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. The watershed's sediment sources, transport, delivery, and impacts are discussed with implications for effectively implementing best management practices (BMPs) to mitigate sediment issues. This synthesis revealed three key issues to consider when planning actions to reduce sediment loading: Scale, time, and land use. Geology and historical land use generated a template that current land use and climate, in addition to management, are acting upon to control sediment delivery. Important sediment sources in the Chesapeake include the Piedmont physiographic region, urban, and agricultural land use, and streambank erosion of headwater streams, whereas floodplain trapping is important along larger streams and rivers. Implementation of BMPs is widespread and is predicted to lead to decreased sediment loading; however, reworking of legacy sediment stored in stream valleys, with potentially long residence times in storage, can delay and complicate detection of the effects of BMPs on sediment loads. In conclusion, the improved understanding of sediment sources, storage areas, and transport lag times reviewed here can help target choices of BMP types and locations to better manage sediment problems—for both local streams and receiving waters.This article is categorized under: Science of Water > Water Quality Water and Life > Stresses and Pressures on Ecosystems Water and Life > Conservation, Management, and Awareness

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. The main objective of this work was to improve WRTDS estimation by accounting for the autocorrelation structure of model residuals using the first‐order autoregressive model (AR1). This modified approach, called WRTDS‐Kalman Filter (WRTDS‐K), was compared with WRTDS for six constituents including nitrate‐plus‐nitrite (NO_x), total phosphorus, total Kjeldahl nitrogen, soluble reactive phosphorus, suspended sediment, and chloride. Near‐daily concentration records at nine sites were used to generate subsets through Monte Carlo sampling for five different sampling scenarios. Results show that WRTDS‐K provided generally better daily estimates of concentration and flux than WRTDS under these sampling scenarios for all constituents, especially NO_x. The degree of improvement is strongly affected by the underlying sampling scenario, with WRTDS‐K gaining more advantage when more samples are available, and hence more residuals can be exploited. The performance of WRTDS‐K depends on the AR1 coefficient (ρ) and that relationship varies with constituents and sampling scenarios. These results provided recommendations on the optimal ρ for each constituent and sampling scenario. Overall, WRTDS‐K has the potential for broad applications to monitoring records elsewhere, as demonstrated by a pilot application to Chesapeake Bay tributaries.

In Chesapeake Bay in the United States, decades of management efforts have resulted in modest reductions of nutrient loads from the watershed, but the corresponding improvements in estuarine water quality have not consistently followed. Generalized additive models were used to directly link river flows and nutrient loads from the watershed to nutrient trends in the estuary on a station-by-station basis, which allowed for identification of exactly when and where responses are happening. Results show that Chesapeake Bay’s total nitrogen and total phosphorus conditions are mostly improving after accounting for variation in freshwater flow. Almost all of these improving nutrient concentrations in the estuary can be explained by reductions in watershed loads entering through 16 rivers and 145 nearby point sources, with the nearby point source reductions being slightly more effective at explaining estuarine nutrient trends. Overall, these two major types of loads from multiple locations across the watershed are together necessary and responsible for the improving estuarine nutrient conditions, a finding that is highly relevant to managing valuable estuarine resources worldwide.

In the Chesapeake Bay, excess nitrogen (N) from both landscape and atmospheric sources has for decades fueled algal growth, disrupted aquatic ecosystems, and negatively impacted coastal economies. Since the 1980s, Chesapeake Bay Program partners have worked to implement a wide range of measures across the region—from the upgrading of wastewater treatment plants to implementation of farm-level best management practices—to reduce N fluxes to the Bay. Despite widespread implementation of such measures and notable reductions in N inputs, water quality across the region has been slow to improve. Such lack of response has in some cases been attributed to N legacies—accumulations of surplus N in soils and groundwater—that can contribute to time lags between implementation of conservation measures and improvements in water quality. Here, we use the ELEMeNT-N modeling framework to explore the role of legacy N in slowing reductions in N loading to the Bay, and to provide estimates of the time required to meet water quality goals in nine major tributary watersheds. Our results first show that recent improvements in water quality can be attributed to decreases in N surplus magnitudes that began to occur in the 1970s and 1980s, and that such improvements will continue in the coming decades. Future simulations suggest that, even with no additional changes in current management practices, goals to reduce N loads across the region by 25% can nearly be met within the next two decades. The present results also suggest that time lags to achieving water quality may vary considerably in the individual study watersheds, with the longest lag times being found in the highly agricultural Choptank watershed, where N surplus magnitudes remain high and where legacy N remains a major control on water quality.