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3.2.2 Data Availability and Quality <br />FEMA FIRM maps are largely available, while DFIRM mapping in Colorado is in progress under the <br />coordination of the CWCB. FIRM and DFIRM maps have clear and wide utility in flood decision <br />support, and should thus form the core of flood information for the DSS. Digital FIRM maps should be <br />used where DFIRM products are not yet available, thus if FIRM maps have not yet been scanned or <br />digitized for use within a GIS then the local agency might be given assistance to do this via the full Flood <br />DSS implementation. This would be most important if a proposed DFIRM is far from completion. In <br />general, it is expected that the State's relationship with Certified Floodplain Managers (CFMs) across <br />Colorado should greatly assist with the process of collecting and integrating FIRM and DFIRM data into <br />the DSS. CFMs exist in many state and city agencies, and typically have understanding of FIRM - DFIRM <br />data and experience using it in a GIS format. Some discussion may be warranted regarding the inclusion <br />of both FIRM and DFIRM information in the DSS. <br />Other data layers typically available at the county scale, such as "Geologic Hazards" (6b in Table A.1), <br />are far less likely to be of consistent content and quality and may require considerable interpretation and <br />processing to be of value in the DSS. Data layers describing aspects of historical floods in detail such as <br />"Damage Zone Buffer" (3d in Table A.1) are likely to be less common. Disadvantages to including these <br />ad -hoc data are that they will be inconsistent in structure and quality from region to region, and may also <br />be contentious — the Fort Collins data identify flood damage zones (and individual properties) outside of <br />FEMA delineated floodways. Advantages are that they provide an example of the utility of this type of <br />information that may encourage others to collect spatial data in future floods, and may lead to guidelines <br />and standardization of the data produced in this process, enhancing its utility. <br />Data at the statewide scale should generally provide fewer problems in terms of availability and <br />consistent content and quality. State - maintained data such as "DWR Dams" (5a in Table A.1) will only <br />require general maintenance to update for new dams over time, and to update attributes such as hazard <br />status. <br />Incorporating data that require more regular update, specifically layers in the "Flood Outlook" data group <br />(Table A.1), will require development of a process to receive, process, and integrate this information at <br />the appropriate time intervals (see Section 3.3.3 below). In the future, it may be possible to utilize web <br />services to retrieve data from other sites, although such services have limited availability at this time. <br />3.2.3 Digitizing data <br />Digitizing was kept to a minimum for the Flood DSS prototype. However, it is likely that for the full <br />DSS, digitizing FIRMS may be necessary. Counties and cities may have in some cases digitized their <br />own FIRM maps (e.g. City of Fort Collins), although this may be less likely in more rural areas. DFIRMs <br />will of course replace FIRMs, but in several locations these may not be completed for some time. <br />3.2.4 Background Data <br />National Agriculture Imagery Program (NAIP) color aerial photographs are available for the entire state. <br />These data have a one -meter ground sample distance (GSD) with a horizontal accuracy that matches <br />within five meters of a reference orthoimage. These image data are recommended for use in the full <br />Flood DSS. <br />3.2.5 Develop Data Standards for Coordination Between Agencies <br />The focus of several important data layers is county or municipality. However, counties and <br />municipalities have varying levels of resources to apply to flood - related spatial data. If the Flood DSS is <br />to serve as a clearinghouse for flood - related data, it will be important to develop standards for data <br />(format, attributes, metadata, etc.), and procedures to process and exchange the data. For example, well- <br />10 <br />