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Estuaries, in particular freshwater estuaries, provide valuable economic, social, and ecological services, but their ecosystems are often heavily stressed. Located in the Laurentian Great Lakes basin, Green Bay is a large freshwater estuary and a prominent example of a degraded ecosystem due to intensive human interventions and rapid development. Excessive amounts of contaminants and nutrients were discharged to the bay by inflowing tributaries for almost half a century, while in contrast Green Bay's seasonal‐, morphological‐, and physically‐restricted mixing is unable to export a significant portion of those materials out of the bay, that is, Green Bay behaves as an efficient retention basin for the Lake Michigan. Consequently, several environmental and public health‐related issues have risen in Green Bay and turned the lower bay into an area of environmental concern since the 1980s. To address these challenges, restoration programs were developed, including the development of monitoring programs, scientific research, and remedial action plans. There is a consensus that accelerated loading rates of contaminated and nutrient‐rich sediments are a major driver of the environmental crisis in the bay, yet the fate and transport patterns of Green Bay sediments are not clearly understood. While field observations in Green Bay are season‐limited and costly, advanced computing techniques provided opportunities to refine our understanding of sediment dynamics in this estuarine system. This review of existing knowledge on Green Bay sediment processes can help to better understand the interplay between sediments, and physical/biogeochemical activities in estuarine systems and contributes conceptually to the restoration of degraded aquatic ecosystems. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Water QualityWater and Life > Stresses and Pressures on Ecosystems [ABSTRACT FROM AUTHOR]

Summary It is a common practice to conduct the water budget or water balance analysis in a given area within a specified time in order to investigate the balance between the inputs and outputs of the water system. Such an analysis can be used for water management and water allocation in a designated study area. Due to appearance of an error in water balance equation because of difficulty in accurate estimation of its individual components, the main objective of the current paper was to apply a set of fuzzy coefficients to the components of the water balance equation in order to reduce this error. The fuzzy coefficients reflect the uncertainty and imprecision in evaluating each component, and minimize the overall error of the water balance equation. These coefficients are adjusted by an error minimization procedure, based on fuzzy regression concepts and using available recorded data for a given study area within a specified time scale. The adjusted coefficients can effectively estimate the water balance components in the future. In this study, four different models, representing different types of fuzzy coefficients, were considered and used for annual water balance of Azghand catchment in Khorasan Razavi Province, Iran as a case study. Analysis of results showed that all models were effective in reducing water balance error in Azghand catchment. The best model reduced the error up to 79% in terms of mean absolute error compared with error in water balance equation when conventional (with no correction coefficients) water balance analysis was conducted. Moreover, the results indicated that the performance of the proposed fuzzy models was not significantly sensitive to selection of confidence level in data ( h ) and improved slightly as h increased. [ABSTRACT FROM AUTHOR]