Machine-learning predictions of groundwater specific conductance in the Mississippi Alluvial Plain, south-central United States, with evaluation of regional geophysical aerial electromagnetic data as explanatory variables

Automated construction of Streamflow-Routing networks for MODFLOW—Application in the Mississippi Embayment region

“…Although the SFR Package allows for input of both K v and thickness (m), these parameters are often not well understood at the scale of the model, and simulation of interactions between groundwater and surface water often lump them together conceptually into a resistance (m/K v ; units of days) or leakance (K v /m; units of days −1 ) term that describes the total resistance to flow between the aquifer and a surface water body. Initial leakance values for the SFR packages described here were derived from the results of waterborne electrical resistivity surveys of streams in the MERAS region (Killian, 2018;Adams and others, 2019). The results of Adams and others (2019) consist of depth-integrated estimates of streambed K v (see Killian, 2018 for methodology) and the total survey depth (the thickness of sediments contributing to the K v values) at point locations along the surveyed streams.…”

“…Initial leakance values for the SFR packages described here were derived from the results of waterborne electrical resistivity surveys of streams in the MERAS region (Killian, 2018;Adams and others, 2019). The results of Adams and others (2019) consist of depth-integrated estimates of streambed K v (see Killian, 2018 for methodology) and the total survey depth (the thickness of sediments contributing to the K v values) at point locations along the surveyed streams. Leakance values were computed from these data at each survey point.…”