Long-Term and Seasonal Trend Decomposition of Maumee River Nutrient Inputs to Western Lake Erie

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.

Water quality trend and change-point analyses using integration of locally weighted polynomial regression and segmented regression

“…In general, the LWPR approach is able to extract long-term trend information from statistically compromised water quality time series (WQ i ), providing a smoothed data series (SM i ) for subsequent analysis (Bodo 1989;Stow et al 2015). Further, the subsequent SegReg is able to quantify the relationships between the smoothed water quality time series (SM i ) versus the corresponding times (t i ).…”

“…This results from STL holding the seasonal patterns the same throughout the whole time period (Cleveland et al 1990); however, the seasonal pattern is somewhat variable from year-to-year. For LWPR, the seasonal patterns were designed to be functions of time and were removed locally according to their corresponding times (Bodo 1989;Stow et al 2015) resulting in the smoothed data trends (green circles in Fig. 4 and Fig.…”

“…Advantage of the LWPR-SegReg approach STL, LOWESS and LWPR are graphical methods that have been improved to assess long-term trends in environmental quality time series records (Bodo 1989;Lee et al 2010;Stow et al 2015). SegReg has also been widely used in trend and change-point analyses in many research fields (Mathews and Hamilton 2005;Wu and Chang 2012;Kazemnejad et al 2014), including hydrology (Shao and Campbell 2002).…”

“…An important advantage of the integrated LWPR-SegReg approach is the ability of LWPR to extract long-term trend information from water quality time series (Cleveland 1979;Bodo 1989;Stow et al 2015) and subsequently providing an appropriate dataset for SegReg analysis. Another notable advantage is the ability of SegReg to quantitatively detect change-points and trends with statistical rigor (Shao and Campbell 2002;Kazemnejad et al 2014).…”

“…Soon afterwards, LOWESS was developed for seasonal-trend decomposition using loess (STL) (Cleveland et al 1990). These graphical methods are based on various local smoothing techniques (Harding et al 2016) and have been widely applied in environmental quality trend analysis including air quality (Li et al 2014;Gong et al 2015), water quality (Lee et al 2010;Stow et al 2015), etc. In general, graphical methods can play an important role in trend analysis in typical time series data, both as a diagnostic tool and as visual corroborative evidence when required assumptions for formal statistical tests are not met (Bodo 1989).…”