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Particle cycling rates in marine systems are difficult to measure directly, but of great interest in understanding how carbon and other elements are distributed throughout the ocean. Here, rates of particle production, aggregation, disaggregation, sinking, remineralization, and transport mediated by zooplankton diel vertical migration were estimated from size-fractionated measurements of particulate organic carbon (POC) concentration collected during the NASA EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) cruise at Station P in summer 2018. POC data were combined with a particle cycling model using an inverse method. Our estimates of the total POC settling flux throughout the water column are consistent with those derived from thorium-234 disequilibrium and sediment traps. A budget for POC in two size fractions, small (1–51 µm) and large (> 51 µm), was produced for both the euphotic zone (0–100 m) and the upper mesopelagic zone (100–500 m). We estimated that POC export at the base of the euphotic zone was 2.2 ± 0.8 mmol m−2 d−1, and that both small and large particles contributed considerably to the total export flux along the water column. The model results indicated that throughout the upper 500 m, remineralization leads to a larger loss of small POC than does aggregation, whereas disaggregation results in a larger loss of large POC than does remineralization. Of the processes explicitly represented in the model, zooplankton diel vertical migration is a larger source of large POC to the upper mesopelagic zone than the convergence of large POC due to particle sinking. Positive model residuals reveal an even larger unidentified source of large POC in the upper mesopelagic zone. Overall, our posterior estimates of particle cycling rate constants do not deviate much from values reported in the literature, i.e., size-fractionated POC concentration data collected at Station P are largely consistent with prior estimates given their uncertainties. Our budget estimates should provide a useful framework for the interpretation of process-specific observations obtained by various research groups in EXPORTS. Applying our inverse method to other systems could provide insight into how different biogeochemical processes affect the cycling of POC in the upper water column. 

Understanding the physics of nitrate contamination in surface and subsurface water is vital for mitigating downstream water quality impairment. Though high frequency sensor data have become readily available and computational models more accessible, the integration of these two methods for improved prediction is underdeveloped. The objective of this study was to utilize high‐frequency data to advance our understanding and model representation of nitrate transport for an agricultural karst spring in Kentucky, USA. We collected 2‐years of 15‐min nitrate and specific conductance data and analyzed source‐timing dynamics across dozens of events to develop a conceptual model for nitrate hysteresis in karst. Thereafter, we used the sensing data, specifically discharge‐concentration indices, to constrain modeled nitrate prediction bounds as well as the uncertainty of hydrologic and nitrogen processes, such as soil percolation and biogeochemical transformation. Observed nitrate hysteresis behavior at the spring was complex and included clockwise (n = 11), counterclockwise (n = 13), and figure‐eight (n = 10) shapes, which contrasts with surface systems that are often dominated by a single hysteresis shape. Sensing results highlight the importance of antecedent connectivity to nitrate‐rich storages in determining the timing of nitrate delivery to the spring. After integrating hysteresis analysis into our numerical model evaluation, simulated nitrate prediction bounds were reduced by 43 ± 12% and parameter uncertainty by 36 ± 20%. Taken together, this study suggests that discharge‐concentration indices derived from high‐frequency sensor data can be successfully integrated into numerical models to improve process representation and reduce modeled uncertainty.

 

The oceans teem with heterotrophic bacterioplankton that play an appreciable role in the uptake of dissolved organic carbon (DOC) derived from phytoplankton net primary production (NPP). As such, bacterioplankton carbon demand (BCD), or gross heterotrophic production, represents a major carbon pathway that influences the seasonal accumulation of DOC in the surface ocean and, subsequently, the potential vertical or horizontal export of seasonally accumulated DOC. Here, we examine the contributions of bacterioplankton and DOM to ecological and biogeochemical carbon flow pathways, including those of the microbial loop and the biological carbon pump, in the Western North Atlantic Ocean (∼39–54°N along ∼40°W) over a composite annual phytoplankton bloom cycle. Combining field observations with data collected from corresponding DOC remineralization experiments, we estimate the efficiency at which bacterioplankton utilize DOC, demonstrate seasonality in the fraction of NPP that supports BCD, and provide evidence for shifts in the bioavailability and persistence of the seasonally accumulated DOC. Our results indicate that while the portion of DOC flux through bacterioplankton relative to NPP increased as seasons transitioned from high to low productivity, there was a fraction of the DOM production that accumulated and persisted. This persistent DOM is potentially an important pool of organic carbon available for export to the deep ocean via convective mixing, thus representing an important export term of the biological carbon pump. 

Nitrogen removal rates can vary with time, space, and external environmental drivers, but are underreported for karst environments. We carried out a multi‐year study of a karst conduit where we: (a) measured inputs and outputs of sediment nitrogen (SN and δ¹⁵N_Sed) and nitrate (NO₃⁻and δ¹⁵N_NO3); (b) developed, calibrated, and applied a numerical model of nitrogen physics and biogeochemistry; and (c) forecasted the impacts of climate and land use changes on nitrate removal and export. Data results from conduit inputs (SN = 0.43% ± 0.07%, δ¹⁵N_Sed = 5.07‰ ± 1.01‰) and outputs (SN = 0.36% ± 0.09%, δ¹⁵N_Sed = 6.45‰ ± 0.71‰) indicate net‐mineralization of SN and increase of δ¹⁵N_Sed(p < 10⁻²). However, δ¹⁵N_Sedincrease cannot be explained by SN mineralization alone and is instead accompanied by immobilization of isotopically heavier mineral nitrogen (δ¹⁵N_NO3 = 11.25‰ ± 6.96‰). Modeled SN and δ¹⁵N_Sedsub‐routines provided a boundary condition for DIN simulation and improved NO₃⁻model performance (from NSE = 0.06 to NSE = 0.68). Modeled spatial zones of removal occur in close proximity to conduit entrances, where deposition of labile organic matter promotes a three‐fold increase in denitrification (∼60 mg N m⁻² d⁻¹). Modeled temporal periods of removal occur during the dry‐season where longer residence times cause up to 90% removal of NO₃⁻inputs. Projected effects of environmental drivers suggest an increase in denitrification (+14.1%); however, this removal is largely offset by greater nitrate soil leaching (+28.1%) from wetter regional climate. Results suggest that conduits underlying mature karst terrain experience spatiotemporal removal gradients, which are modulated by solute and sediment delivery.

 

Abstract Purpose The equilibrium sediment exchange process is defined as instantaneous deposition of suspended sediment to the streambed countered by equal erosion of sediment from the streambed. Equilibrium exchange has rarely been included in sediment transport studies but is needed when the sediment continuum is used to investigate the earth’s critical zone. Materials and methods Numericalmodeling in the watershed uplands and streamcorridor simulates sediment yield and sediment source partitioning for the Upper South Elkhorn watershed in Kentucky, USA.We simulate equilibrium exchange when uplandderived sediment simultaneously deposits to the streambed while streambed sediments erode. Sediment fingerprinting with stable carbon isotopes allowed constraint of the process in a gently rolling watershed. Results and discussion Carbon isotopes work well to partition upland sediment versus streambed sediment because sediment deposited in the streambed accrues a unique autotrophic, i.e., algal, fingerprint. Stable nitrogen isotopes do not work well to partition the sources in this study because the nitrogen isotope fingerprint of algae falls in the middle of the nitrogen isotope fingerprint of upland sediment. The source of sediment depends on flow intensity for the gently rolling watershed. Streambed sediments dominate the fluvial load for low and moderate events, while upland sediments become increasingly important during high flows and extreme events.We used sediment fingerprinting results to calibrate the equilibrium sediment exchange rate in the watershed sediment transport model. Conclusions Our sediment fingerprinting and modeling evidence suggest equilibrium sediment exchange is a substantial process occurring in the system studied. The process does not change the sediment load or streambed sediment storage but does impact the quality of sediment residing in the streambed. Therefore, we suggest equilibrium sediment exchange should be considered when the sediment continuumis used to investigate the critical zone.We conclude the paper by outlining future research priorities for coupling sediment fingerprinting with watershed modeling. 

This paper proposes a new distribution-free model of social networks. The definitions are motivated by one of the most universal signatures of social networks, triadic closure—the property that pairs of vertices with common neighbors tend to be adjacent. Our most basic definition is that of a c-closed graph, where for every pair of vertices u, v with at least c common neighbors, u and v are adjacent. We study the classic problem of enumerating all maximal cliques, an important task in social network analysis. We prove that this problem is fixed-parameter tractable with respect to c on c-closed graphs. Our results carry over to weakly c-closed graphs, which only require a vertex deletion ordering that avoids pairs of non-adjacent vertices with c common neighbors. Numerical experiments show that well-studied social networks with thousands of vertices tend to be weakly c-closed for modest values of c.