Environ. Sci. Technol. 2000, 34, 4474-4482
Seasonal and Long-Term Nutrient Trend Decomposition along a Spatial Gradient in the Neuse River Watershed SONG S. QIAN* Environmental Sciences and Resources, Portland State University, Portland, Oregon 97207-0751 MARK E. BORSUK AND CRAIG A. STOW Nicholas School of the Environment, Duke University, Durham, North Carolina 27708-0328
The Neuse River Estuary in North Carolina has recently received considerable public attention for severe algal blooms, large fish kills, and outbreaks of toxic microorganisms. To investigate the belief that nutrient enrichment has worsened in recent years, we analyzed long-term and seasonal trends in nutrient concentrations along the river and estuary employing seasonal trend decomposition using local regression analysis (STL). The nonparametric nature of the STL approach makes it possible to identify nonlinear trends and seasonal interactions that would be missed by traditional trend detection methods. The results indicate that while there may have been minor increases in nitrogen concentrations at upstream locations over the past twenty years, those changes are not reflected in the lower river and estuary. However, the pronounced decreases in phosphorus concentrations that occurred upstream, corresponding to a phosphorus detergent ban in 1988, do persist downstream. The net result is that the ratio of nitrogen to phosphorus concentrations in the estuary has increased considerably in the last 10 years. When compared with the Redfield value, ambient nutrient ratios suggest that phytoplankton growth in the estuary may be experiencing a shift from nitrogen to phosphorus limitation during much of the year. This shift may be inducing a change in the biotic community that would help explain the perception of worsening eutrophication, despite an overall reduction in nutrient concentrations.
Introduction Nutrient enrichment is a serious water quality problem in many coastal rivers and estuaries worldwide (1). Excessive nutrient loading may result in algal blooms, bottom water hypoxia, massive fish kills, and outbreaks of toxic microorganisms. Management of coastal eutrophication can be enhanced by information on the spatial and temporal trends in historical nutrient concentration data. Clearly documented historical trends reveal the response of a natural system to either the unintentional consequences of human activity or the deliberate results of past management (2, 3). Many statistical methods have been proposed for the detection of environmental trends (4, 5). The choice of a trend detection * Corresponding author phone: (503)725-8190; fax: (503)725-3888; e-mail:
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method depends on the type of trend expected (continuous versus step function), the adherence of the data to various assumptions (normality, independence, homoscedasticity), and the occurrence of censored data (6, 4). Traditionally, environmental trend analyses have used linear regression analysis or nonparametric methods based on order statistics, including Kendall’s tau test for correlation (7) and variations (8, 9). However, these methods are constrained to linear or monotonic trends and cannot detect intermediate reversals in direction. Short-term departures from a long-term trend may be important in assessing the impacts of preliminary or spatially limited management actions. Additionally, traditional trend detection methods decompose a time series into a long-term component and an additive seasonal component. However, this approach assumes that long-term patterns for all seasons differ only in magnitude. This additive assumption is often not appropriate for environmental data since different seasons often involve different forcing functions. Discerning systematic changes in the seasonal pattern is essential for accurately assessing ecosystem response to environmental changes. One solution is to use locally weighted regression (loess) methods to describe nonlinear trends that do not have an obvious functional form (10, 11). By using the generalized additive modeling approach (12), seasonal trend decomposition using loess (STL) (13) provides a nonparametric graphical method for describing nonlinear trends with seasonal interaction. We present an analysis of spatial and temporal trends in nutrient concentrations in the Neuse River and Estuary, North Carolina, U.S.A. using the STL methodology. The Neuse estuary has recently received considerable public attention due to characteristic symptoms of excessive eutrophication. The general perception is that these problems are due to a recent increase in watershed nutrient inputs (14, 15, 1), and management options, including riparian buffers and source reductions, are planned. While conventional trend detection methods based on monotonic trends and an additive seasonal component have provided useful information about other estuaries in the past, a more flexible method, such as STL, is desirable for the Neuse. Sudden changes have occurred in the watershed, including the impoundment of a large reservoir in the headwaters of the river in 1983 and a phosphate detergent ban in 1988, which would be improperly described by a monotonic trend analysis. Additionally, discerning changes in seasonal patterns is essential given the seasonal nature of both proposed nutrient management actions and biotic response. Finally, an assessment of spatial patterns provides information about source changes, subwatershed specific management actions, and biotic response that would not be obtained from trends described at one location only. Therefore, we will present our analysis in a format that facilitates the assessment of both temporal and spatial pattern.
2. Materials and Methods 2.1. Study Site and Data Description. The Neuse River originates north of Durham, NC, at the confluence of the Flat and Eno rivers. Shortly below this confluence, a 35 km reach was impounded in 1983 to create Falls Lake, a multipurpose recreation and drinking water reservoir (16). Below the dam, the river flows approximately 320 km through the central piedmont to the coastal plain and comprises a watershed area of 16,108 km2 (Figure 1). The major land uses in the basin are agriculture (35%) and forestry (34%) with the remainder consisting primarily of urban areas, wetlands, 10.1021/es000989p CCC: $19.00
2000 American Chemical Society Published on Web 09/22/2000
FIGURE 1. Map of the Neuse River and Estuary showing monitoring stations used in this study (triangles) and locations of urban areas (squares). scrub, and open water. Just above New Bern, the Neuse begins mixing with saltwater and opens into a wide shallow estuary, with an average depth of 3.6 m (17). The Neuse estuary extends 70 km before joining Pamlico Sound, an important fish habitat and nursery for the Atlantic coast. The North Carolina Division of Water Quality maintains 16 mid-channel ambient monitoring stations on the Neuse River and Estuary. Periodic nutrient sampling began at some of these stations in the late 1960s, and regular monitoring, on approximately a monthly basis, began in the late 1970s. In this analysis, we use data from 12 stations from 1979 to 1998, the locations and time period of most consistent monthly monitoring. Nutrient concentration data are from surface grab samples following standard collection and analytical protocol (18). To assess the potential for longterm flow effects on nutrient concentration trends, we also analyze flow data recorded by the United States Geological Survey from 1979 to 1998 at Kinston (Station 5), the most downstream location with long-term flow measurements. Flow data are recorded daily at Kinston, and, to maintain consistency with the nutrient data, we have used the monthly medians in our analysis. 2.2. Statistical Method. A time series of monthly environmental monitoring data at a selected location may be considered as a sum of two components: one high-frequency seasonal component and one low-frequency long-term component (or trend). Each individual observation can be decomposed as
Yyear,month ) Tyear,month + Syear,month + Ryear,month
(1)
where Yyear,month is the observed value for a given year and month, Tyear,month is the trend component, Syear,month is the seasonal component, and Ryear,month is the remainder, or residual, term (13). 2.2.1. Estimation of Missing Values. In the first step of our analysis we used median polish (19) to perform the seasonal trend decomposition (eq 1) to impute missing monthly concentration values in the time series. This is necessary because the current implementation of the seasonal trend analysis method does not allow missing values. In a median polishing method, T is estimated by the median of a given year and S is estimated by the median of a given month. When a value of Yyear,month is missing but the first two terms on the right-hand side of eq 1 are available, their sum provides a “fitted value” for the concentration in the year and month. These two values were available for all missing observations in the Neuse time series, other than those at the beginning
or end of a time series. Median polish is implemented in S (20), by fitting the two component model (eq 1) iteratively. First, the seasonal component (median of each month) is fit. Then the long-term component (median for each year) is fit to the residuals. This completes the first iteration. In the subsequent iterations, each component is fit to the residuals from the other component. The procedure stops when the estimates of one iteration do not change significantly from the estimates of previous ones. Imputation of missing values using median-polish has been applied previously in the study of trends in urban ozone levels in the Chicago area (21). In our study, from 7% to 10% of the monthly observations at each station required imputation. Missing months occurred at the beginning or end of a time series were not imputed, resulting in shorter time series for some stations. When compared with observed values, the imputed data have similar distributions. Additionally, the estimated long-term and seasonal trend fit the observed data very well. 2.2.2. Seasonal Trend Analysis Using Loess. Seasonal trend decomposition using loess (STL) is a graphics based statistical method for time series analysis (13), implemented in S (22). It is an iterative nonparametric regression procedure using repeated loess fitting. As in eq 1, the time series is decomposed into trend, seasonal, and remainder (or residual) components. However, while the median polish process uses median values for the trend and seasonal components, STL uses one continuous loess line for the long-term trend component and 12 month-specific loess lines for the seasonal component. As with median polishing, fitting is done on each component iteratively until the resulting trend and seasonal components are no longer different from the estimates of the previous iterations. Generally three iterations are sufficient (13). The nonparametric nature of STL makes it flexible in revealing nonlinear patterns. Because each season (month) is a subseries in the fitted loess model, seasonal interactions are captured. While imputation of missing values using median polishing may artificially reduce residual variance, the low number of missing values in this case minimizes this effect. As with all nonparametric regression methods, STL requires subjective selection of smoothing parameters. There are two smoothing parameters in our model, representing the window widths of the seasonal and long-term components. We chose window widths of 21 months and 99 months respectively in order to visually elucidate trends.
3. Results The seasonal trend decomposition of flow at the Kinston station (Figure 2) suggests that there has been (1) little longVOL. 34, NO. 21, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Seasonal trend decomposition using loess (STL) plots showing the three components in the trend model of river flow (in 107 m3 /day) at Station 5. The left panel shows the long-term trend, the center panel shows the seasonal cycle, and the right plot is the residual term (variation not explained by time). Because the seasonal and residual components are centered at 0, the mean of the long-term trend has been subtracted from the trend term to center the long-term loess line at 0, thus easing the visual comparison of the magnitude of each component. term change and (2) a stable seasonal pattern in river flow in the Neuse in the last 20 years. The slight increase in flow during the last couple of years is probably due to the high flows from Hurricane Fran in September, 1996 and an unusually wet spring in 1998. The seasonality of flow has not changed noticeably, with high spring and low summer flows being a consistent pattern. The residuals of the seasonal trend model show that there is substantial variation in the magnitude of flow that is not explained by either the seasonal nor the long-term trend component. This is to be expected and represents the effects of natural variability. Since nutrient concentrations may be related to river discharge (4) it is important that neither the magnitude nor the seasonality of flow have changed substantially over the period of record. For our analysis of nutrient trends, we present interstation comparisons of the long-term trend component alone as well as seasonal trends for three representative stations. Spatial comparison of long-term trends in the concentration of total nitrogen (TN), total phosphorus (TP), dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), the molar inorganic N:P ratio (DIN:DIP), and molar total N:P ratio (TN:TP) shows substantial upstream to downstream patterns for all six variables (Figure 3). Nutrient concentrations at the most upstream station (Station 1), just downstream of Falls Lake dam, are relatively low compared to other riverine stations, especially after a pronounced decline in the early 1980s. This decline probably resulted from the impoundment of Falls Lake in 1983, effectively creating a large retention basin for nutrients to settle from the river water. Concentrations rise sharply in the river by the next station (Station 2), just downstream of Raleigh and a major wastewater treatment plant. While the early decline from the dam is still evident, nitrogen concentrations increased through the late 1980s and early 1990s, until a decline in the recent five years. Although this pattern of long-term change is minor relative to the residual component (not shown), its persistence across stations substantiates its existence. While the long-term pattern in nitrogen is minor, the phosphorus signal is substantial. After a period of modest change, phosphorus concentrations declined rapidly in the late 1980s. This drop corresponds with the phosphate detergent ban taking full effect in 1988 and demonstrates the impact of point source inputs on this section of the river. (Figure 3 shows the decline starting slightly before 1988. This is due to the smoothing effect resulting from estimating the trend value at a given point in time using neighboring data points.) The temporal signals of both nitrogen and phosphorus are maintained moving down4476
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stream, although the amplitudes are damped along with the concentrations. This suggests that the temporal signal of the sources upstream of Station 2 dominates that of the downstream stations. By the time flow reaches the estuary (Station 7) the temporal signal in nitrogen of the upstream stations is almost entirely damped, except for the suggestion of a decline in the last five years. The phosphorus signal is also damped downstream, although a mid 1980s hump is apparent, indicating that inputs were increasing rapidly prior to the ban and that the management action was well-timed and effective. Concentrations of both nutrients continue to decline out into the estuary, reaching very low levels by the mouth of Pamlico Sound (Station 12). Spatial and temporal patterns in the dissolved inorganic components of nitrogen and phosphorus generally reflect the patterns in total nutrient concentrations, consistent with the fact that they compose the dominant fraction. Our main interest in DIN and DIP concentrations, however, is as components of the molar DIN: DIP ratio (Figure 3), a value often used to infer nutrient limitation of phytoplankton growth. At Station 1, the DIN: DIP ratio increased during the early 1980s, suggesting that phosphorus is retained in Falls Lake more readily than nitrogen. This is consistent with the general results of crosssectional lake models developed for the southeastern U.S. (23). The ratio then declined after 1985, due almost entirely to the decline in DIN concentrations (Figure 3). Further downstream, at Station 2, the DIN:DIP ratio was relatively steady until the mid 1980s when DIN increased and DIP decreased almost simultaneously (Figure 3), resulting in a sharp rise in the ratio. There is very little change in this temporal pattern downstream, although the rise is not quite as high at the lower river stations since DIP was not as high to begin with. Further into the estuary, the increasing trend is barely noticeable and the average value of the ratio decreases as well. Because phytoplankton can assimilate some organic nutrient forms and all forms are relatively labile, it is also useful to examine the ratio of total nutrient concentrations (TN:TP). Except for Station 1, the overall TN:TP ratio does not change very much throughout the river basin. The temporal patterns of the TN:TP ratio is largely determined by the 1988 phosphorus detergent ban and subsequent slight rebound upstream. Values shifted from below to above 16 around 1988. To investigate seasonal changes in nutrient concentrations, we chose Stations 2, 7, and 12 representing the spatial gradient from upstream river conditions to downstream conditions in the estuary. Intermediate stations showed
FIGURE 3. Spatial patterns of the long-term nutrient concentration (in mg/L) trends (seasonal component removed) for TN, TP, DIP, DIN, molar DIN:DIP ratio (DIN:DIP) and molar TN:TP ratio (TN:TP). Each column represents one sampling station from upstream to downstream (left to right). The dashed line represents the long-term mean for each station, and the shaded bar in the two N:P rows represents the approximate intracellular ratio of phytoplankton (16:1). VOL. 34, NO. 21, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Seasonal patterns of the long-term nutrient concentration (in mg/L) trends at Station 2 for TN, TP, molar DIN:DIP ratio (DIN:DIP) and molar TN:TP ratio (TN:TP). Each column represents the long-term trend in a given month (calculated as the sum of first two terms in eq 1 for the month). The dashed line represents the long-term mean for each month, and the shaded bar in the two N:P rows represents the approximate intracellular ratio of phytoplankton (16:1). seasonal patterns that were consistent with the trends indicated by these stations. TN concentrations at Station 2 (Figure 4) show a distinct seasonal cycle, with higher values in the late summer. Long-term trends are relatively consistent across months and follow the long-term pattern discussed above. The August trend differs from the rest primarily due to an unusually large value (25.6 mg/L versus overall median about 2.5 mg/L) reported in 1994. When this outlier is removed, the August trend is similar to those for July and September. TP concentrations, on the other hand, had a strong seasonal cycle early in the time series, but this cycle has diminished over time as concentrations have decreased. Sharp decreases have occurred in all months after 1987, although there has been a rebound in the last five years in winter and spring. Together with continued declines in late summer and autumn when concentrations are generally the highest, this has further weakened the seasonal cycle. Seasonal changes are apparent in both DIN:DIP and TN:TP, with high values in the fall and winter and low values in spring and summer. Largely because of decreases in phosphorus concentrations, both TN:TP and DIN:DIP ratios have 4478
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increased substantially since 1987, going from average values below 10 for most of the year to values regularly above 25 for TN:TP and above 50 for DIN:DIP. In the spring months, DIN:DIP ratios are as high as 150. Station 7 is the furthest downstream station that is strictly freshwater and represents riverine inputs to the estuary. Here, nutrient concentrations are much lower than upstream, although similar trends are apparent. Relatively consistent across all months, the seasonal trend in TN at Station 7 (Figure 5) is marked by irregular changes for most of the series followed by notable decreases in recent years. As at Station 2, the seasonal cycle in TN is weak, and the long-term trend accounts for a minor fraction of the total variation compared with the seasonal and residual terms (not shown). TP shows a slightly more pronounced seasonal cycle with high concentrations throughout the summer and autumn months, rather than the pronounced late summer peak at Station 2. However, sharp declines in all months in the last 10 years have somewhat reduced the amplitude of this cycle. The decline in TP concentrations that was evident in the early 1980s during most months at Station 2 is not apparent at
FIGURE 5. Seasonal patterns of the long-term nutrient concentration (in mg/L) trends at Station 7 for TN, TP, molar DIN:DIP ratio (DIN:DIP), and molar TN:TP ratio (TN:TP). Each column represents the long-term trend in a given month. The dashed line represents the long-term mean for each month, and the shaded bar in the two N:P rows represents the approximate intracellular ratio of phytoplankton (16:1). Station 7, but the mid 1980s hump is much more apparent. The DIN:DIP ratio experienced minor seasonal variation at Station 7 in the early 1980s, with average values ranging from just over 40 in the winter to between 10 and 20 in the summer. However, upon reductions in phosphorus concentrations in the mid 1980s, the ratio greatly increased, together with the amplitude of the seasonal cycle. By the 1990s, the DIN:DIP ratio varied from over 140 in the winter to near 30 in the summer. The TN:TP ratios had similar seasonal patterns to those of the DIN:DIP rations but at much lower values. The reduction in nutrient concentrations continues from Station 7 through the estuary (Figure 3), with DIN being preferentially removed as indicated by a steady reduction in the DIN:DIP ratio while the TN:TP ratio remains almost constant (Figure 3). By Station 12, concentrations of both N and P are low (Figure 6), as is the DIN:DIP ratio. Here, the long-term trends are different from the river stations. TN concentrations show large decreases in all months during the early 1980s and reached a relatively steady state in the early 1990s. The seasonal cycle is weak compared to the longterm trend in N concentrations. TP concentrations show steady declines in all months without a mid 1980s hump. As
at upstream locations, TP concentrations are slightly higher in the summer. DIN:DIP ratios show increases throughout the past twenty years, with the sharpest increases in the last 10 years. The increase in TN:TP ratios are almost linear throughout all months except in May and June. While the average DIN:DIP ratio was rarely above 5 in the early 1980s, it is now above 10 in all but the late summer months. The average TN:TP ratio was near 16 in the early 1980s but is now almost always above 20. Our STL model does not explain all of the variation in the nutrient concentration data. Residual components are still typically large, especially at higher average values (Figure 7). However, despite the high residual component, the validity of the observed trends is supported by the persistence of trend patterns across stations. Thus, while all variation may not be explained by STL, the method reveals an overall picture that may be interpretable ecologically.
4. Discussion The general trends seen in Figures 3-6 may have important implications for management of eutrophication in the Neuse VOL. 34, NO. 21, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 6. Seasonal patterns of the long-term nutrient concentration (in mg/L) trends at Station 12 for TN, TP, molar DIN:DIP ratio (DIN:DIP), and molar TN:TP ratio (TN:TP). Each column represents the long-term trend in a given month. The dashed line represents the long-term mean for each month, and the shaded bar in the two N:P rows represents the approximate intracellular ratio of phytoplankton (16:1). Note that the scales of TP and TN are larger than those in previous figures. River estuary. Eutrophication is believed to have worsened in recent years as a result of the population expansion and development that are occurring throughout the basin, together with the growth in the commercial hog-farming industry (15, 24). Since nitrogen is believed to be the limiting factor controlling algal growth in the Neuse (25), the North Carolina General Assembly has set a goal to reduce nitrogen loading to the river by 30%. However, our results indicate that while nitrogen concentrations have not changed significantly, they may have declined slightly overall in the lower river and estuary in the last twenty years. Additionally, phosphorus concentrations have dropped considerably at all locations since the mid 1980s. Nutrient concentration patterns have led to a sharp rise in the nitrogen-to-phosphorus ratio in the river and estuary in the last 10 years, suggesting possible changes in the factors controlling algal growth. The average intracellular N:P ratio of marine organisms (16:1, the Redfield ratio) is often used as the basis for inferring which nutrient is limiting algal production (26, 27). Nitrogen is considered the limiting nutrient when the ambient N:P ratios are considerably below 4480
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16 and phosphorus is considered limiting when this value is well above 16. Dissolved inorganic nutrient inputs to the estuary, while previously below 10:1 in the summer are now above 20:1 at all times of the year (Figure 5). The change in the ratio of total nutrients is similar. While biological and physical processes reduce this ratio down-estuary, even at the mouth near Pamlico Sound the DIN:DIP is now very near the Redfield ratio for most of the year (Figure 6). These changes suggest that, while nitrogen may still be limiting in the lower mesohaline section of the estuary especially during the summer, there is a much greater potential for phosphorus limitation in the upper oligohaline section. It is in the highly productive upper Neuse estuary where the most severe symptoms of eutrophication have been observed. Similar nutrient-ratio changes to those we suggest are occurring in the Neuse have been observed in the Chesapeake Bay as well. Investigators of that system suggest using caution when employing older data to infer the role of nutrients in controlling algal productivity, especially when nutrient ratios may be changing with time (28). Not only do these changes affect predictions regarding the response of the present algal
FIGURE 7. Long-term, seasonal, and residual components of the STL model for molar DIN:DIP at station 2, 7, and 12. All plots are centered at 0 as described in Figure 2. Apparent R2 values of the model (long-term and seasonal component) are 0.65, 0.67, and 0.37, respectively. community to future nutrient management, but also they suggest the potential for a shift in the composition of the community itself (29). Phytoplankton species in all environments have optimal DIN:DIP ratios in which they thrive (30, 31, 29), and the species assemblage present in many systems has been shown to depend on the long-term DIN:DIP supply ratio (32, 33). Depending on the specific community structure induced by shifting nutrient ratios, undesirable changes may occur in the system, including an increase in nuisance algal blooms (34) or the increased presence of toxic phytoplankton (35, 36). This may, in part, explain a reported increase in the
presence of toxic dinoflagellates in the Neuse (37) and the perception of overall worsening conditions, despite reductions in riverine nutrient loading and estuarine nutrient concentrations. If this is the case, management actions that focus exclusively on nitrogen to reduce estuarine eutrophication may not achieve the desired results.
Acknowledgments We thank K. H. Reckhow, E. C. Lamon, III, Lisa Eby, and Tara Stow for their constructive comments and suggestions on an earlier version of this paper. The authors also thank the VOL. 34, NO. 21, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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associate editor and three referees for their insightful comments and suggestions. Data used herein were provided by the U.S. Geological Survey and the North Carolina Department of Environment and Natural Resources. S. Qian’s work is partly supported by an EPA STAR grant in environmental statistics and an EPA contract through the Cadmus Group, Inc. M. Borsuk is supported by an EPA STAR graduate fellowship. This work was also supported by a grant from the Water Resources Research Institute of the University of North Carolina.
(18) (19) (20) (21)
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Received for review February 9, 2000. Revised manuscript received August 1, 2000. Accepted August 11, 2000. ES000989P
