Anthropogenic inputs of nutrients via river runoff are the primary drivers of ecosystem degradation in Chesapeake Bay (CB) and the northern Adriatic Sea (NAS). The annual cycle of river flow is typically unimodal in CB (seasonal peak during spring) and bimodal in the NAS (peaks during April–June and October–December). Dissolved inorganic nitrogen accounts for most of the total nitrogen (TN) in both systems.

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Nutrient and sediment transport exhibit strong spatial and temporal inequality, with a small percentage of locations and events contributing to the vast majority of total annual loads. The processes for determining how to reduce total annual loads at a watershed scale often target spatial, but not temporal, components of inequality.

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This review aims to synthesize the current knowledge of sediment dynamics using insights from long-term research conducted in the watershed draining to the Chesapeake Bay, the largest estuary in the U.S., to inform management actions to restore the estuary and its watershed. The sediment dynamics of the Chesapeake are typical of many impaired watersheds and estuaries around the world, and this synthesis is intended to be relevant and transferable to other sediment-impaired systems.

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Controlling phosphorus is fundamental to limit the risk of eutrophication of continental aquatic ecosystems. Integrated modelling of its concentration in the aquatic continuum requires specific tools for water bodies. However, although simple static empirical models and complex biogeochemical models are numerous, there are few relatively simple and flexible models able to simulate seasonal variations in phosphorus concentrations in water bodies and particularly in reservoirs.

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Accurate quantification of riverine water‐quality concentration and flux is challenging because monitoring programs typically collect concentration data at lower frequencies than discharge data. Statistical methods are often used to estimate concentration and flux on days without observations. One recently developed approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS), which has been shown to provide among the most accurate estimates compared to other common methods.

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Flux quantification for riverine water-quality constituents has been an active area of research. Statistical approaches are often employed to make estimation for days without observations. One such approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS) method. While WRTDS has been used in many investigations, there is a general lack of effort to identify factors that influence its estimation bias.

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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.

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Low dissolved oxygen (DO) conditions are a recurring issue in waters of Chesapeake Bay, with detrimental effects on aquatic living resources. The Chesapeake Bay Program partnership has developed criteria guidance supporting the definition of state water quality standards and associated assessment procedures for DO and other parameters, which provides a binary classification of attainment or impairment.

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To protect the aquatic living resources of Chesapeake Bay, the Chesapeake Bay Program partnership has developed guidance for state water quality standards, which include ambient water quality criteria to protect designated uses (DUs), and associated assessment procedures for dissolved oxygen (DO), water clarity/underwater bay grasses, and chlorophyll-a. For measuring progress toward meeting the respective states' water quality standards…

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River water-quality time series often exhibit fractal scaling, which here refers to autocorrelation that decays as a power law over some range of scales. Fractal scaling presents challenges to the identification of deterministic trends because (1) fractal scaling has the potential to lead to false inference about the statistical significance of trends and (2) the abundance of irregularly spaced data in water-quality monitoring networks complicates efforts to quantify fractal scaling.

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