How network structure can affect nitrogen removal by streams

Abstract

Streams and rivers can be highly reactive sites for nitrogen (N) transformation and removal. Empirical and model-based research show how location in a stream network affects rates of N removal. Because the structure of stream networks can vary widely and N cycling in headwater streams may affect N cycling in downstream reaches, we hypothesised that network structure may affect whole stream network processing of N. We generated three stream networks with the same catchment area but differing shapes, based on optimal channel network theory. We applied a model of nitrate (NO3-) transport and denitrification, and implemented model scenarios to examine how network shape affects NO3- removal with (1) increased NO3- loading from the catchment, (2) altered spatial distributions of NO3- loading and (3) decreased drainage density (i.e. loss of headwater streams). For all stream networks, the fraction of total NO3- removed decreased with increasing NO3- loading from the catchment. Stream networks in narrow catchments removed a higher fraction of NO3-, particularly at intermediate NO3- loading rates. Network shape also controlled the distribution of removal in small versus large streams, with larger streams removing a higher fraction of the total NO3- load in narrower networks. The effects of network shape on NO3- removal when the spatial distribution of NO3- loading was altered varied with the magnitude of NO3- loading. At low loads, NO3- was entirely removed when added to distal parts of the stream network, and about 50% removed when added near the outlet; there was no effect of network shape. At intermediate and high loads, the fraction of total NO3- load removed by the narrow stream network was 1.5Ã\u97 higher than the rectangular and square networks when NO3- was added to distal parts of the networks. Network shape did not have an effect when NO3- load occurred near the outlet, regardless of the magnitude of the NO3- load. The fraction of total NO3- removed by the stream network was up to 5% lower when drainage density was reduced from 1.0 to 0.74 km-1, with the least change for the narrow network. Reducing the drainage density also altered the role of small relative to large streams, with the net effect of moving the location of NO3- removal downstream. Overall, effects of network shape contributed up to 20% of the variation in the fraction of NO3- removed by stream networks. Network shape was most important at intermediate to high NO3- loads and when NO3- was loaded to distal parts of the catchment. The narrow network removed more NO3- across model scenarios, with elevated removal in larger streams explaining most of the difference. We suggest the shape of the catchment may modulate the degree to which large streams contribute to whole network NO3- removal

The Abundance, Size, and Spacing of Lakes and Reservoirs Connected to River Networks

“…By coupling a new understanding of river network topology with geospatial data sets and network modeling, we can examine how different types of river networks with lakes/reservoirs transport sediment, propagate geomorphic adjustment (Benda et al, ; Czuba & Foufoula‐Georgiou, ; Czuba et al, ; Gran & Czuba, ), process carbon and nutrients (Bertuzzo et al, ; Helton et al, ; Wollheim et al, ), and disperse species (Fuller et al, ). Previous descriptions of river networks and their scaling laws have been instrumental for understanding of geomorphic patterns (Dietrich et al, ; Tarboton et al, ), timing of discharge (Kirkby, ; Mantilla et al, ), and dispersal and production of aquatic insects (Sabo & Hagen, ).…”

Contextualizing Wetlands Within a River Network to Assess Nitrate Removal and Inform Watershed Management

“…For example, some studies have linearly upscaled nitrate removal rates for individual wetlands using hydraulic loading rates but have not taken into account the variability in denitrification rate with organic carbon availability or the dynamics that arise from the hierarchical spatial arrangement of wetlands (Crumpton, ; Crumpton et al, ; Mitsch et al, ). At the network scale, there have been a range of approaches to try to understand nitrogen dynamics—those range from data‐driven empirical methods (such as the SPARROW model; Alexander et al, ; Smith et al, ) to multiparameter process‐based modeling (e.g., Botter et al, ; Donner et al, ) and many approaches in between (Alexander et al, ; Helton et al, ; Seitzinger et al, ; Wollheim et al, ). However, these approaches consider only the river network and do not explicitly incorporate wetlands, which due to their connectivity to the river network change the system biogeochemistry spatially and over time.…”

Generation and application of river network analogues for use in ecology and evolution

“…spreading of human [Bertuzzo et al, 2010;Gatto et al, 2013;Mari et al, 2019] and animal [Carraro et al, 2018] waterborne pathogens; ecosystem processes, such as carbon [Bertuzzo et al, 2017;Koenig et al, 2019] and nitrogen cycling [Helton et al, 2018]; migration fronts of human populations [Campos et al, 2006]; cross-ecosystem subsidies [Harvey et al, 2019]; riverine biodiversity patterns from a theoretical viewpoint [Muneepeerakul et al, 2019] or by means of mesocosm experiments [Carrara et al, 2012[Carrara et al, , 2014Harvey et al, 2018].…”

“…However, this high biodiversity, as well as the associated ecosystem functions, are threatened by various anthropogenically induced causes, including pollution, biological invasions, or damming and modification of the network structure [Vörösmarty et al, 2010;Darwall et al, 2018]. An understanding of many of these processes requires a spatially explicit approach, such as how pollution and chemicals are transported in riverine networks [Helton et al, 2018], how organisms spread along rivers and invade riverine ecosystems [Mari et al, 2014;Giometto et al, 2017], or how the modification of network structures across drainage basins affects local diversity [Leuven et al, 2009]. Consequently, there has been a rapid increase in ecological and evolutionary studies considering the effect of river-like network structures on ecological dynamics over the last two decades [Fagan, 2002;Campbell Grant et al, 2007], paralleled by an increase in methodological tools to analyse such spatial datasets [Muneepeerakul et al, 2008;Rodriguez-Iturbe et al, 2009;Peterson et al, 2013;Welty et al, 2015;Duarte et al, 2019;Rinaldo et al, 2020].…”