37
8/12/2019 CHOW, Ven Te. Stochastic Models http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 1/37 WRC RESEARCH REPORT NO 26 STOCHASTIC ANA LYSIS OF HYDROLOGIC SYSTEMS Ven Te Chow Principal Investigator FI N AL REPORT Project No. A-029- ILL The work upon which this publication is based was supported by funds provided by the U.S. Department of the Interior as authorized under the Water Resources Research Act of 1964 P.L. 88-379 Agreement No. 14-0 1-000 1 1632 UNIVERSITY OF ILLINO IS WATER RESOURCES CENTER 3220 Civil Engineering Building Urbana l llinois 61801 December 1969

CHOW, Ven Te. Stochastic Models

Embed Size (px)

Citation preview

Page 1: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 1/37

WRC RESEARCH REPORT NO 26

STOCHASTIC ANA LYSIS OF HYDROLOGIC SYSTEMS

Ven Te Chow

P r i n c i p a l I n v e s t i g a t o r

F I N A L R E P O R T

P r o j e c t N o. A -0 29 - I L L

Th e w o r k u po n w h i c h t h i s p u b l i c a t i o n i s b a se d was s u p p o r t e d by f u nd s

p r o v i d e d b y t h e U.S. D e pa rtm e nt o f t h e I n t e r i o r as a u t h o r i z e d u n d e r

t h e W a t e r R e s o u r ce s R e s e a r ch A c t o f 1 9 64 P .L . 8 8 -3 7 9

Agreement No. 14-0 1-000

1

1632

UNIVERSITY OF IL LI NO IS

WATER RESOURCES CENTER

3220 C i v i l E n g i ne e r in g B u i l d i n g

U r b a n a

l l l i n o i s 61801

December 1969

Page 2: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 2/37

ABSTRACT

STOCHASTIC ANALYSIS OF HYDROLOGIC SYSTEMS

Hydro logic phenomena a re i n re a l i t y s tochas t i c i n na tu re; tha t i s th e i r

behav ior changes w i th the t ime i n accordance w i th the law o f p r ob ab i l i t y

as wel l

as w i t h the sequent ia l re la t i on sh ip between the occurrences o f

the phenomenon.

I n ord er t o analyze t he hyd ro lo gi c phenomenon a mathe-

mati c model o f the s to cha st i c hy dro log ic system t o s im ulat e t he phenom-

enon must be formulated. I n t h i s study a watershed i s tr ea te d as the

sto cha st i c hydrologi c system whose components of pr ec ip i t at io n runof f

storage and eva pot ran spi rat ion are simulate d as sto ch as ti c processes by

time se rie s models t o be determined by correlograms and spe ct ra l analy sis.

The hydr olo gic system model i s then formulated on the basis o f the p r i n c i -

p l e of con ser vat ion o f mass and composed o f the component st oc ha st ic proc-

esses.

To demonstrate the pr ac t i ca l a pp l i cat io n o f the method of analys is

so developed the upper Sangamon Riv er bas in above Mon t ic el lo i n ea st

ce nt ra l I l l i n o i s i s used as the sample watershed. The watershed system

model so form ulat ed can be employed t o generate s to ch as ti c streamflows

fo r pr ac t i ca l use i n the analys is of water resources systems. This i s

o f pa r t ic u l a r va lue i n the economic planning o f water supply and i r r i ga -

t io n pr o jec t s wh ich i s concerned w i th the long- range water y i e l d o f the

watershed.

Chow Ven Te

STOCHASTIC ANALYSIS OF HYDROLOGIC SYSTEMS

Research Report No.

6 Water Resources Center Un i ver s i t y o f I l l i n o i s

a t Urbana-Champaign December 1969

4

pp.

KEYWORDS--systems analysis/stochastic processes/synthetic hydrology/

water resources development/watershed studies precipitation streamflow

evapotranspiration storage water

y ie ld/hydrologic models /hydrology

Page 3: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 3/37

CONTENTS

I n t r o d u c t i o n

I Form ula t io n of the Hy dro lo gi c System Model 3

I Mathematical Techniques 5

A Mathemat ical Models f o r Time Ser ie s 5

. Moving Average Model

2. Sum of Harmonics Model 5

Autoregression Model

6

TheCor re logram 6

.

The Spectrum Analysis 8

I V

A n a l y s i s o f t h e H y d r o l o g i c S y s t e m

The Watershed under Study

T h e H y d r o l o g i c D a t a

. P r e c i p i t a t i o n

2 Streamflow

Temperature 12

. Po t en t i a l Evapo t r ansp i r a t i on 12

C

Es ta bl is h i ng t he Records f o r Conceptual Watershed

Storage and Actual Evap ot ra nspi rat i on 3

Ana lys is o f the Hyd ro log ic Processes 5

. Det ermi nat i on o f th e System Model 17

V Conclusions

V I Acknow edgments

V I I . References

V I I I

Figures

Page 4: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 4/37

I

l NTRODUCT

ON

t i s gene r a l l y no ted t h a t t he na t u r a l h yd r o lo g i ca l s ys tem and

hydro log i c p rocess a re t r u l y s tochas t c ; t h a t i s , t h e b e h av i o r o f t h e

sys tem o r t h e p rocess va r ie s w i t h a sequen t ia l t im e f unc t i on o f t he p roba -

b i l i t y o f occu r rence [1,2].9:

I n ot he r words, th e hy dr ol og ic phenomenon

changes w i t h t he t im e i n accor dance w i t h t he l aw o f p r o ba b i l i t y as we l l

as w i th the sequent ia l re la t i on sh ip between i t s occur rences . For example ,

t h e o cc u rr e nc e o f a f l o o d i s c on si d er e d t o f o l l o w t h e law o f p r o b a b i l i t y

and a l s o t h e r e l a t i o n s h i p w i t h t he a n te ce da nt f l o o d c o n d i t i o n .

Mos t convent iona l methods f o r hyd ro l og i c des igns a r e de te r -

m i n i s t i c , t h a t i s , t h e b e h av i o r o f t h e h y d r o l o g i c s ys te m o r p ro ce ss i s

assumed independent o f t ime va r i a t io ns . For example, a u n i t hydrograph

d e r i v e d f o r a g i v e n r i v e r b as i n f o r f l o o d - c o n t r o l p r o j e c t d e si g n i s based

on h i s t o r i ca l f l o od r eco rds . Once de r i ved, t he un i t hydr og r aph i s used

f o r a na l y s i s o f f u t u r e de si g n f l o o d s. Thus,

t

i s au tom at i ca l l y assumed

unchanged w i th t ime ( f rom the pas t t o the fu tu re ) and there fo re i s

d e t e r m i n i s t i c .

Some convent i onal methods employ t he concept of p r ob a b i l i ty t o

t he ex t e n t t h a t n o s e q ue n ti a l r e l a t i o n s h i p i s i n v o l v e d i n t h e p r o b a b i l i t y .

Fo r examp le , t he f l o od r ecor d i s ana lyzed and f i t t e d w i t h a ce r t a i n pr oba-

b i l i t y d i s t r i b u t i o n t o determi ne t h e r e cu rr en ce i n t e r v a l s o f t h e f l o od o r

the f l oo d f requenc ies . Such methods are p robab i i s t i c bu t no t i n the

t r u e sense s tochast c .

Numbers i n pa r en theses r e f e r t o r e f e r ences l i s t e d a t t he end o f t he

r e p o r t .

Page 5: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 5/37

T he s t o c h a s t i c m e th od , t h a t i s t o e m plo y t h e c o nc e p t o f p r o ba -

b i l i t y as w e l l as i t s se q u e nt ia l r e l a t i o n s h i p , has n o t been w e l l i n t r o -

duced i n t h e p r a c t i c a l

d e s i g n and p l a n n i n g o f h y d r o l o g i c p r o j e c t s , b e ca us e

s u c h m e th od s h a v e n o t be e n f u l l y d e v e l o p e d .

W h i le t h e n a t u r a l h y d r o l o g i c

phenom enon i s s t o c h a s t i c , t i s i m p o r t a n t t o d e ve lo p t h e s t o c h a s t i c m etho d

o f h y d r o l o g i c a n a l y s i s f o r h y d r o l o g i c s y st em d e s ig n . C o n v e nt io n a l m e th od s,

d e t e r m i n i s t i c and p r o b a b i l i s t i c , w h i c h d o n o ' t c o nf or m m ore c l o s e l y t o t h e

n a t u r a l phenom enon, w i l l p ro d uc e r e s u l t s t h a t d e p a r t f ro m t h e t r u e b e h a v io r

o f t h e h y d r o l o g i c p henomenon and h en ce ha ve t h e p o s s i b i l i t y t o e i t h e r o v e r -

d e s i g n o r u nd er de s i g n t h e h y d r o lo g i c p r o j e c t

[ 3 ]

The o b j e c t i v e o f t h i s s t u dy i s t o f o r m u l a t e t h e m at he m at ic a l

m od el o f a s t o c h a s t i c h y d r o l o g i c s y st em and t h e m a t h e m a ti c al m o de ls o f

t h e h y d r o l o g i c p r oc e s s es i n t h e s ys te m , u s i n g t h e w a t e r s h e d as an exam -

p l e o f t h e h y d r o l o g i c sys tem . I n t h i s s t u d y , i n o t h e r w ord s, t h e fra me -

w o rk o f a m eth od was d e ve lo p ed t o u t i l i z e m a th e m a t ic a l m od els t o s i m u l a t e

t h e s t o c h a s t i c b e h a v i o r o f a wa te r sh e d a s

t h e h y d r o l o g i c s y ste m .

The

m a t h e m a ti c al m o de ls s o de v el op e d s h o u ld h av e a p ' r a c t i c a l a p p l i c a t i o n t o

t h e a n a l y s i s o f h y d r o l o g i c sy ste ms i n t h e w a t e r r e so u r ce s p l a n n i n g an d

d e v e l o p m e n t .

The i n i t i a l s t e p o f t h e s t u d y i n v o l v e d a c om p re he ns iv e re v ie w

o f t h e a p p l i c a t i o n o f t h e t he o r y o f s t o c h a s t i c p ro ce ss i n h y d ro lo g y . The

r e s u l t s o f t h i s i n i t i a l s t e p o f i n v e s t i g a t i o n a r e r e p o r t e d s e p a r a te l y as

W a te r R es o ur ce s S ys te ms A n a l y s i s A n n o t a t e d B i b l i o g r a p h y o n S t o c h a s t i c

P r o c e s s e s

[4.] and Water Resources Sys tems Ana l y s s

-

R ev ie w o f S t o c h a s t i c

P rocesses ' ' [5]

Page 6: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 6/37

11

FORMULATION OF THE HYDROLOGIC SYSTEM MODEL

I n t he f o r m u la t i on o f t he hyd r o l og i c syst em model ,

a watershed

i s used as th e hy dr o l og ic system al tho ugh the mathematica l approach would

be equa l l y app l i c ab l e t o o t he r k inds o f hyd r o l og i c sys tems w i t h some mod i-

f i ca t i on s depending on the na ture o f th e sys tem. The watershed i s t rea ted

as a hyd ro l og i c system wh ich has an in pu t , ma in ly r a i n f a l l , and an ou tpu t ,

ma in l y runo f f and evapo t ransp i ra t ion . The inp u t and ou tpu t a re to be

t r ea t ed as t im e se r ie s o r s t och as t i c p rocesses which desc r ibe t he s tochas-

t i c behav io r o f t he inpu t and ou tpu t p rocesses . The amount o f water

s t o r e d i n t h e w at er sh ed i s a l s o t r e a t e d as a t i m e s e r i e s o r s t o c h a s t i c

p ro ce ss w h ic h de s cr i b es t h e s t o c h a s t i c n a t u r e o f i n f i l t r a t i o n , s u bs ur fa ce

ru no f f and the s o i l mo is tu re and groundwater s to rages .

To fo rmu la te a mathemat ical model f o r the watershed hydro log ic

sys tem, the ru no f f i s cons idered as th e i n t eg ra l p roduc t o f t h re e compo-

nent s tochas t ic p rocesses ; namely,

1 )

a conceptual watershed storage

a t t h e end o f t h e t - t h t i me i n t e r v a l r e pr e se n t in g t h e s t o r ag e o f w a te r

on the ground su rfa ce , such as la kes , ponds, swamps and streams, as w e l l

as below the ground sur face, such as s o i l mo is t ure and groundwater res er -

v o i r s , 2 ) t h e t o t a l r a i n f a l l i n p u t d u ri n g t h e t - t h t i me i n t e r v a l , and

3 ) t h e t o t a l l o ss es , m a i n l y ev a po tr an s pi r a t i o n , d u r i n g t h e t - t h t i me

i n t e r v a l . These three component stochastic processes can be mathemat i-

c a l l y r e p re se n te d r e s p e c t i v e l y b y t i m e s e r i e s f u n c t i o n s [ ~ t ) ;ET],

[ ~ ( t ) tET] and [ E t ) ; ~ G T ] here T is the t ime range under cons idera t ion

o r the leng t h o f th e hy dro lo g i c record . These s to ch as t i c p rocesses can

be s imply denoted by S t ,

X t

and E t r e s p e c t i v e l y .

They are not cons idered

as independent bu t as a s toc ha s t i c vec t o r

[

( t ) x ( t ) E ( t ) ; ~ C T ] The

t heo r y o f t im e se r i es can t he r e f o r e be used t o f o r m u la t e t he s t och as t i c

Page 7: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 7/37

m od el o f t h i s v e c t o r . A r i g o r o u s m a th e m a ti ca l a n a l y s i s o f t h i s v e c t o r

w o u l d

r e q u i r e

t h e us e o f t h e t h e o ry o f m u l t i p l e t i m e s e r i e s

a n a l y s i s [ 6 1 .

I n v ie w o f t h e ac c ur ac y o f t h e n a t u r a l h y d r o l o g i c d a t a and f o r t h e p u r -

p os e o f p r a c t i c a l a p p l i c a t i o n w i t h o u t r e s o r t i n g t o e x c e ss iv e m at he m at ic a l

i nv o lv e m en t , t h e s t o c h a s t i c v e c t o r i s t o be an a ly z ed by t h e s i n g l e t im e

s e r i e s a n a l y s i s t ec h ni qu e s o f c o r r e lo g r a m and s p ec tr u m i n c o m b i na t io n

w i t h t h e c r os s -s p ec tr um t h e o r y w h i ch p r o v i d e s a p o w e r f u l t o o l i n t h e

a n a l y s is o f m u l t i p l e t im e s e r i e s .

By t h e b a s i c c o nc e pt o f sy s te m c o n t i n u i t y , t h e r u n o f f , w h i ch i s

a s t o c h a s t i c p ro ce ss o f t o t a l r u n o f f o u t p u t d u r i n g t h e t - t h t im e i n t e r v a l

as d e n o te d b y [ ~ t ) ;

€ ~

r s i m p l y

Y t

can be r e l a t e d t o t h e o t h e r t h r e e

co mpone nt s t o c h a s t i c p ro ce ss es o f t h e h y d r o l o g i c s ys te m as f o l l o w s :

w h e re S t m l i s t h e c o n c ep t u al w a te r sh e d s t o r a g e a t t h e b e g i n n in g o f t - t h

t im e i n t e r v a l .

Page 8: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 8/37

111

MATHEMATICAL TECHNIQUES

A. M a t h e m a t i c a l M o d e ls f o r T im e S e r i e s

I n t h i s s t u d y t h r e e m o de ls o f t i m e s e r i e s w h ic h h a ve b een u se d

i n h y d r o l o g i c s t u d y w e re r e v ie w e d . T he se m o de ls o r t h e i r c o m b i n a ti o ns

w o u ld b e e m plo ye d t o s i m u l a t e t h e h y d r o l o g i c s t o c h a s t i c p r o c es s e s . The

h y d r o l o g i c t i m e s e r i e s i s d e n ot ed b y [ u t ; t E T] w he re

u

i s t h e h y d r o l o g i c

t

v a r i a b l e a t t r i b u t e d t o t h e t - t h t im e i n t e r v a l a nd T i s t h e l e n g t h o f t h e

h y d r o

1

og i r e c o r d .

1

Mov i ng -Ave rage Mode l . T h i s model may be exp ress ed as

whe re

E

i s a ra nd om v a r i a b l e ; a l , a 2,

...

am a r e t h e w e i g h t s ; a nd

m

i s

t h e e x t e n t o f t h e m o vin g a v er a ge . T h i s e q u a t i o n may be ta k e n as t h e

m od el r e p r e s e n t i n g t h e r e l a t i o n b e tw e en , s a y , a n n u a l r u n o f f u a nd , s a y ,

annua l e f f e c t i v e p r e c i p i t a t i o n

E

where m i s t h e e x t en t o f t h e c a r r y ov e r

due t o t h e w a t e r - r e t a r d a t i o n c h a r a c t e r i s t i c s o f t h e w a te rs he d. F o r s uc h

a m o d e l, t h e w e i g h t s a l , a 2 ,

...

a

m ust be a l l p o s i t i v e and sum t o

m

u n i t y . By v i r t u e o f t h e m o vin g a ve ra ge o n t h e

E S ,

t h e s i m u l a t e d t i m e

s e r i e s u i s n o t random b u t s t o c h a s t i c .

2 . Sum-o f -Harmon ics Mode l . T h is mode l may be ex pre ss ed as

N

2IT t 2 IT - t

t

= A.

J cos

-J-+

B . s i n

+) + E t

where

A

and

0

a r e t h e a m p li t u de s; 2 r j t / T i s t h e p e r io d o f c y c l i c i t y

J

w i t h j

=

1,2,

...

and

N

b e i n g t h e num ber o f r e c o r d i n t e r v a l s i n m o nth s,

Page 9: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 9/37

ye ars o r o th e r u n i t s u sed i n t h e a n a l ys i s ; and

E~

i s a random variable.

Th is equat ion may be taken as a model repres ent ing a r egu lar o r os c i l la -

to ry form of va r i a t io ns , such as d i ur na l , seasonal and sec ular changes

th a t ex i s t f requen t l y i n hydr o log ic phenomena. Such va r i a t io ns a re o f

ne ar ly c onstan t pe ri od and they may be assumed sinu so ida l

as s imulated

i n the model .

3. Aut ore gre ssi on Model. The genera l for m o f t h i s model may

be expressed as

t

U

=

f (u t - l , t -2

E t

t - k

where

f

i s a mathemat i ca l f unct ion , k i s an in t eger , and E~ i s a r an -

dom v ar ia bl e.

A

sp e c ia l case o f t h i s model i s t h e l i n e a r a u to re g re ssi ve

model o f t he n- t h ord er :

where a

I,

a2,

. - a

a a r e t h e r e g r e s s i o n c o e f f i c i e n t s . For n

=

1

t h e

n

above equat ion becomes the f i rst-order Markov process:

where a i s the Markov-process co ef f ic i e n t .

The au to re gr es si on model may be used as a model represent ing

hy dr ol og ic sequences whose nonrandomness i s due t o st ora ge i n t he hydro-

l o g i c system, such as a watershed.

B . The Correlogram

The choice o f an appr opr i a te t im e ser i es model f o r a g iv en

hy dr ol og ic process i s no t an easy task because the above-mentioned thr ee

Page 10: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 10/37

m od e ls a l l e x h i b i t o s c i l l a t i o n s r es em b li n g t h e f l u c t u a t i o n s w h ic h one

u s u a l l y ob se rv es on m os t h y d r o l o g i c d a t a b y v i s u a l i n s p e c t i o n .

A

w e l l -

known a n a l y t i c a l a pp ro ac h w h i ch can h e l p o ne t o s e l e c t t h e b e s t mo del i s

t h e a n a l y s i s o f t h e sa m ple c o r r e lo g r a m .

The c o r re l og r am i s a g r a p h i c a l r e p r e s e n t a t i o n o f t h e s e r i a l

c o r r e l a t i o n c o e f f i c i e n t

r as a f u n c t i o n o f t h e l a g k w he re t h e v a lu e s

r

k

a r e p l o t t e d as o r d i n a t e s a g a i n s t t h e i r r e s p e c t i v e va l ue s o f k as a bs c is s a s

I n o r d e r t o r e ve a l t h e f e a tu r e s o f t h e c o r r e l og ra m b e t t e r , t h e p l o t t e d

p o i n t s a r e j o i n e d each t o t h e n e x t by a s t r a i g h t l i n e . The s e r i a l c o r r e -

l a t i o n c o e f f i c i e n t o f l a g k i s c om puted by

w h e re c o v u t , u

)

i s t h e sam ple a u t oc o v a r ia n c e and v a r u t ) and ~ a r u ~ + ~

t + k

a r e t h e s am p le v a r i a n c e ; o r

and

Page 11: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 11/37

The c o rr e lo g ra m p r o v i d e s a t h e o r e t i c a l b a s i s f o r d i s t i n g u i s h i n g

among t h e th r e e ty p es o f o s c i l l a t o r y t i m e s e r i e s m e nt io ne d p r e v i o u s l y . I t

has bee n p ro v ed a n a l y t i c a l l y t h a t i t h e t i m e s e r i e s i s s i m u la t e d b y a

m o v i n g- a v er a g e m o de l f o r ra ndo m e l e m e n t s o f e x t e n t m, t h e n t h e c o r r e l o -

gram w i l l show a d e c re a s in g l i n e a r r e l a t i o n s h i p and v a n is h es f o r a l l

v a lu e s o f

k

> m F o r a s um - of -h ar m on ic s m o de l, t h e c o r r e l o g r a m i t s e l f i s

a h a rm o n i c w i t h p e r i o d s e q u a l t o t h os e o f t h e h a rm o n ic c om ponents o f t h e

model and i t w i l l t h e r e f o r e show t h e same o s c i l l a t i o n s . I n t h e c ase o f

an a u t o r e g r e s s i o n m o de l, t h e c o r r e lo g r a m w i l l s how a da mp ing o s c i l l a t i n g

c u rv e . I n t h e ca se o f a f i r s t - o r d e r M arkov p ro ce ss w i t h a s e r i a l c o r r e l a t i o n

c o e f f i c i e n t r l

i t

w i l l o s c i l l a t e w i t h p e r i o d u n i t y a bove t h e a b s c i s sa

w i t h a d e c re a s in g b u t n o n v a n is h in g a m p l i t u d e

i f r l

i s n e g a t i ve [ 7 ]

t may b e n o t e d t h a t , w hen t h e t i m e s e r i e s i s t o o s h o r t , t h e

c om pu te d c o r re l o g r a m may e x h i b i t s u b s t a n t i a l s a m p l i n g v a r i a t i o n s a nd t h u s

may c o n c e a l i t s a c t u a l f o rm .

C

T he S p e c t ru m A n a l y s i s

T h i s m ethod i s an o th e r d i a g n o s t i c t o o l f o r t h e a n a l y s i s o f

t i m e s e r i e s i n t h e f r e q u e n c y do ma in , w h i c h c an h e l p d e v e l o p an a p p r o p r i a t e

t i m e s e r i e s m odel f o r t h e h y d r o l o g i c p r oc e s s .

A l l s t a t i o n a r y s t o c h a s t i c p ro ce ss es can b e re p re s e n te d i n t h e

f o r m

Page 12: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 12/37

w h e re

i = J i

and z ( w ) i s a c om p le x, ra nd om f u n c t i o n . U s i n g t h i s a s a

g e n e r a t i n g p r o c e s s ,

i t

c an be shown t h a t t h e a u t o c o v a r i a n c e f o r a s t a -

t o n a r y p ro c es s i s [ a ]

w h e re

=

k i s t h e t im e l ag , w i s t h e a n g u l a r f r e q u e n c y , a nd F ( w )/ y o

i s a d i s t r i b u t i o n f u n c t i o n m o n o t o n i c a l l y i n c r e a s i n g a nd b ou nd ed b etw ee n

F( - IT )

=

0 and F( IT)

=

Y o

=

o2

w he re i s . t h e s t a n d a rd d e v i a t i o n . The f u nc -

t i o n ~ ( w ) s c a l l e d t h e power s p e c t ra l d i s t r i b u t i o n f u n c t i o n . F o r k = 0 ,

E q. ( 1 2 ) g i v e s

w h i ch shows t h a t d F(w ) r e p r e s en t s t h e v a r i a n c e a t t r i b u t e d t o t h e f re q ue n cy

band (w, w+dw)

Thu s, dF (w) = f (w) dw w he re f (w)

i

ca ed

th e p o w e r

s p ec tr u m o f t h e p r o ce s s .

I n t h e p r a c t i c a l h y d r o l o g i c a p p l i c a t i o n o f t h e s p e c t r a l t h e or y

t h e pr o c es s e s a r e r e a l an d t h e i m a g i n a r y c om po ne nt i s d ro p p ed o f f , t h u s

Eq. (1 2) becomes

k

= IT

coskw

f

(w)dw

0

T he m a t h e m a ti c al i n v e r s i o n o f t h e a b ov e e q u a t i o n g i v e s t h e p o we r s p e c tr u m

F o r a f i n i t e a mount o f d a t a [ u t ; ~ E T ] n e s t i m a t e o f t h e p ow er s p e c tr u m i s

Page 13: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 13/37

w he re i s t h e a u t o c o v a r ia n c e f o r a t i m e l a g k .k

T he e s t i m a t e o f t h e p ow er s p e c t r u m b y Eq.

( 16 ) i s c a l l e d t h e

ra w s p e c t r a l e s t i m a t e b ec au se

t

d oe s, n o t g i v e a s m oo th p o w er s p e c t r a l

d ia g ra m . To a d j u s t f o r t h e s m oo th n es s,

i t

i s common t o use th e smoo thed

s p e c t r a l e s t i m a te ' ' i n t h e fo rm

w h e r e h

(w) a r e s e l e c t e d w e i g h t i n g f a c t o r s and

m

i s a n um be r t o b e c h o se n

k

much l es s than

T.

A co mm only u s e d w e i g h t i n g f a c t o r i s t h e T uk ey -H am m in g

w e i g h t s

[91:

57k

hk (w ) 0 .54 + 0.46 cos

whe re m i s t a k e n as l e s s t h a n T /1 0 .

The s i g n i f i c a n c e o f t h e s p ec t r um i s t h a t i t e x h i b i t s l e s s

s a m p li n g v a r i a t i o n s t h a n t h e c o r r e s p o n d in g c o rr e l o g ra m . C o n s e q ue n t ly , t h e

e s t i m a t e d s p e ct ru m w o u ld p r o v i d e a b e t t e r e v a l u a t i o n o f t h e v a r i o u s param -

e t e r s i n v o l v e d i n a m od el. f t h e g e n e r a t in g p r oc es s c o n t a i n s p e r i o d i c

t er m s , t h e f r e q u e n c i e s o f t h e s e te rm s w i l l a p p e a r as h i g h and s h a rp pe ak s

i n t h e e s t im a t e d s p ec tr um and t h e h e i g h t o f t h e p eaks w i l l g i v e a ro ug h

e s t i m a t e o f t h e amp1 i t u d e .

Page 14: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 14/37

I V

ANALYSIS OF THE HYDROLOGIC SYSTEM

A.

T he W a te rshed unde r S tudy

T he wa te rshed chosen as t h e h y d r o l o g i c sy s t em t o b e a na ly z ed i n

t h i s s t u d y i s t h e u p p er Sangamon R i v e r b a s i n o f 55 0 s q. m i i n s i z e , a bo ve

M o n t i c e l l o , I l l i n o i s , and l oc a te d i n e a s t c e n t r a l I l l i n o i s . The c r i t e r i a

f o r s e l e c t i n g t h i s w a t er sh ed a r e t h a t t h e a v a i l a b l e h y d r o l o g i c da t a su ch

as t h e p r e c i p i a t o n , s t r e a m f l ow and t e m p e r a t u r e r e c o r d s h av e a r e a s o n a b l y

c o n c u r re n t p e r i o d and t h a t a d d i t i o n a l d a t a

i

nee ded can be r e l a t i v e l y

e a s i l y c o l l e c t e d due t o co n ve n ie n t access t o i t s l o c a t i o n and t o i t s

d a ta c o l l e c t i n g a ge nc ie s . F i g u r e

1

shows th e map o f th e Sangamon R i ve r

b a s i n above M o n t i c e l l o , I l l i n o i s w i t h t h e l o c a t i o n s o f t h e st re am ga g i n g

s t a t i o n a t M o n t i c e l l o a nd t h e p r e c i p i t a t i o n g ages w h ere d a t a w ere o b se rv ed

f o r u s e i n t h e a n a ly s i s .

B. Th e H y d r o l o g i c D a ta

1. P r e c i p i t a t i o n . The m o n th ly p r e c i p i t a t i o n s i n in ch es w ere

used i n t h e a n a l y s i s a s t h e h i s t o r i c a l h y d r o lo g i 'c i n p u t s t o t h e w a te rs he d

sys tem.

The d a t a w e r e ta k e n f r o m t h e C l i m a t i c Summ ary o f t h e U n i t e d

S t a t e s p u b l i s h e d b y t h e U.S. W e ath er B u re au f o r I l l i n o i s . Th e p e r i o d

o f r e c o r d s u s e d i n t h e a n a l y s i s e x t e n d s f r o m O c t o b er 1 91 4 t h r o u g h S ep-

te m be r 1965 f o r s t a t i o n s a t U rb an a, C l i n t o n , B l o om i n g to n and R o b e r t s ,

f r o m M ar ch 194 0 t h r o u g h S e pte m be r 1 965 f o r t h e s t a t i o n a t R a n t o u l , a nd

f r o m J u ne 1 9 42 t h r o u g h S e pt em b er 1 9 65 a t M o n t i c e l l o . T he a v e r a g e m o n t h l y

p r e c i p i t a t i o n s o v e r t h e w a te r s he d w e re c om pu te d by t h e T h i e s s e n p o ly g o n

method.

2. S t r e a m f l o w . The m o n t h l y s t r e a m f l o w r e c o r d s f o r t h e

Sangamon R i v e r a t M o n t i c e l l o , I l l i n o i s , w ere u se d as t h e h i s t o r i c a l

Page 15: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 15/37

h y d r o l o g i c o u t p u t s o f t h e w a te rs h ed s y s te m i n t h e a n a l y s i s . The U.S.

G e o lo g ic a l Su rv ey , i n i t s c o o p er a t i ve p ro gra m w i t h t h e I l l i n o i s S t a t e

W a te r S u rv e y a nd o t h e r s t a t e , l o c a l a nd f e d e r a l a g e n c ie s , c o l l e c t s l o n g -

t e r m s t r e a m f lo w r e c o r d s t o d e t e r m i n e t h e p e r f o rm a n c e of r i v e r s a nd s tr e a m s .

The g a g i n g s t a t i o n o n t h e Sangam on R i v e r a b o u t o n e - h a l f m i l e w e s t o f

M o n t i c e l l o had p u b l is h e d d a t a a v a i l a b l e f o r t h e p e r io d s o f F e br ua ry 1908

t o December 1912 and June. 1914 t o S eptember 1968.

T h e m o n t h l y s t r e a m -

f l o w s f r o m S e pt em b e r 1 91 4 t h r o u g h S e p te m be r 1 96 5 w e r e u s e d i n t h e a n a l y s i s .

3 T e m p er at u re . I n t h e a n a l y s i s , t h e a v e r a g e m o n t h l y t em p er a-

t u r e s f r o m O c t o b e r 1 91 4 t h r o u g h S e p te m be r 1 96 5 w e r e t a k e n f r o m t h e

C l i m a t i c Summary o f t h e U n i t e d S t a t e s p u b l i s h e d b y t h e U S Weather

B u r e a u f o r l n o i s . The mean o f t h e m o n t h l y av e r ag e te m p e r a tu r e s a t t h e

s t a t i o n s i n U rb an a a nd B lo o m i n g t o n was c o n s i d e r e d a s t h e a v e ra g e m o n t h l y

t e m p e ra t u re o f t h e w a te rs h ed . The r e l a t i v e l o c a t i o n o f th e s e tw o s t a t i o n s

w i t h r e s p e c t t o t h e w at er sh e d has s u g ge s te d t h i s c h o i c e .

4. P o t e n t i a l E v a p o tr a ns p i r a t o n . N e ce ss ar y t o t h e a n a l y s i s o f

t h e w a te rs h ed h y d r o l o g i c sy ste m i s t h e e s t i m a t i o n o f t h e m o n th ly p o t e n t i a l

e v a p o t r a n s p i r a t i o n . T he re a r e s e v e r a l m etho ds f o r t h e c o m p u ta t io n o f t h e

p o t e n t i a l e v a p ot r an s p i r a t o n .

The method propo sed by Hamon [ I 0 1 was used

because i t h as be en t e s t e d i n

l

I 1 n o i s

[ ]

w i t h s a t i s f a c t o r y r e s u l t s and

t h e c o m p u t a t io n an d t h e d a t a r e q u i r e m e n t a r e r a t h e r s im p l e .

T he fo rm u l a p roposed by Hamon i s

whe re E i s t h e d a i l y p o t e n t i a l e v a p o t ra n s p i ra t i o n i n in ch es , D i s t h e

P

p o s s i b l e h o ur s o f s u n s h in e i n u n i t s o f 12 h o ur s and

P t

i s t h e s a t u r a t i o n

Page 16: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 16/37

v ap or d e n s i t y ( a b s o l u t e h u m i d i t y ) i n gram s p e r c u b i c m e t er a t t h e d a i l y

m ean t e m p e r a t u r e . T he v a l u e o f

D

depends o n t h e l a t i t u d e o f t h e w a te rs h ed

an d t h e m on th o f t h e y e a r .

Th e v a l u e o f P t d ep en ds o n t h e t e m p e r a t u r e .

T ab le s f o r e u a l u a t i n g t h e va lu e s o f

D

and P t a re p r ov i d ed by Harnon [121 .

The v a l u e o f

D

i s e s s e n t i a l l y t h e m o n t hl y d ay ti m e c o e f f i c i e n t o f t h e

H a rg r ea v es e v a p o t r a n s p i r a t i o n f o rm u l a [ 1 3 ] .

T he v a l u e o f P t c a n b e f o u n d

f r o m t h e S m i t h s o n i a n M e t e o r o l o g i c a l T a b l e s . F o r t h e w a t e r s h e d u n d e r c on -

s i d e r a t i o n , i t s a ve ra ge l a t i t u d e i s 40 N

The v a lu e s o f

D~

f o r t h e

t w e l v e m o nt hs a r e 0 .6 4 ( J a n . ) , 0 . 79 ( ~ e b . ) , 0 . 9 9 ( M a r . ) , 1 .2 2 ( A p r . ) ,

1 .4 4 ( M ay ), 1.56 ( ~ u n e ) , 1.51 ( ~ u l y ) , .3 1 ( A ug . ), 1 .0 8 ( s e p t . ) , 0 .8 6

( ~ c t . ) , 0 .69 ( N O V. ) , and 0 .6 1 ( ~ e c . ) .

The m o n t h l y p o t e n t i a l e v a p o t r a n s p i r a t i o n c an t h e n be co mp ute d b y

Epm 0.0055 ~ K D ~ P ~ ( 20 )

w h er e n i s t h e n um ber o f d ay s f o r e ac h m o nth an d K i s a c o r r e c t i o n f a c t o r

e q u a l t o 1 . 04 b e c a us e

P t

i s e s t i m a t e d f o r t h e m o n t h ly mean t e m p e r a t u r e

i n s t e a d o f t h e d a i l y m ean t e m p e ra t u re .

C E s t a b l i s h i n g t h e R ec or ds f o r C o n c e pt u al W a te rs he d S t o r a g e

a nd A c t u a l E v a p o t r a n s p i r a t i o n

R e w r i t i n g E q. ( 1 ) g i v e s

S i nc e t h e v a lu e s o f m o nt h l y p r e c i p i t a t i o n

X t

and m o n t h l y r u n o f f Y t a r e

known f ro m t h e h i s t o r i c a l r e co r d s, i t i s o b v i o u s f r o m t h e a b ov e e q u a t i o n

t h a t

i

t h e r e c o r d f o r t h e c o n c e pt ua l w a t er sh e d s t o r a g e

S t

were known

t he n t h e r e co r d f o r t h e a c t u a l m o n th ly e v a p o t r a n s p i r a t i o n E t c o u l d b e

Page 17: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 17/37

e a s i l y e s t a b l i s h e d .

On th e o t h e r hand , i f t h e r e c o rd o f E t were known and

an i n i t i a l v a lu e o f

S t

w e r e assu med, t h e n t h e r e c o r d o f

S t

c o u l d a l s o b e

e s t a b l i s h e d . U n f o r t u n a t e l y n e i t h e r S n o r E c an b e c om pu te d i n a d i r e c t

t

t

manner.

I t

i s know n, h o w ev e r, t h a t i n l a t e S ep te m be r an d e a r l y O c t o b e r

o f e ac h y e a r i n I l l i n o i s t h e am ount o f s u r f a c e w a t e r o n t h e w a te rs h ed a nd

t h e s o i l m o i s t u re a re a t a m inim um . E s p e c i a l l y i n t h e c as e o f v e r y lo w

a mount o f p r e c i p i t a t i o n d u r i n g t h e mo nth s o f A u gu s t, S ep te mb er a n d O c t o b e r ,

t h e w a t e r s h e d s t o r a g e m u st b e t h e l o w e s t . T h i s l o w e s t am ount o f s t o r a g e c an

b e c o n s i d e re d as t h e r e f e r e n c e p o i n t o f t h e c o n c e p t u a l w a t e rs h e d s t o r a g e .

.

I n o t h e r w o rd s, t h e c o n c e pt u a l w a t e rs h e d s t o r a g e i s t a k en a s z e r o a t t h e

b e g i n n in g o f t h e Oc to be r o f t h e y e a r h a v in g v er y lo w p r e c i p i t a t i o n d u r i n g

t h e mo nth s o f A u g u s t , S ep te m be r a nd O c t o b e r . I n t h e p r e s e n t a n a l y s i s ,

t h i s ha ppens t o b e t h e c as e f o r t h e y e a r o f 1914.

Once t h e i n i t i a l s t a g e o f t h e c o n c ep tu a l w a te rs he d s t o ra g e i s

e s t a b l i s h e d , t h e f o l l o w i n g p r o c ed u r e may b e f o l l o w e d t o e s t a b l i s h t h e

r e c o r d s o f c o n c e p t u a l w a t e rs h e d s t o r a g e and a c t u a

1

e v a p o t r a n s p

i

a t i o n .

I f

S t - l

+

X t

Y t E p t where E

i s t h e p o t e n t i a l e v ap o tr an -

P

t

s p i r a t i o n f o r t h e t - t h t im e i n t e r v a l , t he n th e a c tu a l ev a p o t ra n s p ir a -

- E p t .

T hus, t h e i n i t i a l s t o ra g e

S t

f o r t h e ne x t t i m e i n t e r v a l

i o n E t -

can be computed by Eq. 1 ) .

I f

X t

- Y t

<

E p t t h e n E t = S t - l +

X t -

Y t and Eq. 1 )

g i v e s

S t

= 0.

T he mass c u r v e s o f

X t

Y t E t and

S t

St

a r e s hown i n F i g . 2 .

Th e d i f f e r e n c e b et we e n C X t and C Y t i s e s s e n t i a l l y e qu a l t o C E t s i n c e

C s t -

S t-l) i s r e l a t i v e l y s m a l l as p l o t t e d i n an e n la r ge d s c a l e .

The

Page 18: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 18/37

m ass c u r v e f o r

S t

- S t-1 r e p r e s en t s t h e v a r i a t i o n i n co n c e pt u a l w a te r sh e d

s t o r a g e w i t h a mean o f

3.5

i n c h e s .

D

A n a l y s i s o f t h e H y d r o l o g i c Pr oc es se s

I n t h i s a n a l y s i s , t h e s t o c h a s t i c p ro ce ss es of p r e c i p i t a t i o n ,

c o n c ep t u al w a te rs h ed s t o r a g e a nd e v a p o t r a n s p i r a t i o n a r e n o t t o b e t r e a t e d

i n d e p e n d e n tl y o f e a ch o t h e r b u t t h e y a r e c o n s i d e r e d as a t h r e e -d i m e n s i o n al

v e c t o r o r a m u l t i p l e - t i m e s e r i e s . W i th o ut i n t r o d u c i n g t h e t h e or y o f

m u l t i p l e - t i m e s e r i e s , w h i ch ha s y e t t o be f u r t h e r d e ve lo p ed and

r e f i n e d ,

t h e f o l l o w i n g a s su m pt io ns a r e t o be made i n t h e p r e s e n t a n a l y s i s :

a )

Each s t o c h a s t i c p r oc e s s c o n s i s t s o f t w o p a r t s ; n am e ly , o ne

d e t e r m i n i s t i c and t h e o t h e r random and u n c o r r e l a t e d t o t h e d e t e r m i n i s t i c

p a r t a nd t h e p a r t s o f o t h e r p ro ce ss es .

b )

The d e t e r m i n i s t i c p a r t o f e ac h s t o c h a s t i c p ro ce ss c o n s i s t s

a l s o o f tw o p a r t s ; o ne p a r t d ep en din g o n l y o n t i m e and t h e o t h e r p a r t

d ep en din g on t h e v e c t o r o f

t h e s o c h as

t

p ro ce ss es o f p r e c i p i a t o n ,

c o n c ep t u al w a te rs h ed s t o r a g e and a c t u a l e v a p o t r a n s p i r a t i o n a t p r e v i o u s

t im e i n t e r v a l s .

Based on t h e a bo ve as s um p ti on s , t h e f i r s t s t e p i s t o d e t e rm i n e

t h e d e t e r m i n i s t i c p a r t o f ea ch p ro c e s s w h i c h dep ends o n t i m e . From t h e

e x p e ri en c e i n h y d ro lo g y and t h e e x h i b i t i o n o f h y d r o l o g i c d at a , t h e

d e t e r m i n i s t i c p a r t ap pe ars t o be a p e r i o d i c f u n c t i o n r a t h e r t ha n a p o l y -

n o m i a l o f t i m e . H en ce , t h e s a m p le c o r r e l o g r a m s c a n b e c om p ut ed f o r e a ch

p ro c es s t o t e s t t h e e x i s t e n c e o f h a rm o n ic co mp onen ts i n t h e p ro c es s .

The s e r i a l c o r r e l a t i o n c o e f f i c i e n t s r k f o r t i m e l a g k f o r t h e

p r oc e ss e s o f p r e c i p i t a t i o n , c o n c e p t u a l w a t e rs h e d s t o r a g e an d t h e ev ap o-

t r a n s p i r a t i o n w e r e c om p ute d b y Eqs.

7),

8 ) , 9) and 10 ) f o r 1 ,2 ,. . T.

Page 19: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 19/37

I n t h e p re s e n t s t ud y , T i s t h e l e n g t h o f t h e r e co rd s e qu a l t o

6 12 m o n th s a nd k i s f r o m z e r o t o n / l O , sa y 6 0. T he c o r r e l o g r a m s , o r t h e

p l o t s o f v er su s k , f o r p r e c i p i t a t i o n , c o n c ep tu a l w a te rs he d s t o r a g e and

k

e v a p o t r a n s p i r a t i o n a r e shown i n F i g s .

3 ,

and 5 r e s p e c t i v e ly . F or a l l

t h r e e p ro ce ss es t h es e c o rr e l og ra m s a r e o s c i l l a t i n g w i t h o u t any i n d i c a t i o n

o f d am pin g, t h u s r e v e a l i n g t h e pr e se n ce o f h a r m o n i c c om po ne nts i n a l l t h e

p r o c e s s e s

I n o r d e r t o d e t e r m i ne t h e p e r i o d s o f t h e h a r m o n ic c om po ne nts

w h ic h w i l l b e i n c lu d e d i n t h e model t o s i m u l a t e t h e h y d r o l o g i c p r oc es se s

and t h e h y d r o l o g i c s y st em ,

t h e p ow er s p e c t ru m f o r e a ch o f t h e p r oc e ss e s

shou l d be compu ted .

F ro m E qs . ( 1 6 ) a nd ( 1 7 ) , t h e ra w a n d s m oo th e d s p e c t r a l e s t i m a t e s

may be w r i t t e n r e s p e c t i v e l y as

and

1

Ub,) ( c0 c o s - + X Ck t cos

IT^

m m m

S u b s t i t u t i n g Eq . ( 1 8 ) f o r t h e T uk ey -H am m in g w e i g h t s i n Eq . ( 23 )

a n d s i m p l i f y i n g ,

Page 20: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 20/37

 

i

ce

Trkt

COS

~ r k ~ r k

k c o s - ( t + l ) + cos - ( t - 1 )

OS

m m m m

and

1

cos T r t = . c o s T r ( t + l ) + c o s T r ( t - l ) ]

2

Eq. (2 4) becomes

m-

1

Trk

+

0 23

[ c +

2 k COS - ( t + l )

+

Cm c o s ( t + l ) ]

2Tr 0 m

Trk

=C

+

2

C k cos

- ( t - 1 ) + Cm cos ~ ( t - 1 ) (2 7 )

2Tr

0

. . m

As t h e r aw s p e c t r a l e s t i m a t e s c a n b e r e p r e s e n t e d b y E q. ( 2 2 ) , E q. ( 2 7 ) may

b e w r i t t e n as

u ( w t )

=

0.23 L ( U ~ - ~ ) 0 .54 L (w t )

+

0 .23 L (o t+ , )

C om pu te r p ro g ra m s w e r e w r i t t e n t o c om pu te t h e a u t o c o v a r i a n c e b y

Eq.

(8 ) and th e raw and smoothed s p e c t r a l es t i m a t es by Eqs (22 ) and (28 )

The s mo oth ed s p e c t ra f o r p r e c i p i t a t i o n ,

c o n c e p t u a l w a t e r s h e d s t o r a g e a n d

e v a p o t r a n s p i r a t i o n a r e shown i n F i g s . 6 , 7 a nd 8, r e s p e c t i v e l y . T he s h a r p

peaks e x h i b i t e d i n t he s e s p e c t ra i n d i c a t e a s i g n i f i c a n t amount o f t h e

v a r i a n c e w i t h , h e p e r i o d i c i t i e s o f 12-m onth a nd 6-m onth w h ic h a r e

a p p r o p r i a t e f o r u se i n t h e m od el.

E . D e t e r m i n a t i o n o f t h e S ys te m M od el

The p r o po s ed model f o r t h e h y d r o l o g i c p r o ce s s es i s a c o m b i n a t i on

Page 21: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 21/37

o f the sum-of-harmoni cs and. the autogress o n time se ri es models.

i ce

the re su l t s of the corre logram and spectr a l analyses in di ca te the presence

of the 12-month and 6-month pe ri od ic i ie s, t h e general model f o r t he

hyd rol ogi c s to cha st i c processes under s tudy may be wr i t t en i n the form

2lT t 2lTt

U t = c1 +

c

s i n

- +

c3 C O S -

2 12

4lTt 4Tt

+

c 4 s i n

+

c5

os

2 U

where cl, c2, c3, c4 and c are the co ef f i c i en t s t o be estimated and u

t

i s the res idu al st och ast ic process w i t h zero mean. This model was there-

fo re used to i t the hydro log ic processes o f p re c i p i ta t i on , conceptua l

watershed stora ge, and ev ap ot ra ns pi ra ti on by th e lea st-sq uare method such

as the one descr ib ed by Brown

[14 ] .

The coe f f ic ie n t s o f the model de ter -

mined f o r pr ec ip i t a t on, conceptual watershed stora ge and evapotranspi ra-

t i on a re as fo l l ows :

The f i r s t f i v e terms i n the t ime ser ies model represented by

Eq. (29) are a po r t io n o f the deter min is t ic par t o f the s imula ted hydro-

lo g i c s tochas t ic processes. The f i r s t term is a cons tant wh i l e the second,

th i r d , fo ur th and f i f t h terms are det erm in i s t i c harmonics as func t ions o f

t ime. The la s t te rm u represents the res idua l s tochas t ic process which

may consis t of a de te rm in is t i c po r t io n and

the random p a r t o f th e model.

Page 22: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 22/37

T h i s d e t e r m i n i s t i c p o r t i o n may be c o r r e l a t e d w i t h t h e v e c t o r o f t h e p ro c -

e s s e s o f precipitation c o n c e p t u a l w a t e rs h e d s t o r a g e a nd e v a p o t r a n s p i r a -

t i o n a t p r e v i o u s t i m e i n t e r v a l s , w h i l e t h e random p a r t o f t h e p ro c es s may

b e s i m u l a t e d by a r e p r e s e n ta t i v e p r o b a b i l i t y d i s t r i b u t i o n .

T h e d e t e r m i n a -

t i o n o f a s u i t a b l e model f o r t h e r e s i d u a l s t o c h a s t i c p ro ce ss w i l l r e q u i r e

f u r t h e r i n v e s t i g a t io n . I n f u r t h e r i n v e s t i g a t io n ,

t

may be suggested

t h a t t h e d e t e r m i n i s t i c p o r t i o n o f t h e r e s i d u a l s t o c h a s t i c p ro ce ss es be

a n a l y z e d b y t h e c r o s s - s p e c t rum t h e o r y

[ e l

A t h o ug h t h e r e s u a l s o c h a s -

t i c p ro ce ss i s a s i g n i f i c a n t com ponent o f t h e m od el , i t s m ag n i t ud e i s o f

r e l a t i v e l y lo w o r d e r . As a f i r s t a p p ro x im a t io n t h e re s i d u a l s t o c h a s t i c

p r o c e s s e s i n t h e w a t e r s h e d s y s t e m may b e c o n s i d e r e d c o m p l e t e l y ra nd om

w i t h t h e i r means equa l t o ze ro . T hus, f o r t he p r es en t s tu dy , X;=E;=S;=O

a nd t h e i r v a r i a n c e s w er e f o u n d t o b e 2 .7 54 , 0 .4 65 a nd 4 .1 36 r e s p e c t i v e l y .

T h e i r p r o b a b i l i t y d i s t r i b u t i o n s may be r o u g h l y assumed a s no rm a l a t p r e s e n t

u n t i l b e t t e r p r o b a b i l i t y d i s t r i b u t i o n m ode ls a r e t o b e fo un d i n f u t u r e

i n v e s t i g a t i o n .

W i t h t h e h y d r o l o g i c p ro ce ss es o f p r e c i p i t a t i o n , c o nc e pt ua l

w a t e rs h e d s t o r a g e and e v a p o t r a n s p i r a t i o n b e i n g d e t er m i ne d , t h e r u n o f f

p rocess may be fo rm u l a te d f r o m Eqs. (1 ) a nd (29 ) as

n t Tl t

n t 0 . 0 3 0 3 s i n

t = 0.8036 + 0 . 5 0 2 4 s i n

T

.7778 cos g

3

+

0.6064 cos

nt

0 . 5 7 8 6 s i n

n ( t - 1 )

3

m(t- l) 2.3821 cos 6

+ 0 ,5583 s i n Tl (t - l)

-

0.1366

cos

+ X; - E

-

5 ;

-

S;-l)

( 31 )

3 3

Page 23: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 23/37

T h i s i s t h e sy s te m m od el e x p re s s e d f o r t h e r u n o f f p r o c e s s o f t h e u p p e r

Sangamon R i v e r b a s i n a bo ve M o n t i c e l l o I l l i n o i s . T h i s m od el c an b e

e m plo ye d t o g e n e ra t e s t o c h a s t i c m o n t h ly s t r e a m f l o w v a l u e s f o r u s e i n t h e

a n a l y s i s o f w a t e r r e s ou r c es s y st em s .

t

i s o f p a r t i c u l a r v a l u e i n t h e

e co no mic p la n n i n g o f w a t e r s u p p l y and i r r i g a t i o n p r o j e c t s w h i ch i s

con-

c e rn e d w i t h t h e lo ng -r an ge w a t e r y i e l d o f t h e w a te rs h ed .

Page 24: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 24/37

V

CONCLUSIONS

The u l t i m a t e o b j e c t i v e o f t h e r e s e a rc h o n t h e s t o c h a s t i c a n a ly -

s i s o f s t o c h a s t i c h y d r o l o g i c sy ste ms i s t o f o r m u l a t e t h e m a th em a ti c a l

model

f o r a s t o c h a s t i c h y d r o l o g i c sy st em f o r w h i ch a w a te r sh e d i s c on -

s i d e r e d . The u p pe r Sangamon R i v e r b a s i n a bo ve M o n t i c e l l o I l l i n o i s i s

t a k e n a s a n e x am p l e o f t h e w a te r s h e d .

T h i s s t u d y h as d e m o n s tr a te d t h a t

su ch a mode l i s f e a s i b l e and i t s a p p l i c a t i o n t o a p r a c t i c a l p ro b l e m i s

w o r k a b l e .

F or t h i s s t u d y t h e 1 i t e r a t u r e on s t o c h a s t i c p ro ce ss es a nd t h e i r

a p p l i c a t i o n i n h y d r o lo g y w e re re vi ew e d. I t was fo u nd t h a t t h e a p p l i c a -

t i o n o f t h e t h e o r y o f s t o c h a s t i c p r oc es se s i n h y d r o l o gy has b a r e l y begu n

and t h e t h e o r y has a p p l i e d m o s t l y t o s i n g l e p r oc es se s b u t n o t t o c o m po si t e

h y d r o l o g i c s ys te m s. The m a t h e m a ti c al t h e o r y o f s t o c h a s t i c pr oc e ss e s i s

v e r y e x t e n s iv e b u t u n f o r t u n a t e l y m os t o f

t

i s w r i t t e n n o t f o r p r a c t i c -

i n g e n g in e e rs and h y d r o l o g i s t s . F u rt he rm o r e a s y s t e m a t i c t h e o r y f o r

t h e f o r m u l a t i o n o f a s t o c h a s t i c s y st em m od el i s u n a v a i l a b l e be ca us e t h e

f o r m u l a t i o n o f t h e model r e q u i r e s t h e p r a c t i c a l k no wle dg e o n t h e p h y s i -

c a l c h a r a c t e r i s t i c s o f t h e p ro ce ss and t h e sy st em w h i ch i s u s u a l l y l a c k -

i n g o n th e p a r t o f t h e m a th e m a ti ci an . T h i s s t u dy t h e r e f o r e a t te m p t s t o

i n t r o d u c e t h e u se bf a t h e o r e t i c a l m ode l t o th e. s i m u l a t i o n o f p r a c t i -

c a l h y d r o l o g i c sy ste m .

Based on t h e p r i n c i p l e o f c o n s e r v a t i o n o f m ass t h e wa te r sh e d

s y s te m i s r e p r e s e n te d b y t h e mass b a l a n c e e q u a t i o n i n w h i c h t h e s y s te m

co mpone nts o f p r e c i p i t a t i o n c o n c e pt u a l w a te rs h ed s t o r a g e e v a p o t r a n s p i r -

a t i o n and r u n o f f a r e c o ns i de r e d as s t o c h a s t i c p ro c es s es . Whi l e t h e d a t a

o f p r e c i p i t a t i o n and r u n o f f a r e g i v e n a m eth od was d e ve lo p ed t o e s t ab -

Page 25: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 25/37

l i s h t h e u nknow n r e c o r d s o f c o n c e p t u a l w a te r s he d s t o r a g e a nd e v a p o t r a n-

s p i r a t i o n .

d e t e r m i n i s t i c p o r t i o n o f t h e s ys te m com ponent p r oc e ss i s

a n a l y z e d b y t h e t h e o r y o f c o r r e l o g r a m a nd s p e c tr u m . C om p ute r s u b r o u t i n e s

w e re p rog ra mm ed f o r t h e c o m p u t a t io n o f c o r r e l o g r a m s a nd s p e c t r a o f a

d i s c r e t e t i m e s e r i e s o f f i n i t e l e n g t h . The e xp ec te d v a lu e s o f t h e s ys te m

c om po ne nts o f p r e c i p i t a t i o n c o n c e p t u a l w a t e rs h e d s t o r a g e and e v a p o t r a n -

s p i r a t i o n w e re t h u s f ou n d t o be b e s t s i m u l a t e d b y h a rm o n ic s o f 12-m on th

and 6-m onth p e r i o d i c i t i e s . T h i s a n a l y s i s c o n s t i t u t e s an i m p o r t a n t s t e p

i n t h e a tt e m p t o f c o n s i d e r in g t h e n o n s t a t i o n a r i t y o f t h e pr oc es se s i n v o l v e d

i n t h e h y d r o l o g i c s y st e m b ec au se t h e e x p e c t e d v a l u e s a r e t a k e n as f u n c -

t i o n s o f t im e b u t n o t c o n st a n ts .

The h y d r o l o g i c s y s te m m od el s o f o r m u l a t e d f o r t h e u p p e r Sangam on

R i v e r b a s i n ca n b e used t o g e ne r at e s t o c h a s t i c s t r e a m f lo w s f o r t h e u se i n

t h e p l a n n in g o f w a t e r s u p p ly and i r r i g a t i o n p r o j e c t s i n t h e b a s i n . The

m ethod de ve lo pe d i n t h i s s t u d y i s t h e r e f o r e f o rm e d t o b e o f p r a c t i c a l

v a l u e i n t h e a n a l y s i s o f w a t e r r es o ur ce s s ys te m s;

Page 26: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 26/37

V I

ACKNOWLEDGMENT

T h i s r e p o r t i s t h e r e s u l t o f a r e se ar ch p r o j e c t on S t oc h as t ic

Ana ly si s of Hyd rol ogi c Systems sponsored by the U.S. Of f i c e o f Water

Resources Research, which began i n Ju l y 1968 and was completed i n June

1969. Under t he d i r e c t i o n o f t he P ro j ec t I nves t i ga to r , t he hyd ro l og i c

da ta used i n t h i s s t udy were ma in l y co l l e c ted by

M r

Gonzalo Cortes-

Rivera, Research As sis tan t i n C i v i l Engineer ing, and the mathematical

ana ly si s and computat ions were la rg el y performed by M r S o t i r i o s J .

Ka re l i o t i s , Research Ass i s t an t i n C i v i

1

Engineering.

Page 27: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 27/37

V I I . REFERENCES

1 .

Chow,

V .

T., S t a t i s t i c a l and p r o b a b i l i t y an a l y s i s o f h y d r o l o g i c

da ta : Par t

1 .

Frequency anal ysi s, Sec t ion 8 i n Handbook o f Ap pl ie d

:yd;r-;;gy

ed. by

V .

T. Chow, McGraw-Hi

1 1

Book Co., New York , 1964,

2. Chow, V . T.

A

genera l r epo r t on new ideas and s c i e n t i f i c methods i n

hydro logy (S imulat io n o f the hydro lo g

i

beha vio r of watersheds) ,

Proceedings

o r t Co l l i n s , Colo-

rado, 6-8 September 1967, pp. 50-65.

3. Chow,

V .

T., Hy dr ol og ic systems f o r wa ter resour ces management,

Conference Procee di ngs o f Hyd rol ogy i n Water Resources Management,

Water Resources Research I n s t i t u t e Report No. 4 Clemson Universi ty,

Clemson, S outh Ca ro li na , March 1968, pp. 8-22.

4. Chow,

V .

T., and Mer edit h,

D . D .

Water resources systems analysis

-

p a r t . I

~ n n o t a t e d i b1 ography on s t oc h as t ic proce;ses, ~ i v i ~ n ~ i -

neer ing Stud ies, Hydr au l i c Engineer ing Ser ies No.

19

U n i v e r s i t y o f

Il l inois Urbana, I l l i n o i s , Ju ly 1969.

5. Chow,

V .

T., and Mer edit h,

D . D .

Water resource s systems ana ly si s

-

P a r t I l l . Review o f s toc has t i c p rocesses, C i v i l Eng ineer ing S tud ies,

Hyd rau l ic Engineer ing Ser ies No. 21, Un iv er si ty o f I1 in o i s, Urbana,

I l l i n o i s , J u l y 1969.

6 . Quenou i l l e ,

M . H .

The a n al ys i s o f m u l t i p l e t ime se r i e s , Hafne r

Pu bl i sh in g Co., New York , 1957.

7. Dawdy, D . R . and Matalas, N . C . Ana lys i s o f va r iance , covar iance ,

and t ime ser ies , Sect ion 8-111 , Par t I l l i n Handbook o f App l ied

Hydrology, ed. by

V .

T. Chow, McGr aw-H il l Book Co., New Yo rk , 1964,

8. Granger,

C .

W. J. , and Hatanaka,

M.

Spectra l an a ly sis o f economic

t im e se ri es , Pr inc et on Un iv er si ty P.ress, Prin cet on, New Jersey, 1964.

9. Blackman,

R.

B .

and Tukey, J. W . The measurement of power spectra,

Dover Pu bl ic at io ns , In c. , New York, 1959.

10. Hamon, W.

R.

Est imat ing po te n t ia l evapo t ra nsp i ra t i on , Proceedings,

American Soc ie ty o f C i v i l Eng ineers , Journa l o f Hydrau l i cs D iv i s i on ,

Vol. 87 No.

H Y 3

pp. 107-120, May 1961.

1 1 .

Jones, D M. A., V a r i a b i l i t y o f e va po tr an sp ir at io n i n I l l i n o i s ,

l 1 1

no is S ta t e Water Survey C i r cu la r 89

1966.

12. Hamon,

W.

R.

Est ima t ing po te n t ia l evapo t rans p i ra t ion , Massachuset ts

I n s t i t u t e o f Technology Depar tment o f C i v i l and San i ta ry Eng ineer ing ,

unpubl ished

M . S .

thesis, 1960.

Page 28: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 28/37

13

Veihmeyer

F J.

Evapot ransp i ra t ion Sect ion

1 1

i n Handbook of

11-30

pp lie d Hydrology ed. by V . T Chow McGraw-Hi ll Book Co. p.

1964.

14. Brown

R .

C .

Smoothing for eca s t in g and pr ed ic t io n o f d isc re te t ime

ser ies Prent ic e Ha l l Inc . Englewood C l i f f s

N . Y .

1962.

Page 29: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 29/37

VIII F GURES

Fig . 1

F ig .

2

F ig . 3

F ig . 4

Fig . 5

F ig . 6

Fig . 7

F ig . 8

Sangamon River bas in above Mon t ice l lo I l l i n o i s

Mass cur ves o f p r ec ip i t a t i o n evapo t r ansp i r a t i on r uno f f

and conceptual watershed storage

C o r r e l o g r a m f o r p r e c i p i t a t i o n

Corre logram f o r conceptual watershed storage

Correlogram

qr

e v a p o t r a n s p i r a t i o n

S p e c t r u m o f p r e c i p i t a t i o n

Spect rum o f conceptual watershed storage

Spect rum o f evapo t r ansp i r a t i on

Page 30: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 30/37

Page 31: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 31/37

3O

 

VON

Page 32: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 32/37

Page 33: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 33/37

Page 34: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 34/37

Page 35: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 35/37

Page 36: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 36/37

Page 37: CHOW, Ven Te. Stochastic Models

8/12/2019 CHOW, Ven Te. Stochastic Models

http://slidepdf.com/reader/full/chow-ven-te-stochastic-models 37/37