3.3 Vulnerability Assessment

Transcription

1 3.3 Vulnerability Assessment Overview Vulnerability describes an asset s level of exposure or susceptibility to damage from natural hazards. The vulnerability of an asset such as residential and commercial property, critical facilities or infrastructure depends on a variety of factors, including its construction, contents and/or economic value of its functions. A vulnerability assessment provides policy-makers, emergency managers and planners with information on the extent or severity of loss of life, injuries and/or property damage that may result from a hazard event of a given intensity in a given area. A vulnerability assessment includes an inventory of assets and an estimation of potential losses. The vulnerability assessment attempts to combine information related to hazard identification with an inventory of commercial property, residential property, public facilities (including critical facilities) and infrastructure. Vulnerability assessments also include estimates of potential injuries, property damage and economic losses from hazards in a geographic area, measured in dollars. Basic vulnerability assessments were developed for Cass, Clay, Jackson, Platte and Ray counties for the hazards addressed in this plan. These vulnerability assessments provide estimates of the number of people, buildings and infrastructure potentially at risk from a natural hazard in each county, as well as the approximate value of those buildings and infrastructure. The vulnerability assessments divide each county by building-stock exposure category, which includes total valuations for the following types of property: Residential Commercial Industrial Agriculture Religion Government Education Table 3.32 shows the overall vulnerability assessments for Cass, Clay, Jackson, Platte and Ray counties and their incorporated villages and cities. Overall vulnerability is synonymous with overall risk discussed in Section 3.1, and calculated according to the methodologies outlined therein. Detailed vulnerability assessments are discussed in Sections 3.3. Mid-America Regional Council January 2010

2 TABLE 3.32: VULNERABILITY ASSESSMENT (OVERVIEW) HAZARD Jurisdiction Tornado Floods Winter Weather Drought Heat Wave Earthquake Dam Failures Wildland Fire HAZMAT Incident Emerging Infectious Disease Civil Disorder Mass Trans Accidents Cass County H H H M M M L M M M L L Clay County H M M L L L L L L L M M Jackson County H H M M H L M L M M L M Platte County H H M L L L L L M L L M Ray County H H M L L L L L M L L L Blue Springs M L M N/A L N/A N/A N/A L L N/A N/A Garden City H M H M M L N/A M M L L L Gladstone H M H M H M N/A N/A M H H M Grandview H M M M M N/A N/A N/A M N/A L M Houston Lake H M M L L L M L H L L L Independence H H H L H L L L H M L M Kansas City, MO H H H N/A H M M N/A H H M H Kearney M L M M M L L L L L L L Lee s Summit M M M L L L M L M M L M Liberty H M M L L L L L L M L M Loch Lloyd H L M L L L L L L M L L North Kansas City H H H L L N/A N/A H M L M M Northmoor H N/A H N/A N/A N/A N/A N/A N/A N/A N/A N/A Oak Grove H H H L M M L M H H L H Parkville M M M L L L M L M M L L Peculiar M L M L L L N/A N/A L L N/A N/A Platte City M M M L M L L L M L L L Pleasant Hill H H H H H M H M H M L M Mid-America Regional Council January 2010

3 TABLE 3.32: VULNERABILITY ASSESSMENT (OVERVIEW) HAZARD Jurisdiction Tornado Floods Winter Weather Drought Heat Wave Earthquake Dam Failures Wildland Fire HAZMAT Incident Emerging Infectious Disease Civil Disorder Mass Trans Accidents Raytown M L M L L L L L M L L L Raymore H M M M M L L L M M L L Riverside H H M L M L L L M M L L Smithville H L H H H N/A N/A N/A M L L L Sni Valley FPD H H H L M M L M H H L H Strasburg M H N/A N/A M L N/A N/A H N/A N/A N/A Tracy H H M M M N/A N/A N/A N/A N/A N/A N/A Weatherby Lake H H H H M L M L L L L L Weston M M M M M L L L L L L L School Districts Archie R-V H H H M M L L L M M L L Fort Osage R-I H M H L L L L L H M L M Harrisonville M L M N/A N/A M N/A N/A M H L H Lone Jack C-6 M L L L L L L L L L L L Park Hill M M M L L L L L M M L M Pleasant Hill R-III Sherwood Cass R-VIII H L H L L L L L M M H M West Platte R-II H M M L M L L L M M L M Metropolitan Community Colleges H N/A H N/A L N/A N/A N/A M L L L Mid-America Regional Council January 2010

4 3.3.2 Structures and Critical Facilities Identification Table 3.33 below lists the total valuations for the seven categories of building stock in each county. This information is taken from HAZUS-MH runs conducted by SEMA. TABLE 3.33: BUILDING STOCK EXPOSURE (All values thousands of $'s) County Residential Commercial Industrial Agricultural Religion Government Education Total Cass 337,075 68,568 15,449 24,665 4,214 11,300 4,230,985 4,692,256 Clay 10,567,047 1,709, ,302 15, ,555 17,393 33,849 12,766,168 Jackson 35,387,349 7,562,801 1,263,440 53, , , ,461 45,083,895 Platte 4,330, ,756 76,277 7,668 26,435 5,450 20,094 4,908,320 Ray 1,081,798 78,844 44,222 4,787 13,561 3,101 4,448 1,230,761 Source: HAZUS-MH Figures 3.45 and 3.46 are maps for key community and critical facilities in the region. These are supplemental the information provided in the regional profile. Appendix C contains further detailed maps by facility. Source: MARC Research Department Figure 3.45: Community Facilities in the Kansas City Region Mid-America Regional Council January 2010

5 Figure 3.46: Critical Facilities in the Kansas City Region Source: MARC Research Department Potential Loss Estimates Loss estimates provided here are based on available data, and the methodologies applied resulted in an approximation of risk. The effect of inconsistencies in historical records, lack of scientific consensus and a wide array of environmental variables on hazards makes estimating potential losses difficult. Uncertainties also result from approximations and simplifications, such as those made as a result of incomplete inventories, demographics or economic parameters, which are necessary for a comprehensive analysis. Loss estimations were conducted for all the hazards presented in this plan based on the best available data and methodologies. For floods and earthquakes, FEMA s Hazards United States Multi-Hazards (HAZUS-MH), loss-estimation software was used. To achieve a loss estimation for all other hazards, a statistical risk-assessment methodology, which is described in greater detail below, was used where applicable. These approaches provide estimates for potential impacts by using a common, systematic framework for evaluation. Insurance claims for drought losses were also analyzed. Mid-America Regional Council January 2010

6 HAZUS-MH HAZUS-MH is FEMA s standardized loss-estimation software program built upon an integrated geographic information system. The HAZUS-MH risk assessment methodology is parametric in that distinct hazard, vulnerability and inventory parameters (earthquake spectral ordinates, building construction, and building classes) were modeled using the HAZUS-MH software to determine the impact on the built environment (damage and losses). This risk assessment applied HAZUS-MH to produce regional profiles and estimate losses for two hazards: earthquakes and riverine flooding. This information was provided by SEMA and derived from inventory data associated with FEMA s loss estimation software HAZUS-MH MR 2 (May 2006). Building inventory counts are based on the 2000 census data adjusted to 2002 numbers using the Dun & Bradstreet Business Population Report. Inventory values reflect 2005 valuations, based on RSMeans (a supplier of construction cost information) replacement costs. Population counts are 2005 estimates from the U.S. Census Bureau. (Missouri State Mitigation Plan) Statistical Risk Assessment Methodology The statistical risk-assessment methodology was applied to analyze five of the remaining 10 hazards not covered by HAZUS-MH. This approach could not be used for drought, hazardous materials incidents, emerging infectious disease, mass transportation accidents or civil disorder due to specific data set limitations. Risk assessment methodologies for these hazards are discussed separately in the sections that follow. The statistical risk assessment methodology is modeled after the one used in the Missouri State Hazard Mitigation Plan and was modified based on local information. Historical data for each hazard (where available and appropriate) is used, and statistical evaluations are performed using manual calculations. Specific data limitations regarding frequency of occurrence are discussed in Section 3.1. The general steps used in the statistical risk assessment methodology are summarized below: Compile data from national and local sources Conduct statistical analysis of data to relate historical patterns within data to existing hazard models (minimum, maximum, average and standard deviation) Categorize hazard parameters for each hazard to be modeled Develop model parameters based on analysis of data, existing hazard models and risk engineering judgment Apply hazard model including: Analysis of frequency of hazard occurrence Analysis of intensity and damage parameters of hazard occurrence Analysis of total exposure (building stock exposure) Development of simple damage function to relate hazard intensity to a level of damage (e.g., one tornado = a certain monetary amount of estimated damage) Development of annualized loss estimates (Missouri State Hazard Mitigation Plan) Economic Impact A portion of the overall loss estimation calculation is conducted using annualized losses. In general, presenting results in the annualized format is useful for three reasons: Contribution of potential losses from all (long-term) future disasters is accounted for with this approach In this format, results for different hazards are readily comparable and therefore easier to rank When evaluating mitigation alternatives, use of annualized losses is an objective approach Mid-America Regional Council January 2010

7 The annualized loss expectancy (ALE) is the estimated expected long-term value of losses to the general building stock for a specified geographic area (i.e., county). The ALE factors in historic patterns of frequent small events with infrequent larger events to provide a balanced presentation of the risk. In HAZUS-MH, losses are annualized for earthquake return periods of 100; 250; 500; 750; 1,000; 1,500; 2000 and 2,500 years. The ALE is available only through HAZUS-MH. (Missouri State Hazard Mitigation Plan) Social Vulnerability A Social Vulnerability Index compiled by the Hazards and Vulnerability Research Institute in the Department of Geography at the University of South Carolina measures the social vulnerability of U.S. counties to environmental hazards for the purpose of examining the differences in social vulnerability among counties. Based on national data sources, primarily the 2000 census, it synthesizes 42 socioeconomic and built-environment variables (e.g., social vulnerability) that research literature suggests contribute to reduction in a community s ability to prepare for, respond to and recover from hazards. Eleven composite factors were identified that differentiate counties according to their relative level of social vulnerability: personal wealth, age, density of the built environment, single-sector economic dependence, housing stock and tenancy, race (African-American and Asian), ethnicity (Hispanic and Native American), occupation, and infrastructure dependence. (Missouri State Hazard Mitigation Plan) The index can be used by the region to help determine where social vulnerability and exposure to hazards overlaps. Although this information isn t considered in the final loss estimation calculations, it is provided here as a reference for additional planning considerations. Figure 3.47illustrates Missouri s geographic variation in social vulnerability. Figure 3.47: Social Vulnerability to Environmental Hazards, County Comparison within the State, 2000 Mid-America Regional Council January 2010

8 Derivation of Statistical Risk Assessment Methodology Loss Estimation Calculation The final derivation of loss estimation using the statistical risk assessment methodology takes the information presented in Sections 3.1 and 3.2, plus the Economic Impacts discussed above, and synthesizes them into an overall loss estimation. In order to include a viable range of potential loss, two separate calculations were performed, one for a maximum loss estimation and one for a minimum loss estimation. Maximum loss estimation was determined by averaging four variables: Overall Risk (probability and severity), Magnitude, Total Exposure (value of all assets) and Total Population. Maximum loss assumes total vulnerability of all assets and population of a county to a hazard event. Since it is unlikely that an entire county will be affected in a hazard event, the variables of Overall Risk and Magnitude are introduced here to provide a realistic outer bound to maximum loss estimation. The variables were assigned simple numeric values for ease of calculation as follows: Overall Risk (probability and severity): Low = 1; Medium = 2; High = 3 Magnitude (percentage of county affected): Less than 10 percent = 1; 10 to 25 percent = 2; 25 to 50 percent = 3; More than 50 percent = 4 Total Exposure (dollar value, thousands): <1,000,000 = 1; 1,000,000 10,000,000 = 2 >10,000,000 = 3 Total Population (in thousands): = 1; = 2; >500 = 3 The solution was then qualified into a range representing maximum loss estimation potential as follows: = Low = Medium Greater than 2.5 = High Table 3.34 below is a sample maximum loss estimation. Overall Risk (probability and severity) TABLE 3.34: SAMPLE MAXIMUM LOSS ESTIMATION Magnitude Total (percent of Total Exposure Population county ($ value, (in Maximum Loss affected) thousands) thousands) Estimation Med = % = 3 11,000,000 = = = Medium Minimum loss estimation attempts to quantify the average likely damage of a hazard based on historic damages and loss ratios. The minimum loss estimation provides a realistic lower bound for which damage can be expected. The minimum loss was calculated using the same basic formula as the maximum loss. However, averages for magnitude, loss and deaths/injuries from historical events were used instead of the total county-wide maximum loss estimations values. Averages were desirable because they provided real numbers while incorporating available data. Overall risk remained constant from the maximum loss calculations. The ranges used for minimum loss estimation are on the same general order as those used for maximum loss estimation. However, the ranges have been scaled, where appropriate, based on the available data. They are described as follows: Mid-America Regional Council January 2010

9 Overall Risk (probability and severity): Low = 1; Medium = 2; High = 3 Magnitude (percentage of county affected): Less than 10percent = 1; 10 to 25 percent = 2; 25 to 50 percent = 3; More than 50 percent = 4 Annualized Loss (crop/property $ value, thousands): <100= 1; = 2; >500 = 3 Average Deaths/Injuries (summed together): <1 = 1; 1-5 = 2; >5 = 3 The solution was then qualified into a range representing minimum loss estimation potential as follows: = Low = Medium Greater than 2.5 = High Table 3.35 below is a sample minimum loss estimation. Overall Risk (probability and severity) TABLE 3.35: SAMPLE MINIMUM LOSS ESTIMATION Average Magnitude Annualized (percent of Loss ($ Average county value, Deaths and Minimum Loss affected) thousands) Injuries Estimation Medium = 2 <1 % = 1 $289 = 2.39 = = Low By comparing maximum and minimum loss estimations, mitigation planners will be provided with a reasonable target on which to focus their planning efforts. For instance, if the maximum loss estimation for a given hazard is high and the minimum loss estimation is medium, the logical conclusion should be that the hazard is of sufficient concern to warrant high-priority mitigation actions. Likewise, if the maximum loss estimation is medium and the minimum is loss estimation is low, it is logical to assume the hazard warrants a lower-priority rating for mitigation actions. When maximum and minimum loss estimations diverge, the likely threat of the hazard can be assumed to reside somewhere in between, qualifying a medium-priority ranking for hazard mitigation actions. Data limitation: The data obtained from HAZUS-HM software for earthquakes and floods relied upon valuation and population estimates. For consistency of data across all hazard spectrums, these same estimates were used for the remaining 10 hazards. Mid-America Regional Council January 2010

10 Tornado Risk Assessment Note: For background information about tornadoes, see the profile in Section A statistical risk assessment methodology was used to determine tornado loss potential by county. This methodology used National Oceanic and Atmospheric Administration (NOAA) data for tornado losses between 1950 and March It is important to realize that one limitation to this data is that many tornadoes that might have occurred in uninhabited areas, as well as some inhabited areas, have not been reported. Additionally, the random nature of tornadoes makes accurate predictions impossible, therefore only a generic, statistical methodology could be used. The NOAA data for tornadoes required little cleanup for use in the final calculations. The data is already adjusted for inflation and other economic effects. Therefore, no additional compensation was required. NOAA tornado events are defined as follows: Tornadoes may contain multiple segments. A tornado that crosses a county line or state line is considered a separate segment. Also, a tornado that lifts off the ground for less than five minutes or 2.5 miles is considered a separate segment. If the tornado lifts off the ground for greater than five minutes or 2.5 miles, it is considered a separate tornado. Tornadoes reported in Storm Data and the Storm Events Database are in segments. (NOAA Web site) Average magnitude for minimum loss estimation was calculated by multiplying the length (miles) of the tornado by its width (miles) then dividing by the total county area (square miles). Total county areas are: Cass = square miles Clay = square miles Jackson = square miles Platte = square miles Ray = square miles Tables 3.36 and 3.37 illustrate the maximum and minimum loss estimates for tornadoes for the fivecounty region. TABLE 3.36: MAXIMUM LOSS ESTIMATION, TORNADOES Magnitude (percent of county affected) Total Exposure ($ value, thousands) County Overall Risk (probability Total Population (in and severity) thousands) Cass High = 3 4 4,692,256=2 98 =1 Medium Clay High = ,766,168=3 215 = 2 High Jackson High = ,083,895=3 668 = 3 High Platte High = 3 4 4,908,320=2 85 = 1 Medium Ray High = 3 4 1,230,761=2 23 = 1 Medium Maximum Loss Estimation Sources: NOAA National Climatic Data Center Storm Event Database and HAZUS-MH, 2005 (Total Exposure 2005) Mid-America Regional Council January 2010

11 TABLE 3.37: MINIMUM LOSS ESTIMATION, TORNADOES Average Magnitude (percent of county affected) County Overall Risk Annualized (probability and severity) Loss ($ value, thousands) Average Deaths and Injuries Cass High = = 3.5 =1 Medium Clay High = 3 1 1,935 = 3.5 =1 Medium Jackson High = =2 3.7 = 2 Medium Platte High = =3.29 = 1 Medium Ray High = =2.39 = 1 Medium Minimum Loss Estimation Sources: NOAA National Climatic Data Center Storm Event Database and HAZUS-MH, 2005 (Total Exposure 2005) Riverine Flooding Risk Assessment Note: For background information about riverine flooding, see the profile in Section HAZUS-HM. Data for flooding was produced and provided by SEMA. SEMA s methodology for flood-loss estimation for the 2007 Missouri State Hazard Mitigation Plan is excerpted below. Table and figure numberings have been changed to correspond to sequencing in this Plan: During the 2007 plan update, the state used the most recent release of HAZUS-MH MR2 (May 2006) to model flood loss for every Missouri county and St. Louis City. HAZUS-MH can assess flood loss for an entire county if digital terrain data exists. Since digital elevation models (DEMs) were available for the entire state, the state was able to use HAZUS-MH to develop computer generated floodplain boundaries for the flood elevation that has a 1-percent chance of being equaled or exceeded each year (hereafter referred to as the base flood, also known as the 100-year flood) on major streams in each county HAZUS-MH computes the potential flood impact on a building inventory database based on the extent and depth of the modeled floodwaters, enabling a consistent methodology for a county-by-county assessment of potential flood losses. To develop county-wide probabilistic analyses for each county, the following parameters were used: Thirty-meter resolution DEMs as the terrain base to develop hydrologic and hydraulic models Streams and rivers with a minimum drainage basin area of 10 square miles all experiencing a base flood at the same time U.S. Geological Survey hydrologic regional regression equations and stream gage data included in HAZUS-MH HAZUS-MH building inventory defaults summarized to the census-block level with 2005 building valuations Note: In some cases, 10-meter resolution DEMs were used as a substitute for problem areas in the corresponding 30-meter DEMs. A sensitivity analysis comparing 10-meter DEMs with 30-meter DEMs was run for some counties. Although the 10-meter DEM produces slightly more accurate floodplain boundaries, the slight difference in the impact results did not justify the additional processing time it would have taken to run 10-meter DEMs for all counties. HAZUS-MH produces a flood polygon and flood-depth grid that represents the base flood. Although not as accurate as official flood maps, such as Mid-America Regional Council January 2010

12 digital flood insurance rate maps, these floodplain boundaries are available for use in GIS and could be valuable to communities that have not been mapped by the National Flood Insurance Program. Also, a statewide digital flood hazard layer was created by appending floodplain boundaries created in each county run. Flood Damage Estimates, Societal Impacts, and Agriculture Impacts The intent of this analysis was to enable the state to estimate where flood losses could occur and the degree of severity using a consistent methodology. The computer modeling helps quantify risk along known flood-hazard corridors such as along the Mississippi and Missouri rivers. In addition, flood losses are estimated for certain lesser streams and rivers where the flood hazard may not have been previously studied. HAZUS-MH impact analyses were run for direct economic losses for buildings and societal impacts (displaced people and shelter needs) to see which counties ranked the highest on these risk indicators. Using GIS, HAZUS-MH flood results were mapped to show flood-loss potential and how it varies across the state. The primary indicators used to assess flood losses were: Direct building losses combined with income losses Direct building losses Loss ratio of the direct building losses compared to overall building inventory Population displaced by the flood and shelter needs The loss ratio of the direct building losses compared to overall building inventory per county gives an indication of the severity of impacts on community sustainability. Although a large urban area may have the greatest dollar losses, it may be able to absorb the impact better than a more rural area where a flood could impact a significant amount of the infrastructure in the entire region. The HAZUS-MH methodology provides the number of buildings impacted, estimates of the building repair costs, and the associated loss of building contents and business inventory. Building damage can also cause additional losses to a community as a whole by restricting a building s ability to function properly. Income loss data accounts for losses such as business interruption and rental income losses as well as the resources associated with damage repair and job and housing losses. These losses are calculated by HAZUS-MH using a methodology based on the building damage estimates. Flood damage is directly related to the depth of flooding. For example, a 2-foot-deep flood generally results in about 20 percent damage to the structure (which translates to 20 percent of the structure s replacement value). HAZUS-MH takes into account flood depth when modeling damage (based on FEMA s depth-damage functions). The HAZUS-MH reports capture damage by occupancy class (in terms of square footage impacted) by damage percent classes. Occupancy classes in HAZUS-MH include agriculture, commercial, education, government, industrial, religion, and residential. Damage percent classes are grouped by 10 percent increments: 1 10 percent, percent, etc., up to 50 percent. Buildings that sustain more than 50 percent damage are considered to be substantially damaged. Data Limitation Note: The damaged building counts generated by HAZUS-MH are susceptible to rounding errors and are likely the weakest output of the model due to the use of census blocks for analysis. HAZUS-MH reports include this disclaimer: Unlike the earthquake and hurricane models, the flood model performs its analysis at the census block level. This means that the analysis starts with a small number of buildings within each census block and applies a series of distributions necessary for analyzing the potential damage. The application of these distributions and the small number of buildings make the flood model more sensitive to rounding errors that introduces uncertainty into the building count results. Please use these results with suitable caution. The counts of buildings at risk collected from the local hazard mitigation plans could potentially provide a more realistic estimate of the actual numbers of buildings in the base-flood hazard areas. The displaced population is based on the inundation area. Mid-America Regional Council January 2010

13 Individuals and households will be displaced from their homes even when the home has suffered little or no damage either because they were evacuated (i.e., a warning was issued) or there was no physical access to the property because of flooded roadways. Displaced people using shelters will most likely be individuals with lower incomes or those who do not have family or friends within the immediate area. Age plays a secondary role in shelter use because there are some individuals who will go to a public shelter even if they have the financial means to go elsewhere. These will usually be younger, lessestablished families and elderly families (HAZUS-MH Users Manual). HAZUS-MH does not model flood casualties given that flood-related deaths and injuries typically do not have the same significant impact on the medical infrastructure as those associated with earthquakes. HAZUS-MH also has the ability to model flood losses to agriculture, in particular crop losses. It uses the National Resources Inventory (NRI) data as the default agriculture inventory. The NRI provides crop type and units data captured approximately every five years. A hypothetical flood date of September 1 was used as the target date because it has the potential to harm not only the crop in the field but also the next growing season. A flood in late May or early June could also prove devastating because crops would have been recently planted. HAZUS-MH can analyze additional impacts, including what type of infrastructure could be impacted and how bad those impacts could be. Project files for each county are available for use by local governments from SEMA if more details on the impacts discussed here, or information about other impacts, such as vehicle losses, agricultural losses, utility system losses, essential facility impacts and transportation impacts are desired. Data Limitation Note: Levees may not be detected on the computer terrain models. Thus, some communities that may be protected from 100-year floods by levees may be modeled by HAZUS-MH as inundated and the risk may be overestimated. Those counties with levee protection should be considered as the worst-case scenario and may represent losses that could result from a levee breach. (Missouri State Hazard Mitigation Plan) In addition to the HAZUS-MH analysis, repetitive loss properties were also analyzed and discussed further in Section Attachment 1 to this section contains HAZUS-MH results for Cass, Clay, Jackson, Platte and Ray counties. Riverbed degradation is related to flooding and has the potential to cause serious problems. The Missouri River is experiencing substantial degradation, or down-cutting, of the riverbed, which is impacting public infrastructure such as water intake and discharge structures, pipeline crossings, levee systems and potentially major bridges. The degradation is lowering the elevation of the river and undermining the banks. The most severe problems are within the Kansas City reach of the Missouri River, between river miles 340 and 400. The problem has intensified in recent years. The U.S. Army Corps of Engineers completed a reconnaissance study of the Missouri River beddegradation problem in fall The study area covers the length of the river from Rulo, Neb., to the mouth of the river in St. Louis. The reconnaissance study has made a preliminary determination of impacts, causes and strategies to deal with the problem. The reconnaissance study concluded that the federal government has a significant interest in the issue (i.e., that federal infrastructure and operations could be significantly impacted) and recommended that a feasibility study be undertaken. Mid-America Regional Council January 2010

14 ISSUES Increased maintenance costs associated with bed degradation for adjustments in order to maintain the navigation channel. OPPORTUNITIES Addressing the problem will reduce the need for future adjustments in the navigation channel. Increased risk of levee system failure. The reliability of the federally constructed levee systems located along the Missouri River, where the system features are founded upon the revetment-protected slopes, is threatened and catastrophic failure could occur during a large flood event. Some federally constructed levee systems on the tributaries, at their confluence with the Missouri River, may also be at increased risk due to the head cuts causing an undermining of those features. Addressing the problem could reduce the risk of levee system failure of the federally constructed levee systems located on the Missouri River and tributary confluences. Increased demand for releases from upstream federal dams. Under low-flow conditions, releases from upstream dams will be necessary to meet regional water supply needs. This need for increases in flow is the result of bed degradation that causes river stages that are below design elevations for water supply intake structures. Addressing the problem could reduce the need for supplemental releases from upstream dams during periods of low-flow and eliminate the need for costly redesign of water-supply intake structures. Public infrastructure is being damaged due to Missouri River bed degradation. This is not fully inventoried but is most notable in the Kansas City reach. Infrastructure such as pipeline crossings, intakes, bridge abutments and other infrastructure on the main stem will continue to be impacted. Addressing the problem could reduce or eliminate damage to public infrastructure along the main stem of the lower Missouri River channel. Bed degradation is damaging to aquatic and riparian habitat in and along the lower Missouri River, including shallow water habitat and wetlands. These habitat areas consist of remnants of natural habitat and restored habitat constructed under the Missouri River Recovery Program. Addressing the problem could protect or restore natural and constructed shallow water habitat and wetlands along the lower Missouri River in degrading reaches of the river. Reduce or minimize damage to ecological resources due to head cutting related to Missouri River bed degradation. Head cutting and erosion on tributaries with confluences in degrading channel reaches of the Missouri River. The eroding tributary channels will damage infrastructure (e.g., bridge/pipeline crossings and levee structures located on tributaries). Addressing the problem could reduce erosion of tributary channels, thereby reducing damage to infrastructure located on those tributaries Severe Winter Weather Risk Assessment Note: For background information on severe winter weather, see Section Mid-America Regional Council January 2010

15 The same statistical risk assessment methodology to determine tornado loss potential was used for winter weather. Again, this methodology uses NOAA data for winter storm losses between 1994 and March Data Limitations: As noted previously, there are serious data limitations associated with winter storm losses. The NOAA NDC database groups damage estimates cumulatively for all counties affected by a winter storm event. Therefore, it is impossible to calculate an accurate annualized loss at the county level. Additionally, the economic impacts of a winter storm differ greatly from those of a tornado. In a tornado, the majority of the economic impact is generally isolated to the direct losses (infrastructure, property, etc.) in the affected area. A severe winter storm, however, can have wide-ranging secondary effects such as utility and transportation disruption, debris removal, etc. The exact cost of these secondary effects is difficult to quantify. In order to achieve a measurable figure for damages, annualized loss was calculated using the gross sum of all damages for all storms that affected the individual counties. For instance, Cass County has been affected by 28 different winter storm events over a 15-year period, which have caused a total of $32.15 million in damages. Several other counties were also involved in these same storm events. It is unknown what Cass County s share of these damages was, but annualizing the total damages provides a measurable analysis of the probable economic impact on Cass County. To compound the problem further, the magnitude of the historic winter storm events is unknown. Citing the general nature of storms that have affected the entire region, it seems reasonable to assume a magnitude of four for all counties. Tables 3.38 and 3.39 are maximum and minimum loss estimates for winter weather. TABLE 3.38: MAXIMUM LOSS ESTIMATION, SEVERE WINTER WEATHER Magnitude (percent of county affected) Total Exposure ($ value, thousands) County Overall Risk (probability Total Population (in and severity) thousands) Cass High = 3 4 4,692,256=2 98 =1 Medium Clay High = ,766,168=3 215 = 2 High Jackson High = ,083,895=3 668 = 3 High Platte High = 3 4 4,908,320=2 85 = 1 Medium Ray High = 3 4 1,230,761=2 23 = 1 Medium Maximum Loss Estimation Sources: NOAA National Climatic Data Center Storm Event Database and HAZUS-MH, 2005 (Total Exposure 2005) TABLE 3.39: MINIMUM LOSS ESTIMATION, SEVERE WINTER WEATHER Average Magnitude (percent of county affected) County Overall Risk Annualized (probability and severity) Loss ($ value, thousands) Average Deaths and Injuries Cass High = 3 4 2,143 = 3 0 =1 Medium Clay High = 3 4 2,500 = 3 0 =1 Medium Jackson High = 3 4 2,370= 3 0 =1 Medium Platte High = 3 4 2,486= 3 0 =1 Medium Ray High = 3 4 2,470 =3 0 =1 Medium Minimum Loss Estimation Sources: NOAA National Climatic Data Center Storm Event Database and HAZUS-MH, 2005 (Total Exposure 2005) Mid-America Regional Council January 2010

16 Drought Risk Assessment Note: For background information on droughts see Section A statistical risk assessment methodology could not be used to analyze droughts due to specific data set limitations. The onset, duration and end of droughts are difficult to classify because local conditions may vary from mathematical models used to determine when a drought has occurred. Using past drought events as a means to predict future probability is insufficient and does not allow for incorporation into the statistical risk assessment model developed in this report. As discussed in Section 3.2.4, there is not a consistent national methodology to determine the impact (severity) of droughts. In response to this gap, the National Drought Mitigation Center developed the Drought Impact Reporter to fulfill the need for a national drought impact database for the United States. The principle goal of the Drought Impact Reporter is to collect, quantify and map reported drought impacts for the United States and provide access to the reports through interactive search tools. Information for the reporter is collected from a variety of sources including: online drought-related news stories and scientific publications, reviewed by NDMC staff; members of the public who visit the website and submit a drought-related impact for their region; members of the media; members of government agencies such as National Oceanic and Atmospheric Administration (NOAA) and U.S. Department of Agriculture (USDA). (National Drought Mitigation Center Web site, online data) Table 3.40 summarizes the data found in the Drought Impact Reporter as a measure of severity. The reporter measures severity based on six types of impacts a drought can have on a county. These impact types are: agriculture, water/energy, environment, fire, social and other. Detailed information on each event can be found at However, no methodology or threshold is discussed for determining what qualifies as an impact. The reporter recognizes the limitations of this data, and presents the following disclaimer: Users should be aware that the Drought Impact Reporter is based on news reports collected from more than 5,000 online media sources and also reports submitted by private citizens, government officials, and others. The geographic coverage of these submissions and reports may reflect personal/media perceptions or biases rather than actual drought conditions. (National Drought Mitigation Center Web site, online data) TALBE 3.40: DROUGHT IMPACTS Impact Category County Agricultural Water/Energy Environment Fire Social Other Total Cass Clay Jackson Platte Ray Mid-America Regional Council January 2010

17 Source: National Drought Mitigation Center website Crop insurance indemnity payments for which drought was the primary cause of loss were also analyzed as an additional indicator of severity. The information contained in Table 3.41 below was gathered from online data provided by the United States Department of Agriculture (USDA) Risk Management Agency (RMA). Total payments from 1990 to 2008 and annualized payments for Cass, Clay, Jackson, Platte and Ray counties are listed in Table TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 1990 Cass SOYBEANS 90 APH Drought $22, Clay SOYBEANS 90 APH Drought $1, Jackson GRAIN SORGHUM 90 APH Drought $1, Jackson SOYBEANS 90 APH Drought $1, Platte TOBACCO 70 TQ Drought $ Ray CORN 90 APH Drought $4, Ray SOYBEANS 90 APH Drought $13, Cass CORN 90 APH Drought $263, Cass GRAIN SORGHUM 90 APH Drought $6, Cass SOYBEANS 90 APH Drought $400, Clay CORN 90 APH Drought $7, Clay SOYBEANS 90 APH Drought $17, Jackson CORN 90 APH Drought $40, Jackson SOYBEANS 90 APH Drought $154, Platte WHEAT 90 APH Drought $ Platte CORN 90 APH Drought $17, Platte GRAIN SORGHUM 90 APH Drought $ Platte TOBACCO 70 TQ Drought $6, Platte SOYBEANS 90 APH Drought $21, Ray CORN 90 APH Drought $47, Ray GRAIN SORGHUM 90 APH Drought $5, Ray SOYBEANS 90 APH Drought $181, Cass CORN 90 APH Drought $ Cass SOYBEANS 90 APH Drought $ Jackson CORN 90 APH Drought $ Jackson SOYBEANS 90 APH Drought $ Ray CORN 90 APH Drought $2, Ray SOYBEANS 90 APH Drought $1, Cass CORN 90 APH Drought $4, Cass GRAIN SORGHUM 90 APH Drought $ Cass SOYBEANS 90 APH Drought $3, Mid-America Regional Council January 2010

18 TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 1994 Clay CORN 90 APH Drought $1, Clay SOYBEANS 90 APH Drought $8, Jackson SOYBEANS 90 APH Drought $ Platte CORN 90 APH Drought $6, Platte SOYBEANS 90 APH Drought $ Ray CORN 90 APH Drought $2, Ray GRAIN SORGHUM 90 APH Drought $11, Ray SOYBEANS 90 APH Drought $ Cass CORN 90 APH Drought $34, Cass GRAIN SORGHUM 90 APH Drought $2, Cass SOYBEANS 90 APH Drought $53, Clay CORN 90 APH Drought $ Jackson SOYBEANS 90 APH Drought $35, Platte CORN 90 APH Drought $ Platte GRAIN SORGHUM 90 APH Drought $ Platte SOYBEANS 90 APH Drought $1, Ray CORN 90 APH Drought $3, Ray GRAIN SORGHUM 90 APH Drought $ Ray SOYBEANS 90 APH Drought $8, Cass WHEAT 90 APH Drought $2, Cass GRAIN SORGHUM 90 APH Drought $4, Clay WHEAT 90 APH Drought $ Jackson WHEAT 90 APH Drought $6, Platte WHEAT 90 APH Drought $ Ray SOYBEANS 90 APH Drought $ Cass CORN 44 CRC Drought $ Cass CORN 90 APH Drought $ Cass SOYBEANS 44 CRC Drought $2, Cass SOYBEANS 90 APH Drought $1, Clay SOYBEANS 90 APH Drought $14, Clay CORN 90 APH Drought $2, Platte SOYBEANS 90 APH Drought $ Ray CORN 44 CRC Drought $4, Ray CORN 90 APH Drought $7, Ray SOYBEANS 44 CRC Drought $19, Ray SOYBEANS 90 APH Drought $24, Cass CORN 90 APH Drought $14, Cass GRAIN SORGHUM 44 CRC Drought $ Mid-America Regional Council January 2010

19 TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 1998 Cass GRAIN SORGHUM 90 APH Drought $2, Cass SOYBEANS 44 CRC Drought $49, Cass SOYBEANS 90 APH Drought $105, Clay SOYBEANS 44 CRC Drought $ Clay SOYBEANS 90 APH Drought $ Jackson SOYBEANS 44 CRC Drought $14, Jackson SOYBEANS 90 APH Drought $1, Platte CORN 44 CRC Drought $ Platte SOYBEANS 44 CRC Drought $ Platte SOYBEANS 90 APH Drought $6, Ray CORN 90 APH Drought $ Ray SOYBEANS 90 APH Drought $10, Cass CORN 44 CRC Drought $22, Cass CORN 90 APH Drought $47, Cass GRAIN SORGHUM 44 CRC Drought $2, Cass GRAIN SORGHUM 90 APH Drought $1, Cass SOYBEANS 44 CRC Drought $46, Cass SOYBEANS 90 APH Drought $59, Clay CORN 44 CRC Drought $14, Clay CORN 90 APH Drought $2, Clay SOYBEANS 44 CRC Drought $ Clay SOYBEANS 90 APH Drought $19, Jackson CORN 44 CRC Drought $65, Jackson CORN 90 APH Drought $16, Jackson SOYBEANS 44 CRC Drought $39, Jackson SOYBEANS 90 APH Drought $9, Platte CORN 90 APH Drought $14, Platte SOYBEANS 44 CRC Drought $32, Platte SOYBEANS 90 APH Drought $57, Platte BURLEY TOBACCO 70 TQ Drought $24, Ray CORN 44 CRC Drought $9, Ray CORN 90 APH Drought $17, Ray SOYBEANS 44 CRC Drought $17, Ray SOYBEANS 90 APH Drought $64, Cass OATS 90 APH Drought $ Cass CORN 44 CRC Drought $16, Cass CORN 90 APH Drought $9, Cass SOYBEANS 44 CRC Drought $679, Mid-America Regional Council January 2010

20 TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 2000 Cass SOYBEANS 90 APH Drought $387, Clay CORN 90 APH Drought $2, Clay SOYBEANS 44 CRC Drought $9, Clay SOYBEANS 90 APH Drought $28, Clinton WHEAT 90 APH Drought $3, Jackson CORN 44 CRC Drought $ Jackson SOYBEANS 44 CRC Drought $268, Jackson SOYBEANS 90 APH Drought $17, Platte CORN 90 APH Drought $2, Platte SOYBEANS 44 CRC Drought $38, Platte SOYBEANS 90 APH Drought $76, Ray WHEAT 90 APH Drought $2, Ray CORN 44 CRC Drought $ Ray CORN 90 APH Drought $6, Ray SOYBEANS 44 CRC Drought $9, Ray SOYBEANS 90 APH Drought $13, Cass WHEAT 90 APH Drought $1, Cass CORN 25 RA Drought $5, Cass CORN 44 CRC Drought $4, Cass GRAIN SORGHUM 44 CRC Drought $ Cass GRAIN SORGHUM 90 APH Drought $1, Cass SOYBEANS 90 APH Drought $74, Jackson SOYBEANS 44 CRC Drought $ Jackson SOYBEANS 90 APH Drought $5, Platte CORN 44 CRC Drought $3, Platte CORN 90 APH Drought $1, Platte SOYBEANS 90 APH Drought $ Ray CORN 44 CRC Drought $ Ray SOYBEANS 44 CRC Drought $ Ray SOYBEANS 90 APH Drought $5, Cass CORN 25 RA Drought $197, Cass CORN 44 CRC Drought $32, Cass CORN 90 APH Drought $11, Cass POPCORN 90 APH Drought $30, Cass GRAIN SORGHUM 44 CRC Drought $ Cass SOYBEANS 25 RA Drought $254, Cass SOYBEANS 44 CRC Drought $56, Cass SOYBEANS 90 APH Drought $64, Mid-America Regional Council January 2010

21 TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 2002 Clay CORN 25 RA Drought $15, Clay CORN 44 CRC Drought $15, Clay CORN 90 APH Drought $32, Clay SOYBEANS 25 RA Drought $52, Clay SOYBEANS 44 CRC Drought $18, Clay SOYBEANS 90 APH Drought $21, Jackson CORN 25 RA Drought $272, Jackson CORN 44 CRC Drought $35, Jackson CORN 90 APH Drought $38, Jackson POPCORN 90 APH Drought $17, Jackson GRAIN SORGHUM 90 APH Drought $ Jackson SOYBEANS 25 RA Drought $117, Jackson SOYBEANS 44 CRC Drought $27, Jackson SOYBEANS 90 APH Drought $83, Platte CORN 25 RA Drought $122, Platte CORN 44 CRC Drought $98, Platte CORN 90 APH Drought $113, Platte SOYBEANS 25 RA Drought $139, Platte SOYBEANS 44 CRC Drought $168, Platte SOYBEANS 90 APH Drought $93, Ray CORN 25 RA Drought $99, Ray CORN 44 CRC Drought $78, Ray CORN 90 APH Drought $97, Cass WHEAT 90 APH Drought $6, Cass CORN 25 RA Drought $478, Cass CORN 44 CRC Drought $99, Cass CORN 90 APH Drought $60, Cass GRAIN SORGHUM 44 CRC Drought $2, Cass GRAIN SORGHUM 90 APH Drought $ Cass SOYBEANS 25 RA Drought $1,115, Cass SOYBEANS 44 CRC Drought $131, Cass SOYBEANS 90 APH Drought $129, Clay WHEAT 44 CRC Drought $8, Clay CORN 25 RA Drought $140, Clay CORN 44 CRC Drought $4, Clay CORN 90 APH Drought $53, Clay SOYBEANS 25 RA Drought $320, Clay SOYBEANS 44 CRC Drought $9, Mid-America Regional Council January 2010

22 TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 2003 Clay SOYBEANS 90 APH Drought $124, Jackson CORN 25 RA Drought $212, Jackson CORN 44 CRC Drought $9, Jackson CORN 90 APH Drought $24, Jackson GRAIN SORGHUM 44 CRC Drought $1, Jackson GRAIN SORGHUM 90 APH Drought $ Jackson SOYBEANS 25 RA Drought $441, Jackson SOYBEANS 44 CRC Drought $6, Jackson SOYBEANS 90 APH Drought $240, Platte WHEAT 44 CRC Drought $3, Platte CORN 25 RA Drought $768, Platte CORN 44 CRC Drought $42, Platte CORN 90 APH Drought $207, Platte GRAIN SORGHUM 90 APH Drought $1, Platte SOYBEANS 25 RA Drought $377, Platte SOYBEANS 44 CRC Drought $38, Platte SOYBEANS 90 APH Drought $202, Ray CORN 25 RA Drought $189, Ray CORN 44 CRC Drought $97, Ray CORN 90 APH Drought $175, Ray GRAIN SORGHUM 90 APH Drought $5, Ray SOYBEANS 25 RA Drought $191, Ray SOYBEANS 44 CRC Drought $132, Ray SOYBEANS 90 APH Drought $607, Cass SOYBEANS 25 RA Drought $7, Cass CORN 25 RA Drought $109, Cass CORN 44 CRC Drought $19, Cass CORN 90 APH Drought $4, Cass GRAIN SORGHUM 90 APH Drought $5, Cass SOYBEANS 25 RA Drought $35, Cass SOYBEANS 44 CRC Drought $5, Cass SOYBEANS 90 APH Drought $14, Clay CORN 25 RA Drought $61, Clay SOYBEANS 25 RA Drought $18, Clay SOYBEANS 44 CRC Drought $2, Clay SOYBEANS 90 APH Drought $ Jackson CORN 44 CRC Drought $ Jackson CORN 90 APH Drought $1, Mid-America Regional Council January 2010

23 TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 2005 Jackson SOYBEANS 25 RA Drought $29, Jackson SOYBEANS 44 CRC Drought $3, Jackson SOYBEANS 90 APH Drought $1, Platte CORN 25 RA Drought $20, Platte CORN 44 CRC Drought $2, Platte CORN 90 APH Drought $ Platte SOYBEANS 25 RA Drought $18, Ray CORN 25 RA Drought $57, Ray CORN 44 CRC Drought $9, Ray CORN 90 APH Drought $13, Ray GRAIN SORGHUM 44 CRC Drought $2, Ray SOYBEANS 25 RA Drought $34, Ray SOYBEANS 44 CRC Drought $42, Ray SOYBEANS 90 APH Drought $32, Cass WHEAT 44 CRC Drought $ Cass WHEAT 90 APH Drought $3, Cass CORN 25 RA Drought $102, Cass CORN 44 CRC Drought $4, Cass CORN 90 APH Drought $ Cass GRAIN SORGHUM 90 APH Drought $2, Cass SOYBEANS 25 RA Drought $102, Cass SOYBEANS 44 CRC Drought $33, Cass SOYBEANS 90 APH Drought $5, Clay CORN 25 RA Drought $66, Clay CORN 44 CRC Drought $13, Clay SOYBEANS 25 RA Drought $113, Clay SOYBEANS 44 CRC Drought $6, Clay SOYBEANS 90 APH Drought $8, Jackson WHEAT 44 CRC Drought $ Jackson CORN 25 RA Drought $22, Jackson CORN 44 CRC Drought $24, Jackson SOYBEANS 25 RA Drought $31, Jackson SOYBEANS 44 CRC Drought $ Jackson SOYBEANS 90 APH Drought $10, Platte CORN 25 RA Drought $27, Platte CORN 44 CRC Drought $24, Platte CORN 90 APH Drought $14, Platte SOYBEANS 25 RA Drought $70, Mid-America Regional Council January 2010

24 TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 2006 Platte SOYBEANS 44 CRC Drought $2, Platte SOYBEANS 90 APH Drought $19, Platte BURLEY TOBACCO 90 APH Drought $3, Ray CORN 25 RA Drought $9, Ray CORN 44 CRC Drought $6, Ray CORN 90 APH Drought $27, Ray SOYBEANS 25 RA Drought $46, Ray SOYBEANS 44 CRC Drought $38, Ray SOYBEANS 90 APH Drought $24, Cass CORN 25 RA Drought $18, Cass CORN 90 APH Drought $ Cass SOYBEANS 25 RA Drought $15, Cass SOYBEANS 44 CRC Drought $34, Cass SOYBEANS 90 APH Drought $2, Clay CORN 25 RA Drought $24, Clay CORN 44 CRC Drought $27, Clay SOYBEANS 25 RA Drought $24, Clay SOYBEANS 44 CRC Drought $34, Clay SOYBEANS 90 APH Drought $ Jackson CORN 25 RA Drought $19, Jackson CORN 44 CRC Drought $2, Jackson CORN 90 APH Drought $6, Jackson SOYBEANS 25 RA Drought $87, Jackson SOYBEANS 44 CRC Drought $22, Jackson SOYBEANS 90 APH Drought $8, Platte CORN 25 RA Drought $12, Platte CORN 90 APH Drought $5, Platte SOYBEANS 25 RA Drought $18, Platte SOYBEANS 44 CRC Drought $1, Platte SOYBEANS 90 APH Drought $7, Ray CORN 25 RA Drought $19, Ray CORN 44 CRC Drought $34, Ray CORN 90 APH Drought $6, Ray GRAIN SORGHUM 44 CRC Drought $ Ray GRAIN SORGHUM 90 APH Drought $1, Ray SOYBEANS 25 RA Drought $106, Ray SOYBEANS 44 CRC Drought $42, Ray SOYBEANS 90 APH Drought $10, Mid-America Regional Council January 2010

25 TABLE 3.41: CROP LOSSES DUE TO DROUGHT ( ) Crop Year County Crop Name Insurance Plan Code Cause of Loss Total Indemnity Amount 2008 Cass CORN 25 RA Drought $40, Cass SOYBEANS 25 RA Drought $53, Cass SOYBEANS 90 APH Drought $7, Clay SOYBEANS 25 RA Drought $48, Platte CORN 25 RA Drought $9, Platte SOYBEANS 25 RA Drought $30, Platte SOYBEANS 90 APH Drought $ Ray SOYBEANS 25 RA Drought $8, Source: USDA Risk Management Agency TABLE 3.42: DROUGHT INDEMNITY TOTALS County Total Indemnity ( ) Annualized Loss Cass $5,643, $313, Clay $1,400, $77, Jackson $2,485, $138, Platte $2,982, $165, Ray $2,749, $152, Source: USDA Risk Management Agency Heat Wave Risk Assessment Note: For background information on heat waves, see Section Data Limitation: Although the statistical risk assessment methodology was used for heat waves, the results are somewhat limiting. As discussed in Section 3.2.5, county-specific data for heat-wave-related deaths/injuries is unavailable. As in severe winter weather, NOAA provides the data in aggregate form for forecasting zones, of which Cass, Clay, Jackson, Platte, Ray and several other counties are a part. These aggregate numbers were used in the minimum loss equation for calculating averages for annualized loss, deaths and injuries. Use of these numbers most likely over-represents risk to a single county, but probably closely approximates risk to the entire region. Therefore, this information is still useful for planning purposes, as heat waves generally affect the region as a whole. Tables 3.43 and 3.44 contain the maximum and minimum loss estimations for heat waves. Mid-America Regional Council January 2010

26 Overall Risk (probability and severity) TABLE 3.43: MAXIMUM LOSS ESTIMATION, HEAT WAVE Magnitude (percent of county affected) Total Exposure ($ value, thousands) County Total Population (in thousands) Cass High = 3 4 4,692,256=2 98 =1 Medium Clay High = ,766,168=3 215 = 2 High Jackson High = ,083,895=3 668 = 3 High Platte High = 3 4 4,908,320=2 85 = 1 Medium Ray High = 3 4 1,230,761=2 23 = 1 Medium Maximum Loss Estimation Sources: NOAA National Climatic Data Center Storm Event Database and HAZUS-MH, 2005 (Total Exposure 2005) County Overall Risk (probability and severity) TABLE 3.44: MINIMUM LOSS ESTIMATION, HEAT WAVE Average Magnitude (percent of county affected) Annualized Loss ($ value, thousands) Average Deaths and Injuries Cass High = =1 7.6 = 3 High Clay High = =1 7.6 = 3 High Jackson High = =1 7.6 = 3 High Platte High = =1 7.6 = 3 High Ray High = =1 7.6 = 3 High Minimum Loss Estimation Sources: NOAA National Climatic Data Center Storm Event Database and HAZUS-MH, 2005 (Total Exposure 2005) Earthquake Risk Assessment Note: For background information on earthquakes, see Section Like the flooding data, HAZUS-HM data for earthquakes was produced and provided by SEMA. As such, SEMA s methodology for development of earthquakes loss estimation for the 2007 Missouri State Hazard Mitigation Plan is excerpted below. Table and figure numberings have been changed to correspond to sequencing in this plan: As part of the risk assessment update, two new analyses were run with HAZUS-MH to estimate potential losses from earthquakes. All HAZUS-MH runs used the default inventory data associated with the May 2006 release of HAZUS-MH MR2, which includes 2005 building valuations. An annualized loss scenario that enabled an apples to apples comparison of earthquake risk for each county was run. A second scenario, based on event with a 2,500 year return period, was done to model a worst case earthquake using a level of ground shaking recognized in earthquake-resistant design. Annualized Loss Scenario The total annualized expected losses (including building and income losses) are presented in Table The annualized loss ratio and a ranking based on this loss ratio are included in the table. A modification to the default HAZUS-MH seismic design building occupancy mapping was made for this analysis. HAZUS-MH assigns seismic design levels to all the building stock associated with a particular census tract. Default HAZUS-MH design levels assigned to census tracts used a combination of two Mid-America Regional Council January 2010

27 types: MO1, or Missouri low seismic design, and MO2, or Missouri moderate seismic design. MO2 assumes that the majority of structures have moderate seismic design levels incorporated into the building stock of a particular census tract. Because seismic code provisions have only recently been required in Missouri for new construction in certain counties, it is likely that most of the existing building stock is MO1. (This does not mean that buildings using seismic design, such as the International Building Code 2003, will not be damaged or destroyed. The codes only address life safety, not building survivability.) This parameter in HAZUS-MH was changed so that all census tracts were assigned to MO1. Total annualized losses for the state are $77,654,000 with this modification and $73,337,000 with the default mix of low and moderate design. The difference, $4,317,000, represents a measure of average annualized savings from earthquake losses from the use of building codes with seismic provisions. County TABLE 3.45: HAZUS-MH EARTHQUAKE LOSS ESTIMATION: ANNUALIZED LOSS SCENARIO Building Loss Total (thousands of $'s) Loss Ratio Income Loss Total (thousands of $'s) Cass $63 0 $9 $72 Clay $159 0 $34 $193 Jackson $615 0 $163 $778 Platte $57 0 $10 $67 Ray $15 0 $2 $17 2,500-Year Earthquake Scenario Total Loss (thousands of $'s) Source: HAZUS-MH MR2 A second scenario, based on an event with a 2,500-year return period, was done to model a worst-case scenario. The methodology includes probabilistic seismic hazard contour maps developed by the U.S. Geological Survey (USGS) for the 2002 update of the National Seismic Hazard Maps that are included with HAZUS-MH. The USGS maps provide estimates of potential ground acceleration and spectral acceleration at periods of 0.3 second and 1.0 second, respectively. The 2,500-year return period analyzes ground shaking estimates with a 2 percent probability of being exceeded in 50 years. The International Building Code uses this level of ground shaking for building design in seismic areas. The building code classifications in HAZUS-MH were set to low design throughout the state for this scenario as well. Scenario Results The results of this probabilistic scenario include total losses exceeding $44 billion (statewide) in building and income losses, with overall economic losses exceeding $58 billion. Over 27 percent of the total number of buildings in the state would be at least moderately damaged. Thirteen percent of the building and income losses would be related to business interruption. Table 3.46 summarizes the results from the HAZUS-MH run for the Kansas City region. (HAZUS-MH Earthquake Event Summary Report) The loss ratio is a measure of the disaster impact to community sustainability, which is generally considered at risk when losses exceed 10 percent of the built environment (FEMA). Limitations to the HAZUS-MH loss modeling include inability to accurately assess the impact to long-span bridges, such as those crossing the Mississippi River. Damage to major infrastructure, such as power and other utility distribution systems, is estimated based on a proxy of the population within the study area and not on actual data representing these systems. Improvements to future HAZUS-MH runs may include using more extensive geologic mapping (as it becomes available), using more extensive ground shaking mapping, Mid-America Regional Council January 2010

28 adding utilities infrastructure, and adding groundwater depth maps to the analysis. More extensive geologic and ground shaking mapping north of St. Louis would enable more accurate representation of the earthquake hazard in northeastern Missouri. County Structural Damage* TABLE 3.46: HAZUS-MH EARTHQUAKE LOSS ESTIMATION: 2,500-YEAR SCENARIO RESULTS BUILDING IMPACTS BY COUNTY Non-Structural Damage* Loss Ratio** Contents Damage and Inventory Loss* Income Loss*,*** Total Building Losses* Jackson 120, , , , ,876 Clay 31,995 79, ,653 23, ,408 Cass 12,270 30, ,506 6,285 57,640 Platte 11,109 29, ,816 7,032 56,515 Ray 2,914 6, ,892 1,493 12,855 Source: HAZUS-MH MR2 Note: *All monetary values are in thousands **Loss ratio is the sum of structural and nonstructural damage divided by the entire building inventory value within a county ***Total income loss includes relocation loss, capital-related loss, wages loss, and rental income loss Table 3.47 shows social impact estimates by county for the same event. Table 3.48 provides definitions for casualty severity, displaced households and short-term shelter needs as used in Table Casualties resulting from an earthquake will vary depending on if the earthquake occurs during the middle of the night, middle of the day, or rush hour. HAZUS-MH provides casualty estimates for three different times of day: 2 a.m., 2 p.m. and 5 p.m. Table 3.47 represents the 2 a.m. timeframe. (Missouri State Hazard Mitigation Plan) TABLE 3.47: SOCIAL IMPACT ESTIMATES BY COUNTY FROM THE 2,500-YEAR SCENARIO, 2 A.M. TIME OF OCCURRENCE Casualty Severity Level County MMI Zone Total Displaced Households Cass VI Clay VI Jackson VI Platte VI Ray VI Short- Term Shelter needs Source: HAZUS-MH MR2 Mid-America Regional Council January 2010

29 Casualty Severity 1 Casualty Severity 2 Casualty Severity 3 Casualty Severity 4 Displaced Households Short-Term Shelter Needs TABLE 3.48: CASUALTY SEVERITY LEVEL DEFINITIONS Injuries requiring basic medical aid that could be administered by paraprofessionals. These types of injuries would require bandages or observation. Examples include a sprain, a severe cut requiring stitches, a minor burn (first degree or second degree on a small part of the body) or a bump on the head without loss of consciousness. Injuries of lesser severity that could be self-treated are not estimated by HAZUS-MH. Injuries requiring a greater degree of medical care and use of medical technology, such as X- rays or surgery, but not expected to progress to a life threatening status. Examples include third- or second-degree burns over large parts of the body, a bump on the head that causes loss of consciousness, fractured bone, dehydration or exposure. Injuries that pose an immediate life-threatening condition if not treated adequately and expeditiously. Examples include uncontrolled bleeding, punctured organ, other internal injuries, spinal column injuries or crush syndrome. Instantaneously killed or mortally injured. People evacuating their residence due merely to physical damage to the building (there may be others who need to evacuate for sole reasons of utility service disruption, etc.) Results taking into account the functionality of a residence depending on its degree of physical damage. People, among the displaced, in need of shelter. Results taking into account the influence on choice for shelter based on income level, age group, ethnicity and ownership Source: Missouri State Hazard Mitigation Plan Dam Failure Risk Assessment Note: For background information on dam failures, see Section The statistical risk assessment methodology was used to estimate loss for dam failures. Tables 3.49 and 3.50 are the maximum and minimum loss estimate calculations. Data limitations: The statistical risk assessment methodology algorithm does not take into account the number of high-hazard dams in a county. As there have been no recorded incidents of dam failures in the metro area, it was assumed that a county with more high hazard dams than its neighbor was at no significantly greater risk for failure. A lack of historic data therefore lends itself to generally overall low loss estimation, which may or may not be appropriate for use on a local level. Loss estimation is best conducted at the local level and should be incorporated accordingly. Mid-America Regional Council January 2010

30 Overall Risk (probability and severity) TABLE 3.49: MAXIMUM LOSS ESTIMATION, DAM FAILURE Magnitude (percent of county affected) Total Exposure ($ value, thousands) County Total Population (in thousands) Cass Low = 1 1 4,692,256=2 98 =1 Low Clay Low = ,766,168=3 215 = 2 Medium Jackson Low = ,083,895=3 668 = 3 Medium Platte Low = 1 1 4,908,320=2 85 = 1 Low Ray Low = 1 1 1,230,761=2 23 = 1 Low Overall Risk (probability and severity) Maximum Loss Estimation Sources: Missouri Department of Natural Resources, Dam Safety Office and HAZUS-MH, 2005 (Total Exposure 2005) TABLE 3.50: MINIMUM LOSS ESTIMATION, DAM FAILURE Average Magnitude (percent of county affected) Annualized Loss ($ value, thousands) County Average Deaths and Injuries Cass Low = = 1 0 = 1 Low Clay Low = = 1 0 = 1 Low Jackson Low = = 1 0 = 1 Low Platte Low = = 1 0 = 1 Low Ray Low = = 1 0 = 1 Low Minimum Loss Estimation Sources: Missouri Department of Natural Resources, Dam Safety Office and HAZUS-MH, 2005 (Total Exposure 2005) Wildland Fire Risk Assessment Note: For background information on wildland fires, see Section The statistical risk assessment methodology was used to calculate loss estimation for wildland fires. Like dam failures, there is a lack of historic incidents of wildland fires within the region. However, as the urban-wildland interface shrinks, this is to be expected. Tables 3.51 and 3.52 provide the minimum and maximum loss estimations for wildland fires. Overall Risk (probability and severity) TABLE 3.51: MAXIMUM LOSS ESTIMATION, WILDLAND FIRE Magnitude (percent of county affected) Total Exposure ($ value, thousands) County Total Population (in thousands) Cass Low = 1 1 4,692,256=2 98 =1 Low Clay Low = ,766,168=3 215 = 2 Medium Jackson Low = ,083,895=3 668 = 3 Medium Platte Low = 1 1 4,908,320=2 85 = 1 Low Ray Low = 1 1 1,230,761=2 23 = 1 Low Maximum Loss Estimation Sources: NOAA National Climatic Data Center Storm Event Database and HAZUS-MH, 2005 (Total Exposure 2005) Mid-America Regional Council January 2010

31 Overall Risk (probability and severity) TABLE 3.52: MINIMUM LOSS ESTIMATION, WILDLAND FIRE Average Magnitude (percent of county affected) Annualized Loss ($ value, thousands) County Average Deaths and Injuries Cass Low = = 1 0 = 1 Low Clay Low = = 1 0 = 1 Low Jackson Low = = 1 0 = 1 Low Platte Low = = 1 0 = 1 Low Ray Low = = 1 0 = 1 Low Minimum Loss Estimation Sources: NOAA National Climatic Data Center Storm Event Database and HAZUS-MH, 2005 (Total Exposure 2005) Hazardous Materials Incident Risk Assessment Note: For background information on hazardous materials incidents, see Section Data limitations: As previously discussed, the statistical risk assessment methodology could not be used for hazardous materials incidents. Due to the complexity of the databases for hazardous materials incidents maintained by the National Response Center and Missouri Department of Natural Resources, information required for statistical loss estimation injuries, deaths, damages and quantity are not easily queried. Therefore, risk assessments are best accomplished at a local level. The Mid-America LEPC (the LEPC for the Kansas City Region) has been fostering relationships between private industry and emergency responders since 1987 in order to better prepare for and mitigate the hazards of chemical releases. Additionally, the Clean Air Act Amendments passed by Congress in 1990 required the Environmental Protection Agency to publish regulations and guidance for chemical accident prevention at facilities using extremely hazardous substances. The EPA s Risk Management Program Rule was written to comply with Section 112 (r) of these amendments. The rule, Chemical Accident Prevention Provisions (Parts 68 of Title 40 of the CPR), applies to a wide variety of facilities that handle, manufacture, store or use toxic substances. Facilities that contain any of the extremely hazardous toxic and flammable substances listed at 40 CFR in an amount above the threshold quantity specified for that substance are required to develop and implement a Risk Management Program (RMP). The phrase risk management program refers to all the requirements of Part 68, which must be implemented on an ongoing basis. The phrase risk management plan refers to a document facilities must submit to the EPA describing their risk management programs. The RMP requires facilities to maintain: (a) (b) (c) (d) An analysis of the potential off-site consequences of a worst-case accidental release A five-year accident history A release prevention program that includes safety precautions and maintenance, monitoring and employee training measures An emergency response program that spells out emergency health care, employee training measures and procedures for informing the public and response agencies should an accident occur Mid-America Regional Council January 2010

32 The Mid-America LEPC maintains a listing of RMP participating facilities. For additional information, consult the Mid-America LEPC s Regional Hazardous Materials Emergency Preparedness Plan Emerging Infectious Disease Risk Assessment Note: For background information on emerging infectious disease, see Section Referring back to the data limitations discussed in Section for emerging infectious disease, a statistical risk assessment methodology could not be used to estimate loss. Instead, potential losses, described in terms of potential deaths and hospitalizations, of a severe flu pandemic in the Kansas City region were estimated using the CDC-developed software, FluAid 2.0. Direct and indirect economic impacts of a severe pandemic flu were not calculated due to a lack of consistent and proven methodology. FluAid 2.0 is software designed to provide a range of estimates of impact in terms of deaths, hospitalizations and outpatient visits due to pandemic influenza. The methodology used to design the software is similar to that used to calculate national-level estimates of impact. The one notable difference is that, unlike the model used to calculate national-level estimates, the software does not use Monte-Carlo methodologies to provide ranges of estimates. Instead, the software requires that the user supply minimum, most likely and maximum estimates of some inputs (e.g., rates of death per 1,000 population). These data are then used by the program to provide estimates of the minimum, most likely and maximum impact. Another important difference between the state-, local- and national-level model is that the former included a pre-defined age-distribution of cases. For simplicity, this assumption was omitted from the state and local level model. (FluAid 2.0 User Manual) The software only provides estimates of the total impact (i.e., after the event estimates). The model is not an epidemiological model, and cannot describe when or how people will become ill. That is, the software does not provide a description of how a pandemic may spread through society over time. This lack is due to the difficulty of mathematically modeling the epidemiology of influenza. As in the model used to estimate the potential national-level impact, the state- and local-level model uses different levels of gross attack rates. Gross attack rate is the percentage of population that becomes clinically ill due to influenza. Clinical illness is defined as a case of influenza that causes some measurable economic impact, such as one-half day of lost work or a visit to a physician s office. (FluAid 2.0 User Manual) Figures 3.48 through 3.50 are the results of FluAid 2.0 for the region. Population estimates for the region from the 2000 Census were used to calculate the results below. Mid-America Regional Council January 2010

33 FIGURE 3.48: DEATHS DUE TO INFLUENZA PANDEMIC IN THE KANSAS CITY REGION 8,000 Cases 6,000 4,000 maximum most likely mimimum 2, % 25% 35% Gross attack rate Source: FluAid 2.0 FIGURE 3.49: HOSPITALIZATIONS DUE TO INFLUENZA PANDEMIC IN THE KANSAS CITY REGION 25,000 Cases 20,000 15,000 10,000 maximum most likely minimum 5, % 25% 35% Gross attack rate Source: FluAid 2.0 Mid-America Regional Council January 2010

34 FIGURE 3.50: OUTPATIENT VISITS DUE TO PANDEMIC INFLUENZA IN THE KANSAS CITY REGION 1,200 Cases ('000) 1, % 25% 35% Gross attack rate maximum most likely minimum Source: FluAid Civil Disorder Risk Assessment Note: For background information on Civil Disorder, see Section Data limitation: Civil disorder can occur in any number of locations for any number of reasons. The severity of the event is determined by several variables including group size, the cause of the incident, tactics used to de-escalate the incident and police presence. Each of these can significantly alter the severity of an event. Similarly, lack of historic examples often hampers efforts to estimate loss potential. For these reasons, use of the statistical risk assessment methodology to calculate loss potential is not appropriate. Therefore, only a general statement of loss potential can be made for civil disorder events. Owing to the generally confined nature of civil disorder, potential loss is best characterized as low Mass Transportation Accident Risk Assessment Note: For background information on Mass Transportation Accidents, see Section Data limitation: The statistical risk assessment methodology could not be used to calculate loss estimation for mass transportation accidents. The vast majority of motor vehicle accidents involve personal vehicles, which makes estimating the costs associated with an accident difficult, as these property values aren t factored into the total valuation of a county. Instead, general information on vehicle accidents at the federal, state and regional levels are presented in the paragraphs below as a means of estimating loss potential. Nationwide, the total economic cost of motor vehicle crashes in 2000 was $230.6 billion. This represents the present value of lifetime costs for 41,821 fatalities, 5.3 million non-fatal injuries and 28 million Mid-America Regional Council January 2010

35 damaged vehicles, in both police-reported and unreported crashes. Lost market productivity accounted for $61 billion of this total, and property damage accounted for $59 billion. Medical expenses totaled $32.6 billion, and travel delay accounted for $25.6 billion. Each fatality resulted in an average discounted lifetime cost of $977,000. Public revenues paid for roughly 9 percent of all motor vehicle crash costs, costing taxpayers $21 billion in 2000 the equivalent of over $200 in added taxes for every household in the U.S. (U.S. Department of Transportation, The Economic Impact of Motor Vehicle Crashes 2000) Table 3.53 below shows the total economic costs of motor vehicle accidents in Missouri for the year TABLE 3.53: ECONOMIC COSTS DUE TO MOTOR VEHICLE CRASHES (2000) State (Millions $) Percent Total Cost Per Capita Percent Per Capita Personal Income Missouri $4, % $ % Source: U.S. DOT Mid-America Regional Council January 2010

36 Unfortunately, injury and death often accompany traffic accidents. Tables 3.54 through 3.56 below show fatality data for the Kansas City region over a five-year period from All data was compiled by the MARC Transportation Department from information provided by Missouri Department of Transportation, Missouri State Highway Patrol and local media outlets. USE RESTRICTED. Information in these graphs is exempt under discovery or admission under 23 USC 409. County Cass TABLE 3.54: ROADWAY FATALITIES +/- to 5 Yr Avg Yr Avg percent Clay 31.00% Jackson -9.10% Platte % Ray % Source: MARC Transportation Department County TABLE 3.55: FATALITIES/ 10,000 PEOPLE +/- to 5 Yr Avg 2009* 5 Yr Avg Cass % Clay 27.50% Jackson -9.40% Platte % Ray % *Based on 2007 estimates Source: MARC Transportation Department County TABLE 3.56: FATALITIES/ 100 MILLION VMT +/- to 5 Yr Avg 2009* 5 Yr Avg Cass % Clay 29.40% Jackson -8.70% Platte % Ray % *Based on 2007 estimates Source: MARC Transportation Department This page is exempt under discovery or admission under 23 USC 409. PRELIMINARY DATA - Sources: KDOT and MoDOT. Some information calculated by MARC. Mid-America Regional Council January 2010

37 3.3.4 Repetitive Loss Properties In addition to the HAZUS-MH flood runs, National Flood Insurance Program (NFIP) flood-loss data was analyzed to determine areas of the region with the greatest flood risk and highest potential for mitigation. In-force flood insurance information was taken from the NFIP Web site and presented in Table Flood-loss information was provided by SEMA and culled from FEMA s Policy and Loss Data by Community with County and State Data, which documents losses from 1978 to the present (this analysis is based on the report dated Nov. 26, 2006) and presented in Table There are several limitations to this data, including: Only losses to participating NFIP communities are represented Communities joined the NFIP at various times since 1978 The number of flood insurance policies in effect may not include all structures at risk to flooding Some of the historic loss areas have been mitigated with property buyouts Despite these limitations, the data depict a pattern of historic flood losses in the region. Cities along the Missouri River corridor have the greatest number of losses, with jurisdictions in Clay County experiencing the most total losses, both in claims and dollar amount. TABLE 3.57: KANSAS CITY AREA FLOOD INSURANCE POLICIES Policies In-force Insurance In-force (whole dollars) Written Premium Inforce COUNTIES CLAY COUNTY 36 6,543,400 23,954 CASS COUNTY 79 14,149,800 55,690 JACKSON COUNTY 84 17,046,000 46,219 PLATTE COUNTY ,895,400 73,903 RAY COUNTY 70 7,412,100 42,650 CITIES AVONDALE,CITY OF ,700 5,994 BALDWIN PARK, VILLAGE OF 4 94,000 2,765 BELTON, CITY OF 22 3,200,600 11,210 BLUE SPRINGS, CITY OF 32 5,589,700 15,541 BUCKNER, CITY OF 17 1,823,800 12,211 CLAYCOMO, VILLAGE OF 21 2,912,800 18,126 CRYSTAL CITY,CITY OF 39 8,388,000 47,680 EAST LYNNE, CITY OF 1 98, EDGERTON, CITY OF 1 46, EXCELSIOR SPRINGS, CITY 26 4,256,200 41,967 FARLEY, VILLAGE OF 1 105, FREEMAN, CITY OF 4 357,800 2,628 GLADSTONE, CITY OF 41 8,215,400 35,979 GLENAIRE, CITY OF 2 187,500 1,678 Mid-America Regional Council January 2010

38 TABLE 3.57: KANSAS CITY AREA FLOOD INSURANCE POLICIES Policies In-force Insurance In-force (whole dollars) Written Premium Inforce GRAIN VALLEY, CITY OF 37 5,688,100 13,565 GRANDVIEW, CITY OF 15 2,893,200 6,718 GREENWOOD, CITY OF 4 1,154,000 6,649 HARDIN, CITY OF 129 8,741,900 71,326 HARRISONVILLE, CITY OF 11 2,157,200 6,509 HOLT, CITY OF 4 367,600 2,028 HOUSTON, CITY OF 4 389,800 1,919 INDEPENDENCE,CITY OF ,749, ,175 KANSAS CITY, CITY OF ,111,000 1,317,542 KEARNEY, CITY OF 5 1,115,500 2,709 LAKE LOTAWANA, CITY OF 7 2,360,000 5,421 LAKE WINNEBAGO, CITY OF 20 5,462,200 12,501 LAWSON, CITY OF 1 30, LEE'S SUMMIT, CITY OF 94 23,215,600 45,392 LEVASY, CITY OF ,300 5,120 LIBERTY, CITY OF 49 10,604,000 78,680 MISSOURI CITY, CITY OF 23 1,555,100 8,977 MOSBY, CITY OF 42 3,274,100 37,042 NORTH KANSAS CITY, CITY 89 18,674,500 75,795 NORTHMOOR, CITY OF 5 1,141,500 3,455 OAK GROVE, CITY OF 3 522,500 1,827 ORRICK, CITY OF 48 5,576,900 24,347 PARKVILLE, CITY OF 29 7,590,400 35,776 PECULIAR, CITY OF 13 2,033,700 12,623 PLATTE CITY, CITY OF 3 128,600 1,163 PLEASANT HILL, CITY OF 41 2,812,000 20,688 PLEASANT VALLEY, CITY OF 2 337, PRATHERSVILLE, VILLAGE O 4 417,300 3,886 RANDOLPH, VILLAGE OF 1 34, RAYMORE, CITY OF 15 2,659,300 5,724 RAYTOWN, CITY OF 25 2,393,100 14,210 RICHMOND, CITY OF 5 417,400 3,402 RIVERSIDE, CITY OF 59 24,157,200 85,459 SMITHVILLE, CITY OF ,690,400 84,465 SUGAR CREEK, CITY OF 14 1,081,500 6,925 WESTON, CITY OF 4 185,000 1,374 Source: NFIP Web site Mid-America Regional Council January 2010

39 Community Name TABLE 3.58: KANSAS CITY AREA REPETITIVE LOSSES Building Contents Total Average Comm Payments Payments Payments Payment Number ($ s) ($ s) ($ s) ($ s) Losses Properties Cass County Lee's Summit, City Of , , , , Belton, City Of , , , , Cass County * , , , , Freeman, City Of , , , , Harrisonville, City Of , , , Lake Winnebago, City Of , , , , Peculiar, City Of , , , , Pleasant Hill, City Of , , , , Raymore, City Of , , , , Clay County Avondale,City Of , , , , Clay County * , , , , Claycomo, Village Of , , ,471, , Excelsior Springs, City Of , , ,061, , Gladstone, City Of , , , , Independence,City Of , , , , Kansas City, City Of ,522, ,735, ,257, , Liberty, City Of , , , Missouri City, City Of , , , , Mosby, City Of , , , , North Kansas City, City Of , , , , Smithville, City Of , , , , Sugar Creek, City Of , , , , Jackson County Raytown, City Of , , , , Blue Springs, City Of , , , , Buckner, City Of , , , , Grandview, City Of , , , , Jackson County * , , , , Lake Lotawana, City Of , , , , Levasy, City Of , , , , Platte County Edgerton, City Of , , , , Parkville, City Of , , , , Platte City, City Of , , , , Platte County* , , , , Riverside, City Of , , , , Tracy, City Of , , , , Weston, City Of , , , Ray County Hardin, City Of , , , , Orrick, City Of , , , , Ray County * , , , , Source: SEMA Mid-America Regional Council January 2010

40 TABLE 3.58A: REPETITIVE LOSS PROPERTIES (UNMITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss BELTON, CITY OF NO Single-Family 08/13/1982 FREEMAN, CITY OF NO Single-Family 06/28/1999 FREEMAN, CITY OF NO Single-Family 09/27/1986 FREEMAN, CITY OF NO Single-Family 09/27/1986 HARRISONVILLE, CITY OF NO Nonresidential 06/28/1999 HARRISONVILLE, CITY OF NO Nonresidential 05/01/1983 RAYMORE, CITY OF NO Single-Family 05/07/2007 CLAY COUNTY * NO Two to Four- 08/13/1982 Family CLAY COUNTY * NO Single-Family 07/10/1993 CLAY COUNTY * NO Single-Family 04/22/1999 CLAY COUNTY * NO Single-Family 10/04/1998 CLAY COUNTY * NO Single-Family 10/04/1998 CLAYCOMO, VILLAGE OF NO Other Residential 10/04/1998 CLAYCOMO, VILLAGE OF NO Nonresidential 07/27/2008 CLAYCOMO, VILLAGE OF NO Two to Four- 08/13/1982 Family CLAYCOMO, VILLAGE OF NO Single-Family 05/19/2004 CLAYCOMO, VILLAGE OF NO Two to Four- 10/04/1998 Family CLAYCOMO, VILLAGE OF NO Two to Four- 10/04/1998 Family CLAYCOMO, VILLAGE OF NO Two to Four- 10/04/1998 Family CLAYCOMO, VILLAGE OF NO Two to Four- 10/04/1998 Family CLAYCOMO, VILLAGE OF NO Two to Four- 10/04/1998 Family CLAYCOMO, VILLAGE OF NO Two to Four- 10/04/1998 Family CLAYCOMO, VILLAGE OF NO Nonresidential 10/04/1998 EXCELSIOR SPRINGS, CITY OF NO Single-Family 10/04/1998 EXCELSIOR SPRINGS, CITY OF NO Nonresidential 10/04/1998 EXCELSIOR SPRINGS, CITY OF NO Nonresidential 10/04/1998 EXCELSIOR SPRINGS, CITY OF NO Single-Family 08/11/1993 EXCELSIOR SPRINGS, CITY OF NO Two to Four- 08/11/1993 Family EXCELSIOR SPRINGS, CITY OF NO Single-Family 08/11/1993 EXCELSIOR SPRINGS, CITY OF NO Single-Family 08/12/1993 EXCELSIOR SPRINGS, CITY OF NO Nonresidential 10/04/1998 EXCELSIOR SPRINGS, CITY OF NO Nonresidential 10/04/1998 GLADSTONE, CITY OF NO Single-Family 08/13/1982 GLADSTONE, CITY OF NO Single-Family 08/13/1982 MISSOURI CITY, CITY OF NO Single-Family 10/04/1998 MOSBY, CITY OF NO Single-Family 10/04/1998 Mid-America Regional Council January 2010

41 TABLE 3.58A: REPETITIVE LOSS PROPERTIES (UNMITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss MOSBY, CITY OF NO Single-Family 05/07/2007 MOSBY, CITY OF NO Single-Family 05/07/2007 MOSBY, CITY OF NO Single-Family 10/04/1998 MOSBY, CITY OF NO Single-Family 05/07/2007 MOSBY, CITY OF NO Single-Family 10/05/1998 NORTH KANSAS CITY, CITY OF NO Single-Family 07/07/1984 BLUE SPRINGS, CITY OF NO Single-Family 05/01/1983 BLUE SPRINGS, CITY OF NO Single-Family 02/25/1985 BUCKNER, CITY OF NO Single-Family 08/13/1982 GRANDVIEW, CITY OF NO Single-Family 08/13/1982 GRANDVIEW, CITY OF NO Single-Family 06/15/1984 INDEPENDENCE,CITY OF NO Single-Family 06/28/1999 INDEPENDENCE,CITY OF NO Single-Family 05/19/2004 INDEPENDENCE,CITY OF NO Single-Family 05/07/2007 INDEPENDENCE,CITY OF NO Single-Family 05/07/2007 INDEPENDENCE,CITY OF NO Single-Family 07/25/1998 INDEPENDENCE,CITY OF NO Single-Family 05/17/1995 INDEPENDENCE,CITY OF NO Single-Family 07/25/1998 INDEPENDENCE,CITY OF NO Single-Family 10/04/1998 INDEPENDENCE,CITY OF NO Nonresidential 10/04/1998 INDEPENDENCE,CITY OF NO Single-Family 06/28/1999 INDEPENDENCE,CITY OF NO Single-Family 10/04/1998 INDEPENDENCE,CITY OF NO Single-Family 04/19/2001 INDEPENDENCE,CITY OF NO Nonresidential 10/04/1998 INDEPENDENCE,CITY OF NO Two to Four- 10/04/1998 Family KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 06/27/1999 KANSAS CITY, CITY OF NO Single-Family 06/27/1999 KANSAS CITY, CITY OF NO Nonresidential 05/19/2004 KANSAS CITY, CITY OF NO Nonresidential 07/10/1993 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 06/28/1999 KANSAS CITY, CITY OF NO Nonresidential 06/28/1999 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 04/14/1999 KANSAS CITY, CITY OF NO Single-Family 06/28/1999 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Nonresidential 06/28/1999 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 07/13/1993 KANSAS CITY, CITY OF NO Single-Family 10/04/1998 KANSAS CITY, CITY OF NO Nonresidential 05/17/1995 KANSAS CITY, CITY OF NO Nonresidential 05/17/1995 KANSAS CITY, CITY OF NO Nonresidential 09/11/2002 KANSAS CITY, CITY OF NO ASSMD 10/04/1998 Mid-America Regional Council January 2010

42 TABLE 3.58A: REPETITIVE LOSS PROPERTIES (UNMITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss KANSAS CITY, CITY OF NO ASSMD CONDO 05/16/1990 KANSAS CITY, CITY OF NO Single-Family 05/15/1990 KANSAS CITY, CITY OF NO Single-Family 09/23/1986 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Nonresidential 06/30/1981 KANSAS CITY, CITY OF NO Single-Family 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 05/01/1983 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Nonresidential 06/09/1984 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Single-Family 07/20/1993 KANSAS CITY, CITY OF NO Nonresidential 09/18/1986 KANSAS CITY, CITY OF NO Single-Family 05/15/1990 KANSAS CITY, CITY OF NO Nonresidential 09/14/2008 KANSAS CITY, CITY OF NO Single-Family 06/09/1984 KANSAS CITY, CITY OF NO Single-Family 05/15/1990 KANSAS CITY, CITY OF NO Nonresidential 08/27/2004 KANSAS CITY, CITY OF NO Nonresidential 08/13/1982 KANSAS CITY, CITY OF NO Single-Family 06/09/1984 KANSAS CITY, CITY OF NO Nonresidential 07/13/1993 KANSAS CITY, CITY OF NO Nonresidential 05/15/1990 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Nonresidential 05/15/1990 KANSAS CITY, CITY OF NO Nonresidential 06/09/1984 KANSAS CITY, CITY OF NO Single-Family 04/15/1990 KANSAS CITY, CITY OF NO Nonresidential 05/17/1995 KANSAS CITY, CITY OF NO Nonresidential 09/18/1986 KANSAS CITY, CITY OF NO Nonresidential 05/01/1983 KANSAS CITY, CITY OF NO Single-Family 06/08/1984 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Single-Family 05/15/1990 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Single-Family 07/15/1981 KANSAS CITY, CITY OF NO Single-Family 06/25/1981 KANSAS CITY, CITY OF NO Other Residential 09/18/1986 KANSAS CITY, CITY OF NO Other Residential 09/18/1986 KANSAS CITY, CITY OF NO Single-Family 06/08/1984 KANSAS CITY, CITY OF NO Nonresidential 05/07/2007 KANSAS CITY, CITY OF NO Single-Family 06/09/1984 KANSAS CITY, CITY OF NO Nonresidential 06/12/1987 KANSAS CITY, CITY OF NO Single-Family 10/04/1998 Mid-America Regional Council January 2010

43 TABLE 3.58A: REPETITIVE LOSS PROPERTIES (UNMITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss KANSAS CITY, CITY OF NO Single-Family 06/09/1984 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Two to Four- 06/09/1984 Family KANSAS CITY, CITY OF NO Two to Four- 04/29/1983 Family KANSAS CITY, CITY OF NO Single-Family 06/26/1983 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Single-Family 06/08/1984 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Nonresidential 06/08/1984 KANSAS CITY, CITY OF NO Single-Family 06/09/1984 KANSAS CITY, CITY OF NO Other Residential 09/18/1986 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Single-Family 05/15/1990 KANSAS CITY, CITY OF NO Single-Family 05/16/1990 KANSAS CITY, CITY OF NO Nonresidential 05/15/1990 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Nonresidential 07/29/2008 KANSAS CITY, CITY OF NO Nonresidential 07/10/1993 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 05/15/1990 KANSAS CITY, CITY OF NO ASSMD CONDO 05/17/1995 KANSAS CITY, CITY OF NO Single-Family 07/10/1993 KANSAS CITY, CITY OF NO Nonresidential 07/08/2008 KANSAS CITY, CITY OF NO Single-Family 05/16/1990 KANSAS CITY, CITY OF NO Single-Family 08/13/1982 KANSAS CITY, CITY OF NO Nonresidential 06/08/1984 KANSAS CITY, CITY OF NO Single-Family 05/15/1990 KANSAS CITY, CITY OF NO Two to Four- 06/09/1984 Family KANSAS CITY, CITY OF NO Nonresidential 07/10/1993 KANSAS CITY, CITY OF NO Nonresidential 07/10/1993 KANSAS CITY, CITY OF NO Nonresidential 07/10/1993 KANSAS CITY, CITY OF NO Nonresidential 07/10/1993 KANSAS CITY, CITY OF NO Single-Family 07/11/1993 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Nonresidential 04/20/2002 KANSAS CITY, CITY OF NO Single-Family 05/18/1995 KANSAS CITY, CITY OF NO Nonresidential 05/18/1995 KANSAS CITY, CITY OF NO Nonresidential 05/17/1995 KANSAS CITY, CITY OF NO Single-Family 05/17/1995 KANSAS CITY, CITY OF NO Two to Four- Family 05/18/1995 Mid-America Regional Council January 2010

44 TABLE 3.58A: REPETITIVE LOSS PROPERTIES (UNMITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 05/17/1995 KANSAS CITY, CITY OF NO Single-Family 05/23/1995 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 05/19/2004 KANSAS CITY, CITY OF NO Single-Family 07/02/2008 KANSAS CITY, CITY OF NO Single-Family 04/22/1999 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 10/04/1998 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 10/04/1998 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 10/04/1998 KANSAS CITY, CITY OF NO Nonresidential 10/04/1998 KANSAS CITY, CITY OF NO Single-Family 10/03/1998 KANSAS CITY, CITY OF NO Single-Family 10/05/1998 KANSAS CITY, CITY OF NO Single-Family 10/10/1998 KANSAS CITY, CITY OF NO Single-Family 10/04/1998 LEE'S SUMMIT, CITY OF NO Nonresidential 05/06/2007 LEE'S SUMMIT, CITY OF NO Single-Family 05/12/2002 LEE'S SUMMIT, CITY OF NO Single-Family 04/01/1983 LEE'S SUMMIT, CITY OF NO Single-Family 09/23/1986 LEE'S SUMMIT, CITY OF NO Nonresidential 09/23/1986 LEE'S SUMMIT, CITY OF NO Other Residential 05/18/2004 LEE'S SUMMIT, CITY OF NO Other Residential 05/18/2004 LEE'S SUMMIT, CITY OF NO Other Residential 06/06/2001 LEVASY, CITY OF NO Single-Family 05/01/2007 LEVASY, CITY OF NO Single-Family 05/12/1995 LEVASY, CITY OF NO Single-Family 10/05/1998 RAYTOWN, CITY OF NO Single-Family 06/29/1999 RAYTOWN, CITY OF NO Other Residential 04/03/2001 RAYTOWN, CITY OF NO Nonresidential 05/19/2004 RAYTOWN, CITY OF NO Two to Four- 05/18/2004 Family RAYTOWN, CITY OF NO Two to Four- 05/18/2004 Family RAYTOWN, CITY OF NO Other Residential 05/19/2004 RAYTOWN, CITY OF NO Single-Family 06/04/1979 RAYTOWN, CITY OF NO Single-Family 06/02/1987 Mid-America Regional Council January 2010

45 TABLE 3.58A: REPETITIVE LOSS PROPERTIES (UNMITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss RAYTOWN, CITY OF NO Single-Family 08/13/1982 RAYTOWN, CITY OF NO Single-Family 05/01/1983 RAYTOWN, CITY OF NO Single-Family 09/23/1986 RAYTOWN, CITY OF NO Single-Family 08/13/1982 RAYTOWN, CITY OF NO Nonresidential 04/30/1983 SUGAR CREEK, CITY OF NO Single-Family 07/02/1999 EDGERTON, CITY OF NO Single-Family 08/13/1982 EDGERTON, CITY OF NO Nonresidential 07/07/1987 EDGERTON, CITY OF NO Single-Family 09/13/1993 PARKVILLE, CITY OF NO Single-Family 04/08/1983 PARKVILLE, CITY OF NO Single-Family 06/25/1981 PARKVILLE, CITY OF NO Nonresidential 10/10/1986 PLATTE CITY, CITY OF NO Single-Family 10/10/1985 PLATTE CITY, CITY OF NO Single-Family 07/14/1993 RIVERSIDE, CITY OF NO Nonresidential 08/13/1982 RIVERSIDE, CITY OF NO Nonresidential 07/25/1993 RIVERSIDE, CITY OF NO Nonresidential 07/25/1993 RIVERSIDE, CITY OF NO Two to Four- 10/04/1998 Family TRACY, CITY OF NO Single-Family 06/10/1984 WESTON, CITY OF NO Nonresidential 06/19/1984 HARDIN, CITY OF NO Single-Family 09/25/1993 HARDIN, CITY OF NO Single-Family 05/18/1995 ORRICK, CITY OF NO Single-Family 10/04/1998 ORRICK, CITY OF NO Other Residential 09/24/1993 ORRICK, CITY OF NO Single-Family 06/17/1996 PLATTE COUNTY* NO Single-Family 09/16/2001 PLATTE COUNTY* NO Two to Four- 05/07/2002 Family PLATTE COUNTY* NO Single-Family 05/09/2007 PLATTE COUNTY* NO Single-Family 07/09/1993 PLATTE COUNTY* NO Single-Family 08/13/1982 PLATTE COUNTY* NO Single-Family 07/30/1993 PLATTE COUNTY* NO ASSMD CONDO 10/04/1998 PLATTE COUNTY* NO Single-Family 06/07/1982 PLATTE COUNTY* NO Single-Family 08/13/1982 PLATTE COUNTY* NO Single-Family 07/23/1993 PLATTE COUNTY* NO Single-Family 10/04/1998 JACKSON COUNTY * NO Single-Family 07/03/1984 JACKSON COUNTY * NO Single-Family 05/08/2002 LAKE LOTAWANA, CITY OF NO Single-Family 10/04/1998 RAY COUNTY * NO Nonresidential 10/04/1998 RAY COUNTY * NO Single-Family 08/19/2000 RAY COUNTY * NO Single-Family 10/04/1998 Mid-America Regional Council January 2010

46 TABLE 3.58A: REPETITIVE LOSS PROPERTIES (UNMITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss RAY COUNTY * NO Single-Family 10/04/1998 CASS COUNTY * NO Single-Family 06/04/2008 CASS COUNTY * NO Single-Family 05/19/2004 CASS COUNTY * NO Single-Family 09/27/1986 CASS COUNTY * NO Single-Family 10/04/1998 CASS COUNTY * NO Single-Family 05/06/2007 CASS COUNTY * NO Single-Family 06/04/2008 CASS COUNTY * NO Nonresidential 10/04/1998 PECULIAR, CITY OF NO Single-Family 05/07/2007 PECULIAR, CITY OF NO Single-Family 06/03/2008 PECULIAR, CITY OF NO Single-Family 08/13/1982 PECULIAR, CITY OF NO Single-Family 09/27/1986 PECULIAR, CITY OF NO Single-Family 09/26/1986 PLEASANT HILL, CITY OF NO Single-Family 10/04/1998 PLEASANT HILL, CITY OF NO Single-Family 05/17/1995 PLEASANT HILL, CITY OF NO Single-Family 09/24/1986 PLEASANT HILL, CITY OF NO Single-Family 09/14/1998 PLEASANT HILL, CITY OF NO Single-Family 10/04/1998 SMITHVILLE, CITY OF NO Two to Four- 08/13/1982 Family SMITHVILLE, CITY OF NO Nonresidential 08/13/1982 SMITHVILLE, CITY OF NO Single-Family 08/13/1982 SMITHVILLE, CITY OF NO Single-Family 08/13/1982 SMITHVILLE, CITY OF NO Single-Family 08/13/1982 SMITHVILLE, CITY OF NO Single-Family 05/12/1983 SMITHVILLE, CITY OF NO Two to Four- 08/13/1982 Family SMITHVILLE, CITY OF NO Single-Family 08/13/1982 Source: SEMA TABLE 3.58B: REPETITIVE LOSS PROPERTIES (MITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss AVONDALE,CITY OF YES Single-Family 07/13/1992 AVONDALE,CITY OF YES Single-Family 09/08/1989 AVONDALE,CITY OF YES Single-Family 06/23/1998 INDEPENDENCE,CITY OF YES Single-Family 05/01/1983 INDEPENDENCE,CITY OF YES Single-Family 06/28/1999 INDEPENDENCE,CITY OF YES Single-Family 08/13/1982 INDEPENDENCE,CITY OF YES Single-Family 08/13/1982 INDEPENDENCE,CITY OF YES Single-Family 08/13/1982 INDEPENDENCE,CITY OF YES Single-Family 05/01/1983 INDEPENDENCE,CITY OF YES Single-Family 08/13/1982 INDEPENDENCE,CITY OF YES Single-Family 08/13/1982 INDEPENDENCE,CITY OF YES Single-Family 10/04/1998 Mid-America Regional Council January 2010

47 TABLE 3.58B: REPETITIVE LOSS PROPERTIES (MITIGATED) Community Name Comm Nbr Mitigated? Occupancy Date of Most Recent Loss LEVASY, CITY OF YES Single-Family 07/07/1993 TRACY, CITY OF YES Single-Family 07/26/1993 PLATTE COUNTY* YES Single-Family 07/06/1993 PLATTE COUNTY* YES Single-Family 07/15/1993 PLATTE COUNTY* YES Single-Family 06/10/1984 PLATTE COUNTY* YES Single-Family 07/06/1993 PLATTE COUNTY* YES Single-Family 06/10/1984 PLATTE COUNTY* YES Single-Family 07/08/1993 Source: SEMA Development Trends The rate of a region's population growth, household formation and income growth is reflected in data on residential construction. As new households are formed, the demand for starter homes and multi-family housing increases. As these households age, they begin to have children and search for larger, more expensive homes in family neighborhoods. How housing construction changes, then, indicates changes in life cycle and income levels. Likewise, the rate of a region's labor force and other economic growth is reflected in the growth of the number of dollars invested in nonresidential construction. This includes nonresidential buildings such as retail stores, manufacturing plants, warehouses and office complexes, and non-building construction such as roads, bridges, airports, sewer systems and telecommunications infrastructure. The value of any construction, building or non-building, is a strong indicator of the health of the area s economy. If the demand for new homes is strong, this demand points to the area s growth in population, employment and earnings. The value of the construction is also a major economic component in terms of the dollars created by an industry within any area, primarily in new job creation and in the multiplier effect of those wages and salaries. Construction affects both the economy and is in turn affected by it. An economic downturn that causes unemployment and loss of earnings for the industries in the area, especially major employers, will be soon reflected in a slowdown in construction. For example, if a large employer no longer requires its office park, and this space becomes available, it will affect the vacancy rate for office space in the area. Not only will prices for square footage of office space come down, but eventually, new office construction will slow down. Depending on the extent of the economic downturn, infrastructure construction might also be affected as fewer roads, sewers, etc., are needed. Finally, home construction will also slow down as the area s population reduces or postpones its demand for housing. (F. W. Dodge Local Construction Potentials, Home Builders Association of Greater Kansas City US Census Bureau, Population Division) Understanding the above development trends is essential to mitigation planning. Table 3.59 below shows housing estimates for the Kansas City area from 2000 to Table 3.60 lists non-building construction values from 2005 to Table 3.61 lists population changes from April 2000 to July As is seen, the entire are is experiencing some level of growth in terms of both population and construction. As development increases, so does the need to protect these investments from the effects of natural and other hazards. Mid-America Regional Council January 2010

48 TABLE 3.59: ANNUAL ESTIMATES OF HOUSING UNITS FOR COUNTIES IN MISSOURI: APRIL 1, 2000 TO JULY 1, 2008* April 1, 2000 Housing Unit Estimates (thousands) Geographic Area Estimates Census 7/1/00 7/1/01 7/1/02 7/1/03 7/1/04 7/1/05 7/1/06 7/1/07 7/1/08 Base Missouri 2,442,000 2,442,000 2,449,000 2,477,000 2,504,000 2,534,326 2,565,804 2,595,309 2,624,003 2,648,009 2,663,977 Cass County 31,677 31,677 31,886 32,719 33,705 34,841 35,744 36,792 37,913 38,829 39,267 Clay County 76,230 76,230 76,480 77,481 78,447 79,718 81,063 82,638 83,832 84,846 85,386 Jackson County 288, , , , , , , , , , ,024 Platte County 30,902 30,901 31,077 31,781 32,582 33,420 34,137 34,935 35,747 36,615 36,979 Ray County 9,371 9,371 9,397 9,498 9,594 9,717 9,850 9,988 10,137 10,180 10,226 Source: U.S. Census Bureau, Population Division *Note: The April 1, 2000 Housing Unit Estimates Base reflects changes to the Census 2000 housing units from the Count Question Resolution program and geographic program revisions. Annual Estimates of Housing Units for the United States and States: April 1, 2000 to July 1, 2008 (HU-EST ) TABLE 3.60: NON-BUILDING CONSTRUCTION VALUE ($000'S) Month Total Cass Clay Jackson Platte Ray Jan-05 37,382 1,565 12,106 20,142 3,569 0* Jan-06 38,707 1,005 11,951 24, Jan-07 20,769 0* 3,756 16,302 0* 711 Jan ,809 0* , Jan-09 20,204 0* 891 2,292 16, TABLE 3.61: POPULATION, PERCENT CHANGE, APRIL 1, 2000 TO JULY 1, 2008 Cass Clay Jackson Platte Ray Source: U.S. Census Bureau Web site Source: F. W. Dodge Construction Potentials *Note: Where values equal 0, data unavailable. Mid-America Regional Council January 2010

49 Another important aspect when considering mitigation planning is current and anticipated land use. Land use data allows for planners to focus efforts and maximize resources on areas likely to be affected by hazards. Figures 3.51 through 3.53 present existing land use for Cass, Clay, Jackson and Platte counties. Data is currently unavailable for Ray County. All data compiled by MARC GIS staff with assistance from the participating jurisdictions and is current through Figure 3.51: Existing Land Use Map for Cass County Source: MARC Research Department Mid-America Regional Council January 2010

50 Figure 3.52: Existing Land Use Map for Clay County Source: MARC Research Department Mid-America Regional Council January 2010

51 Figure 3.53: Existing Land Use Map for Jackson County Source: MARC Research Department Mid-America Regional Council January 2010

52 Figure 3.54 Existing Land Use Map for Platte County Source: MARC Research Department Figures 3.55 through 3.58 depict planned land use for Cass, Clay, Jackson and Platte counties through 2030 to Data is unavailable for Ray County. All data compiled by MARC GIS staff with assistance from the participating jurisdictions and is current through Mid-America Regional Council January 2010

53 Figure 3.55: Planned Land Use Map for Cass County Source: MARC Research Department Mid-America Regional Council January 2010

54 Source: MARC GIS Department Figure 3.56: Planned Land Use Map for Clay County Mid-America Regional Council January 2010

55 Figure 3.57: Planned Land Use Map for Jackson County Source: MARC Research Department Mid-America Regional Council January 2010

56 Figure 3.58: Planned Land Use Map for Platte County Source: MARC Research Department Mid-America Regional Council January 2010

57 Attachment 1 ATTACHMENT 1: HAZUS-MH FLOOD LOSS TABLES TABLE 3.62: DIRECT ECONOMIC LOSSES FOR BUILDINGS IN THE KANSAS CITY AREA *Note: This table combined from four tables with differing study dates. Dates are as follows: Cass 11/17/06; Clay/Jackson 11/20/06; Platte 1/23/07; Ray 1/02/07. Source: SEMA Mid-America Regional Council 3-A-1 January 2010

58 Attachment 1 TABLE 3.63: BUILDING DAMAGE BY GENERAL OCCUPANCY (CASS COUNTY) Source: SEMA Mid-America Regional Council 3-A-2 January 2010