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      Sediment sources and transport dynamics in large, regulated river systems with multiple lakes and reservoirs in the subarctic region of Canada

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          Abstract

          The Burntwood River (BR) and Upper Nelson River (UNR) are regulated rivers in the subarctic region of Canada. They merge at Split Lake and then discharge into Hudson Bay via the Lower Nelson River (LNR). The BR water discharge was increased eight‐fold by a cross‐watershed diversion in 1976. The UNR drains the 11 th largest lake in the world, Lake Winnipeg, which itself receives discharge from a large North American Interior Plains watershed. Sediment loads and the source fingerprinting approach in these rivers were used to: (a) identify the sediment sources; (b) examine the impact of climate change and flow regulation on the BR and UNR sediment loads; and (c) assess the influence of Split Lake on downstream delivery of sediment into the LNR. Lake Winnipeg effectively decouples the UNR from the sediment sources in its prairie watershed. Fluvial riverbank and reservoir shoreline erosion in the UNR increased in the late 1990s, in response to a multi‐decadal increase in discharge forced by climate change in the Lake Winnipeg watershed. The BR sediment load was increased seven‐fold by diversion. Since diversion, flow regulation near the licenced limit has muted the response to variability in local precipitation and runoff; however, erosion processes independent from discharge (bank failures and subaerial processes) add variability in the sediment load record. Based on sediment budgeting, Split Lake conveys almost 80% of the BR and UNR sediment load into the LNR. The greater sediment load in the UNR (~1100 Gg year −1, compared to ~530 Gg year −1 from the BR) reveals that the UNR is the primary sediment source into the LNR, so that downstream sediment transport dynamics are more sensitive to the environmental changes in the UNR than to disturbances in the BR. Whether this may change in the future depends on changes in climate and engineering responses to increasing demand for hydroelectric power.

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          The sediment delivery problem

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            Is Open Access

            Analyzing mixing systems using a new generation of Bayesian tracer mixing models

            The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g., stable isotope) mixing model framework implemented as an open-source R package. Using MixSIAR as a foundation, we provide guidance for the implementation of mixing model analyses. We begin by outlining the practical differences between mixture data error structure formulations and relate these error structures to common mixing model study designs in ecology. Because Bayesian mixing models afford the option to specify informative priors on source proportion contributions, we outline methods for establishing prior distributions and discuss the influence of prior specification on model outputs. We also discuss the options available for source data inputs (raw data versus summary statistics) and provide guidance for combining sources. We then describe a key advantage of MixSIAR over previous mixing model software—the ability to include fixed and random effects as covariates explaining variability in mixture proportions and calculate relative support for multiple models via information criteria. We present a case study of Alligator mississippiensis diet partitioning to demonstrate the power of this approach. Finally, we conclude with a discussion of limitations to mixing model applications. Through MixSIAR, we have consolidated the disparate array of mixing model tools into a single platform, diversified the set of available parameterizations, and provided developers a platform upon which to continue improving mixing model analyses in the future.
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              Anthropogenic stresses on the world’s big rivers

              Jim Best (2018)
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Hydrological Processes
                Hydrological Processes
                Wiley
                0885-6087
                1099-1085
                September 2022
                September 02 2022
                September 2022
                : 36
                : 9
                Affiliations
                [1 ] Department of Soil Science University of Manitoba Winnipeg Canada
                [2 ] Department of Geography and Environment Studies, Centre for Earth Observations Sciences University of Manitoba Winnipeg Canada
                [3 ] Department of Geography and Environment Brandon University Brandon Canada
                [4 ] Department of Geography, Earth, and Environmental Sciences, and Quesnel River Research Centre University of Northern British Columbia Prince George Canada
                Article
                10.1002/hyp.14675
                0a4c500f-c7f9-4b06-bedd-f83c3cb9f602
                © 2022

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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