US Army Corps

of Engineers

Framework for Estimating National

Economic Development Benefits

and Other Beneficial Effects of

Flood Warning and Preparedness

Systems

AWR
DEPARTUEOT OF THE ARIMY

CORPS OP 8900in1i60, WATU RGSOURCOS SUPPORT CMTER

10611WTl PON WATG ReSOUACIS

7MI ULU.mAP ROA0

ALIMANIIA. VA Um31•lSM

•maw June 24, 1994

CEWRC-IWR

MEMORANDUM FOR COMMANDER, Defense Technical Information Center,

Cameron Station, Alexandria, VA 22314

SUBJECT: Transmittal of IWR Report 94-R-3

1. Reference AR 70-31.

2. Two copies of IWR Report 94-R-3, "Framework for Estimating National Economic Development Benefits and Other Beneficial

Effects of Flood Warning and Preparedness Systems", has hereby

been submitted.

3. Initial distribution of this report has been made to

appropriate Corps of Engineers agencies. It is recommended that

copies of this report be forwarded to the National Technical

Information Center.

4. Request for the DTIC Form 50 (Incl 2) be completed and

returned to WRSC-IWR.

FOR THE DIRECTOR:

Enclosure Kyle E. Schilling

Director
FRAMEWORK FOR ESTIMATING NATIONAL

ECONOMIC DEVELOPMENT BENEFITS AMD

OTHER BENEFICIAL EFFECTS OF FLOOD

WARNINfG AND PREPAREDNESS SYSTEMS

by

Charles Yoe, Ph.D.

of

The Greeley-Polhemus Group

105 South High Street

West Chester, PA 19382

for

U.S. Army Corps of Engineers

Water Resources Support Center

Institute for Water Resources

Alexandria, Virginia 22315-3868

DTIC VUALMLT WZEBIID 3

94-20432

March 1994 IWR Report 94-R-3

.947 081
Framework for Estimated National

PREFACE 

Economic Development Benefits

This report was completed under the Flood Mitigation, Formulation, Planning, and Analysis

research work unit at the Corps of Engineers Institute for Water Resources (IWR). Mr. Stuart

A. Davis is the principal investigator for the research unit. The Flood Mitigation work unit is

part of the Planning Methodologies research program, which is under the direction of Mr.

Michael R. Krouse, Chief of the Technical Analysis and Research Division at IWR. Mr.

Steven R. Cone is the technical monitor of the Flood Mitigation work unit under the direction

of Mr. Robert M. Daniel, Chief of Economics and Social Analysis Branch at the Office of the

Mr. Kenneth Zwickl of the Floodplain Management Services and Coastal Resources Branch of

the Office of the Chief of Engineers, Mr. Michael W. Burnham, Chief of the Planning

Division of the Corps of Engineers' Hydrologic Engineering Center, Dr. David A. Moser,

and Mr. William J. Hansen of IWR all provided comments on earlier drafts of this report.

Mr. Robert F. Norton provided the technical editing of the document. Ms. Arlene Nurthen

was responsible for the document preparation and publication.

Accession NTIS GRA&IToW

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Framework for Estimated National

Economic Developmem Benefits

TABLE OF CONTENTS

PREFACE .............. .................................... iii

CHAPTER I - INTRODUCTION ......... .......................... 1

INTRODUCTION ......... .............................. 1

AUDIENCE ......................... .................. 1

ORGANIZATION OF THE REPORT ........................... 2

CHAPTER 2 - CONCEPTS ........ ............................. 3

INTRODUCTION ......... .............................. 3

EXPECTED ANNUAL DAMAGES ...................... 3

THE DAMAGE FUNCTION ......................... 5

CHAPTER 3 - BEHAVIORAL RESPONSE TO FLOOD WARNING AND

PREPAREDNESS . ............................. 19

INTRODUCTION ............................. 19

ORGANIZATIONAL RESPONSE ..................... 20

DISASTER WARNING AS A PROCESS .................. 21

INITIAL RESPONSES ........................... 23

MESSAGE QUALITIES .......................... 24

DEMOGRAPHIC QUALITIES OF WARNING RECEIVER .......... 25

CONFIRMATION BEHAVIOR ....... ................. 27

PRE-EVACUATION RESPONSES ..................... 28

EVACUATION RATES, REASONS, AND FACTORS ............ 29

FOCUS ON KEY ISSUES ......................... 31

CHAPTER 4- FLOOD WARNING AND PREPAREDNESS BENEFITS ........ 33

INTRODUCTION ............................. 33

BENEFIT TAXONOMY .......................... 33

DIRECT TANGIBLE BENEFITS ...................... 34

Temporary Removal of Property ................... 35

Moving Property to a Safe Elevation Within the Floodplain ......... 40

Temporary Flood-proofing ........ ...................... 40

Opportune Maintenance ........ ....................... 43

Early Alerting of Emergency Services ...................... 45

Orderly Disruption of Network Systems ...................... 46

Suspension of Sensitive Works ....... .................... 46

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Related Effects on Emergency Costs, Cleanup Costs and Business Losses . 47

Traffic Control ......... ........................... 47

DIRECT INTANGIBLE BENEFITS ............................ 48

Human Health and Safety ........ ...................... 48

Use of System for Other Disasters ....... .................. 49

Cost of Employment Disruption ....... ................... 49

Reduced Stress ......... ........................... 49

Family Disruption ........ .......................... 50

Loss of Memorabilia .......... ........................ 50

INDIRECT TANGIBLE BENEFITS ............................ 50

Production Externalities ....... ....................... 50

Consumption Externalities ........ ...................... 51

INDIRECT INTANGIBLE BENEFITS ....... ................... 51

Reduced Stress ......... ........................... 51

Flood warning and preparedness COSTS ......................... 51

CHAPTER 5 - FLOOD FORECAST AND WARNING BENEFIT EVALUATION

MODEL ........... ................................. 55

INTRODUCTION ......... ............................. 55

WITHOUT PROJECT CONDITION WARNING ASSUMPTIONS .......... 57

CURRENT METHODS ........ .......................... 57

Shifting the Damage Curve ............................ 57

The Day Curve ......... ........................... 58

MODEL OVERVIEW ......... ........................... 58

Warning Time .......... ........................... 59

Without Project EAD ........ ........................ 61

With Project Response Scenarios ....... ................... 61

Damage Curve Shifts ........ ........................ 62

The Remainder of the Chapter ....... .................... 64

APPLICATION ONE (ASSUMED PERCENTAGE STAGE-DAMAGE

REDUCTION) ......... ........................... 68

APPLICATION TWO (UNCERTAIN STAGE-DAMAGE PERCENTAGE

REDUCTION) ......... ........................... 70

MODELING BEHAVIORAL RESPONSE TO FLOOD WARNING ......... 72

APPLICATION THREE (UNCERTAIN BEHAVIORAL STAGE-DAMAGE

RESPONSE) ......... ............................ 76

APPLICATION FOUR (PARALLEL STAGE-DAMAGE SHIFTING

SCENARIO) ......... ............................ 82

SHIFTING INDIVIDUAL DEPTH-PERCENT DAMAGE CURVES ........ 83

APPLICATION FIVE (DEPTH-PERCENT DAMAGE REDUCTION) ....... 85

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Framework for Estimated National

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CHAPTER 6 - FURTHER CONSIDERATIONS ....... .................. 91

INTRODUCTION ......... ............................. 91

SHORTCUTS ............ .............................. 91

FLOOD WARNING AS A COMPONENT OF A PLAN ................ 92

WHAT LEVEL OF DETAIL IS APPROPRIATE? .............. 93

FUTURE RESEARCH ........................... 93

APPENDIX 1 - ESTIMATES OF BEHAVIOR RESPONSE PARAMETERS ....... 97

RECENT IWR RESEARCH ........................ 104

REFERENCES ................................. 105

LIST OF FIGURES

Figure 1 - Hydroeconomic EAD Model ...................... 4

Figure 2 - Generation of Stage Damage Curve ................... 6

Figure 3 - Events and Time Intervals Associated with Warning Systems .......... 9

Figure 4 - Linear Stage-Damage Curve ...... ..................... 11

Figure 5 - Shifting Linear Damage Curve ....... .................... 12

Figure 6 - Linear 3-D Damage Function ....... ..................... 14

Figure 7 - Non-Linear 3-D Damage Function ....... .................. 15

Figure 8 - Damage Contour in Stage - Warning Space ...... .............. 16

Figure 9 - Warning Contours in Stage - Damage Space ..... .............. 17

Figure 10 - Moveable Items at Middle Elevations ...... ................. 37

Figure 11 - Moveable Items Throughout Structure ...... ................ 38

Figure 12 - Moveable Items at Greater Elevations ...................... 39

Figure 13 - Items Removed from Structure ....... .................... 41

Figure 14 - Property Rearranged in Structure ...... ................... 42

Figure 15 - Floodproofing a Structure ........ ...................... 44

Figure 16 - Basic Benefit Estimating Model ....... .................... 60

Figure 17 - Sample of 4 Random Stage-Damage Curves ..... .............. 65

Figure 18 - Sample of 500 Random Stage-Damage Curves ..... ............. 66

Figure 19 - Distribution of Damages at 15 NGVD ...................... 67

Figure 20 - Stage-Damage Reduced 4.4% ....... ..................... 69

Figure 21 - Distribution of Benefits ........ ....................... 71

Figure 22 - Response Model Event Tree ....... ..................... 73

Figure 23 - Expected Damage Reductions with 0-4 Hours Warning ............. 78

Figure 24 - Expected Damage Reductions with 4-8 Hours Warning ............. 79

vii
Framework for Estimated National

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Figure 25 - Expected Damage Reductions with 8-12 Hours Warning ............ 80

Figure 26 - Expected Damage Reductions with Over 12 Hours Warning .......... 81

Figure 27 - FIA Content and Structure Curves ....... .................. 84

Figure 28 - Content Damage With & Without Warning .................... 87

Figure 29 - Level of Detail and Budget ...... ...................... 94

LIST OF TABLES

Table 1 - Examples of Benefits Types ........ ....................... 34

Table 2 - Potential Cost Items: Flood Warning-Emergency Preparedness Plan ........ 52

Table 3 - Response Model Assumptions and Results . . .. ... .............. 77

Table 4 - Benefit Estimates Response Model Simulations ..... ............... 82

Table 5 - Percent-Content Damage Estimating Model ...................... 88

Table 6 - Perception of Threat By Warning Response ...................... 100

Table 7 - Indirect Damages As % of Total Damages ..... ................. 101

Table 8 - Flood Losses Per Square Meter For Manufacturing Firms (1/84) ......... 102

Viii
Framework for Estimated National

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CHAPTER 1

INTRODUCTION

INTRODUCTION

Methods for estimating benefits to flood damage reduction measures are well established. There

are, however, contexts in which the estimation of benefits are .more difficult. Estimating benefits of flood

warning and preparedness alternatives is one of the more problematic contexts that Corps' planners face.

The "National Economic Development Procedures Manual-Urban Flood Damage" prepared by the

Institute for Water Resources in March 1988, presents a thorough documentation of flood damage reduction

benefit estimating procedures. Chapter IX of the manual provides an introduction to the evaluation of flood

warning and preparedness system benefits.

The purposes of this report are to present a conceptual framework for planners to evaluate benefits

accruing to flood warning and preparedness alternatives and to demonstrate methods suitable for estimating

these benefits under a variety of planning circumstances. This report presents a simplified workable model,

consistent with or adaptable to existing evaluation models and tools used by most Corps' planners.

A single definitive approach is not offered in this report. A frequently contradictory literature is

reviewed for the insight it offers rather than for the problems it resolves. A great variety of potential

benefits are described. Many of them will be impossible or infeasible to estimate for most studies. A basic

modeling approach is presented and some suggested uses of the model are offered. In the final analysis,

however, professional judgment and experience will continue to play a major role in the evaluation of flood

warning and preparedness systems.

Every evaluation is different and there are no set answers. The benefits accruing to a warning and

response system depend on a complex of factors. This report considers many of those factors in the hope

of providing reasonable estimates of system benefits for a variety of planning circumstances.

AUDIENCE

This report has been written for Corps' planners and others working in flood damage reduction

planning and analysis. It assumes a working knowledge of the Corps' planning principles as articulated in

the Economic and Environmental Principles and Guidelines for Water and Related Land Resources

Implementation Studies (P&G) (Water Resources Council, 1983) and the National Economic Development

Procedures Manual-Urban Flood Damage (Davis, et. al., 1988). It further assumes some experience with

methods and tools used to conduct these analyses.

Flood warning and preparedness systems are being considered by an increasing number of

communities as one alternative for dealing with flood problems. Compared to structural projects, these

systems are inexpensive. A flood warning and preparedness system is more and more becoming an integral
Framework for Estimated National

Economic Development Benefits

part of any structural alternative. The diversity of situations in which the Corps planner evaluates these

systems results in a need for a flexible, yet robust, method for evaluating the systems.

In the remainder of this report, a conceptual framework based on the P&G and National Economic

Development (NED) Manual series familiar to experienced Corps planners is built. Though an overview

of the benefit estimation procedure is provided, you will not find the details necessary to perform many of

the procedures described in this report. For example, the compilation of damage curves is discussed but

the methods and tools available for doing this are not described. The reader is assumed to be aware of the

variety of programs available for compiling stage-damage curves for a floodplain.

ORGANIZATION OF THE REPORT

Chapter 2 introduces the conceptual framework needed to evaluate benefits of flood warning and

preparedness systems. The emphasis is on inundation reduction benefits, the primary benefit type. It

begins with a review of the expected annual damage computation to allow us to focus on the role of the

damage curve. The effect of warning time on the stage- damage curve is explored. This is followed by

discussion in Chapter 3 of the range of behavioral responses to flood warning and preparedness systems

contained in the social sciences literature. To understand the effect of these responses on the damage curve

one must understand the different points in the flood warning process where things can "breakdown."

Chapter 4 examines flood warning and preparedness system benefits in some conceptual detail.

Using an old, but very serviceable, taxonomy the direct and indirect benefits of such systems are

considered. Direct benefits are, roughly, benefits to floodplain occupants and indirect benefits are benefits

to others. These distinctions are followed up with a discussion of intangible benefits that can accrue to

floodplain occupants and others.

The next chapter presents a flood warning and preparedness benefit evaluation model that builds

on the familiar hydroeconomic model used by Corps' planners to estimate expected annual damages (EAD).

Construction and use of the model is developed by considering a range of options for shifting the stagedamage curve in the model. For example, the model provides for low budget evaluations and for very

detailed surveys that consider shifts in the stage-damage curves of individual structures.

Chapter 6 discusses some of the evaluation options for the model. Suggestions are offered on

appropriate estimation techniques for a variety of time and budget options. The report concludes with

references and two appendices. The first appendix provides a few technical notes on using the model of

Chapter 5. The second presents some quantitative estimates of various warning response parameters found

in the literature that can be used with the model.

2
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CHAPTER 2

CONCEPTS

INTRODUCTION

This chapter begins with a review of the model used by the Corps to estimate expected annual

damages. This model is the basis for estimating inundation reduction benefits, the major category of

benefits accruing to most flood warning and preparedness systems. Once the basic model is presented,

attention is turned to the stage-damage relationship because of its critical role in estimating flood warning

and preparedness benefits.

The second section develops the idea of a damage function. The most important point of this

discussion is that even though most analyses tend to consider damages a function of the elevation of water

only, damages are determined by more than the flood stage. "Response to flood warning" will be

introduced as a variable of particular interest in addition to flood stage, in evaluating flood warning and

preparedness systems.

EXPECTED ANNUAL DAMAGES

Expected annual damages (EAD) are typically estimated using a hydroeconomic model familiar to

Corps' analysts. Floods are random events that cause damages, hence flood damages are random events.

More pointedly, flood damages are probabilistic events. The probability of any specific amount of flood

damage depends on the probability of the flood event necessary to cause those damages.

Figure 1 shows the three relationships needed to develop the damage-frequency curve. Figure la

shows the "damage curve" (aka, stage-damage when representing an entire reach or depth-percent damage

curve when representing an individual structure or its contents), a relationship showing damage in dollars

realized at varying elevations, or stages, of water. Figure lb shows the "rating curve" (aka, stage-discharge

curve), a relationship that indicates the flow or the quantity of water, usually measured in cubic feet per

second (CFS) that it takes to reach varying stages. Figure lc displays the "frequency curve" (aka,

discharge-frequency), which shows the annual probability of obtaining a given quantity of water or greater.

By linking these relationships through their common dimensions', it is a simple matter to construct

a damage-frequency curve which shows the annual probability of realizing any given level of damages or

greater. The damage-frequency curve in Figure ld is, in essence, a cumulative distribution of flood

damages. The mean of this distribution is obtained by integrating the area under the curve. The mean of

this distribution is more commonly known as expected annual damages (EAD).

'The stage-damage and rating curves share an elevation dimension. The rating and frequency

curves share a flow dimension. The rating curve combines the damage and frequency curves.

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11w damage, rating and frequency curves are estimated for the without project condition and

C.'A%2c',id annual damages without the project are estimated. For each flood damage reduction al:.rnative

ruider com.lderation, these same three relationships are estimated anew. For example, a floodwall or levee

rtojcci will result in %n altered damage curve. A channel project would result in a modified rating curve

ia reservoir would result in a new frequency curve. Depending on the alternative, one or more of these

itrce basic relationships will be affected. The new relationships generate a new damage-frequency curve.

the expected annual damages obtained from these new relationships represents the with project expected amnial damages. '[he difference between without and with project expected annual damages are project twoielit,..

rilt DAMAGE FUNCTION

Expected etnnual damage tables for Corps' studies have used the hydroeconomic model described

aIboveý ini one form or another since soon after the

1936 Flood Control Act. ,_

STAGE-DAMAGE AND DEPTH-PERCENT

Damages have always been assumed to DAMAGES CURVES

increaiwe as the depth of water in a reach increases

;e; shown by the stage-damage curve. Nonetheless, To develop an understanding of how to

it has long been recognized that damages depend evaluate the benefits of flood waring, xystm ofe

(on a lot more than the depth of water, must first understand the relationsihipbetween a

stage-damage curve and the depth-percent danup

Damages are a function of many variables, curves of which it is comprised. 'The, dnu•e

Anyone who has ever done a damage survey function discussed in the foll6,ing p age s

presented as if it Is the damage fuhction for a quickly learns this lesson. Ask a plant manager hypothetical damage reach rather than for an

hiow much damage he wcmild have with four feet of individual structure. Total reach dafmag Isiimply

water and he responds with a series of questions. the sum of the damage sustained by 4ach property

"How much warning time do I have?" "Does the in the reach. Hence, astage.damage6110"aIsW " flood occur at night or during the day?" "Will upon many individual depthpercent damaie

riggers and trucks be available to remove my curves. Before proceeding to a dhluus" 4of,11

equipment?" "Will there be ice in the water?" damage function for a reach It is useful to kfy

"What kind of sediment load will be carried?" how these damages depend on the' dia to

"Should I assume oily water like last timtre or clear individual structures.

like the time before?" "My inventory and goods in

process vary from day-to-day, week-to-week, and Figure 2 Illustrates how Indhvddalt depthmonth-to-month with seasonality of my business, percent damage curves relate to thet

what should I assume?" "Do you want conditions curve for the reach. Though the point Wi'emade

for the recession we're in now or for one of our again In Chapter 5, It Is Important to ril4 • that

foyers?" "What is the duration of the flood?" different structures have different post" 6te hfor

bt years'.... Wreducing damages given warning of in Mipet I he questrow could go on and on. flood. The changes In t 'age.d"met

discussed throughout this report ate,

depictions of the aggregate chatnge In 10.I-

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The role of other variables in determining flood damages can extend beyond the concerns of the

floodplain occupant. For example, coastal flooding analysts have invented methods of varying ingenuity

to account for the fact that wave energy is often more important than the depth of water in determining

damages to certain coastal properties. The nature of the event itself is an important determinant of flood

damages.

In order to evaluate flood warning and preparedness systems the basic hydroeconomic model is

complicated a little, but not too much. In the past, analysts have always recognized that more than one

variable determines flood damages (Day, 1969:938). In most cases, the effect of water depth dominates

the effect of other variables and for the sake of simplicity, analyses have focused on the relationship

between damages and the stage of flood waters.

It is not possible to continue to focus on the depth or stage of flooding variable only, when flood

warning and preparedness systems are evaluated. Damages still depend on stage but they also depend on

the response of the individual and community to flood warnings. 2 In the next chapter, the nature of flood

forecast and warning benefits is considered in specific detail. This section concentrates on understanding

the need for a method by which flood warning responses can be evaluated.

The actual damage function can involve dozens of variables and a very complex functional form.

A simplified example is used in order to gain some insight into the problems involved in estimating flood

warning and preparedness benefits. Consider the simplified general damage function:

(1) D=d(property value, stage, duration, warning response)

Where D is damages measured in dollars; stage and duration are properties of the flood waters; property

value is the value of floodplain structures and their contents; and warning response is action(s) taken to

prevent or mitigate flood damages.3

2

This is an oversimplification. Analysts have always dealt with more than two variables in the

damage function. Damages at various depths of water vary with the value of the property at risk, the

style of structure, etc. and standardized depth-percent damage curves have been developed for certain

types of residential structure to allow analysts to account for these variables as well. Thus, though this

chapter focusses on the two-variable stage-damage relationship, it is expected that the analyst realizes

that the actual practice of flood damage estimating is far more complex.

3

Effective response, itself, like any of the other arguments in the damage function, can be expressed

as a function of other variables. Warning response depends on the flood forecast system considered,

the amount of warning time given, the nature of the warning message, the number of people that hear

the warning, the number of people that confirm it, etc. A literature review describing some of the

variables that affect warning time is presented later in this chapter.

7
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VAkNiN.G TI[ME

I lie histoduction of a new variable, effective response, to the estimation of LAD b the primary purpose of thk repst

Ulvctive response is the monetized value of effective actions taken by persons to reduce the susceptibility of propety and xconii activity to damage by floods, between the time of the first public issue of a flood warninS until the time a property oets

ývt or aa economic activity Is affected.

1here are a numbe of operational difficulties with this definition. First, planners are constrained to consdefn only tloue monetized values that contribute to National Economic Development (NED). Losses of Income or tax revenum that wer

critically important to local interests are most often mere transfers of Income, from the Federal perspectiv, and are not NED

Uneflts or costs. This difference In perspective frequently requires the careful education of local Interests about what beet

And costs are actually considered in the evaluation of a Federal project.

Second, though it Is people who react to warning systems, it is property and economic activity that accosut for most

of the coits of flooding and for benefits of flood warning systems. It is difficult to empirically link the actions of people to actiom

that lower the susceptibility of property and economic activity to damage. Very little research has been done In this area.

This report proceeds on the basic assumption that warning systems must effectively reach arid convinc persom with

tie opportunity to reduce the susceptibility of property and economic activity to ad in a way that produces benefits. For this

ie on, warning response literature is reviewed to establish the critical links in the process from Issue of a warning to takln action.

Finally, warning time is a vaguely defined concept. Figure 3 presents a sequence of time Intervals representing major

.,ctivitles that comprise the warning and evacuation process. As the figure illustrates, only a portion of the time from Initial

detection of a hazard until the water arrives at a property is available in which people can respond. The amount of respon time

or warning time can vary from property to property, based on a structure's topography. Though the time of fRirt warning is fixed

for everyone, water will inundate different properties at different times depending on a property's location In the flood plain. in

this report warning time and response time are used Interchangeably and it extends from the first warning until water arrives at

the first property.

"For example, production of goods and services, transportation and activities commonly recognized as part of our ordinary busness

enterprise.

Suppose it was possible to estimate this function and it turned out to be the simple linear function, with

hypothetical parameters, that follows:

(2) 1 D 32 + .02P + 1.IS + .4H - .8W

D - flood damages in $1,000s

P property value in $1,000s

S depth of water in feet

i1 duration of flood in hours

W response to warning in hours of warning time

In a traditional flood damage reduction analysis, analysts effectively hold everything but flood depth

Consltant in this function. Let's say the value of the property in the floodplain is estimated to be $1 million.

The hydrograph used for the analysis implies a fixed duration of flooding, say, four hours. In actual

practice, explicit or implicit assumptions about the duration of flooding and response to warning are made

a'; the damage survey is conducted. These assumptions are reflected in the stage-damage curves and during

tht: damage survey interviews. To facilitate the discussion that follows, assume response to warning is

meawsured in terms; of the hours of warning time, in this example 8 hours.

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Thus far, values have been assigned to the following variables: P = $1,000, H = 4, and W = 8.

Plugging these values into equation (2) a new equation is obtained in which damage is a function of stage

only. Equation (3) shows the hypothetical relationship between damages and stage. This relationship holds

only for the case where P = $1,000, H = 4 and W = 8. If any of these variables change, the constant in

equation (3) will also change resulting in a new damage function.

(3) D = + 47.2 + 1.IS

Figure 4 shows the stage-damage curve generated by equation (3). The curve is presented as a

linear function, rather than the more typical curve shown in Figure la, to keep the analysis simple. Using

the curve in Figure 4 and the model previously presented, expected annual damages can be calculated.

Now consider what happens to damages when a flood warning and preparedness system is installed.

Staying with the simple linear model of equation (2), assume warning time can be increased to 12 or 15

hours, depending on the warning system implemented. The revised stage-damage functions for 12 and 15

hours of warning are:

(4) D = 44 + 1.IS

(5) D = 41.6 + 1.IS

These functions are shown, along with the without project condition, in Figure 5.

Changing the warning time results in an entirely new stage-damage curve, hence unique estimates of

expected annual damages with the project and project benefits. Changing any of the variables that are

normally held constant will cause a shift in the entire demand function and new benefit estimates. The

change in this example is a parallel shift because a linear damage function was assumed. The fact that the

actual damage function is likely to be far more complicated than the one in our example only makes the

nature of the shift more complex, it does not change the fact that a shift occurs.

For every possible response time there is a unique stage-damage curve, all other things equal. If

duration or property value is changing in addition to response to warning, the number of new functions

quickly becomes unmanageable. Hence, response to flood warning and preparedness alternatives are

evaluated by only slightly relaxing the model and allowing response time to change, all other things equal.

In effect, it is assumed that the duration of flooding remains four hours and the property value is fixed at

$1,000,000 for all the possible response times.

The damage function of interest in our example becomes:

(6) D = 53.6 + 1.IS- .8W

10
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This is a three-dimensional function as shown by the plane surface in Figure 6. The true damage function

may never be known, but it is almost certainly far more complex than the linear function used here. The

response to warning time is likely to be nonlinear.

Figure 7 shows a nonlinear function of damages, stage and response. Response time reduces

damages as the level of flooding increases, but only up to the point at which stage becomes the dominant

factor. Figure 8 shows curves in stage and warning space that result in the same amount of damage,

derived from the three dimensional function of Figure 7. The height in Figure 7 is the damage dimension.

If a slice is cut through the function at any given height, i.e. a specific amount of damage, parallel to the

depth-warning axes, all the possible combinations of flood- depth and warning time that can result in

identical amounts of damage are contained on that slice.

By cutting a slice of the three dimensional function parallel to the stage-damage axes, a stagedamage curve associated with a given amount of warning time is obtained. Change the warning time and

you move to a new location in the warning dimension. Cut another slice of the function and you obtain a

second stage-damage curve. Thus, a shift in the stage-damage curve that results from a change in warning

time is the same as moving to a new position on the three-dimensional function. Figure 9 shows the

damage curves that result from different amounts of warning time.

These "slices" projected in two-dimensions are simply contours. Contours are routinely used to

show three dimensions in two-dimensional space. Topography maps are the most common example.

Topographic maps show distance in the north-south, east-west directions and height is given by the value

associated with the contours. These contours are simply "slices" of the terrain under examination.

Estimating the effects of a flood warning and preparedness system can be more or less complicated.

Dealing %vith a function as shown in Figure 7, the mathematics can become very complex, assuming the

actual function could ever be identified.

A pragmatic alternative is to identify a rang,. of reasonable alternative functions and complete the

analysis over that range of alternatives. For example, Figure 5 shows stage-damage curves for three

different response times. Each response time will produce a different estimate of expected annual damages.

An appropriately weighted mean of these expected annual damages is a reasonable approach to estimating

the variable effects of a flood warning and preparedness system. This technique is extended and explained

in detail in Chapter 5.

13
Framework for Estimated National

Economic Development Benefits

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Framework for Estimated National

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CHAPTER 3

BEHAVIORAL RES.PONSE TO FLOOD

WARNING AND PREPAREDNESS

INTRODUCTION

The extent to which a damage curve shifts as a result of a flood warning system depends entirely

on the behavioral response of the individuals at risk from flooding. When people respond to a warning,

they may take steps to mAimize their damage from the impending flood. The cumulative effect of these

actions is a shift in the depth-percent damage curve for the contents of a structure that reduces damages as

compared to the without project, e.g., no flood warning and preparedness system, condition. The

accumulative shift of the individual curves causes a shift in the stage-damage curve.

To estimate benefits you need to know how the damage curve shifts. To know how the damage

curve shifts requires knowledge of how people respond to the warning. This is a complex issue, the

resolution of which is well beyond the scope of this report. In this section, a flood warning system is seen

as an ongoing process. The process is described so analysts can see the potential for behavioral responses

at different points in the process that can affect the shift in the individual or aggregate demand curves.

Flood warning and preparedness systems are often unbalanced, in that attention can be focussed on

the hardware required for warning and response dissemination with little consideration given to

development of a formal response plan. The response phase of a system is critical to the determination of

benefits. Thus, the benefits of flood warning and preparedness systems described of this report are entirely

dependent on the behavioral aspects of the system that determine or affect individual and community

responses to a warning.

Behavioral responses can be considered among the variables that determine the effect of the warning

response variable shown on the three-dimensional damage surfaces presented in Chapter 2. For example,

more effective warning messages will result in less damage at any depth of water, all other things held

equal. Though the mathematical dimension of the evaluation concepts will not be pursued further, it is not

difficult to see that the extensive literature on warning systems fits the evaluation context presented above

rather nicely, once behavioral responses are seen as arguments in the damage function.

What is missing from the literature is reliable information about what actions people do or do not

take that may reduce their susceptibility to flood damages after hearing a warning. There is an abundance

of data describing initial reactions, how many people heard a given warning, etc. Evaluation of flood

warning systems requires some knowledge of the actions people take to avoid damages and there is a dearth

of such information. Until more information becomes available, analysts will have to rely on inferences

from the existing data, their own experience, and area-specific survey results.

There is a voluminous literature on behavioral responses to warnings of all types. The bulk of the

literature has developed around natural disaster warning systems, chiefly hurricanes and floods. Before the

analyst can provide a quantitative answer to the question, "How much does the damage curve shift?" the

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Framework for Estimated National

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analyst needs a clear understanding of the nature of individual and community responses to warning

systems. While there is no substitute for experience in gaining this understanding, the literature goes a long

way to facilitate that understanding.

Figures 6 and 7 depict damages as a funcdion of water depth and warning response. In actuality,

warning response is, itself, a function of numerous variables. The literature review that follows examines

some of the variables that determine warning response. The reason for doing so is that changes in these

variables change warning response, which in turn shifts the damage curve. Thus, the literature is

considered to learn what it is about a community that affects the shift in its damage curve.

The link between the literature and an effect on the damage curve is not a direct one. You will not

find specific recommendations or empirical results instructing you in how much to shift a damage curve.

This is a context contrived for this report; intended to help analysts make judgments about whether the

warning response in a project area will be greater or lesser, depending on the extent to which conditions

described below exist in a project area.

The paragraphs that follow present research findings that, at times, are contradictory. At other

times, the findings may be so specific as to seem trivial. The utility of these research findings will become

more evident when the model for estimating flood warning and preparedness benefits is presented in

Chapter 5 and when empirical results from the literature are presented in Appendix 2. Many of the results

that follow here and in the appendix can be used to set the ranges of key variables that can be used with the

model.

The presentation that follows, closely follows the presentation method employed by Drabek in his

book. It begins by defining warnings as a process. In this context, the warning process encompasses all

of the events and time intervals presented in Figure 3. The review concentrates on those events that are

typically part of the public warning process. This includes initial reactions to warnings, the content of the

message, qualities of the persons hearing the message, and the very important stage of confirming behavior.

The remainder of the review concentrates on evacuation issues; specifically what people do before

evacuation and the rates at which they evacuate.

ORGANIZATIONAL RESPONSE

The literature identifies the most important variables as the speed with which warnings are issued

to the public (i.e., response time) and previous disaster history. Experience tends to result in less

skepticism and more effective action. Sorenson and Mileti (1987) reviewed accounts of 39 disasters and

'Though there is an extensive literature, two works are singled out for special attention. Human

System Responses to Disaster: An Inventory of Sociological Findings, by Thomas Drabek, provides

exceptional access to the literature in a well organized and documented review of the literature through

the early to mid eighties. Drabek's work appears to build on the earlier effort, Human Systems in

Extreme Environments: A Sociological Perspective by Dennis Mileti, Drabek, and J. Eugene Haas.

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identified 19 specific categories of uncertainties in organizational decision-making. Their review makes

explicit the many areas in which the warning process can falter from the time a disaster has been detected

until the warning is disseminated to the public.

The detail of this and other work points out the need for planners to deal with reasonable

simplifying assumptions. The planners' knowledge of the study area is of the utmost importance in deciding

which factors can be safely ignored, assumed constant, or in need of explicit handling.

The literature shows that community planners should be aware of certain factors, many of which

will be reviewed here, that will affect public response to warnings. Corps' analysts are also well-advised

to be aware of the presence of these factors in the warning system under consideration. Drabek (1986:93)

summarizes these factors more generally as:

1. The warning must be clear;

2. The warning must convey what is the appropriate response;

3. The warning must be perceived as coming from a credible source;

4. The warning must be reinforced socially and at the local level;

5. The medium used to disseminate the warning is important; and,

6. The type of appeal must be considered and assessed.

Systems that address these concerns will result in larger shifts in the damage curve, all other factors

equal, and NED benefits will be larger the more a community's warning system adheres to the above

factors. Though little of this kind of work is included in most warning system planning studies, the

literature suggests it would be wise to do so. It would appear that benefit estimates can be increased as

communities attend to more and more of these factors.

DISASTER WARNING AS A PROCESS

Researchers have argued, since the 1950s, that disaster warnings must be regarded as a social

process. Worth and McLuckie (1977) have developed a model of this process consisting of three stages:

forecast, alert, and confirmation. Though there are many alternative model formulations they all share the

same basic elements.

The forecast stage is evaluative. In this stage, forecasting technology plays the greatest role.

Events are detected and measured as data are collated and interpreted. Once the potential for, or nature of,

a disaster has been determined, someone must decide who to warn, what to warn them about and how to

warn them. The final step of the forecast stage is the transmission of the warning.

In this report, for purposes of developing benefit evaluation techniques, it is assumed that the

forecasts are accurate and the system is dependable. While this is clearly not going to be true in all cases,

addressing the situations in which this is not true unnecessarily complicates our treatment of the topic

without adding any additional insights. The model presented in Chapter 5 could be modified to reflect

uncertainty about the system's accuracy or reliability in the forecast stage, though this is not done.

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The alert or warning stage is where most decisions are made. It begins as the forecast stage ends,

with decision makers deciding who should be warned about what danger and in what way. Speed is the

key to warning. The more warning time that is provided the more time there is to respond. Warning

messages need to be clear, complete, specific and non-technical. There is a crucial trade-off between

timeliness and accuracy. The longer one waits to issue a warning the better the available information on

which to issue a warning will be, but the less time is left for people to respond to the warning. Warnings

given quickly provide maximum response time, but they run the risk of costly over-reaction to preliminary

forecasts or to false warnings.

The third and critical stage, from the perspective of estimating flood warning and preparedness

benefits, is the confirmation stage. Technology dominates the'first stage, decision makers the second, and

the reaction of the individual dominates the confirmation stage. People need to be able to confirm the

warning and get additional information. The extent to which an individual believes the information that is

received depends largely on his ability to confirm that information. People need a second message that

says, this is really it!

Warnings must give behavioral advice as well as factual information. Hence, flood fighting and

other individual responses to a warning depend critically on confirmation of the message.

During the confirmation stage the message is interpreted. Receivers of the message offer feedback

to the message providers ("Sheriff, is it true the Sleepy Hollow dam broke?") and the message may be

revised as necessary. ("The dam did not break but residents of Sleepy Hollow have evacuated to the county

high school.")

Flood warning and preparedness benefits depend on the quality of the forecast, the effectiveness

of the alert, and the individual response. Only the last two of these stages of the warning process will be

addressed in this report.

Keeping in mind the warning system is a social process, Mileti's notions about how people respond

to warnings are useful:

"1. Even though several persons may listen to the same warning message, there may be

consideiable variation in what they hear and believe; 2. People respond to warnings on the

basis of how what they hear stimulates them to behave; and, 3. People are stimulated

differently depending on who they are, who they are with, and who and what they see."

(Mileti, 1975:xvi)

Drabek says:

"Individuals exhibit a tendency of inertia. That is, no behavioral aAiol, will be directed

toward a warning response until a sequence of information-processing steps have

occurred." (Drabek, 1986:71)

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The conclusion drawn is that shifts in the damage curve must take into account the fact that warning

response is a social process. It is not sufficient to assume for simplicity that everyone hears the warning

and takes appropriate action. Such assumptions, though adequate for theoretical work, are inappropriate

for evaluating the benefits of flood warning systems.

Every warning system should include elements that specifically address the range in behavioral

responses of the community. The following literature review suggests some behavioral responses and

community characteristics that should be considered in developing a plan. Any plan that ends with the

installation of hardware and identification of the person who is to issue a warning is only half complete.

INITIAL RESPONSES

One of the most widespread disaster myths is that, when warned of an impending disaster, the

public will panic. Time and again, research shows that, quite to the contrary, the public's primary reaction

to such warnings is disbelief.

"..most people's immediate reaction to the first warning received is disbelief and a

continuation of normal routine." (Perry, Lindell and Greene, 1981:153)

This "normalcy bias" is an attempt to neutralize the threat. This phenomenon would tend to suggest there

is a built-in bias toward not taking any immediate action upon receiving a warning. In situations where

warning times are short, this normalcy bias will limit the extent of damage mitigating activities. Other

things equal, short warning times will result in smaller shifts in the damage curve.

We will see in subsequent paragraphs that there are circumstances that mitigate this normalcy

response. The literature is, unfortunately, devoid of multivariate analyses that indicate the cumulative

effects when a number of variables, considered individually important, are present. Thus, we know little

about how people will react in a situation where one factor alone results in inaction and a second factor

alone normally results in some action.

The literature consistently shows an initial reaction of denial. People believe they are not in

immediate personal danger until they are unable to continue in this belief (Quarantelli, 1980b: 107).

Members of threatened communities will seize upon any vagueness in the warning message to reinterpret

the situation in a non-threatening manner (Greene, Perry and Lindell, 1981:60).

"What must be stressed is the diversity in response. There is no uniform pattern."

(Drabek, 1986:73)

Due to the diversity of initial responses to warnings, global assumptions about a project area's

response to flood warnings are to be avoided whenever possible. There is no basis in the literature to

support an assumption that 100 percent of a community or group of people will respond in any one way.

Thus, adjustments to damage curves should account for the fact that not everyone responds in the same

way. This can be done rather simply by, for example, assuming 70 percent respond in one way and 30

percent respond in another; the result being a weighted average of the two responses.

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Framework for Estimated National

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A second point that would seem to arise from the inertia inherent in the normalcy bias and denial

stages of initial response is that people will not use the entire warning time available to them for mitigating

damages. They will be engaged in confirming activities for some of that time. Thus, a four hour warning

time does not mean that people will spend four hours mitigating their damages. Taking these factors into

account will result in smaller shifts in the damage function than would otherwise occur.

MESSAGE QUALITIES

The content and quality of the warning message, though not independent of the sender or receiver,

have proven important in evoking common response patterns. With regard to qualities of the message,

Drabek finds:

"Three qualities have been found to matter: (1) content; (2) source; and (3) number."

(Drabek, 1986:74)

It has been found that both personal risk and belief in the truth of the warning are positively related to

warning response (Perry and Greene, 1983:101). Those closest to the river would tend to take more action

that those somewhat removed. This fact would provide justification for analysts to consider expected

annual damage computation reaches based on relative risk (for example, assuming greater shifts in the

damage curve of occupants of the 10-year floodplain than for occupants of the 50-year floodplain suggests

developing a separate stage-damage curve for structures in the 10-year floodplain), rather than traditional

hydraulics and hydrology or damage considerations, in certain cases.

Specific messages from sources with high credibility sent out repeatedly prove to be most successful

in evoking an appropriate response. This becomes important in evaluating the benefits of a warning systemnMost warning and response systems concentrate on getting the hardware into place with the rudiments of

a communication network outlined. Little thought is typically given to the quality of the message. The

literature suggests that when a message is specific, credible and frequent the damage curve shifts more, all

other things equal. The more carefully the system is planned with regard to the message quality, the greater

the shift in the damage curve.

The best warning messages must be personal, specific, unambiguous, prescribe appropriate

measures, be issued by a credible source and distributed by several media. Messages should also account

for existing beliefs and attitudes toward the hazard whenever possible and provide opportunities for

confirmation (Rowsell, 1978). Neal and Parker (1989:54) conducted a study of customer response to, and

satisfaction with, selecte-i flood warning systems in England. They found:

"..it is important for flood warning messages to impart 'behavioral'

information as well as factual information."

Warning systems that devote attention to these warning characteristics will provide more opportunities for

reducing damages prior to flooding. Shifts in the damage curve will be greater in communities where the

message is specific, the provider of the message has credibility, and the message is repeated frequently

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through a variety of media. Plans that do not address such details can be expected, other things being

equal, to result in smaller shifts in the damage curve and fewer benefits.

DEMOGRAPHIC QUALITIES OF WARNING RECEIVER

Different responses to flood warnings have been documented among different categories of persons.

While it is clear that characteristics of those who receive warnings do matter, it is not always clear how they

matter.

People experienced with flooding situations tend to take warnings more seriously and begin to react

sooner than others. They are also more likely to evacuate (Perry, Lindell and Greene, 1981:153). Thus,

given two communities alike in every respect except that one has had recent flooding and the other has no

flood history, one can expect a greater shift in the damage curve of the experienced community to result

from a warning and response system. There is always some danger that people will overestimate their

experience 5, however, causing the experienced community to fail to react appropriately.

Experience is a factor that can also work to minimize the shift in a damage curve, if the experience

has been negative.

"..if warnings are issued and events predicted do not materialize, the

consequence of the experience may be to neutralize future warnings. Thus

a "cry-wolf" syndrome may emerge." (Drabek, 1986:77)

Parker and Neal (undated), on the other hand, found the empirical results of their 1986-7 research on

warning systems:

"..suggest that flood victims would rather receive a warning than not

receive one, even if flooding does not follow."

Sims and Bauman (1983:173) assert that many natural hazards are rather rare events and:

"..people can be mislead by their experience because that experience is

limited or biased; it does not constitute an adequate sample."

Carter (1979) found that people without hurricane experience evacuate earlier than those who have

experienced a hurricane. The latter group waited for total confirmation of the necessity for evacuation.

Hence, the assumption of experience leading to greater reaction and larger shifts in the damage curve must

be tempered by specific knowledge of the study area population.

5"Hurricane parties" are frequently reported phenomena that could result in disastrous consequences.

Individuals who believe they have weathered worse storms fail to evacuate in the belief they can

weather this storm, too.

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Demographic characteristics of a population have, individually, been shown to be important in

determining response to warnings. If you know the composition of the floodplain population, it may be

possible to make better judgments about whether to shift a damage curve more or less based on what is

known about certain groups' behavior. For example:

"There appears to be a tendency for persons of low and high education to disregard the

formal meaning of a signal, while persons of middle socio-economic status are more likely

to accept the formal meaning." (Mileti, Drabek and Haas, 1975:47)

The implication is that both wealthy and poor communities afe less likely to act as effectively as middleclass people are. This suggests larger shifts in the damage curve result in middle-class communities than

in higher or lower income communities, all other things equal. Women are more likely to interpret a signal

as valid than are men (Mileti, Drabek and Haas, 1975:47).

While some argue that the elderly are more reluctant to believe warning messages, it appears clear

that the elderly are less likely to receive the message in the first place. Thus, damage curve shifts in

communities with a large elderly population are likely to be smaller than in other communities. Physical

ability of the elderly to take mitigating actions would appear to be another limiting factor, Neal and Parker

(1989) found:

"The evidence suggests that a major reason for flood warnings not leading

to higher levels of flood damage reduction is the physical inability of the

elderly and others in the community to respond adequately to a flood

warning."

They further suggest that emergency preparedness plans should recognize this fact and plan to assist the

elderly. Plans that heed this warning would seem to be justified in assuming larger shifts in the damage

curve.

Small town residents and people from small town backgrounds are less likely to interpret warning

messages as valid than are urbanites (Mack and Baker, 1961). The closer people are to the threatened area,

the more seriously they take the message (Diggory, 1956). Tightly knit and active communities tend to

respond more (Drabek and Boggs, 1968), a possible contradiction of the small town finding above.

Though there is little evidence on response by racial group, Turner (1976:183) found:

"..members of minority groups will assign little credibility to the official sources that

disseminate warnings, and consequently will not be predisposed to take appropriate

precautionary actions."

This is consistent with the earlier reported finding that stressed the importance of the credibility of

the message giver. A number of studies have shown that individual hazard behavior is related to people's

limited capacity to process information. Slovic (1980) has shown that people use certain rules of thumb

in estimating risks, and these are not always accurate tools. For example, if a series of coin tosses has

26
Framework for Estimated National

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resulted in six heads in a row, it is human nature to assume the seventh toss has more than a 50-50 chance

of being a tail. It does not. Similarly, people who have experienced a 100-year flood tend to believe that

flood will not be experienced again in their lifetime and most certainly not in the next year, despite the fact

that the probability of such a flood is undiminished.

Taking several of these demographic findings together, one might expect that small town

communities of elderly, minority men will respond less to flood warnings (hence, a smaller shift in the

damage curve) than would an urban middle-class community of young white women. Though this

comparison is offered tongue-in-cheek, the point is nonetheless valid.

CONFIRMATION BEHAVIOR

As mentioned earlier, people do not panic when they receive a disaster warning. They tend to

disbelieve the message. As a result, the most probable reaction of people receiving a warning is to try to

gather additional information. They are, in essence, seeking to either confirm or neutralize the warning.

Assuming the warning to be a true one, confirmation behavior becomes important in the warning process.

It is equally important to the analyst because confirmation takes time and time is a critical determinant of

shifts in the damage curve.

Perry, Lindell and Greene (1981:28) describe the major processes involved in behavior following

receipt of a warning. The initial confirmation process focuses on gathering additional information. This

information comes from talking to family, neighbors, co-workers; calling officials; listening to the media;

and, personally investigating (e.g., looking at the river), among other things.

The second stage of the confirmation process focuses on assessing personal risk. Once a person

believes the message, he must determine his proximity to the impact area (floodplain) and assess the

certainty of the threat and its probable severity. All of this confirming activity takes time. In situations

where there is plenty of warning time this may be of little consequence. In areas with little warning time,

confirming activities can reduce the available response time substantially. The shift in the damage curve

is assumed to increase with warning time, all other things equal.

The timing of the warning can also be an important factor in confirming behavior. Time of day,

week and year can affect the individual's ability to confirm a warning through alternative sources. A

warning heard over the radio at 3 a.m. cannot be easily confirmed by neighbors. Neal and Parker found

that "customers" of warning systems consider it a high priority to receive warnings during the daylight

hours.

Warnings given during the day when families are separated can slow down the response actions of

people.

"..unless all members are accounted for, families will be slow to undertake any kind of

protective action." (Perry and Greene, 1983:66)

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Families tend to act as units, taking mitigating or evacuating action together. It is more likely they will take

no action when they are separated. The significance of this for damage curve shifting would appear to lie

in an assessment of how easy or difficult it would be for families to reunite during an emergency. Damage

mitigating activity is more likely once the family is reunited. In a small town where jobs, home and schools

are geographically close this will be less difficult than in a large urban area where things are more spatially

dispersed.

Worth and McLuckie (1977) reviewed the warning process in a number of Colorado communities

flooded in June 1965. They found three basic responses to warning: immediate response to warning,

attempts to confirm the warning, and ignoring the warning. Confirmation took the form of appeals to

authorities, e.g., calls to local authorities, radio and tv stations. Appeals to peers, i.e., families, friends,

neighbors, etc. were also common confirmation behaviors. These appeals were a poor source of factual

information.

Observational confirmation was frequently used in the Colorado floods. People went to the river

or watched their neighbors' actions. The fourth confirmation technique was inadvertent confirmation, an

unintentional or accidental way of confirming the flood, for example, by observing a road closed by

flooding.

Confirming the flood was important in each case and the confirmation problem becomes bigger if

there is not enough time to complete the confirmation process. Neal and Parker say:

"..the customer's need to confirm flood warnings, once received, appears

to be very important to the damage reduction process. Systems designed

to provide telephone callers with a confirmatory service should be

maintained.."

Thus, plans that provide explicitly for confirming behavior can be expected to result in more damage

reductions. Of importance to planners is that time spent confirming a warning is time taken away from

damage mitigating activities. Thus, confirmation opportunities should be provided to facilitate the most

timely completion of the confirmation process.

PRE-EVACUATION RESPONSES

After receiving a warning, the pre-evacuation response, i.e., the activities that could lead to a

reduction in damages, depends on the factors reviewed above. The question of most interest in this report,

i.e., what do people do with their time before they evacuate, is not addressed in the literature. Little, if

anything, has been done to determine what people do between the time they confirm a warning message

and the time they evacuate. This does not bode well for analysts who want to know how to adjust damage

curves. Recent work by IWR and a few other sources providing some quantitative estimates of behavior

responses is reviewed in Appendix 2.

We pay particular attention to the literature on evacuation response for two reasons. First,

evacuation is a critical response in any effort to reduce the potential for loss-of-life due to flooding. The

28
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second reason is purely a conjectural one on the author's part, unsubstantiated by any empirical evidence.

We are inclined to believe that once people become convinced of an impending disaster, particularly people

who voluntarily evacuate the impact area (i.e., floodplain), they will take action to mitigate the effects of

the disaster before they evacuate. The reader is cautioned, that despite the common sense appeal of this

argument, it is speculation and not an established fact.

Carter (1977) found that in two communities

"..the "earliest leavers" are those experienced with hurricanes and who do

not feel that staying will contribute to the protection of their property

(e.g., from direct damage or looting)."

This finding contradicts the subculture of experienced hurricane veterans determined to stick it out at any

cost. It also indicates that hurricane behavior and flood behavior may differ in significant ways when it

comes to behaviors that influence the shifting of a damage curve. No evidence has been found to support

the existence of "early leavers" in the floodplain, those pessimistic about their ability to limit their damages.

Ferrel and Krzysztofowicz (1983) define an individual's degree of response to a warning system

as a function of the degree of response already undertaken, the number of warnings, the current flood level,

and the forecasted flood crest. The latter of these two is obtained from the forecast.

EVACUATION RATES, REASONS. AND FACTORS

Worth and McLuckie identified four types of individual evacuation decisions during their study of

the Colorado floods. First, there is evacuation by default. People who went to work, shopping, to comfort

a neighbor, etc. are unable to return to their homes because of rising waters or roadblocks. Second, is

evacuation by invitation. Family or friends express concern and ask a resident to leave the hazard area.

Evacuation by compromise, the third type, results when a member of a family leaves to placate other

members of the family. The most commonly thought of evacuation type is evacuation by decision, i.e.,

an evacuation order is given by an authority or, in the absence of a formal authority, a family or

organizational head decides to evacuate the floodplain.

Images of mindless hysteria and countless auto accidents have not been documented in the literature

(Drabek, 1986:123). The research indicates that some communities begin to 'specialize in their disasters'

(Wenger, 1972:39) and devAop subcultures for coping effectively with them. Strope, Devaney, Nehnevasja

(1977:4) reviewed 228 events where evacuation occurred and found:

"...the average hurricane evacuation has involved about 40,000 evacuees as compared with

about 4,000 in other types of disasters."

The available research shows that when people are properly warned they do evacuate in large

proportions. Perry, Lindell and Greene (1981) indicate that approximately 50% of the population,

threatened by a natural disaster, will evacuate upon receiving an official advisory. In volcano eruption

studies 11.1 % of the population has failed to evacuate. At Three Mile Island, 39% of the total population

29
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Economic Development Benefits

within 15 miles of the reactor, evacuated (Perry, 1983:43). Carter (1979) found that at least one-third of

the public will not automatically follow official evacuation orders. However, 90% will evacuate if

convinced of the seriousness of the threat.

People are generally motivated to evacuate when they see the hazard, are urged to by officials or

relatives, or when their neighbors leave (Perry and Greene, 1983:89). Fear of looting is frequently given

as a reason for not evacuating (Perry, 1983:43).

Before people evacuate, they need to know there is a high probability a disaster will occur, that the

disaster will have serious consequences, and the individual is at risk of being personally affected by the

hazard (Hultaker, 1976:8). When assessing one's vulnerability to the hazard, timing is a key factor. A

shorter time period "increases an individual's vigilance and propensity to evacuate" (Hultaker, 1976:9).

People in this circumstance will tend to undertake fewer actions that will shift the damage curve.

Past history is another important factor in determining evacuation behavior. Hultaker argues that

people tend to believe history will repeat itself. Those that have been flooded before are more likely to

evacuate and to take action to reduce their damage. As noted above, the likelihood of evacuating decreases

with age. Likewise women are more likely to evacuate (Yamamoto and Quarantelli, 1982:A-86) and

minorities are less likely (Perry and Mushkatel, 1984:106, 215).

People are less inclined to leave their homes when they believe they will not be allowed to take their pets

with them. For example, some people whose only shelter option is a mass shelter where pets are not

welcome, will stay with their pet.

Quarantelli writes of another particularly troublesome problem:

"...organizations typically have serious problems with the movement of institutionalized

populations such as in hospitals, jails, nursery homes, mental hospitals.. .When hospitals

have had to be evacuated as in the Wilkes Barre, Pennsylvania flood, or jails as in a

propane threat in Everett, Washington, questions arise as to who can be released, how

'difficult' cases can be transported, where those moved can be taken, what facilities are

necessary at the new relocation place, etc." (1980b: 123).

Communal institutions merit special consideration when considering risk to life, health and safety. It is easy

to imagine the requirements of such an evacuation would be so overwhelming as to limit the potential for

damage -- reducing behavior prior to evacuation.

The literature provides considerable insight into what kinds of communities are most likely to

evacuate. Planners with an intimate knowledge of their communities can use the literature to help them

gauge the risk to life, health and safety in their study areas in qualitative terms. Unfortunately, there has

been little research that identifies in specific terms what people do between the time they confirm a warning

and evacuate.

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Framework for Estimated National

Economic Development Benefits

FOCUS ON KEY ISSUES

Benefits of flood warning and preparedness systems depend on shifting damage curves. These

damage curves are contours that are derived from complex, multivariate damage functions. Substantial

complexity is added to the analysis by expanding the estimation of innundation reduction benefits of flood

warning and preparedness systems to include not only stage but also response to flood warning. The

literature is rife with accounts of the many variables that influence people's response to flood warnings.

Worse yet, the results found in the literature are usually inconclusive or contradictory. Therefore, analysis

of flood warning systems requires risk-based analysis.

Community differences are important in plan formulation; i.e., what is required in a preparedness

system? Only those factors that are important to the analysis should be considered in plan formulation and

NED benefit evaluation. The literature review is intended to point out examples of the type of factors that

may be important in certain communities. Are they important in yours? The answer to that question

depends entirely on the planners' judgment and knowledge of the community under study.

For example, experience is clearly a factor to be investigated. What has been the communities'

response to past flood events? Almost all communities receive some kind of warning, regardless of the

existence of a formal warning system. How did people respond to prior warnings? If flooding has been

relatively frequent, one could expect people to be better prepared to deal with a flood threat once a warning

is given. Such a community can be expected to respond in ways that cause larger shifts in the damage curve

than inexperienced communities will. If there has been a history of false warnings, a community may be

more inclined to wait longer before taking action, thus diminishing the damage-reducing actions taken.

Differences in response due to age may be so much esoterica for most studies. But, it's important

to realize that the elderly receive warnings less frequently than others and a large elderly population risks

a more significant threat to life than do other demographic groups. Is it important enough to worry about?

Only specific knowledge of the community can answer that question. Planners, thus, must be aware of the

multitude of factors that can affect pre-evacuation damage-reducing actions in any community. Focus only

on those that are relevant to your community. If you are going to conduct surveys on a floodplain

population, it may be enlightening to consider some of the factors found to be significant in prior research.

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Framework for Estimated National

Economic Development Benefits

CHAPTER 4

FLOOD WARNING AND

PREPAREDNESS BENEFITS

INTRODUCTION

Benefits accrue to flood warning and preparedness systems only as a result of actions taken or

deferred as a result of the warning. Thus, the response of individuals and the community, as discussed

above, is of critical importance. How many households will receive the message at all? How many will

have enough time to confirm it and still act? What actions will they take? Will those actions be effective in

reducing damages?

In the preceding chapter the rudiments of flood warning and preparedness benefits are discussed

in general terms; warning systems can result in shifts in the stage-damage relationship. In this chapter,

some of the more specific forms of benefits to a warning system are considered. Some of these are

reflected in the shifting damage curve, others are not. Many of the examples provided are not NED

benefits and are not appropriate for evaluating the economic feasibility of Federal involvement in a water

resource project. They are mentioned because they may be of concern to local interests.

BENEFIT TAXONOMY

The Flood Hazard Research Center of Middlesex Polytechnic in Enfield, England has done a

considerable amount of research in the area of flood warning and preparedness benefits. One focus of their

early research was identifying examples of the different benefits types. Building on their work, flood

warning and preparedness benefits are first classified as tangible or intangible. Tangible benefits can be

assigned a monetary value. Intangible benefits cannot be assigned a monetary value, but may be otherwise

quantified. This taxonomy is illustrated in Table 1.

Few extramarket effects could be assigned a monetary value a few decades ago. Now, however,

it is not inconceivable in the professional literature to see monetary values assigned to the lives saved,

improvements in health, and unique environmental resources. The cost of conducting such analyses may

be prohibitive or the NED benefits so identified may be trivial. The techniques for valuing such

consequences may be impractical for many Corps' projects but their measurement can no longer be

considered impossible.

What makes a benefit "impractical" to measure in dollars is a matter of debate. While measurement

may be conceivable from a theoretical perspective, it may be impossible from a practical perspective. Lack

of time, budget, data, or technical training frequently render measurement of some effects impossible. Lack

of professional or policy consensus on the appropriate means of measurement renders other values

pragmatically impossible to measure.

Direct benefits accrue to those who put project outputs to their intended use. Direct benefits

include what are commonly known as inundation reduction benefits and emergency and recovery costs

33
Framework for Estimated National

Economic Development Benefits

avoided. These comprise the major portion of NED benefits. Indirect benefits result from externalities.

These may be technological externalities, such as, when flood warning and preparedness systems can be

used for other disasters; or pecuniary externalities where the well-being or income of others is increased

as a result of project outputs.

Benefits can be categorized as tangible or intangible and direct or indirect as shown in Table 1.

The benefits described in this chapter are broadly defined. Some of the benefits described are NED

benefits; others are not. Still other benefits are NED, benefits under some circumstances, but not others.

Determining which benefits are NED and which are not, must often be determined on a case-by-case basis.

NED benefits should be identified consistent with the Economic and Environmental Principles and

Guidelines for Water and Related Land Resources Implementation Studies (P&G) (1983) and the National

Economic Development Procedures Manual, Overview Manual for Conducting Economic Development

Analysis (1991).

Table 1

Examples of Benefits Types

Direct Indirect

Reduced physical Reduced time closed

Tangible Damage to flood due to loss of flood

plain occupants plain markets

Flood plain Reduced stress to

Intangible occupants lives family of flood

saved plain occupants

Source: Pennington-Rowsell and Chatterton, 1979.

The following discussion is orgaiized into the four benefit types defined by the categories of

Table 1. The exclusion of an example under one benefit type does not imply it cannot be an example of

another type in other circumstances. Though specific examples of benefits types must be placed in one or

another of these categories, the reader is cautioned that these benefit types are more a continuum than

discrete categories with precise definitions.

DIRECT TANGIBLE BENEFITS

The primary direct tangible benefit from flood warning and preparedness systems is the reduction

of property damage. Techniques for evaluating these benefits are well known to analysts. Benefits can be

34
Framework for Estimated National

Economic Development Benefits

realized by: 1) temporarily removing property from the floodplain; 2) moving property to a safe elevation

within the floodplain; 3) temporary flood-proofing; 4) opportune maintenance; 5) early alerting of

emergency services; 6) orderly disruption of network systems; 7) suspension of sensitive works; and, 8)

related effects on emergency costs, cleanup costs, and business losses.

The first three types have long been recognized and estimated in a number of flood damage

reduction studies. The last five examples are rarely investigated and estimated in a systematic way. This

may be for a variety of reasons. First, it is because these benefit types are largely dependent upon the

response time available. When analysts focus on water depth and damage only, they must make some

assumptions about response time that are effectively the same for the with and without i'-oject condition.

In this case, there is little if any change in residual damages in these areas.

A second possibility is that analysts are unaware of the potential for such damage reductions or,

more likely, the data necessary for analysis are unavailable or too expensive to gather. Third, analysts may

reason that reductions in these damage categories are trivial in comparison to other benefit categories and

the'r magpritude does not warrant the expense of estimating these effects.

Because ff the modest costs of warning and response systems, investigation of these benefit types

.vill nit always be warranted in economic analyses of warning alternatives. The latter categories of benefits

are going to be more likely wk_ý, warning and response systems that include: 1) emergency action plans, 2)

trained personnel to carry out the plan, 3) the means to foster community interest and education. Thus, if

the system under consideration consists of the forecasting hardware with very little detail on the remainder

of the warning process, there are likely to be few benefits from these latter categories. These require a

rather sophisticated planning process and warning system. When such planning has been done, however,

the benefits could be substantial.

Temporary Removal of Property

One obvious way to reduce damages is to remove damageable property from the floodplain. This

alters the shape of an individual structure's depth-percent damage curve.

Automobiles can be filled with property and driven from the floodplain. In the U.S., this is the

primary way of leaving an area subject to a disaster warning. Given enough time, residents can contact

relatives who can provide additional vehicles for removing valuable property from the floodplain.

Televisions, stereos, VCRs, and other high value but mobile equipment is easily moved from a home.

Residents fearing looting can be expected to remove high value and mobile property first. Items of a

personal nature, including medicines, irreplaceable photos, personal papers, etc. are also among the first

items removed from the floodplain by individuals and families who have given some thought to planning

for a flood.

Businesses typically have equipment, inventory and goods-in-process of much greater value than

the typical home. They also have problems in moving that equipment. Relatively few businesses will have

sufficient vehicles for removing their property. Rental trucks are rarely available in adequate numbers,

35
Framework for Estimated National

Economic Development Benefits

particularly when everyone wants them all at once. Firms may call in vehicles from other locations if there

is sufficient time.

Many firms require the services of riggers and trained specialists, electricians, engineers, etc. to

disassemble and move their more valuable equipment. The ability to do this depends on the size of the area

in which the floodplain is located6 and the available warning time. Some businesses, on the other hand, find

significant advantages to their floodplain locations and are well prepared for floods. Inventory may stored

on skids for easy removal with a forklift.

Non-profit organizations that operate as businesses have experiences similar to most businesses.

Some organizations, like churches and fraternal organizations, may have difficulty competing with firms

paying for scarce manpower, trucks and equipment. Neither can they rely on volunteers whose time and

loyalty may be divided between protecting their home and their job.

Farmers may be able to move their livestock out of the floodplain if given enough time. Harvested

crops can also be removed under some circumstances. Neal and Parker found that British farmers' ability

to successfully move property from the floodplain depended on their receiving warning and being able to

work during daylight hours.

A particularly important class of properties to move includes valuable papers (e.g., papers with

sentimental value7, accounts receivable, telephone lists, files), photographs, stocks, bonds, etc. Businesses

inexperienced in flood recovery often protect valuable equipment first only to learn the hard way that they

cannot resume their business without customer lists and accounts receivable records.

If property is stored outdoors, losses prevented must be net of any residual damage that might be

incurred due to the elements during storage.8

Removal of property from the floodplain results in a downward shift in the stage-damage curve.

The shift will not likely be a parallel shift in the curve. The nature of the shift in the damage curve will

depend on the location of the removed property in the structure. If there is nothing removable at the lower

elevations of the structure the without- and with-project condition damage curves may coincide at lower

levels as shown in Figure 10. If the items removed are spread throughout the elevations of the structure,

a shift like that shown in Figure 11 is more likely. For contrast, Figure 12 shows a case with no removable

6Large urban areas, for example, may have a source of available emergency labor that a smaller

area may not.

7

These may more appropriately be considered direct intangible benefits.

3

Businesses that removed property from a Pennsylvania flood plain to an open meadow on high

ground sustained substantial property damage during a storm that occurred before the flood plain could

be reoccupied.

36
Framework for Estimated National

Economic Dewtopmem Beneflts

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Framework for Estimated National

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property at lower levels and increasingly more at upper levels. Figure 13 reflects the removal of one or

more items of considerable value from the structure.

Moving Property to a Safe Elevation Within the Floodplain

Residents and businesses occupying multi-story buildings may have the opportunity to protect

moveable property by relocating it from basements and ground floors to higher levels. Carpets, furniture,

electrical equipment and other residential property are frequently moved by residents to higher floors in

their homes. Businesses may be able to use higher floors and rack storage to move property out of the

reach of flood stages. For minor floods, it may be sufficient to move property to higher elevations in the

same room.

Even when it is not possible to move property to higher floors in a building, property owners may

be able to move goods to high ground on their property. Businesses and homes located on lots with varying

topography may find that part of their land is flooded while part is not. It may be feasible to move

belongings out of the reach of floodwaters without removing them from one's property.

The success of such actions depends on the accuracy of the forecast and a knowledgeable response

to the forecast. During the 1972 Agnes event, the Pennsylvania owner of a fleet of cement trucks moved

the trucks to high ground on his lot expecting them to be well out of the reach of floodwaters, based on the

official forecast available at the time he took action. The forecast was revised as the owner drove home.

Returning to his business, his access was blocked by high water. The trucks were inundated.

The success of moving property to higher elevations to avoid damages depends on the actions taken

and the depth of flooding. Moving property offers limited opportunities for reducing damages in one story

homes without basements during floods with great depths of water. Removing property from a basement

to a first floor is effective only if the first floor is not flooded. Likewise moving property to higher floors

is effective only when those floors are not flooded.

Moving property within a structure does not alter the total value of property at risk of flood

damage. It does, however, alter the stage at which that damage occurs. Hence, shifts in damage curves

for this type of measure will typically have the without and with project curves diverging at lower

elevations, only to converge at higher elevations. Figure 14 presents an example of such a curve.

Temporary Flood-proofing

The existence of warning systems makes it sensible to undertake purchases of equipment or

structural changes in one's building that will be available for use during the time of flooding. Warnings

issued with sufficient lead time allow property owners to implement temporary flood-proofing activities that

can reduce flood damages. These include such things as sandbagging openings and anchoring property that

could be carried off by flood waters or broken loose and damaged by rising waters. Intentional flooding

of basements and floors can equalize hydrostatic pressures that would otherwise damage structures and

property.

40
Framework for Estimated National

Economic Development Benefits

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Framework for Estimated National

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Given time, some property owners will be able to implement semi-permanent flood proofing

measures. For example, a newspaper publisher in the Susquehanna basin installs seals over windows and

doors during the time of a flood. Many businesses have found it desirable to protect in-place equipment

by disconnecting electrical equipment and gas lines, and greasing or wrapping sensitive parts of their

equipment.

While businesses may be expected to use these techniques in greater portions than homeowners,

homeowners may also practice temporary flood-proofing. The extent of their effort may range from

stuffing towels under doors to installing backflow valves an4 installing closure structures in ring levees.

A number of residents in Georgia's Peachtree-Nancy Creek basins have decorative perimeter walls that

louble as ring levees when gates are replaced by closure structures. Removable utilities, such as furnaces,

electric motors, etc. have also been used by some homeowners. The greatest potential for temporary

flood-proofing measures to reduce damages significantly would appear to be among businesses with formal

flood emergency plans.

The effects of temporary flood-proofing on a structure's damage curve can be modeled the same

way a levee or floodwall project is modeled for a community. Another alternative is to model the effect

as shown in Figure 14 above; it simply depends on the nature of the flood-proofing measure. A measure

that flood-proofs the entire structure would be modeled as in Figure 15. A measure that flood-proofs only

certain elements of the structure would be modeled as in Figure 14.

Opportune Maintenance

A substantial amount of damage found when floodplain occupants return to their homes and

businesses after the flood waters recede is due to the indirect effects of a flood rather than to actual flood

damage. For example, fires and explosions are not uncommon in floods. The damages that result from

fires and explosions is not a direct flood damage; but they would never have occurred in the absence of a

flood. Natural and propane gas lines rupture in homes as appliances are moved about by rising waters.

Gas leaks fill rooms and structures with devastating consequences if sparks are present.

Water supplies can be contaminated and sewage spills can result if the flood stage is not anticipated

in sufficient time. Wastes routinely discharged into sewage systems by businesses can end up in

watercourses increasing damages to property9 and the environment. This can be even more troublesome

in communities where combined storm and sewage water systems exist. A warning and response system

can provide officials and individuals with more time to undertake what is here called opportune

maintenance rhis maintenance can be as simple as closing a shut-off valve on a gas line and halting

9Many businesses -outinely discharge a variety of organic and inorganic materials and solutions into

the sewage system. For example, a corned beef processor in one flood plain routinely discharged animal

fat into the sewage system, despite the fact that it was against the law to do so. A combined sewer

overflow released these fatty wastes in the vicinity of a machine shop where they contributed greatly to

the damages caused to the property there.

43
Framework for Estimated National

Economic Development Benefits

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Framework for Estimated National

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discharge of certain materials into the sewage system, or complex safeguarding of water supplies and

sewage treatment plants.

The major difficulty with estimating these NED damages/benefits is that they are difficult to

measure because they are risk-based events. Fires, entry of toxic materials into the watercourse, etc., may

or may not occur. For pragmatic reasons, these may not be estimated in either the with or without project

condition for a flood. Nonetheless, they may, in some circumstances, be important NED effects of a

warning system with significant value to floodplain occupants.

Opportune maintenance benefits can be modeled by downward shifts in the expected stage-damage

curve (assuming a risk-based probabilistic approach to the analysis), provided the without condition

expected stage-damage curve included these damages.

Early Alerting of Emergency Services

A major hidden cost of flooding is nature's element of surprise. Hundreds of people converging

on the local high school gymnasium that is still locked and set up for last night's basketball game are minor

inconveniences in the scheme of things but a one hour wait for 200 people costs society 200 hours. The

opportunity cost of this time is an NED cost; estimating it may be impractical. Contacting and calling in

all available emergency personnel also takes time, time during which lives can hang in the balance.

Volunteers can be mobilized to save lives, minimize damages and ease the transition to recovery, given

sufficient warning.

Lack of access to emergency supplies and provisions can also result in increased costs to a

community. Stockpiled sandbags are no good unless they are ready for use at the place they will be needed.

Four wheel drive vehicles, boats, two-way radios, etc. are often in short supply during disasters. It takes

time to arrange for such resources. Mass shelters will need emergency water, food, clothing, medicine,

blankets, cots, telephones, etc. for the people descending upon them.

Increased warning time can reduce the costs of emergency shelter and emergency care as individuals

have more time to arrange to stay with relatives, friends, or elsewhere. Public assistance and long-term

emergency shelter for evacuees could be reduced if they have time to secure/prepare their property before

evacuation. Emergency expenses saved by the public sector may be financial savings without any reduction

in economic costs if individuals incur personal costs equal to the public sector's savings. Hence, these

benefits are likely to be transfers and not NED in nature.

Communities faced with limited personnel and other resources can benefit greatly from additional

time to ready these and other emergency services. Deployment of personnel and equipment to assure

medical, fire, police and other services are continued and available to all parts of the community is a service

so invaluable that it probably should be considered an intangible benefit. Costs of delays in alerting and

deploying emergency services are rarely estimated in flood damage reduction analyses, thus reductions in

these costs have not often been quantified.

45
Framework for Estimated National

Economic Development Benefits

Warning makes it possible to avoid costs associated with unnecessary mobilization of manpower

and other emergency actions. A preparedness plan can identify necessary resource deployments that can

avoid knee-jerk over-reaction that often comes from a misdirected desire to do something or to help out.

Early alerting of emergency services can be modeled by downward shifts in the stage-damage

curve, provided the without condition damage curve included these damages.

Orderly Disruption of Network Systems

Phone systems, utilities, pipelines, cable TV services, transportation patterns and traffic levels, local

area networks (i.e., computer network systems) and other network systems that unexpectedly "go down"

can do so in ways that affect an area much broader than the floodplain or in ways that are much more

difficult to bring back up. Warning and response systems offer opportunities for these network systems

to prepare for disruptions in a more orderly and, ultimately, cost-effective manner. More alternatives to

continue network services can be investigated, considered and implemented if more time is available.

For example, businesses with large and dynamic data bases may routinely back-up their systems

once or more a day. However, if a flood disrupts the systems operation at an inopportune time the costs

to the business can be overwhelming.

This benefit type presents an area in which the analyst must know the floodplain and look for

potential disruptions to networks. One key is in defining a network. Though a number of examples of

networks have been identified above, networks may also be found in less obvious places. Publishing a

newspaper, for example, may be likened to a network. In the aftermath of the 1972 Agnes event, one

newspaper publisher was eventually able to make arrangements to have several small, independent printers

publish the paper until it could reoccupy its premises. In some cases, it may be possible for businesses to

make alternative plans for thier network services if they have sufficient warning time.

Rival businesses have, at times, been found to be surprisingly supportive of their competitors during

times of natural disasters. The Agnes experience in Pennsylvania during 1972 yields numerous examples

of businesses helping their competitors to meet their commitments and obligations while temporarily

dislocated. There may be instances where a business could make arrangements to avoid or minimize

disruption to network services if they have sufficient warning prior to flooding.

Orderly disruptions to network services can be modeled by downward shifts in the stage-damage

curve, provided the without condition damage curve included these damages.

Suspension of Sensitive Works

Closely related to the two previous benefit types is the suspension of sensitive works. Many

production processes take days to complete. For example, at least one method of producing vinegar is a

continuous process that takes a week to complete. The vinegar is always somewhere inside the series of

vats and pipes that make up the production equipment. While some product is finishing the process, raw

materials are being fed into the beginning of the production line.

46
Framework for Estimated National

Economic Development Benefits

Given sufficient warning of an event that could destroy an entire batch of product, the vinegar can

be stored in homogenous batches that will save goods-in-process and speed up the process of getting back

into production. There are many industries with similarly sensitive processes that could be suspended with

sufficient warning to allow protection of goods-in-process.

A number of production processes involve the use/production of hazardous materials. Warning

systems may provide opportunities to suspend production processes to minimize the possibility of hazardous

materials entering the waterway.

In addition to readily predictable effects like that above, chance will provide opportunities for other

sensitive processes to be suspended. For example, consider something as mundane as the repair of a water

main. Given sufficient warning, repair crews could suspend their repair work in a way that minimizes

disruption to the utility. This could be by temporarily capping a pipe, shoring up an excavation, etc.

The suspension of sensitive processes can be modeled by downward shifts in the stage-damage

curve, provided the without condition damage curve included these damages.

Related Effects on Emergency Costs, Cleanup Costs and Business Losses

The emphasis in the above discussion of direct tangible benefits has been on physical damages. It

should be noted that any or all of the above benefit types could have an effect on emergency and cleanup

costs as well as business losses. For example, a more orderly response to a flood can result in a reduced

need for overtime payments of employees in flood fighting and clean-up activities. If these economic costs

of operating from a floodplain location can be eliminated, they are NED benefits. Business losses, defined

as total revenue generated by the sale of a product less the economic costs of generating that product, may

be NED benefits. Losses that are not merely postponed to another time, or transferred to another

geographic location, represent real decreases in national product and are NED benefits if prevented.

Many of the actions described above will result in a faster and less expensive return to normalcy

during the post-flood period. Reduced unemployment and income loss, smaller losses in sales (and,

consequently net income), and smaller reductions in taxes collected can result from a quick recovery. These

losses are usually regional transfers and are not NED effects. Costs for flood insurance could be reduced

as warnings result in decreases in the amount of coverage required by residents and businesses.

These costs and losses can be modeled by downward shifts in the stage-damage curve, provided

the without condition damage curve included these damages.

Traffic Control

Implementing appropriate traffic controls takes time. Forecasts provide the opportunity for

authorities to decide which roads to close and which to keep open before flooding begins. Traffic can be

re-routed in a more efficient manner and personnel can be deployed in a timely manner to block access to

potentially dangerous areas as well as to direct traffic on detour routes, etc. The value of the time saved

and the avoidance of increased economic costs of transportation through such efforts can be NED benefits.

47
Framework for Estimated National

Economic Development Benefits

DIRECT INTANGIBLE BENEFITS

Many of the effects of a warning system accrue directly to the residents and users of the floodplain,

but they cannot be readily measured in monetary terms. In an effort to categorize some of these effects,

a number of benefit types are described below. There is considerable overlap among some of the

categories. The distinctions and examples of benefit types that follow are offered merely to suggest some

potential benefits. Experienced planners will be able to expand on these examples.

Human Health and Safety

Flood warning and preparedness systems can result in the timely and orderly evacuation of a

floodplain. Evacuation of a floodplain prior to flooding reduces risks to evacuees. As people are removed

from the hazard area they are personally immune to its dangers. Warning time is especially necessary for

the evacuation of insdtutionalized populations in hospitals, nursing homes, schools, prisons, etc. Likewise,

timely warnings protect volunteers and emergency personnel by minimizing the need for them to conduct

rescues.

The provision of early alerts furnishes opportunities to arrange needed assistance for individuals

who are invalids, handicapped, or that need more than the normal amount of time to evacuate. Warning

systems provide additional opportunity to make and implement decisions about closing schools and places

of employment to minimize persons exposure to danger. Action can be taken to prevent travel into

hazardous areas.

In addition to providing substantial threat to the health and safety of floodplain occupants, floods

produce a 'isk of injury or death to patrons, students, patients, inmates, visitors, and employees of public

and private facilities. Though questions of liability for such injury or death are beyond the scope of this

report'°, warnings can contribute to a significant lessening of whatever liability may exist.

Public health problems in the post-flood period can be minimized if there is sufficient warning time

to undertake opportune maintenance, suspend sensitive works, and provide for an orderly disruption of

network services. Equipment and materials necessary for basic health and hygiene needs are more readily

available when and where needed, if they are readied and deployed prior to the flooding rather than during

or after. For example, vaccinations against disease can be more effective if available before the onset of

a problem.

Safety can be better assured if there is sufficient time to evacuate everyone, especially those

requiring extra time to evacuate; and if access to dangerous areas can be controlled at the earliest

opportunity.

'(The 1989 study by Neal and Parker (p.52) points out that the legal position over responsibility to

provide flood warnings is still evolving. Their opinion is that the obligation to provide flood warnings

may be stronger than previously believed based on common law developments in Australia.

48
Framework for Estimated National

Economic Development Benefits

Use of System for Other Disasters

Flood warning and preparedness programs can serve a number of purposes. Significantly, they can

serve as the core around which other disaster preparedness plans can be organized. Communities vary in

the range of disasters they face; earthquake, storms, fire, hazardous materials accidents, etc. are among

the possibilities. A warning system helps to put in place the process for dealing with these emergencies.

Lines of communication are established, warning dissemination patterns exist, etc. To the extent that

education or experience spawn a disaster subculture in a community, it will be better prepared to deal with

any emergency. Thus, a flood warning system could contribute indirectly to the protection of lives and the

enhancement of health and safety during other disasters.

Cost of Employment Disruption

Many of the costs of employment disruption are direct and tangible. Lost productivity is an NED

cost. Lost income is of paramount concern to the individual. As income is lost, tax revenues are

decreased. If production and income are made up at a later time or another place they are not NED costs.

The decline in tax revenues is accompanied by a rise in public expenditures making a balanced

budget more elusive. The public sector bears some share of the costs of recovery from the flood and is

faced with increasing demands for public assistance as individuals lose their means of support, further

straining the public sector's finances.

Warning systems provide time that allows firms to suspend business and prepare for the flood in

a manner that will minimize the time and expense of getting back to business after the flood. Less well

known are the intangible effects of unemployment. Even if an individual or family survives the flood they

may not survive !he unemployment that results. Unemployment has been shown to cause an increase in

crime, suicide, spouse and child abuse, and substance abuse of all kinds; increases in mental breakdowns,

stress-related illnesses, and inattention to health problems (often due to loss of health insurance). To the

extent that flooding limits unemployment, it simultaneously limits the extent of all these problems as well.

Reduced Stress

Loss of life and injury can cause incredible stress to the family of the victim and to the injured

victim. Reducing the number of these events through warnings will lessen flood-related stress. Not all of

the stress-inducing events need be as serious as death and injury. It is a way of life for some floodplain

occupants to cancel outings, trips and vacations because of weather that has produced flood conditions in

the past. Many occupants claim they cannot be away from home for fear of flood damages. Others can't

sleep when it rains heavy.

The presence of flood warning systems in England has resulted in considerable "customer

satisfaction" (Neal and Parker). The mere presence of a warning system provides many floodplain

occupants with the reassurance that if there is a need to worry, someone will tell them to worry. Thus, the

warning program eliminates a lot of unnecessary worry.

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Family Disruption

One form of stress is so common and so debilitating that it deserves to be separated from the other

stress issues. Disruption of the family unit is a common consequence of some types of flooding. It is

common for a family to be widely dispersed during the course of a normal day; parents at separate

workplaces, children at one or more schools or day care facilities, an aging grandparent at home. When

a flood is imminent or occurs, the family's energy is often channeled into reuniting at any cost. With roads

blocked, communications down and water rising, the stress can become unbearable, often producing

irrational behavior.

A warning system can be expected to provide authorities with the time they need to make better

decisions about closing schools and other facilities. Families will have more time to reunite and verify the

safety of other family members, thus reducing stress significantly.

Loss of Memorabilia

Most property losses are readily accounted for through traditional estimation of changes in expected

annual damages without and with the warning program. Traditional methods of valuing property may be

inadequate for that class of property called memorabilia. The beat-up and scratched old table by the door

may have negligible value to an antique collector but it may be invaluable to the owner because it has been

in the family for generations. Wedding albums, photographs of first haircuts and the like are essentially

worthless to people outside the household; but they are a family's history and are irreplaceable to a few.

Forecasts and warnings can provide the time and instruction necessary for people to gather and

remove those most prized possessions.

INDIRECT TANGIBLE BENEFITS

Every direct tangible benefit described above can produce similar indirect tangible benefits (e.g.,

planned and careful disruption of network services can benefit floodplain occupants and others). This

discussion of indirect tangible benefits is separated into production and consumption externalities roughly

according to whether they involve the non-residential or residential populations of the community.

Production Externalities

A major producer of canned foods is located in the floodplain of the Susquehanna River. A few

miles away, well out of the floodplain is a small manufacturer of steel cans. Over ninety percent of their

product is sold to the floodplain firm. When a flood halts production at the cannery, it indirectly halts

production at the can manufacturer's as well. A warning system that limits the impact of a flood on the

cannery limits the impact of the flood on the manufacturer. These impacts could include NED benefits if

the lost production is not deferred in time or transferred in space.

The production relationships in our economy are complex and varied. Firms far from the

floodplain may have their fate tied to the fates of floodplain firms or services. They may depend on

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floodplain firms to provide their inputs or to buy their products. They may rely on travel, communication

or other network linkages with the floodplain or located in the floodplain. When there are physical or

economic ties between floodplain and non-floodplain firms and activities, direct tangible benefits to

floodplain firms and activities are likely to be accompanied by indirect tangible benefits to non-floodplain

firms. The significance of these effects can only be determined on a case-by-case basis.

Consumption Externalities

Repeated flooding can degrade property and cause reductions in property values that are either

short-term (in the immediate aftermath of a flood) or long-term (more or less permanent) in duration. The

value of adjacent non-floodplain property can be likewise diminished by its proximity to low value

properties. These effects can also lead to consequent reductions in tax revenues for the community.

Warnings that allow property owners to mitigate the effects of floods on their property may also have the

effect of allowing the real property to better hold its value. This is a benefit to the floodplain property

owner, adjacent property owners, and local tax authorities but it is almost certainly a regional or local

benefit, not an NED benefit.

Residents of non-floodplain properties can realize positive externalities from a warning and response

program. Many people may be dependent upon floodplain activities for their jobs. A warning system

could get an employee of a floodplain firm back to work sooner; or, an employee of the can manufacturer

may avoid a lay-off because of the system. Consumers who shop, study, recreate in or otherwise use the

floodplain will also benefit from a more rapid recovery from flooding.

INDIRECT INTANGIBLE BENEFITS

Reduced Stress

It is not just the occupants of floodplains who suffer stress-related maladies as a result of flooding.

The families and friends of floodplain occupants are affected by the death and injuries of loved ones. Even

when no harm occurs, the mental health of families and friends can suffer tremendously as word of a flood

arrives and it is impossible to establish contact with the floodplain occupant. Catastrophic events can affect

the mental health of an entire community.

Warning and response programs can mitigate the stress of non-floodplain occupants by reducing

death and injury. The orderly disruption of communication networks could contribute to less time between

news of a disaster and the reassuring contact from a loved one.

Flood warning and preparedness COSTS

The costs of flood warning and preparedness systems have been well documented in both the

literature and in project reports. Davis and Burnham (1986:15) provide a concise summary of flood

warning and preparedness costs that is modified and reproduced in Table 2. A 1990 Report for IWR,

"Procedures for Calculating the Costs and Benefits of Flood Warning and Preparedness Systems," provides

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Table 2

Potential Cost Items:

Flood Warning-Emergency Preparedness Plan

............ ~ .......................... ........................................................ ~........

"* Development of plans

"* Outfitting/equipping administrative facilities

"* Purchase/installation of equipment and hardware

"* Development/printing brochures and instructions

"* Stockpiling equipment and materials

U2 AxsurbirkxtCbste

"* Updating flood recognition methods and formal warning, response, recovery, and

continuous management plans

"* Updating/pringing brochures, instructions, etc.

"* Operation drills

"• Supplement/replace stockpiled materials

"• Equipment/hardware operations, maintenance and replacement

"* Personnel overtime and emergency hiring

"* Equipment purchase and rental

"* Transportation/storage of personnel property

"* Materials supplied/consumed

"* Mass care operations

"* False warning

(Davis and Burnham, 1986)

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a detailed description of system first costs as well as operation and maintenance costs. These costs are not

considered further here.

Flood warning and preparedness systems can impose hidden costs on a community. For example, the

Colorado experience detailed by Worth and McLuckie indicated that successful and well-publicized mass

media warnings had the undesirable side effect of attracting large crowds of "curiosity seekers" or "gawkers" to the disaster site. These crowds present crowd-control/access problems that divert scarce

resources from disaster response to crowd control activities. The opportunity cost of these diverted

resources can be very high in some cases. In some cases the curiosity seekers have foolishly placed their

own lives and safety in jeopardy, creating a new population of people at risk of losing their lives and

endangering volunteers and emergency personnel assigned to dispatch them.

Perhaps the most important hidden cost of a successful warning system is the cost to the individual of

additional time for responding to the warning. Each response action undertaken by a resident or business

person has an opportunity cost. Warning systems are assumed to provide additional time for individuals

to respond to the impending flood. Actions taken during that additional time can be costly. For example,

with sufficient lead time individuals may opt to take leave from work in order to reduce the damages to their

home.

To the extent that the productivity of workers who take off in this fashion cannot be recaptured, this

represents NED cost.

The costs of removing property from the flood's path can be substantial. And, to the extent a warning

system causes an increase in these costs, they must be considered part of the system's costs. These costs

include wages, the value of personal or volunteer time and the opportunity costs of the vehicles and other

special equipment used.

Implicit costs can include the value of time spent moving property, protecting it from vandalism, etc.

One hidden cost of removing property that must be considered is the cost of storage. If a flood lasts any

length of time or if reoccupation of the floodplain is delayed for any reason, property owners must find a

place to store their property. This storage may have explicit costs, such as will be incurred when storage

space is rented; or implicit costs, as may be incurred when relatives give up the convenience of the use of

a garage.

Unsuccessful efforts to move property out of the reach of flood waters can actually increase the damage

incurred during a flood. The opportunity cost of a family's time and materials expended in a flood fighting

effort may be a significant addition to the physical damages they sustain when floodwaters extend their

reach beyond the expectations of the family's flood fighting efforts.

Efforts to move property to higher ground have costs as well. Explicit costs include any expenditures

for equipment or labor, etc. Implicit costs include the value of time spent in moving items. Increased risk

of injury and heart attack induced by strenuous and stressful efforts to relocate property may be significant

in communities with elderly populations.

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Successful evacuations also impose costs on society. Evacuees typically stay with relatives, friends,

motels/hotels, or mass shelters. Costs imposed upon friends and relatives may be substantial but as a

practical matter they are ignored, despite the fact they may be NED costs. Costs of motels are easily

estimated, but frequently overlooked. Mass shelters can be costly to operate in both financial and economic

terms because these shelters frequently have important alternative uses. As with the estimation of benefits,

these costs should be compared for with and without project scenarios.

Perimeter control of a warned area can be very expensive. Security is provided by people who typically

do not know the residents. This can impose hardship and delay on people with a legitimate right to reenter

their communities.

A final cost to consider is the cost of a false warning. Decision makers face a sometimes difficult tradeoff (addressed in some detail by Haimes, Krzystofowicz, and Li: 1990) between accuracy of forecasts and

the probability of false warnings. False warnings can be costly because they entail substantial costs of

mobilization of emergency personnel and resources as well as costly private responses to the warning.

False warnings are an inevitable risk with any warning system and they must be evaluated.

False warnings can be avoided by holding back on the issuance of a warning until flooding is certain.

The cost of this delay is to diminish the amount of response time available. In extreme situations, there may

be no response time left. At the opposite extreme, warnings could be issued with every heavy rainfall,

resulting in frequent false warnings and a "cry wolf" syndrome. Every evaluation of a flood warning and

preparedness system should consider the costs of this trade-off between response time/system benefits and

the costs of false warnings to the extent warranted by the study's circumstances.

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CHAPTER 5

FLOOD FORECAST AND WARNING

BENEFIT EVALUATION MODEL

INTRODUCTION

A benefit-cost analysis should be performed for every flood warning and preparedness system

considered for implementation by a Federal agency. In these cases, NED is the proper accounting

perspective for identifying benefits and costs. Benefit-cost analysis should be performed for any systems

under consideration by non-Federal interests where the resources of the sponsor of the system are limited.

In these cases, it may be appropriate to consider regional or local benefits and costs which may differ

substantially from NED effects.

Evaluating a flood warning system is a complex problem. Neal and Parker investigated a number

of alternative evaluation techniques; each with a different performance indicator. First, they considered

the proportion of flood prone properties within a region served by a flood warning service. Identifying the

number of properties served is amenable to average cost computations and saturation of coverage

comparisons.

Flood loss reductions was the second indicator considered. The third alternative is to measure

customer satisfaction with the system. In a round of surveys, they tried to determine the level of customer

satisfaction of flood warning recipients with the warning they received. This measure has become

controversial, however, because though it can be foolish and self-defeating to ignore the views of one's

customer base, in this case, the customers have neither the technical expertise nor the intuitive basis for

offering truly valuable commentary on the system's performance.

A fourth indicator proposed is to identify, categorize and record flood warning failures and to

compare these failures to system successes, for the purpose of improving the service. The fifth indicator

is a count of the number of flood victims who receive an official flood warning within a given time before

flooding of their property begins. This number could, perhaps, be compared to the number of people

covered by a warning system. The sixth indicator was a pure performance measure; compare the forecast

(say timing and depth) with what actually happened.

While each of these six indicators has its utility and proponents the seventh indicator considered by

the authors is the one used by the Corps of Engineers. It is a full benefit-cost analysis. The difficulty with

this indicator is that flood warning and preparedness system evaluations can be conducted at a variety of

levels of planning.

Continuing authority programs, planning assistance to states, floodplain management services,

reconnaissance studies, feasibility studies, reformulation studies, and special investigations are some of the

planning settings in which warning and response programs might be evaluated. Each setting has different

data, time, budget, and personnel constraints. The level of detail appropriate for a feasibility study is

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simply not possible in other settings. The analysis that suffices for a $25,000 warning and response system

would be woefully inadequate for a feasibility study.

Though the requirement for a benefit-cost analysis may be the same for all planning settings, the

level of detail required and the sophistication possible run the gamut from negligible to appreciable. In this

chapter, a model that can be adapted for use to many planning settings is presented. It is a simple expected

annual damage computation model created in a spreadsheet environment using Lotus 123 and @RISK, a

Lotus add-in software product." The model can be used at different, broad levels of detail. Three levels

are presented here. The simplest application of the model requires nothing more than a stage-damage

curve, some basic assumptions, and the model presented here.

The model is an after-the-fact application of the model using existing data. The most sophisticated

application of the model requires the careful collection of structure-specific data as illustrated in Figures

10 through 14, a stage-damage compilation program like the Hydrologic Engineering Center's Structural

Inventory for Damages and Expected Annual Damages programs or their equivalent, and the model

presented here or its equivalent. It is a before-the-fact application of the model. Between these two

extremes is a middle ground application of the model, that can be used before, during, or after the

collection of stage-damage data.

Development of the model begins with a discussion of the state of existing warning systems in the

without project condition. The basics of the model are then presented. After the initial presentation of the

model a simple application is presented, followed by a number of more complex applications with

accompanying explanations. The applications are numbered for ease of reference.

Data were obtained from a Corps' project for use in most applications. The data are from a large

and densely developed urban area. Hence, the magnitudes of EADs and benefits are likely to be much

larger than the benefits obtained in most project areas where flood warning and preparedness systems are

being considered. It should be clear that the bulk of damage reductions that result from warning systems

will accrue to the contents of structures rather than the structures themselves.'2 Though the stage-damage

curve used includes all types of damages for simplicity, it is treated as if it is content damage only. The

results and applicability of the model are not affected by this fact.

Two basic models are presented. The first is the benefit model; the second is called the "Response

Model" and it is described in a later section. Chapter 5 presents some empirical results found in the

literature that could be of use in using both of these models.

"Lotus 123 is available from the Lotus Development Corporation, 55 Cambridge Parkway,

Cambridge, MA 02142. @RISK is available from Palisade Corporation, 2189 Elmira Rd., Newfield,

NY 14867.

'2Though flood-proofing, opportune maintenance and other benefit types described in the previous

chapter may result in reductions in structural damages, we will not make distinctions in the nature of

the damage reductions and will effectively assume all damage reductions are to content damages.

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WITHOUT PROJECT CONDITION WARNING ASSUMPTIONS

There is no such thing as a flood prone community that has no flood warning and preparedness

system. It may be informal and unsophisticated, ranging from aching joints to watching the river rise. The

dissemination of the message may be limited to police driving through with a bullhorn, neighbors calling

each other, or to asking everyone why they're leaving an area in a such a hurry. The point is, any without

project condition includes some level of damage reducing activity that peopl, already undertake. This

activity should be reflected in the without project condition stage-damage curve.

Damage estimates prepared from interviews can easily account for flood fighting activities that

would reduce damages. Normal flood fighting activities are readily determined during an interview.

Damage estimates obtained from standardized curves, however, are a different matter. Curves based on

empirical damages, i.e. Flood Insurance Administration (FIA) curves, already reflect reductions in damages

due to the actions of individuals. Whether the damage reductions reflected in these curves, generalized

from data over a wide geographic area, are representative of the study area is a problem the analyst should

consider in unique areas.

The general applicability of curves standardized from a large area to any one specific area raises

issues of more importance than the representativeness of the damage-reducing behavior imbedded in them.

This latter issue is rarely addressed in practice because the difference in damage-reducing behavior among

regions of the country is likely to be trivial. Using these standardized curves, estimates of warning system

benefits are as likely to be a little too high as a little too low. In the absence of any consistent bias, however

small, there appears to be no reason to worry about this difference.

In some studies, District personnel develop stage-damage curves specifically for the study area.

These curves are usually constructed in one of two general ways. First, they may be empirical, i.e., based

on actual damages that resulted from past floods. Such curves are likely to include the damage-reducing

activities that result from the existing warning system. Curves generated in this manner will likely yield

the truest estimates of flood warning and preparedness system benefits.

The second approach is to survey homes, noting the value and elevation of property within the

home. Depth-percent damage curves are then generated from this information, based on susceptibility of

structures and contents to flood damages. Surveys such as these do not necessarily reflect damage reducing

activities of floodplain occupants unless these activities are expressly explored during the data collecting

surveys. Benefit estimates prepared from these curves are likely to be biased in the direction of overestimating benefits because the without project condition expected annual damages are overstated if damage

reducing activities are not explicitly considered.

CURRENT METHODS

Shifting the Damage Curve

Corps Districts primarily use one of two methods to estimate warning system benefits. One is to

effect a parallel shift in the stage-damage curve to reduce the amount of damages at every stage of flooding.

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These shifts are normally a one- or two-foot shift in the stage damage curve. This method is illustrated later

in this chapter.

The Day Curve

The most common method of modeling the benefits of a warning system is through use of modified

Day curves. Harold Day, in a series of publications, developed a method that introduced the consideration

of warning time to the stage-damage relationship. The basis for the original work was an early 1960s

synthetic data base built by the Baltimore District based on information obtained in the Susquehanna River

Basin for the development of the District's Damage Assessment Program (DAPROG). Baltimore personnel

developed detailed estimates of flood damages for varying depths of water to different classes of residential

property types classified by size, number of stories, class ( a condition variable), basement, and a subjective

measurement of the value of the structure's contents. For each structure type, a depth of water-dollar

damage relationship was expressed. These individual structure curves were not depth-percent damage

curves; they were depth-dollar damage curves. Damages were expressed in 1963 price levels.

The curves were constructed from detailed data about the contents, their value and susceptibility

to damage. For example, from file data it was possible to identify the precise dollar damage assumed to

be incurred by a television set with two feet of water on the first floor of a house. Using this rich detail,

Day used these detailed data to make judgments about what items could be removed from the path of flood

waters. By re-adding the content damage, he obtained a new depth-dollar damage curve for each structure

type. These damage reductions were standardized by converting them from dollar reductions to percentage

reductions.

Day performed this tedious work for three basic warning scenarios: maximum practical evacuation,

limited warning time, and limited response to warning time. Thus, Day identified a shift in the damage

curve for each of these warning scenarios. This is conceptually consistent with the presentation of

Chapter 2.

Over time, these curves have been produced and reproduced showing the damage reduction as a

percentage of without project condition damages that occurs with varying amounts of response time. The

Day curve methodology is perfectly applicable today. The actual Day curves, however, should not be used.

The Day curve methodology was surely a pathfinding work at the time, but continued use of curves based

on the contents of a typical house in the early 1960s likely do not apply to current floodplain situations.

There is nothing inherently wrong with the Day curve approach to estimating warning system benefits. The

model presented in this report simply builds upon it. There is, however, no rational justification to continue

to use the actual curves developed by Day.

MODEL OVERVIEW

A risk-based model has been developed to estimate flood warning and preparedness benefits in a

spreadsheet environment. Figure 16 displays the basic workings of the model. Without project condition

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Framework for Estimated National

Economic De lopment Benefits

EAD are calculated as usual. With project condition EAD are calculated for as many response scenarios

as the analyst cares to identify.

A warning time is randomly generated from a distribution of warning times specified by the analyst.

The generated warning time serves as a filter, allowing only the response scenario that includes the warning

time to be considered further. This one and only response scenario, i.e., the with project condition

response scenario that includes the randomly generated warning time, is the one used to estimate benefits.

The with project EAD that pass through this "warning time filter" are subtracted from without project EAD

to obtain an estimate of benefits. The calculation is repeated as many times as desired. A distribution of

project benefits is obtained and the mean of this distribution is the estimate of project benefits. The values

of any variable or relationship used in the model can be varied or saved in an output file for future analysis.

Each of the major steps in the functioning of the flood warning and preparedness benefit model is

discussed below:

Warning Time

The amount of warning time provided by a flood warning and preparedness system will vary from

project-to-project as discussed in Chapter 2. It will vary with the characteristics of each flood event, the

natural characteristics of the basin, the social characteristics of the community, and the technical

characteristics of the system itself. Small basins with steep terrain will yield less warning time than very

large basins with mild slopes. Widely dispersed rural populations may have less time to respond than

populations in dense urban areas with multiple opportunities for disseminating warning messages. Systems

that rely on visual inspections of precipitation or stream gages will not generally provide as much warning

time as more sophisticated systems with automatic reporting of rainfall and streamflow information.

The analyst will always have some information about the warning time available. The amount of

warning time might be identified in a number of ways, for example an average warning time, a minimum

and maximum warning time, a minimum, maximum, and most likely warning time, a mean warning time

and standard deviation, etc. At the same time, the analyst will never know precisely how much warning

time will be available for any one event. Some range of possible warning times can always be identified.

Some warning times within the range of possibilities will be more likely than others. Any range of values,

with some probability of the values in that range occurring, can be described as a probability distribution.

In the hypothetical application of this model, the warning system is assumed to provide from 1 (a

minimum) to 24 (a maximum) hours of warning. Such a range in warning times allows us to present a

sufficient number of with project scenarios to demonstrate the flexibility of the model.

A uniform distribution that runs from 1 to 24 has been assumed for simplicity. Any number in this

range is as likely to be selected as any other. Thus, 4.3 hours of warning is as likely as 15.9, 11.0 or any

other amount of warning time.

59
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1 6-

lop I C.• "* a.

ar

60*6

.3

a ~ I

U S

6O*
Framework for Estimated National

Economic Development Benefits

In an actual study, the analyst is likely to have a better idea of what the warning time is likely to

be.' 3 The range probably be narrower, or all values may not be equally likely. Other distributions can be

used to describe these circumstances. 14

Without Project EAD

Data for the hypothetical example have been taken from a single reach of an actual Corps' study.

Thus, the stage-damage, rating and frequency curves are realistic. No explicit adjustments were made to

the without project condition damage curves to account for the existing level of response to impending

floods. It is assumed, for simplicity, that this was done when the data were originally collected.

Damages estimated to occur at each stage can be treated as a known value or they can be allowed

to vary according to an analyst-specified distribution. In this example, damages at each stage are assumed

to vary according to a normal distribution. EAD are estimated in accordance with the hydroeconomic

model of Chapter 2. Appendix 1 provides some discussion of the construction and use of all the models

presented in this chapter.

With Project Response Scenarios

Four different response scenarios have been identified for this sample analysis. They are based on

assumed warning times of 4 hours or less, 4 to 8 hours, 8 to 12 hours, and more than 12 hours of warning

131n some studies, there may be a legitimate concern that no warning will be given. In such cases,

there must be some possibility of zero warning time. Within the spreadsheet framework of this model,

such a possibility is easily accommodated. Let us suppose there is a 20 percent chance a warning will

not be given. We further suppose that if a warning is given the resulting warning time will be uniformly

distributed from 1 to 24 hours. The distribution of warning times can be specified as a discrete

distribution with, say, a 0.2 probability of a value of zero and a 0.8 probability of a value from

a uniform distribution from 1 to 24 hours.

1

4

For example, with a minimum, maximum and most likely amount of warning time the analyst has

estimates of the three parameters necessary to describe a triangular distribution. Triangular

distributions are often used when the actual distribution is not otherwise known. With a single estimate

of the mean, an exponential distribution can be described. Add a standard deviation to the mean and a

normal distribution can be described. Analysts are cautioned, however, that these distributions are

significantly different from one another and there should be some theoretical or empirical basis for the

assumed distributions. A Guide to Probability Theory and Application by C. Derman, L. Glaser, and I.

Olkin, though out of print, is well worth the search for analysts looking for a good, accessible

introduction to probability distributions and their parameters. The book was published by Holt,

Rinehart and Winston, Inc. of New York in 1973.

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time. 15 The designation of these periods is entirely arbitrary as far as the model is concerned. One can

consider 2, 8 or any number of scenarios as easily as one considers 4.

Most studies will include minimum and maximum response scenarios and as many intermediate

scenarios as are necessary to describe the significantly different responses possible over the range from

minimum to maximum response. A most likely response will usually be included among the intermediate

scenarios. The probability or likelihood of the with project response scenarios is dependent upon the

warning time distribution.

Each of the four response scenarios leads to a different estimate of expected annual damages with

the project. In general, a 4 hour warning is expected to result in fewer damage reductions (i.e., a smaller

shift in the damage curve) than a 4-8 hour warning. The model can accommodate other possibilities as

well. For example, it is possible that some 4 hour warnings may result in greater damage reductions than

a 4-8 hour warning. A 7 hour warning initially given at 1 am may not be as effective in reducing damages

as a 4 hour warning given at noon. 16

Based on the amount of warning time randomly determined from the distribution of warning times,

a response scenario is identified as the relevant one for that warning time. For example, if warning times

are uniformly distributed from 1 to 24 hours, and a 6.1 hour warning time is randomly selected, the

relevant response scenario for this warning is the 4-8 hour response. Expected annual damages obtained

for the 4-8 hour scenario are subtracted from the without project condition expected annual damages for

this iteration of the model to obtain an estimate of warning and response system benefits.

A single estimate of benefits, so obtained, is obviously not sufficient for identifying project benefits.

Warning times can vary from event-to-event. The shift in a damage curve can vary from event-to-event

within response scenarios. As indicated in the literature review of Chapter 3, the response to a warning

depends on a great variety of variables. Thus, the computation is repeated numerous times.

Damage Curve Shifts

The major difference in the distinct levels of detail (called applications in the sections that follow)

at which this model can be used is the method by which the damage curve is shifted at each level. Under

1

5The asymmetric distribution of time across these four response scenarios will figure prominently

in the distribution of project benefits later in this chapter.

16For example, suppose a 0-4 hour warning can reduce damages from 0 to 5 percent. A simple

method of allowing for the possibility of a longer warning resulting in less damage than a shorter

warning may produce is to simply allow overlap in the percentage reductions. If the damage reductions

for 4-8 hours warning are 3 to 10 percent, it is evident that randomly selected reductions from a longer

warning, say 4%, will be less than the reductions from a shorter warning, say 5%, in some cases.

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the simplest applications of the model a stage-damage curve is assumed to already be available.17 The curve

is used as is for the without project condition estimate of expected annual damages.

In order to reflect the presence of a flood warning and preparedness system, it is necessary to shift

the damage curve. The simplest way to do this is to assume some percentage reduction in damages at each

stage. The extent of the assumed reduction in damages used in the model can be determined based on

explicit knowledge of the floodplain community, results from similar studies, the literature, a delphi or

other consensus building approach, or professional judgment.

The following assumptions provide the starting point for the applications to follow. With 4 hours

of warning or less, damages are assumed to be reduced from 0 to 2 percent.' 8 These reductions were

assumed to be uniformly distributed, i.e., any reduction in this range is as likely as another. For each stage

and each iteration of the model, a damage reduction was separately selected. Thus, it is possible that for

one iteration of the model, damage reductions at 4 national geodetic vertical datum (NGVD) could be 1.9

percent and reductions at 9 NGVD could be 0.5 percent,' 9 while the next iteration could yield the opposite

result. For response scenarios of 4-8, 8-12, and over 12 hours of warning time the damage reductions are

assumed to be uniformly distributed with reductions of 0 to 4 percent, 2 to 6 percent, and 4 to 10 percent,

respectively. These percentages have been arbitrarily chosen to illustrate a range of possible outcomes and

should not be considered default values for any real analyses.

Figure 17 shows damage curves for the 4-8 hour response scenario generated during four random

iterations of the model. Each curve represents a different shift from the without project condition damage

curve.

At the beginning elevation of 1 NGVD there is substantial difference in the damage curves. EAD

calculations for the four damage curves shown would reflect widely varying levels of success in reducing

'

7Damages at any stage can vary for a lot of reasons besides the response time. As outlined in

Chapter 2, damages depend on a number of variables. To reflect this reality and to encourage similar

thinking among Corps' analysts, we have specified a distribution of damages at each flood stage in the

without project condition. This means that for each iteration of the model, a different without project

condition damage curve was used. The without project damage curve was used for each of the four

response scenarios within each iteration, however.

1'rhe model multiplies each of the damages at each stage by a number randomly selected from a

uniform distribution with a minimum of .98 and a maximum of 1.00. Thus, damages with 4 hours of

warning or less range from 98 to 100 percent of the damages without the project. This corresponds

exactly to an assumption of uniformly distributed damage reductions ranging from 0 to 2 percent.

'

9

Because each stage represents a flood event that is independent of other flood events, we have

assumed that damage reductions at each stage should also be treated independently of each other. This

is not a necessary constraint of the model and - wide variety of dependencies can be built into the

model by making use of Lotus and @RISK programming capabilities.

63
Framework for Estimated National

Economic Development Benefits

damages from the frequent minor floods that reach 1 NGVD. Over the range of floods that would reach

2 to 6 NGVD there is relatively less difference in damages. At 7 NGVD and again at 9 NGVD there are

substantial differences in damages, despite the fact these curves all reflect 4-8 hours of warning time. The

many different variables that affect people's warning response, reviewed in Chapter 3, can be assumed to

vary from one event to the next, resulting in the types of variation shown among stage-damage curves in

Figure 17. Another four-iteration sample can be expected to result in an entirely different distribution of

stage-damage curves.

Figure 18 extends this example by showing the distribution of damage curves for 500 iterations of

the model. The mean, one standard deviation, and approximate two standard deviation2D confidence

intervals are shown. Figure 18 can be considered the plan view of a three-dimensional stage-damage

distribution. Figure 19 shows a cross section of Figure 18 at an NGVD of 15.

These three figures taken together illustrate a point essential to this model. Damage curves shift

in reaction to changes in warning time, as discussed in Chapter 2. Four possible shifts are shown in Figure

17, the range of 500 possible shifts is shown in Figure 18. Each shift has its own probability or likelihood

of occurring. This probability of occurrence for possible damages at one elevation is shown by the height

of Figure 19.

With project EAD are calculated for each damage curve. Because some damage curves are more

likely than others, the EAD resulting from these damage curves will be more likely than other EAD.

Similarly, because benefits depend on with project EAD, the likelihood of some benefits will be greater than

others. If the calculation is repeated a large number of times, the most likely benefits will show up most

often, but the occurrence of extremely low and extremely high benefit estimates will also be observed. The

range of benefits obtained ultimately depends on the assumptions built into the model.

The Remainder of the Chapter

Estimation of flood warning and preparedness benefits as presented in this procedures manual is

a risk-based analysis. The amount of warning time, the response of people to that warning, and the amount

by which they are able to reduce their damages for any specific event cannot be known with certainty.

Hence, the extent of the shift in the with project stage-damage curve as compared to the without project

curve is uncertain.

The models presented in this chapter provide some options for dealing with this uncertainty. The

five applications that follow present different techniques for shifting the damage curve to reflect the effect

of a warning on the damage relationship.

20'lhe wider confidence limits show the 5 and 95 percent confidence limits.

64
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APPLICATION ONE (ASSUMED PERCENTAGE STAGE-DAMAGE REDUCTION)

Let's begin with the simplest case where one flood warning and preparedness system alternative is

assumed to result in one warning response scenario. With a basic understanding of the literature,

knowledge of the community, and exercise of professional judgment, one can simply assume a fixed

percentage reduction in damages. Figure 20 shows the with project condition stage-damage curve is

assumed to be 4.4% less than the without project stage-damage curve. Expected annual damages without

the project are $10,175,000. Expected annual damages with the project and its assumed 4.4% reduction

are $9,727,000, resulting in expected annual benefits of $448,000.

This simple application hardly requires a new model. One need only assume the shift in the damage

curve. The key, of course, is assuming a reasonable reduction. A 4.4% reduction has been assumed

because that was the average reduction obtained in the application that follows, so it lends itself readily to

some simple comparisons.

The same percentage reduction in damages need not be taken for every stage of flooding; variable

percentage reductions can be taken at each stage. 21 The determinate percentage reduction" in the stagedamage curve approach is perhaps most useful when it can be shown that the assumed reductions are

considered reasonable by all interested parties and economic feasibility under these assumptions is clear one

way or the other.

This type of damage curve shifting can also be used with a distribution of warning times and the

varying response scenarios. Each response scenario is defined based on an assumed percentage decrease

in stage-damages.

An assumed percentage reduction in stage-damages, whether that percentage varies from flood stage

to flood stage or is constant for the entire stage-damage curve, represents a risk-based expected value of

damage reductions. As pointed out in the Chapter 3 literature review, there is a wide range of responses

people can make to a flood warning Hence, the actual reduction in damages is expected to vary from eventto-event and the identified value is the simplest estimate of an unknown parameter.

21

For example, one could assume a 7% percent reduction at stage = 1, a 6.4% reduction at stage =

2, a 2.1% reduction at stage = 5, a 0% reduction at stage = 12, etc. The only constraint is the

analyst's ability to discern such differences from stage-to-stage.

"2We distinguish the determinate percentage reduction from the uncertain percentage reduction.

When a single value is assumed to be the reduction of damages, we call that a determinate reduction.

This contrasts with the case where damage reductions are expected to fall within some range of values.

This latter case is considered to be uncertain, and it is the subject of subsequent applications.

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APPLICATION TWO (UNCERTAIN STAGE-DAMAGE PERCENTAGE REDUCTION)

This second application improves on the first by admitting that the reduction in stage-damages that

results from a warning is unknown. The second application allows analysts to treat the warning time as an

uncertain value" in the analysis. Warning times were assumed to be uniformly distributed from 1 to 24

hours. Damage reductions for the four responses were as described above, ranging from a low reduction

of 0 to 2% to a high of 4 to 10%. A 2,000-iteration simulation using the flood warning and preparedness

benefit model was run. A different with project condition stage-damage curve was generated for each

iteration (as demonstrated in Figure 17) and used as the basis for calculating EAD with the project. The

results described below are from that simulation.

Expected annual benefits for the flood warning and preparedness system ranged from a low of

$3,800 to a high of $981,000. These results represent extremes in possible outcomes. For example, if very

little warning time is given and people take little action to reduce their damages the benefits could be slight,

as small as $3,800. On the other hand, if there is ample warning time and people take effective action,

benefits could be close to $1 million.

The distribution of benefit estimates obtained from the simulation is shown in Figure 21. The mean

of this distribution, i.e., expected annual benefits of the system, is $445,000. Decision makers are used

to seeing a single-valued estimate of project benefits, rather than a distribution, and the mean value of the

distribution is that single, best estimate of project benefits.

The distribution presented in Figure 21 is somewhat odd in appearance. The mean, a measure of

central tendency, is found in a "slump" in the distribution. The shape of this distribution results from the

identification of response scenarios with asymmetric durations. Three of the four scenarios are four hours

in duration, the fourth one is twelve hours. Thus, we have, in effect, two distributions overlapping; one

from the first three responses, the other from the fourth response. A more symmetric identification of

response scenarios can be expected to yield a distribution of benefits that is more normal in shape.

A $445,000 reduction in EAD represents a 4.4 % reduction from the without project condition EAD

of $10,175,000. The value of the current approach over a straightforward estimate of a 4.4% reduction

in damages, as was done in application one, is that the credibility of the analysis doesn't rise and fall on the

assumption that damages are reduced by 4.4% (i.e., reviewers, in the former case, might ask why you

didn't assume 4.2% or 3.1%?).

23Damage reduction percentages are uncertain for a variety of reasons. One major factor

contributing to this uncertainty is the behavioral response to a warning. How many people will receive

the message? How many will respond? What will they do? And how can we translate all of this to

damage reductions? A 4.4% reduction in damages may be a reasonable assumption but it does not

imply that everyone reduces her damage by 4.4%. Rather, it implies that the average reduction across

all individuals is 4.4%.

70
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This approach is one of the simplest and least costly when stage-damage data exists, since the

analyst can individually specify the warning time distribution and the range in damage-reductions (i.e,,

curve shifting) for each response scenario as a distribution. These ranges of various judgments will

ultimately result in benefit estimates. No one assumption need be critical to the benefit estimate as was the

case in application one, however.

Any analyst can be more confident of being correct if he can say the reduction will be between "this

percent" and "that percent", rather than being required to specify a single reduction as "something point

something". Thus, it is preferable to say damages are assumed to be reduced from, say, 2 to 6% rather

than to say damage reductions are assumed to be 4.4%. By specifying the nature of the distribution of

reductions, the analyst has the opportunity to elevate the likelihood of some values, including 4.4, over

others, if appropriate.

MODELING BEHAVIORAL RESPONSE TO FLOOD WARNING

In application two, the analyst enters a distribution of possible damage reductions for each flood

stage. It was assumed that the distribution of damage reductions was determined by the analyst. In this

section, a method for generating a distribution of damage reductions is offered.

By allowing the analyst to make judgments about specific behavioral responses in the study area,

distributions can be generated. The basis for the analyst's behavioral judgments may be detailed knowledge

of the study area or reported behavioral responses found in the literature as reviewed in Appendix 2.

There is nothing magical about the modeling of behavioral responses and damage reductions offered

in this section. There are many ways to do this; some are complex, others are simple. The response model

described here can be made more or less simple by adding or deleting components of the model. Figure

22 describes the construction of the response model. The assumptions described in the following

paragraphs are used in Application Three.

The alternative method in this procedures manual for determining percentage reductions to damages

at different stages begins with the assumption that certain parts of the warning process are essential to

damage reductions. The analyst has considerable leeway in determining what elements of the process are

important in the study. The literature reviewed in Chapter 3 provides insight into what are likely to be

important elements of any warning system, as well as the variables that are important in those elements.

We begin with the obvious. In order for people to take action as a result of a warning and response

system, a warning must be given. There is always some possibility this won't happen. The probability of

"no warning" can be discounted by the analyst whenever appropriate. For this example, it is assumed there

is at least a 90% chance, most likely a 99% chance, and at most a 100% chance that a warning will be given

for any event. Lacking specific knowledge of the distribution of these different likelihoods, a triangular

distribution with a minimum of 0.9, a most likely value of 0.99 and a maximum value of 1.00 is assumed.

72
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The probabilities of a warning being issued depend on the type of system. Visual inspection

systems that depend on human observation and reporting are more likely to fail to result in a warning than

other systems. As redundancy is added to a system the probability of no warning decreases.

The literature suggests that not everyone hears a warning. The elderly are one group known to hear

warnings less frequently than other groups, all other things equal. The analyst can adjust the proportion

of people who hear a warning (or equivalently, adjust the probability that any one person will hear the

warning) up or down depending on the characteristics of the population served by the system that affect its

likelihood of hearing warnings. The literature is one source of general information on this topic.

We have assumed that no less than 80% and no more than 97% hear the warning, with 90% of the

people most likely hearing a warning.24 Once again, these three parameters were used to describe a

triangular distribution.

It is worth noting that the proportion of people hearing a warning could be explicitly expressed as

a function of as many other variables as the analyst chooses. Thus, the probability of hearing a warning

could be conditioned on the age, race, sex, income, location, occupations, etc. of a community. Such

modifications can be accomplished in complex ways using precise mathematical functions or, more simply,

these functions can be streamlined by assumptions.

Confirming the validity of the message appears to be as close to an example of universal behavior

as can be found in the literature. It is assumed that taking effective action depends on a person's ability to

confirm the warning message. To this end, it is assumed that between 80 and 95 % of all people who hear

the warning are able to confirm it. It is assumed any percentage in this range is as likely as any other.

Thus the probability of confirming a message is uniformly distributed over this range. 25

The percentage of people who are able to confirm a message will depend on the geographic

dispersion of people and the provisions the warning system makes for opportunities for people to confirm

a message. For example, a flood warning and preparedness alternative that concentrates on the hardware,

to the exclusion of the behavioral aspects of the flood warning and preparedness process, will likely have

smaller percentages of people hearing and confirming messages. A planning process that pays attention to

behavioral details can be expected to produce more benefits. If planners provide procedures for

24This is equivalent to saying there is a probability of at least 0.8 that any one person will hear the

warning and at most a probability of 0.97 that any individual hears the warning. Our best estimate of

the probability of a person hearing the warning is 0.9.

25

The assumptions made in the hypothetical examples were usually chosen to facilitate discussion of

the model. A uniform distribution is chosen here, more to illustrate that one need not always use a

triangular distribution. The parameters used in the distributions are arbitrarily chosen to assure a

variation in the range of results rather than to represent realistic values. We have purposely avoided the

use of "reasonable" and "representative" default values to avoid prejudicing the analysts judgment of

these values.

74
Framework for Estimated National

Economic Development Benefits

disseminating messages, proven effective message formats, opportunities for confirmation through multiple

media, etc., the percent of people hearing and confirming the message will be higher.

Once a warning is confirmed, not everyone will take action. It is assumed that from 90 to 100%

of all people will take action. A uniform distribution was assumed. Of those who take action, not all

actions taken will be effective in reducing damages. Some people will not know what to do to prepare for

the impending flood; others will pour their energy into gathering their families and preparing to evacuate.

Of those who take action, it is assumed that 80 to 97% will take effective action, with 94% the most likely

proportion that will take effective ?ction. A triangular distribution was used to describe this response.

The components of the model to this point serve to identify the expected portion of the population

at risk that takes effective action. Assuming the events described above to be independent of each other,

an estimate of the expected probability of an individual taking effective action can be obtained as the

product of all the events:

(1) P(warning)*P(Heard)*P(Confirmed)*P(Action)*P(Effective)= P(Any individual takes effective action)

Using the minimum probability presented for each event, one can obtain the following probability that any

individual takes effective action to reduce damages as a result of a flood warning and preparedness system:

(2) 0.9 * 0.8 * 0.8 * 0.9 * 0.8 = 0.41

This means there is a 0.41 chance that any individual will take effective action. Alternatively, it can be

interpreted to mean 41% of the community takes effective action.

The analyst has two basic choices in modeling the effects of such behavioral responses on system

benefits. First, he can make adjustments to individual damage curves or classes of individual depth-percent

damage curves (e.g., structures with two stories and no basement) or second, he can make adjustments to

the entire stage-damage curve or types of stage-damage curves (e.g., residential, commercial, etc.). For

now, adjustments will continue to be made to the total stage-damage curve. Adjustments to individual

damage curves will be demonstrated in application five. Thus, the probabilities assumed for the

components of the model reflect average behavior for all floodplain occupants rather than the probability

of any one individual taking effective action.

The next critical step in developing this refinement of the model is to estimate the damage reduction

percentage that results from the effective action taken. The average damage reduction of all people who

take effective action is uncertain but is assumed for this example to have a uniform distribution over a range

of 0.5 to 15%.

Multiplying the proportion of people taking effective action given by equation (1) by the percent

reduction in damages, yields the expected damage reduction, as shown in equation (3).

(3) Expected damage reduction = P(Any individual takes effective action)*(Average damage reduction

percentage)

75
Framework for Estimated National

Economic Development Benefits

For example, if there is a 0.41 chance a person will take effective action and that effective action will

reduce damages by 10% then the expected damage reduction is 4%:

(4) 0.41 * 0.1 = 0.04

The diskette included with this report contains a spreadsheet model that performs this calculation.

The strength of this approach over that described in applications one and two is that it takes more variables

explicitly into account. By recognizing more of the warning process it is possible to be more realistic about

the uncertainty surrounding each of the variables.

Results are no longer as sensitive to the single assumption about the percentage of damage

reductions or to the assumed distribution of damage reduction percentages. It is also possible to calibrate

the model separately for each response scenario considered. It is reasonable to expect that the more

advanced the first warning, the more opportunities there are to update and reenforce the warning message.

This provides more opportunities for the warning to be heard, confirmed and effectively acted upon.

Table 3 presents the results of a series of 1,000-iteration simulations of the damage-reducing

response to a flood warning for the four response scenarios described previously. Distributions of expected

damage reductions obtained from these simulations are shown in Figures 23 through 26. T ( ) indicates a

triangular distribution with minimum, most likely, and maximum parameters given in the parenthesis. U

()indicates a uniform distribution with the minimum and maximum parameters given in parenthesis.

No difference in the probability of warning being given is assumed in each of the scenarios. All

other assumptions reflect the fact that as the amount of available time increases more people are able to

hear, confirm, and act effectively on the warning. The expected damage reductions (EDR), shown in Table

3, average 0.1%, 2.6%, 4.9% and 9.7% for the four scenarios examined.

APPLICATION THREE (UNCERTAIN BEHAVIORAL STAGE-DAMAGE RESPONSE)

The preceding applications were after-the-fact analyses of a flood warning and preparedness system

appropriate for cases where: the data were collected without thought to the special requirements for

analyzing a warning system; where file data are available and the magnitude of the project budget does not

warrant detailed study; or, there is simply not enough time or money to do a more detailed analysis.

Though these circumstances may describe a large number of planning circumstances, they do not describe

them all. There are situations in which some, but not all, data may exist. That data may include a stagedamage curve, a simple structure count, residential damage estimates only, or any number of other

possibilities. In this application, we concentrate on an approach for estimating expected percentage

reductions of damages based on knowledge of relevant behavioral factors. The results of the previous

section are used to define distributions of damage reductions in this application.

Using the mean and standard deviation from the results of the simulations summarized in Table 3

triangular, normal or other distributions of possible expected damage reductions can be described. These

distributions can be used in lieu of the assumed unifoan distributions applied in the previous example.

76
Framework for Estimated National

Economic Development Benefits

Table 3

Response Model Assumptions and Results

Lou tha 1 14 1-12 Ovr 12

Warning given T(.9, .99, 1) T(.9, .99, 1) T(.9, .99, 1) T(.9, .99, 1)

Hear warning T(.5, .75, .9) T(.7, .85, .95) T(.8, .9, .97) T(.8, .95, .97)

Confirm U(.65,.8) U(.75, .9) U(.8, .95) U(.9, .98)

Take action U(.6, .95) U(.8, .98) U(.9, 1) U(.95, 1)

Reduce damage T(.5, .75, .95) T(.7, .85, .95) T(.8, .92, .97) T(.9, .95, .99)

% Reduction U(.0005, .05) U(.005,. 1) U(.005, .15) U(. 15, .5)

Results

Mean EDR 0.72% 2.59% 4.96% 24.63%

Minimum EDR 0.01% 0.23% 0.30% 10.00%

Maximum EDR 2.11% 6.43% 11.19% 43.51%

Standard Deviation 0.45% 1.41% 2.73% 7.80%

77
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Two 2,500-iteration simulations of flood warning and preparedness benefits were run. The first

one assumed triangularly distributed damage reductions using the results from Table 3 to identify

parameters. The second simulation assumed a truncated normal distribution with the mean and standard

deviation above. The distribution is truncated at 0 and 1 (or 0 to 100 in percentages), to force the values

to fall within the range of permissible percentage reductions.

Table 4 presents the results of the two simulations. The first simulation, based on triangular

distributions, yields mean expected annual benefits of $686,000, with a range of about $1.7 million. The

second simulation, based on truncated normal distributions, yields mean expected annual benefits of

$652,000, with a range of over $1.8 million.

TABLE 4

Benefit Estimates

Response Model Simulations

STriangular Truncated Normal

Distribution Distribution

Mean $ 686,000 $ 652,000

Minimum 0 0

Maximum 1,671,000 1,827,000

Standard Deviation 412,000 412,000

The truncated normal distribution mean is about 5 % less than the triangular distribution mean in

this case. The significance of this difference will depend on the circumstances of the study and the costs

of the warning and response system. The assumed distribution can make a difference, however, so the

distribution chosen should be selected based on empirical or theoretical criterion, or a sensitivity analysis

should be conducted as was done here.

APPLICATION FOUR (PARALLEL STAGE-DAMAGE SHIFTING SCENARIO)

A recent IWR report (Jack Faucett Associates 1990:77) indicates that one Corps District has

estimated benefits to flood warning and preparedness systems by assuming that on average, people would

raise the contents of their structures by one foot. By affecting a parallel one-foot downward shift in the

damage curve, benefits can be estimated. Shifting the without project condition damage curve of the first

three applications down one foot yields project benefits of $839,000, substantially higher than the other

methods considered so far. A two-foot shift yields benefits of $1,659,000.

82
Framework for Estimated National

Economic Development Benefits

The model can be modified to account for different average content-raising scenarios. For example,

with less than 4 hours warning people might be assumed to, on average, raise their contents by one foot.

With 4 to 8 hours warning me average could increase to 1.5 feet, etc. Thus, the damage curves in each

response scenario would represent a different parallel shift in the demand curve.'

Allowing for a legitimate role for professional judgment in the estimation of benefits, this approach

suffers a weakness that others do not. As shown in Figures 10 and 14, it is quite likely that the stagedamage curves with and without a project will coincide at greater depths when raising contents is the only

or primary method of response to the warning. Thus, assuming a downward shift throughout the domain

of the damage curve may be conceptually flawed.

SHIFTING INDIVIDUAL DEPTH-PERCENT DAMAGE CURVES

Up until this point, the options explored for shifting the damage curve have focussed on ways to

shift the stage-damage curve. The applications discussed have treated the damage curve as if it was only

content damage. Complications introduced by considering firther disaggregation of the damage curve have

been avoided without weakening the applicability of the model. In practice, the techniques described may

have to be applied separately to commercial, residential or other land uses. Some analysts may prefer to

treat structural, content and other types of damage separately within each land use category. These are

more or less book keeping matters well known to experienced analysts.

There are cases where it would be appropriate to consider the damage curve shifting effects of a

warning system on an individual structure basis. One such case is where it is known at the outset of a study

that flood warning and preparedness systems will play a significant role in the formulation process and there

are sufficient resources to conduct the required analysis. The basic technique is not so different from that

applied to the stage-damage curves in the applications above.

Consider a typical residential property. Figure 27 presents 1973 Flood Insurance Administration

content and structure depth-percent damage curves for a two-story house with no basement. This curve

could be shifted by any of the methods described in applications one through four above. One could reduce

the content damage at every stage by some fixed percentage. Content damage at each stage could be

reduced by a different percentage. Then one could do the same to the structure curve using different

percentages.

The curves could be shifted down a half foot, a foot, two feet. One could use available information

from other studies, the literature, expert opinion and professional judgment to create a distribution of

2Shifting the total stage-damage curve was done here as a matter of convenience. If this technique is

used it would be more appropriate to go into the individual structure data for the flood plain and shift

each structure's damage curve by one foot. This can be done by simply changing the structure's first

floor elevation and/or ground levcl elevation. When the revised stage-damage curve is compiled we

would not expect a parallel shift.

83
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Framework for Estimated National

Economic Development Benefits

percentage damages at each stage.27 Though the options are many, the basic task is to shift the depthpercent damage curve downward to reflect damage reducing behavior that results from additional response

time.

APPLICATION FIVE (DEPTH-PERCENT DAMAGE REDUCTION)

This application follows the same basic method described in the first three applications above. For

each flood stage there is a distribution of damages. The distributions in this application are distributions

of damage percentages rather than of damages or reductions in damages as described in earlier applications.

The method used to develop the distribution of damages is an example of one approach to identifying

damage reductions for individual structure types.

While earlier applications are applied stage-damage curves that were built up from individual

damage relationships, this application begins with those individual relationships. Some Corps Districts have

developed depth-percent damage curves specifically for a study area. One method for developing these

curves involves entering homes and cataloguing the value and location of the contents of the most common

structure types. An estimate of the average total value of all contents can be prepared from this survey.

Using professional judgment, items that would be damaged by floods of varying depths are identified. The

lost value of these contents are accumulated and expressed as a percentage of the total value of contents.

For example, suppose a survey determines a house has $40,000 in contents.2s If two feet of water on the

first floor is judged to cause $10,000 in damage this is 25% damage at +2 feet.

If flood warning benefits are going to be evaluated, the ideal approach would be to gather the

necessary information at the time of the development of the depth-percent damage curve. The typical

survey instrument is a room-by-room listing of the contents of the house. It would be a simple matter to

indicate which items are readily movable, movable with sufficient time, and immovable. Alternatively, it

may be feasible to ask the occupant what items would be moved: "if you had one hour of warning, four

hours of warning, etc." This information could subsequently be used to construct estimates of depth-percent

damage curves under various response time scenarios.

Continuing the example above, if 5 hours of warning time would reduce damages at +2 from

$10,000 to $5,000, content damages at +2 would fall from 25% to 12.5%, thus shifting the damage curve

2TFor example, with 7 feet of water on the first floor FIA estimates 44% loss of the value of the

structure's contents. With a system in place we might estimate the damage as ranging from 40 to 44%

of content value.

28Alternatively, the value of a structure's contents is often expressed as a percentage of the

structure's value. Thus, contents worth $40,000 in a $100,000 house would result in a content-tostructure ratio of 40%. The examples provided can be readily illustrated in this framework, but the

extra step of converting content damages to a percentage of structure value would unnecessarily

complicate the example. Therefore, we treat content damages as a percentage of the total content value

despite the fact that many stage-damage models do not handle contents in this fashion.

85
Framework for Estimated National

Economic Development Benefits

downward. Potential reductions in structure damage could also be investigated at the time of the original

survey and handled in a similar manner.

These adjustments must be made to the individual structure's depth-percent damage curve. For

example, the depth-percent damage curves entered with the SID-EAD model would be different without

and with the project. In Figure 28, the without-project condition content damage curve would be used for

the no warning system scenario, the with-project condition curve reflects the system in place.

Constructing such curves requires some additional thought, however. If everyone in the floodplain

hears the warning and moves everything that is movable, one would observe the maximum possible

reduction of damages. This would result in the maximum shift of the damage curve. Clearly, this will not

happen except under abnormal circumstances. Not everyone will hear the warning and among those who

do, relatively few will minimize their damages. Only some proportion of the population will act and they

will reduce their damages only some proportion of what is possible.

For example, let us assume that at a stage of +2, damages in a single structure could be cut by

50%, from $10,000 to $5,000, as above. Let's assume that 90% of the population hears the warning and

acts. So far 90% of the 50% reduction, or 45%, could be achieved. But we know that not everyone will

realize the maximum reduction of 50%. If, on average, people will take 80% of the actions available to

them to reduce damages? then 80% of 90% of 50% or an estimated 36% damage reduction is obtained.

Hence, damages with a system in place would fall from $10,000 to $6,400, a 36% reduction. The percent

damage would fall from 25% to 16% rather than to the 12.5% in the example above.

Included on the enclosed diskette is a simple model that provides an example of how the above

described adjustment can be made for one given warning/response scenario. The example uses the 1973

FIA curve for a two-story residence with no basement. Table 5 shows the columns included in the model.

Stage is simply the amount of water, in feet, on the first floor of the structure. Structure damage is

expressed as a percentage of the structure's market value. Content damage is expressed as a percentage of

the market value of the structure's contents.

The maximum reduction in content damages is the key parameter for this model. At a stage of +2,

content damages are 17%. If everyone in the floodplain eliminated all of their content damages the

maximum reduction would be 100%. Thus, it is in this column that adjustments, as described above, are

entered. The values entered in the sample program were arbitrarily chosen and have no particular

significance.

The weighted damage curve of column 5 is the output of the model. It is obtained by specifying

some distribution of possible percentages of content damage at each stage. For example, at a stage of +2,

without project condition damages are estimated to be 17%. This is the without project condition damage

'The reasons for this would depend on the community under study. For example, the inability to

realize the fullest possible reduction could be due to age, lack of automobiles, the number of singleparent families, etc.

86
Framework for Estimated National

Economic Development Benefits

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Framework for Estimated National

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Table 5

Percent-Content Damage Estimating Model

NGVD % Structure % Content Max. % Content Weighted %

Damage Damage Damage Reduction Conent Damage

(1) 0 0 0 0.00

0 5 7 20 6.300

1 9 9 25 7.875

2 13 17 30 14.450

3 18 22 30 18.700

4 20 28 30 23.800

5 22 33 30 28.050

6 24 39 25 34.125

7 26 44 25 38.500

8 31 50 20 45.000

9 36 55 20 49.500

10 38 58 20 52 ?0

11 40 65 20 5b

12 42 72 20 64.800

13 44 78 20 70.200

14 46 79 20 71.100

15 47 80 20 72.000

16 48 81 20 72.900

17 49 81 20 72.900

18 50 81 20 72.900

88
Framework for Estimated National

Economic Development Benefits

and is also considered to be the maximum possible percentage of content damage in the distribution,

corresponding to no reductions as a result of the warning. The maximum reduction in damages from

column 4 is used to determine the minimum amount of content damages in the distribution The maximum

reduction in damages at +2 is 30% .3

The distribution used in the sample is a simple uniform distribution. The minimum damage with

the response system is estimated to be 11.9%31, the maximum damage is 17%. Fr simplicity, assume any

value in this range is as likely as any other.32 A simulation using a uniform distribution would be expected

to return a depth-damage curve roughly similar to the weighted curve in Table 5. The simulation results

also reveal information about extreme value possibilities and the likelihood of curves equal to or greater

than curves with specified values.

Another model, provided on the enclosed diskette, provides an example that uses the basic benefit

model, described above, to estimate warning system benefits for a single structure using a depth-percent

damage curve. Allowing warning time to vary uniformly from 1 to 24 hours, and using the same four

response scenarios in application one, the benefits to a two-story residence with no basement are estimated

to be $37.33 The minimum estimate of benefits was $12, the maximum estimate was $69. In this example,

without project condition expected annual content damage is $301 based on the FIA curve and a $100,000

home. Allowing the reductions from a warning system and the amount of warning time to vary, with

project condition EAD range from $232 to $288 with a mean of $264.

Demonstrating the technique for one structure establishes the feasibility of estimating benefits for

any number of structures by this technique Development of the software to do this was beyond the scope

of this investigation so no community-wide estimate of benefits is prepared under this application. Existing

models used by Corps Districts, particularly the models developed by the Hydrologic Engineering Center

(HEC), could be modified to incorporate methods such as those modeled here should sufficient demand for

such tools arise. The models presented here are inadequate for summing damages across many structures.

3°A maximum damage reduction of 30% means that 5.1% of total content value can be saved (i.e.,

30% of 17%). The 30% estimate reflects the proportion of the population responding and the average

effectiveness of their response.

31

A 30% reduction in damage means that 70% of without-project condition damages is the minimum

with condition damage. 70% of 17% is 11.9%. Hence, damages at +2 will range from 11.9% to 17%.

32Other distributions could be used. For example, a truncated normal distribution with a mean of

14.45 % (the midpoint between 11.9 and 17), a standard deviation of 0.85 % (divide the range by 6, the

number of standard deviations needed to include about 99% of all observations), and a minimum value

of 11.9% and a maximum of 17%.

3The small magnitude of the expected annual values reported here have no particular significance.

The same rating and frequency curves used for earlier applications were used for this application. One

foot below :he first floor corresponds to the initial frequency point for the hydroeconomic model.

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Framework for Estimated National

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The approach for a commercial structure is similar. System benefits to commercial property based

on standardized curves can be estimated in any of the ways indicated above. If commercial damages are

estimated on the basis of interviews, the interviewer should collect information at the time of the interview

that will facilitate the preparation of damage curves for different response scenarios.

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Framework for Estimated National

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CHAPTER 6

FURTHER CONSIDERATIONS

INTRODUCTION

The damage curve shifting framework has been presented, the literature has now been reviewed and

the basic model has presented some alternatives for shifting the damage curve to represent the community's

response to a flood warning and preparedness system. Many questions and issues remain for the analyst.

A number of them are addressed in the following sections.

First, a few shortcuts, adapted from the experience of Corps personnel, that may be helpful in some

cases are presented. The second topic covered is how to analyze a flood warning and preparedness system

when it is only one component of a larger flood damage reduction plan. Third, suggestions are offered for

when to use which of the applications present. The chapter concludes with the identification of some

potential research topics.

SHORTCUTS

Despite the purported flexibility of the model presented here there will always be circumstances.

where a "seat of the pants" estimate is needed. Most experienced analysts have developed their own rules

of thumb for providing these estimates. One of the most common techniques is "the similar areas method."

In the absence of any reliable data for a study area, it is a common practice to find an area as similar

in the most important respects as possible and to use data from it. For example, suppose a warning system

was evaluated for community A, roughly the same size and in the same basin as community B which is to

be evaluated. A rough approximation of benefits for B may be obtained by multiplying the average benefits

per structure of A by the number of structures in B.

For example, the Smallville warning system was estimated to produce $50,000 in expected annual

benefits. There are 1,000 structures covered by the Smallville system. Tinytown, 70 miles downstream

of Smallville, has 500 structures in its floodplain that would be served by a warning system. Using the $50

per structure average from Smallville, the Tinytown warning system benefits are estimated to be about

$25,000 in expected annual benefits, based on the fact that it is a similar area that provides the best available

data.

Per structure average EAD for a basin can provide a rough estimate of the total EAD for a

community, if a structure count is available. With a rough estimate of EAD for the community and known

annual costs of a warning system, one can work backwards to figure out what percentage reduction in EAD

91
Framework for Estimated National

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is needed to make the system feasible. If the reduction seems reasonable34, this may suffice as an indicator

of economic feasibility.

For example, in the Dry Creek basin there are 10,000 structures in the floodplain, according to

District files. The without-project condition EAD for all 10,000 structures is $1,000,000. This works out

to be an EAD of $100 per structure. Tinytown has 500 structures, so its without-project condition total

EAD is estimated to be $50,000.

A simple warning system for Tinytown has annual costs of $2,000. To justify this expenditure one

would need at least a 4% reduction in damages. Is this reasonable?

Numbers like these are most useful for very rough estimates of economic feasibility. In the absence

of numbers even this rough, it may be advisable to use some of the performance indicators mentioned in

the opening of Chapter 4. Favorable comparisons35 with communities where flood warning and

preparedness has been justified or is being successfully used now may suffice in situations where budgets

are so limited as to make most analysis impractical. The use of other indicators may be more useful in

convincing local interests than in satisfying Corps' study requirements unless benefit-cost analysis

requirements are waived or revised for low budget studies of warning systems.

FLOOD WARNING AS A COMPONENT OF A PLAN

In some planning settings, flood warning and preparedness systems may be the only alternative

under consideration. When this is the case, benefit estimation can be conducted as outlined in this report.

In other circumstances, a warning system may be one ccnponent of a larger flood damage reduction plan.

Indeed, it may be an insignificant part of the larger plan. No matter which setting the analyst considers,

the basic approach is the same.

In Chapter 2, the basic hydroeconomic model was reviewed. Any flood damage reduction

alternative will affect either the damage, rating or frequency functions. Warning systems generally can be

shown to shift the damage curve.

It is a simple matter to include shifts in the damage curve attributable to a warning system with a

shift in the rating curve attributable to a channel improvement. In fact, it is not uncommon to have more

340f course the definition of reasonable is the key here. As always, it goes without a precise

definition. What is reasonable will be defined as a result of the normal negotiation process among the

many parties to the study, its review and implementation.

3For example, it may be compelling evidence to identify a town with a system that is known to have

prevented substantial damages and injury and to point out that it serves only half the number of people

the proposed system would. Other indicators can be used similarly to try to develop favorable

comparisons.

92
Framework for Estimated National

Economic Development Benefits

than one of the three basic relationships affected by a plan. The effects of a warning system can be easily

and straightforwardly incorporated into the calculation of with project condition EAD.

WHAT LEVEL OF DETAIL IS APPROPRIATE?

Common sense rules in determining the appropriate level of detail for a flood warning and

preparedness benefit evaluation. Four levels of detail that may be appropriate under varying circumstances

are distinguished. Proceeding from the least to the most detailed these levels are: 1) Unit values; 2) Shifting

existing damages curves; 3) Estimating response-dependent shifts of the damage curve; and, 4) Modification

of individual damage curves.

The relevant circumstances include reporting requirements of the study and budget constraints. If

a benefit-cost analysis is not required, other performance indicators can be used to evaluate a system. In

most cases, however, a benefit-cost analysis will be required and that is our working assumption. The most

common constraint will be that imposed by a limited budget.

Figure 29 shows the correlation between the available budget and the preferred level of detail.

Predictably, the more available budget the more detail should be vested in the analysis.

FUTURE RESEARCH

Six different research topics are suggested below. They are italicized to make them easier to

identify.

Individual districts may benefit by culling the information from their own files to develop a unit

value data base. Estimating warning benefits or estimating without-project condition EAD on a per

structure or per acre basis for different river basins could facilitate most general level screening evaluations

within a district. Research is needed to prepare a compilation and summary of existing warning system

benefit estimates on per structure and per acre bases. Compiling these values for all systems that have been

evaluated will help Corps' planners and analysts determine reasonable parameters and unit value estimates

in their planning activities.

Methods for developing detailed individual structure damage curves are well established. No

further research is required in this area. The only real constraint on this level of analysis is budget. There

must be funds available to finance the considerable data collection tasks associated with this approach.

Research is needed for those levels of detail between the most general and the most specific.

Analysts need guidance in making reasonable judgments about the behavioral responses of people to

warning systems. Research is needed to prepare a comprehensive review of the literature related to

behavioral response, summarizing the quantitative estimates of these responses in a concise way for use by

Corps' planners and analysts. The literature reviewed here only begins the process.

93
Framework for Estimated National

Economic Development Benefits

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Primary research is needed to determine in more precise terms what actions people take between

the time they receive a warning and the time they evacuate. This information is needed to help determine

the nature of the shift in the damage curve.

Research is needed to develop analytical tools that can be made available to all Corps' personnel.

For example, the HEC-developed SID-EAD program could be modified along the lines of the model

presented here. The models presented here are regarded as suggestions to be used, adapted and improved

by Corps' analysts. At present, these models are available only to those with the requisite hardware and

software. This situation must be corrected.

Research is needed to extract the maximum utility from data collected in recent WR research efforts

for use in evaluating flood warning and preparedness plans. For example, content surveys of homes in

Wilkes-Barre, Pennsylvania may provide useful information in determining the types of contents that could

be potentially removed from a home or moved to higher elevations during a flood alert. The value of these

contents could be calculated as a percent of total content value and the results used to assist planners in

evaluating systems at less detailed levels of planning.

Research is needed to establish a method for explicitly planning for the behavioral response of the

public in the development of a flood warning and preparedness plan. The literature shows time and again

that the benefits of a system depend on what the people do. Most flood warning and preparedness plans

focus on the development of floodplain maps, forecasting hardware and software, communications

technology, etc. Planning too often stops at the point where a reasonably reliable forecast of the timing and

crest of a flood can be provided.

What is done with that forecast is rarely the focus of planning efforts, yet the true value of a system

lies in what is done with the forecast. It is evident that local authorities must be actively and intimately

involved in the preparation of this part of the plan. Plans must be expanded to include matters such as

message content, message dissemination by multiple media, providing for confirmation of warning

messages, considering a community's special needs such as the elderly or single mother populations, the

prevalence and attitudes about pets, and whatever else is important to the community.

Case studies would be helpful in demonstrating the feasibility of changing the way flood warning

and preparedness plans are developed. The reason for doing this is twofold. First, the literature suggests

that it will improve the performance of the plan. Second, it may increase the benefits of a system.

95
Framevork for Estimated National

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APPENDIX 1

ESTIMATES OF BEHAVIOR

RESPONSE PARAMETERS

Chapter 3 reviewed some of the warning response literature to identify broad types of behavioral

response. In Chapter 5, a simple model for use in modeling response behavior was introduced. In this

appendix, the literature is revisited to obtain some quantitative estimates of behavior. These estimates help

provide a range for some of the behavior responses when no better information is available for a site. This

report does not provide a comprehensive review of the literature and the reader is encouraged to further

research the literature for behavioral responses of interest to him.

Bearing in mind the ultimate goal is to shift the stage-damage curve in a manner that represents the

community's response to warnings for a range of floods, a common starting point for many warning

response studies is to consider how many people hear the warning. Some time is spent discussing the first

results presented and how they might be used. Subsequent summaries of the literature are offered without

the discussion and examples because the general approach to using the literature will be identical.

Leik, Carter, and Clark (1981) have provided one of the few quantitative descriptions of preevacuation behavior available from the literature. The authors studied three hurricane sites (Miami, Mobile,

and New Orleans), eight flood sites (Atlanta, Boise, Wheeling, Sedona, Rochester, Clarksburg, Palo Duro,

and New Orleans), and four tornado sites (Tupelo, Tulsa, Council Bluffs, and Minneapolis-St. Paul). The

percentages of respondents who were aware of the official warning before the event by community were

for hurricanes 97.4, 97.8, and 92.5 percent; for floods 80.5, 41.5, 72.7, 63.4, 88.8, 38.4, 22.7, and 15.0

percent; and, for tornados 38.4, 71.3, 90.8, and 81.0 percent.

The implications of these results for analysts could be many. First, it appears that it is reasonable

to assume a higher percentage hear warnings for hurricanes, all other things equal. The number hearing the flood warning is quite variable, however. To use these results in a reasonable manner it makes sense

to go back to the original documents to determine the characteristics of the high response and low response

communities. Comparing one's study area to those in the literature, it is easier to determine which response

is more likely.

Most post-disaster reports and articles are oriented toward determining what went right and what

went wrong. There are many lessons to be learned from the literature. Planning a flood warning and

preparedness system should include a heavy emphasis on the behavioral aspects of the plan. The literature

provides a rich source of mistakes to avoid and, concomitantly, a rich source of suggestions that will help

ensure a higher response rate, including a larger percentage hearing the warning.

Leik et al's results can be used to provide an example of how the literature can be used to develop

parameter estimates for the benefit analysis. Assume a benefit estimation approach similar to that of

Application Three in Chapter 5. The number of people who hear the warning can be estimated based on

data obtained directly from the community. The number of households that have heard warnings in the past

97
Framework for Estimated National

Economic Development Benefits

are a prime source of such information; residents can be surveyed to determine their likely behavior.3

Systems can be designed to maximize the likelihood that people will hear the warning. Such plans require

some knowledge of the community's formal and informal communication networks (i.e., do they listen to

radio? watch TV? gather at the general store?).

In the absence of better data, the literature can be used to help estimate the response. The ideal

approach would be to conduct a thorough search of the literature relating to the analyst's concerns. The

review presented here should provide a fruitful starting point for most such searches. By reviewing the

source documents, it should be possible to determine which results in the literature are most applicable to

the analyst's study area.

For the sake of an example, let us assume that Leik et al's flood area results are applicable. One

could use the mean of the eight communities, 53.0%. Using this number ignores a lot of information,

specifically the variation that was observed in different communities. One could take the minimum (15.0%)

and the maximum (88.8%) and define a uniform distribution. By adding the mean to the minimum and

maximum a triangular distribution can be defined. Taking the eight communities as a sample, one could

calculate the standard error and use it and the mean to describe a normal distribution. It may be desirable

to truncate the normal distribution at a lower value of 0 and an upper value of 1.0. There are any number

of other distributions that could be used, including cumulative distributions designed by the analyst to

include information from the literature and the community. No matter how the literature is used, a

reasonable effort must be made to ensure some comparability of circumstances or a clear description of the

limitations of the analysis.

The surveys further indicate that the percentages of survey respondents who initially took action

at the hurricane sites were 41.5, 39.9, and 27.1; at the flood sites the percentages were 7.9, 8.8, 13.0,

11.1, 13.6, 16.6, 19.7, and 9.8; and, at the tornado sites 9.4, 21.8, and 4.5% took action. The response

to hurricane warning appears to be more immediate than to floods or tornados. In the absence of sitespecific data, this information from the literature can serve as a starting point for estimating the response

or range of responses from any individual community, bearing in mind that these data represent the initial

response only and do not reflect the total numbers that eventually took actionY Thus, these values

represent minimum numbers of people who took action to minimize damages.

36The discussion of sampling contained in the appendix is as applicable for a sample designed to

determine behavioral responses to floods as it is for conducting a damage survey.

37Suppose we want some kind of idea how many people will take some kind of action in response to

the warning, but have no budget for a site-specific survey of past or likely future behavior. Using data

from the flood sites we see the minimum response observed was 7.9% and the maximum was 19.7%.

The weighted average (using the number of respondents from each community as the weight) is 13.5%.

Planners could look at the list of communities, investigate the circumstances of flooding and

assume their community is like one of those previously studied and use that percentage. Alternatively,

planers could say the actual response will lie somewhere between 7.9 and 19.7% with a most likely

response of 13.5%. These three parameters describe a triangular distribution that can be used in models

such as those described in this report.

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Framework for Estimated National

Economic Development Benefits

The actions that people reported taking do not always lead to reductions in flood damages, however.

A more detailed study of the actions taken in the hurricane sites revealed the most common action was to

turn on the radio or TV. Gathering food and supplies, securing objects and putting gas in the car were the

next most common actions taken. Taping windows, turning utilities off, calling the weather service and

doing nothing were other responses. Damage-reducing actions could fall under the category of "Other"

protective actions taken. In New Orleans, Miami, and Mobile 16.7, 13.2, and 22.8% of the respondents

took other actions not specified.

The work by Leik et al contains a large number of tables summarizing the findings of the authors

in 15 communities. These tables provide some quantitative information on differences in response to

warnings by age, family status, and risk perception.

Ferrel and Krzysztofowicz (1983), summarizing information from prior studies of Victoria, Texas,

Tucson, Arizona, and Shrewsbury, England found that 19, 24, and 43 % of surveyed floodplain occupants

made some partial response to the warning by elevating possessions, moving their cars, sandbagging, etc.

The percentage of people whose initial response to the hazard warning was to take action, ranged

from a low of 4.5 to a high of 41.5 %. These were the people with the most time available to them to take

effective damage-reducing action.

The Illinois Department of Transportation (1985) has reported that 60% to 80% of the people in

a threatened area will take some type of protective action the first time a flood warning is issued. If a flood

occurs, the percent responding will increase to about 90%-95% with the next flood. Hence, experience is

important.

Perry et al (1981) provide numerous summaries of response behavior in four communities during

floods. Table 6 provides an example of the type of information available in this report. Dividing floodplain

occupants into five groups based on their perception of the flood threat ranging from "totally disbelieved"

to "totally believed", the authors looked at three responses: followed normal routine, took protective action,

and evacuated. These results show that 17.3% followed their normal routine, 33.1% took protective action

and 49.6% evacuated.3s Perry et al provide an excellent source of quantitative information on evacuation

rates and other types of behavioral responses.

Parker and Rowsell (1981) have done a great deal of research in the area of quantifying the indirect

benefits of flood damage reduction in general and flood warning and preparedness systems in particular.

Table 7 summarizes some of their research results presenting the indirect costs of flooding as a percentage

of total flooding costs. Though the method has been largely abandoned, it may be cost effective to use a

fixed ratio between direct and indirect flood losses in some low budget studies. Building on the work

summarized in Table 6, Parker, Green and Thompson (1985) present additional ratios. They have also

done work on manufacturing firms that found:

3TIhese figures represent the sum of all normal routine rows, protective action rows, etc. divided by

the total of 538 responses.

99
Framework for Estimated National

Economic Development Benefits

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Table 7

Indirect Damages

As % of Total Damages

LOCATION RETURN PERIOD YEARS PERCENTAGE

Whitstable. UK 12.0 4.70

20.0 6.90

40.0 4.70

80.0 4.80

150.0 4.80

300.0 4.60

1,000.0 4.40

Chesil, UK .5 93.40

5.0 43.20

50.0 48.10

Pulborough. UK 5.0 53.00

12.0 54.00

50.0 40.00

40.0 21.00

TwetrUK2.0 18.00

3.0 13.00

5.0 11.00

10.0 9.00

50.0 8.00

70.0 7.00

75.0 7.00

Gillingham, UK* 4.0 .90

8.0 5.57

12.0 3.09

25.0 2.62

50.0 1.49

100.0 1.06

Swalecliffe., UK 3.0 5.49

5.0 7.32

15.0 6.75

24.0 8.17

37.0 10.87

114.0 12.26

250.0 19.47

Forbes Sire, AUS 20.0 1.17

100.0 3.72

From Parker, Green and Thompson (1985). All others from Parker and Rowsell (1981)

101
Framework for Estimated National

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"The ratio of indirect to direct national losses is 12.7% at 1 meter flood

depths.."

Estimates of indirect flood losses for manufacturing firms have also been estimated in British pounds on

a square meter basis. Using this exchange rate for that period Table 8 converts Parker et al's finding

(1985:16) to US dollars.

Table 8

Flood Losses Per Square Meter

For Manufacturing Firms (1/84)

Floodwater Depth in Meters

Mean Loss Category 0.15 0.30 0.60 1.00

Direct $ 24.88 $ 53.87 $ 91.73 $128.73

Indirect Firm 10.47 14.57 26.61 30.06

Indirect Region 9.02 12.18 21.22 24.

Indirect Nation 7.00 8.55 14.98 16.38

As far as the damage-reducing effects of peoples' response to flood warnings, little has been done.

Pennington-RowselU and Chatterton (1979) wrote what amounts to a handbook that far surpasses any Other

single work on the estimation of inundation reduction benefits. They have identified the likely damagereducing actions taken with different lengths of warning time (p.32). These are not reproduced here. The

contents differ significantly from what might be found in an American home; in part, because of cultural

differences but more importantly because of technological changes since the original data were collected.'

30VCRs, Nintendo, compact disk players, microwave ovens, and personal computers are a few of

the more obvious examples of changes in contents since the late 1970s.

102
Framework for Estimated National

Economic Development Benefits

That information would provide an excellent starting point for anyone trying to develop empirical estimates

of damage reductions. Preceding this information in Pennington-Rowsell and Chatterton's report (pp. 30,

31) is an example of how a depth-percent damage curve could be estimated for either the without or with

project condition.

Pennington-Rowsell and Chatterton (1979:29) report that the value of potential damage saved for

residential properties is:

"..in the order of 3 % of property value per hour of warning up to 4

hours."

Using these as default values results in, say, a 0 to 3 % reduction in damages with one hour of warning; a

0 to 6% reduction with two hours of warning, etc.40

Neal and Parker (1989) conducted surveys of a number of flooded areas to determine the response

to flood warnings. Of those who were warned and subsequently flooded, 50 to 75 % of the people reported

moving property. Of the people without flood experience, 50% feel they would be able to do something,

while 80% of those with experience said they would do something to reduce damages.

Those who indicated they would not move things were asked why. Ten percent said other actions

are more important, 39 % are physically unable, 28 % felt it would not be necessary, 3 % felt it would not

be effective, and 20% gave other reasons. Thus, the percent of people taking action could arguably be

increased in areas where the physically limited are few in number. Likewise, plans that provide the type

of education and training that would lessen the numbers of people thinking action is unnecessary or

ineffective will also have more people taking effective action to reduce damages.

In 1976, Day and Lee estimated benefits for warning systems for a few scenarios. Of potential

interest is the fact that their study provides estimates of reducible damages per structure for residential,

trailer and commercial properties; and reducible damages per acre for the same property types. Thus, in

the absence of any better data the literature provides some per structure and per acre estimates. These

estimates are dated and of limited applicability and are not reproduced here.

Krzysztofowicz and Davis define three values for a flood warning and preparedness system. The

first is actual value (AV); this is what Corps planners would consider the best estimate of benefits for a

forecasting system. The second is optimal value (OV) and it represents the benefits that would accrue if

every individual decision maker (i.e., each resident, business manager, etc.) in the floodplain made an

optimal response to the warning given. The potential value (PV) represents benefits that would result from

perfect forecasts and optimal responses to these perfect forecasts. PV > OV > AV is considered to be

"4°The ranges presented are examples only. It may be reasonable to expect two hours warning to

provide say 3 to 6% reductions. If a model like the response model of Chapter 4 is used, this percent

reduction must be weighted by the number of people who are expected to undertake damage-reducing

activities.

103
Framework for Estimated National

Economic Development Benefits

true by definition. It may be helpful to estimate these different values to aid the decision making process.

The PV and OV provide upper limits on the potential benefits.

The authors go on to define three efficiency concepts. Forecast efficiency (FE), a measure of how

well the forecast system meets the needs of the response system4', is defined as OV/PV. Response

efficiency (RE) is a measure of how well the response system is utilizing the forecasts it obtains and is

defined as AV/OV. Total efficiency (TE), is AV/PV, or FE times RE. These values must be less than

or equal to one and higher values indicate more efficient systems. The efficiency values provide an

alternative to benefit-cost or monetary-based evaluation criteria. Differences in warning times among

warning system alternatives can be evaluated in terms of the changes in system efficiencies.

RECENT IWR RESEARCH

We have indicated a need for additional research to aid analysts in dealing with behavioral responses

to warning systems. It is worth noting that IWR has taken the most significant step forward in this area that

has been taken. Comprehensive surveys of communities in West Virginia, Kentucky, Pennsylvania, and

Texas have been undertaken for a number of research efforts. These surveys may yield a bonanza of data

for use in evaluating warning systems.

Of particular value to planners and analysts were the recent surveys of the West Virginia and

Kentucky communities. The survey of businesses in these communities asked a number of questions that

are of direct interest to evaluating flood warning systems. The percent of people who received a first

warning is available (Question 6 of the OMB-approved survey form), as is a rather detailed description of

the behavioral response to the first warning message (Question 12). Respondents are probed on the receipt

and response to second warnings as well as their reasons for their response. The surveys also probed for

the costs of responding to the flood threat and the potential savings realized from hypothetical warning times

(Questions 58-61).

For example, the Kentucky business survey indicates that 94.6% of the respondents received the

first warning message. About 48.6% elevated valuables, files or furniture as a response to the first

warning. Considerable detail is provided on other response behaviors. Similar questions were asked of

residential floodplain occupants as well.

These data represent a valuable resource to any planners or analysts working in the evaluation of

flood warning and preparedness systems.

4

'The authors work within a flood forecast response process framework that consists of a forecast

system involving the technical aspects of the system and the response system which deals with decision

makers' responses to the warning.

104
Framework for Estimated National

Economic Development Benefits

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Framework for Estimated National

Economic Development Benefits

Ferrell, William R. and Roman Krzysztofowicz (1983) "A Model of Human Response to Flood Warnings

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106
Framework for Estimated National

Economic Development Benefits

Parker, Dennis J. (1969) "Flood Warning Dissemination: The British Experience". Enfield England:

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Economic Development Benefits

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University.

108
REPORT DOCUMENTATION PAGE IwNo. 0/0__I

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I. AGENCY USE ONLY (teave biarl) ,. REPO.7T DATE 3. REPORT TYPE AND DATES COVERED

MARCH 1994 FINAL

4L TITLf -Mo SUUITITL S. pFpjmNG MULMMM

FRAMEWORK FOR ESTIMATING NATIONAL ECONOMICS

DEVELOPMENT BENEFITS & OTHER BENEFICIAL EFFECTS

OF FLOOD WARNING & PREPAREDENSS SYSTEMS

*. AUThOMS)

CHARLES YOE, PH.D.

GREELEY-POLHEMUS GROUP

7. PERFPRIMIG W.ANIZATION NAME($) AND ADORESS(ESJ L. PERFORMING OR"IMIZATION

U.S. ARMY CORPS OF ENGINEERS REPORT NUMPR

WATER RESOURCES SUPPORT CENTER

INSITUTE FOR WATER RESOURCES IWR REPORT 94-R-3

CASEY BUILDING

7701 TELEGRAPH ROAD

ALEXANDRIA, VA 22315-3868

I. SPONSORINIGIMONII'RUWG AGENCY NAMEM(S AND AOORESS(ES) 10. SPONSORIMG ilMITOIiNG

U.S. ARMY CORPS OF ENGINEERS, HEADQUARTERS AGECNCYIMPORT NUMIR

CIVIL WORKS - PLANNING DIVISION

PULASKI BUILDING

20 MASSACHUSETTS AVE., N.W.

WASHINGTON, DC 20314-1000

11. SIIPPLEMINTA'RY NOTES

AVAILABLE FROM NATIONAL TECHNICAL INFORMATION SERVICE, 5285 PORT

ROYAL ROAD, SPRINGFIELD, VA 22161

12a. OISTMlMUTION/IAVAILAUILITY S7ATEMENT 12Lt. DISTMISUTION C.ODE

APPROVED FOR PUBLIC RELEASE/

DISTRIBUTION UNLIMITED

13. ABSTRACT (M&Xfmv,-?0 wOiiJ

THIS REPORT PROVIDES A CONCEPTUAL FRAMEWORK FOR PLANNERS TO

EVALUATE BENNEFITS ACCRUING TO FLOOD WARNING AND PREPAREDNESS

ALTERNATIVES AND TO DEMONSTRATE METHODS SUITABLE FOR ESTIMATING THESE

BENEFITS UNDER A VARIETY OF PLANNING CIRCUMSTANCES. THE REPORT

SUMMARIZES DEMOGRAPHIC FACTORS AND SYSTEM ATTRIBUTES THAT CONTRIBUTE

TO FLOOD WARNING RESPONSE. THE SPECIFIC ACTIONS THAT CAN BE TAKEN FOR

REDUCING FLOOD DAMAGES IN AN EMERGENCY SITUATION ARE DESCRIBED ALONG

WITH A TAXOMONY OF THE RESULTING BENEFITS. THE REPORT INCLUDES

PROCEDURES OF VARYING LEVELS OF DETAIL FOR CONDUCTING RISK-BASED

ECONOMIC ANALYSIS OF FLOOD WARNING AND PREPAREDNESS SYSTEMS.

14. SUBJECT TERMS IS. NUMEIR Of PAGES

FLOOD WARNING AND PREPAREDNESS, FLOOD DAMAGE, COST- 116

BENEFIT ANALYSIS, RISK ANALYSIS, FLOOD WARNING 16.PR1m CODE

RESPONSE

17. SECURITY CLA5SI-CATION 1I. SE*URITY CLASSIFICATION 19. SICURITY CIwASSWICATIOiN 0. LWITATIONOF, ABSTuACT

OF REPORT OF Ti7s PAGE Of AISTRACT

UNCLASSIFIED UNCLASSIFIED UNCLASSIFIED UNLIMITED

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PWri-.d " Ahsi Yd /j4,11 IN-.32