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Using GIS and Digital Terrain Data to Model Groundwater Interaction in Prairie Wetlands
Research : Prairie potholes are water-holding depressions of glacial origin in the northern portion of the Great Plains. They are significant hydrologic features because of the role they play in flood control, water supply, and biological activity of prairie communities. This role is often a function of the existing water balance within the wetland. Hydrologic investigations (e.g. Shjeflo, 1968; Woo and Roswell, 1992) found that direct precipitation and spring runoff from snowmelt were the major sources of water supply for prairie potholes, while evapotranspiration was the major cause of water loss. Their results indicated that northern prairie potholes have a negative water balance with respect to the atmosphere. With more water leaving than entering from the atmosphere the persistence of a prairie pothole in the landscape is directly related to its groundwater budget (Sloan, 1972). An understanding of groundwater dynamics has important applications in predicting the spatial and temporal distribution of wetlands. The thesis is focused on working with spatial datasets and groundwater modeling (Gerla, 1999) to explain the distribution and persistence of wetlands within the upper Turtle River drainage basin in Grand Forks and Nelson counties, North Dakota. Chris Laveau is working on a model to describe, quantitatively, the spatial and temporal distribution of prairie potholes in the upper Turtle River watershed. Gerla (1999) used a method of integrating digital terrain data with groundwater modeling to estimate the local configuration of the water table. The estimation technique combines the use of digital elevation models (DEMs) with numerical modeling to solve the groundwater equation for transient, unconfined flow. The thesis is taking the next steps with this initial research. The model has been redesigned to incorporate the heterogeneity of hydraulic conductivity. The model output on groundwater conditions is being quantitatively compared to field data using a statistical program. The current goal is to incorporate the statistical program into the groundwater model. The result will be a groundwater model with input parameters derived from readily available spatial datasets and output that is immediately and quantitatively compared to field data. Presentations: University of North Dakota RNEST Biocomplexity Workshop (2003) -Speaker 4th Biennial ND/SD Joint EPSCoR Conference (2003)
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