A dynamic model for assessing the impact of coupled sulphur and nitrogen deposition scenarios on...

A dynamic model for assessing the impact of coupled sulphurand nitrogen deposition scenarios on surface water acidification

Alan Jenkinsa,*, Robert C. Ferrierb, Bernard J. Cosbyc

aInstitute of Hydrology, Wallingford OX10 8BB, UKbMacaulay Land Use Research Institute, Craigiebuckler, Aberdeen AB15 8QH, UK

cDepartment of Environmental Science, University of Virginia, Charlottesville, VA, US

Received 15 March 1996; revised 5 September 1996

Abstract

MAGIC-WAND (Model of Acidification of Groundwaters In Catchments—With AggregatedNitrogen Dynamics) has been specifically developed for wide application and scenario assessment.It maintains the sulphur driven acid/base chemistry dynamics of MAGIC, and considers in additionthe impacts of changes in nitrogen deposition from the atmosphere and changes in nitrogen utilisa-tion within the catchment. The model uses estimates of nitrification, mineralisation, N fixation anddenitrification and changes in these soil processes through time. Plant uptake is nonlinear anddependant upon inorganic nitrogen concentrations in soil solution.

Calibration of the model requires specification of values for the soil N fluxes and for the para-meters which describe the hyperbolic uptake function. Literature data can provide ranges for thesevalues but specific catchment related values are not obtainable since the model is conceptual.Selection of uptake parameters must reflect current catchment vegetation and vegetation changethrough time, and this has been investigated at a catchment in SW Scotland. Changes in soilprocesses through time are also important to model functioning, but are not considered in this study.

The model has been applied to 25 acid sensitive (Acid Neutralising Capacity (ANC), 50meq l−1)lochs in SW Scotland to examine the interaction of afforestation and changes in nitrogen deposition,assuming recently agreed reductions in sulphur deposition. The model results suggest that a 50%reduction in nitrogen deposition coupled with agreed sulphur reductions is not sufficient to promotereversibility of acidification at the most sensitive sites within 15 years. Increased nitrogen depositionin conjunction with agreed sulphur reductions will most likely cause continued acidification atafforested lochs in the region.q 1997 Elsevier Science B.V.

0022-1694/97/$17.00q 1997– Elsevier Science B.V. All rights reservedPII S0022-1694(96)03266-0

Journal of Hydrology 197 (1997) 111–127

* Corresponding author.

1. Background and introduction

The emission of sulphur (S) and nitrogen (N) compounds to the atmosphere as a resultof fossil fuel combustion has increased since the Industrial Revolution of the mid 1800s(Mylona, 1993). As a result, surface waters in geologically sensitive areas of Europe andN. America have become progressively acidified (Dochinger and Seliga, 1976; Drablosand Tollan, 1980; Martin, 1986; Last and Watling, 1991). The acidifying effects of S andN depend on the mobility and retention of sulphate (SO4) and nitrate (NO3) anions in theecosystem. Soils in glaciated regions typically have low-to-moderate SO4 adsorptioncapacities, and so SO4 inputs and outputs are generally in steady state causing changesin SO4 deposition to promote relatively rapid changes in the SO4 concentration of surfacewaters (Wright and Hauhs, 1991). If SO4 concentrations decline, acid stresses on the soilsand surface waters are reduced.

Nitrogen, on the other hand, is generally retained within terrestrial systems, largelybecause N is the growth-limiting nutrient (Tamm, 1992). Chronic elevated deposition of Nas oxidised (NOx) or reduced (NHy) species, however, can produce quantities of inorganicN in soils in excess of that needed by the biota for growth (Agren, 1983; Aber et al., 1989).The unassimilated N can lead to eutrophication and/or acidification in soils and can beleached from soils to appear as NO3 in runoff potentially promoting eutrophication and/oracidification in surface waters. Excess ammonium (NH4) is quickly converted to nitrate inforest soils, and so rarely contributes to N leaching except under conditions of extremelyhigh N deposition (Bobbink et al., 1992). The term ‘nitrogen saturation’ has been used todescribe the situation whereby the supply of inorganic N exceeds the biotic (plant andmicrobial) requirement and is manifest through increased leaching of inorganic N belowthe rooting zone (Aber et al., 1989). Prediction of N saturation is difficult at best and theprocesses responsible are difficult to quantify but NO3 leaching to surface waters may beindicative of N saturation. The best information currently available on the magnitude andtiming of N saturation (or retention) comes from empirical observations (Westling, 1991;Emmett et al., 1993; Dise and Wright, 1995).

International agreements aimed at reducing emissions of S and N have been negotiatedunder the auspices of the UNECE Convention on Long Range Transboundary Air Pollu-tion since the mid 1980s. The most recent agreement, the Second S Protocol signed in Osloin 1995, was based on the concept of critical loads (Posch et al., 1995) and called forreductions in UK emissions to 70% of 1980 levels by 2005 and further to 80% of 1980levels by 2010. Agreement on levels of N emissions is yet to be negotiated. It is clear,however, that the amount of S deposition that can be tolerated by catchment systemswithout harmful effects on aquatic biota is dependent on the amount of N deposition,and vice versa (Henriksen et al., 1993). The specification of a critical load for S, therefore,in the absence of knowledge of how the N dynamics within the catchment system mightchange in the future, is inappropriate.

The development of dynamic modelling approaches enables an assessment of the influ-ence of N dynamics in the context of total acidity. That is, given that protocols for Semission reductions are now in place, the most important question relates to the potentialfor N leaching to offset the expected reversibility. The importance of dynamic models inthis context was recognised at a UNECE sponsored workshop on critical loads for N held

112 A. Jenkins et al./Journal of Hydrology 197 (1997) 111–127

in Lokeberg, Sweden in April 1992, where it was identified that, ‘‘Strong efforts should bemade to develop and test dynamic models for assessing the consequences of the excee-dance of critical loads’’ (Grennfelt and Thornelof, 1992).

The strength of dynamic model applications in an applied sense is in answering keypolicy questions such as: (i) what degree, in time and space, of soil and water recovery canwe expect from a given emissions reduction strategy? (ii) what level of emissions reduc-tion is necessary to achieve a given level of soil and water recovery within a given timescale? (iii) what are the consequences for soils and surface waters of not achieving acritical load? (iv) what is the potential for N leaching to offset the expected recoveryfrom agreed S emissions reductions? This paper presents a modelling scheme for addres-sing these questions, and uses it to determine the potential future response to changing Sand N deposition scenarios in the Galloway region of SW Scotland.

2. The MAGIC-WAND model

MAGIC-WAND represents an extension to the MAGIC model (Cosby et al., 1985a,b)to incorporate the major N fluxes and changes in fluxes through time (Fig. 1). The Ndynamics are fully coupled to the existing S driven model. The model structure is designedto enable assessment of future surface water chemistry response to a given N depositionscenario. Assumptions relating to the uptake capabilities of the vegetation and future landuse change and the sensitivity of a catchment or region to these components can also beassessed.

Fig. 1. Schematic of the catchment N stores and fluxes in MAGIC-WAND.

113A. Jenkins et al./Journal of Hydrology 197 (1997) 111–127

The MAGIC model combines a number of key soil chemical processes lumped at thecatchment scale to simulate soil and surface water chemistry: (i) soil–soil solution equili-bria equations in which the chemical composition of soil solution is assumed to begoverned by simultaneous reactions involving SO4 adsorption, cation exchange, dissolu-tion and precipitation of aluminum, and dissolution of inorganic carbon; (ii) mass balanceequations in which the fluxes of major ions to and from the soil and surface waters areassumed to be governed by atmospheric inputs, mineral weathering, net uptake in biomass,and loss in runoff. MAGIC has been used successfully to simulate the observed responseof water chemistry to acid deposition reduction at whole-catchment experimentalmanipulations (Wright et al., 1990), has been demonstrated to compare well with palaeo-limnological reconstructions of lake acidification (Jenkins et al., 1990a) and to captureobserved changes in regional lake chemistry (Jenkins et al., 1990b). In addition several ofthe assumptions in MAGIC have been tested experimentally (Grieve, 1989).

MAGIC-WAND considers two species of inorganic N, NO3 and NH4. Both species areassumed to be present only in solution in soil water. The model explicitly incorporates themajor terrestrial fluxes of N, such that:

NO3 leaching=deposition+nitrification+external addition−uptake−denitrification

and

NH4 leaching=deposition+external addition+mineralisation−nitrification−uptake

Fluxes of N into the system (meq m2 year−1) are in the form of inorganic N added to thesoil solution and are specified at each time step. The primary inputs are atmosphericdeposition and mineralisation. Mineralisation in the model represents the net release ofinorganic N that was formerly bound in organic matter. The mineralisation product is NH4.

Nitrogen losses from the model system are as inorganic N. The primary output is inhydrologic runoff from the soils. The runoff fluxes are calculated as the product of thesimulated concentrations of NO3 and/or NH4 at any time step and the hydrologic dischargeat that time. Provision is also made in the model for losses of inorganic N by denitrificationfrom soil or surface water. The magnitude and timing of this additional output of N may bespecifieda priori as a function of catchment soil characteristics or moisture conditions, ormay be keyed to soil water NO3 or NH4 concentrations using first order reaction kinetics.

The nitrification process (microbial mediated transformation of NH4 to NO3) is repre-sented in the model by a first order reaction. The rate of loss of NH4 (equal to the rate ofproduction of NO3) is given by the product of a rate constant and the concentration of NH4

at each time step, such that:

d[NH4]=dt = −Kn p [NH4]

where, [NH4] is the concentration of NH4 in soil solution (meq m−3) and Kn is a rateconstant (year−1).

Plant uptake is modelled as a nonlinear process that depends upon the concentration ofavailable NO3 or NH4. The equation is hyperbolic (a typical Michaelis–Menten uptakeprocess representation) such that P:

d[N]=dt =Kmax p ([N]=(Ks + [N])


where, [N] is the concentration of either NO3 or NH4, Kmax is the maximum uptake rate(meq m−2 year−1) andKs is the half-saturation constant of the reaction (meq m−3).

The same uptake parameters are assumed for both NO3 and NH4 so there is no differ-ential or preferential uptake of either form of inorganic N. The values ofKmax andKs canbe varied through timea priori, to represent the characteristics of a changing or damagedvegetation.

The process equations are solved sequentially at each time step such that the NH4

deposition flux, mineralisation flux and external additions are added to the soil waterNH4 concentration from the previous time step. The requirement for nitrification is thensubtracted first, followed by the calculated uptake flux. The resulting soil water concen-tration (if any NH4 remains) is then available for leaching at a rate dependant upon thehydrologic turnover of the catchment. The NO3 deposition flux, external additions and theinput from nitrification of NH4 are then added to the soil water NO3 from the previous timestep. From this pool is first subtracted the uptake flux and then the loss through denitrifica-tion. The resulting soil water pool is then available for leaching, again dependant upon thecatchment hydrologic turnover. Further loss of NO3 by denitrification from surface wateris then accounted for. The model is designed for application over a long time scale(decades), and operates at a yearly time step. All ion concentrations represent mean annualvalues.

3. Model calibration

Calibration of the N dynamics for a given site requires selection of values for the rateconstants describing mineralisation and nitrification and to describe the uptake function.The values of these parameters must provide for a good match between present daysimulated and observed (and historical if known) surface water NO3 and NH4 concentra-tions. Measurement of rate coefficients is, however, difficult in the field and in any case,can only usually be made at plot scale whereas MAGIC-WAND requires catchmentaverage values.

Published nitrification rates for a variety of environments indicate that nitrification israpid in forest soils (van Miegroet et al., 1992). In most UK waters NH4 is not detectedimplying that the combination of plant uptake and nitrification processes remove all NH4

from the system. A very high nitrification rate (365 year−1), expressed as the number oftimes the soil N pool ‘turns over’ at each time step, is usually set to effectively nitrify allincoming NH4, (i.e. from deposition plus mineralisation) at each time-step, since NH4 israrely observed in surface waters in upland areas.

Mineralisation of organic N in catchments is determined by many factors, includingtemperature, soil type, soil moisture status, vegetation cover, etc., and so varies widelyacross whole catchments. A constant value is applied to all catchments based on reportedliterature studies (Gosz, 1981; Melilo, 1981; Batey, 1982; Emmett et al., 1995a).Denitrification is assumed to be zero since net loss of gaseous N and net addition viamineralisation are not known specifically for each catchment.

Measured plant uptakes show a wide range both between different vegetation types,such as grassland and forest, and between ecosystems under the same vegetation (Gosz,


1981; Melilo, 1981; Woodmansee et al., 1981; Batey, 1982; Emmett et al., 1995a). Thevarious features of an ecosystem which can influence the rate of plant uptake include: soiland soil solution processes, in particular the available N; the size, location and length ofroots and root hairs, including presence and type of mycorrhizal association; specificchemical and biochemical reactions at the soil solution–root interface and within theroot, including selectivity between NO3 and NH4; reduction of NO3 within the plant;presence of inhibitory agents and translocation processes; N requirements of the plant,including rate of primary production, nutrition and maturity of the plant and rate ofinternal recycling once N has been taken up. In forest ecosystems, for example, the reasonsfor the wide range of uptake rates include: rate of production; availability of N in the soil;duration of needle retention by conifer species; translocation of N back to living tissue atthe time of senescence; age or maturity of the trees.

The two parameters required to describe the plant uptake function, maximum uptakerate (Kmax) and half saturation or slope (Ks), cannot be measured in reality. These arecalibrated using observed surface water NO3 and NH4 concentrations. The calibration iscarried out by adhering to a set of rules, such that:

(i) If NO 3 concentrations in surface waters are negligible thenKmax should be greaterthan, or at least equal to, the sum of external N sources (i.e. mineralisation+ deposi-tion).(ii) If NO 3 is observed in surface water then eitherKmax is less than the sum of externalinputs orKs is high, that is, the slope of the uptake function to the maximum rate isvery low. Both will produce leakage of NO3 to match the observations.(iii) Ks cannot be too high as to cause historical breakthrough at a site where this doesnot presently occur and so is unlikely to have occurred historically unless land use orN deposition has changed substantially.

A wide range of estimated uptake rates have been reported for different vegetation types(Jenkins and Renshaw, 1995) and as a final check, calibrated uptake rates should be inbroad agreement with reported ranges. It is assumed that other plant nutrients are notlimiting plant uptake at each time step.

4. Model sensitivity: application to Loch Grannoch

MAGIC-WAND has been calibrated to Loch Grannoch a forested catchment in theGalloway Region of SW Scotland to provide a measure of model sensitivity to parame-terisation of the uptake function. The site receives an estimated total deposition (wet+ dry)of 170 meq N m−2 year−1, dominated (63%) by NH4 (UKRGAR, 1990) and mean surfacewater NO3 concentration of 14meq l−1 (Jenkins et al., 1996). Nitrate deposition is assumedto have increased linearly from low levels in pre-industrial times, in accordance with SO4

deposition and historical NH3 emissions are assumed to have increased byca. 20% sincethe 1940s in line with increased livestock numbers (UKRGAR, 1990).

Mineralisation is set at 400 meq m−2 year−1 and assumed to remain constant throughoutthe 140 year historical simulation and 50 year future predictions. Nitrification rate,expressed as the number of times the pool turns over per year, is set at 365 year−1 since


NH4 is not detected in surface water at the site. Three combinations ofKmax andK1/2 areused to describe the hyperbolic uptake function (Fig. 2) and each satisfies the rules forcalibration outlined above to give a match to observed present day chemistry includingNO3 concentrations which have been constant between 1975 and present (Harriman et al.,1996).

Model (i) : Kmax=565 meq m−2 year−1, Ks =15 meq m−3

This represents the minimum level of uptake required to match observed NO3 concentra-tion and is equal to mineralisation plus total present day N deposition. The lowKs

effectively precludes any nonlinearity in uptake except at very low external N concentra-tions, and assumes that the plants are always attempting to take N at the rate ofKmax. Inaddition,Kmax is linearly decreased to 500 meq m−2 year−1 50 years after planting, in linewith measured nutritional requirements for 50 year old Sitka spruce of yield class 16 andexcluding internal translocation (MLURI, unpublished data). This represents the worstcase scenario with respect to N leakage.

Model (ii ) : Kmax=900 meq m−2 year−1, Ks =15 meq m−3

This represents the upper range of reported values for coniferous trees.Kmax is linearlydecreased to 500 meq m−2 year−1 in line with model (i). This represents a best case scenarioin which N leaching will be minimised.

Model (iii ) : Kmax=900 meq m−2 year−1, Ks =1285 meq m−3

This represents the highest possible value (and hence the lowest slope) ofKs givenKmax,under the constraint of no historical NO3 breakthrough. The nonlinearity introduced in thiscase allows uptake to increase or decrease in proportion to a change in externalconcentration.Kmax is linearly decreased to 500 meq m−2 year−1 in line with model (i).

Having defined these three plant N uptake scenarios, the resulting dynamics in N uptakeflux is dependant upon the change in soil water N concentration which in turn is a functionof changes in mineralisation and N deposition. Three N deposition scenarios are chosen todemonstrate these dynamics: constant to 2010; a linear increase to 150% of present levelby 2010; a linear decrease to 50% of the present level by 2010. No changes in netmineralisation rates are considered in this conceptual study although such changes mayoccur in systems as N accumulates in the biota.

Under constant deposition (Fig. 3(a)), plant uptake declines in both models (i) and (iii)in response to the assumed linear decrease inKmax as the forest ages over the same period.Plant uptake is maintained at a constant rate in model (ii), whilst external inputs remainconstant and belowKmax, but decreases sharply when total input flux exceedsKmax as theforest ages. As N deposition is increased (Fig. 3(b)), N uptake in model (i) behavesidentically to the constant deposition situation since total input flux is always greaterthanKmax. In model (iii) N uptake decreases asKmax decreases with forest age, althoughthe decrease is less steep than under constant deposition. In model (ii), N uptake increaseswhilst Kmax exceeds the increasing total input flux but decreases rapidly asKmax declineswith forest age. With decreased N deposition (Fig. 3(c)), the levels of uptake in models(i) and (ii) are identical and decline largely in response to decliningKmax, since thisdeclines more steeply than the deposition. The final level of uptake in all three models


is lower than under constant and increased deposition, because input fluxes eventually fallbelow Kmax.

Superimposing these scenarios for N dynamics and N deposition on scenarios forchanges in S deposition provides an assessment of model sensitivity with respect tototal acidity. Clearly, the sensitivity of the model, demonstrated by surface water alkalinityconcentration (meq l−1), to future S and N deposition scenarios is dependant upon thevalues ofKs and Kmax selected (Fig. 4). Future S deposition is derived from HARMmodel predictions (Metcalfe et al., 1995) based upon the currently agreed emissionsreduction strategy (the Second S Protocol) for the UK to 30% of 1980 levels by 2005and to 20% by 2010 (Whyatt and Metcalfe, 1995). The same S deposition scenario isassumed in all cases.

Under constant N deposition (Fig. 4(a)), surface water alkalinity shows no change formodel (ii), and complete uptake of N. Only during the latter 5 years of the simulation doesalkalinity decline in response to N leaching as the forest matures.Kmax declines and NO3leaks into the surface water. Under model (i), the decrease in S compensates to someextent for the increase in NO3 leaching, but a slight decrease in alkalinity is predicted.Model (iii) predicts an extreme acidification effect in response to leaching of high levels ofNO3 in response to the marked decline in plant uptake. If N deposition is increased, theseeffects are enhanced (Fig. 4(b)). If N deposition is reduced (Fig. 4(c)), again the expectedimprovement in alkalinity in response to S deposition reduction alone is not marked due tothe buffering of mobile anions by base cations derived from weathering and ion-exchangewithin the catchment. The acidification shown by model (iii) is an unlikely behaviour butrepresents an extreme possibility. In most cases, the improvements resulting fromdecreased S deposition are negated by NO3 leakage.

5. Regional response in Galloway

MAGIC has been previously calibrated to 37 catchments in the Galloway region of SWScotland (Wright et al., 1994). Across the region as a whole, a reduction in loch waterconcentration of SO4 was observed between 1979 and 1988, presumably as a result ofdecreased S deposition, but no continued recovery is observed between 1989 and 1993(Ferrier et al., 1996).

For this study, MAGIC-WAND was applied to acid sensitive lakes (ANC, 50meq l−1)in the region, involving 25 of the original 37 lakes, by employing the calibrated MAGICmodel describing responses to S deposition for the region, described in Wright et al.(1994). The regional calibration of N dynamics assumed constantKs for all catchmentswhile Kmax was determined on an individual catchment basis such that predicted surfacewater chemistry matched observations at both forested and moorland catchments (Fig. 5).

The effect of forest maturation on the temporal response ofKmax was representedassuming a linear decline inKmax to 70% of the maximum value from year 20 to 50, to

Fig. 2. The three models describing uptake used at Loch Grannoch. (a), (b) and (c) correspond to models (i), (ii)and (iii), respectively.Kmax decreases linearly from time of planting to 500 meq m−3 after 50 years in all models.


simulate observed N breakthrough response (Emmett et al., 1995a). Fifty years is thenormal rotation length for commercial forests in the region. The determination of thetemporal response of individual catchmentKmax values represents the spatial integrationof a mosaic of different ages and extents of plantations.

The calibrated model was used to assess the regional response of surface water chem-istry to three N deposition scenarios coupled to the recently agreed S reduction protocolover 50 years from 1988. The three N scenarios were: (i) constant deposition; (ii) a linearincrease in N deposition to represent a doubling within 50 years; (iii) a linear decrease in Ndeposition to represent a 50% decrease within 50 years. Future S deposition was assumedto follow HARM model predictions. Land use change was assumed to follow a ‘steadystate’, with forest harvested at 50 years of age and immediately replanted to the same arealextent, thereby assuming no regional increase in the total amount or distribution of forestcover. Soil mineralisation is again assumed to remain constant.

Under constant N deposition the predicted response in 2010 is a general increase inANC across the region (Fig. 6(a)), largely as the result of S deposition reductions. Atapproximately half of the sites, however, ANC is predicted to remain below zero, indi-cating that agreed S reductions are not sufficient to promote a regional recovery fromacidification within the time frame of the agreed S emissions reduction protocol (20 years)or within a 50 year timescale (Fig. 6(b)). The moorland sites generally show the biggestrecovery as a consequence of the steady-state N dynamics, i.e.Kmax is set equal to the sumof external N supply. At the forested sites, recovery is a function of the changing Nrequirement of the trees, i.e. Kmax is calibrated to change with forest age and so N break-through can occur to offset the predicted recovery in response to decreased SO4 input.More surprisingly, six sites are predicted to continue to acidify (lose ANC) to 2010. Thesesites are all afforested and the decrease in N requirement by the ageing forest promotes Nbreakthrough and increased NO3 concentration in the surface water. This is sufficientlylarge to offset the reduction in SO4 concentration and further decrease the ANC. Within 50years, however, all but one site are predicted to increase in ANC, although the majorityremain below zeromeq l−1 (Fig. 6(b)).

Under increased N deposition (Fig. 6(c)), ANC recovery is small at all sites by 2010. Atthe moorland sites this is attributed to the increased leaching of NO3. It has been suggestedthat eutrophication of heathlands, and therefore higher availability of N, might be asignificant factor in the replacement ofCalluna by grasses (De Smidt, 1979). Thiswould have the net effect of increasingKmax over time and so enhance the ecosystemretention of N. In this application, uptake of N by moorland vegetation is assumed to havea maximum value set to present day external N supply, and consequently any extra N leaksfrom the system to the surface water. At most forested sites, further significant acidifica-tion is predicted as the external N supply far exceeds the requirement of the ageing forests.By 2038, ANC is predicted to have declined further at the moorland sites as NO3 leakageincreases in response to increased N deposition (Fig. 6(d)).

If N deposition is reduced, the impact of N saturation and breakthrough in the ageing

Fig. 3. The effect of constant (a), increased (b) and decreased (c) N deposition scenarios and the three modelstructures on N uptake fluxes.


forests is lessened, and this scenario is predicted to promote the best recovery responsethrough the region by 2010 (Fig. 6(e)). The degree of recovery is, however, small and themajority of the sites still remain below zero ANC. By 2038, however, all sites are pre-dicted to improve significantly, and many sites regain positive ANC (Fig. 6(f)).

6. Summary and conclusions

There exists a clear need for the development and application of dynamic modelscapable of predicting the response of soils and surface waters to coupled N and S deposi-tion scenarios. The MAGIC-WAND model provides one such tool and offers wide appli-cation within the UNECE Convention on Long Range Transboundary Air Pollution, and inparticular within the critical loads programme. The model describes the major dynamicsand transformations of N within catchment soils and surface waters, and couples this to theexisting S driven model. The relatively simple structure of the model and ease of calibra-tion provide the scope for regional application at large spatial scales.

Calibration and subsequent application of the model emphasises the sensitivity of thesoil and water chemistry to those parameters describing the N uptake dynamics. Furtherobservational and experimental work on N fluxes, and dynamics, from different eco-systems is essential to facilitate the site specific and regional model calibrations. Furtherwork is required to identify ranges of values appropriate to given forest species, siteconditions and climatic conditions. This includes more rigorous model sensitivity analysis,analysis of existing literature and data describing N concentrations and catchmentcharacteristics and, possibly, further field surveys of relevant parameters.

This study examines the impact of changes in plant N uptake alone on recovery fromacidification in surface waters under future S and N deposition scenarios. While the modelhas the capability of varying mineralisation and nitrification rates as a function of Ncontent of soil organic material, this capability was not used in this study. Data are onlyrecently becoming available to allow development of simple empirical relationshipsspecifying changes in these rates as a function of changing N content of soils. Modelestimates of acidification responses to coupled N and S deposition scenarios will besensitive to these changes. Only when such data are available can a further study beundertaken.

The model application and predicted response at the forested sites accords well with theobservations of NO3 output fluxes from mature forests at well drained sites where the Nstatus of catchment soils (%N) is relatively high (Emmett et al., 1995b). At sites where soil%N is relatively low, decreased plant uptake alone is not thought to be sufficient topromote increased N leakage to surface waters, and so the model may be inappropriate.Furthermore, at catchments where soils are less well drained, denitrification may be animportant factor in reducing surface water NO3 concentrations, and so the predictionsrepresent a worst case scenario.

Fig. 4. Predicted response of surface water ANC at Loch Grannoch under constant (a), increased (b) anddecreased (c) N deposition scenarios for the three model structures.


Simulations using MAGIC-WAND in Galloway indicate that emissions reductionsagreed under the Second S Protocol will not alone be sufficient to reverse surface wateracidification in the region. Further reductions in S deposition will be required to promotereversal of acidification, particularly in moorland areas, and these must be coupled tosignificant N reductions in forested areas. The simulation results clearly demonstrate theimportance of quantifying the time component of emissions reductions and surface water

Fig. 5. Observed and predicted surface water NO3 concentrations (a) and ANC concentrations (b), at 25 acidsensitive lochs in Galloway. Moorland sites and forested sites are represented as M and F, respectively.

Fig. 6. Predicted (2010 and 2038) relative to observed (1988) surface water ANC at 25 acid sensitive lochs inGalloway under three N deposition scenarios. (a) 2010 under constant N deposition. (b) 2038 under constant Ndeposition. (c) 2010 under increased N deposition. (d) 2038 under increased N deposition. (e) 2010 underdecreased N deposition. (f) 2038 under decreased N deposition. Moorland sites and forested sites are representedas M and F, respectively.


recovery in the determination of S and N emissions reductions. However, further informa-tion is required on the potential for increased N uptake through species change in moor-land vegetation in response to changes in external N supply, and on the potential forchange in N mineralisation is required, to refine the model predictions.

Acknowledgements

This work has been funded in part by the Department of the Environment (undercontract numbers EPG 1/3/65 and EPG 1/3/51), the National Rivers Authority, the EUDYNAMO project (ENV4-CT95-0030), the Institute of Hydrology, and the ScottishOffice Agriculture, Forestry and Environment Department. The research has also beenfunded in part by the US Environmental Protection Agency agreement (CR820461-01-1)to the University of Virginia and by a grant from the US Department of Energy to E and SEnvironmental Chemistry Inc. (DE-FG06-94ER30235), but has not been subjected toeither Agencies’ review and, therefore, does not necessarily reflect the views of theAgencies and no official endorsement should be inferred. The authors are indebted tothe anonymous reviewers whose suggestions have led to improvements in the model andin this paper.

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