197 9 Predictive Capabilities The treatment of mathematical modeling in this chapter, and throughout this book, is focused almost exclusively on the Model of Acidification of Ground- water in Catchments (MAGIC, Cosby et al., 1985a,b). This is not to imply that MAGIC is necessarily the best or most accurate acid–base chemistry model available. There are several reasons for this bias in treatment of modeling approaches in favor of MAGIC for the purposes of this book: 1. MAGIC is the most widely used acid–base chemistry model in the U.S. and Europe. 2. Because the model is highly generalized, it does not have extensive input data requirements and, therefore, can be applied to a large number of potential sites without incurring inordinate costs asso- ciated with data collection. 3. In part because of the second reason, MAGIC has been extensively tested 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 than with other models. In recent years, a number of models have been developed to simulate N dynamics 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 of this 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 Direct Delayed Response Project (DDRP, Church et al., 1989) to project surface water acidification response: MAGIC, the Integrated Lake Watershed Acidification Study model (ILWAS, Gherini et al., 1985), and the Trickle Down Model (Lin and 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). 1416/frame/ch09 Page 197 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC 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 making projections of likely future changes in surface and soil water chemistry in response to various levels of acidic deposition. MAGIC also provided the technical foundation for the reduced-form modeling in the aquatic and soils components of NAPAP’s Tracking and Analysis Framework (TAF) and has been 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 or stream and then used to simulate the response of that system to changes in atmospheric deposition. MAGIC includes a section in which the concentra- tion of major ions is governed by simultaneous reactions involving S adsorption, cation weathering and exchange, Al dissolution/precipita- tion/speciation, and dissolution/speciation of inorganic C. A mass balance section of MAGIC calculates the flux of major ions to and from the soil in response to atmospheric inputs, chemical weathering inputs, net uptake in biomass, and losses to runoff. The model simulates soil solution chemistry and surface water chemistry to predict the annual average concentrations of the major ions. MAGIC generally represents the watershed with one or two soil-layer compartments. These soil layers can be arranged vertically or horizontally to represent the vertical or horizontal movement, respectively, of water through the soil. A vertical two-layer configuration was used for the NAPAP assessment, and the soil compartments were assumed to be really homogeneous. The meteorological and deposition input requirements for MAGIC include the amount and ionic concentrations of precipitation and annual average air temperature. Also needed are details of the hydrological budget for each watershed. The spatial/temporal scales in the model reflect the intended use for assessment and multiple scenario evaluations. MAGIC does not use a Gran ANC in simulating watershed response. Rather, it uses a calculated alkalinity or ANC defined as follows: CALK = SBC + NH 4 + − SSA (9.1) where SBC = Ca 2+ + Mg 2+ +Na + + K + (9.2) 1416/frame/ch09 Page 198 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC Predictive Capabilities 199 SSA = Cl - + NO 3 - + SO 4 2- (9.3) MAGIC is calibrated using an optimization procedure that selects parame- ter values so that the difference between the observed and predicted mea- surements is minimized. The calibration exercise is a three-step process. The first step is to specify the model inputs such as precipitation, deposition (both wet and dry), an estimate of historical deposition inputs and fixed parame- ters or parameters whose values correspond directly to (or can be computed directly from) field measurements (e.g., soil depth, bulk density, cation exchange capacity). This approach, in effect, assigns all of the uncertainty associated with sampling and intrinsic spatial variability to the “adjustable” parameters. The adjustable parameters are those that are calibrated or scaled to match observed field measurements. The second step is the selection of optimal values for the adjustable param- eters. These adjustable parameters are specified using optimization by the method of Rosenbrock (1960). Optimal values are determined by minimizing a loss function defined by the sum of squared errors between simulated and observed values of system state variables. The final step is to assess the structural adequacy of the model in reproduc- ing the observed behavior of the criterion variables and parameter identifi- ability, or the uniqueness of the set of optimized parameters. Structural adequacy is assessed by examining the mean error in simulated values of observed state variables for those variables used in the calibration procedure as well as for an additional state variable that was not used during calibra- tion. Parameter identifiability is assessed using approximate estimation error variances for the optimized parameters (Bard, 1974). Model calibration to a specific catchment is accomplished by specifying deposition and hydrological forcing functions, setting the values of those parameters that can be measured (fixed parameters), and determining the values of the remaining parameters that cannot be measured (adjustable parameters) through an optimization routine that adjusts those parameters to give the best agreement between observed and predicted surface water and soil chemistry (Cosby et al., 1985a,b, 1989). Atmospheric deposition of base cations, strong acid anions, and NH 4 + are assumed to be uniform over the catchment. Atmospheric fluxes in the pro- gram codes are calculated from concentrations of the ions in precipitation and estimated precipitation volume measured or interpolated to each catch- ment. These annual average concentrations and annual precipitation are used as input parameters for the model. Atmospheric fluxes of the mass balance ions are corrected for estimated dry 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 P CO 2 (partial pressure of CO 2 ) are needed as inputs to the model. Mean annual soil temperatures are set equal to the mean annual air temperatures. Soil P CO 2 is 1416/frame/ch09 Page 199 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC 200 Aquatic Effects of Acidic Deposition derived from a regression on soil temperature constructed from mean grow- ing season soil P CO 2 data from 19 regions of the world (Brook et al., 1983): log 10 (P CO 2 ) = 0.03 * TEMP – 2.48 (9.4) where P CO 2 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 P CO 2 of 0.0066 atm (approximately 20 times atmospheric P CO 2 ). Depth, bulk density, cation exchange capacity, maximum SO 4 2- adsorption capacity, and the SO 4 2- adsorption half-saturation constant are provided from soil 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 and Brezonik (1988) model using the values of relative lake area to the watershed area and the discharge. Significant amounts of S can be retained in lakes through dissimulatory reduction, with SO 4 2- used as an electron acceptor and H 2 S, ester sulfates, or metal sulfides as end products (Rudd et al., 1986; Brezonik et al., 1987). Reduction rates are approximately first order for SO 4 2- at concentrations typically encountered in softwater lakes. In-lake reduction rates 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 represented effectively as: (9.5) where K SO 4 = sulfate mass transfer coefficient (m/year) Z = mean lake depth (m) τ w = 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 to 9.05. The assumed value represents a solid phase of Al(OH) 3 intermediate between 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 of mathematical 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 and observation. Such confirmation is inherently partial. It is, therefore, critical that policy-relevant models be tested in a variety of settings and under a vari- ety of conditions (Sullivan, 1997). o o ⁄ SO 4 retention K SO 4 ∗ 100 Z τ w ⁄ K SO 4 + = 1416/frame/ch09 Page 200 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC Predictive Capabilities 201 The MAGIC model has been widely used throughout North America and Europe to project changes in the chemistry of drainage waters impacted by atmospheric S deposition. MAGIC projections of the effects on surface water chemistry of various S emissions scenarios formed the technical foundation for a large part of the National Acid Precipitation Assessment Program's Inte- grated Assessment (IA; NAPAP, 1991). Subsequently, a research effort was conducted from 1990 to 1996 to improve the performance of MAGIC and to provide testing and confirmation of the model at multiple sites. Model eval- uations have included hindcast comparisons with diatom reconstructions* of pre-industrial lake-water chemistry in the Adirondack Mountains of New York, 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 in Norway (Wright et al., 1993). It is critical that policy-relevant environmental models such as MAGIC be confirmed under a variety of conditions. Since 1990, the MAGIC model has been tested in a variety of settings and under quite varying environmental conditions. These analyses have elucidated a number of potentially impor- tant deficiencies in model structure and method of application, and have resulted in changes to the model and its calibration procedures. The work has included in-depth evaluation of issues related to regional aggregation of soils data, background pre-industrial S deposition, natural organic acidity, N, and Al mobilization. The result has been an improved and more thoroughly tested 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 and model 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 µ eq/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. 1416/frame/ch09 Page 201 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC 202 Aquatic Effects of Acidic Deposition weighted-average values for each soil layer. Within the DDRP (Church et al., 1989) that formed the technical foundation for NAPAP modeling efforts in the Northeast, soil characteristics were aggregated on the basis of attributes of soil sampling classes across the entire northeastern U.S. Subsequent to the DDRP, there was concern that Adirondack soils might differ sufficiently in their chemical properties from similar soils in other areas of the Northeast that MAGIC projections for Adirondack watersheds might be biased because they were based on soil attributes that actually reflected conditions elsewhere than the Adirondacks. The DDRP soils data, therefore, were reaggregated to characterize Adirondack watershed attributes using only soil data collected from pedons in the Adirondacks (Sullivan et al., 1991). Modeling for the DDRP and IA also assumed that the deposition of S in pre-industrial times was limited to sea salt contributions. Based on analy- ses presented by Husar et al. (1991), this assumption was modified such that 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 the regionally corrected soils and background SO 4 2- data resulted in approxi- mately 10 µ eq/L lower estimates of 1984 ANC. A substantial downward shift was 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 SO 4 2- concentrations and higher p CO 2 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 the failure to include organic acids in the MAGIC model formulations used by NAPAP. MAGIC hindcasts of pre-industrial lake-water pH showed poor agreement with diatom-inferences of pre-industrial pH (Sullivan et al., 1991), and preliminary analyses suggested that these differences could be owing, at least in part, to the presence of naturally occurring organic acids in Adiron- dack lake waters. Previous projections of future lake-water chemistry in Adirondack lakes using MAGIC (Church et al., 1989; Cosby et al., 1989) did not consider the acid–base chemistry of dissolved organic acids in the model formulations or their role in the response of lake chemistry to acidic deposition. It has been suggested, however, that organic acids can make significant contributions to surface water acidity (Krug and Frink, 1983). A significant fraction of organic acids in surface waters are characterized by strongly acidic pK a values, below 4.0 (Perdue et al., 1984; Kramer and Davies, 1988). Furthermore, considerable evidence suggested that organic acids influence the response of surface waters to changes in strong acid inputs, potentially by loss of DOC (Krug and Frink, 1983; Almer et al., 1974) and most likely by changes in the protonation of organic acid anions (Wright, 1989). 1416/frame/ch09 Page 202 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC Predictive Capabilities 203 There is not a method available for direct determination of organic acid concentration 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). Some studies report TOC (unfiltered) and others report DOC (filtered); the former are slightly higher owing to the presence in most water samples of small amounts 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 are fairly laborious and time-consuming, and are seldom used in water quality assessments and surveys. Indirect methods available for estimating organic acid anion contributions to acidity include charge balance calculations and the empirical methods of Oliver et al. (1983) that are based on measured pH and DOC, and Driscoll et al. (1994). The latter study was based on empirical data from the Adirondack Lakes Survey (ALSC). From 1984 to 1987, the ALSC surveyed 1469 lakes within the Adirondack Ecological Zone (Kretser et al., 1989; Baker et al., 1990b). This database provided an unparalleled data resource 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 of survey lakes having high DOC concentrations. The median DOC of the study lakes was 500 µ M C and 20% of the lakes had DOC concentrations greater than 1650 µ M 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 µ eq/L). 3. High in pH (greater than 7) or ANC (greater than 400 µ eq/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 data reduction procedure reduced the variability in the initial data set and allowed application of nonlinear methods for fitting the various organic acid analog 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, vari- able pH calculations were conducted. pH was calculated based on conditions of electroneutrality, concentrations of major solutes, and important pH buff- 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). 1416/frame/ch09 Page 203 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC 204 Aquatic Effects of Acidic Deposition FIGURE 9.1 Comparison of calculated (from charge balance) mean organic anion concentration (A n - ) at 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 Adirondack, NY, lakes, Water Resour. Res ., Vol. 30, p. 303, Figure 2; copyright by the American Geophysical Union. With permission.) 1416/frame/ch09 Page 204 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC Predictive Capabilities 205 They included mono-, di-, and triprotic analog models and the model of Oliver et al. (1983). The model calibration involved adjustments of the H + dis- sociation constants and site density of the DOC that specifies the number of dissociation sites per mole of organic C. The object of the fitting routine was to minimize the observed differences across all lakes between the organic charge simulated by the organic acid analog model and the organic anion concentration estimated from the measured charge balance. A nonlinear least squares technique was used in the calibration, with pK a 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 using ALCHEMI (Schecher and Driscoll, 1994) for the diprotic and triprotic mod- els. Additional details are provided by Driscoll et al. (1994). The best agreement ( r 2 = 0.92) was obtained between predicted and observed pH values using the triprotic analog representation, with fitted pK a values of 2.62, 5.66, and 5.94, and a calibrated site density of 0.055 mol sites per mol C. The fitted values for pK a and site density obtained by Driscoll et al. (1994) were used in the revised MAGIC applications conducted by Sulli- van et al. (1996a) and described below. In the Adirondack region of New York, 33 lakes were included in both the DDRP study and the Paleoecological Investigation of Recent Lake Acidifica- tion (PIRLA-II; Charles and Smol, 1990). This data set, therefore, provided an opportunity to evaluate the potential importance of organic acids to the mod- eling efforts. The hindcast comparison focused on pH reconstructions for these lakes because of the underlying importance of pH and its influence on the mobilization of potentially toxic Al and controls on the biological responses 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 esti- mates as directly comparable as possible, MAGIC was recalibrated using soils data specific to the Adirondack subregion. 2. A more realistic pre-industrial S deposition, equal to 13% of 1984 values (Husar et al., 1991), was assumed. 3. The partial pressure of CO 2 in lake water was calculated from measured values of dissolved inorganic carbon (DIC) and pH. The earlier model projections (NAPAP, 1991; Cosby et al., 1989) had been calibrated using soils and surface water data from sampling sites across the entire northeastern region of the U.S., had assumed zero pre-industrial S deposition, and had calibrated P CO 2 in the absence of consideration of organic acids. Changes in the first two factors improved the agreement between MAGIC and diatom estimates of historical pH, owing largely to differences in the calibrated values of strong acid anion concentrations. The 1416/frame/ch09 Page 205 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC 206 Aquatic Effects of Acidic Deposition last change lessened the agreement because the earlier calibration of P CO 2 had effectively resulted in a partial compensation for the missing organics. Additional uncertainties that might have affected the comparison between the MAGIC and diatom approaches include the failure of the process model to account for historic changes in landscape cover, disturbance, N dynam- ics, or changes in base cation deposition (Sullivan et al., 1991). Model sce- narios using the original version of MAGIC without organic acids were designated MAGIC 1 , and those that included the triprotic organic acid ana- log were designated MAGIC 2 . Unmodified MAGIC 1 hindcasts yielded pre-industrial pH values that were substantially 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, MAGIC 1 hindcast pH estimates were greater than 6.0 for all lakes investi- gated, whereas diatom estimates of pre-industrial pH ranged from as low as 5.2 to above 7.0. Previous comparisons between diatom and MAGIC 1 (with- out organic acids) model estimates of historical acidification had been con- ducted primarily for clearwater (DOC less than 300 µ M C) lakes, most of which had experienced substantial acidification (Wright et al., 1986; Jenkins et al., 1990). These comparisons generally showed somewhat better agree- ment for pre-industrial pH than the comparisons reported in Figure 3.3a. The failure to consider proton binding reactions involving organic solutes in the MAGIC 1 hindcast simulations could contribute to the observed dis- crepancy between model-predicted and diatom-inferred pH values because of the influence of dissolved organic acids on the acid–base chemistry of dilute waters (Hemond, 1994). Even low concentrations of dissolved organic acids (less than 250 µ M C) can appreciably affect the pH of dilute waters either in the presence or absence of strong inorganic acids (Kramer and Davies, 1988; Hemond, 1994). Although other factors might also contribute to the observed discrepancies, including, for example, uncertainties in weather- ing, SO 4 2- adsorption, base cation deposition, or hydrological routing, the pat- tern of effect (Figure 3.3a) suggested the importance of organic acids. Organic acids exert a disproportionately larger influence on pH at pH values below 6.5, where the greatest offset was observed. Thus, three independent data sets (DDRP, PIRLA-II, and ALSC) and three interpretive models (MAGIC 1 with no organic acid representation, diatom reconstructions, and MAGIC 2 with Driscoll et al.'s triprotic organic acid ana- log) were employed to test for consistency among the results of these models for estimating pre-industrial lake-water pH (Sullivan et al., 1996a). When the organic acid model was incorporated into MAGIC 2 and simulated pH values were compared with diatom-inferred pH, the comparison yielded consider- ably closer agreement between model estimates of pre-industrial pH (Figure 3.3b) than did the simulations that did not consider the effects of organic acids (Figure 3.3a). The mean difference in MAGIC 1 vs. diatom estimates of pre-industrial pH was 0.6 pH units when organic acids were omitted from the modeling scenarios with the greatest discrepancy being for lakes with 1416/frame/ch09 Page 206 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC [...]... the 4-year period 198 9 to 199 2 Modeling efforts reported by Sullivan et al ( 199 4) and Cosby et al ( 199 6) at Bear Brook provided a continuation of the MAGIC model forecasting efforts presented by Norton et al ( 199 2) Comparisons of MAGIC forecasts from 198 9 through 199 6 and annual average measured values of key parameters in East (reference) and West (treatment) Bear Brooks from 198 9 through 199 2 are... on aspects of the N retention capacity of ecosystems and the ecological effects of excess N availability (e.g., Skeffington and Wilson, 198 8; van Breemen and Van Dijk, 198 8; Schulze, 198 9; Aber et al., 198 9, 199 1; Stoddard, 199 4; Dise and Wright, 199 2) The concept of N saturation has emerged as a central focus of much of the recent research By definition, N saturation implies the initiation of limitation... results of these model testing efforts have been described by Sullivan et al ( 199 4, 199 6, 199 8), Cosby et al ( 199 5, 199 6), and Sullivan and Cosby ( 199 5) and are summarized in the following section The experimental studies are described in Chapter 8 9. 1.4.1 Lake Skjervatjern (HUMEX) Chemical responses of Lake Skjervatjern to the whole-catchment manipulation were simulated by Cosby et al ( 199 5) using... saturation of watershed soils (Sullivan and Cosby, 199 5) These TABLE 9. 2 Cumulative Effects of Some of the Recent (Post- 199 0) Changes to the Structure and Method of Application of the MAGIC Model MAGIC Predictions (Sullivan and Cosby, 199 5) of the Percentage of Adirondack DDRP Lakes having pH, ANC, and Al Above or Below Threshold Values in the Year 2034 Subsequent to an Hypothesized 30% Decrease in S Deposition. .. treatments of (NH4)2SO4 applied to West Bear Brook starting at the beginning of the 199 0 water year (A) SO4 2-; (B) ANC defined as (CB - CA); (C) Al3+; (D) CB (Source: Sullivan et al., 199 4.) Continued © 2000 by CRC Press LLC 1416/frame/ch 09 Page 226 Wednesday, February 9, 2000 2:21 PM 226 Aquatic Effects of Acidic Deposition FIGURE 9. 6 (Continued) (E) Ca2+; (F) Mg2+; G) pH; (H) DOC; (I) NO 3-; (J) Discharge... performance under changing levels of deposition in the U.S © 2000 by CRC Press LLC 1416/frame/ch 09 Page 224 Wednesday, February 9, 2000 2:21 PM 224 Aquatic Effects of Acidic Deposition No long-term historical trends in deposition were assumed in the modeling efforts for any ions except SO4 2-, NO 3- and NH4+ The historical trend used for SO4 2- was based on the U.S Of ce of Technology Assessment scenario... examination of the effects on model output of including N dynamics in the model simulations A suite of simulations was conducted based on the application of an assumed deposition scenario to derive a 50-year forecast using each model structure The deposition scenario assumed constant S deposition from 198 4 (the calibration year) to 199 4, followed by a 30% decrease in S deposition from 199 5 to 20 09, with... 1416/frame/ch 09 Page 2 19 Wednesday, February 9, 2000 2:21 PM Predictive Capabilities 2 19 FIGURE 9. 4 Volume-weighted average annual concentrations in lake water of key variables measured in Side A (treatment) and Side B (control) of Lake Skjervatjern for the period 198 9 through 199 2, and results of MAGIC model simulated concentrations through 199 3 MAGIC simulations were based on the pretreatment chemistry of the... Sullivan et al., 199 4.) Based on the preliminary results of Norton et al ( 199 2), the results of modeling efforts through year 3 of the treatment by Sullivan et al., ( 199 4) and the response trajectories for SO4 2-, it appears that MAGIC overpredicted the increase in stream-water SO4 2- concentrations at Bear Brook by nearly a factor of 2 This overprediction of the increase in stream-water SO4 2- concentration... historical trends in deposition were assumed for any ions except SO4 2-, NO 3-, and NH4+ The historical trend used for SO4 2- deposition was based on the data on S emissions summarized by Bettleheim and Littler ( 197 9) for northern Europe The historical trends in NO 3- and NH4+ deposition were assumed to parallel that of SO4 2- For the period of observation ( 198 5 to 199 2), yearly observed deposition was used . ( 199 0) and Eary et al. ( 198 9). 1416/frame/ch 09 Page 197 Wednesday, February 9, 2000 2:21 PM © 2000 by CRC Press LLC 198 Aquatic Effects of Acidic Deposition 9. 1 Model of Acidification of. Adirondack Mountains (Sullivan et al., 199 2), Sweden (Renberg and Hultberg, 199 2), Scotland (Allott et al., 199 2), and Canada (Dixit et al., 198 7, 199 1, 199 2). Diatom-inferred pH histories generally. 9. 2 Cumulative Effects of Some of the Recent (Post- 199 0) Changes to the Structure and Method of Application of the MAGIC Model. MAGIC Predictions (Sullivan and Cosby, 199 5) of the Percentage of Adirondack