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RANDOMLY LAYERED FLUVIAL SEDIMENTS INFLUENCED GROUNDWATER-SURFACE WATER INTERACTION
Abstract
The sedimentological sequences along the lower sections of rivers have been created by the alluviation of meandering rivers in historical times. For the rehabilitation measures (focusing on water replenishment to oxbows) with the purpose of enhancing ecosystem services, the detailed hydrogeological study of alluvial deposits and soils is indispensible. Our investigation covers the area lying between the CпїЅn-Szaporca oxbow lake system, under rehabilitation, and the Drava River, which controls groundwater flow. The sedimentological sequences were investigated using satellite images, GPR profiles, boreholes for revealing layering and in situ studies of hydraulics of seepage. The data obtained provide the background for hydraulic modelling. The origin and extension of layers was identified through geomorphological interpretation (distinguishing between point bar, dune and other deposits). However, from the viewpoint of hydraulic modelling, a random spatial pattern of such deposits was reconstructed. Their hydrological properties show a wide range (k = 10-4пїЅ10-7). Running several scenarios in MODFLOW, it was found that the application of a multilayered structure provided the most realistic result. Subsurface water monitoring and previous calculations also support that multilayered structures are characterized by considerable water retention capacity. The theory for creating our modelling space is that the generated (sub) structures which characterized by different hydraulic properties and formed during the sedimentation are analogous with fluvial features such as point bars, traces of lateral migration of river channel, and lateral accretion. The results of running of scenarios characterized by extended continuous layering in whole model space were shown to be unsuccessful. The multilayered scenario result shows that the difference between input to groundwater (i.e. seepage from the oxbow lakes) and output to the main river channel amounts to 67 % on the average.
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