Ferry-Based Monitoring of Surface Water Quality in North Carolina ...

Estuaries

Vol. 26, No. 4A, p. 975–984

August 2003

Ferry-Based Monitoring of Surface Water Quality in North Carolina Estuaries C. P. BUZZELLI1*, J. RAMUS1, and H. W. PAERL2 1

Duke University, Marine Laboratory, 135 Duke Marine Lab Road, Beaufort, North Carolina 28516 2 Institute of Marine Sciences, University of North Carolina at Chapel Hill, 3431 Arendell Street, Morehead City, North Carolina 28557 ABSTRACT: Several interrelated factors affect water quality in the Albemarle-Pamlico Estuarine System (APES) including land use change in the upland and coastal watersheds, legislatively mandated basin-wide nutrient management plans, intense storms, and global and local changes in sea level. Despite its importance as an essential fish habitat, the APES has not been monitored as intensively or extensively for habitat impacts associated with decreased water quality as other estuaries have been, such as with the North Carolina tributary estuaries or Chesapeake Bay. To support the sustainable use of these estuaries, we are developing an automated water quality monitoring system aboard ferries that traverse the APES. This program, FerryMon, provides a unique, long-term, and cost-effective monitoring system to evaluate status and trends in APES water quality. Intensive temporal and spatial data obtained from all ferry routes provide an environmental baseline and are used to assess the patterns and variability in surface water hydrography, dissolved constituents, and particulate matter. The data are useful to calibrate estimates of ocean color and sea surface temperature from aircraft and satellite sensors. We are creating a searchable geographic database that is intended for scientists, managers, and the general public. Using ferries as sampling platforms to monitor estuarine water quality is a tractable approach and FerryMon represents a model for use in other large bodies of water traversed by ferries.

certain before the hurricanes of 1999, the recovery of the APES will remain poorly understood due to the scarcity of baseline hydrographic and biogeochemical data. The watersheds of the APES experienced relatively slow development until the late 20th century when land use changes and associated watershed impacts accelerated (Riggs 2001). Intensive row crop agriculture, industrial scale animal husbandry, and urbanization resulted in altered hydrology and increased nitrogen loading to the rivers in the Piedmont area, particularly the Neuse River (Stow et al. 2001). Although there have been indications of eutrophication in the upstream reaches of the Neuse River including nuisance algal blooms and loss of submersed vegetation (Paerl 1983; Copeland and Gray 1991), there has been no discernable increase in nutrient loading to the estuary (Stow et al. 2001). This is most likely due to the high nutrient removal potential of the upstream floodplain wetlands that normally buffer the coastal water bodies from inputs, but can become a source of organic material sources during times of high river discharge. Discerning patterns of material loading, transport, and biogeochemical transformation requires several years of intensive monitoring data to discern water quality patterns and trends in the North Carolina estuaries (Stanley 1993; Luettich et al. 2000; Stow et al. 2001).

Introduction The pulse of freshwater that accompanied the passage of Hurricane Floyd over the watersheds of the Albemarle-Pamlico Estuarine system (APES) was a major coastal perturbation due to the large input of water and dissolved and particulate materials from upstream and watershed sources (Paerl et al. 2001). The specific effects of this significant meteorological event on the Albemarle and Pamlico Sounds remain difficult to ascertain because there has been no systematic monitoring program for these large estuaries. Although there are active, long-term monitoring programs in the APES sub-estuaries (e.g., Neuse and Pamlico River estuaries; Stanley 1993; Luettich et al. 2000), similar efforts are required in Pamlico Sound to maintain water quality and sustain fishery populations. Given both the size of the Albemarle and Pamlico Sounds (6,600 km2) and their importance as essential habitat for many ecologically and commercially important species (Steele 1991; Dame et al. 2000), the lack of a monitoring program is potentially crippling for research and management efforts. Not only was the ecological status of the APES un* Corresponding author current address: University of South Carolina, Baruch Marine Field Lab, Georgetown, South Carolina 29442. tele: 843/546-6219; fax: 843/546-1632; e-mail: [email protected]. Q 2003 Estuarine Research Federation

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TABLE 1. Specifications for FerryMon sampling. The surface water variables that are digitally monitored include temperature (T), salinity (S), pH, dissolved oxygen (DO), turbidity (NTU), and total chlorophyll (chl). Ferries 1 and 2 include a refrigerated, programmable ISCO device to collect discrete water samples for laboratory analyses of dissolved inorganic nutrients, particulate matter, total chlorophyll, and photopigments. Ferry Route: Crossing:

1

2

Cherry Branch, Minnesott Beach

Cedar Island, Ocracoke Inlet

Date started Ferry name Average speed (knots) # of crossings d21 # of data points d21

November 2000 Floyd Lupton 8.0 40 300

Instruments YSI 6600 ISCO 6700FR YSI Interval # of data points/d

water/lab

In response to concerns over changes in watershed nutrient loading, the North Carolina legislature mandated remediation measures including basin-wide nutrient management plans. Although these plans are desirable, it is difficult to detect responses to the remediation measures because there are no established programs to monitor water quality and ecological signatures in the Sounds. As demonstrated by the hurricanes of 1996 and 1999, meteorologically driven, short-term, high volume discharge events complicate detection of potential impacts of altered watershed land use. This is an important consideration as Atlantic hurricanes are predicted to increase in frequency and intensity for the next several decades (Goldenberg et al. 2001). The dearth of background water quality data for the Sounds makes assessment of estuarine status under combined conditions of altered land use, mitigation efforts, and severe meteorological impacts an important challenge for North Carolina and other coastal states. Faced with this challenge we recognized the potential of the North Carolina ferries as monitoring platforms to establish a water quality record for the Sounds. Following the widespread effects of the 1999 hurricanes on the coastal watersheds and estuaries (Paerl et al. 2001), the state of North Carolina helped to expedite a water quality monitoring program using the Department of Transportation Ferry Service. Although underutilized in aquatic studies in the United States, ferries have served as oceanographic platforms in Scandinavia and Japan where researchers used high frequency surface water data from ferry routes to monitor water quality (Althuis et al. 1994; Harashima et al. 1997; Rantajarvi et al. 1998). There are examples of smaller, vessel-mounted, real-time sampling systems used to characterize estuarine water bodies (Madden and Day 1992). The Ferry Monitoring

February 2001 Carteret 10.7 4 200

3 Swan Quarter, Ocracoke Inlet, Cedar Island

May 2001 Governor Hyde 10.4 2 200–300

T, S, pH, DO, NTU, chl (all 3 ferries) water/lab none 3 min (April 2001 to present) n 5 700–800

(FerryMon) program was designed to track surface water quality status and trends by recording data across temporal scales ranging from diel to interannual. In this study we describe FerryMon and summarize an initial year of surface water data from along a North Carolina ferry route to better understand the observed spatial and temporal patterns and appreciate the capacity of ferries as coastal research platforms. Our long-term goal is to create a web-accessible, interactive geo-database for researchers, managers, educators, and interested citizens (www.ferrymon.org). Materials and Methods The North Carolina Department of Transportation’s Ferry Division operates a fleet of ferries that cross the APES at 6 locations (http:// www.ncferry.org). These vessels carry passengers and vehicles and provide a critical transportation link across the APES. The routes span riverine inputs to the Pamlico Sound (the lower Neuse and Pamlico River routes), the open water (the Pamlico and Currituck Sound routes), and exchanges with the coastal ocean (the Hatteras and Ocracoke Inlet routes). We chose 3 routes over which to develop the FerryMon system that included Minnesott Beach to Cherry Branch (route 1, aboard the M/V Floyd Lupton), Cedar Island to Ocracoke Inlet (route 2, aboard the M/V Carteret), and Ocracoke Inlet to Swan Quarter (route 3, aboard the M/V Governor Hyde; Table 1). Route 1 traverses the lower Neuse River, while routes 2 and 3 connect the Outer Banks with the mainland by crossing the southern basin of the Pamlico Sound. The vessels range in length from 1509 to 2209 and in cruising speeds from 8 to 10 kts (Table 1). The ferries operate on a daily schedule with route 1 running from 0600–0100 h and routes 2 and 3 from approximately 0700–

Automated Water Quality Monitoring by Ferries

1900 h. Ferry service ceases only in dense fog or when winds exceed 40 knots. The M/V Floyd Lupton crosses the lower Neuse River 40 times daily (20 round trips), the M/V Carteret crosses the southern Pamlico Sound 4 times daily (2 round trips), and the M/V Hyde travels from Swan Quarter to Ocracoke to Cedar Island twice daily (1 round trip). Each ferry houses a flow-through system for sampling of near surface water (0.0–1.5 m). The automated monitoring system takes raw water at rates of . 15 L min21 through a sea chest near the front of the ship using an impeller pump. The water passes through a coarse strainer, into a vortex debubbler (MSRC VDB-1, Ocean Instrument Lab, SUNY at Stony Brook), and then into a 20.32 3 40.64 cm cylindrical PVC plenum (13 L). A YSIMA 6600 (Endeco/YSI, Marion, Massachusetts) unit equipped with conductivity, temperature, pH, DO, turbidity, and chlorophyll fluorescence probes is plugged through an O-ring sealed port into the PVC plenum. The YSI 6600 units are switched with newly calibrated units approximately monthly in the winter, bi-weekly in the spring and fall, and weekly in the summer. The YSI 6600 is interfaced with a shipboard computer dedicated to data storage and retrieval. Navigation is provided by a Furuno 1850 differential geo-positional system (DGPS; 6 10 m precision) interfaced with the computer to log sample locations by date, time, latitude, and longitude. Logged data are downloaded nightly to a computer located at the Duke University Marine Laboratory, Beaufort, North Carolina, via modem and cellular phone. We can obtain aqueous grab samples for laboratory analyses of particulate and dissolved matter using an ISCO 6700FR refrigerated sampler. The ISCO unit holds 24 1-L bottles and can be programmed to sample at regular intervals while the ferry is underway. Both the YSI data loggers and ISCO sampler are activated when the ship’s speed exceeds 6 knots to avoid sampling water in the ferry turning basin. Several steps are required to obtain and process the intensive data (Fig. 1). The current state of database development includes the accumulation of metadata with the appropriate content and format, automated data input with empirically derived range tolerances for the different variables, and ongoing attempts to calibrate in situ estimates of chlorophyll a concentrations. Briefly, surface water data acquisition occurs during ferry operating hours and concludes with nightly downloads (Fig. 1). Following weekly data transfers to a server, the data are filtered by ferry speed over ground (SOG) to remove potential near-basin sampling locations, combined with the latitude and longitude data

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Fig. 1. Information flow diagram for processing, analyses, and application of ferry-acquired data.

from the DGPS, and re-sorted by time and date for further processing. In this study we processed and used the first full year of data from ferry route 3 to assess spatial and temporal patterns and variability (Fig. 2). Included are surface water temperature (T; 8C), salinity (psu), pH, turbidity (NTU), and fluorometric estimate of chlorophyll a (mg L21) collected at 3 min intervals from May 23, 2001–May 26, 2002. All values , 0.0 were assumed to be spurious and eliminated from the data set. Throughout the year, the data for T, salinity, and pH exhibited little overall variability and sensitivity to environmental or mechanical factors and therefore did not require much post-processing. The situation was reversed with the turbidity and chlorophyll a data. The levels of variability for these parameters were large and sporadic so we compared the value ranges to those from shipboard water column profiles sampled by researchers at the University of North Carolina Institute of Marine Sciences (Peierls unpublished data). Those studies helped to establish maximum tolerances of 500 NTU and 100 mg L21 for turbidity and chlorophyll a, respectively. We are developing curves to calibrate in situ estimates of chlorophyll a relative to in vitro determinations using high performance liquid chromatography (HPLC; unpublished data). Although route 3 includes a stretch between Ocracoke Island and Cedar Island from May to September (same as route 2), those data were omitted from these analyses. This omission resulted from our desire for a complete year of edited data along a distinct linear transect with which to assess spatial and temporal patterns and variability (Fig. 2). First, we generated descriptive statistics and time-space contour plots for the 5 variables to de-

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TABLE 2. Descriptive statistics for 5 surface water variables sampled during the first year of data collection along ferry route 3 between Swan Quarter and Ocracoke Inlet. The statistics include mean, standard deviation (SD), standard error (SE), minimum and maximum values (min and max), the coefficient of variation (CV), skewness and kurtosis, and the median and modal values. Data collected at 3 min intervals while the ferry was underway resulted in a total sample size of 36,053 for these variables from May 23, 2001–May 26, 2002.

Mean SD SE Minimum Maximum CV Skewness Kurtosis Median Mode

T (8C)

S (psu)

pH

Turbidity (NTU)

CHL (mg L21)

18.5 6.5 0.034 3.0 31.4 0.35 20.24 20.93 18.2 15.2

21.9 2.8 0.015 9.3 36.4 0.13 0.36 2.2 22.0 20.3

8.2 0.2 0.001 7.6 8.6 0.02 21.02 0.14 8.2 8.3

12.9 32.1 0.169 0.4 497.7 2.49 8.37 86.9 5.5 3.1

6.4 10.0 0.053 0.0 100.0 1.56 5.68 37.3 4.1 2.9

variograms allowed us to assess spatial scaling in the salinity data as a function of distance across Pamlico Sound for different times of the year. Finally, we performed 3-way Analysis of Variance (ANOVA) with time of day, season, and location along the ferry route as independent variables to help understand the overall patterns observed during the first full year of data collection. Fig. 2. Distribution of surface water sampling locations along a linear transect from Swan Quarter to Ocracoke Inlet, May 23, 2001–May 26, 2002.

scribe and examine overall patterns. Second, we split the transect into 10 km segments to derive time series and examine temporal properties. We selected the time series located .40 km from Swan Quarter, close to Ocracoke Inlet, for the temporal analysis of variability because of the observed salinity changes. Each data point was coded by day of the year, month, and season in order to group the values at different time intervals. We then generated box-and-whisker plots to examine the effects of aggregation at time intervals of minutes, days, months, seasons, and years for all 5 variables (McCormack and Ord 1979). Geo-statistical tools such as semi-variograms permitted assessment of spatial and temporal patch sizes over the irregularly spaced data of route 3 (Rossi et al. 1992; Childers et al. 1994; Haining 1997). Additionally, we plotted the semi-variance over a range of time lags with a 1 d base increment to assess temporal scaling in the near Ocracoke Inlet salinity data. Third, we split the entire edited data set into monthly intervals to perform monthly analyses of salinity semi-variance over a range of spatial lag intervals with a 1000 m base increment. The series of semi-

Results The edited data set contained 36,053 surface water recordings from May 23, 2001–May 26, 2002, along ferry route 3 (Table 2). Mean and median values were similar in the cases of temperature, salinity, and pH but the distribution of values for turbidity and chlorophyll a were positively skewed and leptokurtic. Temperature exhibited temporal but no obvious spatial variability while salinity fluctuated from 15 to approximately 30 psu with time and location (Fig. 3a,b). Turbidity values and chlorophyll a concentrations were generally low from May to September and December 2001, but increased in magnitude and patchiness in October 2001 and the spring of 2002 (Fig. 3d,e). Salinity was fairly constant in time at different locations along the transect length with the greatest variations observed near Ocracoke Inlet (Fig. 4). Salinity at Ocracoke Inlet demonstrated an essentially random pattern with no obvious correlation between successive values lagged in time (Fig. 5). We used the time series data from this location to further examine temporal patterns. Aggregating the data into a series of discreet time intervals influenced the variances and shapes of the data distributions (Fig. 6). The number of values beyond the 10th and 90th percentiles (outside error bars) decreased for all 5 variables as the


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Fig. 3. Interpolated space-time plot of (A) temperature, (B) salinity, (C) pH, (D) turbidity, and (E) chlorophyll a from ferry route 3 between Swan Quarter and Ocracoke Inlet. The x-axis is May 24, 2001–May 24, 2002. The y-axis is distance from Swan Quarter (km). See Figure 2 for map view orientation.

temporal inter val progressed from minute to month. The variance and skewness in the turbidity and chlorophyll a measurements resulted from the large number of values beyond the 90th percentile, which inflated the means well above the median values (Fig. 6d,e). Overall, temperature, salinity, and pH maintained narrower distributions and overall less variance than turbidity and chlorophyll a at the minute and daily levels of aggregation (Fig. 6). The monthly level of aggregation provided the best compromise among the variance and shape of the resulting distributions so we used it to examine patterns from the first year of sampling using all

of the data from ferry route 3 (Fig. 7). First, temperature exhibited a distinct annual cycle while salinity varied little between May 2001 and May 2002 (Fig. 7a,b). Second, average chlorophyll a estimates were highly variable in September 2001, October 2001, and February–April 2002 (Fig. 7e). These values likely contributed to the observed variability in the turbidity measurements in October 2001 and March 2002 (Fig. 7d). All of the monthly semi-variograms of salinity had a similar logarithmic shape so we selected four ( June 2001, September 2001, December 2001, and April 2002) to represent seasonal intervals (Fig. 8).

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Fig. 4. Time series of salinity values May 24, 2001–May 24, 2002 for different segments of the route between Swan Quarter and Ocracoke Inlet.

Fig. 5. Temporal analysis of salinity values using a semi-variogram. Over 200 d of average daily salinity values from near Ocracoke Inlet (. 40 km from Swan Quarter) were analyzed using a base 1 d lag interval. The y-axis is the semi-variance or the sum of the squared difference between successive data values lagged at multiples of the base interval.

Fig. 6. Box and whisker plots of (A) temperature, (B) salinity, (C) pH, (D) turbidity, and (E) chlorophyll a from the Swan Quarter data aggregated at different temporal intervals (minute, day, month, season, and year). The horizontal line splitting each box is the median value with the lower and upper inflections representing the 95% confidence interval about the median and the box edges as the 25th and 75th percentiles, respectively. Values shown as points located outside the lower and upper error bars were beyond the 10th and 90th percentiles, respectively. The sample size for each of the distributions is provided along the bottom of plot (A).


Fig. 7. Monthly averages and standard deviations for (A) temperature, (B) salinity, (C) pH, (D) turbidity, and (E) chlorophyll a from ferry route 3 over all data points, May 24, 2001– May 24, 2002.

The logarithmic shape of the semi-variograms indicated a non-stationary relationship with no discernable upper threshold (or sill) for the semi-variance (Haining 1997). A lower threshold was evident as the semi-variance did not change dramatically with spatial lags of up to 5,000 m. However, the semi-variance increased at a higher rate with spatial lags of 7,000–22,000 m so that salinity values from sampling locations greater than 5 km apart were negatively correlated. The salinity differences, and therefore the range in semi-variances, across Pamlico Sound were most pronounced in the spring (Figs. 3b, 8a,d). Mean salinity values varied significantly with time of day (morning, midday, afternoon), season, and

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Fig. 8. Spatial analysis of salinity values using a series of semivariograms for different months. Although we performed monthly analyses, (A) June 2001, (B) September 2001, (C) December 2001, and (D) April 2002 were selected to demonstrate seasonal patterns.

distance from Swan Quarter (Fig. 9a). Although the temporal and spatial variations in mean salinity were slight in some cases, all tests were significant due to the large sample size. The overall spatial gradient in salinity between Swan Quarter and Ocracoke Inlet provided one of the most compelling statistical results (p # 0.0001; Fig. 9a inset). Despite widely variable estimates of chlorophyll a concentrations, it varied significantly with time of day, season, and distance (Fig. 9b). However, the results were not as obvious as those for salinity as different combinations of the independent variables influenced the distribution of the chlorophyll a mean concentrations differently. Seasonal differ-

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Fig. 9. Interactive bar plots (mean 6 SE) resulting from 3way ANOVA of (A) salinity and (B) chlorophyll a data from ferry route 3. The independent variables were season (spring, summer, fall, winter), time of day (am, mid, pm), and location along the route (legend). The inset plots demonstrate the effects of individual independent variables.

ences in observed chlorophyll a provided another interesting result (p # 0.0001; Fig. 9b inset). As in the Neuse River and other Atlantic temperate estuaries, the phytoplankton biomass maximum and minimum occurred in the spring and winter, respectively. Mean chlorophyll a concentrations were , 8 mg L21 for all seasons. Discussion The FerryMon program was created to expedite development of a cost-effective water quality monitoring tool for the APES. Spatially and temporally intensive surface water hydrographic and biogeochemical information provide an environmental baseline for analysis of both short term and longterm patterns and trends (Althuis et al. 1994; Rantajarvi et al. 1998). Ferry-based monitoring is an opportunistic and effective approach to data acquisition and provides an important complement to shipboard, platform, and satellite oceanograph-

ic studies. By using the ferry-based automated system to provide data for satellite image calibration, we can monitor large areas of the APES not accessible by traditional research vessels. FerryMon provides a model for other locations with ferry crossings including the Gulf of Maine, Narragansett Bay, Nantucket Sound, Long Island Sound, Delaware Bay, James River, Galveston Bay, Laguna Madre, San Francisco Bay, and Puget Sound. Our analyses revealed few compelling temporal or spatial patterns in the surface water variables over the first year of data collection from Swan Quarter to Ocracoke Inlet. Salinity provided a conservative variable to examine temporal and spatial patterns that would respond to inputs of freshwater and salt water. Although there were some spatial differences, surface water salinity did not vary considerably over the time of this study because Pamlico Sound is a large, bar-built estuary that had minimal freshwater input in 2001 and the spring of 2002. Drought conditions developed throughout 2002 in North Carolina. Coupled with the overall lack of spatial patchiness was the lack of temporal signals from the near inlet salinity record. Upon initial inspection the salinity data near Ocracoke Inlet appeared to vary approximately weekly in the summer of 2001, but our analyses indicated only random noise over the entire time series (Figs. 6e and 7). We feel that longer time series will permit identification of the major temporal scales of organization for material flux through the inlet, particularly if the area experiences subsequent hurricanes or large frontal passages. For variables such as chlorophyll a, the large sample size helped to mitigate the extreme variability and provided statistically significant results regarding the time and location of surface water biomass maxima. However, laboratory experiments have demonstrated that in situ, fluorometric estimates of chlorophyll biomass can differ greatly from in vitro determinations (Hall unpublished data). To address this issue we are developing postcalibration curves to correct the in situ estimates of chlorophyll a derived from the ferry routes. Despite the observed homogeneity of salinity and the observed variability of chlorophyll a, we now possess background information against which to compare and contrast future conditions related to both chronic (e.g., watershed inputs) and episodic (e.g., hurricane) effects. The ability to establish environmental baseline information is one of the most valuable aspects of FerryMon. There are several positive attributes of the FerryMon program, and using ferries in general, to monitor water quality. The ferries operate every day, year after year, and yield intensive data streams that are repetitive in time and space. The spatial


and temporal resolution is controllable and presently sampled at 3 min, which results in an average 0.8 km spacing between data points within a particular day. As demonstrated in part by this study, we can address diel, tidal, synoptic, event, seasonal, annual, and interannual scales. The ferries are more reliable than any other transportation mode over coastal waters as they cease operation only when the wind velocity exceeds 40 knots or in dense fog. The automated systems are sufficiently portable that they can be moved among ferries when a ferry is out of service for repair or maintenance. The certified and dedicated ferry personnel function in compliance with U.S. Coast Guard regulations for vessels carrying passengers for hire. As platforms utilized to acquire water quality data in the coastal zone, and as oceanographically capable vessels, ferries are free of fees and charges to the monitoring program, and therefore are extraordinarily cost-effective. One of the negative attributes of the FerryMon program, and ferries in general, is that data are logged in a limited spatial dimension, namely surface waters along a defined route. The spatial domain can be extended by using the ferry platforms as sources of surface truth data to calibrate spectral images from aircraft or satellite flyovers. Satellite platforms offer wide scale sampling over coastal waters not possible with traditional shipboard methods (Khorram and Cheshire 1985; Joint and Groom 2000). However, the water-emerging signals obtained by remote sensors must be calibrated and referenced relative to material concentrations in the water column (Sathyendranath et al. 2000). Materials such as phytoplankton pigments, suspended solids, and colored dissolved organic matter (CDOM) establish the optical properties of water bodies, particularly in optically complex waters (Case II) such as the APES (Woodruff et al. 1999; Sathyendranath et al. 2000). The algorithms used to process spectral imagery for Case II waters must account for highly variable water column materials, may involve constituent spectra that overlap, and must resolve potential difficulties that include bottom reflectance and near-surface aerosols (Harding et al. 1995; Sathyendranath et al. 2000). The FerryMon program is ideally suited to collect surface water quality data to calibrate remotely-sensed images as concentrations of CDOM, pigment biomass, and suspended solids from along the ferry route can be used to modify algorithms and calibrate the coincident image pixels. After the pixels from the ferry route are corrected, the final algorithm can be applied to all relevant pixels within a particular image. The FerryMon program has great potential to monitor huge portions of the

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APES that historically were not studied due to logistic challenges. ACKNOWLEDGMENTS Funding for the FerryMon Project was provided by the North Carolina General Assembly in a special appropriation for Hurricane Floyd relief and administered by the North Carolina Department of Environment and Natural Resources (DENR), Water Quality Division. FerryMon is a cooperative effort between DENR, the Ferry Division of the North Carolina Department of Transportation, Duke University Marine Laboratory (DUML), and the University of North Carolina at Chapel Hill Institute of Marine Sciences (UNC-IMS). We would like to thank the AllTel Corporation for providing cellular phone service for nightly data downloads. Timothy Boynton of DUML played a key role by installing, calibrating, and maintaining the automated water quality monitoring systems. Jerry Gaskill, Director, and Dan Noe, Marine Quality Assurance Specialist, North Carolina Ferry Division, and the officers and crews of the ferries were most helpful in implementing this project. We also thank Tom Gallo, Christina Tallent, and Patrick Sanderson of the UNC-IMS for providing logistic and laboratory support. Mark Fonseca provided invaluable geo-statistical knowledge. We appreciate the comments of Kyle Shertzer, Larissa Nojek, and three anonymous reviewers that greatly improved various versions of the manuscript.

LITERATURE CITED ALTHUIS, J. A., W. W. C. GIESKES, L. VILLERIUS, AND F. COLIJN. 1994. Interpretation of fluorometric chlorophyll registrations with algal pigment analysis along a ferry transect in the southern North Sea. Netherlands Journal of Sea Research 33:37–46. CHILDERS, D. L., F. H. SKLAR, AND S. E. HUTCHINSON. 1994. Statistical treatment and comparative analysis of scale-dependent aquatic transect data in estuarine landscapes. Landscape Ecology 9:127–141. COPELAND, B. J. AND J. GRAY. 1991. Status and trends reports of the Albemarle-Pamlico estuary. In J. Steel (ed.), AlbemarlePamlico Estuary Study Report 90–01. North Carolina Department of Environment, Health, and Natural Resources, Raleigh, North Carolina. DAME, R., M. ALBER, D. ALLEN, M. MALLIN, C. MONTAGUE, A. LEWITUS, A. CHALMERS, R. GARDNER, C. GILMAN, B. J. KJE¨RFVE, J. L. PINCKNEY, AND N. SMITH. 2000. Estuaries of the south Atlantic coast of North America: Their geographical signatures. Estuaries 23:793–819. GOLDENBERG, S. B., C. W. LANDSEA, A. M. MESTAS-NUN˜EZ, AND W. M. GRAY. 2001. The recent increase in Atlantic hurricane activity: Causes and implications. Science 293:474–478. HAINING, R. 1997. Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, Cambridge, U.K. HARASHIMA, A., R. TSUDA, Y. TANAKA, T. KIMOTO, H. TATSUTA, AND K. FURUSAWA. 1997. Monitoring algal blooms and related biogeochemical changes with a flow-through system deployed on ferries in the adjacent Seas of Japan, p. 85–112. In M. Kahru and C. W. Brown (eds.), Monitoring Algal Blooms: New Techniques for Detecting Large-Scale Environmental Change. Springer-Verlag and Landes Bioscience, New York. HARDING, L. W., E. C. ITSWEIRE, AND W. E. ESAIAS. 1995. Algorithm development for recovering chlorophyll concentrations in the Chesapeake Bay using aircraft remote sensing, 1989– 91. Photogrammetric Engineering and Remote Sensing 61:177–185. JOINT, I. AND S. GROOM. 2000. Estimation of phytoplankton production from space: Current status and future potential of satellite remote sensing. Journal of Experimental Marine Biology and Ecology 250:233–255. KHORRAM, S. AND H. M. CHESHIRE. 1985. Remote sensing of wa-

984


ter quality in the Neuse River estuary, North Carolina. Photogrammetric Engineering and Remote Sensing 51:329–341. LUETTICH, R. A., J. E. MCNINCH, J. L. PINCKNEY, M. ALPERIN, C. S. MARTENS, H. W. PAERL, C. H. PETERSON, AND J. T. WELLS. 2000. Neuse River Estuary Modeling and Monitoring Project: Monitoring Phase Report. Report No. 325-B. University of North Carolina, Water Resources Research Institute, Raleigh, North Carolina. MADDEN, C. J. AND J. W. DAY. 1992. An instrument system for high-speed mapping of chlorophyll a and physico-chemical variables in surface waters. Estuaries 15:421–427. MCCORMACK, R. M. AND J. K. ORD (eds.). 1979. Spatial and Temporal Analysis in Ecology. International Co-Operative Publishing House, Burtonsville, Maryland. PAERL, H. W. 1983. Factors regulating nuisance blue-green algal bloom potentials in the lower Neuse River. Report No. 177. University of North Carolina Water Resources Research Institute, Raleigh, North Carolina. PAERL, H. W., J. D. BALES, L. W. AUSLEY, C. P. BUZZELLI, L. B. CROWDER, L. A. EBY, J. M. FEAR, M. GO, B. L. PEIERLS, T. L. RICHARDSON, AND J. RAMUS. 2001. Ecosystem impacts of three sequential hurricanes (Dennis, Floyd, Irene) of the United State’s largest lagoonal estuary, Pamlico Sound. Proceedings of the National Academy of Sciences, U.S.A. 98:5655–5660. PAERL, H. W., J. L. PINCKNEY, J. M. FEAR, AND B. L. PEIERLS. 1998. Ecosystem response to internal and watershed organic matter loading: Consequences for hypoxia in the eutrophying Neuse River estuary, N.C., U.S.A. Marine Ecology Progress Series 166: 17–25. RANTAJARVI, E., V. GRAN, S. HALLFORS, AND R. OLSONEN. 1998. Effects of environmental factors on the phytoplankton community in the Gulf of Finland-unattended high frequency measurements and multivariate analyses. Hydrobiologia 363: 127–139. RIGGS, S. R. 2001. Anatomy of a flood, p. 29–45. In J. R. Maiolo, J. C. Whitehead, M. McGee, L. King, J. Johnson, and H. Stone (eds.), Facing Our Future: Hurricane Floyd and Recovery in the Coastal Plain. Coastal Carolina Press, Wilmington, North Carolina. ROSSI, R. E., D. J. MULLA, A. G. JOURNEL, AND E. H. FRANZ. 1992.

Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecological Monographs 62:277–314. SATHYENDRANATH, S., R. ARNONE, M. BABIN, J. F. BERTHON, R. BUKATA, J. CAMPBELL, C. DAVIS, R. DOERFFLER, M. DOWELL, H. GORDON, N. HOEPFFNER, C. HU, J. KIRK, M. KISHIHNO, O. KOPELEVICH, H. KRAWCZYK, A. NEUMANN, J. PARSLOW, P. SCHLITTENHARDT, AND A. TANAKA. 2000. Remote Sensing of Ocean Color in Coastal and Other Optically-Complex Waters. International Ocean Color Coordinating Group, Dartmouth, Nova Scotia. STANLEY, D. W. 1993. Long-term trends in Pamlico River estuary nutrients, chlorophyll, dissolved oxygen, and watershed nutrient production. Water Resources Research 29: 2651–2662. STEELE, J. (ed.). 1991. Status and trends report of the Albemarle-Pamlico Estuarine Study. Albemarle-Pamlico Estuarine Study Report 90-01. North Carolina Department of Environment, Health, and Natural Resources. Raleigh, North Carolina. STOW, C. A., M. E. BORSUK, AND D. W. STANLEY. 2001. Long term changes in watershed nutrient inputs and riverine exports in the Neuse River, North Carolina. Water Resources 35:1489– 1499. WELSCHMEYER, N. A. 1994. Fluorometric analysis of chlorophyll a in the presence of chlorophyll b and phaeopigments. Limnology and Oceanography 39:1985–1992. WOODRUFF, D. L., R. P. STUMPF, J. A. SCOPE, AND H. W. PAERL. 1999. Remote estimation of water clarity in optically complex estuarine waters. Remote Sensing of the Environment 68:41–52.

SOURCES

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UNPUBLISHED DATA

HALL, N. unpublished data. University of North Carolina at Chapel Hill, Institute of Marine Sciences, Morehead City, North Carolina 28557. PEIERLS, B. L. unpublished data. University of North Carolina at Chapel Hill, Institute of Marine Sciences, Morehead City, North Carolina 28557 Received for consideration, April 24, 2002 Revised, September 12, 2002 Accepted for publication, October 15, 2002