introduction and overview - usehazus · web view30/09/2013 · deterministic scenario options...

Vsn. 11.04.13 Hazus-MH Toolkit

FEMA Regional OfficeHazus-MH

Toolkit

FEMA Training Course 1


This document provides annotations for a series of slides to be used during a one day workshop. The purose of the workshop is to empower FEMA regional staff with the knowledge to understand and credibly represent the capabilities of Hazus-MH when working with state and local government officials.



This workshop is divided into three components. The first provides an overview of Hazus-MH. The second addresses the opportunities that are available for enhancing Hazus-MH outputs. The last involves a facilitated conversation with participants about how FEMA can encourage communities to use Hazus-MH.



Dr. Snow is often recognized as the Father of both GIS and epidemiology for discovering the etomology of cholera and using GIS techniques to explain and analyze that disease.



Hazus-MH operates on a Geographic Information Systems (GIS) platform, so it is necessary to have a basic uderstanding of the concepts of GIS.



These are the same components that are utilzed by modern computer driven GIS tools like Hazus-MH. You need a good map of your area of interest that displays accurately the location of the assets that that are to be studied. You need good information about the situation that you want to study. Finally, you need clever tools for identifying and visualizing your analysis on the map.



Hazus works very much like Dr. Snow’s epidemiological study to analyze the impact of various natural disasters on the built enviornment and the population. To get the most out of Hazus, you need a good map that shows you where the assets are located. You need good scientific data about the natural disaster to define the affected area and the degree to which it is affected. Finally you need good algorithms that can calculate the degree of impact on your area of interest. As with any modeling tool, the better your map and input data, the better will be the results of your analysis.



This slide identifies the elements that distinguish Hazus-MH from the many other similar loss estimation software solutions that currently exist. Key elements to point out include:

Hazus is a product of the Federal Emergency Management agency. This implies that it has the endorsement of that agency as an officially approved technology for supporting loss estimation.

Hazus addresses multiple natural hazards rather than any one specific hazard. You should also point out, however, that Hazus is not intended to calculate losses for other types of manmade or natural hazards.

Hazus is more than a software tool. It is supported by a robust education program, user groups, a website, a national conference and other resources that support the Hazus user community.

Hazus-MH is free of charge. HOWEVER, it does require users to have a licensed version of ArcGIS on their computer. One of the drawbacks to the currently release of Hazus-MH is that it requires an outdated version of ArcGIS – specifically, ArcGIS 10 service pack 2. The currently release of ArcGIS is version 10.2. FEMA may be releasing a more up-to-date version of Hazus in 2014.



The Hazus program has been in existence for over 20 years. It originally supported loss estimation modeling only for earthquakes but was expanded with the 2003 release to include both selected flood (not including flash flooding for example) and hurricane wind analysis. Most recently it has been enhanced to allow for combined analysis of both hurricane wind and storm surge – although separate loss estimates are still generated for those areas impacted by wind and flood outside of the storm surge impact area.

The Hazus-MH project team has been relatively consistent throughout the history of the program. Some highlights of the development history include:

• Hazus has been sponsored by FEMA throughout its history• Management under FEMA was conducted by the National Institute of Building Sciences

from the origin of the program through September 2009. The Hazus-MH application shifted to a new management team under the Risk MAP program starting in late 2009. This effort was led by Zimmerman and Associates up to 2012 when it transitioned to the current contract that is led by IBM.

• Technical oversight of Hazus has included a number of committees staffed by industry experts for each of the hazard models.

• Through the current release of Hazus-MH, technical development has been done the same consultants. These include:

o Flood model developed by ABS Consultingo Earthquake model developed by Atkins Global (formerly PBS&J Consulting)o Hurricane model developed by Applied Research Associates

• User involvement has been a key element of the Hazus-MH development process.



The point to make about this slide is that the types of hazard related impacts that Hazus-MH addresses are important to nearly every sector of society. The dominant user of Hazus-MH is local and state government agencies that address hazard mitigation and other aspects of the emergency management lifecycle. However, Hazus has seen considerable interest from other organizations as well. For example, various educational institutions have engaged in the use of Hazus to support the Disaster Resistant Universities program requirements as well as providing Hazus training and services such as mitigation planning support. Many private sector firms have also become well versed in Hazus-MH especially since the initiation of the RiskMAP program.

Over the past few years Hazus-MH has received increasing international recognition for its accomplishments. One of the best examples of this to date is the decision of the Canadian government – led by Natural Resources Canada – to develop a Canadian version of Hazus-MH in collaboration with FEMA. Canada selected Hazus as the right solution for their needs after conducting a global study of existing loss estimation tools. Hazus-MH was selected from a pool of approximately twenty candidates based on the capabilities of the software as well as the existence of a comprehensive support network that included user groups, a robust education program and technical support. Other countries have also expressed interest in adopting the Hazus-MH program to their needs. These include Singapore, Turkey, and others.



This slide graphically illustrates the major steps that Hazus-MH goes through when assessing the risk from hazards. Starting with the bottom tier, the major points that should be presented to the participants are as follows. (1) Hazard analysis begins with an assessment of the environment. Examples of environmental factors that Hazus-MH considers include things such as soil composition for earthquakes, terrain changes for flooding, and land cover for hurricanes. (2) With input from the user, Hazus-MH assesses the hazard. Examples might include an earthquake of a specific magnitude, a flood of a specific return period, or a hurricane of a specific intensity. (3) The inventory defines the exposure to the hazard faced by the community. Point out that the inventory is connected across all three hazards to reflect the fact that many—although not all—inventory elements are identical for all of the hazards. Examples include the demographic data and building square footage. (4) The fourth step in the process is the analysis, which involves comparing the hazard to the inventory to assess losses. (5) The final step reflects the output of the model, which includes dollar losses and many other items.



The purpose of Hazus-MH is to provide information to communities that can help them minimize losses to life and property. Hazus is intended to support these goals by generating information that can be used to facilitate effective mitigation of risk.



FEMA devised a scheme comprised of three levels of use that are intended to classify the types of work that Hazus users can do with the Hazus-MH software.

The Polis Center believes that the use of this scheme over the past few years has, at times, been counterproductive due to the inconsistency with how the definitions of Level 1, 2 and 3 have applied. As noted below, the definitions of Level 1, 2 and 3 analysis can vary widely. Yet, there have been numerous references in literature, presentations, requests for proposal, etc. to these levels of analysis without adequate explanation of what was actually done to the model inputs to justify those classifications. We recommend that terms such as ‘Basic’ (which implies the minimal amount of user input based on the selected hazard and scenario) and ‘Enhanced’ (which implies anything beyond minimal required inputs) analysis would be more appropriate when describing hazard data inputs, inventory modifications, and parameter modifications that have been or will be implemented. However, even with terms of that type in place, the most appropriate solution is simply to require that – in each instance – careful documentation of what is being or will be changed be put in place. This is necessary due to the simple fact that there are literally dozens of different changes that could be made to inventory, hazard inputs, and analysis parameters that could be made and without the appropriate documentation in place there is no reliable way to know what has been done.

Given that FEMA still uses the Level 1, 2 and 3 scheme, we have provided the following explanation that can be referenced when discussing these terms with communities.



Level 1Characteristics of a Level 1 analysis include:

Inventory No changes are made to the Hazus-MH provided inventory.

Hazard Hazus-MH, not the user, defines the hazard. The user will provide only limited

inputs. A Level 1 flood model requires that the user define the hazard by asking Hazus to generate a depth grid based on a return period or based on a user provided discharge value for each reach in the scenario. Either approach requires the user to provide inputs such as a DEM and a stream network drainage area value. While Hazus-MH can produce a reasonable approximation of flood inundation extent in many instances, it is important to note that a Hazus-MH generated depth grid based on one of these options is subject to potential error due to a variety of assumptions made by the model. These include not accounting for varying conditions around bridges, assuming that the same conditions apply across a stream channel, and others. For this reason, a Hazus-MH generated depth grid needs to be carefully evaluated for accuracy to ensure that it is used properly.

A Level 1 earthquake model will generate ground motions based on a range of default assumptions. These include applying constant values for soil type, liquefaction potential, landslide susceptibility, and more across an entire study region.

A Level 1 hurricane model generates a wind field based on default assumptions after the user manually selects a historic storm path.

Analysis No modifications are made to default assumptions (such as shelter parameters, direct

economic loss parameters, etc.) related to analysis parameters. No modification of damage curves are made

Level 2Describing a Hazus-MH study as a Level 2 analysis does not imply any specific changes were made to the model – only that the user has done something to influence the accuracy of the model outputs beyond what is required for a Level 1 analysis. The amount of effort required to perform a Level 2 analysis can vary considerably depending on exactly what is done to the modeling inputs. Given the range of possibilities for what could be considered a Level 2 analysis, it is vital that documentation is created that specifies what is expected or has been completed that qualifies a study to be termed Level 2.

Characteristics of a Level 2 analysis may include:Inventory

The user may change the inventory. Changes to inventory are often cited as ‘Level 2’ analysis. However, it is very important to document exactly what has been or will be changed since the level of effort can vary considerably depending on what is done. For example, simply changing the name of a fire station is technically a ‘Level 2’ analysis. Changing the entire inventory is also a ‘Level 2’ analysis.

Hazard In a Level 2 analysis the user may elect to provide their own hazard definition. This

can range from a flood depth grid generated from a powerful model such as HEC-RAS to something much less refined such as a flood boundary that is used to perform a Quick Look analysis.



Analysis Users may elect to make modifications to selected default assumptions (such as

shelter parameters, direct economic loss parameters, etc.) related to analysis parameters.

In a Level 2 analysis no modification are made to damage curves.

Level 3Describing a Hazus-MH study as a Level 3 analysis does not imply any specific changes were made to the model – only that the user has done something to influence the accuracy of the model outputs beyond what is required for a Level 2 analysis. Level 3 users normally possess expertise in one or more fields such as economics, seismology, hydrology, etc. that enable them to influence how loss estimations are generated. This can be accomplished by creating new damage functions, defining new building categories, and so forth.



Study regions are self-contained collections of information about a user-specified geographic area. The study region creation process requires the user to identify the area of interest (county, state, watershed, census tract, or census block) and extracts the inventory information for that area to a study region folder. When extracted, there is no connection between the state data and the study region data. It is important that Hazus-MH users understand that study region extents cannot be modified after the initial study region creation process is finished.

Questions to consider when defining a study region: Are you interested in studying the impacts of floods, hurricane winds, and/or earthquakes? Will the hazard impact areas outside your community that may affect your community? For example, does your county use hospitals in neighboring counties? Would it be important for you to know what hospitals in your county and these neighboring counties may be affected by a hazard?

You need to choose which of the three hazards to include and what areas you want to assess when you create your study region. Damage estimates are calculated only within the boundaries of a study region. However, it is important for the participants to realize Hazus-MH recognizes that hazards occurring outside a study region will impact the study region; therefore those hazards are included in damage estimates. Examples of this might include flooding that originates from a watershed that extends outside the study region.



Aggregate inventory represents a sum of values associated with building counts, square footage, building values, etc. within a geographic boundary. The majority of the aggregate inventory categories are reported for census tract and census block boundaries. The earthquake and hurricane models generate losses to aggregate inventory at the Census Tract level and the flood model reports losses by census block. A key point to make with regard to this slide is that aggregate inventory – especially the General Building Stock- in Hazus-MH is the source of most of the Hazus-MH loss estimation outputs. This includes debris, building losses (with limited exceptions), shelter estimates, direct economic impact, and indirect economic impact to name a few. The site specific inventory components in Hazus-MH are limited to the categories listed on the slide – although there are many different types of utilities, for example, that can be modeled within those categories. It could be argued that one of the most important site specific inventory components is the User-Defined Facility inventory. This is not populated out-of-the-box, but users can enter individual points representing local buildings if they have the appropriate data available. This ability permits them to model – as points – every type of building that can be modeled in the General Building Stock.

It should be noted that that some of the building types in the site-specific inventory—such as schools—also are represented in the aggregated data tables. However, losses for site-specific inventory and aggregated inventory are calculated and reported independently of one another. The reason for this redundancy is to allow an estimation of overall losses to buildings via the aggregated GBS data and to allow more accurate loss calculations for important structures such as essential facilities.



This demonstration is optional. If your audience is already familiar with the Hazus-MH inventory your should skip this part of the workshop. However, if the audience is not familiar with the structure of the Hazus-MH inventory it may be helpful to do a live demonstration of the Hazus-MH inventory at this point. Open one of the study regions that you created for this workshop for this purpose.



In the next part of the workshop you will introduce the major features of the Hazus-MH flood model.



Spend a few minutes on this slide. Discuss the different inputs that are required for each of the flood model hazard delineation options.

Option Required InputsReturn Period DEM in Esri grid formatDischarge Value DEM in Esri grid formatStorm Surge DEM in Esri grid formatFlood Information Tool DEM in Esri Grid format, cross sections

attributed with flood surface elevation, and flood boundary

Quick Look Flood boundaryEnhanced Quick Look DEM in Esri Grid format and a Flood

boundaryUser Provided Depth Grid Depth grid in Esri Grid formatHEC-RAS Depth grid in HEC-RAS flt format

It should be stressed that the quality of Hazus-MH loss estimations is closely aligned with the quality of the hazard delineation. The most credible flood delineation option in this list is generally a User Provided Depth Grid or HEC-RAS.



Discuss how the amount of damage typically increases more rapidly at the start of the curve than at the end of the curve. In other words, water does not need to be extremely deep to cause significant damage. A typical depth-damage function is shown on this slide. There are several hundred damage functions available for the flood user in the model.



The flood model can generate loss estimates for each of the components listed on this slide. Two of the items listed, agriculture and vehicle losses, are unique to the flood model.

Remind the participants that most of these outputs are generated from aggregate inventory. These include the general building stock related losses, vehicle losses, agriculture losses, shelter requirements and indirect economic losses.



If the audience is unfamiliar with the design of the flood model you should take this opportunity to demonstrate its main features.



In the next part of the workshop you will introduce the major features of the Hazus-MH hurricane model.



Hazus-MH can model the impact of hurricanes on the 22 gulf and east coast states as well as the state of Hawaii. In addition, some work has been done for Puerto Rico that makes it possible to run a Hazus hurricane analysis for that island as well. The Puerto Rico modeling capabilities are not currently available ‘out-of-the-box’ in Hazus-MH 2.1 SP1.



The Hazus hurricane model for the United States was run to simulate 100,000 hurricane seasons. This is because there is only around a 150+ year historic record for hurricanes in the US, making it difficult to truly estimate the hurricane risk with such a short historic timeframe (e.g., 0.2% chance event, or 500 year event isn’t easy to catch if it did not occur in the 150 year history). From this simulation 300,000+ synthetic storms were created.

Hazus also comes packaged with 100+ years of historic storms that made landfall in the US and at a Category 3 intensity (ONLY). A user can also import a storm from HURREVAC, create their own, or use a forecast advisory.



The analysis menu provides access to the damage functions and to the “Run” window, just as it does in the flood and earthquake models. One significant difference in the hurricane model is that users only can view, rather than change, the damage functions. The complexity of those functions resulted in a decision by the Hazus-MH oversight committee to restrict users to a viewing capability. Point out that when the “+” sign is clicked, the lower-left part of the window shows a unique set of characteristics for that Specific Building Type.

The damage curves represent the “mean” or “expected” probability of a level of building damage (minor, moderate, severe, or destruction), given a peak gust wind speed (maximum 3-second gust 10 meters above ground in open terrain) for a specific building type with specific characteristics (such as roof shape, secondary water resistance, etc.). Point out that damage states always are reported as a probability.

These functions cannot be modified. However, you can modify the fraction of buildings in the Specific Building Type that are assigned to a given combination of wind characteristics.



One of the key differences between outputs in the flood, hurricane, and earthquake models is the fact that damages in most hurricane and earthquake tables are reported as probabilities of being in a damage state rather than providing specific counts of damaged buildings in each census area.

In order to understand how to apply damage states to ‘real world’ conditions, users are recommended to consult the Hazus-MH technical manuals. For instance, the following example shows a description of the conditions that might be expected in various damage states for residential construction.



Essential facility losses are reported both in terms of loss of days and damage state probability.



The Hazus-MH hurricane model estimates debris impacts for brick, wood, reinforced concrete, and steel. This material typically goes to a landfill following a major event. The Hazus-MH Hurricane Wind model has grouped these four sub-types into two categories: Brick/Wood and Reinforced Concrete/Steel (based on the type of equipment needed to break up or move the material). With regard to generating estimates of tree debris, the user has the choice of editing the following variables:

1. Number of stems per acre2. The percentage of trees within each height participants (< 40 ft., 40–60 ft., and > 60 ft.)3. Collection Factor

The collection factor is based on the underlying premise that if trees fall in close proximity to highways, streets, and buildings, they are going to be collected and brought to the curb for pickup. As such, an area reduction factor was developed, based on a predetermined collection area around each of the streets and buildings within a study region. When the collection factor is low, this generally indicates less-populated areas and fewer buildings. When the calculation factor is high, this generally indicates more buildings.



The hurricane model provides estimates, based on the General Building Stock inventory of losses to buildings as well as business interruption impacts.



The Hurricane model applies much the same methodology that is used by the earthquake and flood model. As is the case in all three Hazus-MH models, the modification factors describe percentage of each population participants that likely will use short-term shelter (i.e., one ethnic population might be more likely to use shelter than another ethnic population). However, it should be noted that the default for the Hurricane model is to use only ethnicity and income as factors in shelter predictions; income is the dominant factor (0.73). Share with the participants that this is a parameter that they may wish to review to ensure that it reflects local circumstances.



If you have created a hurricane study region you should take a few minutes to demonstrate the features and outputs of the Hurricance model.



In the next part of the workshop you will introduce the major features of the Hazus-MH earthquake model.



Deterministic scenario options include selecting a historic epicenter event from the database of approximately 6,200 epicenters of magnitude 5.0 or greater (magnitude 5.0 is generally the level at which damage begins to occur); a source event (this option allows the user to identify a point on a fault where the epicenter is located and is only available for the West Coast); or an arbitrary event that allows the user to define the location, strength and other parameters arbitrarily.Probabilistic scenarios are based on the seismic hazard contour maps developed by USGS for the National Seismic Hazard Mapping Project. They can be based on one of eight specific return periods—either 100, 250, 500, 750, 1000, 1500, 2000, or 2500 with a defined moment magnitude—or they can be based on annualized losses.

A user can decide to create a probabilistic scenario or examine the annualized losses expected for a region. They can base a probabilistic scenario on a specific return period event when they unsure how to define a specific event in an area but want to analyze the impacts of ground shaking for a certain type of event. Another option is the Annualized Earthquake Loss (AEL) which provides the estimated long-term value of earthquake losses to the general building stock in any single year in a specified geographic area. AEL averages potential losses based on eight different return periods—including the 100, 250, 500, 750, 1000, 1500, 2000, and 2500 year return period—and incorporates historic patterns of frequent smaller earthquakes with larger, infrequent events to create a balanced assessment of earthquake risk. A user may want to use an AEL to provide a baseline for policy development and to prioritize areas for mitigation strategies and actions. When used in conjunction with a loss ratio or per capita basis, comparisons of relative risk can be performed. This also makes it easier to compare risks against other hazard



types in an apples-to-apples type comparison. You should mention FEMA’s 366 publication: Estimated Annualized Earthquake Losses for the U.S., April 2008, which provides some good background and results for AEL. You also need to define the magnitude so the model can determine whether the event is considered short, moderate, or long duration—which will impact the extent of damages.

User-Defined Scenarios are usually based on outputs from the ShakeMap. Required inputs include feature classes which define Peak Ground Acceleration, Peak Ground Velocity, Spectral Acceleration at 1.0 second and Spectral Acceleration at 0.3 seconds. Additional hazard definition maps can also be integrated into the modeling process if they are available.



Ground motion has two key components: ground shaking and ground failure. This slide elaborates on how the shaking is evaluated and what kind of output is obtained.

You may want to provide some explanation of attenuation. This relates to how the waves of energy released from a fault rupture disperse with distance from the rupture. To some extent, attenuation is similar to how waves of water in a lake shrink and disappear with distance from the impact of an object such as a rock. However, unlike the way that waves of water disperse as concentric circles, earthquake energy waves typically do not disperse evenly due to interaction with the geology where the earthquake occurred (e.g., the map above does not radiate in a perfect concentric form). Note that soil type dramatically affects the shaking.

Earlier version of Hazus only allowed for scenarios of magnitudes ranging from 5 to 8.5 and only estimated damages for distances ranging from 1 km to 200 km in increments of 1 km. The current version of Hazus supports moment magnitudes up to 9.0 and estimates losses out to 1000 km.



General Building Stock Damages are reported as a probability for each damage state and for each census tract. It is important for the participants to understand what slight, moderate, extensive and completely damaged buildings mean. The definition will be different for each specific building type. You can find this information in the Technical Manual.

The Capacity-Response Spectrum method – which is used by Hazus-MH – considers the capacity of a structure to sustain specified ground motions.

If a participant asks a question about how specifically the damages are calculated, the following is a brief summary. In order to calculate damage state probabilities Hazus-MH completes the following steps: (1) Calculates the spectral accelerations and spectral displacements at the site in question in the form of a response spectrum. (2) Modifies the response spectrum to account for the increased damping that occurs at higher levels of building response. (3) Creates a capacity curve for the model building type which shows how the building responds as a function of the laterally applied earthquake load. (4) Overlays the building capacity curve with the modified response spectrum and estimates the building displacement from the intersection of these curves. (5) Compares the estimated building displacement with the fragility curve (which describes the probability of a building being in a damage state as a function of the size of an earthquake) to determine the amount of damage that has been sustained.

Fragility curves define the probability of a building being in a specific damage state as a function of the size of the earthquake.



Economic losses are generated for all of the information listed on this slide. Explain that losses are generally reported in thousands of dollars. Also remind them that losses can be viewed either in the “raw” tables or in reports.



Discuss with the participants how you would use this data. For instance, if Hazus-MH estimates a hospital would be at only 20% functionality after 90 days, what does that mean? It is important for the Hazus users to understand that Hazus-MH is a planning tool and that site-specific results should be used with caution. In the example of the hospital at only 20% functionality after 90 days, a user could use that information for a “what if” scenario. More specifically, if a Hazus-MH model run results in a hospital only having 20% functionality after 90 days, what plans need to be in place if the hospital is inoperable over a long period of time? It is best not to think about the exact numbers; rather, it is better to think in terms of generalities.



Having usable transportation routes is extremely important. Without usable roads, bridges, railways, etc, a community will have a hard time with rescue efforts, bringing in supplies, and providing other essential services to the community.

The ability to assess the number of households without electricity and potable water is useful for planning purposes. This can lead to a better understanding of the community’s needs after an earthquake disaster. Without potable water the local jurisdiction might need to provide water to a number of residences.

Once again, discuss with the participants the best ways to use this data. For instance, if Hazus-MH estimates a waste water facility would be at only 20% functionality after 90 days, what does that mean? It is important for the participants to understand this is a planning tool and that site-specific results should be used with caution. In the example of the waste water facility at only 20% functionality after 90 days, a user could use that information for a “what if” scenario. In a community, what plans need to be in place if the waste water facility is inoperable over long periods of time?



Fires are often a common issue after an earthquake. This is another example of an effective tool for earthquake planning and mitigation.

Note for the participants that Hazus-MH provides a simplified fire following earthquake model. Users are encouraged to update the analysis parameters to reflect local conditions at the time of the simulated earthquake. Recommendations for how to define the required parameters are provided in the earthquake model user manual.

Debris is divided into two categories to reflect differences in the types of equipment that will be required to remove the material. Note that Hazus–MH only models building debris – it does not account for collapsed bridges due to lack of details about dimensions in the bridge inventory which would potentially lead to misleading debris estimates.

Debris results only refer to building debris. Knowing the locations of the highest debris could assist a community in prioritizing clean-up efforts. In the planning arena, this information could be beneficial in targeting mitigation projects.



Hazus-MH models four types of casualties in the earthquake model based on the extent of injuries. In addition casualties are calculated based upon where population is likely to be located at various times of day – 2 a.m. 2 p.m., and 5 p.m. Casualties are estimated for damaged buildings as well as damaged bridges.



Shelter requirements consider both the number of displaced households and the number of people who are expected to be requiring shelter. It is assumed that many individuals will find alternative shelter with friends, family or in hotels while others will seek public shelters. The number of displaced persons who are expected to seek shelter will be a function of the age, ethnicity, income and home ownership status of the displaced population. Users have the option to modify the weights assigned to these parameters if desired.



If you created an earthquake study region this is the point in the workshop where you should demonstrate the design and outputs of the Hazus-MH earthquake model.



The following slides address the issues that impact the ability of Hazus-MH to generate credible information about how risk impacts communities.



You will likely ask or be asked how accurate Hazus-MH outputs are. The answer to that question is in large part dependent on the inputs. Those inputs can be organized into three categories – the quality of the inventory (which defines what is at risk), the quality of the hazard definition (such as the extent and depth of flooding) and the quality of the loss estimation agorithms that drive the loss estimation. These three inputs act very much like the legs of a chair. If any one leg is weak, regardless of how strong the other two legs are, the chair will fall over. When applied to Hazus this means that, for example, if your inventory is perfect but your hazard definition if flawed the model outputs will not correctly reflect the risk to the community. Most Hazus users should focus their attention on refining the quality of the hazard and inventory inputs. Refining the loss estimation algorithms is a task best left to domain experts in building sciences, economics, etc. and is rarely necessary given that the methodologies built into Hazus are designed to address loss estimation calculations assuming that inventories and hazard definitions are credible.



There are many different options for integrating user provided hazard data into the Hazus-MH analysis process.

Hazus-MH model is very much like an airplane model. Just as better materials help the airplane modeler build a more realistic airplane, better model inputs (local building data, detailed hazard maps, etc.) help the Hazus-MH modeler create loss estimations that more accurately reflect the actual losses from an event.

Note that CDMS currently (as of the release of Hazus 2.1 SP1) requires the user to import Office 2003 formatted files.



As previously discussed, there are a number of options for users to influence the quality of the hazard delineation in a flood model. The most desirable of these options is typically providing Hazus-MH with a User Provided Depth Grid – this assumes, of course, that the quality of the provided depth grid is appropriate for the purposes of the study being conducted.

One of the activities that FEMA regional offices, likely in collaboration with appropriate state agencies, can help facilitate is identifying the locations of hazard data within the states that they serve. The availability of these hazard data resources, along with recommendations for how and when they should be applied for Hazus-MH supported studies, should be made known to Hazus-MH users within the region.



This graphic shows the status of the DFIRM development for FEMA Region III as of September 30, 2013. This should be replaced with a different map if the presentation is used in another region or on a later date in FEMA Region III.

If the participants are unfamiliar with DFIRM products it may be useful to spend a few minutes describing those that are most relevant to Hazus-MH users – namely the flood boundaries, cross sections, and other flood study inputs that can be used by Hazus flood delineation tools.



As previously discussed, there are a number of options for users to influence the quality of the hazard delineation in an earthquake model. The most desirable of these options is typically providing Hazus-MH with a Shakemap. Shakemaps typically describe ground shaking based on specific earthquake scenarios. The other ‘common’ inputs listed on this slide can be used by Hazus-MH to refine the earthquake hazard delineation process.

In the absence of a Shakemap, the most important of the user defined hazard inputs for earthquakes is soils. Be sure that it is well understood that Hazus-MH requires NEHRP-compliant (National Earthquake Hazard Reduction Program) soils polygons in an Esri personal geodatabase format. The required file structure for user provided soils data as well as the other optional hazard delineation inputs is provided in the Hazus-MH technical documentation.



User enhanced hazard inputs for the hurricane model generally focus on importing Hurrivac storm advisories, a capability built directly into the Hazus-MH GUI. The remaining options require expert input. H*Wind (http://storm.aoml.noaa.gov/hwind/) is a research level product that is unlikely to be used by the majority of Hazus-MH users. Hazus-MH windfields are easily imported into Hazus-MH if available. These may be developed in collaboration with expert Hazus-MH users who also possess knowledge of hurricane hazard development. Roughness factors in the Hazus-MH hurricane model characterize the type of landscape conditions that exist in a hurricane study region. Default values have been applied within Hazus-MH but it is valuable to change the defaults if conditions (e.g clear cutting of forests, development of new built up areas, etc.) have changed in the community being analyzed in Hazus-MH.


http://storm.aoml.noaa.gov/hwind/


Area weighting works as follows: Each grid cell with a unique depth value is identified and cumulatively added to a 1/2 ft “flood depth storage bins” representing identical flood depths within 1/2 ft. For example, a grid cell with a unique depth of 3.14 ft is categorized into a 3- to 3.5- ft depth bin. When plotted along the horizontal axis of depth, these “depth bins” are representing the specific census block’s (CB’s) depth histogram at 1/2 ft resolution. When each “depth bin” is divided by the total number of grid cells within the CB, the result represents the percentage of the area within the census block at that 1/2 ft depth. Then each “depth bin’s” damage percentage is determined from the corresponding depth-damage function. The process of calculating damage to the general building stock is very CPU intensive because the model is determining the amount of damage at each given flood depth for each occupancy category, number of stories, and foundation type (in reality, the foundation type is narrowed down to basement/no basement).

If you have access to a white board or a chalk board, it may be helpful to draw a rectangle—representing a census block—and a flood polygon that covers a part of the census block. Point out that the model assumes the inventory for each occupancy category is distributed evenly across the census block. Thus, if half of the census block is flooded, it is assumed that half of the inventory is impacted—as long as the flood depths are sufficient to cause damage. This approach can generate results that are not realistic when reviewing results on a block-by-block basis. When reviewing results for large geographic areas, it is assumed that some damage estimates will be high while others will be low, thus resulting in a reasonable overall damage assessment. For users



who are interested in assessing damage for small geographic areas, it is more effective to enter site-specific data.



The Comprehensive Data Management System (CDMS) is a tool, installed as part of the Hazus-MH installation, that supports the incorporation of user provided building data into the Hazus inventory. There are a number of things that need to understood with regard to this tool.

CDMS has two specific roles. It validates your data to ensure that it is Hazus compliant and it loads your data into the Hazus data structure. CDMS is very easy to use once your data is Hazus-MH compliant.

In the vast majority of cases, CDMS does not directly consume your local data. It is very likely that you will need to manipulate your data – sometimes in significant ways – prior to loading it into CDMS. There are a variety of things necessary to make a dataset CDMS compliant. For example, your it must confirm to specific file format requirements and it must contain certain attributes – many of which must be populated in specific ways. Most of the data processing effort involved with updating the Hazus-MH inventory happens before CDMS is used to integrate your data into the Hazus-MH data tables.

CDMS has no editing capabilities. You can only import data through CDMS. CDMS does not import every category of the Hazus-MH inventory. The

most important dataset not supported by CDMS is User Defined Facilities. User Defined Facility data can only be imported directly into a Hazus-MH



study region if you are limited to out-of-the box tools. In addition, large quantities of User Defined Facilities are very slow to import into a study region – approximately one hour per 10,000 records. Due to the many challenges with this method of processing User Defined Facilities, power users are advised to develop external scripts for importing their User Defined Facility data into Hazus-MH study regions.



If the participants are unfamiliar with CDMS you may wish to take the opportunity to briefly demonstrate its interface. A simple and informative demonstration would be the following:

1. Query one of the essential facility classes for a single county – schools are usually a good choice since they exist in nearly every county.

2. Export that the queried data to an Excel workbook, then delete the original data.3. Open the Excel workbook and edit one of the values – the address of the first

record is usually a good choice. 4. Save the Excel workbook (be SURE to save it to Excel 2003 since CDMS does

not currently import later versions of Excel) and then exit Excel. 5. Open CDMS and import the updated data. 6. Query the same essential facility class to show that the change has been made to

the data.



It is very important to acknowledge that there are often significant challenges to aquiring data that is suitable for use in a Hazus-MH model – or for many other purposes. The issues listed on this slide are often region, state, local and sometime even department specific. Successful collaborative efforts – including those discussed as best practices in the upcoming slides – have addressed these issues in a variety of creative ways.



Following two presidentially declared disasters in Georgia during 2008 the Georgia Department of Community Affairs (DCA) applied for the US Department of Housing and Urban Development’s Disaster Recovery Enhancement Fund. The Disaster Recovery Enhancement Fund (DREF) was a $311.6-million set-aside under the disaster recovery supplemental appropriation P.L. 110-329. DREF awards were announced August 26, 2010. Georgia received $640,000 the second smallest amount of 13 eligible states. They devoted about $150,000 of that amount to their Hazus-MH initiative. DCA studied how the land use maps originally compiled for community comprehensive plans were being used in hazard mitigation plans. They also explored how land use classifications and related information from property assessment records might provide better information for both types of planning. Land records and improvements information were immediately identified as being more useful for hazard mitigation planning in general and Hazus supported risk assessment studies in particular.

The project partners included the most important local, regional, state and federal participants in hazard mitigation planning. The Georgia Emergency Management Agency was involved in all aspects of the project including Hazus education and pilot studies as well as development of the disaster resilient building codes. Most Georgia Regional Commissions provide a wide range of planning services to their city and county constituent local governments, especially hazard mitigation planning. The WinGAP Group is a non-profit consortium of Georgia counties that have partnered to develop the



states most popular tax assessment software. FEMA Emergency Management Institute agreed to provide Hazus training materials and certification for their educational initiative. Seven classes were held over a period of a year. Three locations were connected via Adobe Connect and Skype, serving 12 to 40 participants. All classes were based on FEMA authorized curriculum but supplemented with examples unique to Georgia. All classes allowed for ample opportunity for strategic discussion, collaboration and planning. Participants received FEMA Hazus-MH certificates plus Professional and Practitioner status.

DCA contracted with The Polis Center at Indiana University Purdue University Indiananpolis to provide FEMA certified instruction and project leadership during a pilot project study and for the development of a Hazus-based risk assessment workflow and implementation program.



Fundamental to a successful implemention of Hazus in Georgia was the update of the default statewide Essential Facilities data that are included with the Hazus software. For this purpose the Critical Facility information collected by local governments for the Georgia Emergency Management Agency during compilation of their hazard mitigation plans was used. Certain demographics from Census 2000 to the newest Census 2010 Summary File 1 counts were also updated. Just as importantly, a documented workflow was developed to facilitate the future maintenance of these databases.



To improve the accuracy, detail and quality of the analyses, local parcel maps and associated tax assessor records were used to create a User Defined Facility layer or Building Inventory. In Georgia 139 of the 159 counties use a common mass appraisal system known as WinGAP so the developed processes and workflows for created to support those counties.



Four counties were selected for the development and testing of the workflow for Level 2 Hazus-MH flood and hurricane studies to produce multi hazard risk assessments.



The high quality of most local data maintained in the WinGAP system has allowed for the easy creation of the UDFs needed to enhance the Hazus analyses. The example shown here is Camden County, the first pilot project, which is located in the southeastern corner of the state.



The User Defined Facility building inventory, populated from local assessor and parcel data, was used in detailed structure level analysis – including estimates of counts of buildings impacted by occupancy types as well as econonomic losses to those buildings - for flood hazards. In addition, the same data was used to update the General Building Stock for aggregate analyses for the wind hazard of the hurricane model as well as for selected losses – debris and shelter – in the flood model. Finally, the User Defined Facility inventory was valuable in assessing the potential impact from tornados. Hazus-MH does not support tornado loss estimation but the availability of the User Defined Facility inventory, combined with the application of peer reviewed loss estimation methodologies, made it possible to calculate impacts from this important hazard.



The project team analyzed the nearly 200 WinGAP tables to identify the 8 tables essential to constructing Hazus-compliant data. Polis then created tools for transforming the local assessor/parcel data into a Hazus-compliant format. The importance of the development of these tools cannot be understated. Key points to note:

The tools and associated workflow significantly reduce the amount of time necessary to process detailed information about the built environment – information that is critical to deriving credible flood loss estimates.

The tools incorporate multiple tasks into one fully automated workflow. If each of these tasks were completed manually it would require far more time to complete a project – quite possibly more time than an organization would be willing to devote to the process.

The tools can be easily modified to incorporate changes in the data structure of a county.

These tools can now be applied, with minimal modification, to any other county that uses the same property management system.



It is important to note that the CDMS application IS a part of the Georgia workflow. However, the data that is fed into it is Hazus-MH ready thanks to being pre-processed through the tools developed for Georgia.



As stated earlier, this project created a completely documented workflow for the processing of local inventory, the integration of local hazard data and the analysis of hazards. The intent of this project was to leave local and state government officials with the ability to create and maintain quality risk assessments in support of mitigation planning activities in their state.



Another community that could be considered a model for Hazus-MH collaboration is the City of Des Moine, Iowa. When a decision was made to utilize Hazus-MH in the mitigation planning process this city was able to take advantage of existing collaborations rather than having to create new ones. The counties and cities that shared the Des Moine metropolitan area had already established a coordinated data management strategy. The existing tools and workflows that populated this system were simply modified to collect and process Hazus-MH required data that were not already being collected as part of the ongoing regional data management strategy. Translators were then developed to extract and convert the data needed for a Hazus-MH analysis to Hazus-MH required formats that could be consumed into the Hazus-MH state database structure.

The best practice that can be derived from this project is that, when possible, existing collaborations and existing local data stewards should be part of any strategy that involves securing local data for Hazus-MH analysis. This will avoid duplication of effort in many instances as well as resulting in potential cost savings.



Another model to consider is the one that has been employed in the State of Indiana. Indiana has one of the most successful histories of Hazus-MH implementation. To date Hazus-MH analysis has been performed in every county and inventory data – to include both site specific and general building stock inventory – has been enhanced with local data in 74 of the 92 counties in the state.

The things that make the Indiana strategy successful are cited on the slide. First, data used to update the Hazus-MH inventory is acquired from two locations rather than having to be collected from each individual county. The first of these is the IndianaMAP, a statewide repository of information which is populated directly from local government data via web services created with funding from the Indiana Department of Homeland Security. The second key source is the Indiana Department of Local Government Finance. This state agency collects property assessment data from each county on an annual basis. For hazard mitigation planning purposes it is possible to obtain property assessment building characteristic data directly from this single location. That information can then be related to parcel data obtained from the IndianaMAP. These resources are brought together and processed into a Hazus-MH ready form using tools developed by The Polis Center in partnership with the Indiana Department of Homeland Security.



Hazus-MH – and the data that support it – can be of value in a variety of ways. The next few slides provide guidance that addresses some of the areas that Hazus can impact.



The original and still dominant value that Hazus-MH offers in in support of Mitigation Planning projects. There are, of course, four basic phases to the Mitigation Planning process. These include organizing resources, assessing risks, developing a mitigation plan and implementing the plan and monitoring progress. While Hazus-MH can assist in each of these mitigation planning activities it is by far most valuable in the second phase of the process, the risk assessment.



Hazus is well designed to identify where and how communities are at risk from flood, earthquake and hurricane related hazards. Much of the capability that Hazus-MH offers is based on the functions offered by GIS – namely the ability to map and analyze spatially related phenomena. Using these capabilities Hazus-MH users can identify the locations where risk is most significant and thereby know where to apply resources to mitigate that risk.



Lifelines in Hazus-MH are those facilities and infrastructure that are considered vital to the ability of a community to provide basic services. In Hazus-MH this includes things associated with transportation and utility infrastructure. Damage to selected lifelines can be analyzed in both the Hazus-MH earthquake and flood models. The hurricane model does not currently support lifeline analysis. The earthquake model is the most robust in terms of the ability to assess impacts to lifelines. It has the ability to analyze the majority of the lifeline components found in the Hazus-MH inventory. The flood model is less powerful. For example, the only transportation elements that can be modeled in Hazus-MH for floods are bridges.



All three Hazus-MH models have the ability to assess impacts to buildings and populations.



One of the abilities of Hazus-MH is determining the impacts of mitigation actions. This graphic reflects on the ability of Hazus-MH to define the impacts of a buy-out.



While Hazus-MH is not approved as a tool that can be used to substantiate a request for grant funding, it does provide the ability to identify potential actions that may be grant fundable. These actions can then be further explored and justified using FEMA authorized tools such as the Benefit Cost Analysis Tool.



Hazus-MH has considerable value as a tool that could be used to develop table top exercises.



One of the areas where Hazus-MH is seeing increasing use is in support of disaster operations. Caution needs to be exercised if Hazus-MH is used in this manner. It is especially important that the Hazus-MH inventory is updated if used in support of disaster options. It is also important that Hazus-MH not be used to define the extent of a hazard (e.g. the boundary of a floodplain or the amount of shaking from an earthquake) unless steps are taken to ensure that it generates credible hazard extent and strength. (e.g. supplying appropriate inputs for the Flood Information Tool, providing soils, liquefaction and other inputs for earthquake analysis, etc.)



The purpose of this slide is to promote the concept that Risk MAP, Hazus-MH and mitigation both benefit from and support each other. Hazus-MH produces products that serve the needs of both the Risk MAP and mitigation planning.



One of the most important goals of this workshop is to identify the appropriate message for FEMA to convey to states and communities about how Hazus-MH can support their goals. The following elements should be shared with Hazus-MH stakeholders.

- Hazus-MH is first and foremost a model. It attempts to depict what will occur in the event of a disaster. However, there are a multitude of issues that may result in the estimates that it produces being less than perfect.

- As is the case with most models, Hazus-MH is only as accurate as the input from which it generates loss estimations.

- Hazus-MH supports the the goals of the Federal Insurance and Mitigation Administration.



If possible, show the students the EMI website and discuss when future courses will be held. You might event print them out a schedule of courses at EMI.



Explain the Hazus-MH certificate program – encourage the participants to pursue these certificates which are increasingly being sought by Hazus users.



Hazus-MH user groups provide an ongoing opportunity for Hazus users to share best practices, learn new skills, and build relationships with other users.



Time permitting; be sure to spend a few minutes exploring this site with your participants. Be sure to emphasize to them that this is a site they should check frequently because updates are posted on a regular basis. A particularly important component of this section is the Frequently Asked Questions area where questions that are frequently asked by the user community are addressed.



Add your contact information to this slide.


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