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Jun 19, 2014 - We deployed sensors from April to December 2011 in a suburbanizing watershed (479 km2) to characterize storm-event nitrate-N (NO3–N) ...
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Characterizing Storm-Event Nitrate Fluxes in a Fifth Order Suburbanizing Watershed Using In Situ Sensors Richard O. Carey,*,†,‡ Wilfred M. Wollheim,†,‡ Gopal K. Mulukutla,‡ and Madeleine M. Mineau‡ †

Department of Natural Resources and Environment, University of New Hampshire, Durham, New Hampshire 03824, United States Earth Systems Research Center, University of New Hampshire, Durham, New Hampshire 03824, United States

‡

S Supporting Information *

ABSTRACT: Land use influences the distribution of nonpoint nitrogen (N) sources in urbanizing watersheds and storm events interact with these heterogeneous sources to expedite N transport to aquatic systems. In situ sensors provide high frequency and continuous measurements that may reflect storm-event N variability more accurately compared to grab samples. We deployed sensors from April to December 2011 in a suburbanizing watershed (479 km2) to characterize stormevent nitrate-N (NO3−N) and conductivity variability. NO3−N concentrations exhibited complex patterns both within and across storms and shifted from overall dilution (source limitation) before summer baseflows to subsequent periods of flushing (transport limitation). In contrast, conductivity generally diluted with increasing runoff. Despite diluted NO3−N concentrations, NO3−N fluxes consistently increased with flow. Sensor flux estimates for the entire deployment period were similar to estimates derived from weekly and monthly grab samples. However, significant differences in flux occurred at monthly time scales, which may have important implications for understanding impacts to temporally sensitive receiving waters. Evidence of both supply (nutrient-poor) and transport (nutrientrich) limitation patterns during storms is consistent with watersheds undergoing land use transitions. Tracking shifts in these patterns could indicate N accumulation in developing watersheds and help identify mitigation opportunities prior to N impairment.

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Temporal variability in watershed N fluxes may indicate interactions among different sources in addition to spatial and flowpath heterogeneity.1,4 For example, many studies have focused on storm hysteresis patterns, which are cyclical concentration-discharge relationships that develop when solute concentrations at a particular discharge rate differ during the rising and falling limb of storm hydrographs.16−18 Evans and Davies16 proposed a conceptual model based on catchment flowpath and characteristic concentrations within each flow path to explain the relative contribution of different sources (i.e., groundwater, soil water, and stormwater runoff) in commonly observed hysteresis patterns. Rose18 used this conceptual framework to investigate stormwater mixing dynamics in an urbanized headwater basin. In larger watersheds, hysteresis may reflect the distance of influential sources from the basin mouth as well as dominant flowpaths that deliver N during storms. Understanding N hysteresis patterns during storms in developing watersheds, and how these

INTRODUCTION

Nitrogen (N) fluxes in rivers draining human dominated watersheds reflect the mobilization of anthropogenic N and the capacity of aquatic ecosystems to regulate this pollutant.1−5 Watershed land use influences the distribution of nonpoint N sources (e.g., atmospheric deposition, excess fertilizer, septic systems)6−8 and storm events interact with these N sources to accelerate nonpoint N transport to rivers.9,10 Understanding storm events is therefore critical to understanding N fluxes in urbanizing watersheds, especially with ongoing and expected changes in precipitation rates.9,11,12 Quantifying N fluxes using periodic grab samples (e.g., weekly or monthly) can lead to inaccurate storm N export estimates,13,14 given hydrologic response time scales for shortterm storm events. Autosamplers have been used to collect samples throughout individual storms, but this approach generally focuses on only a few storms because of analytical costs and logistical difficulties. The recent development of in situ nitrate (NO3−N) sensors allows multiple storms to be characterized to better understand how N fluxes vary with storm event size across seasons.15 Sensors are especially useful to investigate N fluxes in urbanizing watersheds with complex land management and increasingly flashier hydrology. © 2014 American Chemical Society

Received: Revised: Accepted: Published: 7756

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Figure 1. A map of the Lamprey River watershed in southeastern New Hampshire, with 2006 land use/land cover, a USGS gaging station, and the sensor deployment site.

sensors and periodic grab samples; and (3) identify possible controls of storm event NO3−N patterns that could be used to better manage N in developing watersheds. We tested the following hypotheses regarding storm fluxes in urbanizing watersheds: (1) alternating supply and transport limitation of NO3−N occurs due to changing importance of nonpoint sources, producing varying storm patterns across different seasons; and (2) continuous in situ NO3−N sensors provide improved estimates of N fluxes over periodic grab samples that may misrepresent the range of NO3−N concentrations during storms.

patterns vary over time for different storms, may therefore reveal useful information about N source variability.16,19 Watersheds transitioning from rural to urban land uses may have considerable temporal variability in concentrations that reflects shifts in the relative influence of each type of land use and N sources across flow conditions and seasons. Basu et al.20 suggested relatively stable interannual chemical concentrations in large, heavily impacted watersheds produced an effective biogeochemical stationarity (reduced variability) due to the accumulation of solutes associated with legacy sources and concentrations exceeding saturation thresholds. Annual flowweighted concentrations in these impacted watersheds remain consistent over time and result in transport limited nutrient exports.20 Tracking N fluxes in less impacted, transitional watersheds with in situ sensors could reveal evidence of impending stationarity due to seasonal N inputs, accumulation of N in various flowpaths (e.g., groundwater), shifts between supply (nutrient-poor) and transport (nutrient-rich) limitation of N exports, and discharge variability. In southeastern New Hampshire, the Great Bay estuary was recently classified as N-impaired by the U.S. Environmental Protection Agency (USEPA). Increasing N loads have resulted in nuisance macroalgal growth and the reduction of both eelgrass coverage and adult oyster populations.21 Point and nonpoint sources both contribute to excess N loading to the bay, but increased total N loads in recent years have been primarily attributed to nonpoint pollution and stormwater runoff.21 The increased intensity and frequency of storm events in recent years have also altered historical hydrological patterns in the region.22 Understanding the influence of storm events on watershed N exports is therefore an important aspect of developing strategies to reduce total N loads delivered to the bay and estuaries in general. In this study we used an in situ NO3−N sensor to (1) characterize seasonal and storm-event NO3−N variability in a suburbanizing, fifth order coastal watershed in New Hampshire; (2) compare the accuracy of NO3−N flux estimates using

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METHODS

Study Site. The Lamprey River at the sensor site drains an approximate watershed area of 479 km2 (Figure 1). Forested (64%) and wetland (20%) land cover classes are predominant in the watershed; suburban/developed areas and agricultural land uses represent 9% and 7% of the watershed area, respectively. However, the watershed is experiencing rapid land use change due to ongoing suburbanization.23 Continuous in situ measurements of aquatic chemistry were collected on the main stem of the river (43.105556, −70.947778), 7.6 km upstream of the basin mouth (Figure 1), at 30 min intervals from April to December 2011. The U.S. Geological Survey (USGS) has a gaging station (USGS 01073500) located approximately 500 m upstream (43.1025, −70.953056) from the deployment location (Figure 1). We used discharge data (15 min intervals) from the gaging station, and assumed flows were similar at our sensor location. Daily precipitation data for the watershed were obtained from the National Weather Service, National Oceanic and Atmospheric Administration. In Situ Sensors. An optical sensor (Submersible Ultraviolet Nitrate Analyzer; Satlantic, Halifax, NS, Canada), with a manufacturer provided analytical linear range of −5 μM to +100 μM (−0.71 mg L−1 to 1.43 mg L−1), was used to measure continuous NO3−N concentrations at the study site from April 7757

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e

04−29 05−07 05−28 06−16 06−30 08−14 08−23 09−03 09−13 09−29 10−04 10−11 10−19 10−25 11−08 11−18 11−29

date end 6 3 14 5 8 6 8 9 6 7 5 7 6 6 12 8 6

durationa (days) 5 7 14 6 39 1 2 4 9 0 0 2 0 2 2 5

days between storms 23.16 11.03 79.91 17.48 43.90 26.34 51.05 68.27 29.77 30.11 35.74 39.00 28.31 23.50 46.04 29.71 32.99

precipitationb (mm) 1.01 0.99 1.02 0.98 1.01 0.92 1.03 1.01 1.00 1.05 1.08 1.05 1.01 0.99 0.99 1.02 1.00

precipitation skewnessc min 12.43 7.53 4.87 3.17 2.21 0.37 0.59 0.76 1.73 1.27 2.27 6.23 5.38 9.15 7.53 11.47 10.02

18.24 10.93 36.81 7.96 14.67 1.16 3.68 14.16 7.96 3.71 8.30 23.73 15.72 17.70 19.65 24.95 20.53

max 5.80 3.40 31.94 4.79 12.46 0.79 3.09 13.39 6.23 2.44 6.03 17.50 10.34 8.55 12.12 13.48 10.51

range

min 30.73 38.27 20.29 115.35 70.13 13.29 23.00 19.97 59.34 84.82 60.85 37.52 24.60 31.99 45.89 38.94 59.43

45.70 61.20 78.75 152.59 181.34 44.11 71.54 103.43 114.39 139.60 114.75 102.28 74.39 57.83 78.49 74.22 99.25

max

NO3-N (μg L−1)e 14.96 22.93 58.45 37.25 111.20 30.82 48.55 83.47 55.05 54.78 53.89 64.76 49.79 25.83 32.60 35.27 39.82

range

season spring spring spring early summer early summer late summer late summer late summer early fall early fall early fall early fall early fall early fall mid/late fall mid/late fall mid/late fall

Hydrograph response period for storms. bTotal watershed precipitation. cAn index representing the spatial distribution of precipitation. dThe minimum, maximum, and range of discharge measurements. The minimum, maximum, and range of NO3−N concentrations.

04−23 05−04 05−14 06−11 06−22 08−08 08−15 08−25 09−07 09−22 09−29 10−04 10−13 10−19 10−27 11−10 11−23

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

a

date start

storms

discharge (m3 s−1)d

Table 1. Characteristics of 17 Storms Monitored with Deployed In Situ Sensors in the Lamprey River Watershed from April to December 2011

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Table 2. Concentration-Discharge Characteristics for NO3−N Concentrations and Specific Conductivity during 17 Storms Monitored with Deployed In Situ Sensors in the Lamprey River Watershed from April to December 2011 storms

date start

date end

NO3-N overall patterna

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

04−23 05−04 05−14 06−11 06−22 08−08 08−15 08−25 09−07 09−22 09−29 10−04 10−13 10−19 10−27 11−10 11−23

04−29 05−07 05−28 06−16 06−30 08−14 08−23 09−03 09−13 09−29 10−04 10−11 10−19 10−25 11−08 11−18 11−29

incomplete data C (--) C (--) C (+) C (--) A (+) A (+) A (+) C (+) C (+) C (--) C (--) C (--) C (+) C (+) C (--) C (--)

NO3-N pulse on rising limbb

sp. cond. overall patterna

sp. cond. pulse on rising limbb

season

yes yes yes yes yes no yes yes no yes yes yes yes no yes yes

C (--) A (--) C (--) C (--) C (--) C (--) C (--) A (--) A (+) A (+) C (--) C (--) ? (--) A (--) C (--) C (--) C (--)

no no yes no yes no no no no no no no yes no no no no

spring spring spring early summer early summer late summer late summer late summer early fall early fall early fall early fall early fall early fall mid/late fall mid/late fall mid/late fall

a NO3−N and specific conductivity characteristics during storms. C: Clockwise hysteresis pattern; A: Anticlockwise hysteresis pattern; (+): Flushing (increased concentrations over an entire storm hydrograph); (--): Dilution (decreased concentrations over an entire storm hydrograph); ?: Inconclusive. bShort-term increased concentrations on the rising limb of storm hydrographs.

the offset was 127.0 ug L−1 (n: 15). This break straddled a long period of low flows, suggesting a different chemical matrix preand post baseflow. The offsets were however constant within each period across the NO3−N concentration (i.e., the slope was approximately one), and the residuals were not correlated with discharge, allowing us to make a simple correction in absolute NO3−N. Separate regression equations were developed to correct the NO3−N sensor data for the two periods: (1) April to late August (R2: 0.94; corrected sensor data [ug L−1] = (0.8) * (raw sensor data ug L−1) − 73.1 ug L−1) and (2) late August to December (R2: 0.85; corrected sensor data [ug L−1] = (0.9) * (raw sensor data ug L−1) − 127.0 ug L−1). Using these two periods, the relationship between continuous, adjusted NO3−N sensor data and grab sample NO3−N concentrations for the deployment period (April to December 2011) produced an R2 value of 0.93; grab sample data [ug L−1] = (1) * (corrected sensor data ug L−1) − 9.1 × 10−14 ug L−1) (Supporting Information, Figure S1). Grab Samples. Weekly grab samples were collected at the study site from April to December 2011 to evaluate the accuracy of in situ NO3−N measurements (see the Supporting Information for quality assurance/quality control procedures) and to estimate N fluxes during the deployment period. Additional grab samples were collected during the rising limb of storm hydrographs to independently confirm stormflow NO3− N measurements. Samples for dissolved constituents were filtered in the field using precombusted (5+ hours at 450 °C) glass fiber filters (0.7 μm; Whatman Ltd., Maidstone, UK). Acid-washed high-density polyethylene (HDPE) 60 mL bottles were triple-rinsed with river water prior to sample collection and frozen within hours for later analysis. The Water Quality Analysis Laboratory at the University of New Hampshire analyzed grab samples for NO3−N (EPA method 353.2). Data Analysis. All statistical analyses, unless otherwise specified, were conducted in the statistical software package R (R Foundation for Statistical Computing; Vienna, Austria). Weekly and monthly NO3−N fluxes were estimated using grab

to December 2011. Nitrate ions absorb ultraviolet light in the wavelength range of 200 to 240 nm, with a strong peak observed at 220 nm;24 the sensor estimates NO3 −N concentrations after accounting for other sources that absorb ultraviolet light in the same wavelength range (dissolved organic carbon, bromide, etc.).25 The sensor used in this study was configured to estimate NO3−N concentrations by examining absorbance in the 217 to 240 nm manufacturer recommended range. The sensor was deployed with a copper biofouling guard to reduce measurement interference due to sediment and biological growth. Sensor optics were also cleaned weekly with cotton swabs and isopropyl alcohol to maximize measurement accuracy. The instrument was checked for accuracy prior to deployment using deionized water and a range of NO3−N standards. Blank measurements within a range of ±0.03 mg L−1 were used as a threshold to indicate proper calibration. During the deployment, we routinely checked for instrument drift with deionized water and NO3− N standards. A data logger (CR 1000; Campbell Scientific, Logan, UT) was used with the sensor for data collection and storage. Regular grab samples were collected to validate sensor measurements (see below). A multiprobe water quality sonde (Hydrolab MS5; Hach Hydromet, Loveland, CO) was used to simultaneously measure specific conductivity. The specific conductivity probe was calibrated prior to deployment and checked periodically to ensure accurate measurements. The Hydrolab sonde was also cleaned weekly with deionized water to reduce biofouling. Nitrate Sensor Calibration. Sensor reported NO3−N concentrations were consistently higher than grab sample concentrations, indicating a slight positive bias in the sensor measurement. In addition, the difference between the sensor reported NO3−N concentrations and grab samples changed slightly during the middle of the deployment. NO3−N concentrations from the sensor between April and late August were on average 73.1 ug L−1 greater than grab sample concentrations (n: 27). For the remainder of the deployment 7759

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Figure 2. Example of storm hysteresis patterns for nitrate concentrations and specific conductivity before (Storm 5) and after (Storm 12) the dry period in the Lamprey River watershed. Each figure includes locally weighted scatter plot smoothing regression lines and data points on the rising (green triangles) and falling (red dots) limbs of storm hydrographs. Overall dilution, but short-term flushing on the rising limb, is evident for (a), (b), and (c).

Figure 3. Concentration vs storm flow (as runoff) hysteresis patterns for (a) nitrate concentrations and (b) specific conductivity for all 17 storms between April and December 2011 in the Lamprey River watershed. The numbers on the plots refer to particular storms and the dots indicate the rising limb of storm hydrographs. In (a), storms 12 and 15 are obscured by storms 16 and 17.

using discharge measurements from the nearby USGS gaging station and a partitioning of the baseflow using a recursive digital filter approach (see Eckhardt27 and the Supporting Information). In situ sensor data were used with storm event discharge data to compare the direction of concentration change during storms (increasing [flushing] or decreasing [dilution]), concentration differences between rising and falling limbs (i.e., considering the maximum and minimum concentrations), and rotation pattern (i.e., clockwise or anticlockwise) of concentration-discharge hysteresis loops. We also conducted analyses to evaluate the spatial distribution within the watershed of both different land uses (land use weighted

sample data in conjunction with daily mean discharge measurements, and the USGS Load Estimator (LOADEST) software (see Runkel et al.26 and the Supporting Information). We used two approaches to estimate fluxes: (1) weekly grab samples (including additional samples during storms) and (2) monthly grab samples (one sample collected during the middle of each month). NO3−N fluxes estimated from weekly and monthly grab samples were compared with flux calculations using half-hourly measurements from the in situ NO3−N sensor and instantaneous discharge measurements. The rising and recession limbs of the 17 storm hydrographs during the eight-month deployment period were delineated 7760

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(Table 2). Five of the ten storms with contrasting storm responses for NO3−N and conductivity occurred immediately after the low flow period in early summer. Flux Estimates Using In Situ Sensors. Despite NO3−N concentrations diluting during a few storms, elevated fluxes are consistently evident during storms (Supporting Information, Figures S2 and S3). However, the relationship varied seasonally, with summer having the highest rate of flux for a given level of discharge based on the coefficients in a power function fit to the flux data during different periods (Supporting Information, Figure S3). The constants and storm responses (slopes) for different periods indicated the lowest rate of flux relative to discharge occurred during spring and the rate of change during storms was similar during summer and fall. Maximum fluxes during each season were similar (spring: 153 kg day−1; summer: 142 kg day−1; fall: 167 kg day−1), suggesting the same amount of NO3−N is available for mobilization regardless of flow. Estimates of NO3−N flux at monthly time scales differed with measurement frequency (Table 3). Total NO3−N flux

mean index) and precipitation (skewness index) during each storm event (see the Supporting Information).

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RESULTS Storm-Event Characteristics. Storm event characteristics (i.e., timing, duration, precipitation, discharge, NO3−N concentrations, etc.) are reported in Table 1. Baseline discharge (Discharge Min; Table 1) generally declined from the beginning of sensor deployment in April to early August and increased between August and December (Supporting Information, Figure S2). The NO3−N sensor and Hydrolab sonde provided data for 17 storms during the study, with incomplete NO3−N data for the first storm (Table 2). The hysteresis patterns for NO3−N and conductivity generally exhibited clockwise rotation (Figure 2), indicating later flows during storms provided lower concentrations than the initial runoff. However, during three successive storms in mid-August following the low flow period, NO3−N hysteresis patterns were anticlockwise (Table 2). NO3−N hysteresis loops declined with increasing flow, excluding the extremely low flow period during summer (Figure 3a). In contrast, conductivity consistently declined with increasing seasonal flow, peaking at the lowest runoff period (Figure 3b). The underlying hysteresis patterns were therefore different for NO3−N and conductivity, particularly considering the storms before and after the low flow period in summer (Figure 3). The storm responses of NO3−N follow complex and changing patterns through the deployment period (Figure 3). NO3−N storm patterns consistently show a relatively short period of increased concentrations, or a pulse, on the rising limb, followed by dilution (Figures 2a, c and 3). In this study, flushing refers to concentrations increasing over an entire storm, and pulses refer to short-term increased concentrations during any point in the hydrograph, even during an overall storm dilution (e.g., Figure 2a, c). NO3−N exhibited at least a short-term pulse on the rising limb for 13 of the 16 storms with complete NO3−N data (Table 2), though the effect was most prominent in the fall (Figure 3). In spring and early summer, NO3−N concentrations alternated between overall dilution and flushing during storms (storms 2 to 5; Table 2; Figure 2a). After a month long dry spell, there was a transition period in late summer and early fall (storms 6 to 10; Table 2) where flushing consistently occurred. Both flushing and dilution of storm-event NO3−N were evident (storms 11 to 17; Table 2) following the transition period. However, a NO3−N pulse during the rising limb of storms, followed by dilution during the falling limbs, was the predominant pattern through to the end of the deployment (short NO3−N pulses that occur at beginning of each storm in Figure 2c; Supporting Information, Figure S2). In contrast to NO3−N concentrations, specific conductivity showed at least short-term pulses on the rising limb in only three storms, indicating greater source limitation (Table 2). For storms with NO3−N pulses, but not a similar response for conductivity, this may represent a concentrated surface water input, which is low in conductivity, but high in NO3−N, either from atmospheric deposition or fertilizers. Conductivity increased across the entire hydrograph during two storms in early fall, but short-term pulses occurred during storms where there was an overall dilution in conductivity (Table 2). NO3−N and conductivity had similar storm responses, considering hysteresis direction and flushing or dilution, during six of the 16 monitored storms with complete data for both parameters

Table 3. Total NO3−N Fluxes from the Lamprey River Watershed (2011) Calculated Using USGS Discharge Data, an In Situ NO3−N Sensor, Grab Samples, and Load Estimation (LOADEST) Models NO3-N sensor month May June July August September October November total load (kg)

weekly sample (LOADEST)

monthly samples (LOADEST)

total flux (kg)

total flux (kg)

% of sensor fluxa

total flux (kg)

% of sensor fluxa

1530 1465 534 371 693 1748 2536 8877

2789 1123 351 418 553 1669 2516 9419

182 77 66 113 80 95 99 106

2795 1393 636 542 617 1170 1731 8884

183 95 119 146 89 67 68 100

The “% of sensor flux” is the relative percentage difference each month between the load estimation model and the NO3−N sensor. a

between May and November during the deployment (April 18th to December second, 228 days), calculated using the sensor data, was 8,877 kg. Total fluxes over this period estimated from weekly and monthly grab samples to calibrate the load estimation model were 9,419 kg and 8,884 kg, respectively, within 106 and 100% of continuous data. However, the similarity in flux estimates is somewhat misleading, as considerable offsetting errors occurred at monthly scales (Table 3), and even greater errors associated with individual storms (data not shown). Flux estimates based on monthly grab samples particularly underestimated NO3−N fluxes compared to weekly samples in the fall (Table 3). Overestimated fluxes using infrequent sampling were evident during May, as a large storm diluted NO3−N concentrations, which was captured by the sensor, but not by the grab samples and load estimation technique.

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DISCUSSION Supply and Transport Limitation of NO3−N in Urbanizing Watersheds. In the urbanizing Lamprey River watershed, supply and transport limitation of NO3−N is 7761

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effect (Figure 2). This is a characteristic response of mixed land use watersheds with a certain distribution of loading.16 Typically, rising limbs for storm hydrographs in this study had initially stable NO3−N concentrations and then short-term pulses, while falling limbs have reduced concentrations. The initial period on the rising limb with stable concentrations indicates that early runoff reaching the stream has concentrations similar to groundwater because baseflow concentrations are often assumed to reflect groundwater.16 However, the general pattern of stable concentrations on the rising limb was not consistent across all storms (Table 2). During the fall, NO3−N concentrations increased immediately during the rising limb rather than later. Inconsistent NO3−N concentrations on the rising limbs of storms may reflect the interaction between precipitation and land use patterns in the watershed. The spatial characteristics of precipitation events in larger, spatially complex watersheds influence the volume, distribution, and timing of chemical constituents entering and leaving river networks.9,11 However, the precipitation skewness index indicated precipitation was generally uniformly distributed throughout the watershed (Table 1), and thus the spatial variability of precipitation was not likely a significant factor influencing concentration-discharge relationships. In another large, mixed land-use watershed, shifts in the storm event characteristics of dissolved solutes occurred as a result of an accumulation of nutrients during low flow periods, flushing during periods of greater discharge, and increased hydrological connectivity that facilitate the transport of newly available sources.31 These patterns have generally been found in smaller headwater catchments,10,32,33 but we found a similar response as well in our larger study watershed with complex development patterns. For example, anticlockwise hysteresis patterns with greater NO3−N concentrations on the falling limbs of storm hydrographs were only observed for three storms immediately following the extended period without rainfall in the summer (Table 2), suggesting antecedent conditions and hydrological flowpaths interact to produce specific storm-event patterns. Instances of NO3−N flushing during storm events may be linked to soil N dynamics. The flushing of NO3−N following a prolonged summer period between precipitation events, such as between storms 5 and 6, may be due to the mobilization of accumulated soil NO3−N caused by a reduced supply of labile dissolved carbon to soil microbes during the dry period.29,34 This mechanism occurring in the forest soils, combined with longer flow paths from the more heavily forested areas, may contribute to the anticlockwise pattern of NO3−N concentrations measured at the basin mouth during the three successive storms after the dry period. However, NO3−N concentrations typically diluted and were lower on the falling than rising limbs of storm hydrographs in this study, which suggests that forested areas in the watershed generally export less NO3−N during storms than the developed areas closer to the study site. NO3−N Flux Estimates and Sample Frequency. NO3− N fluxes delivered to receiving water bodies are determined by both spatially heterogeneous sources in the landscape and environmental conditions (e.g., variability in hydrology and climate) that control the supply and transport of NO3−N.35−37 Several studies have indicated a strong relationship between increased development, both urban and agricultural, and elevated inorganic N concentrations and fluxes in streams and rivers.37−39 However, monitoring programs that usually

evident both across and within storms. Supply limitation develops when solutes mobilized during storms are limited by the mass contributed by specific sources, whereas transportlimitation indicates that discharge variability determines when readily available solutes are mobilized.28 Source limitation of nitrate is common in small forested catchments,15 while transport limitation is more common in small urban catchments.10 In larger, mixed land use watersheds such as the Lamprey, source limitation can coexist with transport limitation within and across storms if sources change and different parts of the watershed contribute to the hydrograph at different points in time (e.g., distant parts of the watershed are less developed and take longer to get to the mouth).16,18 Elevated concentrations in headwater streams draining agricultural and suburban catchments relative to forested streams in the watershed (Wollheim, unpublished data) suggest that concentration-discharge variability in the mainstem of the river are attributed to inputs from more developed areas. NO3−N concentrations during the rising limb of storm hydrographs were mostly greater than the pre-event flow in this study, which suggests that NO3−N source areas are closer to the basin mouth leading to NO3−N pulses, with the effect becoming more prominent after the summer transition period (Table 2). However, storm-event flushing was not consistent as NO3−N concentrations also diluted for specific storms as would occur in more pristine watersheds.29 At least some dilution was evident for storms during every period except after the long dry period in early summer, where NO3−N increased during the falling limb (Table 2). NO3−N generally diluted during the falling limb outside of this transition period, with the exception of an early summer storm and two fall storms that also had increasing concentrations on the falling limb (storms 4, 14, and 15; Figure 3). Urbanizing watersheds that remain predominantly forested, such as the Lamprey, retain supply limited characteristics that produce a dilution capacity for solutes such as NO3−N during storms. The resultant effect is intermediate storm event patterns that are characteristic of both pristine conditions and watersheds with a long history of spatially extensive nutrient inputs. Basu et al.20 suggested that intensively managed, transport-limited watersheds have an invariant supply of readily available nutrients for export, which results in relatively chemostatic responses. The occurrence of both supply and transport limitation of NO3−N in predominantly forested but urbanizing watersheds therefore reflects both pristine, supply limited conditions and the increasing availability of NO3−N sources that are associated with land use change.30 At the larger watershed scale of our study, both the flushing and dilution of NO3−N concentrations likely occur because of the nonrandom distribution of urban, agricultural, and forested land use/land cover classes within the watershed. The weighted mean distance of these areas in the watershed from headwaters to the gaging station (agriculture: 27.53 km; residential: 30.72 km; and forested: 34.27 km) indicates that land uses potentially delivering NO3−N to the river network are concentrated closer to mouth of the river (near to the study site), while the more pristine parts of the watershed are further away (Figure 1). This suggests short-term NO3−N pulses measured by the sensor reflect inputs from nearby developed areas, which get to the mouth earlier in the hydrograph on the rising limb. Forested areas of the watershed have relatively less NO3−N concentrations than the developed areas, producing a general dilution 7762

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rely on weekly or monthly grab samples to determine flux estimates may undersample NO3−N concentration variations that occur during both high and low flow periods.13 Less frequent weekly or monthly grab samples may be particularly erroneous in flashy or complex larger watersheds because storm samples tend to be missed.14 Some of the patterns evident with high-resolution data, that may be useful for management purposes (e.g., NO3−N pulses during the rising limb of storm hydrographs, Figure 2), would be entirely missed using infrequent grab sampling. The differences between calculated NO3−N fluxes for the sensor and flux estimations (based on weekly and monthly grab samples) may be due to short-term dilution and NO3−N pulses during different storm events. If storms dilute NO3−N concentrations, less frequent sampling will overestimate NO3−N fluxes. However, if storms increase NO3−N concentrations, less frequent sampling will underestimate NO3−N fluxes. In complex larger watersheds, flux estimations based on grab samples need to capture the range of NO3−N dynamics such as short-term pulses and seasonality to reflect subannual variation that may be critical for watershed management. Rozemeijer et al.40 suggested load estimates from low-frequency grab samples could be improved through high-frequency concentration measurements during peak discharges associated with storm events. In our study watershed, increased NO3−N concentrations during periods of low discharge (July) suggest either enriched groundwater derived from leaky septic wastewater systems or excess N from fields and lawns accumulated in groundwater. NO3−N concentrations were very low during small storm events at the beginning of August but gradually increased between August and September as discharge increased. NO3−N concentrations further increased during October and November storms. Conductivity and NO3−N storm-event patterns were not consistent during the study (Table 2; Figures 2 and 3), and since conductivity is associated with legacy road salt applications evident at baseflow,23 NO3− N patterns indicate unique and variable N sources that affect fluxes. Implications for Watershed Management. High frequency monitoring to characterize nutrient dynamics in large, mixed land use watersheds could inform nutrient management strategies.31,40 Data from one year may not be indicative of an overall watershed pattern, but analysis of increasingly available in situ sensor NO3−N data within the context of historical and concurrent grab sample data could provide additional information on long-term trends and nonpoint N sources. Tracking and identifying N sources through the use of isotopes7 could also be used with high frequency sensor measurements to delineate NO3−N flowpaths and ultimately determine how different sources respond during storms. High frequency measurements of parameters such as dissolved organic matter and dissolved phosphate, coupled with NO3−N measurements, would also be useful to investigate overall water quality variability in urbanizing watersheds. The present study indicates managers need to get fluxes right for the right reason and at the right times. Flux estimates from weekly and monthly grab samples are similar to estimates from the in situ NO3−N sensor on an annual time scale. However, comparisons on subannual time scales reveal the utility of continuous sensor data. The sensor revealed lower NO3−N flux estimates in spring but higher fluxes in the fall, compared to less frequent grab samples. In systems where the timing of nutrient fluxes are critical, understanding seasonal storm responses

becomes important. For example, receiving waters may get less nutrients during the critical spring growing season than expected when storms dilute, and only grab samples are used to characterize fluxes. Watershed management strategies may therefore be improved with more detailed information such as differences in seasonal fluxes.15 Tracking changes in NO3−N concentration-discharge relationships over time can also be used as an indicator of N accumulation in urbanizing watersheds and could help to prioritize mitigation. Further, long-term monitoring could also help evaluate the effectiveness of mitigation. These patterns can only be identified using high resolution in situ sensors that reflect NO3−N responses during both baseflow and stormflow. For example, the NO3−N sensor used in this study revealed recurring nutrient spikes on the rising limbs of several storms. These nutrient spikes are likely associated with a particular source “hot spot” in the watershed−an area with relatively elevated nutrient concentrations−that mobilizes NO3−N during storms. Understanding and managing biogeochemical hot spots is an important aspect of watershed management.41 The key to effective N management in urbanizing watersheds is to identify and target influential factors contributing to elevated NO3−N fluxes and exports, such as hot spots. Grab samples represent a snapshot of the N present in river networks, but in situ aquatic sensors facilitate detailed analysis of underlying patterns that provide information on potential sources and N flux thresholds that may affect sensitive receiving water bodies.

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ASSOCIATED CONTENT

S Supporting Information *

Additional methods and results from this study include baseflow partitioning to delineate storms, discharge measurements for each storm, and storm event patterns for nitrate and specific conductivity. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: 603 862-2603. Fax: 603 862-0587. E-mail: richard. [email protected]. Corresponding author address: Earth Systems Research Center University of New Hampshire, Durham, NH 03824. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS

This work was funded by the New Hampshire Agricultural Experiment Station, the National Sea Grant College Program of the U.S. Department of Commerce’s National Oceanic and Atmospheric Administration grant NA10OAR4170082 to the N.H. Sea Grant College Program, and by the NSF Experimental Program to Stimulate Competitive Research (EPSCoR) program Research Infrastructure Improvement Award # EPS 1101245. We would also like to thank Dick Lord for providing access to the study site, the UNH Water Quality Analysis Lab for analyzing grab samples, Stanley Glidden for data analysis, and Allison Price for helping to complete various tasks associated with the research. 7763

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