Impact of changing land use practices on nitrate export by the Mississippi River

Transcription

1 GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 18,, doi: /2003gb002093, 2004 Impact of changing land use practices on nitrate export by the Mississippi River Simon D. Donner Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, New Jersey, USA Christopher J. Kucharik and Jonathan A. Foley Center for Sustainability and the Global Environment (SAGE), Gaylord Nelson Institute for Environmental Studies, University of Wisconsin, Madison, Wisconsin, USA Received 19 May 2003; revised 18 December 2003; accepted 9 January 2004; published 19 February [1] The increased use of nitrogen fertilizer in the Mississippi River Basin since the 1950s has been blamed for declining water quality, the degradation of aquatic ecosystems and the growth of a seasonal hypoxic zone in the Gulf of Mexico. In this study, we use the IBIS terrestrial ecosystem model and the HYDRA aquatic transport model to examine how agricultural practices and climate influenced terrestrial and aquatic nitrogen cycling across the Mississippi Basin and the nitrate export to the Gulf. The modeling system accurately depicts the observed trends and interannual variability in nitrate export by the Mississippi River (r 2 > 0.83), and several of the major tributaries, between 1960 and The challenge of simulating nitrate export from the central western sub-basins highlights the key role of processes like denitrification. The simulations demonstrate that three factors led to the doubling of nitrate export by the Mississippi River since 1960: (1) an increase in fertilizer application rates, particularly on maize; (2) an increase in runoff across the basin; and (3) the expansion of soybean cultivation. By the early 1990s, fertilized crops may have accounted for almost 90% of the nitrate leached to the river system, despite representing only 20% of the watershed area. The majority of the nitrate exported to the Gulf appears to originate from hot spots, including a stretch of the Corn Belt across Iowa, Illinois, and Indiana. The relative contribution of such heavily fertilized lands, particularly those in close proximity to higher order streams, can be even greater during wet years. INDEX TERMS: 1860 Hydrology: Runoff and streamflow; 1871 Hydrology: Surface water quality; 4845 Oceanography: Biological and Chemical: Nutrients and nutrient cycling; 4805 Oceanography: Biological and Chemical: Biogeochemical cycles (1615); KEYWORDS: agricultural land use, Mississippi River, nitrogen Citation: Donner, S. D., C. J. Kucharik, and J. A. Foley (2004), Impact of changing land use practices on nitrate export by the Mississippi River, Global Biogeochem. Cycles, 18,, doi: /2003gb Introduction [2] The dramatic increase in crop production in the United States was facilitated in part by the development of nitrogen (N) fertilizers. Since 1950, the total application of N-fertilizers to crops across the United States has increased by almost 20-fold [Alexander and Smith, 1990; Battaglin and Goolsby, 1996]. However, there have been several serious unintended environmental consequences of the widespread increase in fertilizer use. The massive increase in N-fertilizer application in the Mississippi River Basin, the heart of American maize, soybean, and wheat cultivation, has contributed to a nearly three-fold increase in nitrate export by the Mississippi River to the Gulf of Mexico since the 1950s [Turner and Rabalais, 1991, 1994; Goolsby and Battaglin, 2001]. The export of nitrate-n to the Gulf of Mexico is believed to Copyright 2004 by the American Geophysical Union /04/2003GB dramatically enhance primary production in Gulf waters along the continental shelf, causing a significant increase in the severity and extent of bottom water hypoxia [Turner and Rabalais, 1991, 1994; Rabalais et al., 2001]. [3] A number of recent studies have attempted to quantity the N budget of the Mississippi and other large river basins in hopes of improving understanding of the fate of N inputs [Howarth et al., 1996; Goolsby et al., 1999; Burkart and James, 1999; McIsaac et al., 2001; Boyer et al., 2002]. For example, McIsaac et al. [2001] found that the annual nitrate flux by the Mississippi River from 1960 to 1999 was related to annual streamflow and the net anthropogenic input of N to the river basin over the previous 2 9 years. Most studies agree that only a small fraction of total anthropogenic N inputs to large river basins like the Mississippi is actually exported to the ocean; the remainder is held in the soil, stored in groundwater, consumed by plants, or released to the atmosphere [Van Breemen et al., 2002]. 1of21

2 [4] Understanding the nitrogen biogeochemistry across a large river basin requires that we consider a number of interacting domains: human activity and land use practices, climate and atmospheric chemistry, land surface hydrology, terrestrial ecology, aquatic ecology, and river hydrology. Predicting the behavior of such a complex system over time requires an understanding of the processes within each subsystem and also how the system operates as a whole. Integrated simulation models that describe N cycling in both terrestrial and aquatic ecosystems are necessary to determine how land use, soil conditions, and climate influence the fate of N in large river basins [Alexander et al., 2002; Donner et al., 2002]. [5] In a preliminary integrated modeling study of N cycling in a large river basin, Donner et al. [2002] linked the IBIS terrestrial ecosystem model and HYDRA hydrological routing model to demonstrate the role of hydrology in nitrate export by the Mississippi River. That study used an empirical algorithm to estimate local nitrate leaching to the river system, where the leaching rate was determined from the land cover type (including cropland and natural vegetation types) and the temporal variability in runoff. However, Donner et al. [2002] concluded that a processbased model of terrestrial N cycling and leaching to the river system was necessary to specifically evaluate the impact of cropping practices on nitrate export by the Mississippi River. In a subsequent study of the Upper Mississippi Basin, Donner and Kucharik [2003] updated IBIS to include process-based representation of N cycling in crop ecosystems. The study showed that the coupled IBIS-HYDRA model could simulate key aspects of the water (e.g., river discharge), carbon (e.g., crop yields), and nitrogen (e.g., nitrate export) cycles across a large river basin over a long time frame. [6] In this study, we use the IBIS-HYDRA modeling system to examine how changes in land use practices, including the increasing rates of fertilizer use and shifting pattern of crop cover, and climate variability have affected N cycling in the Mississippi Basin and nitrate export to the Gulf of Mexico over the latter half of the twentieth century. First, we evaluate the ability of the modeling system to simulate terrestrial N cycling across the Mississippi Basin and aquatic N export by the Mississippi River and its main tributaries over the period. Second, we specifically examine the impact of the changes in fertilizer use and land cover on nitrate loading to the Mississippi River system. Finally, we examine the sensitivity of N loading to fertilizer use, climate, and soil characteristics and identify possible hot spots that may be disproportionately responsible for the nitrate exported to the Gulf of Mexico. 2. Methodology [7] In this study, we use the IBIS terrestrial ecosystem model [Foley et al., 1996; Kucharik et al., 2000; Kucharik and Brye, 2003] and the HYDRA hydrology/aquatic biogeochemistry model [Coe, 1998, 2000; Donner et al., 2002] to simulate terrestrial N cycling and aquatic nitrate export in the Mississippi River Basin over the period. IBIS simulates the cycling of water, carbon, and nitrogen, including leaching of nitrate to the river system, at a latitude-longitude spatial scale (approximately 42.7 km by 55.7 km at 40 N) using historical climate forcing for the Mississippi Basin from 1940 to HYDRA employs the IBIS simulated runoff, including surface runoff and groundwater drainage, and nitrate leaching to simulate river discharge, nitrate export and in-stream nitrate removal at a spatial scale (approximately 7.1 km by 9.3 km at 40 N) from 1960 to We evaluate the model output using U.S. Geological Survey (USGS) estimates of river nitrate flux across the Mississippi Basin and published estimates of the Mississippi Basin N budget [Goolsby et al., 1999]. [8] Here we offer descriptions of the modeling systems, including new wheat and maize-soybean rotation submodels, and the various input data, including the two methods employed to create data sets of N-fertilizer use across the entire Mississippi Basin. We also evaluate N inputs not included in the modeling system, to identify possible sources of error in the simulations IBIS Description [9] IBIS is a dynamic terrestrial ecosystem model that simulates a wide range of phenomena, including land surface processes, canopy physiology, vegetation phenology, long-term ecosystem dynamics, and soil biogeochemistry in natural- and agro-ecosystems [Foley et al., 1996; Kucharik et al., 2000; Kucharik and Brye, 2003]. These processes are organized in a hierarchical framework, operating at time steps ranging from 1 hour to 1 year, which allows for coupling of ecological, biophysical, and physiological processes occurring on different timescales. IBIS uses climate forcing and basic physical principles to explicitly simulate the time-transient surface energy and water budget, including surface runoff, subsurface drainage, and nitrate leaching. The model has been extensively tested and applied toward understanding water and biogeochemical cycling in grasslands, croplands, and forests [Foley et al., 1996; Delire and Foley, 1999; Lenters et al., 2000; Kucharik et al., 2000, 2001; Kucharik and Brye, 2003; Dargaville et al., 2002; Donner and Kucharik, 2003]. [10] IBIS was recently adapted to describe the response of maize, soybeans, winter wheat, and spring wheat cropping systems to management (e.g., fertilizer application and planting date), climate (e.g., temperature and soil moisture), and N availability [Donner, 2002; Kucharik and Brye, 2003; Donner and Kucharik, 2003]. The IBIS crop submodel simulates hourly carbon fluxes, water fluxes, soil moisture, surface runoff, and groundwater drainage; daily carbon and N dynamics like net mineralization (minus denitrification and immobilization); daily dissolved inorganic N (DIN) leaching; daily plant development (in terms of biomass and leaf area index); and annual crop yield, harvest index, and carbon balance. Each soil layer contains pools of soil organic N, soil inorganic N, and DIN, from which a mechanistic leaching module determines nitrate leaching losses via subsurface drainage [Kucharik and Brye, 2003; Donner and Kucharik, 2003]. The IBIS crop model was evaluated using field data obtained at the University of Wisconsin s agricultural research site in Arlington, Wisconsin; for a complete description, see Kucharik and Brye [2003]. 2of21

3 Figure 1. Map of Mississippi River Basin and the major sub-basins examined in this study. The basin definition is based upon the resolution map of river flow directions used by HYDRA. [11] As in previous studies, we resolve six soil layers with thicknesses of 0.10, 0.15, 0.25, 0.50, 0.50, and 2.5 m, and nitrate leaching to base flow occurs at 1.5 m depth, assuming that nitrate is 95% of DIN in crop systems, 35% in grasslands, and 50% in forests [Reckhow et al., 1980; Brye, 1999]. In natural ecosystems, there is assumed to be an ample supply of inorganic N for growth; plant uptake is controlled by photosynthetic demand and fixed C/N ratios of growing vegetative components (e.g., leaves, roots, and wood). [12] The common practice of annually rotating maize and soybean between fields is emulated by combining twin IBIS simulations. In one simulation, maize are grown in the first year and alternated with soybeans; in a second simulation, soybeans are grown in the first year and alternated with maize. The model output for rotated maize and rotated soybeans from the two simulations is incorporated into HYDRA, based on the area of the crops subject to rotation each year (see section 2.4). Sensitivity analysis indicated that incorporating maize-soybean rotations reduces the large difference in leaching rates between the maize and soybean monoculture systems that was noted by Donner and Kucharik [2003] and slightly decreases in mean annual nitrate export by the Mississippi River [Donner, 2002] HYDRA Description [13] HYDRA is a hydrological transport model that simulates the time-varying flow and storage of water and nitrate in terrestrial hydrological systems, including rivers, wetlands, lakes, and human-made reservoirs [Coe, 1998, 2000; Donner et al., 2002]. River discharge, nitrate flux, and surface water volume are determined hourly from upstream inputs, local surface, and subsurface runoff (from IBIS), local nitrate leaching (from IBIS), precipitation over surface waters (from climate data), evaporation from water surfaces (estimated by a simple energy balance model), and river velocity (based on topography). IBIS and HYDRA have been extensively tested and applied together to biophysical and hydrological problems at large temporal and spatial scales [Kucharik et al., 2000; Lenters et al., 2000; Coe and Foley, 2001; Coe et al., 2002; Foley et al., 2002]. More recently, the models have been used to specifically analyze hydrology and nitrate export in the Mississippi River Basin since 1950 [Donner and Kucharik, 2003; Donner et al., 2002]. [14] In HYDRA, nitrate is treated like a semi-conservative tracer in the simulated river system [Donner et al., 2002]. The only process that permanently removes nitrate from river systems is benthic denitrification, in which nitrate is reduced to N 2 O and N 2 gas in the aquatic sediments [Seitzinger, 1988; Howarth et al., 1996; Peterson et al., 2001; Seitzinger et al., 2002]. Denitrification is a notoriously difficult process to simulate across large regions because it is very sensitive to a number of environmental characteristics and tends to occurs in small anaerobic pockets of sediment. In HYDRA, the denitrification rate is estimated at each time step from the nitrate concentration, the river bed area, a seasonal Q10 water temperature function, and a rate parameter; for a thorough description, see Donner et al. [2002]. This function estimates the rate at which nitrate contacts the bottom sediment (concentration and bed area) and the rate at which that nitrate is likely to be denitrified (rate parameter and temperature). Donner et al. [2002] found that HYDRA captures the relationship between nitrate removal and water residence time noted in studies of large river systems [Howarth et al., 1996; Alexander et al., 2000; Seitzinger et al., 2002] and the expected seasonal variation in nitrate removal [Sjodin et al., 1997], but that the simulated removal of river nitrate and microbial denitrification rates were on the lower end of the range reported in the literature. A survey of morphological data [e.g., Moody and Meade, 1993] indicated that the previous study underestimated channel width, key to determining bed area in low-flow streams where significant nitrate removal occurs. A new dischargebased rating curve was developed specifically to estimate width of rivers with low discharge (<120 m 3 /s). [15] For this study, HYDRA has also been adapted to simulate the impact of the Old River Diversion near the mouth of the Mississippi, which maintains the discharge of the Atchafalaya at approximately 25% of the total discharge of the Mississippi and Atchafalaya rivers [Goolsby et al., 1999]. The simulated Mississippi Basin therefore includes the land that drains into the Atchafalaya River (Figure 1); however, only 75% of the total discharge of water and nitrate from the basin flows out to the Gulf via the Mississippi River Climate and Soils Data [16] Long-term monthly mean climate data at spatial resolution is obtained from the Climate Research Unit (CRU) of the University of East Anglia [New et al., 2000] for the period and from the National Center for Environmental Prediction (NCEP) climate reanalysis data set for the period The daily variability for each meteorological variable from the NCEP climate reanalysis is combined with the monthly values from the CRU-05 data set for the period This provides realistic representation of the daily weather events that are vital to simulating crop development (e.g., planting date and maturity) while preserving the monthly values from the CRU-05 data set. Hourly average values of air 3of21

4 Table 1. Mississippi Basin Land Cover (1992) a Basin Station Location Area, km 2 % Maize, Soybean, % Wheat, % Other Crops, % Upper Mississippi Clinton, Iowa 214, Upper Missouri Omaha, Nebraska 784, Lower Missouri Hermann, Missouri 552, Upper Ohio Owensboro, Ohio 247, Lower Ohio Metropolis, Illinois 268, Middle Mississippi Thebes, Illinois 283, Arkansas Little Rock, 409, Arkansas Red/Ouachita Melville, Louisiana 264, Lower Mississippi St. Francisville, 208, Louisiana Mississippi Basin 3,234, a Other crops and urban land are not considered in this study. Urban, % temperature, precipitation, relative humidity, solar radiation, and wind speed are computed by a weather generator and other mathematical functions within IBIS [Foley et al., 1996; Levis et al., 1996]. [17] Soil texture, as a function of soil depth, is derived from the Pennsylvania State University Earth System Science Center s CONUS data set [Miller and White, 1998], based on the USDA State Soil Geographic Database. The 30- arcsecond resolution data set was aggregated to 0.5 resolution and used to determine the dominant soil type [Donner et al., 2002]. Soil physical and hydraulic properties are assigned to each soil layer in IBIS based on a classification of the soil texture into one of 11 major categories (sandy loam, clay loam, etc.) and a table of soil parameters, such as saturated hydraulic conductivity, air-entry potential, wilting point, and field capacity [Campbell and Norman, 1998] Land Cover Data [18] We classify the land cover in the Mississippi Basin as either maize, fertilized and unfertilized soybeans, spring wheat, winter wheat, or natural vegetation, according to the IBIS classification scheme (Table 1). Maize, soybean, and wheat are the primary crops in the Mississippi Basin, comprising almost three quarters of the total cropland area [U.S. Department of Agriculture (USDA), 2001b], and receiving the vast majority of total N-fertilizer sold [Donner, 2002]. The land cover input data sets are described below. [19] Natural vegetation type in IBIS is derived from the 1-km DISCover land cover data set [Loveland and Belward, 1997]. Ramankutty and Foley [1999a] aggregated the original 94 land cover classes into 15 biomes and converted the data to 0.5 resolution by selecting the most dominant biome within each grid cell. Here we assume the land in each grid cell not covered by fertilized maize, soybeans, or fertilized wheat is covered by natural vegetation. We have therefore neglected other croplands and urban or built-up land, which constitute less than 8% of the total land area (Table 1). [20] We use the fractional crop cover data sets from Donner [2003] and USDA state-level data on cropland area treated with N-fertilizer to describe the crop cover in the Mississippi River Basin from 1950 to 1994 (e.g., Figure 2). Donner [2003] determined the area of maize, soybean, and the three varieties of wheat in 1950, 1970, and 1992 at a latitude by longitude spatial resolution in the United States by synthesizing the planted area in each county [USDA, 2001a] with satellite-derived estimates of total cropland area of Ramankutty and Foley [1999a, 1999b]. The historical snapshots of crop distribution from Donner [2003] portray the massive increase in soybean cultivation and the shifting patterns of maize and wheat cultivation in the Mississippi since 1950 (Figure 3). For the purposes of this study, the fractional area of each crop for the intervening years was determined by linear interpolation. In each grid cell, it was assumed that the change in crop area occurred at a steady rate between each time period and there was no change in crop area since [21] The fractional fertilized area for each crop is determined from the fractional crop area and the percent of corn, soybean, and wheat treated with N-fertilizer according to state surveys [USDA, 1994, 2001a]. The vast majority of corn (>90% by area since the 1960s) and wheat (>75%) in the Mississippi Basin are treated with N-fertilizer, so for simplicity, unfertilized corn and wheat lands are assumed to be natural vegetation in the modeling system. However, since less than 20% of the soybeans in the Mississippi Basin are generally treated with N-fertilizer, both fertilized and unfertilized soybeans are explicitly simulated by IBIS. We divide maize and soybean area into monoculture and rotations by assuming that all maize and soybean in a given grid cell is subject to rotation if the crops each comprise more than 20% of the total grid cell area that year. The estimated extent of maize land subject to rotation in the major producing states in 1992 correlates well with USDA survey data [Padgitt et al., 1997]. [22] A small portion of the Upper Missouri Basin (simulated area of 26,721 km 2 ) lies in the Canadian provinces of Alberta and Saskatchewan, where spring wheat is the dominant crop. We estimated the fractional area of spring wheat in each Canadian grid cell during 1950, 1970, and 1992 by multiplying the fractional cropland area for that grid cell [Ramankutty and Foley, 1999a, 1999b] by the fraction of total cropland area in Alberta and Saskatchewan planted as spring wheat according to the 1993 Canadian census. The fractional area of spring wheat in each grid cell in the intervening years was determined using linear interpolation Nitrogen Fertilizer Data [23] Nitrogen fertilizer use is extremely heterogeneous, dependent on a variety of climatic, environmental and 4of21

5 Figure 2. Fractional area of (a) maize, (b) soybeans, (c) winter wheat, and (d) spring wheat in 1992 at resolution. socioeconomic factors. To simulate both local N cycling in crop systems and the variation in nitrate leaching to rivers across the Mississippi Basin, we must represent the spatial variation in the fertilizer application rates on maize, soybean, and wheat. However, no data set of N-fertilizer application rates for individual crops across the United States currently exists. Widespread data on fertilizer usage is available from surveys of farmer practices [e.g., USDA, 1994], which only report application rates by state, or from county-level estimates of fertilizer sales [e.g., Alexander and Smith, 1990], which do not report how fertilizer was apportioned to the crops. Neither data source provides sufficient information to generate a reliable high-resolution data set of the variation in fertilizer application rates across the Mississippi Basin over time that could serve as input for the modeling system. [24] As a compromise, we developed two likely scenarios of N-fertilizer application from 1950 to Rather than trust a single scenario as the truth, we employ both Figure 3. Change in Mississippi Basin cropland area from 1950 to The planted area of maize, soybean, and wheat (winter and spring) is derived from Donner [2003]. In this study, the small areas of unfertilized maize and wheat are treated as natural vegetation. 5of21

6 scenarios suggest the three major crops received 68 74% of the total N-fertilizer sold in the Mississippi River Basin from 1985 to 1994 (Tables 2a and 2b). The majority of this difference between our scenarios and fertilizer sales is likely non-agricultural fertilizer use, which could represent up to 20% of total fertilizer sales [Goolsby et al., 1999]. The remainder (<10%) is the fertilizer applied to crops like rice, cotton, and sorghum, mostly found in the Arkansas, Red, and Lower Mississippi Basins, that are not included in our modeling system [Donner, 2002]. It is also possible that a discrepancy exists between the reported fertilizer use in the USDA surveys and the reported fertilizer sales. [26] The use of homogeneous N-fertilizer application rates for each crop across each state, in Scenario One, or sub-basin, in Scenario Two, does hinder the ability of the modeling system to describe the finer-scale variability in N cycling. We tested the influence of spatial pattern in fertilizer use by randomly distributing maize N-fertilizer application rates in a series of 20-year IBIS and HYDRA simulations of the Upper Mississippi Basin [Donner, 2002]. While the pattern of nitrate leaching was sensitive to the distribution of fertilizer rates, the annual nitrate export from the basin was not sensitive, varying by less than 10% between the simulations and the control. Since the primary objective of this study is to evaluate the nitrate export from Figure 4. Nitrogen fertilizer application in 1992 (kg ha 1 ) in (a) Scenario One and (b) Scenario Two. The total nitrogen fertilizer application was determined from the individual crop application rates, defined by state in Scenario One and sub-basin in Scenario Two, and the resolution fractional crop area maps (see Figure 2). scenarios to better evaluate model sensitivity, the uncertainty in simulated N cycling and nitrate export, and the relationship between N-fertilizer use and N loading the river system. Scenario One uses application rates by crop for each state from agricultural survey data; Scenario Two uses application rates by crop for each major sub-basin, based on a combination of sales data and the relative application of N-fertilizer to the major crops. In each case, the application rates for maize, soybean, and wheat were determined for 1950, 1970, and 1992, to correspond with the original historical crop cover data sets (Figures 4 and 5). The rates for the intervening years were estimated using linear interpolation, assuming no change since 1985 [Padgitt et al., 1997]. For a more thorough description of the fertilizer scenarios, see Donner [2002]. [25] The scenarios present slightly different spatial patterns in modern-day N-fertilizer use and trends in N-fertilizer use over time (Figures 4a and 4b). The two Figure 5. Estimated total N-fertilizer use by basin in (a) and (b) The total nitrogen fertilizer application in each basin was determined from the individual crop application rates, defined by state in Scenario One and sub-basin in Scenario Two, and the resolution fractional crop area maps (see Figure 2). 6of21

7 Table 2a. Annual N-Fertilizer Application ( ), Scenario One Basin N-fertilizer Application, ton yr 1 Maize Soybean Wheat Total Percent of Sales Rate, kg ha 1 Upper Mississippi 487,760 4,537 14, ,529 85% 23.6 Upper Missouri 320,621 5, , ,714 86% 6.8 Lower Missouri 730,370 21, , ,775 69% 17.4 Upper Ohio 219,142 4,082 21, ,741 75% 9.9 Lower Ohio 494,485 15,681 49, ,111 67% 20.8 Middle Mississippi 1,277,960 22,802 73,052 1,373,814 93% 48.5 Arkansas 106,216 3, , ,073 72% 11.3 Red/Ouachita 38, , ,198 59% 7.4 Lower Mississippi 64,054 16,252 62, ,986 27% 6.9 Mississippi Basin 3,739,537 95,408 1,145,998 4,980,943 74% 15.4 the major Mississippi sub-basins, we concluded it is acceptable to use state-level or basin-level fertilizer application rates. However, further analysis of the spatial variation in fertilizer practice, and the influence on N losses and crop yields, is clearly warranted Other Nitrogen Inputs [27] In this section, we review the other sources of N in the model and contrast total N inputs to other estimates of the Mississippi Basin N budget Atmospheric Deposition [28] The deposition of nitrogen from the atmosphere is determined from the existing IBIS precipitation-based function [Kucharik et al., 2000], the observed change in N emissions since 1950, and the present-day spatial pattern in total N-deposition. The observed present-day rate of total N- deposition in each grid cell is obtained from the National Atmospheric Deposition Program (NADP) 2.5 km 2.5 km resolution map of present-day inorganic wet N deposition across the U.S. [National Atmospheric Deposition Program, 2001]. Dry deposition is assumed to be equal to 50% of wet deposition [Lawrence et al., 2000]. The annual rate of N-deposition from in each grid cell is then approximated by the present-day pattern and the change in national N emissions over time, due to industrial, power generation and motor vehicle emissions [U.S. Environmental Protection Agency, 2000]. The daily N-deposition rate is then determined from the annual rate and the distribution of precipitation over the year in that grid cell. The resulting estimated total N-deposition is highest in Middle Mississippi and Ohio sub-basins and within 10% of other published estimates for the period [Goolsby et al., 1999] Nitrogen Fixation by Legumes [29] Soybeans, which cover 23% of total cropland area in the Mississippi Basin (Table 1), are the only leguminous crop represented in IBIS. It is possible that other crops like alfalfa, dry beans, and non-alfalfa hay that together represent over 15% of the croplands in the Mississippi are responsible for much of the legume N-fixation [Burkart and James, 1999; Goolsby et al., 1999; Carey et al., 2001; Donner, 2002]. Assuming the soybean N-fixation rate for from Donner and Kucharik [2003], the total legume N-fixation simulated by IBIS for the Mississippi Basin would only be 25% of the estimate from Goolsby et al. [1999] that included alfalfa, non-alfalfa hay, lentils, peanuts, and dry beans. Excluding these other N- fixing crops may not have a substantial impact on the simulated N balance in this study. Nitrogen fixation by the microbes operating symbiotically with leguminous plants is moderated by the soil-n levels [Goolsby et al., 1999]. For example, a soybean plant could derive anywhere from 25% to 50% of its N from fixation, depending on the rate of soil N-mineralization and the inorganic-n availability [Gentry et al., 1998]. Since the crop will generally not acquire more N via fixation than is required for growth, the N that is derived from fixation should be almost entirely removed through uptake and crop harvest. The net effect of excluding unfertilized N-fixing crops like alfalfa on the simulated soil N balance, which is the term of most concern Table 2b. Annual N-Fertilizer Application ( ), Scenario Two a Basin N-fertilizer Application, ton yr 1 Maize Soybean Wheat Total Percent of Sales Rate, kg ha 1 Upper Mississippi 442,718 6,715 11, ,011 77% 21.5 Upper Missouri 247,758 4, , ,517 67% 5.3 Lower Missouri 713,431 10, , ,229 69% 17.4 Upper Ohio 238,141 8,508 23, ,104 83% 10.9 Lower Ohio 586,104 26,505 52, ,136 79% 24.7 Middle Mississippi 1,110,726 24,930 44,605 1,180,261 80% 41.6 Arkansas 88,200 2, , ,091 63% 9.7 Red/Ouachita 26,713 1,010 93, ,741 37% 4.6 Lower Mississippi 60,404 16,361 52, ,769 24% 6.2 Mississippi Basin 3,514, , ,600 4,650,858 68% 14.2 a The percent difference from Goolsby et al. [1999] estimates of fertilizer sales is shown. The large differences in the Lower Mississippi and Red/Ouachita basins are due to the exclusion of cotton and rice from the model. 7of21

8 in simulating N loss to the aquatic system, should therefore be small Other Anthropogenic Nitrogen Sources [30] Nitrogen from manure, non-agricultural fertilizer, fertilizer applied to other crops, and point sources are excluded from this study because of data and model limitations. Manure is the most significant omission, as Goolsby et al. [1999] estimated that it represented 14% of the total N inputs to the Mississippi Basin from 1980 to However, given the wide variability in both manure application and N content in manure (only 8% of U.S. croplands received manure applications from 1990 to 1997, and only 8% of that land was surveyed for nutrient content [Padgitt et al., 1997]), we chose to omit manure from this study. Point sources of N were also excluded, as they likely represent less than 1% of the total basin N inputs [Goolsby et al., 1999; Carey et al., 2001]. In sum, approximately 24% of the total estimated annual N anthropogenic inputs to the basin (excluding legume fixation differences) from 1980 to 1996, according to Goolsby et al. [1999], are excluded from this study. 3. Description of Simulations [31] All of the IBIS simulations were performed on a terrestrial grid across the Mississippi Basin with an hourly time step. The model was subjected to a 200-year spin-up period ( ) during which potential vegetation (i.e., vegetation that could exist without human intervention) competes for light and water in each grid cell in order to establish an equilibrium state for soil biogeochemistry and vegetation structure for natural ecosystems [Kucharik et al., 2000; Donner and Kucharik, 2003]. During this spin-up period, a 37-year climate record ( ) derived from the CRU monthly mean anomalies and NCEP daily anomalies are recycled six times. [32] A series of separate IBIS simulations were then conducted over the period , using the simulated soil and vegetations conditions from the final year of the natural vegetation spin-up period as the initial conditions. The model was executed nine times, each time assuming the entire basin was covered by a particular cropping system (continuous maize, continuous soybean, maize-soybean, soybean-maize (once each for fertilized and unfertilized soybeans), continuous winter wheat, or continuous spring wheat) for each fertilizer scenario. The crop simulations were driven by the CRU monthly mean data from 1950 to 1957, and the NCEP/ CRU combined climate data set from 1958 to The planting date for each crop was determined automatically across the basin according to local temperature thresholds (i.e., 10-day average soil temperatures at 10 cm depth and minimum air temperature). Fertilizer was applied as a pulse input to the surface each year at the time of planting, and volatilization losses were ignored. [33] The monthly results for the period from the various IBIS crop simulations and the final cycle of the natural vegetation simulation were integrated with the fractional crop cover data to determine the nitrogen, carbon, and water budgets and the inputs for HYDRA. Conducting multiple IBIS simulations and integrating the results in HYDRA offers greater computing flexibility and the option to investigate different land cover scenarios. Simulated surface runoff, subsurface drainage, and nitrate leaching served as inputs for the HYDRA simulations. HYDRA was executed twice under each fertilizer scenario, once with and once without benthic denitrification, at an hourly time step over the period. The output of nitrate export, denitrification losses, and river discharge during the period were averaged to monthly and annual values for analysis. [34] The results of the IBIS/HYDRA simulations of N cycling and nitrate flux across the Mississippi Basin from 1960 to 1994 are presented in the three following sections. In section 4, we compare simulated nitrate flux to the available historical estimates for the mouth of the Mississippi River and the major sub-basins to validate the modeling system. In section 5, we examine the impact of changes in cropping practices on N cycling and aquatic nitrate export across the Mississippi Basin from the 1960s to the 1990s. Finally, in section 6, we examine the impact of fertilizer use, climate, and soils on simulated nitrate leaching across the Mississippi Basin from 1985 to 1994 and identify the potential hot spots of nitrate leaching. 4. Simulated Nitrogen Flows [35] In this section, we compare the simulated annual and monthly nitrate flux across the Mississippi Basin with USGS estimates, first, near the mouth of the Mississippi River, and second, for the outlet of the eight major subbasins. The USGS developed time series of total N and nitrate flux for a series of river stations using a regression model and point measurements of total N and nitrate concentrations (available at midconherb). The record of nitrate flux for the Mississippi River at St. Francisville, Louisiana, just upstream of the Mississippi delta, begins in 1955; the nitrate flux record for most of the major sub-basins begins in the mid-1970s (standard error in annual flux estimates from each sub-basin is less than 10%). The difference between simulated and USGS estimated nitrate flux across the basin is used to evaluate the modeling system and illuminate likely sources of error. River discharge and other water budget terms are not specifically evaluated here, since the simulation of the Mississippi basin hydrology by the modeling system has been discussed in two recent studies [Lenters et al., 2000; Donner et al., 2002] Annual Nitrate Export by the Mississippi River [36] The simulated annual nitrate flux by the Mississippi from 1960 to 1994 at St. Francisville in both fertilizer scenarios is strongly correlated (r 2 = 0.85 with Scenario One, r 2 = 0.80 with Scenario Two) to USGS estimates (Figure 6). The index of agreement, a measure of the difference between observed and model simulated means and variances often used with hydrologic data similar to a correlation coefficient [Wilmott et al., 1985], also indicated significant correlation (d = 0.95 for Scenario One, d = 0.85 for Scenario Two). An autoregressive model confirmed that autocorrelation in the nitrate export time series does not influence the results of the regression. There is also a 8of21

9 Figure 6. Annual nitrate export (ton yr 1 ) by the Mississippi River at St Francisville, Louisiana, upstream of the mouth. Simulated and USGS estimated nitrate export for the period are displayed. highly significant correlation between simulated and USGS estimated annual nitrate concentration at St. Francisville (r 2 = 0.73 for Scenario One, r 2 = 0.64 for Scenario Two). [37] Both scenarios also capture the statistically significant increase in nitrate flux observed since the 1960s. In Scenario One, nitrate export increased by 29,575 tons yr 1 (r 2 = 0.50) from 1965 to 1994, which is 9% greater than the increase of 27,102 tons yr 1 (r 2 = 0.52) in the USGS time series. The greater trend in Scenario One is primarily due to the strong influence of the 1993 peak in nitrate export on the regression. In Scenario Two, the anomalous peak in 1993 is smaller than Scenario One, so the increasing trend of 25,793 tons yr 1 (r 2 = 0.58) from 1965 to 1994, 4% lower than the trend in the USGS time series. However, since mean annual nitrate export is lower in Scenario Two, the normalized annual rate of increase (annual trend divided by the mean) is greater than that of the USGS time series. [38] We expected the simulated nitrate export to be lower than USGS observations in both scenarios due to excluded fertilizer not applied to the three primary crops and manure, which could account for up to 24% of the anthropogenic N inputs in the 1980s and 1990s [Goolsby et al., 1999]. However, in Scenario One, mean annual export is 2% greater than the USGS estimates, due in part to the 33% overestimate of nitrate export during In Scenario Two, the mean annual export is 23% lower than the USGS estimate, but due mostly to lower fertilizer inputs and nitrate export from 1975 to The mean nitrate export in Scenario One and sharper relative trends in both scenarios indicates the modeling system underestimates the N retention capacity of the Mississippi Basin. During wet years like 1973 and especially 1993, simulated nitrate loading to the river system was overly responsive to greater rainfall and runoff resulting in exaggerated peaks in nitrate export. [39] The results are a considerable improvement over the previous application of the modeling system to the Mississippi Basin, in which the simulated nitrate leaching over time varied only as a function of runoff [Donner et al., 2002]. That study found a reasonable correlation between simulated and USGS estimated nitrate export at St. Francisville, but could only explain 25% of the increase in export from 1965 to Here, using a dynamic ecosystem model to describe the impact of climate, land cover, fertilizer use, and soils on terrestrial water, carbon and nitrogen cycling, we are able to more accurately depict the increase in nitrate export. [40] To further assess the accuracy of the modeling system, we contrast the simulated N budget in both scenarios (Table 3) with the estimated Mississippi N budget for the period from Goolsby et al. [1999] and other sources in the literature. As noted, the total simulated input of N to the Mississippi Basin (21.4 million tons yr 1 in Scenario One, 20.0 million tons yr 1 in Scenario Two), is lower than the Goolsby et al. [1999] estimate of total N inputs (23.1 million tons yr 1 ). The simulated N inputs are also more dominated by the net mineralization of organic matter since the Goolsby et al. [1999] estimate only considers net mineralization from agricultural lands. The simulated uptake of N by crops (8.2, 8.1 million tons yr 1 )is smaller than the Goolsby et al. [1999] estimate of N removal through crop harvest (8.6 million tons yr 1 ), as is expected given the exclusion of some crops from this study. [41] IBIS predicts that 12.6% of the N inputs to the Mississippi Basin in Scenario One (10.5% in Scenario Two) are either stored in the soil or leached to the river system in the form of DIN. The majority of this total residual N (leaching plus storage) is exported to the river system in the form of nitrate, with the remainder being stored in the soil or leaching as other forms of DIN. Annual soil storage is only 1% of total N inputs, ranging from near zero in the Middle Mississippi Basin to 2 3% in the Lower Missouri and Arkansas basins. Van Breemen et al. [2002] found that soil storage accounted for an average of 9% of N inputs in 16 northeastern U.S. river basins; however, a significant portion of the soil storage in that study occurred under row crops that had been converted to urban or suburban land. Here storage is a lower fraction of total residual-n in areas with high N fertilizer inputs, demonstrating that the storage capacity of soil tends to be overwhelmed in heavily fertilized lands. [42] In both scenarios, HYDRA estimates that 22% of the nitrate that entered the river system from 1980 to 1994, or 2% of the total basin N inputs, was lost via benthic denitrification before reaching the Gulf of Mexico. The annual rate of removal varies from a low of 18% during 1993 to a high of 28% during 1981, one of the five driest years since This result is at the high end of some other estimates (5 20%) for the Mississippi Basin [Howarth et al., 1996; Goolsby et al., 1999; Alexander et al., 2000] but below some estimates for other large river basins [Seitzinger and Kroeze, 1998; Seitzinger et al., 2002]. The simulated rate of nitrate removal is greater, particularly in the west, in this study than in that of Donner et al. [2002] due to improved representation of the area of substrate available for denitrification in low-flow streams. [43] The simulated proportion of total Mississippi Basin anthropogenic N inputs exported to the Gulf is similar to the result from Goolsby et al. [1999]. The total simulated export of nitrate to the Gulf (export by the Mississippi at St. Francisville, Louisiana, and the Atchafalaya at Melville, Louisiana) is 6.6% of the total N inputs in Scenario One and 9of21

10 Table 3. Simulated Mississippi Basin Annual Mean Nitrogen Budget, a Scenario One Scenario Two Budget Component Mean, kg ha 1 Contribution Percent Mean, kg ha 1 Contribution Percent Inputs Atmospheric deposition 6.8 ± % 6.0 ± % Fertilizer 14.5 ± % 13.7 ± % Net mineralization 42.1 ± % 41.7 ± % Crops 13.5 ± % 13.2 ± % Natural vegetation 28.6 ± % 28.6 ± % Fixation 2.8 ± % 2.8 ± % Outputs Plant uptake 57.5 ± % 57.1 ± % Crops 25.4 ± % 25.0 ± % Natural vegetation 32.1 ± % 32.1 ± % Leaching (DIN) 7.8 ± % 6.0 ± % Crops 6.4 ± % 4.7 ± % Natural vegetation 1.4 ± % 1.4 ± % Storage (annual) 0.5 ± % 0.7 ± % a The annual mean represents the basin-wide mean for the period. For comparison with the results of Goolsby et al. [1999], the 1994 data was counted included 3 times in the average. The standard deviation of the annual mean budget terms is also shown. 5.1% of total N inputs in Scenario Two from 1980 to Like denitrification, this fraction varied annually due to climate, from a low of % in dry 1988 to a high of % in Goolsby et al. [1999] determined that total N export was 7% of basin inputs; assuming that two thirds of river N is in the form of nitrate, the flux of nitrate to the Gulf was 5% of basin inputs, very similar to the result here. The strong agreement over the N processing capacity of the Mississippi Basin between the two studies, despite the radically different approaches, provides confidence in the ability of this modeling system to evaluate the fate of N inputs to a river basin Nitrate Flux From the Major Sub-Basins [44] The annual variability and relative trend in nitrate export from the major Mississippi sub-basins is reasonably well represented in both scenarios. The magnitude and interannual variability in nitrate export is best represented in the Upper Mississippi (Figure 7a) and Ohio sub-basins (Figures 7b and 7c). The simulated annual nitrate export from the Upper Mississippi is similar to the USGS estimates for (r 2 > 0.73 in both scenarios) and to the results of the previous application of the models [Donner and Kucharik, 2003]. The simulated annual increase in export from 1974 to 1994 (6006 ton yr 1 for Scenario One, 6145 ton yr 1 for Scenario Two) is only marginally greater than the annual increase in the USGS data (5882 ton yr 1 ). The model agreement in both the Upper Ohio and the entire Ohio basin is also strong; however, the simulations predict an increase in export from the Upper Ohio (4215 ton yr 1, 4718 ton yr 1,r 2 > 0.38 from 1976 to 1994) and the entire basin (9264 ton yr 1, 12,628 ton yr 1, r 2 > 0.55 from 1965 to 1994) which are not apparent in the USGS data (Figures 7b and 7c). The lack of an increasing trend in the USGS estimates of nitrate export by the Ohio River is surprising, given the well-documented increase in fertilizer use [USDA, 1994], river discharge [Donner et al., 2002], and NO x emissions [Lawrence et al., 2000]. Nitrogen cycling and historical water quality in the Ohio Basin is worthy of future analysis. Figure 7. Annual nitrate export (ton yr 1 ) for the (a) Mississippi River at Clinton, Iowa, (b) Ohio River at Owensboro, Ohio, and (c) Ohio River at Metropolis, Illinois. Results are shown for years in which USGS data was available. 10 of 21

11 Table 4. Mean Annual Nitrate Export, a USGS Estimate Simulation Basin NO 3 TN Scenario 1 Scenario 2 Upper Mississippi 113, , ,022 97,199 Upper Missouri 25,754 66,380 63,672 39,995 Lower Missouri 98, , , ,678 Upper Ohio 148, , , ,642 Lower Ohio 174, , , ,524 Middle Mississippi 287, , , ,886 Arkansas 21,600 64,441 89,500 59,792 Red/Ouachita 8,262 34,959 53,144 32,112 Lower Mississippi 78, ,622 81,996 66,176 a Nitrate export in ton yr 1 within each sub-basin is reported (i.e., export from the Lower Missouri is the export by the Missouri at Hermann, Missouri, minus the export at Omaha, Nebraska). [45] There is also reasonable agreement over the annual variability and relative trend in nitrate export between simulations and observations within the central and western sub-basins, despite the simulations overestimating nitrate export. Although simulated mean annual nitrate export by the Missouri River (at Hermann, Missouri) is more than double the USGS estimates (Table 4), there is a strong correlation between the USGS time series and both scenarios (r 2 = 0.82 and r 2 = 0.83 from 1973 to 1994). This is demonstrated by the anomalies in nitrate export (Figure 8a). Both scenarios also describe a weakly significant increase in export from 1976 to 1994 (8980 ton yr 1 for Scenario One, 8080 ton yr 1 for Scenario Two) that is double that of the USGS data (4098 ton yr 1 ). The trend, relative to the longterm mean, is still very similar between Scenario One (0.032 yr 1 ) and the USGS time series (0.037 yr 1 ). [46] The simulations also capture much of the annual variability in nitrate export by the Arkansas River (r 2 = 0.59 from 1976 to 1994 in both scenarios), despite vastly overestimating mean export (Figure 8b; Table 4). The simulations describe a much greater increase in export (2664 ton yr 1, 2133 ton yr 1 ) than the USGS time series (442 ton yr 1 ), but the trend is not significant at the 90% level in any of the time series when the anomalous 1993 peak is removed from the regression. A very similar relationship is observed in the Red River at Alexandria, Louisiana, the outlet of the Red/Ouachita Basin, and farther downstream in the Atchafalaya River at Melville, Louisiana, which includes flow from the Old River Diversion on the Mississippi (Figure 8c). [47] The largest portion of the simulated increase in total nitrate export to the Gulf (export by the Mississippi at St. Francisville and the Atchafalaya at Melville, Louisiana) over the time period (taken as to ) in both scenarios originated from the Middle Mississippi Basin (40% in Scenario One; 36% in Scenario Two), followed by the Lower Ohio (16%; 23%), Lower Missouri (15%; 13%), and Upper Mississippi (10%; 11%). As expected, the less cultivated sub-basins, like the Upper Missouri and the Upper Ohio, contributed less to the total increase. It should be noted that the contribution of the Red/ Ouachita (3%, 2%) and Lower Mississippi (1%; 1%) may be underestimated because rice and cotton cultivation are excluded from this study. However, the role of rice and cotton in the observed increase in nitrate export probably very minor, because there was a much smaller increase in rates of N-fertilizer application (14% increase on cotton from the mid-1960s to 1990s) on rice and cotton than on the crops in this study. [USDA, 1994]. [48] The modeling system predicts that the majority of nitrate exported to the Gulf under more current land cover and fertilizer use ( ) originates from the Middle Mississippi, Lower Ohio, and Lower Missouri sub-basins (Table 4). There is some clear disagreement with USGS estimates, due to the large predicted N loading in the Middle Mississippi Basin and the western sub-basins. The fertilizer input data is a considerable source of error, but only partially explains the discrepancies in simulated nitrate export. For example, although nitrate export from the western sub-basins was considerably lower in Scenario Two than Scenario One (32% lower in the Missouri, 33% lower in the Arkansas), it is still substantially greater than Figure 8. Annual nitrate export anomalies for the (a) Missouri River at Hermann, Missouri, (b) Arkansas River at Little Rock, Arkansas, and (c) Atchafalaya River at Melville, Louisiana (downstream of the Old River diversion). The normalized nitrate export anomalies are calculated as (annual - long-term mean)/long-term mean for each time series, at each river station. Anomalies are calculated for years in which USGS data was available. 11 of 21

12 Table 5. Simulated Annual Mean Nitrate Removal Due to Benthic Denitrification ( ), Scenario One Annual Removal, % Basin Mean Range Upper Mississippi Upper Missouri Lower Missouri Upper Ohio Lower Ohio Middle Mississippi Arkansas Red/Ouachita Lower Mississippi the USGS estimates (Table 4). While the use of IBIS to simulate terrestrial N cycling has dramatically improved our ability to represent the temporal and spatial variation in nitrate export by the river system, the regional errors highlight the challenge of simulating N cycling across a large basin with cells, given the heterogeneity of farm management practices and soil conditions Possible Sources of Error [49] The analysis of nitrate export across the Mississippi Basin indicates some potential limitations of the modeling system and input data in the central and western sub-basins. The crop models were first developed in conjunction with field research conducted in southern Wisconsin, and could be overly tuned to the soils and climate of the Upper Mississippi Basin. The overestimate of nitrate flux from the central and western Mississippi sub-basins, particularly during latter wet years, demonstrates the importance of explicitly representing terrestrial N cycling processes and in-stream nitrate retention in large-scale models and the challenge of accurately representing N inputs across a broad region. [50] First, the generic structure of soil N cycling in IBIS may primarily explain why simulated nitrate export is higher than expected, especially during wet years. The soil N cycle in IBIS does not explicitly represent nitrification, immobilization, or denitrification and assumes that nitrate leaching occurs below a homogeneous, assigned depth. Rather than simulate nitrification, IBIS maintains the pools of inorganic N in reach soil layer in a dynamic equilibrium (with 93% as soil inorganic N (ammonium) and 7% as leachable DIN (95% nitrate in crop systems)), based on field measurements in continuous maize plots in Wisconsin [Kucharik and Brye, 2003]. The removal of nitrate due to soil denitrification and the immobilization of organic-n (in the crop models) are both assumed to be small and contained within the calculation of net mineralization. Subsurface nitrate leaching is assumed to occur at 1.5 m depth, as in the previous application of IBIS to the shallow soils of the Upper Mississippi Basin. [51] The results of the model demonstrate the importance of explicitly representing these processes, especially denitrification, and the spatial variability in soil depth in largescale N cycling models. Denitrification in agricultural soils and adjacent riparian zones may provide a vital sink for residual N that would otherwise leach to the river system. For example, in a wet year like 1993, the rate of an aerobic process like nitrification would likely decrease, leading to less nitrate available for leaching. The available nitrate is also more likely to be denitrified due to more the anaerobic soil conditions. This is especially true in the deeper soils of the Middle Mississippi Basin (see the CONUS soil data set, available at where the extra time required for infiltration would permit more opportunities for denitrification. Not only would this result in lower nitrate export, it would increase the mean residence time of N in soils, affecting the seasonal variability of nitrate export. Future versions of IBIS and other large-scale biogeochemistry models should represent how these key N cycling processes vary with soil chemistry, hydrology, and geology. [52] Second, the simulation of nitrate loss during transport downstream is a likely source of error. HYDRA estimates that the relative removal of river nitrate via denitrification is highest in the upstream sub-basins, particularly in the west, due to greater opportunity for sediment contact and, often, greater nitrate concentrations, in low-flow streams (Table 5). The pattern is very similar to the predictions of in-stream N removal across the Mississippi Basin using SPARROW from Alexander et al. [2000] and predictions of loss due to denitrification in 16 northeastern U.S. river using Riv-N from Seitzinger et al. [2002]. However, the total percent removal in the low-flow rivers is smaller in this study; for example, Alexander et al. [2000] predicts 80% removal in the Arkansas Basin, more than twice the mean simulated removal for Direct comparisons with that study are problematic, since it predicted total N removal including processes like sedimentation that are important in dammed rivers, but the large difference in removal rates in the Arkansas suggests HYDRA may underestimate denitrification losses. [53] The simulation of denitrification is limited by the scale of the river network in this study and the inherent challenges of simulating a complex process like denitrification across a large region. In upstream regions not dominated by a high-order river, the simulated river is often an amalgam of the actual rivers and streams that wind through each grid cell. In these cases, HYDRA may underestimate the opportunity for river nitrate to contact the bottom sediments, and, in turn, the rate of benthic denitrification. The difference would be greatest in parts of the low relief Central Plains like the Arkansas Basin that feature widely meandering low-flow rivers. This issue could be addressed in the future by digitizing information on river sinuosity for use in HYDRA. Further refinement of simulated denitrification, however, will depend on improving our general understanding of the sensitivity of the rate of denitrification in the sediments to environmental variables. As more field studies of denitrification and in-stream nitrate removal emerge, it will become possible to develop more complex representation of denitrification for numerical models like HYDRA, incorporating other key variables like organic carbon availability, and thoroughly validate the results. [54] Third, the accuracy of simulated nitrate export is clearly limited by the quality of available N input data. The 12 of 21

13 some error is to be expected in the wheat-dominated western basins. For example, in these basins, USGS observations suggest nitrate represents a much smaller percentage of total river N than the Upper Midwest (Table 4). The model assumption that 95% of DIN leaching from croplands, based on measurement from maize fields [Donner and Kucharik, 2003], may not be applicable to wheat systems in the Central Plains. Figure 9. Percent contribution of each land cover class to (a) annual simulated plant N uptake and (b) annual simulated DIN leaching, in the Mississippi Basin under Scenario One. The percent contribution is the total for the area of the individual land cover class divided by the total for the entire basin. For example, the percent contribution of soybeans decreased over time in the simulations because the rate of increase in leaching from soybean fields was lower than the rate of increase in leaching from all land. change in anthropogenic N inputs over time from cultivation of the three major crops and atmospheric deposition are based on coarse data sets and broad assumptions about the details of agricultural management and the emissions trends. The uncertainty in simulated N inputs, as well as the exclusion of other input sources like manure and other fertilizer application, could be primary sources of error in some regions. First, the error in simulated nitrate export in the Middle Mississippi basin, especially in Scenario One, could be a result of overestimated rates of N-fertilizer application on maize; simulated fertilizer use in the Middle Mississippi is 93% of sales, the highest of the all the subbasins. Moreover, the simulated trend in nitrate export from the Ohio may be due to overestimating the contribution of increasing NO x emissions by power plants since 1950 to deposition within the Ohio Basin. Finally, the exclusion of rice and cotton from the modeling system led to a large underestimate of fertilizer application in the Lower Mississippi and Red/Ouachita basins. [55] Finally, it should also be noted that this is the first application of the spring and winter wheat submodels, so 5. Impact of Changing Land Use Practices [56] In this section, we examine the possible impact of increasing fertilizer application and change in crop cover since 1960 on N cycling across the Mississippi Basin. First, we compare the difference between the major N budget components between the and periods. Second, we examine the possible impact of fertilization practices on the seasonality of N leaching and nitrate export between the same two time periods. Finally, we examine the change in simulated N loss from the average maize, soybean, and wheat field in the Mississippi Basin Changes in the Mississippi Basin Nitrogen Budget [57] The modeling system demonstrates that the increase in fertilizer application and change in crop cover caused a profound change in N cycling in the Mississippi River Basin since In Scenario One, the application of N-fertilizer increased by 179%, over 3 million tons, from the period to the period. There was a small related 3% increase in net mineralization, as greater crop yields increased the input of plant residue to the soil; an increase in precipitation and nighttime air temperature may also have increased mineralization, both directly and by influencing crop growth. There also were increases in legume N-fixation from the expansion of soybean cultivation, and atmospheric deposition from the increase in NO x emissions. [58] By the period, the three primary crops accounted for almost half of the total plant N uptake (Figure 9a) and an astonishing 86% of the nitrate leaching (Figure 9b), despite covering only 20% of the land area. Mean annual nitrate leaching across the basin almost tripled, from 3.3 kg ha 1 yr 1 during the period to 9.6 kg ha 1 yr 1 during period (from 2.9 to 7.7 kg ha 1 yr 1 in Scenario Two). Under modern fertilizer and land cover during the period, the model predicts that almost two thirds of the leaching originates from maize systems, with the remaining third split evenly between the other croplands and natural vegetation (Figure 9b). The error analysis indicates that the contribution of croplands, mostly maize and wheat, may each be exaggerated by as much as 20%, due to simulated nitrate losses from central and western sub-basins. Even with the maximum error, the results clearly show that croplands, especially maize lands, now dominate the N cycle of the Mississippi Basin. [59] The increase in nitrate leaching is predominately due to an increase in N-fertilizer application on maize and wheat and more indirectly to the large increase in the area of soybean cultivation. The mean annual rate of fertilizer application on maize increased by approximately 74 kg ha 1 from the period to the period in Scenario 13 of 21

14 highly cultivated zone from Nebraska to Ohio, stretching north into Minnesota and south into Kentucky. There is also a large increase in leaching in parts of Oklahoma, Kansas, and Texas as well as along the Mississippi Valley. The geographic concentration of production in these areas between the 1960s and the 1990s came at the expense of production in other regions. As a consequence, there is actually a slight decrease in nitrate leaching in the western wheat-growing regions, due to a shift in wheat cultivation patterns, and in the southeastern states, due to the reduction in maize cultivation. Figure 10. Change in DIN leaching (kg ha 1 yr 1 ) from the period to period in (a) Scenario One and (b) Scenario Two. One (88 kg ha 1 in Scenario Two, which had lower maize fertilizer use in the 1960s), a total increase of over 2.2 million tons of N-fertilizer to the Mississippi Basin. The mean rate of application on spring and winter wheat increased by 41 kg ha 1 in Scenario One (33 kg ha 1 in Scenario Two), a total increase of over 900,000 tons between the two time periods. The majority of the extra fertilizer was applied to winter wheat, which comprises the vast majority of the wheat grown in the Mississippi Basin. The total increase in fertilizer use on soybeans was an order of magnitude smaller (slightly less 90,000 tons), despite a 68% increase in soybean area between the time periods, since less than 20% of soybean area is treated with fertilizer. However, this small change in total N-fertilizer application on soybeans obscures the key role that the increase in soybean area played in the increase in nitrate leaching. The expansion of soybean cultivation in the Corn Belt led to widespread maize-soybean rotation, in which residual soil-n from soybean N-fixation and fertilizer applied to corn enabled greater production of both crops. [60] The map of change in nitrate leaching across the Mississippi Basin reflects the change in fertilizer application on specific crops and the shift in cropping practices (Figure 10). As expected, the increase in nitrate leaching in both scenarios is greatest across the Corn Belt, a 5.2. Changes in the Seasonality of Nitrate Export [61] The large increase in nitrate export by the Mississippi River was accompanied by a substantial shift in seasonality [Rabalais et al., 2002; Justic et al., 2003]. There was no seasonal peak in N export to the Gulf in the early 1900s [Rabalais et al., 2002]. In the early 1960s, there was a small seasonal peak in nitrate export during March and April, but by the early 1990s, both simulated and USGS estimated nitrate export peaked sharply in May (Figure 11). The dramatic increase in nitrate export in late spring likely contributed to enhanced phytoplankton productivity in Gulf of Mexico, leading to the expansion of the seasonal hypoxic zone [Rabalais et al., 2001]. [62] This shift in seasonality appears to be a result of the increase in fertilizer application during the spring and the change in the seasonality of runoff. The majority of additional fertilizer applied in the latter period occurred at the time of planting of crops in the spring, both in the model and in reality [Padgitt et al., 1997]. The average simulated date of fertilizer application for maize, soybeans, and spring wheat, representing over 80% of the total N-fertilizer applied in the model, was in late April or early May during the period. The seasonality of runoff in the Mississippi Basin decreased since the 1960s [Baldwin and Lall, 1999], such that a greater proportion of runoff occurred in May during the 1990s. The coincidence of planting and fertilizer application with the forward shift in runoff probably fueled greater leaching of recently applied N-fertilizer and residual soil-n. The short water travel times in the central Mississippi River system suggests N-fertilizer Figure 11. Seasonal nitrate export (tons month 1 )bythe Mississippi River at St. Francisville, Louisiana, for the and periods. USGS estimates and Scenario One results are displayed. 14 of 21

15 Figure 12. Simulated annual rate of DIN leaching (kg ha 1 yr 1 ) from each crop system across the Mississippi Basin, , Scenario One. The annual leaching rate is the average for all fertilized cropland in the Mississippi Basin. applied to fields near the Mississippi River at spring planting in late April could reach St. Francisville, Louisiana, just upstream of the mouth, in only a few weeks. Better representation of the time lag in nitrate transport due to soil and in-stream N cycling processes is necessary to compute the difference between water and nitrate travel times. [63] There is evidence that changes in the climate of the central United States may continue to shift in the seasonality of nitrate export. The simulated average planting date for maize across several major growing states, including Illinois, Iowa, Minnesota, and Wisconsin, was up to 14 days earlier in the 1990s than in the 1960s due to increases in air temperature. Further increases in winter and spring air temperatures would permit even earlier planting of crops and earlier fertilizer application. The combination of change in the growing season and the seasonal water budget could lead to a further shift in the timing of nitrate leaching from agricultural fields Nitrogen Losses by Crop Type [64] The increase in N loading to the Mississippi River system since 1960 is clearly illustrated by the increase in simulated nitrate leaching from the average agricultural field (Figure 12). The simulated annual rate of nitrate leaching from the average maize field increased from 5.7 kg N ha 1 during the early 1960s to 87.4 kg N ha 1 during the early 1990s in Scenario One (5.7 kg N ha 1 to 67.4 kg N ha 1 in Scenario Two). The increase is exaggerated by some anomalously large simulated nitrate leaching rates from maize fields in the Middle Mississippi, Lower Missouri, and Arkansas Basins in 1993; excluding 1993, the average for the 1990s drops to 74.6 kg ha 1 in Scenario One and 58.0 kg ha 1. The variation across the basin is much greater during the early 1990s in both scenarios, with rates over 200 kg N ha 1 in northern Texas, between 30 and 100 kg N ha 1 across the highly fertilized Corn Belt, and less than 20 N kg ha 1 in parts of drier western Minnesota and North Dakota. The results for the Corn Belt, with the exception of 1993, fall within the range of field observations for maize fields from a number of field studies in that region [Reckhow et al., 1980; Randall and Mulla, 2001]. [65] There is also a substantial increase in simulated nitrate leaching from winter wheat, from 13.6 to 28.1 kg ha 1 in Scenario One (5.9 to 19.0 kg ha 1 in Scenario Two), due to a near doubling in the rates of fertilizer application. The change in nitrate leaching from the average spring wheat field (5.3 to 8.0 kg ha 1 in Scenario One, 5.3 to 7.5 kg ha 1 in Scenario Two) was much smaller, since spring wheat is grown exclusively in dry Northern Plains, where both plant growth and N losses are limited by a short growing season, poor soils, and low precipitation. Simulated nitrate leaching rates from winter wheat fields across the same region are also less than 10 kg N ha 1, the lower end of some field observations for cereals and grasslands [Reckhow et al., 1980; Johnes et al., 1996]. The simulated nitrate leaching rates from winter wheat increase to kg N ha 1 farther south in central Kansas and Oklahoma in Scenario One (10 40 kg N ha 1 in Scenario Two), the high end of field observations, and reach in excess of 100 kg N ha 1 at a few locations in northern Texas, Mississippi, and Louisiana due to wetter conditions and higher fertilization rates. [66] Like the other crops, there was also a large increase in the annual rate of nitrate leaching from the average fertilized soybean field (25.2 to 45.8 kg ha 1 in Scenario One, 18.6 to 35.6 kg ha 1 in Scenario Two). The increase in nitrate leaching rate was actually smaller than the increase in fertilizer application on soybeans (23 28 kg ha 1 ), since residual-n from the fertilizer applied to maize in the rotated systems leaches out during soybean cultivation. The difference between soybean and maize nitrate leaching rates is lower than in the previous application of the model because of the addition of crop rotations [Donner and Kucharik, 2003]. This result points to an opportunity to reduce total N-fertilizer application by rotating crops, as has been noted in field studies [Owens et al., 2000; Jaynes et al., 2001; Randall and Mulla, 2001]. Research specifically studying the effect of different rotations in IBIS and the nature of the soybean N credit [Jaynes et al., 2001] is necessary to clearly identify the potential benefits of crop rotations. 6. Patterns of Nitrate Leaching: Hot Spots From Fertilizer Use, Climate, and Soil Texture [67] In this section, we specifically evaluate the variation in nitrate leaching during the period across the Mississippi Basin. The period is selected because it featured consistent land cover and fertilizer input, but substantial climate variability, including the 1988 drought and the 1993 Mississippi flood. First, we identify the hot spots of simulated nitrate leaching, the regions with the greatest mean annual nitrate leaching. Second, we evaluate the relative influence of fertilizer use, climate, and soil texture on nitrate leaching across the Mississippi Basin. Finally, we examine the influence of variability in precipitation on the variability in nitrate leaching across the Mississippi Basin Hot Spots [68] In Scenario One, simulated annual mean nitrate leaching is greatest across the Corn Belt, especially central 15 of 21

16 Figure 13. Simulated mean annual nitrate leaching (kg ha 1 yr 1 ) from (a) Scenario One and (b) Scenario Two. Illinois and western Indiana (Figure 13a). Other hot spots include eastern Ohio; the lower Ohio River valley near the Kentucky, Illinois, and Indiana border; a stretch along the Mississippi River in southern Missouri; the Iowa-Missouri border along the Missouri River; and parts of both central Nebraska and central Oklahoma. The pattern in simulated mean annual nitrate leaching in Scenario Two is similar, except the central hot spot is shifted eastward toward Indiana (Figure 13b). [69] There are few other studies of the spatial variation in N loading to river systems across the United States available for comparison. In a modeling study of the central United States with EPIC, Wu and Babcock [1999] also predicted total N-leaching rates (runoff plus drainage inputs) in 1992 were greatest in a stretch across Illinois, Indiana, and Ohio. In a study of the N budget of the Mississippi Basin, Burkart and James [1999] predicted that residual total-n (all N available for leaching or storage) was greatest in the southern third of the Upper Mississippi Basin and parts of northern Indiana, Kansas, and Nebraska. In each study, the exact location of the nitrogen hot spots is influenced by the coarse resolution of the fertilizer input data and the model design. Here the overestimates of mean nitrate export from the Arkansas, Red/Ouachia, and Lower Missouri Basins implies that the hot spots in central Oklahoma, central Nebraska, and along the Iowa-Missouri border are due to model error. Also, the overestimate of nitrate export from the Middle Mississippi, especially in Scenario One, implies that the magnitude of the Illinois/Indiana hot spot could be exaggerated. [70] The key similarity between the two scenarios, and the other studies, is that spatial pattern of nitrate leaching resembles the pattern of N-fertilizer application. In both cases, less than one quarter of the basin has annual mean leaching rates greater than 10 kg ha 1, but that land contributes over 75% of the nitrate leached to the river system. The central Illinois/Indiana hot spot in Scenario One that features annual mean leaching rates greater than 40 kg ha 1 contributes almost 12% of total nitrate loading in the Mississippi Basin, despite representing less than 2% of the basin area (Figure 14). In reality, the distribution of nitrate loading to the river system should be even more nonlinear, due to wide variation of fertilizer application and land use activities within each of the grid cells studied here. [71] The simulations suggest that the hot spots can become even more prominent during some years due to climate. The distribution of leaching rates is much wider in 1993, when the central and upper portions of the Mississippi Basin experienced record late spring runoff (Figure 14). In Scenario One, the simulated leaching rates in the Illinois/ Indiana hot spot and the Nebraska hot spot are above 80 kg ha 1, more than double the average. Together, these lands contribute 22% of the simulated nitrate loading to the river system in 1993, despite representing less than 3% of the Mississippi Basin. In the drought year of 1988, the simulated nitrate leaching rates dropped below 10 kg ha 1 everywhere west of the Mississippi River, so the Illinois/Indiana hot spot contributed 30% of the total nitrate loading to the river system, despite itself experiencing simulated nitrate leaching rates 15 40% below the mean. Figure 14. Distribution of nitrate leaching in the Mississippi basin, Scenario One. A frequency distribution of grid cell nitrate leaching rates (kg ha 1 yr 1 ) was used to determine the contribution of land with different ranges of nitrate leaching to the total nitrate leaching in the Mississippi Basin. 16 of 21

17 Table 6. Relationship Between Simulated Mean Nitrate Leaching and Mean Precipitation, Fertilizer, kg ha 1 Number of Cells r 2 Slope Number of Cells r 2 Slope Scenario One Scenario Two > , , , , , , , , , , , , , , , , [72] The location of these nitrate leaching hot spots within the river network is also a crucial factor controlling nitrate export to the Gulf of Mexico. Nitrate loaded more directly into higher order rivers has far fewer opportunities to be denitrified during transport than nitrate entering into small headwater streams or tributaries far upstream [Seitzinger et al., 2002]. In this example, parts of the Illinois/Indiana hot spot and the smaller hot spots have short hydrological connections to the main stem of the Ohio, Missouri, and Mississippi rivers. The close proximity to the high-order rivers may give these hot spots a disproportionate influence on the nitrate eventually transported to the Gulf of Mexico Impact of Fertilizer, Climate, and Soil Texture [73] We use multiple regression analysis to examine the relative impact of N-fertilizer use, precipitation, and soil texture on simulated nitrate leaching in each grid cell across the Mississippi Basin during the period. As expected, N-fertilizer use is the primary factor influencing nitrate leaching in our modeling system. In both scenarios, the spatial variation in nitrate leaching is strongly positively correlated to total annual N-fertilizer application, a combination of the local crop cover and the annual mean N-fertilizer application rate on each crop. Total fertilizer application explains approximately two thirds of the variation in simulated annual mean nitrate leaching across the Mississippi Basin (r 2 = 0.78 in Scenario One, r 2 = 0.68 in Scenario Two, both significant at 99% level). The relationships suggest that on average across the Mississippi Basin, if annual N-fertilizer use increases by 10 kg ha 1, annual nitrate loading to the river system will increase by 3.3 to 4.3 kg ha 1. This leaching-fertilizer ratio is a composite of the response of different crops across the Mississippi basin to fertilizer application, but still lies within the range of observations in a number of field and modeling studies [Smith et al., 1997; Sogbedji et al., 2000; Jaynes et al., 2001; Kucharik and Brye, 2003]. [74] Precipitation and soil texture play secondary roles in determining the extent of nitrate leaching. The correlation between leaching and N-fertilizer application improves when mean annual precipitation is included in the regression (r 2 = 0.82; r 2 = 0.76). Regions with both high N-fertilizer application and high precipitation like southern Illinois, where annual precipitation is roughly 200 mm greater than in neighboring Iowa, tend to have the greatest nitrate leaching (Figure 13). The addition of soil texture causes only a very minor further improvement in the regression. Regions with high fraction of clay in the top 2 m of soil tend to experience less nitrate leaching, while regions with a high fraction of sand tend to experience more nitrate leaching. [75] To better isolate the role of precipitation and soil texture, we examined the relationship between these variables and nitrate leaching in grid cells with similar rates of total fertilizer application. In both fertilizer scenarios, the influence of precipitation on simulated nitrate leaching becomes greater at higher rates of fertilizer application (Table 6, Figure 15). For example, in the 192 grid cells in Scenario One with annual fertilizer application greater than 90 kg ha 1, a highly significant relationship (r 2 = 0.75) indicates that on average, a 100-mm increase in precipitation would cause a 5.6 kg ha 1 in nitrate leaching (Figure 15). However, in the 2317 grid cells with annual fertilizer application between 30 and 40 kg ha 1, the correlation is weaker (r 2 = 0.37) and the slope is more gradual (a 100-mm increase in precipitation leads to only a 1.5 kg ha 1 increase in leaching). A very similar pattern is evident in the results of Scenario Two. [76] Soil texture was a weaker tertiary factor in determining nitrate leaching in the simulations. The clay fraction in the upper 2 m of soil did appear to limit leaching losses in highly fertilized land. For example, in Scenario One, the clay fraction was negatively correlated with leaching in the grid cells with annual fertilizer application between 70 and 80 kg ha 1 (r 2 = 0.22, significant at 99% level). Previous studies have found higher rates of nitrate leaching in soils with high sand or silt fraction [Sogbedji et al., 2000, 2001]. The greater hydraulic conductivity of clay-poor soils results in greater subsurface runoff and leaching potential, which can lead to lower fertilizer use efficiency and greater nitrate leaching. Grid cells in Butler County, Kentucky, and Marion County, Illinois, have similar rates of total fertilizer application (67 kg ha 1 ), applied to maize and soybean, and precipitation (982 mm). However, the mean annual leaching rate in sandier Butler County (44% sand versus 16% sand in Marion) is one quarter greater than in Macoupin County (43 kg ha 1 versus 34 kg ha 1 ). There are only a few strong examples of the influence of soils on nitrate leaching in this study, since most heavily fertilized croplands are found on productive soils with low sand fractions Precipitation Variability and Nitrate Leaching [77] We use the coefficient of variation (CV) in annual nitrate leaching from 1985 to 1994 to identify the regions 17 of 21

18 Figure 15. Sensitivity of nitrate leaching to precipitation. The figure depicts the average simulated increase in nitrate leaching caused by a 100-mm increase in precipitation, determined from the slope of the regression of nitrate leaching and precipitation for all grid cells in the given range of total fertilizer application (see Table 6). that experience the greatest variability in nitrate leaching to the river system (Figure 16a). The results are extremely similar between the scenarios, suggesting that the temporal variation in leaching across the basin is due to climate variability (Figure 16b) rather than N-fertilizer application. There is a strong positive correlation between the annual precipitation CV and the nitrate leaching CV from 1985 to 1994 (r 2 = 0.39 for Scenario One, r 2 =0.49for Scenario Two, both significant at the 99% level, excluding grid cells with annual mean nitrate leaching less than 5 kg ha 1 ). Seasonally, the correlation between nitrate leaching CV and precipitation CV is strongest over the spring months of March, April, and May due to the influence of late winter snowmelt and early spring rainfall on runoff and leaching losses from fertilized fields (r 2 = 0.33, r 2 = 0.43, also significant at 99% level). In general, Figure 16. Coefficient of variation from 1985 to 1994 in (a) simulated annual nitrate leaching in Scenario One and (b) annual precipitation, over region the where nitrate leaching >4kg ha 1 yr of 21