A Bayesian changepoint-threshold model to examine the effect of TMDL implementation on the flow-nitrogen concentration relationship in the Neuse River basin.

In-stream nutrient concentrations are well known to exhibit a strong relationship with river flow. The use of flow measurements to predict nutrient concentrations and subsequently nutrient loads is common in water quality modeling. Nevertheless, most adopted models assume that the relationship between flow and concentration is fixed across time as well as across different flow regimes. In this study, we developed a Bayesian changepoint-threshold model that relaxes these constraints and allows for the identification and quantification of any changes in the underlying flow-concentration relationship across time. The results from our study support the occurrence of a changepoint in time around the year 1999, which coincided with the period of implementing nitrogen control measures as part of the TMDL program developed for the Neuse Estuary in North Carolina. The occurrence of the changepoint challenges the underlying assumption of temporal invariance in the flow-concentrations relationship. The model results also point towards a transition in the river nitrogen delivery system from a point source dominated loading system towards a more complicated nonlinear system, where non-point source nutrient delivery plays a major role. Moreover, we use the developed model to assess the effectiveness of the nitrogen reduction measures in achieving a 30% drop in loading. The results indicate that while there is a strong evidence of a load reduction, there still remains a high level of uncertainty associated with the mean nitrogen load reduction. We show that the level of uncertainty around the estimated load reduction is not random but is flow related.

[1]  U. Aswathanarayana,et al.  Assessing the TMDL Approach to Water Quality Management , 2001 .

[2]  R. E. Turner,et al.  Global patterns of dissolved N, P and Si in large rivers , 2003 .

[3]  T. Sauer,et al.  Chapter 8 – The Importance and Role of Watersheds in the Transport of Nitrogen , 2008 .

[4]  Hans W. Paerl,et al.  Nitrogen Loading Sources and Eutrophication of the Neuse River Estuary, North Carolina: Direct and Indirect Roles of Atmospheric Deposition , 1995 .

[5]  Dubravko Justic,et al.  Predicting the response of Gulf of Mexico hypoxia to variations in Mississippi River nitrogen load , 2003 .

[6]  J. Kelly.  Nitrogen Effects on Coastal Marine Ecosystems , 2008 .

[7]  Richard P. Hooper,et al.  The National Stream Quality Accounting Network: a flux‐based approach to monitoring the water quality of large rivers , 2001 .

[8]  Hans W. Paerl,et al.  Ecosystem Responses to Internal and Watershed Organic Matter Loading: Consequences for Hypoxia in the Eutrophying Neuse River Estuary, North Carolina, USA , 1998 .

[9]  Carl J. Walters,et al.  Large‐Scale Management Experiments and Learning by Doing , 1990 .

[10]  Stefan Van Dongen,et al.  Prior specification in Bayesian statistics: three cautionary tales. , 2006, Journal of theoretical biology.

[11]  W. McDowell,et al.  Nitrogen yields from undisturbed watersheds in the Americas , 1999 .

[12]  S. Whalen,et al.  Denitrification in the mainstem Neuse River and tributaries, USA , 2008 .

[13]  C. Stow,et al.  Confounding Effect of Flow on Estuarine Response to Nitrogen Loading , 2004 .

[14]  Kenneth H. Reckhow.  On the Need for Uncertainty Assessment in TMDL Modeling and Implementation , 2003 .

[15]  Peter Congdon.  Bayesian statistical modelling , 2002 .

[16]  N. Rabalais,et al.  LSU Digital Commons LSU Digital Commons Coastal Eutrophication near the Mississippi River Delta Coastal Eutrophication near the Mississippi River Delta , 2022 .

[17]  Hans W. Paerl,et al.  Assessing and managing nutrient-enhanced eutrophication in estuarine and coastal waters: Interactive effects of human and climatic perturbations , 2006 .

[18]  C. Stow,et al.  Monitoring Design and Data Analysis for Trend Detection , 1990 .

[19]  A. Gelman.  Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) , 2004 .

[20]  Stefan Van Dongen,et al.  Prior specification in Bayesian statistics: three cautionary tales. , 2006 .

[21]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[22]  J. Burkholder,et al.  Comprehensive trend analysis of nutrients and related variables in a large eutrophic estuary: A decadal study of anthropogenic and climatic influences , 2006 .

[23]  C. Stow,et al.  Long-term changes in watershed nutrient inputs and riverine exports in the Neuse River, North Carolina. , 2001, Water research.

[24]  J. Hatfield,et al.  Nitrogen in the Environment: Sources, Problems, and Management , 2001, TheScientificWorldJournal.

[25]  Gregory E. Schwarz,et al.  Regional interpretation of water‐quality monitoring data , 1997 .

[26]  Craig A Stow,et al.  Assessing TMDL effectiveness using flow-adjusted concentrations: a case study of the Neuse River, North Carolina. , 2003, Environmental science & technology.

[27]  W. Dodds.  Misuse of inorganic N and soluble reactive P concentrations to indicate nutrient status of surface waters , 2003, Journal of the North American Benthological Society.

[28]  Mark E. Borsuk,et al.  Comparison of Estuarine Water Quality Models for Total Maximum Daily Load Development in Neuse River Estuary , 2003 .

[29]  Brian E. Haggard,et al.  USING REGRESSION METHODS TO ESTIMATE STREAM PHOSPHORUS LOADS AT THE ILLINOIS RIVER, ARKANSAS , 2003 .

[30]  Gerard McMahon,et al.  On the application of multilevel modeling in environmental and ecological studies. , 2010, Ecology.

[31]  Timothy A. Cohn,et al.  Load Estimator (LOADEST): A FORTRAN Program for Estimating Constituent Loads in Streams and Rivers , 2004 .

[32]  J. Park.  Modeling Structural Changes: Bayesian Estimation of Multiple Changepoint Models and State Space Models , 2006 .

[33]  C. Stow,et al.  Assessing the Effects of Nutrient Management in an Estuary Experiencing Climatic Change: The Neuse River Estuary, North Carolina , 2006, Environmental management.

[34]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[35]  Gregory E Schwarz,et al.  Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River Basin. , 2008, Environmental science & technology.

[36]  Andrew Gelman,et al.  Manipulating and summarizing posterior simulations using random variable objects , 2007, Stat. Comput..

[37]  William M. LewisJr..  Yield of nitrogen from minimally disturbed watersheds of the United States , 2002 .

[38]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[39]  Arthur H. Johnson,et al.  Estimating solute transport in streams from grab samples , 1979 .

[40]  C. Richardson,et al.  Estimating the long‐term phosphorus accretion rate in the Everglades: A Bayesian Approach with risk assessment , 1997 .

[41]  Kyoichi Otsuki,et al.  Effects of discharge level on the load of dissolved and particulate components of stream nitrogen and phosphorus from a small afforested watershed of Japanese cypress (Chamaecyparis obtusa) , 2007, Journal of Forest Research.

[42]  Gregory E. Schwarz,et al.  Effect of stream channel size on the delivery of nitrogen to the Gulf of Mexico , 2000, Nature.

[43]  Wayne R. Ott,et al.  ENVIRONMENTAL STATISTICS and DATA ANALYSIS , 1995 .

[44]  D. Conley,et al.  Scales of nutrient-limited phytoplankton productivity in Chesapeake Bay , 1996 .

[45]  Hans W. Paerl,et al.  Coastal eutrophication and harmful algal blooms: Importance of atmospheric deposition and groundwater as “new” nitrogen and other nutrient sources , 1997 .

[46]  B. Western,et al.  A Bayesian Change Point Model for Historical Time Series Analysis , 2004, Political Analysis.

[47]  Robert M. Summers,et al.  The validity of a simple statistical model for estimating fluvial constituent loads: An Empirical study involving nutrient loads entering Chesapeake Bay , 1992 .