Modeling geospatial events during flood disasters for response decision-making

Transcription

1 University of Iowa Iowa Research Online Theses and Dissertations Fall 2013 Modeling geospatial events during flood disasters for response decision-making Shane A. Hubbard University of Iowa Copyright 2013 Shane Hubbard This dissertation is available at Iowa Research Online: Recommended Citation Hubbard, Shane A.. "Modeling geospatial events during flood disasters for response decision-making." PhD (Doctor of Philosophy) thesis, University of Iowa, Follow this and additional works at: Part of the Geography Commons

2 MODELING GEOSPATIAL EVENTS DURING FLOOD DISASTERS FOR RESPONSE DECISION-MAKING by Shane A. Hubbard A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Geography in the Graduate College of The University of Iowa December 2013 Thesis Supervisor: Associate Professor Kathleen Stewart

3 Copyright by SHANE A. HUBBARD 2013 All Rights Reserved

4 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL PH.D. THESIS This is to certify that the Ph.D. thesis of Shane A. Hubbard has been approved by the Examining Committee for the thesis requirement for the Doctor of Philosophy degree in Geography at the December 2013 graduation. Thesis Committee: Kathleen Stewart, Thesis Supervisor Marc Armstrong George Malanson David Bennett Larry Weber

5 To Laura, the anchor to every storm and the anchor to our family; To Mark and Nolan, the greatest joys a father could have ever imagined; To my parents, who always put their children first and built this life for me. ii

6 Freedom is never more than one generation away from extinction. We didn't pass it to our children in the bloodstream. It must be fought for, protected, and handed on for them to do the same. Ronald Reagan iii

7 ACKNOWLEDGMENTS This research would not be possible without the continued guidance from my advisor Dr. Kathleen Stewart. Dr. Stewart s mentoring has grown my abilities as an academic and continued to foster my desires to examine new research questions. I would also like to thank my committee (Dr. Marc Armstrong, Dr. David Bennett, Dr. George Malanson, and Dr. Larry Weber). Your comments, suggestions, and guidance while pursuing my Ph.D. have been enormously helpful. I would also like to thank Dr. Sue Grimmond and Dr. Pao Wang. You both continued to push my academic interests and encouraged that I pursue a Ph.D. I would also like to thank my family. Laura, you have been very encouraging all along the way. You have picked up the slack in the family at times when my schedule has been full. You have raised our two boys to be incredible children that we are both so proud of. When everything is turned upside down you manage to turn it all right-side-up. I certainly would not have been able to complete this dissertation without you. To Mark and Nolan, thank you for being patient over the past two years. I would also like to thank my parents. I would not be finishing a Ph.D. if it were not for you. You always placed your children first and yourselves second. You provided every possible opportunity to me and set me up for success, every chance you could. You continue to push me to succeed in life and have continued to support my interests in completing a Ph.D. Finally, I would like to thank Duane and Judy Wagner. I have enjoyed having you in my life. You have provided incredible support to our family and especially while I have been trying to complete this dissertation. iv

8 ABSTRACT A model that emphasizes possible alternative sequences of events that occur over time is presented in paper 1 (chapter 2) of this dissertation. Representing alternative or branching events captures additional semantics unrealized by linear or non-branching approaches. Two basic elements of branching, divergence and convergence are discussed. From these elements, many complex branching models can be built capturing a perspective of events that take place in the future or have occurred in the past. This produces likely sequences of events that a user may compare and analyze using spatial or temporal criteria. The branching events model is especially useful for spatiotemporal decision support systems, as decision-makers are able to identify alternative locations and times of events and, depending on the context, also identify regions of multiple possible events. Based on the formal model, a conceptual framework for a branching events model for flood disasters is presented. The framework has five parts, an event handler, a query engine, data assimilator, web interface, and event database. A branching events viewer application is presented illustrating a case study based on a flood response scenario. A spatiotemporal framework for building evacuation events is developed to forecast building content evacuation events and building vulnerabilities and is presented in paper 2 (chapter 3) of this dissertation. This work investigates the spatiotemporal properties required to trigger building evacuation events in the floodplain during a flood disaster. The spatial properties for building risks are based on topography, flood inundation, building location, building elevation, and road access to determine five categories of vulnerability, vulnerable basement, flooded basement, vulnerable first-floor, flooded first-floor, and road access. The amount of time needed to evacuate each building is determined by the number of vulnerable floors, the number of movers, the mover rate, and the weight of the contents to be moved. Based upon these properties, six possible evacuation profiles are created. Using this framework, a model designed to track v

9 the spatiotemporal patterns of building evacuation events is presented. The model is based upon flood forecast predictions that are linked with building properties to create a model that captures the spatiotemporal ordering of building vulnerabilities and building content evacuation events. Applicable to different communities at risk from flooding, the evacuation model is applied a historical flood for a university campus, demonstrating how the defined elements are used to derive a pattern of vulnerability and evacuation for a campus threatened by severe flooding. Paper 3 (Chapter 4) of this dissertation presents a modeling approach for representing event-based response risk. Surveys were sent to emergency managers in six states to determine the priorities of decision makers during the response phase of flood disasters. Based on these surveys, nine response events were determined to be the most important during a flood response, flooded roads, bridges closed, residential evacuations, residential flooding, commercial flooding, agricultural damage, power outage, sheltering, sandbagging. Survey participants were asked to complete pairwise comparisons of these nine events. An analytic hierarchy process analysis was completed to weight the response events for each decision-maker. A k-means clustering analysis was then completed to form 4 distinct profiles, mixed rural and urban, rural, urban, and high population - low population density. The average weights from each profile were calculated. The weights for each profile were then assigned to geospatial layers that identify the locations of these events. These layers are combined to form a map representing the event-based response risk for an area. The maps are then compared against the response events that actually occurred during a flood disaster in June 2008 in two communities. vi

10 TABLE OF CONTENTS LIST OF TABLES... ix LIST OF FIGURES... x CHAPTER 1. INTRODUCTION Event Modeling for Hazard Domains Scope Motivation Research Goals and Objectives Research Questions Approach Major Results Overview of Dissertation MODELING ALTERNATIVE SEQUENCES OF EVENTS IN DYNAMIC FLOOD SCENARIOS Introduction Modeling Events for Information Systems A Model for Alternative Events Diverging Events Converging Events Modeling the Past, Present, and Future Incorporating Event Durations Initial and Final States A Conceptual Spatiotemporal Branching Events Model for Decision- Makers During Flood Response Conceptual Framework for a Spatiotemporal Branching Events Model for Flood Response Events Assimilating Flood Response Events Queries for a Branching Events Model Modeling a Flood Scenario Using the Branching Events Model Discussion Conclusions and Future Work TRACKING SPATIOTEMPORAL PATTERNS OF EVACUATIONS EVENTS DURING FLOOD HAZARDS Introduction Modeling Spatiotemporal Hazards for Decision Support Temporal Aspects of Evacuation Events During Flooding Determining Building Vulnerability for Flood Evacuation Events Building Vulnerability Identification Road Inundation and Access Simulating Evacuation Time for Evacuation Events Modeling Spatiotemporal Building Evacuation Events Vulnerable Buildings vii

11 3.6.2 Linking Flood Forecast Information to Building Vulnerabilities Flood Inundation Map Repository Evacuation Model Viewer Validating and Tracking the Spatiotemporal Progression of Building Evacuation Events Building Vulnerability Results Building Evacuation Recommendations in 1993 Predicted by Model Discussion Conclusions and Future Work IDENTIFYING PATTERNS OF SPATIOTEMPORAL FLOOD RESPONSE EVENTS BASED UPON THE PRIORITIES OF DECISION MAKERS Introduction Modeling Event-Based Risk for Flood Disasters The Priorities of Emergency Managers Based Upon Flood Surveys Surveying Flood Decision Makers Survey Results Analytic Hierarchy Process and Weights Groups of Response Decision Makers Identifying Areas of Event-based Response Risk Data Collection and Processing Event-based response risk maps during floods Identifying Critical Risk Stages within a Floodplain Validating Risk-Based Response Values The flood of June 2008 in Johnson County, Iowa The flood of June 2008 in Polk County Iowa Discussion Conclusions and Future Work CONCLUSIONS AND FUTURE WORK Results and Major Findings Future Work REFERENCES viii

12 LIST OF TABLES Table 2.1 An event table representing flood events for a hazard being modeled Table 2.2 Table 3.1 An example of an event id table with the antecedent and descendent attributes Actual flooded campus buildings in 1993 versus predicted flooded buildings by the model Table 3.2 Observed versus calculated evacuation times for campus buildings Table 4.1 Surveyed states, predominate flood types, and population rank Table 4.2 The list of response events and frequency that each was selected as a concern by surveyed decision makers Table 4.3 Survey response rate among each state Table 4.4 The ranking of each of the nine events for each group of decisionmakers Table 4.5 Discriminant analysis for each participant and group Table 4.6 Response events and weights by group Table 4.7 The events recorded for Johnson County, Iowa by the county EOC during the flood of Table 4.8 The events recorded for Polk County, Iowa during the flood of ix

13 LIST OF FIGURES Figure 2.1 Representing events in the branching model: (a) an event, represented by a circle, (b) a branch, shown as an arrow and (c) two events linked by a branch Figure 2.2 Sequence of events without branching Figure 2.3 An example of divergence where multiple events are possible Figure 2.4 A branching events model capturing convergence Figure 2.5 Figure 2.6 Modeling the present (shaded in gray) and possible past and future events Branching events model that combines branching and non-branching with selected events in red and alternative events in white Figure 2.7 Intervals for airport events using a branching model.. 23 Figure 2.8 Figure 2.9 Five possible cases for initial and final timesteps for branching events modeling (a) multiple events in the first timestep ending with a single event in the final timestep, (b) one event in the first timestep ending with multiple events in the final timestep, (c) multiple events in the first and final timesteps with convergence and divergence in-between, (d) one event in the first and final timesteps, and (e) multiple events in the first and final timesteps Conceptual Framework of a Branching Events Model for Flood Response Figure 2.10 Branching Events Viewer application. The left panel shows a description of events and the remainder is the main map viewer for representing domain events Figure 2.11 Spatiotemporal boundaries of the selected and alternative floods. 34 Figure 2.12 Modeling the set of possible emergency response events during a flood scenario with selected events in white, alternative flood 1 events in red, and alternative flood 3 events in blue. 36 Figure 2.13 Web application with selected and alternative events 37 Figure 2.14 Spatiotemporal tornado paths with selected and alternative events 42 Figure 3.1 Evacuation time profiles (possible variations shown with dashed lines) Figure 3.2 GIS analysis for modeling building vulnerability Figure 3.3 Number of flooded buildings with increasing flood magnitude Figure 3.4 Representing building inundations and vulnerabilities Figure 3.5 Vulnerable buildings modeled during the 1993 flood event Figure 3.6 Figure 4.1 Recommended evacuation times for (a) July 5 th, (b) July 7 th, and (c) the progression of recommended building evacuations State maps representing the locations of surveyed decision makers and associated group x

14 Figure 4.2 The (a) flood risk using the inundation boundary and asset layers only versus (b) event-based response risk maps within the floodplain during response events Figure 4.3 Event-based response risk maps for Iowa City, Iowa for (mixed) group 1 (a), (low population density) group 2 (b), (urban) group 3 (c), and (rural) group 4 (d) Figure 4.4 Figure 4.5 Percentage change maps showing (a) an increasing change in red hues and a decreasing change in blue for comparisons of group 1 to group 2, (b) group 1 to group 3, and (c) group 1 to group The amount of acres at risk for low, moderate and high risk with the total risk and critical stages plotted for Johnson County, Iowa, a group 1 (mixed) county xi

15 1 CHAPTER 1: INTRODUCTION 1.1 Event Modeling for Hazard Domains During a flood disaster decision makers rely on the best available information for planning and responding to it. As the waters rise, flood decision makers, normally plan their response around a single flood forecast crest and set of response events. However, changes in rainfall forecasts can change the expected level of water for a given location. For example, on June 10 th, 2008, Iowa City, Iowa was forecasted to have a flood stage of 24.5(ft), but because of higher than expected rainfall amounts, the forecast rose to 31 (ft) by June 13 th (Advanced Hydrologic Prediction Service 2008) for the crest on June 15 th. Decision-makers have begun to ask flood prediction officials to provide alternative flood forecasts and as a result the National Weather Service is now issuing alternative flood forecasts based on expected, minimum, and maximum potential rainfall (National Weather Service 2013, Demargne et al. 2013). While flood decision makers are requesting information about other possible floods during response, current geographic information systems (GIS) cannot model multiple possible spatiotemporal outcomes (Hubbard and Stewart Hornsby 2011). Researchers in geographic information science (GIScience) have increasingly investigated topics in hazards research. The evacuation of people during wildfires has been studied through the establishment of evacuation zones (Cova and Johnson 2002) and spatiotemporal buffers (Cova et al. 2005, Dennison et al. 2007, Larsen et al. 2011). For flooding, the assessment of flood defense systems (Yu et al. 2012), the use of varying elevation datasets in flood models (Coveney and Fotheringham 2011), the cellular automata simulations of dam breaches (Li et al. 2013), and site suitability of shelters during floods (Kar and Hodgson 2008) have been investigated. Additional work has

16 2 focused on identifying the social and economic vulnerabilities of people during hazards (Cutter 2006, Schidtlein et al. 2010, Burton 2012, Tate 2012, Thomas et al. 2013). In flood response, decision-makers and responders must react to dynamic changes inside and outside of the floodplain such as water rising over roads, the evacuation of people and building contents, and the construction of sandbag walls. These dynamic occurrences of movement and change are not fully captured in today s GISs. The use of events to model response activities has been shown to be effective at capturing dynamic hazard scenarios such as the movement of wildfires (Pultar et al. 2010) for evacuations and detecting the similarities of rainfall patterns to detect weather phenomena like hurricanes (McIntosh and Yuan 2005). This work investigates the uses of events to model flood response activities for decision-making. One important flood response event is evacuation. Evacuations help to protect the lives of citizens who are at risk during a disaster. An extensive literature has investigated the spatiotemporal simulation of evacuating people from fires (Cova et al. 2005, Dennison et al. 2007, Larsen et al. 2011, Shenhar et al. 2012), hurricanes (Chen et al. 2006, Chiu et al. 2008, Naghawi and Wolshon 2010, Wang et al. 2010, Fries et al. 2011), and floods (Simonovic and Ahmad 2005, Kim et al. 2011). Two spatial decision support systems (SDSS) exist that aid in estimating evacuations during disasters such as hurricanes, floods and earthquakes (HURREVAC, and Hazus- MH, While much literature has covered the evacuation of people, little research has covered the modeling of building content evacuation events. Building content evacuation events capture the movement of vulnerable contents to dry locations prior to flooding. Moving building contents can mitigate substantial losses. For example, during the 2008 floods, the University of Iowa sustained over 50 million dollars in building content damages on its campus (

17 3 Defining hazard risk has also been the focus of considerable research. This is an active area of research with hundreds of articles over the past few years (see for example, Chen and Tseng 2012, Hsu et al. 2012, Shepard et al. 2012, and Hsu et al. 2013). In addition, the vulnerabilities of populations to hazards have also been discussed through research on socio vulnerability indexes (Schidtlein et al. 2010, Finch et al. 2010, Wood et al. 2010, Burton 2012). While these types of risks and vulnerabilities have been addressed, they do not translate directly to what decision-makers and responders react to during flood events. These are measures of vulnerability based-upon the demographic make-up of the population in an area. Decision-makers and emergency responders act upon occurrences within the floodplain called events. Identifying the areas around a floodplain with the highest and lowest risk for events provides the decision-maker with a view of the areas requiring more (or less) action during a flood disaster. New geospatial approaches for modeling activities during a flood response have been increasing over the past few years (Luccio 2005, Kawasaki et al. 2013). These systems are used to locate flood-related information such as, the flood boundary, flood depth, locations of inundated buildings, and places for short-term sheltering, among others. ESRI s situational awareness viewer ( and Google s Crisis Map ( are public-driven websites where users can post geographic information about flood disasters that are taking place. These websites are also used by local officials attempting to post relevant geographic information for public safety (e.g., closed roads, landslides). Other websites present data for decision-makers to assist in planning efforts as a flood is occurring, such as The Iowa Flood Center s Iowa Flood Information System ( and the US Geological Survey s Flood Inundation Mapper ( Geospatial approaches are an integral part of current flood response efforts and have changed dramatically in the past 15 years (Cova 1999, Gunes and Kovel 2000).

18 4 This dissertation investigates approaches for integrating event modeling into flood disaster response. In this three paper dissertation, paper 1 (chapter 2) examines a method for modeling alternative events for decision making to provide the decision maker with a holistic view of what is possible. Paper 2 (chapter 3) presents a modeling approach for simulating the movement of building content evacuation events that provides decision makers with a spatiotemporal timeline of when and where evacuations should take place. Paper 3 (chapter 4) investigates what decision-makers consider to be the most important response events during a flood and, based on those priorities, presents a methodology for creating event-based response risk maps. 1.2 Scope Motivation Decision-makers are beginning to express the need to simulate alternatives when hazards are affecting their jurisdictions. Recently, the National Weather Service has begun to produce flood forecast alternatives based-upon the expressed need of flood decision-makers (Demargne et al. 2013). The National Weather Service has developed a website (Ensemble Quantitative Precipitation Forecast Hydrographs, that provides decision-makers with forecasts of the most likely flood, the maximum potential flood, and the minimum potential flood based upon possible ranges of precipitation values. In the last few years, the NWS has also been issuing storm based warnings for severe thunderstorms and tornadoes that depict the possible future locations of those hazards (Stumpf et al. 2008, Sutter et al. 2010). Decision-makers understand the importance of considering what-if scenarios when responding to hazards (Jacks and Ferree 2007). During the flooding of June 2013, the University of Iowa put into place campus-wide flood protections, not for the forecasted flood (21,000 cfs), but for an alternate flood of greater magnitude (24,000 cfs) (Pradarelli 2013). The motivation for paper 1 of this dissertation is based upon the

19 5 expressed needs of decision-makers, that response scenarios should consider alternatives for floods events and the inability of current geospatial systems to model alternatives. When responders react to actions that are occuring during a flood they are often reacting to actions such as, water moving over a roadway, a bridge collapsing, or a water rescue. These actions involve movement and change and are dynamic occurences. While responders are interested in the events that occur in the floodplain, what is often tracked are the static objects (see for example, Google s Crisis Map or ESRI s situal awareness viewer). In paper 1 of this dissertation a formal model for branching events is presented and a conceptual model for a branching model during flood response is developed for modeling flood response events to provide a more holistic view of what is possible during a flood disaster. Each year during floods, the damage occurs not only to the structures to buildings, but also their contents. Even though there has been considerable research on the evacuations of people during natural hazards, the evacuation of building contents has not been addressed. To reduce the overall losses for a community, the mitigation of content losses can be substantial (e.g., the 50 million dollar loss on the University of Iowa campus). The evacuation of a building s contents is not as simple as loading up a truck and moving the contents out of harms way. It requires an understanding of the spatiotemporal development of the flood, the characteristics of the building (first floor height, amount of contents to be moved), and coordinating the movement of contents with adjacent buildings to prevent the obstruction of moving routes. The motivation for paper 2 (chapter 3) of this dissertation is based-upon this need to reduce the losses of building contents from flood disasters that have enormous economic impacts to communities. As discussed in the previous section, the risks and vulnerabilities to people have been researched to identify the socio-economic vulnerabilities between areas, the risks of hazards to locations based upon deterministic or statistical models, among others. While

20 6 many of these topics are important in understanding risks and vulnerabilities to better plan and mitigate, they do not provide insights into the risks associated with flood response during the disaster itself. For example, flood decision-makers respond to residential building evacuations, flooded roadways, and failing bridges, but have no method to identify where those risks exist. Even less understood are the priorities of decision-makers during response. Emergency managers have been interviewed and surveyed to acquire knowledge on topics such as the economic vitality of their community emergency management agencies, the use of GIS in emergency management, and the number of employees in their agency (National Center for the Study of Counties 2006). Decision-makers have also been asked about their perceptions of uncertainty and uses of uncertainty in weather forecasts and how those perceptions are used in response scenarios (Morss et al. 2010). Understanding how decision-makers respond and what priorities they use during flood hazards helps to build better methods and strategies for responding in the future. In Chapter 4 (paper 3), the priorities of decision-makers are determined through surveying local emergency managers in six states. From these priorities a methodology to build event-based risk maps is presented Research Goals and Objectives The first paper is presented in chapter 2 of this dissertation. The goal of this paper is to investigate modeling alternative events during flood scenarios. Paper 2 has three objectives: first, to build a formal model for modeling branching time for events, second, to present a conceptual model for branching events, and third, to present a hypothetical flood scenario that uses a branching time model and simulates alternative events. This work will demonstrate how using a branching model of events and considering alternatives can produce a more holistic view of what might be possible when a flood occurs.

21 7 The second paper of this research examines the dynamic spatiotemporal properties of building evacuation events. This is presented in Chapter 3 of this dissertation. This work has three objectives. The first objective is to determine, through interviews of flood responders, what the important factors are when evacuating the contents of a building during a flood disaster. The second objective is to present a methodology for modeling the spatiotemporal progression of building evacuation events. The final objective is to simulate two historical floods on a University campus and track the progression of building evacuations. This research will present how tracking the progression of building evacuation events can help decision-makers make better judgments on where and when to evacuate a structure. The third paper of this dissertation researches the preferences and priorities of decision-makers during flood response and develops a methodology for building eventbased response risk maps. This work is presented in Chapter 4. There are two objectives of this research. The first objective is to determine what events flood decision-makers are most concerned about and respond to and how those events are prioritized. The second objective is to create a methodology for creating event-based response risk maps Research Questions The research objectives of this dissertation are: What are the elements for a branching model of events? What unique queries are possible for sequences of events when a branching model is used? What are the components for creating a model for building evacuation events? How can the components of building evacuation events be linked to flood information to track the progression of evacuations over space and time? What are the response preferences and priorities of decision-makers during flood disasters?

22 8 How can the preferences and priorities of flood responders be used to create event-based maps that represent the risk of event occurrences in a flood disaster area? 1.3 Approach Chapter 2 of this dissertation presents a formal model for branching events in order to put forth a structure that considers alternatives in an information system. After the formal model a conceptual framework for building a branching events model is given, followed by a prototype branching events viewer that models multiple possible floods, alternative events and their sequences. The work in Chapter 3 is accomplished first by interviewing flood responders and decision-makers responsible for evacuating the building contents on a University campus. Based on the interviews, a framework for building an evacuation events model is presented, highlighting the components that are required to simulate building vulnerability and evacuation events. From this model, the spatiotemporal evolution of building evacuation events is presented by simulating a historical flood on a University campus. To build event-based response risk maps, chapter 4 begins with a survey of decision-makers from six states. The results of these surveys are used to identify what response events decision-makers are most concerned with during a flood disaster. Once the set of response events is determined, the decision-makers are asked to prioritize each response event. Using the analytic hierarchic process (AHP) the events are weighted. Based on these weights a k-means grouping is applied to create four individual weighting profiles. Based on these weights, event-based flood response maps are created highlighting areas at risk to response events during a flood. This work is validated using actual response events from two Iowa counties during the Midwestern floods of 2008.

23 9 1.4 Major Results Two key elements are presented for branching events, convergence and divergence. Convergence is the case where after multiple possible events, only a single event is possible. Divergence is the case where after a single event, multiple possible events are possible. These elements are especially helpful when modeling what might have occurred in the past or future, especially when multiple events are possible. This model of time is different than other models (e.g., linear, cyclic) in that for other models only a single sequence of events is possible, but when divergence and convergence are considered alternative sequences are possible. This work also presents a conceptual framework for branching events modeling and describes the unique queries associated with a branching events model that are not possible with other models of time (e.g., linear, cyclic). The parameters for modeling building evacuation events are topography, flood inundation, building location, building elevation, and road access. Computing building evacuation times has the following components: vulnerable floors, total number of movers, and mover rate. Based on the evacuation times of groups of buildings on a floodplain, six evacuation profiles are presented. Tracking the spatiotemporal progression of buildings during flood disasters shows patterns of evacuations within the floodplain not noticed by other methods. Nine flood response events were chosen most frequently by over 400 surveyed flood decision-makers. These are closed roads, damaged bridges, residential evacuation, power outage, sheltering, residential flooding, commercial flooding, agricultural flooding, and sandbagging operations. Survey respondents were asked to complete pairwise comparisons of these nine events using AHP. Weights were created from an AHP analysis and the weights of each decision-maker were placed into a k-means analysis to form four profiles. Based on these profiles and their average weights a

24 10 methodology for creating event-based flood response maps is presented. This work is validated using the actual events recorded by two emergency management offices during the Midwestern Floods of While the results do not directly agree with the weights of the decision-makers for each group due to the results, the results do agree closely with the overall frequency of events that decision-makers chose. 1.5 Overview of the Dissertation This dissertation is based upon a 3 paper format and is given as follows. Chapter 2 describes a formal model and a conceptual framework for branching events. At the end of the chapter a flood scenario containing alternative flood disasters is presented with the multiple possible events and sequences that result. Chapter 3 discusses the elements of calculating evacuation time for building evacuation events and also the components required for determining building vulnerability. The work also presents a methodology for modeling evacuation events and tracking their spatiotemporal progression during a flood disaster. The work in Chapter 4 describes the interviews of flood decision-makers in six states and the results of those interviews. An AHP analysis is completed and from this analysis a set of weights is computed. Based on the weights event-based risk maps are created. This work is then validated by collecting events from emergency managers during a flood disaster.

25 11 CHAPTER 2: MODELING ALTERNATIVE SEQUENCES OF EVENTS IN DYNAMIC FLOODS SCENARIOS 2.1 Introduction Many geographic applications involve circumstances where piecing an unclear past or future are important. In forensic science, when investigators reconstruct the events leading to a crime, they often use facts and possible events to put the crime timeline together. When an airplane crashes, National Transportation Safety Board investigators study many possible events leading up to the incident. In a similar way, a city planner is faced with understanding and weighing the many possible situations that may arise from decisions today that will impact a city in the future. In this paper, a modeling approach is described that captures expected and alternative events and presents these possibilities to a user for further analysis. Any information system that is designed to model dynamic domains must contain a means for modeling change and movement to capture the evolution of objects and events over time and space. In geographic information science (GIScience), event modeling approaches have been developed that capture these dynamic aspects (Claramunt and Thériault 1995, Peuquet and Duan 1995, Yuan 1997, Worboys and Hornsby 2004, Galton and Worboys 2005, McIntosh and Yuan 2005, Worboys 2005). These studies typically focus on describing sequences of events using a linear approach where a single timeline of events exists (e.g., Stewart Hornsby and Cole 2007, Beard et al. 2008, Klippel et al. 2008, Jiang and Worboys 2009). This work focuses on representing sequences of events using a more expressive, formal model that emphasizes the different alternatives that may occur through the representation of branching events. In this dissertation, an event refers to an occurrence that captures changes to objects within a geographic information system (Peuquet and Duan, 1995, Worboys and Hornsby 2004, Galton and Worboys 2005, Worboys 2005). A formal model for branching events

26 12 is presented that captures scenarios where multiple likely and related sequences of events are possible. This formal model has applications for domains where queries about the set of possible events that may have occurred in the past or will occur in the future are posed such as scenarios for natural and man-made hazards, environmental planning, or spatial decision-making. The branching model lends users the opportunity to examine multiple possible sequences of events extending the range of representation, analyses, and computation of change for dynamic geographic scenarios. Moving beyond the single timeline that is commonly presented, this model captures alternative events and their sequences. A branching approach offers a holistic view of how events in the past and future may take place (Peuquet 1999). For example, in the days and hours before a hurricane disaster, a set of disaster-related events is identified including alternative events that might transpire. Providing a user with the means to model selected events and alternative events can lead to more informed decisions especially when possible combinations of events are considered (Frank 1998). A representation of these possible futures or pasts affords the user the ability to gain a more complete understanding of the range of possible events, their order, and their relationship to one another over time (Hubbard and Stewart Hornsby 2011). Including multiple alternative events in a branching model generates hypothetical sequences of events. In this work, a flood scenario is modeled emphasizing the alternative sequences of events that may occur over a spatial region. These alternative sequences of events can be compared against each other. Users, such as emergency managers can then apply their domain knowledge to filter in or out events and explore sequences that are most relevant. Key locations that are the site of many of these alternatives can be identified, as well as times when more than one alternative event may occur. This work contributes especially to disciplines where hypothetical situations arise.

27 13 One discipline is planning, where strategies are created to carry out future goals. Decision support systems (DSS) aid planners by selecting possible solutions (decisions) from multiple possible decision choices given multiple criteria (Sugumaran and DeGroote 2011). An extension of DSS, spatial decision support systems (SDSS), integrate spatial analysis, information systems, and decision support tools to create alternative spatial solutions to aid in the decision making process (Densham and Armstrong 1987, Rushton 2001). SDSSs have been shown to be effective in the decision-making processes for the suitability planning of crops in developing countries (Tellez et al. 2013), for damage analysis and risk assessments (Ahola, et al. 2007), and for weather hazard warning support (Lakshmanan, et al. 2007). Recent research has focused on improving SDSS for web support that includes vehicle routing decision-making (Santos et al. 2011), collaborative decision-making (Malczewski and Jelokhani-Niaraki 2012), and for using the web ontology language (OWL) for the house selection problem (Jelokhani-Niaraki and Malczewski 2012). The incorporation of branching events allows a decision-maker to model multiple possibilities, asking what if questions and to plan courses of action based upon a holistic view of the sequences of events in which that decision is included. The remainder of this paper is organized as follows: the next section discusses previous research in event modeling and branching time with respect to geographic information science. Section 2.3 introduces the basic elements of the branching model. This model is subsequently expanded to capture more complex branching scenarios. The next section presents a conceptual framework for modeling branching events for flood response. Section 2.5 provides a case study that uses the branching model for geographical analysis based on a flood response scenario. The final section discusses these results and gives conclusions and suggestions for future work. 2.2 Modeling Events for Information Systems In GIScience, events are typically discussed within the broader topic of

28 14 geographic dynamics research. Geographic dynamics is the field within GIScience where questions about modeling the change and movement of real world entities over space and time within an information system are studied for broader discussions; see for example, (Peuquet 2002, Drummond et al. 2006, Yuan and Stewart Hornsby 2008). With event modeling approaches, the events that drive changes to objects are the primary focus. This approach provides a more comprehensive method for modeling change (Grenon and Smith 2004, Worboys and Hornsby 2004, Galton and Worboys 2005, Worboys 2005, Beard et al. 2008, Jiang and Worboys 2009, Kulik et al. 2011). Event modeling for GIS captures change by including the dynamics associated with change within a GIS (Claramunt and Thériault 1995, Peuquet and Duan 1995, Claramunt and Thériault 1996). Events are occurrences that can be observed (Galton 2000, Grenon and Smith 2004). Events, unlike processes, come into existence and then go out of existence, and can be counted and distinguished from one another (Galton 2000, Galton and Worboys 2005, Galton 2008). Events have attributes, can be grouped into classes, and exist in relation to other events (Grenon and Smith 2004, Worboys and Hornsby 2004, Worboys 2005, Galton 2008, Pei et al. 2013). Events can be compared to find similarities, and similar events can be categorized (McIntosh and Yuan 2005). Researchers have examined how event models that use a linear temporal order can express sequences of events (Hall and Hornsby 2005, Stewart Hornsby and Cole 2007). The conceptualization of events has revealed that humans group similar movement events based on their size and direction of movement with respect to other events (Klippel et al. 2008, Klippel and Li 2009). Recent work on context-aware event detection has proven to show further explanations on why movements are occurring in space-time (Andrienko et al. 2011). Events are situated in time and space therefore making questions on representation and modeling of objects in time relevant. Time geography examines how people move along a path and the relationships and arrangements that are a result of these movements (Kwan and Lee 2004, Raubal et al. 2004, Miller 2005, Kang and Scott 2008,

29 15 Miller and Bridwell 2009, Long et al. 2013). More recently, research is extending previous work with space-time prisms (Hariharan and Hornsby 2000, Hornsby and Egenhofer 2002) to directly investigate how to model the potential (or alternative) movements and uncertainties in space-time (Winter and Yin 2010, Delafontain et al. 2011, Chen and Kwan 2012, Kuijpers et al. 2013). Alternative models of time for the modeling of geographic phenomenon have also been discussed. One model is based on cycles, to capture phenomena that repeat in a predictable manner (Allen 1983, Snodgrass 1992, Frank 1998, Hornsby et al. 1999, Campos and Hornsby 2004). In computer science, research on dynamics has investigated another temporal model, branching time (Allen 1984). For software design and implementation, branching time has been explored as an important means for capturing future events (McDermott 1982). For database design, branching time has been examined for more quickly and accurately reflecting changes that occur in the real world (Mays 1983). Branching time concepts have also been developed to add additional complexity to temporal modeling through capturing multiple possible future computations (Vardi 1998). Recently, branching time has been shown to aid in decision support diagnosis, prognosis and therapy within medicine (Augusto 2005). Within GIScience, discussions on the use of branching time as a temporal model have been suggested as a means for reasoning about multiple possible past and future events (Frank 1998). Branching time can be used in a GIS to model hypothetical sequences of events and to model an indeterminate future or past (Worboys 2005, Galton 2008). In this paper, we investigate a branching events model and show the applicability of this approach for dynamic geographic domains. Branching time concepts are combined with event modeling to provide a user with the ability to describe spatiotemporal scenarios where more than one sequence of events is possible. This combination provides users with additional perspectives about dynamics in their geographic domain.

30 A Model for Alternative Events In this paper, a model that emphasizes spatiotemporal alternatives a branching events model is introduced to capture alternative sequences of events, where some of the sequences may have events in common. This model is especially powerful when questions about what might occur in the future are asked. In the future, the exact sequence of events is often unknown and modeling different possible events for a certain time gives the user flexibility to model different feasible scenarios. Event branching can also occur in the past, whereby an observer may know there is a past, but may not know the sequence of events leading up to the present event (Allen 1984). In meteorology, for example, an understanding of the set of possible future or past sequences of weather events is often desired for forecasting. In the case of a serious traffic accident, reconstruction of possible past movements by the vehicles involved is also commonly undertaken. Modeling these different possible sequences is an important step for comprehension and prevention of natural hazards or accidents. When linear or nonbranching models of time are used, a query often has only one outcome and only one sequence of events is modeled. This model typically supports queries based on the previous (before) or following (after) events (e.g., what happened before the traffic accident?). A branching model on the other hand, returns multiple responses to a query, lending the user the ability to plan and prepare for alternative outcomes. This work presents a formal model that is developed where each event (denoted with an open circle (Figure 2.1 (a)) is linked by a branch (represented by an arrow (Figure 2.1 (b)). An arrow symbol depicts the flow of time with later events on the right side of the arrow and prior events on the left. Linking a pair of events together with an arrow sequentially orders these events (Figure 2.1 (c)). Each event is distinguished with an identifying criterion (eid) such that a set of events ( eid i, eid j,, eid n ) is the basis for describing changes or activities in a domain. In modeling events through time, a notation

31 17 ( ) is used that captures important attributes of the events. The time of an event t describes its time of occurrence. This could refer to an event s start time (st), end time (et), a start time, end time pair (st,et), or a representative time (t), i.e., a time attributed to an event. Here we assume that the start time of one event comes after and is disjoint from the end time of a preceding event, such that the two events do not meet (i.e., we assume there is a gap in time between events). A more detailed discussion on duration of events for the branching model will follow in section Similarly, the location l refers to the starting location (sl), ending location (el), the starting and ending locations (sl,el) over an interval (st,et), or a representative location (l) attributed to the event. Events are ordered by time (t), such that t 0 < t 1 < < t n, and < <. For this work we assume temporal scales where the dynamics occur from t 0 t n and no single event occurs later than t n. Figure 2.1 Representing events in the branching model: (a) an event, represented by a circle, (b) a branch, shown as an arrow and (c) two events linked by a branch. Ordering events is important for event modeling because ordering captures relevant temporal semantics about the events. The sequential ordering of commuting and harbor events, for example, have been shown to create temporal patterns of events that can be used to create a comprehensive picture of movement in a geospatial domain and highlight unexpected event patterns (Stewart Hornsby and Cole 2007). Orders of events (S), where there is no branching (Figure 2.2), describe a linear order of events (i.e., only before and after relations) and are given as follows:

32 18 S i ={,,,, } (1) Figure 2.2 Sequence of events without branching Diverging Events This non-branching perspective can be expanded and revised to account for alternative events. Two basic elements for a branching events model are introduced. In the first case, an event, is followed by two or more possible events and the sequence of events is split into n branches (Figure 2.3), where. The splitting of the event sequence into two or more branches is called divergence. For example, with the three sequences S 1, S 2, S 3, event is common to all three sequences (given below). The subsequent events in S 1, S 2, S 3 (,, ) representing alternatives that could occur after are all different, capturing the semantics of divergence. S 1 = {,,, } S 2 = {,,, } S 3 = {,,, }

33 19 Figure 2.3 An example of divergence where multiple events are possible. Note that the angle of the branches in Figure 3 does not indicate a preference for one event or another, but is simply the time between two events. Branches can have varying lengths, and the representation of a branch captures a sequential connection. A sequence of events describing a passenger s movements at an airport (S) can now be modeled with multiple possibilities. The same event,, occurs in each sequence and precedes alternative events,, and modeling divergence. These events for this example scenario are assumed to have a temporal granularity of minutes and occur over a period of two hours within one terminal of an airport, although for conciseness, the times and locations of the events have been abstracted from the notation. Based on these events and diverging branches, three sequences of events, S 1, S 2, and S 3 are possible: = {, } = {, } = {, }

34 Converging Events The second basic element for branching sequences, convergence, occurs when multiple possible events precede a single event (Figure 2.4). Convergence describes the case where after multiple alternative events only a single event will occur. Figure 2.4 A branching events model capturing convergence. In the airport example, three events (,, in the following sequences. ) are followed by the same single event,, as shown = {, } = {, } = {, } Modeling the Past, Present, and Future Decision-makers are often interested in modeling future and past events. In the airport example, event may be the event that is modeled for the present time (i.e., now). In the future there are three possible alternative events (,, ). Three events in the past, (,, ) are possible events that

35 21 occur before the current event or any future events. This scenario combines both elements of branching events where convergence leads up to the current time event (shaded in gray), and then divergence is possible in the future (Figure 2.5). Figure 2.5 Modeling the present (shaded in gray) and possible past and future events. Divergence and convergence serve as the building blocks of the branching events model and can be combined to describe more complex geographic semantics such as those associated with hazard scenarios. When convergence and divergence occur, each element portrays a different understanding of the semantics within the sequence. Divergence occurs where multiple events are present in subsequent timesteps, capturing the uncertainty of what events might take place next. Convergence in a branching model shows increased certainty in the event sequence as events merge to only one possible event. These distinctions (converging and diverging events) make these two types of events critical events within a sequence, indicating parts of the timeline with more and less certainty. For many dynamic scenarios, there may be a need to combine both branching and non-branching elements to describe the sequences of events that can occur. Combining branching and non-branching allows cases to be modeled where, for example, after branching the subsequent events do not have multiple possibilities. Consider seven

36 22 airport events, where is the current event, and in the future there exists six different events,,,,,, and (Figure 2.6). In this example, a sequence is selected (shown in red in the figure) with three selected events and four alternatives. Figure 2.6 Branching events model that combines branching and non-branching with selected events in red and alternative events in white Incorporating Event Durations So far in this work, only the temporal relationships of before and after have been considered for events. However, additional relations are possible when an event s duration is included. In the branching events model, this takes on additional significance when comparing alternative sequences of events. Suppose, for example, durations are modeled for the upper branch of the airport scenario (as in Figure 2.6). The first event (P1_arrivesGate) captures a passenger arriving at their gate in the airport. The first event is before the next event that represents when the passenger changes their gate (P1_changesGate) and the final event (P1_departsInAirplane) occurs after the previous two events and is the event where the passenger s flight departs the airport. The event relations that exist between the first and second events determine when the final event will occur. Event durations cannot be longer than the total time of the scenario (i.e., t 0 t n ).

37 23 Based on the durations of events, thirteen possible relations may hold between them (before <, after >, equals =, meets m, met-by mi, overlaps o, overlapped-by oi, during d, contains di, starts s, started-by si, finishes f, and finished-by fi (Allen, 1983)). Now, when the model of the airport scenario is extended to include the duration of events, we see how different branches can have quite different temporal properties (Figure 2.7). In this figure, interval relation notations are assigned to each branch indicating the relationship between the preceding and following events. Possible queries that exploit duration information include, for example, what events occur during another event and what events overlap an event. Figure 2.7 Intervals for airport events using a branching model. given by: The relations between the events within each sequence in the airport example are S 1 = P1_arrivesGate < P1_changesGate o P1_departsInAirplane S 2 = P1_arrivesGate m P1_remainsAtGate m P1_departsInAirplane

38 24 S 3 = P1_arrivesGate < P1_departsSecuredArea di P1_leavesTerminal_C < P1_leavesAirport Durations of events are important for capturing the semantics of how events occur along the branches and for distinguishing the differences between each branch, i.e., between different alternative sequences of events. Durations of sequences can be compared and analyzed, and then used for decision-making. For example, it may be important to select a sequence with a shorter duration for time-critical applications, or a longer sequence when time is not so relevant. The different possible orders of events and sets of events allows a modeler to explore and understand differences in possible event sequences as well as different outcomes. The length of the sequence depends on the times associated with events and their durations. The next section describes the importance of initial and final events in sequences in more detail Initial and Final States The beginning and ending conditions of a dynamic scenario can either be modeled with one event or multiple events with respect to initial and final timesteps. This is more expressive than the non-branching case where only a single event initiates or terminates a sequence. Combinations of these two conditions create five possible scenarios for branching events modeling (Galton 2000). They are: Multiple events corresponding to the first timestep and a single event in the final timestep (Figure 2.8(a)) A single event in the first timestep and multiple events in the final timestep (Figure 2.8(b)) Multiple events in the first and final timesteps with convergence and divergence in-between (Figure 2.8(c))

39 25 A single event in the first and final timesteps (Figure 2.8(d)) Multiple events in the first and final timesteps without any convergence or divergence (Figure 2.8(e)) Figure 2.8 Five possible cases for initial and final timesteps for branching events modeling (a) multiple events in the first timestep ending with a single event in the final timestep, (b) one event in the first timestep ending with multiple events in the final timestep, (c) multiple events in the first and final timesteps with convergence and divergence in-between, (d) one event in the first and final timesteps, and (e) multiple events in the first and final timesteps Consideration of the initial and final conditions is relevant for modeling geographic dynamics. For example, if a wildfire is modeled using the branching events model such that more than one event occurred at the first timestep, but only a single event in the final timestep, then this captures the semantics that multiple occurrences initiated the fire e.g., strongwinds, arson and lightning. Conversely, if there is only one beginning event, this suggests a single occurrence for the start of the fire, e.g., a dropsmatch event or a lightning event. Representation of dynamics using the branching model captures

40 26 these important distinctions. This corresponds to the interest by the simulation community in modeling situations involving multifinality (where a single event can lead to multiple end states) or equifinality (where multiple initial events can lead to one single end state). The last case (multiple events in the first and final timesteps) is not considered further here because this case reflects a set of (non-branching) timelines occurring over the same time period. In the next section we introduce a conceptual framework for branching events where alternative events are explicitly modeled, highlighting the sets of possible events, both selected and alternative, and the spatiotemporal queries afforded by the model. 2.4 A Conceptual Spatiotemporal Branching Events Model for Decision-Makers During Flood Response The conceptual framework in this section is based on the previous research on branching time (Allen 1984, Frank 1998, Worboys 2005, Galton 2008), event modeling approaches (Grenon and Smith 2004, Worboys and Hornsby 2004, Worboys 2005), and the formal model presented in this paper. While a branching events model can be constructed for other domains (e.g., transportation, crime), this framework s design is for flood response scenarios. The objective of this framework is to present a method for the creating, developing, and the executing of a system for modeling branching events for flood response scenarios. The goal of the framework is to call attention to the components that are important to the development of such a system and how these components interact and function together. During flood disasters, emergency managers respond to dynamic occurrences such as flood water rescue, water moving onto roadways, and residential evacuations. These types of response actions are best represented as events in any geographic response system. Across the country flood decision-makers rely on commercial software packages such as WebEOC ( or E-Sponder

41 27 ( to assist in their response efforts. These systems have varying capabilities, but the main goal is to share information about what has occurred, what is occurring, and what will occur to response officials. Data is assimilated into these software systems by users and disseminated largely by a web interface. These systems have GIS capabilities but do not represent events in the system. In addition, these systems do not capture branching time or have the ability to consider alternatives. Considering alternatives has recently been shown to be a need by emergency responders during flood events (National Weather Service 2013, Demargne et al. 2013). As a result the National Weather Service has developed a website (Ensemble Quantitative Precipitation Forecast Hydrographs, that presents flood forecast alternatives based on precipitation variations that are only available to emergency managers and decision-makers. To build a branching events model for flood response that models alternatives, the system should consider past, current, and future events. To include all three event types, the system must be designed to store historical events, acquire real-time events, and model (forecast) future events. For decision-making, a branching events model should have a means for querying so that a decision-maker can view alternative sequences of events, including critical events (i.e., events that are more important to the decisionmaker over other events). This section presents a conceptual framework for designing a system that includes these components for flood hazard modeling Conceptual Framework for a Spatiotemporal Branching Events Model for Flood Response Events In this dissertation the focus is on flood response events. Response is defined as the capabilities necessary to save lives, protect property, and the environment, and meet the basic human needs after an incident has occurred (FEMA 2013). This definition

42 28 provides a bounds for the temporal scope of modeling. Flood response events are those events that occur once the flood event has occurred and end at the moment in time the incident is over and the recovery phase takes place. Spatially, response events are modeled within the region a decision-maker is responding (e.g., a county emergency manager may require knowledge of events in adjacent counties, but is not concerned with events occurring hundreds of miles away). Also, response events are not required to fall directly inside a flood boundary. For example, a power outage event for a commercial district or residential neighborhood is an event that would be important to model for flood response, however this event may not lie entirely within the flood boundary. As previously discussed, branching can occur in the past or in the future. Even though there is only one past, the events that have led up to the current moment may be unrealized and therefore many possible events may have led to the current moment. The model also must be bounded by a domain to only include those events that are of interest to the user based-upon a modeling scenario; modeling anything and everything is inefficient and provides unnecessary information to the decision-maker. The model for branching events must have the following broad capabilities, Model events in the past, future, and store real-time data Model alternatives in the past and future An ability to query events and sequences A framework for a branching events model for flood hazards has 5 parts: data assimilation model, web interface, query engine, event handler, and event database (Figure 2.9).

43 29 Figure 2.9: Conceptual Framework of a Branching Events Model for Flood Response Assimilating Flood Response Events Assimilating events into the model requires two main processes. During a flood disaster the majority of flood events are recorded through calls to 911 from the public and from emergency operations center (EOC) software (e.g., WebEOC). The events from these sources must be entered into the model creating a database of timestamped events. For example, when an agency decides to evacuate residential structures that information is entered into EOC software to be shared with cooperating agencies. Fires that occur in the floodplain, related to rising flood waters (e.g., interacting with live electrical wires) are called into E911. The event handler processes these raw observations and builds them into events. As new real-time events are recorded they become past events stored in the database. Another real-time method of collecting past events is the collection of events that are realized in real-time that actually occurred in the past. The historic database can also be used to simulate what events may occur in the future. It can be assumed that some events will occur again in the future if they occurred in the past. For example, if a

44 30 residential structure was evacuated in a past flood, it can be assumed it will be evacuated again in future floods of the same magnitude or greater. Some future events may be reported such as, road X will be flooded tomorrow or the levee will breach tonight. Other future events can be estimated using GIS tools and flood forecasts that model the future level of high water. Table 2.2 An example of an event id table with the antecedent and descendent attributes. As each is created, it is attributed with the flood boundary that it is associated with, and stored in a table within a database (Table 2.1). As events are added to the database, relational tables are created that model the events (Table 2.2). During table creation, a tuple is added for each event with the timestamp for time of occurrence. Based on the timestamp, every event and its table will have at least one descendent and antecedent event, except the initial and final events that will have one descendent or antecedent event respectively. Table 2.1 An event table representing flood events for a hazard being modeled.

45 Queries for a Branching Events Model Response actions are centered on a single sequence of events, while considering the multiple possibilities that may occur. Out of the multiple possible sequences, a decision-maker will select a single sequence to plan response activities. In the database each event must be linked to its corresponding flood (the flood scenario number field). Selecting a sequence has three steps. First select all of the events that have the flood scenario number that is being selected. Next select the EventID tables for each of those events. To find the event that begins the sequence, find the table that has no antecedent events. The rest of the sequence falls into place by connecting successive events based on the timestamp and EventID tables. To select a sequence, a selection will have to occur on one or more events. An example of this type of query is given in the following psuedocode. Select EventID from eventidtable where floodscenarionumber=1 Write EventID to file1 For each EventID in file1 { Select EventID from eventidtable Append file1 } Being able to select converging and diverging events (critical events) depicts more certain and less certain areas within the simulation. Events with only one antecedent and multiple descendants (in the event_id tables) represent events that begin a new set of branches indicating divergence. Events with many antecedents and one descendent represent events that mark a convergence. For a decision-maker understanding where more and less uncertainty exists within the model can be used as a part of their decisionmaking. The following queries in pseudocode demonstrate how the model could select

46 32 converging and diverging events using the table structure given in the previous subsection. foreach eventidtable { SELECT SUM(Antecedent) AS TotalItemsOrdered FROM eventidtable; SELECT SUM(Descendent) AS TotalItemsOrdered FROM eventidtable; If (valueantecedent > 1 AND valuedescendent = 1) Return convergence If (valueantecedent = 1 AND valuedescendent > 1) Return divergence } In the next section, a flood response scenario is modeled using the elements of the formal model and conceptual framework. The possible floods, events, and sequences are represented and displayed in a viewer. 2.5 Modeling a Flood Scenario Using the Branching Events Model In hazards modeling, situations arise where different response events are possible as well as a variety of event sequences. In order to analyze these different cases, an emergency manager can represent the individual events as well as multiple sequences that may occur. In this case study, a branching events model is used to analyze three likely floods, and the sequences of events associated with each flood to spatiotemporally mobilize protective measures before a flood. A web-based branching events application has been developed that displays selected events and a corresponding set of alternative events, providing users, such as emergency managers, with a visualization for geographically-based decisions (Figure 2.10).

47 33 Figure 2.10: Branching Events Viewer application. The left panel shows a description of events and the remainder is the main map viewer for representing domain events. The viewer shows a scenario that is based upon an actual flood that occurred in Iowa City, IA in June of The model is initialized on June 10 th with the currently forecasted flood (selected flood) based upon the most likely rainfall projection. Two additional possible spatiotemporal boundaries are modeled, representing the varying boundary based upon the projected minimum (alternative flood 1) and maximum (alternative flood 2) rainfall forecasts and resulting flood boundaries from those rainfall forecasts. Each boundary represents a possible location of the flood as an alternative to the currently forecasted flood. The flood boundaries were collected from the Iowa Flood Center ( The events associated with each flood are manually entered into the system from actual events that were recorded in the Johnson County, Iowa EOC in June Since these events have occurred in the past, each is assumed to

48 34 occur again in the future (Figure 2.11). The approximate times of occurrence are included as timestamps for each event. Figure 2.11: Spatiotemporal boundaries of the selected and alternative floods. Unlike other types of hazards, alternate flood boundaries are contained within each other. More specifically, lesser floods in magnitude have flood extents that are completely within the boundaries of greater magnitude for the same region. While a range of events are possible (e.g., debriscleanup, reconstructbuilding), only response events associated with the response phase during a flood disaster are considered here. These include, flooded roads, building evacuations, bridges closed, and sandbag wall construction. In this example, based on the primary focus by emergency managers on the selected flood, the events that comprise sequence (S 2 ) are selected, while events

49 35 associated with sequences (S 1 and S 3 ) serve as alternative events based on time. These sequences are given as follows: S 1 = {floodedroad1, artbuildingflooded, sandbagwall1constructed} S 2 = { floodedroad1, artbuildingflooded, floodedroad2, technologybuildingflooded, auditoriumevacuation, sandbagwall2constructed, theatreevacuated} S 3 = { floodedroad1, artbuildingflooded, floodedroad2, technologybuildingflooded, theatrebuildingflooded, auditoriumflooded, floodedroad3, bridgeclosed, musicbuildingflooded} A branching model diagram represents these events, and shows the possible sequences (Figure 2.12). The diagram is constructed from the event tables within the database and is based upon the timestamps, the flood sequence(s) for each event, and each events relational table. The selected event sequence (S 2 ) is represented in white. Each of the floods contains a slightly different set of events. Because alternative flood 1 is a lesser flood than the other two, it has fewer events in this case. Alternative flood 2 is a much greater flood, than the other two, based upon the maximum potential rainfall A major difference between alternative flood scenario 2 and the other floods is that it does not contain any protective types of events such as, sandbag wall construction or building evacuations because the heavy rain and high water rise provide little time for putting flood protections in place. Based on these three sequences, alternative events are sandbagwall1constructed, theatrebuildingflooded, auditoriumflooded, floodedroad3, bridgeclosed, musicbuildingflooded. Two events, floodedroad1 and artbuildingflooded will occur in all three scenarios and floodedroad2 and

50 36 technologybuildingflooded will occur for both the selected flood and alternative flood 2. These latter events are important to a decision-maker because they represent events that have a high likelihood of occurrence because they will occur in multiple sequences. Figure 2.12 Modeling the set of possible emergency response events during a flood scenario with selected events in white, alternative flood 1 events in red, and alternative flood 2 events in blue. Each event is visualized in the web application by the geographic information stored in each event s kml file. When the user selects events (e.g., the events in the selected flood), salient details about the event are displayed in a window over the point. The application displays selected events and also displays the alternative events that arise due to a change in the predicted flood disaster (Figure 2.13). Examining the events associated with a single flood correspond to the more usual single order of events, whereas capturing all possible events demonstrates a branching perspective. A decision

51 37 maker is able to not only analyze a selected sequence of events for planning, but is also able to examine those events that make up the other possible sequences. The selected flood may serve as the focus for a response plan for a coming flood, but the alternatives should be considered when other floods are possible (e.g., rainfall forecasts depicting a range of possible precipitation values). In this scenario, six possible events, sandbagwall1constructed, theatrebuildingflooded, auditoriumflooded, floodedroad3, bridgeclosed, and musicbuildingflooded are alternative events, but all are significant events if the other floods occur. If the application is queried for cases where more than one event in the future is possible (i.e., a diverging event), the application returns artbuildingflooded and technologybuildingflooded. No events are returned for queries of convergence for these scenarios because there are no converging events modeled. Figure 2.13: Web application with selected and alternative events.

52 38 It is important to note that not every possible event related to the floods are considered necessary for representation in this model. The intersection of spatiotemporal flood boundaries and key spatial features pre-selected by domain experts as being most salient for this application help establish the granularity for this application of the model. We discuss this further in the final section of the chapter. 2.6 Discussion This work investigates a branching events model for response events during flood disasters. This work extends previous work on event modeling approaches, such as the event-based spatiotemporal data model (ESTDM) (Peuquet and Duan 1995) and the geospatial event model (GEM) (Worboys and Hornsby 2004). In previous work on the orders of events, linear orders of events were shown to be automatically generated by partial orders based on how events are situated in space (Hall and Hornsby 2005). Linear orders of events have also been researched and applied to the orders of ships in a harbor (Stewart Hornsby and Cole 2007). More recent applications of event modeling have used a linear order of time for trend changes as events (Beard et al. 2008, Kulik et al. 2011) and for modeling topological changes (Jiang and Worboys 2009). This work presents a model for representing multiple possible events and sequences of geospatial events using a branching model, providing a holistic view of the set of possible events over time. This model is applicable to situations where hypothetical situations arise such as in planning, SDSSs, and hazard scenarios. Current work in the spatiotemporal modeling of evacuations from fires (e.g., trigger buffers, Cova et al. 2005, Dennison et al. 2007, Larsen et al. 2011), could benefit from a branching events model. This work only models a single fire scenario based upon the current weather conditions (e.g., wind, humidity) and characteristics of the natural environment (e.g., terrain, fuel) and simulates a single (linear) sequence of evacuation events. Research on the evacuation of other hazards such as hurricanes (see for example, Naghawi and Wolshon 2010, Wang et al. 2010, Fries et

53 39 al. 2011) can also benefit from a branching model of time when multiple possible hurricane paths are possible creating alternative possible evacuation events in the impact area. This work also extends work in time geography and computer science. One area of time geography focuses on the movements of a person or object along a path and the patterns that result from those movements (Hariharan and Hornsby 2000, Hornsby and Egenhofer 2002, Kwan and Lee 2004, Raubal et al. 2004, Miller 2005, Kang and Scott 2008, Miller and Bridwell 2009, Long et al. 2013). More recently, researchers working in space-time prism research have investigated modeling potential (or alternative movements and uncertainties in space-time (Winter and Yin 2010, Delafontain et al. 2011, Chen and Kwan 2012, Kuijpers et al. 2013). This work contributes to these areas by using an event-based approach over the object-oriented approaches that are used in these models. Previous research in temporal modeling for databases, software design, and modeling geographic events has explored branching time as a method for capturing future or past events (Allen 1984, McDermott 1982, Frank 1998, Worboys 2005, Galton 2008). This work presents a formal model for branching events and contributes to these areas of research. Flood decision makers respond to actions that involve movement and change and a modeling approach that utilizes event modeling captures these dynamic occurrences. Flood decision makers also realize that modeling alternatives that capture what-if scenarios are helpful when planning for flood hazards (Jacks and Ferree 2007, Demargne et al. 2013). Many of the current approaches for modeling the response actions of hazard events neglect using an event modeling approach (e.g. Google s Crisis Map, ESRI s situal awareness viewer) or the modeling of alternatives. This work presents a formal model for branching events that captures multiple possible events and the alternative sequences that result.

54 40 Different temporal models have been researched as an alternative for modeling geographic domains. The cyclic model captures phenomena that repeat in a predictable manner (Allen 1983, Snodgrass 1992, Frank 1998, Hornsby et al. 1999, Campos and Hornsby 2004). The formal model presented in this paper extends the work in temporal modeling. Current GISs only support the linear model of time and do not support alternative temporal models of time (e.g., cyclic, branching). Incorporating branching events modeling as an alternative temporal model in future GISs will be especially helpful as a spatial analysis tool for spatiotemporal decision support. 2.7 Conclusions and Future Work In section 1.2.2, the question, what are the elements for a branching model of events, was presented. This paper describes a formal model from branching events and demonstrates there are two key elements, divergence and convergence when modeling alternatives. Divergence describes the case where two or more events may happen next, while convergence captures the case where multiple alternative events are followed by a single event. Many dynamic scenarios when modeled from this perspective can be described by different sequences of diverging and converging events. In addition, these elements of branching can be coupled with non-branching sequences of events to describe how events occur in a domain such as natural hazards. This paper investigates the foundations for a branching model of events. This perspective provides information about the dynamics in a domain and through highlighting alternative events is useful to decision-makers and planners. This model offers several unique qualities that are not present in other approaches based on the more common underlying linear temporal model. Instead of a single sequence of events modeled over time, alternative possible sequences are also modeled. Branching events modeling identifies locations where more than one event (i.e, selected events plus alternative events) can occur, as well as key times when likely and alternative events may

55 41 happen. Cases where the initial or ending timesteps correspond to multiple events describe scenarios where there may be more uncertainty or choice, in contrast with cases where a single event begins or ends the model. Modeling the duration of events captures further details about the way events occur and the relations between events. Incorporating event relations highlights possible distinctions between different patterns of occurrence (captured through branching in the model). This approach supports queries about what may have occurred in the past or what might occur in the future especially where different alternatives may exist. This work answers the following research question, what unique queries are possible for sequences of events when a branching model is used. This model presents different queries not possible when the linear model of time is used. In a branching model queries about possible future events can be asked, but in a linear model only queries about successive events can be asked. For example in a linear model a query to return all events that occur after another event can be asked and returns successive events, but in a branching model the query can be asked to return all of the possible events that can occur after a single event and will include multiple alternatives. In the flood scenario, events are associated with spatiotemporal boundaries modeling the expected and possible locations of a flood. For hazards modeling and other dynamic scenarios, spatial and temporal boundaries can frequently serve as guides for delineating selected and alternative events. For example, tornado paths can be defined using a selected tornado hazard based on a given intensity and trajectory and an alternative tornado hazard based upon an alternative intensity and trajectory (Figure 2.14). These alternatives of tornado paths are considered when the National Weather Service (NWS) creates its storm based warning products that includes a best track and a statistical representation of the bounds of the possible locations of the tornado in the future (National Weather Service 2007). In this case, events that occur within a set of spatiotemporal boundaries may be selected or be alternative respectively. With flooding,

56 42 spatiotemporal boundaries are irregular based on ground elevation and the amount of water present over time. The temporal granularity of tornado events is minutes, while the temporal granularity for flood events could be days based on the rate of rising waters. Other hazards or dynamic domains will involve their own set of boundaries, as well as selected and alternative events, with a range of spatial and temporal granularities. Wildfire footprints, for example, are based on windspeed, wind direction, elevation, among others (Cova et al. 2005, Dennison et al. 2007, Larsen et al. 2011). The speed of the wildfire produces varying temporal granularities (from minutes to hours) for spatiotemporal fire footprints. Figure 2.14: Spatiotemporal tornado paths with selected and alternative events. Future research should investigate methods for incorporating probabilities as well as evidential belief functions that can be used with a branching model to assist with selecting branches and sequences of interest using both knowledge-based and data-driven inputs. In this work we consider flood response events, but in future work other hazards should be applied to the branching model of events to examine the unique requirements that result from modeling those hazards. For example, tornado hazards require the

57 43 modeling of topography and local weather conditions as additional parameters that identify areas of tornado risk and have implications to tornado response.

58 44 CHAPTER 3: TRACKING SPATIOTEMPORAL PATTERNS OF EVACUATION EVENTS DURING FLOOD HAZARDS 3.1 Introduction As flooding threatens, vulnerable groups begin the process of moving themselves and their belongings to higher ground. Actions such as sandbag levee construction and the placement of pumps serve as preventative measures against floodwaters, shielding the surrounding infrastructure. All flood response efforts, including protection strategies, require careful planning to effectively position resources as the situation evolves. These activities demand a well-planned management strategy that is organized within the floodplain so that the impacts of flooding are minimized. In this way, the spatiotemporal dynamics of a forecasted flood are highly important for resource allocation decisions during a serious flood event. In this paper, we present a framework for a spatiotemporal building evacuation model developed to forecast building vulnerabilities and necessary building content evacuations. The main focus of this research is to investigate the underlying spatiotemporal properties of a model for building evacuations events, and the key factors that affect evacuations during a flood disaster. The model tracks the progression of building evacuations over space and time, revealing the buildings and areas that become vulnerable if the flood event continues. Time is a key element of this model, invoked through aspects including the expected time of flood inundation for both buildings and roads, the time needed to conduct an evacuation, as well as the recommended date-time to evacuate building contents. Tracking the spatiotemporal pattern of building risks highlights both individual and clusters of building vulnerabilities, giving flood responders important advanced warning. The model output provides building evacuation planning support for decisionmakers, for example, staff responsible for facilities management, risk management, public safety, and hydrologists among others. We use buildings on a university campus as

59 45 an exemplar of the kind of domain for which such a model is useful. While research on trigger buffers for wildfires has identified topography, windspeed, wind direction, and pre-fire fuels as critical elements for modeling evacuations from dangerous wildfires (Cova et al. 2005, Larsen el al. 2011), in this work, we identify topography, flood inundation, building location, building elevation, and road access as key elements for predicting building vulnerabilities and triggering evacuation events of building contents during a flood. The model determines five different classes of vulnerabilities for the evacuation model including vulnerable basement, flooded basement, vulnerable first floor, flooded first floor, and road access. Although the geographic space used in testing this model is a university campus, there are many elements in the model that are applicable to other geographic domains including urban evacuations, and the results are generalizable for other communities considering flood mitigation strategies. Geographic information systems (GIS) provide a means to analyze and visualize a time-varying flood boundary, representing how this boundary intersects with different parts of a floodplain. This work demonstrates an approach where locations in a domain that are vulnerable to flooding over time are mapped, and form the basis for prioritizing evacuations using information about the spatiotemporal properties of the flood boundary with data about building location, contents and road access to buildings. The contents of buildings at risk from flooding vary widely depending on the purpose for which the building is used. For example, on a campus such contents may include files and office furniture, computers, research laboratory equipment, recreation equipment, and personal belongings in dormitories among others. It should be noted that for this research, our focus is on building content evacuation events, and while this does include evacuating occupants of buildings, we do not discuss here route evacuations, i.e., the path that evacuees must take to leave the hazard region. This will be considered in future work with the model.

60 46 The rest of this paper is organized as follows: in the next section, previous work on GIS-based flood analysis, floodplain vulnerability mapping, and the use of hazard models as a means for response and preparedness decisions are discussed. The temporal aspects of building evacuations are presented in Section 3.3. Section 3.4 presents the approach for identifying building vulnerabilities. Section 3.5 discusses the methodology for generating decision-based evacuation recommendations. Section 3.6 introduces the modeling elements for tracking spatiotemporal campus building evacuations during floods. A validation of the model using a historic flood on a university campus is presented in Section 3.7. The final section provides some future directions for this research Modeling Spatiotemporal Hazards for Decision Support The use of inundation maps for flood prediction have largely centered on the precision of those maps. Comparisons of inundation maps using varying qualities of terrain data for urban environments have shown that when buildings are captured in the terrain, flood depths and flood extents are improved (Fewtrell et al. 2008, Kang 2009, Leitão 2009). Urban environments also make flood prediction difficult if, for example, sewage systems are not often integrated into flood models and can affect the results (Chen et al. 2009). Considering the impacts of future urban growth has also shown an impact to the flood boundary (Correia et al. 1999). Many inputs that are required for flood model calculations have variables that must be adjusted to fit local conditions. Sensitivity and uncertainty analyses as well as statistical approaches are important methods for providing decision makers with a range of possible outcomes (Crosetto and Tarantola 2001, Qi and Altinakar 2011). Research related to spatial decision support systems (SDSS) for hazard scenarios have investigated spatiotemporal analysis techniques and modeling components. The basic components of an SDSS combine the use of spatial analyses, an information

61 47 system, and decision support modeling (Densham and Armstrong 1987, Sugumaran and DeGroote 2011). Recently, the focus has been on enhancing the web support for SDSSs, such as in vehicle routing decisions (Santos et al. 2011), making collaborative decisions (Malczewski and Jelokhani-Niaraki 2012), and using the web ontology language (OWL) (Jelokhani-Niaraki and Malczewski 2012). Research on the design of decision support systems for floods has examined the advantages of using a high performance computing platform with complex models (e.g., statistical and forecast models) (Adorni 2000) and the visualization of hazard maps for decision makers (Hagemeier-Klose and Wagner 2009). A real-time system has been developed that utilizes measured or forecasted flood data, exposure information and damage functions to provide decision makers with estimates of damages and losses from flooding (Luino et al. 2009). Methods for ranking flood decisions (priorities) based upon a set of alternatives have also been investigated (Levy 2005). Flood modeling depth grids have been used as inputs to modeling risk and damage. Loss estimations for future scenarios, that use current development patterns and current mitigation practices as inputs, have been found to underestimate estimated losses by greater than 50% when these datasets are not included in simulations (Hardmeyer and Spencer 2007). Work on using remote sensing to create flood boundaries and loss estimation provides analyses in areas that would be difficult otherwise (Forte et al. 2006, Choi et al. 2007). Other uses include estimating damages and losses within and without levees (Remo et al. 2011), accounting for the social impacts (e.g., loss of life, evacuations) due to flooding (Jonkman et al. 2008), and determining the overall risk of flooding impacts to a geographic area (Meyer et al. 2009). GIS-based approaches have been widely adopted to provide flood analysis, mapping and decision support. Research on real-time flood response systems examines the ability to create flood extents on the fly (e.g., The Iowa Flood Center) and methods for displaying decision-making information on the web (e.g.,

62 In addition, due to the development of web technology, web-based GIS has also been discussed and developed to help the decision-making in flood emergencies. The use of NEXRAD, runoff model outputs, hydrologic and hydraulic models has shown to be effective in producing real-time flood extents (Yang and Tsai 2000, Knebel et al. 2005, Yu et al. 2005, Whiteaker et al. 2006, Van Der Knijff et al. 2010). Other work focuses on methods for creating web-based systems for decision-making and alerting. One model uses data collection routines from geosensors, temporal and spatial models on a webserver, and delivers flood outputs in real-time to the web (Al-Sabhan et al. 2003). Another model utilizes a hydrologic model that is initialized by observations from geosensors and weather forecast data for Europe (Thielen et al. 2009). In this work, the results of spatiotemporal building vulnerability analyses are displayed using a web-based interface that maps real-time flood conditions and campus vulnerabilities. Although little attention has been given to building content evacuations, research on evacuating people from hazards is ongoing. For communities and individuals faced with the possibility of being flooded, risk perception is a function of the location and spatial variation of risk (Siebeneck and Cova 2012). Work on evacuations for hurricanes (involving coastal flooding and hurricane winds) have examined the ability for transportation networks to handle the traffic volumes during an evacuation (Naghawi and Wolshon 2010, Wang et al. 2010, Fries et al. 2011). Similar work has examined the ability for transportation networks to support evacuations from fires as well (Cova and Johnson 2002). In the modeling of the evacuations of people from wildfire hazards, evacuation triggers have been used to determine when people need to leave an area. The triggers are based on the location of a fire, its spread rate, and the distance to a community (Larsen et al. 2011). This method is ideal for planning fire-based scenarios because it does not rely on a single fire ignition location but works for any fires that begin outside of the buffer (Cova et al. 2005). Worst case scenarios have been developed

63 49 based on the worst case meteorological conditions to drive evacuation route planning during a fire (Dennison et al. 2007). Other evacuation trigger examples exist for other hazards. HURREVAC, a hurricane evacuation model developed for the Federal Emergency Management Agency, utilizes forecasted hurricane information to produce storm information (e.g., projected windspeeds and storm surge) that provides decision makers with the necessary information to produce evacuation zones (FEMA 2000). In geographic dynamics research, the modeling of internal and external variation in the attributes of moving objects has been tested in the tracking of storms and near-by vehicles (Pultar 2010). Events are dynamic, involving change and movement across space and time (Grenon and Smith 2004, Worboys and Hornsby 2004, Galton and Worboys 2005, Worboys 2005, Beard et al. 2008, Jiang and Worboys 2009). Events go into existence and out of existence and can be grouped into classes (Galton 2000, Galton and Worboys 2005, Galton 2008). In emergency response, decision-makers respond to dynamic actions such as, levees breaching, residential building evacuations, and water rescues. The focus of this research is modeling building content evacuation events for decision-based support for response activities during flood disasters. Building content evacuation events model the change and movement of building contents that are vulnerable to flooding. The next section describes the temporal aspects of building content events. 3.3 Temporal aspects of evacuation events during flooding The timely evacuations of individuals from natural hazards, is a common topic in the hazards literature (see, e.g. recent research by Horner et al. 2011, Siebeneck and Cova 2012, Wu et al. 2012). For example, the time required to evacuate residents from wildfires along road networks including the number of lead hours required to handle the evacuation traffic, the establishment of evacuation zones (Cova and Johnson 2002, Shekhar et al. 2012) and the development of temporal trigger buffers (Cova et al. 2005,

64 50 Dennison et al., 2007, Larsen et al., 2011) have been discussed. The travel times and level of risk have been used to construct regional public hurricane evacuations (Apivatanagul et al. 2012) and network route times during hurricane evacuations have also been examined (Shekhar et al. 2012), as well as models for the evacuation of people within buildings during a fire (Luh et al. 2012). In each of these works, an integral part of the modeling approach is to compute the length of time required to evacuate individuals from these disasters. The focus of this work is on evacuation events. An evacuation event is the occurrence of moving vulnerable contents away from flood water before a building becomes inaccessible. This work does not focus on the capacity of a road network for moving individuals out of an area. Building-based evacuation events are derived based on a flood depth grid linked to a hydrograph, building location, and content-based building evacuation requirements. From this set of variables, a timeline of evacuation events is constructed and then spatial regions of evacuation lead times are established. The set of regions of evacuation lead times capture the evolution of evacuations within the floodplain (see section 3.7 for further discussion on this topic). While the evacuation of building contents during hazards has not been a research topic, the movements of containers in shipping yards have. In a similar way to the movements of building contents, shipping yards are attempting to move shipping containers quickly while minimizing their costs (Kim and Kim 1998, Kim and Kim 1999, Lee et a. 2013). A number of variables are used to factor in the movement times of shipping containers, for example, the number of cranes that are moving containers, the space where the containers are stored, the speed of the cranes and the total number of containers that are scheduled to be moved, among others (Kim and Kim 2002). More recent researched has focused on the routing of trucks that are exporting shipping containers (Braekers et al. 2013, Boysen et al. 2013).

65 51 Estimating the movement of a home or a business has some common variables to the shipping container research problem. While academic research has not investigated questions regarding the movement of contents within residential or commercial structures, many moving companies list how they calculate the time to move building contents (e.g., As with shipping containers and measuring the impact on the number of cranes on the movement time, movers estimate the movement time based upon the number of movers and their rate. Moving companies also examine the weight of the contents to be moved, similar to the number of containers that are required to be moved. While the location and method of movement (e.g., containers moved to another building using moving trucks) are important factors in evacuation time, they are not considered in this work, but are discussed in future work. This work assumes that moving contents to a higher floor or out of the building require the same basic amount of time and the time required to transport contents to another location is not considered. In this work, the estimated length of time to evacuate a building s contents (E) is given as, The evacuation time (E) is computed from the summation of the weight of items (W) for each vulnerable floor (i), the total number of movers (M), the mover rate (R) (in pounds per minute) required to move vulnerable contents, and the total number of vulnerable floors (n). R varies with each vulnerable floor depending on the number and weight of the contents. For this research, the spatial domain is a university campus. If a building with 2 vulnerable floors (40,000ft 2, 3,716m 2 ) housing a computer lab, small library, and offices requires evacuation, the time for evacuation is estimated at 17.6

66 52 hours. This assumes the following variables: a total weight of approximately 159,000 lbs (72,121 kg), an average worker rate of 10 lbs/min, and 15 movers. The University relies on a substantial volunteer force (e.g., employees, students, etc.) to help with the movement of items during extreme flood events in addition to professional movers. In this model, volunteers are grouped with professional movers, but in future work the model could be refined by considering each type of mover separately. The weights of items were determined by online moving weight metrics (see for example, The calculated evacuation times of vulnerable buildings in a case study are discussed further in Section 3.7. Other elements to evacuating the contents of structures (e.g., costs, distance of travel), are discussed in Section 3.8 as future work. In general, six possible temporal evacuation profiles exist for moving building contents with variations shown with dashed lines (Figure 3.1). These are: The same evacuation time for all buildings (Figure 3.1(a)) The same number of buildings for each evacuation time (Figure 3.1(b)) Greater number of buildings require longer evacuation time (Figure 3.1(c)) Greater number of buildings require shorter evacuation time (Figure 3.1(d)) High numbers of short and long evacuation times, but low mid-range evacuation times (Figure 3.1(e)) and High number of mid-range evacuation times, but low numbers of short and long evacuation times (Figure 3.1(f)).

67 53 Figure 3.1: Evacuation time profiles (possible variations shown with dashed lines) These profiles represent the temporal profiles of evacuations for a group of buildings by comparing the expected time for building flooding and the length of time required to evacuate the buildings. The dashed lines represent alternative variations and demonstrate the range of possibilities for each profile. From a planning perspective, cases where a gradual or constant number of buildings require evacuations over time is desirable (Figure 3.1(b) and (c)) so movers can evacuate small groups of buildings over time. Less desirable cases occur when large numbers of buildings are required to be evacuated at the same time or when very abrupt changes to the total number of buildings occur over time (Figure 3.1(a), (d), (e), and (f)), making evacuations difficult due to a possible lack of movers and resources for moving contents. The evacuation profile for a group of buildings is typically not fixed, rather it is dependent on the evolution of a flood.

68 54 Floods of varying magnitudes will create different evacuation profiles for the same group of buildings. Computing building evacuation time is discussed further in Section Determining building vulnerability for flood evacuation events Tracking the spatiotemporal progression of building evacuation events during floods is especially important for decision-making groups that are tasked with flood mitigation and evacuation planning. In order to track evacuations the time when a building becomes vulnerable must be determined. The level of vulnerability is dependent on the characteristics of the building and the relationship to the flood elevations within the floodplain. Four types of building vulnerabilities are identified that are related to flooding inside of the building and one type related to flooding that affects the access to a building Building vulnerability identification Vulnerable buildings can have a very diverse set of needs and times for evacuation. On a university campus, for example, research projects, such as experiments involving highly delicate equipment, can be sensitive to relocation because movement or disassembly of these experiments means important research results are lost. Valuable art, theater, and music equipment require extra time and care to evacuate. Residence halls require the retrieval of personal effects and temporary housing organized for student evacuees. When buildings are flooded, they are often inaccessible for many days, if not weeks or months, impacting faculty, staff, and students that need time to move key items to either higher floors or other locations. As discussed in Section 3.5, the evacuation time for each of these relocations varies from short period (e.g., half day) for small units with straightforward relocation needs (e.g., at risk contents are simply moved one story higher in the same building), to up to longer times (e.g., seven days) for large buildings with many subunits to relocate.

69 55 For each building, the different indoor spaces are monitored with respect to vulnerability to flood inundation. This involves matching the floor elevations within a building to the specific flood elevations outside of the building. Representing first-floor flooding in addition to basement flooding, i.e., explicitly mapping those buildings where first-floor flooding is a risk, helps personnel to make decisions about moving contents up in a building vs. moving contents out to a different location. The four different levels of vulnerability for buildings when water enters the building are given as follows: Vulnerable basement: flood level is forecast to be within 6 inches (15.24cm) of historic basement flooding elevation Flooded basement: basement is forecast to be flooded in the next seven days based on historic flooding information. Vulnerable first floor: flood level in the next seven days is forecast to be within 6 inches (15.24cm) of the first floor elevation Flooded first floor: first floor is forecast to be flooded in the next seven days The modeling of basement flooding is a complex process. There are various ways a basement can flood, one way is from the intrusion of surface water into the basement (Chen et al. 2005). Basement flooding can also occur from sewer system backups (Schmitt et al. 2004). From interviews with building personnel, it was determined that basements were also flooded from groundwater intrusion and by water entering the basement from the campus tunnel system in previous flood events. In many cases of basement flooding, flooding occurs in the basement before surface floodwater is present. Currently, the model has been constructed so that basement flooding is directly tied to the flood elevations that have caused flooding in previous flood events with the idea that flooding in the future will occur at the same flood elevation as in the past. Section 3.8

70 56 discusses how the process for identifying evacuation events can be extended to include a more detailed basement analysis. To compute first-floor flooding, a layer representing building footprints is intersected with flood elevations. When such an intersection exists, the flood elevation is compared to the first floor elevation for the building. If the first floor elevation is less than or equal to the flood elevation, the first floor is considered flooded at that flood elevation (Figure 3.2). After this flood elevation has been determined, a building s first floor is considered vulnerable at the flood depth grid that is 6 inches (15.24cm) less than the flood elevation that has been found to cause first-floor flooding. This is to account for the uncertainty between depth grids, uncertainty in the exact entry point level, and any local characteristics in terrain not accounted for in the flood model. For the flood depth within a building, the zonal statistics toolset in ArcGIS is used to calculate minimum, maximum and mean water levels for all first floors, with the maximum level being used in the model. Figure 3.2: GIS analysis for modeling building vulnerability.

71 Road inundation and access For tracking the spatiotemporal vulnerabilities of buildings, an understanding of how the road network is being affected by rising water and where navigation is possibly being compromised is required. Road inundation affects access to a building. It should be noted that road inundation is not being used to determine possible route evacuations for people, but rather is used to determine the accessibility of buildings for content evacuation. When a road becomes inundated, this may result in reduced or complete loss of access to a building, and without road access, movers may not be able to evacuate the contents of a building. For this research, a road is considered vulnerable once water depths on the road are greater than 1ft (0.3048m). However the model threshold could be set to other levels as required for different spatial domains. Water velocity is not currently included in the determination of road access, but in the future, velocity layers in conjunction with the flood depth data will be used to create a more comprehensive flooded roadways layer. 3.5 Simulating evacuation time for modeling evacuation events Building evacuation events modeling relies on a best estimate of the length of time required to evacuate. Planning is improved when there is a stable timeline of events. Even though flooding represents an unstable planning environment, it is possible to develop a set of building evacuation recommendations. For a university campus, for example, decision makers require flood recommendations at least once a day as new flood forecast information becomes available from the National Weather Service s Advance Hydrologic Prediction Services (AHPS) website ( Evacuations are reported from the current day through the end of the forecast period, which is typically seven days. During times of peak flooding, decision makers may require more frequent updates (e.g., twice or three times a day) to respond to changing conditions, and provide support in the floodplain as needed.

72 58 Evacuation start times are determined based on: 1. The day/time estimated to evacuate the building (section 3.3). 2. One flood elevation depth grid (6 in, 15.24cm) prior to the expected day/time onset of flooding of the lowest floor. 3. The estimated day/time when the building will become inaccessible as a result of road inundation (section 3.4.2). The final duration of an evacuation event is calculated by subtracting the necessary time to evacuate the building from the expected time of onset of flooding or the time when the building is no longer accessible by road. This represents a conservative estimate for two reasons. First, the expected time of arrival of flooding is based upon the vulnerable lowest floor inundation time. In the case of buildings with basements, first floors may still be accessible and evacuations could potentially continue or in cases where there are no contents in the basements, basement flooding would not impact evacuations. From interviews, we determined that there are uncertainties due to health safety concerns when buildings, even at the lowest levels, become flooded and therefore may be closed, and for this reason we assume the buildings are closed when basements are flooded. Maintaining access to buildings is essential for evacuations during a flood. Once roads become flooded, access to a building may be impossible even though the building itself is not necessarily being inundated. Evacuation times are grouped into six groups based on two factors; hydrologic forecasts by the NWS include 24-hour and sometimes 48-hour rainfall forecasts and discussions with the building stakeholders. The five groups are as follows: Evacuated Evacuations in Progress Evacuations Today

73 59 Evacuations Tomorrow Evacuations beginning in +2 Days Evacuations beginning in 3 7 days Another factor in determining when a structure is to be evacuated is related to a cost-benefit analysis. A set of evacuation recommendations may be modified by a decision-maker based on the costs to evacuate a structure (e.g., movers required, financial considerations) and the benefits to evacuating the structure. This is especially true during flash flooding events, when water rise quickly and decision-makers must choose the buildings to save versus the buildings that will flood. This work does not consider a cost benefit analysis, but can be added in future developments of the model. 3.6 Modeling Spatiotemporal Building Evacuation Events The modeling of evacuation events requires building vulnerability, evacuation start and end times, a flood inundation library, and for flood response, a flood forecast. Building vulnerabilities identify at what level the building is at risk. Some buildings are only at risk of having a basement flooded while other buildings may have multiple floors flooded. The degree of vulnerability determines the time required for a building evacuation (i.e., more vulnerable floors require more time). The evacuation duration identifies the length of time between the time the building will flood (or become inaccessible) and the time required to evacuate. A flood inundation library provides the flood details on flood depth in a building and the flood depths on roadways. A flood forecast provides the magnitude and the temporal information that is linked to the flood depth information. The spatiotemporal dynamics of a flood disaster are unique, and each flood disaster is different from the next. This section discusses how each of these components are used together to model evacuation events.

74 Vulnerable Buildings To model building evacuation events, information about the buildings is of critical importance for accurately monitoring and predicting the status of buildings during a flood event. The database serves two main tasks. It provides specific details on the location, elevation, and other properties of a building (presence or absence of basement, number of floors, details on when the building flooded in the past), and the database also provides key information that is especially relevant for flooding scenarios, e.g., where contents will be relocated and the length of time to evacuate the building. In order to create a database about buildings, historical data should be collected on the degree to which buildings in the domain have been flooded from previous flood events. Existing plans can be collected that list the vulnerable buildings and structures that lie within the floodplain. As much as possible, interviews should be conducted with representatives from vulnerable buildings as well as staff, for example, facilities management representatives to provide base data for the evacuation model. For this research, the model takes into account: when buildings were flooded in previous flood events the time needed for evacuation during previous flooding the estimated time needed for future evacuations and where in the buildings did flooding occur and to what depths Linking flood forecast information to building vulnerabilities Flood forecasts can come from regional or local forecasting centers, however the most widely used flood forecasting information comes from the National Weather Service s (NWS) Advanced Hydrologic Prediction Service s (AHPS) river forecasts ( Based upon current river elevations, soil conditions, and other antecedent factors, the NWS computes the most likely water elevation for locations

75 61 across the US along US Geological Survey (USGS) streamgages. The flood forecasts represent forecast points every six hours over a span of 7 days. These forecasts provide the timing for the flood event and the temporal dynamics of the flood. Flood forecast information can then be linked a to flood inundation model or a library of pre-computed flood inundation maps. The advantage to a map library is the shortened processing time. Flood forecast elevations for each forecast time are then matched to a flood inundation map, providing a spatiotemporal progression of the flood. For a given location, the water level can be tracked over time. In this work a flood inundation library is used and linked to the AHPS forecast information Flood inundation map repository Building a flood inundation map library requires the development of flood layers that can be spatiotemporally linked to flood gage and flood forecast information and are used as inputs in determining building vulnerability. For this research, the model has been initialized on the University of Iowa (UIowa) campus and flood inundation maps were collected from the Iowa Flood Center, ( a UIowa center specializing in hydrologic research and education about floods, and used in the evacuation model. The grids are produced using a 1D/2D coupled model (Gilles et al. 2011). Two well-known hydrologic models are used to create these maps. MIKE 11 is used in the river channel and MIKE 21 is used in the floodplain and then coupled using MIKE FLOOD. For topography, the model uses a LiDAR-derived DEM created from the statewide LiDAR project organized by the Iowa Department of Natural Resources ( To prepare the model, building vulnerabilities are computed for 34 flood layers that represent the flood depth grids, beginning at 17ft (5.18m) and continuing every 6 inches (15.24cm) of flood stage to 34ft (10.36m) at 55,000cfs ( m 3 s -1 ). From this analysis, an interesting pattern emerges on this campus (Figure 3.3). Buildings at some

76 62 locations on campus begin to be at risk from flooding at approximately 21ft at 12,300cfs (6.4m, m 3 s -1 ). Two very steep changes in the number of vulnerable buildings exist. The first increase in flooded buildings occurs at approximately the 100-year flood elevation and the second occurs close to the 200-year flood elevation. This is most likely due to construction requirements in the floodplain in the past (e.g., construction above historic base flood elevations). Basement flooding occurs before first floor flooding until the flood waters become high enough that all basements are flooded and first floors also take on water (30ft in stage at 34,900cfs, or /9.14m at 988m 3 s -1 ). Some buildings do not have basements causing the total number of first floor vulnerabilities to be greater than basement vulnerabilities for the greatest flood discharges. On average, beginning at 25,000cfs (707.92m 3 s -1 ), another building becomes flooded every 1,600cfs (45.30m 3 s -1 ). Figure 3.3: Number of flooded buildings with increasing flood magnitude

77 63 After the flood surfaces have been calculated, additional processing is required to create layers ready for graphical display. Each layer starts with a water surface profile that must be modified into a depth grid and then adapted into layers that are easily displayed in the GUI of the evacuation model. A geoprocessing script is used to process the grids and currently runs in Python and utilizes ArcObjects, providing automated support for a real-time environment that produces the necessary flood depth grids. There are two main goals that the geoprocessing routines accomplish. First, the flood depth grids and flood boundaries are created to provide quick rendering in the GUI for timely display. Second, it provides a quality assurance and quality control step by creating each dataset the exact same way, minimizing the amount of error involved that may occur when dealing with hundreds of datasets. The first component of the geoprocessing script is directed towards computing the flood depth grids that are used in the evacuation model to identify building vulnerability. To calculate depth grids, each water surface profile is converted into a flood depth grid and then reclassified (to provide visual representations of flood depth ranges). The second component of the geoprocessing script contains the steps necessary to compute the flood boundary for each flood depth layer and KMZ creation. To minimize calculations and the duplication of data, the model iterates through the raster images that are created as output from the first component of the geoprocessing script. The final outputs are boundaries of the flood extent for each flood. Each of these boundaries is converted to a KMZ file for web mapping Evacuation model viewer A map-based visualization of vulnerable buildings and roads is generated using Google Maps API. The user selects the date and time they would like to view. Once a user selects the date and time, the following layers can be viewed:

78 64 Flood boundary Flood depth Inundation depth on roads Road inundation boundary Building vulnerability Building evacuation priorities Each layer is created as a KMZ file. The building vulnerability map has the four levels of building vulnerabilities thematically mapped for decision makers. From this layer, a spatiotemporal pattern of vulnerability emerges (Figure 3.4). The current recommendations for building evacuations are also displayed and each of the five levels of recommendations are presented. In addition to these layers, other important layers are also available such as the locations of loading docks, floodwalls, and pumps. Figure 3.4: Representing building inundations and vulnerabilities

79 Validating and tracking the spatiotemporal progression of building evacuation events To validate the model, data from a historical flood hydrograph representing severe flooding experienced on the UIowa campus in 1993, has been obtained and is compared with predictions from the evacuation model about the progression of building vulnerabilities and recommended building evacuations during this event. The 1993 flood also resulted in numerous road closures and was the first major flood event in forty years to damage campus buildings. At the time, the campus did not have a detailed flood evacuation plan in place and as a result evacuations were sporadic and limited. Our model is validated against the best known dates when buildings became inundated and when roads were closed. The validation data for the historical flood impacts were gathered from personal interviews with campus employees who experienced the 1993 flood, accounts in (archived) local newspapers (The Daily Iowan and The Iowa City Press Citizen), and interviews with flood responders. The model is initialized for nine days in the summer of 1993 beginning on July 5 th. Each day is simulated with a seven day forecast. Forecasts are not available for the 1993 event, instead the actual historical flows from the USGS were used as the forecast data ( Building vulnerability results During the 1993 flood event, nine buildings had basement flooding and no buildings had first floor flooding (Figure 3.5). The model results match closely with the actual building inundations during the 1993 event, returning nine total buildings with flooding. However, only seven matched exactly. Two buildings that flooded in 1993 were not predicted by the model and two other buildings were predicted by the model, but they were not actually flooded (Table 3.1). Art Building #2 was not flooded because it was not constructed until The Library and English buildings were not predicted by the

80 66 model because these buildings were flooded from heavy rains that entered the buildings (from unknown entry points) and not from riverine flooding (this model only simulates riverine flooding). The Technology Building was flagged by the model as having a vulnerable basement, but did not actually flood. While it s possible the model requires an adjustment for that building, this building uses pumps year round to keep it dry and could be the reason flooding did not occur in Figure 3.5: Vulnerable buildings modeled during the 1993 flood event.

81 67 Table 3.1: Actual flooded campus buildings in 1993 versus predicted flooded buildings by the model. Building Flooded in 1993 Flooded in Simulation Theatre Building Art Building #1 Art Museum Residence Hall (Dorm) English Building Not Flooded Power Plant Library Not Flooded Hydraulics Liberal Arts Art Building #2 Not Flooded Technologies Building Not Flooded Building evacuation recommendations in 1993 predicted by the model Details about flood evacuations during the 1993 flood are limited, however, based on archived information from The Daily Iowan (a campus newspaper) and the Iowa City Press Citizen at that time, the approximate dates of building evacuations were collected. The evacuation times for vulnerable buildings were computed based upon the equation in Section 3.3 (Table 3.2). The average evacuation time, determined by the model, for all of the buildings is 5.7 days. In 1993 the average evacuation time was 1 day. The model predicted two buildings would be evacuated that were not evacuated during the flood, the Technology Building and the Power Plant. As mentioned previously, the Technology Building did not receive water in the basement and is the reason it was not evacuated. Preparations were made in the Power Plant to evacuate the vulnerable floors in 1993, however there was not specific content that required evacuation. The model shows that three buildings would have had evacuation times that occurred before the modeled start time. These evacuations actually occurred one day earlier due to water seepage from heavy rain. Two buildings were not evacuated until several days after the recommended evacuation time. This likely occurred because this was the first flood in forty years to affect the campus and building vulnerabilities were not completely understood.

82 68 Table 3.2: Observed versus calculated evacuation times for campus buildings. Building Calculated Evacuation Time (days) Actual Evacuation Start Date Evacuation Time (Days) Modeled Evacuation Start Date Theatre Building 5 7/6/ /7/1993 Art Building #1 7 7/6/ /7/1993 Art Museum 7 7/6/ /7/1993 Residence Hall (Dorm) 6 Before 7/1/1993 N/A Before 7/1/1993 English Building 3 7/13/ No Evacuation Predicted Power Plant 3 Not Evacuated N/A 7/9/1993 Library 7 Not Evacuated N/A No Evacuation Predicted Hydraulics 5 7/13/ /9/1993 Liberal Arts 5 7/12/ /7/1993 Art Building #2 6.5 Did not exist N/A 7/7/1993 Technologies Building 7 Not Evacuated N/A 7/7/1993 Based on the model, on day one (July 5 th ), one building (a residence hall) would have already completed evacuations because of the already high water levels (Figure 3.6 (a)). Two other buildings are marked to begin evacuations within the next two days (Liberal Arts Building) and another within the next 3 to 7 days (Power Plant). Two days later on July 7 th, six additional buildings are marked for evacuations (Figure 3.6 (b)). The evacuation time profile for the simulation shows a gradual increase to the number of required evacuations each day (Figure 3.1(c)). As each day passed and new buildings became vulnerable, the model shows new recommended building evacuations to the south of the previous recommendations, producing a pattern of evacuations (Figure 3.6 (c)). The pattern shows that groups of buildings required evacuations that were close in proximity to each other and vulnerable at the same time. It is important to note this pattern is a product of both a changing flood boundary and the time need to evacuate each building, and cannot be explained by physical processes alone (e.g., terrain).

83 69 Figure 3.6: Recommended evacuation times for (a) July 5 th, (b) July 7 th, and (c) the progression of recommended building evacuations. Road access is also modeled to determine if the timeline of building evacuations would change if a road becomes inundated by flood water. Iowa Avenue, North Riverside Drive, North Madison Street, and Dubuque Street all flooded in 1993 on campus and the model predicted inundation on all 4 roads. Road accessibility did not modify the existing order of building evacuations, and did not add any new buildings to the list of recommended evacuations. 3.8 Discussion This work presents a spatiotemporal model for tracking building content evacuation events. This research extends previous work in spatiotemporal evacuation modeling during hazard scenarios. Past work on spatiotemporal evacuation trigger buffers (Dennison et al. 2007, Larsen et al. 2011) and the use of volunteered geographic information (Pultar et al. 2010) have been shown to effectively model the safe evacuation of people from wildfires. Research in transportation networks and how they can be

84 70 optimized to evacuated citizens from hurricanes has also proven successful at simulating the movement of people away from the hurricane s impacts (Naghawi and Wolshon 2010, Wang et al. 2010, Fries et al. 2011). The model in this paper extends this work by including the evacuations of building contents rather than the evacuations of people. In the evacuations of people during wildfires with the use of trigger buffers, the emphasis is to identify evacuation zones by creating temporal regions (zones) of evacuation without consideration of the individual evacuation events (e.g., evacuations of individual structures). As this model has shown, tracking the individual events produces spatiotemporal patterns that can influence how evacuations zones can be created. The same is true for the work on evacuating people during hurricanes. The focus of that work is to determine the number of people evacuating, the capacity of the network, and the travel time. Little consideration is given to the evacuation events of individuals at a local scale where the individual evacuation events are occurring (Lindell and Prater 2007). This work also contributes to research in the area of flood risk modeling. Several approaches to estimating buildings at risk to flooding and the potential losses from high water have been examined (Hardmeyer and Spencer 2007, Forte et al. 2006, Choi et al. 2007, Remo et al. 2011). In this previous research only the risk to buildings is considered, this paper extends that research by also considering the risk and vulnerability to building contents. The model can track the progression of building risk and vulnerability over time for a group of buildings. When the spatiotemporal modeling of building risk is simulated patterns in building risk emerge. Computing building evacuation time extends previous work in moving contents such as in the movement of shipping containers in shipping yards (Kim and Kim 1998, Kim and Kim 1999, Lee et al. 2013). These models consider a number of variables that are useful when moving shipping containers. The building evacuation model extends this work by considering the variables that are important when evacuating building contents during flood disasters.

85 Conclusions and Future Work The evacuation scenario on a University campus was used as the example for this model of evacuations. This model is generalizable to other domains and not just university campuses. The model could be applied to urban evacuations centered on individuals instead of building contents or the evacuations of a collection of buildings such as on a hospital campus or commercial district in a city. This model could also be used for other hazards that have a suitable lead time and that would allow for evacuations, such as hurricanes or wildfires. This model does not work in scenarios where there is not sufficient lead time to prompt evacuations, such as hazards like flash floods and earthquakes. Modeling basement flooding requires several variables for the analysis (e.g., water table elevation, storm water system modeling) (Dutt and Hemphill 2004, Schmitt et al. 2004, Chen et al. 2005, Sommer et al. 2009). Not every variable is possible in such a study. Because of this, basement flooding is currently modeled using flood elevations that were the cause of basement inundation in past floods, but in the future analysis will be extended to incorporate the movement of water in the subsurface soils. These improvements may provide additional accuracy in the 1993 historical flood that was used as validation for one of the buildings. In this work, the spatiotemporal patterns of building vulnerabilities and evacuation events during flood hazards is presented. This dissertation presented the following research question, what are the components for creating a model for building evacuation events. The spatial properties for building evacuations are topography, flood inundation, building location, building elevation, and road access. Additional key elements for the model are detailed building footprints, floor elevations, real-time and forecasted flood elevations data, and flood depth grids for a range of flood elevations, for example, a 500-year event. The amount of work required, the vulnerable floor area to be

86 72 moved, and the volume of contents are the temporal properties for building evacuations and from these properties, six evacuation profiles can be calculated for groups of buildings. This dissertation also presented the following question, how can the components of building evacuation events be linked to flood information to track the progression of evacuations over space and time. From the elements of this framework a model is developed that tracks the progression of building evacuations to expose the buildings and roadways on campus that become vulnerable during a flood event by linking building floor elevations to flood elevations over time using a flood hydrograph. Identifying the spatiotemporal progression of evacuations based on flood forecast information and building vulnerabilities, provides decision makers with advanced warning up to seven days into the future. Tracking the spatiotemporal pattern of building risks highlights individual and clusters of building vulnerabilities, maximizing the available time for evacuations by evacuating one cluster at a time. Five different building vulnerabilities are supported by this model including vulnerable basement, flooded basement, vulnerable first floor, flooded first floor, and road access. A historical flood simulation based on a historic flood and hydrograph was conducted and served to validate the model. Evacuation time modeling involves an understanding of a number of factors such as the weight of items for each vulnerable floor (W i ), the total number of movers (M), and the mover rate (R). Based on these factors, the building evacuation times for a group of buildings can be computed and the resulting temporal profile of building evacuations depicts the type of evacuation timeline that will occur. When evacuation recommendations are presented spatiotemporally for a group of buildings, patterns of evacuations emerge that highlight hotspots of evacuations over time. The modeling of evacuation time is a complex process. Future work should focus on adding the effects of mitigation to the model (e.g., permanent floodwalls and HESCO barriers) to model the changes to evacuation times

87 73 when these protections are in place. When flood barriers are operating correctly, some evacuation times can be delayed or even eliminated, as is the case with a permanent floodwall. Including additional evacuation time parameters (e.g., costs, travel distance) are also being investigated to further develop the calculations for evacuation times. Future extensions should also include incorporating the evacuations of people to model the interaction between all movements in the floodplain during a flood, flood evacuees, flood responders, and flood evacuation volunteers. Future work should also extend the model to include additional variables that would impact modeling time, such as, the location where contents will be moved, the method for moving contents, and the added time required when moving fragile or sensitive objects. When these variables are added, the model can be optimized such that the evacuation of a group of buildings can be evacuated in the least amount of time possible.

88 74 CHAPTER 4: IDENTIFYING PATTERNS OF SPATIOTEMPORAL FLOOD RESPONSE EVENTS BASED ON THE PRIORITIES OF DECISION MAKERS 4.1 Introduction During flood disasters, decision makers such as emergency managers, first responders, and elected officials, lead response efforts within the community, protecting lives, property, and community assets. Many decision makers use GIS to aid in their response efforts (Cova 1999, Gunes and Kovel 2000, Cutter 2003, Clark 2006, Emrich et al. 2011). At the local level, the maps that are created are depictions of what lies inside and outside of the flood hazard area (see for example, The Google Crisis Response, Common examples are representations of the selected parcels or critical infrastructure in the floodplain (FEMA 2012, Pinellas County Property Appraiser 2013). These types of maps are then commonly used as a means for determining what is at risk and how decision makers will respond to events that will occur when these areas flood. Each map provides the user with a general description of vulnerable assets, but lacks the preferences of responding to events from the decision maker or a quantitative assessment of where the risk for events is the greatest or lowest. Risk is the likelihood over a specified time period of severe alterations in the normal functioning of a community or a society due to hazardous physical events interacting with vulnerable social conditions, leading to widespread adverse human, material, economic, or environmental effects that require immediate emergency response to satisfy critical human needs and that may require external support for recovery (Field et al. 2012, p.32). The typical geographic information systems (GIS) approach for

89 75 determining risk in the floodplain is the use a digital flood insurance map (DFIRM) to identify the flood hazard boundary (FEMA 2013a). DFIRMs are produced by the Federal Emergency Management Agency (FEMA) to determine structures that lie within and outside of flood hazard areas for the purpose of deciding the infrastructure that is required (or recommended) to carry flood insurance (FEMA 2013b). A DFIRM outlines the area representing the 1 percent annual chance flood for communities, and is used by the National Flood Insurance Program within FEMA as the boundary to determine what properties are required to carry flood insurance. This approach helps FEMA determine long-term risk to communities and helps identify "at-risk" structures. These maps are also an effective mitigation tool, highlighting the areas of risk for communities, emphasizing areas where mitigation projects can be beneficial. However, this method is only a snapshot of one flood boundary and does not identify the spatiotemporal progression of the flood hazard in the same way as flood inundation map libraries do (Demir and Krajewski 2013). Flood inundation map libraries are a series of flood depth grids and boundaries for the same area that represent consecutive river levels. During floods, the flood response efforts by a community require the identification of risk over space and time. Instead of a single snapshot, an estimated progression of risk aids decision-makers with the knowledge of how risk changes throughout the duration of the flood. In this paper a modeling approach for representing event-based response risk is presented. This model uses the preferences of flood decision-makers during flood response by surveying local emergency management officials from six states. To accomplish this, surveys were sent to over 450 flood emergency managers in six states to determine what the response priorities of decision-makers are during flood disasters. The priorities were linked to geographic information to create maps of event-based risk for response. This fills a gap in the current literature on responding to flood disasters, because while risk has been discussed for other areas, it is rarely discussed from a response perspective.

90 76 Identifying areas of risk to natural hazards has been researched in a variety of ways. The geospatial socio-economic vulnerabilities for populations to hazards (e.g., Cutter et al. 2003, Burton 2010, Finch et al 2010, Schmidtlein et al. 2010, Tate et al. 2010, Wood et al. 2010, Fekete 2011), techniques for using hydrologic and hydraulic modeling to identify hazard risk (e.g., Merwade et al. 2008, Van Der Knijff et al. 2010), and the economic loss risks (e.g., Jonkman et al. 2008, Scawthorn et al. 2006) have been addressed. While each of these are important for understanding the risk to a location, they provide only the basic details necessary to support flood response activities. Recently, during hurricane Sandy, a set of risk maps was created by FEMA s modeling task force (MOTF) (ESRI 2013a). These maps used real-time flood depth information and estimates of building location to calculate risk to buildings. What these maps failed to capture were the response events or the occurrences that decision-makers use to based their actions during a flood. The development of new geospatial approaches to model hazards and their impacts during flood response has increased in its use and has been successful in recent major disasters such as Hurricane Katrina 2005, Hurricane Ike 2008, Southern California Wildfires 2007, and Colorado Flooding 2013 (Luccio 2005, Theodore 2007, ESRI 2013b, Google Crisis Response 2013). However, at the local level, geospatial technologies are still not widely used during response, as often as in planning and mitigation activities (National Center for the Study of Counties 2006). When GIS is used in response, typically the GIS analyses are presented through real-time web-based GIS such as Google s Crisis Map ( ESRI s Situational Awareness Viewer ( USGS s Flood Inundation Mapping Initiative ( and National Weather Service s Advanced Hydrologic Prediction Service Inundation Mapping ( In each case, most of the map layers represent hazards (e.g.,

91 77 flooding, wildfires) that are mapped and overlaid with vulnerable infrastructure (e.g., roadways, bridges) to define risk. There is a need for representing of time-varying risks (Pine 2004), decision maker-based response priorities (Jain and McLean 2006), or a quantification of response risk to measure the level of risk from one location to another (Faith et al. 2011). Flood response events are occurrences that emergency managers react to within the response phase of a flood disaster (e.g., sandbagging levee failure, residential evacuations). This work investigates the types of response events that flood decisionmakers are faced with during hazardous flood events based upon a survey of flood emergency managers from six states. Flood decision makers are asked to rate 22 flood response events (and can add to the list) based on their priorities during floods to filter out the most important events from the less important events. To capture the strength of the most important priorities, flood response events are weighted by the decision-makers using analytic hierarchy process (AHP) from pairwise comparisons. Based on the weights of the AHP analysis, a k-means analysis is undertaken to determine how the events can be grouped so that different response event profiles can be created. The profiles are used to form an event-based response risk map based on the weights for each profile. The rest of this paper is organized as follows. Current research in decisionmaking by emergency managers, risk mapping, and multi-criteria methods is discussed in the next section. Section 4.3 describes the results from the interviews of emergency managers in six flood prone states (California, Florida, Iowa, North Dakota, Oregon, and Pennsylvania). To capture how the priorities of decision-makers are found in space, Section 4.4 describes the reaction of flood response risk maps for communities basedupon the priorities of decision-makers. The following section provides an evaluation of the methods by comparing the priorities calculated in Section 4.3 with actual response events recorded in two county emergency operation centers (EOC) during the

92 78 Midwestern floods of The final section gives a discussion and the future work for this study. 4.2 Modeling Event-Based Risk for Flood Disasters Using flood risk maps for decision-makers has been used determined to be the best mitigation strategy in floodplains (Tkach and Simonovic 1997, Simonovic and Nirupama 2005). The use of multicriteria methods for decision making during hazards has steadily increased in the GIScience literature since 1990 (Rashed and Weeks 2003, Malczewski 2006, Levy et al. 2007, Raaijmakers et al. 2008, Chen et al. 2011, Zou et al. 2013, Oh et al. 2013). Recent research on the mapping of flood risks provides decisionmakers with spatially differentiated values of flood risk for economic, environmental, and societal indicators in Germany using multicriteria evaluation (MCE) and multiple attribute utility theory (MAUT) (Meyer et al. 2009). In this work, the focus is on response events and their risk of occurence in the floodplain. For example, the possible locations of residential evacautions and flooded roads are tracked and weighted based-upon a decision-maker profile and combined with other response events to form the the eventbased risk maps. In this paper, we extend previous work of AHP methods and identifying flood risk using flood response events to create event-based maps of response risk that include the knowledge of flood responders. As the advancement of data collection techniques and data processing have increased, GIS-based approaches have been widely adopted to provide flood analysis, mapping and decision support. Research on real-time flood response systems examines the ability to create flood extents on the fly that are important for decision-making and methods for displaying decision-making information on the web (e.g., The Iowa Flood Center In addition, due to the development of web technology, web-based GIS has also been discussed and developed to help the

93 79 decision-making in flood emergency. The use of NEXRAD, runoff model outputs, hydrologic and hydraulic models has shown to be effective in producing real-time flood extents (Yang and Tsai 2000, Knebel et al. 2005, Yu et al. 2005, Whiteaker et al. 2006, Van Der Knijff et al. 2010). Other work focuses on methods for creating web-based systems for decision-making and alerting (Levy et al. 2007). One model uses data collection routines from geosensors, temporal and spatial models on a webserver, and delivers flood depth grids in real-time to the web (Al-Sabhan et al. 2003). Another model utilizes a hydrologic model that is initialized by observations from geosensors and weather forecast data for Europe (Thielen et al. 2009), however this model is applied to large geographic areas with low spatial granularity. Flood risk mapping using GIS and applied to decision support research has addressed a number of areas. The advantages of high performance computing (Adorni 2000), methods for constructing hazard maps for decision-makers (Hagemeier-Klose and Wagner 2009), and strategies for weighting decisions for floods (Levy 2005) have all been investigated. The risks of flooding have also been examined through modeling flood damages. Research on how the uncertainty inherent in model input datasets can affect the results in flood damage models (Hardmeyer and Spencer 2007), with and without levees (Remo et al. 2011), and the social impacts (Jonkman et al. 2008) have shown that each can have a significant impact on flood losses. Risk perception by decision-makers can determine the impacts to citizens during flood hazards. The use of ensemble flood forecasts (a range of flood forecasts based upon precipitation variations) to inform decision makers during flood hazads (Dermeritt et al. 2007), weights created for spatial risk perception using MCE (Raaijmakers et al. 2008), and the perceptions of flood decision-makers when presented with complex scientific information about flood risk (Morss et al. 2005) have been examined. The public s perceptions of risk also play a role, for example, the perceived risks to the public that live in the floodplain lessen with every year they do not experience a flood. The

94 80 spatiotemporal variations in evacuee risk perception during floods (Siebeneck and Cova 2012) and the use of weather forecasts by decision-makers for severe weather (Morss et al. 2010) have been researched. This work focuses on the perceptions and priorities of decision-makers during flood disasters and integrate each into a spatiotemporal perspective of flood event risk. 4.3 The Priorities of Emergency Managers Based Upon Surveys During a flood, decision-makers respond to events such as evacuating citizens, closing flooded roads, and coordinating the construction of a sandbag levee. The decision-maker must prioritize actions based upon the risk to the community from flooding. The priorities of the decision-maker will change from place to place. For example, the need to evacuate citizens may be a lower priority in a rural community, but a higher priority in an urban community. The set of response events that a decision-maker acts upon may also change depending on the assets in the community that are vulnerable. For example, the need to respond to commercial property flooding might be a major concern in a more economic vital community versus a community with little commerce. To understand the priorities of decision-makers during flood response a survey was sent to county emergency managers from six states Surveying Flood Decision Makers To identify the response-based events that are of greatest concern to flood decision makers in different locations, a web-based survey was created and sent to emergency managers in California, Florida, Iowa, North Dakota, Oregon, and Pennsylvania. The six states were selected for the distribution of the survey based upon population size and the typical causes of flooding (e.g., coastal, tropical systems, snowmelt, heavy rainfall), to examine the possible differences in flood response concerns based upon these differing community characteristics (Table 4.1). The addresses

95 81 for flood decision makers were collected from each state s emergency management website. The group largely consisted of county emergency managers and emergency managers from major cities in each State. The survey URL and instructions were sent in an to each decision-maker. Table 4.1: Surveyed states, predominate flood types, and population rank State Predominate Flood Types US State Population Rank California coastal, snowmelt 1 Florida tropical systems 4 Iowa snowmelt, thunderstorms 31 North Dakota snowmelt 49 Oregon coastal, snowmelt 27 Pennsylvania snowmelt, thunderstorms 6 The survey process was split into two parts. First, an initial test survey of 13 questions was sent to a small sample of flood decision makers from a geographically representative group (i.e., location, population) from the six states. The survey asked respondents a variety of questions relating to their background as a flood responder and the community they support, such as, their level of education, flood response experience, community size. This small, initial, set of participants was used to ensure the survey design was operating as intended. This first set of participants was asked to select and rank the top response events they are concerned with during a flood within their communities from an initial list of 22 events (Table 4.2). This event list was compiled from situational reports, websites, newspapers, s, and incident action plans during the severe flooding in 2008 in eastern Iowa. This list of events is grouped into four broad categories: societal, economic, environmental, and protective measures. Survey

96 82 respondents were also able to add new events to the list and rank them. The survey was then sent out to over 400 flood decision-makers from the six states. Table 4.2: The list of response events and frequency that each was selected as a concern by surveyed decision makers. Highlighted events are those events chosen for pairwise comparisons. Criteria Alternative Percent Responded Societal School Closure 19% Looting 4% Power Outages 60% Waste Water Treatment Plan affected 36% Public Sheltering 51% Loss of Drinking Water 22% Economic Residential Flooding 84% Commercial Property Flooding 61% Flooded Critical Facilities 22% Agricultural crops damaged 55% Bridges Closed or Damaged 66% Building Content Evacuations 23% Fires 7% Environmental Ice Jams 15% Debris Jams 36% Protective Sandbagging Operations 60% Flood Water Rescue 46% Residential Evacuations 61% Hospital Evacuation 5% Prison Evacuation 4% Levee Breached or Overtopped 15% Flooded Roads 96% In decision-making there are some criteria that are intangible and do not have a quantifiable value associated with their importance to the decision-making process. For example, when buying a house it is difficult for a person to quantify the importance of the proximity of good schools. However, it is easier to make a decision about buying a home when proximity of good schools is compared with resale value of the home. When two

97 83 criteria are compared against each other judgments can be made about the importance of one over another. AHP is a method for placing the value on intangible criteria that are used in making a decision (Saaty 2008). A final set of questions was added to the survey asking the participants to complete pairwise comparisons of the top nine of the 22 flood events (the AHP analysis is discussed further in section 4.3.3) Survey Results The survey was created and disseminated using Qualtrics ( a sophisticated survey and analysis software that is web-enabled. Of the 459 surveys that were ed, 104 respondents were collected for a 23% response rate. Of this group, 50 completed the first 13 questions and stopped before completing the pairwise comparisons. Fifty-four respondents completed the entire survey (Table 4.3). Recent flooding in Iowa, North Dakota, and Pennsylvania may have played a role in participation as flooding was occurring during the month the survey was sent out. Oregon, California, and Florida have not had major flooding in more than four years based on disaster declaration data (FEMA 2013c) and these states had the lowest response rates. Non-response errors may exist in the results. Very low response rates occurred in California and Oregon over the other states. In these states, emergency management directors are typically the fire chief or police chief in the community, whereas in the other states the emergency management director is a stand-alone position. Given the responsibilities of persons with both titles, it is possible time was a factor in their ability to fill out the survey. In addition, while response rates were higher in those areas that were experiencing flooding, some emergency managers were still responding to flood waters and likely did not answer. The results of the survey indicate 80% of participants have at least 5 years of flood response experience, making this an experienced group collectively. North Dakota

98 84 and Oregon participants were the least experienced, with approximately 50% of the respondents indicating they had less than 5 years of experience. Every participant had at a minimum a high school diploma, 65% had a post high school degree, and 16% have graduate degrees, which is slightly higher (57%) than previous research that surveyed local emergency managers on their education level (National Center for the Study of Counties 2006). Four states had over 60% of their participants with greater than a Bachelor s degree, however, Iowa and Pennsylvania had less than 40% with a Bachelor s degree. These two states had the highest rates of technical training however, indicating their preparedness. This training likely comes from the Federal Emergency Management Agency (FEMA) training certification program or through the International Association of Emergency Managers (IAEM) certification program that are the two leading certification programs in emergency management in the country. Table 4.3: Survey response rate among each state State Surveyed Decision- Makers Responses Rate Completed Pairwise Comparisons California % % Florida % % Iowa % % North Dakota % % Oregon % % Pennsylvania % % Total % % Rate Most of the participants answered that flooding occurs within their community once every 5 years or less on average, further emphasizing the flood response experience in their communities. Participants were also asked how helpful they believed DFIRMs

99 85 are during a flood disaster. The majority of participants (79%) indicated the maps were only somewhat helpful or occasionally helpful during flood response Analytic Hierarchy Process and Weights AHP is used to quantify intangible criteria during the decision making process. In this work respondents indicated what events (criteria) were important to them during the response to a flood disaster. The importance to intangible criteria can vary between decision-makers. Therefore using AHP provides a common platform by which decisionmakers can weight their preferences (Saaty 1980, 2008). The decision maker is asked to rank how important two events are to each other using a scale from 1 to 9. For example, if event A is very strongly more important than event B, a value of 7 will be assigned. The reciprocal (or negative) is used when the opposite is true (B over A). The value 9 is extreme importance, 5 strong importance, 3 moderate importance, and a value of 1 means no importance of one event over the other. Even values have intermediate strengths to their odd numbered neighbors. These values are loaded into a decision matrix and the eigenvector (and eigenvalues) are calculated. From the list of 22 response events, the most important events were computed based upon the frequency each event was selected. If an event was selected by over 50% of the respondents, then it was used in the AHP analysis. Nine such events were identified and represent the most important events of the group of decision-makers surveyed. Nine response events (sandbagging operations, agricultural damage, commercial flooding, flooded roads, power outages, residential flooding, sheltering, bridges closed, and residential evacuations) were considered alternatives to three criteria (social, economic, and protective), while there were no events in the group of nine that fell into the environmental criteria group (Table 4.3). The AHP analysis was completed using the AHP calculator from Business Performance Management (

100 Groups of Response Decision Makers Each decision maker s decision matrix was computed to determine how each individual weighted the response events. As a whole, the event that had the highest average weight was sandbagging operations, followed by agricultural damage, commercial flooding, flooded roads, power outages, residential flooding, sheltering, bridges closed, and residential evacuations. However, as discovered in the surveys, the decision makers worked in different locations, had different backgrounds, and different flood response experience. A k-means clustering analysis (Doran and Hodson 1975) was performed in Minitab 16 ( to identify groups of weighting profiles with common characteristics. Four groups were created based on the k-means statistics using the weights calculated for each decision maker. These four groups explain 87% of the variance in the data. Each decision maker s IP address was geocoded using online IP address geolocators (see for example and mapped based upon the latitude and longitude value returned (Figure 4.1). Geographically, spatial clusters are not apparent. An Anselin Local Morans I (Anselin 1995) analysis was conducted on the State, education level, experience level, frequency of flooding, and group number, but no significant relationships were found. However, population does appear to play a role the groupings. Group 4 represents a group of decision makers that primarily reside in low population, low population density counties. Group 3 has decision makers that work in mostly urban areas. Group 2 has high population counties, but low population density, indicating counties of a large geographic size. Group 1 is mixed with counties that have urban and rural areas.

101 87 Figure 4.1: State maps representing the locations of surveyed decision makers and associated group. Each group ranked the events differently (Table 4.4 and Table 4.5). Group 1 ranked economic events as the most important response events with agricultural damages and commercial damages ranked 1 and 2 respectively. Social events were ranked second most important while protective events were ranked the least important. Groups 2 ranked sandbagging as the most important event and overall ranked protective events as the most important types of events. Group 2 also had to economic events (agricultural damage and bridges closed or damaged) ranked the least important events. Group 3 also had sandbagging as the most important response event and also had another protective event ranked in the top 3. Societal events were ranked toward the bottom. Group 4 had two economic events ranked 1 and 2 and two economic events ranked 8 and 9. Societal and protective measures were mixed in the middle.

102 88 Table 4.4: The ranking of each of the nine events for each group of decision-makers. Rank Group 1 Group 2 Group 3 Group 4 1 Agricultural Residential Sandbagging Sandbagging Damage Flooding 2 Commercial Agricultural Commercial Sheltering Flooding Damage Flooding 3 Sheltering Residential Evacuations Flooded Roads Flooded Roads 4 Power Outages Residential Flooding 5 Flooded Roads Power Outages Bridges Closed or Damaged Residential Flooding Residential Evacuations 9 Sandbagging Commercial Flooding Flooded Roads Bridges Closed or Damaged Agricultural Damage Bridges Closed or Damaged Commercial Flooding Power Outages Residential Flooding Sheltering Residential Evacuations Residential Evacuations Power Outages Sandbagging Sheltering Agricultural Damage Bridges Closed or Damaged Table 4.5 Response events and weights by group

103 89 A discriminant analysis was performed on each participant s set of weights and the group assigned to each participant using Minitab 16. The analysis showed that the weight for bridges could not be distinguished from the other variables, meaning its value varied in the same manner as other variables. When the bridge weight was removed from the analysis, the discriminant analysis associated participants with the same groups as assigned in the k-means analysis (Table 4.5). The k-means clustering was undertaken again with the bridge weights removed to see if the grouping results from the k-means clustering was affected by removing the bridge weights. The groups remained the same for each participant. Table 4.6: Discriminant analysis for each participant and group In the next section we discuss how these weights are used to construct eventbased response risk maps. These groups can be used for other decision-makers with the same preferences to construct those maps. For example, if a decision-maker is interested in viewing event-based risk maps for their community, they can rank the nine events and match the group that best corresponds to their preferences. In future work, (Section 4.6) the inclusion of events not analyzed in the nine events is discussed as well as the ability to create weights for those events.

104 Identifying Areas of Event-based Response Risk As discussed previously, current research and methodologies define hazard risk for varying uses such as societal, economic, socio-economic, and environmental (among others) risks. These approaches neglect the inclusion of events and rarely define risk based during the response phase of the emergency management cycle (response, recovery, mitigation, preparedness). Approaches for modeling flood response activities are increasing (see for example, National Center for the Study of Counties 2006, Google Crisis Response 2013, ESRI 2013). In the next section the decision-maker s priorities that were weighted based upon the AHP analysis are used to represent event-based response risk to highlight where response events are of the greatest concern to the decision-maker within the community Data collection and processing To create event-based risk maps for flood response in ArcGIS 10.0, GIS layers representing the nine events are collected. For this work, the following layers were collected (with events they identify in parenthesis) for Johnson County, Iowa, residential and commercial parcels (residential and commercial damage) o Johnson County, Iowa, Geographic Information System Division, ( ) residential building footprints - (residential evacuations) o Johnson County, Iowa, Geographic Information System Division, ( ) roads - (flooded roadways) o Iowa Department of Transportation, ( bridge locations (flooded bridges) o Iowa Department of Transportation, ( landcover (agricultural damages) o Iowa Department of Natural Resources, ( sandbagging locations - (sandbagging operations)

105 91 o Johnson County, Iowa, Department of Emergency Management, ( shelter locations - (sheltering) o Johnson County, Iowa, Department of Emergency Management, ( power outage locations (power outages) o Johnson County, Iowa, Department of Emergency Management, ( All of these layers are in vector-based format except the landcover layer which was in a raster format. To create event-based risk maps for a community for a given flood event, first the features that intersect the flood boundary are chosen. Next, the weight for a given response event is given a new attribute in the attribute table of each vector that corresponds to that event. Each layer is then converted into a raster format using the weight as the value assigned to each grid cell. The landcover layer is handled differently. Landcover is used to determine vulnerability related to agricultural damage events for a community. For this work, the Iowa high resolution landcover dataset created by the Iowa Department of Natural Resources ( is used. The pixels representing crops are selected and moved into a new dataset. The new dataset of only crops is vectorized, assigned the weights by decision-makers, and then rasterized again using the weights as the value for each cell. Flood water also presents an amount of risk associated with response events. As waters rise, the water itself presents an amount of risk. The amount of risk is dependent on the depth of water. For this work, the weights for flood depth are taken from the Flood Insurance Agency (FIA) depth-damage curves ( The FIA has a range of curves based upon varying structure types. To total the entire risk, an average for each flood depth, from each structure type is used. With each increase in flood depth, the flood damage increases, however, the increase in damage between flood depths decreases. For example, for single family residential structures, the estimated damage at 1 foot of flood depth is

106 92 18% and at 2 feet of depth it is 22%. However, at 9 feet the damage is 45% and at 10 feet it is 46%. The relationship of depth of water to the weight of importance is approximated by the following best fit curve, y = x , where x > 0.5 This equation is a power function showing that lower flood depths are of greater risk than higher flood depths. A person is at less risk at higher flood depths because you must move through lower depths of flood water first before higher flood water. Lower flood depths are also contributors to car fatalities. It takes only 6 inches of water to lose control of a vehicle, 1 foot of water to float a vehicle, and 2 feet of water to carry away a vehicle (Ready 2013). Once the weights for flood depth are calculated, each flood depth grid cell is then assigned the appropriate weight and the raster layers are added together to create another element for event-based response risk determinations Event-based response risk maps during floods Flood risk maps typically represent all of the vulnerable components that intersect the floodplain (Figure 4.2 (a)). The result is usually multiple maps of risk and vulnerability because one map cannot adequately represent the large number of vulnerable elements. These types of maps also do not quantify how much risk is present for a given area. An analyst could count the number of vulnerable structures, but that requires an additional step. Event-based response risk maps provide three details traditional flood risk maps do not provide (Figure 4.2 (b)). First, the maps are driven by the response priorities of the community. Second, the maps provide a single representation of risk that is quantified based-upon those priorities. Third, the change in risk can also be quantified when these risk maps are computed for multiple flood hazards.

107 93 Figure 4.2: The (a) flood risk using the inundation boundary and asset layers only versus (b) event-based response risk maps within the floodplain during response events To demonstrate the differences in event-based risk between decision-maker groups, the weights for each group 1 (Figure 4.3(a)), group 2 (Figure 4.3(b)), group 3 (Figure 4.3 (c)), and group 4 (Figure 4.3(d)) were applied to the GIS data for Johnson County, IA for the 500 year flood. Depending on the group assignment, the locations of high and low risk change throughout the area. These maps emphasize the amount of risk associated with different events that decision-makers have indicated are important for their community. Some areas remain at higher levels of risk regardless of the group because there are multiple events possible in those locations (the region denoted with number 1 in Figure 4.3(a)). Other areas are more sensitive to changes in the weights because there are fewer response events possible in those locations or the events that are possible are of lesser significance overall (the region denoted with number 2 in Figure 4.3 (d)).

108 94 Figure 4.3: Event-based response risk maps for Iowa City, Iowa for (mixed) group 1 (a), (low population density) group 2 (b), (urban) group 3 (c), and (rural) group 4 (d). To demonstrate the differences in weights (and resulting risk) between groups, percent difference maps for Johnson County, Iowa were created between group maps. Johnson County, Iowa had a participant that completed the survey that falls into group 1. When comparing the differences of group 1 with the other groups, group 1 only has slight changes when compared to group 2 with an average percentage change of only 0.25 (Figure 4.4 (a)). Group one depicts slightly more risk throughout the areas than group 3 with an average percentage change of only 0.07 (Figure 4.4 (b)) and group compared to group four has an average percentage change of 0.28 (Figure 4.4 (c)).

109 95 Figure 4.4: Percentage change maps showing (a) an increasing change in red hues and a decreasing change in blue for comparisons of group 1 to group 2, (b) group 1 to group 3, and (c) group 1 to group Identifying critical risk stages within a floodplain To quantify the change in risk within the floodplain from varying flood stages, 34 flood inundation depth grids (from the University of Iowa Flood Center) were used to determine how risk changes over time within the floodplain in Iowa City, IA (Figure 4.5). Because Iowa City, Iowa falls into a group one community (based-upon the fact that it is a mixed rural and urban county and because a decision-maker that completed the survey fell into group 1). The area was calculated for each of the four groups, low, medium, high, and total risk for each depth grid. For each group, low risk areas (values 10) include places with one response event present or low flood depths. Moderate risk (values