Updating Flood Maps Efficiently Using 

Existing Hydraulic Models, Very-HighAccuracy Elevation Data, and a Geographic 

Information System— A Pilot Study on the 

Nisqually River, Washington 

By Joseph L. Jones, Tana L. Haluska, and David L. Kresch 

U.S. GEOLOGICAL SURVEY


Water-Resources Investigations Report 01-4051


Tacoma, Washington 

2001 
U.S. DEPARTMENT OF THE INTERIOR 

GALE A. NORTON, Secretary 

U.S. GEOLOGICAL SURVEY 

Charles G. Groat, Director 

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

For additional information write to:


District Chief


U.S. Geological Survey


1201 Pacific Avenue – Suite 600


Tacoma, Washington 98402


http://wa.water.usgs.gov 

Copies of this report can be purchased


from:


U.S. Geological Survey


Information Services


Building 810


Box 25286, Federal Center


Denver, CO 80225-0286

CONTENTS 

Abstract ................................................................................................................................................................ 1


Introduction .......................................................................................................................................................... 2


Need for New Maps and Methods .............................................................................................................. 2


Purpose and Scope ...................................................................................................................................... 3


Nisqually River Flood Mapping Pilot .................................................................................................................. 4


Description of the Pilot Study Area ............................................................................................................ 4


Very-High-Accuracy Land-Surface Elevation Data ................................................................................... 6


Digital Elevation Maps ............................................................................................................................... 6


Updating Flood Information................................................................................................................................. 11


Flood Frequency.......................................................................................................................................... 11


Flood Stages................................................................................................................................................ 12


Stage-Discharge Relations from Existing Hydraulic Model Output ................................................. 12


Comparison with Original Hydraulic Model ..................................................................................... 12


Updating Flood Maps........................................................................................................................................... 16


Overview of Data Processing...................................................................................................................... 16


Creating a 3-Dimensional Digital Representation of the Land Surface...................................................... 17


Creating a 3-Dimensional Digital Representation of the Flood Surface..................................................... 17


Creating an Inundation Map........................................................................................................................ 21


Additional Benefits of GIS-Generated Flood Maps.................................................................................... 21


Data Management ................................................................................................................................................ 28


Limitations and Opportunities for Further Study................................................................................................. 28


Summary .............................................................................................................................................................. 29


References Cited .................................................................................................................................................. 30


Contents III 
FIGURES 

Figure 1.	 Map showing location map of the pilot study area on the lower reach of the 


Nisqually River Basin in western Washington .................................................................................. 5


Figure 2.	 Map showing differences in elevation between field survey points tied to elevation 


monuments from the 1970’s flood study and points on the Digital Elevation Model 


derived from Thurston County Geodata Center data......................................................................... 7


Figure 3. Map showing availability of very-high-accuracy elevation data in the 


Puget Sound Region, Washington, 1998 ........................................................................................... 8


Figure 4. Map showing comparison of level of detail in a standard U.S. Geological Survey 10-meter


Digital Elevation Model and one produced from newer 1-foot vertical accuracy elevation data ..... 9


Figure 5. Map showing examples of production artifacts found in standard U.S. Geological Survey 


Digital Elevation Models ................................................................................................................... 10


Figure 6. Graph showing differences between flood elevations estimated by log transform


regression and by the step-backwater model ..................................................................................... 14


Figure 7. Graph showing differences between flood elevations estimated by square-root


transform regression and by the step-backwater model..................................................................... 15


Figure 8. Map showing example of very-high-accuracy elevation data for the 


Nisqually River from Thurston County Geodata Center ................................................................... 18


Figure 9.	 Map showing comparison of land surface Digital Elevation Models generated by the 


TOPOGRID and TIN/TINLATTICE generation methods: (A) shaded relief maps, and 


(B) difference assessments................................................................................................................. 19


Figure 10. Map showing comparison of digital elevation models at 10-meter and 

1-meter horizontal resolutions ...........................................................................................................20


Figure 11. Map showing updated flood inundation map for the 100- and 500-year floods................................ 22


Figure 12. Map showing comparison of the original Flood Insurance Rate Map (FIRM) for the 


100-year flood with updated flood maps made using high-accuracy land elevation 

models and with an aerial photograph of the Nisqually River flood of February 9, 1996 ................ 23


Figure 13. Map showing flood depths for the 100-year flood............................................................................. 25


Figure 14. Map showing flood depths for the 100-year flood, with roads and buildings. .................................. 26


Figure 15. Map showing areas of uncertain flood hazard for the 100-year flood............................................... 27


Contents IV 
TABLES 

Table 1.	 Comparison of flood discharges from the Flood Insurance Study (FEMA, 1982) 

and those updated for the pilot study (1997) for selected recurrence intervals ................................. 11 

Table 2.	 Percentages of flood elevations estimated by logarithmic regression that were within 

0.1, 0.2, and 0.3 foot of elevations calculated with the step-backwater model, for the 

four recurrence intervals .................................................................................................................... 13 

Table 3.	 Statistical comparison of differences, in feet, between flood elevations estimated by 

logarithmic regression and calculated from the step-backwater model, for the four 

recurrence intervals............................................................................................................................ 13 

Table 4.	 Percentages of estimated 500-year flood elevations within 0.1, 0.2, and 0.3 foot of 

elevations calculated from the step-backwater model ....................................................................... 13 

LIST OF ACRONYMS 

GIS Geographic Information System


FIS Flood Insurance Study


NFIP National Flood Insurance Program


FIRM Flood Insurance Rate Map


FHBM Flood Hazard Boundary Map


FEMA Federal Emergency Management Agency


LOMR Letter of Map Revision


LOMA Letter of Map Amendment


LIDAR LIght Detection And Ranging


NFIRA National Flood Insurance Reform Act


TCGC Thurston County Geodata Center


RMSE Root Mean Squared Error


USGS U.S. Geological Survey


DEM Digital Elevation Model


TEC Technical Evaluation Contractor


ESRI Environmental Systems Research Institute


TIN Triangular Irregular Network


TOPOGRID (an Arc/INFO command)


Arc/INFO (a GIS)


TINLATTICE (an Arc/INFO command)


Contents V 
Updating Flood Maps Efficiently Using Existing 

Hydraulic Models, Very-High-Accuracy Elevation 

Data, and a Geographic Information System— 

A Pilot Study on the Nisqually River, Washington 

By Joseph L. Jones, Tana L. Haluska, and David L. Kresch 

ABSTRACT 

A method of updating flood inundation 

maps at a fraction of the expense of using 

traditional methods was piloted in Washington 

State as part of the U.S. Geological Survey Urban 

Geologic and Hydrologic Hazards Initiative. 

Large savings in expense may be achieved by 

building upon previous Flood Insurance Studies 

and automating the process of flood delineation 

with a Geographic Information System (GIS); 

increases in accuracy and detail result from the 

use of very-high-accuracy elevation data and 

automated delineation; and the resulting digital 

data sets contain valuable ancillary information 

such as flood depth, as well as greatly facilitating 

map storage and utility. The method consists of 

creating stage-discharge relations from the 

archived output of the existing hydraulic model, 

using these relations to create updated flood 

stages for recalculated flood discharges, and using 

a GIS to automate the map generation process. 

Many of the effective flood maps were 

created in the late 1970’s and early 1980’s, and 

suffer from a number of well recognized 

deficiencies such as out-of-date or inaccurate 

estimates of discharges for selected recurrence 

intervals, changes in basin characteristics, and 

relatively low quality elevation data used for flood 

delineation. FEMA estimates that 45 percent of 

effective maps are over 10 years old (FEMA, 

1997). Consequently, Congress has mandated the 

updating and periodic review of existing maps, 

which have cost the Nation almost 3 billion 

(1997) dollars. The need to update maps and the 

cost of doing so were the primary motivations for 

piloting a more cost-effective and efficient 

updating method. New technologies such as 

Geographic Information Systems and LIDAR 

(Light Detection and Ranging) elevation mapping 

are key to improving the efficiency of flood map 

updating, but they also improve the accuracy, 

detail, and usefulness of the resulting digital flood 

maps. GISs produce digital maps without manual 

estimation of inundated areas between cross 

sections, and can generate working maps across a 

broad range of scales, for any selected area, and 

overlayed with easily updated cultural features. 

Local governments are aggressively collecting 

very-high-accuracy elevation data for numerous 

reasons; this not only lowers the cost and 

increases accuracy of flood maps, but also 

inherently boosts the level of community 

involvement in the mapping process. These 

elevation data are also ideal for hydraulic 

modeling, should an existing model be judged 

inadequate. 

Abstract 1 
INTRODUCTION 

Flood inundation maps for the Nation are 

produced primarily for Flood Insurance Studies (FIS) 

as part of the National Flood Insurance Program 

(NFIP). Many of these maps—Flood Insurance Rate 

Maps (FIRMs), and Flood Hazard Boundary Maps 

(FHBM)—were produced in the late 1970’s and early 

1980’s, prior to the advent of digital mapping technologies and the very-high-accuracy elevation data that 

are now available, and they have not been updated. 

Also, changes in flood frequency estimates create the 

need to assess the applicability of existing flood maps. 

Flood frequency estimates may change for a variety of 

reasons, including changes in land use or other 

watershed characteristics, development of new regional 

regression methods, or simply an increase in the 

number of years of peak flow record available for the 

calculations. 

The National Flood Insurance Reform Act 

(NFIRA) of 1994 (Public Law 103-325) addresses the 

need to update maps using modern mapping technologies and updated flood frequency statistics. The 

Federal Emergency Management Agency (FEMA), 

which administers the NFIP, estimates that between 

18,000 and 30,000 FIRM maps need updates, and 

estimates costs of between $800 and $1,000 million for 

updating or conducting new detailed studies for 30,000 

to 50,000 map panels (Federal Emergency 

Management Agency, 1997; 1998; and 2000). 

The use of modern very-high-accuracy elevation 

data that are being developed in many areas can 

produce the most accurate maps and minimize the need 

for subsequent adjustments such as Letters of Map 

Revision (LOMRs) and Letters of Map Amendment 

(LOMAs). In places where existing hydraulic models 

from previous FISs still apply, their use, as appropriate, 

can minimize the cost of map revisions. 

In 1997, the U.S. Geological Survey (USGS) 

conducted a pilot study on a 10-mi (mile) stretch of the 

Nisqually River in the Puget Sound Lowlands of 

western Washington State to evaluate a new and more 

time- and cost-effective method of updating flood 

inundation maps. The pilot study was conducted under 

the auspices of the USGS Urban Geologic and 

Hydrologic Hazards Initiative, also referred to as the 

Seattle Area Natural Hazards Project. The initiative is 

being conducted by the Geologic, Water Resources, 

and National Mapping Divisions of the U.S. Geological 

Survey, and addresses natural hazards in five counties 

in the Seattle area: Snohomish, King, Pierce, Thurston, 

and Kitsap. 

Need for New Maps and Methods 

FEMA and others in the flood-plain management 

community acknowledge the need for new maps: 

•	 Existing paper maps are difficult to use and 

archive and are costly to publish; 

•	 The hydrologic analyses sometimes need updating 

because of available improved flood frequency 

information or changes in a watershed that affect 

its hydrologic characteristics, or both; 

•	 Many original maps were made with elevation 

data of insufficient accuracy (many maps were 

based on 1:24,000-scale topographic maps 

accurate to only around 10 feet). 

Each of 15 million mortgages and all building 

permits approved annually require the inspection of the 

appropriate flood map. Existing paper maps are large 

scale (typically 1:12,000 and as large as 1:4,800) and 

are so numerous that they are difficult to store and 

distribute. Many maps also lack up-to-date features 

such as buildings, roads, and other landmarks, so that 

once the appropriate map is located and acquired, a 

specific property or location on it can be difficult to 

find with confidence. 

In some areas the original flood flow statistics 

were based on limited peak-flow data (either in the 

basin under study, or a nearby basin serving as the basis 

for regional flow estimation). Since then, 20 years or 

more of additional peak-flow data have been collected, 

allowing the calculation of more accurate flood 

frequency estimates. Especially significant to flow 

statistics are any exceptionally large peak flows that 

have occurred in the intervening period. 

Flood plains are characteristically of low relief, 

and vertical inaccuracies in a land-surface elevation 

map used for inundation mapping can result in 

relatively large inaccuracies in mapping an area of 

flood inundation. At the time many of the original FISs 

were conducted, the best available topographic map, 

which was used to delineate the 100-year flood 

inundation, was a USGS 1:24,000-scale quadrangle 

2 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
map. These maps are considered accurate to within 

half a contour interval (typically 20 feet). For some 

areas, more accurate maps were available, such as a 

USGS quadrangle with 10-foot contours (which were 

sometimes prepared for low relief areas) or a 5-foot 

contour map prepared for a FIS. Elevation data 

accurate to 1 ft (foot) would greatly improve the 

accuracy of flood maps and are necessary to credibly 

map differences in 100- and 500-year floods that differ 

in elevation by less than 2 ft. The general availability 

of very-high-accuracy (1 ft or better vertical accuracy) 

digital elevation data, comparable in price to 

traditional photogrammetrically derived elevation 

models, is a recent development. Traditional 

methods, such as low-altitude aerial photography with 

photogrammetric interpretation, are capable of 

providing 1-foot vertical accuracy at reasonable costs 

(on the order of $1,000 per square mile); however they 

are time consuming to process, and in difficult terrain, 

such as one with dense vegetation, the vertical 

accuracy may not reach 1 ft, and the cost would 

increase. New data collection methods, such as Light 

Detection and Ranging (LIDAR), offer increased 

accuracy and lower costs, and if appropriately 

gathered and processed, can effectively remove 

structures and most vegetation from the elevation 

model. (LIDAR— analogous to RADAR—combines 

laser ranging, global positioning systems and inertial 

navigation from an airborne platform to gather 

tremendously dense and accurate elevation data very 

quickly.) The data are gathered in digital form and are 

thus well suited to import into a GIS, whereas 

photogrammetric products are traditionally (though 

not necessarily) processed into contour maps, which 

by nature are more difficult to make into representative 

digital models. 

FEMA also recognizes the need for a more 

efficient mapping method. Between 1968, when the 

flood mapping program began, and 1997, $2.7 billion 

(1997 dollars) had been spent on preparing, 

maintaining, and updating flood maps. That alone is 

strong incentive to seek less costly mapping 

procedures. In addition, the NFIRA also requires a 

review of the adequacy of existing maps every 5 years, 

which can require costly map updates. Currently, map 

updates take nearly 5 years to complete (FEMA, 

1997), and a FEMA objective is to streamline the 

process by implementing more efficient analyses and 

mapping, increasing coordination with communities, 

and conducting reviews concurrently with the analyses 

(FEMA, 1997). 

The method developed in the pilot study has the 

potential to address two of these objectives directly. 

First, it greatly reduces the time required for analysis 

and mapping. Time-consuming hydraulic analyses are 

not repeated where the original model remains valid. 

The actual inundation areas of original flood maps 

were manually determined by visually interpolating on 

a paper map between cross sections (where specific 

flood water elevations were determined by hydraulic 

modeling); using a GIS allows the inundation areas 

associated with given flood elevations at and between 

cross sections to be determined more efficiently, and 

yields detailed depth-of-flood information across the 

entire flood plain. Second, it increases community 

involvement by promoting the use of locally produced 

very-high-accuracy digital elevation data for creating 

land-surface elevation models. FEMA’s objective of 

conducting reviews concurrently with analyses also 

fits in well with the approach presented here, in that 

the decision to reuse an existing hydraulic model 

should be made with the concurrence of FEMA’s 

technical staff. 

Purpose and Scope 

This report presents a time- and cost-efficient 

method of updating flood inundation maps using 

existing hydraulic models, very-high-accuracy 

elevation data, and GIS mapping technology. The 

method assumes that information from existing 

hydraulic models can be used to derive new watersurface elevation data. Descriptions and details of the 

development and application of the method include 

updating flood frequency statistics; using GIS for 

flood elevation modeling; accuracy requirements, 

availability, and cost of elevation data; detailing of 

quality-assurance steps for flood surface and land 

surface generation; generating flood inundation maps 

with a GIS; comparing results with the aerial 

photographs of an actual flood on the Nisqually River; 

and assessing the advantages of using GIS and this 

procedure. 

Introduction 3 
NISQUALLY RIVER FLOOD MAPPING 

PILOT 

The flood mapping method developed in the 

pilot study includes these steps: 

•	 Obtain peak-flow data representative of current 

conditions; 

•	 Calculate updated discharges for the desired flood 

frequencies; 

•	 Develop stage-discharge relations for each cross 

section using the output from the original 

hydraulic model; 

•	 Use the stage-discharge relations to update the 

flood stage(s); 

•	 Create digital representations of the elevations of 

the flood and land surfaces; and 

•	 Subtract the land elevation from the flood 

elevation: the resulting data set represents the 

depth of inundation (negative values indicate no 

inundation). 

The approach was based on traditional flood 

mapping procedures used since the inception of the 

flood mapping program. Steps in the traditional 

procedures were evaluated with regard to cost, time 

required, and value added. The conclusion was that of 

all of the steps used traditionally, constructing, 

verifying, and applying the hydraulic model appeared 

to require the most time and effort. Consequently, if the 

results of a previous study were available, significant 

time and cost savings could be realized in situations 

where it is reasonable to use the results of a previous 

hydraulic model to estimate flood stages for updated 

flood flows. (It is not appropriate to use existing 

hydraulic models in all cases, and additional study is 

needed to develop generally applicable guidelines 

about when their use is most appropriate.) The new 

approach differs from the traditional approach in two 

other ways: the use of very-high-accuracy elevation 

data and the application of modern Geographic 

Information Systems (GISs) to produce inundation 

maps and valuable ancillary flood maps, such as depthof-flood maps. 

Description of the Pilot Study Area 

The area selected for the pilot study is about 10 

miles of the lower reach of the Nisqually River in 

western Washington (fig. 1). The pilot study area was 

selected because of local concern about inaccuracies in 

the existing flood maps and the availability of highaccuracy elevation data, and aerial photography of an 

approximate 100-year flood, and the original hydraulic 

model code and input data sets. 

The Nisqually River originates in the foothills of 

the Cascade Range in western Washington, empties 

into Puget Sound, and drains more than 700 mi2 

(517 mi2 at the USGS gage at McKenna, about 

22 miles upstream from the mouth). At the upper end 

of the study reach, adjacent to Fort Lewis Military 

Reservation, the flood plain is less than a mile wide and 

is incised into 91st Division Prairie, a broad (about 

3-miles wide) flat, geologically young (less than 

20,000 years old) unconsolidated glacial outwash plain. 

The sides of the flood plain are formed by 7th Infantry 

Bluff and 38th Infantry Bluff and are about 150 feet 

high. The Nisqually River, since the end of the most 

recent glacial advance, has incised the valley and flood 

plain. The streambed (Holocene alluvium) is reworked 

unconsolidated glacial deposits and sediments from the 

Cascade Range. The lower reach occupies the broad 

glacial outwash plain; because the volumes of glacial 

meltwater that created the valley were much larger than 

current streamflow, the river is underfit in the 

approximately 2-mile-wide valley. 

The U.S. Geological Survey conducted the 

original FIS for the study area during the late 1970’s. 

The flood frequency discharges for the 10-, 50-, 100-, 

and 500-year recurrence intervals were computed using 

a combination of methods. The hydraulic model used 

for that study was J635 (Shearman, 1977), a steadystate one-dimensional step-backwater computer 

program developed by the USGS. Cross-sectional 

elevation data were acquired from photogrammetric 

interpretation of 1:9,600-scale aerial photography 

supplemented by field surveys of channel cross 

sections and bridge openings. Original maps and Mylar 

transparencies were available for the pilot study as 

sources of cross-section location information. The 

inundation areas were manually interpolated between 

cross sections on 4- and 5-foot contour maps (1:4,800 

and 1:9,600 scale). 

4 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Figure 1. Location map of the pilot study area on the lower reach of the Nisqually River Basin in 

western Washington. 

Nisqually River Flood Mapping Pilot 5

Very-High-Accuracy Land-Surface 

Elevation Data 

Perhaps the most significant aspect of the pilot 

approach is the use of digital elevation data with very 

high accuracy (better than 1-foot vertical accuracy) 

and high resolution (around 3 feet horizontal, in order 

to preserve vertical accuracy). In the pilot study, these 

data were made available by the Thurston County 

Geodata Center (TCGC) in the form of digital 2-foot 

contours produced from low-altitude aerial 

photography with automated photogrammetric 

interpretation. To ensure consistency with the 

elevation data used in the original study, the vertical 

datum was verified with the TCGC. The data were also 

verified by field surveys that were tied vertically to 

elevation monuments used in the original 1970’s field 

surveys. The field verification consisted of surveying 

310 elevation points in the vicinity of cross sections 

used in the original study. The field surveys could not 

precisely locate the survey points horizontally, making 

the comparison somewhat subjective; however, the 

comparison appeared to verify the accuracy of the data 

as well as the datums reported by the contractor who 

executed the elevation project for TCGC. The 

differences between the TCGC data and the field 

survey points are shown in figure 2. The median 

difference is 0.39 ft, the mean is 0.87 ft, the standard 

deviation is 3.5 ft, and the root mean squared error 

(RMSE) is 4.0 ft. The standard deviation and RMSE 

are large because there were differences larger than 3 

ft. The nominal accuracy of the TCGC data is 1 ft, or 

one-half contour interval, based on USGS National 

Mapping Division criteria, which state that the RMSE 

of verifiable elevations be within one-half contour 

interval and that no errors exceed one contour interval. 

However the RMSE of the differences (4.0 ft) exceeds 

the expected value of 1 ft, and a number of differences 

are considerably larger than the contour interval of 2 

ft. Inspection of figure 2, however, reveals that the 

survey points that are more than a foot different from 

the TCGC data are close to a steeply sloped surface 

such as the river channel or oxbow, a valley wall, or a 

transportation embankment. Visual inspection also 

indicates that the elevation errors of survey points that 

are not located near a sloped surface are consistently 

within 1 ft of the Digital Elevation Model (DEM) 

derived from TCGC data. Thus, the apparent cause of 

the large RMSE is poor relocation of the original 

surveyed elevation points, and so, at least qualitatively, 

the accuracy and the consistency of TCGC data with 

the original elevation data are verified. 

A survey of the local governments in the Puget 

Sound region revealed that very-high-accuracy 

elevation data are being aggressively acquired in the 

region (fig. 3). The data are highly sought by local 

governments for a host of reasons other than their 

usefulness in mapping flood inundation, such as 

stormwater runoff analysis and design; road and utility 

mapping; facility planning and construction (for 

wastewater, for example); watershed planning; 

evaluation of building and development permits; 

identifying buildable or critical areas; and for public 

information (P. Drury, Kitsap County Department of 

Public Works, written commun., 1998). In the Puget 

Sound region, these data were typically acquired at a 

cost of about $1,000 per square mile. New data 

collection methods, such as LIDAR, promise to 

increase the accuracy and reduce the cost of highaccuracy elevation data in the near future. Figure 4 

illustrates the level of detail available in the highaccuracy TCGC data as compared to USGS 10-meter 

level II DEMs. 

Digital Elevation Maps 

The most common DEM currently available is 

the standard USGS level II 30 m (or, in our case, 10 m) 

gridded DEM. However, because of the importance of 

the high-accuracy data to the study method described 

here, these were deemed unsuitable because of low 

vertical accuracy, low horizontal resolution, and 

production artifacts inherent in them. The level of 

accuracy is especially relevant to this and other 

methods that propose the use of DEMs as a means of 

identifying inundation areas. Standard USGS DEMs 

are derived from the elevation contours on USGS 

1:24,000-scale topographic maps and have a vertical 

accuracy of half a contour interval (typically 10 feet, 5 

feet in some low-relief areas). This vertical accuracy is 

not sufficient for delineating inundations for flood 

stages that may differ by as little as 1 ft. For the 

Nisqually River, the average difference in flood 

elevation for the 100- and 500-year floods (from the 

original FIS) is 2.2 feet. 

6 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Figure 2. Differences in elevation between field survey points tied to elevation monuments from the 1970’s flood study 

and points on the Digital Elevation Model derived from Thurston County Geodata Center data. 

Nisqually River Flood Mapping Pilot 7

Figure 3. Availability of very-high-accuracy elevation data in the Puget Sound Region, Washington, 1998. 

8 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Figure 4. Comparison of level of detail in a standard U.S. Geological Survey 10-meter Digital Elevation Model and one 

produced from newer 1-foot vertical accuracy elevation data. 

Existing USGS DEMs include artifacts of 

production that can impede flood delineation (fig. 5). 

Artifacts formed during the automated DEM 

generation process include these: 

•	 ‘Starburst’ – an artificially elevated or depressed 

star-shaped area generated because of a 

discontinuity in the input contours; the star may 

have up to 16 radial arms that correspond to the 

16-way interpolation used in DEM creation 

algorithm; and 

•	 ‘Rice Paddy’ or ‘Pocket Terrace’ – a contourbiasing artifact that occurs where contours are 

strongly curved; the DEM creation algorithm 

incorrectly interpolates between one side of the 

curved section and the other, generating a flat area 

that causes a ‘layered’ or ‘stepped’ appearance. 

Other artifacts may be created in subsequent 

editing processes. They include these: 

•	 Outlines around bodies of water – cells in a body 

of water (a lake, for example) are set to an 

arbitrary value as large as one-half contour 

interval below the edge of the water, effectively 

recessing the water body into the terrain; and 

•	 ‘Stream Editing’ – streams represented by double 

lines on the source 1:24,000-scale topographic 

quadrangle may be edited such that the stream 

retains the elevation of the last contour that 

crossed the stream, and jumps in elevation up to 

the next contour at the point they intersect. 

The USGS is currently adopting new DEM 

production methods that will eliminate or minimize 

these artifacts (J. E. List, USGS National Mapping 

Division, Elevation Program, written commun., 1998). 

Nevertheless, the bulk of DEMs currently available 

include production artifacts. 

Nisqually River Flood Mapping Pilot 9 
Figure 5. Examples of production artifacts found in standard U.S. Geological Survey Digital Elevation Models. 

10 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
UPDATING FLOOD INFORMATION 

The information required to prepare a flood map 

are the flood flow to be mapped and the elevation 

associated with that flood. Flood flows are determined 

statistically and are based on flood recurrence intervals 

of 10, 50, 100, and 500 years (a 100-year recurrence 

interval correlates to a 1 percent chance of that flow 

occurring any year). These flood frequency statistics 

are based on historical records of annual peak flows. 

Flood stages, or elevations, for each of the flood flows 

are traditionally determined with numerical computer 

models; in this pilot, flood stages are determined using 

the results of the numerical model developed for the 

existing flood map. 

Flood Frequency 

The original flood frequency discharges used for 

the Nisqually River downstream from its confluence 

with Horn Creek, at river mile 25.8, are in the FEMA 

FIS Reports for Thurston County (1982) and Pierce 

County (1987). They were determined by summing 

the discharges from the outlet of La Grande Dam with 

the discharges from tributaries between the dam and 

the farthest downstream gaging station on the 

Nisqually River (station 12089500, located at river 

mile 21.8). The La Grande Dam outlet discharges were 

furnished by Tacoma City Light, the owner and 

operator of a hydroelectric facility at the dam. The 

inflow discharges from downstream tributaries were 

estimated from regional regression equations 

developed for the two FIS reports. Similarly, 

discharges at the mouth of the Nisqually River were 

estimated by adding the estimated tributary discharges 

for the entire reach of the Nisqually River between the 

dam and the river’s mouth to the discharges from the 

outlet of the dam. These discharges were all within 

about 1 percent of those determined for just the 

upstream reach, which indicates that there is very little 

tributary inflow along the lower 21.8 miles of the river. 

For comparison, flood frequency discharges 

were also determined for the original study by means 

of a flood frequency analysis of the annual peak 

discharges at gaging station 12089500. These 

discharges were calculated using USGS computer 

program J407 (Kirby, 1981), which follows the 

guideline set forth by the Interagency Committee 

Advisory on Water Data (1982) of using a log-Pearson 

Type III distribution of the annual peak-discharge data 

to conduct the analysis. The discharges thus 

determined were all within 4 percent of those used in 

the Pierce and Thurston County FIS reports. 

The updated flood frequency discharges used 

for this pilot study were determined using program 

J407 with all available annual peak-discharge data 

(including the approximately 20 years of additional 

data gathered since the original study) for gaging 

station 12089500. Most of the updated discharges, 

especially those for longer recurrence intervals, are 

significantly higher than those used in the Pierce and 

Thurston County FIS reports (table 1). The large 

increases reflect the effect of including the 1996 peak 

discharge, 50,000 ft3/s (cubic feet per second), in the 

log-Pearson Type III analysis. The 1996 flood was 

nearly twice as large as the next highest discharge 

(25,700 ft3/s in 1965) in the entire 40-year period of 

record analyzed (1948-68, 1978-96). 

Many basins are ungaged, however, and in those 

cases FISs have used flow estimates based on regional 

regressions. Additional flow information for the gaged 

basin(s) serving as the basis for these regional 

estimates, also allow better estimates to be made for 

the ungaged basin. In these cases, the regional 

regressions should be updated as opposed to the direct 

statistical estimation described here for the Nisqually 

River. 

Table 1. Comparison of flood discharges from the Flood 

Insurance Study (FEMA, 1982) and those updated for the 

pilot study (1997) for selected recurrence intervals 

[ft

3/s, cubic foot per second; FIS, Flood Insurance Study] 

Recurrence 

Interval 

FIS 

Discharges 

(ft3/s) 

Updated 

Discharges 

(ft3/s) 

Percentage 

of change 

10-year 21,500 21,500 0 

50-year 29,000 33,500 +16 

100-year 33,000 40,000 +21 

500-year 45,000 56,000 +24 

Updating Flood Information 11 
Flood Stages 

The hydraulic model used to determine the 

original FIS flood stages—the elevations of the flood 

surface at cross sections along the length of the river— 

was the U.S. Geological Survey step-backwater 

computer program J635. Step-backwater models, in 

general, use elevation cross sections from selected 

locations along a stream reach, together with estimates 

of the effective roughness of the channel at and 

between cross sections, to calculate the height to 

which the water surface of the stream at a particular 

cross section will rise in order to pass a certain 

streamflow. The approach used in this pilot study was 

to use the archived results from the original model to 

estimate flood stages for updated 10-, 50-, 100-, and 

500-year flood discharges. This was accomplished by 

developing a stage-discharge relation for each cross 

section based on the original hydraulic model results. 

Stage-Discharge Relations from Existing 

Hydraulic Model Output 

Stage-discharge relations are regularly 

developed for USGS streamflow gaging stations. They 

take the form of log-log plots of stage against 

discharge, with a log offset applied to obtain the most 

linear relation possible. The log offset is used to 

determine the logarithmic depth of flow that is linearly 

related to the logarithm of discharge for a given size, 

shape, and slope of the channel. The results of a 

previous step-backwater analysis can be used as data 

points for an analogous stage-discharge relation; the 

model computes the stages associated with specified 

discharges. If the results from a previous FIS study are 

used, which is the case for the reach of the Nisqually 

River used for the pilot study, then there are four data 

points available for each cross section—the computed 

elevations associated with the 10-, 50-, 100-, and 500-

year discharges. These data points should be obtained 

from the original step-backwater analyses output files 

rather than from the flood profile plots contained in 

FIS reports because the resolution of the profile plots 

is low (in the pilot study, 5 feet to the inch). The 

original output files are generally archived and 

maintained by Technical Evaluation Contractors 

(TECs) under contract with FEMA. 

The stage estimates for the updated discharges 

corresponding to the 10-, 50-, 100-, and 500-year 

recurrence intervals were estimated using linear 

regression of the logarithmic transform of the stage 

and discharge data (a standard transform applied to 

these relations). A square-root transform was also 

applied to the data to estimate the stage of the 500-

year flood discharge in order to evaluate the 

extrapolation process for floods higher than the 

highest FIS discharge. This is discussed below. 

Comparison with Original Hydraulic Model 

To evaluate the quality of the updated stage 

estimates, the original step-backwater hydraulic model 

was rerun using the updated flood discharges. The 

starting water surface elevation used in the original 

model run, the mean higher-high tide of Puget Sound 

at the mouth of the Nisqually River, was also used in 

the model rerun. The differences were generally small 

for either log or square-root transform regression (figs. 

6 and 7); for the log transform regression, 84 percent 

were within 0.2 foot (table 2). Using either transform, 

the differences were largest for the 500-year discharge, 

because the updated discharge (56,000 ft3/s) is 24 

percent larger than the previous 500-year discharge 

(45,000 ft3/s) and required extrapolation of the stagedischarge relation. Table 3 presents the means, 

medians, and standard deviations of the differences 

between estimates from the pilot study and the 

updated model results for each of the four recurrenceinterval discharges. As would be expected, the larger 

differences for the 500-year elevations illustrate the 

need to perform extrapolations with caution. 

Nevertheless, 81 percent of the estimates for the 500-

year flood elevation were within 0.2 ft when using the 

regression estimate with the best fit at each cross 

section (table 4). 

12 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Table 2. Percentages of flood elevations estimated by 

logarithmic regression that were within 0.1, 0.2, and 0.3 foot 

of elevations calculated with the step-backwater model, for 

the four recurrence intervals 

Percentage of elevations 

Recurrence Interval 

0.1 foot 0.2 foot 0.3 foot 

10-year 82 99 100 

50-year 84 100 100 

100-year 97 100 100 

500-year 20 38 61 

Average 71 84 90 

Table 3. Statistical comparison of differences, in feet, 

between flood elevations estimated by logarithmic regression 

and calculated from the step-backwater model, for the four 

recurrence intervals 

Recurrence Interval (discharge, in 

cubic feet per second) 

Statistic 10-year 

(21,500 

ft3/s) 

50-year 

(33,500 

ft3/s) 

100-

year 

(40,000 

ft3/s) 

500-year 

(56,000 

ft3/s) 

Mean 0.04 -0.05 -0.02 0.25 

Maximum 0.17 0.11 0.05 0.86 

Minimum -0.23 -0.17 -0.13 -0.48 

Median 0.05 -0.05 -0.02 0.25 

Standard deviation 0.07 0.06 0.03 0.25 

Table 4. Percentages of estimated 500-year flood 

elevations within 0.1, 0.2, and 0.3 foot of elevations 

calculated from the step-backwater model 

Percentage of elevations 

Transformation applied within (in feet) 

0.1 0.2 0.3 

Logarithm (base-10) 20 38 61 

Square root 38 49 68 

Best of either 57 81 98 

This may not be true, however, for other streams 

where extrapolation is required because extrapolations 

implicitly assume that the stage-discharge relation 

(appropriately transformed) continues to be linear 

above the highest data point (the 500-year discharge), 

an assumption that could be unjustified in many 

situations. For example, an updated 500-year flood 

discharge could conceivably raise the flood elevation to 

a point where it breaks out of a large historical river 

channel onto a wide flood plain. Changes in channel 

geometry or land cover could also significantly alter 

the stage-discharge relation for the 500-year discharge 

and other discharges. Regardless of the physical 

setting, great caution should be exercised if the new 

500-year discharge is significantly larger than the one 

used in the original FIS analyses. 

The typical flood discharge used for planning 

purposes is the 100-year event, however, and 

interpolated flood elevation estimates for these floods 

are well within the errors associated with original 

hydraulic models. In some cases flood insurance 

studies have been conducted that did not estimate the 

500-year discharges or flood elevations. In the case 

where this has occurred and the revised 100-year 

discharge estimate has increased significantly, this 

situation would require the revised 100-year flood 

elevation estimate to be extrapolated, and the 

associated cautions would apply. 

On average, the increase between the original 

and revised flood elevations for this investigation are 

1.3 feet for the 100-year flood and 1.7 feet for the 

500-year flood. 

Updating Flood Information 13 
Figure 6. Differences between flood elevations estimated by log transform regression and by the stepbackwater model. 

Flood discharges in cubic feet per second (ft3/s). 

14 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Figure 7. Differences between flood elevations estimated by square-root transform regression and by the stepbackwater model. 

Flood discharges in cubic feet per second (ft3/s). 

Updating Flood Information 15

UPDATING FLOOD MAPS 

Once the flood information has been updated 

(updated flood flows and stages), a flood map may be 

prepared based on that information. While the technical 

details are somewhat involved, the concept is quite 

straightforward. Using a GIS, digital representations of 

the land and flood surfaces are generated and compared 

to determine where the flood elevation is greater than 

the land elevation—indicating flooding. 

Although the pilot study method was developed 

using ARC/INFO®, the proprietary GIS software from 

Environmental Systems Research Institute, Inc. 

(ESRI), any GIS incorporating vector and raster (cellbased) data can be used. The use of ARC/INFO ® in the 

pilot study, or the use of ARC/INFO ®-specific 

terminology in this report, does not constitute 

endorsement by the U.S. government, and is not 

intended to suggest that any specific GIS is needed to 

perform the methods described below. 

GIS data models are typically based on (1) 

points, lines, and polygons; (2) raster data sets; and 

(3) Triangulated Irregular Networks (TINs) used to 

represent spatially distributed information, such as land 

cover, roads, or geology. Point-line-polygon data sets 

are commonly called ‘layers’. A GIS layer containing 

digitized cross sections from an original FIS and a 

layer containing a polygon bounding those cross 

sections are examples of a line and a polygon layer, 

respectively. Point-line-polygon layers are typically 

linked with relational databases that manage the 

information associated with features. Electronic file 

sizes of this type of GIS layer are generally nominal; 

raster data sets can be very large. 

A raster data set comprises point or cell data that are 

evenly spaced in both horizontal dimensions. 

Raster data sets may contain either continuous or 

categorized numerical data. Data that represent 

characteristics of a surface such as slope or elevation 

are continuous data. Land use or soil type are examples 

of categorized data. Raster data sets can be used to 

represent the same data as points, lines, or polygons in 

GIS layers; one strength of raster data sets that is used 

in this pilot study is the ability to add or subtract raster 

data sets to or from one another. A DEM is an example 

of a raster data set; the elevations in standard USGS 

DEMs are typically 30 meters apart and are described 

as having a cell size or horizontal resolution of 30 

meters. The size of raster data sets is dependent 

primarily on the resolution and the format of the 

numerical content (for example, integer or floating 

point). 

A TIN is a collection of data points connected by 

triangulated lines, resulting in a surface model 

consisting of triangular planes, like the sides of a 

pyramid. TINs are used to depict continuous variables 

and can be used as a source of data for a DEM (a raster 

model). The TIN model strictly honors the input data 

used to make it, and as such is a good basis for the 

creation of raster DEMs. Other programs are available 

that attempt to improve raster DEM generation by 

eliminating sharp edges where TIN triangles meet. This 

type of routine is represented in this study by 

TOPOGRID, although there are a variety of such 

programs. 

Overview of Data Processing 

Water surfaces were created in the GIS for 

recurrence intervals of 10-, 50-, 100-, and 500-year 

floods using existing FIS flood elevations and 

estimated flood elevations (using log-10 regression for 

the purposes of the pilot study). The land surface was 

created using very-high-accuracy elevation data. The 

two surfaces were then intersected, to produce in a 

flood inundation area. Surfaces should be generated 

and manipulated by a competent GIS specialist, and 

should be carefully checked to assure that they are 

representative of the input data before an inundation 

layer is created. 

The land and flood elevations for each cross 

section were initially represented by lines. These line 

layers were used to create TIN surfaces for both land 

and water. In order to subtract one surface from the 

other, the two surfaces (now represented by TINs) were 

then converted into a raster format. The land surface 

was then subtracted from the water surface to produce a 

final raster data set that indicates depth of inundation. 

Specifically, at ‘cells’ where the water elevation was 

higher than land, subtracting the land elevation from 

the water elevation resulted in a depth value (negative 

values identify areas that are not inundated). The 

resulting raster data were then converted into a polygon 

layer for display or further manipulation in a GIS. 

16 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Creating a 3-Dimensional Digital 

Representation of the Land Surface 

The very-high-accuracy land elevation data 

currently being acquired by many local governments 

provide an ideal source of elevation data for flood 

mapping. These data typically have a vertical accuracy 

of 1 ft or less and are composed of digital contours or 

digital points and contour fragments; they may or may 

not include breaklines (lines that indicate ridge crests 

or valley bottoms, for example). The ideal format is 

digital points and contours/contour fragments with 

breaklines. For this pilot, TCGC provided a 1-foot 

vertical accuracy data set of 2-foot contours without 

breaklines (fig. 8). In the future, high-accuracy 

elevation data will more likely be delivered as DEMs 

derived from TINs based on very-high-density LIDAR 

elevation data. 

Two surface generation programs, TINLATTICE 

and TOPOGRID, were tested for the creation of both 

the land and water surface; both routines are specific to 

ARC/INFO ®, however other GISs have analogous 

programs. For the purposes of the pilot study, 

TINLATTICE serves as an example of a DEM 

generator that strictly honors the elevation surface 

represented by a TIN, whereas TOPOGRID serves as 

an example of a DEM generator that attempts to create 

smooth changes in slope. Careful examination of the 

differences between the two generated surfaces (fig. 9) 

revealed that TINs more faithfully depict contoured 

elevation data than do TOPOGRID-type routines. In 

fact, the land surface generated by TOPOGRID 

contained excursions of 3 ft or more in areas with 

sparse contour lines. 

The horizontal resolution specified for the DEM 

generation affects the preservation of information 

contained in the input data. The larger the cell size, or 

raster spacing, the greater the loss of information due to 

the aggregation of elevations over the larger cell size. 

This can be seen by comparing DEMs with 10-meter 

and 1-meter horizontal resolution both created from 

TCGC data using TINLATTICE (fig. 10). For the pilot 

study, the 1-meter horizontal resolution DEM produced 

with TINLATTICE was used to analyze inundated 

areas. 

Creating a 3-Dimensional Digital 

Representation of the Flood Surface 

Existing FIS data provided water elevations for 

various flood stages that were updated in the pilot 

study, as described earlier in the section “Flood 

Stages”, to reflect current discharges. These water 

elevations are associated with specific cross sections 

established along the Nisqually River during the 

original FIS. 

Cross sections for this study were digitized from 

Mylar maps included in records retained from the 

original FIS. These maps were not georeferenced 

(registered to a real-world coordinate system). Mylar 

maps will in general not be available, so alternatives 

are to digitize cross sections from FIRMs or to extract 

them from digital FIRMs FEMA has made available 

from some locations. The lack of georeferencing 

causes difficulty with registering maps on a digitizer, 

regardless of the source of the information, and ideally 

a digital FIRM should be used. In this pilot, the Mylar 

maps were registered by laying them over a map of 

streams with georeferencing tics at the same scale, 

adjusting the position of the Mylar using easily 

recognizable stream features, and transferring the 

georeferencing tics to the Mylar. Care should be taken 

in selecting features used to align the Mylar because of 

possible changes in the physical environment since the 

original Mylar maps were prepared. Note, however that 

cross sections were rarely located very precisely in the 

original field surveys, because of georeferencing 

problems. After georeferencing, the cross sections 

from the Mylar maps were digitized as a line layer in 

the GIS. The updated flood levels for each cross 

section were added to the GIS relational database as the 

information source for generating the water surface 

DEMs, which is analogous to generating the land 

surface DEMs. 

Updating Flood Maps 17 
Figure 8. Example of very-high-accuracy elevation data for the Nisqually River from Thurston County Geodata 

Center. 

18 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Figure 9. Comparison of land surface Digital Elevation Models generated by the TOPOGRID and 

TIN/TINLATTICE generation methods: (A) shaded relief maps, and (B) difference assessments. 

Updating Flood Maps 19 
Figure 10. Comparison of digital elevation models at 10-meter and 1-meter horizontal resolutions. 

20 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
As with land surface generation, TINLATTICE 

was the program used to generate the flood surface. 

TOPOGRID was found inadequate because of its 

treatment of water surface contours along sinuous 

reaches of the river such as oxbows: elevation 

information was found to ‘leak’ between river reaches 

that were not contiguous. TINLATTICE allows the use 

of a boundary polygon that eliminates this undesirable 

effect by automatically prohibiting such inter-reach 

inter-pollution. 

As with creation of the land surface DEM, 

re-creation of flood-surface elevation contours is an 

important quality-assurance step that reveals problems 

with DEM generation. The cross sections used for this 

pilot were a mile or more apart, and a quality-assurance 

check revealed significant triangular faceting between 

cross sections. This was remedied by using an 

automated GIS feature called densification that adds 

nodes to the cross sections and polygon boundary to 

provide a denser network and correspondingly smaller 

(and therefore less variable) triangular facets. 

Creating an Inundation Map 

Creating an updated inundation map is trivial 

once the digital representations of the flood and land 

surfaces have been created and checked. The DEM 

representing the land elevation is subtracted from the 

flood DEM, yielding positive values (inundation) 

where the flood elevation is numerically greater than 

the land elevation and negative values elsewhere. The 

resulting raster data set is then converted to a polygon 

for storage, display, and further analyses. The updated 

inundation map for the 100- and 500-year floods on the 

Nisqually River is shown in figure 11. A comparison of 

(a) the original FIRM, (b) the inundation map 

determined using the unrevised flood discharge and 

new elevation data, (c) the updated inundation map 

determined using the revised flood discharge and new 

elevation data, and (d) an aerial photograph of an 

approximate 100-year flood from February 9, 1996, 

(fig. 12) demonstrates the effectiveness of the pilot 

study approach. The aerial photograph confirms that 

the updated map more accurately reflects inundations 

than does the existing FIRM. These maps also illustrate 

the significance of using high-accuracy, high-resolution 

elevation data—the flood maps prepared using new 

elevation data and the unrevised and revised discharges 

are more similar to each other than to the original 

FIRM. 

Additional Benefits of GIS-Generated 

Flood Maps 

The raster data set created from intersecting the 

land and water surfaces not only yields areas of inundation, but also provides the depths of inundation 

(fig. 13). In addition to the obvious value of flood depth 

information—having some idea of whether the water 

would be 1 or 5 ft deep—this information can be used 

for loss estimation, such as the application of depthdamage curves. FEMA is currently developing a floodloss estimation method that will utilize depth 

information. Adding GIS layers such as roads, 

buildings, levees, or critical facilities to the flood depth 

map (fig. 14) produces even more useful information 

about flood damage. An estimated error factor can be 

added to or subtracted from the flood depths to map 

areas of uncertain flood hazard (fig. 15). For 

demonstration purposes, the error estimate in figure 15 

is the standard deviation of the differences between the 

elevation model and the field-surveyed elevations used 

to check the elevation model, and is about 1 ft. 

Updating Flood Maps 21 
Figure 11. Updated flood inundation map for the 100- and 500-year floods. 

Updating Flood Maps 22

Figure 12. Comparison of the original Flood Insurance Rate Map (FIRM) for the 100-year flood with updated flood maps 

made using high-accuracy land elevation models and with an aerial photograph of the Nisqually River flood of February 9, 

1996. 

23 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Figure 12.Continued.

24 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
Figure 13. Flood depths for the 100-year flood. 

Updating Flood Maps 25

Figure 14. Flood depths for the 100-year flood, with roads and buildings. 

Updating Flood Maps 26

Figure 15. Areas of uncertain flood hazard for the 100-year flood. 

Updating Flood Maps 27

DATA MANAGEMENT 

The digital format of a GIS provides a more 

efficient means of storing and generating flood maps 

than the current method of producing, storing, and 

retrieving large paper maps. Maps stored in a GIS can 

be generated for any particular location quickly and 

precisely, as opposed to the current situation of locating 

the map or maps depicting an area, and they can be 

generated at appropriate scales and sizes as needed. 

The extent of the contour data sets and the cell 

resolution and precision of the raster data sets greatly 

affects storage requirements and processing space and 

time. To maintain the integrity of the high-resolution 

contours, a 1-meter cell size was used in generating the 

raster surfaces. Although a 1-meter cell size results in a 

large data set, size can be substantially reduced, if 

needed, by converting the values from floating-point to 

integer format, with the added benefit of faster 

processing speeds in subsequent raster operations. For 

example, floating-point values in meters can be 

converted to integer values in decimeters without 

losing accuracythe accuracy of the input elevation 

data does not support precision greater than a 

decimeter. On some computers, disk space can be a 

consideration when working with high-resolution data. 

If disk space is a concern, storage requirements can be 

greatly reduced by (1) reducing the precision of 

floating-point values to integer format; (2) creating 

final products, such as flood plains and flood depths, as 

polygon coverages instead of raster data sets; and (3) 

deleting intermediate raster products. Original land and 

water raster surfaces should be retained for surface 

profiling or display purposes. 

LIMITATIONS AND OPPORTUNITIES FOR 

FURTHER STUDY 

In some flood plains, land use has changed 

significantly since the original flood study was 

conducted, and the estimated effective roughness of the 

land cover has changed accordingly. For example, if 

significant development has taken place in a flood 

plain, the stage-discharge relation is likely to have 

changed in response to the substitution of buildings and 

roads for trees and bushes. In this case, it is unlikely the 

existing hydraulic model remains applicable, and the 

cost, in time and money, to prepare a new hydraulic 

model is justified. Additionally, major land-use 

changes, such as clear-cutting forests or major 

development, could change the flood plain 

characteristics, causing fundamental changes in the 

stage-discharge relations described by the existing 

hydraulic model. In either case, however, the 

preparation of a new model could still be accelerated, 

and the accuracy of the model improved, by the 

acquisition and use of very-high-accuracy elevation 

data. Land-use changes outside of the flood plain do 

not necessarily void the results of an existing hydraulic 

model. These changes may affect the flood frequency 

relation by changing the amount and timing of runoff in 

the basin, necessitating a revision of the 100-year flood 

discharge. Because this in itself has no direct effect on 

the stage-discharge relations in the flood plain, the 

approach from the pilot study can be applied. 

Another situation where a new model would be 

justified is where major changes to the stream channel 

have occurred as a result of meandering, avulsion, 

aggradation, or scour. This would be especially true 

where the normal stream channel conveys a significant 

percentage of the flood discharge being considered. In 

contrast, if the normal stream channel conveys a very 

small portion of the flood flow, changes in the channel 

might not be significant enough to warrant preparing a 

new hydraulic model. 

Manmade hydraulic structures that have been 

built or modified since the original flood study will in 

all but the most rare of circumstances void an existing 

hydraulic model in the vicinity of and for a limited 

distance upstream from the structure. 

Wide application of the pilot study mapping 

approach requires the development of guidelines for 

evaluating the applicability of existing hydraulic 

models. Considerations would include degree of landuse changes within the flood plain and the importance 

of changes in the normal stream channel and other 

geomorphologic characteristics that either lend 

themselves to, or resist, significant stream channel 

changes (for example, the geology of the river bed or 

the relative size of the normal channel compared with 

the flood plain). 

28 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington 
SUMMARY 

The need to develop a more efficient method of 

producing flood inundation maps, the need to update 

old and aging maps as quickly as feasible, and the need 

to bring flood mapping into the digital age are widely 

recognized. This report presents a methodology piloted 

in Washington State that used existing hydraulic 

studies, locally produced high-accuracy elevation data, 

and Geographic Information System (GIS) technology 

to improve map detail and accuracy, reduce time and 

cost requirements, generate useful new types of flood 

maps, and improve map usability. 

$2.7 billion (1997 dollars) has been spent 

creating existing Flood Insurance Rate Maps (and 

similar flood maps, revisions, and updates). The 

National Flood Insurance Reform Act of 1994 

mandates that the Nation’s flood maps be reviewed and 

revised in the near term and subsequently reviewed 

every 5 years. Accordingly, FEMA’s Map 

Modernization Plan emphasizes that more costeffective methods to produce, store, and use flood maps 

are essential. 

Additional peak-flow data collected, or new 

regional regression equations developed, since the 

original flood studies were conducted may significantly 

improve the hydrologic analyses used to determine the 

size of the 100-year flood discharge—the standard 

flood used for planning purposes—simply due to the 

increased amount of data available. In this pilot study, 

the 100-year discharge increased from 33,000 to 

40,000 cubic feet per second (a 21 percent increase). 

Changes in watershed characteristics also have the 

potential to change the discharge associated with the 

100-year flood, and require the use of up-to-date peakflow data. 

Advances in elevation mapping allow higher 

accuracy and resolution that translate into more 

accurate and detailed flood inundation maps. One-foot 

vertical accuracy elevation data has recently become 

readily available, and although some previous studies 

were conducted using cross-sectional elevation 

information accurate to that level, few inundation maps 

were delineated from information with this degree of 

accuracy. 

The digital mapping abilities provided by GIS 

technology allow the delineation of flood inundation to 

be executed by computer, speeding the process and 

removing the need for a hydrologist to manually 

estimate inundated areas between cross sections. GISs 

also allow the generation of depth-of-flood maps and 

maps with up-to-date (and easily updated) cultural 

features such as roads, houses, and critical facilities. 

Additionally, the maps are stored digitally and can be 

reproduced easily for any location at a broad range of 

map scales, simplifying the storage and usefulness of 

the maps; digitally georeferenced maps allow specific 

geographic locations to be located quickly and 

precisely. 

A key time and cost saving aspect of the pilot 

method is the reuse of the hydraulic modeling results 

from an existing detailed Flood Insurance Study 

(FIS)—thus eliminating expensive and timeconsuming field collection of elevation and structural 

information (bridge openings, for example). 

Mathematical stage-discharge relations are developed 

for each original cross section and are then used to 

determine flood elevations associated with updated 

discharges for selected recurrence intervals (as 

determined from updated hydrologic analyses). Landsurface elevation data and the flood stages calculated 

for each cross section (for each of the flood discharges 

selected for mapping) provide the basis for raster (cellbased) digital representations of the land and flood 

surfaces in a GIS. Quality assurance is an important 

aspect of surface generation. Subtraction of the land 

surface from the flood surface yields a raster 

representation of the inundation for each of the selected 

recurrence intervals. Positive values indicate 

inundation and inherently contain depth-of-flood 

information; negative values indicate no inundation. In 

addition to depth-of-flood maps, GISs allow the 

mapping of other useful information such as areas that 

are subject to uncertain flooding due to the errors 

associated with hydrologic and hydraulic 

computations, elevation data, or other sources of 

uncertainty. 

The continued validity of the existing hydraulic 

analyses is a requirement of this method and hinges on 

whether stream channel, stream valley, and structural 

changes are large enough to significantly alter the 

stage-discharge relation. Because existing hydraulic 

models will not remain applicable in all cases, close 

consultation with FEMA’s technical specialists is 

appropriate. 

Summary 29 
REFERENCES CITED 

Federal Emergency Management Agency, 1982, Flood 

insurance study, Thurston County Washington, 

unincorporated areas: 63 p., 67 panels. 

———1987, Flood insurance study, Pierce County 

Washington, unincorporated areas, 81 p., 117 panels. 

———1997, Modernizing FEMA’s flood hazard mapping 

program—A progress report: November 1997. 

———1998, Modernizing FEMA’s flood hazard mapping 

program—A progress report, Dec. 98. 

———2000, FEMA updates cost estimate for the Map 

Modernization Plan, Jan. 2000. 

Interagency Advisory Committee on Water Data, 1982, 

Guidelines for determining flood flow frequency: 

U.S. Water Resources Council Bulletin 17B, 28 p., 

14 appendices. 

Kirby, William, 1981, Annual flood frequency analysis using 

U.S. Water Resources Council guidelines (Program 

J407), chapter I, section C of WATSTORE user’s guide: 

U.S. Geological Survey Open-File Report 76-435, v. 4, 

p. C-1 to C-57. 

Shearman, J.O., 1977, Step-backwater and floodway 

analyses, computer program J635: U.S. Geological 

Survey Water-Resources Division, Surface Water 

Branch, unpublished user’s guide, 47 p. 

30 Updating Flood Maps Efficiently — A Pilot Study on the Nisqually River, Washington