199 1-94 for the modelled catchments.

Annual deposition:

Area precipit. 5045 NO3N NH4N H Ca Mg Na K

(mm) meq/(m2*a)

DEOl 1229.8 50.8 4I.7 •3.I 39.2 20.4 4.8 II

F103 625.3 21.4 11.5 9.2 19.6 3.1 1.1 3.0 0.9

GBO2 2579.2 87.8 35.6 44.4 34.9 33.3 53.4 242.2 6.8

NOOl 283.6 75.9 52.8 48.0 57.8 9.0 20.0 86.9 3.5

5E04 1073.0 59.! 40.6 38.4 44.6 10.0 18.7 76.8 4.6

Annual runoff:

Area runoff 5045 NO3N NH4N H ta Mg Na K

(mm) meq/(m2*a)

DEOI 870.2 74.4 39.6 5.7 i25.4 39.5 81.4 11.4

t103 391.3 15.5 0.7 0.4 0.3 25.4 10.6 19.1 4.2

GBO2 2064.6 188.3 45.4 33.2 88.4 134.6 415.7 11.3

N001 837.3 97.5 11.2 28.0 42.6 25.8 117.9 3.3

5E04 517.9 144.6 0.8 0.7 41.7 35.5 83.5 258.0 7.6

2.5 Methods

The modeis require deposition and nutrient uptake by forest growth as external input. The method used for generation of this input originates from the work of the SAFE modelling group (Alveteg et al, 1996). The derivation method ofhistorical development and future scenarios for deposition and uptake is explained in more detail in the Annual Synoptic Report 4 (1995) with updates in Johansson et al (1996).

2.5.1 Deposition scenarios

The deposition of each compound was divided into different components using bulk and ffiroughfall deposifion measurements: originating from sea or anthropogenic sources, and these both in dry and wet fractions, Historical and future development and forest filtering effect were then allocated to proper components.

The total dry component for sulphur, chloride and sodium was assumed to be the difference between throughfall and bulk deposition, as they are mobile ions in the tree canopy. For base cations other than sodium, the minimum of sodium or chloride filtering was used. For nitrogen compounds the total dry component was bulk deposition times minimum filtering factor of sulphur,50-

dium or chloride.

The wet marine components were estimated with seasait correcfion factors from the sodium or chloride wet deposition. The dry marine component for sulphur was calculated using the same ratio to its wet marine component as between its total dry and total wet component. For other compounds, except for nitrogen which is completely of non-marine origin, the dry marine component was estimated wiffi seasait correction factors using the sodium or chloride dry deposition. The obtained wetmarine value for any compound cannot be larger than its measured total wet deposition.

The Enneh Environrnent 27

0

The wet anthropogenic components were calculated by subtracting the wet marine component from total wet deposition. The same applies to the dry anthropogenic component. The remaining unallocated deposition was finaily assigned to canopy exchange, serving as an indication of internal circulation in the forest. The wet anthropogenic component for both nitrogen compounds was the measured bulk deposition.

The wet marine component was assumed to remain constant in time. Other wet and dry anthropogenic components were dependent on deposition histories, calibrated to the present measurements of one year. Sulphur deposition history for 1880-1991 was extracted from Mylona (1993) for each EMEP grid cell separately.

The earlier scaling was extended back to 1800 using an average for Europe estimated by Sverdrup et al (1995). Average nitrogen histories for the whoie of Europe were estimated on basis of work by Sverdrup et ei (1995) and Asman and Drukker (1988). The non-marine dry deposition component of base cations was assumed to be partly connected to anthropogenic activities reflected by measurement data during a period of considerable emission reductions (Hedin et ei, 1994), and was assumed to foiiow the historical sulphur deposition curve.

Ali dry deposition components are affected by forest filtering, which was assumed to depend linearly on the tree needle volume represented by the needle mass in internal calculation. The present filtering factor was caiibrated to current canopy volume and dry deposition estimates.

The deposition to open land was assumed to equal the sum of wet com ponents plus 20 ¾ of the dry deposition. The open land deposition does not have a big effect in the mainly forested IM sites.

In the scenarios, base cation and chloride depositions were kept at present levels. For acidifying deposition three future scenarios for the model application were employed (Table 2.3). The deposition was calculated with the EMEP fransfer matrices and official UN-ECE emissions for the target years 2000, 2005 and 2010.

The alternatives for sulphur deposition were current reduction plans (according to second sulphur protocol, SF2) and maximum feasible reductions (MFR) (Cofala and Schöpp, 1995). For nitrogen, NO and NH depositions were frozen at the present level. For NO, a reduction scenario of fiat 30 ¾ reduction from present deposition was used to demonstrate the possible effects of such a measure.

Table 2.3 The three scenaros employed in the acidification model runs, roughly representing the possible range of future acidifying deposition.

scenario SO NO NH

A) best prediction Second sulphur protocol present evel present evel B) Iower imit Maximum feasibe reductions -30% from present eve present eve C) upper imit present eve present eve present eve

2.5.2 Nutrient uptake scenarios

The uptake by forest growth was based on biomass density and element content together with the annual increment of each free compartment: stem over bark, branches and needies. The growth estimate is most suitable for a temporally homogenous forest with one major tree species, or a well-known tree species distribution each of known age, after clearcut or forest fire.

Potential anriual growth and standing volume were calculated from a Iogistic growth curve, representing the whole forest and initially based on conditions

The

Finnish Environment 27

ftom southern Germany. The branch growth was assumed to he one fifth of stem over bark growth (Mälkönen, 1975). The needle growth is described with a growth curve similar to that of stem over bark. Ali growth curve parameters were calibrated to current volume and growth.

Forest growth was assumed to he nifrogen limited. The growth of ali above ground compartments must be reduced if the nitrogen via atmosphere and mineraiization is not enough to satisfy the estimated potential growth.

2.5.3 Model calibrations: MAGIC, SAFE and SMART

Application of the modeis at the catchment scale requires averaging of physical and chemical properties of the soils into one or several homogenous layers characteristic for the entire catchment.

The modeis were calibrated to reproduce present-day (1990-94) soil and sfreamwater chemistry. The weathering rate of base cations is a key model parameter in ali modeis. In SAFE these are obtained from information on soil texture and minerology, and in the other modeis by calibration.

The deposifion and uptake patterns were used to drive the modeis and produce the chemicai changes in soil and sfreamwater over time. The modeis were ali using a common data set for each site, but calibrated independently of one another. Yearly time-steps were used in ali model runs.

2.6 Results and discussion

2.6.1 Deposition and uptake scenarios

In figure 2.1 the average sulphur and nitrogen depositions onto the whole IM piot are shown for scenario A. The forest filtering effect can he seen at most sites, especialiy at clearcut and for future development assuming present level emissions.

Figure 2.2 shows the net uptake by forest, which is required as model input and obtained by extracting mineralized material flux from total forest uptake values. In Afon Hafren, there was no forest at the piot before planting in 1940’s.

For Foreilenbach, forest growth representative for the whole piot was difficult to describe with the current method. Growth estimate could not handie growth for subpiot areas, where various management practices were reported, but their effect was considered for an aggregated forest.

The ion baiance in modelled deposition was calculated in the period under study (1800^-2050). The calcuiated pH was often slightiy above four for present conditions and even the historical pH has been rather low. This may indicate an imbalance between estimated historical deposifion compound values, since the calibrated modeis tended in some cases to overestimate acidification effects in comparison to measurements. The magnitude of historical deposition is sensitive to the measured deposition values, to which it is caiibrated, calling for use of measurement data from several years. Further uncertainty is brought in by the relationship of the forest needle volume to the fiitering effect,which may notbe linear in ali phases of the forest cycle. Ä more thorough comparison against measured pH values wiil he carried out.

The nifrogen deposition histories are estimated averages for the whole of Europe. The difference between actual and derived history is likely to be largest

The Fnneh EnvFronment 27

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figure2.!. Simulated forest and open Iand deposftions of sulphur and nitrogen compounds for scenario A for the five IM sites.

The

Fnnish Envronment 27 200

180 160 140 120 100 80 -o 60 40 20

18000 1850 1900 1950 2000 2050

year

903 Hietajärvi

te

1

180 160

t 140 c’J2

te 120 2 100

180

° 60

3602 Afon Hafren

2

te

2 c0 te

a

te

•0

200 180 160 140 120 100 80 60 40 20

1800

200/\

s

Ntot

NHz 40 NOx

1850 1900 1950 2000

year

2050

/

-

20

1800 1850 1900 1950

year

2000 2050

180 160 140 120 100

0

80

0

-ote

1850 1900 1950 2000 2050

year

50 45 40 .35 f30 25 20 c 15 10 5 18000

E

0E

0) (0 0•D

0)c .35

E 30

0)

E 25 20 c 15 10 5

1850 1900 1950 2000 2050

year

:103 Hietalärvi

45 40 .35

O30

25

(00.20

D 0c 15

10 5

1850 1900 1950 2000 2C

year 50

DEO1 Forellenbach

1850 1900 1950 2000 2050

year

50 45 40 cl 35 c’JE

0) 30 E 25 cl o.20

0c 15 10 5

f800 1850 1900 1950 2000

year

Figure 2.2. Estimated average net uptake of divalent base cations (BC2Ca+Mg), potassium and total nitrogen by forest at the five IM sites.

The Fnneh Enwonnt 27

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at sites where the short-range transport of pollutants is significant, as for ammonia.

Growth estimates could he refined by using locally estimated growth curves for the region and free species concerned. A more complete description would, however, cali for data not availahle for ali sites in the IM program. The growth curve calibration to present voiume and growth only may lead to fauity predictions in the early and iate phases of the rotation period. To depict in more detail the growth of ali tree compartments, various biomass functions (Marklund, 1988) could be applied in the derivation method itself. However, the data availahle from IM sites restricts this approach.

In some cases the potentiai growth curve could only be fitted to the present volume and growth by aliowing a rather high final standing volume. The future growth and uptake may thus he overestimated, and a potential maximum forest volume will he appiied to avoid unrealistic scenarios.

This derivation method was the first attempt to suppiy site-specific deposition and uptake scenarios for acidification model application at IM sites.

A more straightforward approach would have been to estimate a modeiied open land deposition with a constant filtering factor and historical scaling and a simple growffi curve, not restricted by nutrient nitrogen avaiiable, to estimate uptake.

Instead, steps were taken into an aiready more compiex description of hypothesized deposition breakdown and of a nitrogen-limited tree growth with a dynamic filtering capacity of dry deposition, based on earlier work on SAFE model applications.

The advantage of the current approach is the rather modest requirement of input data. The results, in turn, retain considerable uncertainty due to e.g. too few calibration values, although it may he smail when compared to potential uncertainties involved in the whole modelling exercise. The algorithms in the method can stiil be refined, especially the forest growth depiction, even with the current data avaiiable. Presentiy the scheme even as it is illustrates one promising way of compiling scenarios at a level sufficient for dynamic acidification models,

2.6.2 MAGIC, SAFE and SMART results

The simuiated values for soii base saturation for the scenarios A-C are shown in Figures 2.3-2.5. Base saturafion is a key measure for soil acidification, and therefore this variahle was chosen to iliustrate the behaviour of the modeis at the different sites. The compiete results are shown in the detailed project report (Forsius et al, 1996).

In most cases the calibrated modeis yield consistent results for historicai hase saturation in 1900. At ali sites the modei resuits indicate at ieast a slight decrease in base saturation until present day. It should, however, he recognised that SAFE is a multilayer model and in the figures aggregated restilts for the whole soil profile are shown for this model.

At each site the predicted response varies depending on the future deposition scenario. The response to the different scenarios was aiso consistent. As expected, seenario 5, where ‘Maximum Feasihle Reductions’ for S emissions and -30% for NOx emissions have been assumed, always resulted in the most significant improvement. For this scenario either a stabiiisation of the current situation or a significant improvement was generally shown. The only ciear exceptions were MAGIC and SMART simulations for GBO2 where continued soil acidification was indicated also for the B-scenario.

The A-scenario (Second S-protocol for 5 and present level for N emissions⁼

‘Best prediction’) resuited in a slower response than the B-scenario. In many cases this emission reduction for S was enough to stop continued soil acidification, aithough significant improvement vas not always shown. For GB02 the

0

simulations of MAGIC and SMART again indicate continued acidification (as well as SMART for DEO1) using this scenario.

The model results demonstrate that these ecosystems respond in a dynamic way to changes in deposition (Figures 2.3-2.5). The deposition load a site can tolerate therefore depends on the time taken for a response to occur (see aiso Wright et at. 1991). Dynamic modeis are therefore needed in addition to steady state techniques, which presently are used for mapping of critical Ioads and their exceedance. However the large amount of input data required by the dynamic modeis limits the possibility to use them on the regional scale.

The three modeis appiied here give in most cases consistent results. The same resuit has been obtained also in a previous study (Wright et al. 1991).

Aithough the modeis contain in many cases similar process descriptions there are stili important differences in the model structure, e.g. in the estimation of weathering rates. The similarity of the predictions gives confidence in the use of such modeis for scenario assessments and other poiicy oriented work.

It should stiil be recognised that much work is stili needed regarding the modeiiing of nifrogen processes. Ali modeis applied in tMs study contain rather crude descriptions for nifrogen cycling and saturation. Different more detaiied concepts are presently being developed and should be used for scenario assessment when properly tested and evaiuated. It is likely that nitrogen processes in the iong-term also will he affected by future climate change. Synergistic effects between atmospheric deposition and climate change should therefore be considered in future model deveiopment and scenario assessment.

2.7 Conclusions

- Dynamic modeis can be successfuily applied to data ftom ICP IM sites.

- Catchments/plots respond in a dynamic way to changes in emissions/

deposition. Dynamic modeis should therefore complement to steady-state techniques, when adequate data is available.

- The three modeis appiied in this study yielded generally consistent results, which gives confidence in the scenario assessment.

- The ‘Best prediction’-scenario (inciuding the effects of the Second S protocol and present ievel for NO- and NH-emissions), resulted in many cases in a stabilisation in the soil acidificatidn, although significant

improvements were not always shown.

- The modeis should he applied to more ICP IM sites to increase the sensitivity gradient and geographical coverage. This would require that more sites report compiete datasets according to agreed formats.

- More work is needed to improve the description of N processes in the dynamic modeis.

The Fnnrsh Environment 27

MAGIC DE01 MAGC F103

0,16 0,7

0,6 0,12

0

2 Q4 0,08

0 03

0 (u

0,04 02

0,1

0 0

1900 1950 2000 2050 1900 1950 2000 2050

Year Year

SAFE DEO1 SAFE F103

0,16 0,7

0,6

0,12 0,5

.2 .2

cu 0,08 2’

(0 0

0 0

(0 (0

0,04 02

0,1

0 0 ^L

1900 1900 1950 2000 2050

Year

SMART DEO1 SMART F103

0,16 0,7

0,6

0,12 0,5

.2 .2

cul,08

0 0

0.) 0)

0 0

£0 (0

0,04 02

0,1

0 0

1900 1950 2000 2050 1900 1950 2000 2050

Year Year

Figure 2.3. Simulated soil base saturation (fraction) for DEOI Forellenbach and F 103 Hietajärvi using three dynamic modeis and scenarios A-C (see Table 21).

0

1950 2000 2050

Year

MAGIC GBO2 MAGIC N001

0,5 0,7

0,4 0,6

0,5 0,3 .2

0,4

(0 cl)(0

0,2

(1

0,2 0,1

---Ö 0,1

0 _• 0

1900 1950 2000 2050 1900

Vear

SAFE GBO2 SAFE

0

cl cl,cn

0

D (0 cl)(0

1950 Year 2000 2050

NOOl

0,5 0,7

0,6 0,4

0,5

0,3 — 0,4

1900 1950 2000 2050 1900

Year

SMART GBO2

1950 2000 2050

Year

0,5 0,4

0

0,3

(‘3

0 1900

SMART NOOl

1950 2000 2050

Year

0,7 0,6 0,5

0

0,4 0,3 0,2 0,1 0

1900

Figure 2.4. Simulated soil base saturation (fraction) for GBO2 Afon Hafren and NOOl Birkenes using three dynamic modeis and scenarios A-C (see Table 2.1).

The Finnish Enwonment 27

0

1950 2000 2050

Year

MAGIC SEO4 0,6

0,5 0,4 0,3 0,2 0,1

0—

1900

0,6 0,5

.0 0,4 0,3 0,2 0,1 0 c

0 (‘3 :3 (‘3(‘3 0(‘3 0

1950 2000 2050

Year

SAFE SE04

0,6 0,5

0 0,4

(‘3 :3

i 0,3

(0

‘3)

0,2 0,1 0

1900 1950 2000 2050

Year

SMART SEO4

1900 1950 2000 2050

Year

Figure 2.5. Simulated soil base saturation (fraction) for SE04 Gårdsjön using three dynamic modeis and scenarios A-C (see Tabe 2.1).

0

Acknowledgements

Sophia Mylona and Erik Berge at EMEP/MSC-W are gratefully acknowledged for the use of source-receptor matrices for atmospheric pollutant transfer and data on historical sulphur deposition.

References

Alveteg, M., Walse, C. and Warfvinge, P. 1996. Amethod for reconstructing historic deposition and uptake from present day values. Manuscript submitted to WASP Annual Synoptic Report 4, 1995. IC? IM Programme Centre, finnish Environment Agency.

Asman, W. and Drukker, B. 1988. Modelled historical concentrations and depositions of ammonia and ammonium in Europe. Atmospheric Environment 22(4)725-735.

Barrett, K., Seland,O., Foss, A., Mylona, S., Sandnes, H., Styve, H. and Tarrason, L. 1995.

European transboimdary acidifying air pollution; ten years calculated fields and budgets to the end of the first sulphur protocol. EMEP/MSC-W Report 1/95, Oslo.

Bleeker, A., Posch, M., forsius, M., Kämäri,J. 1994. Calibration of the SMART acidification model to integrated monitoring catchments in Europe. Mimeograph Series of the National Board of Waters and Environment 568, Helsinki, Finland.

Bleeker, A., Posch, M., Forsius, M., Kämäri,J. 1995. Calibration of the SMART acidification model to selected IM catchments. In Annual Synoptic Report 1995, p. 24-33, ICP IM Programme Centre, Finnish Environment Agency

Cofala,J. and Schöpp, W. 1995. Assessing future acidification in Europe; current state of the RAINS model development. Note prepared for the lSth meeting of the UN-ECE Task Force on Integrated Assessment Modelling, May 95, The Hague, The Netherlands.

Cosbv, B.J., Hornbergei G.M. and Galloway, J.N. 1985. Modeling the effects of acid

deposition: Assessment of a lumped parameter model of soil and water acidification.

Water Resources Research 21:51-63.

De Vries,W., Posch, M. and Kämäri,J. 1989. Simulation of the long-term soil response to acid deposition in various buffer ranges. Water, Air and Soil Pollution 48:215-246.

Forsius, M., Alveteg, M.,Jenkins, Ä.,Johansson, M., Kleemola,5., Ltikewille,A., Posch, M., Sverdrup, H., and Walse, C. 1996. Dynamic model applications for assessing the effects of SO2 and NO reduction strategies on ICPIntegrated Monitoring sitesin Europe. Tema Nord, Nordic Council of Ministers, Copenhagen (in preparation).

Hedin, L.O., Granat, L.,Likens, G.E., Buishand, T.A., Galloway, J.N., Butler, T.J. and Rohde, H. 1994. Steep declinesinatmospheric base cations in regions of Europe and North America. Nature 367:351-354.

Jenkins, A., Whitehead, PG., Musgrove, T.J., and Cosby, B.J. 1990. A regional model of acidification in Wales. Journal of Hydrology 116: 403-416.

Johansson, M., Alveteg, lvi., Walse, C. and Warfvinge,P. 1996 (in press) Derivation of deposition and uptake scenarios. Forthcoming proceedings of UN-ECE/WGE International Workshop on Critical Ioads and levels, spatial and temporal

interpretation for elements in landscapes sensitive to atmospheric pollutants. Vienna 22-24 Nov 95.

Kämäri,

J.,

Science of the Total Environment 160/161: 687-701.

Marklund, L.G. 1988. Biomass function for pine, spruce and birch in Sweden. Swedish Agricultural University, 45:1-73.

Mälkönen, E. 1975 Annual primary production and nutrientcyclein some Scots pine stands. Communicationes Instituti Forestalis Fenniae 84. Helsinki, Finland.

Mylona, 5. 1993. Trends of sulphur dioxide emissions, air concentrations and depositions of sulphur in Europe since 1880. EMEP/MSC-W Report 2/93, Oslo, Norway.

Posch, lvi., Reinds, G.J. and de Vries, W. 1993. SMART^-Simulation model for Acidification’s Regional Trends: Model description and users manual. Mimeograph Series of the National Board of Waters and Environment 477, Helsinki, Finland.

The Finn:sh Enronment 27

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Sverdrup, 1-1., Warfvinge, P., Biake L. and Goulding K. 1995. Modelling recent and historic soil data from the Rothamsted experimental station, UK, using SAFE. Agriculture Ecosystems and Environment 53:161-177.

Wright, R, Holmberg, M., ?osch, M. and Warfvinge, P. 1991. Dynamic modeis for predicting soil and water acidification: Application to three catchments in fenno-scandia. Acid Rain research report 25/1991. Norwegian Institute for Water Research, Oslo. 40 pp.

0

Assessment of nitrO9en precesses

ICP IM sites

OO9OOOOOOOOOOOOOOOOOOOOOOeOOOOOeO*O,OOOOO,,OOOOO,Ø,,OO

MartinForsius, Jussi Vuorenmaa and Sirpa Kleemola Finnish Environment lnstitute

Impacts Research Division RO.Box 140

FIN-00251 Helsinki Finland

The UN/ECE Working Group on Effects presentiy gives the highest priority to the environmental effects of atmospheric nifrogen pollutants. Therefore, since additional data has become available in the ICP IM database, the calculations regarding the effects of N-deposition presented in the ICP IM Annual Synoptic Report 1995 (ASR 1995), have been updated. The calculations were made for ali IM sites with available data, at both catchment (n=39, input data; n=21, output data) and piot scaie (n=16). Proton budgets, indicating the relative importance of N-processes in the production and consumption of protons on the ecosystem scale, were presented in the ASR 1995.

3.! Materials and methods

3.1.1 Catchment-scale input-output budgets

The calculations were done using bulk and throughfall (when available) deposition and runoff data. The budgets were calculated for the iast 4-year period, normally for the period 1991-1994.

Deposition to the basins for each of the iast four years was caicuiated as the sum of the measured monthly deposition values. The throughfall deposition esfimates were caiculated as the sum of the months when throughfall was recorded (May to October/November or the whole year), for the other months (snow/

frost period), bulk deposition measurements were used, if throughfall measurements were not avaiiabie. In order to obtain the best possible estimate of the total deposition (wet+dry) of N (N03-N+NH4-N) to the catchment, the larger of bulk and throughfall nitrogen deposition vaiues was used.

Output fluxes from the catchments were caiculated from the quaiity and quantity of the runoff water.

The Fnsh Envrrcnment 27

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3.1.2 PIot-scale input-output budgets

The budgets were calculated for the period including observations in the years 1991 ^- 1994, normally for the period 1992^- 1993.

Two different estimates of N deposition were calculated: (1) Station-specifics input fluxes were calculated by multiplying the bulk and/or throughfall deposition by the open and forest stand area of station and deviding the obtained value by the total area of station. This estimate was assumed to give the most accurate value for the input flux of N reaching the soil; (2) In order to obtain an estimate of the total N deposition to the ecosystem, the larger value of estimated throughfall and bulk deposition was also used. This estimate is comparable to the one used for catchment-scale calculations.

The station-specific output fluxes in soil water were calculated using the values measured from the depth of 20 ^-40 cm. The data was best available from these depths and this soil layer was assumed to be in many soil water stations below the root zone. Due to the lack of hydrological measurements or modelled soil water flow estimates, the soil water recharge needed for output calculations were calculated using the chloride-balance method (e.g.Allison and Hughes 1983, Sharma and Hughes 1985, Dettinger 1989, Walkeret at. 1991). This technique is based on the assumption of steady-state chloride fluxes in which the chloride input via deposifion is equal to the chloride output below the root zone. Naturally it has to he assumed that lithological input is negiigihle and for a vegetated land area where surface runoff is unimportant, precipitation is the only source of water and chloride and water movement is predominantly one-dimensional.

For soil profiles under relatively undisturbed soil-vegetation system, steady state can he assumed (i.e. no change in cffloride storage within the root zone) and recharge rate of water (R) can he estimated from:

R C1IN/C1sw

where C1 is the station-specific chloride input obtained by similar technique as in the case of nifrogen and C1, is the soil water cffloride concentration measured from the depth of 20^- 40 cm. Rough output fluxes for nitrogen in this soil layer were obtained by multiplying estimated soil water recharge by nitrogen

(N03-N+NH4-N) concentrations.

The number of sites included in the calculations was 16. Total average input to the basin and average output in the soil profile for the period was calculated as the sum of monthly average values of the stations, The number of stations included in the calculations for each of the IM areas varied hetween one and four.

The assessment included those months when ground was unfrozen and soil water had been possihle to sample. In summer months, if soil water data was missing obviously due to dryness, the nifrogen flux was assumed to be 0.001 kg N/ha/mo.

3.1.3 Correlation analysis

In order to explore the reasons for nitrogen leaching and saturation in more detail a correlation analysis on key ecosystem variabies was also carried out. The analysis focused on ambient nitrogen input and output and on the nitrogen concentrations in needies and various ecosystem compartments. The results presented in this report are updates of those in the ICP IM Annual Synoptic Report 1995.

Due to the lack of relevant data availahle from the IM sites in the IM datab ase, data from confrol plots from 11 sites in two EC ecosystem manipulation projects (NITREX and EXMAN) was also included (Tietema and Beier 1995, Wright and Tietema 1995).

0

The dataset comprises inorganic nifrogen fluxes in bulk deposifion, throughfall and output, stand age and various parameters characterising the internal nifrogen cycling in the system (Table 3.1, and 3.2). The fluxes calculated from the IM areas were averages for the last 4-year period, for the other sites average values of 2-3 years. The nitrogen flux in drainage is catchment output in IM sites, and leaching of nitrogen below the rooting zone in the other sites. The internal nitrogen parameters include the nitrogen concentrations in needies (current and first-year), in litter production, in organic iayer, and the total amount of nitrogen in lifterfall. Additional soil and forest health parameters calculated only for the IM sites were: carbon pool, average C/N ratio of the soil, soil pH, discoloration and defoliation. Ali parameters were calculated as area-weighted averages for the whole catchment, areas were derived from the best available source: Iand-use and forest interpretation (from satellite images), maps, area descriptions.

The following parameters were used in correlation analysis:

nh4nd NH flux in bulk precipitation (kg/ha/a), no3n•d N03 flux in bulk precipitation (kg/ha/a), ntotd NO3+NH4 flux in bulk precipitafion (kg/ha/a), nh4n•t NH4 flux in throughfall (kg/ha/a),

no3n•t NO; flux in throughfail (kg/ha/a), ntoLt NO;+NH4 flux in throughfall (kg/ha/a), nh4no NH4 flux in output (kg/ha/a),

no3n•o NO; flux in output (kg/ha/a), ntoLo NO;+NH4 flux in output (kg/ha/a), NfJf N flux in litterfall (kg/ha/a),

age Stand age (years),

nee&c N in current year needies (g/kg), nee&f N in first year needies (g/kg), NJf N in litterfali (g/kg),

N.org N in organic layer (g/kg),

C•min Cpool in mineral soil layers (kg/m2),

C•mino C pool in mineral and organic soil layers (kg/m2), C/N•min C/N ratio in mineral soil layers,

C/Nmin•o C/N ratio in mineral and organic soil iayers, ph.EK ph•EK of soil at abroximately 50 cm levei, ph•EW ph•EW of soil at abroximately 50 cm level, discoi discolorafion (¾),

defoli defoliafion (¾).

The Ennish Environment 27

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3

=

0•

0

0=

=

0•

0

0•

0

3 30

=

0•

0•

0•

=

0

0•

nJ 0•

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