Flood Warning Systems

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NovaLynx Systems, Inc.

3235 Sunrise Blvd, Suite 3, Rancho Cordova, CA 95742


NEXRAIN Corporation

9477 Greenback Lane, Suite 523A, Folsom, CA 95630

KEY ISSUES IN DEVELOPING AND MAINTAINING SUCCESSFULFLOOD WARNING SYSTEMS

THE IMPORTANCE OF HIGH QUALITY RAINFALL MONITORING INFLOOD WARNING SYSTEMS

CHOOSING A HYDROLOGIC MODEL FOR FLOOD FORECASTING

By

David C. Curtis, Ph.D.

NEXRAIN Corporation

9477 Greenback Lane

Suite 523A

Folsom, CA 95530


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Preparing for the CostIntroduction

Flood warning systems help mitigate flood damages

through early detection of potential flooding and the

issuance of flood warnings. Upon receipt of flood warn-

ings by those individuals affected initiate response activ-

ities designed to protect life and property.

ALERT (Automated Local Evaluation in Real-Time)

flood warning systems and the related IFLOWS (Inte-

grated Flood Warning Observation and Warning Sys-

tems) are the most popular and widely used automatedflood warning systems in the U.S. today. These and

similar automated systems are now helping reduce flood

damages and save lives in more than 500 communities

throughout the U.S. Many more such systems are in

operation throughout the world.

Flood Preparedness Programs

Flood warning systems are only one component of a flood

preparedness program. A typical flood preparedness pro-

gram consists of the following components: planning,

flood threat detection and warning, flood response, flood

recovery, and review. After the flood review process, the

flood preparedness plan is revisited and updated.

Response planning and other preparedness plans are

completed well in advance of any flood activity. The

flood warning system detects a potential problem during

the early stages of the event. Once the warning is issued,

response activities begin as planned. When the water

begins to recede, tasks switch from response to recovery.

After the event, a review and evaluation team studies

community performance during the event and recom-

mends improvements to the preparedness plan.

As the time to execute the flood detection and warning

phase decreases, the time available for response increas-

es. This is a key objective of the flood warning system.

Therefore, goals of the flood threat detection and warning

component are to:

n minimize the time that it takes to detect a potential

problem,

n minimize the time to disseminate the warning, and to

n minimize the time to get populations at risk torespond.

Flood Forecast Value

The value of a flood warning system is derived from the

warning systems contribution to saving lives and prop-

erty. Implicit in any flood warning system is the flood

forecast. (i.e. the prediction of a flood threat based on

current weather conditions, rainfall amounts, and/or river

elevations etc.) Assessing the value of a flood forecast

involves two issues: accuracy and timing.

A flood forecast must be accurate to have value. A flood

forecast must also be timely to have value. Unfortunately,

it is generally not possible to simultaneously have the

most accurate forecast andthe most timely forecast. For

example, a very accurate forecast can be made by taking

a river level measurement (say 16.5 for discussion) and

forecast that the river will rise to a 16.51 in five minutes.

While this may be a very accurate forecast, it has little

value because it doesnt provide any time to respond and

mitigate damages.

On the other hand, if a flood peak of 16.51 is forecast to

occur two weeks from now, no one is likely to take it

seriously enough to engage flood response activities. In

this case, the forecast is timely but current technology

doesnt exist to provide a flood forecast on most streams

thats credible enough for people to take action that far in

advance..

To achieve added value in a flood warning system, some

investment in time, effort, and money is required. The

amount of investment should be in proper balance with

KEY ISSUES IN DEVELOPING AND MAINTAINING SUCCESSFULFLOOD WARNING SYSTEMS

Preparedness Planning

Flood Warning &Detection

ResponseRecovery

Review &Evaluation

Figure 1: The circular nature of a flood warning

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some flood warning systems that trainees are frequently

overwhelmed with new information that they dont even

know what questions to ask. Follow-up training six to

nine months after the initial installation has proven to be

extremely effective and valuable.

Training on several different levels for different job

functions is required. Training for electronics technicians

on field sensors, telemetry, and computer equipment isrequired. Other users require training on central station

and computer software operation. The flood warning

system manager also requires more advanced training for

overall system configuration and management.

Training for new personnel and refresher training on an

annual basis keeps personnel up-to-date on the latest

equipment and software developments. Attendance at

group training sessions frequently brings together flood

warning system personnel from many communities that

eagerly share their experiences and ideas.

Regional and national users groups offer many opportu-nities for professional development for flood warning

system operators. Newsletters, meetings, Internet web

sites, and symposiums are excellent forums to learn and

exchange ideas. The costs of user group memberships and

conference attendance should be considered in flood

warning system training budgets and professional devel-

opment budgets.

Services and Support

A variety of services and support can be purchased from

manufacturers or third-party service providers. Thesecosts must also be recognized in the flood warning system

budget. Recurring costs for services essential to flood

warning system operation such as telephone lines and/or

data fees must also be included in the budget as necessary.

Maintenance

A car needs regular oil changes, tune-ups, and occasional

new tires to operate smoothly. In addition, car owners

have to face repairs for the dents and dings that inevitably

occur from normal use. Flood warning systems are no

different.

Flood warning systems need regular care. Batteries need

replacing, solar panels need cleaning, instruments need

calibration, and radios need tuning. In addition, accidents

occur. Hunters shoot gauges, vandals destroy antennas

and steal solar panels, and, occasionally, bulldozers run

over gauges. As unfortunate and unplanned as they are,

repairs and replacements are still required and must be

considered in operational budgets.

Computers and software evolve rapidly. Upgrades from

manufacturers are frequent and a fact of life. Users must

plan for the cost of updating these items several timesduring the lifetime of the project.

Personnel

Successful flood warning systems all have one common

element - a highly dedicated and skilled staff. Large flood

warning systems may require several full-time personnel

for operations and maintenance. Small systems may only

require one or two people assigned to the project part-

time. In any case, a commitment to adequate staffing

levels is vital to long term success.

Summary

Costs associated with flood warning systems arise from

several sources. Some sources such as capital equipment

costs are obvious. Others, such as training, system up-

dates, and staffing are equally important but often over-

looked. Consideration of these cost elements and other

that may be unique to an individual system must be

considered to provide more realistic total cost estimates

when considering flood warning system investments.

References

National Weather Service,Role of the National WeatherService, Flood Warning - Preparedness Programs, Flood Warning - Preparedness Programs, Flood Warning - Preparedness Programs, Flood Warning - Preparedness Programs, Flood Warning - Preparedness Programs, US

Army Corps of Engineers Short Course, Hydrologic

Engineering Center, Davis, CA March 21-25, 1994

Curtis, David C., Designing Rain Gauge Networks for

Automated Flood Warning Systems, Paper presented at

the Flood Plain Management Association Spring Confer-

ence, Solvang, CA, March 30-April 2, 1993


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IntroductionConsistent rainfall data is, perhaps, the most significant

ingredient in developing accurate hydrologic analyses.

Without consistent rainfall data from storm to storm or

even within storms, accurate streamflow simulations and

forecasts are extremely difficult to achieve. Even though

most hydrologists readily acknowledge this fact, rainfall

records are rarely scrutinized to the degree necessary to

develop an engineered data set that best indexes the true

rainfall entering the watershed. This section will review

some of the factors that lead to inadvertent inconsisten-

cies in rainfall data, provide some insights into the sensi-tivity of streamflow simulations to rainfall errors, and

offer some suggestions to improve gauge and data man-

agement procedures in order to help improve data consis-

tency.

Historical Perspective

Some of the problems associated with rainfall measure-

ment have been known for hundreds of years. For exam-

ple, a demonstration performed in 1769 showed that a rain

gauge located on the top of a 30 foot tall house caught just

80% of amount measured in a ground-level rain gauge.Similarly, a gauge on top of a 150 foot abbey tower caught

just over 50% of the ground level catch. It took until the

late 19th century to fully understand that the reduced rain

gauge catch associated with height above ground was due

to the turbulent airflow around exposed gauges in strong

winds. (Frisinger, 1977)

Similar observations were reported a century later by

Symons (1881). Symons compared rain gauge catch at

various elevations with the catch at two inches above

ground level. Symons results as shown in Figure 2

represent conditions prevalent at the time of his experi-ments. He did not consider the gauge catch variation with

varying wind conditions as later researchers would.

Inadvertent Inconsistencies

Inconsistent rainfall records had been brought about by a

variety of actions, many of them well intentioned, and

most of them quite inadvertent. Rain gauges have been

moved short distances to accommodate the wishes of a

cooperative observer who wanted to raise vegetables

where the gauge stood. Or, the observer might want to put

in a walk way, or build a chicken coop, or provide space

for children to play; at other times, a rose garden might be

desired so that it wouldnt be necessary to look at that

eyesore called a rain gauge. It would be difficult to

determine how many records have lost consistency be-

cause someone wanted to beautify the area around that

dented, dirty old can with an attractive planting.

The National Weather Service occasionally contributed

to this confusion by moving gauges as vegetation altered

site characteristics - the correct procedure would have

been to keep the height of the surrounding vegetation

constant. Unfortunately, the cost and politics of such

actions were well beyond the capability of a government

agency. Another problem impairing data consistency

occurred when an observer terminated their observation

program. At such times, the gauging equipment would

frequently be moved to another property in the general

area. A location which was considered by the NWSsubstation specialist as being a compatible site. All too

often, the term compatible was used to describe any site

which had its mail delivered by the same post office.

Unfortunately, many of these records were published as

a continuing record with inadequate documentation of the

change in location.

Even the best intentions of those who were running the

rainfall data program led to a variety of inadvertent

THE IMPORTANCE OF HIGH QUALITY RAINFALL MONITORING INFLOOD WARNING SYSTEMS

Figure 2: Variation of gauge catch with height for a given set of windconditions as report by Symons (1881)

0

2

4

6

8

10

12

14

16

18

20

GaugeHeight(ft)

84%86%88%90%92%94%96%98%100%Percent of Catch at 2 " Elevation

Variation of Gauge Catch with Heightfrom Symons (1881)

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Figure 3: Expected gauge undercatch due to 15 M.P.H. wind

A

B

10 mph

20 mph

0

5

10

15

20

Height(ft)

0 2 4 6 8 10 12 14 16 18 20 22 24

Velocity (mph)

Expected Gauge Undercatch Due to WindLogarithmic Wind Profile

ALERT Rain Gauge15% undercatch

12% undercatch

Velocity Profile

Relative positions of two rain gauges in a wind profile

with winds of 15 mph at the height of the ALERT gauge.

15 mph

Ground ConditionsMowed Grass and

Wet Soil

changes in record through the necessary, but frequently

injurious, effort to maintain and improve the quality of

data collection. This occurred when equipment wore out

and was replaced with equipment which had an orifice at

a different elevation and/or was designed with a shape

that had different aerodynamic properties. Changes which

confused the hydrologic consistency of the data might be

as obscure as installing a platform so the observer didnt

have to stand in the mud, or as physically obvious as

installing a wind shield to improve the catch under

windy conditions. But all of these actions had one thing in

common, they altered the exposure of the gauge orifice to

the wind and in so doing modified the representativeness

of the resulting rain catch.

Many different factors that affect rain gauge records have

been identified and, to some degree, quantified. Some of

these factors are reviewed in the following sections.

Natural Variation in Rainfall

Rain gauge measurements taken by identical gauges

located a few feet apart have experienced differences as

much as 20%.

Size of the Tipping Bucket

At the end of the storm, the expected amount of unreport-

ed rainfall is one half the tipping bucket size. If all or part

of this rainfall evaporates before the next tip, this amount

of rainfall is unrecorded. In areas with 50 storms per year,

a 1 mm (0.04 in.) tipping bucket might fail to report 25

mm (0.98 in.) of rainfall over an annual cycle. Under the

same conditions, a 0.01 in. tipping bucket gauge might

fail to report 0.25 in. of rainfall during the same annual

cycle.

Tip Time

Tipping buckets miss a small amount of rainfall during

each tip of the bucket due to the bucket travel and tip time.

As rainfall intensities increase, the volumetric loss of

rainfall due to tipping tends to increase. At rainfall inten-

sities above six inches per hour, 1 mm tipping buckets will

under report rainfall in the range of 0-5% depending on

how the gauge was calibrated. Smaller tipping buckets

can have higher volumetric losses due to higher tip

frequencies.

Gauge Height

The height of the gauge above ground can have a dramatic

affect on gauge catch. As gauge height increases from

ground level, wind speed at the gauge orifice increases

due to decreasing frictional effects on the air stream

caused by the ground surface. Larson and Peck (1974)

show results that indicate wind induced undercatch is on

the order of 1% for each mile per hour of wind at the gauge

orifice. Assuming a logarithmic wind profile with height(Figure 3), a 15 mile per hour wind at the standard

ALERT gauge height of 10 feet could be expected to

induce a 15% loss compared to a 12% loss if the same

gauge were just 4 feet high.

Discrete Exposure

Gauges located in an area with variable protection rela-

tive to different wind directions will produce different

results. For example, consider Figure 4 with two gauges

located in the same general area. In the wind field shown,

gauge AAAAA

is protected by nearby vegetation and experienc-es a 10 m.p.h. hour wind. However, gauge BBBBB, located a few

feet away, may experience 20 m.p.h. winds due to more

direct exposure to the general wind field. Under these

conditions, Gauge AAAAA might experience a 10% reduction in

catch due to wind but gauge BBBBB could experience a 20%

Figure 4: Location of gauge relative to local wind field affects gaugeperformance.


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reduction in rainfall catch due to higher wind speeds at its

location.

Time Variability

Site exposure conditions that change with time will also

affect rain gauge performance. As vegetation grows and/

or changes relative to the gauge site or as the number, size,

and shape of nearby buildings change, site aerodynamics

change over time and can produce significant changes to

the rain gauge performance. Figure 5 shows how growth

of vegetation can modify wind at the gauge and alter

precipitation catch.

Gauge Change

Each gauging system has its own unique rainfall measure-

ment characteristics. Both external and internal gauge

system attributes affect gauge performance. External

factors include site and locational characteristics. Inter-nal factors include the physical, mechanical, and electri-

cal characteristics of the gauge itself. As long as these

attributes stay the same, gauge records remain consistent.

Any errors or biases present in a consistent rainfall record

are overcome in hydrologic model calibration.

Table 1 presents some of the important systematic errors

identified in a World Meteorological Organization report

that are associated with rain gauges. Each gauge has a

specific set of systematic errors. If a gauge is changed

either for a new model or a new gauge type, these

systematic errors change, making the record inconsistent.

Gauge Calibration

Rain gauge calibration has an impact on record consisten-

cy, especially if the method of calibration changes period-

ically. For example, if a static calibration is used one time

and a dynamic calibration is used another, rainfall mea-

surement will be inconsistent.

Static calibration of an ALERT tipping bucket usually

sets the 1 mm tipping bucket to tip after the accumulation

of 72.94 grams of water ( i.e. the weight of 1 mm of water

across the area of the 12.0 in. ALERT gauge orifice).

Dynamic calibration, on the other hand, sets the bucket

to tip the correct number of times associated with a certain

rainfall rate, usually 6 in./hr. for ALERT gauges.

Unfortunately, it's impossible to have an ALERT tipping

bucket calibrated to tip at exactly 1 mm (72.94 grams) andandandandand

be calibrated for zero error at a rate of 6 in./hr. In other

words, you cant have your cake and eat it too!

Table 1: Main components of systematic error in precipitation measurement and their meteorological and instrumental factors listed in orderof importance. (Sevruk, 1982)

a: 20 mph

b: 15 mph

c: 10 mph

ab

c

Figure 5: Growth of vegetation over time significantly changes windvelocity at gauge site and alters catch characteristics.

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The reason for this paradox is that in dynamic operation,

the tipping bucket takes a finite amount of time (e.g. on

the order of 0.5 sec) to tip. If the bucket is calibrated to tip

at exactly 1 mm, rain will still accumulate in the bucket

until the bucket moves past the midpoint of the tip and the

rain begins to accumulate in the second bucket. This

extra rain accumulating in the first bucket during the tip

is unmeasured. A bucket calibrated to tip at exactly 1 mm

will tip fewer times and under report rainfall at higher

rates.

Dynamic calibration takes the tip time into account im-

plicitly. In order to calibrate a tipping bucket to have zero

error at a rainfall rate of 6 in./hr., the bucket must be

calibrated to tip at a lower volume (e.g. 69.6 grams,

approximately). This lower volume plus the volume

associated with the tipping time will total 1 mm and the

tipping bucket will exhibit zero error at the dynamic

calibration rate of 6 in./hr.

Calibrating a tipping bucket to zero error at 6 in./hr. usinga smaller volume to initiate a bucket tip implies that the

tipping bucket might tip more frequently at lower rainfall

rates and, therefore, over report the rainfall. However, at

least in some ALERT tipping buckets, the tipping time is

slightly longer at lower rainfall rates which compensates

for the lower calibrated volume. A properly functioning

ALERT tipping bucket dynamically calibrated to zero

error at 6 in./hr. shouldnt over report at lower rainfall

rates by more than 1-2%.

Implications for Hydrologic Analysis

How systematically must the precipitation indexing ca-

pability be maintained in order to be useful in determining

runoff? Figure 6 shows the effect which an inconsistent

gauging network would produce in evaluating the storms

contribution to peak discharge. Assuming moderately dry

Figure 7: Errors in rainfall estimates produce relatively greater errorsin runoff estimates.

0

200

400

600

800

TotalRunoffError(%)

0 50 100 150 200

Total Rainfall Error (%)

Magnification of Precipitation ErrorSCS Runoff Model

8"

6"

4"

2"

NoError

Magnification of precipitationerror in runoff estimates

Figure 6: Errors in rainfall estimates can generate large errors inestimates of peak discharge

0

100

200

300

400

500

600

700

800

900

1000

PercentVariability

0 1 2 3 4 5 6 7 8 9 10 11 12Catchment Rainfall (inches)

Variablity of Peak DischargeDue to Errors in Rainfall Estimates

Small California Coastal Catchment

Moderately Dry

(Runoff using rainfall x 1.05) - (Runoff using rainfall x 0.95)Runoff using rainfall x 0.95

x 100

initial soil moisture conditions, a variety of storms of

various magnitudes were analyzed to determine the rela-

tive difference in the expected contribution to peak flow

if the precipitation was overvalued by 5% compared to

being undervalued by 5%. These relatively small changes

in precipitation indexing capability produce errors whichare inversely related to the quantity of runoff. The propor-

tional variation in peak flow can readily exceed three

hundred percent when runoff volumes are small. In a

major flood event, with high runoff volumes, the runoff

error converges very slowly toward the rainfall error. This

convergence is however, so slow that for the storm

rainfall values used to produce Figure 6, the proportional

runoff variation is just dropping below 20% for very large

storm rainfall volumes. Thus, for even major events, we

can conclude that inconsistency in evaluating precipita-

tion which is as little as 5%, can have substantial impacts

upon runoff determination.

Figure 7 again shows how rainfall errors are magnified.

When soils are nearly saturated, runoff nearly equals the

rainfall and the runoff error is just slightly larger than the

rainfall errors in large events. However, for smaller

events and dryer soils, the effect of errors is much more

pronounced. In this case, a 100% rainfall error is magni-

fied by a factor of 3.5 to 4 for a 2 inch event.

Summary

Unfortunately, there are so many factors which can influ-ence the accuracy of precipitation measurements that no

one has yet been able to devise a gauge that will consis-

tently measure true rain. The measurements which

have been judged most accurate are those which have

been observed with pit gauges. But pit gauges are ex-

tremely expensive to operate and have major constraints

which substantially limit their application to research

projects. The best that can be hoped for is that the gauging


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equipment will operate close to the scale of reality and

with a degree of consistency which will provide a stable

index to the rainfall-runoff process. Inasmuch as it takes

many years of consistent data to define the rainfall-runoff

relationship, it is a mistake to continually modify an

operational gauge seeking a better approximation oftrue

rain.

There are two essential elements in the effective applica-tion of rainfall data to streamflow forecasting. The rain

gauge network must adequately index the precipitation

falling on a catchment and it must do so in a consistent

manner which does not alter the indexing capability with

time. The first of these elements is achieved by installing

an adequate network of appropriately sited and consis-

tently measuring precipitation gauges. Regardless of the

density of the gauge network, a second element is neces-

sary. A consistent representation of the runoff regime is

dependent upon effective systematic maintenance of

both the equipment and the gauging site. Only through

these steps can the investment in real-time data produce

the flood warning capability for which the flood warning

system was intended.

References

Alena, Thomas; et. al.,Measurement Accuracy Enhancement

of Tipping Bucket Rain Gauges at High Rainfall Rates, Fifth

International Conference on Interactive Information and

Processing Systems, AMS publication, 1989

Frisinger, H. Howard, The History of Meteorology: toThe History of Meteorology: toThe History of Meteorology: toThe History of Meteorology: toThe History of Meteorology: to

18001800180018001800, Science History Publications, AMS, 1977, pp 88-91

Larson, Lee and Eugene L. Peck,Accuracy of Precipitation

Measurements for Hydrologic Forecasting, Water Resourc-Water Resourc-Water Resourc-Water Resourc-Water Resourc-

es Researches Researches Researches Researches Research, Vol 10, No. 4, 1974

Sevruk, B., Methods of Correction for Systematic Error in

Point Precipitation Measurement for Operational Use,

Operational Hydrology Report 21Operational Hydrology Report 21Operational Hydrology Report 21Operational Hydrology Report 21Operational Hydrology Report 21, World Meteorological

Organization, Geneva, Switzerland, 1982

Symons, G. J. On the Rainfall Observations Made Upon

Yorkminster by Professor John Phillips, F.R.S, BritishBritishBritishBritishBritish

RainfallRainfallRainfallRainfallRainfall, pp 41-45, 1881

Weiss, L. L. and W. T. Wilson, Precipitation Gauge Shields,

International Union of Geodesy and GeophysicsInternational Union of Geodesy and GeophysicsInternational Union of Geodesy and GeophysicsInternational Union of Geodesy and GeophysicsInternational Union of Geodesy and Geophysics, GeneralAssembly of Toronto, 1957, Volume 1, pp 462-484


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IntroductionThe most common application programs in automated

flood warning systems are the runoff and river forecast

programs. These programs use observed and, in some

cases, forecast rainfall amounts to compute the amount of

water that will enter the stream system.

Forecast Models

The purpose of a forecast model is to estimate future river

flows and elevations based on observed or forecast amounts

of rainfall. In flash flood situations, certain portions of the

forecast hydrograph are more important than others.Accurate forecasts of the rising limb, the time to hydro-

graph peak, and the magnitude of the peak are critical.

These are the elements of model output that have the most

impact on the flood warning. The model implemented in

a flood warning system must consistently perform well in

these three areas.

Before model selection, one very important element,

rainfall estimation, must be considered. The volume of

water under the rising limb of a flash flood hydrograph is

primarily surface runoff. Basins with short response

times are often characterized by low infiltration rates andsteep slopes which efficiently generate runoff. Because

these basins efficiently generate runoff, especially during

periods of high intensity rainfall, the volume of runoff is

very sensitive to the volume of rainfall. This implies that

the output of a flash flood forecast model will also be very

sensitive to the rainfall inputs.

Flash flood forecast sensitivity to rainfall inputs serves to

emphasize the importance of establishing a good mea-

surement system first. The phrase commonly heard in the

computer industry, Garbage in. Garbage out. is equally

applicable to flash flood forecasting. Good model perfor-mance, no matter what model is used, cannot be expected

without a good measurement system. The implication for

forecast system design is to invest in the measurement

and detection systems first, then consider hydrologic

models.

There are many different hydrologic forecast models in

use. The most commonly used models in local flood

warning systems fall into two categories: simple index-

type models and conceptual rainfall-runoff models. In-dex models keep a running index that reflects current

moisture conditions. The moisture index, a time of

year index, current rainfall, and rainfall duration is

generally all thats needed to estimate surface runoff

with these models. Conceptual models attempt to

provide a more physically-based approach to basin

modeling by more explicitly accounting for

evapotranspiration, interception storage, retention stor-

age, infiltration, surface runoff, percolation, interflow,

etc.

Table 2 shows the most widely available models forlocal flood warning systems.

CHOOSING A HYDROLOGIC MODEL FOR FLOOD FORECASTING

API ModelAPI ModelAPI ModelAPI ModelAPI Model The API (Antecedent Precipitation Index)

Model was developed by the National Weather Service

and has been used in various forms since the 1950s. The

antecedent precipitation index reflects the current soil

moisture based on recent rainfall. A high index means

high soil moisture content while a low index indicates dry

conditions. The API for a given period is used with a

rainfall-runoff relationship, the rainfall amount, and the

storm duration to estimate runoff. A unit hydrograph is

applied to distribute the runoff. At each computational

period, the index is updated based on the additional

rainfall and by a seasonally dependent factor. The season-

ally dependent factor empirically accounts for changes inthe rainfall-runoff relationship due to seasonal changes in

evapotranspiration, infiltration, etc.

Complex basins can be modeled by applying the API

technique to individual sub-basins that are hydrologically

homogeneous. Outflows from sub-basins can be routed

downstream and combined with other tributary flows and

inflows calculated by the API model for local areas.

Table 2: Flood Forecast Models

Index Models Conceptual ModelsAPI Sacramento Soil Moisture

ADVIS HEC1-F

Flood Advisory Tables SSARR

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Many versions of the API model exist. Most National

Weather Service River Forecast Centers that use API

have added modifications to customize the technique

for conditions in basins within their area of responsibility.

At least eight different implementations of API are used

by the National Weather Service.

The API model is simple and relatively easy to under-

stand. It is also relatively easy to adjust. Forecasters can

easily change model parameters or model runoff based on

his or her assessment of the current event to improve

model performance.

ADVISADVISADVISADVISADVIS The ADVIS program (Sweeny, 1988), devel-

oped by the National Weather Service for local flood

warning, includes an API model as its primary hydrolog-

ic forecast technique. (All National Weather Service

implementations of API are available in ADVIS) ADVIS

is a simplified implementation of hydrologic modeling

that produces output appropriate for the local user de-

pending upon what type of information is available. For

example, ADVIS output includes:

Categorical forecasts for ungaged

watersheds. Categorical forecasts are general forecasts of

minor, moderate, or severe flooding based on the

antecedent precipitation index and rainfall estimates.

Crest stage forecast. ADVIS will generate a

crest forecast if the unit hydrograph peak is available.

Forecast hydrograph. Where the complete unit

hydrograph is available, ADVIS generates a complete

forecast hydrograph.

The ADVIS program is intended to address relatively

simple hydrologic situations at the local level.

Flood Advisory TablesFlood Advisory TablesFlood Advisory TablesFlood Advisory TablesFlood Advisory Tables Flood advisory tables are used

to provide a quick estimate of peak stage forecasts usingindices produced by the API or other modelling tech-

niques. The tables are computed in advance for a variety

of antecedent conditions. The current index can be com-

puted on-site or provided by a local National Weather

Service office. Local users apply the current index with

the latest rainfall estimate to the table to determine the

estimated peak stage. An estimated time to peak is usually

available based on previous analysis of basin response

times.

Sacramento Soil Moisture Accounting ModelSacramento Soil Moisture Accounting ModelSacramento Soil Moisture Accounting ModelSacramento Soil Moisture Accounting ModelSacramento Soil Moisture Accounting Model

The Sacramento Soil Moisture Accounting Model is aconceptual model designed as a comprehensive represen-

tation of the hydrologic processes of the upper soil man-

tel. Evapotranspiration, direct runoff from impervious

areas, surface runoff, percolation, interflow, and two

types of base flow are explicitly represented. Runoff

calculated for each period is distributed using a unit

hydrograph.

Figure 8: Sacramento Soil Moisture Accounting Model

Evapotranspirat ionDem and Precipitation

E T

E T

E T

E T

E T

Pervio us Im p e rvio us D ire c t Runo ff

Sur face Runof f

Interflow

Tens ionWater

FreeWater

Excess

Percolat ion

1 -PFre e PFre e

TotalC h an n e l

Inflow

DistributionFunction

Stream Flow

SubsurfaceDischarge

SideTotal Base

Flow

SupplementalBase Flow

PrimaryBase Flow

TensionWater

FreeP

FreeS

Reserve

Sacramento Soil Moisture Accounting Model


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Each hydrologic process is represented by a function orseries of functions with adjustable parameters. The model

is calibrated with historical rainfall and streamflow data

by adjusting parameters until the model output adequate-

ly represents basin response. The model is applied to

individual basins that are hydrologically homogeneous.

Complex basins are modeled by combining outflows

from individual basins using a variety of available routing

techniques.

HEC1-FHEC1-FHEC1-FHEC1-FHEC1-F The Hydrologic Engineering Center (HEC)

has developed a forecasting system for COE offices that

is also available for local flood warning systems. Theforecast technique uses an initial and uniform loss rate to

compute runoff which is applied to a unit hydrograph to

produce a basin forecast. Results from each basin can be

combined and routed to develop forecasts for complex

systems. HEC1-F uses observed streamflows to set prop-

er loss rate parameters.

HEC1-F can be calibrated relatively easily. Most of the

necessary parameters can be easily obtained from maps.

Infiltration parameters and certain characteristics of the

unit hydrograph can be estimated initially. During a flood

event, HEC1-F evaluates model performance against

observed stream flow and automatically adjusts the ap-

propriate parameters.

HEC1-F is the forecast version of HEC-1, a widely used

hydrologic design tool. Many different public and private

organizations throughout the United States have usedHEC1 to generate flood hydrographs for a variety of

purposes from bridge design to flood plain mapping. As

a result, many local engineers understand the model and

the transition to HEC1-F is relatively easy.

SSARRSSARRSSARRSSARRSSARR The Synthesized Streamflow and Reservoir Reg-

ulation (SSARR) model was developed jointly by the

National Weather Service and the COE. It is a tool used

by the respective agencies in the Pacific Northwest for

flood forecasting and reservoir regulation. The SSARR

model provides a continuous accounting of soil moisture

to determine how much of the incident rainfall andsnowmelt will become runoff. Three phases of runoff are

computed: direct runoff, interflow, and baseflow. Each

phase is routed through a series or cascade of linear

reservoirs to produce the total streamflow.

Hydrologic Model Selection

Choosing the appropriate hydrologic model is a task

open to much debate. A widely cited study by the World

Meteorological Organization indicated that the API tech-

nique, the Sacramento model, and the SSARR model all

gave about the same results in humid climates. However,explicit soil moisture accounting models like SSARR and

the Sacramento model were clearly superior to the API

model for arid and semi-arid climates. In humid environ-

ments, soil moisture conditions are less variable than in

arid or semi-arid climates. The added complexity of the

explicit soil moisture accounting models to handle wide

ranging conditions doesnt contribute significantly to

model performance when conditions are relatively stable.

However, when conditions are rapidly changing, some

researchers have found that explicit soil moisture ac-

counting models offer a significant performance advan-

tage.

When reviewing studies comparing the complex explicit

soil moisture accounting models with simpler index ap-

proaches, an important insight was noted. While the

simpler models performed well, statistically, compared

to the explicit soil moisture accounting models, signifi-

cant deviations occurred at key points. These deviations,

while significant, were rare and tended to have little effect

on the overall statistics. However, the deviations were

Figure 9: The SSARR Model

AreaElevation Prec ip ita tion Tem pe rature

Snow

Accum ulaton

Snowm eltRainfall

MoistureInput

Soi lMoisture

Runoff

BaseFlow

DirectRunoff

Su bSurface

Surface

SM I

BI I

S-SS

Routing

Rou ting Ro uting

Evaporat ion

Stream flow

SSARR Mo del


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NEXRAIN Corporation

References

Burnash, Robert J. C., Ferral, R. L., and McGuire, Rich-

ard L., A Generalized Streamflow Simulation System,

National Weather Service, California Department of Water

Resources, Sacramento, California, 1973

Dotson, Harry W., John C. Peters, Hydrologic Aspects

of Flood Warning - Preparedness Programs, Technical

Paper No. 131, U.S. Army Corps of Engineers Hydrolog-

ic Engineering Center, Davis, CA, August 1990

Federal Interagency Advisory Committee, Guidelines on

Community Local Flood Warning and Response Sys-

tems, National Technical Information Service, Spring-

field, VA, August 1985

Kitanidis, P. R., and R. L. Bras, Real-Time Forecasting

with a Conceptual Hydrologic Model, 2: Application and

Results, Water Resources Research, Volume 16, No. 6,1980, pp. 1034-1044

Linsley, Ray K., Max Kohler, and Joseph L. H. Paulhus,

Hydrology for Engineers, Third Edition, McGraw-Hill,

1982

Nemec, J., Design and Operation of Forecasting Opera-

tional Real-Time Hydrological Systems (FORTH), World

Meteorological Organization, Geneva, Switzerland, un-

published manuscript, Figure 12, 1984

Pabst, Art, State-of-the-Art Flood Forecasting Technol-

ogy, Proceedings of a Seminar on Local Flood Warning

- Response Systems, US Army Corps of Engineers, 10-12

December, 1986

Sittner, Walter T., Catchment Response in the Computer

Age, Computer Applications in Water Resources, Harry

C. Toro, ed., ASCE, 1985, pp. 749-757

Sittner, W.T., C.E. Schauss, and John Monroe, Contin-

uous Hydrograph Synthesis with an API-Type Hydro-

graph Model, Water Resources Research, Vol. 5, No. 5,

1969, pp. 1007-1022

Sorooshian, Soroosh,Identifiability in Conceptual Rain-

fall-Runoff Models, Computerized Decision Support

Systems for Water Managers, Proceedings of the 3rdWater Resources Operations Management Workshop,

ASCE, 1988, pp 172-183

Sweeny, Timothy L., Flash Flood Hydrologic Forecast

Model, ADVIS, Computerized Decision Support Sys-

tems for Water Managers, Proceedings of the 3rd Water

Resources Operations Management Workshop, ASCE,

1988, pp. 683-692

U.S. Army Corps of Engineers, General Guidelines for

Comprehensive Flood Warning/Preparedness Studies,

Hydrologic Engineering Center, Davis, CA, October 1988

U.S. Army Corps of Engineers Hydrologic Engineering

Center, Floodway Determination Using Computer Pro-

gram HEC-2, Training Document No. 5, January 1988

U.S. Army Corps of Engineers, Water Control Software:

Forecast and Operations, Hydrologic Engineering Cen-

ter, Davis, California, December 1989

U.S. Army Corps of Engineers, SSARR Users Manual,

North Pacific Division, Portland, Oregon, May 1991

World Meteorological Organization, Intercomparison

of Conceptual Models Used in Operational HydrologicForecasting, Operational Hydrology Report No. 7, Annex

III, p 47, Geneva, Switzerland, 1975

frequently observed when extreme hydrologic conditions

existed. The complex models could manage the extremes

where the simpler approaches were not capable. These

rare events are precisely the events that offer the greatest

potential for hazard mitigation.

The choice of models in specific situations remains

difficult. After all the analysis of which model performs

the best for a given basin, it ultimately depends upon thecapabilities and resources of local users. Complex models

requiring a high level of support might be appropriate in

cases where local skills and resources can handle it.

However, the same model may be entirely inappropriate

in situations with lower levels of local hydrologic skill

and resources.

To summarize model selection:

Choose a model that is within the capabilities of the

local user to understand, operate, and maintain,

Choose a model that is appropriate for the localhydrologic regime, and

Choose a model that will provide the best estimate of

the rising limb, the time to peak, and the flood peak.

Documents

Flood Warning Systems