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Using shallow groundwater modeling to frame the restoration of a wet prairie in the Oak Openings Region, Ohio, USA: GSSHA model implementation

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Abstract

The hydrologic characteristics of the Oak Openings Region in northwest Ohio, USA, a globally rare ecosystem are not well understood. Currently, the Oak Openings supports globally rare oak savanna and wet prairie habitats. The wet prairies in the region have been drained by ditches and encroached by invasive plants, which have altered the natural flow making it an unusually variable and artificial system. A shallow groundwater model was implemented using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) software to simulate continuous, long-term groundwater and surface water interaction in a small subwatershed in the Oak Openings Region. The implemented GSSHA model simulates physical processes such as infiltration, evapotranspiration, snowmelt, overland flow, and interaction of groundwater with ditches. The model was calibrated using a time series of water table elevations collected in the field. Although the model tends to slightly underestimate water table elevations (mean and standard deviation percent differences of less than 1%), statistical analysis indicate a good fit between observed and simulated water table elevations (Nash Sutcliffe Efficiency Index of 0.78). The model would be a useful tool for the preservation of natural areas such as wet prairies.

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Acknowledgements

The work for this manuscript was performed while the first author, Dayal Buddika Wijayarathne, was a graduate student at Bowling Green State University. Funding from The Nature Conservancy for another project (OHFO-GLRI-BGSU-10/10 − 02) allowed for the collection of field data used in the calibration and testing of the GSSHA model in this study.

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Correspondence to Enrique Gomezdelcampo.

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Wijayarathne, D.B., Gomezdelcampo, E. Using shallow groundwater modeling to frame the restoration of a wet prairie in the Oak Openings Region, Ohio, USA: GSSHA model implementation. Environ Earth Sci 78, 216 (2019). https://doi.org/10.1007/s12665-019-8208-1

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