Nutrient inputs from subarctic rivers into the Hudson Bay system

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Lee, Jang Han
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Little information exists concerning the riverine supply of inorganic nutrients and its consequences on primary production in the Hudson Bay system (HB), a large subarctic inland sea that is impacted by rapid climate change and anthropogenic disturbance. In order to provide a reference point by which future changes can be evaluated, we estimated fluxes of nitrate (N), phosphate (P) and silicate (Si) using contemporary and historical nutrient data in conjunction with discharge rates generated by 3 different global climate models. Several key points can be highlighted. Firstly, the N:P and Si:N molar ratios of river nutrient fluxes exhibit large contrasts between different sectors of HB, which is attributed to variable geological settings in the watersheds. Generally, low N:P and high Si:N ratios imply that river waters are characterized by a severe deficit of nitrate with respect to the needs of primary producers. Secondly, seasonality in nutrient concentrations and ratios were apparent in the sampled rivers at different times of years. While the regulation of river flow in the Nelson and La Grande rivers had no discernible impact on nutrient concentrations and ratios, it clearly shifted nutrient transports toward the winter when biological activity in the estuaries is reduced. Thirdly, the southwestern rivers made the largest contributions of each nutrient flux to the total annual nutrient deliveries, with the modest contributions from the south and east rivers, and with the lowest contributions from the northwestern rivers. Finally, the combined nitrate input by all rivers was nearly two orders of magnitude (ca. 2.0 × 10^10 g N) lower than the estimated vertical re-supply of nitrate to the surface during winter in offshore waters of HB (ca. 1.2 × 10^12 g N). The potential contribution of river nutrients to new primary production is therefore small at HB scale but can be significant locally.
Hudson Bay, Rivers, Nutrients, Climate change, Modeling