Article pubs.acs.org/est
Evolution of Sediment Plumes in the Chesapeake Bay and Implications of Climate Variability Guangming Zheng,*,†,‡,§ Paul M. DiGiacomo,† Sujay S. Kaushal,‡ Marilyn A. Yuen-Murphy,† and Shuiwang Duan‡ †
NOAA/NESDIS/Center for Satellite Applications and Research, 5830 University Research Court, College Park, Maryland 20740, United States ‡ Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Court, College Park, Maryland 20740, United States § Global Science & Technology, Inc., 7855 Walker Drive, Suite 200, Greenbelt, Maryland 20770, United States S Supporting Information *
ABSTRACT: Fluvial sediment transport impacts fisheries, marine ecosystems, and human health. In the upper Chesapeake Bay, river-induced sediment plumes are generally known as either a monotonic spatial shape or a turbidity maximum. Little is known about plume evolution in response to variation in streamflow and extreme discharge of sediment. Here we propose a typology of sediment plumes in the upper Chesapeake Bay using a 17 year time series of satellite-derived suspended sediment concentration. On the basis of estimated fluvial and wind contributions, we define an intermittent/wind-dominated type and a continuous type, the latter of which is further divided into four subtypes based on spatial features of plumes, which we refer to as Injection, Transport, Temporary TurbidityMaximum, and Persistent Turbidity-Maximum. The four continuous types exhibit a consistent sequence of evolution within 1 week to 1 month following flood events. We also identify a “shift” in typology with increased frequency of Turbidity-Maximum types before and after Hurricane Ivan (2004), which implies that extreme events have longer-lasting effects upon estuarine suspended sediment than previously considered. These results can serve as a diagnostic tool to better predict distribution and impacts of estuarine suspended sediment in response to changes in climate and land use.
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INTRODUCTION Whereas fluvial suspended sediment plays important geological1−3 and biogeochemical4 roles, excessive riverine input of suspended sediment can lead to many environmental problems in estuaries (e.g., pathogen proliferation,5 transport of pollutants,6,7 loss of seagrass habitat,8 and decline of fish and shellfish harvests).9,10 Management and restoration in response to these environmental problems require understanding the distribution of suspended sediment in response to river discharge, which can exhibit significant variability in the context of climate change.11,12 Such an understanding has become increasingly important because the interaction between land use and climate variability is expected to amplify riverine discharge pulses of sediments as well as other contaminants.13 The Chesapeake Bay is the largest estuary in North America, and the upper part is essentially the estuary of the Susquehanna River (Figure 1). Suspended sediment in the upper bay is known to have two main sources: discharge from the Susquehanna River and resuspension from the bay’s bottom,14−16 the latter of which appears to be dominated by wind-generated waves as opposed to tides based on a case study.17 Little is known about the evolution of sediment plumes following flood events and time scales of different evolution © 2015 American Chemical Society
stages, owing to a lack of sufficiently long time series of observations.18 Son and Wang19 illustrated seasonal mean patterns of surface-suspended particles in the Chesapeake Bay using ocean color data derived from MODIS Aqua. Liu and Wang20 found that the suspended sediment concentration and the Susquehanna streamflow is in phase from a monthly perspective and that elevated concentration of sediment can last 10−20 days after a freshet (flood of the river resultant from either rain or melted snow). One important subject that remains to be characterized for this region is the stages of river sediment plumes after freshets. Such research is difficult to conduct with data from only one satellite, which provides limited operational lifecycle and day-to-day data continuity. So far, our knowledge about estuarine suspended sediment distribution associated with freshets is generalized as a twostate pattern:16,21 During high streamflow, turbid waters are confined within the landward reaches of the bay; during low flow, a turbidity maximum, normally defined as a segment with Received: Revised: Accepted: Published: 6494
January 12, 2015 March 30, 2015 May 4, 2015 May 4, 2015 DOI: 10.1021/es506361p Environ. Sci. Technol. 2015, 49, 6494−6503
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surface can be ascribed to suspended particles,27,28 leaving a sizable “fingerprint” of suspended particles on optical measurements. Additionally, a series of five ocean-color observing satellites (see next section) available since the 1990s has provided a good opportunity to achieve synoptic sampling of surface estuarine waters with daily revisit frequency. Here we synthesize multisatellite observations of suspended sediment concentrations along a main-stem transect in the upper Chesapeake Bay and obtained quality-verified data (see next section) for ∼30% of the days during 1997−2013, which at least doubled the temporal coverage from any single satellite alone and enabled us to study the effects of Susquehanna River freshets on evolution of sediment plumes over weekly to interannual time scales.
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DATA AND METHODS To examine the dynamics of suspended sediment in the upper Chesapeake Bay, we inverted ocean color radiometric data provided by multiple satellites to derive the concentration of suspended particulate matter, hereafter referred to as [SPM]. Suspended particles in estuaries are generally dominated by fluvial sediment, and therefore, we use [SPM] to represent the concentration of suspended sediment and use “particles” and “sediment” interchangeably in this paper. In view of the dominant effect of river discharge upon sediment distribution in the upper bay, we subsampled the satellite data along a longitudinal transect from the Susquehanna River mouth (defined as the location of 0 km) to 100 km downstream (Figure 1). Noise and intersensor discrepancies were minimized by applying piece-wise cubic regression models to data from all sensors obtained on the same day. Our final [SPM] data set is a 17 year time series of longitudinal profiles comprising 1728 days of quality-verified observations of surface [SPM] from 1997 to 2013 with revisit frequency depending on the number of active sensors available during a specific period of time and sky conditions. Below, we describe the methods for obtaining satellite-derived [SPM] data in detail. Satellite Radiometric Data. To estimate surface [SPM] in the Chesapeake Bay, we used satellite data of remote-sensing reflectance, Rrs(λ), where λ is light wavelength in vacuum. The Rrs(λ) data were obtained from the National Aeronautics and Space Administration (NASA 2013, Supporting Information). A total of five satellite sensors are involved, including the Seaviewing Wide Field-of-view Sensor (SeaWiFS, 1.1 km resolution, 1997−2010), the two MODerate resolution Imaging Spectroradiometers (MODIS, 1 km) onboard Terra (1999−2013) and Aqua (2002−2013), the MEdium Resolution Imaging Spectrometer (MERIS, 1.2 km reduced resolution, 2002−2012), and the Visible Infrared Imaging Radiometer Suite (VIIRS, 0.75 km, 2011−2013). The use of multiple sensors facilitates data quality-control by enabling intersensor comparisons (see section “Final estimation of [SPM]” below) and improves spatial and temporal coverage of the study area owing to separated satellite overpass time, which differs from several minutes to almost 4 h for the data used in this study. Reflectance Inversion Model for [SPM]. An inversion model developed specifically for the Chesapeake Bay by Son and Wang19 was used to calculate [SPM]. The model requires input of Rrs(λ) data measured at the two wavelengths of 488 and 667 nm. The actual wavelengths used in our calculations vary slightly with sensor within the ranges of 486−490 nm and 665−671 nm.
Figure 1. Map of the upper Chesapeake Bay. The thick gray line indicates the satellite [SPM] data sampling transect. Short black horizontal bars are marked every 10 km in distance along the transect. Black solid circles indicate the locations of four field sampling stations of the Chesapeake Bay Program. Two stars denote the locations of the USGS Conowingo Dam station for streamflow measurements and the NOAA Tolchester Beach station for local wind speed measurements. Gray and white areas indicate land and water, respectively. Thin black curves denote the 4 m isobath.
higher turbidity than those found both upstream and downstream,14,16 is produced and maintained near the landward limit of salt intrusion. From a hydrological standpoint, however, the estuary may undergo a series of progressive stages following flood events, including freshwater transport in the surface layer and subsequently near the bottom, rebound of salt intrusion, and recovery of salinity structure.22−24 Driven by these hydrological processes, the postfreshet evolution of sediment plumes is likely to be more complex than the twostate simplification. In fact, one case study along a longitudinal transect in the upper Rappahannock River (another tributary of the Chesapeake Bay) has already provided an indication pointing to the possible existence of diverse structures of suspended particulate distribution several days to a month after a major storm.22 However, the questions of whether such features commonly exist and consistently evolve after freshets remain unanswered. In addition, long-term evolution of sediment plumes after major freshets is unknown, whereas water quality in the Chesapeake Bay has been strongly impacted by extreme climate events,13,25 and one flood event may discharge more sediment than many years of “normal” flow.16,18,26 One of the most promising tools to fill these gaps is satellite water color remote sensing. In estuarine waters, a significant portion of light reflectance emerging from below the water 6495
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CB2.2, CB3.1, and CB3.2 (Figure 1) were obtained from the Chesapeake Bay Program (CBP 2013, Supporting Information); hourly wind speed data measured at the Tolchester Beach station (Figure 1) were obtained from the National Oceanic and Atmospheric Administration (NOAA 2013, Supporting Information). Within the period from 1997 to 2013, we obtained 6110 observations of daily mean streamflow of the Susquehanna River, Qd. The mean, mode, median, first, and third quartiles of Qd are 1155, 286, 816, 388, and 1518 m3 s−1, respectively, with a positive skewness of 3.8 and a significant positive excess kurtosis of 29.1. These statistical descriptors suggest that the histogram of the Susquehanna streamflow is highly peaked at low values (Qd < 300 m3 s−1) with a “heavy tail” at large values (more than half of the Qd observations exceeds 800 m3 s−1). Thus, neither mean nor median is a good choice to identify bigger pulses of Qd, which we intend to use to define a discharge event. In this study, a discharge event is defined as Qd exceeding the third quartile (1518 m3 s−1) of the 6110 observations. A total of 203 discharge events defined as such were identified with an average duration of 7.5 days. We also calculated daily mean wind speed (Ud, the magnitude of a horizontal wind vector) for each individual day within the period from 1997 to 2013, and we obtained 5322 observations of Ud. The mean, mode, median, first, and third quartiles of Ud are 3.6, 2.4, 2.8, 2.0, and 4.5 m s−1, respectively, with a positive skewness of 1.4 and a positive excess kurtosis of 1.8. A wind event is defined with the same statistical descriptor (the third quartile, i.e., Ud = 4.5 m s−1) used to define the discharge events so that the time scales of the two processes are comparable. In total, we identified 728 wind events within this period of time and the average duration of a wind event is 1.9 days. Calculated Tidal Current Speed. Tidal current data used in this study are the same as those used by Shi and Wang,32 which were simulated with a tide model and tidal forcings at the opening of the Chesapeake Bay. The model output covers the entire Chesapeake Bay with a horizontal resolution of about 0.3 km and comprises the amplitudes, inclination angles, frequencies, and phases of eight major tidal current constituents including Q1, O1, K1, N2, M2, S2, M4, and M6, where the subscript number denotes roughly how many cycles a harmonic constituent undergoes over the course of a day and the entire symbol combining the letter and number specifies the exact frequency of that constituent. Based on these constituents we computed tidal current speed at 10, 20, 30, 40, and 50 km along the transect (Figure 1) and at 0−6 h before each satellitederived [SPM] observation using “T_tide” program.33 Calculated tidal current speed in this region is generally less than 0.5 m s−1 with a median value of around 0.27 m s−1.
The original product of satellite-derived SPM has been “ground-truthed” by Son and Wang.19 To illustrate the accuracy of satellite-derived [SPM], we also compared the satellitederived and our final fitted [SPM] with in situ data provided by the Chesapeake Bay Program (CBP 2013, Supporting Information). Our criteria for a valid matchup between satellite overpass and field sampling require the distance of 0.05). The calm-day baseline is considered as the background level of [SPM] when the upper Chesapeake Bay is subject to minimum wind effect. Finally, the fluvial and wind contributions to the surface [SPM] of each day were estimated by matching the corresponding Q10d and U8h with data shown in Figure 3 (numerical values listed in Supporting Information Table S1). A wind-dominated case of surface suspended particles in the upper bay is identified when the estimated wind contribution surpasses fluvial contribution. The time scales of the winddominated cases are typically shorter than 2 days, and therefore, we refer to them as the intermittent type. The short time scale of the wind-dominated intermittent type is associated with the short duration of local wind events (1.9 days on average) and the particle population in this region. Finer particles (∼3 μm in Stokes diameter) can remain in suspension and contribute to the formation of a stable “background” in the surface layer.14,17,35,36 Larger particles (8−12 μm) tend to sink and stay at the bottom unless vertical mixing is sufficiently strong to bring them up to the surface.14,18 Within the entire time series from 1997 to 2013, a total of 451 cases of wind-dominated intermittent type were identified. The 17 year climatology of the intermittent type of [SPM] distribution along the sampling transect is shown in Figure 4a for all flow conditions and in Supporting Information Figure S3a for various flow conditions. The intermittent type often exhibits [SPM] maxima at 20% higher than minimum [SPM] values found both upstream and downstream, which is a typical range of field-observed primary turbidity maximum in the Chesapeake Bay.14,36 The second spatial feature is a sharp turbidity drop in the seaward direction immediately after the region of 10−50 km, which is considered present if [SPM] decreases >20% at the downstream side (50 km) compared with the maximum [SPM] within 10−50 km. The classification boundary of 20% was determined on the basis of the systematic error in satellite-derived [SPM] expressed as the departure of the median ratio between derived and measured values from 1, which is to ensure that a detected feature cannot be caused by errors in data. We restricted this error calculation to [SPM] values greater than 10 mg L−1 which is on average the lower end of [SPM] in the turbidity maximum region (Figure 4). These criteria lead to the identification of four different continuous types, which we refer to as the Injection, Transport, Temporary Turbidity-Maximum, and Persistent TurbidityMaximum (Figure 4b). The Persistent Turbidity-Maximum type is assigned to a [SPM] profile if both spatial features are present. Conversely, the Transport type is assigned if none of the features are present. The Temporary Turbidity-Maximum type contains the first but misses the second feature, whereas the Injection type contains the second but misses the first. Excluded from the analyses above is the question of whether the satellite-derived [SPM] data are influenced by biological organisms in addition to river- and wind-induced suspended sediments. Among all organisms, pigment-rich phytoplankton exhibits strong absorption bands in the blue and red, presumably contributing to satellite-derived [SPM] signal in a distinct fashion from other particles that are nonpigmented or weakly pigmented such as minerals, organic detritus, bacteria, and zooplankton. The worst-case scenario would be during
low-flow summer time when the proportion of algal biomass to total [SPM] reaches a seasonal peak in surface waters of the upper Chesapeake Bay.38,39 To examine algal effect on satelliteobserved spatial pattern of [SPM], we identified four matchups between in situ measured [SPM], chlorophyll concentration ([Chl]), and satellite-derived [SPM] along the axial transect (Figure 1) during low-flow (Q10d = 143−571 m3 s−1) summer of August 2005, June 2006, and June 2012 (Supporting Information Figure S4). The pattern shown in our satellitederived [SPM] is consistent with in situ [SPM] data. In contrast, the [Chl] transect is decoupled from the satellitederived and in situ [SPM] transects with higher algal biomass located downstream of the turbidity maximum. Similar patterns were reported in this area under moderate streamflow conditions in July 1996 (exact date of sampling not reported but Q10d should be within 584−960 m3 s−1)40 and in May 2008 (Q10d = 1392 m3 s−1).41 Therefore, it is unlikely that our satellite-derived [SPM] pattern be dramatically affected by phytoplankton blooms even during low-flow summer time. A more thorough investigation of this issue can be most effectively made in the future using novel algorithms that enable the separation of phytoplankton from other water constituents (e.g., the model developed by Zheng et al.42). Short-Term Evolution of Typology Following Freshet Events. We found that after freshets, the sequence of succession among the four fluvial-dominated continuous types occurs consistently, but the timing varies considerably (Table 1). The Injection type (Figure 4b) appears typically 0−5 days after peak flow which is defined as the occurrence of maximum daily mean streamflow during a discharge event. It is characterized by generally monotonic decrease of [SPM] moving seaward from the Susquehanna River mouth, implying initial injection of sediment. The subsequent Transport type is observed 3−10 days after peak and is characterized by turbid waters confined in the upper reaches of the bay with a turbidityfront on the seaward side, indicating transport of sediments in 6499
DOI: 10.1021/es506361p Environ. Sci. Technol. 2015, 49, 6494−6503
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Environmental Science & Technology the form of suspended load. Moving to the next stage, the Temporary Turbidity-Maximum type occurs normally 5−17 days after peak. Its spatial shape is approximately symmetric with comparable [SPM] gradient between the ascending and descending sides. In contrast, the final Persistent TurbidityMaximum type is skewed seaward with steeper upstream (ascending) than downstream (descending) slope. The first day of its formation ranges mostly within 10−18 days, and the characteristic skewed shape is retained until disturbed by a resuspension or discharge event. The evolving sediment plumes after selected flood events are visualized in Supporting Information Figure S5. A landward shift in the center of turbid regions is appreciable following the freshets as the typology evolves from Transport to Persistent Turbidity-Maximum, which confirms that the observations made by Nichols22 are a common feature in estuaries after freshet events. With respect to the timings versus the magnitude of peak streamflow, no obvious trend is identified. Nonetheless, under extreme circumstances, the Temporary Turbidity-Maximum can occupy the estuary up until 20−30 days after peak flow, for example, after a very high pulse (peak daily streamflow > ∼9000 m3 s−1, event nos. 18−20 in Table 1) or a prolonged period of flood (duration >2 weeks, event no. 1). The four evolution stages of sediment plumes can be explained by potential changes in hydrological states. A previous study suggests that following Tropical Storm Agnes (1972), salinity structure in estuaries of the Chesapeake Bay underwent four stages.24 In the initial stage, sediment-laden freshwater is transported predominantly in the surface layer. The Injection type defined in this study can be associated with this stage because gradual settling of sediments out of the surface layer could generate the monotonic shape (Figure 4b). In the second stage, freshwater occupies the upper reaches of the estuary throughout the entire water column,24 which can explain the stepwise [SPM] profile found in the Transport type (Figure 4b). In the third stage, upstream rebound of salt intrusion in the lower layer signifies reestablishment of a twolayer gravitational circulation,24 which is favorable for producing a turbidity maximum.18 Our results show that initially the symmetric Temporary Turbidity-Maximum is produced. The final hydrological stage is vertical mixing between surface and bottom waters, ultimately resulting in recovery of salinity structure to prestorm conditions.24 The stabilizing hydrological status in this stage can explain the production and maintenance of the skewed Persistent Turbidity-Maximum, although its timing relative to salinity recovery is still unclear. Long-Term Shift of Typology Following Hurricane Ivan (2004). In addition to the short-term dynamics, the 17 year time series enabled us to examine trends of sediment plume typology over almost two decades. We binned the typology data based on the streamflow Q10d, and then calculated the probability of occurrence for each continuous type at various streamflow conditions. For simplicity, we combine the Temporary and Persistent Turbidity-Maximum types into one group, and the Injection and Transport types into another. Note that excluding the wind-dominated types the occurrence probabilities of the Turbidity-Maximum and non-Maximum types sum to unity. Figure 5 shows the seasonal time series of the probability of Turbidity-Maximum types at low (Q10d 1750 m3 s−1) flow conditions. We found a shift in sediment plume typology with
Figure 5. Seasonal time series for the occurrence probability of Turbidity-Maximum types at (a) low streamflow (Q10d < 500 m3 s−1), (b) moderate streamflow (Q10d = 500−1750 m3 s−1), and (c) high streamflow (Q10d > 1750 m3 s−1) conditions. The four seasons based on which the data were binned are January−February−March, April− May−June, July−August−September, and October−November−December.
increased frequency of Turbidity-Maximum types after the passage of Hurricane Ivan (September 2004). The probability of Turbidity-Maximum types increased significantly (p-value 0.1, Figure 5c). Before Hurricane Ivan 2004, the Injection and Transport types dominate at both low (Q10d 1750 m3 s−1) flows, whereas the Turbidity-Maximum types dominate at moderate flows (Q10d = 500−1750 m3 s−1, Figure 6a). In contrast, after Hurricane Ivan 2004 and before Storm Lee 2011 the TurbidityMaximum types dominate under essentially all flow conditions except the highest streamflows (Figure 6b). Such a shift cannot be explained by differences in satellite sensors across the decade because the typology was determined based on systematic spatial features which is insensitive to small differences in [SPM] derived from different sensors (see Supporting Information Figure S2). Two lines of evidence suggest that the typology shift is likely associated with Hurricane Ivan 2004. First, the shift in typology occurred in an abrupt manner after Hurricane Ivan (Figure 5b), where probability of Turbidity-Maximum types at moderate flows increased immediately after September 2004. For other flows, there are not enough data to make the change-point analysis partly because of a lack of days with these flow conditions. Second, in the aftermath of Hurricane Ivan, the Susquehanna River delivered ∼9 Tg of sediment into the bay (RIM, 2014, Supporting Information), which is equivalent to ∼8 times the mean annual load during 1997−2003 and 2005− 6500
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Figure 6. Probability of Turbidity-Maximum and non-Turbidity-Maximum types at various streamflow conditions (a) before and (b) after Hurricane Ivan (September 2004).
Turbidity-Maximum types and the annual sediment load is only 0.46. These evidence suggest that some portion of the Ivaninduced sediments might have been trapped in the upper Chesapeake Bay for several years after the hurricane, contributing to increased frequency of Turbidity-Maximum types. It is worth noting that Hurricane Isabel (September 2003) passed the Chesapeake Bay area with strong winds of 50−90 knots (http://weather.unisys.com/), which could have resuspended and relocated unusual amounts of bottom deposits. However, an important role of Hurricane Isabel in the typology shift seems unlikely, because while effect by successive events of Isabel and Ivan is possible, no persistent changes in the typology are observed after the passages of Hurricane Isabel, as well as several other hurricanes with intensities comparable to Isabel (e.g., Hurricanes Bonnie (1998) and Floyd (1999)). Our typology analysis of suspended sediment distribution in the Chesapeake Bay serves as a diagnostic tool to studying estuarine responses to freshet events over weekly-to-monthly and interannual time scales. The succession of the four continuous types provides a new conceptual model for characterizing stages of transport and transformation of river sediment plumes in an estuary. This conceptual model can be incorporated into hydrological and ecological models to reproduce more realistic turbidity dynamics in association with varying streamflows. The typology shift after Hurricane Ivan implies that a single pulse of river sediment input can have longer-lasting effects upon estuarine water turbidity than what was previously considered. Long-term turbidity effects over annual to decadal time scales associated with extreme events need to be addressed in future research, given the increasing interactive effects between land use and climate change on coastal water quality.13,25
2010 (Figure 7a). After such an extreme dose of sediment discharge, the average [SPM] in the upper bay increased
Figure 7. Annual time series of (a) total sediment load from the Susquehanna River and (b) mean surface suspended particulates concentrations in the upper bay (10−50 km) at low streamflows (Q10d < 500 m3 s−1). The horizontal dashed lines in panel (b) represent average satellite-derived [SPM] values before and after Hurricane Ivan.
throughout the water column at low flows, which is when surface [SPM] is subject less to recent fluvial transport and more to resuspension of historical deposits. Based on satellite data, the surface [SPM] increased by ∼16% from 10.2 ± 0.5 mg L−1 during the period 1998−2002 to 11.9 ± 0.8 mg L−1 during the period 2005−2010 (Figure 7b). Field [SPM] data obtained around the same region and during the same period of time demonstrate a consistent result of ∼18% increase. In the bottom layer, the [SPM] increased by ∼39% before and after Hurricane Ivan. Other factors that may affect [SPM] in this area do not show any long-term trend (e.g., fluvial [SPM] in the Susquehanna River during low-flow conditions and intensity of mixing represented by wind speed (data not shown)). Figure 7a also shows a higher mean value of annual Susquehanna sediment load during 2005−2010 than that during 1998−2003 (see also Figure 17 by Hirsch et al.26). However, this trend is insufficient to explain the observed changes in typology because the correlation coefficient between the annual probability of the
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ASSOCIATED CONTENT
* Supporting Information S
Data sources and additional table and figures are included in Supporting Information. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/es506361p. 6501
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(15) Schubel, J.; Pritchard, D. Responses of upper Chesapeake Bay to variations in discharge of the Susquehanna River. Estuaries 1986, 9 (4), 236−249. (16) Schubel, J. R. Effects of tropical storm Agnes on the suspended solids of the northern Chesapeake Bay. In Suspended solids in water; Springer: New York, 1974; pp 113−132. (17) Sanford, L. P. Wave-forced resuspension of upper Chesapeake Bay muds. Estuaries 1994, 17 (1), 148−165. (18) Dyer, K. Sediment processes in estuaries: future research requirements. J. Geophys. Res.: Oceans 1989, 94 (C10), 14327−14339. (19) Son, S.; Wang, M. Water properties in Chesapeake Bay from MODIS-Aqua measurements. Remote Sens. Environ. 2012, 123, 163− 174. (20) Liu, X.; Wang, M. River runoff effect on the suspended sediment property in the upper Chesapeake Bay using MODIS observations and ROMS simulations. J. Geophys. Res.: Oceans 2014, 119 (12), 8646− 8661. (21) Schubel, J. Distribution and transportation of suspended sediment in upper Chesapeake Bay. Mem. - Geol. Soc. Am. 1972, 133, 151−167. (22) Nichols, M. M. Response and recovery of an estuary following a river flood. J. Sediment. Res. 1977, 47 (3), 1171−1186. (23) Nichols, M. M. Response of Coastal Plain Estuaries to Episodic Events in the Chesapeake Bay Region. In Nearshore and Estuarine Cohesive Sediment Transport, American Geophysical Union: 1993; pp 1−20. (24) Consortium, C. R. The effects of Tropical Storm Agnes on the Chesapeake Bay estuarine system; Chesapeake Research Consortium (CRC): Edgewater, MD, 1976. (25) Kaushal, S. S.; Groffman, P. M.; Band, L. E.; Shields, C. A.; Morgan, R. P.; Palmer, M. A.; Belt, K. T.; Swan, C. M.; Findlay, S. E.; Fisher, G. T. Interaction between urbanization and climate variability amplifies watershed nitrate export in Maryland. Environ. Sci. Technol. 2008, 42 (16), 5872−5878. (26) Hirsch, R. M. Flux of nitrogen, phosphorus, and suspended sediment from the Susquehanna River basin to the Chesapeake Bay during Tropical Storm Lee, September 2011, as an indicator of the effects of reservoir sedimentation on water quality; U.S. Department of the Interior, U.S. Geological Survey, 2012. (27) Babin, M.; Morel, A.; Fournier-Sicre, V.; Fell, F.; Stramski, D. Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnol. Oceanogr. 2003, 48 (2), 843−859. (28) Stramski, D.; Boss, E.; Bogucki, D.; Voss, K. J. The role of seawater constituents in light backscattering in the ocean. Prog. Oceanogr. 2004, 61 (1), 27−56. (29) Sokal, R. R.; Rohlf, F. J. Biometry: the Principles and Practice of Statistics in Biological Research. 3 ed.; W. H. Freeman: New York, 1995; p 887. (30) Laws, E. A. Mathematical Methods for Oceanographers: An Introduction; John Wiley & Sons: New York, 1997; p 343. (31) Jakabsons, G. ARESLab: Adaptive Regression Splines toolbox for Matlab/Octave, 2013 (http://www.cs.rtu.lv/jekabsons/). (32) Shi, W.; Wang, M.; Jiang, L. Tidal effects on ecosystem variability in the Chesapeake Bay from MODIS-Aqua. Remote Sens. Environ. 2013, 138, 65−76. (33) Pawlowicz, R.; Beardsley, B.; Lentz, S. Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Comput. Geosci. 2002, 28 (8), 929−937. (34) Nezlin, N. P.; DiGiacomo, P. M.; Diehl, D. W.; Jones, B. H.; Johnson, S. C.; Mengel, M. J.; Reifel, K. M.; Warrick, J. A.; Wang, M. Stormwater plume detection by MODIS imagery in the southern California coastal ocean. Estuarine, Coastal Shelf Sci. 2008, 80 (1), 141−152. (35) Schubel, J. Size distributions of the suspended particles of the Chesapeake Bay turbidity maximum. Neth. J. Sea Res. 1969, 4 (3), 283−309.
AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Author Contributions
G.Z. processed the data, conducted the analysis, and wrote the manuscript; P.M.D., S.S.K., M.A.Y., and S.D. helped conceive main research ideas and provided feedbacks on the manuscript. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This project is funded by NOAA’s Ocean Remote Sensing (ORS) Program with partial support from NASA Carbon Cycle Science Program (Grant NNX11AM28G awarded to S.S.K.). We thank Lide Jiang for providing the simulated tidal current data. We are grateful to two anonymous reviewers for valuable comments. The contents of this article are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of the NOAA or the U.S. Government.
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