Influence of Hydrological Perturbations and Riverbed Sediment Characteristics on Hyporheic Zone Respiration of CO₂ and N₂

Abstract

Rivers in climatic zones characterized by dry and wet seasons often experience periodic transitions between losing and gaining conditions across the river‐aquifer continuum. Infiltration shifts can stimulate hyporheic microbial biomass growth and cycling of riverine carbon and nitrogen leading to major exports of biogenic CO2 and N2 to rivers. In this study, we develop and test a numerical model that simulates biological‐physical feedback in the hyporheic zone. We used the model to explore different initial conditions in terms of dissolved organic carbon availability, sediment characteristics, and stochastic variability in aerobic and anaerobic conditions from water table fluctuations. Our results show that while highly losing rivers have greater hyporheic CO2 and N2 production, gaining rivers allowed the greatest fraction of CO2 and N2 production to return to the river. Hyporheic aerobic respiration and denitrification contributed 0.1–2 g/m2/d of CO2 and 0.01–0.2 g/m2/d of N2; however, the suite of potential microbial behaviors varied greatly among sediment characteristics. We found that losing rivers that consistently lacked an exit pathway can store up to 100% of the entering C/N as subsurface biomass and dissolved gas. Our results demonstrate the importance of subsurface feedbacks whereby microbes and hydrology jointly control fate of C and N and are strongly linked to wet‐season control of initial sediment conditions and hydrologic control of seepage direction. These results provide a new understanding of hydrobiological and sediment‐based controls on hyporheic zone respiration, including a new explanation for the occurrence of anoxic microzones and large denitrification rates in gravelly riverbeds.

Hydrogeologic Controls of Surface Water‐Groundwater Nitrogen Dynamics Within a Tidal Freshwater Zone

“…Simulations with large NO 3 − accumulation also tend to have relatively large maximum specific nitrification rates ( V nit ) and small maximum specific denitrification rates ( V den ), but the inclusion of these parameters does not significantly alter the trend in Figure . It is worth noting that the traditional Damköhler number, which represents the balance between reaction and transport rates, is not a good predictor of equilibrium NO 3 − concentrations near the water table, at least in its simplest form ( V nit /α U , L ), though it has been used in other modeling studies to describe general reactive nitrogen transport behavior (e.g., Marzadri et al, ; Newcomer et al, ; Zarnetske et al, ). In this box model framework, the Damköhler number most aptly describes the efficiency with which ammonium and oxygen are converted to NO 3 − within the upper reservoir before export to the lower reservoir, rather than the equilibrium NO 3 − concentration.…”

Reactive Transport of Nutrients and Bioclogging During Dynamic Disconnection Process of Stream and Groundwater

“…The water table declines are manifested by imposing a gradually lowering head boundary on the bottom of domain as done by Newcomer et al (, ). The dropping head boundary at the bottom is a representation of the effects of long‐period drought after monsoons or pumping (Newcomer et al, , ; Ulrich et al, ).…”

“…For an SW‐GW system with a static streambed, Brunner, Cook, and Simmons () proposed a 1‐D criterion for potential disconnection based on a typical subsurface two‐layer (including a homogeneous clogging layer/streambed and a homogeneous aquifer) system: where K sb (L/T) and K sa (L/T) are the saturated hydraulic conductivities of the streambed and the aquifer, respectively; M sb (L) denotes the thickness of the streambed/clogging layer; and d (L) represents the stream depth. To ensure the occurrence of disconnection as GW tables were gradually lowered, the combinations of hydraulic properties that fulfill the disconnection criterion were often adopted as an initial status to investigate various transient behaviors of disconnection in previous studies which assumed a static streambed (e.g., Banks et al, ; Brunner, Simmons, & Cook, ; Brunner, Cook, & Simmons, ; Rivière et al, ; Shanafield et al, ) or a dynamic streambed (e.g., Newcomer et al, , ). Differing from those studies, we used an initial uniform subsurface system in this study to explore if the clogging layer can naturally develop without a prior clogging layer and if the 1‐D disconnection criterion (Brunner, Cook, & Simmons, ) is necessary to be fulfilled by initial systems to make disconnection taking place in SW‐GW systems with dynamic streambeds.…”

“…Especially, in wastewater effluent‐dominated and dependent streams that are increasingly commonly seen in arid and semiarid regions (Berestov et al, ; Brooks et al, ; Juanico & Friedler, ), high nutrients loading in the SW promotes the accumulation of biomass in pores of sediments and therefore the development of a clogging layer near the surface of a streambed. Consequentially, the permeability of streambed sediments is decreased by several orders of magnitude (Aubeneau et al, ; Battin & Sengschmitt, ; Blaschke et al, ; Caruso et al, ; Newcomer et al, , ; Treese et al, ; Ulrich et al, ). Therefore, in effluent‐dominated systems, the exchange flux between SW and GW is largely determined by the streambed bioclogging process (Treese et al, ).…”

“…However, despite of its importance, none of prior studies quantitatively analyzed the dynamic bioclogging‐disconnection process with simultaneously considering the interactions and feedbacks within a streambed. In this study, inspired by previous field studies (Greskowiak et al, ; Massmann et al, ; Newcomer et al, , ; Treese et al, ; Ulrich et al, ), we developed an innovative model that depicts the processes of bioclogging‐disconnection and captures the interactions and feedbacks between variably saturated flow beneath streams, microbial growth, and microbial metabolism of nutrients. This new model includes the fundamental processes that are conceivable during a bioclogging‐disconnection cycle and thus is expected to be closer to physical reality of the problem.…”