Comparing the Biogeochemical Potential of Hyporheic Zones Driven by Different River Morphologies
Abstract
Channel morphology controls the hydrodynamics of hyporheic exchange and its residence times. As a result, it also constrains the hyporheic zone's biogeochemical processes that transform carbon, nutrients, metals, and contaminants and the hyporheic zone's net effect at the local, reach and watershed scales. Previous studies of different morphologies (e.g., meanders, bars, and smaller bedforms such as dunes) have mainly focused on the amount of exchange or, if biogeochemistry was involved, have been specific to a particular morphology. In this work, we present a quantitative intercomparison of the amount of exchange, residence time distributions (RTDs), and biogeochemical potential for four channel morphologies: ripples, dunes, bars, and meander bends. To this end, simple two-dimensional conceptualizations and semi-analytical solutions for the hyporheic zone's flow and transport are used. In general, all morphologies are characterized by heavy-tail RTDs, implying long-term memory to solute inputs. We hypothesize that even though meander bends induce larger hyporheic exchange per unit length of channel and longer residence times, substrate limitations result in less biogeochemical processing when compared with the cumulative effect of multiple bedforms. The models presented are a function of geometric and physical properties easily measured or constrained with field or remote sensing data. The simplicity of this approach allows for practical calculations of the hyporheic zone's exchange and biogeochemical potential over a broad range of scenarios and morphologies, making it a useful tool for experimental design, sampling, and watershed scale assessment.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2013
- Bibcode:
- 2013AGUFM.H31L..06G
- Keywords:
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- 1830 HYDROLOGY Groundwater/surface water interaction;
- 1832 HYDROLOGY Groundwater transport;
- 0412 BIOGEOSCIENCES Biogeochemical kinetics and reaction modeling