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Assessing Regional Hydrology and Water Quality Implications of Large-Scale Biofuel Feedstock Production in the Upper Mississippi River Basin Yonas Demissie,*,† Eugene Yan,† and May Wu‡ †

Environmental Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States Energy Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States

‡

S Supporting Information *

ABSTRACT: A recent U.S. Department of Energy study estimated that more than one billion tons of biofuel feedstock could be produced by 2030 in the United States from increased corn yield, and changes in agricultural and forest residue management and land uses. To understand the implications of such increased production on water resources and stream quality at regional and local scales, we have applied a watershed model for the Upper Mississippi River Basin, where most of the current and future crop/residue-based biofuel production is expected. The model simulates changes in water quality (soil erosion, nitrogen and phosphorus loadings in streams) and resources (soil−water content, evapotranspiration, and runoff) under projected biofuel production versus the 2006 baseline year and a business-as-usual scenario. The basin average results suggest that the projected feedstock production could change the rate of evapotranspiration in the UMRB by approximately +2%, soil−water content by about −2%, and discharge to streams by −5% from the baseline scenario. However, unlike the impacts on regional water availability, the projected feedstock production has a mixed effect on water quality, resulting in 12% and 45% increases in annual suspended sediment and total phosphorus loadings, respectively, but a 3% decrease in total nitrogen loading. These differences in water quantity and quality are statistically significant (p < 0.05). The basin responses are further analyzed at monthly time steps and finer spatial scales to evaluate underlying physical processes, which would be essential for future optimization of environmentally sustainable biofuel productions.

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INTRODUCTION A rapid increase of U.S. domestic biofuel production is expected in order to meet a congressional mandate to produce 36 billion gallons of biofuel by 2022.1 Much of the feedstock required to achieve this target will come from increased production of corn and cellulosic biofuel feedstock such as switchgrass and corn stover. For instance, the recently updated “Billion Ton Study” BT2 by the U.S. Department of Energy,2 which assessed the sustainable feedstock potential of the United States, estimated that nearly 8.9 million hectares of cropland and 16.6 million hectares of pasture and hay lands might shift to energy crops, with additional 140−270 MDT of corn stover being harvested from most no-till and reduced-till corn farms by 2030. Such widespread changes in land use and crop residue management are likely to affect the associated water resources at both regional and local scales, especially in regions like the Upper Mississippi River Basin (UMRB), which supplies most of the feedstock and whose water quality is already impacted by agricultural runoff.3−9 Previous studies by the National Research Council,10 U.S. Environmental Protection Agency,11 U.S. Government Accountability Office,12 and others have examined potential limitations of increased biofuel production on regional water © 2012 American Chemical Society

use and quality. The results showed that regional effects on water resources can be mixed, as raising more corn and cultivating idle lands for biofuel production could adversely impact stream and groundwater quality,13−16 while growing more perennial grasses such as switchgrass and miscanthus could mitigate water pollution12,13,17,18 but might require substantially more water.19,20 Adoption of no-till and conservation-tillage farming is also expected to offset some of the water quality issues,21−23 while collection of agricultural residue could increase soil erosion and affect its productivity, water retention, infiltration, and nutrient cycles.24−26 The extent of these impacts will also likely vary depending on the nature of the soils, topography, type of land use changes, and climatic conditions. Thus, assessing the potential benefits and costs of the expected large-scale biofuel production on regional water resources requires an integrated modeling analysis of hydrologic and agricultural processes. Received: Revised: Accepted: Published: 9174

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Figure 1. Spatial distributions of projected land use changes in the UMRB.

varying land use dynamics and corresponding management changes by adjusting model inputs and parameters at subbasin and hydrologic response unit (HRU) levels, as well as by improving the SWAT model database to represent spatially varying crop properties. To our knowledge, this study is the first to implement the BT2 feedstock projection to address the sustainability of water resources at both regional and local spatial scales. The early BT2 report32 was the basis of the Energy Independence and Security Act of 2007, and thus it is essential to fully understand the water impacts of land management decisions associated with the BT2 projections. In addition, the BT2 presents unique challenges and opportunities to simulate the projected changes in land use, fertilizer and tillage management, harvest of crop residue, and crop yields with a watershed hydrology model, thus making the study unique from earlier works. The impacts on regional water resources and qualityincluding evapotranspiration; soil moisture content; stream discharge; and

This study has applied the Soil and Water Assessment Tool (SWAT)27 for the UMRB to quantify the potential effects of the biofuel feedstock production projected by the BT2 on regional water resources. SWAT is a physically based, semidistributed, watershed-scale model that can simulate daily or hourly hydrology and nutrient cycles, as well as plant growth under varying soil, climate, land use, and management conditions.27 A relatively conservative feedstock production scenario, which assumes a feedstock price of $50 per dry-ton and an annual crop yield increase of 1%, was selected from the BT2. Unlike previous biofuel-related watershed modeling studies for the UMRB, which are restricted to corn land changes11,14,28,29 or to hypothetical future biomass production,10,14,30,31 the present work represents a more practical future biomass production by simultaneously considering the combined changes in land use types, stover harvest, fertilizer application, tillage practices, and corn yield projections of the BT2. The scenario implementation accounts for all spatially 9175

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Figure 2. Spatial distributions of projected stover harvest, corn yield increase, fertilizer application, and tillage changes in the UMRB.

related to biofuels. We selected year 2006 for the baseline scenario and 2022 for the future biomass production and BAU scenarios. The specific procedure for implementing the BT2 scenario involves (1) identifying the spatial distributions of differences between the baseline and projection years land uses, fertilizer applications, tillage, stover harvest proportions, and corn yield; (2) incorporating these changes into the UMRB-SWAT model by transferring county data from BT2 to subbasin-level and then HRU-level data and adjusting the associated model inputs and parameters; (3) recalibrating corn growth parameters, such as radiation use efficiency and leaf area index (LAI), to simulate the BT2-projected corn yield in 2022; and (4) comparing the projected change in water quality and hydrology to the baseline year results. The BAU scenario, on the other hand, involves increasing corn yield and nitrogen fertilizer application on the basis of the BT2 baseline yield projection for 2022. Land Cover Changes. The BT2 projected biofuel production scenario results in considerable change in current land use and cropping patterns in the UMRB (Figures 1 and S2). Compared to 2006, the corn, wheat, and idle land areas in the UMRB are expected to increase by about 1.5, 0.3, and 0.6 million hectares, respectively, while soybean and pasture-hay lands will decrease by about 0.5 and 1.9 million hectares,

sediment, nitrogen, and phosphorus loadingswere analyzed by comparing the BT2 results with the results obtained from baseline and business-as-usual (BAU) scenarios having no biofuel-related land use and management changes. This study is expected to broaden the sustainability analyses of biofuels by addressing potential implications for water resources, which can then be used to design biofuel feedstock production.

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METHODOLOGY This study used a SWAT model, called UMRB-SWAT, that was specifically developed to quantify the water resource impacts of conventional and cellulosic feedstock production in the UMRB,28 as described in the Supporting Information (S1 and S2). The model contains detailed data on agriculture and energy crops, hydrology, and climate inputs to represent integrated watershed processes in the basin and to quantify long-term water impacts associated with land use and management changes related to increased production of biofuel feedstock. It was also calibrated and validated using observed streamflow, nutrient and sediment loadings, and crop yields. In this study, the model was applied for BT2 feedstock projections and BAU future scenarios, and the effects on hydrology cycle and water quality are evaluated against baseline results representing the watershed condition prior to major changes 9176

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to-grain ratio remains at 1:1 in the future, the aboveground biomass of corn was considered to be half grain and half stover. Thus, if stover is not harvested together with grain, the harvestindex in the model will be 0.5 to restrict the harvest to the grain portion of the biomass, as in the case of the baseline and BAU future scenarios. However, if stover is harvested in a specific subbasin, the corn harvest-index in that subbasin will be great than 0.5, to include the harvested portion for stover. Because the current version of SWAT27 does not allow specification of spatially varying harvest-indices, which are considered by the model as basin-level parameters, we modified the SWAT crop database and the associated HRU management files to incorporate the spatially varying harvest-indices at the subbasin level. By considering a uniform harvest-index for all corn-related HRUs in a given subbasin, we distributed the stover harvest area to the entire corn area of the subbasin. This is different from the BT2 assumption of stover harvest, which applies only for corn area under no-tillage and conservationtillage. However, this assumption is necessary to simplify the scenario implementation, with possible minor effects on the overall results as the actual stover harvest level is adjusted when it is applied to the entire corn lands instead of only to those under no- or conservation-tillage. Changes in Fertilize Application and Tillage. We estimated future fertilizer application rates for corn on the basis of the projected yield31 and the amount of stover harvested from the BT2 study. For each subbasin, the total nitrogen and phosphorus fertilizer application rates in the projection year were computed by using the baseline year application rates and adding additional amounts of (1) nitrogen fertilizer derived from the increased corn yield31 and (2) nitrogen and phosphorus fertilizer to compensate for nutrient removed with harvested stover. The nitrogen and phosphorus contents in the stover were assumed to be 0.0035 gN and 0.0014 gP per gram of stover, respectively.27 Compared to the baseline year fertilizer application, the nitrogen fertilizer was increased by 5.17 kg-ha−1 (4.2%) on average to meet the nutrient demand of genetically improved corn. The nutrient removed with harvested stover was compensated for by adding additional nitrogen fertilizer at 9.43 kg-ha−1 (7.65%) and phosphorus at 3.77 kg-ha−1 (8.10%), on average. Figure 2c shows the spatial distribution of changes in nitrogen application rate between the projection and baseline years. Projected tillage practices for corn were also obtained from the BT2 study. The use of no-tillage on corn lands in the UMRB is anticipated to increase by approximately 1.2 million hectares (101.5%), while reduced-tillage, such as ridge and mulch tillage, is expected to decrease by about 405 thousand hectares (5.8%) as the conventional-tillage area remains the same. Similarly, use of conventional and reduced tillage for soybean are expected to decrease by about 607 thousand hectares each (50.0% and12.4%, respectively), while the no-till area for soybean remains the same. Similar to the other changes, the change in tillage type and area varies across the region, with Figure 2d illustrating the change in no-till areas for corn between the baseline and projection year. These differences were used for the BT2 scenario to adjust the baseline tillage for corn and soybean related HRUs in the UMRB-SWAT model. Climate Variability. The study did not explicitly consider future climate change, but potential variation in climate was incorporated by allowing the baseline and projection year climate data to vary on the basis of 2003−2006 climate data

respectively, in 2022. The spatial distributions of these changes (Figure 1) show that corn gains area from soybean, pasture, and idle lands in most subbasins, except in the northern section of the UMRB. To implement these and the other land use changes in the model, the actual individual land use changes between 2022 and 2006 were determined (Figure S4). Because the BT2 projected future acreage of each land use type at the county level and did not track future changes in land use among various land use types over years, we made assumptions to determine the actual changes among different types of land use between 2022 and 2006 at the subbasin and HRU spatial scales. First, the projected land use changes were assumed to be restricted among the seven major crops considered by the BT2 (i.e., corn, soybean, wheat, pasture, hay, idle land, and perennial grass). Second, croplands were assumed to be converted to other croplands first, then pasture and idle lands. Third, the perennial energy crop was considered to be Alamo switchgrass, which grows primarily on current pasturelands. Finally, the net land use areas at the subbasin level were balanced by discounting extra losses or gains. The resulting actual land use changes between the 2022 projection year and the 2006 baseline year were provided in S3. These changes were implemented in UMRB-SWAT by converting and splitting the corresponding HRUs in each subbasin. Corn Yield and Corn Stover Harvest. In addition to direct land use changes, the BT2 study projected production of biofuel feedstock due to genetically increased crop yield and stover harvest. With the adoption of genetically improved varieties of corn, the study projected an increase in future annual corn yield ranging from 1% for a baseline scenario to 2% for a high-yield scenario. We used the baseline crop yield scenario to determine future corn yields for counties in the UMRB. The county-level data were then aggregated to the model subbasins using counties’ corn areas and the weightedaverage method. This resulted in an increase of basin-wideaverage corn yield from 7.68 dry-ton-ha−1 in 2006 to 8.98 dryton-ha−1 in 2022, a 17% increase. The spatial distribution of the change in corn yield is shown in Figure 2a. The changes in corn yield and biomass production were incorporated into the UMRB-SWAT by adjusting corn growth parameters, such as radiation use efficiency and LAI, until the model reasonably simulated the spatially varying corn yield in 2022. The future BAU scenario used the same corn yield projection as BT2 and was applied to the model similarly. However, unlike the BT2 scenario, the BAU scenario did not involve any of the aforementioned land use changes and stover harvest for the purpose of increasing biofuel production. The BT2 study considered corn stover as the main transitional feedstock from corn to cellulosic-based biofuels. Traditionally, majority of stover was left on the ground to reduce soil erosion and preserve soil carbon and nitrogen.33,34 However, with the increased demand for biofuel, the BT2 study projected harvest of corn stover from corn farms that are under conservation- and no-till practices. For the BT2 baseline corn yield scenario and a price of $50 per dry-ton, anticipated U.S. stover supplies will total about 108 MDT per year by 2022, of which 48 MDT (44%) will come from the UMRB. To implement stover harvest in the model, the projected county-level stover biomass was first distributed to the model subbasins by using a method similar to corn yield. The proportions between harvested stover and corn grain were then computed to determine harvest indices for each subbasin (Figure 2b). Under the BT2 study assumption that corn stover9177

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sediment (about 12%) and total phosphorus (about 45%), but decreases in total nitrogen and nitrate loads (about 3% each) in the streams. The increased phosphorus and sediment loading might be a major water quality concern in the region; particularly the increased phosphorus loading may affect aquatic life in the region and lead to increased size of the Gulf of Mexico hypoxic zone if no appropriate nutrient management is applied. On the other hand, the decrease in nitrogen loading is expected to benefit the watershed and the associated aquatic ecosystem. The subbasin variability also reflects the average responses of the subbasins, with the hydrology showing less variability overall compared to water quality contributions of the subbasins. The expected differences in both water quantity and quality are considered to be statistically significant (p < 0.05). However, the basin average annual results provide little insight into underlying physical causes and mechanisms that are useful to mitigate negative impacts and improve design of biofuel feedstock production. In particular, because the BT2 scenario involves numerous types of changes including land cover, corn yield, stover harvest, fertilizer, and tillage changes, detailed analyses of the model results are required to understand the basin responses. Impacts on Monthly Hydrology and Water Quality. The impacts on monthly water balance are depicted in Figure 3a−c. Compared to the baseline scenario, evapotranspiration under the BT2 scenario is expected to increase throughout the year except in August and September, with higher increases occurring early in the crop growing season. Runoff and soil moisture, however, decrease throughout the year. Evapotranspirationthe main outlet for water from the basin, accounting for about 76% of the annual precipitation in Mississippi basin38has considerable impact on soil moisture and total runoff.39 Evapotranspiration is normally controlled by solar radiant energy, soil moisture content, and plant transpiration, depending on the season. During summer and crop growth, evapotranspiration is controlled mostly by soil moisture and plant transpiration, as solar radiation in the region is abundant. Because most energy crops, including corn, have relatively large LAI values and transpiration rates, the gradual reduction in summer evapotranspiration might be caused by cumulative depletion of soil moisture, which is also evident from the lagged effect of increased soil moisture reduction during the previous months (Figure 3b). The increased evaporation after the crop growing season might be attributed primarily to harvesting of corn stover40,41 and replacement of perennial pasture crops with annual crops that might expose the soil to more evaporation. In general, crop residues reduce light reaching the soil surface and decrease soil−water evaporation. Field experiments42 showed that crop residues reduced evaporation by nearly half compared to bare soil. Similar results43 showed a more significant effect of crop residue on evaporation for irrigated lands than for dry lands. The overall increase in the basin’s evapotranspiration might be the main reason for the observed reductions in soil moisture and runoff. Similarly, the BAU scenario resulted in increased evapotranspiration during the crop growing season, with a gradual decrease during the nongrowing season. As in the BT2 scenario, the genetically improved corn in the BAU scenario had larger biomass and LAI than the baseline corn and possibly used more water during the growing season. However, unlike the BT2 scenario, stover in the BAU scenario was left on the ground after the corn grain was harvested; this could lead to the

and rerunning the baseline and scenario models. The 4-yr average model results were used to analyze the potential impacts on hydrology and water quality.

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RESULTS AND DISCUSSION The land use and corn stover harvest analyses from the BT2 study confirm that the UMRB plays a critical role in the future U.S. crop/residue-based biofuel production. An estimated 48 MDT of stover biomass will be collected from UMRB corn farms, which translates to 4.2 billion gallons of biofuel at a biofuel−stover conversion rate of 87.1 gal d−1 t−1.35 Corn grain production is also expected to increase by 28% in 2022, from nearly 95 MDT in 2006. The increase in future corn yield and area, respectively, would account for 41% and 59% of the biomass increase. Assuming that ethanol will consume about 30% of the annual U.S. corn grain production after 200936 and an ethanol−grain conversion of 2.82 gal bu−1,37 the basin is expected to produce an additional 4.7 billion gallons of ethanol from corn grain in 2022. The potential implications of these biofuel productions on the UMRB’s hydrology and water quality were evaluated against the results obtained under BAU and baseline scenarios. The analyses were further broken down to monthly time steps and to subbasin and HRU spatial scales to assess the impacts and their underlying physical causes. Impacts on Annual Hydrology and Water Quality. Table 1 provides the subbasin average and standard deviation of Table 1. Subbasin Average and Standard Deviation for Annual Hydrology and Water Quality Results for the Baseline and Projection Years subbasins average (standard deviation) hydrology and water quality parameter evapotranspiration (mm) soil moisture (mm) flow (mm) sediment (ton km−2) total nitrogen (kgN km−2) total phosphorus (kgN km−2) nitrate (kgN km−2)

baseline

BAU

BT2

606.2 (102.9)

606.5 (102.9)

617.2 (110.0)

191.0 (61.9) 236.0 (143.5) 96.2 (112.4) 854.0 (805.5)

190.6 (61.8) 235.7 (143.2) 88.8 (110.1) 803.2 (726.6)

187.3 225.0 107.8 830.3

30.6 (32.8)

34.6 (39.6)

44.4 (49.6)

841.6 (770.6)

791.4 (689.7)

818.5 (641.8)

(59.5) (139.5) (140.3) (688.1)

annual water quantity and quality results for the baseline and projection years. The average annual results show that projected biomass production for biofuel is expected to increase the annual rate of evapotranspiration from the UMRB by approximately 1.8% and to decrease soil−water content and discharge to streams by nearly 1.9% and 4.6%, respectively. The BAU scenario, in contrast, has minor impact on the basin’s annual hydrology. Although most of the crop farms in the region do not currently require irrigation, the predicted gradual reduction in soil moisture, along with the need to boost crop yield in the future, might lead to increased irrigation, which in turn could affect streams and groundwater in the region. On the positive side, because the region is frequently affected by floods, the reduction in soil moisture and runoff might decrease flooding if the future climate remains unchanged. Unlike the impacts on regional water quantity, the expected future biomass production has a mixed effect on water quality. The BT2 results indicate significant increases in suspended 9178

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Figure 3. Monthly variations of hydrology and water quality impacts in the UMRB.

Figure 4. Spatial variations of hydrology and water quality impacts in the UMRB.

idle lands to row crops, possibly increasing transport of nutrients with runoff. The BAU scenario also showed a significant decrease in the nitrogen loading throughout the year, which is mostly attributed to improved nitrogen uptake of future corn breeds and the availability of nitrate in the soil. This is evident from the large decrease of nitrogen load during the crop growing season and the gradual decrease after harvest, which adds a relatively large amount of stover residue and nitrogen to the soil (Figure 3e). Sediment loadings in the BT2 scenario increased in all months except May, with a larger increase in winterup to a 50% increase from the baseline scenario’s monthly average

observed reduction in evapotranspiration during the nongrowing season. The impacts of the projected biomass production on monthly water quality are illustrated in Figure 3d−f. Despite the increased nitrogen application discussed previously, the nitrogen loading in the BT2 scenario decreased during the crop growing season and increased after harvest. These findings can be attributed in part to improved crop nitrogen uptake from soil, as nitrate remains longer in soil with reduced flow and soil moisture, which decrease transport and denitrification losses, respectively. The increased loading after crop harvest might be caused by the harvest of stover and conversion of pasture and 9179

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Figure 5. Hydrology and water quality impacts at the HRU level (or farm scale) in the UMRB.

sediment loading of 8 ton-km−2. The higher increase in sediment loading after crop harvest might be caused by harvesting of corn stover and growing cultivated crops (and introducing tilling) on pasture and idle lands. Similar erosion trends were observed for harvesting corn stover44 and for replacing pasture with cultivated crops.45 The sharp decreases in sediment loading during May might be caused by reduction of soil erosion due to typical spring flush runoff in the region. In contrast, sediment loading in the BAU scenario decreased slightly during all projection months. This can be attributed to relatively large corn plants and residues in the BAU scenario that decrease rainfall impacts on the soil surface and reduce surface runoff, which transports sediment and erodes stream banks. Phosphorus loading, which is often associated with suspended sediment particles, increased throughout the year for both BT2 and BAU. In some months, especially after crop

harvest, the BT2 phosphorus loading increased by about 75% from the baseline monthly average load of 2.6 kgP-km−2. Our earlier studies28,31 and others46 showed that existing soil in the basin already has large amounts of phosphorus that are not fully utilized by crops. Thus, extra phosphorus added during the conversion of pasture to row crops in the BT2 scenario will be transported to streams with the increased suspended sediment. A study about possible sources of phosphorus loading from all counties in the Mississippi basin found that counties that add less phosphorus to the soil than is removed each year in crop harvest contribute the most phosphorus to the Mississippi.46 Similarly, increased phosphorus loading in the BAU scenario could be explained by increased phosphorus supplies from the larger amount of corn residue left on the ground. Impacts on Subbasin (Local) Hydrology and Water Quality. In addition to temporal analysis, spatial-level analysis is important for understanding basin responses and designing 9180

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sustainable biofuel feedstock production. Figure 4 compares spatial variations, versus the baseline scenario, of the BT2 and BAU hydrologic and water quality impacts. The UMRB has been divided into 131 subbasins or rivers drainage areas, with the subbasin numbers increasing from north (parts of Minnesota and Wisconsin) to south (southern Illinois and Missouri). The BT2 scenario has relatively little hydrologic and water quality effect in the northern URMB but shows significant impacts in most of the southern subbasins. This spatial variation can be explained by the distribution of land use change types between the BT2 and baseline scenarios (Figure 1). For instance, the southern subbasins account for most of the land-use conversions from pasture and idle lands to row crops, while the northern subbasins involve large changes from row crops to idle and pastureland. Hydrologic outputs and phosphorus loading are negatively impacted in most of the subbasins, while impacts on nitrogen and sediment loadings can vary across the subbasins in the BT2 case. Impacts on HRU (Edge of Farm) Hydrology and Water Quality. Hydrology and water quality impacts for the individual HRUs affected in the BT2 scenario are shown in Figure 5. Each circle represents a single HRU (or unique combination of land use, soil, slope, and management) that was converted from crop A in 2006 to crop B in 2022 (denoted as A-B on the x-axis). Note that corn in 2006 (as in corn-soybean) is different from corn in 2022 (as in soybean-corn) because of improved corn genetics and harvest of stover in 2022. The dashed lines represent area-weighted mean annual differences between the BT2 and baseline scenarios of 33.2 mm, −12.4 mm, −30.7 mm, 114.5 ton-ha−1, −55.0 kgN-ha−1, and 11.0 kgPha−1 for evapotranspiration, soil moisture content, discharge, suspended sediment, total nitrogen, and total phosphorus, respectively. In general, HRUs that involve conversion of pasture and idle land to corn or soybean and vice versa are highly impacted, with annual runoff contribution decreasing as much as 300 mm and annual nitrogen, phosphorus, and sediment loading increasing as much as 4000 kgN-km−2, 500 kgP-km−2, and 4000 ton-km−2, respectively. Compared to other land use changes, conversion of pasture and idle lands to corn and soybean contributes 74% of the evapotranspiration increase, 71% of the soil moisture decrease, 77% of the discharge decrease, 99% of the sediment increase, 91% of the total nitrogen increase, and 96% of the total phosphorus increase. The variations in the HRU results for a given land use conversion can be attributed to variations in topography, soil, climate, and agricultural management. This study was conducted by using one representative biomass price ($50.0 d−1t−1) and the crop yield increase (1% yr−1) scenario of the BT2. Additional modeling work is required to study the associated water quality and quantity impacts for projected feedstock production under different prices for biomass and crop yield increase rates considered by the BT2.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]; tel: 630-252-7553; fax: 630-2525747. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS

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REFERENCES

This study was supported in part by the U.S. Department of Energy, Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Biomass Program, under contract DEAC02-06CH11357. We thank Zia Haq and Alison Goss Eng of that office for supporting this study.

(1) Energy Independence and Security Act of 2007. Public Law 110140, 2007; http://www.gpo.gov/fdsys/pkg/PLAW-110publ140/pdf/ PLAW-110publ140.pdf. (2) Perlack, R. D.; Stokes, B. J. U.S. Billion-Ton update: Biomass Supply for a Bioenergy and Bioproducts Industry; ORNL/TM-2011/224; Oak Ridge National Laboratory: Oak Ridge, TN, 2011; http://www1. eere.energy.gov/biomass/pdfs/billion_ton_update.pdf. (3) Alexander, R. B.; Smith, R. A.; Schwarz, G. E.; Boyer, E. W.; Nolan, J. V.; Brakebill, J. W. Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River Basin. Environ. Sci. Technol. 2008, 42 (3), 822−830. (4) Aulenbach, B. T.; Buxton, H. T.; Battaglin, W. A.; Coupe, R. H. Streamflow and Nutrient Fluxes of the Mississippi-Atchafalaya River Basin and Subbasins for the Period of Record through 2005; Open-File Report 2007-1080, 2007; U.S. Geological Survey: Washington, DC, 2007; http://toxics.usgs.gov/hypoxia/mississippi/flux_ests/index.html. (5) David, M. B.; Drinkwater, L. E.; McIsaac, G. F. Sources of nitrate yields in the Mississippi River basin. J. Environ. Qual. 2010, 39, 1657− 1667. (6) Goolsby, D. A.; Battaglin, W. A.; Lawrence, G. B.; Artz, R. S.; Aulenbach, B. T.; Hooper, R. P.; Keeney, D. R.; Stensland, G. J. Flux and Sources of Nutrients in the Mississippi-Atchafalaya River Basin - Topic 3 Report for the Integrated Assessment on Hypoxia in the Gulf of Mexico; NOAA Coastal Ocean Program Decision Analysis Series No. 17, 1999; NOAA Coastal Ocean Program: Silver Spring, MD, 1999; http:// www.cop.noaa.gov/pubs/das/das17.pdf. (7) Rabalais, N. N.; Turner, R. E.; Justic, D.; Dortch, Q.; Wiseman, W. J.; Sen Gupta, B. K. Nutrient changes in the Mississippi river and system responses on the adjacent continental shelf. Estuaries 1996, 19 (2B), 386−407. (8) Rabotyagov, S.; Campbell, T.; Jha, M.; Gassman, P. W.; Arnold, J.; Kurkalova, L.; Secchi, S.; Feng, H.; Kling, C. L. Least-cost control of agricultural nutrient contributions to the Gulf of Mexico hypoxic zone. Ecol. Appl. 2010, 20 (6), 1542−1555. (9) Hypoxia in the Northern Gulf of Mexico an Update; Hypoxia Panel Advisory Report EPA-SAB-08-003; U.S. Environmental Protection Agency Science Advisory Board: Washington DC, 2007. (10) Water Implications of Biofuels Production in the United States; National Research Council, National Academies Press: Washington DC, 2008. (11) Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis; EPA-420-R-10-006; U.S. Environmental Protection Agency, Assessment and Standards Division Office of Transportation and Air Quality: Washington DC, 2010. (12) Biofuels: Potential Effects and Challenges of Required Increases in Production and Use; GAO-09-446; United States Government Accountability Office: Washington, DC, 2009; http://www.gao.gov/ assets/160/157718.pdf. (13) Costello, C.; Griffin, W.; Landis, A.; Matthews, H. Impact of biofuel crop production on the formation of hypoxia in the Gulf of Mexico. Environ. Sci. Technol. 2009, 43 (20), 7985−7991.

ASSOCIATED CONTENT

* Supporting Information S

Descriptions of the UMRB, UMRB-SWAT model, and the land use changes related to BT2 scenario. This material is available free of charge via the Internet at http://pubs.acs.org. 9181

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dx.doi.org/10.1021/es300769k | Environ. Sci. Technol. 2012, 46, 9174−9182