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56 The analyses conducted for this plan incorporate the best regionally available technical information to answer questions of water supply availability, and much of it was collected through local studies. The information in this chapter reflects guidance by a wide variety of stakeholders based on issues identified as important at this time. However, uncertainty is a constant factor, several questions remain unanswered, and other questions will inevitably emerge over time. Water supply planning must be done in such a way that the plans can adapt to factors such as climate changes, technology and emerging contaminants, and changing cultural priorities and attitudes. There are different types of uncertainties related to the issues discussed in this chapter. For instance, a distinction can be made between monitoring uncertainty and uncertainty regarding future conditions. Also, science has its limitations when dealing with complex societal problems where there are many system uncertainties, and where facts and values are intertwined. And insights may change over time as new information becomes available. Water suppliers and planners work in a dynamic environment that requires ongoing action, even in face of less than 100% certainty. This process of "learning by doing" has also been called "adaptive management' - a structured, iterative process of decision-making, with a goal of reducing uncertainty via system monitoring. Monitoring uncertainty Monitoring uncertainty generally refers to how well measurements represent real world conditions. Factors that commonly contribute to monitoring uncertainty include imprecise or inaccurate measurement equipment, inadequate measurement frequency, the length of the monitoring record, and the spatial distribution of the monitoring sites. When monitoring data is used to model hydrologic conditions, uncertainty in the data contributes to uncertainty in the model results. Informed decisions must be made about what data to include in model analyses and how to weight data with higher accuracy and precision more heavily than data with greater uncertainty. The process to develop and calibrate the regional groundwater flow model (Metro Model 3) illustrates this approach. For example, multiple water level datasets were used to calibrate the model including well logs reported in the Minnesota County Well Index (CWI), DNR observation wells, and synoptic water level measurements made by the DNR and USGS. Data compiled from CWI have the most inherent error; however they have the largest geographic extent. Data from synoptic water level datasets and DNR observation wells have the least amount of error, but they are not available everywhere. All data was used to calibrate the regional groundwater model, but the CWI data was not weighted as heavily as the higher quality data (Metropolitan Council 2014d). In addition to improving analytical results, a thorough examination of monitoring uncertainty identifies gaps in information where resources can be directed. For example, the process of calibrating Metro Model 3 highlighted the importance of expanding monitoring networks to assess the connection between surface waters and the regional groundwater system. Predictive uncertainty The most common focus for discussions of predictive uncertainty related to this Master Water Supply Plan is the Metro Model 3 (Appendix 3) and water demand projections that the model evaluates (Appendix 2). Metro Model 3 is a tool that supports a flexible process for water suppliers and planners to explore a wide variety of different water supply approaches under a range of potential future conditions. Model uncertainty comes from four main factors: WATER SUPPLY MASTER PLAN- Draft June 2015