Hydrologic and topographic controls on storm-event exports of

WATER RESOURCES RESEARCH, VOL. 42, W03421, doi:10.1029/2005WR004212, 2006

Hydrologic and topographic controls on storm-event exports of dissolved organic carbon (DOC) and nitrate across catchment scales Shreeram P. Inamdar1,2 and Myron J. Mitchell3 Received 25 April 2005; revised 28 November 2005; accepted 30 December 2005; published 25 March 2006.

[1] Patterns of DOC and nitrate (NO 3 ) concentrations and fluxes were studied for six

storm events across four forested, glaciated, subcatchments (1.6 to 696 ha) in western New York, USA. End-member mixing analysis (EMMA) showed that catchment runoff was composed of shallow groundwater (SGW) discharged at seeps and throughfall (THF) on the rising limb and THF and riparian water (RW) on the recession limb. High THF NO 3 contributions produced a pronounced rise and maximum in streamflow NO 3 , while low THF concentrations resulted in a dilution trajectory. We did not find any evidence for NO 3 flushing by a rising water table. We propose an alternate model for NO3 where the occurrence of seeps and steep slope gradients are the primary determinants of NO 3 generation and delivery, and VSAs or saturated areas have a secondary role as loci for interception of NO 3 contributions from throughfall. Exports of DOC from the catchments were attributed to THF contributions and the flushing of riparian and hillslope-bench saturated areas. Concentrations of DOC increased with increasing catchment size and percent-saturated area whereas NO 3 values showed an opposite trend. Concentrations of DOC from a wetland catchment were consistently high, but higher percent increases in event DOC concentration were observed for those catchments with riparian and hillslopebench saturated areas. Catchments dominated by riparian zones and catchments with discrete, distal wetlands may have substantial untapped stores of DOC, which become mobilized during high moisture conditions. Thus wetlands in steeply incised valleys may not be effective in buffering hillslope solute loadings. Citation: Inamdar, S. P., and M. J. Mitchell (2006), Hydrologic and topographic controls on storm-event exports of dissolved organic carbon (DOC) and nitrate across catchment scales, Water Resour. Res., 42, W03421, doi:10.1029/2005WR004212.

1. Introduction [2] In recent years there has been considerable interest in identifying the sources and flow mechanisms responsible for the export of nitrate (NO 3 ) and dissolved organic carbon (DOC) within watersheds. Some researchers have attributed the NO 3 and DOC export patterns to the expression of nearsurface and deep groundwater flow paths as a function of watershed topography [Boyer et al., 1997; Burns et al., 1998; Hill, 1993; Hornberger et al., 1994; McHale et al., 2002]. Others have ascribed the catchment-scale NO 3 and DOC patterns to specific landscape units or geographic sources such as hillslopes, riparian zones, and/or wetlands [Creed and Band, 1998; Hinton et al., 1998; McGlynn and McDonnell, 2003]. Improved quantitative understanding of how geomorphic, topographic and hydrologic features of catchments regulate solute flux and exports will be especially beneficial for managing water resources in basins that are nitrogen (N) saturated [Aber et al., 1989; Stoddard, 1 Great Lakes Center and Department of Geography, State University of New York at Buffalo, Buffalo, New York, USA. 2 Now at Bioresources Engineering, University of Delaware, Newark, Delaware, USA. 3 Faculty of Environment and Forest Biology, State University of New York at Syracuse, Syracuse, New York, USA.

Copyright 2006 by the American Geophysical Union. 0043-1397/06/2005WR004212$09.00

1994], are subject to episodic acidification [Wigington et al., 1990], and/or experience high levels of atmospheric N deposition [Aber et al., 2003; Driscoll et al., 2003]. [3] High DOC exports have typically been associated with near-surface hydrologic flow paths that intersect DOCrich forest floor and surficial soil layers in riparian or wetland locations [Boyer et al., 1997; Hagedorn et al., 2000; Hornberger et al., 1994]. The increase in DOC concentrations in surface waters with rising groundwater tables has been described as ‘‘flushing’’ [Creed and Band, 1998]. In contrast to DOC, NO 3 exports from watersheds have been found to occur along both near-surface as well as deep groundwater flow paths [Burns et al., 1998; Hill, 1993; Hill et al., 1999; Inamdar et al., 2004; McHale et al., 2002; Schiff et al., 2002]. Burns et al. [1998] found that surface springs that released groundwater flow were the source of highest NO 3 concentrations during the summer season for a catchment in the Catskill Mountains of NY. Similarly, McHale et al. [2002] working in the Adirondack Mountains of NY found that groundwater springs that discharged deep till-groundwater had the highest NO 3 concentrations and were the controlling end-member for stream chemistry. Hill et al. [1999] identified throughfall as the principal contributor to stream NO 3. [4] The areal extent, spatial position, and hydrologic connectivity of hillslopes, riparian and wetland landscapes also influence the temporal patterns of NO 3 and DOC in

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INAMDAR AND MITCHELL: HYDROLOGIC AND TOPOGRAPHIC CONTROLS ON DOC

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[6] We explored the controls of hydrology and topography on storm-event NO 3 and DOC exports for the Point Peter Brook watershed (PPBW) located in the glaciated, forest region of western New York. Investigations were performed for four subcatchments (1.6 – 696 ha). The PPBW has wide ridgetops, steep sideslopes, and narrow valley-bottom areas, which are moist year-round. Surface saturation was also observed at isolated locations on hillslope benches. Perennial seeps located two-thirds of the way up the hillslope generated surface runoff. We hypothesized that the occurrence of seeps, steep sideslopes, and saturated areas in the valley-bottom and hillslope-benches were important determinants of the hydrologic and solute response for PPBW. This response was evaluated using landscape analysis, hydrometric, and chemical data. Key questions that were addressed included: (1) What are the sources of DOC and NO 3 and how do the sources regulate the within-event patterns of these solutes? (2) How do NO 3 and DOC responses vary across the catchments and what factors influence these responses? (3) Can a conceptual model be developed that explains the NO 3 and DOC patterns in terms of catchment hydrology and topography?

2. Site Description and Methods

Figure 1. Study subcatchments (1.6 –696 ha) and instrumentation in the Point Peter Brook Watershed (PPBW). catchment runoff. Creed and Band [1998] hypothesized that the catchment N exports are likely regulated by the areal extent, location, and rate of change of saturation in variable source areas (VSAs). Hinton et al. [1998] found that catchments with wetlands consistently produced higher DOC concentrations than catchments without wetlands (i.e, hillslope or zero order catchments). Schiff et al. [2002] found an order of magnitude difference in the NO 3 export from two adjoining catchments and hypothesized that the high NO 3 concentrations were due to the steep hillslope gradients that expedited the movement of NO 3 rich waters downslope. Researchers [Hill, 1993; Hill and Waddington, 1993; Roulet, 1990] working in Ontario, Canada found that deeper groundwaters and near-surface waters interacted in a unique fashion at a swamp interface to influence the stormevent export of N from the catchment. The swamp was maintained at near-saturation by upwelling groundwaters that allowed near-surface flows from seeps to move across the saturated swamp surface and thus influence stream N chemistry [Hill, 1993]. [5] These previous investigations have clearly shown that the proportions of near-surface and deep groundwater flows and their spatial expression at key landscape positions such as riparian zones or hillslope seeps have an influence on the DOC and NO 3 release from watersheds. Moreover, these findings suggest that greater insights into process dynamics and solute patterns can be achieved by (1) combining landscape analysis, hydrometric, and isotopic and chemical methods; (2) looking at watershed response during storm events and across multiple events; and (3) studying the response patterns across multiple catchment scales.

2.1. Site Description [7] This study was conducted in the PPBW (Figure 1), located in Cattaraugus County and 55km southeast of Buffalo in New York State (422603000N; 785503000W). Mean annual winter temperature is 3C and the mean summer temperature is 21C. Annual precipitation averages 1006 mm of which 200-250 mm occurs as snow (20-year average based on the National Atmospheric Deposition Program (NADP) Weather Station at Chautauqua, NY; 35 km southwest of PPBW; NADP weather site: http:// nadp.sws.uiuc.edu/sites/siteinfo.asp?net=NTN&id=NY10, 2004). The parent material was derived from glacial till (Kent Drift of Woodfordian formed 19,000 yr B.P.) [Phillips, 1988]. Soils in the watershed belong to the Volusia-Mardin-Erie association [Phillips, 1988]. Vegetation on ridgetops and hillslopes was dominated by deciduous trees including sugar maple (Acer saccharum), black maple (Acer nigrum), American beech (Fagus grandiflora), yellow birch (Betula alleghaniensis) with larger proportions of conifers including hemlock (Tsuga canadensis) and white pine (Pinus strobus) in valley-bottoms. Topography of the entire watershed is fairly distinct with wide ridgetops, steep hillslopes, and narrow valley bottoms. Slope gradients in the watershed range from 0 to 69%, with a mean gradient of 14%. Elevation ranges from 252 to 430 m above mean sea level. A low-permeability clay layer that generates perched water tables and forces water to move as shallow subsurface flow on the steep hillslopes underlies the soils. The depth to the clay/till measured using soil cores varies from 1.2 –1.7 m in the valley-bottom locations, 0.3– 0.5 m along the side slopes and 0.6 m at the ridgetops. [8] The subcatchments that were studied (S1, S2, S3, and S5) are shown in Figure 1. Outlet for S1 (696 ha) was located on the main drainage of PPBW with S2 (3.4 ha) and S3 (1.6 ha) nested within S1. Subcatchment S3 drained a hillslope hollow with streamflow originating from two perennial seeps S3a and S3b that discharged at the channel head (Figure 1). Surface saturation in S3 was limited to the

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channel head downslope of the seeps, with no saturation along the stream channel. Hillslopes in S3 extended to the stream channel. Outlet S2 was located in a valley-bottom riparian area downstream of S3. The riparian area in S2 was variably saturated and the organic matter content of surficial (0-20 cm) soils was between 3 and 11%. In addition to S3, two other intermittent seeps contributed to runoff at S2. Runoff from the seeps traversed rapidly as streamlets over the variably-saturated riparian area before entering the stream S2 (Figure 1). Surface-saturation in S2 was also observed in multiple isolated pockets on the hillslope bench along the eastern hillslope flank (Figure 1). Organic matter content of surficial soils in these isolated saturated areas ranged from 3 to 70%. Subcatchment S5 (1.9 ha) located downstream of S1 enclosed a valley-bottom riparian wetland. This saturated area in S5 was identified as a wetland since it was continuously saturated and the organic matter concentration of the soil was 70%. Runoff at S5 also originated from a seep (S8) located more than two-thirds of the distance along the contributing hillslopes along the northeastern edge (Figure 1). 2.2. Watershed Monitoring, Sampling, and Analysis [9] Hydrologic and surface water chemistry monitoring at PPBW was initiated in November 2002 and intensive stormevent sampling started in May 2003 and continued through May 2004. Precipitation was recorded using a tippingbucket rain gage located near the catchment outlet. Streamflow discharge measurements at S1 were initiated in November 2002, at S2 and S3 in May 2003 and at S5 in April 2004. Stream flow stage was recorded every 15 minutes using a pressure transducer (Global Water Inc.). At S1 a stage-discharge relationship developed was developed for the 3m wide stream channel. Parshall flumes were installed on streams at S2 and S3 and a V-notch weir was installed at the stream channel at S5. Groundwater elevations were recorded using pressure transducers (Global Water Inc.) nested within logging wells that were constructed of 5 cm (ID) PVC tubing. The logging wells were cored to the depth at which an impeding clay or gravel layer was intersected (between 1.5 and 3 m). Three logging wells (R1, R2, and R5) were located in the valley-bottom position and one well was positioned in a saturated area on the hillslope bench (H2) (Figure 1). [10] Grab sampling was performed on a biweekly basis for: valley-bottom and hillslope groundwater wells, surface seeps, and lysimeters located in valley-bottom and hillslopebench saturated areas. Groundwater sampling wells were constructed of 5 cm (ID) PVC tubing and were cored to the depth at which an impeding clay or gravel layer was intersected (between 1.5 and 3 m). The wells were screened from 30 cm below the soil surface to the bottom. Three groundwater-sampling wells (RS1, RS2, and RS5) were established in riparian and wetland valley-bottom locations (Figure 1). Seep samples were collected from surface seeps at S3a and S3b (Figure 1) in the subcatchment S3. Starting in spring 2005 samples were also collected downstream of the seep S8 located in subcatchment S5. Zero-tension lysimeters were constructed of 5 cm (ID) PVC tubing and were inserted at a 45-degree angle to a depth of 30 cm from the soil surface. The lysimeter were screened such that they collected soil water from the A horizon. Lysimeters were installed in valley-bottom riparian and wetland areas (L1,

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L2, and L6) and hillslope-bench saturated areas (L3 and L4) (Figure 1). Sample water was obtained from the groundwater wells and lysimeters using a hand-operated suction pump. [11] Storm event sampling for the four subcatchments was conducted using three ISCO samplers. For S1, stormevent sampling was performed from May 2003 through April 19, 2004. Subcatchment S2 was sampled from May 2003 onwards. S3 was sampled from September 2003 onwards and sampling at S5 was initiated after April 19, 2004. The automated ISCO sampler was triggered for event sampling when the rainfall rate exceeded a threshold of 2.8 mm within a 2-hour period. The sampler was programmed on the ‘‘variable time’’ mode so as to sample more frequently on the hydrograph rising limb than on the recession limb. Composite precipitation samples were collected in a collector placed in the open; throughfall samples were collected from two collectors, one placed under a coniferous canopy (Tc) and one placed under a deciduous canopy (Td) (Figure 1). Precipitation and throughfall collectors were 3.8 L plastic containers connected to funnels, which had a plastic mesh on the mouth to prevent entry of debris. All samples were collected within 24 hours of an event in 250 mL Nalgene bottles. Analyses performed on the samples included: DOC on a Tekmar-Dohrmann Phoenix 8000 TOC analyzer, cations (Ca2+, Mg2+, Si) on a PerkinElmer ICP-AEC Div 3300 instrument, and anions (SO2 4 , NO 3 ) on a Dionex IC. The laboratory is a participant in the United States Geological Survey (USGS) performance evaluation program, that helps ensure data quality [Mitchell et al., 2001]. 2.3. Digital Elevation Model (DEM) and Landscape Analysis [12] A 2m DEM was developed for the PPBW via aerial photogrammetry. The drainage network and subcatchment boundaries were delineated using the PrePro program [Olivera and Maidment, 2000]. The DEM-computed subcatchment boundaries for S2, S3, and S5 were verified by comparing against field-surveyed boundaries using a Trimble GPS (Trimble, Inc.). Both the ln(a/tanb) index [Quinn et al., 1995] and the downslope index (DWI) [Hjerdt et al., 2004] were computed using the 2m DEM. The DWI was computed using the GEASY program (J. Seibert, GEASY manual, unpublished report, 2005) for four downslope elevation ‘‘d’’ values: 2, 3, 4, and 10 m [Hjerdt et al., 2004]. Field surveys of surface-saturated areas were performed in May 2004 for the S2, S3, and S5 subcatchments through visual identification of surface-saturation, soil coring, and the squishy-boot technique. Since the DWI index values better matched the field-observed areal extent of surface-saturation (S. P. Inamdar and M. J. Mitchell, manuscript in preparation, 2006) only the DWI index results are presented here. 2.4. Selection of Events [13] A total of 33, 21, 20, and 10 events were monitored for subcatchments S1, S2, S3, and S5, respectively over the study period (May 2003 to June 2004). Of these, six events (June 8, July 27, August 9, 2003; and May 9, 20, 27, 2004) are analyzed here. These events were selected because they provided the largest set of event data that was common across the four subcatchments. The event of July 27 was

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Table 1. Watershed Characteristics and Topographic Attributes Across the Four Subcatchments Subcatchments Attribute

S1

Area, ha Relief, m Mean catchment gradient Dominant aspect (percent catchment area)

S2

696 254 – 430 14.1 W (17)

3.4 255 – 307 14.3 NW (57)

S3 1.6 260 – 307 15.0 NW (39)

Field Surveyed Saturated Area in Percent and m2 in Parentheses Total 2.0 (675) 0.8 (129) Valley-bottom 0.7 (231) 0 Hillslope 1.3 (444) 0.8 (129) Gradient of contributing slopes to surveyed 18.6 20.3 valley-bottom saturated areas and stream Mean Variance Skew Percent catchment area with DWI > 10 Mean and range of DWI for surveyed valley-bottom saturated areas

Downslope Wetness Index (DWI) Moments 5.12 5.39 1.98 1.34 1.61 0.90 2.1 0.9 8.7 (3.3 – 15.6)

large and intense and produced the highest discharge peak of 2003. Data from S1 and S2 was available for the 2003 events while data from S2, S3, and S5 was available for the 2004 events. The start of the event was defined when a perceptible rise in discharge was observed after precipitation or the occurrence of first ISCO sample, whichever occurred earlier. The end of the event was defined when discharge returned to preevent values or when a subsequent event started, again, whichever occurred earlier. Discharge per unit area (mm) was the total volumetric flow for the event divided by the subcatchment area. Sample concentrations were linearly interpolated between sampling intervals including both event and biweekly samples to calculate solute fluxes. 2.5. End-Member Mixing Analysis (EMMA) and Hydrograph Separations [14] EMMA [Burns et al., 2001; Christopherson and Hooper, 1992; McHale et al., 2002] was performed to identify the controlling end-members and to determine the contribution of the end-members to streamflow. Prior to EMMA, bivariate plots for Si-DOC and Mg2+-DOC were also produced to identify the potential end-members. EMMA was performed for four (June 8, July 27, 2003; and May 20, 27, 2004) of the six selected events using Mg2+, Si, and DOC as tracers. Silica has been the tracer of choice in many studies and high silica concentrations have typically been associated with deep groundwaters or waters with high residence time in the watershed [Hooper and Shoemaker, 1986; McGlynn and McDonnell, 2003; Shanley et al., 2002]. Although we recognized the non-conservative nature of DOC, we chose DOC because we had consistently good data on DOC and it represented the solute that typically accumulates in surficial soil layers and is exported with near-surface runoff. DOC has successfully been adopted in numerous studies to identify flow paths and geographic sources of runoff [Brown et al., 1999; McGlynn and McDonnell, 2003]. [15] Streamflow concentrations for the event were standardized by the mean values and a correlation matrix was developed for the standardized values. Eigenvectors for

5.28 1.36 0.91 0.7 -

S5 1.9 255 – 304 14.9 NW (49) 5.9 (1122) 4.7 (896) 1.2 (226) 22.8

5.67 2.60 1.44 4.3 10.3 (6.2 – 14.6)

the correlation matrix were computed using Mathematica (Wolfram, Inc.). The first two components, U1 and U2 of the eigenvector matrix were then used to project the standardized stream concentrations into the U-space. Selection of the two principal components implied a threecomponent mixing model. A similar methodology was used to project the potential end-members into the U-space. Potential end-member concentrations that were used were means of concentrations measured during baseflow conditions (grab sampling) before and after the storm event. Potential end-members that were projected included rainfall, deciduous (Td) and conifer (Tc) throughfall, seep (S3a), valley-bottom riparian (average of RS1 and R2; and RS5), and average of all lysimeters (L1 through L6). [16] Once the three controlling end-members were identified in U-space, the proportions of streamwater derived from the three end-members were computed by solving the water and solute mass-balance equations [Burns et al., 2001, equations (4) – (6)]. The EMMA model was evaluated by comparing the model-predicted concentrations for Mg2+, Si, and DOC against observed stream concentrations. EMMA predictions were also evaluated by comparing 2+ observed and predicted concentrations for NO 3 , Ca , and 2 SO4 . Conservative mixing is implied in the use of this equation. The mixing model was further verified by comparing against hydrometric data – groundwater elevations (well R1) recorded in the valley-bottom riparian location.

3. Results 3.1. Catchment Attributes, Topographic Index, and Surface-Saturated Areas [17] Based on mean catchment gradient, S3 was the steepest followed by S5, S2, and S1 (Table 1). The dominant slope aspect for all three small subcatchments was northwest, whereas 17% of the hillslopes for S1 were oriented to the west. Field-surveyed surface-saturated areas were highest for S5 at 5.9% of the subcatchment area followed by S2 (2.0%) and S3 (0.8%). The valley-bottom wetland in S5 constituted 4.7% with 1.2% of the saturated

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of 0.7%. Using a threshold of 10, the extent of surfacesaturation for S1 was computed to be 2.1%. Although the value of 2.1% for S1 likely represents the valley-bottom saturated areas and does not include the more dynamic hillslope-bench saturated areas, it provides a useful estimate for comparison against the other subcatchments. 3.2. Streamflow Discharge and Groundwater Elevations [20] Hydrographs for the events of July 27, 2003 and May 20, 2004 are presented in Figure 3 and runoff ratios for the six events are given in Table 2. During low flow conditions discharge per unit catchment area was highest for the headwater seep subcatchment S3 while the large S1 subcatchment recorded the lowest values (Figure 3). However, during peak event periods the discharges for S2 and S5 exceeded those for S3. The peak runoff values indicate higher values (especially for large, intense events) for subcatchments S2 and S5, both of which had a greater areal extent of saturated area than S3 (Table 2). [21] GW elevations for valley-bottom riparian wells (R1 and R5) reached peak values close to the discharge peaks (S2 and S5, respectively) and remained at elevated levels through hydrograph recession (Figure 3). Well H2,

Figure 2. Spatial and probability distributions of the downslope wetness index (DWI) [Hjerdt et al., 2004] for the four study subcatchments. areas on hillslopes. The valley-bottom riparian area in S2 accounted for only 0.7% of the saturated area with the remaining saturation (1.3%) in discrete pockets on hillslope benches. Saturated areas in S3 were limited to the channel head (0.8%). The slope contrast between contributing hillslopes and valley-bottom saturated area was the greatest for S5 with contributing slope gradients averaging 22.8% above the flat valley-bottom wetland. For S2, the slope gradients above the riparian area were slightly less at 18.6%, whereas the mean of the slope gradients contributing to the stream channel in S3 was 20.3%. [18] Visual comparisons of the DWI with field-surveyed valley-bottom saturated areas indicated that the DWI corresponding to ‘‘d’’ = 3m provided the best replication of the areal extent of valley-bottom saturated areas (Figures 1 and 2). The mean of the DWI was highest for the S5 subcatchment followed by S2, S3 and S1 (Table 1). The DWI distribution failed to replicate the discrete pockets of saturation (Figure 2) because the DEM despite its high resolution (2m) could not reproduce the break in slope at the hillslope benches. [19] Surface-saturated areas for S1 (696 ha) could not be surveyed because of the large area involved. To ascertain the extent of saturation in S1 we compared the areas corresponding to various DWI index values against fieldsurveyed saturated area extents for subcatchments S5 and S2. A DWI value of 10 produced the best fits between fieldsurveyed saturated area extent and that generated from the DWI map (Table 1). A threshold value of 10 indicated a DWI area of 4.3% for S5 that was similar to the valleybottom wetland area of 4.7%. For S2 the DWI value was 0.9%, which was again similar to the field measured value

Figure 3. Subcatchment streamflows and groundwater depths for riparian (R1, R5) and hillslope-bench (H2) wells for the events of July 27, 2003 and May 20, 2004. Groundwater depths are from the soil surface (negative values indicate surface ponding).

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Table 2. Hydrologic Attributes for the Six Selected Storm Events Across the Four Subcatchments (S1-S5)

Date

Total Rain, mm

Peak Rain Intensity, mm h1

Jun 8, 2003 Jul 27, 2003 Aug 9, 2003 May 9, 2004 May 20, 2004 May 27, 2004

12.1 24.1 11.2 16.0 11.2 10.7

4.3 8.4 3.0 5.6 6.9 3.8

Event Runoff Ratio

Peak Runoff, mm h1

Subcatchments

Subcatchments

S1

S2

0.23 0.27 0.16

0.25 0.29 0.27 0.40 0.18 0.32

which was located in saturated area on a hillslope bench, showed a delayed response with GW being highest late in the recession period. During the event of July 27, surface ponding occurred at H2 (negative elevation values), with increase in depth of ponding through hydrograph recession. Our on-site observations indicated that surface runoff from these hillslope-bench saturated areas moved downslope along surface drainages as water moved into the stream channel. These observations suggest that while valleybottom saturated areas may respond before isolated hillslope-bench saturated areas, runoff contributions from both locations are at their maxima at or after peak stream discharge. 3.3. Solute Chemistry of Watershed Compartments [22] Silica concentrations for rain and throughfall were the lowest while concentrations in the valley-bottom groundwater wells RS1 and RS2 were highest (Table 3). Silica concentrations for topsoil waters (TSW) (A horizon) sampled by lysimeters were higher than rainfall/throughfall but lower than riparian (RW) and shallow groundwater sampled at seeps (SGW). RW concentrations from RS5 are presented as separate values because DOC concentrations at RS5 were significantly higher than those measured for RS1 and RS2. The difference in silica concentrations between the valley-bottom riparian wells and the seeps suggests that the valley-bottom wells were likely sampling deeper groundwaters whereas a shallow groundwater flux is

S3

0.36 0.28 0.33

S5

S1

S2

S3

S5

0.2 1.5 0.4

1.0 4.2 1.2 4.3 3.4 1.7

3.2 1.6 1.5

4.0 2.6 1.0

0.23 0.17 0.30

discharged at the seeps. Similar observations were also made by Hill [1993]. Mg2+ concentrations followed a trend similar to Si, except that Mg2+ concentrations for RS5 were considerably lower than other locations. [23] Throughfall DOC concentrations under conifers were much higher than under the deciduous canopy. Following conifer, highest DOC concentrations were measured in RW and TSW. SGW discharged at seeps had the lowest DOC values. In contrast to DOC, NO 3 concentrations were highest in SGW and lowest in RW and TSW. After SGW, the highest concentrations of NO 3 were recorded in throughfall (THF), indicating that THF is an important contributor of NO 3 to this watershed. The difference between conifer and deciduous NO 3 throughfall concentrations was small, however, compared to the large difference in DOC concentrations. 3.4. Controlling End-Members for Stream Chemistry [24] Si-DOC bivariate plots and EMMA mixing diagrams for the event of June 8, 2003 show four likely end-members: THF, RW, SGW and TSW (Figure 4). Similar diagrams were also generated across all subcatchments and events but are not included for brevity. For all events, THF, RW, and SGW enclosed the stream concentrations across all events and subcatchments and thus were identified as the controlling end-members. The concentrations used for the THF end-member were an average of the solute concentrations recorded under conifer and deciduous canopies. Both coni-

Table 3. Mean Concentrations of Si (mmol L1), Mg2+ (mmolc L1), DOC (mmol L1), and NO3 (mmolc L1) in Various Watershed Componentsa Watershed Component Rainfall Conifer throughfall Tc Deciduous throughfall Td TSWb (lysimeters – average of L1, L2, L6, L3, and L4) RWb (riparian wells – average of RS1, RS2) RWb (well RS5) SGWb (Seep S3a) S1 S2 S3 S5

NO3, mmolc L1

Si, mmolc L1

Mg2+, mmolc L1

DOC, mmol L1

0.4 (0.4) 4 (2) 2 (1) 160 (34)

8 (2) 31 (16) 14 (4) 1123 (113)

150 (43) 847 (266) 255 (80) 254 (207)

234 (55)

1217 (132)

309 (64)

3 (6)

187 (12) 168 (12)

896 (43) 1039 (89)

576 (115) 45 (11)

0.2 (1.4) 61 (20)

Baseflow Stream Concentrations 152 (26) 1047 (226) 161 (12) 1015 (82) 162 (11) 1028 (59) 151 (18) 1181 (43)

179 (58) 87 (56) 58 (18) 135 (30)

27 (7) 54 (16) 61 (21) 24 (9)

28 27 36 3

(15) (30) (13) (4)

a Standard deviations are provided within parentheses. Refer to Figure 1 for spatial locations of the components in the watershed. b TSW, topsoil water; RW, riparian water; SGW, shallow groundwater.

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Figure 4. Bivariate plots for Si (mmol L1) and DOC (mmol L1) (left) and U-space mixing diagrams (right) for the event of June 8, 2003 for catchments S1 and S2. The selected end-members (THF, SGW, RW) enclose the stream concentrations as shown by the dashed lines. Mg2+, Si, and DOC were used as tracers for EMMA. fer and deciduous tree species were distributed across the watershed. Use of the average THF solute concentrations (as opposed to only conifer or only deciduous concentrations) provided better model fits as quantified by the linear regression (R2) between EMMA-predicted and observed concentrations for Si, Mg2+, and DOC. Across the four subcatchments, six events, and three solutes ([2 3 3] + [3 3 3] = 45 model fits; refer to Table 2 for subcatchment and event combinations) the R2 values ranged between 0.79 – 0.99, indicating that the selected three-component EMMA model was a strong predictor of stream solute concentrations. An average of RS1 and RS2 concentrations represented the RW end-member for S1, S2, and S3, while RS5 was used for S5. [25] Concentrations at S2 for the event of June 8, 2003 were clustered close to the SGW end-member (Figure 4). These concentrations evolved in a counterclockwise pattern moving in the direction of THF and then shifting towards the RW end-member before returning towards SGW at the end of the event. In comparison, concentrations for S1 were displaced further away from SGW indicating a lesser influence of the seeps at this larger subcatchment. Similar to S2, S1 concentrations also evolved in a counterclockwise direction but unlike S2, the directional shift towards THF was not as evident as for S2. Although the evolution of S3 event concentrations is not presented, these values were even more tightly bunched around SGW since streamflow at S3 originated from the seeps. 3.5. Evaluation of EMMA-Predicted Solute Concentrations Against Observed Values [26] The EMMA model was further evaluated by compar2+ ing predicted and observed concentrations for NO 3 , Ca , 2 and SO4 for the events of July 27, 2003 (S1 and S2) and May 20, 2004 (S2 and S3) (Figure 5). Similar results were observed for other events and subcatchments (unpublished data). These comparisons assume conservative mixing. We

recognized that NO 3 is a highly reactive solute and would likely not follow a conservative mixing pattern over longer periods (e.g., days). Therefore by assuming conservative mixing at the time scale of events (hours/minutes) we subjected our mixing model to a fairly stringent evaluation. [27] The EMMA model was able to match the temporal pattern in NO 3 concentrations for the large event of July 27, 2003 for S1 and S2, but did not replicate the magnitude of the concentrations very well. In contrast, predicted NO 3 concentrations for S2 and S3 matched both the timing as well as the concentrations for the event of May 20, 2004 (although the R2 values for May 20 were lower than those observed for the July 27 event). The close match in observed and predicted NO 3 concentrations for the May 20 event suggests that NO 3 may behave conservatively over short duration storm events. EMMA-predictions of Ca2+ fared better than NO 3 with respect to the temporal match between observed and predicted values, but produced lower regression (R2) values. The fit between predicted and observed SO2 4 concentrations were the best, both with respect to the timing and concentrations (R2 = 0.88 and 0.99). 3.6. EMMA-Predicted Riparian Water Contributions and Comparison Against Riparian Groundwater Elevations [28] EMMA-derived hydrograph separations showed that RW contributions reached maxima at or after discharge peak and persisted at high values through hydrograph recession across all subcatchments and events (Figures 6 and 7 – 10). To further evaluate these predictions, groundwater elevations measured for the riparian well at R1 were compared against RW predictions for S2 for the events of July 27, 2003 and May 20, 2004 (Figure 6). For the large event of July 27, there was a close agreement between the timing of the RW contributions and the GW elevations during the early portion of the event, but the greatest RW contributions occurred after the peak in GW. The RW contributions were further delayed

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contributions (%) being found during the larger more intense events (Table 4). Across the nested subcatchments (S1, S2, S3), SGW amounts were highest for the headwater S3 subcatchment, while RW amounts were the highest for the large S1 subcatchment (Table 4). These results are consistent with the importance of seeps (SGW) in headwater catchments versus the increasing contribution of valleybottom riparian areas with catchment size (e.g., S3 to S1). However, for subcatchment S5, which had the largest fraction of saturated area, the contribution of THF was much greater than RW to stream chemistry. 3.8. NO 3 Patterns During Events and Across Catchments [30] Two different temporal patterns in NO 3 can be seen across the events (Figures 7 – 10). During the summer events (June 8 and July 27, 2003), NO 3 concentrations decreased on the rising limb, reached a minimum at or before the discharge peak, and then slowly recovered through hydrograph recession. In contrast, for the spring events (May 20 and 27, 2004), NO 3 concentrations increased through the rising limb, reaching a maximum at or before peak discharge, and then decreasing through recession. [31] We attribute these two different expressions to the relative contributions of NO 3 in SGW and THF. SGW and THF were the two watershed compartments with the highest NO 3 concentrations (Table 3). THF NO3 concentrations for

2+ Figure 5. EMMA-predicted and observed NO 3 , Ca , and SO42 (mmolc L1) concentrations for the events of July 27, 2003 and May 20, 2004.

for the event of May 20. Some time lag between RW contributions and GW elevations is to be expected because (1) the valley-bottom riparian area extends upslope beyond R1; and (2) the time associated with travel of surface runoff and solutes from different points in the riparian area to the outlet at S2. The data, moreover, indicate the late response of GW in valley-bottom riparian locations, supporting the delayed expression of RW during storm events. 3.7. End-Member Contributions During Events and Across Subcatchments [29] Contributions of SGW to streamflow were highest at the start of the event, declined very quickly on the rising limb, with a gradual recovery during hydrograph recession (Figures 7 – 10). THF amounts increased on the hydrograph rising limb and reached a maximum before or near peak discharge. In contrast, maximum RW contributions were delayed and occurred after the peak discharge. Event size did influence the proportions of the contributions of the endmembers, with lower SGW but higher THF and RW

Figure 6. Temporal pattern of RW contributions (fraction) and the groundwater elevations recorded by riparian groundwater well R1. Groundwater elevations are below the soil surface.

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Figure 7. Precipitation, streamflow, endmember-contributions, and DOC (mmol L1) and NO 3 (mmolc L1) concentrations for the event of June 8, 2003 for catchments S1 and S2. the summer events were low and much less than the corresponding SGW concentrations (Table 5). In contrast, THF concentrations for the spring events were much higher than the summer values and were as high as the SGW concentrations for the spring events. Rainfall concentrations for these events (Table 5) suggest higher NO 3 deposition during the spring. High rainfall and THF NO 3 concentrations were also observed for other spring events not presented here. [32] We believe this variation in the THF NO 3 concentrations across events determined whether the stream NO 3 concentrations followed a dilution trajectory or increased on the hydrograph rising limb. When THF concentrations were low, as for summer events, stream NO 3 concentrations were primarily influenced by the amounts and concentrations of SGW. A decrease in SGW amounts led to the decrease in stream NO 3 values (Figures 7 and 8). Alternately, when THF concentrations were high, as for spring events, high NO 3 concentrations occurred when the combined NO 3 contributions from THF and SGW were at their maxima (Figures 9 and 10). Furthermore, between spring events too, the event with higher THF NO 3 concentration (May 27) generated higher stream NO 3 concentrations. NO3 concentrations in RW were low during both summer and spring periods (Table 5). [33] Across catchments, the rise in NO 3 concentrations during spring events was most pronounced and consistent for the S5 subcatchment (Figures 9 and 10) followed by S2 and S3 (Table 6). Event THF amounts were also highest for

S5 followed by S2 and S3, supporting our hypothesis that THF was an important contributor to NO 3 increase for spring events. During summer, except for the June 8 event, the % decreases in NO 3 concentrations were similar for S1 and S2 (Table 6). [34] Flow-weighted concentrations and total flux for the six events are presented in Tables 7 and 8, respectively. Subcatchment S3 that contained the high NO 3 seep exported the largest amount of NO 3 in streamflow. Following S3, NO 3 exports decreased in order from S2, S1, to S5. Between events, NO 3 exports were highest for events that produced the most discharge. Baseflow stream nitrate concentrations were highest for the headwater seep subcatchment S3 with a decrease in concentrations with increasing catchment size or % saturated area (Table 3). 3.9. DOC Concentrations During Events and Across Catchments [35] Contrary to NO 3 , DOC did not display any shift in temporal patterns between seasons (Figures 7 –10). For all subcatchments, DOC concentration steadily increased on the hydrograph rising limb, reaching a maximum at peak discharge, and then decreasing through the recession limb. Occasionally, as seen for S5 (Figures 9 and 10), high DOC concentrations were recorded at the start of the event. We attribute this high initial DOC value to in-stream DOC accumulation in stagnant pools immediately upstream of the V-notch weir. This high initial DOC was observed

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Figure 8. Precipitation, streamflow, endmember-contributions, and DOC (mmol L1) and NO 3 (mmolc L1) concentrations for the event of July 27, 2003 for catchments S1 and S2. especially for low-flow conditions or for events that occurred after an extended dry period. Maximum DOC concentrations typically occurred between the peak THF and RW contributions. THF and RW were the most important sources for DOC, and since DOC was used as a tracer, the match between THF/RW and DOC was expected. [36] As expected, highest DOC concentrations were observed for the largest and most intense events. The event of July 27 produced the highest DOC values for both S1 and S2. However, the % increase in DOC for the 3.4 ha S2 subcatchment exceeded that for S1 for the events of July 27 and August 9, 2003 (Table 6). Furthermore, the maximum DOC concentration for S2 also exceeded that for S1 during the July 27 event (Table 7) although S1 was two orders of magnitude larger than S2 and had a larger proportion of valley-bottom saturated areas. Similarly S2 and S3, which had lower saturated area % than S5 produced higher % DOC increases for the large spring event of May 9, 2004. Also, while there was a wide range in the % increase in DOC for subcatchments S2 and S3, the corresponding values for S5 were consistently around 70% (Table 6). The timing of the DOC peak concentration also differed across the subcatchments. Although the maximum DOC concentrations occurred after the peak discharge across all catchments, the shift in the DOC peak (to the right of the discharge peak) was most for S1, the largest subcatch-

ment. In contrast, the peak DOC values were closest to the discharge peak for the smallest subcatchment S3. [37] Average baseflow DOC concentrations increased with increasing catchment size and saturated area % across the nested subcatchments S3, S2, and S1 (Table 3). Average baseflow DOC concentration recorded for S5 was much higher than S2 and S3 but lower than S1. Flowweighted event concentrations followed the same sequence observed in baseflow concentrations for the nested subcatchments (Table 7). While there was considerable difference in the flow-weighted event concentrations among the catchments the difference in exports of DOC was not marked (Table 8). The reason was that although the DOC concentrations increased with increasing catchment and saturated area % the discharge per unit catchment area decreased. The rise in concentrations was thus cancelled by the drop in discharge. Not surprisingly, the highest exports were associated with large events (July 27 and May 9).

4. Discussion 4.1. What Are the Sources of DOC and NO 3 and How Do They Regulate the Within-Event Patterns of These Solutes? [38] EMMA analysis identified THF, SGW and RW as the three end-members that enclosed the stream chemistry

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Figure 9. Precipitation, streamflow, endmember-contributions, and DOC (mmol L1) and NO 3 (mmolc L1) concentrations for the event of May 20, 2004 for catchments S2, S3, and S5.

Figure 10. Precipitation, streamflow, endmember-contributions, and DOC (mmol L1) and NO 3 (mmolc L1) concentrations for the event of May 27, 2004 for catchments S2, S3, and S5. 11 of 16


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Table 4. EMMA-Derived Percent End-Member Contributions for the Selected Eventsa Subcatchments S1

S2

S3

S5

Events

THF

SGW

RW

THF

SGW

RW

THF

SGW

RW

THF

SGW

RW

Jun 8, 2003 Jul 27, 2003 Aug 9, 2003 May 9, 2004 May 20, 2004 May 27, 2004 Average

33 31 29

23 4 7

45 65 64

18 27 38 14 30 16 24

60 7 37 33 51 65 42

21 66 25 53 19 19 34

15 13 14 14

37 63 60 53

48 24 20 31

43 42 19 35

38 47 46 44

19 12 35 22

31

11

58

a

The end-members included: throughfall (THF), shallow groundwater (SGW), and riparian water (RW).

across all events and catchments. Although TSW did not always enclose the stream concentrations, the position of TSW on the mixing diagrams and the surface ponding observed at the hillslope-bench saturated areas tends to suggests that TSW from saturated areas also likely influenced stream water chemistry, especially during the large events. Topsoil waters from valley-bottom riparian areas influenced stream chemistry since one of the chosen endmembers – RW, includes these chemical attributes. [39] While the deciduous forest was overall the more dominant canopy in our watershed, the EMMA analysis indicated that an average of deciduous and conifer THF concentrations provided the best fits between predicted and observed concentrations (as opposed to the choice of only deciduous or only conifer concentrations). We believe that the average values provided the best fit because (1) conifer DOC concentrations were considerably larger than deciduous values; and (2) the average value likely compensated for the chemical signature of the forest floor (litter layer) which was not sampled for this study period. The forest floor or the litter layer can be an important source of solutes [Michalzik et al., 2001]. We have recently installed forest floor water samplers to characterize the forest floor chemistry. [40] EMMA-derived hydrograph separations showed that (1) SGW contributions were high at the start of the event but declined through the rising limb; (2) THF amounts increased on the rising limb and reached a peak at or before discharge peak; and (3) RW contributions were maximum just after the discharge peak and steadily decreased through event recession. The close correspondence between the 2 2+ model-predicted NO 3 , Ca , and SO4 concentrations and the observed values confirmed this mixing model. The close match also indicates that NO 3 from SGW and THF mix in a conservative manner, very likely on the surface-saturated areas where potential for throughfall interception is high. Furthermore, the channelization of SGW and THF amounts Table 5. Concentrations (mmolc L ) for Rainfall and THF, SGW, and RW End-Members Used in the EMMA Model Predictions a

b

Event Date

Rainfall

THF

SGW

RW

Jun 8, 2003 Jul 27, 2003 May 20, 2004 May 27, 2004

7 12 42 35

9 15 40 63

71 92 51 51

0, NA 7, NA 1, 0.2 1, 0.2

Average of conifer and deciduous. Average of RS1 and RS2; RS5. NA, not available.

b

Table 6. Percent Change in NO 3 and DOC Concentrations for the Eventsa NO 3

DOC

Subcatchments

Subcatchments

1

NO3

a

along streamlets on the wetland surfaces likely reduced the contact and residence time and decreased the opportunity for changes in the solute concentrations. Hill [1993] found a close correspondence between model-predicted and observed NO 3 concentrations and identified throughfall and groundwater as the primary regulators of stream NO 3. Furthermore, Hill [1993] verified the conservative mixing behavior of NO 3 through laboratory experiments. Similarly, McHale et al. [2004] found no marked change in stream water chemistry flowing through wetlands and suggested that the wetlands were exhibiting a ‘‘hydrological isolation effect’’. Although we observed conservative NO 3 mixing at the small time scales (hours), we believe it is highly unlikely that the same behavior would persist over longer periods. [41] Our study demonstrates that the relative amounts and NO 3 concentrations of SGW and THF determined stream NO 3 concentrations and can produce different temporal patterns. Low NO 3 concentrations in THF during summer led to a decrease in NO 3 concentrations in streams. In contrast, when THF NO 3 concentrations were high (as for spring events) the mixing of THF and SGW produced a pronounced NO 3 peak on the hydrograph rising limb. Hill [1993] found similar patterns in NO 3 trajectories and attributed them to the contribution from throughfall. In contrast, Creed and Band [1998] hypothesized that the early NO 3 peaks were due to ‘‘flushing’’ of excess NO3 from VSAs by rising water tables but did not present any direct evidence to support their case. Our data show that saturated areas (RW and TSW) had low or negligible NO 3 thus ruling out the possibility of NO3- ‘‘flushing’’ by rising water table. McHale et al. [2002] also did not find any evidence for NO 3 ‘‘flushing’’ and attributed the NO3 peaks to till-

Event Date

S1

S2


51 62 39

19 58 40 78 18 78

S3

11 16 21

S5

71 130 87

S1

S2

S3

S5

79 130 99

46 250 100 200 83 31

170 25 22

70 72 71

a The percent change was computed using starting or preevent DOC concentration and the peak DOC value during the event.

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Table 7. Flow-Weighted Concentrations of DOC (mmol L1) and 1 NO 3 (mmolc L ) for the Six Events and Four Subcatchments Subcatchments S1 Event Date

NO 3


20 18 23

S2

DOC

NO 3

353 540 450

37 34 49 35 35 46

S3

DOC

NO 3

205 517 250 336 293 166

37 37 41

S5

DOC

NO 3

DOC

226 172 162

35 29 30

359 381 258

groundwater discharge at hillslope seeps. Inamdar et al. [2004] found an early event NO 3 peak accompanied by a peak in base cations which led them to conclude that the early NO 3 expression was due to displacement of till groundwater by the pressure wave associated with infiltrating precipitation. This similarity of NO 3 patterns in storms, but resulting from very different mechanisms and sources suggest that we need to be extremely careful in making inferences about NO 3 sources. The use of the ‘‘flushing’’ term for explaining these patterns has been discussed recently [Burns, 2005]. [42] In addition to identifying THF as a contributor of NO 3 , Hill [1993] also found that NO3 concentrations were high in initial throughfall increments followed by a decline to lower values [Hill, 1993, Figure 3]. We did not perform such measurements for this study. However, the variability in THF concentrations during storm events cannot be disregarded, especially for catchments like ours where THF contributions influence stream chemistry. [43] THF and RW had the highest DOC concentrations in this watershed. Event DOC concentrations were maximal after the peak discharge when EMMA-derived contributions of THF and RW were also at their maximal. Groundwater elevations in valley-bottom riparian areas and hillslopebench saturated areas also peaked during the recession limb indicating the presence of a viable hydrologic mechanism for DOC exports from these source areas. Although we did not find evidence for NO 3 flushing, the correspondence between increasing DOC concentrations and the water table rise indicates DOC flushing. However, in contrast to other studies that observed DOC flushing [Hornberger et al., 1994; Boyer et al., 1997] with a DOC peak on the hydrograph rising limb, our DOC concentrations peaked at or after discharge peak. Delayed DOC peaks were also reported by Hagedorn et al. [2000]. An important distinction between the two contrasting DOC flushing scenarios is that the early DOC peak (on the rising limb) was associated with snowmelt events while the delayed peaks observed in our and Hagedorn et al.’s [2000] study were associated with rainfall-driven spring/summer events. The difference in DOC expressions then could simply be due to the difference in runoff dynamics for the events. Snowmelt events typically occur over days whereas rainfall-driven events are limited to hours/minutes. 4.2. How Do NO 3 Responses Vary Across the Catchments and What Factors Influence the Response? [44] The high NO 3 concentrations in SGW discharged at seeps resulted in high NO 3 concentrations for the hollow

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S3 subcatchment. These concentrations then decreased with increasing catchment and percent-saturated area. The importance of hillslope seeps as NO 3 sources has been found elsewhere [Burns et al., 1998; Hill, 1993; Inamdar et al., 2004; McHale et al., 2002]. McHale et al. [2002] attributed the high NO 3 concentrations in seeps to recharge from well-drained ridge-top soils where high nitrification rates had been measured [Ohrui et al., 1999]. Hill [1993] found that high NO 3 concentrations from seeps and springs moved rapidly across the saturated valley-bottom wetlands without much change in concentrations. Schiff et al. [2002] found an order of magnitude difference in the NO 3 export from two adjoining catchments. They attributed the elevated concentrations in the high NO 3 catchment to steep hillslope gradients that allowed the NO 3 rich waters to move downslope rapidly. We believe mechanisms similar to those identified by McHale et al. [2002] and Schiff et al. [2002] are influencing the NO 3 generation and transport in our catchments. Our catchments have broad, flat, and well-drained ridge tops that are populated by a mix of second-growth deciduous trees (G. G. McGee, personal communication, 2004). Seeps are typically located one-third from the top of the ridge at the soil-till interface. Similar to McHale et al. [2002], we attribute the high NO 3 concentrations in the seeps to soil nitrification that may be occurring at ridge top positions. Mean gradients of contributing slopes in our catchments ranged from 18-23% with maximum values as high as 66%. We believe these steep gradients expedited the movement of high NO 3 seep and shallow groundwater to the valley bottom where they traversed the saturated areas with little change in the NO 3 concentrations. The close fit between our EMMA-simulated NO 3 concentrations (based on conservative mixing) and the observed values support this assertion. [45] Based on the NO 3 ‘‘flushing’’ assumption, Creed and Band [1998] hypothesized that the amount and rate of NO 3 export from a watersheds is regulated by the areal extent and the rate of expansion and contraction (wetting up and drawdown) of the VSAs. Our study suggests that the areal extent of saturated areas affected NO 3 exports, but not as characterized by Creed and Band [1998]. We believe the saturated areas indirectly regulated the NO 3 concentrations as loci for interception and delivery of THF NO 3 contributions. The valley-bottom saturated riparian areas also expedited the delivery of high-NO 3 seep waters (SGW) to the stream. This hypothesis explains the more pronounced rise in NO 3 we observed for S5 compared to the other catchments - the large saturated area in S5 allowed for a 1 Table 8. DOC (mol ha1) and NO 3 Flux (molc ha ) for the Six Events Across the Four Subcatchments

Subcatchments S1 Event Date

NO 3


0.6 1.2 0.4

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S2

DOC

NO 3

10.0 35.5 8.2

1.1 2.4 1.5 2.3 0.7 1.6

S3

DOC

NO 3

6.3 35.9 7.6 21.7 5.8 5.7

2.1 1.2 1.4

S5

DOC

NO 3

DOC

12.9 5.4 5.6

1.2 0.5 0.9

13.0 7.3 8.3

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higher interception of THF NO 3 concentrations. Do we imply that all catchments with high % of saturated areas will produce a sharp rise in NO 3 concentrations associated with high NO 3 throughfall? No. We hypothesize that only catchments with large fraction of valley-bottom saturated areas (like S5) or saturated areas well-connected to the drainage network will reproduce these patterns in NO 3 dynamics. Catchments with isolated saturated areas, even if they occupy a large % of the catchment area, will serve as sites for the loss of THF NO 3 via dissimilatory nitrate reduction before this water reaches the stream. 4.3. How Do DOC Responses Vary Across the Catchments and What Factors Influence the Response? [46] Although the average DOC concentrations from S1 and S5 were consistently high the % change in DOC concentrations during large events were occasionally greater for the catchments with lower saturated area extent (S2 and S3). For the event of July 27, S2 registered a 250% increase in DOC concentration while S1 registered a 130% increase. Similarly for the event of May 9, % changes in DOC for S2 and S5 were 200 and 70%, respectively. Similar observations were made by Hinton et al. [1998], who found that the % change in DOC concentrations during storm events were higher in those catchments dominated by riparian areas versus those dominated by wetlands. This difference in DOC response between the catchments in our study can be explained by the types of saturated areas and their spatial distribution. DOC contributions in S5 largely occurred from the valley-bottom wetland (896 m2), which was constrained by the stream along one edge and by steep hillslopes along the other. Additional surface runoff contributions (and therefore DOC) came from the 226 m2 saturated area located on the hillslope. In comparison, saturated areas in S2 included a valley-bottom riparian area (231 m2) and discrete hillslope-bench saturated areas (444 m2). Our lysimeter data (TSW) showed that isolated saturated areas had high DOC (Table 3). We hypothesize that during low moisture events, DOC and runoff contributions in S2 occurred from the valley-bottom positions but as the soil moisture increased and saturated areas became hydrologically connected, increasing surface runoff (and hence DOC) contributions came from the isolated hillslope saturated areas. These observations suggest that catchments like S2 with discrete saturated areas may have substantial untapped ‘‘reserves’’ of DOC that become mobilized during elevated moisture conditions and result in increasing DOC export with rising soil moisture. [47] A comparison between the temporal patterns of DOC for S3, S2, and S1 also show that DOC peaks for S1 were more delayed after maximum discharge than for the other, smaller subcatchments. This lag in DOC peaks has also been observed in other studies [Brown et al., 1999; Hangen et al., 2001; Hagedorn et al., 2000]. Hangen et al. [2001] attributed this pattern to the lag from stem flow that displaced DOC-rich soil waters. We believe that as catchment area increases there is a greater population of distal, discrete saturated areas. This progression can be seen for S3 and S2, where for S3 (hollow subcatchment) the saturated areas are restricted to the channel head but increase in number and distance from the stream for S2 (Figure 1). Furthermore, we have already highlighted the delayed response of groundwater for the hillslope-bench saturated

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area during the July 27 event (Figure 3). We hypothesize that the larger number of discrete distal saturated areas in S1 and their delayed hydrologic response are responsible for delayed expression of DOC as observed for S1. [48] Since DOC flushing was observed in this study, we believe that the Creed and Band [1998] model of VSA controls on solute concentrations is more relevant for describing DOC response in our catchments. Creed and Band [1998] suggested that catchments with large VSA and small change in VSA (dVSA/dt) would yield high average solute concentrations but small changes in concentrations during events. Conversely, catchments with small VSA but large dVSA/dt will have lower average values but greater ranges in solute concentrations during events. Although we did not measure the change in areal saturation (dVSA/dt), ground water elevations recorded in saturated areas and topographic indices provided some insight into saturated area dynamics for S2 and S5. The large valley-bottom wetland of S5 was spatially constrained by hillslopes and the stream. Ground water elevations (R5) indicate that the water table was near the surface and showed considerable variation during events (Figure 3). The range of topographic indices for the wetland was also grouped towards the high values (Table 1). In comparison for the valley-bottom riparian area in S2, ground water height showed much larger variations during events, and topographic index values corresponding to the riparian area included a wider range of values (Table 1). Furthermore, a large proportion of saturation in S2 was in the form of hillslope-bench saturated areas. Well H2 showed that these hillslope-bench positions had large and rapid fluctuations in water table elevations. These observations indicate that the saturated areas in S2 were more dynamic than those in S5. The dynamic nature of saturated areas and the large changes in event DOC for S2 (as opposed to S5) clearly conform to Creed and Band’s [1998] hypotheses. 4.4. Can a Conceptual Model Be Developed That Explains the NO 3 and DOC Patterns in Terms of Catchment Hydrology and Topography? [49] Topography played an important role in influencing the hydrologic response and the DOC and NO 3 patterns in PPBW. The broad ridgetops, steep sideslopes, hillslope benches and narrow valley-bottom areas, and the glacial till are the key components that influence the response of this watershed. A model describing how these features regulate the hydrologic response has been developed by S. P. Inamdar and M. J. Mitchell (manuscript in preparation, 2006). The steeply incised valley and the occurrence of glacial till are likely producing local (shallow) and regional groundwater flow systems (deep) in this watershed. Local and regional groundwater systems have been shown to influence the occurrence of surface saturation or wetlands in watersheds underlain by glacial-till [Hill, 1993; Richardson et al., 2001]. We hypothesize that local groundwaters were discharged at the seeps while the abrupt slope-breaks at the hillslope-bench and the valley-bottom positions forced deeper groundwater flows to the surface, similar to that observed for edge-focused discharge wetlands [Richardson et al., 2001, Figure 3.23]. During baseflow conditions the valley-bottom and hillslope-bench positions were maintained in a moist state by the slow groundwater seepage, but increased hillslope flux and elevated hydraulic gradients during storm events displace stored water from these land-

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scape positions (S. P. Inamdar and M. J. Mitchell, manuscript in preparation, 2006). [50] The broad well-drained ridgetops were likely responsible (via nitrification) for the high NO 3 concentrations observed at the seeps. The steep sideslopes expedited the movement of high concentration NO 3 seep waters to the stream. The wet valley-bottom conditions resulted in a rapid transmittal and high concentration NO 3 from SGW and THF to the streams. These observations suggest an alternate model where the occurrence of seeps and the slope gradients of contributing hillslopes were the primary factors that dictated stream NO 3 . In our NO3 model, VSAs or saturated areas played a secondary role as loci for interception of throughfall NO 3 contributions. Another factor that may have important implications for NO 3 and needs consideration is the slope aspect [Schiff et al., 2002]. The predominant aspect of hillslopes in our smaller subcatchments (S2, S3, and S5) was northwest whereas the largest proportion of slopes in S1were oriented towards the west. Seeps in the north-facing subcatchments were perennial but were ephemeral or non-existent in the south-facing subcatchments. Field surveys and soil coring also showed much higher wetness in north-facing than south-facing subcatchments. This disparity in moisture has important implications for the catchment-scale NO 3 and DOC exports and needs to be further explored. [51] The valley-bottom riparian and hillslope-bench saturated areas influenced DOC exports both directly as sources of DOC as well as indirectly through interception of DOCrich THF. Our study demonstrates that catchments with large well-connected wetlands may consistently generate high DOC concentrations but those with discrete, isolated, saturated areas have the potential for large DOC contributions during large events and variation between events. 4.5. Implications for Watershed Management [52] Two observations from our study that have important implications for management of water resources are (1) high NO 3 concentrations are released at seeps and are transmitted rapidly downslope over steep slope gradients; and (2) valley-bottom riparian and wetland areas do not act as sinks for NO 3 but rather expedite the movement of the NO 3 rich seep waters to the stream. Many of the watersheds in the glaciated region of the northeast U.S. have similar topography with broad ridgetops and deeply incised valleys. Agricultural operations are generally restricted to the broad ridgetops and include a mix of cropping, dairy cattle, and beef operations. Agricultural landscapes have typically been found to leach greater amounts of NO 3 associated with increased soil nitrification, fertilizer use, and/or animal waste generation [Ritter and Bergstorm, 2001]. Hence, seeps being recharged near these ridge tops with agricultural operations would likely have much greater NO 3 concentrations than those observed for the forested catchments in our study. Thus this agriculturally derived NO 3 would more likely drain into the streams and thus have a negative impact on water quality.

5. Conclusions [53] The storm-event exports of NO 3 from PPBW were regulated by the amounts and concentrations of NO 3 in SGW and THF. Over the short period of these storm events

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NO 3 contributions from these two sources were found to mix conservatively and travel over the valley-bottom saturated areas or wetlands without marked changes in concentration. These results suggest that catchments with similar topographic and hydrogeologic settings may be more vulnerable to NO 3 loss from increased atmospheric N deposition or agricultural sources, both of which may contribute to increased NO 3 in SGW discharged at the seeps. [54] We did not find any evidence for NO 3 flushing by a rising water table and our results did not conform to the VSA model for NO 3 proposed by Creed and Band [1998]. We propose an alternate model for NO 3 for this particular topographic setting where the occurrence of seeps and steep slope gradients are the primary determinants of NO 3 generation and delivery, and VSAs or saturated areas play a secondary role as loci for interception of NO 3 contributions from throughfall. [55] Although DOC flushing was observed in our study, unlike the snowmelt-DOC expression [Boyer et al., 1997], the peak in DOC concentrations occurred on the recession limb. This pattern shows that dissimilar solute expressions can be produced by the same solute export mechanism (flushing) while similar expressions (solute peak on the hydrograph rising limb) can be produced by very different mechanisms (as in case of nitrate – flushing versus throughfall contributions). These observations suggest that export mechanisms cannot be inferred simply by studying temporal patterns of stream solutes and that additional field measurements on solute sources are critical. [56] Our observations underscore the importance of discrete saturated areas for DOC exports. Although wetland catchments may yield consistently high DOC concentrations, riparian catchments with multiple discrete saturated areas have the potential for generating large increases in DOC during events. [57] Our study highlights the complexity of hydrologic and solute response of a glacial till catchment with steep topography. Seeps located higher up on the hillslope and at a greater distance from the stream were able to influence stream conditions during low flow conditions, while riparian areas and wetlands contributed runoff and solutes in response to elevated hydraulic gradients during hydrograph recession. [58] Acknowledgments. We would like to thank the USDA-NRI for funding this project via a New Investigator award to S. Inamdar. We are extremely grateful to the Gowanda Water Department for providing access to the watershed. Joanna Tuk and Julia Graham are thanked for sample collection and data management. Pat McHale and David Lyons provided the water chemistry expeditiously. Assistance of Mike McHale on EMMA is much appreciated. Jan Seibert’s GEASY program was extremely helpful for the DWI analysis. We thank Tom Meixner, two anonymous reviewers, and the editors of Water Resources Research who provided very constructive reviews.

References Aber, J. D., K. J. Nadelhoffer, P. A. Steudler, and J. M. Melillo (1989), Nitrogen saturation in forest ecosystems, Bioscience, 39, 378 – 386. Aber, J. D., et al. (2003), Is nitrogen deposition altering the nitrogen status of northeastern forests?, Bioscience, 53(4), 375 – 389. Boyer, E. W., G. M. Hornberger, K. E. Bencala, and D. M. McKnight (1997), Response characteristics of DOC flushing in an Alpine catchment, Hydrol. Processes, 11, 1635 – 1647. Brown, V. A., J. J. McDonnell, D. A. Burns, and C. Kendall (1999), The role of event water a rapid shallow flow component and catchment size in summer stormflow, J. Hydrol., 217, 171 – 190.

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Burns, D. A. (2005), What do hydrologists mean when they use the term flushing?, Hydrol. Processes, 19, 1325 – 1327. Burns, D. A., P. S. Murdoch, G. B. Lawrence, and R. L. Michel (1998), Effect of groundwater springs on NO 3 concentrations during summer in Catskill Mountains streams, Water Resour. Res., 34, 1987 – 1996. Burns, D. A., J. J. McDonnell, R. P. Hooper, N. E. Peters, J. E. Freer, C. Kendall, and K. J. Beven (2001), Quantifying contributions to storm runoff through end-member mixing analysis and hydrologic measurements at the Panola Mountain Research Watershed (Georgia, USA), Hydrol. Processes, 15, 1903 – 1924. Christopherson, N., and R. P. Hooper (1992), Multivariate analysis of stream water chemical data: The use of principal component analysis for the end-member mixing problem, Water Resour. Res., 28, 99 – 107. Creed, I. F., and L. E. Band (1998), Export of nitrogen from catchments within a temperate forest: Evidence for a unifying mechanism regulated by variable source area dynamics, Water Resour. Res., 34(11), 3105 – 3120. Driscoll, C., et al. (2003), Nitrogen pollution in the northeastern United States: Sources, effects, and management options, BioScience, 53, 357 – 374. Hagedorn, F., P. Schleppi, P. Waldner, and H. Fluhler (2000), Export of dissolved organic carbon and nitrogen from Gleysol dominated catchments—The significance of water flow paths, Biogeochemistry, 50, 137 – 161. Hangen, E., M. Lindenlaub, C. Leibundgut, and K. von Wilpert (2001), Investigating mechanisms of stormflow generation by natural tracers and hydrometric data: A small catchment study in the Black Forest, Germany, Hydrol. Processes, 15, 183 – 199. Hill, A. R. (1993), Nitrogen dynamics of storm runoff in the riparian zone of a forested watershed, Biogeochemistry, 20, 19 – 44. Hill, A. R., and J. M. Waddington (1993), Analysis of storm runoff sources using oxygen-18 in a headwater swamp, Hydrol. Processes, 7, 305 – 316. Hill, A. R., W. A. Kem, J. A. Buttle, and D. Goodyear (1999), Nitrogen chemistry of subsurface storm runoff on forested Canadian Shield hillslopes, Water Resour. Res., 35, 811 – 821. Hinton, M. J., S. L. Schiff, and M. C. English (1998), Sources and flowpaths of dissolved organic carbon during storms in two forested watersheds of the Precambrian Shield, Biogeochemistry, 41, 175 – 197. Hjerdt, K. N., J. J. McDonnell, J. Seibert, and A. Rodhe (2004), A new topographic index to quantify downslope controls on local drainage, Water Resour. Res., 40, W05602, doi:10.1029/2004WR003130. Hooper, R. P., and C. A. Shoemaker (1986), A comparison of chemical and isotopic hydrograph separation, Water Resour. Res., 22, 1444 – 1454. Hornberger, G. M., K. E. Bencala, and D. M. McKnight (1994), Hydrological controls on dissolved organic carbon during snowmelt in the Snake River near Montezuma, Colorado, Biogeochemistry, 25, 147 – 165. Inamdar, S. P., S. Christopher, and M. J. Mitchell (2004), Export mechanisms for dissolved organic carbon and nitrate during summer storm events in a glaciated forested catchment in New York, USA, Hydrol. Processes, 18, 2651 – 2661. McGlynn, B. L., and J. J. McDonnell (2003), Role of discrete landscape units in controlling catchment dissolved organic carbon dynamics, Water Resour. Res., 39(4), 1090, doi:10.1029/2002WR001525. McHale, M. R., J. J. McDonnell, M. J. Mitchell, and C. P. Cirmo (2002), A field-based study of soil water and groundwater nitrate release in an Adirondack forested watershed, Water Resour. Res., 38(4), 1031, doi:10.1029/2000WR000102.

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McHale, M. R., C. P. Cirmo, M. J. Mitchell, and J. J. McDonnell (2004), Wetland nitrogen dynamics in an Adirondack forested watershed, Hydrol. Processes, 18(10), 1853 – 1870. Michalzik, B., K. Kalbitz, J. H. Park, S. Solinger, and E. Matzner (2001), Fluxes and concentrations of dissolved organic carbon and nitrogen— A synthesis for temperate forests, Biogeochemistry, 52, 173 – 205. Mitchell, M. J., G. G. McGee, P. McHale, and K. C. Weathers (2001), Experimental design and instrumentation for analyzing solute concentrations and fluxes for quantifying biogeochemical processes in watersheds, paper presented at 4th International Conference on ILTER in East Asia and Pacific Region, Ulaanbaatar-Hatgal, Mongolia. Ohrui, K., M. J. Mitchell, and J. M. Bischoff (1999), Spatial patterns of N mineralization and nitrification in a forested watershed in the Adirondack Mountains of New York, Can. J. For. Res., 29, 497 – 508. Olivera, F., and D. R. Maidment (2000), GIS tools for HMS modeling support, in Hydrologic and Hydraulic Modeling Support With Geographic Information Systems, edited by D. R. Maidment and D. Djokic, chap. 5, ESRI Press, San Diego, Calif. Phillips, R. A. (1988), Relationship between glacial geology and streamwater chemistry in an area receiving acid deposition, J. Hydrol., 101, 267 – 273. Quinn, P. F., K. J. Beven, and R. Lamb (1995), The ln[a/tanb] index: How to calculate it and how to use it in the TOPMODEL framework, Hydrol. Processes, 9, 161 – 182. Richardson, J. L., J. L. Arndt, and J. A. Montgomery (2001), Hydrology of wetland and related soils, in Wetland Soils, edited by J. L. Richardson and M. J. Vepraskas, Lewis, Boca Raton, Fla. Ritter, W. F., and L. Bergstorm (2001), Nitrogen and water quality, in Agricultural Nonpoint Source Pollution, edited by W. F. Ritter and A. Shirmohammadi, pp. 59 – 89, Lewis, Boca Raton, Fla. Roulet, N. T. (1990), Hydrology of a headwater basin wetland: Groundwater discharge and wetland maintenance, Hydrol. Processes, 4, 387 – 400. Schiff, S. L., K. J. Devito, R. J. Elgood, P. M. McCrindle, J. Spoelstra, and P. Dillon (2002), Two adjacent forested catchments: Dramatically different NO 3 exports, Water Resour. Res., 38(12), 1292, doi:10.1029/ 2000WR000170. Shanley, J. B., C. Kendall, T. E. Smith, D. M. Wolock, and J. J. McDonnell (2002), Controls on old and new water contributions to stream flow at some nested catchments in Vermont, USA, Hydrol. Processes, 16, 589 – 609. Stoddard, J. L. (1994), Long term changes in watershed retention of nitrogen: Its causes and consequences, in Environmental Chemistry of Lakes and Reservoirs, Adv. Chem. Ser., vol. 237, edited by L. A. Baker, pp. 223 – 284, Am. Chem. Soc., Washington, D. C. Wigington Jr., P. J., T. D. Davis, M. Tranter, and K. N. Eshleman (1990), Episodic acidification of surface waters due to acidic deposition, in Acidic Deposition: State of Science and Technology, NAPAP Rep. 12, Natl. Acidic Precip. Assess. Program, Washington, D. C.

S. P. Inamdar, Bioresources Engineering, University of Delaware, Newark, DE 19716, USA. ([email protected]) M. J. Mitchell, Faculty of Environment and Forest Biology, SUNY-ESF, Syracuse, NY 13210, USA.

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