Retrospective Analysis of Nutrient and Sediment Loadings to the Chesapeake Bay: Exploration of Trends and Affecting Factors

Principal Investigator:

William P. Ball

Start/End Year:

2014 - 2016


Johns Hopkins University

Strategic focus area:

Resilient ecosystem processes and responses


Toward controlling hypoxia in Chesapeake Bay, management programs have focused for decades on reducing nitrogen, phosphorus, and suspended sediment loads from the Chesapeake Bay Watershed (CBW). In this context, the Chesapeake Bay Partnership (CBP) is currently working to improve its model-based support for the establishment of Total Maximum Daily Loads and the associated development (by others) of Watershed Implementation Plans. To be successful, the CBP needs to better understand the nature and causes of historical changes in nutrient and sediment loads, using best current methods and data. The proposed project will help the program meet this need.

Our project focuses on application and discovery through the study and use of a state-of-the-art riverine loading estimation method called WRTDS ("Weighted Regressions on Time, Discharge, and Season"; Hirsch et al. 2010), and other methods as needed. The overall goals are to better quantify important nutrient and sediment trends in the major tributaries of the CBW and to develop new understanding of the applicability, uncertainty, and accuracy of the WRTDS method. More specifically, faculty, two graduate students, and professional partners will work over two years on tasks that have been formulated to meet the following specific objectives:

1. Obtain a Bay-wide seasonal analysis of historical loading trends for the 9 major tributaries where long-term datasets are available, including new estimations of uncertainty at important locations of perceived trends;

2. Evaluate the historical changes, trends, and (to the extent possible) causes of changing performance in the Lower Susquehanna River Reservoir System with respect to nutrient and sediment loads emanating from the system's outlet at Conowingo Dam; and

3. Gain new understanding of the applicability, uncertainty, and accuracy of the WRTDS method and, to the extent possible, identify alternative approaches or modifications needed when data are insufficient for accurate use of WRTDS.


Corresponding outcomes will include:

1.  New estimations of concentration and loading data from the nine major tributaries of the CBW, including the first complete set of WRTDS-based seasonal estimates of total and non-point-source nutrient and sediment loadings, which will complement other on-going work by USGS partners and will provide valuable new data to help all stakeholders better understand past loadings as a function of historical activities in the watershed;

2.  New understanding of the nature and causes of decreased sediment retention behind the Conowingo Dam, which will provide timely information to help inform an on-going evaluation by others in the context of re- licensing of the Conowingo Dam; and

3.  New scientific analysis of the strengths, weaknesses, and uncertainties of the estimation methods studied, including an assessment of their applicability to more recently initiated monitoring sites. For these sites with temporally short records, we will also explore and apply other methods of interpretation in a way that should provide important insights to our management stakeholders.

These results will be shared with all partners within the CBP and, through on-going outreach programs of those groups, all other stakeholders involved with CB water quality management.



This section describes how this project has advanced scientific knowledge and made a difference for coastal residents, communities, and environments. Maryland Sea Grant has reported these details to the National Oceanic and Atmospheric Administration (NOAA), one of our funding sponsors.

Summary: Researchers used modeling tools to develop novel insights into trends in the accumulation and loss of sediments and nutrients in the reservoir behind a major hydroelectric dam on the Susquehanna River. The findings can inform management decisions about the dam in ways that support a multi-state effort to reduce nutrients and sediments in Chesapeake Bay to improve water quality.

Relevance: The Conowingo Dam hydroelectric facility on the Susquehanna River has became an important focus of scientific study and public interest in an ongoing discussion about effective ways to improve water quality in Chesapeake Bay. Over decades of the dam’s operation, sediments and associated nutrients have accumulated in the reservoir behind the dam. The potential for these to become resuspended and flow downstream poses significant, negative consequences for water quality in the estuary; the Susquehanna flows into the Chesapeake’s head 10 miles downstream of the dam. The dam’s federal operating license is up for renewal, and some citizens have proposed that the new license require new measures, including dredging of the sediments. Future progress in Chesapeake Bay restoration will depend on accurate predictions of how outputs of sediments and nutrients from the reservoir may change over time.

Response:  Maryland Sea Grant (MDSG) supported principal investigator William Ball of the Johns Hopkins University and colleagues to develop new statistical modeling of rates of sediment accumulation and loss in the Conowingo’s reservoir. This work was a component in a broader, MDSG-funded project by the researchers to use a state-of-the-art riverine loading estimation method, WRTDS (Weighted Regressions on Time, Discharge, and Season), to generate models of nutrient and sediment loadings in the nine major tributaries of the Chesapeake Bay watershed, including the Susquehanna. MDSG funded a graduate fellow and doctoral candidate, Qian Zhang, who made substantial contributions on this project.

Results: The researchers’ modeling offers new information describing historical trends in the Conowingo reservoir’s inputs and outputs of sediments and associated nutrients as the dam has filled and under varying levels of river flow. An important implication for the Chesapeake’s water quality is that the reservoir is trapping less sediment over time as its storage capacity has decreased. The modeling indicated that inputs of suspended sediments and total phosphorus at the reservoir inlet have declined during a recent 30-year period, reflecting the effects of upstream management controls on a variety of sources. However, there was not a corresponding decline in outputs from the reservoir. Although storm-driven scour has been implicated in this diminished trapping, the modeling indicated an increase in output of sediment relative to previous decades that occurred under a range of river flows including ones well below the literature-reported scour threshold. Model results were consistent with monitoring data. The research team presented the findings at scientific conferences; in journal articles, including one in Environmental Science & Technology; and in meetings with agencies studying the Conowingo Dam including the Chesapeake Bay Program Office, the U.S. Geological Survey, and the Maryland Department of Natural Resources. The modeling approaches used in this project are helping to inform – and can be used to calibrate and verify – additional modeling that is underway to quantify temporal changes in the reservoir’s sediment output under differing rates of river flow.  Improvements in modeling informed by this project can improve future decisions about how best to manage the dam and the Susquehanna River Basin in order to help reduce future impacts on water quality in Chesapeake Bay. The results and methods are also applicable to other reservoir systems that may be similarly approaching a state of dynamic equilibrium with respect to sediment storage.

Related Publications:

Zhang, Q. 2018. Synthesis of nutrient and sediment export patterns in the Chesapeake Bay watershed: Complex and non-stationary concentration-discharge relationships. Science of the Total Environment618:1268 -1283. doi:10.1016/j.scitotenv.2017.09.221. UM-SG-RS-2018-01.

Zhang, Q. 2018. Synthesis of nutrient and sediment export patterns in the Chesapeake Bay watershed: Complex and non-stationary concentration-discharge relationships Science of the Total Environment618:1268 -1283. doi:10.1016/j.scitotenv.2017.09.221. UM-SG-RS-2018-01.

Zhang, Q; Harman, CJ; Kirchner, J. 2018. Evaluation of statistical methods for quantifying fractal scaling in water-quality time series with irregular sampling. Hydrology and Earth System Sciences22(2):1175 -1192. doi:10.5194/hess-22-1175-2018. UM-SG-RS-2018-02.

Zhang, Q; Harman, CJ; Kirchner, J. 2018. Evaluation of statistical methods for quantifying fractal scaling in water-quality time series with irregular sampling Hydrology and Earth System Sciences22(2):1175 -1192. doi:10.5194/hess-22-1175-2018. UM-SG-RS-2018-02.

Zhang, Q; Ball, WP. 2017. Improving riverine constituent concentration and flux estimation by accounting for antecedent discharge conditions. Journal of Hydrology547:387 -402. doi:10.1016/j.jhydrol.2016.12.052. UM-SG-RS-2017-01.

Zhang, Q; Ball, WP; Moyer, DL. 2016. Decadal-scale export of nitrogen, phosphorus, and sediment from the Susquehanna River basin, USA: Analysis and synthesis of temporal and spatial patterns. Science of the Total Environment563:1016 -1029. doi:10.1016/j.scitotenv.2016.03.104. UM-SG-RS-2016-12.

Zhang, Q; Harman, CJ; Ball, WP. 2016. An improved method for interpretation of riverine concentration-discharge relationships indicates long-term shifts in reservoir sediment trapping. Geophysical Research Letters43(19):10215 -10224. doi:10.1002/2016GL069945. UM-SG-RS-2016-22.

Zhang, Q; Hirsch, RM; Ball, WP. 2016. Long-Term Changes in Sediment and Nutrient Delivery from Conowingo Dam to Chesapeake Bay: Effects of Reservoir Sedimentation. Environmental Science & Technology50(4):1877 -1886. doi:10.1021/acs.est.5b04073. UM-SG-RS-2016-13.

Zhang, Q; Brady, DC; Boynton, WR; Ball, WP. 2015. Long-Term Trends of Nutrients and Sediment from the Nontidal Chesapeake Watershed: An Assessment of Progress by River and Season. Journal of the American Water Resources Association51(6):1534 -1555. doi:10.1111/1752-1688.12327. UM-SG-RS-2015-16.

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