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Communication

A Comparison of Stream Water and Shallow Groundwater Suspended Sediment Concentrations in a West Virginia Mixed-Use, Agro-Forested Watershed

by
Kaylyn S. Gootman
1 and
Jason A. Hubbart
1,2,*
1
Institute of Water Security and Science, West Virginia University, 3109 Agricultural Sciences Building, Morgantown, WV 26506, USA
2
Division of Forestry and Natural Resources Davis College of Agriculture, Natural Resources and Design, West Virginia University, 322-B Percival Hall, Morgantown, WV 26506, USA
*
Author to whom correspondence should be addressed.
Land 2022, 11(4), 506; https://doi.org/10.3390/land11040506
Submission received: 28 February 2022 / Revised: 23 March 2022 / Accepted: 28 March 2022 / Published: 31 March 2022
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Abstract

:
Suspended sediment is an important constituent of freshwater ecosystems that supports biogeochemical, geomorphological, and ecological processes. Current knowledge of suspended sediment is largely based on surface water studies; however, improved understanding of surface and in situ groundwater suspended sediment processes will improve pollutant loading estimates and watershed remediation strategies. A study was conducted in a representative mixed-use, agro-forested catchment of the Chesapeake Bay Watershed of the northeast, USA, utilizing an experimental watershed study design, including eight nested sub-catchments. Stream water and shallow groundwater grab samples were collected monthly from January 2020 to December 2020 (n = 192). Water samples were analyzed for suspended sediment using gravimetric (mg/L) and laser particle diffraction (µm) analytical methods. Results showed that shallow groundwater contained significantly higher (p < 0.001) total suspended solid concentrations and smaller particle sizes, relative to stream water. Differences were attributed to variability between sites in terms of soil composition, land use/land cover, and surficial geology, and also the shallow groundwater sampling method used. Results hold important implications for pollutant transport estimates and biogeochemical modeling in agro-forested watersheds. Continued work is needed to improve shallow groundwater suspended sediment characterization (i.e., mass and particle sizes) and the utility of this information for strategies that are designed to meet water quality goals.

1. Introduction

Suspended sediment is a natural constituent of freshwater ecosystems that supports a variety of stream biological, geomorphological, and ecological processes [1,2]. Suspended sediment is necessary to maintain supplies of organic and inorganic materials required for aquatic ecosystem functioning [3,4]. Transport of suspended sediment is also important for streambed stability and nutrient supplies that support primary productivity and invertebrate communities [5,6]. Excess suspended sediment can harm lotic ecosystems by reducing sunlight transmission through the water column, thereby restricting photosynthesis, increasing surface water temperatures, clogging streambed pore spaces, and interfering with metabolic processes of aquatic life [7,8]. However, too little suspended sediment can contribute to increased channel erosion, habitat degradation, nutrient depletion, and alter aquatic ecosystem functioning [9,10]. Suspended sediment also serves as an important mechanism of transport for many pollutants of concern [11,12,13].
Suspended sediments are well studied across many river systems globally [14,15]. Estimates of flux are typically derived from surface water bodies (e.g., rivers and streams) using continuous monitoring and turbidity sensors or intermittent monitoring using manual or automatic sampling [5,7,16]. However, shallow groundwater (SGW) suspended sediments are typically overlooked or excluded from suspended sediment flux estimations. This is surprising, given that river restoration efforts require a thorough understanding of suspended sediment transport processes and their interactions, which include linkages between river channel reaches, hyporheic zones, parafluvial zones, and riparian zones [17,18]. For example, seasonal alternations in influent and effluent SGW discharge can significantly influence riverine sediment delivery processes [19]. Additionally, floodplain alluvium may act as a conduit or barrier to water and suspended sediment transport, depending on the floodplain size and hydraulic properties [20]. Permeable alluvium promotes subsurface flow through floodplain sediments, providing opportunity for suspended sediment deposition in SGW over time [21]. Impermeable alluvium has a lower buffering capacity because it deflects influent SGW toward deeper aquifers or across the floodplain surface [13]. Results from Estrany et al. [19], Burt [20], and others [22,23] highlight the complexity of factors that influence suspended sediment dynamics. Ideally, efforts to improve the understanding of watershed suspended sediment regimes must also consider SGW contributions (or lack thereof) to surface water suspended sediment regimes.
Considering the relevance of suspended sediments to aquatic ecosystem functioning and water quality, substantial knowledge gaps remain related to the role of SGW in suspended sediment dynamics relative to SW [24,25]. In fact, there is a surprising lack of information on the relative differences between SW and SGW suspended sediment characteristics (e.g., mass and particle size distributions). This could be important, given that over- or underestimates of SGW suspended materials may invalidate estimated contributions and current assumptions pertaining to pollutant transport and loading processes between SW and SGW, thereby impacting resource management strategies that are designed to meet specific water quality requirements [26,27]. This is of further importance, given that there is an ongoing need to better understand suspended sediment processes in SW and SGW in agricultural catchments [28,29]. Agriculture can be an important source of suspended sediment in SW, given seasonal variability of climate and agricultural practices (e.g., plowing, germination, and harvesting) [30]. Previous studies correlated agricultural land uses with increased suspended sediment export and pollutant loading in many parts of the world [31,32]. Although many non-point source pollutants (e.g., N, P, and pesticides) associated with agricultural land uses are delivered to waterbodies via surface pathways (e.g., overland flow, surface erosion, road runoff), recent studies have shown that subsurface pathways, including natural macropores and artificial drains, are important delivery mechanisms for sediments, and therefore pollutants (including nutrients), in SW and SGW [33,34,35].
Widespread agricultural land use throughout the Chesapeake Bay Watershed (CBW) located in northeastern USA is representative of many global catchments, given that agriculture in the CBW is a major source of suspended sediment [36]. While sediments and nutrients (i.e., N and P) are primary constituents of poor water quality in the CBW [37,38], and nutrient sources and transport in the CBW have been studied since the 1980s, less is known about sediment sources, storage, and delivery to the Chesapeake Bay [39,40]. Furthermore, estimated effects of upstream sediment reducing best management practices are inconsistent with downstream monitored sediment loads in the CBW [3]. The disconnect between suspended sediment model outputs and in situ observations implies that meeting the Chesapeake Bay total maximum daily loading (TMDL) goals for nutrients and suspended sediments [41] will require better knowledge of CBW sediment sources and transport [42]. Although total N loads have decreased since the late 1980s, particulate nutrient (i.e., P) and suspended sediment loads have increased since the mid-1990s [43]. Long-term suspended sediment monitoring from the Chesapeake Bay nontidal network (NTN) showed that suspended sediment loads are still increasing in rivers across the CBW, with recent (i.e., 2009–2018) load increases ranging from 8.78 to 965 kg/ha [44]. Suspended sediment predictions can be improved by monitoring both SW and SGW suspended sediments throughout the CBW [45], which can help assess if TMDL goals will be met by 2025 [46]. On this basis, advancements in understanding of SW and SGW suspended sediments in the CBW are relevant and transferable to any watershed or region where agricultural land use contributes to pollutant loading.
Given the importance of suspended sediment to freshwater ecosystems and current knowledge gaps (e.g., SGW suspended sediment characteristics and relative contributions to suspended sediment dynamics), the primary objective of the current work was to compare SW and SGW suspended sediment in a representative catchment of northeast USA. Sub-objectives were to (a) quantify suspended sediment concentration by measuring total suspended solids (TSS, mg/L) concentration and (b) characterize suspended sediment particle size distribution (PSD) from monthly (n = 12) SW and SGW samples. For the purposes of this study, TSS data were considered as a proxy for suspended sediments that could be compared to PSD results. Results improve the understanding of in situ SW and SGW suspended sediment concentrations in agro-forested watersheds.

2. Materials and Methods

2.1. Study Site Description

Moore’s Run Watershed is a 36 km2 mixed-use, agro-forested watershed located in the West Virginia eastern panhandle within the headwater region of the CBW (Figure 1). Land use/land cover in Moore’s Run Watershed is approximately 87% upland forest, 11% agriculture, and 2% mixed development (Table 1). The upland forest is primarily composed of dry mesic oak forests, dry calcareous forests, and other mixed forest assemblages, while the agricultural lands consist of low vegetation, hay/pasture, and cultivated crops [47]. The downstream portion of the stream corridor is largely agricultural and lacks a forested buffer (Figure 1). Moore’s Run is a tributary of the Cacapon River and flows through Reymann Memorial Farm (RMF; 39°6′12.73″ N, 78°35′8.19″ W), which is owned and operated by West Virginia University. RMF was historically used for beef cattle production and currently operates as a working farm that is also used for research on livestock feed efficiency and environmental quality [48,49].
Long-term (1917–2016) average annual air temperature and total precipitation at RMF were 8.6 °C and 811 mm, respectively [50]. Moore’s Run Watershed was instrumented with a nested-scale experimental watershed study design in 2019, with eight gauging sites (n = 8), dividing the catchment into eight sub-watersheds [51]. A single representative climate station was constructed near RMF4 due to the proximity of the gauging sites and relatively flat/open landscape. Air temperature was measured every 30 min with a Campbell Scientific EE181-L probe (error ± 0.2 °C; Campbell Scientific, Inc.; Logan, UT, USA). Total precipitation was recorded with a Texas Electronics TE525MM-L tipping bucket rain gauge (25.4 mm hr−1, accuracy of 1%; Texas Electronics; Dallas, TX, USA).
Surficial geology throughout RMF includes sandstone, shale, and alluvium, with seven of the eight gauging sites located on alluvial deposits, except for RMF7, which is positioned on a shale deposit [52]. Soils at RMF are moderately drained, Basher fine sandy loam in the floodplains and Monongahela silt loam (3–8% slopes) along the stream terraces [53]. Gootman et al. [54] analyzed 179 soil cores from RMF and found that average (n = 14–27) soil dry bulk density and porosity ranged from 1.03 to 1.30 g cm3 and 0.51 to 0.61, respectively, and average soil composition was 92% sand. Average (n = 6, slug tests per site) saturated hydraulic conductivity (Ksat) ranges from 0.29 to 4.76 m day−1 [54].
Table
Table 1. Cumulative land use and land cover (LULC) and drainage area (km2) corresponding with each study site (n = 8) sub-basin from the studied portion of the Moore’s Run Watershed. Percent cumulative land use type is displayed parenthetically. Table was adapted from Gootman et al. [54].
Table 1. Cumulative land use and land cover (LULC) and drainage area (km2) corresponding with each study site (n = 8) sub-basin from the studied portion of the Moore’s Run Watershed. Percent cumulative land use type is displayed parenthetically. Table was adapted from Gootman et al. [54].
LULC
[km2 (%)]		RMF1RMF2RMF3RMF4RMF5RMF6RMF7RMF8
Agriculture3.83.13.13.12.90.2<0.10.1
(10.5)(8.8)(8.8)(8.8)(8.5)(20.3)(42.8)(16.3)
Upland Forest31.130.830.830.830.20.6<0.10.4
(86.5)(88.3)(88.3)(88.3)(88.5)(79.2)(57.2)(83.4)
Mixed Development0.70.70.70.70.7<0.10<0.1
(2.0)(1.9)(1.9)(1.9)(1.9)(0.2)(0)(0.3)
Open Water0.40.40.40.40.4<0.100
(1.0)(1.1)(1.1)(1.1)(1.1)(0.3)(0)(0)
Total Area35.934.934.934.934.10.8<0.10.5
(100)(100)(100)(100)(100)(100)(100)(100)
RMF1 is located near the confluence of Moore’s Run with the Cacapon River, and RMF2–RMF8 are nested within the RMF1 drainage area (Figure 1; Table 1). Piezometers ranged from approximately 283 (RMF1) to 288 (RMF8) meters above sea level. A single PVC stilling well (3.2 cm inner diameter) and steel drive point piezometer (3.2 cm inner diameter; screened for the bottom 76.5 cm with 1 cm slotted pipe and 0.03 cm diameter mesh screen) were installed at each sub-watershed outlet during the summer of 2019 to monitor SW stage and SGW in the unconfined alluvial aquifer, respectively. Individual piezometer depths ranged from 0.98 m at RMF6 to 2.01 m at RMF4 and were driven into the unconfined alluvial aquifer to a depth of at least 0.50 m below the adjacent streambed. Lateral distance between site stilling wells and piezometers ranged from 1.3 m at RMF4 to 17.8 m at RMF5.

2.2. Data Collection and Analysis

Stream water (SW) and shallow groundwater (SGW) grab samples were collected at each sampling location (n = 8) on the third Wednesday of each month (between 8:00 h and 12:00 h) for the duration of the study period (January 2020–December 2020) (Figure 1). SW samples were collected as per the grab sample method from 60% of stream depth [26] within one meter of the respective site stilling well. SGW samples were collected with a Masterflex E/S Portable Sampler peristaltic pump (Cole Parmer; Vernon Hills, IL, USA). SGW was pumped from the piezometers through size 15 tubing at a rate of 17 mL/s. Each piezometer was purged and allowed to refill before SGW sample collection. Water samples were stored in an insulated cooler in the field, delivered to the laboratory within seven hours of collection, and refrigerated at 2 °C until analysis. Samples were analyzed for TSS (mg/L) using gravimetric suspended sediment methods within one day of sample collection [55], and PSD curves were determined within 28 days of sample collection using laser particle diffraction [9].
Gravimetric suspended sediment methods involve collecting a known sample volume and filtering out the sediment with vacuum filtration [7,56]. Before filtration, glass fiber filters (0.7 µm pore size; MilliporeSigma, Burlington, MA, USA) were rinsed with deionized water, dried at 105 °C, and weighed. Water samples were sub-sampled for additional analyses (excluded from the current study) and laser particle diffraction. The remainder of each water sample (i.e., 37–300 mL) was filtered for TSS. After filtration, the sediment and filter were dried at 105 °C and re-weighed. The weight of the sediment was quantified by subtracting the dry filter weight from the weight of the filter with sediment, which resulted in a TSS mass estimate.
Laser particle diffraction methods involve the measurement of optical light scattering over a wide range of angles, providing a multiparameter measurement corresponding to a wide range of particle sizes [10,57]. For the current work, 200 mL sub-samples were analyzed with a Microtrac BLUEWAVE (Microtrac, York, PA, USA), which utilizes a tri-laser system that estimates particle sizes from 2000 to 0.010 µm with limited operator intervention [58,59]. Prior to analysis, samples were resuspended by inverting the sample bottle until the suspended sediments were homogenously mixed. Instrument settings included a 60 s (s) deionized water flush, a 30 s background calculation, a 60 s turbid sample analysis, and a 60 s post-sample deionized water flush. Ultrasonic mixing was used to maintain a turbid suspension in the sample reservoir while the sample was pumped through the analyzer. Additional sample or deionized water were added to the reservoir if the sample obscuration value was out of range. PSD data were partitioned into 69 particle diameter size classes that ranged from 2000 µm to 0.0152 µm. Each size class was assigned a numeric value that represented the fraction of the total sample particle volume. The resulting PSD curves were binned into three size classes by particle diameter, including sand (2000 to 62.23 µm), silt (52.33 to 4.63 µm), and clay (3.89 to 0.0152 µm). Additional results included sample mean diameter (MZ, µm), calculated surface area (CS, m2/mL), and sample skewness (Ski, unitless) [58]. Positive ski results correspond to a PSD with more fine particles, and negative values correspond to a PSD with more coarse particles [59].
Monthly SW PSD were divided by monthly SGW PSD for each particle size class (n = 69). The PSD ratio represents the relative proportion of particles from each suspended sediment size class present in the SW and SGW samples. PSD ratios were plotted versus particle size class diameter. Source water (e.g., SW or SGW) monthly TSS concentration versus average particle size class were included on a secondary y-axis for reference. Absent particle size classes (i.e., zero percent of the TSS mass) were excluded from the analysis.
Descriptive statistics were generated for SW and SGW, based on monthly samples collected at each study site (n = 12, each source water type) and the entire study area (n = 96, each source water type). Precipitation data were reported as monthly totals. Since hydroclimate data are often non-normally distributed, normality of the suspended sediment characteristics (i.e., TSS, MZ, CS, and Ski) and particle size fractions (i.e., percent sand, silt, and clay) were assessed for each site using the Anderson–Darling test [55]. All suspended sediment characteristic data were found to be non-normal, suggesting the use of distribution-independent significance tests [26]. Non-parametric tests were used to test for significant (α = 0.05) differences between SW and SGW suspended sediment for the study area average (Mann–Whitney test), each study site (Kruskal–Wallis test), and between study sites (Dunn’s post hoc multiple comparison test) [60].

3. Results

3.1. Climate during Study

Monthly average air temperature (Ta) at RMF during the study period ranged from 1.43 °C in December to 24.01 °C in July (Table 1). Ta followed seasonal expectations for the region, with the lowest Ta in the winter months and highest Ta in the summer months. Minimum Ta ranged from 1.08 °C in December to 23.50 °C in July. Maximum Ta ranged from 1.78 °C in December to 24.53 °C in July. Precipitation (PPT) for the entire study period was 961.5 mm, approximately 19% above the 99-year average of 811 mm [50]. Monthly PPT totals ranged from 41.9 mm in September to 128.5 mm in April (Table 2). PPT during the study period showed limited seasonality, with 51% of total PPT occurring from January to June, and 49% from July to December.

3.2. Suspended Sediment Characteristics

Water sample average median total suspended solids (TSS) concentration was 6.17 mg/L for SW (n = 96) and 103.50 mg/L for SGW (n = 96), a difference of 177% (Table S1, Figure 2). Water sample average and site-specific median TSS concentrations were 146–196% higher in the SGW relative to the SW (α = 0.05; p < 0.001). SW median TSS concentrations were similar across the eight study sites (p > 0.05), and SGW median TSS concentrations varied by site location (p < 0.001).
Water sample average (n = 96) median particle sizes (MZ) were 45.63 and 19.93 µm for the SW (n = 96) and SGW (n = 96), respectively (Table S1, Figure 2), and were significantly different (p < 0.001). It should be noted that SGW median MZ were likely influenced by the size of the piezometer mesh screen (i.e., 0.03 cm) but were still smaller than SW median MZ. SGW median MZ values were 44–120% smaller than surface water MZ values at each study site (n = 12 months, each source water type) and are likely biased toward smaller particle sizes given the 0.03 cm diameter mesh screen of the piezometers. SW and SGW median MZ were significantly different (p < 0.01) at all sites, except for RMF4. Site-specific median SW MZ values were not significantly different (p > 0.05), but median SGW MZ values were significantly different (p < 0.001) between study sites. SGW MZ at RMF3 (12.28 µm), which had the highest median TSS (415.00 mg/L), was significantly smaller (p < 0.05) than MZ at RMF1, RMF4, RMF 6, and RMF7 (Table S1, Figure 2).
Water sample average median SW (n = 96) and SGW (n = 96) CS were 0.49 and 1.37 m2/mL, respectively, and were 95% higher (p < 0.001) in the SGW (Table S1, Figure 2). SW water and SGW median CS values were significantly different for each study site (n = 12, each source water type; p < 0.001). Median SW CS at RMF1 (0.57 m2/mL) was significantly higher (p < 0.05) than RMF7, and RMF2 median SW CS was significantly higher (p < 0.05) than the upper watershed sites (i.e., RMF6, RMF7, RMF8). Median SGW CS was significantly higher (p < 0.05) at RMF5 (2.34 m2/mL) relative to RMF6 (1.07 m2/mL) and was the only site pairing with significant differences in median SGW CS.
All median Ski results indicated that there were more fine particles (p < 0.001) in the SGW, relative to the SW, at each study site (n = 12 months, each source water type). SGW Ski was 20% higher than SW for the water sample average (n = 96, both source water types) (Table S1, Figure 2). Median SW Ski values were not significantly different (p > 0.05) between sites (n = 8), whereas median SGW Ski values were significantly different (p < 0.05) between sites, driven by the significantly higher (p < 0.05) Ski at RMF1 (0.74) relative to RMF3 (0.61).

3.3. Sand, Silt, Clay Fractions

Water sample average SW (n = 96) had an 84% higher (p < 0.001) percentage of sand, and watershed average SGW (n = 96) had an 8% and 90% higher (p < 0.001) percentage of silt and clay particles, respectively (Table S2). SW and SGW median sand, silt, and clay particle percentages differed at each study site (n = 12, both source water types; Table S2, Figure 3). SGW had consistently lower sand fractions (28–138%) and higher clay fractions (71–152%), relative to SW (Figure 3). In the lower and middle portions of the watershed, there were significant differences (p < 0.05) between sand and clay particles in SW and SGW, apart from RMF4 where the sand fraction was not significantly different (p > 0.05) between the SW (30%) and SGW (17%). Silt particles were not significantly different (p > 0.05) between SW and SGW. In the upper portions of the watershed (i.e., RMF6, RMF7, RMF8), SW and SGW particle size classes were significantly different (p < 0.05).
SW sand and silt fractions were not significantly different (p > 0.05) by study site. However, SW clay fractions were significantly different (p < 0.05) by study site and higher in the lower and middle portions of the watershed (Table S2). The clay fractions measured at RMF1, RMF2, and RMF5 were significantly (p < 0.05) higher than the clay fractions from RMF7 and RMF8. Each particle size class was significantly different (p < 0.05) between site SW and SGW samples, with more variability in the SGW suspended sediment size classes, relative to SW. The SGW sand fraction at RMF3 was 95–109% lower (p < 0.05) than RMF1, RMF4, RMF6, and RMF7. The SGW silt and clay fractions at RMF3 were 14% and 40% higher (p < 0.05) than the SGW silt fraction at RMF4 and SGW clay fraction at RMF6, respectively.

3.4. Particle Size Distribution Ratios

Particle size distribution (PSD) ratio results displayed consistent spatial and temporal (i.e., monthly) patterns at most study sites (Figure 4). Sand-sized particles were evenly distributed in SW and SGW during most months, except for RMF8 (Figure 4H) where the SGW had higher proportions of sand in February and May, as supported by the lower SW (6.67 and 6.00 mg/L) and higher SGW (233.00 and 147.00 mg/L) TSS concentrations. Results also showed that there were six instances where the SW had higher percentages of fine particles relative to the SGW. SW in January at RMF1 (Figure 4A) and RMF5 (Figure 4E) contained more fine particles relative to the SGW. SW contained more fine particles in March at RMF2 (Figure 4B) and in April at RMF1 (Figure 4A) and RMF4 (Figure 4D). In December, RMF4 (Figure 4D) SW had more fine particles relative to the site SGW.

4. Discussion

4.1. Stream Water and Shallow Groundwater Suspended Sediment

Results of the current investigation showed that SGW samples had more suspended sediments that comprised smaller particles relative to SW (Figure 2 and Figure 3), as confirmed by PSD ratios (Figure 4). This finding is in contrast with what is typically found in the literature, as most SGW samples have little to no suspended material, typically due to slow flow velocities, small pore sizes, and tortuous flow paths normally observed in the unconfined alluvial aquifer [8,33,61,62]. Several mechanisms could explain the observed differences in SW and SGW suspended sediment characteristics at RMF. For example, the presence of a semi-permeable soil matrix may increase the potential of the shallow aquifer to retain particles from runoff or infiltration relative to the stream [63,64], which further demonstrates the importance of alluvial floodplains for suspended sediment storage [23,65]. Aquifer material deflocculation may also increase SGW suspended sediment concentrations [66,67]. In previous work from the same watershed, study site soils were shown to be composed of larger particle sizes [54], relative to suspended sediments shown in the current work (Figure 3), showing that fragmentation of soil aggregates may contribute to increased suspended sediments in the SGW.
Low SW suspended sediment concentrations, relative to SGW, could indicate the influence of dilution, specifically the contribution of low-sediment-concentration water from surrounding forested areas to Moore’s Run [2,68]. Previous studies identified forested landscapes as sediment sinks [67,68,69,70]. For example, Zeiger and Hubbart [26] showed that forested land use was negatively correlated with suspended sediment stream loading in a representative urbanizing mixed-land-use watershed. Similarly, Powers et al. [71] noted that combined physical and biological processes in waterbodies on forested and agricultural lands can enhance suspended sediment retention and reduce annual variability in suspended sediment exports. Given the high percentage of forested land cover and the presence of open water in the study watershed (Table 1), it is conceivable that the low SW suspended sediment concentrations are a result of landscape particle retention.

4.2. Particle Size Variability

Observed PSD variability may be partially explained by hydroclimate, surficial watershed geology, and alluvial composition. For example, seasonal differences in runoff or precipitation inputs [72] may have resulted in SW with greater fine particle fractions relative to the SGW in spring and winter months (Figure 4). Seven of the eight study sites were in alluvial soils, which are typified by a range of grain sizes from gravel to clay [73,74]. Thus, differences in the suspended sediment PSDs may reflect variability present in the alluvium and soil matrix.
Larger particle sizes present in the SW relative to the SGW may be related to overland flow or increases in channel erosion due to agricultural land use practices that include soil tillage and ruminant livestock grazing [62,64]. Moreover, it is conceivable that the lower median TSS concentration in the SW and SGW at RMF5 were a result of site location, as RMF5 is located on a different branch of Moore’s Run, relative to the other study sites (Figure 1). RMF5 was also previously shown to have the highest Ksat, thereby increasing opportunities for greater suspended sediment mobilization and transport due to larger pore spaces in the shallow aquifer [54].

4.3. Methodlogical Considerations

The objective of the current work was to compare SW and SGW suspended sediment concentrations in an agro-forested catchment. The results reflect groundwater and stream water suspended sediment conditions as close to in situ as possible while utilizing an efficient method that is feasible and replicable from labor and cost perspectives. This is important and relevant, considering the consistent differences observed in suspended sediment characteristics between SW and SGW in each sub-watershed of the current study. It is, however, worth acknowledging the possibility that the standardized sampling methodology used in the current work may have biased the results in favor of higher SGW TSS concentrations with smaller MZ values. The use of a peristaltic pump to sample SGW is a common practice in hydrological and water quality studies [75,76] but the method may, by virtue of suction, suspend additional sediments through mobilization of shallow aquifer soil [77]. Furthermore, the size of the piezometer mesh screen may potentially limit the actual particle size classes abstracted from the SGW. Ultimately, if higher fine particle concentrations were extracted from SGW, current estimations of SGW suspended sediments could be overestimated in some cases. Future SGW suspended sediment work may benefit from the use of peristaltic pumps with slower sampling rates or passive groundwater sampling techniques that do not require mesh screen, which may improve confidence in the collection of accurate SGW pollutant concentration estimates [78].

4.4. Study Implications

Moore’s Run Watershed is representative of the headwater regions in the CBW regarding hydrologic processes, water quality, climate, and the dominant land use practices (e.g., agriculture and forested), making the study watershed a useful area for understanding how suspended sediments influence SW and SGW quality [51]. Previous studies have shown the importance of understanding local suspended sediment regimes to meet regional water quality goals [3]. However, most studies do not include comparisons of in situ SW and SGW suspended sediments, which creates uncertainty in the role of SGW. This work provides direct measures of differences in SW and SGW suspended sediment characteristics and characterizes how those observations vary across a single watershed. Although groundwater results may be, at least in part, an artifact of the sampling method, the results hold important water quality implications. For example, potential overestimation (or underestimation) of SGW suspended sediments may result in inaccurate pollutant loading estimates, which influence water quality outcomes and corresponding resource management strategies. As such, SGW suspended sediment characteristics must be accounted for to accurately quantify the mass balance of suspended sediments and pollutant concentrations in small, agro-forested watersheds.

5. Conclusions

A study was conducted to quantitively characterize stream water (SW) and shallow groundwater (SGW) suspended sediment differences in a mixed-land-use watershed using gravimetric and laser particle diffraction methods. The work utilized a nested-scale, experimental watershed study design, with eight co-located SW and SGW gauging sites, partitioning the catchment into eight sub-watersheds of varying size with largely agro-forested land use types.
The results provide in situ observations of differences in SW and SGW suspended sediment regimes, including suspended sediment characteristics and particle size classes in a headwaters catchment of the Chesapeake Bay Watershed (CBW). SW samples were characterized by lower suspended solids concentrations that included larger particle sizes, while SGW samples were characterized by higher suspended solids concentrations that had higher fractions of fine particles, the latter confirmed by PSD ratio curves. The results highlight distinct SW and SGW suspended sediment characteristics and suggest SGW is an important component of the study watershed suspended sediment regime. However, additional, and more detailed suspended sediment characterizations are needed to improve the understanding of watershed suspended sediment dynamics and pollutant loading estimates.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land11040506/s1, Table S1: Suspended Sediment Characteristics; Table S2: Suspended Sediment Particle Size Fractions.

Author Contributions

Conceptualization, J.A.H. and K.S.G.; methodology, J.A.H.; formal analysis, K.S.G.; investigation, K.S.G. and J.A.H.; data curation, K.S.G.; writing—original draft preparation, K.S.G. and J.A.H.; writing—review and editing, K.S.G. and J.A.H.; visualization, K.S.G. and J.A.H.; supervision, J.A.H.; project administration, J.A.H.; funding acquisition, J.A.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Science Foundation under Award Number OIA-1458952, the USDA National Institute of Food and Agriculture, Hatch project accession number 1011536, and the West Virginia Agricultural and Forestry Experiment Station. Additional funding was provided by the USDA Natural Resources Conservation Service, Soil and Water conservation, Environmental Quality Incentives Program No: 68-3D47-18-005. Results presented may not reflect the views of the sponsors, and no official endorsement should be inferred.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to observation of data proprietary rights.

Acknowledgments

The authors appreciate the support of many scientists of the Institute of Water Security and Science and the Interdisciplinary Hydrology Laboratory (https://www.researchgate.net/lab/The-Interdisciplinary-Hydrology-Laboratory-Jason-A-Hubbart; accessed on 10 January 2022). The authors also appreciate the feedback of anonymous reviewers whose constructive comments improved the article.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Land 11 00506 g001 550
Figure 1. (a) The location of West Virginia University Reymann Memorial Farm (RMF) within the West Virginia headwater region of the Chesapeake Bay Watershed (CBW) and (b) the land use/land cover for RMF, where red = mixed development; yellow = agriculture; green = upland forest; and blue = open water, and the locations of eight co-located, nested stilling wells and piezometers in Moore’s Run Watershed.
Figure 1. (a) The location of West Virginia University Reymann Memorial Farm (RMF) within the West Virginia headwater region of the Chesapeake Bay Watershed (CBW) and (b) the land use/land cover for RMF, where red = mixed development; yellow = agriculture; green = upland forest; and blue = open water, and the locations of eight co-located, nested stilling wells and piezometers in Moore’s Run Watershed.
Land 11 00506 g001
Land 11 00506 g002 550
Figure 2. Monthly (n = 12) stream water (SW) and shallow groundwater (SGW) (A) Total suspended solids concentration (TSS; mg/L); (B) mean particle size, MZ, (µm); (C) calculated surface area, CS, (m2/mL); and (D) skewness, Ski, (unitless) at each study site in Moore’s Run Watershed (n = 12, each source water type), located near Wardensville, West Virginia, USA, during the study period (January 2020–December 2020) and the study area average (Avg; n = 96, each source water type). Boxes define the interquartile range (IQR). Vertical lines show the range within 1.5 IQR. Midlines indicate the median. Open circles denote the mean. Filled-in diamonds represent data points.
Figure 2. Monthly (n = 12) stream water (SW) and shallow groundwater (SGW) (A) Total suspended solids concentration (TSS; mg/L); (B) mean particle size, MZ, (µm); (C) calculated surface area, CS, (m2/mL); and (D) skewness, Ski, (unitless) at each study site in Moore’s Run Watershed (n = 12, each source water type), located near Wardensville, West Virginia, USA, during the study period (January 2020–December 2020) and the study area average (Avg; n = 96, each source water type). Boxes define the interquartile range (IQR). Vertical lines show the range within 1.5 IQR. Midlines indicate the median. Open circles denote the mean. Filled-in diamonds represent data points.
Land 11 00506 g002
Land 11 00506 g003a 550Land 11 00506 g003b 550
Figure 3. Monthly (n = 12) stream water (SW; blue) and shallow groundwater (SGW; red) particle size fractions (%), including sand, silt, and clay, from each study site (n = 8, both source water types) during the study period (January 2020–December 2020) in Moore’s Run Watershed, located near Wardensville, WV, USA. Average soil core particle size fractions by depth (i.e., dark brown = 0–5 cm, light brown = 25–30 cm, orange = 45–50 cm) from Gootman et al. [54] are also included for a comparison between water and soil particle size fractions.
Figure 3. Monthly (n = 12) stream water (SW; blue) and shallow groundwater (SGW; red) particle size fractions (%), including sand, silt, and clay, from each study site (n = 8, both source water types) during the study period (January 2020–December 2020) in Moore’s Run Watershed, located near Wardensville, WV, USA. Average soil core particle size fractions by depth (i.e., dark brown = 0–5 cm, light brown = 25–30 cm, orange = 45–50 cm) from Gootman et al. [54] are also included for a comparison between water and soil particle size fractions.
Land 11 00506 g003aLand 11 00506 g003b
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Figure 4. Monthly (n = 12) relationships between stream water (SW) and shallow groundwater (SGW) particle size distribution (PSD) ratios (unitless) and particle diameter (µm) at each study site (n = 8) during the study period (January 2020–December 2020) in Moore’s Run Watershed, located near Wardensville, West Virginia, USA. Size classes are noted with solid vertical lines. Open symbols represent the monthly (n = 12) total suspended solids concentration (TSS; mg/L) versus the average particle diameter (Mz; µm).
Figure 4. Monthly (n = 12) relationships between stream water (SW) and shallow groundwater (SGW) particle size distribution (PSD) ratios (unitless) and particle diameter (µm) at each study site (n = 8) during the study period (January 2020–December 2020) in Moore’s Run Watershed, located near Wardensville, West Virginia, USA. Size classes are noted with solid vertical lines. Open symbols represent the monthly (n = 12) total suspended solids concentration (TSS; mg/L) versus the average particle diameter (Mz; µm).
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Table
Table 2. Monthly precipitation totals and descriptive statistics for 30 min air temperature during the study period (January 2020–December 2020) in Moore’s Run Watershed.
Table 2. Monthly precipitation totals and descriptive statistics for 30 min air temperature during the study period (January 2020–December 2020) in Moore’s Run Watershed.
MonthPPT (mm)Ta (°C)
TotalsMeanMinMax
January792.281.952.66
February58.74.053.674.44
March64.38.828.409.24
April128.59.428.999.88
May51.814.7614.3515.20
June110.620.5120.0620.99
July10624.0123.5024.53
August114.622.1821.7822.59
September41.916.7016.2917.12
October57.912.2711.8512.70
November67.98.508.068.97
December80.31.431.081.78
Note: PPT = precipitation (mm), Ta = air temperature (°C), Min = minimum, Max = maximum.
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Gootman, K.S.; Hubbart, J.A. A Comparison of Stream Water and Shallow Groundwater Suspended Sediment Concentrations in a West Virginia Mixed-Use, Agro-Forested Watershed. Land 2022, 11, 506. https://doi.org/10.3390/land11040506

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Gootman KS, Hubbart JA. A Comparison of Stream Water and Shallow Groundwater Suspended Sediment Concentrations in a West Virginia Mixed-Use, Agro-Forested Watershed. Land. 2022; 11(4):506. https://doi.org/10.3390/land11040506

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Gootman, Kaylyn S., and Jason A. Hubbart. 2022. "A Comparison of Stream Water and Shallow Groundwater Suspended Sediment Concentrations in a West Virginia Mixed-Use, Agro-Forested Watershed" Land 11, no. 4: 506. https://doi.org/10.3390/land11040506

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