An evaluation of processes regulating spatial and temporal patterns in lake sulfate in the Adirondack region of New York

Transcription

1 GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 18,, doi: /2003gb002169, 2004 An evaluation of processes regulating spatial and temporal patterns in lake sulfate in the Adirondack region of New York Limin Chen and Charles T. Driscoll Department of Civil and Environmental Engineering, Syracuse University, Syracuse, New York, USA Received 8 October 2003; revised 9 February 2004; accepted 19 May 2004; published 30 September [1] As a result of the Clean Air Act Amendments of 1970 and 1990, there have been significant decreases in sulfate (SO 4 ) concentrations in surface waters across the northeastern United States. The 37 Direct/Delayed Response Program (DDRP) watersheds in the Adirondacks receive elevated levels of atmospheric S deposition and showed considerable variability in lake SO 4 concentrations. In response to decreases in atmospheric S deposition, these sites have generally exhibited relatively uniform decreases in surface water SO 4 concentrations. In this study, an integrated biogeochemical model (PnET-BGC) was used to simulate the response of lake SO 4 concentrations at these DDRP sites to recent changes in atmospheric S deposition. Using default parameters and algorithms, the model underpredicted lake SO 4 concentrations at sites with high SO 4 concentrations and overpredicted at sites with low SO 4 concentrations. Initial predictions of lake SO 4 were relatively uniform across the region. Initial model simulations also underpredicted decreases in lake SO 4 concentrations from 1984 to We identified seven hypotheses that might explain the discrepancies between model predictions and the measured data. Model inputs, parameters, and algorithms were modified to help test these hypotheses and better understand factors that control spatial and temporal patterns in lake SO 4 in this acid-sensitive region. INDEX TERMS: 1615 Global Change: Biogeochemical processes (4805); 3210 Mathematical Geophysics: Modeling; KEYWORDS: Adirondack mountains, modeling, sulfate Citation: Chen, L. and C. T. Driscoll (2004), An evaluation of processes regulating spatial and temporal patterns in lake sulfate in the Adirondack region of New York, Global Biogeochem. Cycles, 18,, doi: /2003gb Introduction [2] Global sulfur (S) cycle is strongly affected by human activities. The burning of fossil fuels has converted substantial amounts of organic and inorganic bound S into sulfur dioxide (SO 2 ), releasing it into the atmosphere [Hultberg et al., 1994]. In many parts of the world, especially the Northern Hemisphere, anthropogenic S is the major source of S to the atmosphere. Sulfur emissions are transported over long distances and deposited as strong acids (SO 4 ), gases, and particles to terrestrial and aquatic ecosystems. [3] The deposition of sulfate (SO 4 ) and other acidforming substances has caused widespread impacts on soils and surface waters in acid-sensitive regions of North America, Europe, and Asia. Sulfate serves as a counter ion of cations, enhancing the leaching of Al 3+ and H + and nutrient cations (Ca 2+,Mg 2+ ) from acidic soils [Reuss and Johnson, 1986]. Elevated deposition of SO 4 also causes increases in SO 4 concentrations and decreases in acid neutralizing capacity (ANC) in surface waters. [4] Over the last three decades, S emissions have declined in Europe and North America [Hultberg et al., 1994]. Since Copyright 2004 by the American Geophysical Union /04/2003GB the Clean Air Act Amendments (CAAA) of 1970, there have been marked decreases in SO 2 emissions in the United States [Driscoll et al., 2001]. Following the 1970 CAAA, Title IV of the 1990 CAAA has resulted in further decreases in SO 2 emissions and SO 4 concentrations in precipitation throughout the northeastern United States [Lynch et al., 2000]. In response to changes in atmospheric deposition, SO 4 concentrations in more than 300 Massachusetts streams have decreased at an average rate of 1.8 meq L 1 yr 1 during [Mattson et al., 1997]. Lake SO 4 concentrations in New England have declined at an average rate of 1.6 meq L 1 yr 1 during [Stoddard et al., 1998]. Sulfate concentrations in 16 Adirondack Long-Term Monitoring (ALTM) lakes declined at an average rate of 2.1 meq L 1 yr 1 during [Driscoll et al., 2003]. During , SO 4 concentrations in New England and Adirondack lakes and Appalachian streams were found to decrease at average rates ranging from 1.8 to 2.3 meq L 1 yr 1 [Stoddard et al., 2003]. [5] The Adirondack region of New York receives high atmospheric S deposition, with a pattern of decreasing deposition from southwest to northeast (wet SO 4 deposition ranging from 2.3 kg S ha 1 yr 1 to 12.9 kg S ha 1 yr 1 ) [Ito et al., 2002]. The region exhibits considerable variability in bedrock and surficial geology, vegetation composition, and 1of11

2 Figure 1. Location of Direct/Delayed Response Project (DDRP) sites of the Adirondacks of New York. hydrologic flow paths, which affect the response of surface waters to acidic deposition [Driscoll et al., 1991]. Lake SO 4 concentrations in the region also exhibit some variability ( meq/l). In contrast, the response of lake SO 4 concentrations to recent changes in atmospheric deposition has been relatively uniform, although across the region, some variability in lake response was also evident [Driscoll et al., 2003]. [6] The objective of this study thus is to investigate the potential mechanisms that regulate S dynamics in the Adirondack watersheds, through the application of an integrated biogeochemical model (PnET-BGC) [Gbondo- Tugbawa et al., 2001] to 37 U.S. Environmental Protection Agency (EPA) Direct/Delayed Response Program (DDRP) lake-watersheds in the Adirondacks. In this study we developed a series of hypotheses that could explain the discrepancies between model predictions and the observed data. We modified model inputs, parameters, and algorithms in an attempt to test these hypotheses concerning mechanisms that regulate spatial and temporal patterns in lake SO 4 across this acid-sensitive region. 2. Methods 2.1. DDRP Site and Data Set Description [7] The DDRP was initiated by U.S. EPA in 1984 to investigate the acidification of soils and surface waters in the eastern United States [Church et al., 1989]. In the study, 145 catchments were selected to represent acid-sensitive sites in the northeastern United States. Among these sites, 38 lake-watersheds were located in the Adirondack subregion of New York (Figure 1). During the study, soils, vegetation, geology, and land use of each watershed were mapped by Soil Conservation Service (SCS). The identified soils in the northeastern U.S. region were grouped into 38 sampling classes. Two to seventeen randomly selected pedons were sampled for each sampling class. Soil samples were analyzed for a range of characteristics (e.g., bulk density, ph, and cation exchangeable concentrations). For each variable, a sample mean of each soil sampling class was derived. Soil characteristics of each watershed thus were estimated through weighing the means of the sampling classes within that watershed. Surface water chemistry of each lake was surveyed during August of During the summer of 2001, another survey was conducted to evaluate changes in soil and surface water chemistry at these sites (R. Warby et al., unpublished data, 2004) Model Description [8] PnET-BGC is an integrated biogeochemical model developed to simulate the response of soil and surface waters of forest ecosystems to atmospheric deposition and land disturbance. The model was formulated by linking a carbon, water, and N balance model PnET [Aber et al., 1997] with a chemical equilibrium model BGC [Gbondo- Tugbawa et al., 2001] to allow for comprehensive simu- 2of11

3 lations of element cycling through both biotic and abiotic processes within forest ecosystems. A detailed description of the model is given by Gbondo-Tugbawa et al. [2001], including an analysis of the sensitivity of model output to uncertainty in model parameters. A description of the algorithms used to simulate S dynamics in watershed ecosystems is given by Gbondo-Tugbawa et al. [2002] Model Inputs [9] The inputs described here form our base case simulation. The model inputs of typical wet S deposition, dry to wet S deposition ratio, and climatic variables of precipitation, temperature, and solar radiation were derived from the available regional regression models [Ollinger et al., 1993, 1995; Aber and Freuder, 2000]. Time series of wet deposition and precipitation were scaled from long-term records at Huntington Forest (HF), located in the central Adirondacks (obtained from National Atmospheric Deposition Program (NADP), available at Dry to wet deposition ratios were assumed to be constant over time. In PnET-BGC model, a ph-dependent SO 4 adsorption isotherm was used to simulate adsorption/ desorption of SO 4 within the soil [Gbondo-Tugbawa et al., 2001, 2002]. For base case simulation, the same SO 4 adsorption coefficients derived from the experimental data from four northeastern U.S sites as described by Gbondo- Tugbawa et al. [2002] were used. Soil masses of upper soil horizons were derived from soil depth and bulk density estimated during the DDRP study. Other soil parameters (e.g., cation exchange capacity and cation exchange coefficients) used in the calculations were also derived from the DDRP data set. A simple formulation developed by Kelly et al. [1987] was used to simulate the in-lake retention of SO 4. In the formulation, removal coefficients of SO 4 are related with a mass transfer coefficient (0.54 m yr 1 ), lake depth, and water residence time of the lake. 3. Base Case Simulation and Hypotheses to Explain Discrepancies 3.1. Predicted Versus Measured Lake SO 4 Concentrations and Changes From 1984 to 2001 [10] The mean model-simulated lake SO 4 concentrations in 1984 for the 37 lakes agreed well with the observed value (119 meq L 1 versus 118 meq L 1 ). However, the model generally underpredicted lake SO 4 concentrations at sites with high SO 4 concentrations and overpredicted at sites with low SO 4 concentrations (Figure 2a). Model calculations using base case inputs, parameters, and algorithms resulted in predictions of relatively uniform concentrations in lake SO 4 across the region. Model-simulated decreases in lake SO 4 concentrations from 1984 to 2001 were also generally less than the decreases observed from the survey, especially for sites that have shown the largest changes (Figure 3). Model-simulated changes in lake SO 4 concentrations from 1984 to 2001 averaged 21.0 meq L 1, significantly lower than the observed value of 37.0 meq L 1. The observed changes at the DDRP sites from 1984 to 2001 were generally similar to long-term trends observed at the ALTM sites ( 37.1 meq L 1 in 18 years) [Driscoll et al., 2003] Hypotheses to Explain Spatial and Temporal Patterns in Lake SO 4 [11] We developed a series of hypotheses to explain the discrepancy between measured and model-predicted values of lake SO 4 (Table 1). Model inputs, parameter values, and algorithms were then modified to help test these hypotheses and identify important mechanisms regulating spatial and temporal patterns in SO 4 concentrations in Adirondack lakes Deposition and Runoff [12] It has been suggested that for lakes in the northeastern United States, atmospheric S deposition is the most important factor in regulating the spatial pattern in lake SO 4 concentrations across the region [Church et al., 1989]. Sulfur deposition used in model simulations was slightly different from that of the DDRP study, in which wet S deposition was estimated from SO 4 concentration and precipitation measurements at the nearest Acid Deposition System (ADS) and National Oceanographic and Atmospheric Administration (NOAA) National Climate Data Center (NCDC) site [Church et al., 1989]. For the Adirondack subregion, no relationships were found between S deposition estimated in either this study or the DDRP study and lake SO 4 concentrations (Table 2). Thus spatial variations in lake SO 4 concentrations do not appear to be coincide with patterns in atmospheric deposition. Moreover, a spatial pattern of decreasing S deposition from southwest to northeast occurs in the Adirondacks [Ito et al., 2002]. This same pattern was not observed in lake SO 4 concentrations. Instead, lake SO 4 concentrations showed a pattern of decreasing concentrations from north to south, and no significant relationship was found with longitude (SO 4 (meq L 1 )= latitude (degree) longitude (degree) elevation (m), R 2 = 0.14, p = 0.047). Thus it appears that factors other than atmospheric deposition have a large influence on the spatial pattern of lake SO 4 concentrations in the Adirondacks. [13] For DDRP sites in the northeastern United States, a negative relationship was found between lake SO 4 concentrations and runoff, suggesting a dilution effect [Church et al., 1989]. The model-predicted surface runoff was generally lower than values derived from a U.S. Geological Survey runoff contour map during the DDRP study. For the Adirondacks, no relationship was found between lake SO 4 concentrations and the estimated runoff (Table 2). However, mass balance constraints on S would suggest that an underprediction of runoff could lead to an overprediction of lake SO 4 concentrations Vegetation Composition [14] Previous studies have suggested that enhanced scavenging of particulate SO 4 and SO 2 vapor occurs in conifer stands [Mollitor and Raynal, 1982; Lovett et al., 1999]. Sulfur throughfall fluxes under conifer stands have been found to be more than twice those of deciduous stands. In a study conducted at three Adirondack watersheds that receive similar S deposition, the large percentage of conifer cover was believed to be responsible for the higher lake 3of11

4 Figure 2. Model simulated SO 4 concentrations (meq L 1 ) in 1984 (solid circles) of (a) Simulation 2 using precipitation derived from Ito et al. [2002] and SO 4 concentration in precipitation derived from Ollinger et al. [1993]; (b) Simulation 3 enhanced collection of dry S deposition in coniferous and mixed forests; (c) Simulation 4 retention of S within wetlands; (d) Simulation 5 varying soil depth with hydrologic flowpath; (e) Simulation 6 using SO 4 adsorption parameters derived from Woods Lake; and (f) Simulation 7 using reconstructed variations in historical dry to wet S deposition ratio, compared to the observed lake SO 4 concentrations in The base case model simulations are included for comparison (open circles). SO 4 concentrations at one watershed [Cronan, 1985]. Enhanced collection of dry S deposition in coniferous and mixed forest stands could increase S fluxes to watersheds and result in higher SO 4 concentrations in the associated lakes. For the Adirondack DDRP sites, a significant (p < 0.1) positive correlation was found between lake SO 4 concentrations and percentage of mixed forests (Table 2). Sites with larger underprediction in SO 4 concentrations coincided with large mixed forest coverage. Thus vegetation composition could serve as a factor regulating lake SO 4 concentrations Wetland Retention [15] Wetlands occupy about 5% of all the Adirondack vegetation cover [Driscoll et al., 1991]. Soils in wetlands generally show large capacities to retain SO 4 through reduction to sulfide and organic sulfur under anaerobic conditions [Bayley et al., 1986]. The relatively low lake SO 4 concentrations at some southwestern Adirondack sites that receive elevated S deposition suggest retention of SO 4 within these watersheds, potentially associated with wetlands. For the Adirondack DDRP sites, significant (p < 0.10) negative correlation was found between lake SO 4 concentrations and both wetland area and percentage of wetlands within the watershed (Table 2). Negative relations were also found between lake SO 4 concentrations and dissolved organic carbon (DOC) concentrations, with DOC being an indicator of wetlands within watersheds [Driscoll et al., 1994]. Thus retention and reduction of SO 4 within wetlands appear to be partially responsible for the low SO 4 concentrations observed at some Adirondack lake-watersheds. 4of11

5 Figure 3. Model simulated decreases in SO 4 concentrations (meq L 1 ) from 1984 to 2001 for the DDRP lakes under (1) Simulation 2 using precipitation derived from Ito et al. [2002] and SO 4 concentration in precipitation derived from Ollinger et al. [1993]; (2) Simulation 3 enhanced collection of dry S deposition in the conifer and mixed forests; (3) Simulation 4 retention of S within wetlands; (4) Simulation 5 varying soil depth with hydrologic flowpath; (5) Simulation 6 using SO 4 adsorption parameters derived from Woods Lake; (6) Simulation 7 using reconstructed variations in historical dry to wet S deposition ratio, compared to decreases observed in the DDRP lake surveys and trends observed in the ALTM lakes. Table 1. Hypotheses That Might Explain Spatial and Temporal Patterns in SO 4 Concentrations in Adirondack Lakes Hypotheses/ Simulation Number Factors Spatial Pattern Temporal Pattern 1 subregion deposition yes no 2 subregion precipitation yes no 3 vegetation yes no 4 wetlands yes no 5 depth of surficial materials yes yes 6 soil adsorption of SO 4 7 temporal variations in dry to wet S deposition ratio yes no yes yes Surficial Geology [16] It has been suggested that for watersheds in the Adirondacks, surficial geology (in terms of thickness and permeability of surficial deposits) could affect hydrologic flowpaths and thus chemistry of the water entering lakes [April and Newton, 1985]. Soils are generally shallower and less permeable in watersheds draining to lakes with low acid-neutralizing capacity (ANC). Within these watersheds, discharge follows a shallow flowpath and exhibits less contact with surficial materials [Chen et al., 1984]. For the DDRP sites, lake SO 4 concentrations were positively correlated with the percentage of shallow deposit (Table 2). Furthermore, surficial geology might also have a role in mediating temporal changes in lake SO 4 concentrations in response to atmospheric deposition. The model predicted smaller decreases in lake SO 4 concentrations for acidsensitive lakes (ANC < 50 meq L 1 ) than less sensitive lakes (17.7 versus 28.7 meq L 1 ). In contrast, larger decreases in SO 4 concentrations were observed at acid-sensitive sites in the 2001 survey (43.8 meq L 1 versus 23.4 meq L 1 ). Similarly, more rapid decreases in SO 4 concentrations Table 2. Correlations Between Lake SO 4 Concentrations With Deposition, Runoff, Vegetation Structure, Wetland Coverage, and Surficial Geology at 37 Adirondack DDRP Watersheds Factors Correlation Coefficients R p Value a Total S deposition, kg ha 1 yr 1b Total S deposition, kg ha 1 yr 1c Runoff, cm Conifer, % Conifer, % d Deciduous, % Mixed forest, % Wetland percentage, % Wetland area, ha DOC, mmol C L 1e Shallow deposit percentage, % Flowpath, m f a Coefficients significant at p < 0.1 shown in bold. b DDRP estimates [Church et al., 1989]. c Derived using model of Ollinger et al. [1993]. d Excluding three sites with high coniferous percentage but low SO 4 concentrations due to wetlands. e Missing values for two sites; number of sites = 35. f Depth of water routed through deep soil layers estimated based on watershed characteristics [Church et al., 1989]. 5of11

6 twice as fast as wet deposition in response to declines in SO 2 emissions [Downing et al., 1995]. For some sites in the northeastern United States, dry deposition of S has been observed to decrease at faster rates than wet deposition [Palmer et al., 2004]. This pattern implies that the dry-towet ratio for a given site, which has been assumed to be constant over time, could actually vary with the levels of SO 2 in the atmosphere. If dry S deposition has decreased at rates faster than wet S deposition in recent years, decreases in total S deposition would be underestimated by assuming a constant dry to wet deposition ratio. Figure 4. Model-simulated adsorbed SO 4 concentrations within soils (mmol kg 1 ) at the DDRP sites using original soil adsorption parameters (open circles) [see Gbondo-Tugbawa et al., 2001], parameters derived from Woods Lake (solid circles), and parameters derived from the DDRP data set (triangles) compared to DDRP estimated adsorbed SO 4 concentrations at each site. were found at thin till watersheds for the ALTM lakes (2.27 meq L 1 yr 1 versus 1.77 meq L 1 yr 1 at thick till sites) [Driscoll et al., 2003]. This pattern suggests possibly less S accumulation and faster response to changes in atmospheric S deposition at sites with shallow surficial materials. In contrast, watersheds with deeper surficial deposits might show greater retention of S due to deeper flowpaths and more extensive contact of flow with soil. In order to better depict lake SO 4 concentrations and their responses to changes in atmospheric deposition, effects of surficial geology (especially the thickness of surficial material) on watershed S retention need to be considered Sulfate Adsorption [17] Sulfate adsorption coefficients in the model were derived from laboratory experimental data from four northeastern U.S. sites: Woods Lake Watershed, New York, Huntington Forest, New York, the Hubbard Brook Experimental Forest (HBEF), New Hampshire, and Bear Brook Watershed (BBW), Maine [Gbondo-Tugbawa et al., 2001; C. T. Driscoll, unpublished data, 1994]. A comparison between model-predicted and DDRP estimated SO 4 concentrations within the soil revealed that the model greatly overestimated soil adsorbed SO 4 at many sites (Figure 4). Soils at these DDRP sites are generally Spodosols with relatively low SO 4 adsorption capacity. Original adsorption coefficients estimated from the four northeastern sites might have overestimated SO 4 adsorption to the soil and might be partially responsible for the lack of response in lake SO 4 concentrations predicted by the model Changes in Dry to Wet Deposition Ratio [18] Trends in wet and dry deposition of S in the United Kingdom have indicated that dry S deposition has decreased 4. Evaluation of Hypotheses Regulating Lake SO Evaluation Criteria [19] To test hypotheses concerning spatial and temporal patterns in lake SO 4 (Table 1) and improve model simulations, these hypothesized mechanisms were evaluated cumulatively through PnET-BGC simulations. Each model simulation incorporated a new hypothesized factor, and the results were compared to its previous simulation for improvement in model predictions of lake SO 4. Simulation results were evaluated using a criterion to measure errors resulting from model predictions, the root mean square error (RMSE) and another criterion to measure the explained variance (efficiency, Eff). The RMSE and Eff are defined as sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X n 2=n RMSE ¼ ^Yi Yi Eff ¼ P n i¼1 P n i¼1 i¼1 2 ^Yi Yi ð1þ ; ð2þ ðyi YiÞ 2 where ^Yi is the predicted value for site i, Yi is the measured value for site i, n is the number of sites and Yiis the average measured value from all sites. When simulated values agree with measured values, Eff = 1. An Eff value greater than 1 indicates overpredicted variance, and values less than 1 indicate that the model does not fully explain the variance in observation Results on the Evaluation of the Hypotheses Deposition and Runoff (Simulations 1 and 2) [20] Regional regression models for the northeastern United States by Ollinger et al. [1993, 1995] were originally used to derive deposition and precipitation inputs for the base case simulation. However, more recent regression models of S deposition and precipitation were developed for the Adirondacks subregion [Ito et al., 2002]. The regression model of Ito et al. [2002] generally predicted larger variation in S deposition among sites ( kg S ha 1 yr 1 ) than the model of Ollinger et al. [1993] (range: kg S ha 1 yr 1 ), with the latter showing closer agreement with estimates from the DDRP study ( kg S ha 1 yr 1 ). Simulation 6of11

7 Table 3. A Comparison of Results From Different Model Simulations Based on Hypothesized Mechanisms Controlling Lake SO 4 a Simulations/ Hypotheses SO 4 Concentrations in 1984 Mean RMSE Eff Absolute Decreases in SO 4 from1984 to 2001, meq L 1 Observed Base case Simulation Simulation Simulation Simulation Simulation Simulation Simulation a See Table 1. Shown are root mean square error (RMSE) and efficiency (Eff). using S deposition derived from Ito et al. [2002] (Simulation 1), however, did not improve the predictions in lake SO 4 concentrations (Table 3), while the predicted runoff using precipitation values of Ito et al. [2002] showed better agreement with estimates from the DDRP study (R 2 = 0.47 versus R 2 = 0.18). Thus a combination of SO 4 concentration from Ollinger et al. [1993] and precipitation from Ito et al. [2002] were used for a simulation (Simulation 2). The resulting simulation showed slightly better agreement with the observed lake SO 4 concentrations of 1984 (Figure 2a, Table 3) and a slight improvement in predictions of changes in SO 4 concentrations from 1984 to 2001 (Figure 3) Vegetation Composition (Simulation 3) [21] In order to account for the enhanced collection of dry S deposition in coniferous and mixed forests, a new set of dry to wet S deposition ratios (DRW) were estimated based on the vegetation composition within each watershed, DRW n ¼ DRW d * ðp d þ EF c * P c þ EF m * P m Þ; ð3þ where DRW n and DRW d are dry to wet S deposition ratio for the whole watershed and deciduous forests, respectively; P c, P d, and P m are the proportion of conifer, deciduous, and mixed forests within the watershed; EF c and EF m are the enhanced factors in coniferous forests and mixed forests, respectively. In a study conducted at HF in the Adirondacks by Mollitor and Raynal [1982], the increase in S throughfall flux from bulk precipitation under coniferous stands was found to be 2.5 times greater than hardwood stands. Therefore, enhanced factor in coniferous forests (EF c ) was assumed to be 2.5, and the EF m was assumed to be Dry to wet ratios for hardwoods were derived from Ollinger et al. [1993] ( with a median of 0.36). The derived new dry to wet ratios (DRW d ) ranged between 0.33 to 0.58 with a median of Simulated lake SO 4 concentrations using new dry to wet ratios (Simulation 3) showed better agreement with the observed data (Table 3), especially at sites with high SO 4 concentrations (Figure 2b). No clear improvement was found in predictions of changes in lake SO 4 concentrations, however (Figure 3) Wetland Retention (Simulation 4) [22] To account for the retention and reduction of SO 4 within wetlands, an algorithm used to simulate S retention within lakes developed by Kelly et al. [1987] was adopted, q a R s ¼ 1 ; q a þ S s where R s is sulfate removal coefficient, q a is the average areal outflow (m yr 1 ), and S s is the mass transfer coefficient for sulfate (m yr 1 ). The values of q a can be calculated by outflow divided by area, q a ¼ OF A ¼ R o * W a ¼ R o ; A P w where OF is outflow (m 3 yr 1 ), A is wetland area (m 2 ), R o is runoff (m yr 1 ), W a is the watershed area, and P w is the percentage of the wetland coverage in the watershed. Thus S s ð4þ ð5þ R s ¼ : ð6þ ðr o =P w ÞþS s In the study by Kelly et al. [1987], measured S s for eight lakes averaged 0.54 m yr 1. Direct estimates of S s within wetlands, however, are not available. Rates of S retention are expected to be greater in wetlands than lakes due to their shallow depth, and closer interaction of water with organic deposits under anaerobic conditions; thus a higher value of 2 m yr 1 was assumed in the model simulation (Simulation 4). The new simulation lowered the predictions in lake SO 4 at sites with significant wetland coverage (Figure 2c) and showed better agreement with the observed data (Table 3) Thickness of Surficial Geology (Simulation 5) [23] During the DDRP study, the flowpath of each site was estimated along with other geomorphic/hydrologic parameters [Church et al., 1989]. In order to account for the thickness of surficial deposits, we assumed the depth of soil involved in soil reactions was equivalent to flowpath estimated in DDRP. Simulation using flowpath as an index of soil depth (Simulation 5) showed better agreement with the observed lake SO 4 concentrations (Table 3, Figure 2d) and exhibited a marked improvement in predictions of changes in lake SO 4 concentrations (Figure 3, Table 3, 28.8 meq L 1 versus observed value of 37.0 meq L 1 ) Soil SO 4 Adsorption Coefficients (Simulation 6) [24] We hypothesized that soil SO 4 adsorption characteristics observed at Woods Lake watershed in the Adirondacks should better represent adsorption behavior in general for acid-sensitive watersheds in the Adirondacks than the other three northeastern U.S. sites. Thus we used laboratory experimental data (measured adsorbed SO 4 under different ph and SO 4 concentrations) from Woods Lake watershed only to derive a new set of SO 4 adsorption coefficients (C. T. Driscoll, unpublished data). The new adsorption coefficients resulted in predictions of soil adsorbed SO 4 that were closer to the DDRP estimates (Figure 4). During the DDRP study, adsorbed SO 4 concentrations within soils and soil ph were estimated for each site. Using these data 7of11

8 Figure 5. Relationship between air SO 2 concentration and dry to wet S deposition ratio across seven monitoring sites (ABT147, CAT175, HOW132, LYE145 and WST109 of CATSNet and HF and WFM of AIRMon) in the northeastern U.S. and lake SO 4 concentrations as a surrogate for soil solution SO 4 concentrations, we derived another set of adsorption coefficients. Comparing these three sets of parameters, parameters derived from Woods Lake seem to yield the best fit (Figure 4) and thus were chosen for the new simulation. Simulation using adsorption coefficients from Woods Lake (Simulation 6) did not result in much change in predictions of lake SO 4 concentrations of 1984 (Table 3, Figure 2e), but showed marked improvement in predictions of temporal changes in lake SO 4 concentrations ( 33.9 meq L 1 versus the observed value of 37.0 meq L 1, Table 3, Figure 3) Variable Dry to Wet Deposition Ratio (Simulation 7) [25] Air SO 2 concentrations and dry to wet S deposition ratios from seven monitoring sites in the northeastern U.S. by the Clean Air Status and Trends Network (CASTNet) and Atmospheric Integrated Research Monitoring Network (AIRMon) suggested a significant relationship between air SO 2 concentrations and dry to wet S deposition ratio (R 2 = 0.56; Figure 5). Although the record period is short, we attempted to reconstruct the historical dry deposition record using this relationship in conjunction with the empirical relationship between air SO 2 concentrations at the HF and emissions of SO 2 in the northeast source area (R 2 = 0.71). Extending this empirical relationship over the past century suggests that dry to wet deposition ratio has indeed changed over time (Figure 6). Trends in dry to wet S deposition ratios resemble the pattern in SO 2 emissions, which peaked around the 1970s and decreased markedly in recent years. Simulation using variable dry to wet deposition ratios (Simulation 7) showed some improvement in predictions of temporal changes in lake SO 4 concentrations, and so far this simulation has yielded the best fit in predicting temporal changes of lake SO 4 (Figure 3). 5. Discussion 5.1. Factors Affecting Spatial Patterns of Lake SO 4 Concentrations [26] It is clear that several biogeochemical processes combine to contribute to the spatial variability in lake SO 4 concentrations across the Adirondacks. Adirondack DDRP lakes did not show the same pattern of decreasing SO 4 concentrations from southwest to northeast observed in wet S deposition. The presence of conifers and mixed forests in some watersheds appear to contribute to greater S deposition and higher lake SO 4 concentrations. The discrepancies in model-predicted and observed lake SO 4 concentrations were greatly reduced by considering the enhanced collection of dry S deposition associated with conifers and mixed forests. The presence of wetlands may be responsible for the lower SO 4 concentrations observed at some watersheds in the southwestern Adirondacks that receive the highest levels of S deposition. The depth of surficial deposits of each watershed directly affects the ability of the watershed to retain atmospheric deposited SO 4. Incorporating these factors into model calculations Figure 6. Reconstructed historical dry to wet S deposition ratio at Huntington Forest based on an empirical relationship developed between emissions of SO 2 in the northeast source area and annual dry to wet S deposition ratio for 1991 to of11

9 clearly improved results, and decreased the differences between measured and model-predicted values. [27] However, questions still remain as to how to best depict these processes in model simulations. The current algorithm of wetland S retention related retention coefficients to percentage of wetland coverage within the watershed and annual runoff, and a uniform mass transfer coefficient was used for all the wetlands. Although the model results showed good agreement with the observed data, there were still several sites showing large discrepancies with measured SO 4 values (Figure 2c). The simple assumption of varying soil depth with hydrologic flowpath greatly improved model predictions (Figure 2d); however the actual flowpath and extent of contact of flow with the soil is undoubtedly complex and clearly poorly quantified. Better characterization of flowpaths or the routing of water within the watershed by taking into account the thickness and permeability of surficial deposits appear to be critical to successful model simulations. [28] Apart from the factors mentioned above, in-lake processes also play a role in regulating spatial pattern of lake SO 4 concentrations. Estimated in-lake SO 4 retention using model of Kelly et al. [1987] ranged between 0.8% and 10.6% (5th and 95th percentile) of lake SO 4 inputs, with one site showing very large retention of 27%. Thus variations in lake depth and water residence time among these lakes also contribute to the spatial variations in lake SO 4 concentrations across the region. [29] Some of the factors considered in this analysis are autocorrelated. The correlation of wetland percentage with wetland area (r = 0.517; p = 0.001) and DOC concentrations (r = 0.343; p= 0.040) is consistent with the finding that all these three wetland-related variables are negatively related to lake SO 4 concentrations (Table 2). The percentage of deciduous forest is negatively correlated with the percentage of coniferous forest (r = 0.580; p = 0.000) and mixed forests (r = 0.678; p = 0.000). This suggests that the negative relationship between percentage of deciduous forest and lake SO 4 concentrations might be in part due to correlations among different vegetation types. However, it is also possible that other mechanisms (e.g., different soils under different vegetation types) are responsible for this negative relationship. Factors regulating S dynamics function interactively. For example, although wetland percentage is not significantly related with percentage of coniferous forests (r = 0.142; p = ), at some sites, wetlands coexist with coniferous forests and could obscure the relationship between percentage of coniferous forest and lake SO 4 concentrations. At three of the sites, high coniferous forest percentage coincides with high wetland percentage and results in relatively low SO 4 concentrations (91 to 92.5 meq L 1 ). By excluding these three sites, percent coniferous cover was found to be significantly correlated with lake SO 4 concentrations (Table 2). The coniferous forest percentage was found to be positively related to DOC (r = 0.383; p = 0.021), however. High wetland cover also tends to coincide with lower elevation, and high elevation tends to coincide with coniferous and mixed forests, shallower surficial deposit material, less wetland coverage, and higher deposition. Model calculations thus provide a way to separate the effect of each individual factor in regulating lake SO Factors Affecting Temporal Changes in Lake SO 4 From [30] Model-simulated rates of change in lake SO 4 concentrations at acid-sensitive lake-watersheds (ANC < 50 meq L 1 ) during averaged 2.26 meq L 1 yr 1 similar to values observed at the thin-till ALTM sites during (mean of 2.27 meq L 1 yr 1 ) [Driscoll et al., 2003]. Correspondingly, simulated SO 4 output to surface waters decreased at an average rate of 0.23 kg S ha 1 yr 1. For two NADP sites in the Adirondacks (Huntington Forest, NY20 and Whiteface Mountain (WFM), NY98), wet S deposition was found to decrease at 0.17 kg S ha 1 yr 1 from 1982 to 2001 and 0.22 kg S ha 1 yr 1 during , respectively. The relatively rapid decreases in lake SO 4 concentrations and output of SO 4 to surface waters indicated that changes in wet S deposition were fully translated into surface waters at most acid-sensitive sites of the region. Mass balances of S based on model simulations for the acid-sensitive sites indicated negligible changes in net S mineralization and a slight decrease in plant uptake of S at 0.05 kg S ha 1 yr 1 over the period of Model simulated desorption of previously adsorbed S supplied SO 4 to lake water at an average rate of 0.08 kg S ha 1 yr 1. The estimated decrease in total S deposition of 0.38 kg S ha 1 yr 1 was partially offset by net SO 4 desorption and decreases in plant uptake of S. [31] The estimated decrease in total S deposition was much higher than the observed changes in wet S deposition at the HF (0.17 kg S ha 1 yr 1 ). For sites in the southwestern Adirondacks, concentrations of SO 4 in precipitation were higher than values at the HF [Ollinger et al., 1993; Ito et al., 2002]. Precipitation in the Adirondacks also showed a decreasing trend from southwest to northeast [Ito et al., 2002]. Thus decreases in wet S deposition scaled from values for the HF using the model of Ollinger et al. [1993] would be expected to be larger than 0.17 kg S ha 1 yr 1. The reconstructed historical time series of dry to wet S deposition ratio also suggests a slightly lower dry to wet S deposition ratio in recent years. Without considering the possible changes in dry to wet S deposition ratio, original model simulations slightly underestimated the decreases in total S deposition. Although SO 4 concentrations in precipitation were found to decrease at a uniform rate of mg L 1 yr 1 at six monitoring sites in the Adirondacks during [Ito et al., 2002], the decreases in terms of both SO 4 concentrations and S flux were greater at WFM (NY98) than at HF (NY20) during ( 1.28 meq L 1 yr 1 at WFM versus 1.06 meq L 1 yr 1 at HF; 0.22 kg S ha 1 yr 1 at WFM versus 0.17 kg S ha 1 yr 1 at HF). The concentration of SO 4 in precipitation at WFM decreased 50% from 1984 to 2001 compared to 42% at HF. Thus the changes in wet S deposition scaled from HF (NY20) generally represent the lower bound of decreases in atmospheric deposition. [32] The base case model simulations overestimated the adsorption of SO 4 within Adirondack DDRP soils 9of11

10 (Figure 4) and thus overestimated desorption of SO 4 from the soil (0.18 kg S ha 1 yr 1 versus 0.08 kg S ha 1 yr 1 ) during The simulated adsorbed SO 4 in the Adirondack DDRP soils (average of 66.4 kg S ha 1 ) was slightly lower than measured values for a northern hardwood forest at New Hampshire (HBEF) (77.4 kg S ha 1, before harvest) [Mitchell et al., 1989]. The adsorbed SO 4 within the soils at the Adirondack DDRP sites could be greater than the modelestimated values. However, the adsorbed SO 4 would not be expected to desorb at greater rates than the model estimates; the model-estimated rate of soil SO 4 desorption of 0.08 kg S ha 1 yr 1 should represent an upper bound of this flux. Research associated with the Integrated Forest Study concluded that a large proportion of the adsorbed SO 4 is irreversibly bound to soil [Mitchell, 1992]. The current model does not depict irreversible adsorption of SO 4, and both increases in ph and decreases in SO 4 concentrations in soil solution will lead to desorption of SO 4. Without considering the irreversible adsorption of SO 4, model simulations indicated enhanced desorption of SO 4 in recent years in response to the relatively rapid decrease in S deposition. For model simulations, desorption of SO 4 abates after the adsorbed S pool is depleted. Another explanation for the rapid decrease in S output to the surface waters might be changes in the internal cycling of S within the watershed (i.e., decreases in S mineralization, increases in plant uptake of S). We have no evidence for decreases in S mineralization or increases in plant S uptake in current model simulations or field measurements in the region, although it has been speculated that increasing uptake of N (therefore increase in the uptake of S) may explain the observed recent decreases in surface water NO 3 concentrations [Goodale et al., 2003]. Nevertheless, we believe the rapid decrease in SO 4 concentrations observed in acid-sensitive Adirondack lakes can be generally attributed to the rapid decreases in total S deposition and low rates of S desorption from soil. [33] Rates of decreases in lake SO 4 concentrations were found to be lower at watersheds with thick deposits of glacial till than at watersheds with shallow deposits of glacial till [Driscoll et al., 2003]. The relatively slow response in lake SO 4 to changes in atmospheric S deposition in watersheds that are less sensitive to acidic deposition suggests the possibility of greater S accumulation. The model simulations generally underestimated S retention at these sites and therefore overestimated the response in lake SO 4 concentrations. Despite the slower response of watersheds with thicker deposits of glacial till to changes in atmospheric deposition, the long-term records at the ALTM sites suggest that changes in lake SO 4 concentrations have been quite uniform (Figure 3) [Driscoll et al., 2003]. The model-predicted changes in lake SO 4 concentrations were also quite uniform, similar to the pattern observed in the ALTM lakes (Figure 3). This pattern, however, was different from the one obtained for the DDRP lakes from the synoptic survey conducted in 2001 (Figure 3). In the survey, some Adirondack DDRP lakes have shown marked decreases in SO 4. It is not clear what mechanisms are driving these large changes in lake SO Conclusions [34] Across the Adirondack region, spatial variations in wet S deposition did not effectively explain spatial patterns in lake SO 4 concentrations. Rather, model calculations using PnET-BGC suggest that several biogeochemical processes within lake-watershed ecosystems were important to depict lake SO 4 concentrations across the subregion. Enhanced collection of dry S deposition within coniferous stands helped explain some of the high lake SO 4 concentrations observed. Retention and reduction of SO 4 within the wetlands were found to be responsible for low lake SO 4 concentrations at some sites. More importantly, the thickness of surficial deposits at each site directly affects the hydrologic flowpaths and chemistry of the water within the watershed and the response of lake SO 4 concentrations to changes in atmospheric deposition. Model predictions that considered these factors were able to explain a greater proportion of the variance in the better measured data and showed agreement with the measured data. Furthermore, new soil SO 4 adsorption parameters derived using laboratory experimental data from an acid-sensitive watershed in the Adirondacks improved model predictions in temporal changes in lake SO 4 concentrations from 1984 to An attempt to reconstruct the historical dry to wet S deposition ratio indicated that changes in dry to wet ratios of S deposition coincide with temporal changes in SO 2 emissions. Model simulations using the reconstructed historical dry to wet ratio improved the predictions in temporal changes in lake SO 4 concentrations from 1984 to Model simulations also suggested that the rapid changes observed in lake SO 4 concentrations are the result of large decreases in total S deposition and limited desorption of SO 4 from the soil. [35] Acknowledgments. Support for this study was provided by the W. M. Keck Foundation and the U.S. Environmental Protection Agency Clean Air Markets Division. We thank Richard Warby, Chris Johnson, Kim Driscoll, and Jack Cosby for supplying data for this study. We are indebted to two anonymous reviewers who provided suggestions that greatly strengthened the manuscript. References Aber, J. D., and R. Freuder (2000), Sensitivity of a forest production model to variation in solar radiation data sets for the Eastern U.S., Clim. Res., 15, Aber, J. D., S. V. Ollinger, and C. T. Driscoll (1997), Modeling nitrogen saturation in forest ecosystems in response to land use and atmospheric deposition, Ecol. Modell., 101, April, R., and R. Newton (1985), Influence of geology on lake acidification in the ILWAS watersheds, Water Air Soil Pollut., 26, Bayley, S. E., R. S. Behr, and C. A. Kelly (1986), Retention and release of S from a freshwater wetland, Water Air Soil Pollut., 31, Chen, C. W., S. A. Gherini, N. E. Peters, P. S. Murdoch, R. M. Newton, and R. A. Goldstein (1984), Hydrological analysis of acidic and alkaline lakes, Water Resour. Res., 20, Church, M. R., K. W. Thornton, P. W. Shaffer, D. L. Stevens, B. P. Rochelle, and G. R. Holdren (1989), Future effects of long-term sulfur deposition on surface water chemistry in the Northeast and southern Blue Ridge Province, EPA/600/3-89/061a-1c, U.S. Environ. Prot. Agency, Washington, D. C. Cronan, C. S. (1985), Biogeochemical influence of vegetation and soil in the ILWAS watersheds, Water Air Soil Pollut., 26, of 11

11 Downing, C. E. H., K. J. Vincent, G. W. Campbell, D. Fowler, and R. I. Smith (1995), Trends in wet and dry deposition of sulfur in the United Kingdom, Water Air Soil Pollut., 85, Driscoll, C. T., R. M. Newton, C. P. Gubala, J. P. Baker, and S. Christensens (1991), Adirondack mountains, in Acidic Deposition and Aquatic Ecosystems: Regional Case Studies, edited by D. F. Charles, pp , Springer-Verlag, New York. Driscoll, C. T., M. D. Lehtinen, and T. J. Sullivan (1994), Modeling the acid-base chemistry of organic solutes in Adirondack, New York, lakes, Water Resour. Res., 30, Driscoll, C. T., G. B. Lawrence, A. J. Bulger, T. J. Butler, C. S. Cronan, C. Eager, K. F. Lambert, G. E. Likens, J. L. Stoddard, and K. C. Weathers (2001), Acidic deposition in the northeastern United States: Sources and inputs, ecosystem effects and management strategies, BioScience, 51, Driscoll, C. T., K. M. Driscoll, K. M. Roy, and M. J. Mitchell (2003), Chemical response of lakes in the Adirondack region of New York to declines in acidic deposition, Environ. Sci. Technol., 37, Gbondo-Tugbawa, S. S., C. T. Driscoll, J. D. Aber, and G. E. Likens (2001), Evaluation of an integrated biogeochemical model (PnET-BGC) at a northern hardwood forest ecosystem, Water Resour. Res., 37, Gbondo-Tugbawa, S. S., C. T. Driscoll, M. J. Mitchell, J. D. Aber, and G. E. Likens (2002), A model to simulate the response of a northern hardwood forest ecosystem to changes in S deposition, Ecol. Appl., 12, Goodale, C. L., J. D. Aber, and P. M. Vitousek (2003), An unexpected nitrate decline in New Hampshire streams, Ecosystems, 6, Hultberg, H., H. A. Simon, R. M. Church, P. Grennfelt, M. J. Mitchell, F. Moldan, and H. B. Ross (1994), Sulfur, in Biogeochemistry of Small Catchments A Tool for Environmental Research (Scope 51), edited by B. Moldan and J. Cerny, John Wiley, Hoboken, N. J. Ito, M., M. J. Mitchell, and C. T. Driscoll (2002), Spatial patterns of precipitation quantity and chemistry and air temperature in the Adirondack Region of New York, Atmos. Environ., 36, Kelly, C. A., J. W. M. Rudd, R. H. Hesslein, D. W. Schindler, P. J. Dillon, C. T. Driscoll, S. A. Gherini, and R. E. Hecky (1987), Prediction of biological acid neutralization in acid-sensitive lakes, Biogeochemistry, 3, Lovett, G. M., A. W. Thompson, and J. Bowser (1999), Elevational patterns of sulfur deposition in the Catskill Mountains, N.Y., Atmos. Environ., 33, Lynch, J. A., V. C. Bowersox, and J. W. Grimm (2000), Acid rain reduced in eastern United States, Environ. Sci. Technol., 34, Mattson, M. D., P. J. Godfrey, M. F. Walk, P. A. Kerr, and O. T. Zajicek (1997), Evidence of recovery from acidification in Massachusetts streams, Water Air Soil Pollut., 96, Mitchell, M. J. (1992), Retention or loss of sulfur for IFS sites and evaluation of relative importance of processes, in Atmospheric Deposition and Forest Nutrient Cycling, Ecol. Stud. Ser., vol. 91, edited by D. W. Johnson and S. E. Lindberg, Springer-Verlag, New York. Mitchell, M. J., C. T. Driscoll, R. D. Fuller, M. B. David, and G. E. Likens (1989), Effects of whole-tree harvesting on the sulfur dynamics of a forest soil, Soil Sci. Soc. Am. J., 53, Mollitor, A. V., and D. J. Raynal (1982), Acid deposition and ionic movements in Adirondack forest soils, Soil Sci. Soc. Am. J., 46, Ollinger, S. V., J. D. Aber, G. M. Lovett, S. E. Millham, R. G. Lathrop, and J. M. Ellis (1993), A spatial model of atmospheric deposition for the northeastern U.S., Ecol. Appl., 3, Ollinger, S. V., J. D. Aber, G. M. Lovett, and J. M. Ellis (1995), Modeling physical and chemical climate of the northeastern United States for a geographical information system, report, 30 pp., Northeast. For. Exper. Stn., For. Serv., U. S. Dep. of Agric., Radnor, Pa. Palmer, S. M., C. T. Driscoll, and C. E. Johnson (2004), Long-term trends in soil solution and stream water chemistry at the Hubbard Brook Experiment Forest: Relationship with landscape position, Biogeochemistry, 68, Reuss, J. O., and D. W. Johnson (1986), Acid Deposition and Acidification of Soils and Waters, Springer-Verlag, New York. Stoddard, J. L., C. T. Driscoll, J. S. Kahl, and J. H. Kellogg (1998), A regional analysis of lake acidification trends for the northeastern U.S., , Environ. Monit. Assess., 51(1 2), Stoddard, J. L., J. S. Kahl, F. A. Deviney, D. R. DeWalle, C. T. Driscoll, A. T. Herlihy, J. H. Kellogg, P. S. Murdoch, J. R. Webb, and K. E. Webster (2003), Response of surface water chemistry to the Clean Air Act Amendments of 1990, EPA 620/R-03/001, U.S. Environ. Prot. Agency, Washington, D. C. L. Chen and C. T. Driscoll, Department of Civil and Environmental Engineering, 220 Hinds Hall, Syracuse University, Syracuse, NY 13244, USA. (ctdrisco@mailbox.syr.edu) 11 of 11