( 26.r )
2. The sum of the probabilitiesof all possibleoutcomesin a singletrial is 1.
s
.1J
P(8,) : 1
(26.2)
I
3. The probability of a numberof independentandmutually exclusiaeevents is the sum of the probabilitiesof the separateevents. P ( 4 U E 2 ) : P ( E y )+ P ( E 2 )
(26.3)
The probability statement,P(Et U Et), signifies the probability of the "the probability of Et ot Er." union of two eventsand is read 4. The probability of two independenteventsoccurring simultaneouslyor in successionis the product of the individual probabilities. P(q ) E,) : P(E) x P(E)
(26'4)
P(& a E ) is called probability of the intersectionof two eventsor ioint probability and is read "the probability of El and Er." Considerthe following exampleof eventsthat are not independentor mutually exclusive:An urban drainagecanal reachesflood stageeach summer with relative freqlrencyof 0.10; power failuresin industriesalongthe canal occur with probability
674
CHAPTER26
PROBABILITYAND STATISTICS
of 0.20; experienceshowsthat whenthereis a flood the ch'ancesof a powerfailure for whateverreasonare raisedto 0.40. The probability statementsare P(power failure) : P(P) : 0'20 P ( f l o o d ) :P ( F ) : 0 ' 1 0 P(no powerfailure) : P(P) : 0'80 P(no flood) : P(F) : 0'90 P(power failure given that a flood occurs) : 0'40 The last statementis called a conditionalprobability. It signifiesthejoint occurrence of events and is usually written P(P I F).. Rules 3 and 4 no longer are strictly : 0.3. If the eventsreapplicable.If Rule 3 applied,P(F u P) : P(F) + P(P) maineOindependent,theionditional probability P(P I F) would gqualthe marginal probability f (p). T'nor the eventsare independentif the probability of either is not i'conditionedby" or changedby knowledgethat the other has occurred'For independent events,the joint probabilitieswould be P(F)P):0.1 x0.2:0'02 P(FaF):o.tXo.8:0.08 P(FnP):0.9X0.2:0.18
P(FaF):o.gxo.8:0.72
The probability of a flood or a power failure during the summerwould be the sum of the flrst three joint probabilitiesabove. P(FUP):P(F nP)+ P(F.P)+p(F lP):e'23 The eventsare dependent,however,from the statementof conditionalprobability: When a flood o""u,. with P(F) : 0'1, a powerfailure will occurwith probability : 0'1 x 0'4 : 0'04 : 0".4, and true joint probability is P(F') x P(P I F) : P(F) + P(P) P(F a P). The proUaUitityof the union is then P(F U P) P(F ) P) : 0.1 + 0.2 - 0.04 : 0.26' Note the contrast: P(F U P) : 0'30 for mutually exclusiveevents P(r U P) : 0'28 for joint but independentevents P(F u P) : 0.26 otherwise The new, more generalrule for the union of probabilitiesis s. P(& u E,) : P(81) + P(E,) P(h . E2)
(26.s)
and a sixth rule shouldbe addedfor conditional probabilities: 6_ " ' P ( E , , l E P(Er r ) : -nwE2)
{26.6)
)
26.2 CONCEPTSOF PROBABILITY
675
be exceeded.Becausethe probability of any single, exact value of a continuous "occur" can also mean the level will be reachedor exceeded.In the variableis 0.0, long run, the levelwould be reachedor exceededon the averageoncein 10 years'lhus the averagereturn period* Z in yearsis definedas T :
1 P(Ft:
(26.7)
and the following generalprobability relation.hold: 1. The probability that F will be equalledor exceededin any year
P@):+
(26.8)
2. The probability that F will not be exceededin any year
(26.e)
P(F):l-P(F):l-+
3. The probability that F will not be equalledor exceededin any of n successlveyears / l\n
p,(F)x &(Fl x . . . x P,(F): P(F)': ( t - ;l \ T l
(26.10)
4. The probability R, called risk, that F will be equalledor exceededat least once in n successiveyears
R : 1 - ( r - + l : 1- I P ( F ) F
(26.rr)
Table 26.1showsreturn periods associatedwith variouslevelsof risk. TABLE 26,1 RETURNPERIODSASSOCIATEDWITH VARIOUSDEGREES OF RISKAND EXPECTEDDESIGNLIFE Expecteddesignlife (Years) Risk
e/.) 75 50 40 30 25 20 15 10 5 2 I
10 4.O2 2.00 7.74 3.43 10.3 4.44 14.5 6.12 7.46 " 17.9 22.9 9.47 31.3 12.8 4 8 I. 19.5 98.0 39.5 248 99.5 498 198.4
6.69 t4.9 20.1 28.5 35.3 45.3 62.0 95.4 195.s 496 996
100
15
11.0 22,t 29.9 42.6 52.6 6'7.7 90.8 r42.9 292.9 743 1492
14.9 29.4 39.7 56.5 70.0 90.1 t23.6 190.3 390 990 1992
18.0 36.6 49.5 70.6 87.4 1r2.5 154.3 238 488 t238 2488
35.6 72.6 98.4 140.7 174.3 224.6 308 475 976 2475 : 4975
72.7 144.8 196.3 28r 348 449 616 950 1949 4950 9953
* The terms return period arldrecurrenceinterval are usedinterchangeablyto denotethe reciprocal of-the.annualprobability of exceedence.
676
CHAPTER 26 PRoBABILITANDSTATISTICS
EXAMPLE 26.1 If Zis the recurence interval for a flood with magnitudeQ*findtbe probability (risk) that the peak flow rate will equalor exceedQ" atleastoncein two consecutiveyears. Assumethe eventsare i4dependent. Solution. The solutionis easilyobtainedby substitutioninto Eq. 26.11.To assistin understapdingthe equations,an alternativederivation follows. The four possibleoutcomesfor the tyo years are: a.' nonexceedance in both years b.' exceedancein the first year only c.' exceedancein the secondyear only d: exceedance in both years Becausethesefour representall possibleoutcomes,the probability of the union o f a , b , c , a n d d i s1 . 0 , o r f r o m E q . 2 6 . 2 , P ( a U b U c U d): l.0.Exceedanceinat least one year is satisfiedby b, c, or d, but not a. Thus the risk of at least one exceedanceis P(b U c U d), which is the total less the probability of a. From Eqs.26.2and26.3,we find that ' z - y e a r r i s :k P ( b U c U d ) : 1 P(a) From Eq. 26.3, we find that P(a) : P(Q < Q"inYear I) x P(Q I Q"inYear 2)
:(t-l-)ft-1-) r/ \ r/\ and
rr
R i s k :r - P ( a ) - 1 - ( t - 1 - ) ' \
.r./
EXAMPLE 26.2 What return period must a highway engineeruse in designinga critical underpass drain to accept only a 10 percent risk that flooding will occur in the next 5 years? Solution R : 1 - (' -;)" .10: 1-
('-i)'
Z : 48.1years
IT
26.3 PROBABILITY DISTRIBUTIONS Randomvariables,either discreteor continuous,are characteizedby the distribution of probabilitiesattachedto the specificvaluesthat the variablemay assume.A random variablethroughoutits rangeof occurrenceis generallydesignatedby a capital letter, and a specificvalueor outcomeof the randomprocessis designatedby a small letter.
26.3 PROBABILIry DISTRIBUTIONS
^ b 0.2 5 ^
P(0)= 0.0s P(1= ) s.15 P(2)= 0.2s P(3)= g.2s
677
P(4)= s.15 P(s)= s.1s P(6) = 9.63 P(7) = s.s2
o.l
Number of cloudY daYsPer week, x
Figure26.L Probabilitydistributionof cloudydaysper week' For example, P(X : x,) is the probability that random variableX takeson the value x,. A shoiter version is p(x,). Figur" 26.1 showsthe probability distribution of the number of cloudy daysin a weet. ft is a discretedistributionbecausethe number of daysis exact;in ihe rlcord from which the relative frequencieswere taken, a day had to-be describedas cloudy or not. Observethat eachof the seveneventshas a finite probability and the sum is 1; that is,
) r(-t,) = t Another important property of random variablesis the cumulativedistribution in X is lessthan or equal function, CDF, definedur ttr" ptotubility that any outcome to a stated,limiting value x. The cumulative diitribution function is denotedF(;r). Thus F(x):P(X=x)
(26.12)
andthe function increasesmonotonicallyfrom a lower limit of zeroto an upperbound of unity. Figure 26.2 isthe CDF of the numberof cloudy daysin a week derivedfrom fig. Z6JUy taking cumulativeprobabilities.The function showsthat the probability is gOqothai the numberof cloudy daysin the week will be 5 or less.Conversely,there is a 10 percentprobability that it will be cloudy for 6 or ! days.This complementary cumula-tiveprolability is sometimescalled G(x), where3 Q6'13) r(x) = P(X> x) G(x):1continuous variables present a slightly different picture. Figure 26.3 is the histogramof an 85-yearr""oid of annualstreamflows.The observationswere grouped into nine intervalsranging from 0 to 900 cfs and the number falling in eachinterval plot the was plotted as frequeniy on the left ordinate.A convenientalternativeis to record streamflow the for cDF The ordinate. right by the relative frequencyas shown districontinuous the increase, observations of number is shownin Fig. 26.4.As the broken the limit, In the intervals. the of size the reducing bution will be developedby curvesof Figs. 26.3 and'26.4 wilt appearas thosein Fig' 26'5' There ls a difference between the ordinates of Figs. 26'3 and 26.5a. Since relative frequencyis synonymouswith probability, it is convenientto reconstitutethe
.7' CHAPTER 26
PROBABILITYAND STATISTICS
vl
3 o.a P(x)
II
^ -' .F o.o
o
.l
0 1 2 3 4 5 6 7
0.4
U
0.05 0.15 0.25 0.20 0.15 0.10 0.08 0.02
0.05 0.20 0.45 0.65 0.80 0.90 0.98 1.00
o.2
0 Figure 26.2
1 2 3 4 5 6 Numberof cloudy daysper week,.r
7
Cumulative distribution of cloudy days per week.
RIR
b il
lt
a q
I o '5 d
o.top
0
1
Figure 26.3
2
3 4 5 6 7 Mean annualflow, -r (100 cfs)
Frequency distribution
8
9
of mean annual flows.
26.9 PROBABILITYDISTRIBUTIONS 679
'
Q . o- ' "r Y o q d
e
0.6
o I o >
04
(J
0.0 0
1
2
3 4 5 6 7 Meanannualflow, x (100cfs)
8
9
Figure 26.4 Cumulative frequency distribution of mean annual flows.
r
Figure 26.5, Csntinuousprobabilitydistributions:(a) probabilitydensityfunction and(b) cumulativedistributionfunction.
680
CHAPTER26
PROBABILITYAND STATISTICS
histograrfrso that the area in each interval representsprobability; the total area containedis thus unity. To do this, the ordinatein eachinterval, sayn/N for relative frequencyor probability, is divided by the interval width, Ax.-The ratio nfN Lx rs literally the probability per unit length in the interval and thereforerepresentsthe densityof probability.The probability n/N inthe interval is fepresentedon the average as AF(x), or F(x + Lxlz) - F(x - L'xl2).We CDF (beforethelimitingprocess) then can define l\x)
:
AF(x)
dF(x)
.. --;liln Ax-o Ax
(26.r4)
dx
which is called the probability densityfunction, PDF.3This function is the density (or intensity) of probability at any point; f(x) dx is describedas the differential probability. For continuousvariables,f(r) > 0, sincenegativeprobabilitieshaveno meaning. Also, the function has the property that ()61\\
1
f(x) dx:
which again is the requirement that the probabilities of all outcomessum to 1. Furthermore,the probability that x will fall betweenthe limits a and b is written
p(a-x-b):lu,r*,o*
(26.16)
Note that the probability that x takeson a particular value,saya, is zero;that is, fo
J
ft-l dx : o
(26.17)
which emphasizesthat nnite proUalilities are definedonly as areasunder the PDF betweenflnite limits. The CDF can now be deflnedin terms of the PDF as P(-*
< X < x) : P(X= x) : F(x) :
t'
I f(u) du
(26.18)
J_*
whereu is usedas a dummy variableto avoidconfusionwith the limit of integration. The area under the CDF has no meaning,only the ordinates,or the difference in ordinates.For example,P(*r3 X = x2),which is equivalentto Eq. 26.16, can be evaluated as.F(x) - F(xt). that cannotbe summarizedin integral form, there are For discrete.distributions analogousarithmetic statementscorrespondingto the propertiesgiven in Eqs. 26.15, 26.16, and26.18.In particular, the distributionof sampleddatetaken from a continuousdistributionis a specialcaseof discretedistributionsand canbe givenin the form Thus of arithmeticsummations.s
),f(',):
t
(26.re)
x1= b
P(a-X
).f(r) r 7 -
(26.20)
26.4 MOMENTSOF DISTRIBUTIONS
681
k
P(x
(26.2r)
For a finite numberof observationsin the sample,/(x) is the probability of xr for each outcomein the samplespaceand thereforeP(x,) : P(xr) : P(xt) : . . . : I /N. Hence/(x) can be replacedwith P(x,) in Eqs.26.19,26.20,and26.21. DXAMPLE26.3 Table B.1 containsthe areabeneatha "standardnormal" bell-shapedPDF.Because the distributionis symmetrical,areasare providedonly on one sideof the center.Use the distribution to determinethe valuesof 1 . P ( 0< z ' 2 ) . 2.P(-2=z=2). 3. P(z > 2\. 4. P(z< -1) Solution 1. P ( 0 = z < 2 ) : . 4 7 7 2 . ) F r o m s y m m e t r y ,P ( - 2 - z < 0 ) : Since P ( 03 2 3 2 ) : . 4 7 7 2 . P(-Z = z < 2) : P(-2 3 z 30) + P(0 3 z 3 2), thenP(=2 = 7 < 2) : .4772+ .4772: .9544. 3. This is the areaunderthe curvein the right tail beyondz : 2.0. Becausethe area right of center (z : 0) is .5000, P(z = 2) : P(z > 0) - P(0 = z = 2 ) ,o r P ( z > 2 ) : . 5 0 0 0 - . 4 7 7 2 : . 0 2 2 8 . 4 . F r o m t h es o l u t i o n t(o3 ) ,P ( z = - 1 ) : P ( z = 0 ) - P ( - l = z < 0 ) ' B y s y m m e t r y , P ( - 1= z = 0 ) - P ( 0 3 z s ! ) : . 3 4 1 3 , a n d P (=z - 1 ) : .5000- .3413: .1587. rr
16.4MOMENTS OF DISTRIBUTIONS The propertiesof many random variablescan be definedin terms of the momentsof the distribution. The moments representparametersthat usually have physical or geometripalsignificance.Readersshould recognizethe analogybetweenstatistical momentsand the momentsof'areasstudiedin solid mechanics. The rth moment of a distribution about the oriein is definedaso
tri : p::
x'f(x) dx
2*,rr,r:12,,,
(26.22) (26.23)
The first moment aboutthe origin is the mean,or as it is commonly known, the average.It determinesthe distancefrom the origin to the centroid ofthe distribution frequencyfunction. The prime is normally usedto signify momentstaken about the origin, but the mean is often written as p insteadof y'.
682
CHAPTER26
PROBABILITYAND STATISTICS
Moments can be definedabout axesother than the origin; the axis usedextensively in deflninghigher momentsis lhe mean or, as given above,the first moment about the origin. Thus I
p,: or
|
I
(x - rd'f\xl dx
1<
tr,:
;)-G,
- p)'
(26.24\ (26.25)
Wheneverp', or p', are defined for r =l 1, . , the distribution/(x) is completd defined.It seldomis necessaryto computemore than the first three rnoments; ieveral important distributionsrequire only two. The momentsare usedto specifythe parametersand descriptivecharacteristicsof distributions that follow in the next section.Becausevarious characteristicsof distributionsare describedby combinations of the momentsaboutthe meanand origin, the following relationsare occasionally helpfull'a: Ft: 0 -p' Pz: FL ttt
:
- 3plr, * 2p'
tJ"'z
(26.26) (26.27) (26.28)
CHARACTERISTICS 26.5 DISTRIBUTION ,'
Characteristicsof statisticaldistributionsare describedby the parametersof probability functions, which in turn are expressedin terms of the moments.The principal are centraltendency,the groupingofobservationsorprobability about characteristics a central value;variability, the dispersionof the variate or observations;and skewness,thedegreeof asymmetryof the distribution.The theoreticalfunctions shownin Fig.26.6 exhibit approximatelythe samegrouping about a central value,but/2 has a pronouncedright-skewwhilefi is much greatervariability thanfl, andf possesses symmetrical.
Symbol Gonvention In introducingthe parametersof distributions,the usual sequenceof statisticalproblemswill be followed-that is, parametersare derivedfrom the distributionof sample data and usedas estimatesof the parametersof the population distribution' Summa-
Figure 26.6 Symmetrical and skewed probability distribution for continuousvariables.
26.5 DISTRIBUTIONCHARACTERISTICS
683
tion forms of integralsare usedto computemomentsfor samples.For example,the meanof sampledatais designatedTandit is usedasthe bestestimateof the population mean.By convention,Greek letters are usedto denotepopulation parameters.
CentralTendency The familiar arithmetic average,the mean, is the most used measureof central tendency.It is the first moment about the origin and is designated
r:lf*,
(26.2e)
n i=t
of the population mean p. The statistic 'Meansx is the best estimate mean-for example, the geometric mean the arithmetic other than : ,l> (Ilx,)-are alsoused.Two addi. . . : (x624 meani or harmonic x,)1/" T median,which is the middle value of the the are tional measuresof central tendency into equal areas,and the mode, which in the distribution observeddata and divides and in continuousvariables frequently most discretevariablesis the value occurring in Fig. 26.6. illustrated three are probability A11 density. is the peak value of
Variability Dispersioncan be representedby the total rangeof valuesor by the averagedeviation aboutthe mean;however,the parameterof statisticalimportanceis the meansquared deviation as measuredby the secondmoment about the mean. The parameteris termed the varianceand is designatedby
c r : rn f
G , -p ) '
i=l
(26.30)
But the population mean /-{,is not known precisely and therefore it is necessaryto computeinstead n .
s-:
\
r
Z\xt-xf
(26.3r)
- 1 in place of n in As the best estimateof o2, the quantity s2 is found using n loss of a degreeof freedom the Eq. 26.30.The reasoningfor this substitutioninvolves text. of this scope by using 7 insteadof p, but a proof is beyondthe The squareroot of the varianceis a statisticknown as the standard deviation (o or s), in which form variability is measuredin the sameunits asthe variateand the mean,and henceis easierto interpretand manipulate.The coefficientof variation C", definedas cf p, or sfi, is an expressionuseful in comparingrelative variability.
Skewness A fully symmetricaldistribution would exhibit the property that all odd moments weightto either side equalLero.A skeweddistribution,however,would haveexcessive of the centerand the odd momentswould exist. The third moment a is
o : ! n2 @ , - p ) ' i:l
(26.32)
684
CHAPTER26
PROBABILITYAND STATISTICS
The best-estimateof the third moment is computedby n
u -
(n-r)(n-2)
s
Z-J
\xi-
x)'
(26.33)
The cofficient of skewnessis the ratio afc3 and is estimatedby n -- Q---; (--
(26.34)
J
For syfnmetricaldistributions,the third moment is zero and C" : 0; for right skewness(i.e.,thelongtailtotherightside)C">0,andforleftskewnessC"<0.ThePDF forfi shownin Fig. 26.6has a right or positive skew.The property of skewnessis of questionablestatisticalvaluewhenit mustbe estimatddfrom lessthan 50 sampledata points. EXAMPLE 26.4 Determinethe distributionparametersand comparethe distributionsof annualrainfall for the records shown inTable 26.2.
TABLE 26,2 ANNUAL RAINFALLFOR SELECTEDCITIES Annual rainfall(in.)
Year
t928 r927 t926 t925 r924 1923 1922 t92l 1920 1919 1918 t9t7 1916 1915 t914 t913 t9t2 1 9 1I 1910 1909 1908 1907 1906
Anniston, AL 48 49 )) 98 43 53 56 47 69 57 61 64 99 54 40. 47 58 44 44 64 44 51
LosAngeles, CA
Richinond, VA.
9
43 44 38 31 47 49 52 31 51 40 4l 43
lo
t9 9 8 6 15 20 11 o 18 8 23 t7 23 17 10 18 5
3 t
36 J+
)4
38 36 37 43 34
l9 l5 21
49 47
JJ
26.7 CONTINUOUSPROBABILITYDISTRIBUTIONFUNCTIONS
685
Solution Parameter Mean, ;r Standarddeviation,s Coefficientof variation,C, : s/V Coefficient of skewness,C": a/s3
Anniston 57.2 in. 15.5in. 0.27 1.69
LosAngeles
Richmond
14.9 in. 5.9 in. 0.40 -0.16
41.5in. 6.7in. 0.16 0.16
Comments.(1) Anniston's record shows a high annual averageand a fair$ large variability. In particular,Anniston'sdistributionhas a pronouncedright skew,caused principally by two very large observedvaluesin this short period of record. (2) Los Angeleshasa small annualaveragebut a very largevariability and a slightly negative skewness.(3) Richmondhasthe mostuniform distribution:a relativelysmall variability and only a slight positive skewness. I I
FUNCTIONS DISTRIBUTION 26.6 TYPESOF PROBABILITY
r
Many standardtheoreticalprobability distributionshavebeenusedto describehydrologic processes.It should be emphasizedthat any theoretical distribution is not an exactrepresentationof the natural processbut only a descriptionthat approximates the under$ing phenomenonand has proved usefui in describingthe observeddata. Table26.3summarizes{he commondistributions,giving the PDF,mean,andvariance of the functions. The distributions presentedin the table have experiencedwide applicationand are derivedand discussedin many standardtextbookson statistics.In the material to follow, only aspectsof the most usbd distributionsare given. The usesof binomial and Poissondiscreteprobability distributionsinTabIe26.3 arerestrictedgenerallyto thoserandomeventsin which the outcomecanbe described trials are independentand either as a successor failure. Furthermore,the successive to trial.3'a In a sense,the trial from constant probability remains of success the techniques' or enumerating counting are distributions discrete common The binomial distributionis frequentlyusedto approximateother distributions, and vice versa.For example,with discretevalues,when n is large andp small (such thal np ( 5 preferably),the binomial approachesthe Poissondistribution.This is a single-parameterdistribution (i : np) and is very useful in describringarrivals in queueingtheory. When p approachesI and n grows large, the binomial becomes indistinguishablefrom the normal distribution describedin the next section.
FUNCTIONS PROBABILIryDISTRIBUTION 26.7 CONTINUOUS Most hydrologicvariablesare assumedto be continuousrandom processes,and the as in frequency commoncontinuousdistributionsare usedto fit historical sequences, for continalso important are (Chapter applications 27). Other for example analysis, for computing is the basis distribution uniform The elementary distributions. uous
7 686
CHAPTER 26
PROBABILITYAND STATISTICS
^ ,-", i l {
+
N H
n d
, C)N
g b
.gb
G
\
B l
r
q ^
o d
*
J
co.
l
l
, L t ,
i
I
B -'i.l
I
(UN
> o
a
l
,
'\-..7
d
+
.i l \ F t.x
x -:
Nl'\a
t v
bl tl H
T
b
, tl h
x el
r-
a f f tx o o
s
1
+
d
\,
Ir o
o (!
tr
s \/" x rir
: "
" vr x
'
v
I
o \/t :'
x
\/r "'
8 vr '' R \,r vt
-r
IL
tr l dl
o
fr
o
z
r
;
Vl
.,
/\r
o o
a o z o
E U' 0)
o o lt
o tu J o
s
o E E
(!
I
IL
F
a? (o 6|
UJ @
E€E E ;F = .E
d
E E6 a. f
'.
-E
r
E
E "
= i ' 5E
E 3 E !
E6iE
l
s
F[ E S EZ B i ' oO eF E
l
l
-{.
a
-\? .
8
+
+ -
^r
8 vl ^
z o ut a U)
B l
+ x J CO-
N I N t i - l xrnl -il
f
sr
{ o J o
dlJa , +'
o ,? ,9, k
vl x
"'
'
'
\/l
M
u ;
d
6
I
l\t
^
26.7 CONTINUOUSPROBABILIW DISTRIBUTIONFUNCTIONS
687
randomnumberssoimportant in simulationstudies.The wholebody of materialin the area of reliability and estimatingdependson derived distributionslike Student'sl, chi-squared,and the F distribution. The explanationsthat follow concernthe more The readeris referred common distributionsappliedin fitting hydrologicsequences. to standardtexts for more detailedtreatment.3-6
NormalDistribution frequencyfunction, alsoknown The normal distributionis a symmetrical,be,ll-shaped as the Gaussiandistributionor the natural law of errors. It describesmany processes that are subjectto random and independentvariations.The whole basisfor a large body of statisticsinvolving testing and quality control is the normal distribution. Although it often does not perfectly fit sequencesof hydrologic data, it has wide application, for example, in dealing with transformed data that do follow the normal distributionand in estimatingsamplereliability by virture of the central limit theorem. The normal distribution has two parameters,the mean p, and the standard deviationa, for which 7 and s, derivedfrom sampledata, aresubstituted.By a simple transformation,the distribution can be written as a single-parameterfunction only. Definingz : \x - t-i/o, dx : o dz,the PDF becomes
(26.3s) and the CDF becomes F\z) :
-r*l-
e-"2/2du
(26.36)
The variablez is called the standardnormal variate; it is normally distributed with zero mean and unit standard deviation. Tables of areas under the standard normal curve, as given in Appendix B, TableB.1, serveall normal distributionsafter standardizationof the variables.Given a cumulativeprobability,the deviatez is found in the table of areasand x is found from the inversetransform: x:p+za
or x:7lzs
(26.37)
EXAMPLE 26.5 Assumethat the Richmond,Virginia, annualrainfall in Table 26.2 follows a noimal distribution.Usethe standardnormal transformationto find the rain depththat would havea recurrqnceinterval of 100 years. Solution. From example26.4,the meanis 41.5 in. and the standarddeviation is 6.7 in. This gives x:
4L5 + z(6.7)
Equation 26.18 showsthat the areaunder the PDF to the right of z is the exceedence probabilprobability of the event.For the 100-yrevent,F;q.26.7 givesthe exceedence Table 8.1 in From the figure accompanying tlI00:0.01. ity Pk): llT,:
688
CHAPTER26
PROBABILIry AND STATISTICS
Appendix B, F(z) : 0.5 - p(z) : 0.49, and z : 2.326 by interpolating the table' The expected100-yr rain depth is therefore x : 4t.5 + O.32O X 6.7 :57.1 in: The 100-yr event for a normal distribution is 2.326 standarddeviationsabove the mean. rt
Log-NormalDistribution partly dueto the influence Many hydrologicvariablesexhibit a markedright skewness, other lower limit, and or some zero, greater than values phenomena having of natural frequencieswill cases, In such range. the upper in theoreiically, beingunconstrained, distribunormal a follow may logarithms but their distribution, normal not follow the y : substituting from comes log-normal for the 26.3 in Table PDF shown tion.7The ln x in the normal. With p, andcy as the mean and standarddeviation,respectively, the following relations have been found to hold betweenthe characteristicsof the untransformid variatex and the transformedvariatey:r'7
p:exp(p'y+412) oz:p,2lexp(d)_11 a : lexp(3fi) - 3 exp(fi) + 2lC3 C,:lexp(dr) - r1t'' C,:3C" * Cl
(26.38) (26.3e) (26.40) (26.4r) (26.42)
Also p, : lfl M, whereM is the median value and the geometricmean of the x's. The log-normal is especiallyusefulbecausethe,transformationopensthe extensive body oI theoreticaland applied usesof the normal distribution. Since both the normal and log-normal are two-parameterdistributions,it is necessaryonly to compute the mean-andvarianceof the untransformedvariatex and solveEqs. 26.38 and 26.39 simultaneously.Information on three-parameteror truncatedlog-normal distributionscan be found in the literature.r'7
Gamma(and PearsonTYPelll)
-
The gammadistributionhaswide applicationin mathematicalstatisticsandhas been usedincreasinglyin hydrologicstudiesnow that computingfacilities make_iteasyto evaluate the gimma functioi insteadof relying on the painstakingmethod of using tablesof the incompletegammafunction that lead to the CDF, P(X < x). In greater useis a specialcaseof gamma: tbePearsonType/1L This distributionhasbeenwidely adoptedas the standardmethodfor flood frequencyanalysisin a form known as the log-pearson /11 in which the transform y :1og x is used to reduce skewness.8-r0 Aithough all three momentsare requiredto fit the distribution,it is extremelyflexible in that a zeroskewwill reducethe log-PearsonIII distribution to a log-normal and the pearsonType III to a normal. Tablesof the cumulativefunction are availableand A very important property of gamm-avariates will be explainedin a later section.lo'11 aswell asnormal variates(includingtransformednormals)is that the sumof two such variablesretains the samedistribution. This feature is important in generatingsyn-thefie hy-drologic sequences.l''''
26J
CONTINUOUSPROBABILITYDISTRIBUTIONFUNCTIONS
689
Gumbel'sExtremalDistribution The theory of extreme valuesconsidersthe distribution of -the largest(qr smallest) observationsoccurringin eachgroup of repeatedsamples.The distribution of the nt extreme values taken from n, samples,with each samplehaving n2 observations, dependson the distribution of the nrn, total observations.Gumbel was the first to employ extremevalue theory for analysisof flood frequencies.laChow has demonstratedthat the Gumbel distribution is essentiallya log-normal with constantskewness.tsThe CDF of the densityfunction given in Table 26.3 is
P(X - x) : F(x) : exp{-expl-o(, - u)l}
(26.43)
a convenientform to evaluatethe function. Parametersa andu are given asfunctions of the meanand standarddeviationin Table26.3.Tablesof the doubleexponentialare usually in terms of the reducedvariate,y - a(x - u).tuGumbel also has proposed anotherextremevaluedistributionthat appearsto fit instantaneous(minimumannual) droughtflows.17'18
CDFsin Hydrology Normal and Pearsondistributionscan often be usedto describehydrologicvariables if the variableis the sum or mean of severalother random variables.The sum of a numberof independentrandom variablesis approximatelynormally distributed.For example,the annualrainfall is the sumof the daily rain totals,eachof which is viewed as a random variable. Other examplesinclude annual lake evaporation, annual pumpagefrom a well, annual flow in a stream,and mean monthly temperature. The log-normal CDF hasbeen successfullyusedin approximatingthe distribution of variablesthat are the product of powersof many other randomvariables.The logarithm of the variableis approximatelynormally distributedbecausethe logarithm of productsis a sum of transformedvariables. Examplesof variablesthat havebeenknown to follow a log-normal distribution include:
1. Annual seriesof peak flow rates. 2. Daily precipitationdepthsand stremflowvolumes(alsomonthly, seasonal, and annual).
3. Daily peak dischargerates. 4. Annual precipitation and runoff (primarily in the westernUnited States). Earthquakemagnitudgs, 6. Intervalsbetweenearthquakes. 7. Yield stressin steel. 8. Sediment sizes in streamswhere fracturing and breakageof larger into smaller sizesis involved. 5.
The PearsonType III (a form of gamma) has been applied to a number of variablessuch as precipitation depths in the easternUnited Statesand cumulative watershedrunoff at any point in time during a given storm event.The transformed log-PearsonType III is most usedto approximatethe CDF for annualflood peaks.If the skew coefficient C" of the variable is zero, the CDF reverts to a log-normal.
690
ANDSTATISTICS 26 PROBABILIry CHAPTER It hasalso.beenusedwith monthly precipitationdepthandyield strengthsof concrete members. A useful CDF for values of annual extremeis the Gurnbel or extreme value probability of distribution.The meanof the distributionhas a theoreticalexceedance streamshave peaks in natural years. Flood 2.33 T of interval 0.43 and a recurrence 2.33-year with means including disffibution, to this conformance exhibited strong variptraight-line fit for Gumbel a produces paper that Graph recurrenceintervals. is A sample extremes. annual of graphical tests for useful and ables is a available peak rates, discharge annual peak to applie! has been 27 The CDF shownin Fig. .2. wind velocities,drought magnitudesand intervals, maximum rainfall intensitiesof given durations,and other hydrologicextremesthat are independentevents.
AND CORRELATION LINEARREGRESSION 26.8 BIVARIATE Correlation and regressionproceduresare widely used in hydrology and other sciences.teThe premiseof the methodsis that one variableis often conditionedby the value of another,or of severalothers,or the distribution of one may be conditioned by the value of another. Just as there are probability density functions (PDFs) for evaluatingthemarginal probability of a variable(seeSection26.2), so also are there PDFsforlhe conditional probabilities (also describedin Section 26.2) of variables. The conceptis illustrated in Fig. 26.7. For two variables,the bivariatedensityfunction,/(y li,), ptottedin the vertical on the frgure,changesfor eachvalueof x' The one shownappliesonly to variationsin y whenx : xr. Different distributionsmight occur for other valuesof x. A measureof the degreeof linear correlationbetweentwo variablesx and y is thelinear correlation cofficient, P*,y. Avalue of p',, : 0.0 indicatesa lack of linear
Pylr regressionline
Figure 26.7 Bivariate regressionwith conditional probability function.
ANDCORRELATION691 REGRESSION LINEAR 26.8 BIVARIATE correlation andp,,, : + 1.0 meansperfect correlation.The correlationcoefficientis found from cr.v
cov(x, y)
u - , , :
(26.44)
-
(l*ay
aroy
where o, ando, are the variancesof eachvariable,respectively,(seeEq. 26.30), and cov(x, y) is the covariancesharedby the two variables,definedas
y) : c,.,: cov(.r,
f _f _Q'-
p)(y - p,)f(x,v)dvdx
(26'4s)
The samplecorrelationcoefficient,r : s,.rfs*s* is usedto estimatep',r. The sample covarianceis found from the squareroot of
s?,,:
(26.46)
The regressionline shown on Fig. 26.7 is derived to passthrough the mean valuesof the distributions,so that for any given value of x, the mean value of y I x (read"y givenx ") can be estimatedby the regressionline. The standarderror of the estimateof y I x is depictedby the line drawnthrough the conditional distributionsat a distanceof one standarddeviationfrom the mean.If the conditionaldistributionsat all x-valuesare normal,it canbe shownthat the meanvalue,&y1,,of the conditional distribution is related to the meansof .r and H or a & , 1 , : l t ', * ( p 2,( * and the variance is
4t.
: #['" -
-
tr,)
, @- p)'1
d
l
t26.47)
(26.48)
where
a?: 40 - p')
(26.4e)
which is the variance of the residualsof the regression.Just as the mean of the distribution requiressubstitutionof the given value of x into Eq. 26.47, so also does thevariance,Eq.26.48. Whenthe valueof x in Fig. 26.7is setiequalto A the standard error of the meanis O-t=:
ae -----7
(26.s0)
VN
Equation 25.47 is linear and expressesthe linear dependencebetweeny and .r as slrownin Fig. 26J.The meanvalue of y can be computedfor fixed valuesof x' Also, if the correlationbetweenthem is significant,one can predict the valuesof y with less error than the marginal distribution of y alone.In fact, from Eq. 26.49, the fraction of the original varianceexplainedor accountedby the regressionis
o":t-*
(26.5r)
692
CHAPTER26
PROBABILIry AND STATISTICS
It can be seenalso from Eq. (26.47) that the slopeof the regressionline is cy
PA:
tl,yl,
-
lLy
(26.s2)
r - rr^
or, ifx andy are standardized,then p itself is the slope,where - p,r)/a, }rnt. (26.s3) p = (x - t*)/o, The bivariatecasecan be expandedto coverhigher-order,multivariatedistributions.
EQUATIONS 26.9 FITTINGREGRESSION
ing value of x. The line to be fitted is
(26.s4)
-l Bx; Y,: a
N 6 I
Year
, ? ; xbo
A1
J . F
/1 !
Q
43 44
3[i e
o
46 47 48 49 50 51 52
F r ;
h
d
B U r40 120 100 80 Lowest annualflow for 1 day (cfs) JacksonRiver at FallingSprings,Virginia, 1941-1952 60
Mean = Standard deviation =
Jackson River
Cowpasture River
61 92 65 72 82 67 74 t 18 t24 r08 65 88-
58 81 70 63 68 58 74 105 134 108 93 85
84.7
83.1
21.7
23.2
: 0.86. : Figure 26.8 Cross-correlationof low floWs.Regressionline: Iz 4.94 + 0.923X; r
EQUATIONS 693 26.9 FITTINGREGRESSION The best estimates of o and B are sought. Thus to minimize
) 0, - f), : ) ly, (o + px,)1,-
(26.ss)
wherey, are the observedvalues and!,ate the estimatedvaluesfrom Eq. 26.54,take partial derivativesas follows:
- (q+ n')r} *{> b, - (o* B';l'} tv, #{>
(26.s6) (26.s7)
After carrying out the differentiationsand summations,two equationsresultin a and B, callednormal equations.
o
(26.s8)
2*,y,-")xt-F2*?:o
(26.se)
)y,
- n d- B ) " , :
SolvingEqs. 26,58 and 26.59 simultaneouslyyields
2 v , F 2 ^' i : y - B T
e:--
n
(26.60)
n
P: =7 - (>;y[
(26.6r)
Recall the slopeis p(arf o), or as estimatedfrom sampledata B:
rt;
(26.62)
Also, the unexplainedvariancein the regressionequationis
4:4Q-p')
(26.63)
and is the squareroot of which is the standarddeviationof residuals(seeFig' 26'8) cailed the standard error of estimate.Thesecan be estimatedfrom
*-: 4 ns -l (z r - r , )
(26.64)
s2":
(26.6s)
2(y,-il'
wherey, and i, are as definedpreviously(seeEq' 26'55)' ivtanynyarotogicvariablesare linearly related,and after estimatingthe regresrangeof sion coeffici"ntr, p."di"tion of y can be madefor any value of x within the be should but performed often is observedx values.Extrapolationoutsidethe range
7
694
CHAPTER26
PROBABILIry AND STATISTICS
done wfth caution.Equation 26.48 showsthat the variancein the estimateof y for a givenx valuebecomeslargewhenx is severalstandarddeviationsaboveor belowthe mean. EXAMPLE 26.6 The lowestannualflows for a l2-yr period on the Jacksonand CowpastureRivers are tabulatedin Fig. 26.8. The stationsare upstr€amof the confluenceof the two rivers that form the JamesRiver. Find the regressionequationand the correlationbetween low flows. Solution
t
: The basicstatisticsare2 x : 1016;) y : 997;2 x2 : 9 1 , 2 1 6 ; 2 l " 88,777;and2 xy : 89,209. For the two-variableregressiona and B are found from Eqs.26;60and 26.61,. _ [ ( 8 9 , 2 0 e ) ( l 0 t 6 x e e 7 ) / ( 1 2 ) ] :o Q o ? (9r,216)- (tor6)' 102)
o:
ee7 (0.e23x1016) : 4'9r i
The regressionis y : 4.91 + 0.923x. 3. The correlation coefficientfrom Eq.26.62 is
(0.e23)(2r.7) : 23.2
0:86
4. From F;q.26.64the standarderror of estimate,s,, is 11.7,which is plotted line in Fig. 26.8. rl as limits aroundthe regression
Coefficientof Determinationfor the Regression A regressionequationreplaces(and extends)the data used in its development.Becauseit cannot reproduceall the basedata, the processresults in the loss of some information. This not only includeslossof information aboutparticular pairs of data, but alsoaboutthe variability of the data.The variancesf is a statisticalmeasureof the variability of the measuredvaluesof y. The greaterthe valueof sl, the wider the spread of points aroundthe mean.The percentageof information aboutthe variancein y that is retained,or explainedby, the regrdssionequationis called the cofficient of determination, Cr. To determineits value,the residualsor departures(differencesbetween actual and estimatedy values)haveknown variance4, which representsthe unaccountedvariance in the regressionequation.The explainedvariance would be the difference, 4 - o2, and the percentageretained (coefficient of determination)is
(26.66)
695
26.9 FITTINGREGRESSIONEQUATIONS
Comparisonwith Eq. 26.49 revealsthat Co= Pz
,
(26.67)
Thus the squareofthe correlationcoefficientp is the percentageof d, explained by the regression.For any sample of data the coefficient of determinationr2 is estimatedas sl,rlslsl . A large r2 indicatesa goodfit of the regressionequationto the databecausethe equationaccountsfor or is able to explaina large percentageof the variation in the data. EXAMPLE 26.7
Determinethe coefficientof determinationfor the regressionin Example26.6. Solution, From Eq. 26.67,the coefficientof determination,r2, is 0.7396. "accountsfor" about 74 Thus, the regressionequationadequatelyexplainsor pefcent of the original information abouty containedin the raw data. Twentysix percentof the information is lost. I r For examThe bivariateexamplecan be extendedto multiple linear r'bgressions. ple, the linear model in three variables,with y the dependentvariable andx1 andx2 the independentvariables,has the form y:a*F$t*Fzxz
(26.68)
y : an-r Br) t' * FrZ *,
(26.6e)
The normal equationsare )
xtt FrZ*?+ Fr2*,*,
(26.70)
2 yr r : * ) xzI Fr 2 *r *, + F"2 *7
(26.7r)
)y"':
")
The squareof the standarderror of estimateis
s7:
2(y,-r,)'
(26.72)
where y, are the observedvaluesand y-,are predictedby Eq. (26.68). The multiple correlation coefficient is
+) ^2\ | /2
n : ( t -
s;/
(26.73)
in Hydrology LinearTransformations Strongnonlinearbivariateand multivariatecorrelationsare also commonin hydrology, ind various mathematical models have been used to describethe relations. Piiabolic, exponential, hyperbolic, power, and other forms have provided better
696
CHAPTER26
PROBABILITYAND STATISTICS
graphicalfits than straight lines. Becauseof difficulties in the derivation of normal equationsusing least squaresfor thesemodels,many can be transformedto linear forms. The most familiar transformationis a linearizationof mtiltiplicative nonlinear relationsby using logarithms.For example,the equation Y :
(26.74)
g,xft1$z
becomes linear when logarithms are taken, or
log y : log * + Bllo$ x1 -f B"Iog x2
(26.7s)
The log transformationprocedureresultsin a linear form when the logarithms are substitutedin Eqs. 26.60 and 26.61.For of one or both setsof measurements example,if a bivariateparabolicform I : qXb is suggestedby the data, logarithms allow use of the linear form log Y : log a -t b log X. The normal equationscan be usedby redefiningy : log Y,x : logX, e : log a, andF : b,thereby transforming the equationto y : a * Bx. The regressioncannow be performedon the logarithms, valuesof a and B determined,and the estimateof a is found by taking the antilog of a. This transformationis possiblefor severalother nonlinearmodels,someof which are shown in Table 26.4. The variablesx and y must be nonnegative,with values preferably greaterthan 1.0 to avoid problemswith the log transformation. OF NONLINEARFORMS TABLE 26,4 LINEARTRANSFORMATIONS
Equation Y=A+BX Y = BeAx Y:AXB Y:ABx
Abscissa X log X X
Ordinate I
log Y log Y log Y
Eouationin linearform
lY): A + B[x] tbc rl : locB + A(1oge)[x] tbc rl : bc A + B[tocX] lloCrl : loCA + (loCB)[X]
Note.'Variables in brackets are the regressionvariates.
EXAMPLE 26.8 In the following exhibit (Table 26.5) preparedby Beard,20the regionalcorrelationis soughtof the standarddeviationof flow logarithmswith the logarithmsof the drainage areasize'andthe numberof rainy daysper year;X, is setequalto ( 1 t log s) to avoid negativevalues.Find the regressionequationand the multiple correlationcoefflcient. Solution 1. From Eqs.26.69,26.70, and26.7l, the parametersare -0.49 a : 1.34; Fr : -0.013; Fz: and the regressionequationis Xt : 1.34- 0.0I3X2- 0.49X3 log s : 0.34 - 0.013log(DA) - 0.49 log(days) or 2. The multiple correlation coefficient from Eqs' 26.72 and 26.73 is R : 0.56. ll
697
APPLICATIONS AND CORRELATION 26.10 REGRESSION TABLE 26.5 LOGARITHMICDATA FOR 50 GAUGINGSTATIONS Xr:1 + logs Station number (1) I
2 3 4 5 o
7 8 q
t0 11 t2 I J
t4 15 16 17 18 19 20 21 22 LJ
24 25 26 27 28 29 30 3l 32
Xz: logDA
X2 (2)
x3 (3)
x1 (4)
1.61 2.89 4.38 3.20 3.92 1.61 3.2r 3.65
2.11 2.12 2.ll 2.04 2.07 2.04 2.09 1.99
0.29 0.18 o.l7 0.44 0.38 0.3'7 0.30 0.35 0.16 0.11 0.32 0.34 0.25 0.43 0.2'l 0.25 0.52 0.18 0.39 0.40 0.25 0.23 0.54 0.51 0.45 0.63 0.45 0.59 0.46 0.32 0.96 0.12
3.23 z.rs
2.08 4.33 1.60 2.09 2.00 2.82 2.00 2.40 2.09 3.69 2.18 2.19 2.09 2.17 |.91 4.48 1.9s 4.95 2.21. r.97 2.08 3.4r 4.82 l 88 r.93 r.78 L.74 4.39 3.23 2.01 3.58 2.04 1.64 1.78 1 . 76 4.58 3.26 1.93 1.81 4.29 1.23 1.89 1.48 3.44 |.97 2.lt
Per Year & : log numberof rainY-daYs
Station numDer (5) 33 34 35 36 37 38 39 40 4I 42 43 44 46 4'7 48 49 50
>X x 2 XX, 2 X2 X2/n
x2 (6)
xs
1.94
1.87 t.36 1.81 1.58 1.48 1.89
z.tJ
3.63 1.91 2.26 2.97 0.70 0.30 3.38 2.87 2.42 4.53 3.04 4.13 |.49 5.37 1.36 2.31
r.32 1.54 1.62 2.03 2.26 1.93 1.78 2.00 2.Or 1.95 2.tl 2.23
x1 (8) 0.20 0.58 0.64 0.37 o.27 0.54 0.63 0.78 0.46 0.44 0.24 -0.03 0.30 0.17 0.14 0.10 0.27 0.18
r4'7.55 2.951 503.7779 435.4200 68.3579
96.24 1.925 285.5627 284.0042 1.5585
17.89 0.358 51.1527 52.7934 -r.640' 7
1.5585
r8'7.59r2 185.2428 2.3484
33.2598 34.4347 -r.r749
2 XX, 2 X2 X,ln
2X2X'ln 2 xx1
(7\
-1.6407
Note: x = X - X. (AfterBeard.2o)
2 6 ' I o R E GR E S S |o N A N D C o RRELAT|oNAPPL|CAT|oNS
8.1635 6.4010 L7625
698
26 PROBABILIry ANDSTATISTICS CHAPTER desiredJtatisticalparameteras dependentvariable,and the appropriatephysicaland The proceclimatic variableswithin the basinor regionas the independenlvariables. dures are signiflcantly better than using relatively short historical sequencesand point-frequencyanalysis.Not only doesthe methodreducethe inherently large sampling errors but it furnishesa meansto estimateparametersat ungaugedlocations. There are limitations to the techniquesof Section 26.9. First, the analyst assumesthe form of the model that can expressonly linear, or logarithmically linear, dependence.Second,the independentvariablesto be includedin the regressionanalysis are selected.And, third, the theory assumesthat the independentvariablesare indeedindependentand are observedor determinedwithout error. Advancedstatistical methodsthat are beyondthe scopeof this text offer meansto overcomesomeof theselimitations but in practiceit may be impossibleto satisfy them. Therefore,care must be exercisedin selectingthe model and in interpretingresults. Accidental or casualcorrelationmay existbetweenvariablesthat are not functionally correlated.For this reason,correlationshouldbe determinedbetweenhydrologic variablesonly when a physicalrelation can be presumed.Becauseof the natural dependencebetween many factors treated as independentvariables in hydrologic studies,the correlationbetweenthe dependentvariableand eachof the independent variablesis different from the relative effect of the sameindependentvariableswhen analyzedtogetherin a multivariatemodel. One way to guard againstthis effect is by screeningthe variablesinitially by graphical methods.Another is to examine the results of the final regressionequationto determinephysicalrelevance. Alternatively,regressiontechniquesthemselvesaid in screeningsignificantvariables.When electroniccomputationis available,a procedurecanbe followedin which successive independentvariableSare addedto the multiple regressionmodel, and the relative effect of eachis judged by the increasein the multiple correlationcoefficient. Although statisticaltestscan be employedto judge significance,it is useful otherwise to specify that any variable remain in the regressionequation if it contributesor explains,say,1 or 5 percentofthe total variance,or ofR2. A frequentlyusedrqethod is to computethe partial correlation cofficients for each variable.This statistic representsthe relative decreasein the varianceremaining( 1 - R') by the addition of the variablein question.If the varianceremainingwith the variableincludedin the regression is (I - Rz) : pz and the variance remaining after removal is (l - R'') : D'', then the partial regression correlation coefficient is
\D'' - D')lD''.
Most PC spreadsheetsoftware packageshave statistical routines for all the analysesdescribedhere and many more. Most are extremelyflexible,requiringminimal instructions-andinput data other than raw data. Specialmanipulationscan effect an interchangeol dependentand independentvariables,bring one variableat a time into the regression equation, rearrange the independent variables in order of significance,and perform various statisticaltests.
ExtendingHydrologicRecords Regressiontechniquesfrequently can be used to extend short records if significant correlation existsbetweenthe station of short record and a nearby station with a - lorigerreeord.Iq Example26.6,if the JacksonRiver recordswerecompletefromI94l
PROBLEMS 699 to datebut the Cowpasturerecordswere incompleteafter 1952,the cross-correlation could be usedto estimatethe missingyearsby solvingthe regressionequationfor I from 1953 on usingthe X flows as observed.The reliability of suchmethodsdepends on the correlationcoefficientand the length ofthe concurrentrecords.Ifthe concurrent record is too short or the correlation weak, the standarderror of the parameter to be estimatedcan be increasedand nothing is gained.The limiting value of crosscorrelation for estimatingmeansis approximatelyp : l/\/ n, where n is the length of the concurrentrecord.21Thus any correlation above0.3 would improve the Cowpasturerecords.Estirnatesof other parameterswith larger standarderrors require highercross-correlationfor significantimprovement.Extendingor filling in deficient recordsoften is necessaryfor regional studiesin which every record usedshould be adjustedto the samelength.
HydrologicVariables Regionalized Predicting.
'
Cruff and Rantzz2studiedvariousmethodsof regional flood analysisand found the multiple regressiontechniquea better predictor than either the index-floodmethod (Chapter27) or the fitting of theoreticalfrequencydistributionsto individual historical records.They flrst usedregressiontechniquesto extendall recordsto a common base length. Next they extrapolatedby various methods to estimate the 50- and 100-yearflood events and with multiple correlation examined several dependent variables including the drainage areaA, the basin-shapefactor (the ratio of the diameterof a circle of sizeA to the length of the basin measuredparallel to the main channel)Sa,channelslopeS, the annualprecipitationP, and others.They found only A and S, to be significant, which resulted in prediction equations of the form Q, : cAS!,. These equationswere superior to those of the other techniques.The multiple correlation coefficientwas as high as 0.954.It is interestingthat regression techniqueswereemployedin still a third way,that is, to estimateregionalvaluesof the mean and standarddeviation after adjustingthe record length. Example26.8 illustrated the applicationof regressionanalysisto regionalizethe standarddeviationof annual maximum flow logarithms as a function of the drainage area size and the number of rainy dayseachyear.
r summary Statisticsis a diversesubject,and the treatmentin this chapterhasbeennothing more than an introduction. Seriousstudentsand practitionersmust return againand again to the theory in standardworks.23They will find that evaluatingnew developments of statisand techniquesmust claim a large shareof their time. Only certain aspects, tical hydrologyhave been presented,principally the common distributionsand the methodsfor analyzingfrequency of eventsobservedat a single point. In the next chapterthis information is extendedto common applicationsin hydrology.
PROBLEMS 26.1. The probabilitiesof eventsE1 andE2 arc each.3. What is the probability that E1 or E2 will occur when (a) the eventsare independentbut not mutually exclusive,and - (b) whenthe probabilityof Et, given E2is .l?
700
CHAPTER26
ANDSTATISTICS PROBABILITY
26.2. EventsA and B are independenteventshaving marginal probabilities of.4 and .5, respectively.Determine for a single trial (a) the probability that both A and B will occur simultaneously,and (b) the probability that neither occurs. 26.3. The conditional probability, P(E, I E,r),of a power failure (given that a flood occurs) is .9, and the conditionalprobability,P(Ez I E), of a flood (given that a powerfailure occurs)is .2. If the joint probability, P (\ andE), of a power failure and a flood is .1, determinethe marginal probabilities,P(E) and P(E). 26.4. Describetwo random eventsthat are (a) mutually exclusive,(b) dependent,(c) both mutually exclusiveand dependent,and (d)"neithermutually exclusivenor dependent. 26.5. A temporar;1cofferdamis to be built to protect the 5-yearconstructionactivity for a major crossvalley dam. If the cofferdam is designedto withstand the 20-yearflood, what is the probability that the structurewill be overtopped(a) in the flrst year, (b) in the third year exactly,(c) at leastonce in the 5-yearconstrucfionperiod, and (d) not at all during the 5-yearperiod? 26.6. A 33-yearrecord of peak annualflow rateswas subjectedto a frequencyanalysis.The median value is defined as the midvalue in the table of rank-ordered magnitudes. Estimatethe following probabilities. , a. The probability that the annualpeak will equalor exceedthe medianvaluein any singleyear. b. The averageretlrrn period of the median value. c. The probability that the annual peak in 1993 will equal or exceedthe median value. d. The probability that the peak flow rate next year will be less than the median value. yearswill e. The probability that the peak flow rate in all of the next 10 successive value. be lessthan the median f. The probability that the peak flow rate will equal or exceedthe median value at years. leastoncein l0 successive g. The probability that the peak flow rates in both of two consecutiveyears will equal or exceedthe median value. h. The probability that, for a2-yearperiod, the peak flow rate will equal or exceed the median value in the secondyear but not in the first' 26.7. What return period must an engineeruse in his or her designof a bridge openingif there is to be only a 50 percent risk that flooding will occur at least once in two successiveyears?Repeatfor a risk of 100 percent. 26.8. A temporary flood wall has been constructedto protect several homes in the floodplain. The wall was built to withstand any dischargeup to the 20-year flood magnitude.The,wall will be removed at the end of the 3-year period after all the homeshavebeen relocated.Determinethe probabilities of the following events: a. The wall will be overtoppedin any year. b. The wall will not be overtoppedduring the relocation operation. c. The wall will be overtoppedat leastonce before all the homesare relocated. d. The wall will be overtoppedexactly once before all the homesare relocated. e. The wall will be adequatefor the flrst 2 years and then overtoppedin the third year. 26.9. Waveheightsand their respectivereturn periods(shownon the next page)are known for a 40-mi long reservoir.Ownersof a downstreamcampsitewill accepta 25 percent risk that a proiective wall will be overtoppedby wavesat least once in a 2}-yeat period. Determinethe minimum height of the protective wall.
PROBLEMS 701 Waveheight (ft)
Returnperiod (years)
10.0 8.5 7.4 5.0 3.5
100 50 30 10 5
26.10. Assumethat the channel capacityof 12,000cfs near a private home was equaledor exceededin 3 of the past 60 years.Find the following: a. The frequencyof the 12,000-cfsvalue. b. The probability that the home will be floodednext year. c. The return period of the 12,000-cfsvalue. d. The probability that the home will not be floodednext year. e. The probability of two consecutive,safeyears. f. The probability of a flood at leastonce in the next 20 years. g. The probability of a flood in the second,but not the first, of two consecutiveyears. h. The 20-yearflood risk. 26,11, The distribution of mean annual rainfall at 35 stations in the JamesRiver Basin, Virginia, is given in the following summary:
Interval(2-in. groupings) Numberof observations
36 or 37 in.
38 or 39 in. 4
40 or 41 in. j
42 or 43 in.
Z
Interval (2-in. groupings) Numberofobservations
44 or 45 in. 5
46 or 47 in. 4
48 or 49 in. 2
50 or 51 in. 2
Computethe relative frequencies(seeChapter27) andplot the frequencydistribution andthe cumulativedistribution.Estimatethe probability that the meanannualrainfall (a) will exceed40 in., (b) will exceed50 in., and (c) will be betweenthesevalues. 26.12. Write a simpleprogram to READin N data points and compute the mean, standard deviation,and skewnesscoefficient. 26.13. A normally distributedrandom variablehas a mean of 4.0 and a standarddeviation of 2.0. Determinethe value of f@
I
-
dx I f(x)
"8
26.14. For a standardnormal densitv ' function. use Table B.1 to determinethe value of fr+'o
I
fG) dx
J*-ro 26.15. A normal variableX has a meanof 5.0 and a standarddeviationof 1.0.Determinethe value of X that has a cumulativeprobability of 0.330. 26.16. If the mode of a PDF is considerablylarger than the median, would the skew most likely be positive or negative?
l
702
ANDSTATISTICS 26 PROBABILIW CHAPTER 26.17. tomplete the following mathematicalstatementsabout the properties of a PDF by insertingin the boxeson the left the correct item numberfrom the right. Assumethat X is a seriesof annual occuffencesfrom a normal distributibn. I r.Zerc a. I f(x) dx: J t " b.
d.
: f'_ro,dx r
2.Unity
dx: '34 l-.o 'o'
3. Valuewith 5 percentchanceofexceedanceeachyear
f,o"
dx:r
dx: .5 f-to, f(x) dx : Z
I rt, dx: .02
4. 0.68 5. Valueexpectedevery 50 r"urc on the average 6. P(X < mr) 'X'*r) 7.P(m1 8 . P ( m 1- X = m z )
l.
9. Median
10. Standarddeviation 26.18, The mean monthly temperaturefor Septemberat a weather station is found to be normally distributed.The mean is 65.5" F, the varianceis 39.3'F2, and the record is completefor 63 years.With the aid of TableB.1, find (a) the midrangewithin which two thirds of all future mean monthly valuesare expectedto fall, (b) the midrange within which 95 percentof all future valuesare expected,(c) the limit below which 80 percentof all future valuesare expected,and (d) the valuesthat are expectedto be exceededwith a frequencyof oncein l0 yearsand oncein 100years.Verify the results by plotting the cumulativedistribution on normal probability paper. 26.L9. The total annualrunoff from a small drainagebasinis determinedto be approximately normal with a mean of 14.0 in. and a varianceof 9.0 in.2.Determinethe probability that the annualrunoff from the basinwill be lessthan I 1.0 in. in all three of the next three consecutiveyears. 26.20. In the past60 years,a dischargeof 30,000cfs at a streamgaugingstationwasequaled or exceededonly three times. Determine the averagereturn period (years) of this value. 26.21. EventsA and B are independentand havemarginal probabilitiesof .4 and .5, respectively. Determine the following for a single trial: a. The probability that both A and B occur. b. The probability that neither occurs. c. The probability that B, but notA, occurs. Existingrecordsrevealthe following information aboutEventsA and 4 whereA = a ?6il,
IongMerch'warmspellandB 1q
:!94!$99!.
PROBLEMS
703 1n
Year A : warm March? B : April flood?
No Yes
No No
Yes No
No Yes
Yes Yes
No Yes
Yes No
No Yes
Yes Yes
No No
On the basisof the 10-yearrecord, answerthe following: a. Are variablesA and B independent?Prove. b. Are variablesA andB mutually exolusive?Prove. c. Determinethe marginal probability of an April flood. d. Determinethe probability of having a cold March next year. e. Determinethe probability (onevalue)of havingboth a cold March and a floodfree April next year. f. If a long March warm spell hasjust endedtoday,what is the best estimateof the probability of a flood in April? 26.23. Two dependenteventsarc A : a flood will occur in Omaha next year and B : an ice-jam will form near Omahain the Missouri River next year. Useyour judgment to rank from largestto smallestthe following probabilities:P(A), P(A andB), P (A or B), P(A I B). 26.24. The probability of having a specifiedreturn period, [, is definedas: I P(annualvaluewill be equaledor exceeded : /, \'-' exactlyoncein a periodof r : I years) T,f \' Also, p (annualvalue will be equaledor exceeded _ exactly r times in a period of n years)
pn_r(l _ p)r
the secondprobability shouldequal a. According to the descriptionsin parentheses, the first when n and r are equal to what values? b. Showthat both equationsresultin the sameprobability for an annualvaluewhose years.Discusss. frequencyis 33{ percentand the return period is Z.: /:3 26.25. For the function describedbelow, find (a) the number b that will make the function a probability densityfunction, and (b) the probability that a singlemeasurementof x will be lessthanl.
.l \^)
-
forx ( 0 for0
{i",,'
26.26. The random variabler representsdepth of precipitation in July. Betweenvaluesof -r : 0 and x : 30, the probability densityfunction has the equation/(-r) : x/40p',. In the past, the averageJuly precipitation p,, was 30 in. a. Determine the probability that next July's precipitation will not exceed20 in. b. Determinethe singleprobability that the July precipitatjon will equal or exceed 30 in. in all of five consecutiveyears. 26.27. The random variable-r representsdepth of precipitation in July. Betweenvaluesof x : 0 and .r : 30, the probability densityfunction has the equation/(.x) : x/1200. a. Determine the probability that next July's precipitation will not exceed20 in. b. Determinethe probability that next July's precipitationwill equalor exceed30 in.
l---
704
ANDSTATISTICS 26 PROBABILITY CHAPTER 26.28.
26.29.
26.30. 26.31.. 26.32.
26.33. 26.34.
discharge Measured (1000acre-ft)
recharge Estimated (1000acre-ft)
12.2 10.4 10.6 1.2.6 14.2 13.0 14.0 t2.0 10.4 tl.4
t2.o 9.8 I 1.0 t3.z
14.6 14.0 14.0 I z,+
10.4 11.6
26.35. F i t a r e g r e s s i o n e q u a t i o n t o t h e d a t a i n P r o b l e m 2 6 ' 3 4 , t r e a t i l g d i s c h a r g e a s t h e dependentvariable.computethestandarderrorofestimate.Estimatetheexpeoted be the estimateof dischargeif no dischargewhen recharg"ir 13 Kti what would relative improvementprovided the is information were availatie on recharge?what by the regressionestimate? linear regression'The program 26.36. prepare a computerprogramfor simple,two-variable, (b) computethe means'variances' X' should(a) read in N pain oioUt"tuuiions, Y and the regressionconstants'the (c) find and andX, Y and standarddeviationsoiUott' coefficient. verify with the data in standarderror of estimal-, u"J,ir" correlation Problem26.34. of the mean annual rainfall with the 26.37. From the following observations of variation How iinea, predi.tion equationfor the catchment. altitude of the gauge,d";;;;; well correlatedare rainfall and altitude?
)
i
PROBLEMS Gauge number
Mean annual rainfall(in.)
1 2 3
22 28 25 31
4
5 6 7 8 9 10 1l 12
705
Altitudeof gauge (1000ft) A A
4.4 1 < < A
5.6 5.6 5.8 6.0 6.6 6.6 6.8 7.0
JZ J I
36 35 36 46 4l 4I
26.38. Estimatethe expectedrainfall in Problem 26.31 for a gaugeto be installed at an altitude of 5500 ft. estimatesof A and B in the bivariateregressionequationY : A + 26.39. The least-squares definedaslogro)andXis BXarcA: 2.0 andB : 3.0,whereYis atransformation : the valuesof a and b. determine y axb, by related Ify and r are as 1o916;r. defined for 26.40. The time of rise of flood hydrographs(Z), deflnedasthe time a streamto rise from low waterto maximum depthfollowing a storm,is relatedto the streamlength (L) and the averageslope(S). From the information given below for 11 watershedsin Texas, New Mexico, and Oklahoma, derive a functional relation of the form T,: aLbS'.
Watershed number I
2 3 A
5 6 7 8 9 l0 11
Ir
L
D
(min)
(1000ft)
(fv10oo ft)
150 90 60 60 100 75 90 30 30 45 50
18.5 14.2 25.3 tt.7 9.7 8.1 21.'7 3.9 1.2
7.93 19.0 t2.a 13.3 11.0 15.0 16.7 146.0 20.0 64.0 33.0
J.J J.)
26.4r, Repeat the exercise in Problem 26'40 by fitting the relation T,: dF", whete
with t in mi and S in ftimi. Plot the results on log-log paper. 26.42. The squareof the linear correlationcoefficientis calledthe proportion of the variance that is "explainedby the regression."Describethe meaningof this phraseby evaluating the equationsgivenin the text.What varianceis explained,and what doesthe term "explained" mean? 26.43. Twenty measuredpairs of valuesof normally distributedvariablesX and Y are analyzed, yielding valuesof X : 3O,V : 20, s' = 20, ands, : 0. Determinethe values F : L/{S
706
CHAPTER26
PROBABILITY ANDSTATISTICS
d a andb and the standarddeviationof residualsfor a least-squares fit usingthe linear equationY:a+bX. 26.44. The least-squares estimatesof A and B in the bivariateregres*sion equationy : A + BXareA:2.OandB : I.0, whereyis atransformation definedaslog1eLIf Iand X are relatedby Y : a(.b)',determinethe valuesof a and b.
26.45. Given a table of ten valuesof mean annual floods and correspondingdrainageareas for a numberofdrainagebasins,statehow linear regressiontechniqueswould be used to determinethe coefficientand exponent(p and 4) in the equation Qzzz : pAq.
26.46. What choiceof transformedvariablesI and X would provide a linear transformation for y : a/(x3 + b)? Also, if a regressionon these transformed variables yields I : 100 + 10X determinethe correspondingvaluesof a and b. Would the linear transformationbe applicableto all possiblepairs and valuesofx and y? 26.47. Which measureof variation in a regressionY : a * bX is generallylargerin magnitude, the standarddeviation of I or the standarddeviation of residuals?For what condition would the two valuesbe equal?
REFERENCES 1. Ven T. Chow, "Statisticaland ProbabilityAnalysisof HydrologicData," Sec. 8-1, in Handbookof Applied Hydrology (V. T. Chow, ed.). New York: McGraw-Hill, 1964. 2. M. B. Fiering, "Information Analysis," in Water Supply and Waste Water Removal (G. M. Fair, J. C. Geyer,and D. A. Okun, eds.).New York: Wiley, 1966,Chap.4. 3. J. R. Benjamin and C. Cornell, Probability, Statisticsand Decisionfor Civil Engineers. New York: McGraw-Hill. 1969. 4. A. M. Mood and F. A. Graybill, Introduction to the Theory of Statistics, 2nd ed. New York: McGraw-Hill. 1963. 5. A. J. Duncan,Quality Control and Statistics.Homewood,IL: RichardD. Irwin, Inc., 1959. 6. P. G. HoeI, Introduction to MathematicalStatistics,3rd ed. New York: Wiley, 1962. 7. J. Aitchison and J. A. C. Brown, The Log-Normal Distributlon. New York: Cambridge UniversityPress,1957. 8. H. A. Foster,"TheoreticalFrequencyCurves,"Trans.ASCE 87,142-203(1924). 9. L. R. Beatd, Statistical Methods in Hydrology, Civil Works Investigations,U.S. Army Corps of Engineers,SacramentoDistrict, 1962. 10. "A Uniform Techniquefor DeterminingFlood Flow Frequencies,"Bull. No. 178, U.S. GeologicalSurvey,1989. 11. "New Tablesof PercentagePoints of the PearsonType III Distribution," Tech. Release No. 38, CentralTechnicalUnit, U.S. Departmentof Agriculture,1968. 12. M. B. Fiering, StreamflowSynthesis.Cambridge,MA: Harvard University Press,1967. 13. F. E. Perkins,SimulationLecture Notes,SummerInstitute, "Applied MathematicalProgramming in WaterResources,"University of Nebraska,1970. 14. E. J. Gumbel, "The Return Period of Flood Flows," Ann. Math. Statist. l2(2), L63190(June1941). 15. Ven T. Chow, "The Log-Probability and Its EngineeringApplication," Proc. ASCE 80, 1-25(Nov. 1954). 16. "Probability Tables and Other Analysis of Extreme Value Data," Series22, National Bureauof StandardsApplied Mathematics,1953. 17. E. J. Gumbel, Statisticsof Extremes.New York: ColumbiaUniversity Press,1958.
Chapter27
FrequencyAnalysis
r Prologue The purposeof this chapteris to: . Presentmethodsused in hydrology to evaluatethe recurrenceof particular magnitudesand durationsof random hydrologicvariables. . Elaborateon the definitionsof freqUency,reiurrence interval, return period, and risk analysisintroducedin Chapter10, Section10.4. . Illustrate the diverseapplicationsof frequencyanalysisin hydrology. . Teachseveralmethodsfor conductingfrequencyanalyses,includingthe useof frequencyfactors that allow estimationof recurrenceintervals for variables that follow conventionalprobability distribution functions. . Introduce methods of point and regional frequency analysis and describe regional USGSregressionequationsthat have been 4doptedthroughoutthe U.S. for estimating flood flows for use in structure design and floodplain analysis. . Establishhow to estimatethe reliability of estimatesderived from point or regional frequencyanalyses. Explainthe widelyusedBulletin No. 17BLog-PearsonType III proceduresfor performing uniform flood flow frequencyanalyses. Describehow variousfederal agenciesapply frequencymethodsin designor analysisof water resourcessystems. In Chapter 26, probability and statistical characteristicsof random variables were introduced,alongwith common distributionfunctions and principlesof regression and correlatiqn.The presentchapterprovidesapplicationsof theseprinciplesto common hydrologicvariables.
27.1 FREQUENCY ANALYSIS The statisticalmethodspresentedin Chapter26 areusedmostfrequentlyin describing hydrologicdata suchas rainfall depthsand intensities,peak annual discharge,flood flows, low-flow durations,and the like. Frequencyanalysiswas introduced in Sec-
ANALYSIS 709 FREQUENCY 27.2 GRAPHICAL tion 10.4 and is defined as the inve recurrencAor probabilitiesof magnit wise, the frequencYof a hYdrologic discretevariablewill occur or some' exceededin anY given Year'The lat freq-uenc probability or exceedance ir"qo"n"Y is a ProbabilitYand has ceedancsfrequencY,as shownbYEc Two methods of frequencYa plotting techniqueto obtain the cum iu"to.t. The cumulativedistribution the probability of an eventequal to is used to obtain recurrencerntervz tioned whenworking with recordssl ofexpectedhydrologiceventsgreaterthantwicetherecordlength.
ANALYSIS FREQUENCY 27.2 GRAPHICAL The frequencYof an event can be When annualmaximum valuesare imated as the meantime in Years'\ exceededonceon the average'The t be shownto be m
tuf,lj
x:
(21.r)
the mean number of exceedances the number of future trials -oi of values the number to I descendingvalues,with largestequal therunt t : 1' N : T' and If the mean number of exceedances
where
7 = N = n : m:
4 I
-
_
n ) l
(21.2)
m
indicatingthattherecurfenceintervalisequaltothenumberofyearsofrecordplus 1, divided bY the rank of the event' They give different results as Severalpictting p*liion formulas are available'l for 10 yearsof record .1,.The rangein recurrencerntervalsoutained notedin Table2'7' plotting position formulasdo not account is illustratedin the right-hani column.Most formula ihat doesaccountfor samplesize for the samplesizeor length of record. One generalform wa, giuen by Gringorten' and has the .r1 -
r -
n * l - 2 a m - a
(27.3)
710
CHAPTER2TFREQUENCYANALYSIS TABLE 27.1 PLOTTINGPOSITIONFORMULAS
Form:1 andn=10 Method
Solve for P (X > x\
P
m
.10
l0
.05
20
.067
14.9
.091
11
m-0.3 n+0.4
.06'l
14.9
Blom
,m-i n+i
.061
t6.4
Tukey
3m-l 3n*l
.065
15.5
California
n 2m-l
Hazen
1 - (0.5)'/'
Beard
fn
Weibull
n * l
Chegadayev
where n : the number of yearsof record the rank a parameterdependingon n as follows:
n a
10 0.448
20 0.443
30 0.442
40 0.441
50 0.440
n a
60 0.440
70 0.440
80 0.440
90 0.439
100 0.439
In general,a : 0.4 is recommendedin the Gringortenequation.If the distribution is approximatelynormal, , : fi is used.A value of a : 0.44 is usedif the data follows a Gumbel distribution. The techniquein all casesis to arrangethe datain increasingor decreasingorder of magnitudeand to assignorder number m to the ranked values.The most efficient formula for computingplotting positionsfor unspecifieddistributions,l and the one now commonly usedfor most sampledata, is the Weibull equation P _
m n-fI
(27.4)
Whenm is rankedfrom lowestto highest,P is an estimateof the probability of values being equalto or lessthan the rankedvalue,that is, P(X < x); whenthe rank is from the value highestto lowest,P is P(X > x). For probabilitiesexpressedin percentages, is IA\ml@ + 1). The probability that X: .x is zero for any continuousvariable.
27.4 REGIONALFREQUENCYANALYSIS
721
regiond'lstudies.Methodsof "smoothing" and averagingregionalvaluesof skewness havealsobeenproposed.ro'15 Many techniquesused in the past for generalizingregi6nal characteristicsdid not rely on statisticalconsiderations.The so-calledstation-yearmethodof extending rainfall recordshasprovedhelpful but has questionablestatisticalvalidity, especially areas.The method if applied to dependentseriesor to stationsin nonhomogeneous has been used to combine, say,two 25-yearrecords to obtain a single 50-year sequence. In practice, the analyst may have to use imagination and ingenuity to summarizeregional characteristics,while remaining awareof actual and theoretical considerations, lndex Flood Method The index-floodmethodusedin the pastby the U.S. GeologicalSurveyis an example of summarizingregional characteristicssuccessfully.t''tuThe method usesstatistical andgenerally databut combinesthem in graphicalsummaries.It canbe supplemented improved by using statisticalmethods,employing,for example,the regressiontechniquesexplainedin Chapter26.The index method,as illustrated in Fig. 27.4, canbe outlined as follows. 1. Preparesingle-stationflood-frequencycurves for each station within the homogeneous region (Fig. 27.4a). 2. Compute the ratio of flood dischargestaken from the curves at various frequenciesto the mean annual flood for the samestation. 3. Compileratios for all stationsand find the medianratio for eachfrequency (Fi5.27.4b). 4. Plot the median ratios againstrecurrenceinterval to produce a regional frequencycurve (Fig. 27.4c). Two statistical considerationsinvolved are (1) a homogeneitytest to justify definitionof a region, and(2) a methodfor extendingshortrecordsto placeall stations on the samebaseperiod. A somewhatsimilar techniquewas developedby Potterfor the Bureau of Public Roads.rTIt relies on the graphical correlation of floods with physical and climatic variables and is thus a techniquethat refers in part to the in Chapter26 (seealso Chapter16). discussion
U.S.G.S.RegionalRegressionEquations Early in the 1950s,the U.S. Geologicalsurvey institutedaprocessof correlatingflood flow magnitudqsand frequencieswith drainagebasin characteristics.Setsof regressionequationsfor the 2-,5-, l0-,25-,50-, and 100-yearfloodshavebeendeveloped regionin every state.The work was for practically everyhydrologicallyhomogeneous largely inauguratedto developmethodsfor estimatingpeak flow rates for designof highway structuresat ungaugedbasins.Data from gaugedsites was evaluatedby regional analysisto provide the best fit of regressionmodelsto the data. Continuouswater stagerecordersand crest-stagegaugedata were consultedto developfrequencycurvesfor all gaugedwatersheds.Given the frequencycurves,a
6000 5000 o
4000 a
o po
3000 2000 1000 0
I
I ts c") c..l
I
I
i' 2 5 1 0 2 interval(Yr) Recurrence
1.01 1.1
0
50
100
(al Recurrenceintervals (Yr)
station 1
5
1.5
1.1 ^ ALt
n ?<
1 AA
10 1 q?
20 ).55
50 3.03
(b)
3.0 2.8
I
2.2 6
2.0
o
1.8 l.o
o
po
' :
7.4 t.2 1.0 0.8 0.6 0.4 1.01 1.1
I
10 interval(Yr) Recurrence
t.52
5
50
100
(cl
Figure27.4 Index-floodmethod of regional flood frequency an-alysis:(a) single-stationflood frequencycurve, (b) ratios Q' to Qr.rrfor 15 stations,and (c) regional flood frequencycurve'
ANALYSIS 723 FREQUENCY 27.4 REGIONAL numberof correlationtestswere madeusingmultiple linear regressionto predict the peak flows from various easily obtained independentparameterssuch as drainage area,basinslope,watershedaspect,elevation,meantemperatureduringthe snowmelt season,and hundredsof other variables' Each study was reportedby state.The open file or water resourceinvestigation reports are availablefrom the USGS and include discussionsof the equations,com1n"ntron rangeof applicability,information on the reliability of the equations,copies of all the gaugedbasin frequencycurves,and.setsof equationsfor estimatingfloods Equationsfor all stateshavebeen compiledby the U.S. in ungaugid watersheds. Ceotogicit Survey into a PC software packagecalled NFF (National Flood Frequ"n"y;, availablefrom tJreusGS (or FHWA as part of their package,HYDRAIN)' Figure 27.5 showsthe six regionsfor the stateof Texas.Regionalregressionwas conductedindependentlyby regionusingavailablegaugedstationdata.As in many of the reports, the Texasmanual revealsthat different independentvariableswere selectedfor eachregion.18The equationsdevelopedfgr Region2 are: Q, Qs
: : :
389 Ao6a6So'2ta 236 = 485 Ao 668510 Qr, : 555 Ao 6825'0'250 Qro : 628 Ao 6e4s0261 Qrco Qro
(27.r2) (27.r3) (27.r4) (27.rs)
216 Aos74So'12s 184 322 Ao62oso
'
(27.16)
(27.r7)
where g : peak dischargefor given frequency,cfs A : drainagearea,squaremiles S : averageslopeof the streambedbetweenpoints 10 and 85 percentof the distancealons the main streamchannelfrom the mouth to the basin divide, feet per mile
EXAMPLE 27.6 Develop estimates of flood peaks for a 200-square-mile rural watershed near Dallas. The mean slope between the 10 and 85 percent points is 3.4 ft per mile. Solution.
Dallas is in Region 2. Equations 2712-27.17 2 1 6 A o s 1 a s o l 2:s 5 , 2 7 0 c f s : O s : 3 2 2 4 0 6 2 0 5 0 1 8 o 1 0 , 7 7 0c f s : Q r o : 3 8 9 A o ' 6 a 6 s o ' 2 1 a1 5 ' 4 9 0c f s :22'300 cfs Q r r : 4 8 5A 0 6 6 8 5 0 2 3 6 : Q r o : 5 5 5 A o 6 8 2 s o 2 5 o 2 7 , 8 0 0c f s : ll Qrco : 628 Ao6e4so261 34,170 cfs Qr:
-
give:
724
CHAPTER2T FREQUENCYANALYSTS
UNDEFINED
Pacoa
,'7I
N
N D R E
Figure 27.5 Hydrologicregionsin Texasfor 1976USGSregional regressionequations.(From Ref. 18.).
27.4 REGIONALFREQUENCYANALYSIS
725
726
CHAPTER2TFREQUENCYANALYSIS
NationalFlood Frequency(NFD Program equationslike Eqs.27.I2 through27:t7 for estimatingfloodSince1973,regression peak dischargesfor rural, unregulatedwatershedshavebeenpublished,at leastonce, for every stateand the Commonwealthof PuertoRico. In 1993the USGS,in cooperation with the FederalHighwayAdministration and the FederalEmergencyManagement Agency,compiled all of the current statewideand metropolitanarearegression equationsinto a microcomputerprogramtitled the National Flood Frequency(NFF) Program.reThis program summarizesregressionequationsfor estimatingflood-peak techniquesfor estimatinga typical flood dischargesfor all52 states.It also addresses probability peak discharge hydrographfor a given recurrenceinterval or exceedence prograr4 lists statewideregression for unregulatedrural and urban watersheds.The information and reference equationsfor rural watershedsand providesmuch of the for estimating equations input data neededto run the computerprogram.Regression least 13 statesare in at urban flood-peak dischargesfor severalmetropolitan areas also available. Information on computerspecificationsand the computerprogramare given.rT Instructionsfor installing NFF on a personalcomputerand a descriptionof the NFF program and the associateddata base of regressionstatistics are also given. The program is available as part of the Federal Highway Administration package, HYDRAIN, or by itself. Thoughthe USGSand FHWA do not distributeor servicethe software,information about vendorswho provide softwaresalesand servicecan be obtainedby contactingthe agencies.
Flood Frequencyfrom ChannelGeometry
.
Stream channelsin alluvial systemsdeveloptheir width, depth, slope, and other hydraulicgeometrycharacteristicsfrom the compositehydrographsthat flow through their valleys. It has been demonstratedthat the shapeof some streamchannels,if properly evaluatedby trained hydrologists,can be correlatedwith the mean annual flow, peak annual flow, bank-full flow, and the dominant, or channel=forming,discharge.Regressionequations,similar to Eqs. 27.12-27.I7, havebeen successfully derivedfor many perennialstreamswith very reasonablestandarderrors of estimate. Measurementsfor these studiesare normally obtained during low flow. The channelof interestis that channelbeing maintainedby the current flow regime.It is by the activechannel,limited laterally by the point barsand mostrecent characterized geologicfloodplaindeposits.It is felt that theserepresentthe most recentdepositions, and are thereforeindicative of the width neededby the current flow and sediment transport regipe. Figure 27.6 rll:ustratesthe principle in determining the active, floodplain-buildingchannel,established'forthe exampleas the width A-A . Suchstudieshavebeenconductedin Nevadd,California, Kansas,Colorado,and elsewhere.A USGSinvestigationof 53 gaugedstreamsin mountainregionsof Colorado resultedin the following equations.2o Qz = 0.666 Wr'eoaD*o Q, : 1.53 W1.682D-o2stAo'017 Qr6
:
2.38 W1
t43 2se 53o A.o D-o
(27.r8) (27.r9) (27.20)
27.4 REGIONALFREQUENCYANALYSIS
727
Referenceline Low-flow water level
Distance, in feet
Figure27.6 Ref. 20.)
Typical streamcross-section,illustrating active channeldimensions'(From
Q25 Qro
where Q W D A
: = : :
:
3.70 Wr'372D-o263Ao2rs
:
4.93 W127aD-02s6Ao'257
(27.2r) (27.22)
peakflow for the given frequency,cfs iop width of streamat bank-full condition, ft mean depth for bank-full flow, ft cross-sectionareaat bank-full flow, sq ft
The multiple correlation coefficientsfor theseequationsranged from 0-80 for the 42' 1 ranged_from 50-yrflow to 0.89 for the2-yr event.Standarderrors,respectively, use in for tool yet another offer investigations of types These percentto 32.2percent. site-specific by floods evaluate to hydrologist the and allow p"uk flo*r, estimating conditionsversusmore uncertain regionalparameters'
RegionalRainfallCharacteristics The variation of rainfall frequencieswith duration was introduced in Chapter 2' Regressionanalysiscan be usedto I thoseshownin ChaPter15, and the Many formulashavebeenusedin t a form with intensity(i) inverselypt of the form i : AIQ + B) to fit constantsA andB thereforeserveascharacteristicfeaturesof both the rainfall region and the frequencyof occurrencein eacharea'
728
CHAPTER27
FREQUENCYANALYSIS
EXAMPLE 27.7
Fit the following rainfall datato determinethe 10-yearintensity-duration-frequency curve.
r : duration (min) i : intensity (in./hr) Ui
r0
5
7.r o.t4
5.9 o.l7
15 5.1 0.20
30 3.8 0.26
t20
60 z-)
1
0.43
0.71,
A
Solution 1.. A model of the form i : AIG * B) can be expressedin linear form as ,,
lli:tlA+BlA.
The regressionof l.li versust yields l/i : 0.005t + 0'12, from which A : 2 0 0 a n d B: 2 4 . 3. Thus the rainfall formula is i : 200/(t + 24). The correlation coefficient is -0.997. ll
Maximum averagerainfall depths have been publishedby the U.S. Weather for durationsbetween30 min and24hr and for recurrenceintervalsbetween Bureau22
depthrelation showninFig.27.7. c o F
100
o
'a
9u
N
t 8 0
24ft
6hr 3hr
\\
F
e 'ro
lhr mln
q
* 6 0 b - "
50
\
150
350
400
Area(mi2) for use with duration frequency curves Area-depth Figare 27,7 values.(U.S. WeatherBureau.)
FREQUENCY ANALYSIS 729 27.4 REGIONAL The accuracyof arearainfall data dependshedvily on the densityand location of gaugesthroughoutthe areaconsidered.The simpleaveragingof the accumulation in all gaugesgivesno considerationof the effectiveareaaround eachgaugeor to the stormpattern.Two methodsare availablein calculatingthe weightedaverageof gauge records, the Thiessen polygon method and the isohyetal method. The Thiessen methodassumesa linear variation of rainfall betweeneachpair of gauges.Perpendicular bisectorsof the connectinglines form polygonsaround each gauge(or partial polygonswithin the areaboundary).If a sufficientnumberof gaugesare availableto constructcontoursof rainfall depth (isohy.ets),the weightingprocesscan be carried out by using the averagedepth betweenisohyetsand the area includedbetweenthe isohyetsand the areaboundaries.Figure 27.8 showsboth schemes. An exampleof the effect of gaugelocation and density is shown inFig.27.9. stormrainFigure 27.9ashowsthe increasein variability betweenThiessen-weighted falls and rainfall at a singlegaugeas the distanceof singlegaugesfrom the watershed centerincreases.Figure 27.9b showsthe effect of gaugedensityand total areaon the standarderror of the mean.Completestudiesof precipitationpatternsoverlargeareas requiredetailedanalysisof depth-area-duration datathat dependon the masscurves of accumulationfrom a network of gauges.The methodis describedin detail in other Figure 27.I0 depictsthe depth-area relation for the 24-hr storm references.23-25. shownin Fig. 27.8.It also required observationstaken at variousdurationsand the successivedeterminationof averagedepthsby the isohyetalmethod.
(a)
(b)
Figure 27.8 Methods of determiningrainfall averages:(a) Thiessennetwork (24-hr total; averagebasin precipitation = 2.54 in.) and (b) isohyetal map (24-hr total; averagebasin precipitation = 2.50 in.), The arithmetic averageover the basin = 39.10/15 * 2.61 in.
730
OHAPTER27
FREQUENCYANALYSIS H
U.+
>.= O F
(n n
Distance from rain gauge to watershed center (mi) (a)
H 6 6 6 o
b g
9 R 6
500 (mi2) Areapergauge (b) Figure27.9
(a) Relationbetweenthe standarddeviationof the watershed
a4tlt,ililliT,i',l:##-:"::: :1111i:ifli'ffi ill"lH"rffi cipitation as a function of the network density and drainageare for the Muskingumbasin.(U.S. WeatherBureau')
STUDIES 27.5 RELIABILIWOF FREQUENCY A significantdevelopmentof theoretical statisticsis the central limit theorem.As a .on*-rqu"n"" of the law of large numbers,the central limit theorem statesthat for a populationwith finite varianceoz anda meanp, the distributionof samplemeansitrut ir, a numberof equally good meansfrom repeatedsamples-will be distributed themselvesas a normal disiribution with meanp, anda varianceequalto a2fn, where or is the population standard deviation. This theorem does not limit the type of under$ing population distributionbut saysthat the distribution of the sampl" *9-uts will approacha normal distribution as the samplesizeincreases.The statisticalY n is the siandarddeviationof the distributionof meansand is called lhe standarderror
27.5 RELIABILIWOF FREQUENCYSTUDIES
731
r
9
z
o 00
<
1
500
0
1500
1000 Area (mi2)
2000 Figure27.10 Depth-area-duration curves for24-hr stormofFig.27'8. (FromRef' 23')
their of the mean. Listed in Table 27.6 are severalparametersof distributionsand are reliability, therefore and standard errors. It is apparent that standard errors, almost completelya function of the samplesize'
ConfidenceLimits based It is possibleto placeconfidencelimits on the measurementof a samplemean population' underlying the of on the normal distribution of all meansand regardless variate As mentionedearlier,approximatelytwo thirds of the observationsof a normal thirds two Therefore, deviation' shouldfall betweentheiimits of + i and 1 standard +olfi. percent 95 The limits of all sample means should occur between the confidencelimits for the mean are ment requires knowledgeof the u only s2insteadof o2 is known and confidencelimits for a samPlemeat For more the use of samplingdistributionsthat are beyond the scope of this text. and testing hypothesis information in ine neta of inferential statistics-in particular, statisticaldecisiontheory-the readermust turn to other sources. Approximateerror limits or control cufvescan be placedon freqlency curves. curve metdd proposedby Beardsinvolvesplacinglines aboveand below the fitted TABLE27.6 error Standard
Measure Mean Standarddeviation Coefficientof variation Coefficient of skewness
"/{!_ a/\/2n
c,\/r + zcl/vzn x6t"
- D/tn + l)(n - 2\(n + 3)
732
CHAPTER2T FREQUENCYANALYSIS TABLE27.7
Yearsof record (n) 5 10 I.)
20 30 40 50 '70 100
ERROR LIMITSFOR FLOOD FREQUENCYCURVES Exceedancefrequency (7o,at 5ololevel) 99.9
99
r.22 o.94 0.80 0.71 0.60 0.53 0.49 0.42 0.37
1.00 0.76 0.65 0.58 0.49 0.43 0.39 0.34 0.29
0.1
50
10
0.76 0.57 0.48 0.42 0.35 0.31 0.28 0.24 o.2l
0.95 0.58 0.46 0.39 0.31 0.27 0.24 0.20 0,17
2.12 1.07 0.79
10
50
90
Q.64
0.50 0.42 0.36 0.30 0.25
0.1
3.4r 1.65 1.19 0.97 0.74 0.61 0.54 0.44 0.36 99
4.41 2.11 1.52 t.23 0.93 0.77 0.67 0.s5 0.45 99.9
Exceedancefrequency (%, at 95% level) Nate: Tabular valuesare multiples ofthe standarddeviation of the variate. Five percent error limits are addedto the flood value from the fitted curve at the sameexceedancefrequency and the sum plotted. Ninety-five percent limits are subtractedfrom the flood value at the sameexceedancefrequency. Log values are added or subtractedbefore antilogging and plotting.
to form a reliability band. Table27.7 showsthe factorsby which the standarddeviations of the variatemust be multiplied to mark off a 90 percentreliability band above and below the frequencycurve. The 5 percentlevel, for example,meansthat only 5 percentof future valuesshouldfall higherthan the limit, and,similarly,only 5 percent shouldfall under the 95 percentlimit. Nine of ten should fall within the band. EXAMPLE 27.8
The maximum annual instantaneousflows from the Maury River near Lexington, Virginia, for a26-yearperiod are listed in Table 27.8. Plot the log-PearsonIII curve of best fit and determinethe magnitudeof the flood to be equaledor exceededonce in 5, 10, 50, and 100 years.Using Table 27.7, also plot the upper and lower confidencelimits.
Water (year)
Discharge (cfs)
1926 1927 1928 1,929 1930 t93l 1932 1933 1934
6,730. 9,150 6,310 10,000 15,000 2,950 8,650 I 1,100 6,360
1935 t936 1937 1938 1939 1940 t941 1942 t943
13,800 40,000 10,200 13,400 8,950 11,900 5,840 20:t00 12,300
Water (year)
Discharge (cfs)
1944 1945 1946 t947 1948 1949 1950 1951
6,680 6,540 5,560 7,700 8,630 14,500 23,700 15,100
STUDIES 733 OFFREQUENCY 27.5 RELIABILITY Solution 1. The statisticalcalculationsare summarizedas follows:
Log
Arithmetic Mean 7 Variances2 Skew coefficient C"
11,606
4.001
53.87 x 106 " 2.4
0.051b 0.38
2. After forming an array and computingplotting positions,the data are plotted in Fig. 27.11. 3. Plottingdatafor log-PearsonIII (Table27.9) aredevelopedfrom TableB.2' Confidencelimits are plotted in Fig. 27.II usingTable 27.7. r I 0.01 0.05 0.1 0.2 0.5 1 2
Log-Pearson IIIFit
V
5 F t 0
',;/
g r o d
-
:l
"
I bo 30
t.,
? + o d
5r)
'"
360 9" 70
t
d
6 a o
a
o
/-
o . 9 0
a,
95 98 99 99.8 99.9 99.99
r
2
4
6
1 0 2 0 4 0 6 (1000cfs) discharge Annualmaximum
0
1
0
Figure 27.11 Maximum instantaneous annual flows, Maury River, Lexington,Virginia.
0
;
734
CHAPTER2T FREQUENCYANALYSIS TABLE 27,9
Chance
('/") 99 95 90 80 50 20 l0 4 2 I
0.5
/ (vr)
(c" = 0.38) K
1.01 1.05 1.11 1.25 2 5 l0 25 50 100 200
-2.044 - 1.530 -
\.zJ+
-0.855 -0,062 0.818 1.315 t.874 2.251, 2.60r 2.930
(t: 4.001) (sy= 0'227) y + Ksy: logQ 3.537 3.653 3.721 3.760 3.987 4.ft1 4.300 4.426 4.512 4.591, 4.666
1 44?
4,498 < )6n
9,705 15,380 19,950 26,690 32,5t0 39,030 46,360
SERIES ANALYSISOF PARTIALDURATION 27.6 FREQUENCY In earlier examplesof frequency analysis,only the seriesof annual maximum or minimum occurrencesin the hydrologicrecord havebeen described.Theseextremes constitutean annual series thal is consistentwith frequencyanalysisand the manipulation of annualprobabilitiesof occurrence.All the observeddata-say, all floods or all the daily streamflows-would constitutea completeseries.Any subsetof the completeseriesis a partial series.In selectingthe maximumannualeventsfrom a record, it often happensthat the secondgreatesteventin one year exceedsthe annual maximum in some other year. Analysis of the annual seriesneglectssuch events. Although they generally contain the same number of events,the extreme values analyzedwithout regardfor the period (i.e., year)of occurrence,is usuallytermedthe partial duration series. In Table27.10 themaximumrainfall depthsthat occurredfor any 30-min period rainfallsat Baltimore,Maryland,1.945-1954,areshownin the order duringexcessive representa completeseries,The 11 maximum The 65 observations of occurrence. the annual series.the greatest11 events represent and annual eventsare underlined and representthe partial duration asterisk by an are identified throughoutthe record series. The larger numbersoccur in both series,and hencerecurrenceintervalsfor the less-frequenteventsare the same.The theoreticaldifferencesin recurrenceintervals based on annual and partial duration series of the same length are shown in TabIe27.Il. The differencefor intervalsgreaterthan 10 years is negligible.The following exampleis illustrative. EXAMPLE 27.9 Performa frequencyanalysisof the 30-min Baltimore rainfall data in Table27.L0 as an annual and apafiial duration seriesand plot the results. Solution. SeeTable27.I2. The data are plottedin Fig' 27'1'2. r r
DURATION SERIES ANALYSIS OF PARTIAL 27.6 FREQUENCY
735
MD,1945_1954 BALTIMORE, RAINFALL DEPTHS, TABLEi7.1O MMIMUM3O-MIN
Year 1945
Storm num0er 1 z J I
5 6 7 8 9
RF depth (in.)
Year 1.33* 0.65 0.47 0.84 0.68 0.63 0.47
0.38 0.47 0.39 0.76 0.56 0.35 0.43 0.40 0.36
Storm number
1953
RF depth (in.)
5 6 7 8
0.40 0.45 0.53 2.50* r.03 0.75 0.70 1.00*
1 2 3 4
0.42 0.70 0.85 0-30
1 2 3 4 5 6 'l
0.70 0.95 t.o2 0.50 0.65 0.55 0.52 0.45 0.54 0.60 0.80 0.95
1 z J A
0,52 0.49 1 2 J
4
5 6 7 8 9
1947
I z J A
5 6 7 8
0.62 0.55 0.88 0.47 0.36 1.15* 0.75 1.53* 0.51 0.88 2.04* 0.76 0,97 0.71 1.07* 0.94 r.20*
0.55 0.63 0.69 t.27* 1.10*
1955
0.88 0.97 0.59 0.46 0.50 0.55 t952
I 2 3 4 5 6 7 8
, 0.47 1.20* 0.93 0.70 0.57 0.46 0.48 1.30*
Nole: Underlineditems are the annual series.Asterisks identify the partial duration series.
BETWEEN THE TABLE27.1'I RELATION PARTIAL DURATION SERIES SERIES ANDTHEANNUAL Recurrenceinterval(yr) Partialdurationseries
Annualseries
0.5 1.0 1.5 2.0 5.0 10.0
t.2 1.6 2.0 2.5 5.5 10.5
8 9 0 I 2
736
CHAPTER2T FREQUENCYANALYSIS TABLE27.12
Recurrence interval (n + 1)/m
Depth(in.) Annual series
2.50 2.04 1.53 1.33 1.30 1.27 1.02 0.97 0.85 0.76 0.52
1 z J i
5 6 7 8 9 10 l1
Partial series
12 6
2.50 2.04 1.53 1.33 1.30 1.27 r.20 t.20 1.15 1.10 t.o7
A J A A
z
1.7 1.5 1.3 1.2 1.1
The preceding example leads to considerationof the frequency analysis of rainfall depth or iniensity for various durations of rainfall. Design problems often require the estimationof expectedintensitiesfor a critical time period. Frequency analysisof the rainfall record for.periodsother than the 30-min duration-for example, ihe maximum 5-, 10-, 20-, and 60-min occurrences-would yield a family of .oru"r similar to thoseof Fig. 27.10. The usual methodof presentingthesedata is to conveftdepthin inchesto an intensityin in. /hr andto summaize thedatain intensityduration-frequency curves as shown in Fig. 27.I3. Thesecurves are typical of the point analysisof rainfall data.It shouldbe emphasizedthat the frequencycurves.join -o""u.t"tt""t that are not necessarilyfrom the samestorm; that is, they do not repre-
/^
2.5
.i E
t <
0) E
t
_x--
1.0
d
I
zAnnta
1.01
1.r1.21.1 3.5
>-P
series
2,
3
4
5 678910
20
Recunenceinterval (Yr) Figune 27.12 Difference in annual and partial duration series 1 1-year record of maximum 30-min durations' Baltimore, Maryland'
PROBLEMS 741 Expand the computerprogram of Problem 26.12 to include the computationof the mean,standarddeviation;and skewnesscoefflcientofthe logarithmsofthe input data. Also, include a routine to sort the data by placing them in descendingorder and computethe correspondingplotting positions.verify, usiiig the datain Ptoblem27.4. 27.g, perform a completefrequencyanalysison one of the three 33-yearrecordsgiven in the tablebelow.Fit a Pearsontype III or log-PearsonIII and comparewith the normal or log-normal ofbest fit. Plot the dataand placecontrol curvesaroundthe theoretical curve of best flt usins.Table27.7.
27.t
Year t928 t929 1930 r93l 1932 1933 1934 r935 1936 193'7 r938 1939 1940 t94l 1942 1943 1944 1945 t946 1947 1948 t949 1950 1951 1952 1953 1954 1955 1956 1957' 1958 1959 1960
River Trempeuleau Dodge,Wl (DA : 643 mi') Qp..r.(cfs)
Blow River Banff, Alberta, Canada (DA = 858 mi' ) Opua,(cfs)
3,700 1,700 3,360 1,650 3,600 11,000 2,5'70 4,490 7,180 1,780 3,170 6,400 3,120 2,890 5,680 5,060 2,040 8,120 4,570 5,4r0 4,840 1920 3,600 4,840 6,950 4,040 5,710 10,400 r7,400 713
10,200 7,590 9,280 6,610 9,850 11,000 9,490 6,940 7,'720 5,210 7;770 6,270 7,220 4,450 5,850 7,380 5,590 4,450 7,2r0 5,880 r0,320
r,r40
6,730 7,480 6,440
8,000 1,480
4 )qo
10,080 8,570 5,460 9,180 10,120 8,680 9,060 5 160
James River Scottsville,VA (DA : 4570 mi2) Qp""r (cls)
75,600 44,700 45,800
2t,roo 31,400 5q 500
38,800 93,400 126,000 62,200 87,400 68,400 130,000 27,100 80,600 95,200 133,000 57,000 41,200 33,200 59,600 94,200 64900 54 snn 67,000 62,900 70,000 20,400 64,200 44,500 ,o ?no 64.200
742
CHAPTER 27 FREQUENCY ANALYSIS
27.10. Compare results of Problem 27.9 wfih estimatesby Gumbel's extreme-valuedistribu' tion for the 50- and 100-yearevents.
27.11. The pan-evaporationdata (in.) for the'month of July at a site4nMissouri are 9.7 11.2 9.3 9.8
lt.7 8.8 9.2 8.7
It.2 tt.4 9.3 I1.5
11.3 I 1.8 9.3 10.9
l 1.5 8.9 10.4 10.2
Determinethe mean, standarddeviation,and coefficient of variation. What are the standarderrors of thesestatistics?Establishthe approximate95 percentconfldence limits of the mean. 27.12. On which type of plotting paper(probability,log-probability, rectangularcoordinate, log-log, semilog,extreme-value,none)would eachof the following plot as a straight line? a. Normal frequencydistribution. b. Gumbel frequencydistribution. c.Y=3X+4 d. Log-normal frequencydistribution. e. PearsonType III with a skew of zero. t' Q: 43Ao1' g. Log-PearsonType III with a skew of logarithmsequal to zero. h. PearsonType III with a skew of 3.0. 27.13. Determine the 50-yearpeak (cfs) for a log-PearsonType III distribution of annual peaksfor a major river if the skew coefficient of logarithms (base 10) is -0.1, the mean'oflogarithms(base10) is 3.0, and the standarddeviationof basel0logarithms is 1.0. 27.14. A 4Oaear record of rainfall indicatesthat the mean monthly precipitation during April is 3.85 in. with a standarddeviationof 0.92. The distribution is normal. With 95 percentconfidence,estimatethe limits within which (a) next April's precipitation is expectedto fall, and (b) the mean April precipitation for the next 40 years is expectedto fall. 27.15. Given a table of valuesof mean annual floods and correspondingdrainageareasfor a number of basinsin a region, describehow regressionanalysiscould be used to determinethe coefficientsp and q in the relation Qz.tz: pAq. 27.16. The 80-yearrecord of annualprecipitationat a midwesterngaugelocationhasa range between14 in. in 1936and 42 in. in 1965.The recordha5a meanof 27.6 in. anda standarddeviationof 6.06 in. Assuminga normal distribution, (a) plot the frequency curve on probability paper,(b) determinethe probability of a droughtworsethan the 1936value,and (c) determinethe recurrenceinterval.ofthe 1965maximumdepthand comparei&ith the apparentrecurrenceinterval. 27.17. A reservoirin the localeof Problem27.16 wlll overfill when the annualprecipitation exceeds30 in. Determinethe probability that the reservoirwill overfill (a) next year, (b) at leastonce in three successiveyears,and (c) in each of three successiveybars.
PROBLEMS
743
27.18. *Using Eqs. 26.38 and 26.39 and log-probability paper, solve Problems27.16 and 27.17 assumingthat the annual precipitation is log-normal. 27.19. Given the following valuesof peak flow ratesfor a small stre.am,determinethe return period (years)for a flood of 100 cfs by flrst using annual peaksfor an annual series and then using all the data for a partial duration series.
Year
1963 t964 1965 1966 1967
June I Aug.3 June7 July 2 May 18 June 3 July 4
Peak (cfs)
Year
Date
Peak (cfs)
90 300 60 80 r00 90 40
1968
May 11 June 8 Sept.4 Aug. 8 May 9 Sept.8 May 4
800 700 90 400 30 700 80
1969 r970 t97r r972
27,20. Recordedmaximum depths (in.) of precipitation for a 30-min duration at a single station are:
Year
Date
Depth(in.)
Year
Date
Depth (in.)
1963
May 3 June 3 June7 June 2 June 1 Aug. 3 July 4
2.0 1.0 1.0 1.0 1.0 3.0 1.0
1968 1969
Aug.8 May 6 June 8 Sept.4 May 4 Sept.8 May 9
4.O 6.0 5.0 1.0 1.0 5.0 1.0
1964 1965 1966 1967
a. Determine the return period (years) for a depth of 2.0 in. using the california method with an annual series. b. RepeatPart (a) using a partial duration serles. c. Determinefrom the partial duration seriesthe depth of 30-min rain expectbdto be equaledor exceeded(on the average)once every 8 years. 27.21. For a 60-yearrecord of precipitation intensitiesand durations,a 30-min intensity of 2.50 in.lhr was equaledor exceededa total of 85 times. Al1 but 5 of the 60 years experiencedone or more 30-min intensitiesequaling or exceedingthe 2.50-in./hr value.Use the Weibull formula to determinethe return period of this intensity using (a) a paltial seriesand (b) an annual series.
744
ANALYSIS 27 FREQUENCY CHAPTER 27,22. from the data given in the accompanyingtable of low flows, prepare a set of low-flow frequency curves for the daily, weekly, and monthly durations. (cfs)FORTHE LOWESTMEANDISCHARGE NUMBER OFCONSECUTIVE FOLLOWING DAYS,MAURYRIVERNEARBUENAVISTA, VIRGINIA Year
1-day
7-day
1939 1940 194l 1942 1943 t944 1945 1946 1947 t948 1949 1950 195I 1952 1953 1954 1955 t956 1957 1958 1959 1960 1961 1962 1,963 1964 1965
100.0 167.0 22.0 101.0 86.0 62.0 78.0 76.0 97.0 r54.0 136.0 113.0 95.0 115.0 85.0 68.0 83.0 64.0 62.0 88.0 76.0 83.0 99.0 90.0 60.0 51.0 64.0
103.0 171.0 59.4 127.0 93.9 65.9 80.7 78.6 102.0 176.0 138.0 125.0 95.3 116.0 86.1 70.0 96.1 66.3 64.1 92.6 80.9 91.7 103.0 95.0 60.6 54.1 68.7
3O-day
r25.0 t94.0 69.1 1'73.0 103.0 77.4 90.3 87.1 123.0 215.0 163.0 139.0 101.0 r20.0 90.8 81.7 99.9 7r.7 75.8 107.0 117.0 103.0 152.0 105.0 70.8 62.0 76.2
27.23. For the 7-day low flows at Buena Vista given in Problem 27.22, attempt to fit a straight-linefrequencycurve on log-normalor extreme-valueprobability paper' proceedingas follows:From the original plot of the data, estimatethe lowestflow (say;4) - q: at the high recurrenceintervals;subtractthis flow from all observedflows (Q Qr); and rcplot Qr versusthe original recurrenceintervals.Repeatif necessary.The best fittiBg curve will be a three-parameterfrequencydistribution.
PROBLEMS 745 27.24. The following table surirmarizesthe number of occurrencesof intensitiesof various durationsfor a34-yearrecordof rainfall. Maximum intensitiesfor the given durations were determinedfor all excessivestorms and a count made of the exceedances' expectedon a 5-yearfrequencyand Interpolatefor the averagenumberof exceedances plot the 5-yearintensity-duration-frequency curve' Numbei of times stated ihtensitieswere equaledor exceeded Intensity(in./hr) Duration (min)
5 10 15 30 60 120
2.0 68
3s l7
72 29 1 8
5
6 l
3.0
4.0
5.0
6.0
7.0
'73
48 26 11 3 1
2I l1
o
2
3
I
51 LJ
7 2
I I
27.25. The resultsof a multiple regressionanalysisof over 200 flood recordsin Virginia led to the following regional flood frequencyequations: . Qt'z'v': 9'l3AeoeS2e3 Qzzt-v':20'84861S30e 3oo Qs'v,: 38'1A83oS 283 Qn', : 63'0A8o2S : 104A71e5266 Qzs-v, : l18A7esS21e Qso'v'
27.26.
27.27.
21.28.
27,29.
whefe the flood dischargefor the given frequencyis in cfs, A is the drainageareain mi2, and S is the channeislopein ftlmi (measuredbetweenthe points that are 10 and g5 percent of the total rivei miles upstreamof the gauging station to the drainage diviAe;. Devise a method for graphically portraying theseregional flood frequency relations.(Note that there are four factors,Q, T, A' and S') Using the regressionequationsin Problem 2'1.25, flnd the predicted floods for the River, at cootes Store.Drainage atea : 215 mi2and chanNorth Fork, shenandoa-h : nel slope 44.3 ftlmi. Comparethe predictions from the regressionequationsin Problem 27 '25 with the : valuesestimatedby the frequencyanafsis inqxample27.8. Drainageatea 487 fii2 : and channelsloPe 2I.l ftltrl'l. Referringto Fig. 2.6b, comparethe averagestorm rainfall over the city of Baltimore on SeptemberI0, Ig57, computedby the isohyetalmethod, with the simpleaverage of total accumulationat the riin gaugeswithin the city. Neglectthe areato the south of the 1.0-in.isohYet' Fit the forrnula i : AIQ + B) to the data derived in Problem 21.24 for the 5-year intensity-duration-frequency curve.
746
27 FREQUENCYANALYSIS CHAPTER 27.30. Deielop a regional flood index curve for the RappahannockRiver basin from the flood frequency data given in the following table: PEAK FLOOD FREQUENCYDISCHARGES(ft3/sec)FOR STATIONSlN THE RAPPAHANNOCKRIVER BASIN
Station
Drainage Typeof area(mi2) series 192
RappahannockRiver near Warrenton, VA Rush River at Washington,VA Thornton River near Laurel Mills, VA Hazel River at fuxeyville, VA RappahannockRiver at Remington,VA RappahannockRiver at Kellys Ford, VA Mountain Run near Culpeper,VA Rapidan River near Ruckerwille, VA RobinsonRiver near Locust Dale, VA Rapidan Rivet near Culpeper,VA RappahannockRiver near Fredericksburg, VA
15.2 142 286 616 641 14.'7 111 180 456 | sqg
Returnperiodin years
2.33 (mean).
10
25
Annual Partial
4,150 4,600
8,350 8,650
9,000 9,20A
14,000 14,000
19,250 19,250
Annual Partial Annual Partial Annual Partial Annual Partial Annual Partial Annual Partial Annual Partial Annual Partial Annual Partial Annual Partial
530 610 5,900 7,200 7300 8,300 11,000 12,000 12,300 14,000 '750
860 900 11,500 12,500 11,800 t2,400 14,500 15,200 19,000 20,000 t,750 1,900 7,100 7,700 7,000 7,300 16,400 17,600 39,900 42,000
r,290 1,310 19,900 20,500 17,200 18,000 18,100 18,900 26,800 27,500 3,350 3,550 11,600 12,000 9,800 10,100 26,900 27,600 55,000 57,500
2,100 2,100 34,000 34,000 25,000 25,500 24,500 25,000 42,000 42,000 6,000 6,000 21,000 21,000 !5,400 15,800 50,000 50,000 85,000 85,000
3,000 3,000 48,000 48,000 41,000 41,000 31,000 31,000 57,500 57,500 10,000 10,000 34,000 34,000 2r,5oo 21,500 78,000 78,000 117,000 117,000
950 3,950 4,700 4,600 5,150 9,100 10,800 26,000 29.300
27.31. From the information given in Problem 27.30, find the relation betweenthe mean annual flow and the drainagearea.(Note that the functional expressionshouldbe of the form Qz.zz: rA".) 27.32. Using the resultsof Problems27.30 and27.3l, estima{ethe 30-yearflood for an ungaugedwatershedwith a drainagearcaof 540 mi2. 27.33. Annual flood recordsfor a lO-yearperiod are given by:
Year Flood
1 300
2 700
3 200
4 400
5 1000
6 900
7 800
6
500
o
100
10 600
Mean : 550 cfs, median : 550 cfs, standarddeviation : 300 cfs. Use an annual seriesand the definition offrequency in a frequencyanalysisto determinethe magnitude of the 4-yearflood. Comparethis historicalvaiuewith the analytical4-yearflood obtained assuminsfloods follow a normal distribution.
PROBLEMS 747 27.34. For a 60-yearrecordof precipitationintensitiesanddurations,a 30-min intensityof 2.50 in.i hr wasequaledor exceededa total of 85 times.All but 5 of the 60 yearsexperienced one or more 30-min intensitiesequalingor exceedingthe 2.50-in./hr value. Use the Kimball formula to determine the return period of this intensity using (a) a partial duration seriesand (b) an annualseries. 27.35. The total annualrunoff from a small drainagebasinis determinedto be approximately normal with a mean of 14.0 in. and a standarddeviation of 3 in. Determine the probabilitythat the annualrunofffrom the basinwill be lessthan 8.0 in. in the second year only of the next three consecutiveyeafs. 27.36. Six yearsof peakrunoff ratesare given below.Assumethat the floodsfollow exactlya normal distributionand determinethe magnitudeof the 5O-yearpeak.
Year Runoff (cfs)
1 200
2 800
3 500
4 600
5 400
6 500
21.37. Annual floodsfor a streamare normally distributedwith a mean of 30,000 cfs and a returnperiodT,of a32,000-cfsflood varianceof I x 106cfs2.Determinethe average in the stream. 27.38. Annual floodsfor a streamhavea normal frequencydistribution.The 2-yearflood is 40,000cfs and the l0-year flood is 52,820cfs.Determinethe magnitudeof the 25-year flood. 27.39. The 80-yearrecord of annual precipitation at Linclon, Nebraska,yields a range of valuesbetween10 and 50 in. with a meanannualvalueof 25.00 in. and a standard deviationof 5.30 in. Becauseannualprecipitationrepresentsa sum of many random variables(i.e., depth of precipitation for eachday of the year), assumethat annual precipitation is normally distributed. a. In 1936 the precipitation at Lincoln was a mere 14 in. Determinethe probability that the annual precipitation will be 14 in. or lessnext year. b. In 1965Lincoln received42 in. On the average,this amountwould be equaledor exceededonce in how many years? c. Comparethe theoreticaland apparentreturn periods of the record-highvalue of 50.00in. 27.40. Annual floods (cfs) at a particular site on a river follow a zero-skew log-Pearson Type III distributions.If the mean of logarithms(base 10) of annual floods is 2.946 and the standarddeviationof base-10 logarithmsis 1.000,determinethe magnitude of the 50-yearflood. 27,41,. Annual floods (cfs) at a particular site on a river follow a zero-skew log-Pearson 1.733and annualfloodsis Type Illdistribution.Ifthemeanoflogarithms(base10)of the standarddeviationof base-l0logarithms is L.420,determinetfe magnitudeof the 10O-year flood. 27.42 The 100-yearrecord for a drainagebasin gives 10- and 5O-yearflood magnitudesof 12,500 and22,000 cfs. Determinethe magnitudeof the mean annual flood if (a) the flood peaksfollow the index-flood curve of Fig. 27.4c atd (b) the flood peaksfollow a Gumbel extreme-valuedistribution.
748
CHAPTER27
FREQUENCYANALYSIS *The following parameters were computed for a stream: 27.43.
Period ofrecord : 1960-1984, inelusive. Mean annual flood : 7000 cfs Standarddeviationof annual floods : 1000 cfs Skew coefficientof annual floods : 2.0 Mean qf logarithms(base l0) of annual floods : 3.52 Standarddeviationof logarithms : 0.50 Coefficientof skew of logarithms : -2I Determine the magnitudeof the 25-yearflood by assumingthat the peaks follow a (d) log-PearsonType III distribution, (b) Gumbel distribution, and (c) log-normal distribution. 27.44. Peak annual dischargerates in the Elkhorn river at Waterloo,Nebraska,yield the following statistics: Period of record : 1930-1969, inclusive Mean flood : 16,900cfs Standarddeviation : 17,600cfs Skew of annual floods : 0.8 Mean of logarithms(base 10) : 4.0923 Standarddeviationof logarithms : 0.3045 Skew of loearithms : 2.5 Determinethe 100-yearflood magnitudeusing the uniform techniqueadoptedby the U.S. Water ResourcesCouncil for all federal evaluations. b. Determinethe'100-yearflood magnitudeassumingthat the floods follow a twoparametergamma distribution. 27.45. A PearsonType III variableX has a mean of 4.0, a standarddeviationof 2.0, and a of I:f.(X)dX. coefficientofskewof 0.0.Determinethevalue(foursignificantfigures) .
27.46, A timber railroad bridge in Hydrologic Region2 of Texasat Milepost 738.04 on the railroad systemshownin the sketchis to be replacedwith a new concretestructure. The 50- and 100-yearflood magnitudesare neededto establishthe low chord and embankmentelevations,respectively.Determinethe designflow ratesusingthe USGS RegressionEquations.The drainagearea is 0.43 sq. mi, and the streambedslopeis 62 ftoer mi.
REFERENCES t . M. A. Benson,"Plotting Positionsand Economicsof EngineeringPlanning,"Proc. ASCE J. Hyd. Div. 88057-71(Nov. 1962). L I. Gringorten,"A Plotting Rule for ExtremeProbability Paper,"J. Geoplrys.Res'68(3), 8 1 3 - 8 1 4 ( F e b1. 9 6 3 ) .
ucENoftp.*
l l * *l* --) .J$ ^ " : 1- ' .
!
,
I
i
I
t \
\l Sketch for Problem 27.46
I I
Slrip
]
750
CHAPTER2TFREQUENCYANALYSIS 3. VerfT. Chow, "A General Formula for Hydrologic FrequencyAnalysis," Trans. Am. Geophys. Union 32, 231-237 (1951). 4. Water ResourcesCouncil, Hydrology Committee, "Guidelines:for Determining Flood Frequency,"Bulletin 17B, (Revised)U.S. Water ResourcesCouncil, Washington,D.C., Sept.,1981. 5. L. R. Beard, Statistical Methods in Hydrology, Civil Works Investigations,U.S. Army Corps of Engineers,SacramentoDistrict, 1962. 6. A. Hazen, Flood Flows. New York: Wiley, 1930. 7. V. T. Chow, "Statistical and Probability Analyqisof Hydrologic Data," in Handbookof Applied Hydrology.New York: McGraw-Hill, 1964. 8. P. Victorov, "Effect of Period of Record on Flood Prediction," Proc. ASCE J. Hyd. Div. 97(Nov.1971). 9. L. R. Beard, Statistical Methodsin Hydrology, Civil Works Investigations;Sacramento District, U.S. Army Corps of Engineers,1962. 10. M. A. Benson and N.C. Matalas, "Synthetic Hydrology Based on Regional Statistical " Water Re Parameters, sources Res. 3(4)(1967). 11. N. C. Matalas,"MathematicalAssessment of SyntheticHydrology," WaterResourcesRes.
3(4)(re67).
12. Yet T. Chow, "Statistical and Probability Analysis of Hydrologic Data," Sec. 8-I, in Handbookof Applied Hydrology (V. T. Chow, ed.). New York: McGraw-Hill, 1964. 13. T. Dalrymple, "Flood-FrequencyAnalysis,"Manual of Hydrology,Part 3, U.S. Geological $urveyWater-SupplyPaper1543=A.Washington,D.C.: U.S. GovernmentPrinting Offlce, 1960. 14. G. M. Fair,J. C. Geyer,andD. A. Okun,WaterandWasteWaterEngineerlng. New York: Wiley, 1966. 15. "Monthly Stream Simulation," Hydrologic EngineeringCenter, ComputerProgram23C-L267, SacramentoDistrict, U.S. Army Corps of Engineers,July 1967. 16. R.W.CruffandS.E.Rantz,"AComparisonofMethodsUsedinFloodFrequencyStudies for CoastalBasinsin California," Flood Hydrology,U.S.G.S.Water Supply Paper 1580: Washington,D.C.: U.S. GovernmentPrinting Office, 1965. 17. W. D. Potter, "Peak Ratesof Runoff from Small Watersheds,"Hydraulic Design Series No. 2, Bureau of Public Roads,Washington,D.C.: U.S. GovernmentPrinting Offlce, Apr. 1961. 18. U.S. GeologicalSurvey, "Technique for Estimating the Magnitude and Frequencyof Floods in Texas," WaterResourcesInvestigationsReport 77 -110, 7977. t9. M. E. Jennings,W. O. Thomas, Jr., and H. C. Riggs, "Nationwide Summary of U.S. GeologicalSurvey's RegionalRegressionEquationsfor Estimating Magnitude and Frequencyof Floodsat UngaugedSites,"U.S.G.S.WRI 93-1, Reston,VA, 1993. 20. U.S. Geological Survey, "Selected Streamflow Characteristicsas Related to Channel Geometry of Perennial Streamsin Colorado," Open-File Report 12-160, Water ResourcesDivision, Lakewood,Colorado,May 1972. 2 r . E. W. Steel,l{ater Supplyand Sewerage,4th ed. New York: McGraw-Hill, 1960. 22. D. M. Hershfield,"Rainfall FrequencyAtlas of the United States,"Tech.PaperNo. 40, U.S. WeatherBureau.1961. 23. HydrologyHandbook, ASCE Manual of Practice,No. 28, 1949. 24. W. G. Knisel, Jr.,and R. W Baird, in Al?SPrecipitation Facilities and RelatedStudies. Washington,D.C.: U.S. Departmentof Agriculture, Agricultural ResearchService,1971, Chap.14, , \ R. K. Linsley, Jr., M. A, Kohler, and J. L. H. Paulhus,Applied llydrology. New York: McGraw-Hill, 1949.
Appendixes A APPENDIX: FACTORS ANDCONVERSION CONSTANTS. TABLEA.1 WATERPROPERTIES. Heat of vaporizationof water at 1-.0atm Gasconstants(R) 540 callg: 970 Btu/Ib R : 0.0821(atm)(liter)/(g-mol)(K) R : 1.987g-ca1/(g-mol)(K) R : 1.987Btu/(lb-mo1)('R) Specificheat of air Accelerationof gravity (standard) g : 32.17ftlsecz:980.6 cm/sec2 Cp : 0.238call(g)("C) Density of dry air at OoCand 760 mm Hg 0.001293g/cm3 Heat of fusion of water 19.7 callg: l44BtulIb Conversionfactors I second-foot-dayper squaremile : 0.03719 inch
*t" 1inchofrunofrpersquare
: 33:3:::::r1;?t-0"t, : 2,323,200cubic feet
I cubic rootpersecond t3jL":'"i'ff1":* n'"' : ? I horsepower : 3Jr-f,f*:llld,p".,..ond
e = 2.71828 log e = 0.43429 ln 10 : 2.30259 Metricequivalents foot : 0.3048 meter mile : 1.609 kilometers acre : 0.4047 hectare 4047 squaremeters I squaremile (mi') : 259 hectares 2.59 squarekilometers (km2) 1 acre foot (acre-ft) : 1233 cubic meters I million cubic feet (mcf ) : 28,320 cubic meters I cubic foot per second(cfs) : 6.6rta, cubic metersper second 1.699 cubic metersPer minute I acre-in. per hour : 1.008cubic feet per second(cfs) " 1 second-foolday (cfsd) = 2447 cubic meters I million gallons (mg) = 3785 cubic meters 3.785 million liters 1 million gallons per day (mgd) = 694.4 gallons per minute (gpm) 2,629 cubic meters per :ninute 3785 cubic metersper day
752
APPENDIXA TABLE A,2* PROPERTIESOF WATER
Traditional U.S.Units
iz
40 50 60 70 80 90 100
Heat of vaporization (Btu/lb)
Unit weight 0b/ft3)
Temperature Specific gravity ('F) 0.99987 0.99999 0.99975 099907 0.99802 0.99669 0.99510 0.99318
62.416 62.423 62.408 62.366 62.300 62.217 62.11,8 61.998
t073 1066 1059 r054 r049 l044 1039 1033
Vaporpressure
Kinematic viscositY (ft'lsec)
1,.93x L67 x - 1.41 X 1.21x 1.06 x 0.929x 0.828x 0.741X
10*5 10-s 10-5 105 10-s l}-s l0-5 10-5
psl
in.Hg
0.18 0.25 0.36 0.52 0.74 1.03 1.42 r.94
6.11 0.09 8.36 0.12 12.19 0.18 17.51 0.26 24.79 0.36 34.61 0.51 47.68 0.70 64.88 0.95
Sl Units Temperature Specific Density gravity (o/cm1 fC) 0 5 10 15 20 25 JU
35 40 50 60 70 80 90 100
0.99987 0.99999 0.99973 0.99913 0.99824 0.99708 0.99568 0.99407 0.99225 0.98807 0.98323 0.97780 0.97182 0.96534 0.95839
0.99984 0.99996 0.99970 0.99910 0.9982r 0.99705 0.99565 0.99404 0.99222 0.98804 0.98320 0.97777 0.97179 0.96531 0.95836
Heat of vaporization (cal/g)
597.3 594.5 591.7 588.9 586.0 583.2 580.4 577.6 574.7 569.0 563.2 551.4 545.3 539.1
Kinematic viscosity (cs)
1..790 .t.520 1.310 1.140 1.000 0.893 0.801 0.723 0.658 0.554 0,4'14 0.4r3 0.365 0.326 0.294
Vaporpressure (mmHg) 4.58 6.54 9.20 12.78 17.53 23.76 31.83 42.18 55.34 92.56 149.46 233.79 355.28 525.89 760.00
(mb)
(g/cm')
6.23 6.11 8.89 8.72 L:2.27 r2.s1 17.38 17.04 23.83 23.37 32.30 31.67 43.2.7 42.43 5'7.34 56.24 75.23 73.78 123.40 125.83 199.26 203.19 311.69 317.84 473.67 483.01 70r.13 714.95 1013.25 1033.23
B APPENDIX
753
B APPENDIX: TABLE 8.1 AREAS UNDERTHE NORMALCURVE
,<,t=1,f:*"*'"t'a, 01 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 r.0 1.1 r.2 r.3 r.4 1.5 r.6 r.7 1.8 r.9 2.0 2.1 2.2 z-5
2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 J.Z 5.5
3.4
4.0
.0000 .0398 .0793 .1179 .t554 .1915 .2257 .2580 .2881 .3159 .3413 .3643 .3849 .4032 .4192 .4332 .4452 .4554 .4641 .4713 .4772 .4821 .486r .4893 .4918 .4938 .4953 .4965 .4974 .498t .4987 .4990 .4993 .4995 .4997
.0040 .0438 .0832 .1217 .1591 .1950 .2291, .2611 .2910 .3186 .3438 .3665 .3869 .4049 .4207 .4345 .4463 .4564 .4649 .4719 .4778 .4826 .4865 .4896 .4920 .4940 .4955 .4966 .49:15 .4982 .4987 .4991 .499? .4995 .499't
.02 .0080 .0478 .0871 .1255 .1628 .1985 .2324 .2642 .2939 .3212 .3461 .3686 .3888 .4066 .4222 .4357 .44't4 .4573 .4656 .4' 126 .4783 .4830 .4868 .4898 .4922 .4941 .4956 .4967 .4976 .4983 .4987 .4991 .4994 .4996 .4997
.03 .0120 .0517 .0910 .1293 .1664 .2019 .2357 .2673 .2967 .3238 .3485 .3708 .390'7 .4082 .4236 .4370 .4485 .4582 .4664 .4732 .4788 .4834 .487r .490r .4925 .4943 .4957 .4968 .49'77 .4983 .4988 .499r .4994 .4996 .4997
.04 .0159 .0557 .0948 .1331 .1700 .2054 .2389 .2704 .2995 .3264 .3508 .3729 .3925 .4099 .4251 .4382 .4495 .459r .467r .4738 .4793 .4838 .4875 .4904 .4927 .4945 .4959. .4969 .4977 .4984 .4988 .4992 .4994 .4996 .4997
.o7
.05 .0199 .0596 .0987 .1368 .1736 .2088 .2422 .2734 .3023 .3289 .3531 .3749 .3944 .4115 .4265 .4394 .4505 .4599 .4678 .4744 .4798 .4842 .48"18 .4906 .4929 .4946 .4960 .4970 .49'78 .4984 .4989 .4992 .4994 .4996 .4997
.0239 .0636 .1026 .1406 .r772 .2123 .2454 .2'764 .3051 .33t5 .3s54 .3770 .3962 .4t3t .4279 .4406 .4515 .4608 .4686 .4750 .4803 .4846 .4881 .4909 .4931 .4948 .4961 .4971 .4979 .4985 .4989 .4992 .4994 .4996 .4997
.0279 .0675 .1064 .1443 .1808 .21,57 .2486 .2794 .3078 .3340 .3577 .3790 .3980 .4147 .4292 .4418 .4525 .4616 .4693 .4756 .4808 .4850 .4884 .4911 .4932 .4949 .4962 .49' 72 .4980 .4985 .4989 .4992 .4995 .4996 .4997
08 .0319 .07t4 .1103 .1480 .1844 .2190 .2518 .2823 .3106 .3365 .3599 .3810 .3997 .4162 .4306 .4430 .4535 .4625 .4699 .4762 .4812 .4854 .4887 .4913 .4934 .4951 .4963 .4973 .4980 .4986 .4990 .4993 .4995 .4996 .4998
.09 .0359 .0753 .rr4r .1517 .1879 .2224 .2549 .2852 .3133 .3389 .3621 .3830 .4015 .4177 .4319 .444r .4545 .4633 .4606 .4767 .4817 .4857 .4890 .4916 .4936 .4952 .4964 .4974 .498r .4986 .4990 .4993 .4995 .4997 .4998
.499968
(for Source:AfrerC. E. Weatherburn,Mathematical Statistics.London: CambridgeUniversity Press,1957 z : 0 to z : 3,1); C. H. Richardson,An Intoduction to Statistical Analysis. Orlando, FL: Harcourt Brace Jovanovich.1994(for z = 3.2to z: 3.4); A. H. Bowker and G. J. Lieberman,EngineeringStatistics. Eagl€wooGCliffs,Nf: Prentice-Hall, 1959 (for z : 4.0 and 5.0)'
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759 Open channelflow, 131 Orographicprecipitation, 19 Overland flow, 561 Pan, classA, 129 Parshallflume, 131 Partial duration series,734 Partially penetratingwells, 473 Peakflow,3lI-343 Penmanmethod"97 Permeability,438 Piezometrichead,435 Point precipitation, 27 Potentialevapotranspiration,101 Potentialtheory, 463 Precipitablewater, 16 Precipitation,7, 15-39, 164 Probability, 67I-699 Probablemaximum flood. 373 Probablemaximum precipitation (PMP), 34,392 Rainfall. 281.727 Rain gauges,127 Rationalmethod,312 Recession, 574 Regressionequation,692 Relativehumidity, 16 Remotesensing,135 Reservoirs,245 Reynoldsnumber,437 Road ResearchLaboratory (RRL) Method,631 Routing,234-257 Runoff, 153-169, 302 Runoff curve number,73 Saltwaterintrusion. 474 SCS:seeSoil ConservationService SCSAtt-Kin Tr-20 Method,254 SCSmethod,73 SCSTR-55 Method, 320,445 SCSTP-149Method,331 S-hydrograph,198-201 Simulationmodels,548-59 1, 594-628,630
Snow,265-305 Snowmelt,271-284 Snyder'smethod,207 Soil ConservationService(SCS) runoff curve number,73 unit hydrographmethod,211 Soil moisture,25,55, 165 Specificyrelds,432 Standarddeviation,683 Standardproject storm (SPS),395 Stanford WatershedModel IV
(swM-IV),550 Statisticalanalysis,67| -699, 7O8-739 Steadyflow routing, 252 methods,508 Stochastic. Storm drainage,402 Storm Water ManagementModel (swMM), 604,650 111-118, l7 l, 574 Streamflow, StreamflowSynthesisand Reservoir RegulationModel (SSARR), 565 Streamlines,446 448 Surfaceof seepage, Surfacerunoff. 177
swMM,650
Syntheticunit hydrographs,205-227, 335 Temperatureindexes,291 Theis' nonequilibrium approach,467 Thiessenmethod. 30 Thunderstorms,20 Time of concentration,182-185 Time series,535-544 Transmissivity,444 Transpiration,95- 100 Unconfined aquifer, 443, 461 Uniform flow field,463 Unit hydrographs,188-227 U.S. GeologicalSurvey Index-Flood Method.336 U,S. GeologicalSurvey Rainfall-Runoff Model, 597
University of Cincinnati Urban Runoff Model (UCURM), 655-658 Unsteadyflow,442,467 Unsteadyflow routing, 252 Urban drainage,309 Urban runoff models.309-351 Velocity measurement,114 Velocity potential, 440
Water budget,86, 293, 5'l.I Water equivalent,268 : Water t'apor, 16 WatershedHydrology Model
(usDAHL),563
t53,570 Watersheds, Weirs,131 Wellfields,.465 Well function, 468 Wells,460-473