Combined Effects of Stream Hydrology and Land Use on Basin‐Scale Hyporheic Zone Denitrification in the Columbia River Basin

Abstract

Denitrification in the hyporheic zone (HZ) of river corridors is crucial to removing excess nitrogen in rivers from anthropogenic activities. However, previous modeling studies of the effectiveness of river corridors in removing excess nitrogen via denitrification were often limited to the reach‐scale and low‐order stream watersheds. We developed a basin‐scale river corridor model for the Columbia River Basin with random forest models to identify the dominant factors associated with the spatial variation of HZ denitrification. Our modeling results suggest that the combined effects of hydrologic variability in reaches and substrate availability influenced by land use are associated with the spatial variability of modeled HZ denitrification at the basin scale. Hyporheic exchange flux can explain most of spatial variation of denitrification amounts in reaches of different sizes, while among the reaches affected by different land uses, the combination of hyporheic exchange flux and stream dissolved organic carbon (DOC) concentration can explain the denitrification differences. Also, we can generalize that the most influential watershed and channel variables controlling denitrification variation are channel morphology parameters (median grain size (D50), stream slope), climate (annual precipitation and evapotranspiration), and stream DOC‐related parameters (percent of shrub area). The modeling framework in our study can serve as a valuable tool to identify the limiting factors in removing excess nitrogen pollution in large river basins where direct measurement is often infeasible.

Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA

“…Lower water velocities may result in higher heterogeneity in subsurface biogeochemistry, including redox (Briggs et al 2013), resulting in more variable (and therefore weaker) allometric behavior. Conversely, strong, Super-linear scaling at highest cumulative HEFs is consistent with previous observations that HEFs control biogeochemical processes in the hyporheic zone, including denitrification (Son et al 2022a).…”

“…However, it remains unknown how, or even if this relationship is transferable to non-idealized cases (real watersheds), or if cumulative hyporheic metabolism exhibits equivalent allometric scaling behavior. Additionally, although we know that watershed characteristics relate to hyporheic metabolic processes, (Son et al 2022a;Buser-Young et al 2023), it is unknown how differences in watershed characteristics within or between watersheds might impact such scaling relationships.…”

Screening and Optimization of Soil Remediation Strategies Assisted by Machine Learning

“…This change diminished with an increasing distance from the river (e.g., GW1). The frequent reversals of groundwater flow directions would increase the exchange of river water with groundwater, promoting the biogeochemical transformation of soil contaminants [58][59][60]. The effect of MNA was simulated to predict the fate and reactive transport of the contaminants under natural conditions.…”

The Fragility of Bedform‐Induced Hyporheic Zones: Exploring Impacts of Dynamic Groundwater Table Fluctuations

“…The hyporheic exchange zone is a dynamic interface connecting rivers and aquifers, playing a critical role in river corridor ecosystem services (Boulton, 2003; Gómez‐Gener et al., 2021; Harvey & Gooseff, 2015; Harvey et al., 2018; Krause et al., 2017, 2022; Stanley et al., 1997). This exchange has traditionally been assumed as a resilient process contributing to the emergence of hotspots and hot moments for biogeochemical processes in river corridors (McClain et al., 2003; Newcomer et al., 2018; Stegen et al., 2016, 2018), leading to significant efforts to understanding its role in nutrient cycling (i.e., carbon, nitrogen, and oxygen), greenhouse gas emissions (Son, Fang, Gomez‐Velez, Byun, & Chen, 2022; Son, Fang, Gomez‐Velez, & Chen, 2022), contaminant fate and transport, and microbiome dynamics over different spatial (Claret & Boulton, 2009; Gomez‐Velez et al., 2015) and temporal (Briggs et al., 2014; Gomez‐Velez et al., 2017; Gu et al., 2012) scales.…”