Sediments from a Colorado River floodplain that are rich in organic matter have a 70% higher potential for nitrate removal than background sediments.
Biogeochemical "hot spots" are regions with disproportionally high reaction rates relative to the surrounding spatial locations, while "hot moments" are short periods of time manifesting high reaction rates relative to longer intervening time periods. These hot spots and hot moments together affect ecosystem processes and are considered ‘‘ecosystem control points”. However, relatively few studies have incorporated hot spots and/or hot moments in numerical models to quantify their aggregated effects on biogeochemical processes at floodplain and riverine scales. This study quantifies the occurrence and distribution of nitrogen hot spots and hot moments at a Colorado River floodplain site in Rifle, Colorado, using a high-resolution, three-dimensional (3D) flow and reactive transport model.
This study was used to assess the interplay between dynamic hydrologic processes and organic matter–rich, geochemically reduced sediments (i.e., “naturally reduced zones”) within the Rifle floodplain and the impact of hot spots and hot moments on nitrogen cycling at the site using a fully coupled, high-resolution reactive flow and transport simulator. Simulation results indicated that nitrogen hot spots are not simply hydrologically driven, but occur because of complex fluid mixing, localized reduced zones, and biogeochemical variability. Furthermore, results indicated that chemically reduced sediments of the Rifle floodplain have 70% greater potential for nitrate removal than nonreduced zones.
Although hot spots and hot moments are important for understanding large-scale coupled carbon and nitrogen cycling, relatively few studies have incorporated hot spots and hot moments in numerical models, especially not in a 3D framework, thereby neglecting the potential effects of fluid mixing on the biogeochemistry. In this study, scientists from the Lawrence Berkeley National Laboratory integrated a complex biotic and abiotic reaction network into a high-resolution, 3D subsurface reactive transport model to understand key processes that produce hot spots and hot moments of nitrogen in a floodplain environment. The model was able to capture the significant hydrological and biogeochemical variability observed across the Rifle floodplain site. In particular, simulation results demonstrated that hot and cold moments of nitrogen did not coincide in different wells, in contrast to flow hydrographs. This has important implications for identifying nitrogen hot moments at other contaminated sites and/or mitigating risks associated with the persistence of nitrate in groundwater. Model simulations further demonstrated that nitrogen hot spots are both flow related and microbially driven in the Rifle floodplain. Sensitivity analyses results indicated that the naturally reduced zones (NRZs) have a higher potential for nitrate removal than the non-NRZs for identical hydrological conditions. However, flow reversal leads to a reduction in nitrate removal (approximately 95% lower) in non-NRZs, whereas the NRZ remains unaffected by the influx of the river water. This study demonstrates that chemolithoautotrophy, the microbial processes responsible for iron ion Fe+2 and sulfur ion S–2 oxidation, is primarily responsible for the removal of nitrate in the Rifle floodplain.
BER Program Manager
U.S. DOE Office of Biological and Environmental Research, SC-23.1
Susan S. Hubbard
Lawrence Berkeley National Laboratory
Support was by the Office of Biological and Environmental Research (BER), within the U.S. Department of Energy (DOE) Office of Science, as part of its Subsurface Biogeochemical Research (SBR) program under DE-AC02-05CH11231 to Lawrence Berkley National Laboratory (Genomes to Watershed Scientific Focus Area)), and by BER Small Business Innovation Research under DE-SC0009732.
Dwivedi, D., B. Arora, C.I. Steefel, B. Dafflon, and R. Versteeg. “Hot spots and hot moments of nitrogen in a riparian corridor.” Water Resources Research 54(1), 205–22 (2018). [DOI:10.1002/2017WR022346].
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