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The hydrological effects of climate change are documented in many regions; however, climate-driven impacts to the source and transport of river nutrients remain poorly understood. Understanding the factors controlling nutrient dynamics across river systems is critical to preserve ecosystem function yet challenging given the complexity of landscape and climate interactions. Here, we harness a large regional dataset of nitrate (NO₃^–) yield, concentration, and isotopic composition (δ¹⁵N and δ¹⁸O) to evaluate the strength of hydroclimate and landscape variables in controlling the seasonal source and transport of NO₃^–. We show that hydroclimate strongly influenced the seasonality of river NO₃^–, producing distinct, source-dependent NO₃^–regimes across rivers from two mountain ranges. Riverine responses to hydroclimate were also constrained by watershed-scale topographic features, demonstrating that while regional climate strongly influences the timing of river NO₃^–transport, watershed topography plays a distinct role in mediating the sensitivity of river NO₃^–dynamics to future change.

 

Large river systems, particularly those shared by developing nations in the tropics, exemplify the interconnected and thorny challenges of achieving sustainability with respect to food, energy, and water ( 1 ). Numerous countries in South America, Africa, and Asia have committed to hydropower as a means to supply affordable energy with net-zero emissions by 2050 ( 2 ). The placement, size, and number of dams within each river basin network have enormous consequences for not only the ability to produce electricity ( 3 ) but also how they affect people whose livelihoods depend on the local river systems ( 4 ). On page 753 of this issue, Flecker et al. ( 5 ) present a way to assess a rich set of environmental parameters for an optimization analysis to efficiently sort through an enormous number of possible combinations for dam placements and help find the combination(s) that can achieve energy production targets while minimizing environmental costs in the Amazon basin. 

Recent advances in high‐frequency environmental sensing and statistical approaches have greatly expanded the breadth of knowledge regarding aquatic ecosystem metabolism—the measurement and interpretation of gross primary productivity (GPP) and ecosystem respiration (ER). Aquatic scientists are poised to take advantage of widely available datasets and freely‐available modeling tools to apply functional information gained through ecosystem metabolism to help inform environmental management. Historically, several logistical and conceptual factors have limited the widespread application of metabolism in management settings. Benefitting from new instrumental and modeling tools, it is now relatively straightforward to extend routine monitoring of dissolved oxygen (DO) to dynamic measures of aquatic ecosystem function (GPP and ER) and key physical processes such as gas exchange with the atmosphere (G). We review the current approaches for using DO data in environmental management with a focus on the United States, but briefly describe management frameworks in Europe and Canada. We highlight new applications of diel DO data and metabolism in regulatory settings and explore how they can be applied to managing and monitoring ecosystems. We then review existing data types and provide a short guide for implementing field measurements and modeling of ecosystem metabolic processes using currently available tools. Finally, we discuss research needed to overcome current conceptual limitations of applying metabolism in management settings. Despite challenges associated with modeling metabolism in rivers and lakes, rapid developments in this field have moved us closer to utilizing real‐time estimates of GPP, ER, and G to improve the assessment and management of environmental change.

This article is categorized under:

Water and Life > Nature of Freshwater Ecosystems

Water and Life > Conservation, Management, and Awareness

 

Williams et al . claim that the data used in Sabo et al . were improperly scaled to account for fishing effort, thereby invalidating the analysis. Here, we reanalyze the data rescaled per Williams et al . and following the methods in Sabo et al . Our original conclusions are robust to rescaling, thereby invalidating the assertion that our original analysis is invalid.