Seasonal patterns in hydrochemical mixing in three Great Lakes rivermouth ecosystems

Journal of Great Lakes Research 45 (2019) 651–663

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Seasonal patterns in hydrochemical mixing in three Great Lakes rivermouth ecosystems Martha L. Carlson Mazur a,⁎, Jeff Schaeffer b, Jennifer E. Granneman b,1, Natalie Goldstrohm b,2, Faith A. Fitzpatrick c, James H. Larson d, Paul C. Reneau c, Kurt P. Kowalski b, Paul W. Seelbach e a

Bellarmine University, 2001 Newburg Rd., Louisville, KY 40213, USA U.S. Geological Survey (USGS) Great Lakes Science Center, 1451 Green Rd., Ann Arbor, MI 48105, USA USGS Wisconsin Water Science Center, 8505 Research Way, Middleton, WI 53562, USA d USGS Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603, USA e University of Michigan, School for Environment and Sustainability, 440 Church St., Ann Arbor, MI 48109, USA b c

a r t i c l e

i n f o

Article history: Received 18 June 2018 Accepted 15 March 2019 Available online 18 April 2019 Communicated by Ram Yerubandi Keywords: Delta Stable isotopes Geomorphology Hydrology Water chemistry Current profiling

a b s t r a c t Rivermouth ecosystems in the Laurentian Great Lakes represent complex hydrologic mixing zones where lake and river water combine to form biologically productive areas that are functionally similar to marine estuaries. As urban, industrial, shipping, and recreational centers, rivermouths are the focus of human interactions with the Great Lakes and, likewise, may represent critical habitat for larval fish and other biota. The hydrology and related geomorphology in these deltaic systems form the basis for ecosystem processes and wetland habitat structure but are poorly understood. To this end, we examined hydrogeomorphic structure and lake-tributary mixing in three rivermouths of intermediate size using water chemistry, stable isotopes, and current profiling over a fivemonth period. In rivermouths of this size, the maximum depth of the rivermouth ecosystem influenced water mixing, with temperature-related, density-dependent wedging and layering that isolated lake water below river water occurring in deeper systems. The inherent size of the rivermouth ecosystem, local geomorphology, and human modifications such as shoreline armoring and dredging influenced mixing by altering the propensity for density differences to occur. The improved scientific understanding and framework for characterizing hydrogeomorphic processes in Great Lakes rivermouths across a disturbance gradient is useful for conservation, management, restoration, and protection of critical habitats needed by native species. © 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction Rivermouth ecosystems of the Laurentian Great Lakes are dynamic habitats where river and lake waters meet and mix (Larson et al., 2013). Structurally, these ecosystems have three main components: a low-lying river where river flows are influenced by lake backwater and seiches, an outlet to the lake, and a lake nearshore area influenced by the river plume (Larson et al., 2013). Some rivermouth ecosystems may also have a receiving basin just prior to the outlet that is somewhat deeper and where river flow slows and often warms in summer prior to entering the lake. The interplay between these dynamic forces and degree of constraint by local geomorphology affect rivermouth and

⁎ Corresponding author. E-mail address: [email protected] (M.L. Carlson Mazur). Present address: Florida Fish and Wildlife Research Institute, 100 8th Ave SE, St. Petersburg, FL 33701, USA. 2 Present address: Texas Parks and Wildlife Department, Inland Fisheries Abilene District, 5325 N 3rd Street, Abilene, TX, 79603, USA. 1

nearshore water mixing, delivery and accumulation of materials, habitat development, and biological community composition (Fagherazzi et al., 2015; Janetski and Ruetz, 2015; Keough et al., 1999; Nienhuis et al., 2016; Peterson, 2003). A better understanding of freshwater rivermouths as distinct ecosystems with a specific function within the nearshore, and supporting the offshore lacustrine environment, is needed for their effective management and restoration (Larson et al., 2013). As locations with intense human interaction, rivermouth ecosystems provide both ecological (Höök et al., 2008; Kowalski et al., 2014; MacKenzie et al., 2004; Minns and Wichert, 2005; Morrice et al., 2004) and economic benefits (Allan et al., 2013; Braden et al., 2008; Isely et al., 2018; Taylor et al., 2006). Many rivermouths are altered by urban and industrial development, excess nutrient and sediment subsidies (Bellinger et al., 2016; Evans and Seamon, 1997; Lin and Guo, 2016), and geomorphic modifications (Allan et al., 2013; Larson et al., 2013) that can influence ecosystem services in the Great Lakes proper (Smith et al., 2015). Additional information on rivermouth ecosystem function can guide restoration to ameliorate negative externalities on ecosystem services.

https://doi.org/10.1016/j.jglr.2019.03.009 0380-1330/© 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

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M.L. Carlson Mazur et al. / Journal of Great Lakes Research 45 (2019) 651–663

A foundation of rivermouth function is the hydrochemical mixing that occurs in these systems resulting from the dynamic hydrologic and hydraulic interplay between river and lake, which can be affected by the morphology of the rivermouth itself. Narrow rivermouth openings attenuate seiches (Trebitz, 2006), while periodic closure of a rivermouth by sediment can inhibit exchange of lake and river water (Trebitz et al., 2002). In contrast, in large rivers with wide rivermouths, seiche-related displacement of river water by lake water can be substantial (Morrice et al., 2011). How water mixes depends on density gradients, as in marine estuarine settings, but these density gradients in freshwater interactions are governed mostly by temperature differences (Larson et al., 2013), and water temperatures depend on time of year and local geomorphology. These dynamics of mixing are understudied in freshwater rivermouths (Robinson and Shepard, 2011) despite their influence on water temperature (Mortimer, 2004), pollution and sediment transport (Bellinger et al., 2016; MacKenzie, 2001; Sorensen et al., 2004), and biological communities (Bhagat and Ruetz, 2011; Hoffman et al., 2010; Trebitz et al., 2005; Trebitz et al., 2009). Mixing in rivermouth ecosystems is a fundamental lake ecotone process that is as important to ecosystem function in freshwater rivermouths as riverine and saltwater estuarine systems. Natural flow regime of rivers and estuaries determines ecological integrity through its effect on water quality, energy, habitat, and biological interactions (Montalto and Steenhuis, 2004; Poff et al., 1997). In rivermouth ecosystems, we would expect a similar response to restoration of the natural flow regime that can be assessed by examining mixing dynamics along the river-to-lake continuum. Studies of mixing dynamics and their importance in rivermouths, however, have largely focused on nearshore river plumes (Höök et al., 2006; Jameel et al., 2018; Makarewicz et al., 2012; Rao and Schwab, 2007; Reichert et al., 2010; Xu et al., 2017). Because specifics of mixing vary across rivermouth locations and morphologies, comprehensive study should include a range of types. What little we know about mixing in rivermouth ecosystems

Fig. 1. Location of rivermouth study sites along Lake Michigan sampled May–October 2011.

suggests that mixing is fundamental to their function, and restoration plans would benefit from taking such dynamics into account. Our goal was to examine mixing in rivermouths of intermediate size under a range hydrologic conditions, across hydrogeomorphic physiographies, and over multiple seasons to understand mixing processes that might occur within the lower river or receiving basin. Selected areas of study, under specified conditions, were of special interest for restoration due to the prevalent human impacts along the Great Lakes coast. We used intermediate rivermouth size to mean those that fall between small tributaries, and large rivers and connecting channels, typically with drainage areas between 1000 and 5000 km2. Our specific

Fig. 2. Within-site sampling design showing lateral transects (solid lines), longitudinal transects (dashed lines), and sampling stations of depth profiles and water sampling (dots) for the Ford (a), Manitowoc (b), and Pere Marquette (c) rivermouth ecosystems. The evaporation pond referenced in the text is shown in the Pere Marquette. Sampling locations 1 and 2 were located in the river proper upstream of lake influence and are not shown.

M.L. Carlson Mazur et al. / Journal of Great Lakes Research 45 (2019) 651–663

objectives were: 1) determine the degree and spatial distribution of lake-river mixing and 2) describe seasonal variability in hydrochemical structure in the context of understanding the aforementioned processes in the context of river flow, variation in lake level, and rivermouth morphology. Methods

653

unique hydrogeomorphic setting, morphology, and potential upstream distance of lake effect (Fig. 2). Lake Michigan water levels in 2011 were below the average lake level of 176.42 m (datum: International Great Lakes Datum 1985, IGLD85) (Fig. 3), except for some seicherelated oscillations during the summer and fall months that briefly raised water levels in the rivermouths to near the average. In general, July is the month with the highest water levels in Lake Michigan (Wilcox et al., 2007).

Study design Field and laboratory methods Rivermouth ecosystems were identified using the definition outlined in Larson et al. (2013) such that each ecosystem included a lower river affected by lake levels, an outlet, and a plume. Three rivermouth sites on Lake Michigan of intermediate discharge and varying anthropogenic impact were selected for study based on the following criteria: 1) a U.S. Geological Survey (USGS) gage was present in the lower river but above the lake influence, 2) the rivermouth ecosystem did not contain a dam within the lower valley, and 3) public boat access was available to allow field researchers access to the site. The three sites chosen, Ford River near Escanaba, MI; Manitowoc River in Manitowoc, WI; Pere Marquette River in Ludington, MI (Fig. 1), represented different rivermouth types in terms of river flow sourcing (groundwater vs. runoff), coastal slope, geomorphic structure and presence of a receiving basin, and human impacts. Due to the limited number of sites available that fit these criteria, within-site systematic sampling was used rather than random site selection. To measure the degree and spatial distribution of lake-river mixing, sampling was performed longitudinally, laterally, vertically, and on a monthly basis in each of the three sites from May to October 2011. For each studied river, six lateral transects, one longitudinal transect, and up to three vertical profiles were sampled at various locations between the river upstream of lake influence and the lake proper (Fig. 2). The two upstream river sampling stations were not depicted in Fig. 2 to provide better resolution of the rivermouth. Longitudinally, water sampling extended from upriver, outside of direct lake water influence, to the open waters of the lake. Site descriptions The three rivermouths included in this study varied in the physical and hydrologic structure, and degree of human alteration, as indicated by catchment alteration both at the rivermouth and land cover in the upstream watershed (Table 1). The Manitowoc River flows directly into Lake Michigan, the Pere Marquette River flows into a drowned rivermouth lake (Pere Marquette Lake) before emptying into Lake Michigan proper, and the Ford River flows into Lake Michigan at the top of shallower Green Bay (Fig. 1). Along with position of entry into Lake Michigan, local geology and bathymetry contributed to the rivers'

Field crews collected measurements of specific conductance (μS/cm) in the top 0.5 m of the water column by boat along longitudinal and lateral transects at each site (Fig. 2) by continuous logging using a YSI 6600 or 6560 conductivity sensor (calibrated daily, 1 to 100 μS/cm resolution with ±0.5% of reading and 1 μS/cm accuracy). Measurements of specific conductance also were collected by continuous logging vertically at three equidistant locations along longer lateral transects and at one station along short lateral transects (Fig. 2). At these same locations, grab water samples were collected for tracer water chemistry processing, including magnesium (mg/L; detection level = 0.008 mg/L), boron (μg/L, detection level = 1.0 μg/L), and stable water isotopes (δD H2O ‰, δ18O H2O ‰). If a thermocline was present in deeper water, as determined using the YSI, a grab sample was taken from the top and bottom of the water column, midway between the thermocline and the water surface or substrate, respectively. Otherwise, a grab sample was taken in the middle of the water column. Water samples were filtered immediately through a 0.45-micron cellulose acetate filter into sample bottles. Magnesium and boron samples were preserved with nitric acid and sent overnight on ice to the U.S. Geological Survey National Water Quality Laboratory in Denver, CO, where they were analyzed using inductively coupled plasma atomic emissions spectrometry (ICP-AES). Stable water isotope samples were sent to Isotech Laboratories, Inc. in Chicago, IL, where they were analyzed for δD and δ18O using cavity ring-down spectroscopy (CRDS). Laboratory measurement uncertainty indicated by standard deviation in relation to reference waters was minimal for isotopes, 0.28‰ for δD H2O and 0.07‰ for δ18O H2O. To verify that the chemical signatures corresponded to magnitude and direction of flow, velocity was measured at Manitowoc by boat along the six lateral transects (Fig. 2b) using an acoustic Doppler current profiler (ADCP) (SonTek M9 using River Surveyor Live 3.6). The ADCP was integrated with an external differentially corrected global positioning system (DGPS) to georeference the measurements. To ensure data quality standards, procedures outlined in Mueller and Wagner (2009) were followed. Standard data-collection procedures were used consistently. ADCP data are typically noisy, especially at low velocities below 0.03 m/s, which are common in rivermouths. The ADCPs are capable

Table 1 Characteristics of the rivermouth sites in this study. Mean annual discharge from United States Geological Survey gages for Ford River near Hyde, MI, is over 1955–2016, Manitowoc River at Manitowoc, WI, is 1972–2016, and Pere Marquette River at Scottville, MI, is 1940–2016 (https://waterdata.usgs.gov, accessed 31 May 2018). Lake-level data are provided by the National Oceanographic and Atmospheric Administration (Ford: Port Inland, MI; Manitowoc: Kewanee, MI; Pere Marquette: Ludington, MI; https://tidesandcurrents.noaa.gov/, accessed 31 May 2018). Watershed areas were obtained from the USGS Watershed Boundary Dataset (https://www.usgs.gov/core-science-systems/ngp/tnm-delivery/). The National Land Cover Database (https://www.mrlc.gov/) was used to determine primary land cover. Site

Location

Geomorphology

Watershed area (km2)

Ford

Ford River Township, MI Manitowoc, WI

Delta with side channels Artificial barriers

1198

Lagoon with connecting channel

1956

Manitowoc

Pere Ludington, MI Marquette

1364

Primary watershed land cover

Wetlands and forest Agriculture and urban Forest and agriculture

Length of lower River (km)

Maximum width (m)

Maximum depth (m)

Mean Annual discharge (m3/s)

Groundwater contribution

0.6

50

2

10

Low

04059500 9087096

4

75

8

14

Low

04085427 9087068

18

700

13

24

High

04122500 9087023

USGS River gage ID

NOAA station ID

654

M.L. Carlson Mazur et al. / Journal of Great Lakes Research 45 (2019) 651–663

80

a. Ford Mean lake level

176.4

70 60

176.2

50

176.0

40 30

175.8

20 175.6

Data analysis

River Discharge (m3/s)

Lake Michigan Elevation (m IGLD85)

176.6

P ¼ 100  ðRM−RÞ=ðR−LÞ

10

175.4 1-Jan

1-Apr

1-Jul

1-Oct

0 31-Dec

Date 80

b. Manitowoc Mean lake level

176.4

70 60

176.2

50

176.0

40 30

175.8

20 175.6

River Discharge (m3/s)

Lake Michigan Elevation (m IGLD85)

176.6

10

175.4 1-Jan

1-Apr

1-Jul

1-Oct

0 31-Dec

Date 176.6

80 Mean lake level

70 60

176.2

50

176.0

40 30

175.8

20 175.6 Sampling Event

175.4 1-Jan

1-Apr

Daily Lake Hourly Lake 1-Jul

1-Oct

River Discharge (m3/s)

Lake Michigan Elevation (m IGLD85)

c. Pere Marquette 176.4

Mixing was assessed via percent lake water determined from specific conductance values, magnesium and boron concentrations, and stable water isotopes using a two-source mixing model for each tracer. Deuterium excess (d-excess) was calculated from stable water isotope data following Dansgaard (1964); this value combines 18O and 2H contributions in a water sample and is useful for differentiating water sources (Kendall and Coplen, 2001). Mixing models for each of dexcess, magnesium, boron, and specific conductance were used to estimate the percent lake water using the equation.

10 River

0 31-Dec

Date Fig. 3. Hydrographs of river discharge and lake level for January–December 2011 at the Ford (a), Manitowoc (b), and Pere Marquette (c). Lake Michigan NOAA water-level stations were Port Inland, MI; Kewaunee, WI; and Ludington, MI, respectively. USGS discharge gages were Ford River near Hyde, MI; Manitowoc River at Manitowoc, WI; and Pere Marquette River at Scottville, MI.

of creating a vertical profile (or ensemble) of bins of velocity data. With the SonTek M9, the bin size changes depending on the water depth, and the thickness of unmeasured zones at the water surface and at the streambed also vary. The ADCP also recorded water depth along transects. Seiche-related oscillations likely affected the velocity readings during the 20–40 min necessary to travel across the width of the lateral transects while collecting ADCP data (Jackson and Reneau, 2014).

ð1Þ

where P is the percent lake water at a given station within the rivermouth, RM is the tracer value (specific conductance, boron, magnesium, or d-excess) at the rivermouth station, R is the river tracer endmember, and L is the lake tracer end-member (Jameel et al., 2018; Larson et al., 2016; Trebitz et al., 2002). This approach assumes the river and lake water are the only significant sources of water to the rivermouth sample. In these calculations, continuous specific conductance values collected using the sonde were used to determine percent lake water along longitudinal, lateral, and vertical transects. Percent lake water values derived from d-excess, magnesium, and boron data were averaged and compared by simple linear regression (SPSS Statistics 23) to values derived from specific conductance as independent verification of percent lake water; specific conductance is not considered fully conservative, whereas point samples of magnesium, boron, and stable water isotopes are conservative. Boron was not used in Pere Marquette calculations because an industrial salt evaporation pond (Fig. 2c) was an immediate source of boron to the upstream end of the rivermouth, which could have led to a potential error in estimating the percent lake water in the rivermouth. End-members for mixing models represented an upstream river station and a station in Lake Michigan not affected by the river plume (i.e., R and L in Eq. (1), respectively; Table 2). Decision rules to determine the upstream end-member followed the logic that the chemical signature of the end-member must reflect the masses of water in the rivermouth at the time. For example, when rain events diluted the specific conductance of the river end-member in relation to the river water in the rivermouth at the time, steps were taken to select the most appropriate end-member. Likewise, if industrial pollutants concentrated the dissolved load in the rivermouth in relation to the river, the most appropriate end-member was chosen. As a result, we primarily chose Station 2 for our upstream end-member in the Ford, but spatial patterns were not substantially altered by choosing Site 1 or 3 instead. In Pere Marquette and Manitowoc, Station 3 primarily was used as the upstream end-member due to effects of industrial pollution and the considerable distance between Stations 2 and 3 in both rivers. For all three rivermouth sites, the lake end-member represented the minimum (or maximum in Ford in May and June) value from the two Lake Michigan vertical profiles (Station 9 north and south in Fig. 2) after manually removing outliers. These decision rules ensured that the percent lake water in the rivermouth fell between the river and lake end-members. Percent lake water from vertical profiles along the longitudinal transect were converted to contour isopleth plots by inverse distance interpolation in SigmaPlot 12.5. Depths along the longitudinal profile were determined in ArcGIS 10.2 from digital elevation models (DEMs) that had been generated for the rivermouth zones using sidescan sonar. In the Ford and Manitowoc, depths for the outer 963 m and 1459 m of the longitudinal transects, respectively, were obtained from National Oceanographic and Atmospheric Administration (NOAA) nautical charts (http://www.charts.noaa.gov/InteractiveCatalog/nrnc.shtml) and tied to the DEMs using the low water datum as the plane of reference. To create plan-view maps of percent lake water, Euclidean

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655

Table 2 End-members used in mixing models. Stations refer to Fig. 2. Site

FORD FORD FORD FORD FORD FORD MAN MAN MAN MAN MAN MAN PM PM PM PM PM PM

Month

MAY JUN JUL AUG SEP OCT MAY JUN JUL AUG SEP OCT JUN JUL AUG1 AUG29 SEP OCT

SpC (μS/cm)

Mg (mg/L)

B (mg/L)

d-excess

Stations of selected end-members (River, Lake)

River

Lake

River

Lake

River

Lake

River

Lake

254.0 264.3 375.5 316.3 381.0 379.3 695.0 563.0 652.0 706.0 682.7 762.0 403.0 394.0 398.0 400.0 384.0 380.0

285.0 285.0 285.0 294.0 292.0 292.0 296.0 300.0 286.0 285.0 287.0 287.0 299.0 299.0 279.0 294.0 296.0 292.0

14.8 14.9 21.9 17.8 23.2 22.5 41.6 28.6 37.9 44.4 40.4 45.3 15.4 15.0 15.7 15.6 15.9 15.4

12.0 12.7 11.9 12.6 12.3 12.3 12.6 12.0 12.0 11.4 11.7 11.8 12.3 11.8 11.8 11.7 11.9 12.0

8.9 12.9 14.4 17.3 14.2 15.1 25.3 33.6 31.0 29.4 26.8 29.2 19.6 20.6 21.4 21.4 17.1 20.3

22.0 18.4 21.2 20.9 21.9 22.1 21.5 22.5 22.7 22.8 22.2 24.0 23.8 23.6 22.4 23.5 23.4 22.9

8.0 11.1 6.1 5.4 2.6 4.6 5.7 10.1 5.9 1.9 6.2 6.7 −10.4 −9.9 −10.0 −10.0 −10.3 −10.1

2.1 5.1 2.0 1.6 0.8 −1.0 1.1 1.7 1.1 −1.0 0.1 1.6 −6.2 −5.9 −6.1 −5.9 −5.8 −5.9

distance kriging was used in ArcGIS 10.2 to interpolate surface percent lake water values from lateral and longitudinal transects. This method was chosen due to widespread adoption but inherently did not take into account water-land boundaries. Euclidean distance kriging was applicable in this case due the configuration of the rivermouth zones and close approximation between Euclidean and water-distance-based kriging found in other studies (e.g., Murphy et al., 2014). Results Mixing models Regressions between percent lake water as determined by averaging mixing model results of magnesium, boron, and d-excess showed a strong relationship with percent lake water determined from specific conductance, suggesting that specific conductance was a reliable tracer in these three rivermouths (Fig. 4). However, sufficient attention to the appropriate river and lake end-members was needed (Table 2). Outliers were removed from the mixing model in instances of potential error. In the Pere Marquette in October, for example, most transects were sampled during dry conditions, but Stations 3 and 4 (Fig. 2c) were sampled on a day following a nighttime rain event, which diluted the specific conductance. Therefore, those two stations were removed from the mixing analysis. In the Ford in June, Station 3 was removed because sampling occurred on the subsequent day to the other sites, and hydrochemistry had changed. In September, no data were collected at Ford Station 7 due to shallow water. Seasonality and temperature Spatiotemporal variability in mixing was apparent in all three rivermouths in both vertical (Figs. 5, 6, 7) and planform (Fig. 8) dimensions. Some variability was related to temperature differences. For example, layering of lake water below river water occurred in July and August in the Manitowoc and in August in the Pere Marquette. Vertical mixing, instead, occurred at the Ford (Fig. 5) where shallow water depths and higher streambed slopes were present. Differences in temperature between the Ford River and Lake Michigan's shallow region at the top of Green Bay were greater than 9 C ̊ in early spring, but then lake and river temperature converged in summer and into the fall (Table 3). Similarly, a large water temperature difference was observed in the Manitowoc in May, but that difference persisted as water temperature in the lake remained low through August, before water

2, 9 north 2, 9 south 2, 9 north 2, 9 north 3, 9 north 2, 9 south 3, 9 south 3, 9 south 3, 9 south 3, 9 south 3, 9 south 3, 9 south 3, 9 south 3, 9 north 3, 9 north 3, 9 north 3, 9 south 4, 9 south

temperatures converged in the fall. In the Pere Marquette, temperature differences were usually not particularly divergent, except for one occasion in mid-summer that was likely due to an upwelling event (Table 3). Water velocity At Manitowoc, the signatures in water chemistry that were verified with physical measurements of velocity showed congruence and complementarity. Backflows to Transects 3 and 4 in September and August respectively, were matched by lake chemical signatures in the river channel (Figs. 8, 9). In September, in particular, lake levels were still declining overall after peaking in July and August, and river flows were very low, allowing for velocity reversals. Further downstream near Transects 7and 8, noisy velocity patterns indicated prevalent, low magnitude flow reversals over much of the summer (Fig. 9). Relative influences of river, lake, and geomorphology In addition to seasonal temperature differences, variability in observed mixing patterns was due to the relative hydrologic fluxes of river and lake, and the influence of local geomorphology on these flows. During this study, we captured a variety of configurations of river flow and lake levels. In general, higher flows at lower lake levels in spring and fall resulted in more water delivery out into the lake and formation of a plume, but strong plumes only developed after large rain events (Fig. 8). In contrast, higher lake levels and lower river flows allowed lake water to extend into the lower rivers and supported in-river mixing. The notable exception was Pere Marquette, where the drowned rivermouth lake was predominantly river water for most of the study period except at the beginning of June when river flows were declining (Figs. 3c, 8m). Over summer, lake water, when present, was near the bottom of the drowned rivermouth lake (Fig. 7c, d). Low values in Pere Marquette in October near the upstream extent of the drowned rivermouth lake were an artifact of dilution due to a rain event. In mid-summer, Manitowoc and Pere Marquette, the deeper systems with lesser slopes, exhibited a wedge structure (Figs. 7, 8), whereas the Ford with a steeper slope and deltaic geomorphic structure showed vertical mixing (Fig. 6) with minimal mid-range lake water percentages between 30% and 60% (Fig. 4). Additionally, plume formation in the nearshore of the Ford was more prevalent, and the effect of nearshore current on plume direction was more pronounced (Fig. 8a–f). The Manitowoc, in contrast, did not exhibit substantial plume formation

656

M.L. Carlson Mazur et al. / Journal of Great Lakes Research 45 (2019) 651–663

Lake Water (%) Determined from SpC

100

zones, our data showed that substantial mixing occurs within the river channel of the rivermouth zone. We did not sample during conditions that would cause maximum upriver intrusion of lake water, however; and so, this study characterized a subdued expression of lake-water intrusion. The balance of lake and river fluxes on daily, seasonal, and interannual time scales can influence ecosystem structure and function, but local geomorphology also has a strong effect.

a. Ford 80 60 40 20

y = 0.9104x + 5.0478 R² = 0.919

0 0

20

40

60

80

100

Lake Water (%) Determined from Mg, B, and d-excess Lake Water (%) Determined from SpC

100

b. Manitowoc

80 60 40 20 y = 1.0038x + 4.263 R² = 0.917

0 0

20

40

60

80

100

Lake Water (%) Determined from Mg, B, and d-excess

Lake Water (%) Determined from SpC

100

c. Pere Marquette

80 60 40 20 y = 0.9824x - 7.9914 R² = 0.925

0 0

20

40

60

80

100

Lake Water (%) Determined from Mg, B, and d-excess Fig. 4. Regressions (dashed line) of percent lake water, as determined by specific conductance, against percent lake water, as determined by the mean of estimates from boron, magnesium, and d-excess measurements showing that specific conductance is a robust tracer in these systems. Outer black lines indicate 95% confidence interval, and outer gray lines represent 95% prediction interval (df Ford: 1,52; Manitowoc: 1,72; Pere Marquette: 1,72).

under similar high lake and low river flow conditions in mid-summer (Fig. 8g–l). Due to the drowned rivermouth lake in Pere Marquette, most mixing was observed within the pier structure with periodic mixing with Pere Marquette Lake. Very little plume development was observed under these conditions. Instead, high river flows (e.g. as occurred in spring and fall) were needed to push river water into the lake (Fig. 8m–r).

Dynamic hydrology Our data suggested that intra-annual variations in lake level had a lesser effect on mixing than river flow and local morphology, although a larger dataset would be needed to quantify the relative strengths of these components. Within the record of the three closest lake gaging stations to our study sites, Trebitz (2006) found that fluctuation intensity, a composite measure of the frequency and magnitude of daily water-level fluctuations, was similar across Lake Michigan and among our study sites. Differences in mixing, therefore, are more likely due to variability in river flow and local morphology (i.e., overall size, width, depth, slope, and arrangement of the outlet). All three lake gages showed similar ranges in seiches, with sampling spanning one of the larger storm surges of about 30 cm in October 2011. Manitowoc and Pere Marquette, which are almost directly opposite each other, saw opposing but equal magnitude changes in water level during this storm surge. Possibly, storm surges have a larger role in mixing dynamics than we were able to capture in this study, but studying these relatively rare and unpredictable events is logistically difficult due to challenges with mobilizing a field team around stochastic events. Also, we could not sample quickly enough to record instantaneous measurements; the mixing conditions changed as we moved from station to station, posing further challenges in data congruence. Further research in this area is warranted, such as installation of continuously logging specific conductance meters in a three-dimensional net throughout the rivermouth zone. Nonetheless, sufficient evidence exists to suggest that the natural hydrologic flow regime influences ecosystem function, and estimating the elevations and areas that would be inundated under such conditions to better simulate the natural regimes of these rivermouth ecosystems (sensu Poff et al., 1997) is prudent in understanding spatial mixing relevant to restoration planning. Our work parallels similarly designed mixing zone studies for marine deltas except that alternative tracers were used successfully as substitutes for salinity. Marine systems have been investigated thoroughly in terms of hydrogeochemistry from a wide range of perspectives ranging from system modeling (MacCready et al., 2009) to trace element flux (Duinker and Nolting, 1978); we contend that marine investigations are facilitated greatly by the ease at which salinity is measured. Our work measured mixing via stable isotopes, conservative tracers, and specific conductance. Like Jameel et al. (2018), we found that, despite low concentrations of dissolved materials, specific conductance was an easily measured variable that could be used to calculate percent lake water, and was even more useful when distinct boundaries between river and lake water were present. Our work also differed from most prior Great Lakes mixing studies that focused on plumes in the lake environment. Our study is consistent with prior findings that river plumes from runoff events represent strong mixing and transport of river water into the lake. Our results differed, however, in that we observed river and lake water mixing in river during low flows, and showed that lake water, in low proportions, could be found for at least 3 km upriver from the outlet in deeper systems with lower longitudinal slopes.

Discussion Geomorphic constraints This study demonstrated variability in spatiotemporal pattern in rivermouth ecosystems that was related to the magnitude of river discharge and local geomorphology. Whereas prior water-mixing studies primarily focused on plume influences in the nearshore and offshore

Outlet geomorphology, depth and width in particular, affected mixing dynamics in the three rivermouths we studied. Due to similar maximum depths and lack of a prograding delta, the Pere Marquette

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Fig. 5. Ford longitudinal isopleth plots of percent lake water, interpolated from vertical profiles and determined from mixing models of specific conductance. White space on the graph indicates no data, and black indicates substrate.

and the Manitowoc Rivers were functionally more similar to each other than they were to the Ford River, as evidenced by midsummer layering of colder lake water below warmer river water that occurred in the former two systems. Despite similarities, the wedge at the Manitowoc stratified earlier and more distinctly for a longer duration, likely due to a greater temperature and density differential, as well as a stronger hydrologic connection to Lake Michigan at depth. High lake levels and low river flows in the Pere Marquette and Manitowoc in mid-summer allowed for cooler lake water to reside below warmer river water, whereas lower lake levels and higher river flows in spring and fall, along with seasonal changes in water temperature, promoted mixing. The Pere Marquette River, however, had a short constricted channel at the outlet with a large wide drowned rivermouth lake in the upriver direction that attenuated seiche forces (Grabas and Rokitnicki-Wojcik, 2015). In the Ford rivermouth, vertical mixing was prevalent due to

the shallow depth. Backflows were rare in the Ford River channel, and most of the mixing was in the plume in the lake due to the proximity of the rivermouth and delta to the nearshore. Additionally, substantial lateral variability in space was only observed at the Ford because the lack of a flow constriction at the mouth led to a more dynamic interaction between the river channel and the plume. Consideration of uncertainty One caution that we took into account when analyzing this dataset was that in our study design, we maximized spatial coverage in three dimensions and did not take duplicate or triplicate water samples at each station, which would have been cost prohibitive and less logistically feasible (e.g., taking longer at each station while hydrologic conditions were changing). Because the mixing models were highly dependent

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Fig. 6. Manitowoc longitudinal isopleth plots of percent lake water, interpolated from vertical profiles and determined from mixing models of specific conductance. White space on the graph indicates no data, and black indicates substrate.

on the accuracy of the end-members, we took samples at two locations in the lake and two locations in the river but ended up, in some cases, using only one site as the upstream end-member, which better reflected the water masses in the rivermouth at the time of sampling. Some erroneous values were evident but were easily removed from the dataset. In using this two end-member mixing model, we assumed that groundwater was insubstantial in comparison to lake and river water fluxes, which is a fair assumption due to the slow rate of groundwater flow even in locations with high overall groundwater discharge (Morrice et al., 2011). However, if additional dissolved load is added to the system through groundwater pathways, the percent lake water could be underestimated. An additional source of error was in the inherent quickly changing temporal aspect of characterizing rivermouth

ecosystems that can change over a single day. Smaller, frequent changes in the lake level are propagated upriver every 40 min or so. When weather, the size of the rivermouth ecosystem, or other logistical constraints prohibited sampling within a single day, resulting changes in the hydrology and hydraulics of the system could not be measured. This, in turn, affected which data were comparable and could be included in the analysis. Ecosystem structure and function Mixing in rivermouth ecosystems has implications for contaminant distribution. Persistent and emerging contaminants detrimentally affect Great Lakes biota and, therefore, are an active area of research

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Fig. 7. Pere Marquette longitudinal isopleth plots of percent lake water, interpolated from vertical profiles and determined from mixing models of specific conductance. White space on the graph indicates no data, and black indicates substrate.

(Jorgenson et al., 2018; Kiesling et al., 2019). The distribution of contaminants delivered by rivers to rivermouth and lake ecosystems likely is affected by rivermouth structure. In places like the Pere Marquette, where a drowned rivermouth lake slows and retains river water, contaminants may settle out prior to entering the Great Lake. In places like the Manitowoc, although lake-dominated, the deep channel and movement of river water into the lake is likely to deliver contaminants to the lake. Likewise, in locations with a prograding delta, like the Ford, contaminants will settle out in the delta where redistribution of sediment by longshore currents is more prevalent. In this way, contamination is likely to be more localized and concentrated where a lagoon feature is present and more widespread and diffuse where a more direct connection to the lake enhances transport.

Similarly, rivermouth geomorphic structure and mixing dynamics may influence phytoplankton response to excess nutrient loading (Gillett and Steinman, 2011). The Great Lakes are phosphorus limited (Dove and Chapra, 2015), as are some drowned rivermouth lakes (Liu et al., 2018a). Excess phosphorus delivered by river water is likely to have greater impacts on primary productivity in the nearshore zone in areas like the Ford and Manitowoc where delivery to the lake is more direct. In drowned rivermouth lake scenarios, niche partitioning of primary producers may occur. For example, Liu et al. (2018a) found an association between river water and planktonic diatoms, and lake water with benthic diatoms. Wedge mixing structures and temperature-related density layering may, therefore, impact autotrophic community composition in deeper systems. Shallow, deltaic

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Fig. 8. Plan view of percent lake water in the top 50 cm of water at the three sites determined from mixing models of specific conductance. Gray indicates land, white indicates Lake Michigan, and numbered points and lines indicate sampling locations and transects.

systems may have greater influence on the nearshore zone, given similar watershed nutrient exports. As rivermouth mixing and structure may influence nutrient delivery and autotrophic response, better understanding of mixing zones may be vital for management of offshore fisheries, as well. Turschak et al. (2014) found strong evidence that the Lake Michigan deepwater fish community now relies increasingly on nearshore production, likely because energy sequestration by dreissenid mussels has curtailed offshore phytoplankton production. Mixing zones represent potential nearshore production hotspots, and recent evidence shows that river plumes have higher biological production than adjacent nearshore waters (Jameel et al., 2018; Sierszen et al., 2012; Xu et al., 2017). Future studies that compare seasonal production of mixing zones influencing nearshore lake waters may reveal their role in sustaining offshore fish communities. Ecosystem management and restoration Our findings support increased sensitivity in restoration planning to rivermouth water mixing dynamics and locations, emphasizing

restoration of fundamental ecological processes spatially within the various rivermouth zones. Although we studied only intermediate-sized rivers, the mixing zones were generally small compared to the watershed or receiving water. More extreme seiche events would extend mixing even further upriver than we documented. Additionally, upwelling events in the lake provides for greater lake-water intrusion, as does widening of a navigation channel or outlet (Liu et al., 2018b). These findings suggest that restorations would need to be sited carefully to take advantage of mixing processes at potentially different locations as lake levels rise and fall, and lake currents move water into and out of rivermouths. Fortunately, in many cases, simple seasonal specific conductance measurements might provide data needed for siting projects. Because conditions change over both short and long timescales in these dynamic systems, building in resiliency and adaptability to restoration design is also important. We know from decades of research in environmental flow assessment that restoring the natural flow regime in rivers is critical for maintaining ecological integrity due to its effect on water quality, energy sources, physical habitat, and biotic interactions (Poff et al., 1997; Tharme, 2003). Hydrological restoration in rivermouth ecosystems,

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Table 3 Temperatures of river and lake end-members measured in the top 50 cm of the water column, discharge range (m3/s), and lake level range (IGLD85, m) at the three sites. Discharge values were obtained for the Ford River at Hyde, MI; the Manitowoc River at Manitowoc, WI; and the Pere Marquette River at Scottville, MI. Lake levels were obtained from the NOAA Port Inland, MI, station for the Ford; the Kewanee, MI, station for the Manitowoc; and the Ludington, MI, station for the Pere Marquette. For sampling events that spanned multiple days, discharge and lake level ranges are shown.

Difference

9.5 20.1 24.2 21.1 18.6 16.5 9.9 12.6 12.4 12.2 16.5 10.5 19.0 19.9 13.2 19.2 16.0 8.0

10.2 −0.7 6.0 4.0 1.9 0.7 9.4 11.1 14.4 10.6 1.3 0.0 3.1 3.1 9.3 0.4 −2.9 1.2

therefore, is likely an appropriate ecosystem goal. Our research suggests that due to the interacting effects of lake, river, and geomorphology in rivermouth ecosystems, managers might tailor different approaches

Transect 3 0

-3.0

0

58

0

Depth Below Water Surface (m)

Transect 4 0.08

0.34 -2.4 0.27

-0.37

-3.0 0

88

0

0.15

-3.0

0 0

0.13

0

56

0

0.09

-3.0 0

61

0

63 0.17

0

69

0

52

0

54

0

0

183

175

170

0

-0.27 -7.6

0.12

157

0.17

0

49

58

0.09

0

46

0

53

0

465

0

-0.08 -7.6 0.17

366 0.11

0

366

0

373

0

0.22

-0.13 0

762 0.14

-0.17 0

610 0.21

-0.12 0

610

0

-0.12 -7.6 0.08

221

0

-0.15 -7.6 0.12

-0.14 0

0

-0.08 -7.6 0

0.05

0

-0.08 -7.6 0.12

0

-0.24 -7.6 0.24

0

350

0

-0.32 -7.6 0.27

0

0

-0.18 -7.6

Transect 8 0.17

0

-0.06 -7.6 0.18

0

0

-0.11 -7.6 0.46

0

-0.12 -7.6 0

49

0

-0.09 -7.6 0

0

0

-0.14 -7.6 0.14

0.15

0

-0.12 -7.6 0.14

0

0

-0.12 -7.6 0.20

0

-0.12 -7.6 0.18

145

0

-0.02 -7.6 0.06

0

176.07–176.09 176.29 176.25 176.22 176.08 176.09 176.14–176.19 176.25 176.25 176.21 176.13 176.01–176.09 176.24–176.26 176.30–176.31 176.26–176.30 176.29–176.20 176.17–176.19 176.11–176.15

Transect 7

Transect 6 0.12

0

0.17 -7.6 0

0

-0.30 -3.0 0.49

0.37

0

0.01 -3.0

-3.0

53

0

76

0

0.07 -7.6 0

0

-0.15 -3.0

-3.0

0.04

(IGLD85, m)

19.4–22.3 21.8 1.9 2.3 0.8 1.5 10.8–10.9 29.2 6.6 1.4–1.6 1.0–1.1 1.4–1.7 17.0–21.1 17.4–19.0 13.4–15.2 12.7–13.0 13.9–14.8 21.0–22.5

for river versus lake-dominated rivermouths. In lake-dominated systems, mixing ideally would occur within the river channel or drowned rivermouth lake; therefore, the focus may be on providing larger

Transect 5

0

(m /s)

0.09

-0.09 0

701

0

0.08

No Data -0.15 -3.0

-3.0 0

50

0 0

62

-7.6

0

49

-0.09 -7.6

0

404

-0.08 -7.6

May 24

Pere Marquette

Lake

19.7 19.4 30.2 25.1 20.5 17.2 19.3 23.7 26.8 22.8 17.8 10.5 22.1 23.0 22.5 19.6 13.1 9.2

Jun. 29

Manitowoc

River

Lake Level

3

Jul. 26

MAY 17–18 JUN 27 JUL 19 AUG 16 SEP 13 OCT 12 MAY 24–25 JUN 29 JUL 26 AUG 23–24 SEP 20–21 OCT 18–19 JUN 7–10 JUL 5–7 AUG 1–3 AUG 29–30 SEP 27–28 OCT 24–26

Discharge

Aug. 24

Ford

Temperature (°C)

Sep. 21

Date

-0.08 0

Oct. 18

Site

678

Distance Along Lateral Transect (m) Fig. 9. Velocities (m/s) recorded at Manitowoc using an acoustic Doppler current profiler (ADCP). Red indicates a negative velocity (upstream flow), blue indicates a positive velocity (downstream flow), and yellow is zero velocity (stationary water). Legends are provided to the right of each panel. Stations refer to Fig. 2b.

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hydraulic connections to allow lake water to enter the system and shallow habitat in which mixing benefits could be realized. Hydrologic modeling prior to such modifications would be advantageous to avoid unintended results, such as an increase in local flooding along the rivermouth if greater lake water intrusion coincided with river storm events. Nonetheless, for the Manitowoc and Pere Marquette Rivers, construction of additional entry channels would allow greater mixing. In river-dominated systems such as the Ford, mixing will occur in the lake at the plume front. For the Ford River, focusing on nearshore habitat quality or prioritizing rivermouths with distinct embayments that might provide protected shallow habitats could be beneficial. Given the Ford River's intact natural multi-channel entry, protection of shoreline habitat in the adjacent nearshore zone would be a reasonable approach. Conclusion We observed seasonal effects in mixing dynamics within each of the rivermouths we examined. Degree of mixing during a given season or day was due to relative forces of river and lake. Higher river flows at lower lake levels led to flushing, but strong plumes only developed after large rain events. In contrast, higher lake levels and lower river flows allowed lake water to extend into the lower rivers and supported in-river mixing. Likewise, the geomorphology of the outlet, particularly the width, depth, and slope, influenced the location of mixing, temperature-related layering and wedge structures, and water delivery to the lake. These processes have likely effects on nutrient and contaminant distribution, planktonic communities, and fisheries. Because Great Lakes rivermouths represent a continuum between river-dominated and lake-dominated systems, restoration planning that takes the relative influences of river, lake, and local geomorphology into account is preferable. Acknowledgments This work was supported by the Great Lakes Restoration Initiative through the U.S. Environmental Protection Agency's Great Lakes Program Office (Project 82 of the U.S. Geological Survey). We thank the following colleagues for their conceptual and technical contributions to this work: S. Mackey, J. Waide, R. Gaugush, J. Allen, W. Richardson, J.C. Nelson, D. Bennion, S. Antonelli, and E. Idleman. We also thank internal reviewers from the U.S. Geological Survey and anonymous reviewers. Data are available at https://doi.org/10.5066/P9DXIWBJ (Schaeffer, 2009). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. References Allan, J.D., McIntyre, P.B., Smith, S.D.P., Halpern, B.S., Boyer, G., Buchsbaum, A., Burton, A., Campbell, L., Chadderton, L., Ciborowski, J., Doran, P., Eder, T., Infante, D.M., Johnson, L.B., Joseph, C.G., Marino, A.L., Prusevich, A., Read, J., Rose, J., Rutherford, E., Sowa, S., Steinman, A.D., 2013. Joint analysis of stressors and ecosystems services to enhance restoration effectiveness. Proc. Natl. Acad. Sci. 110, 372–377. Bellinger, B.J., Hoffman, J.C., Angradi, T.R., Bolgrien, D.W., Starry, M., Elonen, C., Jicha, T.M., Lehto, L.P., Seifert-Monson, L.R., Pearson, M.S., Anderson, L., 2016. Water quality in the St. Louis River Area of Concern, Lake Superior: historical and current conditions and delisting implications. J. Great Lakes Res. 42, 28–38. Bhagat, Y., Ruetz, C.R., 2011. Temporal and fine-scale variation in fish assemblage structure in a drowned river mouth system of Lake Michigan. Trans. Am. Fish. Soc. 140, 1429–1440. Braden, J., Taylor, L., Won, D., Mays, N., Cangelosi, A., Patunru, A.A., 2008. Economic benefits of remediating the Buffalo River, New York area of concern. J. Great Lakes Res. 34, 631–648. Dansgaard, W., 1964. Stable isotopes in precipitation. Tellus. 16, 436–468. Dove, A., Chapra, S.C., 2015. Long-term trends of nutrients and trophic response variables for the Great Lakes. Limnol. Oceanogr. 60, 696–721. Duinker, J.C., Nolting, R.F., 1978. Mixing, removal and mobilization of trace metals in the Rhine estuary. Neth. J. Sea Res. 12, 205–223. Evans, J.E., Seamon, D.E., 1997. A GIS model to calculate sediment yields from a small rural watershed, Old Woman Creek, Erie and Huron Counties, Ohio. Ohio J. Sci. 97, 44–52.

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