Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA

Journal of Hydrology xxx (2016) xxx–xxx

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Research papers

Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA S. Tian a,⇑, M.A. Youssef a, R.P. Richards b, J. Liu c,d, D.B. Baker b, Y. Liu a a

Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA National Center for Water Quality Research, Heidelberg University Tiffin, OH 44883, USA c School of Environmental Science and Engineering, South University of Science and Technology of China, Shenzhen 518055, China d Ecosystems Services & Management Program, International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria b

a r t i c l e

i n f o

Article history: Received 13 October 2015 Received in revised form 29 July 2016 Accepted 23 August 2016 Available online xxxx This manuscript was handled by L. Charlet, Editor-in-Chief, with the assistance of M. Todd Walter, Associate Editor Keywords: Land use Land cover Climate change Nitrate export Agricultural lands Urbanization

a b s t r a c t Land use/land cover is a critical factor affecting temporal dynamics of nitrate export from watersheds. Based on a long-term (>30 years) water quality monitoring program in the Western Lake Erie area, United States, this study compared seasonal variation of nitrate export from an agricultural watershed and an urbanized watershed. A seasonality index was adapted to quantitatively characterize seasonal variation of nitrate export from the two watersheds. Results showed that monthly nitrate concentrations from the two watersheds exhibited different seasonal variation. Seasonality index of monthly nitrate loading for the agricultural watershed is approximately 3 times of that from the urbanized watershed and the difference is statistically significant (p < 0.0001). Meanwhile, calculated historical seasonality indexes of monthly nitrate loading for both watersheds exhibited significant (p < 0.05) decreasing trends according to the non-seasonal Mann-Kendall test. The identified differences in seasonal nitrate export from the two watersheds were mainly attributed to their distinct nitrogen sources, physical and biogeochemical settings. The declining seasonality index of monthly nitrate loading from the agricultural watershed could be partially caused by historical climate change in the study region, especially increased temperature during winter. Urbanization could be one key factor contributing to the declining seasonality index of monthly nitrate loading from the urbanized watershed. Information derived from this study have practical implications for developing proper management practices to mitigate nitrate pollution in Midwestern United States. Ó 2016 Elsevier B.V. All rights reserved.

1. Introduction Anthropogenic disturbances to natural nitrogen (N) cycles (Canfield et al., 2010; Galloway et al., 2003, 2004) commonly increase the export of biologically available N from terrestrial ecosystems to surface waters (Canfield et al., 2010; Liu et al., 2010; Vitousek et al., 1997). Excessive N loading impairs aquatic ecosystems by causing eutrophication (Carpenter et al., 1998; Conley et al., 2009) and acidification (Murdoch and Stoddard, 1992), resulting in losses of habitat function and biodiversity in surface waters (Allan, 2004), as well as deterioration of drinking water quality (Johnson et al., 2010). Impacts of human activities, including land use and land cover (LULC) changes and associated management practices, on terrestrial N losses are well recognized (Foley et al., 2005) and their legacies are long-lasting (Basu et al.,

⇑ Corresponding author. E-mail address: [email protected] (S. Tian).

2011; Goodale and Aber, 2001; Schlesinger, 2009; Sebilo et al., 2013). Thus, understanding the impacts of different LULC types on temporal dynamics of N export is fundamentally important for developing effective N reduction strategies and guiding watershed management practices. Changes in LULC, especially agricultural intensification and urbanization, elevate N loading to surface waters worldwide (Carpenter et al., 1998), through increasing N inputs (fertilizer application, wastewater effluent, atmospheric deposition, etc.) and/or decreasing watershed N retention capacities (Boyer et al., 2002; Caraco and Cole, 1999; Foster et al., 2003; Howarth et al., 2002; Liu et al., 2010). For instance, agricultural N-fertilizer application was found to be the predominant contributor of nitrate
 NO
1 export to surface waters (Howarth et al., 2011), such as 3 the Mississippi river (David et al., 2010; Gentry et al., 2014; McIsaac et al., 2002), Gulf of Mexico (Donner and Kucharik, 2008; Donner et al., 2004), the Great Lakes (Han and Allan, 2008), coastal waters in Europe (Bouraoui and Grizzetti, 2011),

http://dx.doi.org/10.1016/j.jhydrol.2016.08.042 0022-1694/Ó 2016 Elsevier B.V. All rights reserved.

Please cite this article in press as: Tian, S., et al. Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.08.042

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S. Tian et al. / Journal of Hydrology xxx (2016) xxx–xxx

and many surface water bodies in China (Ju et al., 2009). In addition to fertilizer application, the widespread use of artificial drainage in agricultural fields substantially alters the hydrology of naturally poorly drained soils, accelerating subsurface water

2. Materials and methods

movement and NO
1 export to adjacent surface waters (Blann 3 et al., 2009; Goolsby et al., 2000; Kennedy et al., 2012). Urban area is usually defined as ‘‘areas of intensive use with much of the land covered by structures” (Anderson and Anderson, 1976). Thus, urbanization usually is accompanied by increasing impervious area, replacement of natural vegetation or agricultural fields with lawns, and conveyance of storm water directly into surface waters. These modifications prohibit surface stored N from entering the soil profile and passing through zones of intense N retention or removal within watersheds (Bernhardt et al., 2008; Kaye et al., 2006), which could potentially change the amount, forms and seasonal pattern of N export from watersheds (Bernhardt et al., 2008; Groffman et al., 2004; Kaushal et al., 2006). For example, urbanization was found to be a key factor in decreasing N retention and

This study was carried out using data collected for two watersheds in the Lake Erie basin, one agricultural and one urbanized. The agricultural watershed is the Sandusky River (SRAG) watershed and the urbanized watershed is the Cuyahoga River (CRUB) watershed. The long-term mean annual precipitation is approximately 980 mm for CRUB and 900 mm for SRAG. The long-term mean annual temperature is around 9.3 °C and 9.7 °C for CRUB and SRAG, respectively. The long-term temporal patterns of monthly precipitation (Fig. SI-1) and temperature for both watersheds are comparable.

amplifying NO
1 export from several watersheds in Maryland, 3 USA (Kaushal et al., 2008). Urbanization also caused an increase in the proportion of N export that occurred during higher and less frequent discharge events in the Gwynns Falls watershed in the rural/suburban fringe of Baltimore County, Maryland, USA (Shields et al., 2008). Temporal dynamics of NO
1 3 export from watersheds with different LULC could reflect the inter-system differences with respect to intrinsic watershed properties (e.g. hydrological and biogeochemical settings) and external factors (e.g. climatic conditions, N inputs, etc.) (Barnes and Raymond, 2010). Differentiating and identifying the distinct behaviors of NO
1 export from different LULCs (e.g. 3 agriculture vs. urban land uses) could be fundamentally beneficial for developing proper management strategies and making policies for water quality protection at watershed and regional levels. Several recent studies investigated the effects of LULC on stream NO
1 dynamics during storm events (Poor and McDonnell, 2007; 3 Vidon et al., 2009) and concentration-discharge relationship (Goodridge and Melack, 2012). These studies have improved our understanding of the chemical and physical behavior of mineral N under different human activities. However, the paucity of findings for watersheds with different climatic settings impedes a better quantification of impacts of LULC on temporal dynamics of mineral N export. In addition, quantitative measures of seasonality of NO
1 3 export from different LULC using long-term historical data are not common due to limited data availability. The objectives of this study were to (1) quantitatively compare and characterize the long-term seasonal NO
1 export dynamics 3 (concentration and loading) from agricultural and urbanized (residential) watersheds in the Great Lakes area, and (2) investigate the underlying mechanisms controlling seasonal NO
1 3 export dynamics from the two watersheds. This study utilized existing long-term hydrology and water quality data from the Ohio Tributary Monitoring Program (NCWQR, 2013). Long-term (>30 years) daily measurements of riverine NO
1 concentration and discharge, and 3 monthly temperature, wet deposition, precipitation were used in the analysis. Although water quality management in the Great Lakes region focuses on phosphorus and largely ignores N, results from this study are of general importance for N management in Midwestern USA. Both watersheds selected for this study well represent conditions of the Upper Midwest, which contributes the major N loading to the Gulf of Mexico. More importantly, the availability of this long-term data set provides a unique opportunity for comparing, exploring, and understanding the dominant factors and processes affecting seasonal NO
1 export from watersheds with 3 different LULC.

2.1. Study sites

2.1.1. SRAG watershed The SRAG watershed (HUC: 04100011) is located in North central Ohio and drains into Lake Erie with a drainage area above Fremont, OH of 3240 km2. Two physiographic regions compose the watershed: the Lake Plains in the northern portion, and the till plains in the central and southern portions. The soils typically have silt loam and silty clay loam textures and are characterized as fine– silty, mixed (illitic), mesic Aeric (Typic) Epiaqualfs (OEPA, 2004b; Qi and Grunwald, 2005). Agriculture is the major land use in the SRAG watershed, accounting for about 80% of the total area (Richards et al., 2008) (Table SI-1). The main crops are soybean and corn, followed by wheat and hay. Tillage practices shifted from 86.0% conventional tillage in 1985 to 50.5% in 1995, as farmers increasingly adopted conservation tillage since the late 1980s (Richards and Grabow, 2003). Tile drainage is used extensively throughout the watershed (Qi and Grunwald, 2005). 2.1.2. CRUB watershed The CRUB watershed (HUC: 04110002) is relatively urbanized and located in the Erie-Ontario Lake Plain eco-region of northeast Ohio. The area of the watershed upstream of the gauging station of Independence (04208000) is about 1831 km2. There are three major physiographic regions within the watershed: the glaciated Allegheny Plateau comprising the largest area, and the till plains and lake plains making up the rest. Soils are mainly derived from glacial till and lacustrine deposits, and are light colored, acidic, and moderately to highly erodible. Land use (Table SI-1 and Fig. SI-2) patterns differ greatly from the upper basin that is primarily agricultural, to the lower basin that is densely populated and industrialized (Richards et al., 2008). The main land use type changed from agriculture (46%) in late 1970s and early 1980s to urban (45%) since 2000s according to records from USGS (Fig. SI-1). The middle and lower basins are influenced by municipal wastewater treatment plants (WWTP), urban and construction site outflow, and combined sewer overflows (CSOs). There are about 23 point sources of N (WWTP and industry effluents) throughout the CRUB watershed upstream of the USGS gauging station at Independence (OEPA, 2000, 2003, 2004a; Yuan et al., 2013). Specifically, the Cuyahoga River receives an effluent of about 0.34  107 m3 per day from the Akron WWTP, which can contribute approximately 43% of the total flow in the river during low flow conditions (OEPA, 2003). There are 3 major reservoirs in the Cuyahoga watershed: East Branch Reservoir, LaDue Reservoir, and Lake Rockwell Dam, which have a total capacity of 4.8  107 m3. 2.2. Data compilation The long-term discharge and NO
1 3 concentration data used in this study were obtained from the Ohio Tributary Monitoring

Please cite this article in press as: Tian, S., et al. Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.08.042

S. Tian et al. / Journal of Hydrology xxx (2016) xxx–xxx

program for CRUB and SRAG watersheds, which is operated by the National Center for Water Quality Research at Heidelberg University and supported by the Ohio Department of Natural Resources, Division of Soil and Water Conservation (NCWQR, 2013). Measurements are conducted at U.S. Geological Survey (USGS) gauging stations located near Fremont, Ohio (USGS 04198000) for SRAG, and at Independence, Ohio (USGS 04208000) for CRUB watershed. The water quality monitoring stations have been in continuous operation since 1982 and 1976 for CRUB and SRAG, respectively. The discharge data were derived from stage data provided by the USGS according to rating tables. Four water samples were taken daily before May 1988, thereafter three water quality samples were collected daily using refrigerated ISCO (Lincoln, NE) auto-samplers and returned to the analytical lab for measurements. During days with storm events, all water samples are analyzed; otherwise only one of these samples is analyzed. A total of 400–450 water samples
h i are typically analyzed per year. Nitrate-N concentration NO
1 3 is measured on water samples passing through a 0.45 lm membrane filter according to EPA method 300.1 (USEPA, 1979). More detailed description of data collection procedures can be found elsewhere (Richards and Baker, 2002). Meteorological data such as monthly precipitation, mean temperature, and wet deposition were used to explain the identified seasonality of stream discharge and NO
1 export from the two 3 watersheds. Specifically, monthly mean temperature and monthly precipitation were obtained from NOAA’s National Climatic Data Centre (NCDC). Nine and five weather stations were used for quantifying the spatial mean weather conditions for SRAG and CRUB watershed, respectively. The monthly NO
1 wet deposition data 3 were obtained from two stations (OH71 and OH17) belong to the National Atmospheric Deposition Program and National Trend Network (NADP/NTN). The monthly precipitation and temperature were collected for the whole study period, while the wet deposition data started in 1980. 2.3. Data analysis export from the two waterThe seasonality analysis of NO
1 3 sheds in this study was carried out on monthly basis. Monthly h i values were calculated as: volume-weighted mean NO
1 3

C¼

Pn i¼1 C i  t i  qi P n i¼1 t i  qi

where C is the volume-weighted mean concentration (mg L
1), Ci (mg L
1) and qi (L s
1) are measured concentration and discharge during time interval ti; the time interval ti represents half of the interval between the preceding sample i
1 and the following sample i + 1; n is the number of intervals of measurements during one month. Nitrate-N loading during each interval was calculated as the product of measured flow and concentration during each inter
 val. Monthly nitrate-N loading L NO
1 was calculated to repre3 sent the amount of nitrate passing a sampling station and moving downstream during one month. It was obtained by summing the losses during all intervals of that month. For months with gaps (<5%) of concentration measurements, the monthly loading was corrected by multiplying by the ratio of total monthly discharge h i to the discharge during the period with measured NO
1 . Monthly 3 discharge (Q, mm month
1) was converted to equivalent depth over the upstream catchment area and was calculated as USGS verified monthly discharge (expressed in units of volume per unit time) h i divided by the watershed area. The calculated monthly NO
1 , 3 L NO
1 3 , and Q were further normalized to make the seasonal

3

variations clearer and eliminate the effect of inter-annual changes of magnitudes. The normalized values were obtained by dividing variables of each month by the maximum monthly value during that year, making the normalized values vary from 0 to 1 during each year. In this study, a simple seasonality index (SI) (Walsh and Lawler, 1981) was adapted as a quantitative measure of the magnitude of seasonal variations of NO
1 3 export from the two watersheds. This index has been widely used to quantify seasonality of precipitation (Pryor and Schoof, 2008). Similar to the definition for precipitation (Walsh and Lawler, 1981), SI of L NO
1 3 is defined as the sum of the absolute deviation of monthly L NO
1 3 divided by the total annual L NO
1 3 :

SI ¼

 12   I X M 1
M   M i¼1 12

where SI is the seasonality index, M is the total annual NO
1 3 load, and Mi is the load in month i. This index varies from 0 (if all months have the same amount of export), to 1.83 (if all exports occur in a single month). It was recommended that SI 6 0:2 indicates even seasonal distribution, 0:2 < SI 6 0:4 indicates even seasonal distribution with large values in some months, 0:4 < SI 6 0:6 indicates a rather seasonal distribution with small values in some months, and SI > 0:6 indicates strong seasonal variation (Walsh and Lawler, 1981). The SI of monthly Q was also similarly calculated. In addition, to investigate the underlying causes of SI changes, contributions of individual seasons (spring, summer, fall, and winter) to the annual total L NO
1 3 and Q were calculated for both watersheds during the study period. The four seasons were defined as: Winter, November to January; Spring, February to April; Summer, May to July; and fall, August to October (Richards et al., 2008). A paired-sample two-tailed t-test was performed to determine significant differences between the calculated SI for SRAG and CRUB watersheds. The paired t test was conducted using the SAS statistical software (SAS Institute, Cary, NC). The significance of long-term trends for SI for each watershed was assessed using the non-seasonal Mann–Kendall test, which is well suited to distinguish between random fluctuations and monotonic trends (Hirsch and Slack, 1984). Following conventions were used to define the significance of statistical tests: nonsignificant when p > 0.1, marginally significant when 0.05 < p 6 0.1, and significant when p 6 0.05. 3. Results 3.1. Comparison of monthly NO
1 3 export and discharge from the two watersheds h i , Q and L NO
1 Since values of NO
1 3 3 were normalized to compare the seasonality of NO
1 3 export from the two watersheds, an overview of their actual magnitudes is given in this section. As export summarized in Table SI-2, the amount of monthly NO
1 3 from the two watersheds are clearly different. The mean monthly h i was 4.4 ± 2.1 mg L
1 for the SRAG, compared to the signifiNO
1 3 cantly lower (p < 0.002) concentration of 2.1 ± 0.5 mg L
1 for the h i in the CRUB is comparable to the CRUB watershed. The NO
1 3 h i
1 ranges of NO3 typically observed in urban stormwater runoff in the U.S. (Carey et al., 2013). Additionally, the mean monthly L NO
1 for CRUB watershed was 0.7 ± 0.1 kg ha
1 mo
1, which is 3 significantly (p < 0.001) less than the load for SRAG (1.5 ± 1.0 kg ha
1 mo
1). The two watersheds depict different

Please cite this article in press as: Tian, S., et al. Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.08.042

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S. Tian et al. / Journal of Hydrology xxx (2016) xxx–xxx

historical changes of annual NO
1 losses (Fig. SI-3). Specifically, 3 annual NO
1 3 losses from the agricultural watersheds depict large variations, while annual NO
1 3 losses from the urbanized watershed were rather stable during the study period. Seasonal variations of long-term monthly mean precipitation and discharge are comparable for SRAG and CRUB (Fig. SI-2). For both watersheds, peak precipitation usually occurs during summer from May to July, while the peak discharge typically happens during spring (Richards et al., 2008), especially in March. More specifically, the spring season accounts for 37% and 42% of the annual discharge from the CRUB and SRAG watersheds, respectively (Richards et al., 2008). Compared to SRAG watershed, the CRUB watershed received on average 12% more precipitation through the whole year, while it released 40% more discharge. The discharge-to-precipitation ratio on annual basis for CRUB watershed is about 0.53, slightly different for the SRAG watershed (0.42). 3.2. Seasonal variations of NO
1 3 export from the two watersheds h i from As illustrated in Fig. 1A, the long-term monthly NO
1 3 SRAG and CRUB watersheds depicts distinct seasonal variations. h i The highest monthly NO
1 in the SRAG watershed during each 3 year generally occurred from April to June, with occasional peaks h i during winter and early spring. In contrast, NO
1 in the CRUB 3 watershed peaked during July to October with occasional peaks h i h i of NO
1 during winter months. Peak NO
1 mainly occurred 3 3 prior to seasons with low discharge for the SRAG watershed, but occurred during low flow seasons for the CRUB watershed. Overall, h i peak monthly NO
1 from the CRUB watershed occurred about 3– 3 4 months later than that of SRAG. These findings in terms of the

h i timing of peak NO
1 are consistent with many studies in agricul3 tural watersheds in Midwestern United States (Arheimer and Liden, 2000; Royer et al., 2006; Vanni et al., 2001; Vidon et al., 2009; Yevenes and Mannaerts, 2011) and urbanized watersheds (Liu et al., 1997; Miltner et al., 1996; Petrone, 2010). h i In contrast to NO
1 trend, monthly L NO
1 from the two 3 3 watersheds exhibits similar seasonal variations with peak values commonly occurring during spring or winter (Fig. 1B). The similar seasonal dynamics of L NO
1 from the two watersheds were 3 mainly due to their comparable climate conditions (Fig. SI-1) and similar hydrological responses (Q) (Fig. 1C). Comparison between Fig. 1B and C showed clear overlaps of the critical moments (represented by red color) between monthly L NO
1 3 and monthly Q for both watersheds, revealing the core role of hydrology in delivering to surface waters. More specifically, monthly Q alone can NO
1 3 explain 65% of the variations in monthly L NO
1 3 from SRAG watershed, while it explains 87% for the CRUB watershed. The high correlation between monthly L NO
1 and Q in both watersheds is 3 comparable to findings of several other studies (Basu et al., 2011; Petrone, 2010; Tian et al., 2012). In spite of the similar seasonal trend of L NO
1 3 between the two watersheds, seasonal variations were much more pronounced for the SRAG watershed of L NO
1 3 as indicated by higher color contrast in Fig. 1B.

3.3. Changes in seasonality index (SI) Fig. 2 quantitatively shows SI for monthly L NO
1 3 and Q for the two watersheds. Long-term mean SI (±standard deviation) values are 0.82 ± 0.16 and 0.28 ± 0.08 for SRAG and CRUB, for L NO
1 3 respectively. The long-term mean SI of monthly Q from SRAG is 0.81 ± 0.19, compared to 0.51 ± 0.11 for CRUB. According to criteria

Fig. 1. Comparison between SRAG and CRUB watersheds with respect to temporal variations of (A) monthly NO
1 3 concentration


h

NO
1 3

i


 , (B) loading L NO
1 , and (C) 3

discharge (Q). All values presented in the figure were normalized. (For interpretation of the color in this figure, the reader is referred to the digital version of the article.).

Please cite this article in press as: Tian, S., et al. Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.08.042

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5



1 Fig. 2. The seasonality index (SI) of (A): monthly NO
1 and (B) monthly discharge (Q) for SRAG and CRUB watersheds. The solid and dashed lines, for CRUB 3 loading L NO3 and SRAG watersheds, respectively, are the best-fit regression lines and statistical significance is indicated by R2 and p values.

for grouping SI (Walsh and Lawler, 1981), both monthly L NO
1 3 and Q from SRAG watershed show strong seasonal variations. For the CRUB watershed, however, seasonal distribution of L NO
1 is 3 not pronounced, but monthly discharge is rather seasonal with small drainage in some months. SI of monthly L NO
1 3 and Q from the SRAG watershed are about 3 and 1.6 times respectively of those for the CRUB watershed. The difference are statically significant (p < 0.001) according to paired student t-test. This confirms the visual trends observed in Fig. 1 that the seasonal variations of both monthly L NO
1 3 and Q from the agricultural watershed were more evident than those of the urbanized watershed. As seen in Fig. 1, discharge is a critical factor regulating L NO
1 3 NO
1 3

for both watersheds. SI of L and Q for SRAG watershed are statistically significantly correlated (Pearson correlation coefficient of 0.67; p < 0.001) and their magnitudes are comparable (p = 0.7), confirming that seasonal variation in flow is a good indicator of seasonal variation of L NO
1 from the agricultural watershed. In 3 and Q contrast, the correlation coefficient between SI of L NO
1 3 for CRUB watershed is low (0.15), though statistically significant (p = 0.04). Meanwhile, the SI of monthly Q is significantly higher (p < 0.00001) than the SI of monthly L NO
1 for CRUB watershed. 3 This finding suggested that human activities in the urbanized watershed exerted more influence on the seasonal variation of than on Q, mainly through affecting seasonal changes of L NO
1 3 h i
1 NO3 (Fig. 1A). Another notable finding from Fig. 2 is that the SI of monthly L NO
1 and Q from both watersheds generally displayed a down3 ward trend. The best-fit linear regression equations listed in Fig. 2 indicate a weak statistical relationship (R2 < 0.2) due to large interannual variations over the study period. However, except for the slope of the regression equation for SI of Q in the CRUB watershed, slopes of the other regression equations (SI of L NO
1 3 for both watersheds and SI of Q for the SRAG watershed) are significantly less than zero (p < 0.05) according to the non-seasonal Mann-Kendall test. h i and Q for 3.4. Distinct relationships between mean monthly NO
1 3 the two watersheds Studies have shown that hydrology is the predominant factor regulating NO
1 losses to surface waters (Basu et al., 2011; 3

Donner et al., 2004; Ocampo et al., 2006; Poor and McDonnell, 2007; Tian et al., 2012). Given the high correlation between monthly L NO
1 and monthly Q (R2 = 0.65 and 0.87 for SRAG and 3 CRUB, respectively) for both watersheds, it is clear that the similar seasonal trend of L NO
1 3 was mainly due to the similar seasonal variation of monthly Q for the two watersheds (Fig. SI-1). However, h i showed similar seasonal variations as monthly Q monthly NO
1 3 did in the SRAG watershed, while they showed opposite seasonal dynamics in the CRUB watershed (Fig. 1A). To better understand h i the changes in NO
1 under different hydrological conditions, a 3 scatter plot is further used to depict the relationship between h i monthly Q and NO
1 (the Q-C relationship) for both watersheds 3 (Fig. 3). For SRAG watershed, the relationship follows a positively h i skewed distribution (Fig. 3A). Monthly NO
1 increased with Q 3 during months with lower flow, but decreased once monthly Q reached high monthly flow of around 30 mm mo
1. This finding suggests that both dilution and concentration effects could happen for this agricultural watershed. Poor and McDonnell (2007) found similar phenomena for an agricultural watershed based on data collected during several storm events (Poor and McDonnell, 2007). For the CRUB watershed, however, the relationship between h i monthly Q and NO
1 is logarithmic, indicating clear dilution phe3 nomena of NO
1 3 losses from this urbanized watershed. The dilution effect in suburban or urban areas have been commonly reported (Burns et al., 2009; Kaushal et al., 2011; Poor and h i continuously decrease with McDonnell, 2007). Monthly NO
1 3 monthly flow increasing until it reached a nearly constant concentration of around 1.1 mg L
1 (Fig. 3B). Whether the Q-C relationship of NO
1 losses from terrestrial ecosystems depicts dilution 3 or concentration is mainly controlled by two factors: its availability and mobility. Dilution phenomenon will be observed if NO
1 3 export is limited by its availability, while concentration effect will occur under mobility limited condition (Ocampo et al., 2006; Salmon et al., 2001; Schnabel et al., 1993). Therefore, NO
1 3 export from the agricultural watershed is mainly transport limited during most months, while it could be source limited for months with very high discharge. In contrast, the urbanized watershed is clearly source limited.

Please cite this article in press as: Tian, S., et al. Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.08.042

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S. Tian et al. / Journal of Hydrology xxx (2016) xxx–xxx

h i h i Fig. 3. A scatter plot of NO
1 versus discharge for (A): SRAG and (B) CRUB watersheds. The red cycles and black triangles in (A) are used to illustrate response of NO
1 under 3 3 low and high low regime. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

h i NO
1 . Compared to the sudden large inputs of N-fertilizer to 3

4.1. Why does NO
1 3 export from the two watersheds depict distinct seasonality?

the agricultural watershed, the nearly constant WWTP effluent during months with low flow in the CRUB watershed effectively

NO
1 3

Temporal dynamics of export from a watershed is primarily determined by the complicated interactions among sources, internal transformations, and hydrologic transport processes. The h i for the identified different seasonal variations of monthly NO
1 3 two watersheds can be primarily attributed to their different N sources (Table SI-3). The SRAG watershed receives about 75 kg ha
1 N from chemical fertilizer application and 13 kg ha
1 N from manure (Table SI-3), which account 90% of total N inputs. Thus, the amount and timing of fertilization is critical for the temporal dynamic of NO
1 export from agricultural watersheds (Richards 3 and Baker, 2002; Royer et al., 2006). In the SRAG watershed, famers tended to apply N fertilizer during spring prior to and/or after planting (personal communication with Dr. Tom Bruulsema from International Plant Nutrition Institute) N-fertilizer application immediately enriched the available NO
1 in soil profile, leading 3 to the observed concentration effects (Fig. 3). The timing of fertilh i in izer application also explains the observation that peak NO
1 3 SRAG watershed during late spring and early summer. In contrast, there are three main sources of N inputs to CRUB watershed: atmospheric deposition, fertilizer application to lawn and cropland portion of the watershed, and effluent of WWTP (Table SI-3 and Fig. SI-4). These three sources had comparable contributions to the total N inputs. However, contribution from WWTP effluent can range from less than 5% of the total N loading during high flow months to as high as 70% during low flow months (August to October) (OEPA, 2003). Compared to the other two sources, WWTP effluent is a dominant contributor to NO
1 3 export from the watershed under low flow condition because it is a direct discharge into the river. Given the clear dilution effects in the urbanized watershed (Fig. 3), it is thus expected that the monthly h i was high during the months with low discharge. The diluNO
1 3

mitigated the naturally low flow and L NO
1 3 . Therefore, the urbanized watershed depicts significantly lower SI of monthly L NO
1 3 . The physical settings of the two watersheds also contribute to h i . The SRAG the difference in the timing of peak monthly NO
1 3 watershed, like many other agricultural watersheds in Midwestern USA (Dinnes et al., 2002; Royer et al., 2006), is intensively tile drained, while the major alterations in the CRUB watershed are the increased fraction of impervious area and the existence of storm water culverts. In artificially drained agricultural fields, the drain tiles or ditches function as a ‘‘short-cut” and result in substantial loss of ‘‘leaked” water and agricultural nutrients to surface waters compared to pre-drained conditions (Blann et al., 2009). Impervious surfaces along with storm water culverts change the hydrologic response of urbanized watersheds and can effectively function as a ‘‘funnel” to convey accumulated pollutants on surface to surface waters (Grimm et al., 2008; Groffman et al., 2004; Kaye et al., 2006). Therefore, one similarity is that hydrological modifications in both agricultural and urbanized areas lead to decreased hydraulic retention time of nutrients, reducing the nutrient removal efficiency (Sudduth et al., 2013). However, the impervious surfaces and storm water conduits in urbanized watersheds totally prohibit the contact of nutrient rich water with soil particles, unlike tile drained agricultural fields where most of the water still passes through the soil profile before reaching the tile drains. In addition, the removal of vegetation in the impervious areas also reduces the biological assimilation capacity that usually peak during growing season (Canadell et al., 2007). Overall the reduced biological assimilation capacity in urbanized watersheds may h i partially contribute to the observed peak NO
1 during growing 3 season. Meanwhile, the decline of biological assimilation of NO
1 3 and evapotranspiration losses of water in urbanized watersheds (Canadell et al., 2007; Mao and Cherkauer, 2009), which might otherwise contribute to seasonal patterns, may partially contribute

tion effects in the CRUB watershed also compensated for the sea-

to the lower SI of monthly L NO
1 3 and Q from CRUB.

sonal variations of monthly Q, leading to lower SI for L NO
1 3 , compared to the SI for Q. Researchers have shown that lawn fertilization during fall season in South New England, USA quickly

4.2. Why does the SI of L NO
1 3 for both watersheds show a decreasing trend?

increased NO
1 losses (Guillard and Kopp, 2004; 3 leaching Mangiafico and Guillard, 2006). Therefore, lawn N-fertilizer that has been commonly applied in northern USA during fall (Miltner et al., 2004) could also partially contribute to subsequent peak

Summary of results is given in Table 1. Detailed figures and regression equations with statistical measures are included in supplementary materials (Figs. SI-5–SI-8).

Please cite this article in press as: Tian, S., et al. Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.08.042

7

S. Tian et al. / Journal of Hydrology xxx (2016) xxx–xxx Table 1 Changes in contribution (% year
1) of each season to total annual L NO
1 3 and Q, and wet deposition (WD) in SRAG and CRUB. Asterisks denote significant changes in seasonal contribution at 0.1 (⁄), 0.05 (⁄⁄).

SRAG

L NO
1 3 Q

CRUB

NO
1 3

(1980–2012)

L Q

WD

Winter

Spring

Summer

Fall

0.09


0.36⁄

0.20

0.07 0.02

0.17


0.38⁄

0.19


0.08


0.03


0.01

0.12⁄⁄


0.04


0.07


0.02

0.13

0.02

0.07


0.00


0.09

The decreasing SI for SRAG watershed was mainly caused by a declining contribution during spring and increasing contributions during the other three seasons. Spring contributions to annual total L NO
1 3 and Q from SRAG watershed decreased from approximately 50% at the beginning of the study period to about 38% for the last 10 years. The down trend of spring contributions, with a slope of about 0.36% per year for L NO
1 3 and 0.43% per year for Q, was marginally statistically significant (p = 0.09 for L NO
1 3 and p = 0.08 for Q). The decreasing spring contribution to total annual L NO
1 3 was accompanied with evident (0.2% year
1) increasing contribution in summer and minor (<0.1%) increasing contributions from the other two seasons (Table 1 and Figs. SI-5 and SI-6). The changes in seasonal contribution to total annual L NO
1 and to annual Q were 3 comparable (p > 0.15) based on t test. For CRUB watershed, the decreasing SI was mainly caused by NO
1 3 ,

noticeable (p = 0.03 for L p = 0.15 for Q) increase in fall contribution and minor decrease in contributions (<
0.08% year
1) during the other three seasons. Specifically, fall contribution to

of L NO
1 3 was caused, at least partly, by climate change. In this section, an analysis was conducted to investigate the historical changes of seasonal contribution to annual total precipitation and annual total wet deposition, as well as temporal changes of temperature during each season (Table 2). Changes in seasonal contributions to total annual precipitation for SRAG watershed were minor (<0.06% year
1) and insignificant (p > 0.3), while temperature increased evidently for all seasons. Specifically, air temperature during winter season significantly (p = 0.04) increased from
1.1 °C in 1970s to 0.5 °C in 2010s (Fig. SI-9). The rising temperature in winter is critical because it could potentially increase the rainfall portion of total precipitation and snow melt, yielding higher winter flow and lower spring flow, which may partially explain the decreasing spring contribution to total annual Q and L NO
1 3 . The changes in seasonal contribution to annual precipitation and in seasonal temperature for CRUB watershed are different from that of SRAG watershed. Seasonal temperature did not change clearly in CRUB watershed, except for summer with an average rise of 0.2 °C per decade. Among all seasons, only the fall contribution to annual precipitation for CRUB watershed evidently increased by 3% during the study period, and the increase is marginally significant (p = 0.07). 4.4. Implications Understanding seasonality of NO
1 input to surface waters is 3 critical for developing management practices to mitigate its negative environmental impacts. This study adapted a simple seasonal index to quantitatively compare and characterize seasonal variations of NO
1 3 losses from agricultural and urbanized watersheds.

annual L NO
1 increased from approximately 16% in 1980s to 3 about 22% in 2010s. Likewise, fall contribution to annual Q increased from 8% to 14% during the same period. In addition, lawn N-fertilizer application, which has been mainly applied during fall season, substantially increased during the past 30 years from 4.0 kg ha
1 to 8.5 kg ha
1 (Fig. SI-4). The increased lawn fertilizer application was accompanied by changes of land use from agricultural fields to urbanized area (Fig. SI-1). The combined impacts from increased fall contribution to annual Q and possible increased non-farm N-fertilizer application during fall may have explained

export were mainly attributed to different N variations of NO
1 3 sources, physical and biogeochemical settings for the two LULC types. These results suggest that it is necessary to consider differ-

the increased fall contribution to annual total L NO
1 3 . In addition, increased adoption of modern stormwater management practices in urbanized area (Groffman and Crawford, 2003; McPhillips and Walter, 2015; Walter et al., 2009) may also partially contributed

comprehensive management practices to reduce regional NO
1 3 loading (Bernhardt et al., 2008; Dinnes et al., 2002). The seasonal index we adapted was proven to be useful in quantitatively charac-

to the declining SI of L NO
1 3 for CRUB watershed. 4.3. Has climate change affected the long-term seasonality of NO
1 3 export? Studies have showed that Midwest USA has experienced clear climate change (changing precipitation patterns and increasing temperatures) and the trend will continue in the foreseeable future (Andresen et al., 2012; Hall et al., 2007). Many studies have stated that precipitation is a key factor regulating temporal dynamics of NO
1 export (de Wit et al., 2008; Hatfield and Prueger, 2004; 3 Kaushal et al., 2008; Randall and Mulla, 2001; Tian et al., 2012; Watmough et al., 2004). In addition, temperature is a widely recognized climatic factor controlling N losses from terrestrial ecosystems (Baron et al., 2009; Brookshire et al., 2011; de Wit et al., 2008; Park et al., 2003; Watmough et al., 2004). One study also illustrated that climate has a pronounced and sustained influence on nitrogen losses from large rivers (Howarth et al., 2012, 2006). It is a straightforward question to ask whether the decreasing SI

export from the Results showed that seasonal dynamics of NO
1 3 h i SRAG watershed and CRUB watershed are different. The NO
1 3 exhibited distinct seasonal variations (Fig. 1) and SI of monthly L NO
1 3 from the agricultural watershed was about 2 times higher than for the urbanized watershed (Fig. 2). Their distinct seasonal

ent seasonal NO
1 export from various LULC when developing 3

terizing the seasonality of NO
1 3 losses and conducting comparison between systems. It also enabled us to quantitatively detect the losses from watersheds by using changes in seasonality of NO
1 3 long-term data. The seasonality index is applicable to other water quality variables as well. Though the study watersheds drain to the Great Lakes where phosphorus is the main concern, N has been found as another contributing factor affecting summer eutrophication in Lake Erie (Chaffin et al., 2013). In addition, they are similar to other

Table 2 Changes in contribution (% year
1) of each season to total annual precipitation (P), and changes in seasonal mean temperature (T) is represented as °C/decade Asterisks denote significant changes at each season at 0.1 (⁄), 0.05 (⁄⁄). Winter

Spring

Summer

Fall

SRAG (1975–2012)

P T


0.02 0.4⁄⁄

0.03 0.3

0.05 0.2⁄


0.06 0.2⁄

CRUB (1982–2012)

P T


0.02 0.05

0.00 0.02


0.08 0.20

0.1⁄ 0.07

Please cite this article in press as: Tian, S., et al. Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.08.042

8

S. Tian et al. / Journal of Hydrology xxx (2016) xxx–xxx

Midwestern watersheds which drain to the Gulf of Mexico, where N is the primary management target. Many studies have shown that agricultural watersheds in the upper Midwest are major N source to lower Mississippi river (David et al., 2010; Gentry et al., 2014; Howarth et al., 2011; McIsaac et al., 2002) and Gulf of Mexico (Donner and Kucharik, 2008; Donner et al., 2004). Results of this study showed that the peak NO
1 3 losses from the agricultural watershed occurred during spring and early summer, which could be partially responsible for the largest extent of hypoxic zone in the Gulf of Mexico occurring during the summer of each year (David et al., 2013). This supports the awareness that agricultural watersheds must be targeted with management strategies to reduce NO
1 3 losses during the spring and early summer to the Mississippi River basin and the Gulf of Mexico, in spite of the existing biophysical and social barriers (David et al., 2013). In addition, this study illustrated that climate change could be one of the causes for declining SI of L NO
1 for the agricultural 3 watershed. The US Midwest is expected to experience strong climate change in the near future (Winkler et al., 2012). Annual precipitation, especially during winter, is projected to increase (Hayhoe et al., 2010). Annual mean temperature is expected to increase by approximately 3 °C by the end of this century (Winkler et al., 2012), with a larger increase during the winter and/or spring in the northern portion of the region (Hayhoe et al., 2010). These projected trends for temperature change are consistent with the historical trends we found for the two watersheds (Table 2). Therefore, impacts of future climate change on NO
1 3 losses from this region must be taken into consideration for developing mitigation measures by various stakeholders (Randall and Mulla, 2001; Robertson et al., 2013). More studies are needed to confirm whether the phenomenon we identified for the agricultural watershed is ubiquitous for the upper Midwest USA or not. If positively confirmed, consequences of this change on the eutrophication in the Great Lake region and Gulf of Mexico warrant further investigations. For instance, if summer contribution to total annual NO
1 losses from agricultural watersheds continues to increase, 3 the severity of the cyanobacterial blooms during summer in Lake Erie may become worse because low N availability constrained the late summer cyanobacterial blooms (Chaffin et al., 2013). Acknowledgments Thanks to Dr. Tom Bruulsema from International Plant Nutrition Institute for fertilizer application in Midwest of USA. We also appreciate Dr. Walter and two other anonymous reviewers for their pertinent and professional suggestions and comments, which are very helpful for further improvement of the quality of this paper. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.jhydrol.2016.08. 042. References Allan, J.D., 2004. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annu. Rev. Ecol. Evol. Syst. 35, 257–284. http://dx.doi.org/10.1146/ annurev.ecolsys.35.120202.110122. Anderson, J.R., Anderson, J.R., 1976. A Land use and Land Cover Classification System for use with Remote Sensor Data. U S Geological Survey Professional Paper 964. U.S. Govt. Print. Off., Washington, iii, 28pp. Andresen, J., Hilberg, S., Kunkel, K., Center, M.R.C., 2012. Historical Climate and Climate Trends in the Midwestern USA. Arheimer, B., Liden, R., 2000. Nitrogen and phosphorus concentrations from agricultural catchments - influence of spatial and temporal variables. J.

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Please cite this article in press as: Tian, S., et al. Different seasonality of nitrate export from an agricultural watershed and an urbanized watershed in Midwestern USA. J. Hydrol. (2016), http://dx.doi.org/10.1016/j.jhydrol.2016.08.042