Impact of Climate Change on Three-Dimensional Dynamic Critical

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Environ. Sci. Technol. 2010, 44, 720–726

Impact of Climate Change on Three-Dimensional Dynamic Critical Load Functions WEI WU* AND CHARLES T. DRISCOLL Department of Civil and Environmental Engineering, Syracuse University, Syracuse, New York 13244

Received March 24, 2009. Revised manuscript received November 20, 2009. Accepted November 24, 2009.

Changes in climate and atmospheric deposition of base cations can alter the ionic composition of soil and surface waters, and therefore affect the structure and function of sensitive ecosystems. However, these drivers are not generally explicitly considered in the calculation of critical loads or dynamic critical loads to evaluate the recovery of ecosystems from elevated acidic deposition. Here we explore the importance of accounting for these changes in calculating dynamic critical loads for ecosystems. We developed threedimensional dynamic critical load surfaces as a function of nitrate, sulfur, and base cation deposition under current and future climate change scenarios for the Hubbard Brook Experimental Forest, New Hampshire. This case study indicates that dynamic critical loads for nitrate and sulfur will be lower under conditions of potential climate change or decreases in base cation deposition. This analysis suggests that greater emission controls may be needed to protect sensitive forest ecosystems from acidic deposition under a future climate change or conditions of lower atmospheric deposition of base cations, particularly for watersheds experiencing elevated leaching losses of nitrate. This study should facilitate more informed policy decisions on emission control strategies and assessments of ecosystem recovery.

1. Introduction There is a need to decrease emissions of SO2, NOx, and NHx to facilitate recovery of ecosystems impacted from acidic deposition (1). The concept of “critical loads” as a policy tool for evaluating the ecological effects associated with atmospheric emissions and subsequent deposition of air pollutants and controls to mitigate these effects was introduced in the 1980s (2) and has been widely used in Europe (3-5). This approach has also been applied in Asia, Africa, and Canada, but has not gained prominence in the United States (6). The critical load is defined as a quantitative estimate of the exposure to one or more pollutants below which significant harmful effects on specified sensitive biological indicators of the environment do not occur according to present knowledge (3). It is a potentially important tool to establish a framework to help guide management and protect ecosystems from air pollution based on ecosystem science and resource protection polices that have been advanced (6). The explicit inclusion of the time-scale to reach a critical * Corresponding author current address: Department of Coastal Sciences, University of Southern Mississippi, 703 East Beach Drive, Ocean Springs, MS 39564; tel: 1-228-818-8855; fax: 1-228-818-8848; e-mail: [email protected]. 720

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limit in the critical load analyses defines the dynamic critical load or target load (3). When the critical/dynamic critical load is calculated, a critical limit of a chemical indicator is set at a value to protect biological indicators that are sensitive to acidification stress. Critical biological indicators could include the presence of aquatic species (e.g., brook trout (Salvelinus fontinalis)) or species richness (of fish or invertebrates). These critical limits have included (1) the molar Ca/Al ratio of soil water ) 1.0, or soil percent base saturation ) 20% to protect the acid-base status of soil; or (2) pH ) 6.0, acid neutralizing capacity (ANC) ) 50 µeq/L, or Al ) 2 µmol/L to protect surface waters (1). ANC is probably the most widely used chemical indicator of acidification stress to aquatic ecosystems, because it can be effectively predicted with watershed acidification models. Based on an evaluation of fish and invertebrate population, critical limits of ANC of 0, 20, and 50 µeq/L were suggested for the United Kingdom, Norway, and central Europe (7-9). ANC limits can also vary across a region based on bedrock geology (10). Critical loads can be estimated by empirical methods (3, 11), steady-state models (8, 12), or by running a dynamic model, such as MAGIC (13) or PnET-BGC (14-16) to steady state conditions. Dynamic critical loads can only be calculated by the application of dynamic models because of their explicit time-dependence (17), which steady-state models do not consider. The PnET models depict carbon and N dynamics, while MAGIC does not have a comprehensive carbon cycle in catchments which is important for simulating N retention and cycling (18). Different models are likely to give different critical loads (19). Process-oriented dynamic models have the potential to provide more reliable and accurate simulations of ecosystem responses to imposed changes in atmospheric deposition and climatic conditions than empirical models or steady state models because they are developed to simulate the dynamic nature of ecosystems with a more comprehensive depiction of processes. Dynamic models require more detailed data for application which may be difficult to obtain. However, the data requirements frequently narrow the uncertainties in the direction and magnitude of ecosystem response to changes in drivers. Critical/dynamic critical loads of S or N are typically calculated in dynamic models by varying one element input while keeping the others constant instead of changing them simultaneously (20, 1). The effects of changes in base cation deposition have also largely been ignored in the calculation of critical loads of S or S plus N (21). Steep declines in atmospheric deposition of base cations have occurred in Europe and North America (22), which introduces uncertainties in critical/dynamic critical load calculations (23). The importance of changes in base cation deposition in regulating the acid-base status of soils and surface waters has been recognized in Europe, the U.S., and China (8, 12, 21, 24, 25), revealing a limitation in the two-dimensional function (i.e., S and N) that is commonly used for determining critical/dynamic critical loads of acidity. Thus a threedimensional critical load function based on the steady-state model was developed to quantify how calculated critical loads for S and N would change with decreases in base cation deposition (21). With consideration of changes in base cation deposition, critical/dynamic critical loads can be more informative in guiding air pollution control policies. Most modeling studies of soil and surface water recovery from acidification typically assume a constant climate over the simulation period (26). This assumption is largely due to the lack of understanding of the mechanisms linking changes in climate to changes in soil and water chemistry (18). For 10.1021/es900890t

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example, it is unclear if increases in temperature will increase net retention of N in forest ecosystems since higher temperature can both increase forest growth and increase mineralization of soil organic matter, processes which have opposing effects on N retention. Nevertheless, ecosystem drivers affected by climate change may alter vegetation and soil processes, which complicates assessments of further acidification or recovery (27, 28). Thus the projected future changes in climate and climate-induced changes in biogeochemical cycles would need to be considered in calculating critical loads for S and N, and predicting the recovery of soils and surface water from acidic deposition (18, 26, 29, 30). A biogeochemical model which incorporates atmospheric deposition, climatic factors, and the parameters for vegetation growth, soil, and water processes which are sensitive to climate change can be used to study the interactions of climate change with decreasing deposition of S, N, and base cations on the recovery of surface water from acidic deposition. In this study, we applied the biogeochemical watershed model PnET-BGC (Photosynthesis-net EvapotranspirationBioGeoChemistry (16)) to calculate the chemistry in soil and surface waters used to derive three-dimensional dynamic critical load functions under current and future changing climate scenarios for Watershed 6 (W6) at Hubbard Brook Experimental Forest (HBEF) (43°56′ N, 71°45′ W) in New Hampshire, USA. This ecosystem has experienced elevated atmospheric acidic deposition. Long-term monitoring data are available for model application (1).

2. Methods We applied the PnET-BGC (16) to simulate soil and vegetation dynamics of major elements, organic acids, Al species, and ANC to assess the acid-base status of soil and drainage waters. We selected an ANC of 20 µeq/L in 2100 as a critical chemical limit and recovery target date that would help protect biological indicators such as brook trout, found at the HBEF (31), against the adverse effects associated with episodic acidification. This limit has been used to ensure protection of fish (10). We used normalized mean error (NME), normalized mean absolute error (NMAE), and mean absolute error (MAE) to compare model simulation with the observed soil and surface water chemistry data (DOC: 1982-2005; ANC: 1988-2005; others: 1964-2005) during model calibration. We ran the model under multiple deposition and two climate scenarios to 2100 when the steady state was reached. The deposition scenarios considered were combinations of decreases from 0% to 100% by increments of 20% in S, NO3-, and base cation deposition after 2008 (see SI). Climate change scenarios included simultaneous changes of temperature, precipitation (A1b scenario (32)), and water use efficiency (WUE) in response to increasing atmospheric CO2 concentration. We assume a 100% increase in WUE when the CO2 concentration doubles (14) (SI).

3. Results 3.1. Simulation Results of Surface Water Chemistry (Model Evaluation). Generally, the model predicted the annual streamflow well, with a slight overprediction (NME ) 0.043 and NMAE ) 0.13) (Table S1, Figure 1). The mean monthly streamflow was also reproduced reasonably well (NME ) 0.043, NMAE ) 0.53, MAE ) 3.99 cm/month). Annual volume-weighted stream concentrations of dissolved organic carbon (DOC) were overpredicted by 19% (Table S1). The predicted annual volume-weighted pH compared well with the observed values (NME ) 0.011, NMAE ) 0.022, shown in Table S1). The model under-predicted stream SO42- concentrations by 3.6%, and overpredicted Na+,

FIGURE 1. Simulated and measured discharge and annual volume-weighted water chemistry for W6 at the HBEF. Mg2+, K+, Ca2+, and Cl- concentrations by 4.7%, 2.5%, 5.0%, 11%, and 4.7%, respectively (Table S1). The dynamics of N are complex and involve considerable uncertainty, which was reflected in the larger values of NME and NMAE for this solute. Significant interannual variation in NO3- loss was evident in the measured data, largely due to the interannual variation in climate (33, 34). Although the model predicted stream NO3- concentrations effectively before 1988 (NME ) -0.10, NMAE ) 0.27), it did not capture the decreasing trend from the late 1980s to present (35), resulting in an over prediction in recent years (44%) (Table S1). Predicted concentrations of Al are strongly influenced by the accuracy of predictions of all major solutes. Largely as a result of the over prediction of NO3-, Al concentrations were also overpredicted. The absolute mean error for ANC predictions was 3.8 µeq/L, which we consider indicative of a good simulation. Because ANC values are near 0 µeq/L and can be positive and negative, the absolute error for ANC is more meaningful than relative values as indicated by NME and NAME. 3.2. Predictions of ANC through 2100 under Multiple Deposition and Climate Scenarios. Without decreases in sulfur and nitrate deposition, ANC was predicted to slowly increase from 2009 at an average rate of 0.014 µeq/L-yr until VOL. 44, NO. 2, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Predictions of annual volume-weighted stream ANC of Watershed 6 at the HBEF under multiple scenarios of different strong acid (S decrease (A); NO3- decrease (B)) and base cation deposition (C) at both the constant climate scenario (no cc) and the climate change scenario (cc). 2100 under constant climate. In contrast, ANC values decrease at a rate of 0.065 µeq/L-yr under climate change (Figure 2). When S deposition decreases, model simulations suggest that ANC will increase at a lower rate (0.0081 and 0.079 µeq/ L-yr under 40% and 80% reduction of S, respectively) under the climate change scenario compared to the constant climate scenario (0.12 and 0.20 µeq/L-yr, respectively) (Figure 2A). Model calculations show that the greater the decrease in S deposition, the faster the rate of stream ANC increases or the slower the rate of stream ANC decreases, especially right after the reduction. Following an 80% decrease in S deposition under changing climate, the ANC in 2100 was less than the values predicted with a 40% decrease in S deposition under constant climate. With an 80% decrease in NO3- deposition under the climate change scenario, the ANC was simulated to decrease until 2100 (Figure 2B). The ANC following a 40% decrease in NO3- deposition under climate change was predicted to be lower than the ANC value with no changes in S and NO3- deposition under constant climate. With no change in base cation deposition, the ANC was simulated to increase under the current climate scenario, 722

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while it will decrease under the climate change scenario (Figure 2C). It appears that climatic factors play a more important role than changes in base cation deposition in regulating future changes in ANC. For example, under the scenario of climate change and no decrease in base cation deposition, the ANC will be lower than values under the scenario of an 80% decrease in base cation deposition with constant climate. These results indicate the importance of considering climate change when evaluating the recovery of ecosystems following decreases in acidic deposition. Note, base cation deposition should also be considered since decreases in base cation deposition can change the direction of future changes of ANC. 3.3. 3-D Dynamic Critical Load Surfaces under Current and Changing Climate Scenarios. We evaluated combinations of base cation, NO3-, and S deposition under which predicted stream ANC will reach 20 µeq/L in 2100 for current and changing climate scenarios (Figure 3). Since 20 µeq/L in 2100 is the critical chemical limit, we refer to these deposition values as three-dimensional (3-D) dynamic critical

FIGURE 3. Three dimensional dynamic critical load surfaces at ANClimit)20 µeq/L in 2100 under two different climate scenarios (the colors represent the sulfur deposition in eq/ha-yr). load surfaces. Emission caps have been specified for SO2 in the 1990 Amendments of the Clean Air Act and for NOx and SO2 under different multipollutant emission control proposals. Note however, NH3 emissions are not regulated in the U.S. Thus, we did not consider a dynamic critical load for NH3, although with PnET-BGC we simulate the entire N cycle and recognize the importance of NH4+ inputs in N dynamics and its effects on the acid-base status of forest ecosystems. The dynamic critical load surface under the climate change scenario is positioned below the constant climate scenario with the vertical axis of S deposition (Figure 3). For a given value of base cation deposition, the dynamic critical loads of S and NO3- are predicted to be lower under climate change than under constant climatic conditions (Figure 3). For example, when base cation deposition is 200 eq/ha-yr and NO3- deposition is 0 eq/ha-yr, the dynamic critical load for S deposition decreases from 204 eq/ha-yr under the current climate conditions to 35 eq/ha-yr under changing climate to achieve a stream ANC of 20 µeq/L by 2100 (Figure 3).

4. Discussion 4.1. Control of N or S Emissions? The current S deposition (526 eq/ha-yr) is about 1.6 times current NO3- deposition (327 eq/ha-yr), and similar to current NO3- + NH4+ deposition (∼522 eq/ha-yr) at W6 of the HBEF. Equivalent decreases (e.g., 50% decrease) in S deposition under current climate will result in a slightly larger increase in ANC (0.14 µeq/L-yr from 2009 to 2100) compared to equivalent decreases (e.g., 80% decrease) in NO3- deposition (0.10 µeq/L-yr). This difference is due to the fact that changes in atmospheric S deposition are more conservatively transported through the watershed while inorganic N is less mobile due to plant/ microbial immobilization. The difference in equivalent decreases in S and NO3- deposition on ANC changes is less under changing climate (e.g., +0.0091 µeq/L-yr for 50% reduction in S deposition and -0.0066 µeq/L-yr for an equivalent reduction in NO3-). While decreases in S deposition are somewhat more effective than decreases in NO3deposition under changing climate, both decreases in S and NO3- deposition contribute to increases in ANC at the similar order of magnitude. Therefore, controlling NOx (and NH3) emissions should be considered as important as controlling SO2 emissions, especially under conditions of high NO3leaching in N saturated forests (36-39). Note, it may be difficult to control emissions of NOx from transportation

sources (38) and nonutility energy uses in industry, so disproportionate decreases in sulfur dioxide emissions could be considered as an alternative approach since multiple paths and strategies exist to bring the current deposition down to the dynamic critical load surfaces. (Note: the control on coal combustion may decrease the emissions of SO2 and calcium and therefore the atmospheric deposition of sulfur and calcium, which would make it more difficult to reach dynamic critical load surface.) The costs and benefits of these different paths should be evaluated to find the most cost-effective air management option, which is not necessarily the shortest Euclidian path from the current conditions to the dynamic critical load surface. 4.2. Impact of Climate Change on the Nitrogen Status of Forest Ecosystems. NO3- leaching to streams is a sensitive indicator of the biogeochemical status of forest ecosystems, since N contributes to soil and surface water acidification and is often a limiting nutrient for plants and microbes (40). Given the seasonal pattern of NO3- leaching (Figure S1), the HBEF is in a transition phase to a condition of N saturation (36), due to a change in N cycling from a closed internal cycle to an open cycle where excess N is leached and/or emitted from the forest ecosystem (41). The model-predicted annual mineralization and nitrification rates (9.6 and 0.94 g/m2-yr, respectively) under the current climate scenario compared well with the measured values (10.0 and 1.3 g/m2yr, respectively; 15, 42). These values were predicted to increase to 11.4 and 3.2 g/m2-yr, respectively, under climate change. Even small changes in key processes such as N mineralization or nitrification under changing climate can result in relatively large changes in N flux (29). From the model predictions of ecosystem N fluxes, NO3- uptake by plants is 13% of the total N uptake under current climate conditions, increasing to 26% under future climate conditions. In contrast, NH4+ uptake decreased from 87% to 74% from current to future climate conditions, suggesting an acceleration toward N saturation under climate change (36, 43). 4.3. Impact of Climate Change on Soil and Water Processes. Extreme temperatures, higher frequency of drought, the North Atlantic Oscillation, and interannual variation are likely to have large and varying effects on watershed NO3- losses (29, 33, 34, 44, 45). The depiction of climate-vegetation-soil-stream processes in PnET-BGC makes the model a useful tool to assess the impacts of air pollution on forest and aquatic ecosystems under changing VOL. 44, NO. 2, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. Comparison of different processes of simulated N transformations between changing climate and current climate scenarios (unit: g/m2-year). climate, and ultimately in the calculation of dynamic critical loads of sensitive ecosystems. Both moisture and temperature affect soil decomposition. Model variables, which are derived from the soil moisture and temperature, can be used to evaluate their impacts on soil decomposition. The model simulations show that anticipated precipitation increase under the future climate change scenario will have little impact on soil decomposition processes at this relatively humid site. In contrast, the temperature increase has considerable impact on decomposition at this temperate forest. The increases in air temperature under climate change greatly increased rates of mineralization and nitrification, in excess of rates of vegetation uptake. Mineralization and immobilization of N both were predicted to increase, however the mineralization increases exceeded increased rates of N immobilization, therefore the net mineralization was predicted to increase under climate change (Figure 4). Nitrification was also predicted to increase due to increases in the availability of NH4+. Nitrate uptake by plants and the ratio of NO3- uptake to the total N uptake were predicted to increase, but not as rapidly as net mineralization and nitrification, as a result, leaching of NO3- was predicted to increase, consistent with De Schrijver et al. (41). We also predicted increases in DOC concentrations in streamwater under climate change due to increases in soil carbon mineralization (Figure S2), consistent with the assumptions made in a study using MAGIC (18). Both NO3- and DOC are important factors contributing to acidification of soil and water, and increases in these leaching losses slow the recovery of ecosystems from decreasing SO2 emissions. 4.4. Uncertainties in the Model Simulations. 4.4.1. Uncertainties in Simulating Recent Decreasing Trends in Nitrate Concentration in Streams. The simulation of the 724

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PnET-BGC in this study captured the decreasing trend of NO3- concentration in stream waters from the 1970 to 1980s, but did not depict the continuing decreases through the 1990s to present, similar to other dynamic models (e.g., (13)). Simulated NO3- concentrations started to increase after the late 1980s, due to the predicted increases in nitrification, which in turn was caused by decreasing N retention by the forest after a period of N accumulation according to the nutrient retention hypothesis (37) and N saturation theory (43, 46). Huntington (47) hypothesized increases in immobilization of inorganic N through increased belowground productivity in response to recovery from past disturbances in the Northeast to explain the unexpected decline of NO3concentration in surface waters between the 1970s and 1990s. The recovery hypothesis has been tested using PnET-CN (40) and may be a reasonable explanation for the observed phenomenon. However, we cannot test the hypothesis of increases in belowground productivity since this process is not modeled separately in the PnET-BGC model due to a lack of supporting field data. Alternatively, Goodale et al. (48) suggested that long-term changes in stream NO3- might be partly linked to observed changes in DOC. While the model effectively predicted NO3- from 1964 to 1987, and DOC concentrations from 1982 to present, there was no relation between DOC and NO3-. The model’s failure to show a relationship does not discredit Goodale et al.’s (48) hypothesis since the model does not include algorithms which directly link DOC and NO3-. The failure to depict recent NO3- trends by the model and the lack of an accepted theory to explain this pattern shows a major gap in our understanding on the N dynamics in forest ecosystems, making model predictions problematic. More data are needed to improve N algorithms in the model and narrow uncertainties in predictions (see below). 4.4.2. Other Uncertainties in the Model Simulations. Uncertainties involved in the simulation of N dynamics in the model include lack of consideration of denitrification losses due to the highly variable rates of this process and the lack of quantitative data (49). Abiotic N immobilization (47), mycorrhizal N assimilation (36), and spatial variation of N dynamics (50) are also not considered in the model. PnETBGC also assumes equal preference in NH4+ and NO3- in plant uptake (16). Furthermore, future land disturbances and future interannual climate variations were not considered in model simulations. The weathering rates of all the elements were assumed to be constant over time and space. Other sources of uncertainty lie in the reconstruction of historical atmospheric deposition and land disturbance, estimates of parameter values, the climate change scenario implemented, and changes in WUE assumed to occur under the climate change scenario. 4.5. Summary. Despite these uncertainties, this exploratory case study, which is based on a comprehensive biogeochemical model, identifies some important issues for wider application of critical load/dynamic critical load calculations and policy making, including the importance of accounting for changes in climate and base cation deposition, and controlling N emissions especially for watersheds that are experiencing elevated leaching losses of NO3-. The finding that decreases in base cation deposition lead to lower dynamic critical loads of nitrogen and sulfur may seem obvious, but this quantitative analysis not only raises awareness of the importance of changes of base cation deposition, but also highlights the direction of future research and model development (i.e., understanding how BC deposition covaries with S and/or N deposition). Note that dynamic critical loads are defined based on the current knowledge. We anticipate that model predictions

will improve as we continue to refine and quantify our understanding of biogeochemical processes and their response to changing atmospheric deposition and climate. For example, measurements of in-field denitrification which come from consistent methods and account for spatial and temporal variations, temporally and spatially explicit data on weathering rates, abiotic N immobilization, mycorrhizal N assimilation, predictions of future climate and land change with narrower uncertainties, and the response of water use efficiency to climate change will all help improve our understanding of N and other biogeochemical cycles in response to climate change.

Acknowledgments We thank W.M. Keck Foundation, U.S. Environmental Protection Agency (Contract 68-W03-033 to The Cadmus Group, Inc.), USDA Forest Service (NSRC program), and the National Science Foundation (LTER program) for their financial support of this research. The HBEF is administered by the USDA Forest Service. We thank Ms. Jing Zhai, Dr. Limin Chen, and Dr. Pranesh Selvendiran for their help with the PnET-BGC model. We especially thank the three anonymous reviewers and the editor for their insightful comments to help improve the manuscript.

Supporting Information Available Detailed description of the methods used, one supplementary table of study results, and two supplementary figures for the results and discussion. This material is available free of charge via the Internet at http://pubs.acs.org.

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