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Community <br />Supplied by <br />Another <br />Community <br />No Wells <br />Planned <br />Locations and <br />Sources Updated <br />Locations and Sources <br />Same as Metro Model 2 <br />Waconia <br />X <br />Watertown <br />X <br />Wayzata <br />X <br />West St. Paul <br />X <br />White Bear Lake <br />X <br />White Bear Twp. <br />X <br />Willernie <br />X <br />X <br />Woodbury <br />X <br />Woodland <br />X <br />Business as Usual <br />This scenario was designed to test the hypothesis that, given projected demands, metropolitan area <br />communities can continue to use water and develop supplies using the traditional assumption of aquifer <br />availability. Due to uncertainty regarding future population, the effectiveness of conservation practices, <br />and climate a 20% increase of municipal water use and a 20% decrease of municipal water use was <br />included in the "Business as Usual" scenario. The 20% increase and decrease was applied to all <br />existing and future municipal wells in the seven -county metropolitan area. <br />Model Uncertainty <br />Groundwater models are used to make decisions, to analyze risk, and to manage water systems. While <br />no model can be 100% correct, when properly constructed and evaluated, a model can be a useful and <br />informative tool. Evaluating the uncertainty that exists within a model reinforces the output from the <br />model and makes it more useable to the end user. <br />Sources of Uncertainty <br />Model uncertainty comes from four main factors: <br />1. Conceptual framework <br />2. Model parameter <br />3. Calibration <br />4. Predictive <br />In the Metro Model 3, key contributors to conceptual framework and model parameter uncertainty <br />include old geologic atlases. While the geology hasn't changed in the past 20 years, we are now able to <br />better map the geology of the area. Our evolving understanding about fault systems is one example of <br />uncertainty in our conceptual framework. The following county geologic atlases are over 20 years old: <br />• Dakota <br />• Hennepin <br />• Ramsey <br />• Washington <br />Key contributors to calibration uncertainty include the quality of data in the County Well Index (CWI). <br />CWI was weighted less than other more certain datasets, such as observation wells, but where <br />