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Long-Term and Seasonal Trend Decomposition of Maumee River Nutrient Inputs to Western Lake Erie † ‡ § § § Craig A

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Long-Term and Seasonal Trend Decomposition of Maumee River Nutrient Inputs to Western Lake Erie † ‡ § § § Craig A Article pubs.acs.org/est Long-Term and Seasonal Trend Decomposition of Maumee River Nutrient Inputs to Western Lake Erie † ‡ § § § Craig A. Stow,*, YoonKyung Cha, Laura T. Johnson, Remegio Confesor, and R. Peter Richards † Great Lakes Environmental Research Laboratory (GLERL), National Oceanic and Atmospheric Administration (NOAA), 4840 South State Road, Ann Arbor, Michigan 48108, United States ‡ School of Natural Resources and Environment, University of Michigan, 440 Church Street, Ann Arbor, Michigan 48109, United States § National Center for Water Quality Research, Heidelberg University, 310 East Market Street, Tiffin, Ohio 44883, United States *S Supporting Information ABSTRACT: Cyanobacterial blooms in western Lake Erie have recently garnered widespread attention. Current evidence indicates that a major source of the nutrients that fuel these blooms is the Maumee River. We applied a seasonal trend decomposition technique to examine long-term and seasonal changes in Maumee River discharge and nutrient concentrations and loads. Our results indicate similar long-term increases in both regional precipitation and Maumee River discharge (1975−2013), although changes in the seasonal cycles are less pronounced. Total and dissolved phosphorus concentrations declined from the 1970s into the 1990s; since then, total phosphorus concentrations have been relatively stable, while dissolved phosphorus concentrations have increased. However, both total and dissolved phosphorus loads have increased since the 1990s because of the Maumee River discharge increases. Total nitrogen and nitrate concentrations and loads exhibited patterns that were almost the reverse of those of phosphorus, with increases into the 1990s and decreases since then. Seasonal changes in concentrations and loads were also apparent with increases since approximately 1990 in March phosphorus concentrations and loads. These documented changes in phosphorus, nitrogen, and suspended solids likely reflect changing land-use practices. Knowledge of these patterns should facilitate efforts to better manage ongoing eutrophication problems in western Lake Erie. ■ INTRODUCTION bulletins for potentially affected stakeholders (http://www. Eutrophication problems resulting from excessive nutrient glerl.noaa.gov/res/waterQuality/). Additionally, the updated inputs have plagued Lake Erie for many years. In 1965, an 2012 GLWQA mandated a review of the existing TP target article in The Economist declared that Lake Erie was “literally loads with a directive to update the Lake Erie target and dying”.1 To control eutrophication, the 1978 Great Lakes develop numerical phosphorus concentration criteria within 3 years.12 Water Quality Agreement (GLWQA) established target total ’ phosphorus (TP) loads for each of the Great Lakes, including Lake Erie s most severe algal blooms originate in the western 2 basin of the lake, where nutrient inputs from the Maumee River an 11 000 metric tons per year goal for Lake Erie. Following 6,13 implementation of the 1978 Agreement, notable phosphorus (Figure 1) drive algal growth. Unlike many Great Lakes tributaries where input data are sparse, making long-term reductions and corresponding water quality improvements were 14 documented.3 However, the Lake Erie phosphorus target is still estimation and inference uncertain, nearly continuous records occasionally exceeded,4,5 and since the mid-late 1990s, of approximately daily concentration data are available for symptoms of “re-eutrophication”, including increased phyto- phosphorus, nitrogen, and other constituents in the Maumee plankton biomass and a reappearance of widespread hypoxia, River. These data are collected by Heidelberg University and in have been documented.6,7 In 2011, despite meeting the TP load combination with available United States Geological Survey target, Lake Erie experienced the most extensive algal bloom on (USGS) discharge monitoring records allow for detailed record.8 In August 2014, the city of Toledo drinking water analyses of changes in nutrient loads and concentrations since supply was temporally shut down because of high toxin levels the late 1970s. Previous examination of long-term patterns in resulting from a Microcystis bloom. The resurgence of eutrophication in Lake Erie has prompted Received: December 23, 2014 the National Oceanic and Atmospheric Administration Revised: February 13, 2015 (NOAA) and partners to develop predictive models for Accepted: February 13, 2015 − harmful algal bloom (HAB) occurrence9 11 and issue regular Published: February 13, 2015 © 2015 American Chemical Society 3392 DOI: 10.1021/es5062648 Environ. Sci. Technol. 2015, 49, 3392−3400 Environmental Science & Technology Article Figure 1. Lake Erie map. The upper right inset depicts all five Laurentian Great Lakes with Lake Erie enclosed in a rectangle. The upper left inset shows the mouth of the Maumee River in the western Lake Erie basin. The orange area indicates Ohio region 1, the area represented by the precipitation data. the annual phosphorus loads and flow-weighted mean edu/academiclife/distinctive/ncwqr/data). Data from the concentrations from the Maumee River revealed substantial Maumee River were collected at the Bowling Green Water increases in bioavailable phosphorus forms since the early Treatment Plant, which is located 5.6 km upstream from the 1990s, while TP changed little or decreased slightly.13 USGS stream gage in Waterville, OH (USGS 04193500). In this analysis, we build on previous work13 and use an Samples were collected using an ISCO automated sampler that approach to simultaneously examine both long-term and pulled water from a receiving basin continuously flushed with seasonal fluctuations in nutrient concentrations, loads, Maumee river water via a submersible pump in the river. Discrete River discharge, and regional precipitation. Environmental samples were collected either 3 (1974−1988) or 4 (1988− trend analyses commonly employ linear regression or non- present) times a day, and all samples collected during high flow parametric methods based on order statistics, including or with high turbidity were analyzed. Otherwise, one sample ’ 15 16,17 Kendall s tau test and variations. However, these methods per day was analyzed. Samples were retrieved weekly and are most appropriate for linear or monotonic trends; returned to the laboratory for analysis following standard assessments using them may be inaccurate when the supporting United States Environmental Protection Agency (U.S. EPA) fl assumptions are not well met or when the data uctuate with protocols. Dissolved reactive P (DRP) and TP were both time. Polynomial regression is sometimes applied for nonlinear analyzed using molybdate blue colorimetry (U.S. EPA Method time-series analysis, but this approach uses the entire time range 365.3); TP was pretreated using persulfate digestion. Nitrate of observations to estimate nonlinear patterns, whereas non- (NO −) was analyzed using ion chromatography (U.S. EPA linearities are often temporally local features. Generalized 3 18 Method 300.1). Total Kjeldahl N (TKN) was analyzed using additive modeling is a graphical approach to highlight phenol colorimetry after pretreatment with acid digestion (U.S. nonlinear patterns in data based on various localized smoothing EPA Method 351.2). Total N was calculated as the sum of techniques (also known as smoothers). To highlight seasonal − NO3 and TKN. Finally, total suspended solids (TSS) was and long-term patterns in Maumee River data, we apply a form analyzed by measuring dry weight after filtration through a glass of generalized additive modeling: seasonal trend decomposition fiber filter (U.S. EPA Method 160.2). using loess (STL),19,20 which employs a locally weighted We downloaded average monthly precipitation data for Ohio running-line smoother and has been previously used to analyze region 1 from the NOAA National Climatic Data Center, long-term nutrient concentration and river discharge data.21,22 Midwest Regional Climate Center website (http://mrcc.isws. Our results suggest that long-term precipitation increases are illinois.edu/CLIMATE/index.jsp.). partially responsible for discharge and load increases since the Before analyzing the data, we first averaged the Maumee mid-1990s and that the seasonal timing of delivery has also River concentration data by day (when multiple measurements changed for some constituents. were available) and then averaged the daily data by month to obtain monthly average concentrations. We estimated loads ■ METHODS first on a daily basis by multiplying the average daily Water quality data were downloaded from the National Center concentration by average daily discharge from the USGS for Water Quality Research website (http://www.heidelberg. stream gage (USGS 04193500) and then on a monthly basis by 3393 DOI: 10.1021/es5062648 Environ. Sci. Technol. 2015, 49, 3392−3400 Environmental Science & Technology Article Figure 2. (a) Ohio climate region 1 monthly average precipitation data (1895−2013) with STL long-term trend line (blue) and least-squares line (red). (b) STL long-term trend centered at zero (left), seasonal pattern (middle), and residuals (right). Seasonal pattern and residuals are shown as deviations from the long-term trend. (c) Enhanced resolution depiction of STL long-term trend (blue) and least-squares line (red). (d) STL seasonal patterns depicted by month (blue curves) with corresponding long-term monthly averages (black horizontal lines), shown as deviations from the smoothed, long-term trend. summing
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