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• Overall coordination of the Flood DSS effort has been consistent with CDSS, involving RTi and <br />CWCB staff that could efficiently contribute CDSS capabilities to the project. <br />The following sections provide recommendations for implementing a full -scale Flood DSS. A summary <br />of level of effort estimates to implement a full -scale Flood DSS is provided in Section 3.4. <br />3.1 User Needs Assessment <br />To date, the focus of the Flood DSS has been on development of a data clearing house for State personnel <br />and possibly other stakeholders. If the intention of the State is for the full Flood DSS to support <br />stakeholder decision support as well as internal State needs, it is recommended that some form of user <br />needs assessment be conducted. This should enable stakeholders to provide input on the types of <br />decisions they need to make regarding flood hazard and other flood related issues, and the data and data <br />formats that could support these. Such a user needs assessment might begin with a demonstration of the <br />Flood DSS prototype at the Colorado Association of Stormwater and Floodplain Managers (CASFM) <br />Annual Conference and/or other appropriate venues. Based on identified needs, requirements for the <br />system can be defined and prioritized (e.g., "need" versus "want ") in order to guide system development. <br />To facilitate additional needs assessment, the existing prototype Flood DSS should be configured to allow <br />viewing by appropriate persons. This will require implementing some level of security to protect <br />sensitive data, or removing such data from public viewing. <br />3.2 Data Recommendations <br />The foundation of the DSS has been developed for the prototype; consequently, the greatest costs in <br />developing the full Flood DSS will occur for the collection, processing, and integration of data for input <br />into the DSS. Based on prototype Flood DSS data collection efforts, it is clear that costs for complete <br />development of the full Flood DSS will range depending on how ambitious the initial data integration will <br />be. The recommended baseline — incorporation of FIRM/DFIRM (digital FIRM) data for each County — <br />will require a relatively straightforward, and hence relatively inexpensive process of collecting <br />standardized data sets that create few problems with symbolizing and other processing tasks prior to <br />integration. Integration of more disparate data types by County (e.g. "Geologic- Hazard ") is likely to vary <br />county -by- county, and thus require more effort to locate, interpret, and process to format the data into <br />something useful for the DSS. Collectively, data in the "Flood- Outlook" data group represents a <br />relatively high data integration effort, primarily because development of a system(s) will be required in <br />order to transfer, receive, process and integrate data on a regular basis. However, once completed, these <br />capabilities will result in statewide data availability. <br />3.2.1 Data Collection <br />The experience of obtaining prototype data from Larimer County and the City of Fort Collins highlights a <br />number of issues that are useful in planning for development of the full Flood DSS. Firstly, while face - <br />to -face meetings may not always be necessary to obtain data, they often speed the process. These <br />meetings also create opportunities to interact with people such as Susan Hayes (City of Fort Collins <br />Certified Floodplain Manager), who uncovered the 1997 historical flood data. Secondly, not all data will <br />be freely available, at least not without explaining its application — a conversation that can take time. <br />Thirdly, these two agencies are relatively data rich and well resourced with available and well - trained GIS <br />analysts who can smooth the process of identifying and supplying appropriate data and metadata. It is <br />likely that other county and city GIS departments will not be able to handle data requests as effectively, <br />and in some instances may have little or no quality data. Data collection for the full DSS is unlikely to be <br />a trivial task, may require substantial contact and possibly travel time, and may not yield consistent results <br />across the state. <br />Z <br />