Patterns and Trends in Secchi Disk Depth over Three Decades in the Chesapeake Bay Estuarine Complex

Water clarity is an important ecosystem indicator of eutrophication in Chesapeake Bay and other coastal and estuarine systems across the globe. Although a variety of measures are available to quantify light availability in water, Secchi disk depths have been the most consistent and frequent measure employed in water monitoring programs. Because light availability is influenced by multiple variables, such as phytoplankton biomass, non-living suspended particles, and colored dissolved organic matter (CDOM), understanding the factors driving long-term variability and trends in water clarity is critical for targeting watershed management actions related to eutrophication. Thus, we conducted a comprehensive statistical analysis of spatial and temporal variations in Secchi disk depth and the key internal and external variables that influence its variability in Chesapeake Bay and its tidal tributaries over the past 30 years. Our results indicate that although watershed nutrient, sediment, and freshwater inputs did not correlate with Secchi depth on a monthly timescale outside of low-salinity regions near river outflows, water-column variables that represent the consequences of those inputs (CDOM, chlorophyll-a, and total suspended solids [TSS]) were strongly associated with Secchi depth variability. The inconsistency of these two findings may be explained by controls on chlorophyll-a and TSS that are not directly related to watershed input, such as grazing and resuspension, and by lags of several months between watershed inputs and the associated water-column concentrations. While salinity (a proxy for CDOM) was a dominant spatial covariate with Secchi depth bay-wide, TSS concentrations were strongly associated with temporal changes in Secchi depths in low-salinity regions and indicators of phytoplankton biomass were more important in mesohaline and polyhaline regions. These findings related to spatially dependent controls on Secchi depth enhance our understanding of long-term changes in estuarine light availability and suggest a region-specific response of Secchi depth to variables (TSS and chlorophyll-a) targeted by watershed restoration actions designed to limit nutrient and sediment inputs to Chesapeake Bay.

[1]  Qian Zhang,et al.  Watershed export of fine sediment, organic carbon, and chlorophyll-a to Chesapeake Bay: Spatial and temporal patterns in 1984-2016. , 2018, The Science of the total environment.

[2]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[3]  P. Tango,et al.  Chesapeake Bay's water quality condition has been recovering: Insights from a multimetric indicator assessment of thirty years of tidal monitoring data. , 2018, The Science of the total environment.

[4]  S. Seitzinger,et al.  Global patterns and sources of dissolved organic matter export to the coastal zone: Results from a spatially explicit, global model , 2005 .

[5]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2001, Springer Series in Statistics.

[6]  A. Bouwman,et al.  Estimation of global river transport of sediments and associated particulate C, N, and P , 2005 .

[7]  D F Boesch,et al.  Chesapeake Bay eutrophication: scientific understanding, ecosystem restoration, and challenges for agriculture. , 2001, Journal of environmental quality.

[8]  Carl F. Cerco,et al.  Management modeling of suspended solids in the Chesapeake Bay, USA , 2013 .

[9]  J. Maindonald Statistical Learning from a Regression Perspective , 2008 .

[10]  W. David Miller,et al.  Variable climatic conditions dominate recent phytoplankton dynamics in Chesapeake Bay , 2016, Scientific Reports.

[11]  W. P. Ball,et al.  Long-term seasonal trends of nitrogen, phosphorus, and suspended sediment load from the non-tidal Susquehanna River Basin to Chesapeake Bay. , 2013, The Science of the total environment.

[12]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[13]  W. Kemp,et al.  Nutrient- and Climate-Induced Shifts in the Phenology of Linked Biogeochemical Cycles in a Temperate Estuary , 2018, Front. Mar. Sci..

[14]  Hugh L. MacIntyre,et al.  A dynamic model of photoadaptation in phytoplankton , 1996 .

[15]  P. Tango,et al.  Deriving Chesapeake Bay Water Quality Standards , 2013 .

[16]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[17]  Thomas S. Bianchi,et al.  Temporal variability in terrestrially-derived sources of particulate organic carbon in the lower Mississippi River and its upper tributaries , 2007 .

[18]  Jiangtao Xu,et al.  Modeling biogeochemical cycles in Chesapeake Bay with a coupled physical biological model , 2006 .

[19]  S. Markager,et al.  Carbon‐to‐chlorophyll ratio for phytoplankton in temperate coastal waters: Seasonal patterns and relationship to nutrients , 2016 .

[20]  Michael R. Roman,et al.  Eutrophication of Chesapeake Bay: historical trends and ecological interactions , 2005 .

[21]  Lawrence P. Sanford,et al.  Reconsidering the physics of the Chesapeake Bay estuarine turbidity maximum , 2001 .

[22]  K. Sand‐Jensen,et al.  Optical Changes in a Eutrophic Estuary During Reduced Nutrient Loadings , 2014, Estuaries and Coasts.

[23]  W. P. Ball,et al.  Long-Term Changes in Sediment and Nutrient Delivery from Conowingo Dam to Chesapeake Bay: Effects of Reservoir Sedimentation. , 2016, Environmental science & technology.

[24]  W. Kemp,et al.  Seasonal and regional variations in plankton community production and respiration for Chesapeake Bay , 1995 .

[25]  Robert M. Hirsch,et al.  Comparison of Two Regression-Based Approaches for Determining Nutrient and Sediment Fluxes and Trends in the Chesapeake Bay Watershed , 2014 .

[26]  C. McClain,et al.  Long-term changes in light scattering in Chesapeake Bay inferred from Secchi depth, light attenuation, and remote sensing measurements , 2011 .

[27]  B. Håkansson,et al.  Long‐term trends in Secchi depth in the Baltic Sea , 1996 .

[28]  J. Carstensen,et al.  Recovery of Danish Coastal Ecosystems After Reductions in Nutrient Loading: A Holistic Ecosystem Approach , 2015, Estuaries and Coasts.

[29]  J. Schubel,et al.  Turbidity Maximum of the Northern Chesapeake Bay , 1968, Science.

[30]  T. Fisher,et al.  Chromophoric dissolved organic matter and dissolved organic carbon in Chesapeake Bay , 2002 .

[31]  Ming Li,et al.  Sediment deposition from tropical storms in the upper Chesapeake Bay: Field observations and model simulations , 2014 .

[32]  D. Moyer,et al.  Application of a Weighted Regression Model for Reporting Nutrient and Sediment Concentrations, Fluxes, and Trends in Concentration and Flux for the Chesapeake Bay Nontidal Water-Quality Monitoring Network, Results Through Water Year 2012 , 2016 .

[33]  Rudolph W. Preisendorfer,et al.  Secchi disk science: Visual optics of natural waters1 , 1986 .

[34]  J. Friedman,et al.  Estimating Optimal Transformations for Multiple Regression and Correlation. , 1985 .

[35]  G. Fiske,et al.  Chromophoric dissolved organic matter export from U.S. rivers , 2012 .

[36]  L. Ward,et al.  Phytoplankton, nutrients, and turbidity in the Chesapeake, Delaware, and Hudson estuaries , 1988 .

[37]  Jon Barry,et al.  Relationships between suspended particulate material, light attenuation and Secchi depth in UK marine waters , 2008 .

[38]  Jiangtao Xu,et al.  A simple empirical optical model for simulating light attenuation variability in a partially mixed estuary , 2005 .

[39]  Scott R. Marion,et al.  Multiple stressors threaten the imperiled coastal foundation species eelgrass (Zostera marina) in Chesapeake Bay, USA , 2017, Global change biology.

[40]  Charles L. Gallegos,et al.  Calculating optical water quality targets to restore and protect submersed aquatic vegetation: Overcoming problems in partitioning the diffuse attenuation coefficient for photosynthetically active radiation , 2001 .

[41]  C. McGilliard,et al.  Seasonal and annual variability in the spatial patterns of plankton biomass in Chesapeake Bay , 2005 .

[42]  James N. Kremer,et al.  The relative importance of chlorophyll and colored dissolved organic matter (CDOM) to the prediction of the diffuse attenuation coefficient in shallow estuaries , 2005 .

[43]  W. Cai,et al.  Spatial distribution of riverine DOC inputs to the ocean: an updated global synthesis , 2012 .

[44]  W. Cleveland Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .

[45]  J. M. Landwehr,et al.  Habitat requirements for submerged aquatic vegetation in Chesapeake Bay: Water quality, light regime, and physical-chemical factors , 2004 .

[46]  James D. Hagy,et al.  Hypoxia in Chesapeake Bay, 1950–2001: Long-term change in relation to nutrient loading and river flow , 2004 .

[47]  Vivi Fleming-Lehtinen,et al.  Long-term changes in Secchi depth and the role of phytoplankton in explaining light attenuation in the Baltic Sea , 2012 .

[48]  C. Cerco,et al.  Managing for Water Clarity in Chesapeake Bay , 2004 .

[49]  D. Angeler,et al.  A worldwide view of organic carbon export from catchments , 2012, Biogeochemistry.

[50]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[51]  H. Paerl,et al.  Climate effects on phytoplankton floral composition in Chesapeake Bay , 2015 .

[52]  Bengt Karlson,et al.  The Effect of Optical Properties on Secchi Depth and Implications for Eutrophication Management , 2019, Front. Mar. Sci..

[53]  J. Kremer,et al.  The relative importance of chlorophyll and colored dissolved organic matter (CDOM) to the prediction of the diffuse attenuation coefficient in shallow estuaries , 2005 .

[54]  C. Anderson,et al.  Long-Term Variability of Nutrients and Chlorophyll in the Chesapeake Bay: A Retrospective Analysis, 1985–2008 , 2010 .

[55]  W. P. Ball,et al.  Long‐Term Trends of Nutrients and Sediment from the Nontidal Chesapeake Watershed: An Assessment of Progress by River and Season , 2015 .

[56]  J. Megonigal,et al.  Tidal marshes as a source of optically and chemically distinctive colored dissolved organic matter in the Chesapeake Bay , 2008 .

[57]  Robert M. Hirsch,et al.  Large Biases in Regression‐Based Constituent Flux Estimates: Causes and Diagnostic Tools , 2014 .

[58]  H. Paerl,et al.  Long-Term Trends of Nutrients and Phytoplankton in Chesapeake Bay , 2016, Estuaries and Coasts.

[59]  James P. M. Syvitski,et al.  Global variability of daily total suspended solids and their fluxes in rivers , 2003 .

[60]  J. Hagy,et al.  Dissolved and particulate organic carbon in Chesapeake Bay , 1998 .

[61]  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 , 2012 .

[62]  W. Dennison,et al.  Long-term nutrient reductions lead to the unprecedented recovery of a temperate coastal region , 2018, Proceedings of the National Academy of Sciences.

[63]  R. Hirsch,et al.  Weighted Regressions on Time, Discharge, and Season (WRTDS), with an Application to Chesapeake Bay River Inputs , 2010, Journal of the American Water Resources Association.

[64]  T. R. Fisher,et al.  Scientific Bases for Numerical Chlorophyll Criteria in Chesapeake Bay , 2013, Estuaries and Coasts.

[65]  W. Dennison,et al.  Submersed Aquatic Vegetation in Chesapeake Bay: Sentinel Species in a Changing World , 2017 .

[66]  W. P. Ball,et al.  An improved method for interpretation of riverine concentration‐discharge relationships indicates long‐term shifts in reservoir sediment trapping , 2016 .

[67]  C. W. Keefe The contribution of inorganic compounds to the particulate carbon, nitrogen, and phosphorus in suspended matter and surface sediments of Chesapeake Bay , 1994 .