AQUATIC EFFECTS OF ACIDIC DEPOSITION - CHAPTER 9 pot

198 Aquatic Effects of Acidic Deposition9.1 Model of Acidification of Groundwater in Catchments MAGIC MAGIC has been the principal model used thus far by NAPAP for makingprojections of l

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1 MAGIC is the most widely used acid–base chemistry model in theU.S and Europe.

2 Because the model is highly generalized, it does not have extensiveinput data requirements and, therefore, can be applied to a largenumber of potential sites without incurring inordinate costs asso-ciated with data collection

3 In part because of the second reason, MAGIC has been extensivelytested against independent databases, thereby providing an excel-lent example of the iterative processes of model testing and refine-ment that all environmental models should go through

4 The author has far more personal experience with MAGIC thanwith other models

In recent years, a number of models have been developed to simulate Ndynamics in forested ecosystems, and N has recently been added in vari-ous ways to MAGIC Several of these N models are discussed at the end ofthis chapter

A number of acid–base chemistry models have been developed that focus

on S-driven acidification Three primary models were used in EPA’s DirectDelayed Response Project (DDRP, Church et al., 1989) to project surface wateracidification response: MAGIC, the Integrated Lake Watershed AcidificationStudy model (ILWAS, Gherini et al., 1985), and the Trickle Down Model (Linand Schnoor, 1986) In addition, the Internal Alkalinity Generation (IAG)model (Baker and Brezonik, 1988) was used to generate projections for seep-age lakes in the NAPAP Assessment These and other models were reviewed

by Thornton et al (1990) and Eary et al (1989)

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198 Aquatic Effects of Acidic Deposition

9.1 Model of Acidification of Groundwater in Catchments (MAGIC)

MAGIC has been the principal model used thus far by NAPAP for makingprojections of likely future changes in surface and soil water chemistry inresponse to various levels of acidic deposition MAGIC also provided thetechnical foundation for the reduced-form modeling in the aquatic and soilscomponents of NAPAP’s Tracking and Analysis Framework (TAF) and hasbeen used to estimate critical loads of S, and more recently also N, deposition

to national parks and wilderness areas in many parts of the country

9.1.1 Background and General Structure as Used for the NAPAP 1990 Integrated Assessment

MAGIC is a lumped-parameter model of intermediate complexity (Cosby

et al., 1985a,b) that is calibrated to the watershed of an individual lake orstream and then used to simulate the response of that system to changes inatmospheric deposition MAGIC includes a section in which the concentra-tion of major ions is governed by simultaneous reactions involving Sadsorption, cation weathering and exchange, Al dissolution/precipita-tion/speciation, and dissolution/speciation of inorganic C A mass balancesection of MAGIC calculates the flux of major ions to and from the soil inresponse to atmospheric inputs, chemical weathering inputs, net uptake inbiomass, and losses to runoff The model simulates soil solution chemistryand surface water chemistry to predict the annual average concentrations

of the major ions MAGIC generally represents the watershed with one ortwo soil-layer compartments These soil layers can be arranged vertically orhorizontally to represent the vertical or horizontal movement, respectively,

of water through the soil A vertical two-layer configuration was used forthe NAPAP assessment, and the soil compartments were assumed to bereally homogeneous

The meteorological and deposition input requirements for MAGIC includethe amount and ionic concentrations of precipitation and annual average airtemperature Also needed are details of the hydrological budget for eachwatershed The spatial/temporal scales in the model reflect the intended usefor assessment and multiple scenario evaluations MAGIC does not use aGran ANC in simulating watershed response Rather, it uses a calculatedalkalinity or ANC defined as follows:

where SBC = Ca2+ + Mg2+ +Na+ + K+ (9.2)

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Predictive Capabilities 199

MAGIC is calibrated using an optimization procedure that selects ter values so that the difference between the observed and predicted mea-surements is minimized The calibration exercise is a three-step process Thefirst step is to specify the model inputs such as precipitation, deposition (bothwet and dry), an estimate of historical deposition inputs and fixed parame-ters or parameters whose values correspond directly to (or can be computeddirectly from) field measurements (e.g., soil depth, bulk density, cationexchange capacity) This approach, in effect, assigns all of the uncertaintyassociated with sampling and intrinsic spatial variability to the “adjustable”parameters The adjustable parameters are those that are calibrated or scaled

parame-to match observed field measurements

The second step is the selection of optimal values for the adjustable eters These adjustable parameters are specified using optimization by themethod of Rosenbrock (1960) Optimal values are determined by minimizing

param-a loss function defined by the sum of squparam-ared errors between simulparam-ated param-andobserved values of system state variables

The final step is to assess the structural adequacy of the model in ing the observed behavior of the criterion variables and parameter identifi-ability, or the uniqueness of the set of optimized parameters Structuraladequacy is assessed by examining the mean error in simulated values ofobserved state variables for those variables used in the calibration procedure

reproduc-as well reproduc-as for an additional state variable that wreproduc-as not used during tion Parameter identifiability is assessed using approximate estimation errorvariances for the optimized parameters (Bard, 1974)

calibra-Model calibration to a specific catchment is accomplished by specifyingdeposition and hydrological forcing functions, setting the values of thoseparameters that can be measured (fixed parameters), and determining thevalues of the remaining parameters that cannot be measured (adjustableparameters) through an optimization routine that adjusts those parameters

to give the best agreement between observed and predicted surface waterand soil chemistry (Cosby et al., 1985a,b, 1989)

Atmospheric deposition of base cations, strong acid anions, and NH4+ areassumed to be uniform over the catchment Atmospheric fluxes in the pro-gram codes are calculated from concentrations of the ions in precipitationand estimated precipitation volume measured or interpolated to each catch-ment These annual average concentrations and annual precipitation areused as input parameters for the model

Atmospheric fluxes of the mass balance ions are corrected for estimateddry deposition of particulates and aerosols Dry deposition is represented as

a proportion of wet deposition, using dry deposition factors (DDF) calculated

on the basis of site-specific measurements or regional average estimates.Average annual values for soil and surface water temperature and soil PCO2(partial pressure of CO2) are needed as inputs to the model Mean annual soiltemperatures are set equal to the mean annual air temperatures Soil PCO2 is

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derived from a regression on soil temperature constructed from mean ing season soil PCO2 data from 19 regions of the world (Brook et al., 1983):

grow-log10 (PCO2) = 0.03 * TEMP – 2.48 (9.4)where PCO2 is in atmospheres and TEMP is the soil temperature in degrees C.Using this expression, mean annual soil temperature of 10°C would produce

a soil PCO2 of 0.0066 atm (approximately 20 times atmospheric PCO2)

Depth, bulk density, cation exchange capacity, maximum SO42- adsorptioncapacity, and the SO42- adsorption half-saturation constant are provided fromsoil characterization studies for each soil type All soil horizons are aggre-gated to reflect average soil conditions

Sulfate uptake in the lake sediments is calculated from the Baker andBrezonik (1988) model using the values of relative lake area to the watershedarea and the discharge Significant amounts of S can be retained in lakesthrough dissimulatory reduction, with SO42- used as an electron acceptor and

H2S, ester sulfates, or metal sulfides as end products (Rudd et al., 1986;Brezonik et al., 1987) Reduction rates are approximately first order for SO42-

at concentrations typically encountered in softwater lakes In-lake reductionrates are apparently limited by diffusion into the sediments (Baker et al.,1986; Kelly et al., 1987) The process appears to be rate limited, and Baker et

al (1986) and Kelly et al (1987) showed that this process can be representedeffectively as:

(9.5)

where

KSO4 = sulfate mass transfer coefficient (m/year)

Z = mean lake depth (m)

τw = hydraulic residence time (year) (outflow based)The Al solubility constants in the soil layers (KAL1, KAL2) are given as log-arithms (base 10) and are calibrated or sometimes assumed to be equal to9.05 The assumed value represents a solid phase of Al(OH)3 intermediatebetween natural and synthetic gibbsite (see Cosby et al., 1985a)

It is important to test the veracity of environmental model projections,especially in cases where policy and/or economic interests are considerable

As Oreskes et al (1994) pointed out, however, verification and validation ofmathematical models of natural systems are impossible, because natural sys-tems are never closed and model results are nonunique Model confirmation

is possible, and entails demonstration of agreement between prediction andobservation Such confirmation is inherently partial It is, therefore, criticalthat policy-relevant models be tested in a variety of settings and under a vari-ety of conditions (Sullivan, 1997)

o⁄ SO4 retention KSO4∗100

Z⁄τw+KSO4 -

=

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The MAGIC model has been widely used throughout North America andEurope to project changes in the chemistry of drainage waters impacted byatmospheric S deposition MAGIC projections of the effects on surface waterchemistry of various S emissions scenarios formed the technical foundationfor a large part of the National Acid Precipitation Assessment Program's Inte-grated Assessment (IA; NAPAP, 1991) Subsequently, a research effort wasconducted from 1990 to 1996 to improve the performance of MAGIC and toprovide testing and confirmation of the model at multiple sites Model eval-uations have included hindcast comparisons with diatom reconstructions* ofpre-industrial lake-water chemistry in the Adirondack Mountains of NewYork, and tests of the veracity of model forecasts using the results of whole-catchment acidification experiments in Maine (Norton et al., 1992) and Nor-way (Gjessing, 1992) and whole catchment acid-exclusion experiments inNorway (Wright et al., 1993)

It is critical that policy-relevant environmental models such as MAGIC beconfirmed under a variety of conditions Since 1990, the MAGIC model hasbeen tested in a variety of settings and under quite varying environmentalconditions These analyses have elucidated a number of potentially impor-tant deficiencies in model structure and method of application, and haveresulted in changes to the model and its calibration procedures The work hasincluded in-depth evaluation of issues related to regional aggregation of soilsdata, background pre-industrial S deposition, natural organic acidity, N, and

Al mobilization The result has been an improved and more thoroughlytested version of MAGIC, and one that yields different forecasts than the ver-sion that formed the technical foundation for the 1990 IA

9.1.2 Recent Modifications to the MAGIC Model

9.1.2.1 Regional Aggregation and Background Sulfate

MAGIC model projections of future lake-water chemistry made by NAPAP(1991) for lakes in the northeastern U.S were based on data collections andmodel calibrations performed by the EPA's Direct Delayed Response Project(DDRP; Church et al., 1989; Cosby et al., 1989) The northeastern DDRP anal-yses were based on a probability subsample of the 1984 Eastern Lake Survey(ELS; Linthurst et al., 1986), and included 145 low-ANC (less than 400 µeq/L)lakes, larger than 4 ha in area These lakes provide an unbiased representa-tion of northeastern lakes included in the DDRP statistical frame

The MAGIC model represents the horizontal dimension of the watershed

as a homogeneous unit and the vertical dimension as one or two soil layers.Watershed and soils data required as model inputs are aggregated to provide

* Diatoms are microscopic algae, the remains of which are incorporated into lake sediments that accumulate over time The species composition and relative abundance of diatoms at different levels in the sediment can be used to estimate the pH of lake water in the past using sophisticated mathematical relationships.

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weighted-average values for each soil layer Within the DDRP (Church et al.,1989) that formed the technical foundation for NAPAP modeling efforts inthe Northeast, soil characteristics were aggregated on the basis of attributes

of soil sampling classes across the entire northeastern U.S Subsequent to theDDRP, there was concern that Adirondack soils might differ sufficiently intheir chemical properties from similar soils in other areas of the Northeastthat MAGIC projections for Adirondack watersheds might be biased becausethey were based on soil attributes that actually reflected conditions elsewherethan the Adirondacks The DDRP soils data, therefore, were reaggregated tocharacterize Adirondack watershed attributes using only soil data collectedfrom pedons in the Adirondacks (Sullivan et al., 1991)

Modeling for the DDRP and IA also assumed that the deposition of S inpre-industrial times was limited to sea salt contributions Based on analy-ses presented by Husar et al (1991), this assumption was modified suchthat pre-industrial deposition of S was assumed equal to 13% of 1984 values(Sullivan et al., 1991)

Recalibration of MAGIC to the Adirondack lakes database using theregionally corrected soils and background SO42- data resulted in approxi-mately 10 µeq/L lower estimates of 1984 ANC A substantial downward shiftwas also observed in predicted pre-industrial and current lake-water pH(approximately 0.25 pH units) for lakes having pH greater than about 5.5.These differences were attributed to lower calibrated values for lake-water

SO42- concentrations and higher pCO2 values estimated for Adirondack lakes,compared with the Northeast as a whole (Sullivan et al., 1991)

9.1.2.2 Organic Acids

Concern was raised subsequent to the IA regarding potential bias from thefailure to include organic acids in the MAGIC model formulations used byNAPAP MAGIC hindcasts of pre-industrial lake-water pH showed pooragreement with diatom-inferences of pre-industrial pH (Sullivan et al., 1991),and preliminary analyses suggested that these differences could be owing, atleast in part, to the presence of naturally occurring organic acids in Adiron-dack lake waters

Previous projections of future lake-water chemistry in Adirondack lakesusing MAGIC (Church et al., 1989; Cosby et al., 1989) did not consider theacid–base chemistry of dissolved organic acids in the model formulations ortheir role in the response of lake chemistry to acidic deposition It has beensuggested, however, that organic acids can make significant contributions tosurface water acidity (Krug and Frink, 1983) A significant fraction of organicacids in surface waters are characterized by strongly acidic pKa values, below4.0 (Perdue et al., 1984; Kramer and Davies, 1988) Furthermore, considerableevidence suggested that organic acids influence the response of surfacewaters to changes in strong acid inputs, potentially by loss of DOC (Krug andFrink, 1983; Almer et al., 1974) and most likely by changes in the protonation

of organic acid anions (Wright, 1989)

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There is not a method available for direct determination of organic acidconcentration in the laboratory (Glaze et al., 1990) Measures of total (TOC)and dissolved organic carbon (DOC) are commonly used to represent, in rel-ative terms, the amount of organic acidity present (Aiken et al., 1985) Somestudies report TOC (unfiltered) and others report DOC (filtered); the formerare slightly higher owing to the presence in most water samples of smallamounts of particulate carbon The pool of dissolved organic material in nat-ural waters is generally comprised largely of organic acids (McKnight et al.,1985; David and Vance, 1991) Empirical methods for laboratory determina-tion of organic acidity generally include concentration, fractionation, isola-tion, purification, and titration steps (e.g., Leenheer, 1981; David and Vance,1991; David et al., 1989, 1992; Kortelainen et al., 1992) Such methods arefairly laborious and time-consuming, and are seldom used in water qualityassessments and surveys Indirect methods available for estimating organicacid anion contributions to acidity include charge balance calculations andthe empirical methods of Oliver et al (1983) that are based on measured pHand DOC, and Driscoll et al (1994) The latter study was based on empiricaldata from the Adirondack Lakes Survey (ALSC) From 1984 to 1987, theALSC surveyed 1469 lakes within the Adirondack Ecological Zone (Kretser

et al., 1989; Baker et al., 1990b) This database provided an unparalleled dataresource with which to investigate questions of organic acidity in lake waters

in the U.S because of the large number of lakes sampled and abundance ofsurvey lakes having high DOC concentrations The median DOC of the studylakes was 500 µM C and 20% of the lakes had DOC concentrations greaterthan 1650 µM C

Driscoll et al (1994) constructed a reduced data set from the ALSC database

by deleting lakes that were

1 Missing variables

2 High in salt content (greater than 1000 µeq/L)

3 High in pH (greater than 7) or ANC (greater than 400 µeq/L)

4 Outside QA/QC guidelines

The remaining lakes were grouped into pH intervals of 0.1 pH units from

pH 3.9 to 7.0, whereby each observation represented the mean of from 12 to

94 individual lake measurements of pH and related chemistry This datareduction procedure reduced the variability in the initial data set andallowed application of nonlinear methods for fitting the various organic acidanalog models to estimates of organic anion concentration from the mea-sured anion deficits (Σ cations - Σ anions; Figure 9.1)

To evaluate the ability of model calculations to predict lake-water pH, able pH calculations were conducted pH was calculated based on conditions

vari-of electroneutrality, concentrations vari-of major solutes, and important pH ering systems (DIC, DOC, and Al) A total of four organic acid analog repre-sentations were calibrated to the ALSC reduced data set (Driscoll et al., 1994)

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FIGURE 9.1

0.1 pH unit intervals with calibrated model predicted values for a monoprotic, b Oliver et

al (1983), c diprotic, and d triprotic organic analog models (Source: Driscoll, C.T., M.D Lehtinen, and T.J Sullivan, 1994, Modeling the acid-base chemistry of organic solutes in

Geophysical Union With permission.)

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They included mono-, di-, and triprotic analog models and the model ofOliver et al (1983) The model calibration involved adjustments of the H+ dis-sociation constants and site density of the DOC that specifies the number ofdissociation sites per mole of organic C The object of the fitting routine was

to minimize the observed differences across all lakes between the organiccharge simulated by the organic acid analog model and the organic anionconcentration estimated from the measured charge balance A nonlinear leastsquares technique was used in the calibration, with pKa values fit first, fol-lowed by site density The calibration was accomplished using SAS (Driscoll

et al., 1989a) for the Oliver et al (1983) and monoprotic models, and usingALCHEMI (Schecher and Driscoll, 1994) for the diprotic and triprotic mod-els Additional details are provided by Driscoll et al (1994)

The best agreement (r2 = 0.92) was obtained between predicted andobserved pH values using the triprotic analog representation, with fitted pKavalues of 2.62, 5.66, and 5.94, and a calibrated site density of 0.055 mol sitesper mol C The fitted values for pKa and site density obtained by Driscoll et

al (1994) were used in the revised MAGIC applications conducted by van et al (1996a) and described below

Sulli-In the Adirondack region of New York, 33 lakes were included in both theDDRP study and the Paleoecological Investigation of Recent Lake Acidifica-tion (PIRLA-II; Charles and Smol, 1990) This data set, therefore, provided anopportunity to evaluate the potential importance of organic acids to the mod-eling efforts The hindcast comparison focused on pH reconstructions forthese lakes because of the underlying importance of pH and its influence onthe mobilization of potentially toxic Al and controls on the biologicalresponses to acidification (Baker et al., 1990c)

MAGIC simulations were performed as done earlier by Cosby et al (1989)for the DDRP (Church et al., 1989) and by NAPAP (1991), with three excep-tions (Sullivan et al., 1991)

1 To remove known biases and make the MAGIC and diatom mates as directly comparable as possible, MAGIC was recalibratedusing soils data specific to the Adirondack subregion

esti-2 A more realistic pre-industrial S deposition, equal to 13% of 1984values (Husar et al., 1991), was assumed

3 The partial pressure of CO2 in lake water was calculated frommeasured values of dissolved inorganic carbon (DIC) and pH.The earlier model projections (NAPAP, 1991; Cosby et al., 1989) had beencalibrated using soils and surface water data from sampling sites across theentire northeastern region of the U.S., had assumed zero pre-industrial Sdeposition, and had calibrated PCO2 in the absence of consideration oforganic acids Changes in the first two factors improved the agreementbetween MAGIC and diatom estimates of historical pH, owing largely todifferences in the calibrated values of strong acid anion concentrations The

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last change lessened the agreement because the earlier calibration of PCO2had effectively resulted in a partial compensation for the missing organics.Additional uncertainties that might have affected the comparison betweenthe MAGIC and diatom approaches include the failure of the process model

to account for historic changes in landscape cover, disturbance, N ics, or changes in base cation deposition (Sullivan et al., 1991) Model sce-narios using the original version of MAGIC without organic acids weredesignated MAGIC1, and those that included the triprotic organic acid ana-log were designated MAGIC2

dynam-Unmodified MAGIC1 hindcasts yielded pre-industrial pH values that weresubstantially higher than diatom-based estimates (Figure 3.3a), and the dis-crepancy was greatest for those lakes in the most biologically sensitive por-tion of the pH range (pH 5.0 to 6.0) (Baker et al., 1990c) Furthermore,MAGIC1 hindcast pH estimates were greater than 6.0 for all lakes investi-gated, whereas diatom estimates of pre-industrial pH ranged from as low as5.2 to above 7.0 Previous comparisons between diatom and MAGIC1 (with-out organic acids) model estimates of historical acidification had been con-ducted primarily for clearwater (DOC less than 300 µM C) lakes, most ofwhich had experienced substantial acidification (Wright et al., 1986; Jenkins

et al., 1990) These comparisons generally showed somewhat better ment for pre-industrial pH than the comparisons reported in Figure 3.3a.The failure to consider proton binding reactions involving organic solutes

agree-in the MAGIC1 hindcast simulations could contribute to the observed crepancy between model-predicted and diatom-inferred pH values because

dis-of the influence dis-of dissolved organic acids on the acid–base chemistry dis-ofdilute waters (Hemond, 1994) Even low concentrations of dissolved organicacids (less than 250 µM C) can appreciably affect the pH of dilute waterseither in the presence or absence of strong inorganic acids (Kramer andDavies, 1988; Hemond, 1994) Although other factors might also contribute tothe observed discrepancies, including, for example, uncertainties in weather-ing, SO42- adsorption, base cation deposition, or hydrological routing, the pat-tern of effect (Figure 3.3a) suggested the importance of organic acids Organicacids exert a disproportionately larger influence on pH at pH values below6.5, where the greatest offset was observed

Thus, three independent data sets (DDRP, PIRLA-II, and ALSC) and threeinterpretive models (MAGIC1 with no organic acid representation, diatomreconstructions, and MAGIC2 with Driscoll et al.'s triprotic organic acid ana-log) were employed to test for consistency among the results of these modelsfor estimating pre-industrial lake-water pH (Sullivan et al., 1996a) When theorganic acid model was incorporated into MAGIC2 and simulated pH valueswere compared with diatom-inferred pH, the comparison yielded consider-ably closer agreement between model estimates of pre-industrial pH (Figure3.3b) than did the simulations that did not consider the effects of organicacids (Figure 3.3a) The mean difference in MAGIC1 vs diatom estimates ofpre-industrial pH was 0.6 pH units when organic acids were omitted fromthe modeling scenarios with the greatest discrepancy being for lakes with

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diatom-inferred pH less than 6.0 This mean difference was reduced to only0.2 pH units when the triprotic organic acid model was included, and theagreement for individual low pH lakes improved by as much as a full pHunit (Figures 3.3a,b) The extent to which the incorporation of an organic acidrepresentation into MAGIC1 alters estimates of historic acidification for thepopulation of low-ANC lakes represented by this study is illustrated in Fig-ure 9.2 The diatom model and both versions of MAGIC resulted in cumula-tive frequency distributions of pre-industrial pH higher than currentmeasured pH The diatom model suggested the least amount of acidification,and MAGIC1 without organic acids suggested the greatest acidification.MAGIC2 estimates with a triprotic organic acid were intermediate, but closer

to diatom estimates Differences between the two MAGIC applications weremost pronounced at the lowest end of the pH distribution, and varied by up

to a full pH unit for individual lakes (Sullivan et al., 1996a)

The observed improved agreement between MAGIC2 and diatom hindcasts

of pre-industrial pH was attributable partly to improvement in the calibrated

1984 pH values and partly to lower estimates of ∆pH for those lakes simulated

by MAGIC2 to have experienced the greatest historical acidification (greater

FIGURE 9.2

Cumulative frequency distributions of current measured pH from ELS and estimates of industrial pH using the diatom method and the MAGIC model with and without the organic acid representation Distributions were derived using population weighting factors developed for the DDRP More than 40% of the lakes had measured current pH less than 6 Application

of MAGIC with the triprotic organic acid suggested that one-half of these lakes also had industrial pH less than 6, whereas application of MAGIC without considering organic acids

Vol 91, 1996, p 277, Influence of organic acids on model projections of lake acidification, Sullivan, T.J., B.J Cosby, C.T Driscoll, D.F Charles, and H.F Hemond, Figure 2, copyright 1996 Reprinted with kind permission from Kluwer Academic Publishers.)

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than 1 pH unit) Both effects of adding organic acids to MAGIC2 are importantbecause it is ultimately the projected endpoint pH values that are importantfrom a policy perspective Model forecasts are often generated to answerquestions such as

• How many lakes will acidify to pH less than 5.0 if deposition is

maintained at a certain level for a certain number of years?

• How much will deposition have to be reduced in order for 95% of

the lakes in a region to recover to pH values above 5.5?

Even after adding organic acids to MAGIC2, the model still predictedgreater historical acidification of Adirondack lakes than did the diatommodel (Sullivan et al., 1996a) The differences between MAGIC2 and diatom-based estimates of pre-industrial pH were far more reasonable, however,when the influence of organic acids was included in the modeling effort Theremaining discrepancy may be owing to additional uncertainties in theMAGIC2 model and/or a general tendency for diatom estimates to be conser-vative Diatom estimates of pH have been compared with measured pH val-ues at numerous lake sites where changes in acid–base status have occurred.Such confirmations of the diatom approach have been performed for lakesthat have been acidified and lakes that have recovered from acidification orhave been limed in the Adirondack Mountains (Sullivan et al., 1992), Sweden(Renberg and Hultberg, 1992), Scotland (Allott et al., 1992), and Canada(Dixit et al., 1987, 1991, 1992) Diatom-inferred pH histories generally agreereasonably well with the timing, trend, and magnitude of known acidifica-tion and deacidification periods Sullivan et al (1992) presented data for BigMoose Lake and Constable Pond in the Adirondacks that showed diatominferences of mean pH close to the mean of measured pH values, that showedgreat seasonal variability In other studies, however, the diatom reconstruc-tions did not always fully reflect the magnitude of either the water pHdecline or subsequent recovery, although the observed differences betweenpredicted and measured change in pH were frequently smaller than the rootmean squared error of diatom predictive models

Diatom inferences of pH change may in some cases be slightly less thanmeasured values, although the observed differences are generally less thanthe error of the inference equations Possible explanations include the prefer-ence of many diatom taxa for benthic habitats where pH changes may bebuffered by chemical and biological processes Alternatively, such an attenu-ation could be an artifact of sediment mixing processes or a time-averagingartifact of sediment subsample thickness relative to the sediment accumula-tion rate It is, thus, not surprising that MAGIC2 model simulations thatincluded organic acid representations estimated greater acidification thandiatom-inferences for Adirondack lakes in this study It is not possible todetermine which method provides estimates closer to reality, although dia-tom inferences of 1984 pH agreed somewhat better with measured values

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than either version of MAGIC It is reassuring that the two methods provide

results that are generally in reasonable agreement

The results of this intercomparison reported by Sullivan et al (1996a) are

important for assessment of the effects of acidic deposition in two respects

First, these results were the first to show quantitative agreement between

estimates of pH of natural aquatic systems receiving acidic deposition, as

derived from two independent and conceptually different approaches over

a large geographic region and over a long time span Previous model

test-ing and evaluation studies, other than calibration exercises, had either been

relatively short duration (Norton et al., 1992), site specific (Renberg and

Hultberg, 1992), or had involved comparisons among two or more models

that share many fundamental assumptions (Cook et al., 1992) Second, and

perhaps more important, is the fact that the agreement between MAGIC2

and paleolimnological model hindcast estimates of lake-water pH was

dependent upon consideration of proton binding reactions involving

dis-solved organic acids in the process model The latter result was obtained

despite the relatively low concentrations of DOC in the study

lakes The importance of organic acids in achieving reliable

model results undoubtedly increases with increasing lake-water DOC In

fact, all lakes for which estimates of ∆pH (current pH minus pre-industrial

pH) decreased by more than 0.5 pH units, upon inclusion of organic acids

in the model, had DOC in the range of 400 to 500 µM Such concentrations

of DOC are not considered high, but were at the upper end of measured

DOC concentrations in the 33 study lakes

Organic acids have been shown in other instances to be important

contrib-utors to, and buffers of, ecosystem acidity and, therefore, are important to

include in modeling ecosystem response to acidification For example, Lam

et al (1989) assumed a triprotic organic acid representation for observed data

from Moose Pit Brook and Mersey River in Nova Scotia The objective was to

determine what specific modifications were needed to calibrate the Turkey

Lakes model to colored water systems having DOC values of 800 to 3300 µM

C and 400 to 1200 µM C, respectively They assumed pK1 = pH, for simplicity,

for pH values between 4.5 and 5.5 Calibrated values for pK2 and pK3 were

4.8 to 5.0 and 5.0 to 5.2, respectively, for the 2 stream systems Calibrated

charge densities for DOC in both streams were about 4 µeq/mg C They

found that the assumed charge density of DOC and the assumed pK1 value

were at least as important as the SO42- loading in influencing the pH predicted

by the model Furthermore, because the organic anions both buffer and

con-tribute acidity to the water, the model simulations illustrated that increased

or decreased SO42- input to these two colored stream systems would not cause

as large a change in pH as in clear water systems (Lam et al., 1989)

Inclusion of organic acids in the MAGIC simulations for the experimental

watersheds at Lake Skjervatjern, Bear Brook, and Risdalsheia (see also

Chap-ter 8) also had dramatic effects on model simulations of pH In all cases,

MAGIC simulated considerably higher pH values when organic acids were

omitted from the model Even at Bear Brook, where annual average DOC

x

( = 313µM C)

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concentrations are very low (less than 250 µM C), incorporation of organic

acids into the model reduced simulated pH by 0.1 to 0.3 pH units for the

years of study At Lake Skjervatjern and Risdalsheia, where organic acids

provide substantial pH buffering, omission of the organic acid analog

repre-sentation from MAGIC resulted in consistent overprediction of pH by about

0.2 to 0.5 pH units (Sullivan et al., 1994)

9.1.2.3 Aluminum

Aluminum mobilization is now widely believed to be one of the most

impor-tant ecological effects of surface water acidification Potential effects of Al

mobilization from soils to surface and soil waters include alterations in

nutri-ent cycling, pH buffering effects, toxicity to aquatic biota, and toxicity to

ter-restrial vegetation MAGIC simulates Al solubility based on an assumed

equilibrium with the mineral gibbsite (Al(OH)3):

Al(OH)3(s) + 3 H+ Al3+ + 3 H2O (9.6)The preceding equilibrium expression illustrates a cubic relationship

between the concentrations of Al3+ and H+, such that

where brackets indicate activities and KSO is the solubility product For a

solu-tion in equilibrium with gibbsite, Al3+ changes in proportion to the change in

H+ to the third power, and a plot of pAl3+ vs pH (p indicates -log10) will have

a slope of 3 and an intercept of pKSO

The MAGIC model first calculates the total concentration of acidic cations

(e.g., H+ plus Aln+) on the basis of simulated concentrations of base cations

and mineral acid anions (e.g., SO42-, NO3-, Cl-) using mass balance and

elec-troneutrality constraints The acidic cations are then partitioned between H+

and Aln+ using the gibbsite mineral equilibrium, thermodynamic equations,

the partial pressure of CO2, and the organic acid formulation This

partition-ing is important because inorganic Al in solution can be highly toxic to

aquatic biota, even at low concentrations (Baker and Schofield, 1982)

Model estimates of changes in the concentration of Al3+ in surface waters,

using the MAGIC model have shown a consistent pattern of overestimating

the change in Al3+ concentration in response to experimental treatment

(Sul-livan and Cosby, 1998) This overestimate of the change in Al3+ concentration

calculated by MAGIC was owing to a combination of the cubic relationship

between H+ and Al3+ assumed in the gibbsite model and the model

calibra-tion procedure of selecting a gibbsite solubility product based on measured

pretreatment data

Data sets collected by the EPA in the Eastern Lake Survey-Phase II

(ELS-II), National Stream Survey (NSS), and Episodic Response Project (ERP)

were assessed by Sullivan and Cosby (1998) to evaluate relationships

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between Ali and pH in lake and stream waters in the eastern U.S Watersamples collected within these projects had been analyzed for both totaland nonlabile monomeric Al, thus allowing the labile, or inorganic, mono-meric Al component (Ali) to be determined by difference (c.f., Driscoll,1984) Appreciable concentrations of Ali were found in surface waters ofthe Adirondack Mountains in ELS-II, the Pocono/Catskill Mountains andnorthern Appalachian province in the National Stream Survey, and theAdirondack Mountains and Catskill Mountains in the Episodic ResponseProject Speciation of the Ali was accomplished using the chemical equilib-rium model ALCHEMI (Schecher and Driscoll, 1987) With input data of

pH, Ali, total F, SO42-, dissolved Si, and temperature, ALCHEMI estimatesthe concentration of the various inorganic Al species, including Al3+ andthe Al complexes with hydroxide, fluoride, SO42-, and silica, as well as min-

eral phase saturation indices Plots of pAl i and pAl3+ vs pH were structed to compare empirical patterns across lakes and streams with thosepredicted by the gibbsite formulation

con-For all data sets examined, consistent relationships were evident between

pAl i and pH for the waters of interest (pH 4 to 6) The slope of this

relation-ship was consistently near 1.0, ranging from 0.77 to 1.28 When plots of pAl3+

vs pH were examined, similar results were found The slopes of the ships in this case were consistently near 2.0, and ranged from 1.82 to 2.34(Table 9.1) These results illustrate that, for the surface waters in the U.S thatare of interest with respect to potential Al mobilization, a gibbsite-type equa-tion to model Ali concentration directly should use a power term of about 1.For predicting Al3+ concentration, a power term of about 2 should be used

scatter observed at higher pH.

and were deleted from the analysis.

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None of the data we examined suggested a power term close to 3, the valuepreviously used in model formulations

Model simulations were also conducted by Sullivan and Cosby (1998)with the MAGIC model for two watersheds in which acidic depositioninputs have been experimentally altered: West Bear Brook, ME, and Ris-dalsheia, Norway (Norton et al., 1993; Wright et al., 1993) At the Bear Brooktreatment catchment, ambient deposition has been augmented with addi-tional inputs of S and N At Risdalsheia in southernmost Norway, highambient levels of S and N deposition have been reduced to background lev-els by emplacement of a transparent roof over an entire mini catchment.MAGIC projections at these sites were modified from recent applications(Cosby et al., 1995, 1996) by altering the model algorithms for predicting the

Al response The alteration was based on the results of the empirical spatialanalyses described previously

MAGIC was applied to the Bear Brook data using an exponent of 2 and anintercept, log KSO equal to 4.0 that corresponded approximately to the empir-ical relationships derived for fall samples from Adirondack lakes (Table 9.1).The previous simulation for Bear Brook had been based on an exponent of 3and an intercept of 10, based on calibration to the pretreatment watersheddata We judged that the log KSO value derived from pretreatment data atBear Brook was too high, based on comparison with data from other sites AtRisdalsheia, log KSO equal to 2.6 was calibrated to data from the referencecatchment assuming an exponent of 2

The revised MAGIC projections of Ali concentration at West Bear Brookagreed more closely with measured values than did the projections based onthe gibbsite solubility assumption (Figure 9.3; Sullivan and Cosby, 1998) Theresults of comparing simulated with measured Ali concentrations at the Ris-dalsheia site were not so consistent However, the majority of the annualaverage measured values at Risdalsheia more closely followed the MAGICtrajectory that was constructed assuming an exponent of 2 in Eq 9.7, ratherthan 3 as in the gibbsite model (Sullivan and Cosby, 1998) Neither formula-tion was completely satisfactory for predicting stream-water Ali concentra-tion at these sites This is to be expected given the lumped-parameter nature

of the model and the complexity of the Al hydrogeochemical response van, 1994) In most cases, however, a power term of 2.0 in the model formu-lation for Al3+ provided the most reasonable projections

(Sulli-9.1.2.4 Nitrogen

MAGIC, as originally formulated and applied for the studies described viously, contained an extremely simplified representation of N dynamicswithin catchment soils There were no processes controlling the details of Ncycling in the model The version of the MAGIC model used for the Inte-grated Assessment was not appropriate for simulation of changes in atmo-spheric deposition of N In light of the increasing concern about N saturation

pre-in forested ecosystems, this was a serious shortcompre-ing pre-in the model A major

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FIGURE 9.3

µM) where the simulations are based on gibbsite solubility with a power term of 3.0 (solid line) and a modified relationship for solubility with a power term of 2.0 (dashed line) Data are presented for the watershed manipulation experiment at West Bear Brook, ME, where sulfur

measured as total monomeric Al and corrected to remove organically bound Al using an

model was calibrated twice, once to East Bear Brook (left panels) and once to the manipulated

stream, West Bear Brook (right panels) (Source: Water Air Soil Pollut., Vol 105, 1998, p 654,

Modeling the concentration of aluminum in surface waters, Sullivan, T.J and B.J Cosby, Figure

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efforts are underway to incorporate the results of the European studies (e.g.,Tietema and Beier, 1995) into the model to predict N-saturation status fromforest floor C:N ratios For many recent applications, however, it has beenassumed that current measured N retention in the modeled watersheds willremain constant into the future as a percentage of N inputs (e.g., Sinja et al.,1998; Sullivan et al., 1998) Such an assumption is probably reasonable, aslong as changes in N deposition in the future are modest The difficulty is pre-dicting the timing and magnitude of the changes in the percent N retained by

a watershed that will occur if deposition changes dramatically

A new coupled S and N model, MAGIC-WAND, was developed by ing the MAGIC model to incorporate the major ecosystem N fluxes and theirchanges through time (Ferrier et al., 1995) The Model of Acidification ofGroundwater in Catchments With Aggregated Nitrogen Dynamics (MAGIC-WAND) represents an extension to the MAGIC model In MAGIC-WANDthe N dynamics are fully coupled to the initial S-driven model

extend-MAGIC-WAND considers two species of inorganic N, NO3- and NH4+ Themodel explicitly incorporates the major terrestrial fluxes of N, such that

NO3- leaching = deposition + nitrification + external addition

- uptake - denitrificationand

NH4+ leaching = deposition + external addition + mineralization

- nitrification - uptake

If the net result of these fluxes is positive (surplus NO3 and/or NH4), leaching

to surface waters occurs Nitrogen inputs to the system are in the form ofinorganic N added to soil solution Mineralization in the model representsthe release of inorganic N that was formerly bound in organic matter, and themineralization product is NH4 Nitrogen losses from the model system are asinorganic N, and the primary output is hydrologic runoff from the soils Therunoff fluxes are calculated as the product of the simulated concentrations of

NO3 and/or NH4 at any time step and the hydrologic discharge at that time.Provision is also made in the model for other losses of inorganic N, such asdenitrification from soil or surface water The magnitude and timing of these

additional outputs of N may be specified a priori or they may be keyed to

external inorganic N concentrations using first order reactions The bial-mediated transformation of NH4 to NO3 (nitrification) is represented inthe model by a first order reaction such that the rate of loss of NH4 (equal tothe rate of production of NO3) is given by the product of a rate constant andthe concentration of NH4 at each time step

micro-Plant uptake is modeled as a nonlinear process that depends upon the centration of available NH4 and NO3 The equation is hyperbolic (representa-tion of a typical Michaelis-Menten uptake function) such that

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con-Predictive Capabilities 215

d(N)/dt = Kmax * (N)/(K(1/2) + (N)) (9.8) where (N) is the concentration of either NH4 or NO3, Kmax is the maximumuptake rate (meq/m3 per year), and K1/2 is the half-saturation constant of thereaction (meq/m3) The values of Kmax and K1/2 can be varied through time a

priori, to represent the dynamics of ecosystem response to available N

An important limitation of MAGIC-WAND, as described by Ferrier et al.(1995), was the necessity of specifying rates of mineralization and nitrifica-tion for the watershed being modeled It proved difficult, time-consuming,and expensive to derive watershed-scale estimates of these process rate func-tions, and variability was often high Results of experimental studies inNITREX have recently demonstrated that NO3- leaching can be empiricallyestimated based on the N concentration of various ecosystem compartments,including forest floor, soil, and foliage (see discussion in Chapter 7) Theapproach for modeling N in MAGIC is currently in the process of beingrevised to reflect these new findings (Cosby, personal communication) In theinterim, recent MAGIC applications have calibrated the current watershedretention of N as a percent of total N input These calibrated values of Nretention are used to estimate N retention and leaching under future chang-ing levels of N deposition (c.f., Sinha et al., 1998; Sullivan and Cosby, 1998)

9.1.3 Cumulative Impacts of Changes to the MAGIC Model

In order to evaluate the incremental and cumulative impact of some of themodifications to MAGIC, a suite of model simulations was conducted by Sul-livan and Cosby (1995) for the Adirondack DDRP lakes The baseline modelstructure was used in the DDRP and NAPAP IA studies The changes to themodel that were examined included modifying the assumption regardingbackground S deposition, reaggregating the soils data, recalibrating themodel specifically for the Adirondack subregion, adding the organic acidmodel to the surface water compartment, and changing the Aln+/H+ ion rela-tionship from cubic to quadratic These analyses did not, however, includeexamination of the effects on model output of including N dynamics in themodel simulations

A suite of simulations was conducted based on the application of anassumed deposition scenario to derive a 50-year forecast using each modelstructure The deposition scenario assumed constant S deposition from 1984(the calibration year) to 1994, followed by a 30% decrease in S depositionfrom 1995 to 2009, with constant deposition thereafter until 2034 The mod-eled responses of 33 Adirondack lakes to this deposition scenario were con-sidered The impacts of the changes were illustrated by tabulating thepercentage of lakes predicted to have pH, ANC, or Al values in excess of com-monly accepted thresholds of potential biological effects

The overall effect of the various changes to the model structure and tion procedures was an increase in the percentage of lakes exceeding various

Tiêu đề	Aquatic Effects of Acidic Deposition - Chapter 9 pot
Trường học	CRC Press LLC
Chuyên ngành	Environmental Science
Thể loại	report
Năm xuất bản	2000
Thành phố	Boca Raton

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